PatrickHaller/the-stack-python-100k · Datasets at Hugging Face

56185b126c1c6cd1aee3411e9637e3f4ba5da646

4,933

py

Python

glue/viewers/image/compat.py

sergiopasra/glue

c25a217a122a11818382672c99cb21f57a30636f

[ "BSD-3-Clause" ]

1

2019-12-17T07:58:35.000Z

glue/viewers/image/compat.py

sergiopasra/glue

c25a217a122a11818382672c99cb21f57a30636f

[ "BSD-3-Clause" ]

null

glue/viewers/image/compat.py

sergiopasra/glue

c25a217a122a11818382672c99cb21f57a30636f

[ "BSD-3-Clause" ]

1

2019-08-04T14:10:12.000Z

from __future__ import absolute_import, division, print_function import uuid import numpy as np from glue.viewers.image.state import ImageLayerState, ImageSubsetLayerState from glue.viewers.scatter.state import ScatterLayerState STATE_CLASS = {} STATE_CLASS['ImageLayerArtist'] = ImageLayerState STATE_CLASS['ScatterLayerArtist'] = ScatterLayerState STATE_CLASS['SubsetImageLayerArtist'] = ImageSubsetLayerState class DS9Compat(object): @classmethod def __setgluestate__(cls, rec, context): result = cls() for k, v in rec.items(): setattr(result, k, v) return result def update_image_viewer_state(rec, context): """ Given viewer session information, make sure the session information is compatible with the current version of the viewers, and if not, update the session information in-place. """ if '_protocol' not in rec: # Note that files saved with protocol < 1 have bin settings saved per # layer but they were always restricted to be the same, so we can just # use the settings from the first layer rec['state'] = {} rec['state']['values'] = {} # TODO: could generalize this into a mapping properties = rec.pop('properties') viewer_state = rec['state']['values'] viewer_state['color_mode'] = 'st__Colormaps' viewer_state['reference_data'] = properties['data'] data = context.object(properties['data']) # TODO: add an id method to unserializer x_index = properties['slice'].index('x') y_index = properties['slice'].index('y') viewer_state['x_att_world'] = str(uuid.uuid4()) context.register_object(viewer_state['x_att_world'], data.world_component_ids[x_index]) viewer_state['y_att_world'] = str(uuid.uuid4()) context.register_object(viewer_state['y_att_world'], data.world_component_ids[y_index]) viewer_state['x_att'] = str(uuid.uuid4()) context.register_object(viewer_state['x_att'], data.pixel_component_ids[x_index]) viewer_state['y_att'] = str(uuid.uuid4()) context.register_object(viewer_state['y_att'], data.pixel_component_ids[y_index]) viewer_state['x_min'] = -0.5 viewer_state['x_max'] = data.shape[1] - 0.5 viewer_state['y_min'] = -0.5 viewer_state['y_max'] = data.shape[0] - 0.5 viewer_state['aspect'] = 'st__equal' # Slicing with cubes viewer_state['slices'] = [s if np.isreal(s) else 0 for s in properties['slice']] # RGB mode for layer in rec['layers'][:]: if layer['_type'].split('.')[-1] == 'RGBImageLayerArtist': for icolor, color in enumerate('rgb'): new_layer = {} new_layer['_type'] = 'glue.viewers.image.layer_artist.ImageLayerArtist' new_layer['layer'] = layer['layer'] new_layer['attribute'] = layer[color] new_layer['norm'] = layer[color + 'norm'] new_layer['zorder'] = layer['zorder'] new_layer['visible'] = layer['color_visible'][icolor] new_layer['color'] = color rec['layers'].append(new_layer) rec['layers'].remove(layer) viewer_state['color_mode'] = 'st__One color per layer' layer_states = [] for layer in rec['layers']: state_id = str(uuid.uuid4()) state_cls = STATE_CLASS[layer['_type'].split('.')[-1]] state = state_cls(layer=context.object(layer.pop('layer'))) for prop in ('visible', 'zorder'): value = layer.pop(prop) value = context.object(value) setattr(state, prop, value) if 'attribute' in layer: state.attribute = context.object(layer['attribute']) else: state.attribute = context.object(properties['attribute']) if 'norm' in layer: norm = context.object(layer['norm']) state.bias = norm.bias state.contrast = norm.contrast state.stretch = norm.stretch if norm.clip_hi is not None: state.percentile = norm.clip_hi else: if norm.vmax is not None: state.v_min = norm.vmin state.v_max = norm.vmax state.percentile = 'Custom' if 'color' in layer: state.global_sync = False state.color = layer['color'] context.register_object(state_id, state) layer['state'] = state_id layer_states.append(state) list_id = str(uuid.uuid4()) context.register_object(list_id, layer_states) rec['state']['values']['layers'] = list_id

38.24031

95

0.588283

369fa80b40b091564f7a909c251fea12a1999b8f

48

py

Python

config.py

ydogukan/TRON_Discord_Bot

87c1ec8fa8fb1291ecf332b13fe5f6b856bdf8fc

[ "MIT" ]

null

config.py

ydogukan/TRON_Discord_Bot

87c1ec8fa8fb1291ecf332b13fe5f6b856bdf8fc

[ "MIT" ]

null

config.py

ydogukan/TRON_Discord_Bot

87c1ec8fa8fb1291ecf332b13fe5f6b856bdf8fc

[ "MIT" ]

null

queues = {} global now_playing now_playing = []

12

18

0.708333

22cbeee3d5e38481ae49455caaa4b2d29cb018ea

2,359

py

Python

scripts/Reader_RDM6300.py

emhal/RPi-Jukebox-RFID

0914ef7b1b018f3c39a127ab33d95e3a306e1e06

[ "MIT" ]

null

scripts/Reader_RDM6300.py

emhal/RPi-Jukebox-RFID

0914ef7b1b018f3c39a127ab33d95e3a306e1e06

[ "MIT" ]

null

scripts/Reader_RDM6300.py

emhal/RPi-Jukebox-RFID

0914ef7b1b018f3c39a127ab33d95e3a306e1e06

[ "MIT" ]

null

""" Support for the RDM6300 serial RFID module 1.) Connect the RDM6300 module ------------------------------ Connect the RDM6300 module to the serial GPIO pins 14 and 15. 2.) Enable GPIO serial port --------------------------- Edit the /boot/config.txt (sudo nano /boot/config.txt) and add the following line: enable_uart=1 3.) Install dependecies ----------------------- Be aware not to install the "serial" module, install "pyserial" instead: pip install pyserial 4.) Replace the default Reader.py --------------------------------- Replace the Reader.py file with the Reader_RDM6300.py: mv Reader.py Reader_default.py; mv Reader_RDM6300.py Reader.py """ import serial import string import atexit class Reader: def __init__(self): device = '/dev/ttyS0' baudrate = 9600 ser_timeout = 0.1 self.last_card_id = '' atexit.register(self.cleanup) try: self.rfid_serial = serial.Serial(device, baudrate, timeout=ser_timeout) except serial.SerialException as e: print(e) exit(1) def readCard(self): byte_card_id = b'' try: while True: try: read_byte = self.rfid_serial.read() if read_byte == b'\x02': # start byte while read_byte != b'\x03': # end bye read_byte = self.rfid_serial.read() byte_card_id += read_byte card_id = byte_card_id.decode('utf-8') byte_card_id = '' card_id = ''.join(x for x in card_id if x in string.printable) # Only return UUIDs with correct length if len(card_id) == 12 and card_id != self.last_card_id: self.last_card_id = card_id self.rfid_serial.reset_input_buffer() return self.last_card_id else: # wrong UUID length or aleady send that UUID last time self.rfid_serial.reset_input_buffer() except ValueError as ve: print(ve) except serial.SerialException as se: print(se) def cleanup(self): self.rfid_serial.close()

31.039474

86

0.529462

b98ca19a1ebd063a0776d50ba072626e515fcf84

3,600

py

Python

src/sphinx/conf.py

cvogt/sbt

1432d633e2652c51baa8af1c84411bd0517b6bf9

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

null

src/sphinx/conf.py

cvogt/sbt

1432d633e2652c51baa8af1c84411bd0517b6bf9

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

null

src/sphinx/conf.py

cvogt/sbt

1432d633e2652c51baa8af1c84411bd0517b6bf9

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

null

# -*- coding: utf-8 -*- import sys, os sys.path.append(os.path.abspath('_sphinx/exts')) extensions = ['sphinxcontrib.issuetracker', 'sphinx.ext.extlinks', 'howto', 'codeliteral', 'key'] # Project variables project = 'sbt' version = os.environ['sbt.partial.version'] site_version = os.environ['sbt.site.version'] release = os.environ['sbt.full.version'] scalaVersion = os.environ['scala.binary.version'] scalaRelease = os.environ['scala.full.version'] # General settings needs_sphinx = '1.1' nitpicky = True default_role = 'codeliteral' master_doc = 'home' highlight_language = 'scala' add_function_parentheses = False # TODO: make this an argument # pdf_index should be excluded when generating html # index.rst should be excluded when generating a pdf exclude_patterns = [ 'pdf_index.rst' ] # HTML html_theme = 'sbt' html_theme_path = ['_sphinx/themes'] html_title = 'sbt Documentation' html_domain_indices = False html_use_index = False html_show_sphinx = False htmlhelp_basename = 'sbtdoc' html_use_smartypants = False html_copy_source = False # if true: # the Home link is to scala-sbt.org # if false: # the Home link is to home.html for the current documentation version # TODO: pass this as an argument to sphinx home_site = True # Passed to Google as site:<site_search_base> # If empty, no search box is included site_search_base = 'http://www.scala-sbt.org/' + site_version + '/docs' # passes variables to the template html_context = {'home_site': home_site, 'site_search_base': site_search_base} # Latex (PDF) #latex_documents = [ # ('pdf_index', 'sbt.tex', html_title, '', 'manual', True), # ('Getting-Started/index', 'sbt-Getting-Started.tex', html_title, '', 'manual', True), #] # Issues role issuetracker = 'github' issuetracker_project = 'sbt/sbt' issuetracker_plaintext_issues = True issuetracker_issue_pattern = r'\bgh-(\d+)\b' issuetracker_title_template = '#{issue.id}' # links, substitutions typesafe_base = 'http://repo.typesafe.com/typesafe/' typesafe_ivy_snapshots = typesafe_base + 'ivy-snapshots/' typesafe_ivy_releases = typesafe_base + 'ivy-releases/' launcher_release_base = typesafe_ivy_releases + 'org.scala-sbt/sbt-launch/' launcher_snapshots_base = typesafe_ivy_snapshots + 'org.scala-sbt/sbt-launch/' sbt_native_package_base = 'http://repo.scala-sbt.org/scalasbt/sbt-native-packages/org/scala-sbt/sbt/' rst_epilog = """ .. |scalaVersion| replace:: %(scalaVersion)s .. |scalaRelease| replace:: %(scalaRelease)s .. _typesafe-snapshots: %(typesafe_ivy_snapshots)s .. |typesafe-snapshots| replace:: Typesafe Snapshots .. _sbt-launch.jar: %(launcher_release_base)s%(release)s/sbt-launch.jar .. _MSI: %(sbt_native_package_base)s%(release)s/sbt.msi .. _TGZ: %(sbt_native_package_base)s%(release)s/sbt.tgz .. _ZIP: %(sbt_native_package_base)s%(release)s/sbt.zip .. _DEB: %(sbt_native_package_base)s%(release)s/sbt.deb .. _RPM: %(sbt_native_package_base)s%(release)s/sbt.rpm .. |nightly-launcher| replace:: <%(launcher_snapshots_base)s .. _sbt-dev mailing list: https://groups.google.com/forum/#!forum/sbt-dev .. _adept: https://groups.google.com/group/adept-dev/topics .. _sbt-launcher-package: https://github.com/sbt/sbt-launcher-package .. _Stack Overflow: http://stackoverflow.com/tags/sbt .. _source code: http://github.com/sbt/sbt """ % { 'launcher_release_base': launcher_release_base, 'launcher_snapshots_base': launcher_snapshots_base, 'typesafe_ivy_snapshots': typesafe_ivy_snapshots, 'sbt_native_package_base': sbt_native_package_base, 'scalaRelease': scalaRelease, 'scalaVersion': scalaVersion, 'release': release }

32.727273

101

0.750278

48926be6fe65f47f8f85f12dafc9f1e8f6f9f47b

3,731

py

Python

onadata/apps/logger/management/commands/create_image_thumbnails.py

BuildAMovement/whistler-kobocat

7f61dd0761bb0aa5b27c909bcff8c29453d3311d

[ "BSD-2-Clause" ]

38

2017-02-28T05:39:40.000Z

2019-01-16T04:39:04.000Z

onadata/apps/logger/management/commands/create_image_thumbnails.py

BuildAMovement/whistler-kobocat

7f61dd0761bb0aa5b27c909bcff8c29453d3311d

[ "BSD-2-Clause" ]

48

2019-03-18T09:26:31.000Z

2019-05-27T08:12:03.000Z

onadata/apps/logger/management/commands/create_image_thumbnails.py

BuildAMovement/whistler-kobocat

7f61dd0761bb0aa5b27c909bcff8c29453d3311d

[ "BSD-2-Clause" ]

5

2017-02-22T12:25:19.000Z

2019-01-15T11:16:40.000Z

#!/usr/bin/env python from optparse import make_option from django.contrib.auth.models import User from django.core.management.base import BaseCommand, CommandError from django.core.files.storage import get_storage_class from django.conf import settings from onadata.apps.logger.models.attachment import Attachment from onadata.apps.logger.models.xform import XForm from onadata.libs.utils.image_tools import resize, resize_local_env from onadata.libs.utils.model_tools import queryset_iterator from onadata.libs.utils.viewer_tools import get_path from django.utils.translation import ugettext as _, ugettext_lazy class Command(BaseCommand): help = ugettext_lazy("Creates thumbnails for " "all form images and stores them") option_list = BaseCommand.option_list + ( make_option('-u', '--username', help=ugettext_lazy("Username of the form user")), make_option('-i', '--id_string', help=ugettext_lazy("id string of the form")), make_option('-f', '--force', action='store_false', help=ugettext_lazy("regenerate thumbnails if they exist.")) ) def handle(self, *args, **kwargs): attachments_qs = Attachment.objects.select_related( 'instance', 'instance__xform') if kwargs.get('username'): username = kwargs.get('username') try: user = User.objects.get(username=username) except User.DoesNotExist: raise CommandError( "Error: username %(username)s does not exist" % {'username': username} ) attachments_qs = attachments_qs.filter(instance__user=user) if kwargs.get('id_string'): id_string = kwargs.get('id_string') try: xform = XForm.objects.get(id_string=id_string) except XForm.DoesNotExist: raise CommandError( "Error: Form with id_string %(id_string)s does not exist" % {'id_string': id_string} ) attachments_qs = attachments_qs.filter(instance__xform=xform) fs = get_storage_class('django.core.files.storage.FileSystemStorage')() for att in queryset_iterator(attachments_qs): filename = att.media_file.name default_storage = get_storage_class()() full_path = get_path(filename, settings.THUMB_CONF['small']['suffix']) if kwargs.get('force') is not None: for s in ['small', 'medium', 'large']: fp = get_path(filename, settings.THUMB_CONF[s]['suffix']) if default_storage.exists(fp): default_storage.delete(fp) if not default_storage.exists(full_path): try: if default_storage.__class__ != fs.__class__: resize(filename) else: resize_local_env(filename) if default_storage.exists(get_path( filename, '%s' % settings.THUMB_CONF['small']['suffix'])): print (_(u'Thumbnails created for %(file)s') % {'file': filename}) else: print (_(u'Problem with the file %(file)s') % {'file': filename}) except (IOError, OSError), e: print _(u'Error on %(filename)s: %(error)s') \ % {'filename': filename, 'error': e}

46.061728

79

0.56044

df2a7bb93d330dfda915e0af01b5e654b0e6dac0

11,784

py

Python

lib/py/src/server/TServer.py

Jimexist/thrift

684ee0717472e1c084f4858f3faf650b6f17b451

[ "Apache-2.0" ]

8,514

2015-01-02T12:00:14.000Z

2022-03-31T10:34:56.000Z

lib/py/src/server/TServer.py

Jimexist/thrift

684ee0717472e1c084f4858f3faf650b6f17b451

[ "Apache-2.0" ]

1,623

2015-01-01T08:06:24.000Z

2022-03-30T19:48:52.000Z

lib/py/src/server/TServer.py

Jimexist/thrift

684ee0717472e1c084f4858f3faf650b6f17b451

[ "Apache-2.0" ]

3,849

2015-01-01T02:13:43.000Z

2022-03-31T06:23:34.000Z

# # Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you under the Apache License, Version 2.0 (the # "License"); you may not use this file except in compliance # with the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND, either express or implied. See the License for the # specific language governing permissions and limitations # under the License. # from six.moves import queue import logging import os import threading from thrift.protocol import TBinaryProtocol from thrift.protocol.THeaderProtocol import THeaderProtocolFactory from thrift.transport import TTransport logger = logging.getLogger(__name__) class TServer(object): """Base interface for a server, which must have a serve() method. Three constructors for all servers: 1) (processor, serverTransport) 2) (processor, serverTransport, transportFactory, protocolFactory) 3) (processor, serverTransport, inputTransportFactory, outputTransportFactory, inputProtocolFactory, outputProtocolFactory) """ def __init__(self, *args): if (len(args) == 2): self.__initArgs__(args[0], args[1], TTransport.TTransportFactoryBase(), TTransport.TTransportFactoryBase(), TBinaryProtocol.TBinaryProtocolFactory(), TBinaryProtocol.TBinaryProtocolFactory()) elif (len(args) == 4): self.__initArgs__(args[0], args[1], args[2], args[2], args[3], args[3]) elif (len(args) == 6): self.__initArgs__(args[0], args[1], args[2], args[3], args[4], args[5]) def __initArgs__(self, processor, serverTransport, inputTransportFactory, outputTransportFactory, inputProtocolFactory, outputProtocolFactory): self.processor = processor self.serverTransport = serverTransport self.inputTransportFactory = inputTransportFactory self.outputTransportFactory = outputTransportFactory self.inputProtocolFactory = inputProtocolFactory self.outputProtocolFactory = outputProtocolFactory input_is_header = isinstance(self.inputProtocolFactory, THeaderProtocolFactory) output_is_header = isinstance(self.outputProtocolFactory, THeaderProtocolFactory) if any((input_is_header, output_is_header)) and input_is_header != output_is_header: raise ValueError("THeaderProtocol servers require that both the input and " "output protocols are THeaderProtocol.") def serve(self): pass class TSimpleServer(TServer): """Simple single-threaded server that just pumps around one transport.""" def __init__(self, *args): TServer.__init__(self, *args) def serve(self): self.serverTransport.listen() while True: client = self.serverTransport.accept() if not client: continue itrans = self.inputTransportFactory.getTransport(client) iprot = self.inputProtocolFactory.getProtocol(itrans) # for THeaderProtocol, we must use the same protocol instance for # input and output so that the response is in the same dialect that # the server detected the request was in. if isinstance(self.inputProtocolFactory, THeaderProtocolFactory): otrans = None oprot = iprot else: otrans = self.outputTransportFactory.getTransport(client) oprot = self.outputProtocolFactory.getProtocol(otrans) try: while True: self.processor.process(iprot, oprot) except TTransport.TTransportException: pass except Exception as x: logger.exception(x) itrans.close() if otrans: otrans.close() class TThreadedServer(TServer): """Threaded server that spawns a new thread per each connection.""" def __init__(self, *args, **kwargs): TServer.__init__(self, *args) self.daemon = kwargs.get("daemon", False) def serve(self): self.serverTransport.listen() while True: try: client = self.serverTransport.accept() if not client: continue t = threading.Thread(target=self.handle, args=(client,)) t.setDaemon(self.daemon) t.start() except KeyboardInterrupt: raise except Exception as x: logger.exception(x) def handle(self, client): itrans = self.inputTransportFactory.getTransport(client) iprot = self.inputProtocolFactory.getProtocol(itrans) # for THeaderProtocol, we must use the same protocol instance for input # and output so that the response is in the same dialect that the # server detected the request was in. if isinstance(self.inputProtocolFactory, THeaderProtocolFactory): otrans = None oprot = iprot else: otrans = self.outputTransportFactory.getTransport(client) oprot = self.outputProtocolFactory.getProtocol(otrans) try: while True: self.processor.process(iprot, oprot) except TTransport.TTransportException: pass except Exception as x: logger.exception(x) itrans.close() if otrans: otrans.close() class TThreadPoolServer(TServer): """Server with a fixed size pool of threads which service requests.""" def __init__(self, *args, **kwargs): TServer.__init__(self, *args) self.clients = queue.Queue() self.threads = 10 self.daemon = kwargs.get("daemon", False) def setNumThreads(self, num): """Set the number of worker threads that should be created""" self.threads = num def serveThread(self): """Loop around getting clients from the shared queue and process them.""" while True: try: client = self.clients.get() self.serveClient(client) except Exception as x: logger.exception(x) def serveClient(self, client): """Process input/output from a client for as long as possible""" itrans = self.inputTransportFactory.getTransport(client) iprot = self.inputProtocolFactory.getProtocol(itrans) # for THeaderProtocol, we must use the same protocol instance for input # and output so that the response is in the same dialect that the # server detected the request was in. if isinstance(self.inputProtocolFactory, THeaderProtocolFactory): otrans = None oprot = iprot else: otrans = self.outputTransportFactory.getTransport(client) oprot = self.outputProtocolFactory.getProtocol(otrans) try: while True: self.processor.process(iprot, oprot) except TTransport.TTransportException: pass except Exception as x: logger.exception(x) itrans.close() if otrans: otrans.close() def serve(self): """Start a fixed number of worker threads and put client into a queue""" for i in range(self.threads): try: t = threading.Thread(target=self.serveThread) t.setDaemon(self.daemon) t.start() except Exception as x: logger.exception(x) # Pump the socket for clients self.serverTransport.listen() while True: try: client = self.serverTransport.accept() if not client: continue self.clients.put(client) except Exception as x: logger.exception(x) class TForkingServer(TServer): """A Thrift server that forks a new process for each request This is more scalable than the threaded server as it does not cause GIL contention. Note that this has different semantics from the threading server. Specifically, updates to shared variables will no longer be shared. It will also not work on windows. This code is heavily inspired by SocketServer.ForkingMixIn in the Python stdlib. """ def __init__(self, *args): TServer.__init__(self, *args) self.children = [] def serve(self): def try_close(file): try: file.close() except IOError as e: logger.warning(e, exc_info=True) self.serverTransport.listen() while True: client = self.serverTransport.accept() if not client: continue try: pid = os.fork() if pid: # parent # add before collect, otherwise you race w/ waitpid self.children.append(pid) self.collect_children() # Parent must close socket or the connection may not get # closed promptly itrans = self.inputTransportFactory.getTransport(client) otrans = self.outputTransportFactory.getTransport(client) try_close(itrans) try_close(otrans) else: itrans = self.inputTransportFactory.getTransport(client) iprot = self.inputProtocolFactory.getProtocol(itrans) # for THeaderProtocol, we must use the same protocol # instance for input and output so that the response is in # the same dialect that the server detected the request was # in. if isinstance(self.inputProtocolFactory, THeaderProtocolFactory): otrans = None oprot = iprot else: otrans = self.outputTransportFactory.getTransport(client) oprot = self.outputProtocolFactory.getProtocol(otrans) ecode = 0 try: try: while True: self.processor.process(iprot, oprot) except TTransport.TTransportException: pass except Exception as e: logger.exception(e) ecode = 1 finally: try_close(itrans) if otrans: try_close(otrans) os._exit(ecode) except TTransport.TTransportException: pass except Exception as x: logger.exception(x) def collect_children(self): while self.children: try: pid, status = os.waitpid(0, os.WNOHANG) except os.error: pid = None if pid: self.children.remove(pid) else: break

36.37037

92

0.588425

9ccc0fba52e34bd2b0a37ce9e1b9e5280862df48

9,934

py

Python

federal_spending/usaspending/management/commands/import_updates.py

rlugojr/federal_spending

e06f5a0018c1a0c581e8659472acb1574919dc50

[ "CC0-1.0" ]

20

2015-01-15T18:59:50.000Z

2022-01-31T03:37:53.000Z

federal_spending/usaspending/management/commands/import_updates.py

rlugojr/federal_spending

e06f5a0018c1a0c581e8659472acb1574919dc50

[ "CC0-1.0" ]

null

federal_spending/usaspending/management/commands/import_updates.py

rlugojr/federal_spending

e06f5a0018c1a0c581e8659472acb1574919dc50

[ "CC0-1.0" ]

7

2015-01-16T15:47:06.000Z

2020-01-09T07:14:45.000Z

from federal_spending.usaspending.models import Contract, Grant from federal_spending.usaspending.scripts.usaspending.contracts_loader import Loader from federal_spending.usaspending.scripts.usaspending.fpds import FIELDS as CONTRACT_FIELDS, CALCULATED_FIELDS as CONTRACT_CALCULATED_FIELDS from federal_spending.usaspending.scripts.usaspending.faads import FIELDS as GRANT_FIELDS, CALCULATED_FIELDS as GRANT_CALCULATED_FIELDS from django.core.management.base import BaseCommand from django.core import management from django.conf import settings from django.db import connections, connection, transaction from django.db.models import sql from itertools import izip from dateutil.parser import parse import os import csv import datetime import time from federal_spending.usaspending.management.commands.create_indexes import contracts_idx, grants_idx from federal_spending.usaspending.scripts.usaspending.config import INDEX_COLS_BY_TABLE def notnull(val): if val and val != '' and 'null' not in val.strip().lower(): return True return False class Command(BaseCommand): ALL_CONTRACT_FIELDS = [ x[0] for x in CONTRACT_FIELDS ] + [ x[0] for x in CONTRACT_CALCULATED_FIELDS ] ALL_GRANT_FIELDS = [ x[0] for x in GRANT_FIELDS ] + [ x[0] for x in GRANT_CALCULATED_FIELDS ] contracts_failed = [] grants_failed = [] contracts_idx_drop = contracts_idx[:10] contracts_idx_add = contracts_idx[12:22] grants_idx_drop = grants_idx[:3] grants_idx_add = grants_idx[5:8] @transaction.commit_manually def handle(self, download_file='delta_downloads.txt', **options): OUTPATH = settings.CSV_PATH + 'out/' a="""confirm = raw_input("Clearing out the csvs in the out folder, continue? y/n") if confirm != 'y': return #remove any csvs so we don't reprocess everything for f in os.listdir(OUTPATH): os.remove(OUTPATH + f) print "Downloading links in {0}".format(download_file) management.call_command('download_files', settings.PROJECT_ROOT + '/usaspending/downloads/' + download_file) print "sleeping for a minute" time.sleep(60) print "processing downloaded files into proper format" for fname in os.listdir(settings.CSV_PATH + 'datafeeds/'): if 'Delta' in fname and 'Contracts' in fname: management.call_command('convert_usaspending_contracts') elif 'Delta' in fname and ('Grants' in fname or 'Loans' in fname or 'Insurance' in fname or 'Direct_Payments' in fname): management.call_command('convert_usaspending_grants') print "Processing transaction updates in database" #print "Current number of rows in contract table: {0}".format(Contract.objects.all().count()) #print "Current number of rows in grant table: {0}".format(Grant.objects.all().count()) """ c = connections['default'].cursor() print 'deleting unecessary indexes' for x in self.contracts_idx_drop: print x c.execute(x) for x in self.grants_idx_drop: print x c.execute(x) for tab in ['usaspending_grant', 'usaspending_contract']: for fy in settings.FISCAL_YEARS: for i, colname in enumerate(INDEX_COLS_BY_TABLE[tab]): if 'fiscal_year' not in colname and 'unique_transaction_id' not in colname: del_stmt = 'drop index if exists {0}_{1}_{2}; commit;'.format(tab, fy, i) print del_stmt c.execute(del_stmt) for sname in os.listdir(OUTPATH): line_total = 0 if 'contracts' in sname: print "processing file {0}".format(sname) reader = csv.reader(open(OUTPATH + sname), delimiter='|') for line in reader: self.update_contract_row(line) if line_total % 1000 == 0: print "... on line {0}".format(line_total) line_total += 1 line_total = 0 if 'grants' in sname: print "processing file {0}".format(sname) reader = csv.reader(open(OUTPATH + sname), delimiter='|') for line in reader: self.update_grant_row(line) if line_total % 1000 == 0: print "... on line {0}".format(line_total) transaction.commit() line_total += 1 print 'recreating unecessary indexes' for x in self.contracts_idx_add: print x c.execute(x) for x in self.grants_idx_add: print x c.execute(x) #print "New number of rows in contract table: {0}".format(Contract.objects.all().count()) #print "New number of rows in grant table: {0}".format(Grant.objects.all().count()) self.write_log() def check_fiscal_year(self, line, num): if len(line) >= (num): fy = line[num] if fy and fy != '' and len(fy) == 4: return True else: print "it failed! {0}".format(line[0]) return False else: print "length failed {0} it's only {1}".format(line[0], len(line)) return False def update_contract_row(self, line): c = None status = line[1] if status.strip().lower() == 'inactive': #means that this update deletes a record try: c = Contract.objects.get(unique_transaction_id=line[0], fiscal_year=line[97]) print "Deleting {0}".format(line[0]) c.delete() except Contract.DoesNotExist as e: pass return else: if not self.check_fiscal_year(line, 97): self.contracts_failed.append(line) return try: c = Contract.objects.get(unique_transaction_id=line[0], fiscal_year=line[97]) except Contract.DoesNotExist as e: c = Contract(unique_transaction_id=line[0], fiscal_year=line[97]) except Contract.MultipleObjectsReturned as e: # delete extra objects cset = Contract.objects.filter(unique_transaction_id=line[0], fiscal_year=line[97]).order_by('-id') for i, obj in enumerate(cset): if i == 0: c = obj else: obj.delete() for (i, (column_name, value)) in enumerate(izip(self.ALL_CONTRACT_FIELDS, line)): if i in [13,14,15,16, 68, 69, 158]: if notnull(value): #parse date fields into python date objects try: value = parse(value) except OverflowError as e: value = None else: value = None if value == 'NULL': #convert CSV/Postgresql null values to python null value = None setattr(c, column_name, value) c.save() def update_grant_row(self, line): #To Do: add logging for transactions that fail c = None status = line[1] #print "processing {0}".format(line[0]) if status.strip().lower() == 'inactive': #means that this update deletes a record try: c = Grant.objects.get(unique_transaction_id=line[0], fiscal_year=line[46]) print "Deleting {0}".format(line[0]) c.delete() except Grant.DoesNotExist as e: pass return else: if not self.check_fiscal_year(line, 46): self.contracts_failed.append(line) return try: c = Grant.objects.get(unique_transaction_id=line[0], fiscal_year=line[46]) except Grant.DoesNotExist as e: c = Grant(unique_transaction_id=line[0], fiscal_year=line[46]) except Grant.MultipleObjectsReturned as f: print f cset = Grant.objects.filter(unique_transaction_id=line[0], fiscal_year=line[46]).order_by('-id') # delete extra objects for i, obj in enumerate(cset): print obj if i == 0: c = obj else: obj.delete() #print connection.queries[-1] for (i, (column_name, value)) in enumerate(izip(self.ALL_GRANT_FIELDS, line)): if i in [21, 22, 23, 55]: if notnull(value): #parse date fields into python date objects try: value = parse(value).date() except OverflowError as e: value = None else: value = None if value == 'NULL': #convert CSV/Postgresql null values to python null value = None setattr(c, column_name, value) c.save() #print connection.queries[-1] def write_log(self): today = datetime.datetime.now() print "Writing Log" writer = csv.writer(open(settings.LOGGING_DIRECTORY + '/failed_contracts_{0}.csv'.format(today.strftime('%Y%m%d')), 'w+')) for line in self.contracts_failed: writer.writerow(line) gwriter = csv.writer(open(settings.LOGGING_DIRECTORY + '/failed_grants_{0}.csv'.format(today.strftime('%Y%m%d')), 'w+')) for line in self.grants_failed: gwriter.writerow(line)

39.895582

140

0.567042

336ee2de3be52b01d6cad9e93f839072131d5334

235

py

Python

venv/Lib/site-packages/konlpy/user.py

movierecommend-chatbot/chat-bot

fc40c1937e8f597230578c1957305ad22f8280e4

[ "bzip2-1.0.6" ]

null

venv/Lib/site-packages/konlpy/user.py

movierecommend-chatbot/chat-bot

fc40c1937e8f597230578c1957305ad22f8280e4

[ "bzip2-1.0.6" ]

null

venv/Lib/site-packages/konlpy/user.py

movierecommend-chatbot/chat-bot

fc40c1937e8f597230578c1957305ad22f8280e4

[ "bzip2-1.0.6" ]

null

#! /usr/bin/python2.7 # -*- coding: utf-8 -*- import os from . import utils def addterm(term): dicfilename = os.path.join(utils.installpath, "data", "dictionary.tsv") with open(dicfilename, 'a') as f: f.write(term)

18.076923

75

0.629787

acc3e751b854448b31c3ed7fc7ffff9e3a36fa95

842

py

Python

setup.py

NotFaizen/popcat_wrapper

f2ea07a07cfa8084115645272cdbdee718012b90

[ "MIT" ]

null

setup.py

NotFaizen/popcat_wrapper

f2ea07a07cfa8084115645272cdbdee718012b90

[ "MIT" ]

null

setup.py

NotFaizen/popcat_wrapper

f2ea07a07cfa8084115645272cdbdee718012b90

[ "MIT" ]

2

2021-09-10T00:27:21.000Z

2022-02-02T19:46:48.000Z

import setuptools with open("README.md", "r", encoding="utf-8") as fh: long_description = fh.read() setuptools.setup( name="popcat_wrapper", version="1.6", author="NotFaizen", author_email="munavir370@gmail.com", url = "https://github.com/NotFaizen/popcat_wrapper", description="A wrapper designed for easy image manipulation", long_description=long_description, long_description_content_type="text/markdown", keywords=['python', 'async', 'popcat', 'popcatapi', 'api', 'api wrapper','discord','wrapper'], classifiers=[ "Programming Language :: Python :: 3", "License :: OSI Approved :: MIT License", "Operating System :: OS Independent", ], packages=setuptools.find_packages(), python_requires=">=3.6", install_requires=["aiohttp"] )

35.083333

99

0.649644

159f0d39b309f0056b77a3459098dbe0a5aac1c6

54,356

py

Python

estrategias/2021_Kasparov.py

rsouza/jogos_vorazes

807031a981b18e8fed0a73b5edb35fa4d486c269

[ "MIT" ]

null

estrategias/2021_Kasparov.py

rsouza/jogos_vorazes

807031a981b18e8fed0a73b5edb35fa4d486c269

[ "MIT" ]

null

estrategias/2021_Kasparov.py

rsouza/jogos_vorazes

807031a981b18e8fed0a73b5edb35fa4d486c269

[ "MIT" ]

19

2016-01-15T17:24:45.000Z

2021-01-28T18:12:50.000Z

import pickle import numpy as np import random from .jogadores import Jogador class MeuJogador(Jogador): '''Entao, meu plano inicial era usar uma rede neural, mas acabei desistindo da ideia, porque estava tendo problemas em importar o tensorflow em uma maquina que nao possui instalando usando apenas um pip install no modulo (conversei com o Professor Renato sobre isso em sala que a versao nova estava colidindo com o Cuda). Decidi entao por fazer um k-cluster de 300 clusters em um banco de dados que criei ao modificar o aqruivo simulador.py para criar um csv com a jogada de cada jogador, em funcao dos inputs recebidos. Treinei o k-cluster em 1000000 de instancias e depois, fiz uma estimativa da probabilidade de retorno de "c" para cada parametro fornecido. O resultado foi regular. Apesar de vencer algumas vezes no conjunto oferecido de jogadores, nao era constante como seria se treinasse um algoritmo de reinforcement, o que nao fiz por limitacoes de tempo e espaco. Isso me indica algumas coisas 1) o sample set utilizado era muito heterogeneo; 2)os jogadores divergiam muito em estrategia, especialmente durante rodadas mais avancadas. Entretanto, como exercicio, a experiencia foi divertida!''' def __init__(self): #medias e variancas da data usada para treino, importante pois o algoritmo de cluster precisa que variaveis estejam em mesma escala, caso contrario pode supervalorizar variaveis mais altas na hora de calcular as distancias ate os centroides self.medias = [99.486022, 0.505942, 102.346536, 0.50406664, 0.50368208, 0, 0.86783335] self.var = [3.33037732e+03, 3.92964236e-02, 9.92188611e+03, 4.02969574e-02, 2.70893678e-03, 0.00000000e+00, 1.08038673e-02] #desvio padrao dado como a raiz da variancia self.desvio = [(variancia)**0.5 for variancia in self.var] #o ideal seria importar esses dados, mas como eu achava que so poderiamos fazer o envio de apenas um arquivo, decidi por #simplesmente copiar e colar os dados a seguir #essa lista da as coordenadas do centroide de cada cluster self.centros = [[-1.3426829348155176, 0.6924552266171596, -1.0275578946133381, -2.5086053815815297, 1.1826925858274417, 1.2699594110885652], [0.7646816750653262, 0.23567784797510313, -1.0275578946133381, 0.7354025192197684, -0.563189301306828, -0.6515455280526591], [-0.37646244020598263, 0.017061445306800357, 0.9398433574915842, 0.0025065262759503062, -0.04139868056448892, -0.4480920639082951], [-1.2931779667372716, 0.07542576034266327, -1.0275578946133381, -0.3524564505307079, 1.0657573402534872, 1.2699594110885655], [-1.544828221135023, -0.09132626385313414, 1.1606741102788711, 0.04841172256894984, 1.282849835576562, 1.2699594110885655], [-1.7238711890180138, 1.5330155915144752, -1.0275578946133381, -2.5086053815815292, -9.662092783798043, -8.33756528461756], [1.392775957558075, -0.0738973394704198, 0.815626059048735, 0.06916673153121014, -0.6980909063982096, -0.6335314192482101], [0.9011297433309924, 0.09048803783284924, -1.0275578946133381, -1.4127409083741813, -0.5983629897097297, -0.6515455280526591], [-1.1070392867630656, 0.1300594261719541, -1.0275578946133381, 1.5062435520781192, 0.978411902616491, 1.2699594110885652], [-1.0481283747499528, 0.9900499947530698, 1.1272149053111005, 1.4597523320026558, 0.7902144478614118, 1.2699594110885652], [0.6735194014621467, -2.446293774051437, 0.8595412655689342, 0.1721870487438844, -0.35752341645936847, -0.4681291474982699], [0.2610030033192252, -0.18195093541483157, -1.0275578946133381, 0.09822374407837477, -0.16947068411239485, -0.19251934814670052], [-0.034514153347861246, -0.6178748103634039, 0.9298055960012528, -2.5086053815815297, -0.09010430882111396, -0.3152821637029456], [-1.1549765975188342, -2.475971589931971, 1.147290428291763, -0.856506668185603, 1.1427268200274077, 1.2699594110885652], [1.6635062517359844, -0.48673467925691055, -1.0275578946133381, 0.280867822946266, -0.7858897361929769, -0.9397712689238431], [-1.1347620688868836, 0.42713644270125356, -1.0275578946133381, 0.7789880380405153, 0.9834571869628221, 1.2699594110885652], [0.3553374702683278, -0.03104102342373167, 0.9197678345109216, -0.4663124996951078, -0.21752551640134732, -0.17116929326735356], [-1.0697867982841855, 1.524256521262631, -1.0275578946133381, -2.01048516648728, 0.6895030184461921, 1.2699594110885652], [-1.7065444501906275, 2.4952626277726724, 1.1807496332595337, -2.5086053815815297, 3.5488146210623355, 1.2699594110885652], [-1.0689204613428163, -1.7195043363746776, -1.0275578946133381, 0.0915821412104513, 0.9465663656657026, 1.2699594110885652], [-0.43129647249500475, 0.08245134356632196, 0.9398433574915842, 1.3866947004554993, -0.03495033981306848, -0.49782513292136166], [-1.4540691129915717, -2.0596334952719486, 1.1692779058420126, 2.408552741705988, 1.993785271685662, 1.2699594110885655], [0.29565648097399755, 0.6146128986109288, -1.0275578946133381, 0.5742052829462128, -0.24702197274710247, -0.224544430465721], [-1.658029581473946, 0.30657768169489186, 1.1687043194711362, 2.422784747851537, 1.9219922760929946, 1.2699594110885652], [-1.2820393489196662, 2.371989592973655, 1.1640200307756483, -0.1591383670536541, 1.0863468546260762, 1.2699594110885652], [-1.1520888077142697, 1.9410151645661655, 1.1272149053111005, 2.3563687191723046, 0.8001876843599733, 1.2699594110885652], [-0.6756034899611498, 0.09396849750466822, -1.0275578946133381, -0.5659365427139578, 0.13891765365474984, 0.11705644760383044], [1.2736546281197954, 0.547882658071266, 0.8153751150114766, -1.0640567578082067, -0.7242152568218809, -0.7668358244011327], [0.9011297433309924, -2.1741058457389997, -1.0275578946133381, -2.4504913564872006, -0.4586638053291047, -0.6195204457336388], [0.8318227880214478, -0.09827721499755392, -1.0275578946133381, -0.33348044233664137, -0.5600951378011079, -0.6515455280526591], [0.2266245532648876, 0.2512588492010434, 0.9226357663653021, 0.4943479151295156, -0.19190462242954878, -0.23979446966525453], [0.9236545038065943, 0.9936993755195441, 0.8314355333960067, 0.5299279304933905, -0.6418572873660603, -0.6515455280526591], [1.1061628194550623, -1.1338200593237138, 0.8260820606011635, 0.01520370822933314, -0.5995195641119295, -0.6515455280526591], [-0.6236232734789913, 0.14878385137210492, -0.043857268560877016, -0.49121851044982023, 0.4708023006208418, 0.5013574354320751], [-0.2433976158780169, 0.028740783877281923, -1.0275578946133381, 0.2697984848330605, -0.05256812793748276, -0.3846698420608232], [0.21672355964923834, 0.044544851028180295, 0.9197678345109216, -0.316876435166833, -0.1908263941921861, -0.2672445402244149], [-0.6192915887721449, 1.4798223475207264, -1.0275578946133381, 0.24973530950287556, 0.06431151137365539, 0.14107525934309578], [-0.7189203370296153, -2.5565343125828637, 0.9799944034529091, 1.4265443176630395, 0.22908157821147035, 0.020981200646769102], [-0.7015935982022292, -1.200424028712869, 0.9799944034529091, -0.018004306110283502, 0.18591799730665715, 0.06901882412529978], [0.8716742873244359, -2.447033670924399, -1.0275578946133384, -0.0777787319215934, -0.47201461667157457, -0.6515455280526592], [-1.074118482991032, -2.2682966939785643, 1.1255419450627118, 0.37218986238021173, 0.9463339576658482, 1.2699594110885657], [-0.16099934678778033, 0.15556378972531137, 0.9425200938890058, -1.1968888151666732, -0.060081481936848403, -0.2608395237606108], [0.8924663739172993, 0.787110080190615, 0.8394657425882718, -0.11762834912913345, -0.6126959173983717, -0.6515455280526591], [0.20517240043098087, 0.724449610619481, 0.9231137550076988, -0.25046040648759976, -0.20257831318926492, -0.2512319990649046], [-1.3340195654018248, -0.2046046324163659, 1.1606741102788711, 0.6949302517433605, 1.0774329411753885, 1.2699594110885652], [-1.1867422853690424, -0.22479496257641624, 1.1472904282917626, -2.2097332525249795, 1.0121257291194936, 1.2699594110885652], [1.1485170699220062, -2.3288984303283544, 0.8260820606011633, -0.8316006574308905, -0.5410939574304531, -0.6408705006129858], [-1.1492010179097052, 1.6117866019357951, -1.0275578946133381, 0.38879386955002, 0.9538308079523837, 1.2699594110885652], 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0.9420739711561021, 0.5576012757764045, -0.01143798559091178, -0.28859459510376184], [1.1647436983476536, 0.47744133739690414, -1.0275578946133381, -0.2386004013663082, -0.6850724376626347, -0.6515455280526591], [0.8318227880214477, 0.6189930822416798, 0.8394657425882718, -0.316876435166833, -0.596193712034522, -0.6515455280526591], [1.137515965904618, 0.16746719828784465, 0.8208241855347994, 0.049597723081078945, -0.6747745746492859, -0.6515455280526591], [-0.9303065507237267, 0.5683212030056752, 1.1205230643175466, 0.03678891755008396, 0.747515682956305, 1.2699594110885652], [0.7870620460507, 0.08751287018577458, -1.0275578946133381, -0.09272233837442091, -0.560954627663807, -0.6515455280526591], [0.4939513808874172, 1.4258997517343683, -1.0275578946133381, -1.9606731449778554, -0.35687099359980956, -0.2672445402244149], [-1.3624849220468163, -0.26019732287078856, 1.1606741102788714, -0.010888303037508512, 1.0858714306707582, 1.2699594110885652], [-0.18921717859238069, -0.36952639097820345, 0.9398433574915842, 0.24884580911877868, -0.04725482675710389, -0.36331978718147623], [1.6086382454492612, 0.5936130425771036, -1.0275578946133381, -0.051212320449900144, -0.8277006122831106, -0.9397712689238431], [0.05830766179885042, 0.09872415770581178, 0.9341074937828233, 0.07450373383579142, -0.1179133718628538, -0.2741070578642049], [1.3949417999114986, -0.2202323833423584, 0.8193902196076093, -2.5086053815815297, -0.6926172638921648, -0.6515455280526591], [0.9906512272724877, -1.2769300272889106, -1.0275578946133381, 1.2771082531347644, -0.5747438345914775, -0.6515455280526591], [-0.11826005768022778, -0.5956479510935556, -1.0275578946133381, 0.015203708229333188, -0.04506425521782632, -0.33129470486245577], [-1.065455113577339, 0.9469782202658675, 1.1606741102788714, 2.4725967693609627, 0.9154045143843887, 1.2699594110885652], [-1.4812968454346072, -0.05361401376156978, -1.0275578946133381, 1.1027661778517772, 1.1582821561729344, 1.2699594110885652], [-0.6091843244561694, 0.1926399798169334, -1.0275578946133381, 0.4801159089839658, 0.10237933459748931, 0.16189156285045905]] #essa lista da a acuracia media de cada cluster, calculada da seguinte maneira: numero de "c" no cluster/numero de elementos no #cluster self.accuracy = [0.30763358778625954, 0.5448634590377113, 0.7056499407348874, 0.5014644351464436, 0.5817369093231162, 0.21309036816660468, 0.056481979558902634, 0.013043478260869565, 0.6285963382737576, 0.992707672796449, 0.004991680532445923, 0.40569395017793597, 0.5124421296296297, 0.40532909828833596, 0.5558285558285558, 0.6279854620976116, 0.47768206734534063, 0.43078038173471855, 0.41393168117883455, 0.013022618231665525, 0.76793893129771, 0.0028708133971291866, 0.2601685599120557, 0.4740061162079511, 0.17678173719376392, 0.09327548806941431, 0.41829268292682925, 0.7872073578595318, 0.32897362003990244, 0.31643803766565914, 0.4247231367853936, 0.3897544853635505, 0.33668561434193267, 0.7765834932821497, 0.5605461731943946, 0.22340425531914893, 0.03389330975537872, 0.5380233551694674, 0.798125, 0.7173154362416108, 0.42102590147282887, 0.957667731629393, 0.6845726970033297, 0.011235955056179775, 0.4047568932856241, 0.6038415366146459, 0.48743987172634956, 0.46289517470881864, 0.1949286846275753, 0.9534103615355944, 0.18986486486486487, 0.5478158205430933, 0.5183743003163787, 0.07758620689655173, 0.40902612826603324, 0.6951450189155107, 0.8598207008964955, 0.9305873379099924, 0.38461538461538464, 0.5380631796996375, 0.10408560311284047, 0.4720883534136546, 0.16638935108153077, 0.9914201851433733, 0.6498257839721254, 0.051075268817204304, 0.008159866777685262, 0.5872210953346856, 0.75, 0.2566103164282618, 0.43799111718483086, 0.42420937840785167, 0.0032552083333333335, 0.6185538504078529, 0.15673981191222572, 0.608010801080108, 0.6683435141933166, 0.38020351526364476, 0.9763546798029556, 0.8388342696629213, 0.7485955056179775, 0.11692529772645255, 0.31734006734006737, 0.0205699188526137, 0.015204170286707211, 0.3747252747252747, 0.060496380558428126, 0.6889352818371608, 0.5800193986420951, 0.28110599078341014, 0.3955431754874652, 0.1964461994076999, 0.35591133004926107, 0.30368098159509205, 0.34215133242559814, 0.7472712006717045, 0.20897615708274894, 0.4872512437810945, 0.4685123212655917, 0.5026843741169822, 0.4297924297924298, 0.755165581658647, 0.49142327306444133, 0.018463642580351037, 0.18500923726576934, 0.4385212965443343, 0.8519602106495027, 0.4664905909541103, 0.10808767951625095, 0.27095808383233533, 0.009983361064891847, 0.49170834430113186, 0.4338492261607589, 1.0, 0.921747042766151, 0.8929889298892989, 0.5950611888111889, 0.8984423676012461, 0.11720096518441916, 0.5363438782902681, 0.31161364507474126, 0.8326359832635983, 0.6173988943846378, 0.5621289449015727, 0.5730935251798561, 0.5146081504702195, 0.7330710291985918, 0.6359832635983264, 0.4109396914446003, 0.10874053682037164, 0.5455615728749705, 0.10471204188481675, 0.5269814502529511, 0.820677570093458, 0.9589381999170469, 0.4303630363036304, 0.5357769545720413, 0.7739957068383931, 0.808610400682012, 0.3764564081960627, 0.9941754641427011, 0.9299655568312285, 0.5300751879699248, 0.5327852004110997, 0.4655577299412916, 0.31878999418266435, 0.145935960591133, 0.1977142857142857, 0.48074608904933813, 0.3124202467035304, 0.9819680577022154, 0.6471951784886416, 0.8828754389588928, 0.8472344161545216, 0.17105263157894737, 0.6996197718631179, 0.5493449781659389, 0.33831808585503165, 0.9179400113186191, 0.5075783091950151, 0.31774744027303753, 0.39846005774783444, 0.35416139640779154, 0.9562872551802442, 0.020612653879186944, 0.448972602739726, 0.48043184885290147, 0.0073541701487548055, 0.17417162276975362, 0.29827315541601257, 0.9057154776804339, 0.40791984732824427, 0.3641851106639839, 0.3098503740648379, 0.3705386112913692, 0.6262398557258791, 0.4777542372881356, 0.5383670963781461, 0.4950955085183273, 0.40476190476190477, 0.25924075924075923, 0.7658087067047982, 0.5026680896478122, 0.6924386920980926, 0.731462086300796, 0.7467948717948718, 0.6818663838812301, 0.5140845070422535, 0.305744888023369, 0.4764181007010835, 0.4614168247944339, 0.4318518518518519, 0.21048951048951048, 0.6226904376012966, 0.42008066227977076, 0.5179570474643567, 0.6607629427792916, 0.11895910780669144, 0.6896493594066082, 0.19865319865319866, 0.4741726492733322, 0.764037985136251, 0.5792544956532095, 0.4708209693372898, 0.8691492996241886, 0.4578106203815088, 0.9372099372099372, 0.5889253871421868, 0.5537513997760358, 0.5482832618025751, 0.012973533990659055, 0.40542035398230086, 0.46336272423228947, 0.5070445084854307, 0.5869851007887817, 0.4701335207308503, 0.9561975768872321, 1.0, 0.009992862241256246, 0.6859635210150674, 0.5163269778654394, 0.3633666037226868, 0.5140039447731756, 0.4318330071754729, 0.4681870448447681, 0.0, 0.5506361323155217, 0.45352400408580185, 0.6963912133891214, 0.4004587155963303, 0.010676965015901863, 0.5699448231093801, 0.5457276368491322, 0.018212267113250993, 0.459802538787024, 0.6549405069839628, 0.5215590284315571, 0.4848746758859118, 0.2830188679245283, 0.0, 0.8156424581005587, 0.46451284660391756, 0.6962332928311057, 0.6081235697940504, 0.5730710624740268, 0.9418672930123312, 0.42763713080168775, 0.7689984901862104, 0.5684097421203438, 0.2902843601895735, 0.5897435897435898, 0.7626561472715319, 0.27549751243781095, 0.9632224168126094, 0.5773672055427251, 0.5722150259067358, 0.5267597440372309, 0.6704918032786885, 0.2668410725964683, 0.6773872679045093, 0.4245582238332578, 0.5323590814196242, 0.5153093105602999, 0.6826884722776226, 0.5121562375448386, 0.00987713803902674, 0.7053824362606232, 0.6672519754170325, 0.48134018908608395, 0.5455265241488519, 0.9373737373737374, 0.5304246655031996, 0.5440699935191186, 0.27380952380952384, 0.4193202146690519, 0.4393162393162393, 0.03729071537290715, 0.456645056726094, 0.5629863301787592, 0.3611650485436893, 0.282472613458529, 0.7747220596840257, 0.5293098469535085, 0.45551839464882943, 0.5333013128402178, 0.5858164481525626, 0.5526072911859714, 0.7510755992624463, 0.3282329713721619, 0.5593099768806686, 0.5468113975576662, 0.41034271725826194, 0.6544583526502858, 0.5114116652578191, 0.4516000795070563, 0.5640434639446823, 0.4832914121451671, 0.5309815950920246, 0.7248707639287766, 0.3947887879984208] #transforma em np.array as coordenadas dos centroides, permitindo assim usar np.dot self.centros = np.array(self.centros) #lista que salva a comida do meu jogador em cada rodada self.comida_historico = [] def faz_previsao(self, data): """Funcao recebe os dados e, com base no cluster de centroid mais proximo, devolve o cluster a qual aquele dado povavelemte esta e, utilizando dessa informacao, retorna a probabilidade de aquele cluster possuir "c", que depois sera usado como parametro para decisao de jogada.""" distancia = 10000000000000000000000 centro_min = 0 #procura o cluster de centroide a menor distancia for centro in range(len(self.centros)): erro = self.centros[centro] - data if erro.dot(erro) < distancia: distancia = erro.dot(erro) centro_min = centro #retorna a probabilidade estimada de aqueles valores especificos de input retornarem "c" return self.accuracy[centro_min] def escolha_de_cacada(self, rodada, comida_atual, reputacao_atual, m, reputacoes_dos_jogadores): self.comida_historico.append(comida_atual) #prepara os dados de maneira em que foi treinado media_de_reputacao = sum(reputacoes_dos_jogadores)/len(reputacoes_dos_jogadores) max_de_reputacao = max(reputacoes_dos_jogadores) #retorna a melhor jogada possivel #para isso, se calculamos probabilidade menor de 0.5-r de um jogador descansar, descansamos tambem #se a probailidade de descansar e superior a 0.5+r, entao provavelmente esse jogador vai cacar e entao cacamos com probabilidade de prob_2 #se a probabilidade de descansar e em torno de 50%, cacamos com probabilidade de prob_1 #porem, se estamos bem de comida, jogamos mais agressivamente, cacando menos #no comeco, porem, vamos tomar cuidado para a reputacao cair pouco if rodada<200: if reputacao_atual>media_de_reputacao: r = 0.1 prob_1 = 0.5 prob_2 = 0.3 else: r = 0.3 prob_1 = 0.7 prob_2 = 0.5 #se ainda temos mais de 15 jogadores vivos elif len(reputacoes_dos_jogadores)>15: #se minha comida estiver caindo muito, vou cacar mais ultimas_comidas = [1 for rodada in range(-10,-2) if self.comida_historico[rodada]<comida_atual] if len(ultimas_comidas)<5: r = 0.1 prob_1 = 0.5 prob_2 = 0.2 else: r = 0.3 prob_1 = 0.7 prob_2 = 0.5 #porem, com poucos jogadores, podemos cacar pouco else: r = 0.1 prob_1 = 0.3 prob_2 = 0 #escala os dados utilizando as medias e desvios padroes que foram usados para treinar os dados data_da_rodada = [] for jogador in range(len(reputacoes_dos_jogadores)): rodada_escalada =(rodada-self.medias[0])/self.desvio[0] reputacao_escalada = (reputacoes_dos_jogadores[jogador]-self.medias[3])/self.desvio[3] recompensa_escalada = (m-self.medias[2])/self.desvio[2] input_escalada = (reputacao_atual-self.medias[1])/self.desvio[1] media_escalada = (media_de_reputacao-self.medias[4])/self.desvio[4] max_escalada = (max_de_reputacao-self.medias[6])/self.desvio[6] data_da_rodada.append(np.array([rodada_escalada, reputacao_escalada, recompensa_escalada, input_escalada, media_escalada, max_escalada])) #faz a lista de respostas, tendo em ideia as previsoes resposta = [] for jogador in range(len(reputacoes_dos_jogadores)): previsao = self.faz_previsao(data_da_rodada[jogador]) #prediz qual a chance de sair um determinado resultado if previsao< (0.5-r): solucao = "d" elif previsao> (0.5+r): if random.random() < prob_2: solucao = "c" else: solucao = "d" else: if random.random() < prob_1: solucao = "c" else: solucao = "d" resposta.append(solucao) return resposta

24.551039

1,108

0.745566

e3237089ce1b23c5dc87d26edef94adbe690edd7

2,010

py

Python

dl/models/gcn.py

salemilab/DeepDynaTree

a1cd0b8e6cbc415dd91425667e6ef722eb4138a5

[ "MIT" ]

null

dl/models/gcn.py

salemilab/DeepDynaTree

a1cd0b8e6cbc415dd91425667e6ef722eb4138a5

[ "MIT" ]

null

dl/models/gcn.py

salemilab/DeepDynaTree

a1cd0b8e6cbc415dd91425667e6ef722eb4138a5

[ "MIT" ]

null

#!/usr/bin/env # -*- coding: utf-8 -*- """ gcn.py: """ import torch import torch.nn as nn import torch.nn.functional as F from dgl.nn.pytorch.conv import GraphConv from dl import feat_dict class Net(nn.Module): def __init__(self, args): super(Net, self).__init__() in_feats = len(feat_dict[args.node_feat_cols]) num_classes = len(feat_dict[args.node_label_cols.split("_cat")[0]]) h_feat = 64 self.conv1 = GraphConv(in_feats, h_feat) self.conv2 = GraphConv(h_feat, h_feat) self.conv3 = GraphConv(h_feat, h_feat) self.conv4 = GraphConv(h_feat, h_feat) self.conv5 = GraphConv(h_feat, h_feat) self.conv6 = GraphConv(h_feat, h_feat) self.conv7 = GraphConv(h_feat, h_feat) self.conv8 = GraphConv(h_feat, h_feat) self.conv9 = GraphConv(h_feat, h_feat) self.conv10 = GraphConv(h_feat, h_feat) self.conv11 = GraphConv(h_feat, h_feat) self.fc = nn.Linear(h_feat, num_classes) def forward(self, g): info = dict() node_feat = g.ndata["feat"] edge_feat = g.edata["feat"] h = self.conv1(g, node_feat) h = F.relu(h) h = self.conv2(g, h) h = F.relu(h) h = self.conv3(g, h) h = F.relu(h) h = self.conv4(g, h) h = F.relu(h) h = self.conv5(g, h) h = F.relu(h) h = self.conv6(g, h) h = F.relu(h) h = self.conv7(g, h) h = F.relu(h) h = self.conv8(g, h) h = F.relu(h) h = self.conv9(g, h) h = F.relu(h) h = self.conv10(g, h) h = F.relu(h) h = self.conv11(g, h) h = F.relu(h) h = self.fc(h) return h, info def ce_loss(self, y_pred, y_true, weight=None): # print(y_pred.shape, y_true.shape, weight.shape) ce = F.cross_entropy(y_pred, y_true, weight=weight, size_average=None, reduce=None, reduction='mean') return {"loss": ce}

28.309859

109

0.555224

2f4b313f3251ebe4344d789f7f2be826e3c4fea7

5,424

py

Python

docs/conf.py

lesamouraipourpre/Adafruit_CircuitPython_IL0398

b37e709588b999e8d1fe2ba05621fe274668979a

[ "Unlicense", "MIT-0", "MIT" ]

1

2021-07-13T14:58:31.000Z

docs/conf.py

lesamouraipourpre/Adafruit_CircuitPython_IL0398

b37e709588b999e8d1fe2ba05621fe274668979a

[ "Unlicense", "MIT-0", "MIT" ]

2

2019-09-03T23:21:57.000Z

2021-09-27T16:45:13.000Z

docs/conf.py

lesamouraipourpre/Adafruit_CircuitPython_IL0398

b37e709588b999e8d1fe2ba05621fe274668979a

[ "Unlicense", "MIT-0", "MIT" ]

7

2019-08-21T01:40:25.000Z

2022-03-27T14:09:33.000Z

# -*- coding: utf-8 -*- # SPDX-FileCopyrightText: 2021 ladyada for Adafruit Industries # # SPDX-License-Identifier: MIT import os import sys sys.path.insert(0, os.path.abspath("..")) # -- General configuration ------------------------------------------------ # Add any Sphinx extension module names here, as strings. They can be # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom # ones. extensions = [ "sphinx.ext.autodoc", "sphinx.ext.intersphinx", "sphinx.ext.napoleon", "sphinx.ext.todo", ] # TODO: Please Read! # Uncomment the below if you use native CircuitPython modules such as # digitalio, micropython and busio. List the modules you use. Without it, the # autodoc module docs will fail to generate with a warning. autodoc_mock_imports = ["displayio"] intersphinx_mapping = { "python": ("https://docs.python.org/3.4", None), "CircuitPython": ("https://circuitpython.readthedocs.io/en/latest/", None), } # Add any paths that contain templates here, relative to this directory. templates_path = ["_templates"] source_suffix = ".rst" # The master toctree document. master_doc = "index" # General information about the project. project = "Adafruit IL0398 Library" copyright = "2019 Scott Shawcroft" author = "Scott Shawcroft" # The version info for the project you're documenting, acts as replacement for # |version| and |release|, also used in various other places throughout the # built documents. # # The short X.Y version. version = "1.0" # The full version, including alpha/beta/rc tags. release = "1.0" # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. # # This is also used if you do content translation via gettext catalogs. # Usually you set "language" from the command line for these cases. language = None # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. # This patterns also effect to html_static_path and html_extra_path exclude_patterns = ["_build", "Thumbs.db", ".DS_Store", ".env", "CODE_OF_CONDUCT.md"] # The reST default role (used for this markup: `text`) to use for all # documents. # default_role = "any" # If true, '()' will be appended to :func: etc. cross-reference text. # add_function_parentheses = True # The name of the Pygments (syntax highlighting) style to use. pygments_style = "sphinx" # If true, `todo` and `todoList` produce output, else they produce nothing. todo_include_todos = False # If this is True, todo emits a warning for each TODO entries. The default is False. todo_emit_warnings = True napoleon_numpy_docstring = False # -- Options for HTML output ---------------------------------------------- # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. # on_rtd = os.environ.get("READTHEDOCS", None) == "True" if not on_rtd: # only import and set the theme if we're building docs locally try: import sphinx_rtd_theme html_theme = "sphinx_rtd_theme" html_theme_path = [sphinx_rtd_theme.get_html_theme_path(), "."] except: html_theme = "default" html_theme_path = ["."] else: html_theme_path = ["."] # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". html_static_path = ["_static"] # The name of an image file (relative to this directory) to use as a favicon of # the docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32 # pixels large. # html_favicon = "_static/favicon.ico" # Output file base name for HTML help builder. htmlhelp_basename = "AdafruitIl0398Librarydoc" # -- Options for LaTeX output --------------------------------------------- latex_elements = { # The paper size ('letterpaper' or 'a4paper'). # # 'papersize': 'letterpaper', # The font size ('10pt', '11pt' or '12pt'). # # 'pointsize': '10pt', # Additional stuff for the LaTeX preamble. # # 'preamble': '', # Latex figure (float) alignment # # 'figure_align': 'htbp', } # Grouping the document tree into LaTeX files. List of tuples # (source start file, target name, title, # author, documentclass [howto, manual, or own class]). latex_documents = [ ( master_doc, "AdafruitIL0398Library.tex", "AdafruitIL0398 Library Documentation", author, "manual", ), ] # -- Options for manual page output --------------------------------------- # One entry per manual page. List of tuples # (source start file, name, description, authors, manual section). man_pages = [ ( master_doc, "AdafruitIL0398library", "Adafruit IL0398 Library Documentation", [author], 1, ) ] # -- Options for Texinfo output ------------------------------------------- # Grouping the document tree into Texinfo files. List of tuples # (source start file, target name, title, author, # dir menu entry, description, category) texinfo_documents = [ ( master_doc, "AdafruitIL0398Library", "Adafruit IL0398 Library Documentation", author, "AdafruitIL0398Library", "One line description of project.", "Miscellaneous", ), ]

29.639344

85

0.669432

c94621cc4618b48094f33d32a4ba2fcd11e19b83

205

py

Python

drf_haystack/__init__.py

advantch/drf-haystack

e625264508b505074349c374cd7f0ab0e63f89b6

[ "MIT" ]

201

2015-02-14T08:17:35.000Z

2019-07-10T04:19:04.000Z

drf_haystack/__init__.py

advantch/drf-haystack

e625264508b505074349c374cd7f0ab0e63f89b6

[ "MIT" ]

138

2015-02-17T09:28:33.000Z

2019-07-30T10:29:52.000Z

drf_haystack/__init__.py

advantch/drf-haystack

e625264508b505074349c374cd7f0ab0e63f89b6

[ "MIT" ]

60

2015-04-01T14:51:18.000Z

2019-05-12T15:31:52.000Z

# -*- coding: utf-8 -*- from __future__ import unicode_literals __title__ = "drf-haystack" __version__ = "1.8.11" __author__ = "Rolf Haavard Blindheim" __license__ = "MIT License" VERSION = __version__

18.636364

39

0.731707

51b4192e5b540a160f4c815cd7e08861f4ef00f9

3,142

py

Python

pythonforandroid/recipes/android/__init__.py

wo01/python-for-android

df0866d95c9c508299a6f948302454beb971e3ac

[ "MIT" ]

1

2018-12-21T03:40:18.000Z

pythonforandroid/recipes/android/__init__.py

wo01/python-for-android

df0866d95c9c508299a6f948302454beb971e3ac

[ "MIT" ]

null

pythonforandroid/recipes/android/__init__.py

wo01/python-for-android

df0866d95c9c508299a6f948302454beb971e3ac

[ "MIT" ]

null

from __future__ import unicode_literals from pythonforandroid.recipe import CythonRecipe, IncludedFilesBehaviour from pythonforandroid.util import current_directory from pythonforandroid.patching import will_build from pythonforandroid import logger from os.path import join class AndroidRecipe(IncludedFilesBehaviour, CythonRecipe): # name = 'android' version = None url = None src_filename = 'src' depends = [('pygame', 'sdl2', 'genericndkbuild'), ('python2', 'python3crystax', 'python3')] config_env = {} def get_recipe_env(self, arch): env = super(AndroidRecipe, self).get_recipe_env(arch) env.update(self.config_env) return env def prebuild_arch(self, arch): super(AndroidRecipe, self).prebuild_arch(arch) ctx_bootstrap = self.ctx.bootstrap.name # define macros for Cython, C, Python tpxi = 'DEF {} = {}\n' th = '#define {} {}\n' tpy = '{} = {}\n' # make sure bootstrap name is in unicode if isinstance(ctx_bootstrap, bytes): ctx_bootstrap = ctx_bootstrap.decode('utf-8') bootstrap = bootstrap_name = ctx_bootstrap is_sdl2 = bootstrap_name in ('sdl2', 'sdl2python3', 'sdl2_gradle') is_pygame = bootstrap_name in ('pygame',) is_webview = bootstrap_name in ('webview',) if is_sdl2 or is_webview: if is_sdl2: bootstrap = 'sdl2' java_ns = 'org.kivy.android' jni_ns = 'org/kivy/android' elif is_pygame: java_ns = b'org.renpy.android' jni_ns = b'org/renpy/android' else: logger.error(( 'unsupported bootstrap for android recipe: {}' ''.format(bootstrap_name) )) exit(1) config = { 'BOOTSTRAP': bootstrap, 'IS_SDL2': int(is_sdl2), 'IS_PYGAME': int(is_pygame), 'PY2': int(will_build('python2')(self)), 'JAVA_NAMESPACE': java_ns, 'JNI_NAMESPACE': jni_ns, } # create config files for Cython, C and Python with ( current_directory(self.get_build_dir(arch.arch))), ( open(join('android', 'config.pxi'), 'w')) as fpxi, ( open(join('android', 'config.h'), 'w')) as fh, ( open(join('android', 'config.py'), 'w')) as fpy: for key, value in config.items(): fpxi.write(tpxi.format(key, repr(value))) fpy.write(tpy.format(key, repr(value))) fh.write(th.format( key, value if isinstance(value, int) else '"{}"'.format(value) )) self.config_env[key] = str(value) if is_sdl2: fh.write('JNIEnv *SDL_AndroidGetJNIEnv(void);\n') fh.write( '#define SDL_ANDROID_GetJNIEnv SDL_AndroidGetJNIEnv\n' ) elif is_pygame: fh.write('JNIEnv *SDL_ANDROID_GetJNIEnv(void);\n') recipe = AndroidRecipe()

32.729167

77

0.559835

40c00e8eb4445f185ade400935bee7e2ac1f7559

1,475

py

Python

build/lib/adapter/estimator/xgboost_classification.py

mozjay0619/scikit-optimize-adapter

6550b6cba1667a0f0bf2c082a6ab64981cedb3e0

[ "BSD-3-Clause" ]

null

build/lib/adapter/estimator/xgboost_classification.py

mozjay0619/scikit-optimize-adapter

6550b6cba1667a0f0bf2c082a6ab64981cedb3e0

[ "BSD-3-Clause" ]

null

build/lib/adapter/estimator/xgboost_classification.py

mozjay0619/scikit-optimize-adapter

6550b6cba1667a0f0bf2c082a6ab64981cedb3e0

[ "BSD-3-Clause" ]

null

from .base_estimator import BaseEstimator import xgboost as xgb from sklearn.metrics import mean_absolute_error as mae # add more metrics class XgboostClassification(BaseEstimator): def __init__(self, **kwargs): self.n_jobs = kwargs["n_jobs"] def fit(self, X, y, params): learning_rate = params[0] gamma = params[1] max_depth = int(params[2]) n_estimators = int(params[3]) learning_rate = learning_rate / float(n_estimators) min_child_weight = int(params[4]) colsample_bytree = params[5] subsample = params[6] algo = xgb.XGBRegressor(objective ="binary:logistic", learning_rate=learning_rate, gamma=gamma, max_depth=max_depth, n_estimators=n_estimators, min_child_weight=min_child_weight, colsample_bytree=colsample_bytree, subsample=subsample, n_jobs=self.n_jobs, tree_method='hist') # for fast hyperparameter tuning) self.model = algo.fit(X, y) def predict(self, X): return self.model.predict(X) def score(self, X, y, score_metric="mae"): pred = self.model.predict(X) return mae(pred, y)

33.522727

86

0.527458

2acaeb0de8b6ff094473ca6554657ef7bc07a0ea

267

py

Python

precompute_syllables.py

superMDguy/nanogenmo-2018

26f72dc922948a0f2bc464b33b70a3a37f376b61

[ "MIT" ]

1

2021-05-04T14:11:11.000Z

precompute_syllables.py

superMDguy/nanogenmo-2018

26f72dc922948a0f2bc464b33b70a3a37f376b61

[ "MIT" ]

1

2018-12-20T20:59:48.000Z

2019-01-19T17:11:29.000Z

precompute_syllables.py

superMDguy/nanogenmo-2018

26f72dc922948a0f2bc464b33b70a3a37f376b61

[ "MIT" ]

null

import cmudict from tqdm import tqdm import re with open('cmu_pronouncing.txt', 'w') as f: only_stress = re.compile(r'[^012]') for word, phonemes in tqdm(cmudict.dict().items()): f.write(f"{word} {re.sub(only_stress, '', ''.join(phonemes[0]))}\n")

26.7

76

0.640449

64cac1588112331a9f5b080991b8bc61eee56aec

2,423

py

Python

astrodenoisepygui-package-dist.py

kalgecin/astro-csbdeep

a216642750e0357b9b59002eaff1d69c54eb2316

[ "BSD-3-Clause" ]

1

2022-01-24T13:51:12.000Z

astrodenoisepygui-package-dist.py

kalgecin/astro-csbdeep

a216642750e0357b9b59002eaff1d69c54eb2316

[ "BSD-3-Clause" ]

null

astrodenoisepygui-package-dist.py

kalgecin/astro-csbdeep

a216642750e0357b9b59002eaff1d69c54eb2316

[ "BSD-3-Clause" ]

null

import sys from cx_Freeze import setup, Executable build_exe_options = { "build_exe": "astrodenoisepy\\dist-gui", "packages": ["kivy"], "include_files": [ "LICENSE.txt", ["astrodenoisepy\\dist-models\\main", "models\\default"], ["astrodenoisepy\\data", "astrodenoisepy\\data"], "astrodenoisepygui.kv", #angle ".venv\\share\\angle\\bin\\d3dcompiler_47.dll", ".venv\\share\\angle\\bin\\libEGL.dll", ".venv\\share\\angle\\bin\\libGLESv2.dll", #glew ".venv\\share\\glew\\bin\\glew32.dll", #sdl2 ".venv\\share\\sdl2\\bin\\libFLAC-8.dll", ".venv\\share\\sdl2\\bin\\libfreetype-6.dll", ".venv\\share\\sdl2\\bin\\libjpeg-9.dll", ".venv\\share\\sdl2\\bin\\libmodplug-1.dll", ".venv\\share\\sdl2\\bin\\libmpg123-0.dll", ".venv\\share\\sdl2\\bin\\libogg-0.dll", ".venv\\share\\sdl2\\bin\\libopus-0.dll", ".venv\\share\\sdl2\\bin\\libopusfile-0.dll", ".venv\\share\\sdl2\\bin\\libpng16-16.dll", ".venv\\share\\sdl2\\bin\\libtiff-5.dll", ".venv\\share\\sdl2\\bin\\libvorbis-0.dll", ".venv\\share\\sdl2\\bin\\libvorbisfile-3.dll", ".venv\\share\\sdl2\\bin\\libwebp-7.dll", ".venv\\share\\sdl2\\bin\\LICENSE.FLAC.txt", ".venv\\share\\sdl2\\bin\\LICENSE.freetype.txt", ".venv\\share\\sdl2\\bin\\LICENSE.jpeg.txt", ".venv\\share\\sdl2\\bin\\LICENSE.modplug.txt", ".venv\\share\\sdl2\\bin\\LICENSE.mpg123.txt", ".venv\\share\\sdl2\\bin\\LICENSE.ogg-vorbis.txt", ".venv\\share\\sdl2\\bin\\LICENSE.opus.txt", ".venv\\share\\sdl2\\bin\\LICENSE.opusfile.txt", ".venv\\share\\sdl2\\bin\\LICENSE.png.txt", ".venv\\share\\sdl2\\bin\\LICENSE.tiff.txt", ".venv\\share\\sdl2\\bin\\LICENSE.webp.txt", ".venv\\share\\sdl2\\bin\\LICENSE.zlib.txt", ".venv\\share\\sdl2\\bin\\SDL2.dll", ".venv\\share\\sdl2\\bin\\SDL2_image.dll", ".venv\\share\\sdl2\\bin\\SDL2_mixer.dll", ".venv\\share\\sdl2\\bin\\SDL2_ttf.dll", ".venv\\share\\sdl2\\bin\\zlib1.dll", ] } import astrodenoisepyguiversion setup( name="astrodenoisepygui", version=astrodenoisepyguiversion.version, options={"build_exe": build_exe_options}, description="astrodenoisepygui", executables=[Executable("astrodenoisepygui.py")] )

39.721311

65

0.588114

0859380d064e5fd3ce40f77b016f9678d66b0d7a

9,513

py

Python

test/functional/test_framework/blocktools.py

rednaxus/bitcoin

a47ac9e98bb3c1ead00843cf8068ac298543d536

[ "MIT" ]

1

2020-10-27T09:27:31.000Z

test/functional/test_framework/blocktools.py

blackzilla2126/bitcoin

88271184e82222f556d67511cc64230b0532f40d

[ "MIT" ]

18

2020-10-31T01:04:18.000Z

2020-11-03T19:25:27.000Z

test/functional/test_framework/blocktools.py

blackzilla2126/bitcoin

88271184e82222f556d67511cc64230b0532f40d

[ "MIT" ]

1

2021-09-18T04:39:58.000Z

#!/usr/bin/env python3 # Copyright (c) 2015-2019 The Bitcoin Core developers # Distributed under the MIT software license, see the accompanying # file COPYING or http://www.opensource.org/licenses/mit-license.php. """Utilities for manipulating blocks and transactions.""" from binascii import a2b_hex import io import struct import time import unittest from .address import ( key_to_p2sh_p2wpkh, key_to_p2wpkh, script_to_p2sh_p2wsh, script_to_p2wsh, ) from .messages import ( CBlock, COIN, COutPoint, CTransaction, CTxIn, CTxInWitness, CTxOut, FromHex, ToHex, hash256, hex_str_to_bytes, ser_uint256, sha256, uint256_from_str, ) from .script import ( CScript, CScriptNum, CScriptOp, OP_0, OP_1, OP_CHECKMULTISIG, OP_CHECKSIG, OP_RETURN, OP_TRUE, hash160, ) from .util import assert_equal from io import BytesIO WITNESS_SCALE_FACTOR = 4 MAX_BLOCK_SIGOPS = 20000 MAX_BLOCK_SIGOPS_WEIGHT = MAX_BLOCK_SIGOPS * WITNESS_SCALE_FACTOR # Genesis block time (regtest) TIME_GENESIS_BLOCK = 1296688602 # From BIP141 WITNESS_COMMITMENT_HEADER = b"\xaa\x21\xa9\xed" NORMAL_GBT_REQUEST_PARAMS = {"rules": ["segwit"]} def create_block(hashprev=None, coinbase=None, ntime=None, *, version=None, tmpl=None, txlist=None): """Create a block (with regtest difficulty).""" block = CBlock() if tmpl is None: tmpl = {} block.nVersion = version or tmpl.get('version') or 1 block.nTime = ntime or tmpl.get('curtime') or int(time.time() + 600) block.hashPrevBlock = hashprev or int(tmpl['previousblockhash'], 0x10) if tmpl and not tmpl.get('bits') is None: block.nBits = struct.unpack('>I', a2b_hex(tmpl['bits']))[0] else: block.nBits = 0x207fffff # difficulty retargeting is disabled in REGTEST chainparams if coinbase is None: coinbase = create_coinbase(height=tmpl['height']) block.vtx.append(coinbase) if txlist: for tx in txlist: if not hasattr(tx, 'calc_sha256'): txo = CTransaction() txo.deserialize(io.BytesIO(tx)) tx = txo block.vtx.append(tx) block.hashMerkleRoot = block.calc_merkle_root() block.calc_sha256() return block def get_witness_script(witness_root, witness_nonce): witness_commitment = uint256_from_str(hash256(ser_uint256(witness_root) + ser_uint256(witness_nonce))) output_data = WITNESS_COMMITMENT_HEADER + ser_uint256(witness_commitment) return CScript([OP_RETURN, output_data]) def add_witness_commitment(block, nonce=0): """Add a witness commitment to the block's coinbase transaction. According to BIP141, blocks with witness rules active must commit to the hash of all in-block transactions including witness.""" # First calculate the merkle root of the block's # transactions, with witnesses. witness_nonce = nonce witness_root = block.calc_witness_merkle_root() # witness_nonce should go to coinbase witness. block.vtx[0].wit.vtxinwit = [CTxInWitness()] block.vtx[0].wit.vtxinwit[0].scriptWitness.stack = [ser_uint256(witness_nonce)] # witness commitment is the last OP_RETURN output in coinbase block.vtx[0].vout.append(CTxOut(0, get_witness_script(witness_root, witness_nonce))) block.vtx[0].rehash() block.hashMerkleRoot = block.calc_merkle_root() block.rehash() def script_BIP34_coinbase_height(height): if height <= 16: res = CScriptOp.encode_op_n(height) # Append dummy to increase scriptSig size above 2 (see bad-cb-length consensus rule) return CScript([res, OP_1]) return CScript([CScriptNum(height)]) def create_coinbase(height, pubkey=None, extra_output_script=None, fees=0): """Create a coinbase transaction. If pubkey is passed in, the coinbase output will be a P2PK output; otherwise an anyone-can-spend output. If extra_output_script is given, make a 0-value output to that script. This is useful to pad block weight/sigops as needed. """ coinbase = CTransaction() coinbase.vin.append(CTxIn(COutPoint(0, 0xffffffff), script_BIP34_coinbase_height(height), 0xffffffff)) coinbaseoutput = CTxOut() coinbaseoutput.nValue = 50 * COIN halvings = int(height / 150) # regtest coinbaseoutput.nValue >>= halvings coinbaseoutput.nValue += fees if pubkey is not None: coinbaseoutput.scriptPubKey = CScript([pubkey, OP_CHECKSIG]) else: coinbaseoutput.scriptPubKey = CScript([OP_TRUE]) coinbase.vout = [coinbaseoutput] if extra_output_script is not None: coinbaseoutput2 = CTxOut() coinbaseoutput2.nValue = 0 coinbaseoutput2.scriptPubKey = extra_output_script coinbase.vout.append(coinbaseoutput2) coinbase.calc_sha256() return coinbase def create_tx_with_script(prevtx, n, script_sig=b"", *, amount, script_pub_key=CScript()): """Return one-input, one-output transaction object spending the prevtx's n-th output with the given amount. Can optionally pass scriptPubKey and scriptSig, default is anyone-can-spend output. """ tx = CTransaction() assert n < len(prevtx.vout) tx.vin.append(CTxIn(COutPoint(prevtx.sha256, n), script_sig, 0xffffffff)) tx.vout.append(CTxOut(amount, script_pub_key)) tx.calc_sha256() return tx def create_transaction(node, txid, to_address, *, amount): """ Return signed transaction spending the first output of the input txid. Note that the node must be able to sign for the output that is being spent, and the node must not be running multiple wallets. """ raw_tx = create_raw_transaction(node, txid, to_address, amount=amount) tx = CTransaction() tx.deserialize(BytesIO(hex_str_to_bytes(raw_tx))) return tx def create_raw_transaction(node, txid, to_address, *, amount): """ Return raw signed transaction spending the first output of the input txid. Note that the node must be able to sign for the output that is being spent, and the node must not be running multiple wallets. """ rawtx = node.createrawtransaction(inputs=[{"txid": txid, "vout": 0}], outputs={to_address: amount}) signresult = node.signrawtransactionwithwallet(rawtx) assert_equal(signresult["complete"], True) return signresult['hex'] def get_legacy_sigopcount_block(block, accurate=True): count = 0 for tx in block.vtx: count += get_legacy_sigopcount_tx(tx, accurate) return count def get_legacy_sigopcount_tx(tx, accurate=True): count = 0 for i in tx.vout: count += i.scriptPubKey.GetSigOpCount(accurate) for j in tx.vin: # scriptSig might be of type bytes, so convert to CScript for the moment count += CScript(j.scriptSig).GetSigOpCount(accurate) return count def witness_script(use_p2wsh, pubkey): """Create a scriptPubKey for a pay-to-witness TxOut. This is either a P2WPKH output for the given pubkey, or a P2WSH output of a 1-of-1 multisig for the given pubkey. Returns the hex encoding of the scriptPubKey.""" if not use_p2wsh: # P2WPKH instead pubkeyhash = hash160(hex_str_to_bytes(pubkey)) pkscript = CScript([OP_0, pubkeyhash]) else: # 1-of-1 multisig witness_program = CScript([OP_1, hex_str_to_bytes(pubkey), OP_1, OP_CHECKMULTISIG]) scripthash = sha256(witness_program) pkscript = CScript([OP_0, scripthash]) return pkscript.hex() def create_witness_tx(node, use_p2wsh, utxo, pubkey, encode_p2sh, amount): """Return a transaction (in hex) that spends the given utxo to a segwit output. Optionally wrap the segwit output using P2SH.""" if use_p2wsh: program = CScript([OP_1, hex_str_to_bytes(pubkey), OP_1, OP_CHECKMULTISIG]) addr = script_to_p2sh_p2wsh(program) if encode_p2sh else script_to_p2wsh(program) else: addr = key_to_p2sh_p2wpkh(pubkey) if encode_p2sh else key_to_p2wpkh(pubkey) if not encode_p2sh: assert_equal(node.getaddressinfo(addr)['scriptPubKey'], witness_script(use_p2wsh, pubkey)) return node.createrawtransaction([utxo], {addr: amount}) def send_to_witness(use_p2wsh, node, utxo, pubkey, encode_p2sh, amount, sign=True, insert_redeem_script=""): """Create a transaction spending a given utxo to a segwit output. The output corresponds to the given pubkey: use_p2wsh determines whether to use P2WPKH or P2WSH; encode_p2sh determines whether to wrap in P2SH. sign=True will have the given node sign the transaction. insert_redeem_script will be added to the scriptSig, if given.""" tx_to_witness = create_witness_tx(node, use_p2wsh, utxo, pubkey, encode_p2sh, amount) if (sign): signed = node.signrawtransactionwithwallet(tx_to_witness) assert "errors" not in signed or len(["errors"]) == 0 return node.sendrawtransaction(signed["hex"]) else: if (insert_redeem_script): tx = FromHex(CTransaction(), tx_to_witness) tx.vin[0].scriptSig += CScript([hex_str_to_bytes(insert_redeem_script)]) tx_to_witness = ToHex(tx) return node.sendrawtransaction(tx_to_witness) class TestFrameworkBlockTools(unittest.TestCase): def test_create_coinbase(self): height = 20 coinbase_tx = create_coinbase(height=height) assert_equal(CScriptNum.decode(coinbase_tx.vin[0].scriptSig), height)

37.305882

108

0.706612

de530323577d058947b6914880479ebcbffb6ac7

10,902

py

Python

contextPred/chem/finetune.py

thomasly/slgnn

caa1e7814498da41ad025b4e62c569fe511848ff

[ "MIT" ]

2

2020-08-31T00:55:31.000Z

2020-09-01T19:59:30.000Z

contextPred/chem/finetune.py

thomasly/slgnn

caa1e7814498da41ad025b4e62c569fe511848ff

[ "MIT" ]

null

contextPred/chem/finetune.py

thomasly/slgnn

caa1e7814498da41ad025b4e62c569fe511848ff

[ "MIT" ]

null

import argparse from .loader import MoleculeDataset from torch_geometric.data import DataLoader import torch import torch.nn as nn import torch.nn.functional as F import torch.optim as optim from tqdm import tqdm import numpy as np from .model import GNN, GNN_graphpred from sklearn.metrics import roc_auc_score from .splitters import scaffold_split, random_split, random_scaffold_split import pandas as pd import os import shutil from tensorboardX import SummaryWriter criterion = nn.BCEWithLogitsLoss(reduction="none") def train(args, model, device, loader, optimizer): model.train() for step, batch in enumerate(tqdm(loader, desc="Iteration")): batch = batch.to(device) pred = model(batch.x, batch.edge_index, batch.edge_attr, batch.batch) y = batch.y.view(pred.shape).to(torch.float64) # Whether y is non-null or not. is_valid = y ** 2 > 0 # Loss matrix loss_mat = criterion(pred.double(), (y + 1) / 2) # loss matrix after removing null target loss_mat = torch.where( is_valid, loss_mat, torch.zeros(loss_mat.shape).to(loss_mat.device).to(loss_mat.dtype), ) optimizer.zero_grad() loss = torch.sum(loss_mat) / torch.sum(is_valid) loss.backward() optimizer.step() def eval(args, model, device, loader): model.eval() y_true = [] y_scores = [] for step, batch in enumerate(tqdm(loader, desc="Iteration")): batch = batch.to(device) with torch.no_grad(): pred = model(batch.x, batch.edge_index, batch.edge_attr, batch.batch) y_true.append(batch.y.view(pred.shape)) y_scores.append(pred) y_true = torch.cat(y_true, dim=0).cpu().numpy() y_scores = torch.cat(y_scores, dim=0).cpu().numpy() roc_list = [] for i in range(y_true.shape[1]): # AUC is only defined when there is at least one positive data. if np.sum(y_true[:, i] == 1) > 0 and np.sum(y_true[:, i] == -1) > 0: is_valid = y_true[:, i] ** 2 > 0 roc_list.append( roc_auc_score((y_true[is_valid, i] + 1) / 2, y_scores[is_valid, i]) ) if len(roc_list) < y_true.shape[1]: print("Some target is missing!") print("Missing ratio: %f" % (1 - float(len(roc_list)) / y_true.shape[1])) return sum(roc_list) / len(roc_list) # y_true.shape[1] def main(): # Training settings parser = argparse.ArgumentParser( description="PyTorch implementation of pre-training of graph neural networks" ) parser.add_argument( "--device", type=int, default=0, help="which gpu to use if any (default: 0)" ) parser.add_argument( "--batch_size", type=int, default=32, help="input batch size for training (default: 32)", ) parser.add_argument( "--epochs", type=int, default=100, help="number of epochs to train (default: 100)", ) parser.add_argument( "--lr", type=float, default=0.001, help="learning rate (default: 0.001)" ) parser.add_argument( "--lr_scale", type=float, default=1, help="relative learning rate for the feature extraction layer (default: 1)", ) parser.add_argument( "--decay", type=float, default=0, help="weight decay (default: 0)" ) parser.add_argument( "--num_layer", type=int, default=5, help="number of GNN message passing layers (default: 5).", ) parser.add_argument( "--emb_dim", type=int, default=300, help="embedding dimensions (default: 300)" ) parser.add_argument( "--dropout_ratio", type=float, default=0.5, help="dropout ratio (default: 0.5)" ) parser.add_argument( "--graph_pooling", type=str, default="mean", help="graph level pooling (sum, mean, max, set2set, attention)", ) parser.add_argument( "--JK", type=str, default="last", help="how the node features across layers are combined. last, sum, max or concat", ) parser.add_argument("--gnn_type", type=str, default="gin") parser.add_argument( "--dataset", type=str, default="tox21", help="root directory of dataset. For now, only classification.", ) parser.add_argument( "--input_model_file", type=str, default="", help="filename to read the model (if there is any)", ) parser.add_argument( "--save_model_to", type=str, default="", help="path to save the finetuned model" ) parser.add_argument("--filename", type=str, default="", help="output filename") parser.add_argument( "--seed", type=int, default=42, help="Seed for splitting the dataset." ) parser.add_argument( "--runseed", type=int, default=0, help="Seed for minibatch selection, random initialization.", ) parser.add_argument( "--split", type=str, default="scaffold", help="random or scaffold or random_scaffold", ) parser.add_argument( "--eval_train", type=int, default=0, help="evaluating training or not" ) parser.add_argument( "--num_workers", type=int, default=4, help="number of workers for dataset loading", ) args = parser.parse_args() torch.manual_seed(args.runseed) np.random.seed(args.runseed) device = ( torch.device("cuda:" + str(args.device)) if torch.cuda.is_available() else torch.device("cpu") ) if torch.cuda.is_available(): torch.cuda.manual_seed_all(args.runseed) # Bunch of classification tasks if args.dataset == "tox21": num_tasks = 12 elif args.dataset == "hiv": num_tasks = 1 elif args.dataset == "pcba": num_tasks = 128 elif args.dataset == "muv": num_tasks = 17 elif args.dataset == "bace": num_tasks = 1 elif args.dataset == "bbbp": num_tasks = 1 elif args.dataset == "toxcast": num_tasks = 617 elif args.dataset == "sider": num_tasks = 27 elif args.dataset == "clintox": num_tasks = 2 elif args.dataset in ["jak1", "jak2", "jak3"]: num_tasks = 1 else: raise ValueError("Invalid dataset name.") # set up dataset dataset = MoleculeDataset( "contextPred/chem/dataset/" + args.dataset, dataset=args.dataset ) print(dataset) if args.split == "scaffold": smiles_list = pd.read_csv( "contextPred/chem/dataset/" + args.dataset + "/processed/smiles.csv", header=None, )[0].tolist() train_dataset, valid_dataset, test_dataset = scaffold_split( dataset, smiles_list, null_value=0, frac_train=0.8, frac_valid=0.1, frac_test=0.1, ) print("scaffold") elif args.split == "random": train_dataset, valid_dataset, test_dataset = random_split( dataset, null_value=0, frac_train=0.8, frac_valid=0.1, frac_test=0.1, seed=args.seed, ) print("random") elif args.split == "random_scaffold": smiles_list = pd.read_csv( "contextPred/chem/dataset/" + args.dataset + "/processed/smiles.csv", header=None, )[0].tolist() train_dataset, valid_dataset, test_dataset = random_scaffold_split( dataset, smiles_list, null_value=0, frac_train=0.8, frac_valid=0.1, frac_test=0.1, seed=args.seed, ) print("random scaffold") else: raise ValueError("Invalid split option.") print(train_dataset[0]) train_loader = DataLoader( train_dataset, batch_size=args.batch_size, shuffle=True, num_workers=args.num_workers, ) val_loader = DataLoader( valid_dataset, batch_size=args.batch_size, shuffle=False, num_workers=args.num_workers, ) test_loader = DataLoader( test_dataset, batch_size=args.batch_size, shuffle=False, num_workers=args.num_workers, ) # set up model model = GNN_graphpred( args.num_layer, args.emb_dim, num_tasks, JK=args.JK, drop_ratio=args.dropout_ratio, graph_pooling=args.graph_pooling, gnn_type=args.gnn_type, ) if not args.input_model_file == "": model.from_pretrained(args.input_model_file) model.to(device) # set up optimizer # different learning rate for different part of GNN model_param_group = [] model_param_group.append({"params": model.gnn.parameters()}) if args.graph_pooling == "attention": model_param_group.append( {"params": model.pool.parameters(), "lr": args.lr * args.lr_scale} ) model_param_group.append( {"params": model.graph_pred_linear.parameters(), "lr": args.lr * args.lr_scale} ) optimizer = optim.Adam(model_param_group, lr=args.lr, weight_decay=args.decay) print(optimizer) train_acc_list = [] val_acc_list = [] test_acc_list = [] if not args.filename == "": # fname = "runs/finetune_cls_runseed" + str(args.runseed) + "/" + args.filename # # delete the directory if there exists one # if os.path.exists(fname): # shutil.rmtree(fname) # print("removed the existing file.") writer = SummaryWriter(args.filename) for epoch in range(1, args.epochs + 1): print("====epoch " + str(epoch)) train(args, model, device, train_loader, optimizer) print("====Evaluation") if args.eval_train: train_acc = eval(args, model, device, train_loader) else: print("omit the training accuracy computation") train_acc = 0 val_acc = eval(args, model, device, val_loader) test_acc = eval(args, model, device, test_loader) print("train: %f val: %f test: %f" % (train_acc, val_acc, test_acc)) val_acc_list.append(val_acc) test_acc_list.append(test_acc) train_acc_list.append(train_acc) if not args.filename == "": writer.add_scalar("data/train auc", train_acc, epoch) writer.add_scalar("data/val auc", val_acc, epoch) writer.add_scalar("data/test auc", test_acc, epoch) print("") if not args.filename == "": writer.close() if not args.save_model_to == "": torch.save(model.gnn.state_dict(), args.save_model_to) if __name__ == "__main__": main()

29.786885

95

0.593469

bb3abd3741997f065425533cb5360b15fac3df61

2,874

py

Python

CAIL2020/sfksz/tools/eval_tool.py

ShenDezhou/CAIL

c4cfa98ab4ecedbce34a7a5a186830486047540c

[ "Apache-2.0" ]

71

2020-07-16T01:49:27.000Z

2022-03-27T16:55:00.000Z

CAIL2020/sfksz/tools/eval_tool.py

ShenDezhou/CAIL

c4cfa98ab4ecedbce34a7a5a186830486047540c

[ "Apache-2.0" ]

11

2020-09-18T14:26:25.000Z

2022-02-09T23:49:33.000Z

CAIL2020/sfksz/tools/eval_tool.py

ShenDezhou/CAIL

c4cfa98ab4ecedbce34a7a5a186830486047540c

[ "Apache-2.0" ]

16

2020-07-15T07:24:30.000Z

2022-03-19T05:41:11.000Z

import logging import os import torch from torch.autograd import Variable from torch.optim import lr_scheduler from tensorboardX import SummaryWriter from timeit import default_timer as timer logger = logging.getLogger(__name__) def gen_time_str(t): t = int(t) minute = t // 60 second = t % 60 return '%2d:%02d' % (minute, second) def output_value(epoch, mode, step, time, loss, info, end, config): try: delimiter = config.get("output", "delimiter") except Exception as e: delimiter = " " s = "" s = s + str(epoch) + " " while len(s) < 7: s += " " s = s + str(mode) + " " while len(s) < 14: s += " " s = s + str(step) + " " while len(s) < 25: s += " " s += str(time) while len(s) < 40: s += " " s += str(loss) while len(s) < 48: s += " " s += str(info) s = s.replace(" ", delimiter) if not (end is None): print(s, end=end, flush=True) else: print(s, flush=True) def valid(model, dataset, epoch, writer, config, gpu_list, output_function, mode="valid"): model.eval() acc_result = None total_loss = 0 cnt = 0 total_len = len(dataset) start_time = timer() output_info = "" output_time = config.getint("output", "output_time") step = -1 more = "" if total_len < 10000: more = "\t" for step, data in enumerate(dataset): for key in data.keys(): if isinstance(data[key], torch.Tensor): if len(gpu_list) > 0: data[key] = Variable(data[key].cuda()) else: data[key] = Variable(data[key]) results = model(data, config, gpu_list, acc_result, "valid") loss, acc_result = results["loss"], results["acc_result"] total_loss += float(loss) cnt += 1 if step % output_time == 0: delta_t = timer() - start_time output_value(epoch, mode, "%d/%d" % (step + 1, total_len), "%s/%s" % ( gen_time_str(delta_t), gen_time_str(delta_t * (total_len - step - 1) / (step + 1))), "%.3lf" % (total_loss / (step + 1)), output_info, '\r', config) if step == -1: logger.error("There is no data given to the model in this epoch, check your data.") raise NotImplementedError delta_t = timer() - start_time output_info = output_function(acc_result, config) output_value(epoch, mode, "%d/%d" % (step + 1, total_len), "%s/%s" % ( gen_time_str(delta_t), gen_time_str(delta_t * (total_len - step - 1) / (step + 1))), "%.3lf" % (total_loss / (step + 1)), output_info, None, config) writer.add_scalar(config.get("output", "model_name") + "_eval_epoch", float(total_loss) / (step + 1), epoch) model.train()

29.030303

105

0.549408

e11d1a30dc9a72975b031e3eae91c45ca3bcf30d

687

py

Python

files/question histoire pygame/thorpy_reaction_(show_place_mouse_onclick).py

HenraL/NSI_1ereG6_Programme_Python

9f46b848fa2331daca57e5e2e11cba41da45a67f

[ "Unlicense" ]

1

2021-06-15T13:44:47.000Z

files/question histoire pygame/thorpy_reaction_(show_place_mouse_onclick).py

HenraL/NSI_1ereG6_Programme_Python

9f46b848fa2331daca57e5e2e11cba41da45a67f

[ "Unlicense" ]

null

files/question histoire pygame/thorpy_reaction_(show_place_mouse_onclick).py

HenraL/NSI_1ereG6_Programme_Python

9f46b848fa2331daca57e5e2e11cba41da45a67f

[ "Unlicense" ]

null

import thorpy, pygame def my_func_reaction(event):#Reactions functions must take an event as first arg print("My reaction displays the pos of event:", event.pos) #We declare a Reaction. Note that we do not filter the event here. my_reaction = thorpy.Reaction(reacts_to=pygame.MOUSEBUTTONDOWN, reac_func=my_func_reaction) application = thorpy.Application(size=(300, 300), caption="Reaction tuto") background = thorpy.Background(color=(255,255,255)) background.add_reaction(my_reaction) #add my_reaction to background's reactions menu = thorpy.Menu(background) #create a menu for auto events handling menu.play() #launch the menu application.quit()

36.157895

80

0.754003

bc1ef50c3683fccefcc1680447d2bc2b43077777

27,227

py

Python

ax/plot/helper.py

stevemandala/Ax

8e289a154e3a2ed237bf27ddb90e09963c0d6a97

[ "MIT" ]

null

ax/plot/helper.py

stevemandala/Ax

8e289a154e3a2ed237bf27ddb90e09963c0d6a97

[ "MIT" ]

null

ax/plot/helper.py

stevemandala/Ax

8e289a154e3a2ed237bf27ddb90e09963c0d6a97

[ "MIT" ]

null

#!/usr/bin/env python3 # Copyright (c) Facebook, Inc. and its affiliates. # # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. import math from collections import Counter from typing import Any, Dict, List, Optional, Set, Tuple, Union import numpy as np from ax.core.generator_run import GeneratorRun from ax.core.observation import ObservationFeatures from ax.core.parameter import ChoiceParameter, FixedParameter, RangeParameter from ax.core.types import TParameterization from ax.modelbridge.base import ModelBridge from ax.modelbridge.transforms.ivw import IVW from ax.plot.base import DECIMALS, PlotData, PlotInSampleArm, PlotOutOfSampleArm, Z from ax.utils.common.logger import get_logger from ax.utils.common.typeutils import not_none logger = get_logger(name="PlotHelper") # Typing alias RawData = List[Dict[str, Union[str, float]]] TNullableGeneratorRunsDict = Optional[Dict[str, GeneratorRun]] def _format_dict(param_dict: TParameterization, name: str = "Parameterization") -> str: """Format a dictionary for labels. Args: param_dict: Dictionary to be formatted name: String name of the thing being formatted. Returns: stringified blob. """ if len(param_dict) >= 10: blob = "{} has too many items to render on hover ({}).".format( name, len(param_dict) ) else: blob = "<br><em>{}:</em><br>{}".format( name, "<br>".join("{}: {}".format(n, v) for n, v in param_dict.items()) ) return blob def _wrap_metric(metric_name: str) -> str: """Put a newline on "::" for metric names. Args: metric_name: metric name. Returns: wrapped metric name. """ if "::" in metric_name: return "<br>".join(metric_name.split("::")) else: return metric_name def _format_CI(estimate: float, sd: float, relative: bool, zval: float = Z) -> str: """Format confidence intervals given estimate and standard deviation. Args: estimate: point estimate. sd: standard deviation of point estimate. relative: if True, '%' is appended. zval: z-value associated with desired CI (e.g. 1.96 for 95% CIs) Returns: formatted confidence interval. """ return "[{lb:.{digits}f}{perc}, {ub:.{digits}f}{perc}]".format( lb=estimate - zval * sd, ub=estimate + zval * sd, digits=DECIMALS, perc="%" if relative else "", ) def arm_name_to_tuple(arm_name: str) -> Union[Tuple[int, int], Tuple[int]]: tup = arm_name.split("_") if len(tup) == 2: try: return (int(tup[0]), int(tup[1])) except ValueError: return (0,) return (0,) def resize_subtitles(figure: Dict[str, Any], size: int): for ant in figure["layout"]["annotations"]: ant["font"].update(size=size) return figure def _filter_dict( param_dict: TParameterization, subset_keys: List[str] ) -> TParameterization: """Filter a dictionary to keys present in a given list.""" return {k: v for k, v in param_dict.items() if k in subset_keys} def _get_in_sample_arms( model: ModelBridge, metric_names: Set[str], fixed_features: Optional[ObservationFeatures] = None, ) -> Tuple[Dict[str, PlotInSampleArm], RawData, Dict[str, TParameterization]]: """Get in-sample arms from a model with observed and predicted values for specified metrics. Returns a PlotInSampleArm object in which repeated observations are merged with IVW, and a RawData object in which every observation is listed. Fixed features input can be used to override fields of the insample arms when making model predictions. Args: model: An instance of the model bridge. metric_names: Restrict predictions to these metrics. If None, uses all metrics in the model. fixed_features: Features that should be fixed in the arms this function will obtain predictions for. Returns: A tuple containing - Map from arm name to PlotInSampleArm. - List of the data for each observation like:: {'metric_name': 'likes', 'arm_name': '0_0', 'mean': 1., 'sem': 0.1} - Map from arm name to parameters """ observations = model.get_training_data() # Calculate raw data raw_data = [] arm_name_to_parameters = {} for obs in observations: arm_name_to_parameters[obs.arm_name] = obs.features.parameters for j, metric_name in enumerate(obs.data.metric_names): if metric_name in metric_names: raw_data.append( { "metric_name": metric_name, "arm_name": obs.arm_name, "mean": obs.data.means[j], "sem": np.sqrt(obs.data.covariance[j, j]), } ) # Check that we have one ObservationFeatures per arm name since we # key by arm name and the model is not Multi-task. # If "TrialAsTask" is present, one of the arms is also chosen. if ("TrialAsTask" not in model.transforms.keys()) and ( len(arm_name_to_parameters) != len(observations) ): logger.error( "Have observations of arms with different features but same" " name. Arbitrary one will be plotted." ) # Merge multiple measurements within each Observation with IVW to get # un-modeled prediction t = IVW(None, [], []) obs_data = t.transform_observation_data([obs.data for obs in observations], []) # Start filling in plot data in_sample_plot: Dict[str, PlotInSampleArm] = {} for i, obs in enumerate(observations): if obs.arm_name is None: raise ValueError("Observation must have arm name for plotting.") # Extract raw measurement obs_y = {} # Observed metric means. obs_se = {} # Observed metric standard errors. # Use the IVW data, not obs.data for j, metric_name in enumerate(obs_data[i].metric_names): if metric_name in metric_names: obs_y[metric_name] = obs_data[i].means[j] obs_se[metric_name] = np.sqrt(obs_data[i].covariance[j, j]) # Make a prediction. if model.training_in_design[i]: features = obs.features if fixed_features is not None: features.update_features(fixed_features) pred_y, pred_se = _predict_at_point(model, features, metric_names) else: # Use raw data for out-of-design points pred_y = obs_y pred_se = obs_se in_sample_plot[not_none(obs.arm_name)] = PlotInSampleArm( name=not_none(obs.arm_name), y=obs_y, se=obs_se, parameters=obs.features.parameters, y_hat=pred_y, se_hat=pred_se, context_stratum=None, ) return in_sample_plot, raw_data, arm_name_to_parameters def _predict_at_point( model: ModelBridge, obsf: ObservationFeatures, metric_names: Set[str] ) -> Tuple[Dict[str, float], Dict[str, float]]: """Make a prediction at a point. Returns mean and standard deviation in format expected by plotting. Args: model: ModelBridge obsf: ObservationFeatures for which to predict metric_names: Limit predictions to these metrics. Returns: A tuple containing - Map from metric name to prediction. - Map from metric name to standard error. """ y_hat = {} se_hat = {} f_pred, cov_pred = model.predict([obsf]) for metric_name in f_pred: if metric_name in metric_names: y_hat[metric_name] = f_pred[metric_name][0] se_hat[metric_name] = np.sqrt(cov_pred[metric_name][metric_name][0]) return y_hat, se_hat def _get_out_of_sample_arms( model: ModelBridge, generator_runs_dict: Dict[str, GeneratorRun], metric_names: Set[str], fixed_features: Optional[ObservationFeatures] = None, ) -> Dict[str, Dict[str, PlotOutOfSampleArm]]: """Get out-of-sample predictions from a model given a dict of generator runs. Fixed features input can be used to override fields of the candidate arms when making model predictions. Args: model: The model. generator_runs_dict: a mapping from generator run name to generator run. metric_names: metrics to include in the plot. Returns: A mapping from name to a mapping from arm name to plot. """ out_of_sample_plot: Dict[str, Dict[str, PlotOutOfSampleArm]] = {} for generator_run_name, generator_run in generator_runs_dict.items(): out_of_sample_plot[generator_run_name] = {} for arm in generator_run.arms: # This assumes context is None obsf = ObservationFeatures.from_arm(arm) if fixed_features is not None: obsf.update_features(fixed_features) # Make a prediction try: pred_y, pred_se = _predict_at_point(model, obsf, metric_names) except Exception: # Check if it is an out-of-design arm. if not model.model_space.check_membership(obsf.parameters): # Skip this point continue else: # It should have worked raise arm_name = arm.name_or_short_signature out_of_sample_plot[generator_run_name][arm_name] = PlotOutOfSampleArm( name=arm_name, parameters=obsf.parameters, y_hat=pred_y, se_hat=pred_se, context_stratum=None, ) return out_of_sample_plot def get_plot_data( model: ModelBridge, generator_runs_dict: Dict[str, GeneratorRun], metric_names: Optional[Set[str]] = None, fixed_features: Optional[ObservationFeatures] = None, ) -> Tuple[PlotData, RawData, Dict[str, TParameterization]]: """Format data object with metrics for in-sample and out-of-sample arms. Calculate both observed and predicted metrics for in-sample arms. Calculate predicted metrics for out-of-sample arms passed via the `generator_runs_dict` argument. In PlotData, in-sample observations are merged with IVW. In RawData, they are left un-merged and given as a list of dictionaries, one for each observation and having keys 'arm_name', 'mean', and 'sem'. Args: model: The model. generator_runs_dict: a mapping from generator run name to generator run. metric_names: Restrict predictions to this set. If None, all metrics in the model will be returned. fixed_features: Fixed features to use when making model predictions. Returns: A tuple containing - PlotData object with in-sample and out-of-sample predictions. - List of observations like:: {'metric_name': 'likes', 'arm_name': '0_1', 'mean': 1., 'sem': 0.1}. - Mapping from arm name to parameters. """ metrics_plot = model.metric_names if metric_names is None else metric_names in_sample_plot, raw_data, cond_name_to_parameters = _get_in_sample_arms( model=model, metric_names=metrics_plot, fixed_features=fixed_features ) out_of_sample_plot = _get_out_of_sample_arms( model=model, generator_runs_dict=generator_runs_dict, metric_names=metrics_plot, fixed_features=fixed_features, ) # pyre-fixme[16]: `Optional` has no attribute `arm_name`. status_quo_name = None if model.status_quo is None else model.status_quo.arm_name plot_data = PlotData( metrics=list(metrics_plot), in_sample=in_sample_plot, out_of_sample=out_of_sample_plot, status_quo_name=status_quo_name, ) return plot_data, raw_data, cond_name_to_parameters def get_range_parameter(model: ModelBridge, param_name: str) -> RangeParameter: """ Get the range parameter with the given name from the model. Throws if parameter doesn't exist or is not a range parameter. Args: model: The model. param_name: The name of the RangeParameter to be found. Returns: The RangeParameter named `param_name`. """ range_param = model.model_space.parameters.get(param_name) if range_param is None: raise ValueError(f"Parameter `{param_name}` does not exist.") if not isinstance(range_param, RangeParameter): raise ValueError(f"{param_name} is not a RangeParameter") return range_param def get_range_parameters(model: ModelBridge) -> List[RangeParameter]: """ Get a list of range parameters from a model. Args: model: The model. Returns: List of RangeParameters. """ return [ parameter for parameter in model.model_space.parameters.values() if isinstance(parameter, RangeParameter) ] def get_grid_for_parameter(parameter: RangeParameter, density: int) -> np.ndarray: """Get a grid of points along the range of the parameter. Will be a log-scale grid if parameter is log scale. Args: parameter: Parameter for which to generate grid. density: Number of points in the grid. """ is_log = parameter.log_scale if is_log: grid = np.linspace( np.log10(parameter.lower), np.log10(parameter.upper), density ) grid = 10 ** grid else: grid = np.linspace(parameter.lower, parameter.upper, density) return grid def get_fixed_values( model: ModelBridge, slice_values: Optional[Dict[str, Any]] = None, trial_index: Optional[int] = None, ) -> TParameterization: """Get fixed values for parameters in a slice plot. If there is an in-design status quo, those values will be used. Otherwise, the mean of RangeParameters or the mode of ChoiceParameters is used. Any value in slice_values will override the above. Args: model: ModelBridge being used for plotting slice_values: Map from parameter name to value at which is should be fixed. Returns: Map from parameter name to fixed value. """ if trial_index is not None: if slice_values is None: slice_values = {} slice_values["TRIAL_PARAM"] = str(trial_index) # Check if status_quo is in design if model.status_quo is not None and model.model_space.check_membership( # pyre-fixme[16]: `Optional` has no attribute `features`. model.status_quo.features.parameters ): setx = model.status_quo.features.parameters else: observations = model.get_training_data() setx = {} for p_name, parameter in model.model_space.parameters.items(): # Exclude out of design status quo (no parameters) vals = [ obs.features.parameters[p_name] for obs in observations if ( len(obs.features.parameters) > 0 and parameter.validate(obs.features.parameters[p_name]) ) ] if isinstance(parameter, FixedParameter): setx[p_name] = parameter.value elif isinstance(parameter, ChoiceParameter): setx[p_name] = Counter(vals).most_common(1)[0][0] elif isinstance(parameter, RangeParameter): setx[p_name] = parameter.cast(np.mean(vals)) if slice_values is not None: # slice_values has type Dictionary[str, Any] setx.update(slice_values) return setx # Utility methods ported from JS def contour_config_to_trace(config): # Load from config arm_data = config["arm_data"] density = config["density"] grid_x = config["grid_x"] grid_y = config["grid_y"] f = config["f"] lower_is_better = config["lower_is_better"] metric = config["metric"] rel = config["rel"] sd = config["sd"] xvar = config["xvar"] yvar = config["yvar"] green_scale = config["green_scale"] green_pink_scale = config["green_pink_scale"] blue_scale = config["blue_scale"] # format data res = relativize_data(f, sd, rel, arm_data, metric) f_final = res[0] sd_final = res[1] # calculate max of abs(outcome), used for colorscale f_absmax = max(abs(min(f_final)), max(f_final)) # transform to nested array f_plt = [] for ind in range(0, len(f_final), density): f_plt.append(f_final[ind : ind + density]) sd_plt = [] for ind in range(0, len(sd_final), density): sd_plt.append(sd_final[ind : ind + density]) CONTOUR_CONFIG = { "autocolorscale": False, "autocontour": True, "contours": {"coloring": "heatmap"}, "hoverinfo": "x+y+z", "ncontours": int(density / 2), "type": "contour", "x": grid_x, "y": grid_y, } if rel: f_scale = reversed(green_pink_scale) if lower_is_better else green_pink_scale else: f_scale = green_scale f_trace = { "colorbar": { "x": 0.45, "y": 0.5, "ticksuffix": "%" if rel else "", "tickfont": {"size": 8}, }, "colorscale": [(i / (len(f_scale) - 1), rgb(v)) for i, v in enumerate(f_scale)], "xaxis": "x", "yaxis": "y", "z": f_plt, # zmax and zmin are ignored if zauto is true "zauto": not rel, "zmax": f_absmax, "zmin": -f_absmax, } sd_trace = { "colorbar": { "x": 1, "y": 0.5, "ticksuffix": "%" if rel else "", "tickfont": {"size": 8}, }, "colorscale": [ (i / (len(blue_scale) - 1), rgb(v)) for i, v in enumerate(blue_scale) ], "xaxis": "x2", "yaxis": "y2", "z": sd_plt, } f_trace.update(CONTOUR_CONFIG) sd_trace.update(CONTOUR_CONFIG) # get in-sample arms arm_text = list(arm_data["in_sample"].keys()) arm_x = [ arm_data["in_sample"][arm_name]["parameters"][xvar] for arm_name in arm_text ] arm_y = [ arm_data["in_sample"][arm_name]["parameters"][yvar] for arm_name in arm_text ] # configs for in-sample arms base_in_sample_arm_config = { "hoverinfo": "text", "legendgroup": "In-sample", "marker": {"color": "black", "symbol": 1, "opacity": 0.5}, "mode": "markers", "name": "In-sample", "text": arm_text, "type": "scatter", "x": arm_x, "y": arm_y, } f_in_sample_arm_trace = {"xaxis": "x", "yaxis": "y"} sd_in_sample_arm_trace = {"showlegend": False, "xaxis": "x2", "yaxis": "y2"} f_in_sample_arm_trace.update(base_in_sample_arm_config) sd_in_sample_arm_trace.update(base_in_sample_arm_config) traces = [f_trace, sd_trace, f_in_sample_arm_trace, sd_in_sample_arm_trace] # iterate over out-of-sample arms for i, generator_run_name in enumerate(arm_data["out_of_sample"].keys()): symbol = i + 2 # symbols starts from 2 for candidate markers ax = [] ay = [] atext = [] for arm_name in arm_data["out_of_sample"][generator_run_name].keys(): ax.append( arm_data["out_of_sample"][generator_run_name][arm_name]["parameters"][ xvar ] ) ay.append( arm_data["out_of_sample"][generator_run_name][arm_name]["parameters"][ yvar ] ) atext.append("<em>Candidate " + arm_name + "</em>") traces.append( { "hoverinfo": "text", "legendgroup": generator_run_name, "marker": {"color": "black", "symbol": symbol, "opacity": 0.5}, "mode": "markers", "name": generator_run_name, "text": atext, "type": "scatter", "xaxis": "x", "x": ax, "yaxis": "y", "y": ay, } ) traces.append( { "hoverinfo": "text", "legendgroup": generator_run_name, "marker": {"color": "black", "symbol": symbol, "opacity": 0.5}, "mode": "markers", "name": "In-sample", "showlegend": False, "text": atext, "type": "scatter", "x": ax, "xaxis": "x2", "y": ay, "yaxis": "y2", } ) return traces def axis_range(grid: List[float], is_log: bool) -> List[float]: if is_log: return [math.log10(min(grid)), math.log10(max(grid))] else: return [min(grid), max(grid)] def _relativize(m_t: float, sem_t: float, m_c: float, sem_c: float) -> List[float]: r_hat = (m_t - m_c) / abs(m_c) - sem_c ** 2 * m_t / abs(m_c) ** 3 variance = (sem_t ** 2 + (m_t / m_c * sem_c) ** 2) / m_c ** 2 return [r_hat, math.sqrt(variance)] def relativize_data( f: List[float], sd: List[float], rel: bool, arm_data: Dict[Any, Any], metric: str ) -> List[List[float]]: # if relative, extract status quo & compute ratio f_final = [] if rel else f sd_final = [] if rel else sd if rel: f_sq = arm_data["in_sample"][arm_data["status_quo_name"]]["y"][metric] sd_sq = arm_data["in_sample"][arm_data["status_quo_name"]]["se"][metric] for i in range(len(f)): res = _relativize(f[i], sd[i], f_sq, sd_sq) f_final.append(100 * res[0]) sd_final.append(100 * res[1]) return [f_final, sd_final] def rgb(arr: List[int]) -> str: return "rgb({},{},{})".format(*arr) def infer_is_relative( model: ModelBridge, metrics: List[str], non_constraint_rel: bool ) -> Dict[str, bool]: """Determine whether or not to relativize a metric. Metrics that are constraints will get this decision from their `relative` flag. Other metrics will use the `default_rel`. Args: model: model fit on metrics. metrics: list of metric names. non_constraint_rel: whether or not to relativize non-constraint metrics Returns: Dict[str, bool] containing whether or not to relativize each input metric. """ relative = {} constraint_relativity = {} if model._optimization_config: constraints = not_none(model._optimization_config).outcome_constraints constraint_relativity = { constraint.metric.name: constraint.relative for constraint in constraints } for metric in metrics: if metric not in constraint_relativity: relative[metric] = non_constraint_rel else: relative[metric] = constraint_relativity[metric] return relative def slice_config_to_trace( arm_data, arm_name_to_parameters, f, fit_data, grid, metric, param, rel, setx, sd, is_log, visible, ): # format data res = relativize_data(f, sd, rel, arm_data, metric) f_final = res[0] sd_final = res[1] # get data for standard deviation fill plot sd_upper = [] sd_lower = [] for i in range(len(sd)): sd_upper.append(f_final[i] + 2 * sd_final[i]) sd_lower.append(f_final[i] - 2 * sd_final[i]) grid_rev = list(reversed(grid)) sd_lower_rev = list(reversed(sd_lower)) sd_x = grid + grid_rev sd_y = sd_upper + sd_lower_rev # get data for observed arms and error bars arm_x = [] arm_y = [] arm_sem = [] for row in fit_data: parameters = arm_name_to_parameters[row["arm_name"]] plot = True for p in setx.keys(): if p != param and parameters[p] != setx[p]: plot = False if plot: arm_x.append(parameters[param]) arm_y.append(row["mean"]) arm_sem.append(row["sem"]) arm_res = relativize_data(arm_y, arm_sem, rel, arm_data, metric) arm_y_final = arm_res[0] arm_sem_final = [x * 2 for x in arm_res[1]] # create traces f_trace = { "x": grid, "y": f_final, "showlegend": False, "hoverinfo": "x+y", "line": {"color": "rgba(128, 177, 211, 1)"}, "visible": visible, } arms_trace = { "x": arm_x, "y": arm_y_final, "mode": "markers", "error_y": { "type": "data", "array": arm_sem_final, "visible": True, "color": "black", }, "line": {"color": "black"}, "showlegend": False, "hoverinfo": "x+y", "visible": visible, } sd_trace = { "x": sd_x, "y": sd_y, "fill": "toself", "fillcolor": "rgba(128, 177, 211, 0.2)", "line": {"color": "rgba(128, 177, 211, 0.0)"}, "showlegend": False, "hoverinfo": "none", "visible": visible, } traces = [sd_trace, f_trace, arms_trace] # iterate over out-of-sample arms for i, generator_run_name in enumerate(arm_data["out_of_sample"].keys()): ax = [] ay = [] asem = [] atext = [] for arm_name in arm_data["out_of_sample"][generator_run_name].keys(): parameters = arm_data["out_of_sample"][generator_run_name][arm_name][ "parameters" ] plot = True for p in setx.keys(): if p != param and parameters[p] != setx[p]: plot = False if plot: ax.append(parameters[param]) ay.append( arm_data["out_of_sample"][generator_run_name][arm_name]["y_hat"][ metric ] ) asem.append( arm_data["out_of_sample"][generator_run_name][arm_name]["se_hat"][ metric ] ) atext.append("<em>Candidate " + arm_name + "</em>") out_of_sample_arm_res = relativize_data(ay, asem, rel, arm_data, metric) ay_final = out_of_sample_arm_res[0] asem_final = [x * 2 for x in out_of_sample_arm_res[1]] traces.append( { "hoverinfo": "text", "legendgroup": generator_run_name, "marker": {"color": "black", "symbol": i + 1, "opacity": 0.5}, "mode": "markers", "error_y": { "type": "data", "array": asem_final, "visible": True, "color": "black", }, "name": generator_run_name, "text": atext, "type": "scatter", "xaxis": "x", "x": ax, "yaxis": "y", "y": ay_final, "visible": visible, } ) return traces

32.413095

88

0.592684

681c2dbcba09fc1f35e2ca0aae6848e63780e95e

678

py

Python

temboo/core/Library/Yahoo/Weather/__init__.py

jordanemedlock/psychtruths

52e09033ade9608bd5143129f8a1bfac22d634dd

[ "Apache-2.0" ]

7

2016-03-07T02:07:21.000Z

2022-01-21T02:22:41.000Z

temboo/core/Library/Yahoo/Weather/__init__.py

jordanemedlock/psychtruths

52e09033ade9608bd5143129f8a1bfac22d634dd

[ "Apache-2.0" ]

null

temboo/core/Library/Yahoo/Weather/__init__.py

jordanemedlock/psychtruths

52e09033ade9608bd5143129f8a1bfac22d634dd

[ "Apache-2.0" ]

8

2016-06-14T06:01:11.000Z

2020-04-22T09:21:44.000Z

from temboo.Library.Yahoo.Weather.GetTemperature import GetTemperature, GetTemperatureInputSet, GetTemperatureResultSet, GetTemperatureChoreographyExecution from temboo.Library.Yahoo.Weather.GetWeather import GetWeather, GetWeatherInputSet, GetWeatherResultSet, GetWeatherChoreographyExecution from temboo.Library.Yahoo.Weather.GetWeatherByAddress import GetWeatherByAddress, GetWeatherByAddressInputSet, GetWeatherByAddressResultSet, GetWeatherByAddressChoreographyExecution from temboo.Library.Yahoo.Weather.GetWeatherByCoordinates import GetWeatherByCoordinates, GetWeatherByCoordinatesInputSet, GetWeatherByCoordinatesResultSet, GetWeatherByCoordinatesChoreographyExecution

135.6

201

0.917404

66d5412d318d71e6cdc64b643cf0ad07e785f465

1,671

py

Python

setup.py

arnaud-morvan/papyrus

04ce5730e0af229cbded40fc96dfee132300f4f7

[ "BSD-2-Clause" ]

null

setup.py

arnaud-morvan/papyrus

04ce5730e0af229cbded40fc96dfee132300f4f7

[ "BSD-2-Clause" ]

null

setup.py

arnaud-morvan/papyrus

04ce5730e0af229cbded40fc96dfee132300f4f7

[ "BSD-2-Clause" ]

null

from setuptools import setup, find_packages import os version = '2.3' here = os.path.abspath(os.path.dirname(__file__)) README = open(os.path.join(here, 'README.rst')).read() TODO = open(os.path.join(here, 'TODO.rst')).read() CHANGES = open(os.path.join(here, 'CHANGES.rst')).read() install_requires = [ 'pyramid>=1.1a3', 'geojson>=1.1.0', 'GeoAlchemy2>=0.2.4', 'six', ] if os.environ.get('READTHEDOCS') != 'True': install_requires.append('Shapely>=1.2') setup(name='papyrus', version=version, description="Geospatial Extensions for Pyramid", classifiers=[ 'Framework :: Pyramid', 'Intended Audience :: Developers', 'Operating System :: OS Independent', 'Programming Language :: Python', 'Programming Language :: Python :: 2.7', 'Programming Language :: Python :: 3.3', 'Programming Language :: Python :: 3.4', 'Programming Language :: Python :: 3.5', 'Programming Language :: Python :: 3.6', 'License :: OSI Approved :: BSD License', 'Programming Language :: Python', 'Topic :: Internet :: WWW/HTTP', ], keywords='FOSS4G, Pylons, Pyramid', author='Eric Lemoine', author_email='eric.lemoine@gmail.com', url='https://github.com/elemoine/papyrus', license='BSD', packages=find_packages(exclude=['ez_setup', 'examples', 'tests']), include_package_data=True, zip_safe=False, install_requires=install_requires, entry_points=""" # -*- Entry points: -*- """, long_description=README + '\n\n' + TODO + '\n\n' + CHANGES, )

32.134615

72

0.59605

24ce098b8e56d95ed789f5a04072350ecc609c5c

65,757

py

Python

reviewboard/testing/testcase.py

LloydFinch/reviewboard

563c1e8d4dfd860f372281dc0f380a0809f6ae15

[ "MIT" ]

2

2020-06-19T14:57:49.000Z

2020-06-19T15:17:40.000Z

reviewboard/testing/testcase.py

LloydFinch/reviewboard

563c1e8d4dfd860f372281dc0f380a0809f6ae15

[ "MIT" ]

null

reviewboard/testing/testcase.py

LloydFinch/reviewboard

563c1e8d4dfd860f372281dc0f380a0809f6ae15

[ "MIT" ]

null

from __future__ import unicode_literals import os import re import warnings from contextlib import contextmanager from datetime import timedelta from django.conf import settings from django.contrib.auth.models import AnonymousUser, Permission, User from django.core.cache import cache from django.core.files import File from django.core.files.base import ContentFile from django.core.urlresolvers import ResolverMatch from django.test.client import RequestFactory from django.utils import six, timezone from djblets.siteconfig.models import SiteConfiguration from djblets.testing.testcases import (FixturesCompilerMixin, TestCase as DjbletsTestCase) from oauthlib.common import generate_token from oauth2_provider.models import AccessToken from reviewboard import scmtools, initialize from reviewboard.accounts.models import ReviewRequestVisit from reviewboard.admin.siteconfig import load_site_config from reviewboard.attachments.models import (FileAttachment, FileAttachmentHistory) from reviewboard.diffviewer.differ import DiffCompatVersion from reviewboard.diffviewer.models import (DiffCommit, DiffSet, DiffSetHistory, FileDiff) from reviewboard.notifications.models import WebHookTarget from reviewboard.oauth.models import Application from reviewboard.reviews.models import (Comment, FileAttachmentComment, GeneralComment, Group, Review, ReviewRequest, ReviewRequestDraft, Screenshot, ScreenshotComment, StatusUpdate) from reviewboard.scmtools.models import Repository, Tool from reviewboard.site.models import LocalSite from reviewboard.webapi.models import WebAPIToken class TestCase(FixturesCompilerMixin, DjbletsTestCase): """The base class for Review Board test cases. This class provides a number of convenient functions for creating common objects for testing, such as review requests and comments. They're populated with default data that can be overridden by the callers. This also overcomes an annoyance with default Django unit tests where the cache is not cleared across tests, leading to inconsistent results and useless testing. """ local_site_name = 'local-site-1' local_site_id = 1 ws_re = re.compile(r'\s+') DEFAULT_FILEDIFF_DATA_DIFF = ( b'--- README\trevision 123\n' b'+++ README\trevision 123\n' b'@@ -1 +1 @@\n' b'-Hello, world!\n' b'+Hello, everybody!\n' ) DEFAULT_GIT_FILEDIFF_DATA_DIFF = ( b'diff --git a/README b/README\n' b'index 94bdd3e..197009f 100644\n' b'--- README\n' b'+++ README\n' b'@@ -2 +2 @@\n' b'-blah blah\n' b'+blah!\n' ) DEFAULT_GIT_README_DIFF = ( b'diff --git a/readme b/readme\n' b'index d6613f5..5b50866 100644\n' b'--- a/readme\n' b'+++ b/readme\n' b'@@ -1 +1,3 @@\n' b'Hello there\n' b'+\n' b'+Oh hi!\n' ) DEFAULT_GIT_FILEMODE_DIFF = ( b'diff --git a/testing b/testing\n' b'old mode 100755\n' b'new mode 100644\n' b'index e69de29..bcae657\n' b'--- a/testing\n' b'+++ b/testing\n' b'@@ -0,0 +1 @@\n' b'+ADD\n' b'diff --git a/testing2 b/testing2\n' b'old mode 100644\n' b'new mode 100755\n' ) DEFAULT_GIT_FILE_NOT_FOUND_DIFF = ( b'diff --git a/missing-file b/missing-file\n' b'index d6613f0..5b50866 100644\n' b'--- a/missing-file\n' b'+++ b/missing-file\n' b'@@ -1 +1,3 @@\n' b'Hello there\n' b'+\n' b'+Oh hi!\n' ) DEFAULT_GIT_BINARY_IMAGE_DIFF = ( b'diff --git a/logo.png b/logo.png\n' b'index 86b520c..86b520d\n' b'Binary files a/logo.png and b/logo.png differ\n' ) def setUp(self): super(TestCase, self).setUp() siteconfig = SiteConfiguration.objects.get_current() siteconfig.set('mail_from_spoofing', 'never') siteconfig.save(update_fields=('settings',)) initialize(load_extensions=False) self._local_sites = {} # Clear the cache so that previous tests don't impact this one. cache.clear() def shortDescription(self): """Returns the description of the current test. This changes the default behavior to replace all newlines with spaces, allowing a test description to span lines. It should still be kept short, though. """ doc = self._testMethodDoc if doc is not None: doc = doc.split('\n\n', 1)[0] doc = self.ws_re.sub(' ', doc).strip() return doc def get_local_site_or_none(self, name): """Returns a LocalSite matching the name, if provided, or None.""" if name: return self.get_local_site(name=name) else: return None def get_local_site(self, name): if name not in self._local_sites: self._local_sites[name] = LocalSite.objects.get(name=name) return self._local_sites[name] def create_http_request(self, path='/', user=None, method='get', with_local_site=False, local_site=None, resolver_match=None, view=None, **kwargs): """Create an HttpRequest for testing. This wraps :py:class:`~django.test.client.RequestFactory`, automatically handing some common fields normally set by middleware, including the user, resolver match, and Local Site. Args: path (unicode, optional): The path for the HTTP request, relative to the server root. user (django.contrib.auth.models.User, optional): The user authenticated for the request. If not provided, :py:class:`~django.contrib.auth.models.AnonymousUser` will be used. method (unicode, optional): The method on :py:class:`~django.test.client.RequestFactory` used to create the request. with_local_site (bool, optional): If set, the default Local Site will be assigned to the request, if ``local_site`` is not provided in the call. local_site (reviewboard.site.models.LocalSite, optional): The Local Site to assign to the request. resolver_match (django.core.urlresolvers.ResolverMatch, optional): A custom resolver match to set for the request. This may be used by views to determine which URL entry was invoked. If not provided, a blank one pointing to the provided ``view`` will be used. view (callable, optional): The view used for a default :py:class:`~django.core.urlresolvers.ResolverMatch`. **kwargs (dict): Additional keyword arguments to pass to the request factory method. Returns: django.http.HttpRequest: The resulting HTTP request. Raises: ValueError: One or more of the values provided was invalid. """ factory = RequestFactory() try: factory_method = getattr(factory, method) except AttributeError: raise ValueError('Invalid RequestFactory method "%s"' % method) if local_site is None: if with_local_site: local_site = self.get_local_site(name=self.local_site_name) else: local_site = None if resolver_match is None: resolver_match = ResolverMatch(func=view, args=[], kwargs={}) request = factory_method(path, **kwargs) request.local_site = local_site request.resolver_match = resolver_match request.user = user or AnonymousUser() return request def create_user(self, username='test-user', password='', email='test@example.com', perms=None, **kwargs): """Create a User for testing. Args: username (unicode, optional): The username. password (unicode, optional): The user's password. email (unicode, optional): The user's e-mail address. perms (list of tuple, optional): A list of permissions to assign. Each item is a tuple of ``(app_label, permission_name)``. **kwargs (dict): Additional attributes for the user. Returns: django.contrib.auth.models.User: The new User object. """ user = User.objects.create(username=username, password=password, email=email, **kwargs) if perms: user.user_permissions.add(*[ Permission.objects.get(codename=perm_name, content_type__app_label=perm_app_label) for perm_app_label, perm_name in perms ]) return user def create_webapi_token(self, user, note='Sample note', policy={'access': 'rw'}, with_local_site=False, **kwargs): """Creates a WebAPIToken for testing.""" if with_local_site: local_site = self.get_local_site(name=self.local_site_name) else: local_site = None return WebAPIToken.objects.generate_token(user=user, note=note, policy=policy, local_site=local_site) @contextmanager def assert_warns(self, cls=DeprecationWarning, message=None): """A context manager for asserting code generates a warning. This method only supports code which generates a single warning. Tests which make use of code generating multiple warnings will need to manually catch their warnings. """ with warnings.catch_warnings(record=True) as w: # Some warnings such as DeprecationWarning are filtered by # default, stop filtering them. warnings.simplefilter('always') # Now that we've done that, some warnings may come in that we # really don't want. We want to turn those back off. try: from django.utils.deprecation import RemovedInDjango20Warning warnings.filterwarnings('ignore', category=RemovedInDjango20Warning) except ImportError: pass self.assertEqual(len(w), 0) yield self.assertEqual(len(w), 1) self.assertTrue(issubclass(w[-1].category, cls)) if message is not None: self.assertEqual(message, six.text_type(w[-1].message)) def create_diff_file_attachment(self, filediff, from_modified=True, review_request=None, orig_filename='filename.png', caption='My Caption', mimetype='image/png', **kwargs): """Creates a diff-based FileAttachment for testing. The FileAttachment is tied to the given FileDiff. It's populated with default data that can be overridden by the caller. """ file_attachment = FileAttachment.objects.create_from_filediff( filediff=filediff, from_modified=from_modified, caption=caption, orig_filename=orig_filename, mimetype=mimetype, **kwargs) filename = os.path.join(settings.STATIC_ROOT, 'rb', 'images', 'logo.png') with open(filename, 'rb') as f: file_attachment.file.save(os.path.basename(filename), File(f), save=True) if review_request: review_request.file_attachments.add(file_attachment) return file_attachment def create_diffcommit(self, repository=None, diffset=None, commit_id='r1', parent_id='r0', diff_contents=DEFAULT_GIT_FILEDIFF_DATA_DIFF, parent_diff_contents=None, author_name='Author', author_email='author@example.com', author_date=None, commit_message='Commit message', committer_name='Committer', committer_email='committer@example.com', committer_date=None, **kwargs): """Create a DiffCommit for testing. This also creates a :py:class:`reviewboard.diffviewer.models.filediff.FileDiff` attached to the commit. Args: repository (reviewboard.scmtools.models.Repository, optional): The repository the commit is associated with. diffset (reviewboard.diffviewer.models.diffset.DiffSet, optional): The parent diffset. commit_id (unicode, optional): The commit ID. parent_id (unicode, optional): The commit ID of the parent commit. diff_contents (bytes, optional): The contents of the diff. parent_diff_contents (bytes, optional): The contents of the parent diff, if any. author_name (unicode, optional): The name of the commit's author. author_email (unicode, optional): The e-mail address of the commit's author. author_date (datetime.datetime, optional): The date the commit was authored. commit_message (unicode, optional): The commit message. committer_name (unicode, optional): The name of the committer, if any. committer_email (unicode, optional): The e-mail address of the committer, if any. committer_date (datetime.datetime, optional): The date the commit was committed, if any. **kwargs (dict): Keyword arguments to be passed to the :py:class:`~reviewboard.diffviewer.models.diffcommit. DiffCommit` initializer. Returns: reviewboard.diffviewer.models.diffcommit.DiffCommit: The resulting DiffCommit. """ assert isinstance(diff_contents, bytes) if diffset is None: diffset = self.create_diffset(repository=repository) else: repository = diffset.repository if author_date is None: author_date = timezone.now() if not committer_date and committer_name and committer_email: committer_date = author_date if ((not committer_name and committer_email) or (committer_name and not committer_email)): raise ValueError( 'Either both or neither of committer_name and committer_email ' 'must be provided.') if parent_diff_contents: assert isinstance(parent_diff_contents, bytes) parent_diff_file_name = 'parent_diff' else: parent_diff_file_name = None return DiffCommit.objects.create_from_data( repository=repository, diff_file_name='diff', diff_file_contents=diff_contents, parent_diff_file_name=parent_diff_file_name, parent_diff_file_contents=parent_diff_contents, diffset=diffset, commit_id=commit_id, parent_id=parent_id, author_name=author_name, author_email=author_email, author_date=author_date, commit_message=commit_message, request=None, committer_name=committer_name, committer_email=committer_email, committer_date=committer_date, check_existence=False, **kwargs) def create_diffset(self, review_request=None, revision=1, repository=None, draft=False, name='diffset'): """Creates a DiffSet for testing. The DiffSet defaults to revision 1. This can be overriden by the caller. DiffSets generally are tied to a ReviewRequest, but it's optional. """ if review_request: repository = review_request.repository diffset = DiffSet.objects.create( name=name, revision=revision, repository=repository, diffcompat=DiffCompatVersion.DEFAULT) if review_request: if draft: review_request_draft = \ self.create_review_request_draft(review_request) review_request_draft.diffset = diffset review_request_draft.save() else: review_request.diffset_history.diffsets.add(diffset) return diffset def create_diff_comment(self, review, filediff, interfilediff=None, text='My comment', issue_opened=False, issue_status=None, first_line=1, num_lines=5, extra_fields=None, reply_to=None, **kwargs): """Create a Comment for testing. The comment is tied to the given Review and FileDiff (and, optionally, an interfilediff). It's populated with default data that can be overridden by the caller. Args: review (reviewboard.reviews.models.review.Review): The review associated with the comment. filediff (reviewboard.diffviewer.models.filediff.FileDiff): The FileDiff associated with the comment. interfilediff (reviewboard.diffviewer.models.filediff.FileDiff, optional): The FileDiff used for the end of an interdiff range associated with the comment. text (unicode): The text for the comment. issue_opened (bool, optional): Whether an issue is to be opened for the comment. issue_status (unicode, optional): The issue status to set, if an issue is opened. Defaults to being an open issue. first_line (int, optional): The first line (0-based) of the comment range. num_lines (int, optional): The number of lines in the comment. extra_fields (dict, optional): Extra data to set on the comment. reply_to (reviewboard.reviews.models.diff_comment.Comment, optional): The comment this comment replies to. **kwargs (dict): Additional model attributes to set on the comment. Returns: reviewboard.reviews.models.diff_comment.Comment: The resulting comment. """ if issue_opened and not issue_status: issue_status = Comment.OPEN comment = Comment( filediff=filediff, interfilediff=interfilediff, first_line=first_line, num_lines=num_lines, text=text, issue_opened=issue_opened, issue_status=issue_status, reply_to=reply_to, **kwargs) if extra_fields: comment.extra_data = extra_fields comment.save() review.comments.add(comment) return comment def create_file_attachment(self, review_request, attachment_history=None, draft=False, active=True, **kwargs): """Create a FileAttachment for testing. The attachment is tied to the given :py:class:`~reviewboard.reviews.models.review_request.ReviewRequest`. It's populated with default data that can be overridden by the caller. Args: review_request (reviewboard.reviews.models.review_request. ReviewRequest): The review request that ultimately owns the file attachment. attachment_history (reviewboard.attachments.models. FileAttachmentHistory, optional): An attachment history managing the file attachment. draft (bool or reviewboard.reviews.models.review_request_draft. ReviewRequestDraft, optional) A draft to associate the attachment with. This can also be a boolean, for legacy reasons, which will attempt to look up or create a draft for the review request. active (bool, optional) Whether this attachment is considered active (not deleted). **kwargs (dict): Additional keyword arguments to pass to :py:meth:`create_file_attachment_base`. Returns: reviewboard.attachments.models.FileAttachment: The resulting file attachment. """ file_attachment = self.create_file_attachment_base( attachment_history=attachment_history, **kwargs) if draft: if isinstance(draft, ReviewRequestDraft): review_request_draft = draft else: review_request_draft = \ self.create_review_request_draft(review_request) if active: attachments = review_request_draft.file_attachments else: attachments = review_request_draft.inactive_file_attachments else: if active: attachments = review_request.file_attachments else: attachments = review_request.inactive_file_attachments attachments.add(file_attachment) return file_attachment def create_user_file_attachment(self, user, has_file=False, **kwargs): """Create a user FileAttachment for testing. The :py:class:`reviewboard.attachments.models.FileAttachment` is tied to the given :py:class:`django.contrib.auth.models.User`. It's populated with default data that can be overridden by the caller. Notably, by default the FileAttachment will be created without a file or a local_site. Args: user (django.contrib.auth.models.User): The user who owns the file attachment. has_file (bool, optional): ``True`` if an actual file object should be included in the model. This is ``False`` by default. **kwargs (dict): Additional keyword arguments to pass to :py:meth:`create_file_attachment_base`. Returns: reviewboard.attachments.models.FileAttachment: The new file attachment instance. """ return self.create_file_attachment_base(user=user, has_file=has_file, **kwargs) def create_file_attachment_comment(self, review, file_attachment, diff_against_file_attachment=None, text='My comment', issue_opened=False, issue_status=None, extra_fields=None, reply_to=None, **kwargs): """Create a FileAttachmentComment for testing. The comment is tied to the given Review and FileAttachment. It's populated with default data that can be overridden by the caller. Args: review (reviewboard.reviews.models.review.Review): The review associated with the comment. file_attachment (reviewboard.attachments.models.FileAttachment): The file attachment associated with the comment. diff_against_file_attachment (reviewboard.attachments.models. FileAttachment, optional): The file attachment being diff against, for comments on attachment diffs. text (unicode): The text for the comment. issue_opened (bool, optional): Whether an issue is to be opened for the comment. issue_status (unicode, optional): The issue status to set, if an issue is opened. Defaults to being an open issue. extra_fields (dict, optional): Extra data to set on the comment. reply_to (reviewboard.reviews.models.file_attachment_comment. FileAttachmentComment, optional): The comment this comment replies to. **kwargs (dict): Additional model attributes to set on the comment. Returns: reviewboard.reviews.models.file_attachment_comment.FileAttachmentComment: The resulting comment. """ if issue_opened and not issue_status: issue_status = FileAttachmentComment.OPEN comment = FileAttachmentComment( file_attachment=file_attachment, diff_against_file_attachment=diff_against_file_attachment, text=text, issue_opened=issue_opened, issue_status=issue_status, reply_to=reply_to, **kwargs) if extra_fields: comment.extra_data = extra_fields comment.save() review.file_attachment_comments.add(comment) return comment def create_file_attachment_history(self, review_request=None, display_position=None, **kwargs): """Create a FileAttachmentHistory for testing. Args: review_request (reviewboard.reviews.models.review_request. ReviewRequest, optional): The optional review request to attach the history to. display_position (int, optional): The display position on the review request. If not provided, a proper position will be computed. **kwargs (dict): Additional fields to set on the model. Returns: reviewboard.attachments.models.FileAttachmentHistory: The new file attachment instance. """ if display_position is None: if review_request is None: display_position = 0 else: display_position = \ FileAttachmentHistory.compute_next_display_position( review_request) attachment_history = FileAttachmentHistory.objects.create( display_position=display_position, **kwargs) if review_request is not None: review_request.file_attachment_histories.add(attachment_history) return attachment_history def create_filediff(self, diffset, source_file='/test-file', dest_file='/test-file', source_revision='123', dest_detail='124', status=FileDiff.MODIFIED, diff=DEFAULT_FILEDIFF_DATA_DIFF, commit=None, save=True): """Create a FileDiff for testing. The FileDiff is tied to the given DiffSet. It's populated with default data that can be overridden by the caller. Args: diffset (reviewboard.diffviewer.models.diffset.DiffSet): The parent diff set that will own this file. source_file (unicode, optional): The source filename. dest_file (unicode, optional): The destination filename, which will be the same as ``source_file`` unless the file was moved/renamed/copied. source_revision (unicode, optional): The source revision. dest_detail (unicode, optional): The destination revision or other detail as found in the parsed diff. This may be a timestamp or some other value. status (unicode, optional): The status of the file. This is the operation performed as indicated in the diff. diff (bytes, optional): The diff contents. commit (reviewboard.diffviewer.models.diffcommit.DiffCommit, optional): The commit to attach the FileDiff to. save (bool, optional): Whether to automatically save the resulting object. Returns: reviewboard.diffviewer.models.filediff.FileDiff: The resulting FileDiff. """ filediff = FileDiff( diffset=diffset, source_file=source_file, dest_file=dest_file, source_revision=source_revision, dest_detail=dest_detail, status=status, diff=diff, commit=commit) if save: filediff.save() return filediff def create_repository(self, with_local_site=False, name='Test Repo', tool_name='Git', path=None, local_site=None, extra_data=None, **kwargs): """Create a Repository for testing. The Repository may optionally be attached to a :py:class:`~reviewboard.site.models.LocalSite`. It's also populated with default data that can be overridden by the caller. Args: with_local_site (bool, optional): Whether to create the repository using a Local Site. This will choose one based on :py:attr:`local_site_name`. If ``local_site`` is provided, this argument is ignored. name (unicode, optional): The name of the repository. tool_name (unicode, optional): The name of the registered SCM Tool for the repository. path (unicode, optional): The path for the repository. If not provided, one will be computed. local_site (reviewboard.site.models.LocalSite, optional): The explicit Local Site to attach. extra_data (dict, optional): Explicit extra_data to attach to the repository. **kwargs (dict): Additional fields to set on the repository. Returns: reviewboard.scmtools.models.Repository: The new repository. """ if not local_site: if with_local_site: local_site = self.get_local_site(name=self.local_site_name) else: local_site = None testdata_dir = os.path.join(os.path.dirname(scmtools.__file__), 'testdata') if not path: if tool_name in ('Git', 'Test', 'TestToolSupportsPendingChangeSets'): path = os.path.join(testdata_dir, 'git_repo') elif tool_name == 'Subversion': path = 'file://' + os.path.join(testdata_dir, 'svn_repo') elif tool_name == 'Mercurial': path = os.path.join(testdata_dir, 'hg_repo.bundle') elif tool_name == 'CVS': path = os.path.join(testdata_dir, 'cvs_repo') elif tool_name == 'Perforce': path = 'localhost:1666' else: raise NotImplementedError repository = Repository(name=name, local_site=local_site, tool=Tool.objects.get(name=tool_name), path=path, **kwargs) if extra_data is not None: repository.extra_data = extra_data repository.save() return repository def create_review_request(self, with_local_site=False, create_repository=False, create_with_history=False, publish=False, id=None, local_id=1001, local_site=None, repository=None, time_added=None, last_updated=None, status=ReviewRequest.PENDING_REVIEW, submitter='doc', summary='Test Summary', description='Test Description', testing_done='Testing', branch='my-branch', depends_on=None, target_people=None, target_groups=None, **kwargs): """Create a ReviewRequest for testing. The :py:class:`~reviewboard.reviews.models.review_request. ReviewRequest` may optionally be attached to a :py:class:`~reviewboard.site.models.LocalSite`. It's also populated with default data that can be overridden by the caller. Args: with_local_site (bool, optional): Whether to create this review request on a default :term:`local site`. This is ignored if ``local_site`` is provided. create_repository (bool, optional): Whether to create a new repository in the database for this review request. This can't be set if ``repository`` is provided. create_with_history (bool, optional): Whether or not the review request should support multiple commits. publish (bool, optional): Whether to publish the review request after creation. id (int, optional): An explicit database ID to set for the review request. local_id (int, optional): The ID specific to the :term:`local site`, if one is used. local_site (reviewboard.site.models.LocalSite, optional): The LocalSite to associate the review request with. If not provided, the LocalSite with the name specified in :py:attr:`local_site_name` will be used. repository (reviewboard.scmtools.models.Repository, optional): An explicit repository to set for the review request. time_added (datetime.datetime, optional): An explicit creation timestamp to set for the review request. last_updated (datetime.datetime, optional): An explicit last updated timestamp to set for the review request. status (unicode, optional): The status of the review request. This must be one of the values listed in :py:attr:`~reviewboard.reviews.models. review_request.ReviewRequest.STATUSES`. submitter (unicode or django.contrib.auth.models.User, optional): The submitter of the review request. This can be a username (which will be looked up) or an explicit user. summary (unicode, optional): The summary for the review request. description (unicode, optional): The description for the review request. testing_done (unicode, optional): The Testing Done text for the review request. branch (unicode, optional): The branch for the review request. depends_on (list of reviewboard.reviews.models.review_request. ReviewRequest, optional): A list of review requests to set as dependencies. target_people (list of django.contrib.auth.models.User, optional): A list of users to set as target reviewers. target_groups (list of reviewboard.reviews.models.group.Group, optional): A list of review groups to set as target reviewers. **kwargs (dict): Additional fields to set on the review request. Returns: reviewboard.reviews.models.review_request.ReviewRequest: The resulting review request. Raises: ValueError: An invalid value was provided during initialization. """ if not local_site: if with_local_site: local_site = self.get_local_site(name=self.local_site_name) else: local_site = None if not local_site: local_id = None if create_repository: assert not repository repository = \ self.create_repository(with_local_site=with_local_site) if not isinstance(submitter, User): submitter = User.objects.get(username=submitter) review_request = ReviewRequest( summary=summary, description=description, branch=branch, testing_done=testing_done, local_site=local_site, local_id=local_id, submitter=submitter, diffset_history=DiffSetHistory.objects.create(), repository=repository, status=status, **kwargs) review_request.created_with_history = create_with_history # Set this separately to avoid issues with CounterField updates. review_request.id = id review_request.save() if depends_on: review_request.depends_on = depends_on if target_people: review_request.target_people = target_people if target_groups: review_request.target_groups = target_groups if publish: review_request.publish(review_request.submitter) if time_added and last_updated: ReviewRequest.objects.filter(pk=review_request.pk).update( time_added=time_added, last_updated=last_updated) review_request.time_added = time_added review_request.last_updated = last_updated elif time_added: ReviewRequest.objects.filter(pk=review_request.pk).update( time_added=time_added) review_request.time_added = time_added elif last_updated: ReviewRequest.objects.filter(pk=review_request.pk).update( last_updated=last_updated) review_request.last_updated = last_updated return review_request def create_review_request_draft(self, review_request): """Create a ReviewRequestDraft for testing. Args: review_request (reviewboard.reviews.models.review_request. ReviewRequest) The review request for the draft. Returns: reviewboard.reviews.models.review_request_draft.ReviewRequestDraft: The newly-created draft. """ return ReviewRequestDraft.create(review_request) def create_visit(self, review_request, visibility, user='doc', username=None, timestamp=None): """Create a ReviewRequestVisit for testing. The ReviewRequestVisit is tied to the given ReviewRequest and User. It's populated with default data that can be overridden by the caller. The provided user may either be a username or a User object. """ if not isinstance(user, six.string_types): user = User.objects.get(username=user) return ReviewRequestVisit.objects.create( review_request=review_request, visibility=visibility, user=user) def create_review(self, review_request, user='dopey', body_top='Test Body Top', body_bottom='Test Body Bottom', ship_it=False, publish=False, timestamp=None, **kwargs): """Creates a Review for testing. The Review is tied to the given ReviewRequest. It's populated with default data that can be overridden by the caller. The provided user may either be a username or a User object. If publish is True, Review.publish() will be called. Args: review_request (reviewboard.reviews.models.review_request. ReviewRequest): The review request the review is filed against. user (unicode or django.contrib.auth.models.User, optional): The username or User object owning the review. body_top (unicode, optional): The text for the ``body_top`` field. body_bottom (unicode, optional): The text for the ``body_bottom`` field. ship_it (bool, optional): The Ship It state for the review. publish (bool, optional): Whether to publish the review immediately after creation. timestamp (datetime.datetime, optional): The timestamp for the review. **kwargs (dict): Additional attributes to set in the review. Returns: reviewboard.reviews.models.review.Review: The resulting review. """ if not isinstance(user, User): user = User.objects.get(username=user) review = Review.objects.create( review_request=review_request, user=user, body_top=body_top, body_bottom=body_bottom, ship_it=ship_it, **kwargs) if publish: review.publish() if timestamp: Review.objects.filter(pk=review.pk).update(timestamp=timestamp) review.timestamp = timestamp return review def create_review_group(self, name='test-group', with_local_site=False, local_site=None, visible=True, invite_only=False, is_default_group=False): """Creates a review group for testing. The group may optionally be attached to a LocalSite. It's also populated with default data that can be overridden by the caller. """ if not local_site and with_local_site: local_site = self.get_local_site(name=self.local_site_name) return Group.objects.create( name=name, local_site=local_site, visible=visible, invite_only=invite_only, is_default_group=is_default_group) def create_reply(self, review, user='grumpy', body_top='Test Body Top', timestamp=None, publish=False, **kwargs): """Create a review reply for testing. The reply is tied to the given Review. It's populated with default data that can be overridden by the caller. To reply to a ``body_top`` or ``body_bottom`` field, pass either ``body_top_reply_to=`` or ``body_bottom_reply_to=`` to this method. This will be passed to the review's constructor. Args: review (reviewboard.reviews.models.review.Review): The review being replied to. user (django.contrib.auth.models.User or unicode, optional): Either the user model or the username of the user who is replying to the review. body_top (unicode, optional): The body top text. timestamp (datetime.datetime, optional): The timestamp of the review. publish (bool, optional): Whether the review should be published. By default it's in draft form. **kwargs (dict): Additional arguments to pass to the :py:class:`~reviewboard.reviews.models.review.Review` constructor. Returns: reviewboard.reviews.models.review.Review: The resulting review. """ if not isinstance(user, User): user = User.objects.get(username=user) reply = Review.objects.create( review_request=review.review_request, user=user, body_top=body_top, base_reply_to=review, **kwargs) if publish: reply.publish() if timestamp: Review.objects.filter(pk=reply.pk).update(timestamp=timestamp) reply.timestamp = timestamp return reply def create_screenshot(self, review_request, caption='My caption', draft=False, active=True, **kwargs): """Create a Screenshot for testing. The screenshot is tied to the given :py:class:`~reviewboard.reviews.models.review_request.ReviewRequest`. It's populated with default data that can be overridden by the caller. Args: review_request (reviewboard.reviews.models.review_request. ReviewRequest): The review request that ultimately owns the screenshot. caption (unicode, optional): The caption to use for the screenshot. draft (bool or reviewboard.reviews.models.review_request_draft. ReviewRequestDraft): A draft to associate the screenshot with. This can also be a boolean, for legacy reasons, which will attempt to look up or create a draft for the review request. active (bool): Whether this screenshot is considered active (not deleted). **kwargs (dict): Additional fields to set on the screenshot. Returns: reviewboard.reviews.models.screenshot.Screenshot: The resulting screenshot. """ screenshot = Screenshot(caption=caption, **kwargs) filename = os.path.join(settings.STATIC_ROOT, 'rb', 'images', 'logo.png') with open(filename, 'rb') as f: screenshot.image.save(os.path.basename(filename), File(f), save=True) if draft: if isinstance(draft, ReviewRequestDraft): review_request_draft = draft else: review_request_draft = \ self.create_review_request_draft(review_request) if active: screenshots = review_request_draft.screenshots else: screenshots = review_request_draft.inactive_screenshots else: if active: screenshots = review_request.screenshots else: screenshots = review_request.inactive_screenshots screenshots.add(screenshot) return screenshot def create_screenshot_comment(self, review, screenshot, text='My comment', x=1, y=1, w=5, h=5, issue_opened=False, issue_status=None, extra_fields=None, reply_to=None, **kwargs): """Create a ScreenshotComment for testing. The comment is tied to the given Review and Screenshot. It's It's populated with default data that can be overridden by the caller. Args: review (reviewboard.reviews.models.review.Review): The review associated with the comment. screenshot (reviewboard.reviews.models.screenshot.Screenshot): The screenshot associated with the comment. text (unicode): The text for the comment. x (int, optional): The X location for the comment on the screenshot. y (int, optional): The Y location for the comment on the screenshot. w (int, optional): The width for the comment on the screenshot. h (int, optional): The height for the comment on the screenshot. issue_opened (bool, optional): Whether an issue is to be opened for the comment. issue_status (unicode, optional): The issue status to set, if an issue is opened. Defaults to being an open issue. extra_fields (dict, optional): Extra data to set on the comment. reply_to (reviewboard.reviews.models.general_comment. GeneralComment, optional): The comment this comment replies to. **kwargs (dict): Additional model attributes to set on the comment. Returns: reviewboard.reviews.models.screenshot_comment.ScreenshotComment: The resulting comment. """ if issue_opened and not issue_status: issue_status = ScreenshotComment.OPEN comment = ScreenshotComment( screenshot=screenshot, text=text, x=x, y=y, w=w, h=h, issue_opened=issue_opened, issue_status=issue_status, reply_to=reply_to, **kwargs) if extra_fields: comment.extra_data = extra_fields comment.save() review.screenshot_comments.add(comment) return comment def create_file_attachment_base(self, caption='My Caption', orig_filename='logo.png', mimetype='image/png', uuid='test-uuid', has_file=True, file_content=None, user=None, with_local_site=False, local_site_name=None, local_site=None, **kwargs): """Base helper to create a FileAttachment object. When creating a :py:class:`reviewboard.attachments.models.FileAttachment` that will be associated to a review request, a user and local_site should not be specified. This is not meant to be called directly by tests. Callers should generallly use one of: * :py:meth:`create_file_attachment` * :py:meth:`create_user_file_attachment` Args: caption (unicode, optional): The caption for the file attachment. orig_filename (unicode, optional): The original name of the file to set in the model. mimetype (unicode, optional): The mimetype of the file attachment. uuid (unicode, optional): The UUID used to prefix the filename and reference the file attachment. has_file (bool, optional): ``True`` if an actual file object should be included in the model. This will set the file content based on ``file_content``, if one is provided. If not provided, the Review Board logo is used as the file content. file_content (bytes, optional): The file content. This is only set if passing ``has_file=True``. user (django.contrib.auth.models.User, optonal): The user who owns the file attachment. with_local_site (bool, optional): ``True`` if the file attachment should be associated with a local site. If this is set, one of ``local_site_name`` or ``local_site`` should be provided as well. local_site_name (unicode, optional): The name of the local site to associate this attachment with. local_site (reviewboard.site.models.LocalSite, optional): The local site to associate this attachment with. kwargs (dict): Additional keyword arguments to pass into the FileAttachment constructor. Returns: reviewboard.attachments.models.FileAttachment: The new file attachment instance. """ if with_local_site: local_site = self.get_local_site(name=local_site_name) filename = kwargs.get('filename', '%s-%s' % (uuid, orig_filename)) file_attachment = FileAttachment( caption=caption, mimetype=mimetype, user=user, uuid=uuid, local_site=local_site, orig_filename=orig_filename, **kwargs) if has_file: if file_content is None: logo_path = os.path.join(settings.STATIC_ROOT, 'rb', 'images', 'logo.png') with open(logo_path, 'rb') as fp: file_content = fp.read() assert isinstance(file_content, bytes), ( 'file_content must be passed as bytes, not %s' % type(file_content)) file_attachment.file.save(filename, ContentFile(file_content), save=True) file_attachment.save() return file_attachment def create_general_comment(self, review, text='My comment', issue_opened=False, issue_status=None, extra_fields=None, reply_to=None, **kwargs): """Create a GeneralComment for testing. The comment is tied to the given Review. It is populated with default data that can be overridden by the caller. Args: review (reviewboard.reviews.models.review.Review): The review associated with the comment. text (unicode): The text for the comment. issue_opened (bool, optional): Whether an issue is to be opened for the comment. issue_status (unicode, optional): The issue status to set, if an issue is opened. Defaults to being an open issue. extra_fields (dict, optional): Extra data to set on the comment. reply_to (reviewboard.reviews.models.general_comment. GeneralComment, optional): The comment this comment replies to. **kwargs (dict): Additional model attributes to set on the comment. Returns: reviewboard.reviews.models.general_comment.GeneralComment: The resulting comment. """ if issue_opened and not issue_status: issue_status = GeneralComment.OPEN comment = GeneralComment( text=text, issue_opened=issue_opened, issue_status=issue_status, reply_to=reply_to, **kwargs) if extra_fields: comment.extra_data = extra_fields comment.save() review.general_comments.add(comment) return comment def create_status_update(self, review_request, user='dopey', service_id='service', summary='Status Update', state=StatusUpdate.PENDING, review=None, change_description=None, timestamp=None): """Create a status update for testing. It is populated with default data that can be overridden by the caller. Args: review_request (reviewboard.reviews.models.ReviewRequest): The review request to associate with the new status update. user (django.contrib.auth.models.User or unicode): Either the user model or the username of the user who should own the status update. service_id (unicode): The ID to fill in for the new model. summary (unicode): The summary to fill in for the new model. state (unicode): The state for the new model. This must be one of the valid choices for the state field. review (reviewboard.reviews.models.review.Review, optional): The review associated with this status update. change_description (reviewboard.changedescs.models. ChangeDescription, optional): The change description for this status update. timestamp (datetime.datetime): The timestamp for the status update. Returns: reviewboard.reviews.models.StatusUpdate: The new status update. """ if not isinstance(user, User): user = User.objects.get(username=user) status_update = StatusUpdate.objects.create( review_request=review_request, change_description=change_description, service_id=service_id, summary=summary, state=state, review=review, user=user) if timestamp: StatusUpdate.objects.filter(pk=status_update.pk).update( timestamp=timestamp) status_update.timestamp = timestamp return status_update def create_webhook(self, enabled=False, events=WebHookTarget.ALL_EVENTS, url='http://example.com', encoding=WebHookTarget.ENCODING_JSON, use_custom_content=False, custom_content='', secret='', apply_to=WebHookTarget.APPLY_TO_ALL, repositories=None, with_local_site=False, local_site=None, extra_fields=None): """Create a webhook for testing. It is populated with default data that can be overridden by the caller. Args: enabled (bool): Whether or not the webhook is enabled when it is created. events (unicode): A comma-separated list of events that the webhook will trigger on. url (unicode): The URL that requests will be made against. encoding (unicode): The encoding of the payload to send. use_custom_content (bool): Determines if custom content will be sent for the payload (if ``True``) or if it will be auto-generated (if ``False``). custom_content (unicode): The custom content to send when ``use_custom_content`` is ``True``. secret (unicode): An HMAC secret to sign the payload with. apply_to (unicode): The types of repositories the webhook will apply to. repositories (list): A list of repositories that the webhook will be limited to if ``apply_to`` is ``WebHookTarget.APPLY_TO_SELECTED_REPOS``. with_local_site (bool): Determines if this should be created with a local site. local_site (reviewboard.site.models.LocalSite): An optional local site. If ``with_local_site`` is ``True`` and this argument is ``None``, the local site will be looked up. extra_fields (dict): Extra data to be imported into the webhook. Returns: WebHookTarget: A webhook constructed with the given arguments. """ if not local_site: if with_local_site: local_site = self.get_local_site(name=self.local_site_name) else: local_site = None webhook = WebHookTarget.objects.create( enabled=enabled, events=events.split(','), url=url, encoding=encoding, use_custom_content=use_custom_content, custom_content=custom_content, secret=secret, apply_to=apply_to, local_site=local_site) if repositories: webhook.repositories = repositories if extra_fields: webhook.extra_data = extra_fields webhook.save(update_fields=['extra_data']) return webhook def create_oauth_application( self, user, local_site=None, with_local_site=False, redirect_uris='http://example.com', authorization_grant_type=Application.GRANT_CLIENT_CREDENTIALS, client_type=Application.CLIENT_PUBLIC, **kwargs): """Create an OAuth application. Args: user (django.contrib.auth.models.User): The user whom is to own the application. local_site (reviewboard.site.models.LocalSite, optional): The LocalSite for the application to be associated with, if any. redirect_uris (unicode, optional): A whitespace-separated list of allowable redirect URIs. authorization_grant_type (unicode, optional): The grant type for the application. client_type (unicode, optional): The application client type. **kwargs (dict): Additional keyword arguments to pass to the :py:class:`~reviewboard.oauth.models.Application` initializer. Returns: reviewboard.oauth.models.Application: The created application. """ if not local_site: if with_local_site: local_site = self.get_local_site(self.local_site_name) else: local_site = None return Application.objects.create( user=user, local_site=local_site, authorization_grant_type=authorization_grant_type, redirect_uris=redirect_uris, client_type=client_type, extra_data='{}', **kwargs) def create_oauth_token(self, application, user, scope='', expires=None, **kwargs): """Create an OAuth2 access token for testing. Args: application (reviewboard.oauth.models.Application): The application the token should be associated with. user (django.contrib.auth.models.User): The user who should own the token. scope (unicode, optional): The scopes of the token. This argument defaults to the empty scope. expires (datetime.timedelta, optional): How far into the future the token expires. If not provided, this argument defaults to one hour. Returns: oauth2_provider.models.AccessToken: The created access token. """ if expires is None: expires = timedelta(hours=1) return AccessToken.objects.create( application=application, token=generate_token(), expires=timezone.now() + expires, scope=scope, user=user, ) @contextmanager def siteconfig_settings(self, settings, reload_settings=True): """Temporarily sets siteconfig settings for a test. Args: settings (dict): The new siteconfig settings to set. reload_settings (bool, optional): Whether to reload and recompute all settings, applying them to Django and other objects. Context: The current site configuration will contain the new settings for this test. """ try: with super(TestCase, self).siteconfig_settings(settings): if reload_settings: load_site_config() yield finally: if reload_settings: load_site_config()

36.390149

85

0.567118

87e25da31bde304141f5902a30ed0de46184dbf2

273

py

Python

day5/d5p4.py

Akankshasharmaa/100DaysOfCode

395bd8bd063495af7d04ec7b2f819923f502059f

[ "MIT" ]

2

2021-12-22T07:43:14.000Z

2021-12-24T12:07:33.000Z

day5/d5p4.py

Akankshasharmaa/100DaysOfCode

395bd8bd063495af7d04ec7b2f819923f502059f

[ "MIT" ]

null

day5/d5p4.py

Akankshasharmaa/100DaysOfCode

395bd8bd063495af7d04ec7b2f819923f502059f

[ "MIT" ]

1

2021-12-22T07:43:26.000Z

def make_abba(a, b): newstr = a + b + b + a if len(a) >= 0 and len(b) >= 0: return newstr else: return False result = make_abba('Hi', 'Bye') print(result) result = make_abba('Yo', 'Alice') print(result) result = make_abba('x', '') print(result)

21

35

0.575092

08bcb9abe4d7956bfe9c1ebe44296bce9e3a337f

14,774

py

Python

A1 - Search and Games/fishing_game_core/app.py

NickSmyr/ai-player-agents

f8972d02c53a2ba566b541b1270a0637e3d3e5c7

[ "MIT" ]

null

A1 - Search and Games/fishing_game_core/app.py

NickSmyr/ai-player-agents

f8972d02c53a2ba566b541b1270a0637e3d3e5c7

[ "MIT" ]

null

A1 - Search and Games/fishing_game_core/app.py

NickSmyr/ai-player-agents

f8972d02c53a2ba566b541b1270a0637e3d3e5c7

[ "MIT" ]

null

import json import sys from datetime import datetime from io import UnsupportedOperation from os.path import join from pathlib import Path import numpy as np from kivy.app import App from kivy.clock import Clock from kivy.core.window import Window from kivy.lang import Builder from fishing_game_core.communicator import Communicator from fishing_game_core.player_utils import Player from fishing_game_core.sequences import Sequences from fishing_game_core.shared import SettingLoader from fishing_game_core.shared import TYPE_TO_SCORE from fishing_game_core.widgets import Boat, TimeBoard, Stats, FishingDerby, Fish home = str(Path.home()) class Fishes(SettingLoader): def __init__(self): super().__init__() self.seq_types_fishes = None self.observations_sequence = None self.main_widget = None self.fishes = {} def init_fishes(self): """ Initialize fishes and their parameters :return: """ # Generate fishes exactly according to the custom specification. self.fishes.clear() init_fishes = self.observations_sequence['init_fishes'] for i in range(len(init_fishes)): init_x,init_y = init_fishes[str(i)]['init_pos'] score = init_fishes[str(i)]['score'] obs_seq = self.observations_sequence['sequence'][str(i)] name = "fish"+str(i) # Get the right fish type from the score. type_fish = None for key, value in TYPE_TO_SCORE.items(): if value == score: type_fish = key fish = Fish(init_state=(init_x, init_y), type_fish=type_fish, name=name, observations_sequence=obs_seq, settings=self.settings) self.main_widget.ids.fish_layout.add_widget(fish) self.fishes[name] = fish class PrintScoresAbstract: def __init__(self): self.time = 0 self.total_time = 0 self.main_widget = None self.players = {} class PrintScore2Players(PrintScoresAbstract): def print_score(self): if hasattr(self, 'latest_msg') and self.latest_msg is not None and self.latest_msg['search_time'] is not None: search_time = self.latest_msg['search_time'] # print("Elapsed time:", str(self.time) + '/' + str(self.total_time), # "s\tScore:", self.players[0].score - self.players[1].score, '\tSearch time:', '%.2E' % search_time) return # print("Elapsed time:", str(self.time) + '/' + str(self.total_time), # "s\tScore:", self.players[0].score - self.players[1].score) class PrintScore1Player(PrintScoresAbstract): def print_score(self): # print("Elapsed time:", str(self.time) + '/' + str(self.total_time), # "s\tScore:", self.players[0].score) pass class GamesWithBoats: def __init__(self): self.settings = None self.main_widget = None self.players = None def introduce_boats_to_screen(self, n_boats): """ Introduce and draw the boats on the screen :type n_boats: int. Number of boats to draw. :return: """ colors = [[0, 0.5, 0, 1], [1, 0, 0, 1]] space_subdivisions = 20 for i in range(1, n_boats + 1): if not hasattr(self, 'observations_sequence'): # sanity check raise Exception('wrong settings specification for boats...') init_players = self.observations_sequence['init_players'] init_pos = init_players[str(i-1)] init_pos_x_boat = init_pos[0] init_pos_y_hook = init_pos[1] boat = Boat(init_pos_x_boat, space_subdivisions=space_subdivisions, source=f"fishing_game_core/images/fishing{i}.png", init_hook=init_pos_y_hook) boat.line_rod.color = colors[i - 1] self.main_widget.ids.boats_layout.add_widget(boat) self.main_widget.ids.hooks_layout.add_widget(boat.hook) self.main_widget.ids.line_rods_layout.add_widget(boat.line_rod) self.players[i - 1].boat = boat class FishingDerbyApp(App, SettingLoader, Communicator): def __init__(self): App.__init__(self) SettingLoader.__init__(self) Communicator.__init__(self) # Use the main kivy file to draw the board Builder.load_file('fishing_game_core/main.kv') # Create class variables and set default values self.fishes = {} # Dictionary of fishes self._cnt_steps = 0 # Count of the number of steps taken so far self.move_x = [] # Next moves of the fishes in the x axis self.move_y = [] # Next moves of the fishes in the y axis self.action = "stay" # Actions received from player self.time = 0 # Seconds since start self.total_time = 60 # Total time of the game self.players = [] # Players list self.main_widget = None # Main widget of the game self.time_board = None # Time board widget # PID of the player loop in order to be able to kill it when the game is over self.player_loop_pid = None self.observations_sequence = None self.update_scheduled = None self.timer_scheduled = None # Steps counter is a number that goes from 0 to frames_per_action """ @property def cnt_steps(self): frames_per_action = 10 return self._cnt_steps % frames_per_action @cnt_steps.setter def cnt_steps(self, val): self._cnt_steps = val """ def set_player_loop_pid(self, pid): self.player_loop_pid = pid def create_players(self): """Always 2 players, not necessarily 2 boats""" self.players = [Player(), Player()] def update(self, dt): raise NotImplementedError def init_clock(self): """ Initialize the timer :return: """ n_seq = self.observations_sequence["params"]["n_seq"] self.total_time = n_seq * 10 * 1.0 / self.settings.frames_per_second self.time_board = TimeBoard(seconds=int(self.total_time)) self.time_board.pos_hint['center_x'] = 0.5 self.main_widget.add_widget(self.time_board) self.timer_scheduled = Clock.schedule_interval(self.update_clock, 1.0) def check_fish_near(self, boat): """ Catch a random fish that is on the same position as the boat if possible :param boat: Boat. It must not have a caught fish. :return: """ indices = np.random.permutation(len(self.fishes)) keys = list(self.fishes.keys()) for f in indices: fish = self.fishes[keys[f]] if fish.position == boat.hook.position and fish.caught is None: return fish def new_action(self, msg): """ Assign the new action coming from the message :param msg: dict. Message coming from the receiver. :return: """ self.action = msg["action"] def send_state_or_display_stats(self): """ Send msg in order to indicate the player we have updated the game. If game has ended, display the stats screen. """ msg = { "game_over": self.main_widget.game_over } if self.main_widget.game_over: self.timer_scheduled.cancel() self.update_scheduled.cancel() self.display_stats() self.sender(msg) return False self.update_specific(msg) return True def update_clock(self, dl): """ Increase the clock by 1 second. If the remaining time is 0, the game is over. :param dl: delta-time. Not used. :return: """ if self.time_board.seconds == 0: self.main_widget.game_over = True else: self.time_board.seconds -= 1 self.time += 1.0 def fishes_next_move(self): """ Calculate and store, for every fish, the infinitesimal moving step for the position changing process. After that, increase each fish's updates counter. :return: """ self.move_x.clear() self.move_y.clear() for fish in self.fishes.values(): move_x, move_y = fish.next_movement_and_flip_horizontally() self.move_x += [move_x / self.settings.frames_per_action] self.move_y += [move_y / self.settings.frames_per_action] fish.updates_cnt += 1 def check_fishes_caught(self): """ For every boat in the game, do one of: 1) if no fish is caught by it, check whether any can be caught 2) if a fish has been caught and the player is at the surface, finish pulling the rod :return: """ for player_number, player in enumerate(self.players): boat = player.boat if boat is None: continue elif boat.has_fish is None: fish_near = self.check_fish_near(boat) if fish_near is not None: self.main_widget.ids.fish_layout.remove_widget(fish_near) self.main_widget.ids.fish_layout.add_widget(fish_near) boat.has_fish = fish_near fish_near.caught = boat if boat.has_fish is not None and boat.hook.position.y == 19: self.main_widget.finish_pulling_fish(player_number) def load_observations(self): """ Load the observations file stated in the settings :return: """ try: sequences = Sequences() sequences.load(self.settings.observations_file) self.observations_sequence = sequences.data except AttributeError: print("Observations file not provided", file=sys.stderr) def init_specific(self): """ Specific initialization of App. Abstract. :return: """ raise NotImplementedError def update_specific(self, msg): """ Specific action to perform in the loop with the message from the player controlled. :param msg: :return: """ raise NotImplementedError def update_fishes_position_and_increase_steps(self): """ Change the position of every fish by the amount inside move_x and move_y lists. After that, increase the updates counter of the game. :return: """ for i, fish in enumerate(self.fishes.values()): fish.increase_x_y(self.move_x[i], self.move_y[i]) self._cnt_steps += 1 def calculate_strategy_for_next_frame_action(self): pass def display_stats(self): scores_file = join(home, ".fishing_derby_scores") stats = Stats(self.players, self.settings, self.fishes) with open(scores_file, "a") as f: try: stats_file = json.load(f) except UnsupportedOperation: stats_file = dict() stats_dict = stats.get_stats() stats_file[datetime.now().timestamp()] = stats_dict json.dump(stats_file, f) stats.load(stats_dict) stats.open() def build(self): """Initialize the Kivy screen""" # Set sky color Window.clearcolor = 63 / 255, 191 / 255, 191 / 255, 0.3 # Create main widget self.create_players() self.main_widget = FishingDerby(fishes=self.fishes, players=self.players, settings=self.settings) self.init_clock() self.init_specific() # Run initial update self.fishes_next_move() self.update_scheduled = Clock.schedule_interval( self.update, 1.0 / self.settings.frames_per_second) # Kivy receives main widget and draws it return self.main_widget class FishingDerbyHumanApp(FishingDerbyApp, Fishes, PrintScore1Player, GamesWithBoats): def __init__(self): super().__init__() # Keyboard events self._keyboard = None self.last_action = None def update_clock(self, dl): super().update_clock(dl) self.print_score() def _keyboard_closed(self): self._keyboard.unbind( on_key_down=self._key_down_function, on_key_up=self._key_up_function) self._keyboard = None def _key_down_function(self, keyboard, key_code, text, modifiers): self.last_action = key_code[1] if key_code[1] in [ 'up', 'down', 'right', 'left'] else 'stay' def _key_up_function(self, keyboard, key_code): self.last_action = 'stay' def update_specific(self, msg): msg = {"action": self.last_action} self.new_action(msg) def build(self): """Initialize the Kivy screen""" # Set sky color Window.clearcolor = 63 / 255, 191 / 255, 191 / 255, 0.3 # Create main widget self.load_observations() self.create_players() self.main_widget = FishingDerby(fishes=self.fishes, players=self.players, settings=self.settings) self.init_clock() self.init_specific() # Run initial update self.fishes_next_move() self.update_scheduled = Clock.schedule_interval( self.update, 1.0 / self.settings.frames_per_second) # Attach the keyboard self._keyboard = self.main_widget.keyboard self._keyboard.bind(on_key_down=self._key_down_function, on_key_up=self._key_up_function) # Kivy receives main widget and draws it return self.main_widget def update(self, dt): if self._cnt_steps % self.settings.frames_per_action == 0 and self._cnt_steps > 0: # Check if a fish is to be caught by any of the players self.check_fishes_caught() # Check if game is about to timeout if self.time >= self.total_time: self.main_widget.game_over = True self.send_state_or_display_stats() self.fishes_next_move() self.update_fishes_position_and_increase_steps() self.execute_action() def init_specific(self): self.init_fishes() self.introduce_boats_to_screen(1) def execute_action(self): if self.players[0].boat.has_fish: self.main_widget.act("up", player=0) else: self.main_widget.act(self.action, player=0)

34.438228

119

0.607554

2d8c76401c7b11b1b733325ce1fb21cba63a5b30

1,036

py

Python

Alt/saliencymapper/api.py

MalcolmGomes/SoftwareEng2

2cc9417a30cd1350980bc6e272d1024e866397a6

[ "MIT" ]

null

Alt/saliencymapper/api.py

MalcolmGomes/SoftwareEng2

2cc9417a30cd1350980bc6e272d1024e866397a6

[ "MIT" ]

null

Alt/saliencymapper/api.py

MalcolmGomes/SoftwareEng2

2cc9417a30cd1350980bc6e272d1024e866397a6

[ "MIT" ]

null

from flask import Flask, request, send_file from flask_restful import Resource, Api from saliency_mapper import * import os import requests import torch import time import sys from torchvision import models from torchvision import transforms app = Flask(__name__) api = Api(app) class SaliencyMapAPI(Resource): def get(self): filename = "malcolm.jpg" img = Image.open(filename) output_path = generate_saliency_map(img, filename) return send_file(output_path, attachment_filename=filename) def post(self): image_url = request.form["image_url"] filename = image_url.split('/')[-1] r = requests.get(image_url, allow_redirects=True) open(filename, 'wb').write(r.content) img = Image.open(filename) output_path = generate_saliency_map(img, filename) os.remove(filename) return send_file(output_path, attachment_filename=filename) # return { # 'img': output_path # } api.add_resource(SaliencyMapAPI, '/') if __name__ == '__main__': app.run(host='0.0.0.0', port=80, debug=True)

25.268293

61

0.732625

029c948456cb89b831f5a1ffdc142e7ca4638310

10,364

py

Python

verde/vector.py

djhoese/verde

ad14acf94717ee5c6672559f40576f65989753a5

[ "BSD-3-Clause" ]

null

verde/vector.py

djhoese/verde

ad14acf94717ee5c6672559f40576f65989753a5

[ "BSD-3-Clause" ]

null

verde/vector.py

djhoese/verde

ad14acf94717ee5c6672559f40576f65989753a5

[ "BSD-3-Clause" ]

null

""" Vector gridding using elasticity Green's functions from Sandwell and Wessel (2016). """ import numpy as np from sklearn.utils.validation import check_is_fitted from .base import check_fit_input from .spline import Spline from .coordinates import get_region class Vector2D(Spline): r""" Elastically coupled interpolation of 2-component vector data. Uses the Green's functions based on elastic deformation from [SandwellWessel2016]_. The interpolation is done by estimating point forces that generate an elastic deformation that fits the observed vector data. The deformation equations are based on a 2D elastic sheet with a constant Poisson's ratio. The data can then be predicted at any desired location. The east and north data components are coupled through the elastic deformation equations. This coupling is controlled by the Poisson's ratio, which is usually between -1 and 1. The special case of Poisson's ratio -1 leads to an uncoupled interpolation, meaning that the east and north components don't interfere with each other. The point forces are traditionally placed under each data point. The force locations are set the first time :meth:`~verde.Vector2D.fit` is called. Subsequent calls will fit using the same force locations as the first call. This configuration results in an exact prediction at the data points but can be unstable. [SandwellWessel2016]_ stabilize the solution using Singular Value Decomposition but we use ridge regression instead. The regularization can be controlled using the *damping* argument. Alternatively, we also allow forces to be placed on a regular grid using the *spacing*, *shape*, and/or *region* arguments. Regularization or forces on a grid will result in a least-squares estimate at the data points, not an exact solution. Note that the least-squares solution is required for data weights to have any effect. The Jacobian (design, sensitivity, feature, etc) matrix for the spline is normalized using :class:`sklearn.preprocessing.StandardScaler` without centering the mean so that the transformation can be undone in the estimated forces. Parameters ---------- poisson : float The Poisson's ratio for the elastic deformation Green's functions. Default is 0.5. A value of -1 will lead to uncoupled interpolation of the east and north data components. fudge : float The positive fudge factor applied to the Green's function to avoid singularities. damping : None or float The positive damping regularization parameter. Controls how much smoothness is imposed on the estimated forces. If None, no regularization is used. shape : None or tuple If not None, then should be the shape of the regular grid of forces. See :func:`verde.grid_coordinates` for details. spacing : None or float or tuple If not None, then should be the spacing of the regular grid of forces. See :func:`verde.grid_coordinates` for details. region : None or tuple If not None, then the boundaries (``[W, E, S, N]``) used to generate a regular grid of forces. If None is given, then will use the boundaries of data given to the first call to :meth:`~verde.Vector2D.fit`. Attributes ---------- forces_ : array The estimated forces that fit the observed data. force_coords_ : tuple of arrays The easting and northing coordinates of the forces. region_ : tuple The boundaries (``[W, E, S, N]``) of the data used to fit the interpolator. Used as the default region for the :meth:`~verde.Vector2D.grid` and :meth:`~verde.Vector2D.scatter` methods. See also -------- verde.vector2d_jacobian : Jacobian matrix for the 2D elastic deformation """ def __init__( self, poisson=0.5, fudge=1e-5, damping=None, shape=None, spacing=None, region=None, ): self.poisson = poisson super().__init__( fudge=fudge, damping=damping, shape=shape, spacing=spacing, region=region ) def fit(self, coordinates, data, weights=None): """ Fit the gridder to the given 2-component vector data. The data region is captured and used as default for the :meth:`~verde.Vector2D.grid` and :meth:`~verde.Vector2D.scatter` methods. All input arrays must have the same shape. Parameters ---------- coordinates : tuple of arrays Arrays with the coordinates of each data point. Should be in the following order: (easting, northing, vertical, ...). Only easting and northing will be used, all subsequent coordinates will be ignored. data : tuple of array A tuple ``(east_component, north_component)`` of arrays with the vector data values at each point. weights : None or tuple array If not None, then the weights assigned to each data point. Must be one array per data component. Typically, this should be 1 over the data uncertainty squared. Returns ------- self Returns this estimator instance for chaining operations. """ coordinates, data, weights = check_fit_input( coordinates, data, weights, unpack=False ) if len(data) != 2: raise ValueError( "Need two data components. Only {} given.".format(len(data)) ) # Capture the data region to use as a default when gridding. self.region_ = get_region(coordinates[:2]) self.force_coords_ = self._get_force_coordinates(coordinates) if any(w is not None for w in weights): weights = np.concatenate([i.ravel() for i in weights]) else: weights = None self._check_weighted_exact_solution(weights) data = np.concatenate([i.ravel() for i in data]) jacobian = vector2d_jacobian( coordinates[:2], self.force_coords_, self.poisson, self.fudge ) self.force_ = self._estimate_forces(jacobian, data, weights) return self def predict(self, coordinates): """ Evaluate the fitted gridder on the given set of points. Requires a fitted estimator (see :meth:`~verde.Vector2D.fit`). Parameters ---------- coordinates : tuple of arrays Arrays with the coordinates of each data point. Should be in the following order: (easting, northing, vertical, ...). Only easting and northing will be used, all subsequent coordinates will be ignored. Returns ------- data : tuple of arrays A tuple ``(east_component, north_component)`` of arrays with the predicted vector data values at each point. """ check_is_fitted(self, ["force_", "force_coords_"]) jac = vector2d_jacobian( coordinates[:2], self.force_coords_, self.poisson, self.fudge ) cast = np.broadcast(*coordinates[:2]) npoints = cast.size components = jac.dot(self.force_).reshape((2, npoints)) return tuple(comp.reshape(cast.shape) for comp in components) def vector2d_jacobian( coordinates, force_coordinates, poisson, fudge=1e-5, dtype="float32" ): """ Make the Jacobian matrix for the 2D coupled elastic deformation. Follows [SandwellWessel2016]_. The Jacobian is segmented into 4 parts, each relating a force component to a data component:: | J_ee J_ne |*|f_e| = |d_e| | J_ne J_nn | |f_n| |d_n| The forces and data are assumed to be stacked into 1D arrays with the east component on top of the north component. Parameters ---------- coordinates : tuple of arrays Arrays with the coordinates of each data point. Should be in the following order: (easting, northing, vertical, ...). Only easting and northing will be used, all subsequent coordinates will be ignored. force_coordinates : tuple of arrays Arrays with the coordinates of each vertical force. Should be in the following order: (easting, northing, vertical, ...). Only easting and northing will be used, all subsequent coordinates will be ignored. poisson ; float The Poisson's ratio for the elastic deformation Green's functions. A value of -1 will lead to uncoupled interpolation of the east and north data components (the ``J_ne`` component of the Jacobian is null). fudge : float The positive fudge factor applied to the Green's function to avoid singularities. dtype : str or numpy dtype The type of the Jacobian array. Returns ------- jacobian : 2D array The (n_data*2, n_forces*2) Jacobian matrix. See also -------- verde.Vector2D : Coupled gridder for 2-component vector data """ force_coordinates = [np.atleast_1d(i).ravel() for i in force_coordinates[:2]] coordinates = [np.atleast_1d(i).ravel() for i in coordinates[:2]] npoints = coordinates[0].size nforces = force_coordinates[0].size # Reshaping the data coordinates to a column vector will automatically # build a distance matrix between each data point and force. east, north = ( datac.reshape((npoints, 1)) - forcec for datac, forcec in zip(coordinates, force_coordinates) ) distance = np.hypot(east, north, dtype=dtype) # The fudge factor helps avoid singular matrices when the force and # computation point are too close distance += fudge # Pre-compute common terms for the Green's functions of each component ln_r = (3 - poisson) * np.log(distance) over_r2 = (1 + poisson) / distance ** 2 jac = np.empty((npoints * 2, nforces * 2), dtype=dtype) jac[:npoints, :nforces] = ln_r + over_r2 * north ** 2 # J_ee jac[npoints:, nforces:] = ln_r + over_r2 * east ** 2 # J_nn jac[:npoints, nforces:] = -over_r2 * east * north # J_ne jac[npoints:, :nforces] = jac[:npoints, nforces:] # J is symmetric return jac

40.015444

85

0.65988

2ae10782e28b42c62b64daf4ca0ae47ffbf83626

1,335

py

Python

app/core/tests/test_admin.py

AliSayyah/recipe-app-api

42c689ba2fd709dad35508d5f09452daa33f81ea

[ "MIT" ]

null

app/core/tests/test_admin.py

AliSayyah/recipe-app-api

42c689ba2fd709dad35508d5f09452daa33f81ea

[ "MIT" ]

null

app/core/tests/test_admin.py

AliSayyah/recipe-app-api

42c689ba2fd709dad35508d5f09452daa33f81ea

[ "MIT" ]

null

from django.test import TestCase from django.contrib.auth import get_user_model from django.urls import reverse from django.test import Client class AdminSiteTests(TestCase): def setUp(self): self.client = Client() self.admin_user = get_user_model().objects.create_superuser( email='ali.sayyah79@gmail.com', password='test123' ) self.client.force_login(self.admin_user) self.user = get_user_model().objects.create_user( email='ali.sayyah78@gmail.com', password='test123', name='testUser' ) def test_users_listed(self): """Test that users are listed on user page""" url = reverse('admin:core_user_changelist') res = self.client.get(url) self.assertContains(res, self.user.name) self.assertContains(res, self.user.email) def test_user_change_page(self): """Test that the user edit page works""" url = reverse('admin:core_user_change', args=[self.user.id]) res = self.client.get(url) self.assertEqual(res.status_code, 200) def test_create_user_page(self): """Test that the create user page works""" url = reverse('admin:core_user_add') res = self.client.get(url) self.assertEqual(res.status_code, 200)

31.785714

68

0.643446

eebe676f07082cced8c7fbd96dd3cb896c4776b4

5,125

py

Python

src/pykeen/models/unimodal/pair_re.py

rpatil524/pykeen

b76239ab68f15bbf52af744c2821c73c2115b5aa

[ "MIT" ]

null

src/pykeen/models/unimodal/pair_re.py

rpatil524/pykeen

b76239ab68f15bbf52af744c2821c73c2115b5aa

[ "MIT" ]

null

src/pykeen/models/unimodal/pair_re.py

rpatil524/pykeen

b76239ab68f15bbf52af744c2821c73c2115b5aa

[ "MIT" ]

null

# -*- coding: utf-8 -*- """Implementation of PairRE.""" from typing import Any, ClassVar, Mapping, Optional from torch.nn import functional from torch.nn.init import uniform_ from ..nbase import ERModel from ...constants import DEFAULT_EMBEDDING_HPO_EMBEDDING_DIM_RANGE from ...nn.emb import EmbeddingSpecification from ...nn.modules import PairREInteraction from ...typing import Hint, Initializer, Normalizer __all__ = [ 'PairRE', ] class PairRE(ERModel): r"""An implementation of PairRE from [chao2020]_. --- citation: author: Chao year: 2020 link: http://arxiv.org/abs/2011.03798 github: alipay/KnowledgeGraphEmbeddingsViaPairedRelationVectors_PairRE """ #: The default strategy for optimizing the model's hyper-parameters hpo_default: ClassVar[Mapping[str, Any]] = dict( embedding_dim=DEFAULT_EMBEDDING_HPO_EMBEDDING_DIM_RANGE, p=dict(type=int, low=1, high=2), ) #: The default entity normalizer parameters #: The entity representations are normalized to L2 unit length #: cf. https://github.com/alipay/KnowledgeGraphEmbeddingsViaPairedRelationVectors_PairRE/blob/0a95bcd54759207984c670af92ceefa19dd248ad/biokg/model.py#L232-L240 # noqa: E501 default_entity_normalizer_kwargs: ClassVar[Mapping[str, Any]] = dict( p=2, dim=-1, ) def __init__( self, embedding_dim: int = 200, p: int = 1, power_norm: bool = False, entity_initializer: Hint[Initializer] = uniform_, entity_initializer_kwargs: Optional[Mapping[str, Any]] = None, entity_normalizer: Hint[Normalizer] = functional.normalize, entity_normalizer_kwargs: Optional[Mapping[str, Any]] = None, relation_initializer: Hint[Initializer] = uniform_, relation_initializer_kwargs: Optional[Mapping[str, Any]] = None, **kwargs, ) -> None: r"""Initialize PairRE via the :class:`pykeen.nn.modules.PairREInteraction` interaction. :param embedding_dim: The entity embedding dimension $d$. :param p: The $l_p$ norm. :param power_norm: Should the power norm be used? :param kwargs: Remaining keyword arguments passed through to :class:`pykeen.models.ERModel`. """ entity_normalizer_kwargs = _resolve_kwargs( kwargs=entity_normalizer_kwargs, default_kwargs=self.default_entity_normalizer_kwargs, ) # update initializer settings, cf. # https://github.com/alipay/KnowledgeGraphEmbeddingsViaPairedRelationVectors_PairRE/blob/0a95bcd54759207984c670af92ceefa19dd248ad/biokg/model.py#L45-L49 # https://github.com/alipay/KnowledgeGraphEmbeddingsViaPairedRelationVectors_PairRE/blob/0a95bcd54759207984c670af92ceefa19dd248ad/biokg/model.py#L29 # https://github.com/alipay/KnowledgeGraphEmbeddingsViaPairedRelationVectors_PairRE/blob/0a95bcd54759207984c670af92ceefa19dd248ad/biokg/run.py#L50 entity_initializer_kwargs = self._update_embedding_init_with_default( entity_initializer_kwargs, embedding_dim=embedding_dim, ) relation_initializer_kwargs = self._update_embedding_init_with_default( relation_initializer_kwargs, # in the original implementation the embeddings are initialized in one parameter embedding_dim=2 * embedding_dim, ) super().__init__( interaction=PairREInteraction, interaction_kwargs=dict(p=p, power_norm=power_norm), entity_representations=EmbeddingSpecification( embedding_dim=embedding_dim, initializer=entity_initializer, initializer_kwargs=entity_initializer_kwargs, normalizer=entity_normalizer, normalizer_kwargs=entity_normalizer_kwargs, ), relation_representations=[ EmbeddingSpecification( embedding_dim=embedding_dim, initializer=relation_initializer, initializer_kwargs=relation_initializer_kwargs, ), EmbeddingSpecification( embedding_dim=embedding_dim, initializer=relation_initializer, initializer_kwargs=relation_initializer_kwargs, ), ], **kwargs, ) @staticmethod def _update_embedding_init_with_default( init_kwargs: Optional[Mapping[str, Any]], embedding_dim: int, ) -> Mapping[str, float]: """Update kwargs by dimension-based default init range.""" init_kwargs = dict(init_kwargs or {}) embedding_range = 14 / embedding_dim init_kwargs.setdefault("a", -embedding_range) init_kwargs.setdefault("b", embedding_range) return init_kwargs def _resolve_kwargs(kwargs: Optional[Mapping[str, Any]], default_kwargs: Mapping[str, Any]) -> Mapping[str, Any]: kwargs = dict(kwargs or {}) for k, v in default_kwargs.items(): kwargs.setdefault(k, v) return kwargs

40.674603

177

0.677268

a8c79c79f3f09c0650c3ca9075b2f343a1ca6b1b

1,640

py

Python

constant/base.py

MacHu-GWU/constant-project

de44b1973d0457b856d47d6e17b3997b3611179e

[ "MIT" ]

null

constant/base.py

MacHu-GWU/constant-project

de44b1973d0457b856d47d6e17b3997b3611179e

[ "MIT" ]

null

constant/base.py

MacHu-GWU/constant-project

de44b1973d0457b856d47d6e17b3997b3611179e

[ "MIT" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- """ Similar to ``collections.namedtuple``, ``nameddict`` is a data container class. **中文文档** 和 ``collections.namedtuple`` 类似, ``nameddict`` 是一种数据容器类。提供了方便的方法对属性, 值进行for循环, 以及和list, dict之间的IO交互。 """ try: from .pkg import nameddict, name_convention except: from constant.pkg import nameddict, name_convention SEP = "____" class Base(nameddict.Base): """nameddict base class. """ __attrs__ = None """该属性非常重要, 定义了哪些属性被真正视为 ``attributes``, 换言之, 就是在 :meth:`~Base.keys()`, :meth:`~Base.values()`, :meth:`~Base.items()`, :meth:`~Base.to_list()`, :meth:`~Base.to_dict()`, :meth:`~Base.to_OrderedDict()`, :meth:`~Base.to_json()`, 方法中要被包括的属性。 """ def items(self): return [(key, value) for key, value in super(Base, self).items() if SEP not in key] def _getattr_by_key_value(self, key): """High order function for self.getattr_by_field(value). """ def getattr_by_key_value(value): return getattr( self, "%s____%s" % (key, name_convention.to_index_key(value))) return getattr_by_key_value def __getattr__(self, attr): """ >>> obj.getattr_by_name("John") == obj.name____John >>> True >>> obj.name____John.name == "John" >>> True """ if attr.startswith("getattr_by_"): key = attr.replace("getattr_by_", "") return self._getattr_by_key_value(key) else: return object.__getattribute__(self, attr) #--- Unittest --- if __name__ == "__main__": """ """

26.031746

85

0.590854

41d5809adb7d350a2852159d1a56f85a78d56f9c

6,362

py

Python

code/zoltar-scripts/upload_covid19_forecasts_to_zoltar.py

Yannael/covid19-forecast-hub

a16c9a147df5536ea75f2870b1f8a0227ef6d3ee

[ "MIT" ]

null

code/zoltar-scripts/upload_covid19_forecasts_to_zoltar.py

Yannael/covid19-forecast-hub

a16c9a147df5536ea75f2870b1f8a0227ef6d3ee

[ "MIT" ]

null

code/zoltar-scripts/upload_covid19_forecasts_to_zoltar.py

Yannael/covid19-forecast-hub

a16c9a147df5536ea75f2870b1f8a0227ef6d3ee

[ "MIT" ]

null

from zoltpy.quantile_io import json_io_dict_from_quantile_csv_file from zoltpy import util from zoltpy.connection import ZoltarConnection from zoltpy.covid19 import VALID_TARGET_NAMES, covid19_row_validator, validate_quantile_csv_file import os import sys import yaml import hashlib import pickle # meta info project_name = 'COVID-19 Forecasts' project_obj = None project_timezeros = [] conn = util.authenticate() url = 'https://github.com/reichlab/covid19-forecast-hub/tree/master/data-processed/' try: with open('./code/zoltar-scripts/validated_file_db.p', 'rb') as f: l = pickle.load(f) f.close() except Exception as ex: l = [] db = dict(l) # Get all existing timezeros and models in the project project_obj = [project for project in conn.projects if project.name == project_name][0] project_timezeros = [timezero.timezero_date for timezero in project_obj.timezeros] models = [model for model in project_obj.models] model_names = [model.name for model in models] # Function to read metadata file to get model name def metadata_dict_for_file(metadata_file): with open(metadata_file, encoding="utf8") as metadata_fp: metadata_dict = yaml.safe_load(metadata_fp) return metadata_dict # Function to upload all forecasts in a specific directory def upload_covid_all_forecasts(path_to_processed_model_forecasts, dir_name): global models global model_names # Get all forecasts in the directory of this model forecasts = os.listdir(path_to_processed_model_forecasts) # Get model name or create a new model if it's not in the current Zoltar project try: metadata = metadata_dict_for_file(path_to_processed_model_forecasts+'metadata-'+dir_name+'.txt') except Exception as ex: return ex model_name = metadata['model_name'] if model_name not in model_names: model_config = {} model_config['name'], model_config['abbreviation'], model_config['team_name'], model_config['description'], model_config['home_url'], model_config['aux_data_url'] \ = metadata['model_name'], metadata['team_abbr']+'-'+metadata['model_abbr'], metadata['team_name'], metadata['methods'], url + dir_name, 'NA' try: project_obj.create_model(model_config) models = project_obj.models model_names = [model.name for model in models] except Exception as ex: return ex model = [model for model in models if model.name == model_name][0] # Get names of existing forecasts to avoid re-upload existing_forecasts = [forecast.source for forecast in model.forecasts] # Batch upload json_io_dict_batch = [] forecast_filename_batch = [] timezero_date_batch = [] for forecast in forecasts: over_write = False # Check if forecast is already on zoltar with open(path_to_processed_model_forecasts+forecast, "rb") as f: # Get the current hash of a processed file checksum = hashlib.md5(f.read()).hexdigest() f.close() # Check this hash against the previous version of hash if db.get(forecast, None) != checksum: print(forecast) db[forecast] = checksum if forecast in existing_forecasts: over_write = True else: continue # Skip metadata text file if '.txt' in forecast: continue with open(path_to_processed_model_forecasts+forecast) as fp: # Get timezero and create timezero on zoltar if not existed time_zero_date = forecast.split(dir_name)[0][:-1] if time_zero_date not in project_timezeros: try: project_obj.create_timezero(time_zero_date) project_timezeros.append(time_zero_date) except Exception as ex: return ex # Validate covid19 file errors_from_validation = validate_quantile_csv_file(path_to_processed_model_forecasts+forecast) # Upload forecast if "no errors" == errors_from_validation: quantile_json, error_from_transformation = json_io_dict_from_quantile_csv_file(fp, VALID_TARGET_NAMES, covid19_row_validator) if len(error_from_transformation) >0 : return error_from_transformation else: try: util.upload_forecast(conn, quantile_json, forecast, project_name, model_name , time_zero_date, overwrite=over_write) except Exception as ex: print(ex) return ex json_io_dict_batch.append(quantile_json) timezero_date_batch.append(time_zero_date) forecast_filename_batch.append(forecast) else: return errors_from_validation fp.close() # # Batch upload for better performance # if len(json_io_dict_batch) > 0: # try: # util.upload_forecast_batch(conn, json_io_dict_batch, forecast_filename_batch, project_name, model_name, timezero_date_batch, overwrite = over_write) # except Exception as ex: # return ex return "Pass" # Example Run: python3 ./code/zoltar-scripts/upload_covid19_forecasts_to_zoltar.py if __name__ == '__main__': list_of_model_directories = os.listdir('./data-processed/') output_errors = {} for directory in list_of_model_directories: # if "CovidActNow-SEIR_CAN" not in directory: # continue if "." in directory: continue output = upload_covid_all_forecasts('./data-processed/'+directory+'/',directory) if output != "Pass": output_errors[directory] = output # List all files that did not get upload and its error if len(output_errors) > 0: for directory, errors in output_errors.items(): print("\n* ERROR IN '", directory, "'") print(errors) sys.exit("\n ERRORS FOUND EXITING BUILD...") else: print("✓ no errors") with open('./code/zoltar-scripts/validated_file_db.p', 'wb') as fw: pickle.dump(db, fw) fw.close()

39.515528

172

0.649167

2afcc43ccfa943fff4102358ceb907ab6f08151c

1,031

py

Python

www/SYS/FILE.py

ranyxr/infoVis

307c2ffc4c7d6cf87ed000310a1f2b6233bd7a3b

[ "MIT" ]

2

2020-05-27T11:12:41.000Z

2020-12-17T19:33:41.000Z

www/SYS/FILE.py

ranyxr/infoVis

307c2ffc4c7d6cf87ed000310a1f2b6233bd7a3b

[ "MIT" ]

null

www/SYS/FILE.py

ranyxr/infoVis

307c2ffc4c7d6cf87ed000310a1f2b6233bd7a3b

[ "MIT" ]

3

2020-03-18T19:20:24.000Z

2020-12-17T17:37:24.000Z

from SYS import DIR import os "picture-info.csv" _meta_data_fnm = "metadatas.csv" _meta_data_uri = os.path.join(DIR.raw_data, _meta_data_fnm) meta_data_uri = _meta_data_uri _meta_level_meta_fnm = "picture-description.csv" _meta_level_meta_uri = os.path.join(DIR.raw_data, _meta_level_meta_fnm) pic_desc_uri = _meta_level_meta_uri _data_dump_fnm = "datadump.csv" _data_dump_uri = os.path.join(DIR.raw_data, _data_dump_fnm) data_dump_uri = _data_dump_uri _reproductions_fnm = "picture-info.csv" _reproductions_uri = os.path.join(DIR.raw_data, _reproductions_fnm) pic_info_uri = _reproductions_uri cleaned_data1_fnm = DIR.clean_data1 cleaned_data1_uri = DIR.clean_data1 cleaned_data2_fnm = DIR.clean_data2 cleaned_data2_uri = DIR.clean_data2 word_cloud_data1_fnm = DIR.word_cloud_data1 word_cloud_data1_uri = DIR.word_cloud_data1 result_stack_data_uri = DIR.result_stack_data result_stack_x_axis_uri = DIR.result_stack_x_axis result_stack_art_type_uri = DIR.result_stack_art_type result_word_cloud_uri = DIR.result_word_cloud

30.323529

71

0.845781

1ab40d03f981526d9e031f31a0832b6f0bb50bd7

81,522

py

Python

sectionproperties/pre/nastran_sections.py

audunarn/section-properties

6dd68d0cb6b31adcaffeb5ea9f78f985a8955f95

[ "MIT" ]

2

2021-01-18T08:04:55.000Z

2021-03-06T01:23:39.000Z

sectionproperties/pre/nastran_sections.py

audunarn/section-properties

6dd68d0cb6b31adcaffeb5ea9f78f985a8955f95

[ "MIT" ]

null

sectionproperties/pre/nastran_sections.py

audunarn/section-properties

6dd68d0cb6b31adcaffeb5ea9f78f985a8955f95

[ "MIT" ]

null

from sectionproperties.pre.sections import ( Geometry, RectangularSection, CustomSection, MergedSection ) from sectionproperties.pre.pre import create_mesh import numpy as np class BARSection(Geometry): """Constructs a BAR section with the center at the origin *(0, 0)*, with two parameters defining dimensions. See Nastran documentation [1]_ [2]_ [3]_ [4]_ [5]_ for definition of parameters. Added by JohnDN90. :param float DIM1: Width (x) of bar :param float DIM2: Depth (y) of bar :param shift: Vector that shifts the cross-section by *(x, y)* :type shift: list[float, float] The following example creates a BAR cross-section with a depth of 1.5 and width of 2.0, and generates a mesh with a maximum triangular area of 0.001:: import sectionproperties.pre.nastran_sections as nsections geometry = nsections.BARSection(DIM1=2.0, DIM2=1.5) mesh = geometry.create_mesh(mesh_sizes=[0.001]) .. figure:: ../images/sections/bar_geometry.png :align: center :scale: 75 % BAR section geometry. .. figure:: ../images/sections/bar_mesh.png :align: center :scale: 75 % Mesh generated from the above geometry. """ def __init__(self, DIM1, DIM2, shift=[0, 0]): """Inits the BARSection class.""" # force dimensions to be floating point values DIM1 *= 1.0 DIM2 *= 1.0 self.DIM1 = DIM1 self.DIM2 = DIM2 # assign control point control_points = [[0., 0.]] # shift = [-0.5*DIM1+shift[0], -0.5*DIM2+shift[1]] super().__init__(control_points, shift) # construct the points and facets self.points = [ [-0.5*DIM1, -0.5*DIM2], [0.5*DIM1, -0.5*DIM2], [0.5*DIM1, 0.5*DIM2], [-0.5*DIM1, 0.5*DIM2] ] self.facets = [[0, 1], [1, 2], [2, 3], [3, 0]] self.shift_section() def getStressPoints(self, shift=(0., 0.)): """Returns the coordinates of the stress evaluation points relative to the origin of the cross-section. The shift parameter can be used to make the coordinates relative to the centroid or the shear center. :param shift: Vector that shifts the cross-section by *(x, y)* :type shift: tuple(float, float) :returns: Stress evaluation points relative to shifted origin - C, D, E, F """ C = (0.5*self.DIM1-shift[0], 0.5*self.DIM2-shift[1]) D = (0.5*self.DIM1-shift[0], -0.5*self.DIM2-shift[1]) E = (-0.5*self.DIM1-shift[0], -0.5*self.DIM2-shift[1]) F = (-0.5*self.DIM1-shift[0], 0.5*self.DIM2-shift[1]) return C, D, E, F class BOXSection(Geometry): """ Constructs a BOX section with the center at the origin *(0, 0)*, with four parameters defining dimensions. See Nastran documentation [1]_ [2]_ [3]_ [4]_ [5]_ for definition of parameters. Added by JohnDN90. :param float DIM1: Width (x) of box :param float DIM2: Depth (y) of box :param float DIM3: Thickness of box in y direction :param float DIM4: Thickness of box in x direction :param shift: Vector that shifts the cross-section by *(x, y)* :type shift: list[float, float] The following example creates a BOX cross-section with a depth of 3.0 and width of 4.0, and generates a mesh with a maximum triangular area of 0.001:: import sectionproperties.pre.nastran_sections as nsections geometry = nsections.BOXSection(DIM1=4.0, DIM2=3.0, DIM3=0.375, DIM4=0.5) mesh = geometry.create_mesh(mesh_sizes=[0.001]) .. figure:: ../images/sections/box_geometry.png :align: center :scale: 75 % BOX section geometry. .. figure:: ../images/sections/box_mesh.png :align: center :scale: 75 % Mesh generated from the above geometry. """ def __init__(self, DIM1, DIM2, DIM3, DIM4, shift=[0, 0]): """Inits the BOXSection class.""" # force dimensions to be floating point values DIM1 *= 1.0 DIM2 *= 1.0 DIM3 *= 1.0 DIM4 *= 1.0 self.DIM1 = DIM1 self.DIM2 = DIM2 self.DIM3 = DIM3 self.DIM4 = DIM4 # Ensure dimensions are physically relevant np.testing.assert_(2.0*DIM4 < DIM1, "Invalid geometry specified.") np.testing.assert_(2.0*DIM3 < DIM2, "Invalid geometry specified.") # assign control point control_points = [[0., 0.5*DIM2 - 0.5*DIM3]] super().__init__(control_points, shift) # specify a hole in the centre of the Box self.holes = [[0., 0.]] # construct the points and facets self.points = [ [-0.5*DIM1, -0.5*DIM2], [0.5*DIM1, -0.5*DIM2], [0.5*DIM1, 0.5*DIM2], [-0.5*DIM1, 0.5*DIM2], [-0.5*DIM1 + DIM4, -0.5*DIM2 + DIM3], [0.5*DIM1 - DIM4, -0.5*DIM2 + DIM3], [0.5*DIM1 - DIM4, 0.5*DIM2 - DIM3], [-0.5*DIM1 + DIM4, 0.5*DIM2 - DIM3] ] self.facets = [[0, 1], [1, 2], [2, 3], [3, 0], [4, 5], [5, 6], [6, 7], [7, 4]] self.shift_section() def getStressPoints(self, shift=(0., 0.)): """Returns the coordinates of the stress evaluation points relative to the origin of the cross-section. The shift parameter can be used to make the coordinates relative to the centroid or the shear center. :param shift: Vector that shifts the cross-section by *(x, y)* :type shift: tuple(float, float) :returns: Stress evaluation points relative to shifted origin - C, D, E, F """ C = (0.5*self.DIM1-shift[0], 0.5*self.DIM2-shift[1]) D = (0.5*self.DIM1-shift[0], -0.5*self.DIM2-shift[1]) E = (-0.5*self.DIM1-shift[0], -0.5*self.DIM2-shift[1]) F = (-0.5*self.DIM1-shift[0], 0.5*self.DIM2-shift[1]) return C, D, E, F class BOX1Section(Geometry): """Constructs a BOX1 section with the center at the origin *(0, 0)*, with six parameters defining dimensions. See Nastran documentation [1]_ [2]_ [3]_ [4]_ for more details. Added by JohnDN90. :param float DIM1: Width (x) of box :param float DIM2: Depth (y) of box :param float DIM3: Thickness of top wall :param float DIM4: Thickness of bottom wall :param float DIM5: Thickness of left wall :param float DIM6: Thickness of right wall :param shift: Vector that shifts the cross-section by *(x, y)* :type shift: list[float, float] The following example creates a BOX1 cross-section with a depth of 3.0 and width of 4.0, and generates a mesh with a maximum triangular area of 0.007:: import sectionproperties.pre.nastran_sections as nsections geometry = nsections.BOX1Section( DIM1=4.0, DIM2=3.0, DIM3=0.375, DIM4=0.5, DIM5=0.25, DIM6=0.75 ) mesh = geometry.create_mesh(mesh_sizes=[0.007]) .. figure:: ../images/sections/box1_geometry.png :align: center :scale: 75 % BOX1 section geometry. .. figure:: ../images/sections/box1_mesh.png :align: center :scale: 75 % Mesh generated from the above geometry. """ def __init__(self, DIM1, DIM2, DIM3, DIM4, DIM5, DIM6, shift=[0, 0]): """Inits the Box1Section class.""" # force dimensions to be floating point values DIM1 *= 1.0 DIM2 *= 1.0 DIM3 *= 1.0 DIM4 *= 1.0 DIM5 *= 1.0 DIM6 *= 1.0 self.DIM1 = DIM1 self.DIM2 = DIM2 self.DIM3 = DIM3 self.DIM4 = DIM4 self.DIM5 = DIM5 self.DIM6 = DIM6 # Ensure dimensions are physically relevant np.testing.assert_(DIM5+DIM6 < DIM1, "Invalid geometry specified.") np.testing.assert_(DIM3+DIM4 < DIM2, "Invalid geometry specified.") # assign control point control_points = [[0.5*DIM1, 0.5*DIM4]] shift = [-0.5*DIM1+shift[0], -0.5*DIM2+shift[1]] super().__init__(control_points, shift) # specify a hole in the centre of the Box self.holes = [[DIM6 + 0.5*(DIM1-DIM5), DIM4+0.5*(DIM2-DIM3)]] # construct the points and facets self.points = [ [0., 0.], [DIM1, 0.], [DIM1, DIM2], [0., DIM2], [DIM6, DIM4], [DIM1-DIM5, DIM4], [DIM1-DIM5, DIM2-DIM3], [DIM6, DIM2-DIM3] ] self.facets = [[0, 1], [1, 2], [2, 3], [3, 0], [4, 5], [5, 6], [6, 7], [7, 4]] self.shift_section() def getStressPoints(self, shift=(0., 0.)): """ Returns the coordinates of the stress evaluation points relative to the origin of the cross-section. The shift parameter can be used to make the coordinates relative to the centroid or the shear center. :param shift: Vector that shifts the cross-section by *(x, y)* :type shift: tuple(float, float) :returns: Stress evaluation points relative to shifted origin - C, D, E, F """ C = (0.5*self.DIM1-shift[0], 0.5*self.DIM2-shift[1]) D = (0.5*self.DIM1-shift[0], -0.5*self.DIM2-shift[1]) E = (-0.5*self.DIM1-shift[0], -0.5*self.DIM2-shift[1]) F = (-0.5*self.DIM1-shift[0], 0.5*self.DIM2-shift[1]) return C, D, E, F class CHANSection(Geometry): """ Constructs a CHAN (C-Channel) section with the web's middle center at the origin *(0, 0)*, with four parameters defining dimensions. See Nastran documentation [1]_ [2]_ [3]_ [4]_ for more details. Added by JohnDN90. :param float DIM1: Width (x) of the CHAN-section :param float DIM2: Depth (y) of the CHAN-section :param float DIM3: Thickness of web (vertical portion) :param float DIM4: Thickness of flanges (top/bottom portion) :param shift: Vector that shifts the cross-section by *(x, y)* :type shift: list[float, float] The following example creates a CHAN cross-section with a depth of 4.0 and width of 2.0, and generates a mesh with a maximum triangular area of 0.008:: import sectionproperties.pre.nastran_sections as nsections geometry = nsections.CHANSection(DIM1=2.0, DIM2=4.0, DIM3=0.25, DIM4=0.5) mesh = geometry.create_mesh(mesh_sizes=[0.008]) .. figure:: ../images/sections/chan_geometry.png :align: center :scale: 75 % CHAN section geometry. .. figure:: ../images/sections/chan_mesh.png :align: center :scale: 75 % Mesh generated from the above geometry. """ def __init__(self, DIM1, DIM2, DIM3, DIM4, shift=[0, 0]): """Inits the CHANSection class.""" # force dimensions to be floating point values DIM1 *= 1.0 DIM2 *= 1.0 DIM3 *= 1.0 DIM4 *= 1.0 self.DIM1 = DIM1 self.DIM2 = DIM2 self.DIM3 = DIM3 self.DIM4 = DIM4 # Ensure dimensions are physically relevant np.testing.assert_(2.0*DIM4 < DIM2, "Invalid geometry specified.") np.testing.assert_(DIM3 < DIM1, "Invalid geometry specified.") # assign control point control_points = [[0.5*DIM1, 0.5*DIM4]] shift = [-0.5*DIM3+shift[0], -0.5*DIM2+shift[1]] super().__init__(control_points, shift) # construct the points and facets self.points = [ [0., 0.], [DIM1, 0.], [DIM1, DIM4], [DIM3, DIM4], [DIM3, DIM2-DIM4], [DIM1, DIM2-DIM4], [DIM1, DIM2], [0., DIM2] ] self.facets = [[0, 1], [1, 2], [2, 3], [3, 4], [4, 5], [5, 6], [6, 7], [7, 0]] self.shift_section() def getStressPoints(self, shift=(0., 0.)): """ Returns the coordinates of the stress evaluation points relative to the origin of the cross-section. The shift parameter can be used to make the coordinates relative to the centroid or the shear center. :param shift: Vector that shifts the cross-section by *(x, y)* :type shift: tuple(float, float) :returns: Stress evaluation points relative to shifted origin - C, D, E, F """ C = (self.DIM1-0.5*self.DIM3-shift[0], 0.5*self.DIM2-shift[1]) D = (self.DIM1-0.5*self.DIM3-shift[0], -0.5*self.DIM2-shift[1]) E = (-0.5*self.DIM3-shift[0], -0.5*self.DIM2-shift[1]) F = (-0.5*self.DIM3-shift[0], 0.5*self.DIM2-shift[1]) return C, D, E, F class CHAN1Section(Geometry): """ Constructs a CHAN1 (C-Channel) section with the web's middle center at the origin *(0, 0)*, with four parameters defining dimensions. See Nastran documentation [1]_ [2]_ [3]_ [4]_ for more details. Added by JohnDN90. :param float DIM1: Width (x) of channels :param float DIM2: Thickness (x) of web :param float DIM3: Spacing between channels (length of web) :param float DIM4: Depth (y) of CHAN1-section :param shift: Vector that shifts the cross-section by *(x, y)* :type shift: list[float, float] The following example creates a CHAN1 cross-section with a depth of 4.0 and width of 1.75, and generates a mesh with a maximum triangular area of 0.01:: import sectionproperties.pre.nastran_sections as nsections geometry = nsections.CHAN1Section(DIM1=0.75, DIM2=1.0, DIM3=3.5, DIM4=4.0) mesh = geometry.create_mesh(mesh_sizes=[0.01]) .. figure:: ../images/sections/chan1_geometry.png :align: center :scale: 75 % CHAN1 section geometry. .. figure:: ../images/sections/chan1_mesh.png :align: center :scale: 75 % Mesh generated from the above geometry. """ def __init__(self, DIM1, DIM2, DIM3, DIM4, shift=[0, 0]): """Inits the CHAN1Section class.""" # force dimensions to be floating point values DIM1 *= 1.0 DIM2 *= 1.0 DIM3 *= 1.0 DIM4 *= 1.0 self.DIM1 = DIM1 self.DIM2 = DIM2 self.DIM3 = DIM3 self.DIM4 = DIM4 # Ensure dimensions are physically relevant np.testing.assert_(DIM4 > DIM3, "Invalid geometry specified.") # assign control point control_points = [[0.5*DIM1, 0.5*DIM4]] shift = [-0.5*DIM2+shift[0], -0.5*DIM4+shift[1]] super().__init__(control_points, shift) # construct the points and facets tf = 0.5 * (DIM4 - DIM3) self.points = [ [0, 0], [DIM1+DIM2, 0], [DIM1+DIM2, tf], [DIM2, tf], [DIM2, tf+DIM3], [DIM2+DIM1, tf+DIM3], [DIM2+DIM1, DIM4], [0, DIM4] ] self.facets = [[0, 1], [1, 2], [2, 3], [3, 4], [4, 5], [5, 6], [6, 7], [7, 0]] self.shift_section() def getStressPoints(self, shift=(0., 0.)): """ Returns the coordinates of the stress evaluation points relative to the origin of the cross-section. The shift parameter can be used to make the coordinates relative to the centroid or the shear center. :param shift: Vector that shifts the cross-section by *(x, y)* :type shift: tuple(float, float) :returns: Stress evaluation points relative to shifted origin - C, D, E, F """ C = (0.5*self.DIM2+self.DIM1-shift[0], 0.5*self.DIM4-shift[1]) D = (0.5*self.DIM2+self.DIM1-shift[0], -0.5*self.DIM4-shift[1]) E = (-0.5*self.DIM2-shift[0], -0.5*self.DIM4-shift[1]) F = (-0.5*self.DIM2-shift[0], 0.5*self.DIM4-shift[1]) return C, D, E, F class CHAN2Section(Geometry): """ Constructs a CHAN2 (C-Channel) section with the bottom web's middle center at the origin *(0, 0)*, with four parameters defining dimensions. See Nastran documentation [1]_ [2]_ [3]_ [4]_ for more details. Added by JohnDN90. :param float DIM1: Thickness of channels :param float DIM2: Thickness of web :param float DIM3: Depth (y) of CHAN2-section :param float DIM4: Width (x) of CHAN2-section :param shift: Vector that shifts the cross-section by *(x, y)* :type shift: list[float, float] The following example creates a CHAN2 cross-section with a depth of 2.0 and width of 4.0, and generates a mesh with a maximum triangular area of 0.01:: import sectionproperties.pre.nastran_sections as nsections geometry = nsections.CHAN2Section(DIM1=0.375, DIM2=0.5, DIM3=2.0, DIM4=4.0) mesh = geometry.create_mesh(mesh_sizes=[0.01]) .. figure:: ../images/sections/chan2_geometry.png :align: center :scale: 75 % CHAN2 section geometry. .. figure:: ../images/sections/chan2_mesh.png :align: center :scale: 75 % Mesh generated from the above geometry. """ def __init__(self, DIM1, DIM2, DIM3, DIM4, shift=[0, 0]): """Inits the CHAN2Section class.""" # force dimensions to be floating point values DIM1 *= 1.0 DIM2 *= 1.0 DIM3 *= 1.0 DIM4 *= 1.0 self.DIM1 = DIM1 self.DIM2 = DIM2 self.DIM3 = DIM3 self.DIM4 = DIM4 # Ensure dimensions are physically relevant np.testing.assert_(DIM4 > 2.0*DIM1, "Invalid geometry specified.") np.testing.assert_(DIM3 > DIM2, "Invalid geometry specified.") # assign control point control_points = [[0.5*DIM4, 0.5*DIM2]] shift = [-0.5*DIM4+shift[0], -0.5*DIM2+shift[1]] super().__init__(control_points, shift) # construct the points and facets self.points = [ [0., 0.], [DIM4, 0.], [DIM4, DIM3], [DIM4-DIM1, DIM3], [DIM4-DIM1, DIM2], [DIM1, DIM2], [DIM1, DIM3], [0., DIM3] ] self.facets = [[0, 1], [1, 2], [2, 3], [3, 4], [4, 5], [5, 6], [6, 7], [7, 0]] self.shift_section() def getStressPoints(self, shift=(0., 0.)): """ Returns the coordinates of the stress evaluation points relative to the origin of the cross-section. The shift parameter can be used to make the coordinates relative to the centroid or the shear center. :param shift: Vector that shifts the cross-section by *(x, y)* :type shift: tuple(float, float) :returns: Stress evaluation points relative to shifted origin - C, D, E, F """ C = (0.5*self.DIM4-shift[0], self.DIM3-0.5*self.DIM2-shift[1]) D = (0.5*self.DIM4-shift[0], -0.5*self.DIM2-shift[1]) E = (-0.5*self.DIM4-shift[0], -0.5*self.DIM2-shift[1]) F = (-0.5*self.DIM4-shift[0], self.DIM3-0.5*self.DIM2-shift[1]) return C, D, E, F class CROSSSection(Geometry): """ Constructs Nastran's cruciform/cross section with the intersection's middle center at the origin *(0, 0)*, with four parameters defining dimensions. See Nastran documentation [1]_ [2]_ [3]_ [4]_ for more details. Added by JohnDN90. :param float DIM1: Twice the width of horizontal member protruding from the vertical center member :param float DIM2: Thickness of the vertical member :param float DIM3: Depth (y) of the CROSS-section :param float DIM4: Thickness of the horizontal members :param shift: Vector that shifts the cross-section by *(x, y)* :type shift: list[float, float] The following example creates a rectangular cross-section with a depth of 3.0 and width of 1.875, and generates a mesh with a maximum triangular area of 0.008:: import sectionproperties.pre.nastran_sections as nsections geometry = nsections.CROSSSection(DIM1=1.5, DIM2=0.375, DIM3=3.0, DIM4=0.25) mesh = geometry.create_mesh(mesh_sizes=[0.008]) .. figure:: ../images/sections/cross_geometry.png :align: center :scale: 75 % Cruciform/cross section geometry. .. figure:: ../images/sections/cross_mesh.png :align: center :scale: 75 % Mesh generated from the above geometry. """ def __init__(self, DIM1, DIM2, DIM3, DIM4, shift=[0, 0]): """Inits the CROSSSection class.""" # force dimensions to be floating point values DIM1 *= 1.0 DIM2 *= 1.0 DIM3 *= 1.0 DIM4 *= 1.0 self.DIM1 = DIM1 self.DIM2 = DIM2 self.DIM3 = DIM3 self.DIM4 = DIM4 # Ensure dimensions are physically relevant np.testing.assert_(DIM4 < DIM3, "Invalid geometry specified.") # assign control point control_points = [[0.5*DIM1+0.5*DIM2, 0.5*DIM3]] shift = [-(0.5*DIM1+0.5*DIM2)+shift[0], -(0.5*DIM3)+shift[1]] super().__init__(control_points, shift) # construct the points and facets d = 0.5*(DIM3 - DIM4) self.points = [ [0.5*DIM1, 0], [0.5*DIM1+DIM2, 0], [0.5*DIM1+DIM2, d], [DIM1+DIM2, d], [DIM1+DIM2, d+DIM4], [0.5*DIM1+DIM2, d+DIM4], [0.5*DIM1+DIM2, DIM3], [0.5*DIM1, DIM3], [0.5*DIM1, d+DIM4], [0, d+DIM4], [0, d], [0.5*DIM1, d] ] self.facets = [ [0, 1], [1, 2], [2, 3], [3, 4], [4, 5], [5, 6], [6, 7], [7, 8], [8, 9], [9, 10], [10, 11], [11, 0] ] self.shift_section() def getStressPoints(self, shift=(0., 0.)): """Returns the coordinates of the stress evaluation points relative to the origin of the cross-section. The shift parameter can be used to make the coordinates relative to the centroid or the shear center. :param shift: Vector that shifts the cross-section by *(x, y)* :type shift: tuple(float, float) :returns: Stress evaluation points relative to shifted origin - C, D, E, F """ C = (-shift[0], 0.5*self.DIM3-shift[1]) D = (0.5*(self.DIM1+self.DIM2)-shift[0], -shift[1]) E = (-shift[0], -0.5*self.DIM3-shift[1]) F = (-0.5*(self.DIM1+self.DIM2)-shift[0], -shift[1]) return C, D, E, F class FCROSSSection(Geometry): """ Constructs a flanged cruciform/cross section with the intersection's middle center at the origin *(0, 0)*, with eight parameters defining dimensions. Added by JohnDN90. :param float DIM1: Depth (y) of flanged cruciform :param float DIM2: Width (x) of flanged cruciform :param float DIM3: Thickness of vertical web :param float DIM4: Thickness of horizontal web :param float DIM5: Length of flange attached to vertical web :param float DIM6: Thickness of flange attached to vertical web :param float DIM7: Length of flange attached to horizontal web :param float DIM8: Thickness of flange attached to horizontal web :param shift: Vector that shifts the cross-section by *(x, y)* :type shift: list[float, float] The following example demonstrates the creation of a flanged cross section:: import sectionproperties.pre.nastran_sections as nsections geometry = nsections.FCROSSSection( DIM1=9.0, DIM2=6.0, DIM3=0.75, DIM4=0.625, DIM5=2.1, DIM6=0.375, DIM7=4.5, DIM8=0.564 ) mesh = geometry.create_mesh(mesh_sizes=[0.03]) .. figure:: ../images/sections/fcross_geometry.png :align: center :scale: 75 % Flanged Cruciform/cross section geometry. .. figure:: ../images/sections/fcross_mesh.png :align: center :scale: 75 % Mesh generated from the above geometry. """ def __init__(self, DIM1, DIM2, DIM3, DIM4, DIM5, DIM6, DIM7, DIM8, shift=[0, 0]): """Inits the FCROSSSection class.""" # force dimensions to be floating point values DIM1 *= 1.0 DIM2 *= 1.0 DIM3 *= 1.0 DIM4 *= 1.0 DIM5 *= 1.0 DIM6 *= 1.0 DIM7 *= 1.0 DIM8 *= 1.0 self.DIM1 = DIM1 self.DIM2 = DIM2 self.DIM3 = DIM3 self.DIM4 = DIM4 self.DIM5 = DIM5 self.DIM6 = DIM6 self.DIM7 = DIM7 self.DIM7 = DIM8 # Ensure dimensions are physically relevant # TODO: Finish dimension checks. np.testing.assert_(DIM5 > DIM3, "Invalid geometry specified.") np.testing.assert_(DIM7 > DIM4, "Invalid geometry specified.") np.testing.assert_(DIM7 < DIM1, "Invalid geometry specified.") np.testing.assert_(DIM5 < DIM2, "Invalid geometry specified.") np.testing.assert_(DIM8 < (0.5*DIM2-0.5*DIM3), "Invalid geometry specified.") np.testing.assert_(DIM6 < (0.5*DIM1-0.5*DIM4), "Invalid geometry specified.") # assign control point control_points = [[0.0, 0.0]] shift = [shift[0], shift[1]] super().__init__(control_points, shift) # construct the points and facets self.points = [ [0.5*DIM3, -0.5*DIM4], [0.5*DIM2-DIM8, -0.5*DIM4], [0.5*DIM2-DIM8, -0.5*DIM7], [0.5*DIM2, -0.5*DIM7], [0.5*DIM2, 0.5*DIM7], [0.5*DIM2-DIM8, 0.5*DIM7], [0.5*DIM2-DIM8, 0.5*DIM4], [0.5*DIM3, 0.5*DIM4], [0.5*DIM3, 0.5*DIM1-DIM6], [0.5*DIM5, 0.5*DIM1-DIM6], [0.5*DIM5, 0.5*DIM1], [-0.5*DIM5, 0.5*DIM1], [-0.5*DIM5, 0.5*DIM1-DIM6], [-0.5*DIM3, 0.5*DIM1-DIM6], [-0.5*DIM3, 0.5*DIM4], [-0.5*DIM2+DIM8, 0.5*DIM4], [-0.5*DIM2+DIM8, 0.5*DIM7], [-0.5*DIM2, 0.5*DIM7], [-0.5*DIM2, -0.5*DIM7], [-0.5*DIM2+DIM8, -0.5*DIM7], [-0.5*DIM2+DIM8, -0.5*DIM4], [-0.5*DIM3, -0.5*DIM4], [-0.5*DIM3, -0.5*DIM1+DIM6], [-0.5*DIM5, -0.5*DIM1+DIM6], [-0.5*DIM5, -0.5*DIM1], [0.5*DIM5, -0.5*DIM1], [0.5*DIM5, -0.5*DIM1+DIM6], [0.5*DIM3, -0.5*DIM1+DIM6] ] self.facets = [ [0, 1], [1, 2], [2, 3], [3, 4], [4, 5], [5, 6], [6, 7], [7, 8], [8, 9], [9, 10], [10, 11], [11, 12], [12, 13], [13, 14], [14, 15], [15, 16], [16, 17], [17, 18], [18, 19], [19, 20], [20, 21], [21, 22], [22, 23], [23, 24], [24, 25], [25, 26], [26, 27], [27, 0] ] self.shift_section() def getStressPoints(self, shift=(0., 0.)): """Returns the coordinates of the stress evaluation points relative to the origin of the cross-section. The shift parameter can be used to make the coordinates relative to the centroid or the shear center. :param shift: Vector that shifts the cross-section by *(x, y)* :type shift: list[float, float] :returns: Stress evaluation points relative to shifted origin - C, D, E, F """ C = (-shift[0], 0.5*self.DIM1-shift[1]) D = (0.5*self.DIM2-shift[0], -shift[1]) E = (-shift[0], -0.5*self.DIM1-shift[1]) F = (-0.5*self.DIM2-shift[0], -shift[1]) return C, D, E, F class DBOXSection(Geometry): """ Constructs a DBOX section with the center at the origin *(0, 0)*, with ten parameters defining dimensions. See MSC Nastran documentation [1]_ for more details. Added by JohnDN90. :param float DIM1: Width (x) of the DBOX-section :param float DIM2: Depth (y) of the DBOX-section :param float DIM3: Width (x) of left-side box :param float DIM4: Thickness of left wall :param float DIM5: Thickness of center wall :param float DIM6: Thickness of right wall :param float DIM7: Thickness of top left wall :param float DIM8: Thickness of bottom left wall :param float DIM9: Thickness of top right wall :param float DIM10: Thickness of bottom right wall :param shift: Vector that shifts the cross-section by *(x, y)* :type shift: list[float, float] The following example creates a DBOX cross-section with a depth of 3.0 and width of 8.0, and generates a mesh with a maximum triangular area of 0.01:: import sectionproperties.pre.nastran_sections as nsections geometry = nsections.DBOXSection( DIM1=8.0, DIM2=3.0, DIM3=3.0, DIM4=0.5, DIM5=0.625, DIM6=0.75, DIM7=0.375, DIM8=0.25, DIM9=0.5, DIM10=0.375 ) mesh = geometry.create_mesh(mesh_sizes=[0.01]) .. figure:: ../images/sections/dbox_geometry.png :align: center :scale: 75 % DBOX section geometry. .. figure:: ../images/sections/dbox_mesh.png :align: center :scale: 75 % Mesh generated from the above geometry. """ def __init__(self, DIM1, DIM2, DIM3, DIM4, DIM5, DIM6, DIM7, DIM8, DIM9, DIM10, shift=[0, 0]): """Inits the DBOXSection class.""" # force dimensions to be floating point values DIM1 *= 1.0 DIM2 *= 1.0 DIM3 *= 1.0 DIM4 *= 1.0 DIM5 *= 1.0 DIM6 *= 1.0 DIM7 *= 1.0 DIM8 *= 1.0 DIM9 *= 1.0 DIM10 *= 1.0 self.DIM1 = DIM1 self.DIM2 = DIM2 self.DIM3 = DIM3 self.DIM4 = DIM4 self.DIM5 = DIM5 self.DIM6 = DIM6 self.DIM7 = DIM7 self.DIM8 = DIM8 self.DIM9 = DIM9 self.DIM10 = DIM10 # Ensure dimensions are physically relevant np.testing.assert_((DIM4+DIM5+DIM6) < DIM1, "Invalid geometry specified.") np.testing.assert_((DIM4+0.5*DIM5) < DIM3, "Invalid geometry specified.") np.testing.assert_((DIM7+DIM8) < DIM2, "Invalid geometry specified.") np.testing.assert_((DIM9+DIM10) < DIM2, "Invalid geometry specified.") # assign control point control_points = [[0.5*DIM3, 0.5*DIM8]] shift = [-0.5*DIM1+shift[0], -0.5*DIM2+shift[1]] super().__init__(control_points, shift) # specify a hole in the centre of the Box d2 = 0.5*(DIM1 - DIM6 - DIM3 - 0.5*DIM5) self.holes = [ [DIM4 + 0.5*(DIM3 - DIM4 - 0.5*DIM5), DIM8 + 0.5*(DIM2 - DIM8 - DIM7)], [DIM3 + 0.5*DIM5 + d2, DIM10 + 0.5*(DIM2 - DIM10 - DIM9)] ] # construct the points and facets self.points = [ [0., 0.], [DIM1, 0.], [DIM1, DIM2], [0., DIM2], [DIM4, DIM8], [DIM3-DIM5/2., DIM8], [DIM3-DIM5/2., DIM2-DIM7], [DIM4, DIM2-DIM7], [DIM3+DIM5/2., DIM10], [DIM1-DIM6, DIM10], [DIM1-DIM6, DIM2-DIM9], [DIM3+DIM5/2., DIM2-DIM9] ] self.facets = [ [0, 1], [1, 2], [2, 3], [3, 0], [4, 5], [5, 6], [6, 7], [7, 4], [8, 9], [9, 10], [10, 11], [11, 8] ] self.shift_section() def getStressPoints(self, shift=(0., 0.)): """ Returns the coordinates of the stress evaluation points relative to the origin of the cross-section. The shift parameter can be used to make the coordinates relative to the centroid or the shear center. :param shift: Vector that shifts the cross-section by *(x, y)* :type shift: tuple(float, float) :returns: Stress evaluation points relative to shifted origin - C, D, E, F """ C = (0.5 * self.DIM1 - shift[0], 0.5 * self.DIM2 - shift[1]) D = (0.5 * self.DIM1 - shift[0], -0.5 * self.DIM2 - shift[1]) E = (-0.5 * self.DIM1 - shift[0], -0.5 * self.DIM2 - shift[1]) F = (-0.5 * self.DIM1 - shift[0], 0.5 * self.DIM2 - shift[1]) return C, D, E, F class GBOXSection(Geometry): """ Constructs a GBOX section with the center at the origin *(0, 0)*, with six parameters defining dimensions. See ASTROS documentation [5]_ for more details. Added by JohnDN90. :param float DIM1: Width (x) of the GBOX-section :param float DIM2: Depth (y) of the GBOX-section :param float DIM3: Thickness of top flange :param float DIM4: Thickness of bottom flange :param float DIM5: Thickness of webs :param float DIM6: Spacing between webs :param shift: Vector that shifts the cross-section by *(x, y)* :type shift: list[float, float] The following example creates a GBOX cross-section with a depth of 2.5 and width of 6.0, and generates a mesh with a maximum triangular area of 0.01:: import sectionproperties.pre.nastran_sections as nsections geometry = nsections.GBOXSection( DIM1=6.0, DIM2=2.5, DIM3=0.375, DIM4=0.25, DIM5=0.625, DIM6=1.0 ) mesh = geometry.create_mesh(mesh_sizes=[0.01]) .. figure:: ../images/sections/gbox_geometry.png :align: center :scale: 75 % GBOX section geometry. .. figure:: ../images/sections/gbox_mesh.png :align: center :scale: 75 % Mesh generated from the above geometry. """ def __init__(self, DIM1, DIM2, DIM3, DIM4, DIM5, DIM6, shift=[0, 0]): """Inits the GBOXSection class.""" # force dimensions to be floating point values DIM1 *= 1.0 DIM2 *= 1.0 DIM3 *= 1.0 DIM4 *= 1.0 DIM5 *= 1.0 DIM6 *= 1.0 self.DIM1 = DIM1 self.DIM2 = DIM2 self.DIM3 = DIM3 self.DIM4 = DIM4 self.DIM5 = DIM5 self.DIM6 = DIM6 # Ensure dimensions are physically relevant np.testing.assert_((DIM3+DIM4) < DIM2, "Invalid geometry specified.") np.testing.assert_((2.0*DIM5+DIM6) < DIM1, "Invalid geometry specified.") # assign control point control_points = [[0.5*DIM1, 0.5*DIM4]] shift = [-(0.5*DIM1)+shift[0], -(DIM4 + 0.5*(DIM2-DIM3-DIM4))+shift[1]] super().__init__(control_points, shift) # specify a hole in the centre of the GBOX self.holes = [[0.5*DIM1, 0.5*DIM2]] # construct the points and facets d = 0.5*(DIM1 - DIM6 - 2.0 * DIM5) self.points = [ [0., 0.], [DIM1, 0.], [DIM1, DIM4], [d + 2. * DIM5 + DIM6, DIM4], [d + 2. * DIM5 + DIM6, DIM2 - DIM3], [DIM1, DIM2 - DIM3], [DIM1, DIM2], [0., DIM2], [0., DIM2 - DIM3], [d, DIM2 - DIM3], [d, DIM4], [0., DIM4], [d + DIM5, DIM4], [d + DIM5 + DIM6, DIM4], [d + DIM5 + DIM6, DIM2 - DIM3], [d + DIM5, DIM2 - DIM3] ] self.facets = [ [0, 1], [1, 2], [2, 3], [3, 4], [4, 5], [5, 6], [6, 7], [7, 8], [8, 9], [9, 10], [10, 11], [11, 0], [12, 13], [13, 14], [14, 15], [15, 12] ] self.shift_section() def getStressPoints(self, shift=(0., 0.)): """Returns the coordinates of the stress evaluation points relative to the origin of the cross-section. The shift parameter can be used to make the coordinates relative to the centroid or the shear center. :param shift: Vector that shifts the cross-section by *(x, y)* :type shift: tuple(float, float) :returns: Stress evaluation points relative to shifted origin - C, D, E, F """ C = (0.5*self.DIM1-shift[0], 0.5*self.DIM2-shift[1]) D = (0.5*self.DIM1-shift[0], -0.5*self.DIM2-shift[1]) E = (-0.5*self.DIM1-shift[0], -0.5*self.DIM2-shift[1]) F = (-0.5*self.DIM1-shift[0], 0.5*self.DIM2-shift[1]) return C, D, E, F class HSection(Geometry): """Constructs a H section with the middle web's middle center at the origin *(0, 0)*, with four parameters defining dimensions. See Nastran documentation [1]_ [2]_ [3]_ [4]_ for more details. Added by JohnDN90. :param float DIM1: Spacing between vertical flanges (length of web) :param float DIM2: Twice the thickness of the vertical flanges :param float DIM3: Depth (y) of the H-section :param float DIM4: Thickness of the middle web :param shift: Vector that shifts the cross-section by *(x, y)* :type shift: list[float, float] The following example creates a H cross-section with a depth of 3.5 and width of 2.75, and generates a mesh with a maximum triangular area of 0.005:: import sectionproperties.pre.nastran_sections as nsections geometry = nsections.HSection(DIM1=2.0, DIM2=0.75, DIM3=3.5, DIM4=0.25) mesh = geometry.create_mesh(mesh_sizes=[0.005]) .. figure:: ../images/sections/h_geometry.png :align: center :scale: 75 % H section geometry. .. figure:: ../images/sections/h_mesh.png :align: center :scale: 75 % Mesh generated from the above geometry. """ def __init__(self, DIM1, DIM2, DIM3, DIM4, shift=[0, 0]): """Inits the HSection class.""" # force dimensions to be floating point values DIM1 *= 1.0 DIM2 *= 1.0 DIM3 *= 1.0 DIM4 *= 1.0 self.DIM1 = DIM1 self.DIM2 = DIM2 self.DIM3 = DIM3 self.DIM4 = DIM4 # Ensure dimensions are physically relevant np.testing.assert_(DIM4 < DIM3, "Invalid geometry specified.") d1 = 0.5 * (DIM3 - DIM4) d2 = 0.5 * DIM2 # assign control point control_points = [[0.5*d2, 0.5*DIM3]] shift = [-0.5*(DIM2+DIM1)+shift[0], -0.5*DIM3+shift[1]] super().__init__(control_points, shift) # construct the points and facets self.points = [ [0, 0], [d2, 0], [d2, d1], [d2+DIM1, d1], [d2+DIM1, 0], [DIM1+DIM2, 0], [DIM1+DIM2, DIM3], [DIM1+DIM2-d2, DIM3], [DIM1+DIM2-d2, d1+DIM4], [d2, d1+DIM4], [d2, DIM3], [0, DIM3] ] self.facets = [ [0, 1], [1, 2], [2, 3], [3, 4], [4, 5], [5, 6], [6, 7], [7, 8], [8, 9], [9, 10], [10, 11], [11, 0] ] self.shift_section() def getStressPoints(self, shift=(0., 0.)): """Returns the coordinates of the stress evaluation points relative to the origin of the cross-section. The shift parameter can be used to make the coordinates relative to the centroid or the shear center. :param shift: Vector that shifts the cross-section by *(x, y)* :type shift: tuple(float, float) :returns: Stress evaluation points relative to shifted origin - C, D, E, F """ C = (0.5*(self.DIM1+self.DIM2)-shift[0], 0.5*self.DIM3-shift[1]) D = (0.5*(self.DIM1+self.DIM2)-shift[0], -0.5*self.DIM3-shift[1]) E = (-0.5*(self.DIM1+self.DIM2)-shift[0], -0.5*self.DIM3-shift[1]) F = (-0.5*(self.DIM1+self.DIM2)-shift[0], 0.5*self.DIM3-shift[1]) return C, D, E, F class HATSection(Geometry): """Constructs a Hat section with the top most section's middle center at the origin *(0, 0)*, with four parameters defining dimensions. See Nastran documentation [1]_ [2]_ [3]_ [4]_ for more details. Note that HAT in ASTROS is actually HAT1 in this code. Added by JohnDN90. :param float DIM1: Depth (y) of HAT-section :param float DIM2: Thickness of HAT-section :param float DIM3: Width (x) of top most section :param float DIM4: Width (x) of bottom sections :param shift: Vector that shifts the cross-section by *(x, y)* :type shift: list[float, float] The following example creates a HAT cross-section with a depth of 1.25 and width of 2.5, and generates a mesh with a maximum triangular area of 0.001:: import sectionproperties.pre.nastran_sections as nsections geometry = nsections.HATSection(DIM1=1.25, DIM2=0.25, DIM3=1.5, DIM4=0.5) mesh = geometry.create_mesh(mesh_sizes=[0.001]) .. figure:: ../images/sections/hat_geometry.png :align: center :scale: 75 % HAT section geometry. .. figure:: ../images/sections/hat_mesh.png :align: center :scale: 75 % Mesh generated from the above geometry. """ def __init__(self, DIM1, DIM2, DIM3, DIM4, shift=[0, 0]): """Inits the HATSection class.""" # force dimensions to be floating point values DIM1 *= 1.0 DIM2 *= 1.0 DIM3 *= 1.0 DIM4 *= 1.0 self.DIM1 = DIM1 self.DIM2 = DIM2 self.DIM3 = DIM3 self.DIM4 = DIM4 # Ensure dimensions are physically relevant np.testing.assert_(2.0*DIM2 < DIM1, "Invalid geometry specified.") # assign control point control_points = [[0.5*DIM4, 0.5*DIM2]] shift = [-DIM4-0.5*DIM3+shift[0], -DIM1+0.5*DIM2+shift[1]] super().__init__(control_points, shift) # construct the points and facets self.points = [ [0., 0.], [DIM4+DIM2, 0.], [DIM4+DIM2, DIM1-DIM2], [DIM4+DIM3-DIM2, DIM1-DIM2], [DIM4+DIM3-DIM2, 0.], [2*DIM4+DIM3, 0.], [2.*DIM4+DIM3, DIM2], [DIM4+DIM3, DIM2], [DIM4+DIM3, DIM1], [DIM4, DIM1], [DIM4, DIM2], [0., DIM2] ] self.facets = [ [0, 1], [1, 2], [2, 3], [3, 4], [4, 5], [5, 6], [6, 7], [7, 8], [8, 9], [9, 10], [10, 11], [11, 0] ] self.shift_section() def getStressPoints(self, shift=(0., 0.)): """Returns the coordinates of the stress evaluation points relative to the origin of the cross-section. The shift parameter can be used to make the coordinates relative to the centroid or the shear center. :param shift: Vector that shifts the origin by *(x, y)* :type shift: tuple(float, float) :returns: Stress evaluation points relative to shifted origin - C, D, E, F """ C = (0.5*self.DIM3 - shift[0], 0.5*self.DIM2 - shift[1]) D = (0.5*self.DIM3 + self.DIM4 - shift[0], -self.DIM1 + self.DIM2 - shift[1]) E = (-0.5*self.DIM3 - self.DIM4 - shift[0], -self.DIM1 + self.DIM2 - shift[1]) F = (-0.5*self.DIM3 - shift[0], 0.5*self.DIM2 - shift[1]) return C, D, E, F class HAT1Section(Geometry): """ Constructs a HAT1 section with the bottom plate's bottom center at the origin *(0, 0)*, with five parameters defining dimensions. See Nastran documentation [1]_ [2]_ [3]_ [5]_ for definition of parameters. Note that in ASTROS, HAT1 is called HAT. Added by JohnDN90. :param float DIM1: Width(x) of the HAT1-section :param float DIM2: Depth (y) of the HAT1-section :param float DIM3: Width (x) of hat's top flange :param float DIM4: Thickness of hat stiffener :param float DIM5: Thickness of bottom plate :param shift: Vector that shifts the cross-section by *(x, y)* :type shift: list[float, float] The following example creates a HAT1 cross-section with a depth of 2.0 and width of 4.0, and generates a mesh with a maximum triangular area of 0.005:: import sectionproperties.pre.nastran_sections as nsections geometry = nsections.HAT1Section(DIM1=4.0, DIM2=2.0, DIM3=1.5, DIM4=0.1875, DIM5=0.375) mesh = geometry.create_mesh(mesh_sizes=[0.005]) .. figure:: ../images/sections/hat1_geometry.png :align: center :scale: 75 % HAT1 section geometry. .. figure:: ../images/sections/hat1_mesh.png :align: center :scale: 75 % Mesh generated from the above geometry. """ def __init__(self, DIM1, DIM2, DIM3, DIM4, DIM5, shift=[0, 0]): """Inits the HAT1Section class.""" # force dimensions to be floating point values DIM1 *= 1.0 DIM2 *= 1.0 DIM3 *= 1.0 DIM4 *= 1.0 DIM5 *= 1.0 self.DIM1 = DIM1 self.DIM2 = DIM2 self.DIM3 = DIM3 self.DIM4 = DIM4 self.DIM5 = DIM5 # Ensure dimensions are physically relevant np.testing.assert_((2.0*DIM4+DIM5) < DIM2, "Invalid geometry specified.") np.testing.assert_(DIM3 < DIM1, "Invalid geometry specified.") shift = [-0.5*DIM1+shift[0], shift[1]] # create bottom rectangular plate bottom_plate = RectangularSection(d=DIM5, b=DIM1, shift=shift) # create the hat stiffener d1 = DIM2 - DIM5 d2 = DIM4 d4 = 0.5*(DIM1 - DIM3) # specify a hole in the combined plate and hat structure holes = [[0.5*DIM1, 0.5*DIM2]] # assign control point control_points = [[0.5*d4, DIM5 + 0.5*DIM4]] super().__init__(control_points, shift) # construct the points and facets points = [ [0., DIM5 + 0.], [d4 + d2, DIM5 + 0.], [d4 + d2, DIM5 + d1 - d2], [d4 + DIM3 - d2, DIM5 + d1 - d2], [d4 + DIM3 - d2, DIM5 + 0.], [2. * d4 + DIM3, DIM5 + 0.], [2. * d4 + DIM3, DIM5 + d2], [d4 + DIM3, DIM5 + d2], [d4 + DIM3, DIM5 + d1], [d4, DIM5 + d1], [d4, DIM5 + d2], [0, DIM5 + d2] ] facets = [ [0, 1], [1, 2], [2, 3], [3, 4], [4, 5], [5, 6], [6, 7], [7, 8], [8, 9], [9, 10], [10, 11], [11, 0] ] hat = CustomSection(points, facets, holes, control_points, shift=shift) # Create a list of the sections to merge section_list = [bottom_plate, hat] # Merge the three sections into one geometry geometry = MergedSection(section_list) # Clean the geometry and print information to the terminal geometry.clean_geometry(verbose=False) self.control_points = geometry.control_points self.shift = geometry.shift self.points = geometry.points self.facets = geometry.facets self.holes = geometry.holes def create_mesh(self, mesh_sizes): """Creates a quadratic triangular mesh from the Geometry object. This is overloaded here to allow specifying only one mesh_size which is used for both regions in the Hat1 section. :param mesh_sizes: A list of maximum element areas corresponding to each region within the cross-section geometry. :type mesh_size: list[float] :return: Object containing generated mesh data :rtype: :class:`meshpy.triangle.MeshInfo` :raises AssertionError: If the number of mesh sizes does not match the number of regions """ mesh_sizes *= 2 str = "Number of mesh_sizes ({0}), should match the number of regions ({1})".format( len(mesh_sizes), len(self.control_points) ) assert(len(mesh_sizes) == len(self.control_points)), str return create_mesh(self.points, self.facets, self.holes, self.control_points, mesh_sizes) def getStressPoints(self, shift=(0., 0.)): """ Returns the coordinates of the stress evaluation points relative to the origin of the cross-section. The shift parameter can be used to make the coordinates relative to the centroid or the shear center. :param shift: Vector that shifts the origin by *(x, y)* :type shift: tuple(float, float) :returns: Stress evaluation points relative to shifted origin - C, D, E, F """ C = (-0.5*self.DIM1 - shift[0], -shift[1]) D = (0.5*self.DIM1 - shift[0], -shift[1]) E = (-0.5*self.DIM3 - shift[0], self.DIM2 - shift[1]) F = (0.5*self.DIM3 - shift[0], self.DIM2 - shift[1]) return C, D, E, F class HEXASection(Geometry): """ Constructs a HEXA (hexagon) section with the center at the origin *(0, 0)*, with three parameters defining dimensions. See Nastran documentation [1]_ [2]_ [3]_ [4]_ for more details. Added by JohnDN90. :param float DIM1: Spacing between bottom right point and right most point :param float DIM2: Width (x) of hexagon :param float DIM3: Depth (y) of hexagon :param shift: Vector that shifts the cross-section by *(x, y)* :type shift: list[float, float] The following example creates a rectangular cross-section with a depth of 1.5 and width of 2.0, and generates a mesh with a maximum triangular area of 0.005:: import sectionproperties.pre.nastran_sections as nsections geometry = nsections.HEXASection(DIM1=0.5, DIM2=2.0, DIM3=1.5) mesh = geometry.create_mesh(mesh_sizes=[0.005]) .. figure:: ../images/sections/hexa_geometry.png :align: center :scale: 75 % HEXA section geometry. .. figure:: ../images/sections/hexa_mesh.png :align: center :scale: 75 % Mesh generated from the above geometry. """ def __init__(self, DIM1, DIM2, DIM3, shift=[0, 0]): """Inits the HEXASection class.""" # force dimensions to be floating point values DIM1 *= 1.0 DIM2 *= 1.0 DIM3 *= 1.0 self.DIM1 = DIM1 self.DIM2 = DIM2 self.DIM3 = DIM3 # Ensure dimensions are physically relevant np.testing.assert_(DIM2 > DIM1, "Invalid geometry specified.") # assign control point control_points = [[0.5*DIM2, 0.5*DIM3]] shift = [-0.5*DIM2+shift[0], -0.5*DIM3+shift[1]] super().__init__(control_points, shift) # construct the points and facets self.points = [ [DIM1, 0.], [DIM2-DIM1, 0.], [DIM2, 0.5*DIM3], [DIM2-DIM1, DIM3], [DIM1, DIM3], [0., 0.5*DIM3] ] self.facets = [[0, 1], [1, 2], [2, 3], [3, 4], [4, 5], [5, 0]] self.shift_section() def getStressPoints(self, shift=(0., 0.)): """Returns the coordinates of the stress evaluation points relative to the origin of the cross-section. The shift parameter can be used to make the coordinates relative to the centroid or the shear center. :param shift: Vector that shifts the cross-section by *(x, y)* :type shift: tuple(float, float) :returns: Stress evaluation points relative to shifted origin - C, D, E, F """ C = (-shift[0], 0.5*self.DIM3-shift[1]) D = (-shift[0], -0.5*self.DIM3-shift[1]) E = (0.5*self.DIM2-shift[0], -shift[1]) F = (-0.5*self.DIM2-shift[0], -shift[1]) return C, D, E, F class NISection(Geometry): """Constructs Nastran's I section with the bottom flange's middle center at the origin *(0, 0)*, with six parameters defining dimensions. See Nastran documentation [1]_ [2]_ [3]_ [4]_ for definition of parameters. Added by JohnDN90. :param float DIM1: Depth(y) of the I-section :param float DIM2: Width (x) of bottom flange :param float DIM3: Width (x) of top flange :param float DIM4: Thickness of web :param float DIM5: Thickness of bottom web :param float DIM6: Thickness of top web :param shift: Vector that shifts the cross-section by *(x, y)* :type shift: list[float, float] The following example creates a Nastran I cross-section with a depth of 5.0, and generates a mesh with a maximum triangular area of 0.008:: import sectionproperties.pre.nastran_sections as nsections geometry = nsections.NISection( DIM1=5.0, DIM2=2.0, DIM3=3.0, DIM4=0.25, DIM5=0.375, DIM6=0.5 ) mesh = geometry.create_mesh(mesh_sizes=[0.008]) .. figure:: ../images/sections/ni_geometry.png :align: center :scale: 75 % Nastran's I section geometry. .. figure:: ../images/sections/ni_mesh.png :align: center :scale: 75 % Mesh generated from the above geometry. """ def __init__(self, DIM1, DIM2, DIM3, DIM4, DIM5, DIM6, shift=[0, 0]): """Inits the NISection class.""" # force dimensions to be floating point values DIM1 *= 1.0 DIM2 *= 1.0 DIM3 *= 1.0 DIM4 *= 1.0 DIM5 *= 1.0 DIM6 *= 1.0 self.DIM1 = DIM1 self.DIM2 = DIM2 self.DIM3 = DIM3 self.DIM4 = DIM4 self.DIM5 = DIM5 self.DIM6 = DIM6 # Ensure dimensions are physically relevant np.testing.assert_((DIM5 + DIM6) < DIM1, "Invalid geometry specified.") np.testing.assert_(DIM4 < DIM3, "Invalid geometry specified.") np.testing.assert_(DIM4 < DIM2, "Invalid geometry specified.") # assign control point control_points = [[0.5*DIM2, 0.5*DIM5]] shift = [-0.5*DIM2+shift[0], -0.5*DIM1+shift[1]] super().__init__(control_points, shift) # construct the points and facets db = 0.5*(DIM2 - DIM4) dt = 0.5*(DIM3 - DIM4) self.points = [ [0., 0.], [DIM2, 0.], [DIM2, DIM5], [db+DIM4, DIM5], [db + DIM4, DIM1-DIM6], [db+DIM4+dt, DIM1-DIM6], [db+DIM4+dt, DIM1], [db-dt, DIM1], [db-dt, DIM1-DIM6], [db, DIM1-DIM6], [db, DIM5], [0, DIM5] ] self.facets = [ [0, 1], [1, 2], [2, 3], [3, 4], [4, 5], [5, 6], [6, 7], [7, 8], [8, 9], [9, 10], [10, 11], [11, 0] ] self.shift_section() def getStressPoints(self, shift=(0., 0.)): """Returns the coordinates of the stress evaluation points relative to the origin of the cross-section. The shift parameter can be used to make the coordinates relative to the centroid or the shear center. :param shift: Vector that shifts the cross-section by *(x, y)* :type shift: tuple(float, float) :returns: Stress evaluation points relative to shifted origin - C, D, E, F """ C = (0.5*self.DIM3-shift[0], 0.5*self.DIM1-shift[1]) D = (0.5*self.DIM3-shift[0], -0.5*self.DIM1-shift[1]) E = (-0.5*self.DIM3-shift[0], -0.5*self.DIM1-shift[1]) F = (-0.5*self.DIM3-shift[0], 0.5*self.DIM1-shift[1]) return C, D, E, F class I1Section(Geometry): """Constructs a I1 section with the web's middle center at the origin *(0, 0)*, with four parameters defining dimensions. See Nastran documentation [1]_ [2]_ [3]_ [4]_ for more details. Added by JohnDN90. :param float DIM1: Twice distance from web end to flange end :param float DIM2: Thickness of web :param float DIM3: Length of web (spacing between flanges) :param float DIM4: Depth (y) of the I1-section :param shift: Vector that shifts the cross-section by *(x, y)* :type shift: list[float, float] The following example creates a I1 cross-section with a depth of 5.0 and width of 1.75, and generates a mesh with a maximum triangular area of 0.02:: import sectionproperties.pre.nastran_sections as nsections geometry = nsections.I1Section(DIM1=1.0, DIM2=0.75, DIM3=4.0, DIM4=5.0) mesh = geometry.create_mesh(mesh_sizes=[0.02]) .. figure:: ../images/sections/i1_geometry.png :align: center :scale: 75 % I1 section geometry. .. figure:: ../images/sections/i1_mesh.png :align: center :scale: 75 % Mesh generated from the above geometry. """ def __init__(self, DIM1, DIM2, DIM3, DIM4, shift=[0, 0]): """Inits the I1section class.""" # force dimensions to be floating point values DIM1 *= 1.0 DIM2 *= 1.0 DIM3 *= 1.0 DIM4 *= 1.0 self.DIM1 = DIM1 self.DIM2 = DIM2 self.DIM3 = DIM3 self.DIM4 = DIM4 # Ensure dimensions are physically relevant np.testing.assert_(DIM4 > DIM3, "Invalid geometry specified.") shift = [-0.5*(DIM1+DIM2)+shift[0], -0.5*DIM4+shift[1]] # assign control point control_points = [[0.5*(DIM1+DIM2), 0.5*DIM4]] super().__init__(control_points, shift) # construct the points and facets t = 0.5*(DIM4 - DIM3) self.points = [ [0., 0.], [DIM1+DIM2, 0.], [DIM1+DIM2, t], [0.5*DIM1+DIM2, t], [0.5*DIM1+DIM2, t+DIM3], [DIM1+DIM2, t+DIM3], [DIM1+DIM2, DIM4], [0., DIM4], [0., t+DIM3], [0.5*DIM1, t+DIM3], [0.5*DIM1, t], [0., t] ] self.facets = [ [0, 1], [1, 2], [2, 3], [3, 4], [4, 5], [5, 6], [6, 7], [7, 8], [8, 9], [9, 10], [10, 11], [11, 0] ] self.shift_section() def getStressPoints(self, shift=(0., 0.)): """Returns the coordinates of the stress evaluation points relative to the origin of the cross-section. The shift parameter can be used to make the coordinates relative to the centroid or the shear center. :param shift: Vector that shifts the cross-section by *(x, y)* :type shift: tuple(float, float) :returns: Stress evaluation points relative to shifted origin - C, D, E, F """ C = (0.5*(self.DIM1+self.DIM2)-shift[0], 0.5*self.DIM4-shift[1]) D = (0.5*(self.DIM1+self.DIM2)-shift[0], -0.5*self.DIM4-shift[1]) E = (-0.5*(self.DIM1+self.DIM2)-shift[0], -0.5*self.DIM4-shift[1]) F = (-0.5*(self.DIM1+self.DIM2)-shift[0], 0.5*self.DIM4-shift[1]) return C, D, E, F class LSection(Geometry): """Constructs a L section with the intersection's center at the origin *(0, 0)*, with four parameters defining dimensions. See Nastran documentation [1]_ [2]_ [3]_ for more details. Added by JohnDN90. :param float DIM1: Width (x) of the L-section :param float DIM2: Depth (y) of the L-section :param float DIM3: Thickness of flange (horizontal portion) :param float DIM4: Thickness of web (vertical portion) :param shift: Vector that shifts the cross-section by *(x, y)* :type shift: list[float, float] The following example creates a L cross-section with a depth of 6.0 and width of 3.0, and generates a mesh with a maximum triangular area of 0.01:: import sectionproperties.pre.nastran_sections as nsections geometry = nsections.LSection(DIM1=3.0, DIM2=6.0, DIM3=0.375, DIM4=0.625) mesh = geometry.create_mesh(mesh_sizes=[0.01]) .. figure:: ../images/sections/l_geometry.png :align: center :scale: 75 % L section geometry. .. figure:: ../images/sections/l_mesh.png :align: center :scale: 75 % Mesh generated from the above geometry. """ def __init__(self, DIM1, DIM2, DIM3, DIM4, shift=[0, 0]): """Inits the LSection class.""" # force dimensions to be floating point values DIM1 *= 1.0 DIM2 *= 1.0 DIM3 *= 1.0 DIM4 *= 1.0 self.DIM1 = DIM1 self.DIM2 = DIM2 self.DIM3 = DIM3 self.DIM4 = DIM4 # Ensure dimensions are physically relevant np.testing.assert_(DIM4 < DIM1, "Invalid geometry specified.") np.testing.assert_(DIM3 < DIM2, "Invalid geometry specified.") # assign control point control_points = [[0.5*DIM1, 0.5*DIM3]] shift = [-0.5*DIM4+shift[0], -0.5*DIM3+shift[1]] super().__init__(control_points, shift) # construct the points and facets self.points = [[0, 0], [DIM1, 0], [DIM1, DIM3], [DIM4, DIM3], [DIM4, DIM2], [0, DIM2]] self.facets = [[0, 1], [1, 2], [2, 3], [3, 4], [4, 5], [5, 0]] self.shift_section() def getStressPoints(self, shift=(0., 0.)): """Returns the coordinates of the stress evaluation points relative to the origin of the cross-section. The shift parameter can be used to make the coordinates relative to the centroid or the shear center. :param shift: Vector that shifts the cross-section by *(x, y)* :type shift: tuple(float, float) :returns: Stress evaluation points relative to shifted origin - C, D, E, F """ C = (0.5*self.DIM4-shift[0], self.DIM2-0.5*self.DIM3-shift[1]) D = (self.DIM1-0.5*self.DIM4-shift[0], -0.5*self.DIM3-shift[1]) E = (-0.5*self.DIM4-shift[0], -0.5*self.DIM3-shift[1]) F = (-0.5*self.DIM4-shift[0], self.DIM2-0.5*self.DIM3-shift[1]) return C, D, E, F class RODSection(Geometry): """Constructs a circular rod section with the center at the origin *(0, 0)*, with one parameter defining dimensions. See Nastran documentation [1]_ [2]_ [3]_ [4]_ for more details. Added by JohnDN90. :param float DIM1: Radius of the circular rod section :param int n: Number of points discretising the circle :param shift: Vector that shifts the cross-section by *(x, y)* :type shift: list[float, float] The following example creates a circular rod with a radius of 3.0 and 50 points discretising the boundary, and generates a mesh with a maximum triangular area of 0.01:: import sectionproperties.pre.nastran_sections as nsections geometry = nsections.RODSection(DIM1=3.0, n=50) mesh = geometry.create_mesh(mesh_sizes=[0.01]) .. figure:: ../images/sections/rod_geometry.png :align: center :scale: 75 % Rod section geometry. .. figure:: ../images/sections/rod_mesh.png :align: center :scale: 75 % Mesh generated from the above geometry. """ def __init__(self, DIM1, n, shift=[0, 0]): """Inits the RODSection class.""" # force dimensions to be floating point values DIM1 *= 1.0 self.DIM1 = DIM1 # assign control point control_points = [[0, 0]] super().__init__(control_points, shift) # loop through each point on the circle d = 2.0*DIM1 for i in range(n): # determine polar angle theta = i * 2 * np.pi * 1.0 / n # calculate location of the point x = 0.5 * d * np.cos(theta) y = 0.5 * d * np.sin(theta) # append the current point to the points list self.points.append([x, y]) # if we are not at the last point if i != n - 1: self.facets.append([i, i + 1]) # if we are at the last point, complete the circle else: self.facets.append([i, 0]) self.shift_section() def getStressPoints(self, shift=(0., 0.)): """Returns the coordinates of the stress evaluation points relative to the origin of the cross-section. The shift parameter can be used to make the coordinates relative to the centroid or the shear center. :param float DIM1: Radius of the circular rod section :param shift: Vector that shifts the cross-section by *(x, y)* :type shift: tuple(float, float) :returns: Stress evaluation points relative to shifted origin - C, D, E, F """ C = (-shift[0], self.DIM1-shift[1]) D = (self.DIM1-shift[0], -shift[1]) E = (-shift[0], -self.DIM1-shift[1]) F = (-self.DIM1-shift[0], -shift[1]) return C, D, E, F class TSection(Geometry): """Constructs a T section with the top flange's middle center at the origin *(0, 0)*, with four parameters defining dimensions. See Nastran documentation [1]_ [2]_ [3]_ [4]_ [5]_ for more details. Added by JohnDN90. :param float DIM1: Width (x) of top flange :param float DIM2: Depth (y) of the T-section :param float DIM3: Thickness of top flange :param float DIM4: Thickness of web :param shift: Vector that shifts the cross-section by *(x, y)* :type shift: list[float, float] The following example creates a T cross-section with a depth of 4.0 and width of 3.0, and generates a mesh with a maximum triangular area of 0.001:: import sectionproperties.pre.nastran_sections as nsections geometry = nsections.TSection(DIM1=3.0, DIM2=4.0, DIM3=0.375, DIM4=0.25) mesh = geometry.create_mesh(mesh_sizes=[0.001]) .. figure:: ../images/sections/t_geometry.png :align: center :scale: 75 % T section geometry. .. figure:: ../images/sections/t_mesh.png :align: center :scale: 75 % Mesh generated from the above geometry. """ def __init__(self, DIM1, DIM2, DIM3, DIM4, shift=[0, 0]): """Inits the TSection class.""" # force dimensions to be floating point values DIM1 *= 1.0 DIM2 *= 1.0 DIM3 *= 1.0 DIM4 *= 1.0 self.DIM1 = DIM1 self.DIM2 = DIM2 self.DIM3 = DIM3 self.DIM4 = DIM4 # Ensure dimensions are physically relevant np.testing.assert_(DIM4 < DIM1, "Invalid geometry specified.") np.testing.assert_(DIM3 < DIM2, "Invalid geometry specified.") d = DIM2 b = DIM1 t_f = DIM3 t_w = DIM4 r = 0 n_r = 1 shift = [-DIM1/2.0+shift[0], -(DIM2-DIM3/2.0)+shift[1]] # assign control point control_points = [[b * 0.5, d - t_f * 0.5]] super().__init__(control_points, shift) # add first two points self.points.append([b * 0.5 - t_w * 0.5, 0]) self.points.append([b * 0.5 + t_w * 0.5, 0]) # construct the top right radius pt = [b * 0.5 + t_w * 0.5 + r, d - t_f - r] self.draw_radius(pt, r, np.pi, n_r, False) # add next four points self.points.append([b, d - t_f]) self.points.append([b, d]) self.points.append([0, d]) self.points.append([0, d - t_f]) # construct the top left radius pt = [b * 0.5 - t_w * 0.5 - r, d - t_f - r] self.draw_radius(pt, r, 0.5 * np.pi, n_r, False) # build the facet list for i in range(len(self.points)): # if we are not at the last point if i != len(self.points) - 1: self.facets.append([i, i + 1]) # if we are at the last point, complete the loop else: self.facets.append([len(self.points) - 1, 0]) self.shift_section() def getStressPoints(self, shift=(0., 0.)): """ Returns the coordinates of the stress evaluation points relative to the origin of the cross-section. The shift parameter can be used to make the coordinates relative to the centroid or the shear center. :param shift: Vector that shifts the cross-section by *(x, y)* :type shift: tuple(float, float) :returns: Stress evaluation points relative to shifted origin - C, D, E, F """ C = (-shift[0], 0.5*self.DIM3-shift[1]) D = (0.5*self.DIM1-shift[0], 0.5*self.DIM3-shift[1]) E = (-shift[0], 0.5*self.DIM3-self.DIM2-shift[1]) F = (-0.5*self.DIM1-shift[0], 0.5*self.DIM3-shift[1]) return C, D, E, F class T1Section(Geometry): """Constructs a T1 section with the right flange's middle center at the origin *(0, 0)*, with four parameters defining dimensions. See Nastran documentation [1]_ [2]_ [3]_ [4]_ for more details. Added by JohnDN90. :param float DIM1: Depth (y) of T1-section :param float DIM2: Length (x) of web :param float DIM3: Thickness of right flange :param float DIM4: Thickness of web :param shift: Vector that shifts the cross-section by *(x, y)* :type shift: list[float, float] The following example creates a T1 cross-section with a depth of 3.0 and width of 3.875, and generates a mesh with a maximum triangular area of 0.001:: import sectionproperties.pre.nastran_sections as nsections geometry = nsections.T1Section(DIM1=3.0, DIM2=3.5, DIM3=0.375, DIM4=0.25) mesh = geometry.create_mesh(mesh_sizes=[0.001]) .. figure:: ../images/sections/t1_geometry.png :align: center :scale: 75 % T1 section geometry. .. figure:: ../images/sections/t1_mesh.png :align: center :scale: 75 % Mesh generated from the above geometry. """ def __init__(self, DIM1, DIM2, DIM3, DIM4, shift=[0, 0]): """Inits the T1section class.""" # force dimensions to be floating point values DIM1 *= 1.0 DIM2 *= 1.0 DIM3 *= 1.0 DIM4 *= 1.0 self.DIM1 = DIM1 self.DIM2 = DIM2 self.DIM3 = DIM3 self.DIM4 = DIM4 # Ensure dimensions are physically relevant np.testing.assert_(DIM4 < DIM1, "Invalid geometry specified.") shift = [-0.5*DIM3+shift[0], -0.5*DIM1+shift[1]] # assign control point control_points = [[0.5*DIM3, 0.5*DIM1]] super().__init__(control_points, shift) # construct the points and facets d1 = (DIM1 - DIM4) / 2.0 self.points = [ [0, 0], [DIM3, 0], [DIM3, DIM1], [0, DIM1], [0, d1 + DIM4], [-DIM2, d1 + DIM4], [-DIM2, d1], [0, d1] ] self.facets = [[0, 1], [1, 2], [2, 3], [3, 4], [4, 5], [5, 6], [6, 7], [7, 0]] self.shift_section() def getStressPoints(self, shift=(0., 0.)): """Returns the coordinates of the stress evaluation points relative to the origin of the cross-section. The shift parameter can be used to make the coordinates relative to the centroid or the shear center. :param shift: Vector that shifts the cross-section by *(x, y)* :type shift: tuple(float, float) :returns: Stress evaluation points relative to shifted origin - C, D, E, F """ C = (0.5*self.DIM3-shift[0], -shift[1]) D = (0.5*self.DIM3-shift[0], -0.5*self.DIM1-shift[1]) E = (-0.5*self.DIM3-self.DIM2-shift[0], -shift[1]) F = (0.5*self.DIM3-shift[0], 0.5*self.DIM1-shift[1]) return C, D, E, F class T2Section(Geometry): """Constructs a T2 section with the bottom flange's middle center at the origin *(0, 0)*, with four parameters defining dimensions. See Nastran documentation [1]_ [2]_ [3]_ [4]_ for more details. Added by JohnDN90. :param float DIM1: Width (x) of T2-section :param float DIM2: Depth (y) of T2-section :param float DIM3: Thickness of bottom flange :param float DIM4: Thickness of web :param shift: Vector that shifts the cross-section by *(x, y)* :type shift: list[float, float] The following example creates a T2 cross-section with a depth of 4.0 and width of 3.0, and generates a mesh with a maximum triangular area of 0.005:: import sectionproperties.pre.nastran_sections as nsections geometry = nsections.T2Section(DIM1=3.0, DIM2=4.0, DIM3=0.375, DIM4=0.5) mesh = geometry.create_mesh(mesh_sizes=[0.005]) .. figure:: ../images/sections/t2_geometry.png :align: center :scale: 75 % T2 section geometry. .. figure:: ../images/sections/t2_mesh.png :align: center :scale: 75 % Mesh generated from the above geometry. """ def __init__(self, DIM1, DIM2, DIM3, DIM4, shift=[0, 0]): """Inits the T2Section class.""" # force dimensions to be floating point values DIM1 *= 1.0 DIM2 *= 1.0 DIM3 *= 1.0 DIM4 *= 1.0 self.DIM1 = DIM1 self.DIM2 = DIM2 self.DIM3 = DIM3 self.DIM4 = DIM4 # Ensure dimensions are physically relevant np.testing.assert_(DIM4 < DIM1, "Invalid geometry specified.") np.testing.assert_(DIM3 < DIM2, "Invalid geometry specified.") # assign control point control_points = [[0.5*DIM1, 0.5*DIM3]] shift = [-0.5*DIM1+shift[0], -0.5*DIM3+shift[1]] super().__init__(control_points, shift) # construct the points and facets d1 = 0.5*(DIM1 - DIM4) self.points = [ [0., 0.], [DIM1, 0.], [DIM1, DIM3], [DIM1-d1, DIM3], [DIM1-d1, DIM2], [d1, DIM2], [d1, DIM3], [0, DIM3] ] self.facets = [[0, 1], [1, 2], [2, 3], [3, 4], [4, 5], [5, 6], [6, 7], [7, 0]] self.shift_section() def getStressPoints(self, shift=(0., 0.)): """Returns the coordinates of the stress evaluation points relative to the origin of the cross-section. The shift parameter can be used to make the coordinates relative to the centroid or the shear center. :param shift: Vector that shifts the cross-section by *(x, y)* :type shift: tuple(float, float) :returns: Stress evaluation points relative to shifted origin - C, D, E, F """ C = (0.5*self.DIM4-shift[0], self.DIM2-0.5*self.DIM3-shift[1]) D = (0.5*self.DIM1-shift[0], -0.5*self.DIM3-shift[1]) E = (-0.5*self.DIM1-shift[0], -0.5*self.DIM3-shift[1]) F = (-0.5*self.DIM4-shift[0], self.DIM2-0.5*self.DIM3-shift[1]) return C, D, E, F class TUBESection(Geometry): """Constructs a circular tube section with the center at the origin *(0, 0)*, with two parameters defining dimensions. See Nastran documentation [1]_ [2]_ [3]_ [4]_ for more details. Added by JohnDN90. :param float DIM1: Outer radius of the circular tube section :param float DIM2: Inner radius of the circular tube section :param int n: Number of points discretising the circle :param shift: Vector that shifts the cross-section by *(x, y)* :type shift: list[float, float] The following example creates a circular tube cross-section with an outer radius of 3.0 and an inner radius of 2.5, and generates a mesh with 37 points discretising the boundaries and a maximum triangular area of 0.01:: import sectionproperties.pre.nastran_sections as nsections geometry = nsections.TUBESection(DIM1=3.0, DIM2=2.5, n=37) mesh = geometry.create_mesh(mesh_sizes=[0.01]) .. figure:: ../images/sections/tube_geometry.png :align: center :scale: 75 % TUBE section geometry. .. figure:: ../images/sections/tube_mesh.png :align: center :scale: 75 % Mesh generated from the above geometry. """ def __init__(self, DIM1, DIM2, n, shift=[0, 0]): """Inits the TUBESection class.""" # force dimensions to be floating point values DIM1 *= 1.0 DIM2 *= 1.0 self.DIM1 = DIM1 self.DIM2 = DIM2 # Ensure dimensions are physically relevant np.testing.assert_(DIM2 < DIM1, "Invalid geometry specified.") d = 2.0*DIM1 t = DIM1-DIM2 # assign control point control_points = [[d * 0.5 - t * 0.5, 0]] super().__init__(control_points, shift) # specify a hole in the centre of the CHS self.holes = [[0., 0.]] # loop through each point of the CHS for i in range(n): # determine polar angle theta = i * 2 * np.pi * 1.0 / n # calculate location of outer and inner points x_outer = 0.5 * d * np.cos(theta) y_outer = 0.5 * d * np.sin(theta) x_inner = (0.5 * d - t) * np.cos(theta) y_inner = (0.5 * d - t) * np.sin(theta) # append the current points to the points list self.points.append([x_outer, y_outer]) self.points.append([x_inner, y_inner]) # if we are not at the last point if i != n - 1: self.facets.append([i * 2, i * 2 + 2]) self.facets.append([i * 2 + 1, i * 2 + 3]) # if we are at the last point, complete the circle else: self.facets.append([i * 2, 0]) self.facets.append([i * 2 + 1, 1]) self.shift_section() def getStressPoints(self, shift=(0., 0.)): """Returns the coordinates of the stress evaluation points relative to the origin of the cross-section. The shift parameter can be used to make the coordinates relative to the centroid or the shear center. :param shift: Vector that shifts the cross-section by *(x, y)* :type shift: tuple(float, float) :returns: Stress evaluation points relative to shifted origin - C, D, E, F """ C = (-shift[0], self.DIM1-shift[1]) D = (self.DIM1-shift[0], -shift[1]) E = (-shift[0], -self.DIM1-shift[1]) F = (-self.DIM1-shift[0], -shift[1]) return C, D, E, F class TUBE2Section(Geometry): """Constructs a circular TUBE2 section with the center at the origin *(0, 0)*, with two parameters defining dimensions. See MSC Nastran documentation [1]_ for more details. Added by JohnDN90. :param float DIM1: Outer radius of the circular tube section :param float DIM2: Thickness of wall :param int n: Number of points discretising the circle :param shift: Vector that shifts the cross-section by *(x, y)* :type shift: list[float, float] The following example creates a circular TUBE2 cross-section with an outer radius of 3.0 and a wall thickness of 0.5, and generates a mesh with 37 point discretising the boundary and a maximum triangular area of 0.01:: import sectionproperties.pre.nastran_sections as nsections geometry = nsections.TUBE2Section(DIM1=3.0, DIM2=0.5, n=37) mesh = geometry.create_mesh(mesh_sizes=[0.01]) .. figure:: ../images/sections/tube2_geometry.png :align: center :scale: 75 % TUBE2 section geometry. .. figure:: ../images/sections/tube2_mesh.png :align: center :scale: 75 % Mesh generated from the above geometry. """ def __init__(self, DIM1, DIM2, n, shift=[0, 0]): """Inits the TUBE2Section class.""" # force dimensions to be floating point values DIM1 *= 1.0 DIM2 *= 1.0 self.DIM1 = DIM1 self.DIM2 = DIM2 # Ensure dimensions are physically relevant np.testing.assert_(DIM2 < DIM1, "Invalid geometry specified.") d = 2.0*DIM1 t = DIM2 # assign control point control_points = [[d * 0.5 - t * 0.5, 0]] super().__init__(control_points, shift) # specify a hole in the centre of the section self.holes = [[0., 0.]] # loop through each point of the section for i in range(n): # determine polar angle theta = i * 2 * np.pi * 1.0 / n # calculate location of outer and inner points x_outer = 0.5 * d * np.cos(theta) y_outer = 0.5 * d * np.sin(theta) x_inner = (0.5 * d - t) * np.cos(theta) y_inner = (0.5 * d - t) * np.sin(theta) # append the current points to the points list self.points.append([x_outer, y_outer]) self.points.append([x_inner, y_inner]) # if we are not at the last point if i != n - 1: self.facets.append([i * 2, i * 2 + 2]) self.facets.append([i * 2 + 1, i * 2 + 3]) # if we are at the last point, complete the circle else: self.facets.append([i * 2, 0]) self.facets.append([i * 2 + 1, 1]) self.shift_section() def getStressPoints(self, shift=(0., 0.)): """Returns the coordinates of the stress evaluation points relative to the origin of the cross-section. The shift parameter can be used to make the coordinates relative to the centroid or the shear center. :param shift: Vector that shifts the cross-section by *(x, y)* :type shift: tuple(float, float) :returns: Stress evaluation points relative to shifted origin - C, D, E, F """ C = (-shift[0], self.DIM1-shift[1]) D = (self.DIM1-shift[0], -shift[1]) E = (-shift[0], -self.DIM1-shift[1]) F = (-self.DIM1-shift[0], -shift[1]) return C, D, E, F class ZSection(Geometry): """Constructs a Z section with the web's middle center at the origin *(0, 0)*, with four parameters defining dimensions. See Nastran documentation [1]_ [2]_ [3]_ [4]_ for more details. Added by JohnDN90. :param float DIM1: Width (x) of horizontal members :param float DIM2: Thickness of web :param float DIM3: Spacing between horizontal members (length of web) :param float DIM4: Depth (y) of Z-section :param shift: Vector that shifts the cross-section by *(x, y)* :type shift: list[float, float] The following example creates a rectangular cross-section with a depth of 4.0 and width of 2.75, and generates a mesh with a maximum triangular area of 0.005:: import sectionproperties.pre.nastran_sections as nsections geometry = nsections.ZSection(DIM1=1.125, DIM2=0.5, DIM3=3.5, DIM4=4.0) mesh = geometry.create_mesh(mesh_sizes=[0.005]) .. figure:: ../images/sections/z_geometry.png :align: center :scale: 75 % Z section geometry. .. figure:: ../images/sections/z_mesh.png :align: center :scale: 75 % Mesh generated from the above geometry. """ def __init__(self, DIM1, DIM2, DIM3, DIM4, shift=[0, 0]): """Inits the ZSection class.""" # force dimensions to be floating point values DIM1 *= 1.0 DIM2 *= 1.0 DIM3 *= 1.0 DIM4 *= 1.0 self.DIM1 = DIM1 self.DIM2 = DIM2 self.DIM3 = DIM3 self.DIM4 = DIM4 # Ensure dimensions are physically relevant np.testing.assert_(DIM4 > DIM3, "Invalid geometry specified.") # assign control point control_points = [[DIM1+0.5*DIM2, 0.5*DIM4]] shift = [-0.5*(DIM1+DIM2)+shift[0], -0.5*DIM4+shift[1]] super().__init__(control_points, shift) # construct the points and facets t = 0.5*(DIM4 - DIM3) self.points = [ [DIM1, 0.], [2.*DIM1+DIM2, 0.], [2.*DIM1+DIM2, t], [DIM1+DIM2, t], [DIM1+DIM2, DIM4], [0., DIM4], [0., DIM4-t], [DIM1, DIM4-t] ] self.facets = [[0, 1], [1, 2], [2, 3], [3, 4], [4, 5], [5, 6], [6, 7], [7, 0]] self.shift_section() def getStressPoints(self, shift=(0., 0.)): """Returns the coordinates of the stress evaluation points relative to the origin of the cross-section. The shift parameter can be used to make the coordinates relative to the centroid or the shear center. :param shift: Vector that shifts the cross-section by *(x, y)* :type shift: tuple(float, float) :returns: Stress evaluation points relative to shifted origin - C, D, E, F """ C = (0.5*self.DIM2-shift[0], 0.5*self.DIM4-shift[1]) D = (0.5*self.DIM2+self.DIM1-shift[0], -0.5*self.DIM4-shift[1]) E = (-0.5*self.DIM2-shift[0], -0.5*self.DIM4-shift[1]) F = (-0.5*self.DIM2-self.DIM1-shift[0], 0.5*self.DIM4-shift[1]) return C, D, E, F

36.475168

99

0.590466

477cd6d865b307291a4a798405b8d7b2ff819de1

17,248

py

Python

tests/providers/google/cloud/hooks/test_dataproc.py

dorranh/airflow

1a9a2cadcf8606cfcb729d1323dd33dfacc64633

[ "Apache-2.0" ]

null

tests/providers/google/cloud/hooks/test_dataproc.py

dorranh/airflow

1a9a2cadcf8606cfcb729d1323dd33dfacc64633

[ "Apache-2.0" ]

1

2019-05-14T14:32:40.000Z

tests/providers/google/cloud/hooks/test_dataproc.py

dorranh/airflow

1a9a2cadcf8606cfcb729d1323dd33dfacc64633

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- # # Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you under the Apache License, Version 2.0 (the # "License"); you may not use this file except in compliance # with the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND, either express or implied. See the License for the # specific language governing permissions and limitations # under the License. # import unittest import mock from google.cloud.dataproc_v1beta2.types import JobStatus # pylint: disable=no-name-in-module from airflow import AirflowException from airflow.providers.google.cloud.hooks.dataproc import DataprocHook, DataProcJobBuilder from airflow.version import version AIRFLOW_VERSION = "v" + version.replace(".", "-").replace("+", "-") JOB = {"job": "test-job"} JOB_ID = "test-id" TASK_ID = "test-task-id" GCP_LOCATION = "global" GCP_PROJECT = "test-project" CLUSTER = {"test": "test"} CLUSTER_NAME = "cluster-name" PARENT = "parent" NAME = "name" BASE_STRING = "airflow.providers.google.cloud.hooks.base.{}" DATAPROC_STRING = "airflow.providers.google.cloud.hooks.dataproc.{}" def mock_init(*args, **kwargs): pass class TestDataprocHook(unittest.TestCase): def setUp(self): with mock.patch( BASE_STRING.format("CloudBaseHook.__init__"), new=mock_init ): self.hook = DataprocHook(gcp_conn_id="test") @mock.patch(DATAPROC_STRING.format("DataprocHook._get_credentials")) @mock.patch( DATAPROC_STRING.format("DataprocHook.client_info"), new_callable=mock.PropertyMock, ) @mock.patch(DATAPROC_STRING.format("ClusterControllerClient")) def test_get_cluster_client( self, mock_client, mock_client_info, mock_get_credentials ): self.hook.get_cluster_client(location=GCP_LOCATION) mock_client.assert_called_once_with( credentials=mock_get_credentials.return_value, client_info=mock_client_info.return_value, client_options={ "api_endpoint": "{}-dataproc.googleapis.com:443".format(GCP_LOCATION) }, ) @mock.patch(DATAPROC_STRING.format("DataprocHook._get_credentials")) @mock.patch( DATAPROC_STRING.format("DataprocHook.client_info"), new_callable=mock.PropertyMock, ) @mock.patch(DATAPROC_STRING.format("WorkflowTemplateServiceClient")) def test_get_template_client( self, mock_client, mock_client_info, mock_get_credentials ): _ = self.hook.get_template_client mock_client.assert_called_once_with( credentials=mock_get_credentials.return_value, client_info=mock_client_info.return_value, ) @mock.patch(DATAPROC_STRING.format("DataprocHook._get_credentials")) @mock.patch( DATAPROC_STRING.format("DataprocHook.client_info"), new_callable=mock.PropertyMock, ) @mock.patch(DATAPROC_STRING.format("JobControllerClient")) def test_get_job_client(self, mock_client, mock_client_info, mock_get_credentials): self.hook.get_job_client(location=GCP_LOCATION) mock_client.assert_called_once_with( credentials=mock_get_credentials.return_value, client_info=mock_client_info.return_value, client_options={ "api_endpoint": "{}-dataproc.googleapis.com:443".format(GCP_LOCATION) }, ) @mock.patch(DATAPROC_STRING.format("DataprocHook.get_cluster_client")) def test_create_cluster(self, mock_client): self.hook.create_cluster( project_id=GCP_PROJECT, region=GCP_LOCATION, cluster=CLUSTER ) mock_client.assert_called_once_with(location=GCP_LOCATION) mock_client.return_value.create_cluster.assert_called_once_with( project_id=GCP_PROJECT, region=GCP_LOCATION, cluster=CLUSTER, metadata=None, request_id=None, retry=None, timeout=None, ) @mock.patch(DATAPROC_STRING.format("DataprocHook.get_cluster_client")) def test_delete_cluster(self, mock_client): self.hook.delete_cluster( project_id=GCP_PROJECT, region=GCP_LOCATION, cluster_name=CLUSTER_NAME ) mock_client.assert_called_once_with(location=GCP_LOCATION) mock_client.return_value.delete_cluster.assert_called_once_with( project_id=GCP_PROJECT, region=GCP_LOCATION, cluster_name=CLUSTER_NAME, cluster_uuid=None, metadata=None, request_id=None, retry=None, timeout=None, ) @mock.patch(DATAPROC_STRING.format("DataprocHook.get_cluster_client")) def test_diagnose_cluster(self, mock_client): self.hook.diagnose_cluster( project_id=GCP_PROJECT, region=GCP_LOCATION, cluster_name=CLUSTER_NAME ) mock_client.assert_called_once_with(location=GCP_LOCATION) mock_client.return_value.diagnose_cluster.assert_called_once_with( project_id=GCP_PROJECT, region=GCP_LOCATION, cluster_name=CLUSTER_NAME, metadata=None, retry=None, timeout=None, ) @mock.patch(DATAPROC_STRING.format("DataprocHook.get_cluster_client")) def test_get_cluster(self, mock_client): self.hook.get_cluster( project_id=GCP_PROJECT, region=GCP_LOCATION, cluster_name=CLUSTER_NAME ) mock_client.assert_called_once_with(location=GCP_LOCATION) mock_client.return_value.get_cluster.assert_called_once_with( project_id=GCP_PROJECT, region=GCP_LOCATION, cluster_name=CLUSTER_NAME, metadata=None, retry=None, timeout=None, ) @mock.patch(DATAPROC_STRING.format("DataprocHook.get_cluster_client")) def test_list_clusters(self, mock_client): filter_ = "filter" self.hook.list_clusters( project_id=GCP_PROJECT, region=GCP_LOCATION, filter_=filter_ ) mock_client.assert_called_once_with(location=GCP_LOCATION) mock_client.return_value.list_clusters.assert_called_once_with( project_id=GCP_PROJECT, region=GCP_LOCATION, filter_=filter_, page_size=None, metadata=None, retry=None, timeout=None, ) @mock.patch(DATAPROC_STRING.format("DataprocHook.get_cluster_client")) def test_update_cluster(self, mock_client): update_mask = "update-mask" self.hook.update_cluster( project_id=GCP_PROJECT, location=GCP_LOCATION, cluster=CLUSTER, cluster_name=CLUSTER_NAME, update_mask=update_mask, ) mock_client.assert_called_once_with(location=GCP_LOCATION) mock_client.return_value.update_cluster.assert_called_once_with( project_id=GCP_PROJECT, region=GCP_LOCATION, cluster=CLUSTER, cluster_name=CLUSTER_NAME, update_mask=update_mask, graceful_decommission_timeout=None, metadata=None, request_id=None, retry=None, timeout=None, ) @mock.patch(DATAPROC_STRING.format("DataprocHook.get_template_client")) def test_create_workflow_template(self, mock_client): template = {"test": "test"} mock_client.region_path.return_value = PARENT self.hook.create_workflow_template( location=GCP_LOCATION, template=template, project_id=GCP_PROJECT ) mock_client.region_path.assert_called_once_with(GCP_PROJECT, GCP_LOCATION) mock_client.create_workflow_template.assert_called_once_with( parent=PARENT, template=template, retry=None, timeout=None, metadata=None ) @mock.patch(DATAPROC_STRING.format("DataprocHook.get_template_client")) def test_instantiate_workflow_template(self, mock_client): template_name = "template_name" mock_client.workflow_template_path.return_value = NAME self.hook.instantiate_workflow_template( location=GCP_LOCATION, template_name=template_name, project_id=GCP_PROJECT ) mock_client.workflow_template_path.assert_called_once_with( GCP_PROJECT, GCP_LOCATION, template_name ) mock_client.instantiate_workflow_template.assert_called_once_with( name=NAME, version=None, parameters=None, request_id=None, retry=None, timeout=None, metadata=None, ) @mock.patch(DATAPROC_STRING.format("DataprocHook.get_template_client")) def test_instantiate_inline_workflow_template(self, mock_client): template = {"test": "test"} mock_client.region_path.return_value = PARENT self.hook.instantiate_inline_workflow_template( location=GCP_LOCATION, template=template, project_id=GCP_PROJECT ) mock_client.region_path.assert_called_once_with(GCP_PROJECT, GCP_LOCATION) mock_client.instantiate_inline_workflow_template.assert_called_once_with( parent=PARENT, template=template, request_id=None, retry=None, timeout=None, metadata=None, ) @mock.patch(DATAPROC_STRING.format("DataprocHook.get_job")) def test_wait_for_job(self, mock_get_job): mock_get_job.side_effect = [ mock.MagicMock(status=mock.MagicMock(state=JobStatus.RUNNING)), mock.MagicMock(status=mock.MagicMock(state=JobStatus.ERROR)), ] with self.assertRaises(AirflowException): self.hook.wait_for_job( job_id=JOB_ID, location=GCP_LOCATION, project_id=GCP_PROJECT, wait_time=0, ) calls = [ mock.call(location=GCP_LOCATION, job_id=JOB_ID, project_id=GCP_PROJECT), mock.call(location=GCP_LOCATION, job_id=JOB_ID, project_id=GCP_PROJECT), ] mock_get_job.has_calls(calls) @mock.patch(DATAPROC_STRING.format("DataprocHook.get_job_client")) def test_get_job(self, mock_client): self.hook.get_job(location=GCP_LOCATION, job_id=JOB_ID, project_id=GCP_PROJECT) mock_client.assert_called_once_with(location=GCP_LOCATION) mock_client.return_value.get_job.assert_called_once_with( region=GCP_LOCATION, job_id=JOB_ID, project_id=GCP_PROJECT, retry=None, timeout=None, metadata=None, ) @mock.patch(DATAPROC_STRING.format("DataprocHook.get_job_client")) def test_submit_job(self, mock_client): self.hook.submit_job(location=GCP_LOCATION, job=JOB, project_id=GCP_PROJECT) mock_client.assert_called_once_with(location=GCP_LOCATION) mock_client.return_value.submit_job.assert_called_once_with( region=GCP_LOCATION, job=JOB, project_id=GCP_PROJECT, request_id=None, retry=None, timeout=None, metadata=None, ) @mock.patch(DATAPROC_STRING.format("DataprocHook.wait_for_job")) @mock.patch(DATAPROC_STRING.format("DataprocHook.submit_job")) def test_submit(self, mock_submit_job, mock_wait_for_job): mock_submit_job.return_value.reference.job_id = JOB_ID with self.assertWarns(DeprecationWarning): self.hook.submit(project_id=GCP_PROJECT, job=JOB, region=GCP_LOCATION) mock_submit_job.assert_called_once_with( location=GCP_LOCATION, project_id=GCP_PROJECT, job=JOB ) mock_wait_for_job.assert_called_once_with( location=GCP_LOCATION, project_id=GCP_PROJECT, job_id=JOB_ID ) @mock.patch(DATAPROC_STRING.format("DataprocHook.get_job_client")) def test_cancel_job(self, mock_client): self.hook.cancel_job( location=GCP_LOCATION, job_id=JOB_ID, project_id=GCP_PROJECT ) mock_client.assert_called_once_with(location=GCP_LOCATION) mock_client.return_value.cancel_job.assert_called_once_with( region=GCP_LOCATION, job_id=JOB_ID, project_id=GCP_PROJECT, retry=None, timeout=None, metadata=None, ) class TestDataProcJobBuilder(unittest.TestCase): def setUp(self) -> None: self.job_type = "test" self.builder = DataProcJobBuilder( project_id=GCP_PROJECT, task_id=TASK_ID, cluster_name=CLUSTER_NAME, job_type=self.job_type, properties={"test": "test"}, ) @mock.patch(DATAPROC_STRING.format("uuid.uuid4")) def test_init(self, mock_uuid): mock_uuid.return_value = "uuid" properties = {"test": "test"} job = { "job": { "labels": {"airflow-version": AIRFLOW_VERSION}, "placement": {"cluster_name": CLUSTER_NAME}, "reference": {"job_id": TASK_ID + "_uuid", "project_id": GCP_PROJECT}, "test": {"properties": properties}, } } builder = DataProcJobBuilder( project_id=GCP_PROJECT, task_id=TASK_ID, cluster_name=CLUSTER_NAME, job_type="test", properties=properties, ) self.assertDictEqual(job, builder.job) def test_add_labels(self): labels = {"key": "value"} self.builder.add_labels(labels) self.assertIn("key", self.builder.job["job"]["labels"]) self.assertEqual("value", self.builder.job["job"]["labels"]["key"]) def test_add_variables(self): variables = ["variable"] self.builder.add_variables(variables) self.assertEqual( variables, self.builder.job["job"][self.job_type]["script_variables"] ) def test_add_args(self): args = ["args"] self.builder.add_args(args) self.assertEqual(args, self.builder.job["job"][self.job_type]["args"]) def test_add_query(self): query = ["query"] self.builder.add_query(query) self.assertEqual( {"queries": [query]}, self.builder.job["job"][self.job_type]["query_list"] ) def test_add_query_uri(self): query_uri = "query_uri" self.builder.add_query_uri(query_uri) self.assertEqual( query_uri, self.builder.job["job"][self.job_type]["query_file_uri"] ) def test_add_jar_file_uris(self): jar_file_uris = ["jar_file_uris"] self.builder.add_jar_file_uris(jar_file_uris) self.assertEqual( jar_file_uris, self.builder.job["job"][self.job_type]["jar_file_uris"] ) def test_add_archive_uris(self): archive_uris = ["archive_uris"] self.builder.add_archive_uris(archive_uris) self.assertEqual( archive_uris, self.builder.job["job"][self.job_type]["archive_uris"] ) def test_add_file_uris(self): file_uris = ["file_uris"] self.builder.add_file_uris(file_uris) self.assertEqual(file_uris, self.builder.job["job"][self.job_type]["file_uris"]) def test_add_python_file_uris(self): python_file_uris = ["python_file_uris"] self.builder.add_python_file_uris(python_file_uris) self.assertEqual( python_file_uris, self.builder.job["job"][self.job_type]["python_file_uris"] ) def test_set_main_error(self): with self.assertRaises(Exception): self.builder.set_main("test", "test") def test_set_main_class(self): main = "main" self.builder.set_main(main_class=main, main_jar=None) self.assertEqual(main, self.builder.job["job"][self.job_type]["main_class"]) def test_set_main_jar(self): main = "main" self.builder.set_main(main_class=None, main_jar=main) self.assertEqual( main, self.builder.job["job"][self.job_type]["main_jar_file_uri"] ) def test_set_python_main(self): main = "main" self.builder.set_python_main(main) self.assertEqual( main, self.builder.job["job"][self.job_type]["main_python_file_uri"] ) @mock.patch(DATAPROC_STRING.format("uuid.uuid4")) def test_set_job_name(self, mock_uuid): uuid = "test_uuid" mock_uuid.return_value = uuid name = "name" self.builder.set_job_name(name) name += "_" + uuid[:8] self.assertEqual(name, self.builder.job["job"]["reference"]["job_id"]) def test_build(self): self.assertEqual(self.builder.job, self.builder.build())

37.577342

94

0.659439

661e7836a9d7a82b1ef6be7a3b4f25e82394b3ce

1,643

py

Python

src/python/zquantum/core/bitstring_distribution/distance_measures/clipped_negative_log_likelihood.py

alexjuda2/z-quantum-core

c258100dbd091f0b22495b77b36399426ae9abac

[ "Apache-2.0" ]

24

2020-04-15T17:36:59.000Z

2022-01-25T05:02:14.000Z

src/python/zquantum/core/bitstring_distribution/distance_measures/clipped_negative_log_likelihood.py

alexjuda2/z-quantum-core

c258100dbd091f0b22495b77b36399426ae9abac

[ "Apache-2.0" ]

177

2020-04-23T15:19:59.000Z

2022-03-30T18:06:17.000Z

src/python/zquantum/core/bitstring_distribution/distance_measures/clipped_negative_log_likelihood.py

alexjuda2/z-quantum-core

c258100dbd091f0b22495b77b36399426ae9abac

[ "Apache-2.0" ]

19

2020-06-24T10:56:02.000Z

2021-09-30T13:02:21.000Z

import math from typing import TYPE_CHECKING, Dict if TYPE_CHECKING: from zquantum.core.bitstring_distribution import BitstringDistribution def compute_clipped_negative_log_likelihood( target_distribution: "BitstringDistribution", measured_distribution: "BitstringDistribution", distance_measure_parameters: Dict, ) -> float: """Compute the value of the clipped negative log likelihood between a target bitstring distribution and a measured bitstring distribution. See Equation (4) in https://advances.sciencemag.org/content/5/10/eaaw9918?rss=1 Args: target_distribution: The target bitstring probability distribution. measured_distribution: The measured bitstring probability distribution. distance_measure_parameters: epsilon (float): The small parameter needed to regularize log computation when argument is zero. The default value is 1e-9. Returns: The value of the clipped negative log likelihood """ epsilon = distance_measure_parameters.get("epsilon", 1e-9) value = 0.0 target_keys = target_distribution.distribution_dict.keys() measured_keys = measured_distribution.distribution_dict.keys() all_keys = set(target_keys).union(measured_keys) for bitstring in all_keys: target_bitstring_value = target_distribution.distribution_dict.get(bitstring, 0) measured_bitstring_value = measured_distribution.distribution_dict.get( bitstring, 0 ) value += target_bitstring_value * math.log( max(epsilon, measured_bitstring_value) ) return -value

36.511111

88

0.736458

e219461fe40ed8a26ec6ec4adcd87016828bf126

1,858

py

Python

easy/python3/c0168_690_employee-importance/00_leetcode_0168.py

drunkwater/leetcode

8cc4a07763e71efbaedb523015f0c1eff2927f60

[ "Ruby" ]

null

easy/python3/c0168_690_employee-importance/00_leetcode_0168.py

drunkwater/leetcode

8cc4a07763e71efbaedb523015f0c1eff2927f60

[ "Ruby" ]

null

easy/python3/c0168_690_employee-importance/00_leetcode_0168.py

drunkwater/leetcode

8cc4a07763e71efbaedb523015f0c1eff2927f60

[ "Ruby" ]

3

2018-02-09T02:46:48.000Z

2021-02-20T08:32:03.000Z

# DRUNKWATER TEMPLATE(add description and prototypes) # Question Title and Description on leetcode.com # Function Declaration and Function Prototypes on leetcode.com #690. Employee Importance #You are given a data structure of employee information, which includes the employee's unique id, his importance value and his direct subordinates' id. #For example, employee 1 is the leader of employee 2, and employee 2 is the leader of employee 3. They have importance value 15, 10 and 5, respectively. Then employee 1 has a data structure like [1, 15, [2]], and employee 2 has [2, 10, [3]], and employee 3 has [3, 5, []]. Note that although employee 3 is also a subordinate of employee 1, the relationship is not direct. #Now given the employee information of a company, and an employee id, you need to return the total importance value of this employee and all his subordinates. #Example 1: #Input: [[1, 5, [2, 3]], [2, 3, []], [3, 3, []]], 1 #Output: 11 #Explanation: #Employee 1 has importance value 5, and he has two direct subordinates: employee 2 and employee 3. They both have importance value 3. So the total importance value of employee 1 is 5 + 3 + 3 = 11. #Note: #One employee has at most one direct leader and may have several subordinates. #The maximum number of employees won't exceed 2000. #""" ## Employee info #class Employee: # def __init__(self, id, importance, subordinates): # # It's the unique id of each node. # # unique id of this employee # self.id = id # # the importance value of this employee # self.importance = importance # # the id of direct subordinates # self.subordinates = subordinates #""" #class Solution: # def getImportance(self, employees, id): # """ # :type employees: Employee # :type id: int # :rtype: int # """ # Time Is Money

48.894737

371

0.700215

7a23de5c5144bde724c8cecbda2be9d2ac255baf

8,652

py

Python

pyro/distributions/transforms/planar.py

chiragnagpal/pyro

9b67c84798f39310345a6cf80f602195c0571166

[ "Apache-2.0" ]

null

pyro/distributions/transforms/planar.py

chiragnagpal/pyro

9b67c84798f39310345a6cf80f602195c0571166

[ "Apache-2.0" ]

null

pyro/distributions/transforms/planar.py

chiragnagpal/pyro

9b67c84798f39310345a6cf80f602195c0571166

[ "Apache-2.0" ]

null

# Copyright (c) 2017-2019 Uber Technologies, Inc. # SPDX-License-Identifier: Apache-2.0 import math from functools import partial import torch import torch.nn as nn import torch.nn.functional as F from torch.distributions import Transform, constraints from pyro.distributions.conditional import ConditionalTransformModule from pyro.distributions.torch_transform import TransformModule from pyro.distributions.util import copy_docs_from from pyro.nn import DenseNN @copy_docs_from(Transform) class ConditionedPlanar(Transform): domain = constraints.real codomain = constraints.real bijective = True event_dim = 1 def __init__(self, params): super().__init__(cache_size=1) self._params = params self._cached_logDetJ = None # This method ensures that torch(u_hat, w) > -1, required for invertibility def u_hat(self, u, w): alpha = torch.matmul(u.unsqueeze(-2), w.unsqueeze(-1)).squeeze(-1) a_prime = -1 + F.softplus(alpha) return u + (a_prime - alpha) * w.div(w.pow(2).sum(dim=-1, keepdim=True)) def _call(self, x): """ :param x: the input into the bijection :type x: torch.Tensor Invokes the bijection x => y; in the prototypical context of a :class:`~pyro.distributions.TransformedDistribution` `x` is a sample from the base distribution (or the output of a previous transform) """ bias, u, w = self._params() if callable(self._params) else self._params # x ~ (batch_size, dim_size, 1) # w ~ (batch_size, 1, dim_size) # bias ~ (batch_size, 1) act = torch.tanh(torch.matmul(w.unsqueeze(-2), x.unsqueeze(-1)).squeeze(-1) + bias) u_hat = self.u_hat(u, w) y = x + u_hat * act psi_z = (1. - act.pow(2)) * w self._cached_logDetJ = torch.log( torch.abs(1 + torch.matmul(psi_z.unsqueeze(-2), u_hat.unsqueeze(-1)).squeeze(-1).squeeze(-1))) return y def _inverse(self, y): """ :param y: the output of the bijection :type y: torch.Tensor Inverts y => x. As noted above, this implementation is incapable of inverting arbitrary values `y`; rather it assumes `y` is the result of a previously computed application of the bijector to some `x` (which was cached on the forward call) """ raise KeyError("ConditionedPlanar object expected to find key in intermediates cache but didn't") def log_abs_det_jacobian(self, x, y): """ Calculates the elementwise determinant of the log Jacobian """ x_old, y_old = self._cached_x_y if x is not x_old or y is not y_old: # This call to the parent class Transform will update the cache # as well as calling self._call and recalculating y and log_detJ self(x) return self._cached_logDetJ @copy_docs_from(ConditionedPlanar) class Planar(ConditionedPlanar, TransformModule): r""" A 'planar' bijective transform with equation, :math:`\mathbf{y} = \mathbf{x} + \mathbf{u}\tanh(\mathbf{w}^T\mathbf{z}+b)` where :math:`\mathbf{x}` are the inputs, :math:`\mathbf{y}` are the outputs, and the learnable parameters are :math:`b\in\mathbb{R}`, :math:`\mathbf{u}\in\mathbb{R}^D`, :math:`\mathbf{w}\in\mathbb{R}^D` for input dimension :math:`D`. For this to be an invertible transformation, the condition :math:`\mathbf{w}^T\mathbf{u}>-1` is enforced. Together with :class:`~pyro.distributions.TransformedDistribution` this provides a way to create richer variational approximations. Example usage: >>> base_dist = dist.Normal(torch.zeros(10), torch.ones(10)) >>> transform = Planar(10) >>> pyro.module("my_transform", transform) # doctest: +SKIP >>> flow_dist = dist.TransformedDistribution(base_dist, [transform]) >>> flow_dist.sample() # doctest: +SKIP The inverse of this transform does not possess an analytical solution and is left unimplemented. However, the inverse is cached when the forward operation is called during sampling, and so samples drawn using the planar transform can be scored. :param input_dim: the dimension of the input (and output) variable. :type input_dim: int References: [1] Danilo Jimenez Rezende, Shakir Mohamed. Variational Inference with Normalizing Flows. [arXiv:1505.05770] """ domain = constraints.real codomain = constraints.real bijective = True event_dim = 1 def __init__(self, input_dim): super().__init__(self._params) self.bias = nn.Parameter(torch.Tensor(1,)) self.u = nn.Parameter(torch.Tensor(input_dim,)) self.w = nn.Parameter(torch.Tensor(input_dim,)) self.input_dim = input_dim self.reset_parameters() def _params(self): return self.bias, self.u, self.w def reset_parameters(self): stdv = 1. / math.sqrt(self.u.size(0)) self.w.data.uniform_(-stdv, stdv) self.u.data.uniform_(-stdv, stdv) self.bias.data.zero_() @copy_docs_from(ConditionalTransformModule) class ConditionalPlanar(ConditionalTransformModule): r""" A conditional 'planar' bijective transform using the equation, :math:`\mathbf{y} = \mathbf{x} + \mathbf{u}\tanh(\mathbf{w}^T\mathbf{z}+b)` where :math:`\mathbf{x}` are the inputs with dimension :math:`D`, :math:`\mathbf{y}` are the outputs, and the pseudo-parameters :math:`b\in\mathbb{R}`, :math:`\mathbf{u}\in\mathbb{R}^D`, and :math:`\mathbf{w}\in\mathbb{R}^D` are the output of a function, e.g. a NN, with input :math:`z\in\mathbb{R}^{M}` representing the context variable to condition on. For this to be an invertible transformation, the condition :math:`\mathbf{w}^T\mathbf{u}>-1` is enforced. Together with :class:`~pyro.distributions.ConditionalTransformedDistribution` this provides a way to create richer variational approximations. Example usage: >>> from pyro.nn.dense_nn import DenseNN >>> input_dim = 10 >>> context_dim = 5 >>> batch_size = 3 >>> base_dist = dist.Normal(torch.zeros(input_dim), torch.ones(input_dim)) >>> param_dims = [1, input_dim, input_dim] >>> hypernet = DenseNN(context_dim, [50, 50], param_dims) >>> transform = ConditionalPlanar(hypernet) >>> z = torch.rand(batch_size, context_dim) >>> flow_dist = dist.ConditionalTransformedDistribution(base_dist, ... [transform]).condition(z) >>> flow_dist.sample(sample_shape=torch.Size([batch_size])) # doctest: +SKIP The inverse of this transform does not possess an analytical solution and is left unimplemented. However, the inverse is cached when the forward operation is called during sampling, and so samples drawn using the planar transform can be scored. :param nn: a function inputting the context variable and outputting a triplet of real-valued parameters of dimensions :math:`(1, D, D)`. :type nn: callable References: [1] Variational Inference with Normalizing Flows [arXiv:1505.05770] Danilo Jimenez Rezende, Shakir Mohamed """ domain = constraints.real codomain = constraints.real bijective = True event_dim = 1 def __init__(self, nn): super().__init__() self.nn = nn def _params(self, context): return self.nn(context) def condition(self, context): params = partial(self._params, context) return ConditionedPlanar(params) def planar(input_dim): """ A helper function to create a :class:`~pyro.distributions.transforms.Planar` object for consistency with other helpers. :param input_dim: Dimension of input variable :type input_dim: int """ return Planar(input_dim) def conditional_planar(input_dim, context_dim, hidden_dims=None): """ A helper function to create a :class:`~pyro.distributions.transforms.ConditionalPlanar` object that takes care of constructing a dense network with the correct input/output dimensions. :param input_dim: Dimension of input variable :type input_dim: int :param context_dim: Dimension of context variable :type context_dim: int :param hidden_dims: The desired hidden dimensions of the dense network. Defaults to using [input_dim * 10, input_dim * 10] :type hidden_dims: list[int] """ if hidden_dims is None: hidden_dims = [input_dim * 10, input_dim * 10] nn = DenseNN(context_dim, hidden_dims, param_dims=[1, input_dim, input_dim]) return ConditionalPlanar(nn)

35.170732

106

0.67337

ef6ded6a2a27334d79faf61095e7a422571f9097

668

py

Python

src/ndc/create_hist.py

TEI-EAJ/auto_aozora_tei

5535abef680a1e186f8a7dc6efc30a1dcf4efeec

[ "CC0-1.0" ]

3

2019-02-12T13:28:22.000Z

2021-07-25T20:58:07.000Z

src/ndc/create_hist.py

TEI-EAJ/auto_aozora_tei

5535abef680a1e186f8a7dc6efc30a1dcf4efeec

[ "CC0-1.0" ]

null

src/ndc/create_hist.py

TEI-EAJ/auto_aozora_tei

5535abef680a1e186f8a7dc6efc30a1dcf4efeec

[ "CC0-1.0" ]

1

2019-02-12T22:04:00.000Z

import json with open('data/ndc.json', 'r') as f: data = json.load(f) with open('data/subjects.json', 'r') as f: subjects = json.load(f) arr = data["children"] for lev1 in arr: for lev2 in lev1["children"]: for lev3 in lev2["children"]: for lev4 in lev3["children"]: id = lev4["id"] id = id.split("/")[-1].replace("ndc", "") id = id.upper() if id in subjects: lev4["value"] = subjects[id] with open('../../docs/ndc/hist.json', 'w') as outfile: json.dump(data, outfile, ensure_ascii=False, indent=4, sort_keys=True, separators=(',', ': '))

24.740741

98

0.523952

febc05664ea50c97d67b8f34fd7a95fd90ce24a7

6,471

py

Python

torchseq/models/samplers/parallel_nucleus.py

tomhosking/torchseq

1b08c16822a553ecb77b96289fb21eb0a13d9c6b

[ "Apache-2.0" ]

17

2021-02-25T14:24:06.000Z

2021-12-12T07:12:26.000Z

torchseq/models/samplers/parallel_nucleus.py

tomhosking/torchseq

1b08c16822a553ecb77b96289fb21eb0a13d9c6b

[ "Apache-2.0" ]

null

torchseq/models/samplers/parallel_nucleus.py

tomhosking/torchseq

1b08c16822a553ecb77b96289fb21eb0a13d9c6b

[ "Apache-2.0" ]

null

import torch import torch.nn as nn from torchseq.utils.tokenizer import Tokenizer from torchseq.utils.functions import onehot # FROM: https://gist.github.com/thomwolf/1a5a29f6962089e871b94cbd09daf317 def top_k_top_p_filtering(logits, top_k=0, top_p=0.0, filter_value=-float("Inf")): """Filter a distribution of logits using top-k and/or nucleus (top-p) filtering Args: logits: logits distribution shape (vocabulary size) top_k >0: keep only top k tokens with highest probability (top-k filtering). top_p >0.0: keep the top tokens with cumulative probability >= top_p (nucleus filtering). Nucleus filtering is described in Holtzman et al. (http://arxiv.org/abs/1904.09751) """ # assert logits.dim() == 1 # batch size 1 for now - could be updated for more but the code would be less clear top_k = min(top_k, logits.size(-1)) # Safety check orig_shape = logits.shape logits = logits.view(-1, orig_shape[-1]) if top_k > 0: # Remove all tokens with a probability less than the last token of the top-k indices_to_remove = logits < torch.topk(logits, top_k)[0][..., -1, None] logits[indices_to_remove] = filter_value if top_p > 0.0: sorted_logits, sorted_indices = torch.sort(logits, descending=True) cumulative_probs = torch.cumsum(nn.functional.softmax(sorted_logits, dim=-1), dim=-1) # Remove tokens with cumulative probability above the threshold sorted_indices_to_remove = cumulative_probs > top_p # Shift the indices to the right to keep also the first token above the threshold sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone() sorted_indices_to_remove[..., 0] = 0 indices_to_remove = sorted_indices_to_remove.scatter(dim=1, index=sorted_indices, src=sorted_indices_to_remove) logits[indices_to_remove] = filter_value return logits.reshape(orig_shape) class ParallelNucleusSampler(nn.Module): def __init__(self, config, device): super(ParallelNucleusSampler, self).__init__() self.config = config self.device = device def forward(self, model, batch, tgt_field): curr_batch_size = batch[[k for k in batch.keys() if k[-5:] != "_text"][0]].size()[0] max_output_len = self.config.eval.data.get("max_out_len", 32) prevent_repetition = ( self.config.nucleus_sampling.prevent_repetition if "prevent_repetition" in self.config.nucleus_sampling.data else True ) if not self.config.eval.data.get("shifted_decoding", True): print("Unshifted decoding not supported by nucleus decoder!") beam_width = self.config.nucleus_sampling.beam_width # number of total hypotheses to maintain prob_cutoff = self.config.nucleus_sampling.cutoff # Create vector of SOS + placeholder for first prediction output_seq = torch.LongTensor(curr_batch_size, beam_width, 1).fill_(Tokenizer().bos_id).to(self.device) scores = torch.FloatTensor(curr_batch_size, beam_width, 1).fill_(1).to(self.device) output_done = torch.BoolTensor(curr_batch_size, beam_width).fill_(False).to(self.device) padding = torch.LongTensor(curr_batch_size, beam_width).fill_(Tokenizer().pad_id).to(self.device) pad_probs = ( torch.FloatTensor(curr_batch_size, beam_width, self.config.prepro.vocab_size) .fill_(float("0")) .to(self.device) ) pad_probs[:, :, Tokenizer().pad_id] = float("1") def _tile_batch(x): return x.repeat_interleave(beam_width, dim=0) batch_tiled = {k: (_tile_batch(x) if k[-5:] != "_text" and k[0] != "_" else x) for k, x in batch.items()} seq_ix = 0 memory = {} while torch.sum(output_done) < curr_batch_size * beam_width and seq_ix < max_output_len: new_logits, memory = model(batch_tiled, output_seq.view(curr_batch_size * beam_width, -1), memory) new_logits = new_logits.view(curr_batch_size, beam_width, -1, self.config.prepro.vocab_size) output_done = (output_seq[:, :, -1] == Tokenizer().pad_id) | (output_seq[:, :, -1] == Tokenizer().eos_id) new_logits = top_k_top_p_filtering(logits=new_logits, top_p=prob_cutoff) if prevent_repetition: one_hot_prev = onehot(output_seq[:, :, -1], N=self.config.prepro.vocab_size) new_logits[:, :, -1, :] = new_logits[:, :, -1, :] + (one_hot_prev * float("-1e-16")) new_probs = torch.where( output_done.unsqueeze(-1), pad_probs, nn.functional.softmax(new_logits[:, :, -1, :], -1) ) sampled_indices = ( torch.multinomial(new_probs.view(curr_batch_size * beam_width, -1).cpu(), 1) .view(curr_batch_size, beam_width, -1) .to(self.device) ) sampled_scores = new_probs.gather(index=sampled_indices, dim=-1) new_output = torch.cat([output_seq, sampled_indices], dim=-1) scores = torch.cat([scores, sampled_scores], dim=-1) # Use pad for the output for elements that have completed if seq_ix > 0: output_done = (new_output[:, :, -2] == Tokenizer().eos_id) | ( new_output[:, :, -2] == Tokenizer().pad_id ) new_output[:, :, -1] = torch.where(output_done, padding, new_output[:, :, -1]) output_seq = new_output seq_ix += 1 # Take top-1 beam: hypothesis_len = torch.sum(output_seq != Tokenizer().pad_id, dim=-1) # Length penalty needs to be applied to *overall* score, not score for this token len_alpha = self.config.nucleus_sampling.length_alpha length_penalty = torch.pow((5 + hypothesis_len).float(), len_alpha) / pow(5.0 + 1.0, len_alpha) beam_scores = ( torch.log(scores).where(output_seq != Tokenizer().pad_id, torch.FloatTensor([0.0]).to(self.device)).sum(-1) / length_penalty ) sorted_scores, sorted_indices = torch.sort(beam_scores, descending=True) output_seq = torch.gather(output_seq, 1, sorted_indices.unsqueeze(-1).expand(-1, -1, output_seq.shape[2])) output = output_seq return output, sorted_scores, torch.sum(output_seq != Tokenizer().pad_id, dim=-1), memory

44.321918

119

0.643332

20bebc733d7674e861ad7c07684a3007af031c69

3,160

py

Python

activelearning/settings.py

evanlouie/activelearning

7ee6e9d2d795f85a441ad70e70ac0d8de9c25e31

[ "MIT" ]

null

activelearning/settings.py

evanlouie/activelearning

7ee6e9d2d795f85a441ad70e70ac0d8de9c25e31

[ "MIT" ]

null

activelearning/settings.py

evanlouie/activelearning

7ee6e9d2d795f85a441ad70e70ac0d8de9c25e31

[ "MIT" ]

1

2019-01-03T18:03:18.000Z

""" Django settings for activelearning project. Generated by 'django-admin startproject' using Django 2.1.2. For more information on this file, see https://docs.djangoproject.com/en/2.1/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/2.1/ref/settings/ """ import os # Build paths inside the project like this: os.path.join(BASE_DIR, ...) BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/2.1/howto/deployment/checklist/ # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = "!y4%@!k#=%=&k%!(2em8$rrb-mjt2kup1pa(d@=(x#6y_uloh^" # SECURITY WARNING: don't run with debug turned on in production! DEBUG = True ALLOWED_HOSTS = [] # Application definition INSTALLED_APPS = [ "ensemble.apps.EnsembleConfig", "django.contrib.admin", "django.contrib.auth", "django.contrib.contenttypes", "django.contrib.sessions", "django.contrib.messages", "django.contrib.staticfiles", ] MIDDLEWARE = [ "django.middleware.security.SecurityMiddleware", "django.contrib.sessions.middleware.SessionMiddleware", "django.middleware.common.CommonMiddleware", "django.middleware.csrf.CsrfViewMiddleware", "django.contrib.auth.middleware.AuthenticationMiddleware", "django.contrib.messages.middleware.MessageMiddleware", "django.middleware.clickjacking.XFrameOptionsMiddleware", ] ROOT_URLCONF = "activelearning.urls" TEMPLATES = [ { "BACKEND": "django.template.backends.django.DjangoTemplates", "DIRS": [], "APP_DIRS": True, "OPTIONS": { "context_processors": [ "django.template.context_processors.debug", "django.template.context_processors.request", "django.contrib.auth.context_processors.auth", "django.contrib.messages.context_processors.messages", ] }, } ] WSGI_APPLICATION = "activelearning.wsgi.application" # Database # https://docs.djangoproject.com/en/2.1/ref/settings/#databases DATABASES = { "default": { "ENGINE": "django.db.backends.sqlite3", "NAME": os.path.join(BASE_DIR, "db.sqlite3"), } } # Password validation # https://docs.djangoproject.com/en/2.1/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { "NAME": "django.contrib.auth.password_validation.UserAttributeSimilarityValidator" }, {"NAME": "django.contrib.auth.password_validation.MinimumLengthValidator"}, {"NAME": "django.contrib.auth.password_validation.CommonPasswordValidator"}, {"NAME": "django.contrib.auth.password_validation.NumericPasswordValidator"}, ] # Internationalization # https://docs.djangoproject.com/en/2.1/topics/i18n/ LANGUAGE_CODE = "en-us" TIME_ZONE = "UTC" USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/2.1/howto/static-files/ STATIC_URL = "/static/" # Media Files MEDIA_ROOT = "media" MEDIA_URL = "/media/"

25.901639

90

0.705696

2a9ee1efeb8c721c00cc33cda209834c6a1353bd

783

py

Python

bacchus/celery.py

hudecof/bacchus

1d7bafa2331535b27b336b42f07f8fe328f6d131

[ "Apache-2.0" ]

1

2020-04-15T14:31:48.000Z

bacchus/celery.py

hudecof/bacchus

1d7bafa2331535b27b336b42f07f8fe328f6d131

[ "Apache-2.0" ]

4

2019-04-13T08:35:51.000Z

2019-04-13T15:08:47.000Z

bacchus/celery.py

hudecof/bacchus

1d7bafa2331535b27b336b42f07f8fe328f6d131

[ "Apache-2.0" ]

1

2019-03-25T07:48:29.000Z

from __future__ import absolute_import, unicode_literals import os from celery import Celery # set the default Django settings module for the 'celery' program. os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'bacchus.settings') app = Celery('bacchus') # Using a string here means the worker don't have to serialize # the configuration object to child processes. # - namespace='CELERY' means all celery-related configuration keys # should have a `CELERY_` prefix. app.config_from_object('django.conf:settings', namespace='CELERY') app.conf.timezone = 'Europe/Istanbul' app.conf.enable_utc = False # Load task modules from all registered Django app configs. app.autodiscover_tasks() @app.task(bind=True) def debug_task(self): print('Request: {0!r}'.format(self.request))

29

67

0.773946

998662d5fc197c662c69592197835dcd1578e87b

2,468

py

Python

js/analysis.py

Omar-Ceesay/2048-AI

520a319b2f3cdf56e0f298e49e08cb7f0864b52c

[ "MIT" ]

null

js/analysis.py

Omar-Ceesay/2048-AI

520a319b2f3cdf56e0f298e49e08cb7f0864b52c

[ "MIT" ]

null

js/analysis.py

Omar-Ceesay/2048-AI

520a319b2f3cdf56e0f298e49e08cb7f0864b52c

[ "MIT" ]

null

import matplotlib import matplotlib.pyplot as plt import numpy as np from os import listdir from os.path import isfile, join import json file_path = "./data" all_data_files = [f for f in listdir(file_path) if isfile(join(file_path, f))] all_data = [] for filename in all_data_files: f = open("./data/"+filename, "r") json_data = json.loads(f.read()) for entry in json_data: all_data.append(entry) mc_data = { "10": { "count": 0, "items": [] }, "50": { "count": 0, "items": [] }, "60": { "count": 0, "items": [] }, "100": { "count": 0, "items": [] }, "150": { "count": 0, "items": [] }, "200": { "count": 0, "items": [] }, "250": { "count": 0, "items": [] }, "300": { "count": 0, "items": [] } } for entry in all_data: sim_number_as_string = str(entry["simulation_number"]) mc_data[sim_number_as_string]["count"] += 1 mc_data[sim_number_as_string]["items"].append(entry) count = 0 total_score = 0 total_time = 0 data = [] scores = [] average_scores = [] wins = 0 get_these = ["50", "100", "150", "200", "250", "300"] for simulation_number in get_these: total_score = 0 total_time = 0 wins = 0 count = 0 scores = [] for entry in mc_data[simulation_number]["items"]: count += 1 total_score += entry["score"] scores.append(entry["score"]) if (entry["highestTile"] >= 2048): wins += 1 if "time" in entry: # I have some messed up times in here if (entry["time"] < 150000): total_time += entry["time"] print("*"*20) print("Total number of samples: " + str(count)) print("Total wins: " + str(wins)) print("Average number of wins: " + str(round(wins/count, 3))) average_scores.append(total_score/count) print("Average score for "+ simulation_number +" simulations is " + str(total_score/count)) print("Average time for "+ simulation_number +" simulations is " + str((total_time/count)/1000) + " seconds") print("*"*20) data.append(scores) np.random.seed(19680801) # Data for plotting fig1, ax1 = plt.subplots() ax1.set_title('Final score vs number of simulations') ax1.boxplot(data, labels=get_these) plt.plot([1,2,3,4,5,6], average_scores) # fig.savefig("test.png") plt.show()

22.642202

113

0.561588

b7b81266e65b2023132e0196ca92bab0cd73f2d4

617

py

Python

workload-traces/mysql/tpcc/extract_lock_names.py

spcl/vldb19-distributed-locking

efa9ffa4065cf17ccdf0b59672c173eb2d23934c

[ "MIT" ]

8

2019-11-04T19:05:40.000Z

2022-01-19T06:05:21.000Z

workload-traces/mysql/tpcc/extract_lock_names.py

spcl/vldb19-distributed-locking

efa9ffa4065cf17ccdf0b59672c173eb2d23934c

[ "MIT" ]

null

workload-traces/mysql/tpcc/extract_lock_names.py

spcl/vldb19-distributed-locking

efa9ffa4065cf17ccdf0b59672c173eb2d23934c

[ "MIT" ]

null

#!/usr/bin/env python3 import fileinput import json from collections import OrderedDict locks = set() for line in fileinput.input(): data = json.loads(line) # Get only lock events if data.get('action') not in ['lock', 'unlock']: continue # Insert ware house where missing (home ware house by default) ware_house = data['ware_house'] if 'ware_house' in data else data['home_ware_house'] ware_house = int(ware_house) locks.add((ware_house, data['object_name'])) for l in locks: data = OrderedDict([('ware_house', l[0]), ('object_name', l[1])]) print(json.dumps(data))

24.68

88

0.672609

a9a988c3221786da4c41b780858f77a389d50793

550

py

Python

thumbor/detectors/glasses_detector/__init__.py

jairhenrique/thumbor

fa29ba0efab2dd420c6840616a079756fd75293a

[ "MIT" ]

6,837

2015-01-01T14:33:12.000Z

2022-03-31T22:21:05.000Z

thumbor/detectors/glasses_detector/__init__.py

jairhenrique/thumbor

fa29ba0efab2dd420c6840616a079756fd75293a

[ "MIT" ]

1,055

2015-01-03T22:22:05.000Z

2022-03-31T21:56:17.000Z

thumbor/detectors/glasses_detector/__init__.py

jairhenrique/thumbor

fa29ba0efab2dd420c6840616a079756fd75293a

[ "MIT" ]

744

2015-01-05T03:49:31.000Z

2022-03-30T02:35:16.000Z

#!/usr/bin/python # -*- coding: utf-8 -*- # thumbor imaging service # https://github.com/thumbor/thumbor/wiki # Licensed under the MIT license: # http://www.opensource.org/licenses/mit-license # Copyright (c) 2011 globo.com thumbor@googlegroups.com from thumbor.detectors.local_detector import CascadeLoaderDetector class Detector(CascadeLoaderDetector): def __init__(self, context, index, detectors): super().__init__(context, index, detectors) self.load_cascade_file(__file__, context.config.GLASSES_DETECTOR_CASCADE_FILE)

30.555556

86

0.761818

4aa954cf6c840968e8d972c9934f8f658bedecb6

4,394

py

Python

src/addit/ncf.py

HajimeKawahara/addit

7b19006bd338d7887f1a600b66fc74fc72c21c70

[ "MIT" ]

3

2021-06-28T15:40:50.000Z

2021-07-13T17:53:06.000Z

src/addit/ncf.py

HajimeKawahara/addit

7b19006bd338d7887f1a600b66fc74fc72c21c70

[ "MIT" ]

4

2021-07-13T22:02:56.000Z

2021-08-13T00:47:02.000Z

src/addit/ncf.py

HajimeKawahara/addit

7b19006bd338d7887f1a600b66fc74fc72c21c70

[ "MIT" ]

1

2021-08-10T09:43:36.000Z

"""Neighbouring Contribution Function (new version updated Aug 10th 2021) * Assume a given x-grid xv[k], and a value x. For a 1D case, the Neighbouring Contribution Function gives ncf(k,x) = (x-xv[s])/dv for k=s, (xv[s]-x)/dv for k=s+1, and 0 elsewhere, where s is the nearest index of xv[k] to x but xv[k]<x. * For a 2D case, NCF gives the non-zero values for 4 points around (x,y) """ import jax.numpy as jnp import numpy as np from jax import jit from jax import vmap from jax.lax import scan from jax.ops import index_add from jax.ops import index as joi def getix(x,xv): indarr=jnp.arange(len(xv)) pos = jnp.interp(x,xv,indarr) index = (pos).astype(int) cont = (pos-index) return cont,index @jit def inc1D(w,x,xv): cx,ix=getix(x,xv) a=jnp.zeros(len(xv)) a=index_add(a,joi[ix],w*(1.0-cx)) a=index_add(a,joi[ix+1],w*cx) return a @jit def inc2D(w,x,y,xv,yv): """integrated neighbouring contribution function for 2D (memory reduced sum). Args: w: weight (N) x: x values (N) y: y values (N) xv: x grid yv: y grid Returns: integrated neighbouring contribution function Note: This function computes \sum_n w_n fx_n \otimes fy_n, where w_n is the weight, fx_n and fy_n are the n-th NCFs for 1D. A direct sum uses huge RAM. In this function, we use jax.lax.scan to compute the sum Example: >>> N=10000 >>> xv=jnp.linspace(0,1,11) #grid >>> yv=jnp.linspace(0,1,13) #grid >>> w=np.logspace(1.0,3.0,N) >>> x=np.random.rand(N) >>> y=np.random.rand(N) >>> val=inc2D(w,x,y,xv,yv) >>> #the comparision with the direct sum >>> valdirect=jnp.sum(nc2D(x,y,xv,yv)*w,axis=2) >>> #maximum deviation >>> print(jnp.max(jnp.abs((val-valdirect)/jnp.mean(valdirect)))*100,"%") #% >>> 5.196106e-05 % >>> #mean deviation >>> print(jnp.sqrt(jnp.mean((val-valdirect)**2))/jnp.mean(valdirect)*100,"%") #% >>> 1.6135311e-05 % """ cx,ix=getix(x,xv) cy,iy=getix(y,yv) a=jnp.zeros((len(xv),len(yv))) a=index_add(a,joi[ix,iy],w*(1-cx)*(1-cy)) a=index_add(a,joi[ix,iy+1],w*(1-cx)*cy) a=index_add(a,joi[ix+1,iy],w*cx*(1-cy)) a=index_add(a,joi[ix+1,iy+1],w*cx*cy) return a @jit def inc3D(w,x,y,z,xv,yv,zv): """The lineshape distribution matrix = integrated neighbouring contribution for 3D (memory reduced sum). Args: w: weight (N) x: x values (N) y: y values (N) z: z values (N) xv: x grid yv: y grid zv: z grid Returns: lineshape distribution matrix (integrated neighbouring contribution for 3D) Note: This function computes \sum_n w_n fx_n \otimes fy_n \otimes fz_n, where w_n is the weight, fx_n, fy_n, and fz_n are the n-th NCFs for 1D. A direct sum uses huge RAM. In this function, we use jax.lax.scan to compute the sum Example: >>> N=10000 >>> xv=jnp.linspace(0,1,11) #grid >>> yv=jnp.linspace(0,1,13) #grid >>> zv=jnp.linspace(0,1,17) #grid >>> w=np.logspace(1.0,3.0,N) >>> x=np.random.rand(N) >>> y=np.random.rand(N) >>> z=np.random.rand(N) >>> val=inc3D(w,x,y,z,xv,yv,zv) >>> #the comparision with the direct sum >>> valdirect=jnp.sum(nc3D(x,y,z,xv,yv,zv)*w,axis=3) >>> #maximum deviation >>> print(jnp.max(jnp.abs((val-valdirect)/jnp.mean(valdirect)))*100,"%") #% >>> 5.520862e-05 % >>> #mean deviation >>> print(jnp.sqrt(jnp.mean((val-valdirect)**2))/jnp.mean(valdirect)*100,"%") #% >>> 8.418057e-06 % """ cx,ix=getix(x,xv) cy,iy=getix(y,yv) cz,iz=getix(z,zv) a=jnp.zeros((len(xv),len(yv),len(zv))) a=index_add(a,joi[ix,iy,iz],w*(1-cx)*(1-cy)*(1-cz)) a=index_add(a,joi[ix,iy+1,iz],w*(1-cx)*cy*(1-cz)) a=index_add(a,joi[ix+1,iy,iz],w*cx*(1-cy)*(1-cz)) a=index_add(a,joi[ix+1,iy+1,iz],w*cx*cy*(1-cz)) a=index_add(a,joi[ix,iy,iz+1],w*(1-cx)*(1-cy)*cz) a=index_add(a,joi[ix,iy+1,iz+1],w*(1-cx)*cy*cz) a=index_add(a,joi[ix+1,iy,iz+1],w*cx*(1-cy)*cz) a=index_add(a,joi[ix+1,iy+1,iz+1],w*cx*cy*cz) return a

32.548148

239

0.565316

2aebd98258592fe26c2e32d74e3485c8f047ecb3

6,920

py

Python

plugins/action/node_deployment.py

steinzi/ansible-ise

0add9c8858ed8e0e5e7219fbaf0c936b6d7cc6c0

[ "MIT" ]

null

plugins/action/node_deployment.py

steinzi/ansible-ise

0add9c8858ed8e0e5e7219fbaf0c936b6d7cc6c0

[ "MIT" ]

null

plugins/action/node_deployment.py

steinzi/ansible-ise

0add9c8858ed8e0e5e7219fbaf0c936b6d7cc6c0

[ "MIT" ]

null

from __future__ import (absolute_import, division, print_function) __metaclass__ = type from ansible.plugins.action import ActionBase try: from ansible_collections.ansible.utils.plugins.module_utils.common.argspec_validate import ( AnsibleArgSpecValidator, ) except ImportError: ANSIBLE_UTILS_IS_INSTALLED = False else: ANSIBLE_UTILS_IS_INSTALLED = True from ansible.errors import AnsibleActionFail from ansible_collections.cisco.ise.plugins.module_utils.ise import ( ISESDK, ise_argument_spec, ise_compare_equality, get_dict_result, ) from ansible_collections.cisco.ise.plugins.module_utils.exceptions import ( InconsistentParameters, ) # Get common arguments specification argument_spec = ise_argument_spec() # Add arguments specific for this module argument_spec.update(dict( state=dict(type="str", default="present", choices=["present", "absent"]), fdqn=dict(type="str"), userName=dict(type="str"), password=dict(type="str", no_log=True), administration=dict(type="dict"), generalSettings=dict(type="dict"), profileConfiguration=dict(type="dict"), hostname=dict(type="str"), )) required_if = [ ("state", "present", ["hostname"], True), ("state", "absent", ["hostname"], True), ] required_one_of = [] mutually_exclusive = [] required_together = [] class NodeDeployment(object): def __init__(self, params, ise): self.ise = ise self.new_object = dict( fdqn=params.get("fdqn"), user_name=params.get("userName"), password=params.get("password"), administration=params.get("administration"), general_settings=params.get("generalSettings"), profile_configuration=params.get("profileConfiguration"), hostname=params.get("hostname"), ) def get_object_by_name(self, name): try: result = self.ise.exec( family="node_deployment", function="get_node_details", params={"hostname": name} ).response.get('response', {}) result = get_dict_result(result, 'name', name) except Exception as e: result = None return result def get_object_by_id(self, id): # NOTICE: Does not have a get by id method or it is in another action result = None return result def exists(self): prev_obj = None id_exists = False name_exists = False o_id = self.new_object.get("id") name = self.new_object.get("hostname") if o_id: prev_obj = self.get_object_by_id(o_id) id_exists = prev_obj is not None and isinstance(prev_obj, dict) if not id_exists and name: prev_obj = self.get_object_by_name(name) name_exists = prev_obj is not None and isinstance(prev_obj, dict) if name_exists: _id = prev_obj.get("id") if id_exists and name_exists and o_id != _id: raise InconsistentParameters("The 'id' and 'name' params don't refer to the same object") it_exists = prev_obj is not None and isinstance(prev_obj, dict) return (it_exists, prev_obj) def requires_update(self, current_obj): requested_obj = self.new_object obj_params = [ ("fdqn", "fdqn"), ("userName", "user_name"), ("password", "password"), ("administration", "administration"), ("generalSettings", "general_settings"), ("profileConfiguration", "profile_configuration"), ("hostname", "hostname"), ] # Method 1. Params present in request (Ansible) obj are the same as the current (ISE) params # If any does not have eq params, it requires update return any(not ise_compare_equality(current_obj.get(ise_param), requested_obj.get(ansible_param)) for (ise_param, ansible_param) in obj_params) def create(self): result = self.ise.exec( family="node_deployment", function="register_node", params=self.new_object, ).response return result def update(self): result = self.ise.exec( family="node_deployment", function="update_node", params=self.new_object ).response return result def delete(self): result = self.ise.exec( family="node_deployment", function="delete_node", params=self.new_object ).response return result class ActionModule(ActionBase): def __init__(self, *args, **kwargs): if not ANSIBLE_UTILS_IS_INSTALLED: raise AnsibleActionFail("ansible.utils is not installed. Execute 'ansible-galaxy collection install ansible.utils'") super(ActionModule, self).__init__(*args, **kwargs) self._supports_async = True self._result = None # Checks the supplied parameters against the argument spec for this module def _check_argspec(self): aav = AnsibleArgSpecValidator( data=self._task.args, schema=dict(argument_spec=argument_spec), schema_format="argspec", schema_conditionals=dict( required_if=required_if, required_one_of=required_one_of, mutually_exclusive=mutually_exclusive, required_together=required_together, ), name=self._task.action, ) valid, errors, self._task.args = aav.validate() if not valid: raise AnsibleActionFail(errors) def run(self, tmp=None, task_vars=None): self._task.diff = False self._result = super(ActionModule, self).run(tmp, task_vars) self._result["changed"] = False self._check_argspec() ise = ISESDK(self._task.args) obj = NodeDeployment(self._task.args, ise) state = self._task.args.get("state") response = None if state == "present": (obj_exists, prev_obj) = obj.exists() if obj_exists: if obj.requires_update(prev_obj): response = obj.update() ise.object_updated() else: response = prev_obj ise.object_already_present() else: response = obj.create() ise.object_created() elif state == "absent": (obj_exists, prev_obj) = obj.exists() if obj_exists: response = obj.delete() ise.object_deleted() else: ise.object_already_absent() self._result.update(dict(ise_response=response)) self._result.update(ise.exit_json()) return self._result

34.427861

128

0.606936

6d476c15a429eadd7dbd717a575d515102beb574

39,738

py

Python

robot_sim/robots/ur3_dual/ur3_dual.py

liang324/wrs

46eadec355c61a9c7bac1fa0f3cf419b2aac19aa

[ "MIT" ]

null

robot_sim/robots/ur3_dual/ur3_dual.py

liang324/wrs

46eadec355c61a9c7bac1fa0f3cf419b2aac19aa

[ "MIT" ]

null

robot_sim/robots/ur3_dual/ur3_dual.py

liang324/wrs

46eadec355c61a9c7bac1fa0f3cf419b2aac19aa

[ "MIT" ]

null

import os import math import numpy as np import basis.robot_math as rm import modeling.model_collection as mc import modeling.collision_model as cm import robot_sim._kinematics.jlchain as jl import robot_sim.manipulators.ur3.ur3 as ur import robot_sim.end_effectors.grippers.robotiq85.robotiq85 as rtq from panda3d.core import CollisionNode, CollisionBox, Point3 import robot_sim.robots.robot_interface as ri class UR3Dual(ri.RobotInterface): def __init__(self, pos=np.zeros(3), rotmat=np.eye(3), name='ur3dual', enable_cc=True): super().__init__(pos=pos, rotmat=rotmat, name=name) this_dir, this_filename = os.path.split(__file__) # left side self.lft_body = jl.JLChain(pos=pos, rotmat=rotmat, homeconf=np.zeros(12), name='lft_body_jl') self.lft_body.jnts[0]['loc_pos'] = np.array([0.0, 0.0, 0.0]) self.lft_body.jnts[1]['loc_pos'] = np.array([-0.0, 0.0, 0.0]) self.lft_body.jnts[2]['loc_pos'] = np.array([0.0, 0.0, 0.0]) self.lft_body.jnts[3]['loc_pos'] = np.array([0.0, 0.0, 0.0]) self.lft_body.jnts[4]['loc_pos'] = np.array([-0.0, 0.0, 0.0]) self.lft_body.jnts[5]['loc_pos'] = np.array([0.0, 0.0, 0.0]) self.lft_body.jnts[6]['loc_pos'] = np.array([0.0, 0.0, 0.0]) self.lft_body.jnts[7]['loc_pos'] = np.array([-0.0, 0.0, 0.0]) self.lft_body.jnts[8]['loc_pos'] = np.array([0.0, 0.0, 0.0]) self.lft_body.jnts[9]['loc_pos'] = np.array([0.0, 0.0, 0.0]) self.lft_body.jnts[10]['loc_pos'] = np.array([-0.0, 0.0, 0.0]) self.lft_body.jnts[11]['loc_pos'] = np.array([0.0, 0.0, 0.0]) self.lft_body.jnts[12]['loc_pos'] = np.array([0.0, 0.0, 0.0]) self.lft_body.jnts[13]['loc_pos'] = np.array([.0, .258485281374, 1.61051471863]) self.lft_body.jnts[13]['loc_rotmat'] = rm.rotmat_from_euler(-3.0 * math.pi / 4.0, 0, math.pi, 'rxyz') # body self.lft_body.lnks[0]['name'] = "ur3_dual_lft_body" self.lft_body.lnks[0]['loc_pos'] = np.array([0, 0, 0]) self.lft_body.lnks[0]['collisionmodel'] = cm.CollisionModel( os.path.join(this_dir, "meshes", "ur3_dual_base.stl"), cdprimit_type="user_defined", expand_radius=.005, userdefined_cdprimitive_fn=self._base_combined_cdnp) self.lft_body.lnks[0]['rgba'] = [.3, .3, .3, 1.0] # columns self.lft_body.lnks[1]['name'] = "ur3_dual_back_rgt_column" self.lft_body.lnks[1]['loc_pos'] = np.array([-0.41, -0.945, 0]) self.lft_body.lnks[1]['collisionmodel'] = cm.CollisionModel( os.path.join(this_dir, "meshes", "ur3_dual_column2400x60x60.stl")) self.lft_body.lnks[2]['name'] = "ur3_dual_back_lft_column" self.lft_body.lnks[2]['loc_pos'] = np.array([-0.41, 0.945, 0]) self.lft_body.lnks[2]['collisionmodel'] = cm.CollisionModel( os.path.join(this_dir, "meshes", "ur3_dual_column2400x60x60.stl")) self.lft_body.lnks[3]['name'] = "ur3_dual_front_rgt_column" self.lft_body.lnks[3]['loc_pos'] = np.array([0.73, -0.945, 0]) self.lft_body.lnks[3]['collisionmodel'] = cm.CollisionModel( os.path.join(this_dir, "meshes", "ur3_dual_column2400x60x60.stl")) self.lft_body.lnks[4]['name'] = "ur3_dual_front_lft_column" self.lft_body.lnks[4]['loc_pos'] = np.array([0.73, 0.945, 0]) self.lft_body.lnks[4]['collisionmodel'] = cm.CollisionModel( os.path.join(this_dir, "meshes", "ur3_dual_column2400x60x60.stl")) # x_rows self.lft_body.lnks[5]['name'] = "ur3_dual_up_rgt_xrow" self.lft_body.lnks[5]['loc_pos'] = np.array([-0.38, -0.945, 2.37]) self.lft_body.lnks[5]['loc_rotmat'] = rm.rotmat_from_euler(0, math.pi / 2, 0) self.lft_body.lnks[5]['collisionmodel'] = cm.CollisionModel( os.path.join(this_dir, "meshes", "ur3_dual_column1080x60x60.stl")) self.lft_body.lnks[6]['name'] = "ur3_dual_bottom_rgt_xrow" self.lft_body.lnks[6]['loc_pos'] = np.array([-0.38, -0.945, 1.07]) self.lft_body.lnks[6]['loc_rotmat'] = rm.rotmat_from_euler(0, math.pi / 2, 0) self.lft_body.lnks[6]['collisionmodel'] = cm.CollisionModel( os.path.join(this_dir, "meshes", "ur3_dual_column1080x60x60.stl")) self.lft_body.lnks[7]['name'] = "ur3_dual_up_lft_xrow" self.lft_body.lnks[7]['loc_pos'] = np.array([-0.38, 0.945, 2.37]) self.lft_body.lnks[7]['loc_rotmat'] = rm.rotmat_from_euler(0, math.pi / 2, 0) self.lft_body.lnks[7]['collisionmodel'] = cm.CollisionModel( os.path.join(this_dir, "meshes", "ur3_dual_column1080x60x60.stl")) self.lft_body.lnks[8]['name'] = "ur3_dual_bottom_lft_xrow" self.lft_body.lnks[8]['loc_pos'] = np.array([-0.38, 0.945, 1.07]) self.lft_body.lnks[8]['loc_rotmat'] = rm.rotmat_from_euler(0, math.pi / 2, 0) self.lft_body.lnks[8]['collisionmodel'] = cm.CollisionModel( os.path.join(this_dir, "meshes", "ur3_dual_column1080x60x60.stl")) # y_rows self.lft_body.lnks[9]['name'] = "ur3_dual_back_up_yrow" self.lft_body.lnks[9]['loc_pos'] = np.array([-0.41, -0.915, 2.37]) self.lft_body.lnks[9]['loc_rotmat'] = rm.rotmat_from_euler(-math.pi / 2, 0, 0) self.lft_body.lnks[9]['collisionmodel'] = cm.CollisionModel( os.path.join(this_dir, "meshes", "ur3_dual_column1830x60x60.stl")) self.lft_body.lnks[10]['name'] = "ur3_dual_back_bottom_yrow" self.lft_body.lnks[10]['loc_pos'] = np.array([-0.41, -0.915, 0.35]) self.lft_body.lnks[10]['loc_rotmat'] = rm.rotmat_from_euler(-math.pi / 2, 0, 0) self.lft_body.lnks[10]['collisionmodel'] = cm.CollisionModel( os.path.join(this_dir, "meshes", "ur3_dual_column1830x60x60.stl")) self.lft_body.lnks[11]['name'] = "ur3_dual_front_up_yrow" self.lft_body.lnks[11]['loc_pos'] = np.array([0.73, -0.915, 2.37]) self.lft_body.lnks[11]['loc_rotmat'] = rm.rotmat_from_euler(-math.pi / 2, 0, 0) self.lft_body.lnks[11]['collisionmodel'] = cm.CollisionModel( os.path.join(this_dir, "meshes", "ur3_dual_column1830x60x60.stl")) # table TODO update using vision sensors self.lft_body.lnks[12]['name'] = "ur3_dual_table" self.lft_body.lnks[12]['loc_pos'] = np.array([0.36, 0.0, 1.046]) self.lft_body.lnks[12]['loc_rotmat'] = rm.rotmat_from_euler(0, 0, math.pi / 2) self.lft_body.lnks[12]['collisionmodel'] = cm.CollisionModel( os.path.join(this_dir, "meshes", "ur3_dual_table1820x54x800.stl")) self.lft_body.lnks[12]['rgba'] = [.9, .77, .52, 1.0] self.lft_body.reinitialize() lft_arm_homeconf = np.zeros(6) lft_arm_homeconf[0] = math.pi / 3.0 lft_arm_homeconf[1] = -math.pi * 1.0 / 3.0 lft_arm_homeconf[2] = -math.pi * 2.0 / 3.0 lft_arm_homeconf[3] = math.pi lft_arm_homeconf[4] = -math.pi / 2.0 self.lft_arm = ur.UR3(pos=self.lft_body.jnts[-1]['gl_posq'], rotmat=self.lft_body.jnts[-1]['gl_rotmatq'], homeconf=lft_arm_homeconf, enable_cc=False) # lft hand ftsensor self.lft_ft_sensor = jl.JLChain(pos=self.lft_arm.jnts[-1]['gl_posq'], rotmat=self.lft_arm.jnts[-1]['gl_rotmatq'], homeconf=np.zeros(0), name='lft_ft_sensor_jl') self.lft_ft_sensor.jnts[1]['loc_pos'] = np.array([.0, .0, .0484]) self.lft_ft_sensor.lnks[0]['name'] = "ur3_dual_lft_ft_sensor" self.lft_ft_sensor.lnks[0]['loc_pos'] = np.array([0, 0, 0]) self.lft_ft_sensor.lnks[0]['collisionmodel'] = cm.gen_stick(spos=self.lft_ft_sensor.jnts[0]['loc_pos'], epos=self.lft_ft_sensor.jnts[1]['loc_pos'], thickness=.067, rgba=[.2, .3, .3, 1], sections=24) self.lft_ft_sensor.reinitialize() # lft hand self.lft_hnd = rtq.Robotiq85(pos=self.lft_ft_sensor.jnts[-1]['gl_posq'], rotmat=self.lft_ft_sensor.jnts[-1]['gl_rotmatq'], enable_cc=False) # rigth side self.rgt_body = jl.JLChain(pos=pos, rotmat=rotmat, homeconf=np.zeros(0), name='rgt_body_jl') self.rgt_body.jnts[1]['loc_pos'] = np.array([.0, -.258485281374, 1.61051471863]) # right from robot_s view self.rgt_body.jnts[1]['loc_rotmat'] = rm.rotmat_from_euler(3.0 * math.pi / 4.0, .0, .0) # left from robot_s view self.rgt_body.lnks[0]['name'] = "ur3_dual_rgt_body" self.rgt_body.lnks[0]['loc_pos'] = np.array([0, 0, 0]) self.rgt_body.lnks[0]['meshfile'] = None self.rgt_body.lnks[0]['rgba'] = [.3, .3, .3, 1.0] self.rgt_body.reinitialize() rgt_arm_homeconf = np.zeros(6) rgt_arm_homeconf[0] = -math.pi * 1.0 / 3.0 rgt_arm_homeconf[1] = -math.pi * 2.0 / 3.0 rgt_arm_homeconf[2] = math.pi * 2.0 / 3.0 rgt_arm_homeconf[4] = math.pi / 2.0 self.rgt_arm = ur.UR3(pos=self.rgt_body.jnts[-1]['gl_posq'], rotmat=self.rgt_body.jnts[-1]['gl_rotmatq'], homeconf=rgt_arm_homeconf, enable_cc=False) # rgt hand ft sensor self.rgt_ft_sensor = jl.JLChain(pos=self.rgt_arm.jnts[-1]['gl_posq'], rotmat=self.rgt_arm.jnts[-1]['gl_rotmatq'], homeconf=np.zeros(0), name='rgt_ft_sensor_jl') self.rgt_ft_sensor.jnts[1]['loc_pos'] = np.array([.0, .0, .0484]) self.rgt_ft_sensor.lnks[0]['name'] = "ur3_dual_rgt_ft_sensor" self.rgt_ft_sensor.lnks[0]['loc_pos'] = np.array([0, 0, 0]) self.rgt_ft_sensor.lnks[0]['collisionmodel'] = cm.gen_stick(spos=self.rgt_ft_sensor.jnts[0]['loc_pos'], epos=self.rgt_ft_sensor.jnts[1]['loc_pos'], thickness=.067, rgba=[.2, .3, .3, 1], sections=24) self.rgt_ft_sensor.reinitialize() # TODO replace using copy self.rgt_hnd = rtq.Robotiq85(pos=self.rgt_ft_sensor.jnts[-1]['gl_posq'], rotmat=self.rgt_ft_sensor.jnts[-1]['gl_rotmatq'], enable_cc=False) # tool center point # lft self.lft_arm.tcp_jntid = -1 self.lft_arm.tcp_loc_pos = self.lft_ft_sensor.jnts[-1]['loc_pos'] + self.lft_hnd.jaw_center_pos self.lft_arm.tcp_loc_rotmat = self.lft_ft_sensor.jnts[-1]['loc_rotmat'].dot(self.lft_hnd.jaw_center_rotmat) # rgt self.rgt_arm.tcp_jntid = -1 self.rgt_arm.tcp_loc_pos = self.lft_ft_sensor.jnts[-1]['loc_pos'] + self.lft_hnd.jaw_center_pos self.rgt_arm.tcp_loc_rotmat = self.lft_ft_sensor.jnts[-1]['loc_rotmat'].dot(self.lft_hnd.jaw_center_rotmat) # a list of detailed information about objects in hand, see CollisionChecker.add_objinhnd self.lft_oih_infos = [] self.rgt_oih_infos = [] # collision detection if enable_cc: self.enable_cc() # component map self.manipulator_dict['rgt_arm'] = self.rgt_arm self.manipulator_dict['lft_arm'] = self.lft_arm self.manipulator_dict['rgt_hnd'] = self.rgt_arm # specify which hand is a gripper installed to self.manipulator_dict['lft_hnd'] = self.lft_arm # specify which hand is a gripper installed to self.manipulator_dict['rgt_ftsensor'] = self.rgt_arm # specify which hand is a gripper installed to self.manipulator_dict['lft_ftsensor'] = self.lft_arm # specify which hand is a gripper installed to self.hnd_dict['rgt_hnd'] = self.rgt_hnd self.hnd_dict['lft_hnd'] = self.lft_hnd self.hnd_dict['rgt_arm'] = self.rgt_hnd self.hnd_dict['lft_arm'] = self.lft_hnd self.hnd_dict['rgt_ftsensor'] = self.rgt_hnd self.hnd_dict['lft_ftsensor'] = self.lft_hnd self.ft_sensor_dict['rgt_ftsensor'] = self.rgt_ft_sensor self.ft_sensor_dict['lft_ftsensor'] = self.lft_ft_sensor self.ft_sensor_dict['rgt_arm'] = self.rgt_ft_sensor self.ft_sensor_dict['lft_arm'] = self.lft_ft_sensor self.ft_sensor_dict['rgt_hnd'] = self.rgt_ft_sensor self.ft_sensor_dict['lft_hnd'] = self.lft_ft_sensor @staticmethod def _base_combined_cdnp(name, radius): collision_node = CollisionNode(name) collision_primitive_c0 = CollisionBox(Point3(0.18, 0.0, 0.105), x=.61 + radius, y=.41 + radius, z=.105 + radius) collision_node.addSolid(collision_primitive_c0) collision_primitive_c1 = CollisionBox(Point3(0.0, 0.0, 0.4445), x=.321 + radius, y=.321 + radius, z=.2345 + radius) collision_node.addSolid(collision_primitive_c1) collision_primitive_c2 = CollisionBox(Point3(0.0, 0.0, 0.8895), x=.05 + radius, y=.05 + radius, z=.6795 + radius) collision_node.addSolid(collision_primitive_c2) collision_primitive_c3 = CollisionBox(Point3(0.0, 0.0, 1.619), x=.1 + radius, y=.275 + radius, z=.05 + radius) collision_node.addSolid(collision_primitive_c3) collision_primitive_l0 = CollisionBox(Point3(0.0, 0.300, 1.669), x=.1 + radius, y=.029 + radius, z=.021 + radius) collision_node.addSolid(collision_primitive_l0) collision_primitive_r0 = CollisionBox(Point3(0.0, -0.300, 1.669), x=.1 + radius, y=.029 + radius, z=.021 + radius) collision_node.addSolid(collision_primitive_r0) return collision_node def enable_cc(self): # TODO when pose is changed, oih info goes wrong super().enable_cc() self.cc.add_cdlnks(self.lft_body, [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]) self.cc.add_cdlnks(self.lft_arm, [1, 2, 3, 4, 5, 6]) self.cc.add_cdlnks(self.lft_ft_sensor, [0]) self.cc.add_cdlnks(self.lft_hnd.lft_outer, [0, 1, 2, 3, 4]) self.cc.add_cdlnks(self.lft_hnd.rgt_outer, [1, 2, 3, 4]) self.cc.add_cdlnks(self.rgt_arm, [1, 2, 3, 4, 5, 6]) self.cc.add_cdlnks(self.rgt_ft_sensor, [0]) self.cc.add_cdlnks(self.rgt_hnd.lft_outer, [0, 1, 2, 3, 4]) self.cc.add_cdlnks(self.rgt_hnd.rgt_outer, [1, 2, 3, 4]) # lnks used for cd with external stationary objects activelist = [self.lft_arm.lnks[2], self.lft_arm.lnks[3], self.lft_arm.lnks[4], self.lft_arm.lnks[5], self.lft_arm.lnks[6], self.lft_ft_sensor.lnks[0], self.lft_hnd.lft_outer.lnks[0], self.lft_hnd.lft_outer.lnks[1], self.lft_hnd.lft_outer.lnks[2], self.lft_hnd.lft_outer.lnks[3], self.lft_hnd.lft_outer.lnks[4], self.lft_hnd.rgt_outer.lnks[1], self.lft_hnd.rgt_outer.lnks[2], self.lft_hnd.rgt_outer.lnks[3], self.lft_hnd.rgt_outer.lnks[4], self.rgt_arm.lnks[2], self.rgt_arm.lnks[3], self.rgt_arm.lnks[4], self.rgt_arm.lnks[5], self.rgt_arm.lnks[6], self.rgt_ft_sensor.lnks[0], self.rgt_hnd.lft_outer.lnks[0], self.rgt_hnd.lft_outer.lnks[1], self.rgt_hnd.lft_outer.lnks[2], self.rgt_hnd.lft_outer.lnks[3], self.rgt_hnd.lft_outer.lnks[4], self.rgt_hnd.rgt_outer.lnks[1], self.rgt_hnd.rgt_outer.lnks[2], self.rgt_hnd.rgt_outer.lnks[3], self.rgt_hnd.rgt_outer.lnks[4]] self.cc.set_active_cdlnks(activelist) # lnks used for arm-body collision detection fromlist = [self.lft_body.lnks[0], # body self.lft_body.lnks[1], # back-rgt column self.lft_body.lnks[2], # back-lft column self.lft_body.lnks[3], # head-rgt row self.lft_body.lnks[4], # head-lft row self.lft_body.lnks[5], # up right x_row self.lft_body.lnks[6], # bottom right x_row self.lft_body.lnks[7], # up left row self.lft_body.lnks[8], # bottom left row self.lft_body.lnks[9], # back up y_row self.lft_body.lnks[10], # back bottom y_row self.lft_body.lnks[11], # head up y_row self.lft_body.lnks[12], # table self.lft_arm.lnks[1], self.rgt_arm.lnks[1]] intolist = [self.lft_arm.lnks[3], self.lft_arm.lnks[4], self.lft_arm.lnks[5], self.lft_arm.lnks[6], self.lft_ft_sensor.lnks[0], self.lft_hnd.lft_outer.lnks[0], self.lft_hnd.lft_outer.lnks[1], self.lft_hnd.lft_outer.lnks[2], self.lft_hnd.lft_outer.lnks[3], self.lft_hnd.lft_outer.lnks[4], self.lft_hnd.rgt_outer.lnks[1], self.lft_hnd.rgt_outer.lnks[2], self.lft_hnd.rgt_outer.lnks[3], self.lft_hnd.rgt_outer.lnks[4], self.rgt_arm.lnks[3], self.rgt_arm.lnks[4], self.rgt_arm.lnks[5], self.rgt_arm.lnks[6], self.rgt_ft_sensor.lnks[0], self.rgt_hnd.lft_outer.lnks[0], self.rgt_hnd.lft_outer.lnks[1], self.rgt_hnd.lft_outer.lnks[2], self.rgt_hnd.lft_outer.lnks[3], self.rgt_hnd.lft_outer.lnks[4], self.rgt_hnd.rgt_outer.lnks[1], self.rgt_hnd.rgt_outer.lnks[2], self.rgt_hnd.rgt_outer.lnks[3], self.rgt_hnd.rgt_outer.lnks[4]] self.cc.set_cdpair(fromlist, intolist) # lnks used for arm-body collision detection -- extra fromlist = [self.lft_body.lnks[0]] # body intolist = [self.lft_arm.lnks[2], self.rgt_arm.lnks[2]] self.cc.set_cdpair(fromlist, intolist) # arm-arm collision fromlist = [self.lft_arm.lnks[3], self.lft_arm.lnks[4], self.lft_arm.lnks[5], self.lft_arm.lnks[6], self.lft_ft_sensor.lnks[0], self.lft_hnd.lft_outer.lnks[0], self.lft_hnd.lft_outer.lnks[1], self.lft_hnd.lft_outer.lnks[2], self.lft_hnd.lft_outer.lnks[3], self.lft_hnd.lft_outer.lnks[4], self.lft_hnd.rgt_outer.lnks[1], self.lft_hnd.rgt_outer.lnks[2], self.lft_hnd.rgt_outer.lnks[3], self.lft_hnd.rgt_outer.lnks[4]] intolist = [self.rgt_arm.lnks[3], self.rgt_arm.lnks[4], self.rgt_arm.lnks[5], self.rgt_arm.lnks[6], self.rgt_ft_sensor.lnks[0], self.rgt_hnd.lft_outer.lnks[0], self.rgt_hnd.lft_outer.lnks[1], self.rgt_hnd.lft_outer.lnks[2], self.rgt_hnd.lft_outer.lnks[3], self.rgt_hnd.lft_outer.lnks[4], self.rgt_hnd.rgt_outer.lnks[1], self.rgt_hnd.rgt_outer.lnks[2], self.rgt_hnd.rgt_outer.lnks[3], self.rgt_hnd.rgt_outer.lnks[4]] self.cc.set_cdpair(fromlist, intolist) def get_hnd_on_manipulator(self, manipulator_name): if manipulator_name == 'rgt_arm': return self.rgt_hnd elif manipulator_name == 'lft_arm': return self.lft_hnd else: raise ValueError("The given jlc does not have a hand!") def fix_to(self, pos, rotmat): super().fix_to(pos, rotmat) self.pos = pos self.rotmat = rotmat self.lft_body.fix_to(self.pos, self.rotmat) self.lft_arm.fix_to(pos=self.lft_body.jnts[-1]['gl_posq'], rotmat=self.lft_body.jnts[-1]['gl_rotmatq']) self.lft_ft_sensor.fix_to(pos=self.lft_arm.jnts[-1]['gl_posq'], rotmat=self.lft_arm.jnts[-1]['gl_rotmatq']) self.lft_hnd.fix_to(pos=self.lft_ft_sensor.jnts[-1]['gl_posq'], rotmat=self.lft_ft_sensor.jnts[-1]['gl_rotmatq']) self.rgt_body.fix_to(self.pos, self.rotmat) self.rgt_arm.fix_to(pos=self.rgt_body.jnts[-1]['gl_posq'], rotmat=self.rgt_body.jnts[-1]['gl_rotmatq']) self.rgt_ft_sensor.fix_to(pos=self.rgt_arm.jnts[-1]['gl_posq'], rotmat=self.rgt_arm.jnts[-1]['gl_rotmatq']) self.rgt_hnd.fix_to(pos=self.rgt_ft_sensor.jnts[-1]['gl_posq'], rotmat=self.rgt_ft_sensor.jnts[-1]['gl_rotmatq']) def fk(self, component_name, jnt_values): """ :param jnt_values: nparray 1x6 or 1x12 depending on component_names :hnd_name 'lft_arm', 'rgt_arm', 'both_arm' :param component_name: :return: author: weiwei date: 20201208toyonaka, 20210403osaka """ def update_oih(component_name='rgt_arm'): # inline function for update objects in hand if component_name == 'rgt_arm': oih_info_list = self.rgt_oih_infos elif component_name == 'lft_arm': oih_info_list = self.lft_oih_infos for obj_info in oih_info_list: gl_pos, gl_rotmat = self.cvt_loc_tcp_to_gl(component_name, obj_info['rel_pos'], obj_info['rel_rotmat']) obj_info['gl_pos'] = gl_pos obj_info['gl_rotmat'] = gl_rotmat def update_component(component_name, jnt_values): self.manipulator_dict[component_name].fk(jnt_values=jnt_values) self.ft_sensor_dict[component_name].fix_to(pos=self.manipulator_dict[component_name].jnts[-1]['gl_posq'], rotmat=self.manipulator_dict[component_name].jnts[-1][ 'gl_rotmatq']) self.get_hnd_on_manipulator(component_name).fix_to( pos=self.ft_sensor_dict[component_name].jnts[-1]['gl_posq'], rotmat=self.ft_sensor_dict[component_name].jnts[-1]['gl_rotmatq']) update_oih(component_name=component_name) super().fk(component_name, jnt_values) # examine length if component_name == 'lft_arm' or component_name == 'rgt_arm': if not isinstance(jnt_values, np.ndarray) or jnt_values.size != 6: raise ValueError("An 1x6 npdarray must be specified to move a single arm!") update_component(component_name, jnt_values) elif component_name == 'both_arm': if (jnt_values.size != 12): raise ValueError("A 1x12 npdarrays must be specified to move both arm!") update_component('lft_arm', jnt_values[0:6]) update_component('rgt_arm', jnt_values[6:12]) elif component_name == 'all': raise NotImplementedError else: raise ValueError("The given component name is not available!") def rand_conf(self, component_name): """ override robot_interface.rand_conf :param component_name: :return: author: weiwei date: 20210406 """ if component_name == 'lft_arm' or component_name == 'rgt_arm': return super().rand_conf(component_name) elif component_name == 'both_arm': return np.hstack((super().rand_conf('lft_arm'), super().rand_conf('rgt_arm'))) else: raise NotImplementedError def hold(self, objcm, jaw_width=None, hnd_name='lft_hnd'): """ the objcm is added as a part of the robot_s to the cd checker :param jaw_width: :param objcm: :return: """ if hnd_name == 'lft_hnd': rel_pos, rel_rotmat = self.lft_arm.cvt_gl_to_loc_tcp(objcm.get_pos(), objcm.get_rotmat()) intolist = [self.lft_body.lnks[0], # body self.lft_body.lnks[1], # back-rgt column self.lft_body.lnks[2], # back-lft column self.lft_body.lnks[3], # head-rgt row self.lft_body.lnks[4], # head-lft row self.lft_body.lnks[5], # up right x_row self.lft_body.lnks[6], # bottom right x_row self.lft_body.lnks[7], # up left row self.lft_body.lnks[8], # bottom left row self.lft_body.lnks[9], # back up y_row self.lft_body.lnks[10], # back bottom y_row self.lft_body.lnks[11], # head up y_row self.lft_body.lnks[12], # table self.lft_arm.lnks[1], self.lft_arm.lnks[2], self.lft_arm.lnks[3], self.lft_arm.lnks[4], self.rgt_arm.lnks[1], self.rgt_arm.lnks[2], self.rgt_arm.lnks[3], self.rgt_arm.lnks[4], self.rgt_arm.lnks[5], self.rgt_arm.lnks[6], self.rgt_ft_sensor.lnks[0], self.rgt_hnd.rgt_outer.lnks[1], self.rgt_hnd.rgt_outer.lnks[2], self.rgt_hnd.rgt_outer.lnks[3], self.rgt_hnd.rgt_outer.lnks[4]] self.lft_oih_infos.append(self.cc.add_cdobj(objcm, rel_pos, rel_rotmat, intolist)) elif hnd_name == 'rgt_hnd': rel_pos, rel_rotmat = self.rgt_arm.cvt_gl_to_loc_tcp(objcm.get_pos(), objcm.get_rotmat()) intolist = [self.lft_body.lnks[0], # body self.lft_body.lnks[1], # back-rgt column self.lft_body.lnks[2], # back-lft column self.lft_body.lnks[3], # head-rgt row self.lft_body.lnks[4], # head-lft row self.lft_body.lnks[5], # up right x_row self.lft_body.lnks[6], # bottom right x_row self.lft_body.lnks[7], # up left row self.lft_body.lnks[8], # bottom left row self.lft_body.lnks[9], # back up y_row self.lft_body.lnks[10], # back bottom y_row self.lft_body.lnks[11], # head up y_row self.lft_body.lnks[12], # table self.lft_arm.lnks[1], self.lft_arm.lnks[2], self.lft_arm.lnks[3], self.lft_arm.lnks[4], self.lft_arm.lnks[5], self.lft_arm.lnks[6], self.lft_ft_sensor.lnks[0], self.lft_hnd.rgt_outer.lnks[1], self.lft_hnd.rgt_outer.lnks[2], self.lft_hnd.rgt_outer.lnks[3], self.lft_hnd.rgt_outer.lnks[4], self.rgt_arm.lnks[1], self.rgt_arm.lnks[2], self.rgt_arm.lnks[3], self.rgt_arm.lnks[4]] self.rgt_oih_infos.append(self.cc.add_cdobj(objcm, rel_pos, rel_rotmat, intolist)) else: raise ValueError("hnd_name must be lft_hnd or rgt_hnd!") if jaw_width is not None: self.jaw_to(hnd_name, jaw_width) return rel_pos, rel_rotmat def get_loc_pose_from_hio(self, hio_pos, hio_rotmat, component_name='lft_arm'): """ get the loc pose of an object from a grasp pose described in an object's local frame :param hio_pos: a grasp pose described in an object's local frame -- pos :param hio_rotmat: a grasp pose described in an object's local frame -- rotmat :return: author: weiwei date: 20210302 """ if component_name == 'lft_arm': arm = self.lft_arm elif component_name == 'rgt_arm': arm = self.rgt_arm hnd_pos = arm.jnts[-1]['gl_posq'] hnd_rotmat = arm.jnts[-1]['gl_rotmatq'] hnd_homomat = rm.homomat_from_posrot(hnd_pos, hnd_rotmat) hio_homomat = rm.homomat_from_posrot(hio_pos, hio_rotmat) oih_homomat = rm.homomat_inverse(hio_homomat) gl_obj_homomat = hnd_homomat.dot(oih_homomat) return self.cvt_gl_to_loc_tcp(component_name, gl_obj_homomat[:3, 3], gl_obj_homomat[:3, :3]) def get_gl_pose_from_hio(self, hio_pos, hio_rotmat, component_name='lft_arm'): """ get the loc pose of an object from a grasp pose described in an object's local frame :param hio_pos: a grasp pose described in an object's local frame -- pos :param hio_rotmat: a grasp pose described in an object's local frame -- rotmat :return: author: weiwei date: 20210302 """ if component_name == 'lft_arm': arm = self.lft_arm elif component_name == 'rgt_arm': arm = self.rgt_arm hnd_pos = arm.jnts[-1]['gl_posq'] hnd_rotmat = arm.jnts[-1]['gl_rotmatq'] hnd_homomat = rm.homomat_from_posrot(hnd_pos, hnd_rotmat) hio_homomat = rm.homomat_from_posrot(hio_pos, hio_rotmat) oih_homomat = rm.homomat_inverse(hio_homomat) gl_obj_homomat = hnd_homomat.dot(oih_homomat) return gl_obj_homomat[:3, 3], gl_obj_homomat[:3, :3] def get_oih_cm_list(self, hnd_name='lft_hnd'): """ oih = object in hand list :param hnd_name: :return: """ if hnd_name == 'lft_hnd': oih_infos = self.lft_oih_infos elif hnd_name == 'rgt_hnd': oih_infos = self.rgt_oih_infos else: raise ValueError("hnd_name must be lft_hnd or rgt_hnd!") return_list = [] for obj_info in oih_infos: objcm = obj_info['collisionmodel'] objcm.set_pos(obj_info['gl_pos']) objcm.set_rotmat(obj_info['gl_rotmat']) return_list.append(objcm) return return_list def get_oih_glhomomat_list(self, hnd_name='lft_hnd'): """ oih = object in hand list :param hnd_name: :return: author: weiwei date: 20210302 """ if hnd_name == 'lft_hnd': oih_infos = self.lft_oih_infos elif hnd_name == 'rgt_hnd': oih_infos = self.rgt_oih_infos else: raise ValueError("hnd_name must be lft_hnd or rgt_hnd!") return_list = [] for obj_info in oih_infos: return_list.append(rm.homomat_from_posrot(obj_info['gl_pos']), obj_info['gl_rotmat']) return return_list def get_oih_relhomomat(self, objcm, hnd_name='lft_hnd'): """ TODO: useless? 20210320 oih = object in hand list :param objcm :param hnd_name: :return: author: weiwei date: 20210302 """ if hnd_name == 'lft_hnd': oih_info_list = self.lft_oih_infos elif hnd_name == 'rgt_hnd': oih_info_list = self.rgt_oih_infos else: raise ValueError("hnd_name must be lft_hnd or rgt_hnd!") for obj_info in oih_info_list: if obj_info['collisionmodel'] is objcm: return rm.homomat_from_posrot(obj_info['rel_pos']), obj_info['rel_rotmat'] def release(self, hnd_name, objcm, jaw_width=None): """ the objcm is added as a part of the robot_s to the cd checker :param jaw_width: :param objcm: :param hnd_name: :return: """ if hnd_name == 'lft_hnd': oih_infos = self.lft_oih_infos elif hnd_name == 'rgt_hnd': oih_infos = self.rgt_oih_infos else: raise ValueError("hnd_name must be lft_hnd or rgt_hnd!") if jaw_width is not None: self.jaw_to(hnd_name, jaw_width) for obj_info in oih_infos: if obj_info['collisionmodel'] is objcm: self.cc.delete_cdobj(obj_info) oih_infos.remove(obj_info) break def release_all(self, jaw_width=None, hnd_name='lft_hnd'): """ release all objects from the specified hand :param jaw_width: :param hnd_name: :return: author: weiwei date: 20210125 """ if hnd_name == 'lft_hnd': oih_infos = self.lft_oih_infos elif hnd_name == 'rgt_hnd': oih_infos = self.rgt_oih_infos else: raise ValueError("hnd_name must be lft_hnd or rgt_hnd!") if jaw_width is not None: self.jaw_to(hnd_name, jaw_width) for obj_info in oih_infos: self.cc.delete_cdobj(obj_info) oih_infos.clear() def gen_stickmodel(self, tcp_jntid=None, tcp_loc_pos=None, tcp_loc_rotmat=None, toggle_tcpcs=False, toggle_jntscs=False, toggle_connjnt=False, name='ur3dual'): stickmodel = mc.ModelCollection(name=name) self.lft_body.gen_stickmodel(tcp_loc_pos=None, tcp_loc_rotmat=None, toggle_tcpcs=False, toggle_jntscs=toggle_jntscs).attach_to(stickmodel) self.lft_arm.gen_stickmodel(tcp_jntid=tcp_jntid, tcp_loc_pos=tcp_loc_pos, tcp_loc_rotmat=tcp_loc_rotmat, toggle_tcpcs=toggle_tcpcs, toggle_jntscs=toggle_jntscs, toggle_connjnt=toggle_connjnt).attach_to(stickmodel) self.lft_hnd.gen_stickmodel(toggle_tcpcs=False, toggle_jntscs=toggle_jntscs, toggle_connjnt=toggle_connjnt).attach_to(stickmodel) self.rgt_body.gen_stickmodel(tcp_loc_pos=None, tcp_loc_rotmat=None, toggle_tcpcs=False, toggle_jntscs=toggle_jntscs).attach_to(stickmodel) self.rgt_arm.gen_stickmodel(tcp_jntid=tcp_jntid, tcp_loc_pos=tcp_loc_pos, tcp_loc_rotmat=tcp_loc_rotmat, toggle_tcpcs=toggle_tcpcs, toggle_jntscs=toggle_jntscs, toggle_connjnt=toggle_connjnt).attach_to(stickmodel) self.rgt_hnd.gen_stickmodel(toggle_tcpcs=False, toggle_jntscs=toggle_jntscs, toggle_connjnt=toggle_connjnt).attach_to(stickmodel) self.lft_ft_sensor.gen_stickmodel(toggle_tcpcs=toggle_tcpcs, toggle_jntscs=toggle_jntscs, toggle_connjnt=toggle_connjnt).attach_to(stickmodel) self.rgt_ft_sensor.gen_stickmodel(toggle_tcpcs=toggle_tcpcs, toggle_jntscs=toggle_jntscs, toggle_connjnt=toggle_connjnt).attach_to(stickmodel) return stickmodel def gen_meshmodel(self, tcp_jntid=None, tcp_loc_pos=None, tcp_loc_rotmat=None, toggle_tcpcs=False, toggle_jntscs=False, rgba=None, name='xarm_gripper_meshmodel'): meshmodel = mc.ModelCollection(name=name) self.lft_body.gen_meshmodel(tcp_loc_pos=None, tcp_loc_rotmat=None, toggle_tcpcs=False, toggle_jntscs=toggle_jntscs, rgba=rgba).attach_to(meshmodel) self.lft_arm.gen_meshmodel(tcp_jntid=tcp_jntid, tcp_loc_pos=tcp_loc_pos, tcp_loc_rotmat=tcp_loc_rotmat, toggle_tcpcs=toggle_tcpcs, toggle_jntscs=toggle_jntscs, rgba=rgba).attach_to(meshmodel) self.lft_hnd.gen_meshmodel(toggle_tcpcs=False, toggle_jntscs=toggle_jntscs, rgba=rgba).attach_to(meshmodel) self.rgt_arm.gen_meshmodel(tcp_jntid=tcp_jntid, tcp_loc_pos=tcp_loc_pos, tcp_loc_rotmat=tcp_loc_rotmat, toggle_tcpcs=toggle_tcpcs, toggle_jntscs=toggle_jntscs, rgba=rgba).attach_to(meshmodel) self.rgt_hnd.gen_meshmodel(toggle_tcpcs=False, toggle_jntscs=toggle_jntscs, rgba=rgba).attach_to(meshmodel) self.lft_ft_sensor.gen_meshmodel(toggle_tcpcs=toggle_tcpcs, toggle_jntscs=toggle_jntscs, rgba=rgba).attach_to(meshmodel) self.rgt_ft_sensor.gen_meshmodel(toggle_tcpcs=toggle_tcpcs, toggle_jntscs=toggle_jntscs, rgba=rgba).attach_to(meshmodel) for obj_info in self.lft_oih_infos: objcm = obj_info['collisionmodel'] objcm.set_pos(obj_info['gl_pos']) objcm.set_rotmat(obj_info['gl_rotmat']) objcm.copy().attach_to(meshmodel) for obj_info in self.rgt_oih_infos: objcm = obj_info['collisionmodel'] objcm.set_pos(obj_info['gl_pos']) objcm.set_rotmat(obj_info['gl_rotmat']) objcm.copy().attach_to(meshmodel) return meshmodel if __name__ == '__main__': import visualization.panda.world as wd import modeling.geometric_model as gm base = wd.World(cam_pos=[2, 0, 3], lookat_pos=[0, 0, 1]) gm.gen_frame().attach_to(base) u3d = UR3Dual() u3d.show_cdprimit() # u3d.fk(.85) u3d_meshmodel = u3d.gen_meshmodel(toggle_tcpcs=True) u3d_meshmodel.attach_to(base) u3d.gen_stickmodel().attach_to(base) base.run()

52.424802

121

0.555614

5a2a509d3147a39a338049132f2f0356fb20e1f4

4,513

py

Python

lib/roi_data_layer/roidb.py

aaamourao/faster-rcnn.pytorch

e5f476360fbdc8af9ae6a4981b3eaac3b744cee9

[ "MIT" ]

null

lib/roi_data_layer/roidb.py

aaamourao/faster-rcnn.pytorch

e5f476360fbdc8af9ae6a4981b3eaac3b744cee9

[ "MIT" ]

null

lib/roi_data_layer/roidb.py

aaamourao/faster-rcnn.pytorch

e5f476360fbdc8af9ae6a4981b3eaac3b744cee9

[ "MIT" ]

null

"""Transform a roidb into a trainable roidb by adding a bunch of metadata.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import os import pickle import datasets import numpy as np from model.utils.config import cfg from datasets.factory import get_imdb import PIL import pdb def prepare_roidb(imdb): """Enrich the imdb's roidb by adding some derived quantities that are useful for training. This function precomputes the maximum overlap, taken over ground-truth boxes, between each ROI and each ground-truth box. The class with maximum overlap is also recorded. """ roidb = imdb.roidb if not (imdb.name.startswith('coco')): cache_file = os.path.join(imdb.cache_path, imdb.name + '_sizes.pkl') if os.path.exists(cache_file): print('Image sizes loaded from %s' % cache_file) with open(cache_file, 'rb') as f: sizes = pickle.load(f) else: print('Extracting image sizes... (It may take long time)') sizes = [PIL.Image.open(imdb.image_path_at(i)).size for i in range(imdb.num_images)] with open(cache_file, 'wb') as f: pickle.dump(sizes, f) print('Done!!') for i in range(len(imdb.image_index)): roidb[i]['img_id'] = imdb.image_id_at(i) roidb[i]['image'] = imdb.image_path_at(i) if not (imdb.name.startswith('coco')): roidb[i]['width'] = sizes[i][0] roidb[i]['height'] = sizes[i][1] # need gt_overlaps as a dense array for argmax gt_overlaps = roidb[i]['gt_overlaps'].toarray() # max overlap with gt over classes (columns) max_overlaps = gt_overlaps.max(axis=1) # gt class that had the max overlap max_classes = gt_overlaps.argmax(axis=1) roidb[i]['max_classes'] = max_classes roidb[i]['max_overlaps'] = max_overlaps # sanity checks # max overlap of 0 => class should be zero (background) zero_inds = np.where(max_overlaps == 0)[0] assert all(max_classes[zero_inds] == 0) # max overlap > 0 => class should not be zero (must be a fg class) nonzero_inds = np.where(max_overlaps > 0)[0] assert all(max_classes[nonzero_inds] != 0) def rank_roidb_ratio(roidb): # rank roidb based on the ratio between width and height. ratio_large = 2 # largest ratio to preserve. ratio_small = 0.5 # smallest ratio to preserve. ratio_list = [] for i in range(len(roidb)): width = roidb[i]['width'] height = roidb[i]['height'] ratio = width / float(height) if ratio > ratio_large: roidb[i]['need_crop'] = 1 ratio = ratio_large elif ratio < ratio_small: roidb[i]['need_crop'] = 1 ratio = ratio_small else: roidb[i]['need_crop'] = 0 ratio_list.append(ratio) ratio_list = np.array(ratio_list) ratio_index = np.argsort(ratio_list) return ratio_list[ratio_index], ratio_index def filter_roidb(roidb): # filter the image without bounding box. print('before filtering, there are %d images...' % (len(roidb))) i = 0 while i < len(roidb): if len(roidb[i]['boxes']) == 0: del roidb[i] i -= 1 i += 1 print('after filtering, there are %d images...' % (len(roidb))) return roidb def combined_roidb(imdb_names, training=True): """ Combine multiple roidbs """ def get_training_roidb(imdb): """Returns a roidb (Region of Interest database) for use in training.""" if cfg.TRAIN.USE_FLIPPED: print('Appending horizontally-flipped training examples...') imdb.append_flipped_images() print('done') print('Preparing training data...') prepare_roidb(imdb) #ratio_index = rank_roidb_ratio(imdb) print('done') return imdb.roidb def get_roidb(imdb_name): imdb = get_imdb(imdb_name) print('Loaded dataset `{:s}`'.format(imdb.name)) imdb.set_proposal_method(cfg.TRAIN.PROPOSAL_METHOD) print('Set proposal method: {:s}'.format(cfg.TRAIN.PROPOSAL_METHOD)) roidb = get_training_roidb(imdb) return roidb roidbs = [get_roidb(s) for s in imdb_names.split('+')] roidb = roidbs[0] if len(roidbs) > 1: for r in roidbs[1:]: roidb.extend(r) tmp = get_imdb(imdb_names.split('+')[1]) imdb = datasets.imdb.imdb(imdb_names, tmp.classes) else: imdb = get_imdb(imdb_names) if training: roidb = filter_roidb(roidb) ratio_list, ratio_index = rank_roidb_ratio(roidb) return imdb, roidb, ratio_list, ratio_index

30.910959

77

0.662087

bce455de8873952dd4d49cf03dcb314bc8611f78

1,751

py

Python

test/feature/test_scale_space_detector.py

tdchaitanya/kornia

6dd16563f66f979c7a95846ef86678894b7d54fd

[ "Apache-2.0" ]

1

2019-11-21T13:18:56.000Z

test/feature/test_scale_space_detector.py

tdchaitanya/kornia

6dd16563f66f979c7a95846ef86678894b7d54fd

[ "Apache-2.0" ]

null

test/feature/test_scale_space_detector.py

tdchaitanya/kornia

6dd16563f66f979c7a95846ef86678894b7d54fd

[ "Apache-2.0" ]

2

2020-01-08T17:11:34.000Z

2020-10-14T00:44:18.000Z

import pytest import kornia.testing as utils # test utils import kornia from torch.testing import assert_allclose from torch.autograd import gradcheck from kornia.feature.scale_space_detector import * class TestScaleSpaceDetector: def test_shape(self): inp = torch.rand(1, 1, 32, 32) n_feats = 10 det = ScaleSpaceDetector(n_feats) lafs, resps = det(inp) assert lafs.shape == torch.Size([1, n_feats, 2, 3]) assert resps.shape == torch.Size([1, n_feats]) def test_shape_batch(self): inp = torch.rand(7, 1, 32, 32) n_feats = 10 det = ScaleSpaceDetector(n_feats) lafs, resps = det(inp) assert lafs.shape == torch.Size([7, n_feats, 2, 3]) assert resps.shape == torch.Size([7, n_feats]) def test_print(self): sift = ScaleSpaceDetector() sift.__repr__() def test_toy(self): inp = torch.zeros(1, 1, 33, 33) inp[:, :, 13:-13, 13:-13] = 1.0 n_feats = 1 det = ScaleSpaceDetector(n_feats, resp_module=kornia.feature.BlobHessian(), mr_size=3.0) lafs, resps = det(inp) expected_laf = torch.tensor([[[[6.0548, 0.0000, 16.0], [0.0, 6.0548, 16.0]]]]) expected_resp = torch.tensor([[0.0806]]) assert_allclose(expected_laf, lafs) assert_allclose(expected_resp, resps) def test_gradcheck(self): batch_size, channels, height, width = 1, 1, 31, 21 patches = torch.rand(batch_size, channels, height, width) patches = utils.tensor_to_gradcheck_var(patches) # to var assert gradcheck(ScaleSpaceDetector(2), (patches), raise_exception=True)

35.02

86

0.6008

8b861a4d6360161dcb30202dddad4ded13612036

4,504

py

Python

slot/a/__init__.py

LorentzB/dl

c2af8498ba868abcd2ddb08eb9e4b4bb79594ba2

[ "Apache-2.0" ]

45

2018-12-30T14:19:37.000Z

2021-01-28T08:16:41.000Z

slot/a/__init__.py

LorentzB/dl

c2af8498ba868abcd2ddb08eb9e4b4bb79594ba2

[ "Apache-2.0" ]

23

2019-01-07T22:32:00.000Z

2019-10-04T10:23:02.000Z

slot/a/__init__.py

LorentzB/dl

c2af8498ba868abcd2ddb08eb9e4b4bb79594ba2

[ "Apache-2.0" ]

36

2019-01-11T21:38:02.000Z

2021-01-28T08:16:53.000Z

from slot import * from ability import Ability from collections import defaultdict class Amulet(AmuletBase): a = [] def __init__(self): self.mod = [] self.conf = Conf() self.mmax = { 'a' : 0.20, # attack 's' : 0.40, # skill damage 'cc' : 0.15, # crit chance 'cd' : 0.25, # crit damage 'fs' : 0.50, # force strike 'bt' : 0.30, # buff time 'sp' : 0.15, # skill haste 'bk' : 0.30, # break killer 'od' : 0.15, # od killer 'lo' : 0.60, # lastoffence 'bc' : 0.15, # buffchain 'sts' : 0.06, # striker strength 'sls' : 0.06, # slayer stength 'dc' : 3, # dragon's claw 'ds' : 3, # dragon's skill 'prep' : 100, # skill prep 'resist' : 10000, # resist 'da' : 0.18, # dragon damage 'dt' : 0.20, # dragon time 'eprep' : 5, # energy prep } from core.afflic import AFFLICT_LIST for afflic in AFFLICT_LIST: self.mmax['k_'+afflic] = 0.25 self.mmax['k_burn'] = 0.30 self.base_a = self.a def setup(self, c): abilities = self.base_a if self.a2: abilities += self.a2.base_a self.att += self.a2.att sorted_abilities = defaultdict(lambda: []) for ab in abilities: name = ab[0] sorted_abilities[name].append(ab) self.a = [] for name, ab_list in sorted_abilities.items(): if name in self.mmax: max_value = self.mmax[name] for ab in sorted(ab_list, key=lambda x: '' if len(x) < 3 or x[2] in ('flame', 'water', 'wind', 'light', 'shadow') else x[2]): if len(ab) > 2: new_ab = (ab[0], min(ab[1], max_value), *ab[2:]) else: new_ab = (ab[0], min(ab[1], max_value)) self.a.append(new_ab) max_value -= ab[1] if max_value <= 0: break else: self.a.extend(ab_list) # def oninit(self, adv): # super(Amulet, self).oninit(adv) # for i in self.a: # i.oninit(adv) # def merge(self, a, b): # k = b[0] # if k not in a: # a[k] = b # else: # a[k] = (b[0],a[k][1]+b[1]) # def merge_cond(self, a, b): # k = b[0]+b[2] # if k not in a: # a[k] = b # else: # a[k] = (b[0],a[k][1]+b[1],b[2]) # def setup(self, c): # super(Amulet,self).setup(c) # if self.a2: # self.on(c) # self.a2.on(c) # self.att += self.a2.att # self.tmp = self.a + self.a2.a # self.a = {} # else: # self.on(c) # self.tmp = self.a # self.a = {} # for i in self.tmp: # if len(i)==2 or (len(i)==3 and not isinstance(i[2], str)): # k = i[0] # if k not in self.mmax: # self.merge(self.a, i) # elif self.mmax[k] > 0: # if self.mmax[k] > i[1]: # self.merge(self.a, i) # self.mmax[k] -= i[1] # else : # i = (i[0],self.mmax[k]) # self.merge(self.a, i) # self.mmax[k] = 0 # for i in self.tmp: # if len(i)==3 and isinstance(i[2], str): # k = i[0] # if k not in self.mmax: # self.merge_cond(self.a, i) # elif self.mmax[k] > 0: # if self.mmax[k] > i[1]: # self.merge_cond(self.a, i) # self.mmax[k] -= i[1] # else: # i = (i[0],self.mmax[k],i[2]) # self.merge_cond(self.a, i) # self.mmax[k] = 0 # tmp = [] # for k,i in self.a.items(): # tmp.append(i) # self.a = tmp # def on(self, c): # return from slot.a.all import *

32.637681

141

0.37833

f8ce34cf1949eddb3fbe5b13b838cd17283a54af

397

py

Python

magma/config.py

Kuree/magma

be2439aa897768c5810be72e3a55a6f772ac83cf

[ "MIT" ]

null

magma/config.py

Kuree/magma

be2439aa897768c5810be72e3a55a6f772ac83cf

[ "MIT" ]

null

magma/config.py

Kuree/magma

be2439aa897768c5810be72e3a55a6f772ac83cf

[ "MIT" ]

null

__COMPILE_DIR = 'normal' def set_compile_dir(target): global __COMPILE_DIR assert target in ['normal', 'callee_file_dir'] __COMPILE_DIR = target def get_compile_dir(): return __COMPILE_DIR __DEBUG_MODE = False def set_debug_mode(value=True): global __DEBUG_MODE assert value in {True, False} __DEBUG_MODE = value def get_debug_mode(): return __DEBUG_MODE

15.88

50

0.725441

c09f30d44726e2b15a51003e0329ee3d1c49e5a9

13,739

py

Python

python/oneflow/nn/modules/constant.py

L-Net-1992/oneflow

4dc08d65caea36fdd137841ac95551218897e730

[ "Apache-2.0" ]

1

2022-03-14T11:17:56.000Z

python/oneflow/nn/modules/constant.py

L-Net-1992/oneflow

4dc08d65caea36fdd137841ac95551218897e730

[ "Apache-2.0" ]

null

python/oneflow/nn/modules/constant.py

L-Net-1992/oneflow

4dc08d65caea36fdd137841ac95551218897e730

[ "Apache-2.0" ]

1

2021-12-15T02:14:49.000Z

""" Copyright 2020 The OneFlow Authors. All rights reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. """ from typing import List, Optional, Union import oneflow as flow from oneflow.framework.tensor import register_tensor_op from oneflow.nn.common_types import _size_any_t from oneflow.nn.module import Module from oneflow.nn.modules.utils import _single, _handle_size_arg class _ConstantBase(Module): def __init__( self, size: Union[_size_any_t, flow.Size], value: Union[float, int], dtype: Optional[flow.dtype], device: Union[flow.device, str] = None, placement: flow.placement = None, sbp: Union[flow.sbp.sbp, List[flow.sbp.sbp]] = None, requires_grad: bool = False, ) -> None: super().__init__() assert size is not None, "shape must not be None!" assert isinstance( size, (int, tuple, list, flow.Size) ), "shape should be int or tuple int!" self.device = device if isinstance(self.device, str): self.device = flow.device(self.device) self.requires_grad = requires_grad size = _single(size) if dtype is None: dtype = flow.float32 if placement is None: if device is None: self.device = flow.device("cpu") else: assert device is None self.placement = placement self.sbp = sbp if placement is not None: assert isinstance(sbp, (flow.sbp.sbp, tuple, list)), "sbp: %s" % sbp if isinstance(self.sbp, flow.sbp.sbp): self.sbp = (self.sbp,) else: for elem in sbp: assert isinstance(elem, flow.sbp.sbp), "sbp: %s" % sbp assert len(self.sbp) == len(placement.ranks.shape) else: assert sbp is None, "sbp: %s" % sbp self.shape = size self.value = value self.dtype = dtype def forward(self): if self.placement is not None: res = flow._C.global_constant( self.shape, self.value, dtype=self.dtype, placement=self.placement, sbp=self.sbp, ) else: res = flow._C.constant( self.shape, self.value, dtype=self.dtype, device=self.device ) res.requires_grad = self.requires_grad return res class Ones(_ConstantBase): def __init__( self, size, dtype=None, device=None, placement=None, sbp=None, requires_grad=False, ): super().__init__(size, 1, dtype, device, placement, sbp, requires_grad) def ones_op( *size: Union[_size_any_t, flow.Size, List[int]], dtype: Optional[flow.dtype] = None, device: Union[flow.device, str, None] = None, placement: flow.placement = None, sbp: flow._oneflow_internal.sbp.sbp = None, requires_grad: bool = False, ): """ Returns a tensor filled with the scalar value 1, with the shape defined by the variable argument `size`. Args: size (an integer or tuple of integer values): defining the shape of the output tensor. Can be \\ a variable number of arguments or a collection like a list or tuple. dtype (flow.dtype, optional): the desired data type of returned tensor. device (flow.device, optional): the desired device of returned tensor. Default: if None, uses the current device for the default tensor type placement (flow.placement, optional): the desired placement of returned global tensor. Default: if None, the returned tensor is local one using the argument `device`. sbp (flow.sbp.sbp or tuple of flow.sbp.sbp, optional): the desired sbp descriptor of returned global tensor. Default: if None, the returned tensor is local one using the argument `device`. requires_grad (bool, optional): If autograd should record operations on the returned tensor. Default: False. For example: .. code-block:: python >>> import oneflow as flow >>> y = flow.ones(5) >>> y tensor([1., 1., 1., 1., 1.], dtype=oneflow.float32) >>> y = flow.ones(2,3) # construct local tensor >>> y tensor([[1., 1., 1.], [1., 1., 1.]], dtype=oneflow.float32) >>> placement = flow.placement("cpu", ranks=[0]) >>> y = flow.ones(4, 5, placement=placement, sbp=flow.sbp.broadcast) # construct global tensor >>> y.is_global True """ size = _handle_size_arg(size) return Ones(size, dtype, device, placement, sbp, requires_grad)() class Zeros(_ConstantBase): def __init__( self, size, dtype=None, device=None, placement=None, sbp=None, requires_grad=False, ): super().__init__(size, 0, dtype, device, placement, sbp, requires_grad) def zeros_op( *size: Union[_size_any_t, flow.Size, List[int]], dtype: Optional[flow.dtype] = None, device: Union[flow.device, str, None] = None, placement: flow.placement = None, sbp: flow._oneflow_internal.sbp.sbp = None, requires_grad: bool = False, ): """ Returns a tensor filled with the scalar value 0, with the shape defined by the variable argument `size`. Args: size(an integer or tuple of integer values) - defining the shape of the output tensor. Can be \\ a variable number of arguments or a collection like a list or tuple. dtype (flow.dtype, optional): the desired data type of returned tensor. device (flow.device, optional): the desired device of returned tensor. Default: if None, uses the current device for the default tensor type placement (flow.placement, optional): the desired placement of returned global tensor. Default: if None, the returned tensor is local one using the argument `device`. sbp (flow.sbp.sbp or tuple of flow.sbp.sbp, optional): the desired sbp descriptor of returned global tensor. Default: if None, the returned tensor is local one using the argument `device`. requires_grad (bool, optional): If autograd should record operations on the returned tensor. Default: False. For example: .. code-block:: python >>> import oneflow as flow >>> y = flow.zeros(5) >>> y tensor([0., 0., 0., 0., 0.], dtype=oneflow.float32) >>> y = flow.zeros(2,3) >>> y tensor([[0., 0., 0.], [0., 0., 0.]], dtype=oneflow.float32) """ size = _handle_size_arg(size) return Zeros(size, dtype, device, placement, sbp, requires_grad)() class Full(_ConstantBase): def __init__( self, size, value, dtype, device=None, placement=None, sbp=None, requires_grad=False, ): super().__init__(size, value, dtype, device, placement, sbp, requires_grad) def full_op( size: Union[_size_any_t, flow.Size], value: Union[float, int], dtype: Optional[flow.dtype] = None, device: Union[flow.device, str, None] = None, placement: flow.placement = None, sbp: flow._oneflow_internal.sbp.sbp = None, requires_grad: bool = False, ): """ Creates a tensor of size `size` filled with fill_value. The tensor’s dtype is inferred from `value`. Args: size(int...): a list, tuple, or oneflow.Size of integers defining the shape of the output tensor. fill_value(Scalar): the value to fill the output tensor with. dtype (flow.dtype, optional): the desired data type of returned tensor. device (flow.device, optional): the desired device of returned tensor. Default: if None, uses the current device for the default tensor type placement (flow.placement, optional): the desired placement of returned global tensor. Default: if None, the returned tensor is local one using the argument `device`. sbp (flow.sbp.sbp or tuple of flow.sbp.sbp, optional): the desired sbp descriptor of returned global tensor. Default: if None, the returned tensor is local one using the argument `device`. requires_grad (bool, optional): If autograd should record operations on the returned tensor. Default: False. For example: .. code-block:: python >>> import oneflow as flow >>> y = flow.full((5,),5) >>> y tensor([5, 5, 5, 5, 5], dtype=oneflow.int64) >>> y = flow.full((2,3),5.0) # construct local tensor >>> y tensor([[5., 5., 5.], [5., 5., 5.]], dtype=oneflow.float32) >>> placement = flow.placement("cpu", ranks=[0]) >>> y = flow.full((2,3),5.0, placement=placement, sbp=flow.sbp.broadcast) # construct global tensor >>> y.is_global True """ size = _handle_size_arg(size) if dtype is None: dtype = flow.tensor(value).dtype return Full(size, value, dtype, device, placement, sbp, requires_grad)() def new_ones_op( x, size=None, dtype=None, device=None, placement=None, sbp=None, requires_grad=False ): if isinstance(device, str): device = flow.device(device) if size != None: size = _single(size) new_size = size new_dtype = dtype new_device = device new_placement = placement new_sbp = sbp new_requires_grad = requires_grad if size is None: new_size = x.shape if dtype is None: new_dtype = x.dtype if device is None: new_device = x.device if x.is_local else None if placement is None: new_placement = x.placement if x.is_global else None if sbp is None: new_sbp = x.sbp if x.is_global else None if new_placement is not None: assert device is None assert new_sbp is not None assert isinstance( new_size, (int, tuple, flow.Size) ), f"size parameter not correct, please check!" assert isinstance( new_dtype, flow.dtype ), f"dtype parameter not correct, please check!" if new_placement is not None: assert isinstance( new_placement, flow.placement ), f"device parameter not correct, please check!" assert isinstance( new_sbp, flow.sbp.sbp ), f"device parameter not correct, please check!" else: assert isinstance( new_device, (str, flow.device) ), f"device parameter not correct, please check!" assert isinstance( new_requires_grad, bool ), f"requires_grad parameter not correct, please check!" if placement is not None: res = flow._C.global_constant( new_size, 1.0, dtype=new_dtype, placement=placement, sbp=sbp ) else: res = flow._C.constant(new_size, 1.0, dtype=new_dtype, device=new_device) res.requires_grad = new_requires_grad return res def new_zeros_op( x, size=None, dtype=None, device=None, placement=None, sbp=None, requires_grad=False ): if isinstance(device, str): device = flow.device(device) if size is None or len(size) == 0: new_size = x.shape else: new_size = _handle_size_arg(size) new_dtype = dtype new_device = device new_placement = placement new_sbp = sbp new_requires_grad = requires_grad if dtype is None: new_dtype = x.dtype if device is None: new_device = x.device if x.is_local else None if placement is None: new_placement = x.placement if x.is_global else None if sbp is None: new_sbp = x.sbp if x.is_global else None if new_placement is not None: assert ( device is None ), "argument 'device' must be None when argument 'placement' exist" assert ( new_sbp is not None ), "argument 'sbp' must not be None when argument 'placement' exist" assert isinstance( new_size, (int, tuple, list, flow.Size) ), f"argument 'size' must be tuple of ints, not %s" % (type(new_size)) assert isinstance( new_dtype, flow.dtype ), f"argument 'dtype' must be flow.dtype, not %s" % (type(new_dtype)) if new_placement is not None: assert isinstance( new_placement, flow.placement ), f"argument 'placement' must be flow.placement, not %s" % ( type(new_placement) ) assert isinstance( new_sbp, (flow.sbp.sbp, tuple) ), f"argument 'sbp' must be flow.sbp.sbp, not %s" % (type(new_sbp)) else: assert isinstance( new_device, (str, flow.device) ), f"argument 'device' must be flow.device, not %s" % (type(new_device)) assert isinstance( new_requires_grad, bool ), f"argument 'requires_grad' must be bool, not %s" % (type(new_requires_grad)) if new_placement is not None: res = flow._C.global_constant( new_size, 0.0, dtype=new_dtype, placement=new_placement, sbp=new_sbp ) else: res = flow._C.constant(new_size, 0.0, dtype=new_dtype, device=new_device) res.requires_grad = new_requires_grad return res if __name__ == "__main__": import doctest doctest.testmod(raise_on_error=True)

36.442971

196

0.628576

dd8b44021ee9134ebd2a4d988f8f4d28a476fdbd

3,543

py

Python

lz4steg.py

jvarho/lz77steg

09b78ed226139e554d21fc52b599502661d81a3a

[ "MIT" ]

null

lz4steg.py

jvarho/lz77steg

09b78ed226139e554d21fc52b599502661d81a3a

[ "MIT" ]

null

lz4steg.py

jvarho/lz77steg

09b78ed226139e554d21fc52b599502661d81a3a

[ "MIT" ]

null

#!/usr/bin/env python # # Copyright (c) 2014, Jan Varho <jan@varho.org> # Some rights reserved, see COPYING import argparse import sys from lz77steg import LZ77Steg, _hash class LZ4Steg(LZ77Steg): TOK_LITERAL = 1 TOK_MATCH = 2 def init(self, cover): super(LZ4Steg, self).init(cover) self.end = self.get_littleendian(4) def get_tokens(self): '''Generator for tokens, must be implemented''' while self.pos < self.end: token = self.get_cbyte() llen = token >> 4 mlen = token & 0xf if llen == 15: while self.cover[self.cpos] == 255: llen += self.get_cbyte() llen += self.get_cbyte() if llen: yield (self.TOK_LITERAL, llen) if self.pos == self.end: return opos = self.cpos moff = self.get_littleendian(2) if mlen == 15: while self.cover[self.cpos] == 255: mlen += self.get_cbyte() mlen += self.get_cbyte() mlen += 4 yield (self.TOK_MATCH, mlen, moff, opos) def is_match(self, t): '''Is token a match token?''' return t[0] == self.TOK_MATCH def update_window(self, t): '''Update window with token''' if t[0] == self.TOK_LITERAL: self.update_window_literal(t[1]) elif t[0] == self.TOK_MATCH: self.update_window_match(t[1], t[2]) else: raise TypeError def list_possible_matches_t(self, t): '''Return a list of possible matches for t''' tt, mlen, moff, opos = t return self.list_possible_matches(mlen, moff) def update_match(self, t, nmatch): '''Updates cover token to new match, must be implemented''' self.cover[t[3]] = nmatch & 0xff self.cover[t[3] + 1] = nmatch >> 8 def get_index(self, mlist, t): '''Get the index of the match''' return mlist.index(t[2]) if __name__ == '__main__': parser = argparse.ArgumentParser(description='LZ4 steganography') action = parser.add_mutually_exclusive_group() action.add_argument('-d', '--decode', action='store_true') action.add_argument('-m', '--message') output = parser.add_mutually_exclusive_group() output.add_argument('-i', '--inplace', action='store_true') output.add_argument('-o', '--output') parser.add_argument('FILE') args = parser.parse_args() with open(args.FILE) as f: cover = f.read() if args.decode: message = LZ4Steg().retrieve(cover, nullterm=True) print message elif args.message: assert len(args.message) cover = LZ4Steg().store(cover, args.message, nullterm=True) if args.output: with open(args.output, 'wb') as f: f.write(cover) elif args.inplace: with open(args.FILE, 'wb') as f: f.write(cover) else: sys.stdout.write(cover) else: s = LZ4Steg() cap, pcap = s.scan(cover) clen = len(cover) su = 'Size uncompressed %d' % s.end dl = len(su) - len('Size uncompressed ') d = '%' + str(dl) + 'd' print su print ('Size compressed '+d+' (%.2f%%)') % (clen, clen * 100. / s.end) print ('Storage '+d+' (%.2f%%)') % (cap, cap * 100. / clen) print ('Storage potential '+d+' (%.2f%%)') % (pcap, pcap * 100. / clen)

29.525

80

0.544736

c31eead604399b77c2a3111c2ca12b71f51b722a

12,036

py

Python

zerver/data_import/gitter.py

pranayshahxyz/zulip

3da483487af79fde9dce2d21124dfa39b94936a5

[ "Apache-2.0" ]

1

2020-04-09T18:34:44.000Z

zerver/data_import/gitter.py

pranayshahxyz/zulip

3da483487af79fde9dce2d21124dfa39b94936a5

[ "Apache-2.0" ]

null

zerver/data_import/gitter.py

pranayshahxyz/zulip

3da483487af79fde9dce2d21124dfa39b94936a5

[ "Apache-2.0" ]

null

import os import dateutil.parser import logging import subprocess import ujson from django.conf import settings from django.forms.models import model_to_dict from django.utils.timezone import now as timezone_now from typing import Any, Dict, List, Set, Tuple from zerver.models import UserProfile, Recipient from zerver.lib.export import MESSAGE_BATCH_CHUNK_SIZE from zerver.data_import.import_util import ZerverFieldsT, build_zerver_realm, \ build_avatar, build_subscription, build_recipient, build_usermessages, \ build_defaultstream, process_avatars, build_realm, build_stream, \ build_message, create_converted_data_files, make_subscriber_map # stubs GitterDataT = List[Dict[str, Any]] realm_id = 0 def gitter_workspace_to_realm(domain_name: str, gitter_data: GitterDataT, realm_subdomain: str) -> Tuple[ZerverFieldsT, List[ZerverFieldsT], Dict[str, int]]: """ Returns: 1. realm, Converted Realm data 2. avatars, which is list to map avatars to zulip avatar records.json 3. user_map, which is a dictionary to map from gitter user id to zulip user id """ NOW = float(timezone_now().timestamp()) zerver_realm = build_zerver_realm(realm_id, realm_subdomain, NOW, 'Gitter') # type: List[ZerverFieldsT] realm = build_realm(zerver_realm, realm_id, domain_name) zerver_userprofile, avatars, user_map = build_userprofile(int(NOW), domain_name, gitter_data) zerver_stream, zerver_defaultstream = build_stream_and_defaultstream(int(NOW)) zerver_recipient, zerver_subscription = build_recipient_and_subscription( zerver_userprofile, zerver_stream) realm['zerver_userprofile'] = zerver_userprofile realm['zerver_stream'] = zerver_stream realm['zerver_defaultstream'] = zerver_defaultstream realm['zerver_recipient'] = zerver_recipient realm['zerver_subscription'] = zerver_subscription return realm, avatars, user_map def build_userprofile(timestamp: Any, domain_name: str, gitter_data: GitterDataT) -> Tuple[List[ZerverFieldsT], List[ZerverFieldsT], Dict[str, int]]: """ Returns: 1. zerver_userprofile, which is a list of user profile 2. avatar_list, which is list to map avatars to zulip avatars records.json 3. added_users, which is a dictionary to map from gitter user id to zulip id """ logging.info('######### IMPORTING USERS STARTED #########\n') zerver_userprofile = [] avatar_list = [] # type: List[ZerverFieldsT] user_map = {} # type: Dict[str, int] user_id = 0 for data in gitter_data: if data['fromUser']['id'] not in user_map: user_data = data['fromUser'] user_map[user_data['id']] = user_id email = get_user_email(user_data, domain_name) build_avatar(user_id, realm_id, email, user_data['avatarUrl'], timestamp, avatar_list) # Build userprofile object userprofile = UserProfile( full_name=user_data['displayName'], short_name=user_data['username'], id=user_id, email=email, delivery_email=email, avatar_source='U', pointer=-1, date_joined=timestamp, last_login=timestamp) userprofile_dict = model_to_dict(userprofile) # Set realm id separately as the corresponding realm is not yet a Realm model # instance userprofile_dict['realm'] = realm_id zerver_userprofile.append(userprofile_dict) user_id += 1 logging.info('######### IMPORTING USERS FINISHED #########\n') return zerver_userprofile, avatar_list, user_map def get_user_email(user_data: ZerverFieldsT, domain_name: str) -> str: # TODO Get user email from github email = ("%s@users.noreply.github.com" % (user_data['username'],)) return email def build_stream_and_defaultstream(timestamp: Any) -> Tuple[List[ZerverFieldsT], List[ZerverFieldsT]]: logging.info('######### IMPORTING STREAM STARTED #########\n') # We have only one stream for gitter export stream_name = 'from gitter' stream_description = "Imported from gitter" stream_id = 0 stream = build_stream(timestamp, realm_id, stream_name, stream_description, stream_id) defaultstream = build_defaultstream(realm_id=realm_id, stream_id=stream_id, defaultstream_id=0) logging.info('######### IMPORTING STREAMS FINISHED #########\n') return [stream], [defaultstream] def build_recipient_and_subscription( zerver_userprofile: List[ZerverFieldsT], zerver_stream: List[ZerverFieldsT]) -> Tuple[List[ZerverFieldsT], List[ZerverFieldsT]]: """ Returns: 1. zerver_recipient, which is a list of mapped recipient 2. zerver_subscription, which is a list of mapped subscription """ zerver_recipient = [] zerver_subscription = [] recipient_id = subscription_id = 0 # For stream # We have only one recipient, because we have only one stream # Hence 'recipient_id'=0 corresponds to 'stream_id'=0 recipient = build_recipient(0, recipient_id, Recipient.STREAM) zerver_recipient.append(recipient) for user in zerver_userprofile: subscription = build_subscription(recipient_id, user['id'], subscription_id) zerver_subscription.append(subscription) subscription_id += 1 recipient_id += 1 # For users for user in zerver_userprofile: recipient = build_recipient(user['id'], recipient_id, Recipient.PERSONAL) subscription = build_subscription(recipient_id, user['id'], subscription_id) zerver_recipient.append(recipient) zerver_subscription.append(subscription) recipient_id += 1 subscription_id += 1 return zerver_recipient, zerver_subscription def convert_gitter_workspace_messages(gitter_data: GitterDataT, output_dir: str, subscriber_map: Dict[int, Set[int]], user_map: Dict[str, int], user_short_name_to_full_name: Dict[str, str], chunk_size: int=MESSAGE_BATCH_CHUNK_SIZE) -> None: """ Messages are stored in batches """ logging.info('######### IMPORTING MESSAGES STARTED #########\n') message_id = 0 recipient_id = 0 # Corresponding to stream "gitter" low_index = 0 upper_index = low_index + chunk_size dump_file_id = 1 while True: message_json = {} zerver_message = [] zerver_usermessage = [] # type: List[ZerverFieldsT] message_data = gitter_data[low_index: upper_index] if len(message_data) == 0: break for message in message_data: message_time = dateutil.parser.parse(message['sent']).timestamp() mentioned_user_ids = get_usermentions(message, user_map, user_short_name_to_full_name) rendered_content = None topic_name = 'imported from gitter' user_id = user_map[message['fromUser']['id']] zulip_message = build_message(topic_name, float(message_time), message_id, message['text'], rendered_content, user_id, recipient_id) zerver_message.append(zulip_message) build_usermessages( zerver_usermessage=zerver_usermessage, subscriber_map=subscriber_map, recipient_id=recipient_id, mentioned_user_ids=mentioned_user_ids, message_id=message_id, is_private=False, ) message_id += 1 message_json['zerver_message'] = zerver_message message_json['zerver_usermessage'] = zerver_usermessage message_filename = os.path.join(output_dir, "messages-%06d.json" % (dump_file_id,)) logging.info("Writing Messages to %s\n" % (message_filename,)) write_data_to_file(os.path.join(message_filename), message_json) low_index = upper_index upper_index = chunk_size + low_index dump_file_id += 1 logging.info('######### IMPORTING MESSAGES FINISHED #########\n') def get_usermentions(message: Dict[str, Any], user_map: Dict[str, int], user_short_name_to_full_name: Dict[str, str]) -> List[int]: mentioned_user_ids = [] if 'mentions' in message: for mention in message['mentions']: if mention.get('userId') in user_map: gitter_mention = '@%s' % (mention['screenName'],) if mention['screenName'] not in user_short_name_to_full_name: logging.info("Mentioned user %s never sent any messages, so has no full name data" % (mention['screenName'],)) full_name = mention['screenName'] else: full_name = user_short_name_to_full_name[mention['screenName']] zulip_mention = ('@**%s**' % (full_name,)) message['text'] = message['text'].replace(gitter_mention, zulip_mention) mentioned_user_ids.append(user_map[mention['userId']]) return mentioned_user_ids def do_convert_data(gitter_data_file: str, output_dir: str, threads: int=6) -> None: # Subdomain is set by the user while running the import commands realm_subdomain = "" domain_name = settings.EXTERNAL_HOST os.makedirs(output_dir, exist_ok=True) # output directory should be empty initially if os.listdir(output_dir): raise Exception("Output directory should be empty!") # Read data from the gitter file with open(gitter_data_file) as fp: gitter_data = ujson.load(fp) realm, avatar_list, user_map = gitter_workspace_to_realm( domain_name, gitter_data, realm_subdomain) subscriber_map = make_subscriber_map( zerver_subscription=realm['zerver_subscription'], ) # For user mentions user_short_name_to_full_name = {} for userprofile in realm['zerver_userprofile']: user_short_name_to_full_name[userprofile['short_name']] = userprofile['full_name'] convert_gitter_workspace_messages( gitter_data, output_dir, subscriber_map, user_map, user_short_name_to_full_name) avatar_folder = os.path.join(output_dir, 'avatars') avatar_realm_folder = os.path.join(avatar_folder, str(realm_id)) os.makedirs(avatar_realm_folder, exist_ok=True) avatar_records = process_avatars(avatar_list, avatar_folder, realm_id, threads) attachment = {"zerver_attachment": []} # type: Dict[str, List[Any]] # IO realm.json create_converted_data_files(realm, output_dir, '/realm.json') # IO emoji records create_converted_data_files([], output_dir, '/emoji/records.json') # IO avatar records create_converted_data_files(avatar_records, output_dir, '/avatars/records.json') # IO uploads records create_converted_data_files([], output_dir, '/uploads/records.json') # IO attachments records create_converted_data_files(attachment, output_dir, '/attachment.json') subprocess.check_call(["tar", "-czf", output_dir + '.tar.gz', output_dir, '-P']) logging.info('######### DATA CONVERSION FINISHED #########\n') logging.info("Zulip data dump created at %s" % (output_dir,)) def write_data_to_file(output_file: str, data: Any) -> None: with open(output_file, "w") as f: f.write(ujson.dumps(data, indent=4))

42.083916

108

0.640329

44d7a50a47788884bd8198a0fa340ce382afe25b

3,823

py

Python

dhcp/forms.py

jlin/inventory

c098c98e570c3bf9fadfd811eb75e1213f6ea428

[ "BSD-3-Clause" ]

22

2015-01-16T01:36:32.000Z

2020-06-08T00:46:18.000Z

dhcp/forms.py

jlin/inventory

c098c98e570c3bf9fadfd811eb75e1213f6ea428

[ "BSD-3-Clause" ]

9

2019-03-15T11:39:32.000Z

2019-04-30T00:59:50.000Z

dhcp/forms.py

jlin/inventory

c098c98e570c3bf9fadfd811eb75e1213f6ea428

[ "BSD-3-Clause" ]

13

2015-01-13T20:56:22.000Z

2022-02-23T06:01:17.000Z

from django import forms from django.forms.extras.widgets import SelectDateWidget import models class AddDHCPScopeForm(forms.Form): scope_name = forms.CharField(max_length=32, required=True, widget=forms.TextInput(attrs={'size':'48'})) scope_description = forms.CharField(max_length=32, required=True, widget=forms.TextInput(attrs={'size':'48'})) class DHCPScopeOverrideForm(forms.ModelForm): dhcp_scope = forms.CharField(max_length=32, required=True, widget=forms.TextInput(attrs={'size':'48'})) #override_text = forms.CharField(max_length=32, required=True, widget=forms.Textarea(attrs={'rows':'60', 'cols':'80'})) class Meta: model = models.DHCPOverride class EditDHCPScopeForm(forms.Form): SUBNET_CHOICES = ( ('255.255.224.0', '255.255.224.0'), ('255.255.240.0', '255.255.240.0'), ('255.255.248.0', '255.255.248.0'), ('255.255.252.0', '255.255.252.0'), ('255.255.254.0', '255.255.254.0'), ('255.255.255.0', '255.255.255.0'), ('255.255.255.128', '255.255.255.128'), ('255.255.255.192', '255.255.255.192'), ('255.255.255.224', '255.255.255.224'), ('255.255.255.240', '255.255.255.240'), ('255.255.255.248', '255.255.255.248'), ('255.255.255.252', '255.255.255.252'), ('255.255.255.254', '255.255.255.254') ) YES_NO_CHOICES = ( (0, 'No'), (1, 'Yes'), ) CHOICES = ( ('True', 'True'), ('False', 'False'), ) scope_name = forms.CharField(max_length=32,widget=forms.TextInput(attrs={'size':'48'})) domain_name = forms.CharField(max_length=64, required=False, widget=forms.TextInput(attrs={'size':'48'})) router = forms.CharField(max_length=64, required=False, widget=forms.TextInput(attrs={'size':'48'})) scope_start = forms.CharField(max_length=64, required=True, widget=forms.TextInput(attrs={'size':'48'})) scope_end = forms.CharField(max_length=64, required=True, widget=forms.TextInput(attrs={'size':'48'})) scope_netmask = forms.CharField(max_length=64, required=True, widget=forms.Select(choices=SUBNET_CHOICES)) pool_start = forms.CharField(max_length=64, required=True, widget=forms.TextInput(attrs={'size':'48'})) pool_end = forms.CharField(max_length=64, required=True, widget=forms.TextInput(attrs={'size':'48'})) ntp_server1 = forms.CharField(max_length=64, required=False, widget=forms.TextInput(attrs={'size':'48'})) ntp_server2 = forms.CharField(max_length=64, required=False, widget=forms.TextInput(attrs={'size':'48'})) dns_server1 = forms.CharField(max_length=64, required=False, widget=forms.TextInput(attrs={'size':'48'})) dns_server2 = forms.CharField(max_length=64, required=False, widget=forms.TextInput(attrs={'size':'48'})) allow_booting = forms.CharField(max_length=64, required=False, widget=forms.Select(choices=CHOICES)) allow_bootp = forms.CharField(max_length=64, required=False, widget=forms.Select(choices=CHOICES)) class Meta: fields = [ "scope_name", "scope_start", 'scope_netmask', 'scope_notes', 'filename', 'pool_start', 'pool_end', 'pool_deny_dynamic_bootp_agents', 'allow_booting', 'allow_bootp', 'filename', 'option_subnet_mask', 'ntp_server1', 'ntp_server2', 'dns_server1', 'dns_server2', 'router', 'domain_name', 'option_routers' ]

50.302632

123

0.588805

faeafd869188374215d860a3e647c2b440b2371f

418

py

Python

vtkplotter_examples/volumetric/probePlaneUGrid.py

ismarou/vtkplotter-examples

1eefcc026be169ab7a77a5bce6dec8044c33b554

[ "MIT" ]

4

2020-07-30T02:38:29.000Z

2021-09-12T14:30:18.000Z

vtkplotter_examples/volumetric/probePlaneUGrid.py

ismarou/vtkplotter-examples

1eefcc026be169ab7a77a5bce6dec8044c33b554

[ "MIT" ]

null

vtkplotter_examples/volumetric/probePlaneUGrid.py

ismarou/vtkplotter-examples

1eefcc026be169ab7a77a5bce6dec8044c33b554

[ "MIT" ]

null

"""Probe a vtkUnStructuredGrid with a plane""" from vtkplotter import * # same could be done with vtkRectilinearGrid etc.. data = loadUnStructuredGrid(datadir+"ugrid.vtk") # create the outline of the data outermesh = Mesh(data).alpha(0.2).wireframe() orig = data.GetCenter() pl = probePlane(data, origin=orig, normal=(0.1,0.2,1)) #pl.printInfo() #pl.pointColors('scalars', cmap='hot') show(pl, outermesh, axes=1)

24.588235

54

0.727273

58c6d8c54c31cdf1aae4643f65e4ab4e67bc8c3f

731

py

Python

release/stubs.min/System/Windows/Forms/__init___parts/ListViewItemMouseHoverEventArgs.py

tranconbv/ironpython-stubs

a601759e6c6819beff8e6b639d18a24b7e351851

[ "MIT" ]

null

release/stubs.min/System/Windows/Forms/__init___parts/ListViewItemMouseHoverEventArgs.py

tranconbv/ironpython-stubs

a601759e6c6819beff8e6b639d18a24b7e351851

[ "MIT" ]

null

release/stubs.min/System/Windows/Forms/__init___parts/ListViewItemMouseHoverEventArgs.py

tranconbv/ironpython-stubs

a601759e6c6819beff8e6b639d18a24b7e351851

[ "MIT" ]

null

class ListViewItemMouseHoverEventArgs(EventArgs): """ Provides data for the System.Windows.Forms.ListView.ItemMouseHover event. ListViewItemMouseHoverEventArgs(item: ListViewItem) """ def Instance(self): """ This function has been arbitrarily put into the stubs""" return ListViewItemMouseHoverEventArgs() def __getitem__(self,*args): """ x.__getitem__(y) <==> x[y] """ pass @staticmethod def __new__(self,item): """ __new__(cls: type,item: ListViewItem) """ pass Item=property(lambda self: object(),lambda self,v: None,lambda self: None) """Gets the item the mouse pointer is currently hovering over. Get: Item(self: ListViewItemMouseHoverEventArgs) -> ListViewItem """

28.115385

76

0.70725

79813757233743f26c579fa95ec121acc5f9ff67

261

py

Python

pyvisdk/enums/host_unresolved_vmfs_resolution_spec_vmfs_uuid_resolution.py

Infinidat/pyvisdk

f2f4e5f50da16f659ccc1d84b6a00f397fa997f8

[ "MIT" ]

null

pyvisdk/enums/host_unresolved_vmfs_resolution_spec_vmfs_uuid_resolution.py

Infinidat/pyvisdk

f2f4e5f50da16f659ccc1d84b6a00f397fa997f8

[ "MIT" ]

null

pyvisdk/enums/host_unresolved_vmfs_resolution_spec_vmfs_uuid_resolution.py

Infinidat/pyvisdk

f2f4e5f50da16f659ccc1d84b6a00f397fa997f8

[ "MIT" ]

null

######################################## # Automatically generated, do not edit. ######################################## from pyvisdk.thirdparty import Enum HostUnresolvedVmfsResolutionSpecVmfsUuidResolution = Enum( 'forceMount', 'resignature', )

18.642857

58

0.524904

30ddcd586781074a0952c680467261ec209612bf

5,871

py

Python

C/.ycm_extra_conf.py

Kwpolska/roman_numerals

887e648a39fa73583f4b7cf330436f94bf88325e

[ "BSD-3-Clause" ]

null

C/.ycm_extra_conf.py

Kwpolska/roman_numerals

887e648a39fa73583f4b7cf330436f94bf88325e

[ "BSD-3-Clause" ]

null

C/.ycm_extra_conf.py

Kwpolska/roman_numerals

887e648a39fa73583f4b7cf330436f94bf88325e

[ "BSD-3-Clause" ]

null

# This file is NOT licensed under the GPLv3, which is the license for the rest # of YouCompleteMe. # # Here's the license text for this file: # # This is free and unencumbered software released into the public domain. # # Anyone is free to copy, modify, publish, use, compile, sell, or # distribute this software, either in source code form or as a compiled # binary, for any purpose, commercial or non-commercial, and by any # means. # # In jurisdictions that recognize copyright laws, the author or authors # of this software dedicate any and all copyright interest in the # software to the public domain. We make this dedication for the benefit # of the public at large and to the detriment of our heirs and # successors. We intend this dedication to be an overt act of # relinquishment in perpetuity of all present and future rights to this # software under copyright law. # # 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 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. # # For more information, please refer to <http://unlicense.org/> import os import ycm_core # These are the compilation flags that will be used in case there's no # compilation database set (by default, one is not set). # CHANGE THIS LIST OF FLAGS. YES, THIS IS THE DROID YOU HAVE BEEN LOOKING FOR. flags = [ '-Wall', '-Wextra', '-Werror', '-Wno-long-long', '-Wno-variadic-macros', '-fexceptions', # You 100% do NOT need -DUSE_CLANG_COMPLETER in your flags; only the YCM # source code needs it. # THIS IS IMPORTANT! Without a "-std=<something>" flag, clang won't know which # language to use when compiling headers. So it will guess. Badly. So C++ # headers will be compiled as C headers. You don't want that so ALWAYS specify # a "-std=<something>". # For a C project, you would set this to something like 'c99' instead of # 'c++11'. '-std=c99', # ...and the same thing goes for the magic -x option which specifies the # language that the files to be compiled are written in. This is mostly # relevant for c++ headers. # For a C project, you would set this to 'c' instead of 'c++'. '-x', 'c', ] # Set this to the absolute path to the folder (NOT the file!) containing the # compile_commands.json file to use that instead of 'flags'. See here for # more details: http://clang.llvm.org/docs/JSONCompilationDatabase.html # # You can get CMake to generate this file for you by adding: # set( CMAKE_EXPORT_COMPILE_COMMANDS 1 ) # to your CMakeLists.txt file. # # Most projects will NOT need to set this to anything; you can just change the # 'flags' list of compilation flags. Notice that YCM itself uses that approach. compilation_database_folder = '' if os.path.exists( compilation_database_folder ): database = ycm_core.CompilationDatabase( compilation_database_folder ) else: database = None SOURCE_EXTENSIONS = [ '.cpp', '.cxx', '.cc', '.c', '.m', '.mm' ] def DirectoryOfThisScript(): return os.path.dirname( os.path.abspath( __file__ ) ) def MakeRelativePathsInFlagsAbsolute( flags, working_directory ): if not working_directory: return list( flags ) new_flags = [] make_next_absolute = False path_flags = [ '-isystem', '-I', '-iquote', '--sysroot=' ] for flag in flags: new_flag = flag if make_next_absolute: make_next_absolute = False if not flag.startswith( '/' ): new_flag = os.path.join( working_directory, flag ) for path_flag in path_flags: if flag == path_flag: make_next_absolute = True break if flag.startswith( path_flag ): path = flag[ len( path_flag ): ] new_flag = path_flag + os.path.join( working_directory, path ) break if new_flag: new_flags.append( new_flag ) return new_flags def IsHeaderFile( filename ): extension = os.path.splitext( filename )[ 1 ] return extension in [ '.h', '.hxx', '.hpp', '.hh' ] def GetCompilationInfoForFile( filename ): # The compilation_commands.json file generated by CMake does not have entries # for header files. So we do our best by asking the db for flags for a # corresponding source file, if any. If one exists, the flags for that file # should be good enough. if IsHeaderFile( filename ): basename = os.path.splitext( filename )[ 0 ] for extension in SOURCE_EXTENSIONS: replacement_file = basename + extension if os.path.exists( replacement_file ): compilation_info = database.GetCompilationInfoForFile( replacement_file ) if compilation_info.compiler_flags_: return compilation_info return None return database.GetCompilationInfoForFile( filename ) def FlagsForFile( filename, **kwargs ): if database: # Bear in mind that compilation_info.compiler_flags_ does NOT return a # python list, but a "list-like" StringVec object compilation_info = GetCompilationInfoForFile( filename ) if not compilation_info: return None final_flags = MakeRelativePathsInFlagsAbsolute( compilation_info.compiler_flags_, compilation_info.compiler_working_dir_ ) # NOTE: This is just for YouCompleteMe; it's highly likely that your project # does NOT need to remove the stdlib flag. DO NOT USE THIS IN YOUR # ycm_extra_conf IF YOU'RE NOT 100% SURE YOU NEED IT. try: final_flags.remove( '-stdlib=libc++' ) except ValueError: pass else: relative_to = DirectoryOfThisScript() final_flags = MakeRelativePathsInFlagsAbsolute( flags, relative_to ) return { 'flags': final_flags, 'do_cache': True }

35.79878

80

0.720491

5ce744a9d09ba9ccc548371f8d8c425687b14425

3,558

py

Python

sktime/forecasting/compose/_ensemble.py

MFaroukB/sktime

29932fc071ab04797bc2f5c00cd533726b31eb46

[ "BSD-3-Clause" ]

null

sktime/forecasting/compose/_ensemble.py

MFaroukB/sktime

29932fc071ab04797bc2f5c00cd533726b31eb46

[ "BSD-3-Clause" ]

null

sktime/forecasting/compose/_ensemble.py

MFaroukB/sktime

29932fc071ab04797bc2f5c00cd533726b31eb46

[ "BSD-3-Clause" ]

1

2021-04-30T08:12:18.000Z

#!/usr/bin/env python3 -u # -*- coding: utf-8 -*- # copyright: sktime developers, BSD-3-Clause License (see LICENSE file) __author__ = ["Markus Löning"] __all__ = ["EnsembleForecaster"] import pandas as pd from sktime.forecasting.base._base import DEFAULT_ALPHA from sktime.forecasting.base._meta import _HeterogenousEnsembleForecaster from sktime.forecasting.base._sktime import _OptionalForecastingHorizonMixin class EnsembleForecaster( _OptionalForecastingHorizonMixin, _HeterogenousEnsembleForecaster ): """Ensemble of forecasters Parameters ---------- forecasters : list of (str, estimator) tuples n_jobs : int or None, optional (default=None) The number of jobs to run in parallel for fit. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. aggfunc : str, {'mean', 'median', 'min', 'max'}, (default='mean') The function to aggregate prediction from individual forecasters. """ _required_parameters = ["forecasters"] def __init__(self, forecasters, n_jobs=None, aggfunc="mean"): super(EnsembleForecaster, self).__init__(forecasters=forecasters, n_jobs=n_jobs) self.aggfunc = aggfunc def fit(self, y, X=None, fh=None): """Fit to training data. Parameters ---------- y : pd.Series Target time series to which to fit the forecaster. fh : int, list or np.array, optional (default=None) The forecasters horizon with the steps ahead to to predict. X : pd.DataFrame, optional (default=None) Exogenous variables are ignored Returns ------- self : returns an instance of self. """ self._is_fitted = False self._set_y_X(y, X) self._set_fh(fh) names, forecasters = self._check_forecasters() self._fit_forecasters(forecasters, y, X, fh) self._is_fitted = True return self def update(self, y, X=None, update_params=True): """Update fitted parameters Parameters ---------- y : pd.Series X : pd.DataFrame update_params : bool, optional (default=True) Returns ------- self : an instance of self """ self.check_is_fitted() self._update_y_X(y, X) for forecaster in self.forecasters_: forecaster.update(y, X, update_params=update_params) return self def _predict(self, fh, X=None, return_pred_int=False, alpha=DEFAULT_ALPHA): """return the predicted reduction Parameters ---------- fh : int, list or np.array, optional (default=None) X : pd.DataFrame return_pred_int : boolean, optional (default=False) alpha : fh : float, (default=DEFAULT_ALPHA) Returns ------- y_pred : pd.Series Aggregated predictions. """ if return_pred_int: raise NotImplementedError() y_pred = pd.concat(self._predict_forecasters(fh, X), axis=1) valid_aggfuncs = ("median", "mean", "min", "max") if self.aggfunc not in valid_aggfuncs: raise ValueError(f"Invalid `aggfunc`. Please use one of {valid_aggfuncs}") if self.aggfunc == "median": return y_pred.median(axis=1) elif self.aggfunc == "min": return y_pred.min(axis=1) elif self.aggfunc == "max": return y_pred.max(axis=1) else: return y_pred.mean(axis=1)

31.767857

88

0.61692

e88f5c7e5c5526a23be42528470ea313e6284e45

420

py

Python

app/commands.py

nattesharan/delhivery

3ef419d403b27fc490a8557590d81f50a8dbfc4f

[ "MIT" ]

2

2019-06-02T04:32:54.000Z

2021-01-05T12:27:50.000Z

app/commands.py

nattesharan/delhivery

3ef419d403b27fc490a8557590d81f50a8dbfc4f

[ "MIT" ]

null

app/commands.py

nattesharan/delhivery

3ef419d403b27fc490a8557590d81f50a8dbfc4f

[ "MIT" ]

null

from flask_script import Command from app.settings import ROLES from delhivery.models import DelhiveryHierarchy class CreateRoles(Command): def run(self): print("Creating roles in database") for role in ROLES: DelhiveryHierarchy.objects.create(name=role['name'], role=role['role'], features=role['features']) def add_command(manager): manager.add_command('initroles', CreateRoles())

35

110

0.730952

15552e06d8192e717651395e0c5336c7682d37ce

15,273

py

Python

src/gluonts/model/san/_layers.py

unibeck/gluon-ts

73d0e9de689ab4bb014cdb4423642dff030e7678

[ "Apache-2.0" ]

1

2019-10-15T01:47:40.000Z

src/gluonts/model/san/_layers.py

unibeck/gluon-ts

73d0e9de689ab4bb014cdb4423642dff030e7678

[ "Apache-2.0" ]

null

src/gluonts/model/san/_layers.py

unibeck/gluon-ts

73d0e9de689ab4bb014cdb4423642dff030e7678

[ "Apache-2.0" ]

null

# Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"). # You may not use this file except in compliance with the License. # A copy of the License is located at # # http://www.apache.org/licenses/LICENSE-2.0 # # or in the "license" file accompanying this file. This file is distributed # on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either # express or implied. See the License for the specific language governing # permissions and limitations under the License. import math from typing import List, Optional, Tuple import mxnet as mx # Third-party import import numpy as np from mxnet import init from mxnet.gluon import HybridBlock, Parameter, nn from mxnet.gluon.contrib.nn import HybridConcurrent from gluonts.core.component import validated from gluonts.mx import Tensor from gluonts.mx.block.feature import FeatureEmbedder def _torch_gather(F, data: Tensor, idx: Tensor, axis: int): """ Pytorch-style gather_nd """ ndim = 4 if axis < 0: axis = ndim + axis mx_idx = [] for dim in range(ndim): if dim == axis: d_idx = F.broadcast_like(idx, data) else: d_idx = F.contrib.arange_like(data, axis=dim) for _ in range(dim): d_idx = F.expand_dims(data=d_idx, axis=0) for _ in range(ndim - dim - 1): d_idx = F.expand_dims(data=d_idx, axis=-1) d_idx = F.broadcast_like(d_idx, data) mx_idx.append(d_idx) mx_idx = F.stack(*mx_idx, axis=0) return F.gather_nd(data, mx_idx) class SinusoidalPositionalEmbedding(HybridBlock): @validated() def __init__(self, d_embed: int, **kwargs): super(SinusoidalPositionalEmbedding, self).__init__(**kwargs) if d_embed % 2 != 0: raise ValueError( "sinusoidal embedding must have an even dimension" ) self.d_embed = d_embed def hybrid_forward(self, F, pos_seq: Tensor) -> Tensor: inv_freq = F.arange(0, self.d_embed, 2) inv_freq = F.exp((inv_freq / self.d_embed) * -math.log(1e4)) pos_seq = F.reshape(data=pos_seq, shape=(0, 0, 1)) pos_seq = F.broadcast_mul(pos_seq, inv_freq) return F.concat(F.sin(pos_seq), F.cos(pos_seq), dim=-1) class CausalConv1D(HybridBlock): @validated() def __init__( self, channels: int, kernel_size: int, activation: str = "tanh", **kwargs, ): super(CausalConv1D, self).__init__(**kwargs) self.kernel_size = kernel_size self.channels = channels with self.name_scope(): self.net = nn.Conv1D( channels, kernel_size, use_bias=False, activation="tanh", weight_initializer=init.Xavier(), ) def hybrid_forward(self, F, x: Tensor, *args) -> Tensor: pad = ( F.zeros_like(x) .slice_axis(axis=1, begin=0, end=1) .tile(reps=(1, self.kernel_size - 1, 1)) ) x = F.concat(pad, x, dim=1) x = F.swapaxes(x, dim1=1, dim2=2) x = self.net(x) x = F.swapaxes(x, dim1=1, dim2=2) return x class SelfAttention(HybridBlock): @validated() def __init__( self, d_hidden: int, kernel_sizes: List[int], n_head: int = 1, bias: bool = True, bidirectional: bool = False, dist_enc: Optional[str] = None, share_values: bool = False, dropout: float = 0.0, temperature: float = 1.0, **kwargs, ): """ Self-attention module with q,k,v from the same input Parameters ---------- d_hidden : int hidden dimension kernel_sizes: int kernel sizes of convolutions to generate queries and keys n_head : int, optional number of attention heads, by default 1 bias : bool, optional add bias term in input and output projections, by default True bidirectional : bool, optional if False, add a mask to avoid backward attention, by default False dist_enc : Optional[str], optional add relative distance embeddings to dot-product attention, can be 'add' (linearly combine key and dist), 'dot' (dot product between key and dist), or None (disabled), by default None share_values : bool, optional if True, a value reprensentation is shared by all attention heads, by default False ref. https://arxiv.org/abs/1912.09363 dropout : float, optional dropout rate, by default 0.0 temperature : float, optional softmax temperature, by default 1.0 """ super(SelfAttention, self).__init__(**kwargs) n_groups = len(kernel_sizes) assert ( d_hidden % n_head == 0 ), f"hidden dim {d_hidden} cannot be split into {n_head} heads." assert ( d_hidden % n_groups == 0 ), f"hidden dim {d_hidden} cannot be split into {n_groups} groups." assert ( n_head % n_groups == 0 ), f"num_heads {n_head} cannot be allocated for {n_groups} groups." self.d_hidden = d_hidden self.kernel_sizes = kernel_sizes self.n_groups = n_groups self.d_group = self.d_hidden // self.n_groups self.n_head = n_head self.d_head = self.d_hidden // self.n_head self.bias = bias self.dist_enc = dist_enc self.bidirectional = bidirectional self.share_values = share_values self.temperature = temperature with self.name_scope(): self.qk_proj = HybridConcurrent(axis=-1, prefix="qk_proj_") for ksize in self.kernel_sizes: self.qk_proj.add( CausalConv1D( channels=self.d_group * 2, kernel_size=ksize, prefix=f"conv{ksize}_", ) ) self.v_proj = nn.Dense( units=self.d_head if self.share_values else d_hidden, use_bias=bias, flatten=False, weight_initializer=init.Xavier(), prefix="v_proj_", ) self.out_proj = nn.Dense( units=d_hidden, use_bias=bias, flatten=False, weight_initializer=init.Xavier(), prefix="out_proj_", ) if self.dist_enc is not None: assert self.dist_enc in [ "dot", "add", ], f"distance encoding type {self.dist_enc} is not supported" self.posemb = SinusoidalPositionalEmbedding(d_hidden) self.pos_proj = nn.Dense( units=d_hidden, use_bias=bias, flatten=False, weight_initializer=init.Xavier(), prefix="pos_proj_", ) if self.dist_enc == "add": self._ctt_bias_weight = Parameter( "_ctt_bias_weight", shape=(1, n_head, 1, self.d_head), init=init.Xavier(), ) self._pos_bias_weight = Parameter( "_pos_bias_weight", shape=(1, n_head, 1, self.d_head), init=init.Xavier(), ) self.dropout = nn.Dropout(dropout) def _split_head(self, F, x: Tensor) -> Tensor: """ Split hidden state into multi-heads Args ---------- x : Tensor [batch, length, d_hidden] Returns ------- Tensor [batch, n_head, length, d_head] """ x = F.reshape(data=x, shape=(0, 0, -4, self.n_head, self.d_head)) x = F.swapaxes(data=x, dim1=1, dim2=2) return x def _merge_head(self, F, x: Tensor) -> Tensor: """ Merge multi-heads into one hidden state Args ---------- x : Tensor [batch, n_head, length, d_head] Returns ------- Tensor [batch, length, d_hidden] """ x = F.swapaxes(data=x, dim1=1, dim2=2) x = F.reshape(data=x, shape=(0, 0, self.d_hidden)) return x def _compute_qkv(self, F, x: Tensor) -> Tuple[Tensor, Tensor, Tensor]: qk = self.qk_proj(x) qk = F.split(qk, num_outputs=self.n_groups * 2, axis=-1) q = F.concat(*qk[0::2], dim=-1) k = F.concat(*qk[1::2], dim=-1) q = self._split_head(F, q) k = self._split_head(F, k) v = self.v_proj(x) if self.share_values: v = F.broadcast_like(v.expand_dims(axis=1), k) else: v = self._split_head(F, v) return q, k, v def _apply_mask( self, F, score: Tensor, key_mask: Optional[Tensor] ) -> Tensor: if not self.bidirectional: k_idx = F.contrib.arange_like(score, axis=-1) k_idx = ( k_idx.expand_dims(axis=0) .expand_dims(axis=0) .expand_dims(axis=0) ) q_idx = F.contrib.arange_like(score, axis=-2) q_idx = ( q_idx.expand_dims(axis=-1) .expand_dims(axis=0) .expand_dims(axis=0) ) unidir_mask = F.broadcast_lesser_equal(k_idx, q_idx) unidir_mask = F.broadcast_like(unidir_mask, score) score = F.where(unidir_mask, score, F.ones_like(score) * 1e-9) if key_mask is not None: mem_mask = key_mask.squeeze(axis=-1) mem_mask = mem_mask.expand_dims(axis=1) # head mem_mask = mem_mask.expand_dims(axis=2) # query mem_mask = F.broadcast_like(mem_mask, score) score = F.where(mem_mask, score, F.ones_like(score) * 1e-9) return score def _compute_attn_score( self, F, q: Tensor, k: Tensor, mask: Optional[Tensor], _ctt_bias_weight: Optional[Tensor], _pos_bias_weight: Optional[Tensor], ) -> Tensor: score = F.batch_dot(lhs=q, rhs=k, transpose_b=True) if self.dist_enc is not None: # score_{ij} = <q_i, k_j> + s_{ij} # idx.shape = [klen, klen] # idx[i][j] = i-j idx = F.contrib.arange_like(k, axis=2) idx = F.broadcast_sub( idx.expand_dims(axis=1), idx.expand_dims(axis=0) ) # idx[i][j] = |i-j| idx = idx.abs() # idx.shape = [1, 1, klen, klen] idx = idx.expand_dims(axis=0).expand_dims(axis=0) # dist representation r for attention # r.shape = [1, klen, d_hidden] r = F.contrib.arange_like(k, axis=2).expand_dims(axis=0) r = self.posemb(r) r = self.pos_proj(r) # r.shape = [1, n_head, klen, d_head] r = self._split_head(F, r) # r.shape = [batch, n_head, klen, d_head] r = r.broadcast_like(k) if self.dist_enc == "add": # transformer-xl style: https://arxiv.org/abs/1901.02860 # s_{ij} = <q_i, r_{|i-j|}> + <u, k_j> + <v, r_{|i-j|}> # u = _content_bias_weight # v = _position_bias_weight # qr_{ij} = <q_i, r_j> # qr'_{ij} = qr_{i,idx[i][j]} = qr_{i,|i-j|} qr = F.batch_dot(lhs=q, rhs=r, transpose_b=True) qr = _torch_gather(F, data=qr, idx=idx, axis=-1) # rk_{ij} = <v, r_i> + <u, k_j> # rk'_{ij} = rk_{idx[i][j], j} = rk_{|i-j|, j} u = F.broadcast_to(_ctt_bias_weight, k) v = F.broadcast_to(_pos_bias_weight, r) rk = F.batch_dot(u, k, transpose_b=True) + F.batch_dot( v, r, transpose_b=True ) rk = _torch_gather(F, data=rk, idx=idx, axis=-2) # s_{ij} = qr_{i,|i-j|} + rk_{|i-j|, j} s = qr + rk else: # s_{ij} = <r_{|i-j|}, (q_i+k_j)> # = <q_i, r_{|i-j|}> + <r_{|i-j|}, k_j> # qr_{ij} = <q_i, r_j> # qr'_{ij} = qr_{i,idx[i][j]} = qr_{i,|i-j|} qr = F.batch_dot(lhs=q, rhs=r, transpose_b=True) qr = _torch_gather(F, data=qr, idx=idx, axis=-1) # rk_{ij} = <r_i, k_j> # rk'_{ij} = rk_{idx[i][j], j} = rk_{|i-j|, j} rk = F.batch_dot(lhs=r, rhs=k, transpose_b=True) rk = _torch_gather(F, data=rk, idx=idx, axis=-2) # s_{ij} = qr_{i,|i-j|} + rk_{|i-j|,j} s = qr + rk # add relative positional bias to content-based attention score score = score + s score = self._apply_mask(F, score, mask) score = score / (math.sqrt(self.d_head) * self.temperature) score = F.softmax(score, axis=-1) score = self.dropout(score) return score def _compute_attn_output(self, F, score: Tensor, v: Tensor) -> Tensor: v = F.batch_dot(score, v) v = self._merge_head(F, v) v = self.out_proj(v) return v def hybrid_forward( self, F, x: Tensor, mask: Tensor, _ctt_bias_weight: Optional[Tensor] = None, _pos_bias_weight: Optional[Tensor] = None, ) -> Tensor: q, k, v = self._compute_qkv(F, x) score = self._compute_attn_score( F, q, k, mask, _ctt_bias_weight, _pos_bias_weight ) v = self._compute_attn_output(F, score, v) return v class PosFFN(HybridBlock): @validated() def __init__( self, d_model: int, d_hidden: int, activation: str = "softrelu", pre_ln: bool = True, dropout: float = 0.0, **kwargs, ): super(PosFFN, self).__init__(**kwargs) self.pre_ln = pre_ln with self.name_scope(): self.linear1 = nn.Dense( units=d_hidden, use_bias=True, flatten=False, activation=activation, weight_initializer=init.Xavier(), ) self.dropout = nn.Dropout(dropout) self.linear2 = nn.Dense( units=d_model, use_bias=True, flatten=False, weight_initializer=init.Xavier(), ) self.lnorm = nn.LayerNorm(axis=-1) def hybrid_forward(self, F, x: Tensor) -> Tensor: if self.pre_ln: y = self.lnorm(x) else: y = x y = self.linear1(y) y = self.dropout(y) y = self.linear2(y) y = y + x if not self.pre_ln: y = self.lnorm(y) return y

35.191244

95

0.521443

b9f14e24661a562864244ce1401a8c6a7371104d

2,287

py

Python

logic/reserved/save_row.py

rdevost/pymixup

9004fbdc7939033014b0eefa669056014647a0c8

[ "MIT" ]

5

2017-01-02T15:12:31.000Z

2021-09-03T15:51:39.000Z

logic/reserved/save_row.py

rdevost/pymixup

9004fbdc7939033014b0eefa669056014647a0c8

[ "MIT" ]

null

logic/reserved/save_row.py

rdevost/pymixup

9004fbdc7939033014b0eefa669056014647a0c8

[ "MIT" ]

1

2021-09-03T15:51:41.000Z

from peewee import IntegrityError, DoesNotExist from data.base import obfuscatedb from data.save_row import save_row from logic.reserved import reserved_prefixes def save_reserved(reserved_row, **kwargs): """Save a Reserved row.""" from logic.identifier import get_identifier_by_name, \ get_identifier_by_obfuscated, save_identifier, get_identifier try: for name, value in kwargs.iteritems(): getattr(reserved_row, name) # Make sure column exists setattr(reserved_row, name, value) except AttributeError: raise with obfuscatedb.atomic(): try: reserved_id = save_row(reserved_row, **kwargs) except IntegrityError: raise #################### # Update identifiers #################### if reserved_row.name[0] in [reserved_prefixes.reserved_dir, reserved_prefixes.non_obfuscated_dir]: identifier_name = reserved_row.name[1:] elif reserved_row.name[0] in [reserved_prefixes.reserved_file, reserved_prefixes.non_obfuscated_file]: identifier_name = reserved_row.name[1:-3] else: identifier_name = reserved_row.name # Reassign identifier obfuscated name if it exists for another name try: identifier_row = get_identifier_by_obfuscated(identifier_name) except DoesNotExist: pass else: if identifier_row.name != identifier_name: identifier_row.obfuscated_name = None save_identifier(identifier_row) # Unobfuscate name in identifiers try: identifier_row = get_identifier_by_name(identifier_name) except DoesNotExist: identifier_row = get_identifier(None) save_identifier( identifier_row, name=identifier_name, obfuscated_name=identifier_name) else: if identifier_row.obfuscated_name != identifier_name: save_identifier( identifier_row, name=identifier_name, obfuscated_name=identifier_name) return reserved_id

35.184615

77

0.609532

f01e4e692f228accacfe185a6c2aef5402234333

10,821

py

Python

tensorflow_transform/coders/example_proto_coder_test.py

devidipak/transform

56efe455b29fa3d0a29ce2f8872adc41ed6012c3

[ "Apache-2.0" ]

2

2021-07-19T02:00:30.000Z

2021-07-19T02:00:37.000Z

tensorflow_transform/coders/example_proto_coder_test.py

devidipak/transform

56efe455b29fa3d0a29ce2f8872adc41ed6012c3

[ "Apache-2.0" ]

null

tensorflow_transform/coders/example_proto_coder_test.py

devidipak/transform

56efe455b29fa3d0a29ce2f8872adc41ed6012c3

[ "Apache-2.0" ]

null

# coding=utf-8 # # Copyright 2017 Google Inc. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Tensorflow-transform ExampleProtoCoder tests.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import os import pickle import sys # Note that this needs to happen before any non-python imports, so we do it # pretty early on. if any(arg == '--proto_implementation_type=python' for arg in sys.argv): os.environ['PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION'] = 'python' elif any(arg == '--proto_implementation_type=cpp' for arg in sys.argv): os.environ['PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION'] = 'cpp' elif any(arg.startswith('--proto_implementation_type') for arg in sys.argv): raise ValueError('Unexpected value for --proto_implementation_type') import numpy as np import tensorflow as tf from tensorflow_transform import test_case from tensorflow_transform.coders import example_proto_coder from tensorflow_transform.tf_metadata import dataset_schema from google.protobuf.internal import api_implementation from google.protobuf import text_format _FEATURE_SPEC = { 'scalar_feature_1': tf.FixedLenFeature([], tf.int64), 'scalar_feature_2': tf.FixedLenFeature([], tf.int64), 'scalar_feature_3': tf.FixedLenFeature([], tf.float32), 'varlen_feature_1': tf.VarLenFeature(tf.float32), 'varlen_feature_2': tf.VarLenFeature(tf.string), '1d_vector_feature': tf.FixedLenFeature([1], tf.string), '2d_vector_feature': tf.FixedLenFeature([2, 2], tf.float32), 'sparse_feature': tf.SparseFeature('idx', 'value', tf.float32, 10), } _ENCODE_DECODE_CASES = [ dict( testcase_name='multiple_columns', feature_spec=_FEATURE_SPEC, ascii_proto="""\ features { feature { key: "scalar_feature_1" value { int64_list { value: [ 12 ] } } } feature { key: "varlen_feature_1" value { float_list { value: [ 89.0 ] } } } feature { key: "scalar_feature_2" value { int64_list { value: [ 12 ] } } } feature { key: "scalar_feature_3" value { float_list { value: [ 1.0 ] } } } feature { key: "1d_vector_feature" value { bytes_list { value: [ 'this is a ,text' ] } } } feature { key: "2d_vector_feature" value { float_list { value: [ 1.0, 2.0, 3.0, 4.0 ] } } } feature { key: "varlen_feature_2" value { bytes_list { value: [ 'female' ] } } } feature { key: "value" value { float_list { value: [ 12.0, 20.0 ] } } } feature { key: "idx" value { int64_list { value: [ 1, 4 ] } } } }""", instance={ 'scalar_feature_1': 12, 'scalar_feature_2': 12, 'scalar_feature_3': 1.0, 'varlen_feature_1': [89.0], '1d_vector_feature': [b'this is a ,text'], '2d_vector_feature': [[1.0, 2.0], [3.0, 4.0]], 'varlen_feature_2': [b'female'], 'sparse_feature': ([1, 4], [12.0, 20.0]) }), dict( testcase_name='multiple_columns_ndarray', feature_spec=_FEATURE_SPEC, ascii_proto="""\ features { feature { key: "scalar_feature_1" value { int64_list { value: [ 13 ] } } } feature { key: "varlen_feature_1" value { float_list { } } } feature { key: "scalar_feature_2" value { int64_list { value: [ 214 ] } } } feature { key: "scalar_feature_3" value { float_list { value: [ 2.0 ] } } } feature { key: "1d_vector_feature" value { bytes_list { value: [ 'this is another ,text' ] } } } feature { key: "2d_vector_feature" value { float_list { value: [ 9.0, 8.0, 7.0, 6.0 ] } } } feature { key: "varlen_feature_2" value { bytes_list { value: [ 'male' ] } } } feature { key: "value" value { float_list { value: [ 13.0, 21.0 ] } } } feature { key: "idx" value { int64_list { value: [ 2, 5 ] } } } }""", instance={ 'scalar_feature_1': np.array(13), 'scalar_feature_2': np.int32(214), 'scalar_feature_3': np.array(2.0), 'varlen_feature_1': np.array([]), '1d_vector_feature': np.array([b'this is another ,text']), '2d_vector_feature': np.array([[9.0, 8.0], [7.0, 6.0]]), 'varlen_feature_2': np.array([b'male']), 'sparse_feature': (np.array([2, 5]), np.array([13.0, 21.0])) }), ] _ENCODE_ONLY_CASES = [ dict( testcase_name='unicode', feature_spec={'unicode_feature': tf.FixedLenFeature([], tf.string)}, ascii_proto="""\ features { feature { key: "unicode_feature" value { bytes_list { value: [ "Hello κόσμε" ] } } } }""", instance={'unicode_feature': u'Hello κόσμε'}), ] _DECODE_ONLY_CASES = [ ] _DECODE_ERROR_CASES = [ dict( testcase_name='to_few_values', feature_spec={ '2d_vector_feature': tf.FixedLenFeature([2, 2], tf.int64), }, ascii_proto="""\ features { feature { key: "2d_vector_feature" value { int64_list { value: [ 1, 2, 3 ] } } } }""", error_msg='got wrong number of values'), ] _ENCODE_ERROR_CASES = [ dict( testcase_name='to_few_values', feature_spec={ '2d_vector_feature': tf.FixedLenFeature([2, 2], tf.int64), }, instance={'2d_vector_feature': [1, 2, 3]}, error_msg='got wrong number of values'), ] def _ascii_to_example(ascii_proto): return text_format.Merge(ascii_proto, tf.train.Example()) def _ascii_to_binary(ascii_proto): return _ascii_to_example(ascii_proto).SerializeToString() def _binary_to_example(serialized_proto): return tf.train.Example.FromString(serialized_proto) class ExampleProtoCoderTest(test_case.TransformTestCase): def assertSerializedProtosEqual(self, a, b): np.testing.assert_equal(_binary_to_example(a), _binary_to_example(b)) @test_case.named_parameters(*(_ENCODE_DECODE_CASES + _DECODE_ONLY_CASES)) def test_decode(self, feature_spec, ascii_proto, instance, **kwargs): schema = dataset_schema.from_feature_spec(feature_spec) coder = example_proto_coder.ExampleProtoCoder(schema, **kwargs) serialized_proto = _ascii_to_binary(ascii_proto) np.testing.assert_equal(coder.decode(serialized_proto), instance) @test_case.named_parameters(*(_ENCODE_DECODE_CASES + _DECODE_ONLY_CASES)) def test_decode_non_serialized(self, feature_spec, ascii_proto, instance, **kwargs): schema = dataset_schema.from_feature_spec(feature_spec) coder = example_proto_coder.ExampleProtoCoder( schema, serialized=False, **kwargs) proto = _ascii_to_example(ascii_proto) np.testing.assert_equal(coder.decode(proto), instance) @test_case.named_parameters(*(_ENCODE_DECODE_CASES + _ENCODE_ONLY_CASES)) def test_encode(self, feature_spec, ascii_proto, instance, **kwargs): schema = dataset_schema.from_feature_spec(feature_spec) coder = example_proto_coder.ExampleProtoCoder(schema, **kwargs) serialized_proto = _ascii_to_binary(ascii_proto) self.assertSerializedProtosEqual(coder.encode(instance), serialized_proto) @test_case.named_parameters(*(_ENCODE_DECODE_CASES + _ENCODE_ONLY_CASES)) def test_encode_non_serialized(self, feature_spec, ascii_proto, instance, **kwargs): schema = dataset_schema.from_feature_spec(feature_spec) coder = example_proto_coder.ExampleProtoCoder( schema, serialized=False, **kwargs) proto = _ascii_to_example(ascii_proto) np.testing.assert_equal(coder.encode(instance), proto) @test_case.named_parameters(*_DECODE_ERROR_CASES) def test_decode_error(self, feature_spec, ascii_proto, error_msg, error_type=ValueError, **kwargs): schema = dataset_schema.from_feature_spec(feature_spec) coder = example_proto_coder.ExampleProtoCoder(schema, **kwargs) serialized_proto = _ascii_to_binary(ascii_proto) with self.assertRaisesRegexp(error_type, error_msg): coder.decode(serialized_proto) @test_case.named_parameters(*_ENCODE_ERROR_CASES) def test_encode_error(self, feature_spec, instance, error_msg, error_type=ValueError, **kwargs): schema = dataset_schema.from_feature_spec(feature_spec) coder = example_proto_coder.ExampleProtoCoder(schema, **kwargs) with self.assertRaisesRegexp(error_type, error_msg): coder.encode(instance) def test_example_proto_coder_picklable(self): schema = dataset_schema.from_feature_spec(_FEATURE_SPEC) coder = example_proto_coder.ExampleProtoCoder(schema) ascii_proto = """ features { feature { key: "scalar_feature_1" value { int64_list { value: [ 12 ] } } } feature { key: "varlen_feature_1" value { float_list { value: [ 89.0 ] } } } feature { key: "scalar_feature_2" value { int64_list { value: [ 12 ] } } } feature { key: "scalar_feature_3" value { float_list { value: [ 2.0 ] } } } feature { key: "1d_vector_feature" value { bytes_list { value: [ 'this is a ,text' ] } } } feature { key: "2d_vector_feature" value { float_list { value: [ 1.0, 2.0, 3.0, 4.0 ] } } } feature { key: "varlen_feature_2" value { bytes_list { value: [ 'female' ] } } } feature { key: "value" value { float_list { value: [ 12.0, 20.0 ] } } } feature { key: "idx" value { int64_list { value: [ 1, 4 ] } } } } """ instance = { 'scalar_feature_1': 12, 'scalar_feature_2': 12, 'scalar_feature_3': 2.0, 'varlen_feature_1': [89.0], '1d_vector_feature': [b'this is a ,text'], '2d_vector_feature': [[1.0, 2.0], [3.0, 4.0]], 'varlen_feature_2': [b'female'], 'sparse_feature': ([1, 4], [12.0, 20.0]) } serialized_proto = _ascii_to_binary(ascii_proto) for _ in range(2): coder = pickle.loads(pickle.dumps(coder)) np.testing.assert_equal(coder.decode(serialized_proto), instance) self.assertSerializedProtosEqual(coder.encode(instance), serialized_proto) if __name__ == '__main__': test_case.main()

39.349091

86

0.651049

abe3a1d1717f7c2c95e1e56375fac48249fea210

23,334

py

Python

Main.py

dydx-git/Calcy

fba4510220599a1a148dad15da0cbb508905034c

[ "MIT" ]

null

Main.py

dydx-git/Calcy

fba4510220599a1a148dad15da0cbb508905034c

[ "MIT" ]

null

Main.py

dydx-git/Calcy

fba4510220599a1a148dad15da0cbb508905034c

[ "MIT" ]

null

#<--------------------------------------------------------------------------------------IMPORTS------------------------------------------------------------------------> from tkinter import * import add, subtract, multiply, divide, sin, cos, tan, asin, acos, atan, sinh, cosh, tanh, asinh, acosh, atanh, log, ln, x2, xy, e, fact, x3, sqrt, cubert, mod #<---------------------------------------------------------------------------------------GLOBALS------------------------------------------------------------------------> root= Tk() root.title('Calcy') num1=StringVar() ans = None common = '' condition = '' isOpAllowed = False listOfNumbers = ['1', '2', '3', '4', '5', '6', '7', '8', '9'] isDegreeOn = True mem = None num1.set("Start Calculation") isExtraOff = False finalans = None #<---------------------------------------------------------------------------------------ENTRY MANAGEMENT----------------------------------------------------------------> def Clear(): global ans, common, isOpAllowed, condition ans = None condition = '' common = '' isOpAllowed = False num1.set('0') history = '' def Backspace(event): global common, ans if common=='': pass else: common = str(common)[:-1] num1.set(common) #<---------------------------------------------------------------------------------------SETTERS-------------------------------------------------------------------------> def SetAns(): global ans, finalans print('this is ans:', ans) print(common) try: i = str(ans).index('.') tempans = str(ans)[-1: i: -1] print('this is ans:', ans) if '0.' in str(ans): num1.set(round(ans,12)) else: for digit in tempans: if digit in listOfNumbers: num1.set(round(ans,12)) elif tempans == '0': finalans = str(ans)[:i] num1.set(finalans) else: finalans = str(ans)[:i] num1.set(finalans) except: pass def SetAdd(event): global condition if condition == '': GetAdd() else: decider[condition]() condition = '+' SetAns() global isOpAllowed isOpAllowed = True def SetSubtract(event): global condition, isOpAllowed if condition == '': GetSubtract() else: decider[condition]() condition = '-' SetAns() isOpAllowed = True def SetMultiply(event): global condition, isOpAllowed if condition == '': GetMultiply() else: decider[condition]() condition = '*' SetAns() isOpAllowed = True def SetDivide(event): global condition, isOpAllowed if condition == '': GetDivide() else: decider[condition]() condition = '/' SetAns() isOpAllowed = True def SetMod(): global condition, isOpAllowed if condition == '': GetMod() else: decider[condition]() condition = 'mod' SetAns() isOpAllowed = True def SetSin(): GetSin() SetAns() def SetCos(): GetCos() SetAns() def SetTan(): GetTan() SetAns() def SetASin(): GetASin() SetAns() def SetACos(): GetACos() SetAns() def SetATan(): GetATan() SetAns() def SetSinh(): GetSinh() SetAns() def SetCosh(): GetCosh() SetAns() def SetTanh(): GetTanh() SetAns() def SetASinh(): GetASinh() SetAns() def SetACosh(): GetACosh() SetAns() def SetATanh(): GetATanh() SetAns() def SetLog(event): GetLog() SetAns() def SetLn(event): GetLn() SetAns() def SetX2(event): GetX2() SetAns() def SetX3(): GetX3() SetAns() def SetXy(event): global condition, isOpAllowed if condition == '': GetXy() else: decider[condition]() condition = 'xy' SetAns() isOpAllowed = True SetAns() def SetE(event): GetE() SetAns() def SetSqrt(): GetSqrt() SetAns() def SetCubert(): GetCubert() SetAns() def SetFact(event): GetFact() num1.set(ans) def SetEquals(event): global condition, isOpAllowed, ans decider[condition]() SetAns() condition = '' ans = None common = '' #<---------------------------------------------------------------------------------------GETTERS-----------------------------------------------------------------------> def GetAdd(): global ans, common ans, common = add.Add(ans, common) def GetSubtract(): global ans, common ans, common = subtract.Subtract(ans, common) def GetMultiply(): global ans, common ans, common = multiply.Multiply(ans, common) def GetDivide(): global ans, common ans, common = divide.Divide(ans, common) def GetSin(): global ans, common, isDegreeOn ans, common = sin.Sin(ans,common, isDegreeOn) def GetCos(): global ans, common, isDegreeOn ans, common = cos.Cos(ans,common, isDegreeOn) def GetTan(): global ans, common, isDegreeOn ans, common = tan.Tan(ans, common, isDegreeOn) def GetASin(): global ans, common, isDegreeOn ans, common = asin.ASin(ans,common, isDegreeOn) def GetACos(): global ans, common, isDegreeOn ans, common = acos.ACos(ans,common, isDegreeOn) def GetATan(): global ans, common, isDegreeOn ans, common = atan.ATan(ans,common, isDegreeOn) def GetSinh(): global ans, common, isDegreeOn ans, common = sinh.Sinh(ans,common, isDegreeOn) def GetCosh(): global ans, common, isDegreeOn ans, common = cosh.Cosh(ans,common, isDegreeOn) def GetTanh(): global ans, common, isDegreeOn ans, common = tanh.Tanh(ans,common, isDegreeOn) def GetASinh(): global ans, common, isDegreeOn ans, common = asinh.ASinh(ans,common, isDegreeOn) def GetACosh(): global ans, common, isDegreeOn ans, common = acosh.ACosh(ans,common, isDegreeOn) def GetATanh(): global ans, common, isDegreeOn ans, common = atanh.ATanh(ans,common, isDegreeOn) def GetLog(): global ans, common ans, common = log.Log(ans, common) def GetLn(): global ans, common ans, common = ln.Ln(ans, common) def GetX2(): global ans, common ans, common = x2.X2(ans, common) def GetX3(): global ans, common ans, common = x3.X3(ans, common) def GetXy(): global ans, common ans, common = xy.Xy(ans, common) def GetE(): global ans, common ans, common = e.E(ans, common) def GetSqrt(): global ans, common ans, common = sqrt.Sqrt(ans, common) def GetCubert(): global ans, common ans, common = cubert.Cubert(ans, common) def GetFact(): global ans, common ans, common = fact.Fact(ans, common) def GetMod(): global ans, common ans, common = mod.Mod(ans, common) #<--------------------------------------------------------------------------------------MEMORY MANAGER-----------------------------------------------------------------> def MC(): global mem mem = None def MR(): global mem, common if mem != None: common = mem else: pass print('this is MR:', common) num1.set(common) def MS(): global mem, common, ans if common != '': mem = int(common) else: print(ans) mem = str(ans) #<------------------------------------------------------------------------------------DEGREE/RADIAN------------------------------------------------------------------- > def DegRad(): global isDegreeOn if isDegreeOn == False: isDegreeOn = True degRadButton.config(image=imgDeg) elif isDegreeOn == True: isDegreeOn = False degRadButton.config(image=imgRad) #<----------------------------------------------------------------------------------EXTRAS MANAGER----------------------------------------------------------------------> def ActivateExtra(): global isExtraOff if isExtraOff == False: asinButton.grid(row=3,column=0) acosButton.grid(row=4,column=0) atanButton.grid(row=5,column=0) asinhButton.grid(row=7, column=0) acoshButton.grid(row=7, column=1) atanhButton.grid(row=7, column=2) lnButton.grid(row=7, column=3) x3Button.grid(row=2, column = 0) cubertButton.grid(row=2, column=4) modButton.grid(row=8, column=0) hisLabel.grid(row=0, column=2, columnspan=3, pady=(60,0)) xtraButton.config(image=imgXtra2) isExtraOff = True else: acosButton.grid_forget() asinButton.grid_forget() atanButton.grid_forget() acoshButton.grid_forget() asinhButton.grid_forget() atanhButton.grid_forget() lnButton.grid_forget() x3Button.grid_forget() cubertButton.grid_forget() modButton.grid_forget() hisLabel.place(x=10,y=10) xtraButton.config(image=imgXtra1) isExtraOff = False #<---------------------------------------------------------------------------------------ENTRY-------------------------------------------------------------------------> txtDisplay = Entry(root, textvariable = num1, width=17,justify="right", fg='white', borderwidth=0); txtDisplay.config(readonlybackground='#0b486b', font = ("Segoe UI", 26)) txtDisplay.focus(); txtDisplay.config(state='readonly') txtDisplay.grid(columnspan=7,row=0,ipady=6, pady=(0,24)) #<--------------------------------------------------------------------------------------DECORATING BUTTONS-------------------------------------------------------------> img1 = PhotoImage(file="assets/nb1.png") img2 = PhotoImage(file="assets/nb2.png") img3 = PhotoImage(file="assets/nb3.png") img4 = PhotoImage(file="assets/nb4.png") img5 = PhotoImage(file="assets/nb5.png") img6 = PhotoImage(file="assets/nb6.png") img7 = PhotoImage(file="assets/nb7.png") img8 = PhotoImage(file="assets/nb8.png") img9 = PhotoImage(file="assets/nb9.png") imgPlus = PhotoImage(file="assets/+.png") imgMinus = PhotoImage(file="assets/-.png") imgMultiply = PhotoImage(file="assets/x.png") imgDivide = PhotoImage(file="assets/dvd.png") imgMC = PhotoImage(file='assets/MC.png') imgMR = PhotoImage(file='assets/MR.png') imgMS = PhotoImage(file='assets/MS.png') imgBKSPC = PhotoImage(file='assets/backspace.png') imgX2 = PhotoImage(file='assets/x2.png') imgX3 = PhotoImage(file='assets/x3.png') imgxy = PhotoImage(file='assets/xy.png') imgSin = PhotoImage(file='assets/sin.png') imgCos = PhotoImage(file='assets/cos.png') imgTan = PhotoImage(file='assets/tan.png') imgAsin = PhotoImage(file='assets/asin.png') imgAcos = PhotoImage(file='assets/acos.png') imgAtan = PhotoImage(file='assets/atan.png') imgAsinh = PhotoImage(file='assets/asinh.png') imgAcosh = PhotoImage(file='assets/acosh.png') imgAtanh = PhotoImage(file='assets/atanh.png') imgSinh = PhotoImage(file='assets/sinh.png') imgCosh = PhotoImage(file='assets/cosh.png') imgTanh = PhotoImage(file='assets/tanh.png') imgDec = PhotoImage(file='assets/dec.png') img0 = PhotoImage(file='assets/nb0.png') imgDeg = PhotoImage(file='assets/deg.png') imgRad = PhotoImage(file='assets/rad.png') imgLog = PhotoImage(file='assets/log.png') imgLn = PhotoImage(file='assets/ln.png') imgE = PhotoImage(file='assets/e.png') imgPi = PhotoImage(file='assets/pi.png') imgC = PhotoImage(file='assets/c.png') imgFact = PhotoImage(file='assets/fact.png') imgSqrt = PhotoImage(file='assets/sqrt.png') imgCubert = PhotoImage(file='assets/cubert.png') imgPM = PhotoImage(file='assets/PM.png') imgEquals = PhotoImage(file='assets/equals.png') imgXtra1 = PhotoImage(file='assets/blue.png') imgXtra2 = PhotoImage(file='assets/red.png') imgdydx = PhotoImage(file='assets/dydx.png') imgMod = PhotoImage(file='assets/mod.png') dydxLabel = Label(root, image=imgdydx, bg='#232323') dydxLabel.grid(row=8, column=2, columnspan=2) #<---------------------------------------------------------------------------------------DEFINING BUTTONS-------------------------------------------------------------------------> oneButton = Button(root, height = '32', width='32', borderwidth=0, image=img1, bg='#232323', highlightthickness=0,command = lambda: clck('', 1)) twoButton = Button(root, height = '32', width='32', borderwidth=0, image=img2, bg='#232323', highlightthickness=0,command = lambda: clck('', 2)) threeButton = Button(root, height = '32', width='32', borderwidth=0, image=img3, bg='#232323', highlightthickness=0,command = lambda: clck('', 3)) fourButton = Button(root, height = '32', width='32', borderwidth=0, image=img4, bg='#232323', highlightthickness=0,command = lambda: clck('', 4)) sevenButton = Button(root, height = '32', width='32', borderwidth=0, image=img7, bg='#232323', highlightthickness=0,command = lambda: clck('', 7)) eightButton = Button(root, height = '32', width='32', borderwidth=0, image=img8, bg='#232323', highlightthickness=0,command = lambda: clck('', 8)) nineButton = Button(root, height = '32', width='32', borderwidth=0, image=img9, bg='#232323', highlightthickness=0,command = lambda: clck('', 9)) fiveButton = Button(root, height = '32', width='32', borderwidth=0, image=img5, bg='#232323', highlightthickness=0,command = lambda: clck('', 5)) sixButton = Button(root, height = '32', width='32', borderwidth=0, image=img6, bg='#232323', highlightthickness=0,command = lambda: clck('', 6)) zeroButton = Button(root, height = '32', width='32', borderwidth=0, image=img0, bg='#232323', highlightthickness=0,command = lambda: clck('', 0)) clearButton = Button(root, height = '46', width='46', borderwidth=0, image=imgC, bg='#232323', highlightthickness=0,command=Clear) addButton = Button(root, text="+", height = '32', width='32',borderwidth=0, image=imgPlus, bg='#232323') subButton = Button(root, text="-", height = '32', width='32',borderwidth=0, image=imgMinus, bg='#232323') mcButton = Button(root, text='MC',height = '46', width='67', borderwidth=0, image=imgMC, bg='#232323', highlightthickness=0, command=MC); mrButton = Button(root, text='MR', height = '46', width='67', borderwidth=0, image=imgMR, bg='#232323', highlightthickness=0, command=MR); msButton = Button(root, text='M+', height = '46', width='67',borderwidth=0, image=imgMS, bg='#232323', highlightthickness=0, command=MS); multiplyButton = Button(root, text="*",height = '46', width='46' ,borderwidth=0, image=imgMultiply, bg='#232323') divideButton = Button(root, text="/",height = '46', width='46',borderwidth=0, image=imgDivide, bg='#232323') sinButton = Button(root, text='sin', height = '36', width='36',borderwidth=0, image=imgSin, bg='#232323', highlightthickness=0, command=SetSin); cosButton = Button(root, text='cos', height = '36', width='36',borderwidth=0, image=imgCos, bg='#232323', highlightthickness=0, command=SetCos); tanButton = Button(root, text='tan', height = '36', width='36',borderwidth=0, image=imgTan, bg='#232323', highlightthickness=0, command=SetTan); asinButton = Button(root, text='asin', height = '36', width='36',borderwidth=0, image=imgAsin, bg='#232323', highlightthickness=0, command=SetASin); decButton = Button(root, text='.', height = '36', width='36',borderwidth=0, image=imgDec, bg='#232323', highlightthickness=0, command =lambda: clck('', '.')) degRadButton = Button(root, text='deg', borderwidth=0, bg='#232323', highlightthickness=0, command=DegRad, image=imgDeg) xtraButton = Button(root,borderwidth=0, bg='#232323', highlightthickness=0, command=ActivateExtra, image=imgXtra1); acosButton = Button(root, text='acos', height = '36', width='36',borderwidth=0, image=imgAcos, bg='#232323', highlightthickness=0, command=SetACos); atanButton = Button(root, text='atan', height = '36', width='36',borderwidth=0, image=imgAtan, bg='#232323', highlightthickness=0, command=SetATan); sinhButton = Button(root, text='sinh', height = '36', width='36',borderwidth=0, image=imgSinh, bg='#232323', highlightthickness=0, command=SetSinh); coshButton = Button(root, text='cosh', height = '36', width='36',borderwidth=0, image=imgCosh, bg='#232323', highlightthickness=0, command=SetCosh); tanhButton = Button(root, text='tanh', height = '36', width='36',borderwidth=0, image=imgTanh, bg='#232323', highlightthickness=0, command=SetTanh); asinhButton = Button(root, text='asinh', height = '36', width='36',borderwidth=0, image=imgAsinh, bg='#232323', highlightthickness=0, command=SetASinh); acoshButton = Button(root, text='acosh', height = '36', width='36',borderwidth=0, image=imgAcosh, bg='#232323', highlightthickness=0, command=SetACosh); atanhButton = Button(root, text='atanh', height = '36', width='36',borderwidth=0, image=imgAtanh, bg='#232323', highlightthickness=0, command=SetATanh); logButton = Button(root, text='log', height = '36', width='36',borderwidth=0, image=imgLog, bg='#232323', highlightthickness=0); lnButton = Button(root, text='log', height = '36', width='36',borderwidth=0, image=imgLn, bg='#232323', highlightthickness=0); backspaceButton = Button(root, width = '46', height = '46', image = imgBKSPC, bg = '#232323',borderwidth=0, highlightthickness=0) x2Button = Button(root, width = '27', height = '29', image = imgX2, bg = '#232323',borderwidth=0, highlightthickness=0) x3Button = Button(root, width = '27', height = '29', image = imgX3, bg = '#232323',borderwidth=0, highlightthickness=0, command=SetX3) xyButton = Button(root, width = '27', height = '29', image = imgxy, bg = '#232323',borderwidth=0, highlightthickness=0) eButton = Button(root, width = '27', height = '29', image = imgE, bg = '#232323',borderwidth=0, highlightthickness=0) piButton = Button(root, width = '27', height = '29', image = imgPi, bg = '#232323',borderwidth=0, highlightthickness=0, command= lambda: clck('', 3.14159265358)) factButton = Button(root, width = '36', height = '36', image = imgFact, bg = '#232323',borderwidth=0, highlightthickness=0) sqrtButton = Button(root, width = '36', height = '36', image = imgSqrt, bg = '#232323',borderwidth=0, highlightthickness=0, command=SetSqrt) cubertButton = Button(root, width = '36', height = '36', image = imgCubert, bg = '#232323',borderwidth=0, highlightthickness=0, command=SetCubert) equalsButton = Button(root, width = '36', height = '72', image = imgEquals, bg = '#232323',borderwidth=0, highlightthickness=0) pmButton = Button(root, width = '36', height = '36', image = imgPM, bg = '#232323',borderwidth=0, highlightthickness=0, command= lambda: clck('', '-')) modButton = Button(root, width = '36', height = '36', image = imgMod, bg = '#232323',borderwidth=0, highlightthickness=0, command= SetMod) #<---------------------------------------------------------------------------------------BUTTON PLACEMENTS--------------------------------------------------------------------> mcButton.grid(row = 1, column = 0) mrButton.grid(row = 1, column = 1) msButton.grid(row = 1, column = 2) clearButton.grid(row = 1, column = 3) backspaceButton.grid(row=1, column = 4) x2Button.grid(row=2, column = 0, ipady=12) eButton.grid(row=2, column = 1, ipady=12) xyButton.grid(row=2, column = 3, ipady=12) sinButton.grid(row=3,column=0) cosButton.grid(row=4,column=0) tanButton.grid(row=5, column=0) xtraButton.grid(row=6, column=0) sevenButton.grid(row=3, column=1, ipady=8) eightButton.grid(row=3, column=2) nineButton.grid(row=3, column=3) sixButton.grid(row=4, column=3, ipady=12) fiveButton.grid(row=4, column=2) fourButton.grid(row=4, column=1) threeButton.grid(row=5, column=3, ipady=12) twoButton.grid(row=5, column=2) oneButton.grid(row=5, column=1) decButton.grid(row=6, column=1) zeroButton.grid(row=6, column=2) degRadButton.grid(row=6, column=3) sinhButton.grid(row=7, column=0) coshButton.grid(row=7, column=1) tanhButton.grid(row=7, column=2, ipady=12) logButton.grid(row=7, column=3) piButton.grid(row=2, column =2) addButton.grid(row=3, column=4) subButton.grid(row=4, column=4) multiplyButton.grid(row=5, column=4) divideButton.grid(row=6, column=4) factButton.grid(row=8, column=0, ipady=8) sqrtButton.grid(row=2, column=4) pmButton.grid(row=8, column= 1) equalsButton.grid(row=7, column=4, rowspan=2) #<---------------------------------------------------------------------------------------CLICK/DECIDER------------------------------------------------------------------------> def clck (event, number): global condition global isOpAllowed global ans print(isOpAllowed) print('Condition', condition) global common print(number) if '-' in common: print('this is common', common) common = list(common) common[0] = '' common = ''.join(common) num1.set(common) elif number == '-': common = str(number)+ common num1.set(common) else: common+= str(number) if common[0] == '.': common = '0' + common num1.set(common) elif common.count('.')>1 : num1.set('Not allowed') common = '' else: num1.set(common) decider = {'+': GetAdd, '-': GetSubtract, '*':GetMultiply, '/':GetDivide, 'xy': GetXy, 'mod': GetMod} #<---------------------------------------------------------------------------------------KEYBOARD BUTTON BINDERS------------------------------------------------------------------> root.bind("1", lambda event, arg = 1 : clck(event, arg)) root.bind("2", lambda event, arg = 2 : clck(event, arg)) root.bind("3", lambda event, arg = 3 : clck(event, arg)) root.bind("4", lambda event, arg = 4 : clck(event, arg)) root.bind("5", lambda event, arg = 5 : clck(event, arg)) root.bind("6", lambda event, arg = 6 : clck(event, arg)) root.bind("7", lambda event, arg = 7 : clck(event, arg)) root.bind("8", lambda event, arg = 8 : clck(event, arg)) root.bind("9", lambda event, arg = 9 : clck(event, arg)) root.bind("0", lambda event, arg = 0 : clck(event, arg)) root.bind("<plus>", SetAdd) root.bind("<minus>", SetSubtract) root.bind("<asterisk>", SetMultiply) root.bind("<slash>", SetDivide) root.bind("<BackSpace>", Backspace) root.bind("<period>", lambda event, arg = '.' : clck(event, arg)) root.bind("<Return>", SetEquals) #<--------------------------------------------------------------------------------------MOUSE BUTTON BINDERS------------------------------------------------------------------> addButton.bind("<Button-1>", SetAdd) subButton.bind("<Button-1>", SetSubtract) multiplyButton.bind("<Button-1>", SetMultiply) divideButton.bind("<Button-1>", SetDivide) logButton.bind("<Button-1>", SetLog) lnButton.bind("<Button-1>", SetLn) x2Button.bind("<Button-1>", SetX2) eButton.bind("<Button-1>", SetE) backspaceButton.bind("<Button-1>", Backspace) factButton.bind("<Button-1>", SetFact) xyButton.bind("<Button-1>", SetXy) equalsButton.bind("<Button-1>", SetEquals) root.configure(background='#232323') root.mainloop()

39.085427

180

0.577955

f6eff9e897288c7563a293b83e2a0c82907ad90e

632

py

Python

jass/mongo_utils.py

crim-ca/JASS

8a2d0bdd4cb50021c890fbb3059e75fa6f9adebb

[ "MIT", "Python-2.0", "BSD-2-Clause", "Apache-2.0" ]

1

2017-01-18T13:05:21.000Z

jass/mongo_utils.py

crim-ca/JASS

8a2d0bdd4cb50021c890fbb3059e75fa6f9adebb

[ "MIT", "Python-2.0", "BSD-2-Clause", "Apache-2.0" ]

null

jass/mongo_utils.py

crim-ca/JASS

8a2d0bdd4cb50021c890fbb3059e75fa6f9adebb

[ "MIT", "Python-2.0", "BSD-2-Clause", "Apache-2.0" ]

1

2017-05-18T18:38:53.000Z

#!/usr/bin/env python # coding:utf-8 """ Various utilities for mongoDB usage. """ from bson.objectid import ObjectId def changeDocIdToString(mongoDoc): """ Changes the _id to string. Will crash if mongoDoc is not a valid Mongo Document """ if(mongoDoc is not None): mongoDoc['_id'] = str(mongoDoc['_id']) def changeDocIdToMongoId(jsonDoc): """ Changes the _id to ObjectId. Will crash if jsonDoc is not a simple JSON object with _id field """ if(jsonDoc is not None): jsonDoc['_id'] = ObjectId(jsonDoc['_id']) def isObjectId(strId): return ObjectId.is_valid(strId)

20.387097

68

0.664557

051ca1df8e3e82a4bdd0c9d489e4643cfa3fbb5b

4,386

py

Python

generate.py

CodeProcessor/python-project-template

5dd9c461aca011458d2f52ccd0b67f3ffa90254b

[ "MIT" ]

null

generate.py

CodeProcessor/python-project-template

5dd9c461aca011458d2f52ccd0b67f3ffa90254b

[ "MIT" ]

null

generate.py

CodeProcessor/python-project-template

5dd9c461aca011458d2f52ccd0b67f3ffa90254b

[ "MIT" ]

null

""" Copyright (C) CUBE Content Governance Global Limited - All Rights Reserved Unauthorized copying of this file, via any medium is strictly prohibited Proprietary and confidential Written by Dulan Jayasuriya <dulan.jayasuriya@cube.global>, 11 February 2022 """ import json import os.path from datetime import datetime class Generator: def __init__(self, config_path='config.json'): self.configs = json.load(open(config_path)) self.project_name = self.configs['project_name'] self.library_name = self.configs['library_name'] def write_content(self, file_path, content): """ Write content to file :param file_path: :param content: :return: """ _dir = os.path.dirname(file_path) if not os.path.exists(_dir): os.makedirs(_dir) with open(file_path, 'w') as f: f.write(content) def get_banner(self): banner = f"\"\"\"\n\ Copyright (C) {self.configs['library_author_company']} - All Rights Reserved \n\ Unauthorized copying of this file, via any medium is strictly prohibited \n\ Proprietary and confidential \n\ Written by {self.configs['library_author']} <{self.configs['library_author_email']}>, {datetime.now().strftime('%d %B %Y')} \n\ \"\"\"\n\n" return banner def get_setup_content(self): content = f"import {self.library_name} \n\ from setuptools import setup, find_packages \n\ \n\ \n\ setup(\n\ name='{self.library_name}', \n\ version={self.library_name}.__version__, \n\ description='{self.configs['library_description']}', \n\ url='{self.configs['library_author_url']}', \n\ author='{self.configs['library_author']}', \n\ author_email='{self.configs['library_author_email']}', \n\ license='{self.configs['library_license']}', \n\ packages=find_packages(), \n\ zip_safe=False\n\ )\n" return content def generate(self): """ Main function :return: """ self.write_content(os.path.join(self.project_name, 'README.md'), f"#{self.project_name}") self.write_content(os.path.join(self.project_name, 'LICENSE'), f"#{self.project_name}") self.write_content(os.path.join(self.project_name, self.library_name, '__init__.py'), self.get_banner() + f"__version__ = '{self.configs['library_version']}'\n") self.write_content(os.path.join(self.project_name, 'setup.py'), self.get_banner() + self.get_setup_content()) self.write_content(os.path.join(self.project_name, 'requirements.txt'), "") self.write_content(os.path.join(self.project_name, "tests", 'README.md'), "#Package integration and unit tests.") self.write_content(os.path.join(self.project_name, "docs", 'README.md'), "#Package reference documentation.") if self.configs["add_sample_data"]: self.write_content(os.path.join(self.project_name, self.library_name, 'hello.py'), self.get_banner() + self.get_sample_hello()) self.write_content(os.path.join(self.project_name, "tests", 'test_hello.py'), self.get_banner() + self.get_sample_test()) def get_sample_hello(self): content = "class Hello:\n" \ " def __init__(self, name):\n" \ " self.name = name\n" \ " \n" \ " def say_hello(self):\n" \ " return f'Hello {self.name}'\n" \ " " return content def get_sample_test(self): content = f"from {self.library_name}.hello import Hello \n\ \n\ \n\ def test_say_hello():\n\ h = Hello('{self.configs['library_author']}')\n\ assert h.say_hello() == 'Hello {self.configs['library_author']}'\n\ " return content def clean(self): """ Clean the project :return: """ pass if __name__ == '__main__': import argparse parser = argparse.ArgumentParser(description='Generate a new project') parser.add_argument('--config_file', help='Path to the config file', default='config.json') args = parser.parse_args() # get input filename from user # generate the project gen = Generator(args.config_file) gen.generate() print('Generated!')

36.247934

127

0.618103

f58d2ccac4af29972b878702f522bd7071a45924

2,106

py

Python

generateDates.py

Husterknupp/2020-oster-squash

43e8742c89ad1225119e8d2c4d2dba6a2914dd0d

[ "MIT" ]

1

2020-03-06T16:06:00.000Z

generateDates.py

Husterknupp/2020-oster-squash

43e8742c89ad1225119e8d2c4d2dba6a2914dd0d

[ "MIT" ]

1

2021-06-10T18:36:46.000Z

generateDates.py

Husterknupp/2020-oster-squash

43e8742c89ad1225119e8d2c4d2dba6a2914dd0d

[ "MIT" ]

1

2020-03-05T23:38:21.000Z

# 2017-09-14T03:21:47.070-04:00 # VERSION 3 days = {} for day in range(7, 12): timeFrames = [] for hour in range(14, 18): timeFrames.append(hour) days[day] = timeFrames for day, hours in days.items(): if day < 10: print(f'{{day: "2020-04-0{day}", hours: {hours}}}') else: print(f'{{day: "2020-04-{day}", hours: {hours}}}') """ {day: "2020-04-07", hours: [14, 15, 16, 17]} {day: "2020-04-08", hours: [14, 15, 16, 17]} {day: "2020-04-09", hours: [14, 15, 16, 17]} {day: "2020-04-10", hours: [14, 15, 16, 17]} {day: "2020-04-11", hours: [14, 15, 16, 17]} """ # VERSION 2 # for day in range(7, 12): # for hour in range(14, 18): # if day < 10: # print(f'{{day: "2020-04-0{day}", hour: "{hour}"}}') # else: # print(f'{{day: "2020-04-{day}", hour: "{hour}"}}') """ {day: "2020-04-07", hour: "14"} {day: "2020-04-07", hour: "15"} {day: "2020-04-07", hour: "16"} {day: "2020-04-07", hour: "17"} {day: "2020-04-08", hour: "14"} {day: "2020-04-08", hour: "15"} {day: "2020-04-08", hour: "16"} {day: "2020-04-08", hour: "17"} {day: "2020-04-09", hour: "14"} {day: "2020-04-09", hour: "15"} {day: "2020-04-09", hour: "16"} {day: "2020-04-09", hour: "17"} {day: "2020-04-10", hour: "14"} {day: "2020-04-10", hour: "15"} {day: "2020-04-10", hour: "16"} {day: "2020-04-10", hour: "17"} {day: "2020-04-11", hour: "14"} {day: "2020-04-11", hour: "15"} {day: "2020-04-11", hour: "16"} {day: "2020-04-11", hour: "17"} """ """ 2020-04-07T14:00:00.000+01:00 2020-04-07T15:00:00.000+01:00 2020-04-07T16:00:00.000+01:00 2020-04-07T17:00:00.000+01:00 2020-04-08T14:00:00.000+01:00 2020-04-08T15:00:00.000+01:00 2020-04-08T16:00:00.000+01:00 2020-04-08T17:00:00.000+01:00 2020-04-09T14:00:00.000+01:00 2020-04-09T15:00:00.000+01:00 2020-04-09T16:00:00.000+01:00 2020-04-09T17:00:00.000+01:00 2020-04-10T14:00:00.000+01:00 2020-04-10T15:00:00.000+01:00 2020-04-10T16:00:00.000+01:00 2020-04-10T17:00:00.000+01:00 2020-04-11T14:00:00.000+01:00 2020-04-11T15:00:00.000+01:00 2020-04-11T16:00:00.000+01:00 2020-04-11T17:00:00.000+01:00 """

26.325

65

0.575024

5b9d1385a38db639b838aaf556603ad2f7af03c9

895

py

Python

lightkit/nn/_protocols.py

borchero/lightkit

725cde3dff1cfbccf78bf10b9e922145a43959ca

[ "MIT" ]

1

2022-01-26T07:58:04.000Z

lightkit/nn/_protocols.py

borchero/lightkit

725cde3dff1cfbccf78bf10b9e922145a43959ca

[ "MIT" ]

null

lightkit/nn/_protocols.py

borchero/lightkit

725cde3dff1cfbccf78bf10b9e922145a43959ca

[ "MIT" ]

null

# pylint: disable=missing-class-docstring,missing-function-docstring from typing import Generic, Iterator, OrderedDict, Protocol, Tuple, Type, TypeVar import torch from torch import nn from lightkit.utils import PathType C = TypeVar("C", covariant=True) M = TypeVar("M", bound="ConfigurableModule") # type: ignore class ConfigurableModule(Protocol, Generic[C]): @property def config(self) -> C: ... @classmethod def load(cls: Type[M], path: PathType) -> M: ... def save(self, path: PathType, compile_model: bool = False) -> None: ... def save_config(self, path: PathType) -> None: ... def named_children(self) -> Iterator[Tuple[str, nn.Module]]: ... def state_dict(self) -> OrderedDict[str, torch.Tensor]: ... def load_state_dict(self, state_dict: OrderedDict[str, torch.Tensor]) -> None: ...

26.323529

82

0.64581

7b5a18350f540be7cf4eaf8053f1a8c17a766f2c

1,120

py

Python

setup.py

kbarnhart/corebreakout

fa8dc7575b330b7c1ce47a35b44deca7856bd05c

[ "MIT" ]

20

2019-12-09T23:56:32.000Z

2021-08-11T18:57:59.000Z

setup.py

kbarnhart/corebreakout

fa8dc7575b330b7c1ce47a35b44deca7856bd05c

[ "MIT" ]

13

2019-11-05T00:13:39.000Z

2021-08-20T19:08:13.000Z

setup.py

kbarnhart/corebreakout

fa8dc7575b330b7c1ce47a35b44deca7856bd05c

[ "MIT" ]

12

2019-12-12T17:35:44.000Z

2021-10-05T05:45:49.000Z

#!/usr/bin/env python3 import os from setuptools import find_packages try: from setuptools import setup except ImportError: raise UserWarning('`distutils` is not supported since you must use Python>=3.6') try: import tensorflow except ImportError: raise UserWarning('`tensorflow` or `tensorflow-gpu` must be installed manually!') PACKAGE_PATH = os.path.abspath(os.path.join(__file__, os.pardir)) # Mostly a duplication of requirements.txt # with the addition of pip-only package `imgaug` install_requires = [ 'numpy<=1.16.4', 'scipy', 'dill', 'Pillow', 'cython', 'matplotlib', 'scikit-image', 'keras>=2.0.8,<=2.2.5', 'opencv-python', 'h5py', 'imgaug', 'IPython[all]' ] setup(name='corebreakout', version='0.2', description='Segmentation and depth-alignment of geological core sample images via Mask-RCNN', url='https://github.com/rgmyr/corebreakout', author='Ross Meyer', author_email='ross.meyer@utexas.edu', packages=find_packages(PACKAGE_PATH), install_requires=install_requires, zip_safe=False )

23.829787

100

0.68125

e4bc7012aeb298951ce85df5077a9bd74ccebb69

213

py

Python

project_system/config/desktop.py

pradyotr/frappe-project-sys

ec4bc793e445ddd8f37f286e30f329369b51bb11

[ "MIT" ]

null

project_system/config/desktop.py

pradyotr/frappe-project-sys

ec4bc793e445ddd8f37f286e30f329369b51bb11

[ "MIT" ]

null

project_system/config/desktop.py

pradyotr/frappe-project-sys

ec4bc793e445ddd8f37f286e30f329369b51bb11

[ "MIT" ]

null

from frappe import _ def get_data(): return [ { "module_name": "Project System", "color": "grey", "icon": "octicon octicon-file-directory", "type": "module", "label": _("Project System") } ]

16.384615

44

0.596244

14be349ed04493c43141190033835920909e1dae

369

py

Python

projects/migrations/0004_rename_name_userprofile_username.py

sling254/msHackthorn

82fb627c9e521e1a24c583b28c63df44db7860d9

[ "MIT" ]

null

projects/migrations/0004_rename_name_userprofile_username.py

sling254/msHackthorn

82fb627c9e521e1a24c583b28c63df44db7860d9

[ "MIT" ]

null

projects/migrations/0004_rename_name_userprofile_username.py

sling254/msHackthorn

82fb627c9e521e1a24c583b28c63df44db7860d9

[ "MIT" ]

null

# Generated by Django 3.2.9 on 2021-12-11 20:48 from django.db import migrations class Migration(migrations.Migration): dependencies = [ ('projects', '0003_auto_20211211_2255'), ] operations = [ migrations.RenameField( model_name='userprofile', old_name='name', new_name='username', ), ]

19.421053

48

0.590786

9f6503581e582d13b54be9ade22689a2e1f432d8

2,120

py

Python

utils/models_utils.py

ermekaitygulov/STIT

93dca8d589b555fa99a5c5438a8517a52d8898c3

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

6

2022-03-11T23:42:12.000Z

2022-03-28T09:39:25.000Z

utils/models_utils.py

bycloudai/STIT-Windows

cadb2a01457bfd1c90bcd8d220587b48e1c2327a

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

null

utils/models_utils.py

bycloudai/STIT-Windows

cadb2a01457bfd1c90bcd8d220587b48e1c2327a

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

null

import copy import pickle from argparse import Namespace import torch from configs import paths_config, global_config from models.e4e.psp import pSp from training.networks import Generator def save_tuned_G(generator, pivots, quads, run_id): generator = copy.deepcopy(generator).cpu() pivots = copy.deepcopy(pivots).cpu() torch.save({'generator': generator, 'pivots': pivots, 'quads': quads}, f'{paths_config.checkpoints_dir}/model_{run_id}.pt') def load_tuned_G(run_id): new_G_path = f'{paths_config.checkpoints_dir}/model_{run_id}.pt' with open(new_G_path, 'rb') as f: checkpoint = torch.load(f) new_G, pivots, quads = checkpoint['generator'], checkpoint['pivots'], checkpoint['quads'] new_G = new_G.float().to(global_config.device).eval().requires_grad_(False) pivots = pivots.float().to(global_config.device) return new_G, pivots, quads def load_old_G(): return load_g(paths_config.stylegan2_ada_ffhq) def load_g(file_path): with open(file_path, 'rb') as f: old_G = pickle.load(f)['G_ema'].to(global_config.device).eval() old_G = old_G.float() return old_G def initialize_e4e_wplus(): ckpt = torch.load(paths_config.e4e, map_location='cpu') opts = ckpt['opts'] opts['checkpoint_path'] = paths_config.e4e opts = Namespace(**opts) e4e_inversion_net = pSp(opts) e4e_inversion_net = e4e_inversion_net.eval().to(global_config.device).requires_grad_(False) return e4e_inversion_net def load_from_pkl_model(tuned): model_state = {'init_args': tuned.init_args, 'init_kwargs': tuned.init_kwargs , 'state_dict': tuned.state_dict()} gen = Generator(*model_state['init_args'], **model_state['init_kwargs']) gen.load_state_dict(model_state['state_dict']) gen = gen.eval().cuda().requires_grad_(False) return gen def load_generators(run_id): tuned, pivots, quads = load_tuned_G(run_id=run_id) original = load_old_G() gen = load_from_pkl_model(tuned) orig_gen = load_from_pkl_model(original) del tuned, original return gen, orig_gen, pivots, quads

31.641791

95

0.713208

1cfc4917b463b78e9058131327b07dce7af2d133

39,145

py

Python

job_search-ad_targeting-search_engine-advanced_use_cases.py

MDRCS/Redis

5315ce48d4a771f14129efe8bd2deefcec465f24

[ "MIT" ]

null

job_search-ad_targeting-search_engine-advanced_use_cases.py

MDRCS/Redis

5315ce48d4a771f14129efe8bd2deefcec465f24

[ "MIT" ]

null

job_search-ad_targeting-search_engine-advanced_use_cases.py

MDRCS/Redis

5315ce48d4a771f14129efe8bd2deefcec465f24

[ "MIT" ]

null

import math import re import unittest import uuid import redis # Search Engine - Use case : AVERAGE_PER_1K = {} # <start id="tokenize-and-index"/> STOP_WORDS = set('''able about across after all almost also am among an and any are as at be because been but by can cannot could dear did do does either else ever every for from get got had has have he her hers him his how however if in into is it its just least let like likely may me might most must my neither no nor not of off often on only or other our own rather said say says she should since so some than that the their them then there these they this tis to too twas us wants was we were what when where which while who whom why will with would yet you your'''.split()) #A WORDS_RE = re.compile("[a-z']{2,}") #B def tokenize(content): words = set() #C for match in WORDS_RE.finditer(content.lower()): #D word = match.group().strip("'") #E if len(word) >= 2: #F words.add(word) #F return words - STOP_WORDS #G def index_document(conn, docid, content): words = tokenize(content) #H pipeline = conn.pipeline(True) for word in words: #I pipeline.sadd('idx:' + word, docid) #I return len(pipeline.execute()) #J # <end id="tokenize-and-index"/> #A We pre-declare our known stop words, these were fetched from http://www.textfixer.com/resources/ #B A regular expression that extracts words as we defined them #C Our Python set of words that we have found in the document content #D Iterate over all of the words in the content #E Strip any leading or trailing single-quote characters #F Keep any words that are still at least 2 characters long #G Return the set of words that remain that are also not stop words #H Get the tokenized words for the content #I Add the documents to the appropriate inverted index entries #J Return the number of unique non-stop words that were added for the document #END # <start id="_1314_14473_9158"/> def _set_common(conn, method, names, ttl=30, execute=True): id = str(uuid.uuid4()) #A pipeline = conn.pipeline(True) if execute else conn #B names = ['idx:' + name for name in names] #C getattr(pipeline, method)('idx:' + id, *names) #D pipeline.expire('idx:' + id, ttl) #E if execute: pipeline.execute() #F return id #G def intersect(conn, items, ttl=30, _execute=True): #H return _set_common(conn, 'sinterstore', items, ttl, _execute) #H def union(conn, items, ttl=30, _execute=True): #I return _set_common(conn, 'sunionstore', items, ttl, _execute) #I def difference(conn, items, ttl=30, _execute=True): #J return _set_common(conn, 'sdiffstore', items, ttl, _execute) #J # <end id="_1314_14473_9158"/> #A Create a new temporary identifier #B Set up a transactional pipeline so that we have consistent results for each individual call #C Add the 'idx:' prefix to our terms #D Set up the call for one of the operations #E Instruct Redis to expire the SET in the future #F Actually execute the operation #G Return the id for the caller to process the results #H Helper function to perform SET intersections #I Helper function to perform SET unions #J Helper function to perform SET differences #END # <start id="parse-query"/> QUERY_RE = re.compile("[+-]?[a-z']{2,}") #A #query = "look at that query behind this result +see +request -code -cql" def parse(query): unwanted = set() #B all = [] #C current = set() #D for match in QUERY_RE.finditer(query.lower()): #E word = match.group() #F print(match) print(word) prefix = word[:1] #F print(prefix) if prefix in '+-': #F word = word[1:] #F else: #F prefix = None #F word = word.strip("'") #G if len(word) < 2 or word in STOP_WORDS: #G continue #G if prefix == '-': #H unwanted.add(word) #H continue #H if current and not prefix: #I all.append(list(current)) #I current = set() #I current.add(word) #J if current: #K all.append(list(current)) #K return all, list(unwanted) #L # <end id="parse-query"/> #A Our regular expression for finding wanted, unwanted, and synonym words #B A unique set of unwanted words #C Our final result of words that we are looking to intersect #D The current unique set of words to consider as synonyms #E Iterate over all words in the search query #F Discover +/- prefixes, if any #G Strip any leading or trailing single quotes, and skip anything that is a stop word #H If the word is unwanted, add it to the unwanted set #I Set up a new synonym set if we have no synonym prefix and we already have words #J Add the current word to the current set #K Add any remaining words to the final intersection #END # <start id="search-query"/> def parse_and_search(conn, query, ttl=30): all, unwanted = parse(query) #A if not all: #B return None #B to_intersect = [] for syn in all: #D if len(syn) > 1: #E to_intersect.append(union(conn, syn, ttl=ttl)) #E else: #F to_intersect.append(syn[0]) #F if len(to_intersect) > 1: #G intersect_result = intersect(conn, to_intersect, ttl=ttl) #G else: #H intersect_result = to_intersect[0] #H if unwanted: #I unwanted.insert(0, intersect_result) #I return difference(conn, unwanted, ttl=ttl) #I return intersect_result #J # <end id="search-query"/> #A Parse the query #B If there are no words in the query that are not stop words, we don't have a result #D Iterate over each list of synonyms #E If the synonym list is more than one word long, then perform the union operation #F Otherwise use the individual word directly #G If we have more than one word/result to intersect, intersect them #H Otherwise use the individual word/result directly #I If we have any unwanted words, remove them from our earlier result and return it #J Otherwise return the intersection result #END # <start id="sorted-searches"/> def search_and_sort(conn, query, id=None, ttl=300, sort="-updated", #A start=0, num=20): #A desc = sort.startswith('-') #B sort = sort.lstrip('-') #B by = "kb:doc:*->" + sort #B alpha = sort not in ('updated', 'id', 'created') #I if id and not conn.expire(id, ttl): #C id = None #C if not id: #D id = parse_and_search(conn, query, ttl=ttl) #D pipeline = conn.pipeline(True) pipeline.scard('idx:' + id) #E pipeline.sort('idx:' + id, by=by, alpha=alpha, #F desc=desc, start=start, num=num) #F results = pipeline.execute() return results[0], results[1], id #G # <end id="sorted-searches"/> #A We will optionally take an previous result id, a way to sort the results, and options for paginating over the results #B Determine which attribute to sort by, and whether to sort ascending or descending #I We need to tell Redis whether we are sorting by a number or alphabetically #C If there was a previous result, try to update its expiration time if it still exists #D Perform the search if we didn't have a past search id, or if our results expired #E Fetch the total number of results #F Sort the result list by the proper column and fetch only those results we want #G Return the number of items in the results, the results we wanted, and the id of the results so that we can fetch them again later #END # <start id="zset_scored_composite"/> def search_and_zsort(conn, query, id=None, ttl=300, update=1, vote=0, #A start=0, num=20, desc=True): #A if id and not conn.expire(id, ttl): #B id = None #B if not id: #C id = parse_and_search(conn, query, ttl=ttl) #C scored_search = { id: 0, #I 'sort:update': update, #D 'sort:votes': vote #D } id = zintersect(conn, scored_search, ttl) #E pipeline = conn.pipeline(True) pipeline.zcard('idx:' + id) #F if desc: #G pipeline.zrevrange('idx:' + id, start, start + num - 1) #G else: #G pipeline.zrange('idx:' + id, start, start + num - 1) #G results = pipeline.execute() return results[0], results[1], id #H # <end id="zset_scored_composite"/> #A Like before, we'll optionally take a previous result id for pagination if the result is still available #B We will refresh the search result's TTL if possible #C If our search result expired, or if this is the first time we've searched, perform the standard SET search #I We use the 'id' key for the intersection, but we don't want it to count towards weights #D Set up the scoring adjustments for balancing update time and votes. Remember: votes can be adjusted to 1, 10, 100, or higher depending on the sorting result desired. #E Intersect using our helper function that we define in listing 7.7 #F Fetch the size of the result ZSET #G Handle fetching a "page" of results #H Return the results and the id for pagination #END # <start id="zset_helpers"/> def _zset_common(conn, method, scores, ttl=30, **kw): id = str(uuid.uuid4()) #A execute = kw.pop('_execute', True) #J pipeline = conn.pipeline(True) if execute else conn #B for key in list(scores.keys()): #C scores['idx:' + key] = scores.pop(key) #C getattr(pipeline, method)('idx:' + id, scores, **kw) #D pipeline.expire('idx:' + id, ttl) #E if execute: #F pipeline.execute() #F return id #G def zintersect(conn, items, ttl=30, **kw): #H return _zset_common(conn, 'zinterstore', dict(items), ttl, **kw) #H def zunion(conn, items, ttl=30, **kw): #I return _zset_common(conn, 'zunionstore', dict(items), ttl, **kw) #I # <end id="zset_helpers"/> #A Create a new temporary identifier #B Set up a transactional pipeline so that we have consistent results for each individual call #C Add the 'idx:' prefix to our inputs #D Set up the call for one of the operations #E Instruct Redis to expire the ZSET in the future #F Actually execute the operation, unless explicitly instructed not to by the caller #G Return the id for the caller to process the results #H Helper function to perform ZSET intersections #I Helper function to perform ZSET unions #J Allow the passing of an argument to determine whether we should defer pipeline execution #END # <start id="string-to-score"/> def string_to_score(string, ignore_case=False): if ignore_case: #A string = string.lower() #A pieces = list(map(ord, string[:6])) #B while len(pieces) < 6: #C pieces.append(-1) #C score = 0 for piece in pieces: #D score = score * 257 + piece + 1 #D return score * 2 + (len(string) > 6) #E # <end id="string-to-score"/> #A We can handle optional case-insensitive indexes easily, so we will #B Convert the first 6 characters of the string into their numeric values, null being 0, tab being 9, capital A being 65, etc. #C For strings that aren't at least 6 characters long, we will add place-holder values to represent that the string was short #D For each value in the converted string values, we add it to the score, taking into consideration that a null is different from a place holder #E Because we have an extra bit, we can also signify whether the string is exactly 6 characters or more, allowing us to differentiate 'robber' and 'robbers', though not 'robbers' and 'robbery' #END def to_char_map(set): out = {} for pos, val in enumerate(sorted(set)): out[val] = pos-1 return out LOWER = to_char_map(set([-1]) | set(range(ord('a'), ord('z')+1))) ALPHA = to_char_map(set(LOWER) | set(range(ord('A'), ord('Z')+1))) LOWER_NUMERIC = to_char_map(set(LOWER) | set(range(ord('0'), ord('9')+1))) ALPHA_NUMERIC = to_char_map(set(LOWER_NUMERIC) | set(ALPHA)) def string_to_score_generic(string, mapping): length = int(52 / math.log(len(mapping), 2)) #A pieces = list(map(ord, string[:length])) #B while len(pieces) < length: #C pieces.append(-1) #C score = 0 for piece in pieces: #D value = mapping[piece] #D score = score * len(mapping) + value + 1 #D return score * 2 + (len(string) > length) #E # <start id="zadd-string"/> def zadd_string(conn, name, *args, **kwargs): pieces = list(args) #A for piece in kwargs.items(): #A pieces.extend(piece) #A a = {} for i, v in enumerate(pieces): if i & 1: #B a[pieces[i-1]] = string_to_score(v) #B return conn.zadd(name, a) #C # <end id="zadd-string"/> #A Combine both types of arguments passed for later modification #B Convert string scores to integer scores #C Call the existing ZADD method #END # Ads Targeting - Use case : # <start id="ecpm_helpers"/> def cpc_to_ecpm(views, clicks, cpc): return 1000. * cpc * clicks / views def cpa_to_ecpm(views, actions, cpa): return 1000. * cpa * actions / views #A # <end id="ecpm_helpers"/> #A Because click through rate is (clicks/views), and action rate is (actions/clicks), when we multiply them together we get (actions/views) #END # <start id="index_ad"/> TO_ECPM = { b'cpc': cpc_to_ecpm, b'cpa': cpa_to_ecpm, b'cpm': lambda *args:args[-1], } def index_ad(conn, id, locations, content, type, value): pipeline = conn.pipeline(True) #A if not isinstance(type, bytes): type = type.encode('latin-1') for location in locations: pipeline.sadd('idx:req:'+location, id) #B words = tokenize(content) for word in words: #H pipeline.zadd('idx:' + word, {id: 0}) #H rvalue = TO_ECPM[type]( #C 1000, AVERAGE_PER_1K.get(type, 1), value) #C pipeline.hset('type:', id, type) #D pipeline.zadd('idx:ad:value:', {id: rvalue}) #E pipeline.zadd('ad:base_value:', {id: value}) #F pipeline.sadd('terms:' + id, *list(words)) #G pipeline.execute() # <end id="index_ad"/> #A Set up the pipeline so that we only need a single round-trip to perform the full index operation #B Add the ad id to all of the relevant location SETs for targeting #H Index the words for the ad #C We will keep a dictionary that stores the average number of clicks or actions per 1000 views on our network, for estimating the performance of new ads #D Record what type of ad this is #E Add the ad's eCPM to a ZSET of all ads #F Add the ad's base value to a ZST of all ads #G Keep a record of the words that could be targeted for the ad #END # <start id="target_ad"/> def target_ads(conn, locations, content): pipeline = conn.pipeline(True) matched_ads, base_ecpm = match_location(pipeline, locations) #A words, targeted_ads = finish_scoring( #B pipeline, matched_ads, base_ecpm, content) #B pipeline.incr('ads:served:') #C pipeline.zrevrange('idx:' + targeted_ads, 0, 0) #D target_id, targeted_ad = pipeline.execute()[-2:] if not targeted_ad: #E return None, None #E ad_id = targeted_ad[0] record_targeting_result(conn, target_id, ad_id, words) #F return target_id, ad_id #G # <end id="target_ad"/> #A Find all ads that fit the location targeting parameter, and their eCPMs #B Finish any bonus scoring based on matching the content #C Get an id that can be used for reporting and recording of this particular ad target #D Fetch the top-eCPM ad id #E If there were no ads that matched the location targeting, return nothing #F Record the results of our targeting efforts as part of our learning process #G Return the target id and the ad id to the caller #END # <start id="location_target"/> def match_location(pipe, locations): required = ['req:' + loc for loc in locations] #A matched_ads = union(pipe, required, ttl=300, _execute=False) #B return matched_ads, zintersect(pipe, #C {matched_ads: 0, 'ad:value:': 1}, _execute=False) #C # <end id="location_target"/> #A Calculate the SET key names for all of the provided locations #B Calculate the SET of matched ads that are valid for this location #C Return the matched ads SET id, as well as the id of the ZSET that includes the base eCPM of all of the matched ads #END # <start id="finish_scoring"/> def finish_scoring(pipe, matched, base, content): bonus_ecpm = {} words = tokenize(content) #A for word in words: word_bonus = zintersect( #B pipe, {matched: 0, word: 1}, _execute=False) #B bonus_ecpm[word_bonus] = 1 #B if bonus_ecpm: minimum = zunion( #C pipe, bonus_ecpm, aggregate='MIN', _execute=False) #C maximum = zunion( #C pipe, bonus_ecpm, aggregate='MAX', _execute=False) #C return words, zunion( #D pipe, {base:1, minimum:.5, maximum:.5}, _execute=False) #D return words, base #E # <end id="finish_scoring"/> #A Tokenize the content for matching against ads #B Find the ads that are location-targeted, which also have one of the words in the content #C Find the minimum and maximum eCPM bonuses for each ad #D Compute the total of the base + half of the minimum eCPM bonus + half of the maximum eCPM bonus #E If there were no words in the content to match against, return just the known eCPM #END # <start id="record_targeting"/> def record_targeting_result(conn, target_id, ad_id, words): pipeline = conn.pipeline(True) terms = conn.smembers(b'terms:' + ad_id) #A matched = list(words & terms) #A if matched: matched_key = 'terms:matched:%s' % target_id pipeline.sadd(matched_key, *matched) #B pipeline.expire(matched_key, 900) #B type = conn.hget('type:', ad_id) #C pipeline.incr('type:%s:views:' % type) #C for word in matched: #D pipeline.zincrby('views:%s' % ad_id, 1, word) #D pipeline.zincrby('views:%s' % ad_id, 1, '') #D if not pipeline.execute()[-1] % 100: #E update_cpms(conn, ad_id) #E # <end id="record_targeting"/> #A Find the words in the content that matched with the words in the ad #B If any words in the ad matched the content, record that information and keep it for 15 minutes #C Keep a per-type count of the number of views that each ad received #D Record view information for each word in the ad, as well as the ad itself #E Every 100th time that the ad was shown, update the ad's eCPM #END # <start id="record_click"/> def record_click(conn, target_id, ad_id, action=False): pipeline = conn.pipeline(True) click_key = 'clicks:%s'%ad_id match_key = 'terms:matched:%s'%target_id type = conn.hget('type:', ad_id) if type == 'cpa': #A pipeline.expire(match_key, 900) #A if action: click_key = 'actions:%s' % ad_id #B if action and type == 'cpa': pipeline.incr('type:%s:actions:' % type) #C else: pipeline.incr('type:%s:clicks:' % type) #C matched = list(conn.smembers(match_key))#D matched.append('') #D for word in matched: #D pipeline.zincrby(click_key, 1, word) #D pipeline.execute() update_cpms(conn, ad_id) #E # <end id="record_click"/> #A If the ad was a CPA ad, refresh the expiration time of the matched terms if it is still available #B Record actions instead of clicks #C Keep a global count of clicks/actions for ads based on the ad type #D Record clicks (or actions) for the ad and for all words that had been targeted in the ad #E Update the eCPM for all words that were seen in the ad #END # <start id="update_cpms"/> def update_cpms(conn, ad_id): pipeline = conn.pipeline(True) pipeline.hget('type:', ad_id) #A pipeline.zscore('ad:base_value:', ad_id) #A pipeline.smembers(b'terms:' + ad_id) #A type, base_value, words = pipeline.execute()#A which = 'clicks' #B if type == 'cpa': #B which = 'actions' #B pipeline.get('type:%s:views:' % type) #C pipeline.get('type:%s:%s' % (type, which)) #C type_views, type_clicks = pipeline.execute() #C AVERAGE_PER_1K[type] = ( #D 1000. * int(type_clicks or '1') / int(type_views or '1')) #D if type == 'cpm': #E return #E view_key = 'views:%s' % ad_id click_key = '%s:%s' % (which, ad_id) to_ecpm = TO_ECPM[type] pipeline.zscore(view_key, '') #G pipeline.zscore(click_key, '') #G ad_views, ad_clicks = pipeline.execute() #G if (ad_clicks or 0) < 1: #N ad_ecpm = conn.zscore('idx:ad:value:', ad_id) #N else: ad_ecpm = to_ecpm(ad_views or 1, ad_clicks or 0, base_value)#H pipeline.zadd('idx:ad:value:', {ad_id: ad_ecpm}) #H for word in words: pipeline.zscore(view_key, word) #I pipeline.zscore(click_key, word) #I views, clicks = pipeline.execute()[-2:] #I if (clicks or 0) < 1: #J continue #J word_ecpm = to_ecpm(views or 1, clicks or 0, base_value) #K bonus = word_ecpm - ad_ecpm #L pipeline.zadd('idx:' + word, {ad_id: bonus}) #M pipeline.execute() # <end id="update_cpms"/> #A Fetch the type and value of the ad, as well as all of the words in the ad #B Determine whether the eCPM of the ad should be based on clicks or actions #C Fetch the current number of views and clicks/actions for the given ad type #D Write back to our global dictionary the click-through rate or action rate for the ad #E If we are processing a CPM ad, then we don't update any of the eCPMs, as they are already updated #N Use the existing eCPM if the ad hasn't received any clicks yet #G Fetch the per-ad view and click/action scores and #H Calculate the ad's eCPM and update the ad's value #I Fetch the view and click/action scores for the word #J Don't update eCPMs when the ad has not received any clicks #K Calculate the word's eCPM #L Calculate the word's bonus #M Write the word's bonus back to the per-word per-ad ZSET #END # Job Search - Use case : # <start id="slow_job_search"/> def add_job(conn, job_id, required_skills): conn.sadd('job:' + job_id, *required_skills) #A def is_qualified(conn, job_id, candidate_skills): temp = str(uuid.uuid4()) pipeline = conn.pipeline(True) pipeline.sadd(temp, *candidate_skills) #B pipeline.expire(temp, 5) #B pipeline.sdiff('job:' + job_id, temp) #C return not pipeline.execute()[-1] #D # <end id="slow_job_search"/> #A Add all required job skills to the job's SET #B Add the candidate's skills to a temporary SET with an expiration time #C Calculate the SET of skills that the job requires that the user doesn't have #D Return True if there are no skills that the candidate does not have #END # <start id="job_search_index"/> def index_job(conn, job_id, skills): pipeline = conn.pipeline(True) for skill in skills: pipeline.sadd('idx:skill:' + skill, job_id) #A pipeline.zadd('idx:jobs:req', {job_id: len(set(skills))}) #B pipeline.execute() # <end id="job_search_index"/> #A Add the job id to all appropriate skill SETs #B Add the total required skill count to the required skills ZSET #END # <start id="job_search_results"/> def find_jobs(conn, candidate_skills): skills = {} #A for skill in set(candidate_skills): #A skills['skill:' + skill] = 1 #A job_scores = zunion(conn, skills) #B final_result = zintersect( #C conn, {job_scores:-1, 'jobs:req':1}) #C return conn.zrangebyscore('idx:' + final_result, 0, 0) #D # <end id="job_search_results"/> #A Set up the dictionary for scoring the jobs #B Calculate the scores for each of the jobs #C Calculate how many more skills the job requires than the candidate has #D Return the jobs that the candidate has the skills for #END # 0 is beginner, 1 is intermediate, 2 is expert SKILL_LEVEL_LIMIT = 2 def index_job_levels(conn, job_id, skill_levels): total_skills = len(set(skill for skill, level in skill_levels)) pipeline = conn.pipeline(True) for skill, level in skill_levels: level = min(level, SKILL_LEVEL_LIMIT) for wlevel in range(level, SKILL_LEVEL_LIMIT+1): pipeline.sadd('idx:skill:%s:%s'%(skill,wlevel), job_id) pipeline.zadd('idx:jobs:req', {job_id: total_skills}) pipeline.execute() def search_job_levels(conn, skill_levels): skills = {} for skill, level in skill_levels: level = min(level, SKILL_LEVEL_LIMIT) skills['skill:%s:%s'%(skill,level)] = 1 job_scores = zunion(conn, skills) final_result = zintersect(conn, {job_scores:-1, 'jobs:req':1}) return conn.zrangebyscore('idx:' + final_result, '-inf', 0) def index_job_years(conn, job_id, skill_years): total_skills = len(set(skill for skill, years in skill_years)) pipeline = conn.pipeline(True) for skill, years in skill_years: pipeline.zadd( 'idx:skill:%s:years'%skill, {job_id:max(years, 0)}) pipeline.sadd('idx:jobs:all', job_id) pipeline.zadd('idx:jobs:req', {job_id:total_skills}) pipeline.execute() def search_job_years(conn, skill_years): skill_years = dict(skill_years) pipeline = conn.pipeline(True) union = [] for skill, years in skill_years.items(): sub_result = zintersect(pipeline, {'jobs:all':-years, 'skill:%s:years'%skill:1}, _execute=False) pipeline.zremrangebyscore('idx:' + sub_result, '(0', 'inf') union.append( zintersect(pipeline, {'jobs:all':1, sub_result:0}, _execute=False)) job_scores = zunion(pipeline, dict((key, 1) for key in union), _execute=False) final_result = zintersect(pipeline, {job_scores:-1, 'jobs:req':1}, _execute=False) pipeline.zrangebyscore('idx:' + final_result, '-inf', 0) return pipeline.execute()[-1] class TestCh07(unittest.TestCase): content = 'this is some random content, look at how it is indexed.' def setUp(self): self.conn = redis.Redis(db=15) self.conn.flushdb() def tearDown(self): self.conn.flushdb() def test_index_document(self): print("We're tokenizing some content...") tokens = tokenize(self.content) print("Those tokens are:", tokens) self.assertTrue(tokens) print("And now we are indexing that content...") r = index_document(self.conn, 'test', self.content) self.assertEqual(r, len(tokens)) for t in tokens: self.assertEqual(self.conn.smembers('idx:' + t), set([b'test'])) def test_set_operations(self): index_document(self.conn, 'test', self.content) r = intersect(self.conn, ['content', 'indexed']) self.assertEqual(self.conn.smembers('idx:' + r), set([b'test'])) r = intersect(self.conn, ['content', 'ignored']) self.assertEqual(self.conn.smembers('idx:' + r), set()) r = union(self.conn, ['content', 'ignored']) self.assertEqual(self.conn.smembers('idx:' + r), set([b'test'])) r = difference(self.conn, ['content', 'ignored']) self.assertEqual(self.conn.smembers('idx:' + r), set([b'test'])) r = difference(self.conn, ['content', 'indexed']) self.assertEqual(self.conn.smembers('idx:' + r), set()) def test_parse_query(self): query = 'test query without stopwords' self.assertEqual(parse(query), ([[x] for x in query.split()], [])) query = 'test +query without -stopwords' self.assertIn(parse(query), (([['test', 'query'], ['without']], ['stopwords'],), ([['query', 'test'], ['without']], ['stopwords'],))) def test_parse_and_search(self): print("And now we are testing search...") index_document(self.conn, 'test', self.content) r = parse_and_search(self.conn, 'content') self.assertEqual(self.conn.smembers('idx:' + r), set([b'test'])) r = parse_and_search(self.conn, 'content indexed random') self.assertEqual(self.conn.smembers('idx:' + r), set([b'test'])) r = parse_and_search(self.conn, 'content +indexed random') self.assertEqual(self.conn.smembers('idx:' + r), set([b'test'])) r = parse_and_search(self.conn, 'content indexed +random') self.assertEqual(self.conn.smembers('idx:' + r), set([b'test'])) r = parse_and_search(self.conn, 'content indexed -random') self.assertEqual(self.conn.smembers('idx:' + r), set()) print("Which passed!") def test_search_with_sort(self): print("And now let's test searching with sorting...") index_document(self.conn, 'test', self.content) index_document(self.conn, 'test2', self.content) self.conn.hmset('kb:doc:test', {'updated': 12345, 'id': 10}) self.conn.hmset('kb:doc:test2', {'updated': 54321, 'id': 1}) r = search_and_sort(self.conn, "content") self.assertEqual(r[1], [b'test2', b'test']) r = search_and_sort(self.conn, "content", sort='-id') self.assertEqual(r[1], [b'test', b'test2']) print("Which passed!") def test_search_with_zsort(self): print("And now let's test searching with sorting via zset...") index_document(self.conn, 'test', self.content) index_document(self.conn, 'test2', self.content) self.conn.zadd('idx:sort:update', {'test': 12345, 'test2': 54321}) self.conn.zadd('idx:sort:votes', {'test': 10, 'test2': 1}) r = search_and_zsort(self.conn, "content", desc=False) self.assertEqual(r[1], [b'test', b'test2']) r = search_and_zsort(self.conn, "content", update=0, vote=1, desc=False) self.assertEqual(r[1], [b'test2', b'test']) print("Which passed!") def test_string_to_score(self): words = 'these are some words that will be sorted'.split() pairs = [(word, string_to_score(word)) for word in words] pairs2 = list(pairs) pairs.sort() pairs2.sort(key=lambda x:x[1]) self.assertEqual(pairs, pairs2) words = 'these are some words that will be sorted'.split() pairs = [(word, string_to_score_generic(word, LOWER)) for word in words] pairs2 = list(pairs) pairs.sort() pairs2.sort(key=lambda x:x[1]) self.assertEqual(pairs, pairs2) zadd_string(self.conn, 'key', 'test', 'value', test2='other') self.assertEqual(self.conn.zscore('key', 'test'), string_to_score('value')) self.assertEqual(self.conn.zscore('key', 'test2'), string_to_score('other')) def test_index_and_target_ads(self): index_ad(self.conn, '1', ['USA', 'CA'], self.content, 'cpc', .25) index_ad(self.conn, '2', ['USA', 'VA'], self.content + ' wooooo', 'cpc', .125) for i in range(100): ro = target_ads(self.conn, ['USA'], self.content) self.assertEqual(ro[1], b'1') r = target_ads(self.conn, ['VA'], 'wooooo') self.assertEqual(r[1], b'2') self.assertEqual(self.conn.zrange('idx:ad:value:', 0, -1, withscores=True), [(b'2', 0.125), (b'1', 0.25)]) self.assertEqual(self.conn.zrange('ad:base_value:', 0, -1, withscores=True), [(b'2', 0.125), (b'1', 0.25)]) record_click(self.conn, ro[0], ro[1]) self.assertEqual(self.conn.zrange('idx:ad:value:', 0, -1, withscores=True), [(b'2', 0.125), (b'1', 2.5)]) self.assertEqual(self.conn.zrange('ad:base_value:', 0, -1, withscores=True), [(b'2', 0.125), (b'1', 0.25)]) def test_is_qualified_for_job(self): add_job(self.conn, 'test', ['q1', 'q2', 'q3']) self.assertTrue(is_qualified(self.conn, 'test', ['q1', 'q3', 'q2'])) self.assertFalse(is_qualified(self.conn, 'test', ['q1', 'q2'])) def test_index_and_find_jobs(self): index_job(self.conn, 'test1', ['q1', 'q2', 'q3']) index_job(self.conn, 'test2', ['q1', 'q3', 'q4']) index_job(self.conn, 'test3', ['q1', 'q3', 'q5']) self.assertEqual(find_jobs(self.conn, ['q1']), []) self.assertEqual(find_jobs(self.conn, ['q1', 'q3', 'q4']), [b'test2']) self.assertEqual(find_jobs(self.conn, ['q1', 'q3', 'q5']), [b'test3']) self.assertEqual(find_jobs(self.conn, ['q1', 'q2', 'q3', 'q4', 'q5']), [b'test1', b'test2', b'test3']) def test_index_and_find_jobs_levels(self): print("now testing find jobs with levels ...") index_job_levels(self.conn, "job1" ,[('q1', 1)]) index_job_levels(self.conn, "job2", [('q1', 0), ('q2', 2)]) self.assertEqual(search_job_levels(self.conn, [('q1', 0)]), []) self.assertEqual(search_job_levels(self.conn, [('q1', 1)]), [b'job1']) self.assertEqual(search_job_levels(self.conn, [('q1', 2)]), [b'job1']) self.assertEqual(search_job_levels(self.conn, [('q2', 1)]), []) self.assertEqual(search_job_levels(self.conn, [('q2', 2)]), []) self.assertEqual(search_job_levels(self.conn, [('q1', 0), ('q2', 1)]), []) self.assertEqual(search_job_levels(self.conn, [('q1', 0), ('q2', 2)]), [b'job2']) self.assertEqual(search_job_levels(self.conn, [('q1', 1), ('q2', 1)]), [b'job1']) self.assertEqual(search_job_levels(self.conn, [('q1', 1), ('q2', 2)]), [b'job1', b'job2']) print("which passed") def test_index_and_find_jobs_years(self): print("now testing find jobs with years ...") index_job_years(self.conn, "job1",[('q1',1)]) index_job_years(self.conn, "job2",[('q1',0),('q2',2)]) self.assertEqual(search_job_years(self.conn, [('q1',0)]), []) self.assertEqual(search_job_years(self.conn, [('q1',1)]), [b'job1']) self.assertEqual(search_job_years(self.conn, [('q1',2)]), [b'job1']) self.assertEqual(search_job_years(self.conn, [('q2',1)]), []) self.assertEqual(search_job_years(self.conn, [('q2',2)]), []) self.assertEqual(search_job_years(self.conn, [('q1',0), ('q2', 1)]), []) self.assertEqual(search_job_years(self.conn, [('q1',0), ('q2', 2)]), [b'job2']) self.assertEqual(search_job_years(self.conn, [('q1',1), ('q2', 1)]), [b'job1']) self.assertEqual(search_job_years(self.conn, [('q1',1), ('q2', 2)]), [b'job1',b'job2']) print("which passed") if __name__ == '__main__': unittest.main()

44.994253

192

0.567557

98190be3cb4d7cd798c48d3d94ec4f5848355f07

2,072

py

Python

backend/pharmacy/api/views/medical/medicine.py

rahul007-bit/pharmaService

73191f64569eae7c7851f5b7bf9187f3f01b7a6e

[ "MIT" ]

4

2022-01-28T13:05:07.000Z

2022-01-31T12:24:56.000Z

backend/pharmacy/api/views/medical/medicine.py

rahul007-bit/pharmaService

73191f64569eae7c7851f5b7bf9187f3f01b7a6e

[ "MIT" ]

6

2022-01-30T11:53:31.000Z

2022-02-02T06:17:30.000Z

backend/pharmacy/api/views/medical/medicine.py

rahul007-bit/pharmaService

73191f64569eae7c7851f5b7bf9187f3f01b7a6e

[ "MIT" ]

3

2022-01-28T13:41:03.000Z

2022-01-30T12:23:11.000Z

# pylint: disable=missing-module-docstring # # Copyright (C) 2022 by YadavGulshan@Github, < https://github.com/YadavGulshan >. # # This file is part of < https://github.com/Yadavgulshan/pharmaService > project, # and is released under the "BSD 3-Clause License Agreement". # Please see < https://github.com/YadavGulshan/pharmaService/blob/master/LICENCE > # # All rights reserved. from django.http import Http404 from rest_framework.response import Response from rest_framework.decorators import permission_classes from rest_framework.permissions import IsAuthenticated from pharmacy.models import Medical, Medicine from ...serializers import MedicalSerializer, MedicineSerializer from rest_framework import generics from rest_framework import status @permission_classes([IsAuthenticated]) class MedicineViewList(generics.CreateAPIView): """This class will display or help medical owner view or update the medicines.""" def get(self, request): medicine = Medicine.objects.filter(user=request.user) serializer = MedicalSerializer(medicine, many=True) return Response(serializer.data, status=status.HTTP_200_OK) @permission_classes([IsAuthenticated]) class MedicineViewByID(generics.CreateAPIView): """ "This class will display only the medicine owned by specific medical shop.""" def get(self, request, pk): # First check if the medical shop exists try: medical = Medical.objects.get(pk=pk) except Medical.DoesNotExist: raise Http404 # Check if that medical shop is owned by the user if medical.user != request.user: return Response(status=status.HTTP_403_FORBIDDEN) try: # Get the medicine list of that medical shop medicine = Medicine.objects.filter(medicalId=pk) # medicine = Medicine.objects.get(pk=pk) except Medicine.DoesNotExist: raise Http404 serializer = MedicineSerializer(medicine, many=True) return Response(serializer.data, status=status.HTTP_200_OK)

35.724138

85

0.726834

00345485045dc5f4f7fed4443a3b831e24371cee

4,740

py

Python

test/unit/test_cmdline.py

jsiverskog/pyOCD

8b75633482a2f1856a8ab6af9ebb5c1b2f9d8285

[ "Apache-2.0" ]

1

2020-07-11T09:24:25.000Z

test/unit/test_cmdline.py

ARMmbed/pyOCD-Samsung

03242b6eb57d2170a4b531d00f1a0577e2b0abde

[ "Apache-2.0" ]

null

test/unit/test_cmdline.py

ARMmbed/pyOCD-Samsung

03242b6eb57d2170a4b531d00f1a0577e2b0abde

[ "Apache-2.0" ]

null

# pyOCD debugger # Copyright (c) 2015,2018-2019 Arm Limited # SPDX-License-Identifier: Apache-2.0 # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from pyocd.utility.cmdline import ( split_command_line, convert_vector_catch, VECTOR_CATCH_CHAR_MAP, convert_session_options, ) from pyocd.core.target import Target import pytest import six class TestSplitCommandLine(object): def test_split(self): assert split_command_line('foo') == ['foo'] assert split_command_line(['foo']) == ['foo'] assert split_command_line('foo bar') == ['foo', 'bar'] assert split_command_line(['foo bar']) == ['foo', 'bar'] def test_split_strings(self): assert split_command_line('"foo"') == ['foo'] assert split_command_line('"foo bar"') == ['foo bar'] assert split_command_line(['"foo"']) == ['foo'] assert split_command_line('a "b c" d') == ['a', "b c", 'd'] assert split_command_line("'foo bar'") == ['foo bar'] def test_split_whitespace(self): assert split_command_line('a b') == ['a', 'b'] assert split_command_line('a\tb') == ['a', 'b'] assert split_command_line('a\rb') == ['a', 'b'] assert split_command_line('a\nb') == ['a', 'b'] assert split_command_line('a \tb') == ['a', 'b'] class TestConvertVectorCatch(object): def test_none_str(self): assert convert_vector_catch('none') == 0 def test_all_str(self): assert convert_vector_catch('all') == Target.CATCH_ALL def test_none_b(self): assert convert_vector_catch(b'none') == 0 def test_all_b(self): assert convert_vector_catch(b'all') == Target.CATCH_ALL @pytest.mark.parametrize(("vc", "msk"), list(VECTOR_CATCH_CHAR_MAP.items())) def test_vc_str(self, vc, msk): assert convert_vector_catch(vc) == msk @pytest.mark.parametrize(("vc", "msk"), [(six.b(x), y) for x,y in VECTOR_CATCH_CHAR_MAP.items()]) def test_vc_b(self, vc, msk): assert convert_vector_catch(vc) == msk class TestConvertSessionOptions(object): def test_empty(self): assert convert_session_options([]) == {} def test_unknown_option(self): assert convert_session_options(['dumkopf']) == {} def test_bool(self): assert convert_session_options(['auto_unlock']) == {'auto_unlock': True} assert convert_session_options(['no-auto_unlock']) == {'auto_unlock': False} assert convert_session_options(['auto_unlock=1']) == {'auto_unlock': True} assert convert_session_options(['auto_unlock=true']) == {'auto_unlock': True} assert convert_session_options(['auto_unlock=yes']) == {'auto_unlock': True} assert convert_session_options(['auto_unlock=on']) == {'auto_unlock': True} assert convert_session_options(['auto_unlock=0']) == {'auto_unlock': False} assert convert_session_options(['auto_unlock=false']) == {'auto_unlock': False} assert convert_session_options(['auto_unlock=anything-goes-here']) == {'auto_unlock': False} def test_noncasesense(self): # Test separate paths for with and without a value. assert convert_session_options(['AUTO_Unlock']) == {'auto_unlock': True} assert convert_session_options(['AUTO_Unlock=0']) == {'auto_unlock': False} def test_int(self): # Non-bool with no value is ignored (and logged). assert convert_session_options(['frequency']) == {} # Invalid int value is ignored and logged assert convert_session_options(['frequency=abc']) == {} # Ignore with no- prefix assert convert_session_options(['no-frequency']) == {} # Valid int assert convert_session_options(['frequency=1000']) == {'frequency': 1000} # Valid hex int assert convert_session_options(['frequency=0x40']) == {'frequency': 64} def test_str(self): # Ignore with no value assert convert_session_options(['test_binary']) == {} # Ignore with no- prefix assert convert_session_options(['no-test_binary']) == {} # Valid assert convert_session_options(['test_binary=abc']) == {'test_binary': 'abc'}

41.217391

100

0.65211

662f721e6bf897cea9dd650362875a4f927de112

3,595

py

Python

tests/unit/test_protocol_errors.py

dcolligan/ga4gh-server

dd0b00a52de9684609b7f04a9d70946c36afa8a5

[ "Apache-2.0" ]

83

2015-01-05T22:21:11.000Z

2017-02-20T01:25:28.000Z

tests/unit/test_protocol_errors.py

dcolligan/ga4gh-server

dd0b00a52de9684609b7f04a9d70946c36afa8a5

[ "Apache-2.0" ]

1,508

2015-01-02T14:06:12.000Z

2017-03-08T19:49:18.000Z

tests/unit/test_protocol_errors.py

dcolligan/ga4gh-server

dd0b00a52de9684609b7f04a9d70946c36afa8a5

[ "Apache-2.0" ]

99

2015-01-14T20:48:56.000Z

2017-03-08T18:35:06.000Z

""" Unit tests for frontend error conditions. """ from __future__ import division from __future__ import print_function from __future__ import unicode_literals import unittest import ga4gh.server.frontend as frontend import ga4gh.server.exceptions as exceptions import ga4gh.schemas.protocol as protocol class TestFrontendErrors(unittest.TestCase): """ Tests the frontend for various errors that can occur and verify that the correct exception was raised by the error code sent back. """ @classmethod def setUpClass(cls): frontend.reset() frontend.configure(baseConfig="TestConfig") cls.app = frontend.app.test_client() @classmethod def tearDownClass(cls): cls.app = None def setUp(self): # TODO replace this with ALL post methods once the rest of the # end points have been implemented. This should also add an API # to protocol.py to simplify and document the process of getting # the correct API endpoints and classes. That is, we shouldn't # use protocol.postMethods directly, but instead call a function. supportedMethods = set([ protocol.SearchCallSetsRequest, protocol.SearchVariantSetsRequest, protocol.SearchVariantsRequest, ]) self.endPointMap = {} for endPoint, requestClass, responseClass in protocol.postMethods: if requestClass in supportedMethods: self.endPointMap[endPoint] = requestClass def assertRawRequestRaises(self, exceptionClass, url, requestString): """ Verifies that the specified request string returns a protocol exception corresponding to the specified class when applied to all POST endpoints. """ response = self.app.post( url, headers={'Content-type': 'application/json'}, data=requestString) self.assertEqual(response.status_code, exceptionClass.httpStatus) error = protocol.fromJson(response.data, protocol.GAException) self.assertEqual( error.error_code, exceptionClass.getErrorCode()) self.assertGreater(len(error.message), 0) def assertRequestRaises(self, exceptionClass, url, request): """ Verifies that the specified request returns a protocol exception corresponding to the specified exception class. """ self.assertRawRequestRaises( exceptionClass, url, protocol.toJson(request)) def testPageSize(self): for url, requestClass in self.endPointMap.items(): for badSize in [-100, -1]: request = requestClass() request.page_size = badSize self.assertRequestRaises( exceptions.BadPageSizeException, url, request) @unittest.skip("Gets caught by the protocol buffer checkers") def testPageToken(self): for url, requestClass in self.endPointMap.items(): for badType in [0, 0.0, 1e-3, {}, [], [None]]: request = requestClass() request.page_token = badType self.assertRequestRaises( exceptions.RequestValidationFailureException, url, request) @unittest.skip("TODO: create invalid JSON to test validation") def testInvalidFields(self): for url, requestClass in self.endPointMap.items(): request = self._createInvalidInstance(requestClass) self.assertRequestRaises( exceptions.RequestValidationFailureException, url, request)

38.244681

79

0.664256

8639cebd5b3ff50ceacb82ea2b2485b775145b52

1,115

py

Python

pacific-factbook/flag.py

kaunta/pacific-factbook

ccf3f08c0d6121d852e5dd0319e21e0a9ec44e3d

[ "MIT" ]

5

2020-01-23T04:08:46.000Z

2020-04-02T05:19:34.000Z

pacific-factbook/flag.py

kaunta/pacific-factbook

ccf3f08c0d6121d852e5dd0319e21e0a9ec44e3d

[ "MIT" ]

23

2019-11-22T01:56:54.000Z

2020-02-08T23:45:10.000Z

pacific-factbook/flag.py

kaunta/pacific-factbook

ccf3f08c0d6121d852e5dd0319e21e0a9ec44e3d

[ "MIT" ]

null

from fractions import Fraction from random import choice def generate() -> str: """ Generate a random flag. Outputs SVG blob. """ colors = set("red blue white green yellow black orange brown gray purple".split()) color_background = choice(list(colors)) color_shape = choice(list(colors - {color_background})) aspect_ratio = choice([Fraction("2/3"), Fraction("1/2")]) height = 200 width = height / aspect_ratio shape = choice( [ f"""<circle cx="50" cy="50" r="40" stroke="{color_shape}" stroke-width="4" fill="{color_shape}" />""", f"""<polygon points=" 50,5 20,99 95,39 5,39 80,99 " style="fill:{color_shape};stroke:{color_shape};stroke-width:4;fill-rule:nonzero;" />""", ] ) return f""" <svg width="{width}" height="{height}" style="border: 1px solid black"> <rect width="100%" height="100%" fill="{color_background}"/> {shape} </svg> """ if __name__ == "__main__": print("<h1>Test Flag Report</h1>") for _ in range(12): print(generate()) print("<hr>")

31.857143

152

0.584753

37bfa76cc51a0d3dcd589ba75b34bc72511475c3

6,723

py

Python

lighttpd/datadog_checks/lighttpd/lighttpd.py

glasser/integrations-core

1dd515d49b1690a1369ee5195713605b1b072b1f

[ "BSD-3-Clause" ]

null

lighttpd/datadog_checks/lighttpd/lighttpd.py

glasser/integrations-core

1dd515d49b1690a1369ee5195713605b1b072b1f

[ "BSD-3-Clause" ]

null

lighttpd/datadog_checks/lighttpd/lighttpd.py

glasser/integrations-core

1dd515d49b1690a1369ee5195713605b1b072b1f

[ "BSD-3-Clause" ]

null

# (C) Datadog, Inc. 2010-2017 # All rights reserved # Licensed under Simplified BSD License (see LICENSE) import re import requests from six.moves.urllib.parse import urlparse from datadog_checks.checks import AgentCheck VERSION_REGEX = re.compile(r".*/(\d)") class Lighttpd(AgentCheck): """Tracks basic connection/requests/workers metrics See http://redmine.lighttpd.net/projects/1/wiki/Docs_ModStatus for Lighttpd details See http://redmine.lighttpd.net/projects/lighttpd2/wiki/Mod_status for Lighttpd2 details """ SERVICE_CHECK_NAME = 'lighttpd.can_connect' URL_SUFFIX_PER_VERSION = {1: '?auto', 2: '?format=plain', 'Unknown': '?auto'} GAUGES = { b'IdleServers': 'lighttpd.performance.idle_server', b'BusyServers': 'lighttpd.performance.busy_servers', b'Uptime': 'lighttpd.performance.uptime', b'Total kBytes': 'lighttpd.net.bytes', b'Total Accesses': 'lighttpd.net.hits', b'memory_usage': 'lighttpd.performance.memory_usage', b'requests_avg': 'lighttpd.net.requests_avg', b'traffic_out_avg': 'lighttpd.net.bytes_out_avg', b'traffic_in_avg': 'lighttpd.net.bytes_in_avg', b'connections_avg': 'lighttpd.net.connections_avg', b'connection_state_start': 'lighttpd.connections.state_start', b'connection_state_read_header': 'lighttpd.connections.state_read_header', b'connection_state_handle_request': 'lighttpd.connections.state_handle_request', b'connection_state_write_response': 'lighttpd.connections.state_write_response', b'connection_state_keep_alive': 'lighttpd.connections.state_keep_alive', b'requests_avg_5sec': 'lighttpd.net.requests_avg_5sec', b'traffic_out_avg_5sec': 'lighttpd.net.bytes_out_avg_5sec', b'traffic_in_avg_5sec': 'lighttpd.net.bytes_in_avg_5sec', b'connections_avg_5sec': 'lighttpd.net.connections_avg_5sec', } COUNTERS = { b'requests_abs': 'lighttpd.net.requests_total', b'traffic_out_abs': 'lighttpd.net.bytes_out', b'traffic_in_abs': 'lighttpd.net.bytes_in', b'connections_abs': 'lighttpd.net.connections_total', b'status_1xx': 'lighttpd.response.status_1xx', b'status_2xx': 'lighttpd.response.status_2xx', b'status_3xx': 'lighttpd.response.status_3xx', b'status_4xx': 'lighttpd.response.status_4xx', b'status_5xx': 'lighttpd.response.status_5xx', } RATES = {b'Total kBytes': 'lighttpd.net.bytes_per_s', b'Total Accesses': 'lighttpd.net.request_per_s'} HTTP_CONFIG_REMAPPER = {'user': {'name': 'username'}} def __init__(self, name, init_config, instances): super(Lighttpd, self).__init__(name, init_config, instances) self.assumed_url = {} if 'auth_type' in self.instance: if self.instance['auth_type'] == 'digest': auth = self.http.options['auth'] self.http.options['auth'] = requests.auth.HTTPDigestAuth(auth[0], auth[1]) def check(self, instance): if 'lighttpd_status_url' not in instance: raise Exception("Missing 'lighttpd_status_url' variable in Lighttpd config") url = self.assumed_url.get(instance['lighttpd_status_url'], instance['lighttpd_status_url']) tags = instance.get('tags', []) auth_type = instance.get('auth_type', 'basic').lower() if self.http.options['auth'] is None: msg = "Unsupported value of 'auth_type' variable in Lighttpd config: {}".format(auth_type) raise Exception(msg) self.log.debug("Connecting to %s" % url) # Submit a service check for status page availability. parsed_url = urlparse(url) lighttpd_url = parsed_url.hostname lighttpd_port = parsed_url.port or 80 service_check_tags = ['host:%s' % lighttpd_url, 'port:%s' % lighttpd_port] + tags try: r = self.http.get(url) r.raise_for_status() except Exception: self.service_check(self.SERVICE_CHECK_NAME, AgentCheck.CRITICAL, tags=service_check_tags) raise else: self.service_check(self.SERVICE_CHECK_NAME, AgentCheck.OK, tags=service_check_tags) headers_resp = r.headers server_version = self._get_server_version(headers_resp) response = r.content metric_count = 0 # Loop through and extract the numerical values for line in response.split(b'\n'): values = line.split(b': ') if len(values) == 2: # match metric, value = values try: value = float(value) except ValueError: continue # Special case: kBytes => bytes if metric == b'Total kBytes': value = value * 1024 # Send metric as a gauge, if applicable if metric in self.GAUGES: metric_count += 1 metric_name = self.GAUGES[metric] self.gauge(metric_name, value, tags=tags) # Send metric as a rate, if applicable if metric in self.RATES: metric_count += 1 metric_name = self.RATES[metric] self.rate(metric_name, value, tags=tags) # Send metric as a counter, if applicable if metric in self.COUNTERS: metric_count += 1 metric_name = self.COUNTERS[metric] self.increment(metric_name, value, tags=tags) if metric_count == 0: url_suffix = self.URL_SUFFIX_PER_VERSION[server_version] if self.assumed_url.get(instance['lighttpd_status_url']) is None and url[-len(url_suffix) :] != url_suffix: self.assumed_url[instance['lighttpd_status_url']] = '%s%s' % (url, url_suffix) self.warning("Assuming url was not correct. Trying to add %s suffix to the url" % url_suffix) self.check(instance) else: raise Exception( "No metrics were fetched for this instance. Make sure " "that %s is the proper url." % instance['lighttpd_status_url'] ) def _get_server_version(self, headers): server_version = headers.get("server", "") match = VERSION_REGEX.match(server_version) if match is None: self.log.debug("Lighttpd server version is Unknown") return "Unknown" version = int(match.group(1)) self.log.debug("Lighttpd server version is %s" % version) return version

41.5

119

0.625614

233391949dea48d8da274bc5b7e7be2c1ffac7f7

3,568

py

Python

sdks/python/appcenter_sdk/models/BlobInfo.py

Brantone/appcenter-sdks

eeb063ecf79908b6e341fb00196d2cd9dc8f3262

[ "MIT" ]

null

sdks/python/appcenter_sdk/models/BlobInfo.py

Brantone/appcenter-sdks

eeb063ecf79908b6e341fb00196d2cd9dc8f3262

[ "MIT" ]

6

2019-10-23T06:38:53.000Z

2022-01-22T07:57:58.000Z

sdks/python/appcenter_sdk/models/BlobInfo.py

Brantone/appcenter-sdks

eeb063ecf79908b6e341fb00196d2cd9dc8f3262

[ "MIT" ]

2

2019-10-23T06:31:05.000Z

2021-08-21T17:32:47.000Z

# coding: utf-8 """ App Center Client Microsoft Visual Studio App Center API # noqa: E501 OpenAPI spec version: preview Contact: benedetto.abbenanti@gmail.com Project Repository: https://github.com/b3nab/appcenter-sdks """ import pprint import re # noqa: F401 import six class BlobInfo(object): """NOTE: This class is auto generated by the swagger code generator program. Do not edit the class manually. """ """ Attributes: swagger_types (dict): The key is attribute name and the value is attribute type. attribute_map (dict): The key is attribute name and the value is json key in definition. """ swagger_types = { 'size': 'number', 'url': 'string' } attribute_map = { 'size': 'size', 'url': 'url' } def __init__(self, size=None, url=None): # noqa: E501 """BlobInfo - a model defined in Swagger""" # noqa: E501 self._size = None self._url = None self.discriminator = None self.size = size self.url = url @property def size(self): """Gets the size of this BlobInfo. # noqa: E501 :return: The size of this BlobInfo. # noqa: E501 :rtype: number """ return self._size @size.setter def size(self, size): """Sets the size of this BlobInfo. :param size: The size of this BlobInfo. # noqa: E501 :type: number """ if size is None: raise ValueError("Invalid value for `size`, must not be `None`") # noqa: E501 self._size = size @property def url(self): """Gets the url of this BlobInfo. # noqa: E501 :return: The url of this BlobInfo. # noqa: E501 :rtype: string """ return self._url @url.setter def url(self, url): """Sets the url of this BlobInfo. :param url: The url of this BlobInfo. # noqa: E501 :type: string """ if url is None: raise ValueError("Invalid value for `url`, must not be `None`") # noqa: E501 self._url = url def to_dict(self): """Returns the model properties as a dict""" result = {} for attr, _ in six.iteritems(self.swagger_types): value = getattr(self, attr) if isinstance(value, list): result[attr] = list(map( lambda x: x.to_dict() if hasattr(x, "to_dict") else x, value )) elif hasattr(value, "to_dict"): result[attr] = value.to_dict() elif isinstance(value, dict): result[attr] = dict(map( lambda item: (item[0], item[1].to_dict()) if hasattr(item[1], "to_dict") else item, value.items() )) else: result[attr] = value return result def to_str(self): """Returns the string representation of the model""" return pprint.pformat(self.to_dict()) def __repr__(self): """For `print` and `pprint`""" return self.to_str() def __eq__(self, other): """Returns true if both objects are equal""" if not isinstance(other, BlobInfo): return False return self.__dict__ == other.__dict__ def __ne__(self, other): """Returns true if both objects are not equal""" return not self == other

26.043796

90

0.539238

2501b593dd91359dd470ec59d0b5a796aa1d77c7

13,195

py

Python

GeoHealthCheck/probe.py

SteveR1984/GeoHealthCheck

52a13c1bd42e79dec547fa29324d583aea5e3773

[ "MIT" ]

null

GeoHealthCheck/probe.py

SteveR1984/GeoHealthCheck

52a13c1bd42e79dec547fa29324d583aea5e3773

[ "MIT" ]

null

GeoHealthCheck/probe.py

SteveR1984/GeoHealthCheck

52a13c1bd42e79dec547fa29324d583aea5e3773

[ "MIT" ]

null

import logging import sys import datetime import requests from factory import Factory from init import App from plugin import Plugin from result import ProbeResult LOGGER = logging.getLogger(__name__) class Probe(Plugin): """ Base class for specific implementations to run a Probe with Checks. Most Probes can be implemented using REQUEST_TEMPLATES parameterized via actualized PARAM_DEFS but specialized Probes may implement their own Requests and Checks, for example by "drilling down" through OWS services on an OGC OWS endpoint starting at the Capabilities level or for specific WWW:LINK-based REST APIs. """ # Generic attributes, subclassses override RESOURCE_TYPE = 'Not Applicable' """ Type of GHC Resource e.g. 'OGC:WMS', default not applicable. """ # Request attributes, defaults, subclasses override REQUEST_METHOD = 'GET' """ HTTP request method capitalized, GET (default) or POST. """ REQUEST_HEADERS = {} """ `dict` of optional requests headers. """ REQUEST_TEMPLATE = '' """ Template in standard Python `str.format(*args)`. The variables like {service} and {version} within a template are filled from actual values for parameters defined in PARAM_DEFS and substituted from values or constant values specified by user in GUI and stored in DB. """ # Parameter definitions and possible Checks, # subclassses override PARAM_DEFS = {} """ Parameter definitions mostly for `REQUEST_TEMPLATE` but potential other uses in specific Probe implementations. Format is `dict` where each key is a parameter name and the value a `dict` of: `type`, `description`, `required`, `default`, `range` (value range) and optional `value` item. If `value` specified, this value becomes fixed (non-editable) unless overridden in subclass. """ CHECKS_AVAIL = {} """ Available Check (classes) for this Probe in `dict` format. Key is a Check class (string), values are optional (default `{}`). In the (constant) value 'parameters' and other attributes for Check.PARAM_DEFS can be specified, including `default` if this Check should be added to Probe on creation. """ METADATA_CACHE = {} """ Cache for metadata, like capabilities documents or OWSLib Service instances. Saves doing multiple requests/responses. In particular for endpoints with 50+ Layers. """ def __init__(self): Plugin.__init__(self) self._resource = None # # Lifecycle : optionally expand params from Resource metadata def expand_params(self, resource): """ Called after creation. Use to expand PARAM_DEFS, e.g. from Resource metadata like WMS Capabilities. See e.g. WmsGetMapV1 class. :param resource: :return: None """ pass def get_metadata(self, resource, version='any'): """ Get metadata, specific per Resource type. :param resource: :param version: :return: Metadata object """ return 'md' def get_metadata_cached(self, resource, version='any'): """ Get metadata, specific per Resource type, get from cache if cached. :param resource: :param version: :return: Metadata object """ key = '%s_%s_%s' % (resource.url, resource.resource_type, version) metadata = None if key in Probe.METADATA_CACHE: entry = Probe.METADATA_CACHE[key] delta = datetime.datetime.utcnow() - entry['time'] metadata = entry['metadata'] # Don't keep cache forever, refresh every N mins if delta.seconds > App.get_config()['GHC_METADATA_CACHE_SECS']: entry = Probe.METADATA_CACHE.pop(key) del entry metadata = None if not metadata: # Get actual metadata, Resource-type specifc metadata = self.get_metadata(resource, version) if metadata and App.get_config()['GHC_METADATA_CACHE_SECS'] > 0: # Store entry with time, for expiry later entry = { "metadata": metadata, "time": datetime.datetime.utcnow() } Probe.METADATA_CACHE[key] = entry return metadata # Lifecycle def init(self, resource, probe_vars): """ Probe contains the actual Probe parameters (from Models/DB) for requests and a list of response Checks with their functions and parameters :param resource: :param probe_vars: :return: None """ self._resource = resource self._probe_vars = probe_vars self._parameters = probe_vars.parameters self._check_vars = probe_vars.check_vars self.response = None # Create ProbeResult object that gathers all results for single Probe self.result = ProbeResult(self, self._probe_vars) # # Lifecycle def exit(self): pass def get_var_names(self): var_names = Plugin.get_var_names(self) var_names.extend([ 'RESOURCE_TYPE', 'REQUEST_METHOD', 'REQUEST_HEADERS', 'REQUEST_TEMPLATE', 'CHECKS_AVAIL' ]) return var_names def expand_check_vars(self, checks_avail): for check_class in checks_avail: check_avail = checks_avail[check_class] check = Factory.create_obj(check_class) check_vars = Plugin.copy(check.get_plugin_vars()) # Check if Probe class overrides Check Params # mainly "value" entries. if 'set_params' in check_avail: set_params = check_avail['set_params'] for set_param in set_params: if set_param in check_vars['PARAM_DEFS']: param_orig = check_vars['PARAM_DEFS'][set_param] param_override = set_params[set_param] param_def = Plugin.merge(param_orig, param_override) check_vars['PARAM_DEFS'][set_param] = param_def checks_avail[check_class] = check_vars return checks_avail def get_checks_info_defaults(self): checks_avail = self.get_checks_info() checks_avail_default = {} for check_class in checks_avail: check_avail = checks_avail[check_class] # Only include default Checks if specified if 'default' in check_avail and check_avail['default']: checks_avail_default[check_class] = check_avail return checks_avail_default def get_checks_info(self): return Plugin.copy(Plugin.get_plugin_vars(self))['CHECKS_AVAIL'] def get_plugin_vars(self): probe_vars = Plugin.copy(Plugin.get_plugin_vars(self)) probe_vars['CHECKS_AVAIL'] = \ self.expand_check_vars(probe_vars['CHECKS_AVAIL']) return probe_vars def log(self, text): LOGGER.info(text) def before_request(self): """ Before running actual request to service""" pass def after_request(self): """ After running actual request to service""" pass def get_request_headers(self): if not self._resource: return dict() headers = Plugin.copy(self.REQUEST_HEADERS) return self._resource.add_auth_header(headers) def perform_request(self): """ Perform actual request to service""" # Actualize request query string or POST body # by substitution in template. url_base = self._resource.url request_string = None if self.REQUEST_TEMPLATE: request_string = self.REQUEST_TEMPLATE if '?' in url_base and self.REQUEST_TEMPLATE[0] == '?': self.REQUEST_TEMPLATE = '&' + self.REQUEST_TEMPLATE[1:] if self._parameters: request_parms = self._parameters param_defs = self.get_param_defs() # Expand string list array to comma separated string for param in request_parms: if param_defs[param]['type'] == 'stringlist': request_parms[param] = ','.join(request_parms[param]) request_string = self.REQUEST_TEMPLATE.format(**request_parms) self.log('Requesting: %s url=%s' % (self.REQUEST_METHOD, url_base)) try: if self.REQUEST_METHOD == 'GET': # Default is plain URL, e.g. for WWW:LINK url = url_base if request_string: # Query String: mainly OWS:* resources url = "%s%s" % (url, request_string) self.response = self.perform_get_request(url) elif self.REQUEST_METHOD == 'POST': self.response = self.perform_post_request( url_base, request_string) except requests.exceptions.RequestException as e: msg = "Request Err: %s %s" % (e.__class__.__name__, str(e)) self.result.set(False, msg) if self.response: self.log('response: status=%d' % self.response.status_code) if self.response.status_code / 100 in [4, 5]: self.log('Error response: %s' % (str(self.response.text))) def perform_get_request(self, url): """ Perform actual HTTP GET request to service""" return requests.get( url, timeout=App.get_config()['GHC_PROBE_HTTP_TIMEOUT_SECS'], headers=self.get_request_headers()) def perform_post_request(self, url_base, request_string): """ Perform actual HTTP POST request to service""" return requests.post( url_base, timeout=App.get_config()['GHC_PROBE_HTTP_TIMEOUT_SECS'], data=request_string, headers=self.get_request_headers()) def run_request(self): """ Run actual request to service""" try: self.before_request() self.result.start() try: self.perform_request() except Exception as e: msg = "Perform_request Err: %s %s" % \ (e.__class__.__name__, str(e)) self.result.set(False, msg) self.result.stop() self.after_request() except Exception as e: # We must never bailout because of Exception # in Probe. msg = "Probe Err: %s %s" % (e.__class__.__name__, str(e)) LOGGER.error(msg) self.result.set(False, msg) def run_checks(self): """ Do the checks on the response from request""" # Do not run Checks if Probe already failed if not self.result.success: return # Config also determines which actual checks are performed # from possible Checks in Probe. Checks are performed # by Check instances. for check_var in self._check_vars: check = None check_class = '' try: check_class = check_var.check_class check = Factory.create_obj(check_class) except Exception: LOGGER.error("Cannot create Check class: %s %s" % (check_class, str(sys.exc_info()))) if not check: continue try: check.init(self, check_var) check.perform() except Exception: msg = "Check Err: %s" % str(sys.exc_info()) LOGGER.error(msg) check.set_result(False, msg) self.log('Check: fun=%s result=%s' % (check_class, check._result.success)) self.result.add_result(check._result) # Lifecycle def calc_result(self): """ Calculate overall result from the Result object""" self.log("Result: %s" % str(self.result)) @staticmethod def run(resource, probe_vars): """ Class method to create and run a single Probe instance. Follows strict sequence of method calls. Each method can be overridden in subclass. """ probe = None try: # Create Probe instance from module.class string probe = Factory.create_obj(probe_vars.probe_class) except Exception: LOGGER.error("Cannot create Probe class: %s %s" % (probe_vars.probe_class, str(sys.exc_info()))) if not probe: return # Initialize with actual parameters probe.init(resource, probe_vars) # Perform request probe.run_request() # Perform the Probe's checks probe.run_checks() # Determine result probe.calc_result() # Lifecycle probe.exit() # Return result return probe.result

33.070175

78

0.59371

b20f5bb5243c099ecde48102e2c5b3241ad8c242

1,215

py

Python

blog/feeds.py

josephdubon/boilerplate_dubon_django_blog

1dbe470006be066b12dd6486eb26a41d304206f8

[ "Unlicense", "MIT" ]

null

blog/feeds.py

josephdubon/boilerplate_dubon_django_blog

1dbe470006be066b12dd6486eb26a41d304206f8

[ "Unlicense", "MIT" ]

2

2021-06-10T20:43:00.000Z

2021-09-22T19:55:41.000Z

blog/feeds.py

josephdubon/boilerplate_dubon_django_blog

1dbe470006be066b12dd6486eb26a41d304206f8

[ "Unlicense", "MIT" ]

null

from django.contrib.syndication.views import Feed from django.template.defaultfilters import truncatewords from django.urls import reverse_lazy from .models import Post # First subclass the Feed class of the syndication framework class LatestPostFeed(Feed): # The title, link, and description attributes correspond to the # - <title>, <link>, and <description> RSS elements, respectively. title = "My Blog" link = reverse_lazy('blog:post_list') # reverse_lazy() to generate the URL for the link attribute description = 'New posts of my blog.' # The items() method retrieves the objects to be included in the feed. You are retrieving only the # - last five published posts for this feed. def items(self): return Post.published.all()[:5] # The item_title() and item_description() methods will receive each object returned by items() # - and return the title and description for each item. def item_title(self, item): return item.title # - Use the truncatewords built-in template filter to build the description of the blog post with the # - first 30 words. def item_description(self, item): return truncatewords(item.body, 30)

40.5

105

0.721811

732af64a825fbb52eff7ca5348fcccf84ffaf7b6

6,857

py

Python

tests/unit/python/foglamp/services/core/api/test_plugin_discovery_api.py

christoofar/FogLAMP

3aaae302104038a8534c54ff8a3ed0fefd4f3201

[ "Apache-2.0" ]

null

tests/unit/python/foglamp/services/core/api/test_plugin_discovery_api.py

christoofar/FogLAMP

3aaae302104038a8534c54ff8a3ed0fefd4f3201

[ "Apache-2.0" ]

null

tests/unit/python/foglamp/services/core/api/test_plugin_discovery_api.py

christoofar/FogLAMP

3aaae302104038a8534c54ff8a3ed0fefd4f3201

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- # FOGLAMP_BEGIN # See: http://foglamp.readthedocs.io/ # FOGLAMP_END import json from unittest.mock import patch import pytest from aiohttp import web from foglamp.services.core import routes from foglamp.common.plugin_discovery import PluginDiscovery __author__ = "Ashish Jabble" __copyright__ = "Copyright (c) 2017 OSIsoft, LLC" __license__ = "Apache 2.0" __version__ = "${VERSION}" @pytest.allure.feature("unit") @pytest.allure.story("api", "plugin-discovery") class TestPluginDiscoveryApi: @pytest.fixture def client(self, loop, test_client): app = web.Application(loop=loop) # fill the routes table routes.setup(app) return loop.run_until_complete(test_client(app)) @pytest.mark.parametrize("method, result, is_config", [ ("/foglamp/plugins/installed", {"name": "sinusoid", "version": "1.0", "type": "south", "description": "sinusoid plugin"}, False), ("/foglamp/plugins/installed?config=true", {"name": "sinusoid", "version": "1.0", "type": "south", "description": "sinusoid plugin", "config": { "plugin": {"description": "sinusoid plugin", "type": "string", "default": "sinusoid", "readonly": "true"}}}, True), ("/foglamp/plugins/installed?config=false", {"name": "sinusoid", "version": "1.0", "type": "south", "description": "sinusoid plugin"}, False) ]) async def test_get_plugins_installed(self, client, method, result, is_config): with patch.object(PluginDiscovery, 'get_plugins_installed', return_value=result) as patch_get_plugin_installed: resp = await client.get('{}'.format(method)) assert 200 == resp.status r = await resp.text() json_response = json.loads(r) assert {'plugins': result} == json_response patch_get_plugin_installed.assert_called_once_with(None, is_config) @pytest.mark.parametrize("param", [ "north", "south", "North", "South", "NORTH", "SOUTH", "filter", "Filter", "FILTER", "notify", "NOTIFY" ]) async def test_get_plugins_installed_by_params(self, client, param): with patch.object(PluginDiscovery, 'get_plugins_installed', return_value={}) as patch_get_plugin_installed: resp = await client.get('/foglamp/plugins/installed?type={}'.format(param)) assert 200 == resp.status r = await resp.text() json_response = json.loads(r) assert {'plugins': {}} == json_response patch_get_plugin_installed.assert_called_once_with(param.lower(), False) @pytest.mark.parametrize("param, direction, result, is_config", [ ("?type=north&config=false", "north", {"name": "http", "version": "1.0.0", "type": "north", "description": "HTTP North-C plugin"}, False), ("?type=south&config=false", "south", {"name": "sinusoid", "version": "1.0", "type": "south", "description": "sinusoid plugin"}, False), ("?type=filter&config=false", "filter", {"name": "scale", "version": "1.0.0", "type": "filter", "description": "Filter Scale plugin"}, False), ("?type=notify&config=false", "notify", {"name": "email", "version": "1.0.0", "type": "notify", "description": "Email notification plugin"}, False), ("?type=north&config=true", "north", {"name": "http", "version": "1.0.0", "type": "north", "description": "HTTP North-C plugin", "config": {"plugin": {"description": "HTTP North-C plugin", "type": "string", "default": "http-north"}}}, True), ("?type=south&config=true", "south", {"name": "sinusoid", "version": "1.0", "type": "south", "description": "sinusoid plugin", "config": {"plugin": {"description": "sinusoid plugin", "type": "string", "default": "sinusoid", "readonly": "true"}}}, True), ("?type=filter&config=true", "filter", {"name": "scale", "version": "1.0.0", "type": "filter", "description": "Filter Scale plugin", "config": {"offset": {"default": "0.0", "type": "float", "description": "A constant offset"}, "factor": {"default": "100.0", "type": "float", "description": "Scale factor for a reading."}, "plugin": {"default": "scale", "type": "string", "description": "Scale filter plugin"}, "enable": {"default": "false", "type": "boolean", "description": "A switch that can be used to enable or disable."}}}, True), ("?type=notify&config=true", "notify", {"name": "email", "version": "1.0.0", "type": "notify", "description": "Email notification plugin", "config": {"plugin": {"type": "string", "description": "Email notification plugin", "default": "email"}}}, True) ]) async def test_get_plugins_installed_by_type_and_config(self, client, param, direction, result, is_config): with patch.object(PluginDiscovery, 'get_plugins_installed', return_value=result) as patch_get_plugin_installed: resp = await client.get('/foglamp/plugins/installed{}'.format(param)) assert 200 == resp.status r = await resp.text() json_response = json.loads(r) assert {'plugins': result} == json_response patch_get_plugin_installed.assert_called_once_with(direction, is_config) @pytest.mark.parametrize("param, message", [ ("?type=blah", "Invalid plugin type. Must be 'north' or 'south' or 'filter' or 'notify'."), ("?config=blah", 'Only "true", "false", true, false are allowed for value of config.'), ("?config=False", 'Only "true", "false", true, false are allowed for value of config.'), ("?config=True", 'Only "true", "false", true, false are allowed for value of config.'), ("?config=f", 'Only "true", "false", true, false are allowed for value of config.'), ("?config=t", 'Only "true", "false", true, false are allowed for value of config.'), ("?config=1", 'Only "true", "false", true, false are allowed for value of config.'), ("?config=Y", 'Only "true", "false", true, false are allowed for value of config.'), ("?config=Yes", 'Only "true", "false", true, false are allowed for value of config.'), ("?config=No&type=north", 'Only "true", "false", true, false are allowed for value of config.'), ("?config=TRUE&type=south", 'Only "true", "false", true, false are allowed for value of config.'), ("?type=south&config=0", 'Only "true", "false", true, false are allowed for value of config.') ]) async def test_bad_get_plugins_installed(self, client, param, message): resp = await client.get('/foglamp/plugins/installed{}'.format(param)) assert 400 == resp.status assert message == resp.reason

62.336364

450

0.612221

996b118d17d717560f4a0af453b7ab64ecd26aa2

14,016

py

Python

bcbio/variation/freebayes.py

SciLifeLab/bcbio-nextgen

370b3f316c423b41523accc5e212d51a5b7ecaa9

[ "MIT" ]

3

2015-11-18T07:17:54.000Z

2021-04-28T13:58:37.000Z

bcbio/variation/freebayes.py

SciLifeLab/bcbio-nextgen

370b3f316c423b41523accc5e212d51a5b7ecaa9

[ "MIT" ]

null

bcbio/variation/freebayes.py

SciLifeLab/bcbio-nextgen

370b3f316c423b41523accc5e212d51a5b7ecaa9

[ "MIT" ]

null

"""Bayesian variant calling with FreeBayes. https://github.com/ekg/freebayes """ import os import sys from bcbio import bam, utils from bcbio.distributed.transaction import file_transaction from bcbio.pipeline import config_utils from bcbio.pipeline.shared import subset_variant_regions from bcbio.provenance import do from bcbio.variation import annotation, bedutils, ploidy, vcfutils from bcbio.variation.vcfutils import (get_paired_bams, is_paired_analysis, move_vcf) def region_to_freebayes(region): if isinstance(region, (list, tuple)): chrom, start, end = region return "%s:%s..%s" % (chrom, start, end) else: return region def _freebayes_options_from_config(items, config, out_file, region=None): """Prepare standard options from configuration input. Input BED target files are merged to avoid overlapping regions which cause FreeBayes to call multiple times. """ opts = [] opts += ["--ploidy", str(ploidy.get_ploidy(items, region))] variant_regions = bedutils.merge_overlaps(utils.get_in(config, ("algorithm", "variant_regions")), items[0]) target = subset_variant_regions(variant_regions, region, out_file, items) if target: if isinstance(target, basestring) and os.path.isfile(target): opts += ["--targets", target] else: opts += ["--region", region_to_freebayes(target)] resources = config_utils.get_resources("freebayes", config) if resources.get("options"): opts += resources["options"] return opts def _add_somatic_opts(opts, paired): """Add somatic options to current set. See _run_freebayes_paired for references. """ if "--min-alternate-fraction" not in opts and "-F" not in opts: # add minimum reportable allele frequency # FreeBayes defaults to 20%, but use 10% by default for the # tumor case min_af = float(utils.get_in(paired.tumor_config, ("algorithm", "min_allele_fraction"), 10)) / 100.0 opts += " --min-alternate-fraction %s" % min_af # Recommended settings for cancer calling opts += (" --pooled-discrete --pooled-continuous --genotype-qualities " "--report-genotype-likelihood-max --allele-balance-priors-off") return opts def run_freebayes(align_bams, items, ref_file, assoc_files, region=None, out_file=None): """Run FreeBayes variant calling, either paired tumor/normal or germline calling. """ if is_paired_analysis(align_bams, items): paired = get_paired_bams(align_bams, items) if not paired.normal_bam: call_file = _run_freebayes_caller(align_bams, items, ref_file, assoc_files, region, out_file, somatic=paired) else: call_file = _run_freebayes_paired(align_bams, items, ref_file, assoc_files, region, out_file) else: vcfutils.check_paired_problems(items) call_file = _run_freebayes_caller(align_bams, items, ref_file, assoc_files, region, out_file) return call_file def _run_freebayes_caller(align_bams, items, ref_file, assoc_files, region=None, out_file=None, somatic=None): """Detect SNPs and indels with FreeBayes. Performs post-filtering to remove very low quality variants which can cause issues feeding into GATK. Breaks variants into individual allelic primitives for analysis and evaluation. """ config = items[0]["config"] if out_file is None: out_file = "%s-variants.vcf.gz" % os.path.splitext(align_bams[0])[0] if not utils.file_exists(out_file): with file_transaction(items[0], out_file) as tx_out_file: for align_bam in align_bams: bam.index(align_bam, config) freebayes = config_utils.get_program("freebayes", config) vcffilter = config_utils.get_program("vcffilter", config) input_bams = " ".join("-b %s" % x for x in align_bams) opts = " ".join(_freebayes_options_from_config(items, config, out_file, region)) # Recommended options from 1000 genomes low-complexity evaluation # https://groups.google.com/d/msg/freebayes/GvxIzjcpbas/1G6e3ArxQ4cJ opts += " --min-repeat-entropy 1 --experimental-gls" if somatic: opts = _add_somatic_opts(opts, somatic) compress_cmd = "| bgzip -c" if out_file.endswith("gz") else "" fix_ambig = vcfutils.fix_ambiguous_cl() cmd = ("{freebayes} -f {ref_file} {input_bams} {opts} | " "{vcffilter} -f 'QUAL > 5' -s | {fix_ambig} | " "vcfallelicprimitives --keep-info --keep-geno | vcffixup | vcfstreamsort | " "vt normalize -r {ref_file} -q - 2> /dev/null | vcfuniqalleles " "{compress_cmd} > {tx_out_file}") do.run(cmd.format(**locals()), "Genotyping with FreeBayes", {}) ann_file = annotation.annotate_nongatk_vcf(out_file, align_bams, assoc_files.get("dbsnp"), ref_file, config) return ann_file def _run_freebayes_paired(align_bams, items, ref_file, assoc_files, region=None, out_file=None): """Detect SNPs and indels with FreeBayes for paired tumor/normal samples. Sources of options for FreeBayes: mailing list: https://groups.google.com/d/msg/freebayes/dTWBtLyM4Vs/HAK_ZhJHguMJ mailing list: https://groups.google.com/forum/#!msg/freebayes/LLH7ZfZlVNs/63FdD31rrfEJ speedseq: https://github.com/cc2qe/speedseq/blob/e6729aa2589eca4e3a946f398c1a2bdc15a7300d/bin/speedseq#L916 sga/freebayes: https://github.com/jts/sga-extra/blob/7e28caf71e8107b697f9be7162050e4fa259694b/ sga_generate_varcall_makefile.pl#L299 """ config = items[0]["config"] if out_file is None: out_file = "%s-paired-variants.vcf.gz" % os.path.splitext(align_bams[0])[0] if not utils.file_exists(out_file): with file_transaction(items[0], out_file) as tx_out_file: paired = get_paired_bams(align_bams, items) assert paired.normal_bam, "Require normal BAM for FreeBayes paired calling and filtering" freebayes = config_utils.get_program("freebayes", config) opts = " ".join(_freebayes_options_from_config(items, config, out_file, region)) opts += " --min-repeat-entropy 1 --experimental-gls" opts = _add_somatic_opts(opts, paired) compress_cmd = "| bgzip -c" if out_file.endswith("gz") else "" fix_ambig = vcfutils.fix_ambiguous_cl() py_cl = os.path.join(os.path.dirname(sys.executable), "py") cl = ("{freebayes} -f {ref_file} {opts} " "{paired.tumor_bam} {paired.normal_bam} " "| vcffilter -f 'QUAL > 5' -s " "| {py_cl} -x 'bcbio.variation.freebayes.call_somatic(x)' " "| {fix_ambig} | " "vcfallelicprimitives --keep-info --keep-geno | vcffixup | vcfstreamsort | " "vt normalize -r {ref_file} -q - 2> /dev/null | vcfuniqalleles " "{compress_cmd} > {tx_out_file}") bam.index(paired.tumor_bam, config) bam.index(paired.normal_bam, config) do.run(cl.format(**locals()), "Genotyping paired variants with FreeBayes", {}) ann_file = annotation.annotate_nongatk_vcf(out_file, align_bams, assoc_files.get("dbsnp"), ref_file, config) return ann_file # ## Filtering def _check_lods(parts, tumor_thresh, normal_thresh): """Ensure likelihoods for tumor and normal pass thresholds. Skipped if no FreeBayes GL annotations available. """ try: gl_index = parts[8].split(":").index("GL") except ValueError: return True try: tumor_gls = [float(x) for x in parts[9].split(":")[gl_index].split(",")] tumor_lod = max(tumor_gls[i] - tumor_gls[0] for i in range(1, len(tumor_gls))) # No GL information, no tumor call (so fail it) except IndexError: tumor_lod = -1.0 try: normal_gls = [float(x) for x in parts[10].split(":")[gl_index].split(",")] normal_lod = min(normal_gls[0] - normal_gls[i] for i in range(1, len(normal_gls))) # No GL inofmration, no normal call (so pass it) except IndexError: normal_lod = normal_thresh return normal_lod >= normal_thresh and tumor_lod >= tumor_thresh def _check_freqs(parts): """Ensure frequency of tumor to normal passes a reasonable threshold. Avoids calling low frequency tumors also present at low frequency in normals, which indicates a contamination or persistent error. """ thresh_ratio = 2.7 try: # FreeBayes ao_index = parts[8].split(":").index("AO") ro_index = parts[8].split(":").index("RO") except ValueError: ao_index, ro_index = None, None try: # VarDict af_index = parts[8].split(":").index("AF") except ValueError: af_index = None if af_index is None and ao_index is None: raise NotImplementedError("Unexpected format annotations: %s" % parts[0]) def _calc_freq(item): try: if ao_index is not None and ro_index is not None: ao = sum([int(x) for x in item.split(":")[ao_index].split(",")]) ro = int(item.split(":")[ro_index]) freq = ao / float(ao + ro) elif af_index is not None: freq = float(item.split(":")[af_index]) except (IndexError, ValueError, ZeroDivisionError): freq = 0.0 return freq tumor_freq, normal_freq = _calc_freq(parts[9]), _calc_freq(parts[10]) return normal_freq <= 0.001 or normal_freq <= tumor_freq / thresh_ratio def call_somatic(line): """Call SOMATIC variants from tumor/normal calls, adding REJECT filters and SOMATIC flag. Assumes tumor/normal called with tumor first and normal second, as done in bcbio implementation. Uses MuTect like somatic filter based on implementation in speedseq: https://github.com/cc2qe/speedseq/blob/e6729aa2589eca4e3a946f398c1a2bdc15a7300d/bin/speedseq#L62 Extracts the genotype likelihoods (GLs) from FreeBayes, which are like phred scores except not multiplied by 10.0 (https://en.wikipedia.org/wiki/Phred_quality_score). For tumors, we retrieve the best likelihood to not be reference (the first GL) and for normal, the best likelhood to be reference. After calculating the likelihoods, we compare these to thresholds to pass variants at tuned sensitivity/precision. Tuning done on DREAM synthetic 3 dataset evaluations. We also check that the frequency of the tumor exceeds the frequency of the normal by a threshold to avoid calls that are low frequency in both tumor and normal. This supports both FreeBayes and VarDict output frequencies. """ # Thresholds are like phred scores, so 3.5 = phred35 tumor_thresh, normal_thresh = 3.5, 3.5 if line.startswith("#CHROM"): headers = ['##INFO=<ID=SOMATIC,Number=0,Type=Flag,Description="Somatic event">', ('##FILTER=<ID=REJECT,Description="Not somatic due to normal call frequency ' 'or phred likelihoods: tumor: %s, normal %s.">') % (int(tumor_thresh * 10), int(normal_thresh * 10))] return "\n".join(headers) + "\n" + line elif line.startswith("#"): return line else: parts = line.split("\t") if _check_lods(parts, tumor_thresh, normal_thresh) and _check_freqs(parts): parts[7] = parts[7] + ";SOMATIC" else: if parts[6] in set([".", "PASS"]): parts[6] = "REJECT" else: parts[6] += ";REJECT" line = "\t".join(parts) return line def _clean_freebayes_output(line): """Clean FreeBayes output to make post-processing with GATK happy. XXX Not applied on recent versions which fix issues to be more compatible with bgzip output, but retained in case of need. - Remove lines from FreeBayes outputs where REF/ALT are identical: 2 22816178 . G G 0.0339196 or there are multiple duplicate alleles: 4 60594753 . TGAAA T,T - Remove Type=Int specifications which are not valid VCF and GATK chokes on. """ if line.startswith("#"): line = line.replace("Type=Int,D", "Type=Integer,D") return line else: parts = line.split("\t") alleles = [x.strip() for x in parts[4].split(",")] + [parts[3].strip()] if len(alleles) == len(set(alleles)): return line return None def clean_vcf_output(orig_file, clean_fn, config, name="clean"): """Provide framework to clean a file in-place, with the specified clean function. """ base, ext = utils.splitext_plus(orig_file) out_file = "{0}-{1}{2}".format(base, name, ext) if not utils.file_exists(out_file): with open(orig_file) as in_handle: with file_transaction(config, out_file) as tx_out_file: with open(tx_out_file, "w") as out_handle: for line in in_handle: update_line = clean_fn(line) if update_line: out_handle.write(update_line) move_vcf(orig_file, "{0}.orig".format(orig_file)) move_vcf(out_file, orig_file) with open(out_file, "w") as out_handle: out_handle.write("Moved to {0}".format(orig_file))

46.564784

111

0.625571

059477a5e9673a4c2633fe2f8f66cc7e6ad70934

722

py

Python

dataworkspace/dataworkspace/apps/core/migrations/0009_alter_newslettersubscription_user.py

uktrade/analysis-workspace

2de79c6172cf391c1954ca3789c5c0dc0030ec25

[ "MIT" ]

1

2019-06-10T08:22:56.000Z

dataworkspace/dataworkspace/apps/core/migrations/0009_alter_newslettersubscription_user.py

uktrade/analysis-workspace

2de79c6172cf391c1954ca3789c5c0dc0030ec25

[ "MIT" ]

2

2019-05-17T13:10:42.000Z

2019-06-17T10:48:46.000Z

dataworkspace/dataworkspace/apps/core/migrations/0009_alter_newslettersubscription_user.py

uktrade/analysis-workspace

2de79c6172cf391c1954ca3789c5c0dc0030ec25

[ "MIT" ]

null

# Generated by Django 3.2.13 on 2022-05-30 16:36 from django.conf import settings from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ migrations.swappable_dependency(settings.AUTH_USER_MODEL), ("core", "0008_newslettersubscription"), ] operations = [ migrations.AlterField( model_name="newslettersubscription", name="user", field=models.ForeignKey( on_delete=django.db.models.deletion.CASCADE, related_name="newsletter_signups", to=settings.AUTH_USER_MODEL, unique=True, ), ), ]

26.740741

66

0.620499

92bef292d32f033bf6fc1a3129d3ad91496c15f1

735

py

Python

third_party/WebKit/LayoutTests/http/tests/websocket/count-received-bytes_wsh.py

google-ar/chromium

2441c86a5fd975f09a6c30cddb57dfb7fc239699

[ "Apache-2.0", "BSD-3-Clause-No-Nuclear-License-2014", "BSD-3-Clause" ]

777

2017-08-29T15:15:32.000Z

2022-03-21T05:29:41.000Z

third_party/WebKit/LayoutTests/http/tests/websocket/count-received-bytes_wsh.py

harrymarkovskiy/WebARonARCore

2441c86a5fd975f09a6c30cddb57dfb7fc239699

[ "Apache-2.0", "BSD-3-Clause-No-Nuclear-License-2014", "BSD-3-Clause" ]

66

2017-08-30T18:31:18.000Z

2021-08-02T10:59:35.000Z

third_party/WebKit/LayoutTests/http/tests/websocket/count-received-bytes_wsh.py

harrymarkovskiy/WebARonARCore

2441c86a5fd975f09a6c30cddb57dfb7fc239699

[ "Apache-2.0", "BSD-3-Clause-No-Nuclear-License-2014", "BSD-3-Clause" ]

123

2017-08-30T01:19:34.000Z

2022-03-17T22:55:31.000Z

# Copyright 2014 The Chromium Authors. All rights reserved. # Use of this source code is governed by a BSD-style license that can be # found in the LICENSE file. import codecs def web_socket_do_extra_handshake(request): request.ws_extension_processors = [] request.received_bytes = 0 def web_socket_transfer_data(request): while True: line = request.ws_stream.receive_message() if line is None: return if isinstance(line, unicode): request.received_bytes += len(codecs.encode(line, 'utf-8')) else: request.received_bytes += len(line) def web_socket_passive_closing_handshake(request): return 1000, 'received %d bytes' % request.received_bytes

27.222222

72

0.69932

0dc7a0e33f43683e9d3893c734939f1b47d95db7

3,853

py

Python

Website/site/gravi_site.py

NPPC-UK/Gravimetrics

a4a4bada6da5e2c3dd6f58e7fa4ca226fc374d86

[ "MIT" ]

4

2016-11-19T00:34:45.000Z

2021-12-30T14:27:01.000Z

Website/site/gravi_site.py

NPPC-UK/Gravimetrics

a4a4bada6da5e2c3dd6f58e7fa4ca226fc374d86

[ "MIT" ]

null

Website/site/gravi_site.py

NPPC-UK/Gravimetrics

a4a4bada6da5e2c3dd6f58e7fa4ca226fc374d86

[ "MIT" ]

null

#!/usr/bin/python3 from flask import Flask, render_template, request, redirect, url_for, session, flash, make_response from functools import wraps import pandas as pd from login_manager import login_user from data_manager import get_experiments, get_experiment_plants, get_all_water_data, get_all_balance_data, end_experiment, create_new_experiment, update_target_weights app = Flask(__name__) ALLOWED_EXTENSIONS = set(['csv']) def allowed_file(filename): return '.' in filename and \ filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS def get_uploaded_file_as_df(): # Max said it was okay! # We Trust people! df = pd.DataFrame() # check if the post request has the file part if 'file' not in request.files: flash('No file part') resp = False file = request.files['file'] # if user does not select file, browser also # submit an empty part without filenameX if file.filename == '': flash('No selected file') resp = False if file and allowed_file(file.filename): print(file.filename) resp = True df = pd.read_csv(request.files.get('file')) else: resp = 'Bad upload file' return (resp, df) def checkuser(func): """Checks whether user is logged in or passes to login page.""" @wraps(func) def wrapper(*args, **kwargs): if 'username' not in session: return redirect(url_for('login')) return func(*args, **kwargs) return wrapper @app.route("/", defaults={'path': ''}, methods=['GET', 'POST']) @app.route('/<path:path>', methods=['GET', 'POST']) @checkuser def index(path): exps = get_experiments() return render_template('index.html', experiments=exps, path=path) @app.route("/new_experiment", methods=['GET', 'POST']) @checkuser def new_experiment(): resp = None if request.method == 'POST': resp, df = get_uploaded_file_as_df() if resp: resp = create_new_experiment(df, owner=session['username'][:3]) return render_template('new_experiment.html', resp=resp) @app.route("/data") @checkuser def data(): exp = request.args.get("experiment") data_type = request.args.get("type") if 'end' in data_type.lower(): end_experiment(exp) df = get_all_water_data( exp) if 'water' in data_type.lower() else get_all_balance_data(exp) resp = make_response(df.to_csv()) resp.headers["Content-Disposition"] = "attachment; filename=export.csv" resp.headers["Content-Type"] = "text/csv" return resp @app.route("/experiment", methods=['GET', 'POST']) @checkuser def view_experiment(): resp = None exp = request.args.get("experiment") plants_df = get_experiment_plants(exp) if request.method == 'POST': resp, df = get_uploaded_file_as_df() if resp: resp = update_target_weights(df) return render_template('experiment.html', experiment=exp, error=resp, plants=plants_df) @app.route('/login', methods=['GET', 'POST']) def login(): error = None if request.method == 'GET': # When this page is visited we want to log out the user if 'username' in session: session.pop('username', None) if request.method == 'POST': session['username'] = request.form['username'] pwd = request.form['password'] if login_user(app, session['username'], pwd): return redirect(url_for('index')) else: error = 'Invalid Credentials. Please try again.' return render_template('login.html', error=error) if __name__ == "__main__": app.secret_key = '8080' app.config['SESSION_TYPE'] = 'filesystem' app.run(host='0.0.0.0', port=9666, debug=False)

31.072581

167

0.634051

80c66543b75d2ff9a7750f02411603a70d48e236

3,171

py

Python

louvain_to_gephi_giraph.py

ErathosthemesAmmoro/track-communities

7afd60aaa62ed0b81c7f785974ea0a8687ea136e

[ "Apache-2.0" ]

12

2015-02-02T13:13:52.000Z

2022-03-16T12:35:32.000Z

louvain_to_gephi_giraph.py

ErathosthemesAmmoro/track-communities

7afd60aaa62ed0b81c7f785974ea0a8687ea136e

[ "Apache-2.0" ]

null

louvain_to_gephi_giraph.py

ErathosthemesAmmoro/track-communities

7afd60aaa62ed0b81c7f785974ea0a8687ea136e

[ "Apache-2.0" ]

3

2015-10-05T00:27:38.000Z

2020-03-02T17:51:39.000Z

# # Copyright 2016 Sotera Defense Solutions Inc. # # 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. #!/usr/bin/env python import os import sys from subprocess import call table = sys.argv[1] garbage = open("garbage.out","w") os.system("cat output/giraph/giraph_0/part-m* > output/giraph/giraph_0/output") f = open('output/giraph/giraph_0/output','r') o = open('louvain_to_gephi/giraph/community_itr_1.nodes','w') for line in f: vals = line.split('\t') o.write(vals[0].strip() + '\t' + vals[1].strip() + '\n') f.close() o.close() call("hadoop fs -mkdir /tmp/trackcomms/" + table + "/output/giraph/comm_1", stdout=garbage, shell=True) call("hadoop fs -put louvain_to_gephi/giraph/community_itr_1.nodes /tmp/trackcomms/" + table + "/output/giraph/comm_1", stdout=garbage, shell=True) f = open('edgelist.tsv','r') o = open('louvain_to_gephi/giraph/graph_itr_0.edges','w') for line in f: if len(line.split('\t')) == 3: source,weight,edgelist = line.split('\t') edgelist = edgelist.strip().split(',') for e in edgelist: o.write('\t'.join((source,e.split(':')[0],e.split(':')[1])) + '\n') o.close() f.close() # Here's the looping piece i = 1 pm = 'output/giraph/mapreduce_'+str(i) pg = 'output/giraph/giraph_'+str(i+1) while os.path.exists(pm): os.system("cat " + pg + "/part* > " + pg + "/output") os.system("cat " + pm + "/part* > " + pm + "/output") level = str(i+1) f = open(pg + '/output','r') o = open('louvain_to_gephi/giraph/community_itr_' + level + '.nodes','w') for line in f: vals = line.split('\t') o.write(vals[0].strip() + '\t' + vals[1].strip() + '\n') f.close() o.close() call("hadoop fs -mkdir /tmp/trackcomms/" + table + "/output/giraph/comm_" + level, stdout=garbage, shell=True) call("hadoop fs -put louvain_to_gephi/giraph/community_itr_" + level + ".nodes /tmp/trackcomms/" + table + "/output/giraph/comm_" + level, stdout=garbage, shell=True) f = open(pm + '/output','r') o = open('louvain_to_gephi/giraph/graph_itr_' + str(i) + '.edges','w') for line in f: if len(line.split('\t')) == 3: source,weight,edgelist = line.split('\t') edgelist = edgelist.strip().split(',') for e in edgelist: o.write('\t'.join((source,e.split(':')[0],e.split(':')[1])) + '\n') if int(weight) != 0: o.write('\t'.join((source,source,weight,'\n'))) elif len(line.split('\t')) == 2: source, weight = line.split('\t') weight = weight.strip() if int(weight) != 0: o.write('\t'.join((source,source,weight,'\n'))) o.close() f.close() i = i + 1 pm = 'output/giraph/mapreduce_'+str(i) pg = 'output/giraph/giraph_'+str(i+1)

32.690722

168

0.639546

a911e61793ef8f41cbc108968858cbe169ca9be7

854

py

Python

maza/modules/creds/cameras/avigilon/ssh_default_creds.py

ArturSpirin/maza

56ae6325c08bcedd22c57b9fe11b58f1b38314ca

[ "MIT" ]

2

2020-02-06T20:24:31.000Z

2022-03-08T19:07:16.000Z

maza/modules/creds/cameras/avigilon/ssh_default_creds.py

ArturSpirin/maza

56ae6325c08bcedd22c57b9fe11b58f1b38314ca

[ "MIT" ]

null

maza/modules/creds/cameras/avigilon/ssh_default_creds.py

ArturSpirin/maza

56ae6325c08bcedd22c57b9fe11b58f1b38314ca

[ "MIT" ]

null

from maza.core.exploit import * from maza.modules.creds.generic.ssh_default import Exploit as SSHDefault class Exploit(SSHDefault): __info__ = { "name": "Avigilon Camera Default SSH Creds", "description": "Module performs dictionary attack against Avigilon Camera SSH service. " "If valid credentials are found, they are displayed to the user.", "authors": ( "Marcin Bury <marcin[at]threat9.com>", # routersploit module ), "devices": ( "Avigilon Camera", ) } target = OptIP("", "Target IPv4, IPv6 address or file with ip:port (file://)") port = OptPort(22, "Target SSH port") threads = OptInteger(1, "Number of threads") defaults = OptWordlist("admin:admin,Administrator:", "User:Pass or file with default credentials (file://)")

37.130435

112

0.633489

1a846b94245e2d6772d0bc9c4ad377131f0e9ab0

842

py

Python

molecool/io/xyz.py

aatishpr/molecool

73a52479b41ae2847b32707b2c32ca4e23ca83c4

[ "BSD-3-Clause" ]

null

molecool/io/xyz.py

aatishpr/molecool

73a52479b41ae2847b32707b2c32ca4e23ca83c4

[ "BSD-3-Clause" ]

null

molecool/io/xyz.py

aatishpr/molecool

73a52479b41ae2847b32707b2c32ca4e23ca83c4

[ "BSD-3-Clause" ]

null

""" xyz.py read and write xyz files """ import numpy as np def open_xyz(file_location): # Open an xyz file and return symbols and coordinates. xyz_file = np.genfromtxt(fname=file_location, skip_header=2, dtype='unicode') symbols = xyz_file[:,0] coords = (xyz_file[:,1:]) coords = coords.astype(np.float) return symbols, coords def write_xyz(file_location, symbols, coordinates): # Write an xyz file given a file location, symbols, and coordinates. num_atoms = len(symbols) with open(file_location, 'w+') as f: f.write('{}\n'.format(num_atoms)) f.write('XYZ file\n') for i in range(num_atoms): f.write('{}\t{}\t{}\t{}\n'.format(symbols[i], coordinates[i,0], coordinates[i,1], coordinates[i,2]))

27.16129

100

0.597387

e104fb385828ad6c784935c829f1b6493d240a92

4,142

py

Python

packages/core/minos-microservice-common/minos/common/exceptions.py

sorasful/minos-python

1189330eebf6444627a2af6b29f347670f95a4dd

[ "MIT" ]

247

2022-01-24T14:55:30.000Z

2022-03-25T12:06:17.000Z

packages/core/minos-microservice-common/minos/common/exceptions.py

sorasful/minos-python

1189330eebf6444627a2af6b29f347670f95a4dd

[ "MIT" ]

400

2021-04-03T08:51:40.000Z

2022-01-28T11:51:22.000Z

packages/core/minos-microservice-common/minos/common/exceptions.py

sorasful/minos-python

1189330eebf6444627a2af6b29f347670f95a4dd

[ "MIT" ]

21

2022-02-06T17:25:58.000Z

2022-03-27T04:50:29.000Z

from __future__ import ( annotations, ) from typing import ( Any, Type, ) class MinosException(Exception): """Exception class for import packages or modules""" __slots__ = "_message" def __init__(self, error_message: str): self._message = error_message def __repr__(self): return f"{type(self).__name__}(message={repr(self._message)})" def __str__(self) -> str: """represent in a string format the error message passed during the instantiation""" return self._message class NotProvidedException(MinosException): """Exception to be raised when a dependency is needed but not provided.""" class MinosImportException(MinosException): pass class MinosProtocolException(MinosException): pass class MinosMessageException(MinosException): pass class MinosConfigException(MinosException): """Base config exception.""" class MinosBrokerException(MinosException): """Base broker exception""" class MinosHandlerException(MinosException): """Base handler exception""" class MinosLockException(MinosException): """Base lock exception""" class MinosModelException(MinosException): """Exception to be raised when some mandatory condition is not satisfied by a model.""" pass class EmptyMinosModelSequenceException(MinosModelException): """Exception to be raised when a sequence must be not empty, but it is empty.""" pass class MultiTypeMinosModelSequenceException(MinosModelException): """Exception to be raised when a sequence doesn't satisfy the condition to have the same type for each item.""" pass class MinosModelAttributeException(MinosException): """Base model attributes exception.""" pass class MinosReqAttributeException(MinosModelAttributeException): """Exception to be raised when some required attributes are not provided.""" pass class MinosTypeAttributeException(MinosModelAttributeException): """Exception to be raised when there are any mismatching between the expected and observed attribute type.""" def __init__(self, name: str, target_type: Type, value: Any): self.name = name self.target_type = target_type self.value = value super().__init__( f"The {target_type!r} expected type for {name!r} does not match with " f"the given data type: {type(value)!r} ({value!r})" ) class MinosMalformedAttributeException(MinosModelAttributeException): """Exception to be raised when there are any kind of problems with the type definition.""" pass class MinosParseAttributeException(MinosModelAttributeException): """Exception to be raised when there are any kind of problems with the parsing logic.""" def __init__(self, name: str, value: Any, exception: Exception): self.name = name self.value = value self.exception = exception super().__init__(f"{repr(exception)} was raised while parsing {repr(name)} field with {repr(value)} value.") class MinosAttributeValidationException(MinosModelAttributeException): """Exception to be raised when some fields are not valid.""" def __init__(self, name: str, value: Any): self.name = name self.value = value super().__init__(f"{repr(value)} value does not pass the {repr(name)} field validation.") class DataDecoderException(MinosModelException): """Base data decoder exception.""" class DataDecoderMalformedTypeException(DataDecoderException): """Exception to be raised when malformed types are provided.""" class DataDecoderRequiredValueException(DataDecoderException): """Exception to be raised when required values are not provided.""" class DataDecoderTypeException(DataDecoderException): """Exception to be raised when expected and provided types do not match.""" def __init__(self, target_type: Type, value: Any): self.target_type = target_type self.value = value super().__init__( f"The {target_type!r} expected type does not match the given data type: {type(value)!r} ({value!r})" )

27.986486

116

0.714148

26dda6dadc85793b3ded36754e694ede7a1ba805

2,394

py

Python

tests/test_environment.py

mclaffey/dfx

29f223e4d2be924f25f8903bcbac10b91915d6fb

[ "MIT" ]

null

tests/test_environment.py

mclaffey/dfx

29f223e4d2be924f25f8903bcbac10b91915d6fb

[ "MIT" ]

null

tests/test_environment.py

mclaffey/dfx

29f223e4d2be924f25f8903bcbac10b91915d6fb

[ "MIT" ]

null

#!/usr/bin/env python import os import sys import unittest import unittest_dfx import dfx.ui_curses import dfx.datasets class EnvTest(unittest_dfx.AbstractDfTestCase): """Test functionality of ui_curses.Environment """ def test_load_file(self): """Providing a path to a csv loads a new Dfview named after the path """ env = dfx.ui_curses.Environment() data_path='../sample_data/obesity.csv' data_dir=os.path.dirname(data_path) env.load_file(data_path) self.assertIn(data_path, env.dfvs) self.assertEqual(data_path, env.current_dfv_name) self.assertEqual(data_dir, env.current_dir) self.assertTrue(isinstance(env.dfv, dfx.ui_curses.DfView)) def test_next_file(self): """Specify a directory and calling next file loads new csvs """ env = dfx.ui_curses.Environment() env.current_dir='../sample_data' file_name = env.next_file() self.assertEqual(file_name, '../sample_data/corona.csv') self.assertEqual(env.current_dfv_name, '../sample_data/corona.csv') file_name = env.next_file() self.assertEqual(file_name, '../sample_data/emissions.csv') self.assertEqual(env.current_dfv_name, '../sample_data/emissions.csv') def test_new_dfv(self): """Test convenience method for creating a new DfView """ env=dfx.ui_curses.Environment() df=dfx.datasets.checks dfv_p=dfx.ui_curses.DfView(df) env.new_dfv(df=df, name='child', parent_dfv=dfv_p) self.assertIn('child', env.dfvs) self.assertEqual('child', env.current_dfv_name) self.assertTrue(isinstance(env.dfv, dfx.ui_curses.DfView)) self.assertEqual(env.dfv.parent_dfv, dfv_p) def test_next(self): """Next goes to alphabetically next Dfview """ env=dfx.ui_curses.Environment() df=dfx.datasets.checks env.new_dfv(df=df, name='abc') env.new_dfv(df=df, name='xyz') self.assertEqual(env.current_dfv_name, 'xyz') env.next() self.assertEqual(env.current_dfv_name, 'abc') env.next() self.assertEqual(env.current_dfv_name, 'xyz') if __name__=='__main__': sys.exit(unittest_dfx.main(__file__))

32.351351

67

0.629073

52e16dff99c9662b47b99315d3e8de64bfb65f4e

374

py

Python

contests/20210123/abc189/b/main.py

yamap55/atcoder_python

eb000b8df3037a2bba3d3527014bc12770018cb6

[ "MIT" ]

null

contests/20210123/abc189/b/main.py

yamap55/atcoder_python

eb000b8df3037a2bba3d3527014bc12770018cb6

[ "MIT" ]

7

2021-01-23T06:51:03.000Z

2021-07-26T15:05:44.000Z

contests/20210123/abc189/b/main.py

yamap55/atcoder_python

eb000b8df3037a2bba3d3527014bc12770018cb6

[ "MIT" ]

null

#!/usr/bin/env python3 a = [int(_) for _ in input().split()] N = a[0] X = a[1] l = [input().split() for _ in range(N)] al = 0 result = -1 for i, r in enumerate(l): v = int(r[0]) p = int(r[1]) # al = al + (v * p / 100) # if X < al: # 小数点以下は誤差が出るので100倍して比較 al = al + (v * p) if (X * 100) < al: result = i + 1 break print(result)

17

39

0.475936

7d75044ee36eb1c5bd509405675bedcbc9d547da

2,564

py

Python

library_samples/Python3/ocs_sample_library_preview/SDS/SdsStreamViewMap.py

osi-awoodall/OSI-Samples-OCS

1995ccda20e4fe2ae66f3b67afbc1127d638a6fc

[ "Apache-2.0" ]

null

library_samples/Python3/ocs_sample_library_preview/SDS/SdsStreamViewMap.py

osi-awoodall/OSI-Samples-OCS

1995ccda20e4fe2ae66f3b67afbc1127d638a6fc

[ "Apache-2.0" ]

null

library_samples/Python3/ocs_sample_library_preview/SDS/SdsStreamViewMap.py

osi-awoodall/OSI-Samples-OCS

1995ccda20e4fe2ae66f3b67afbc1127d638a6fc

[ "Apache-2.0" ]

null

# SdsStreamViewMap.py # import json from .SdsStreamViewProperty import SdsStreamViewProperty class SdsStreamViewMap(object): """ SdsStreamViewMap definitions """ @property def SourceTypeId(self): """ required :return: """ return self.__sourceTypeId @SourceTypeId.setter def SourceTypeId(self, baseType): """ required :param baseType: :return: """ self.__sourceTypeId = baseType @property def TargetTypeId(self): """ required :return: """ return self.__targetTypeId @TargetTypeId.setter def TargetTypeId(self, typeCode): """ required :param typeCode: :return: """ self.__targetTypeId = typeCode @property def Properties(self): """ list of SdsStreamViewMapProperty not required :return: """ return self.__properties @Properties.setter def Properties(self, properties): """ list of SdsStreamViewMapProperty not required :param properties: :return: """ self.__properties = properties def toJson(self): return json.dumps(self.toDictionary()) def toDictionary(self): # required properties dictionary = {'SourceTypeId': self.SourceTypeId, 'TargetTypeId': self.TargetTypeId} # optional properties if hasattr(self, 'Properties'): dictionary['Properties'] = [] for value in self.Properties: dictionary['Properties'].append(value.toDictionary()) return dictionary @staticmethod def fromJson(jsonObj): return SdsStreamViewMap.fromDictionary(jsonObj) @staticmethod def fromDictionary(content): streamViewMap = SdsStreamViewMap() if not content: return streamViewMap if 'TargetTypeId' in content: streamViewMap.TargetTypeId = content['TargetTypeId'] if 'SourceTypeId' in content: streamViewMap.SourceTypeId = content['SourceTypeId'] if 'Properties' in content: properties = content['Properties'] if properties is not None and len(properties) > 0: streamViewMap.Properties = [] for value in properties: streamViewMap.Properties.append( SdsStreamViewProperty.fromDictionary(value)) return streamViewMap

24.188679

69

0.583463

b57289300e28fa87dba472d7101a480343463701

7,606

py

Python

graphsaint/pytorch_version/models.py

sandl99/KGraph

bb1a9e90b785315ecb501593a0ac19e6fafc2f28

[ "MIT" ]

null

graphsaint/pytorch_version/models.py

sandl99/KGraph

bb1a9e90b785315ecb501593a0ac19e6fafc2f28

[ "MIT" ]

1

2021-04-14T15:22:45.000Z

graphsaint/pytorch_version/models.py

sandl99/Simple-KGCN-GraphSAINT

bb1a9e90b785315ecb501593a0ac19e6fafc2f28

[ "MIT" ]

null

import torch from torch import nn import torch.nn.functional as F import numpy as np from graphsaint.utils import * import graphsaint.pytorch_version.layers as layers class GraphSAINT(nn.Module): def __init__(self, num_classes, arch_gcn, train_params, feat_full, label_full, cpu_eval=False): """ Build the multi-layer GNN architecture. Inputs: num_classes int, number of classes a node can belong to arch_gcn dict, config for each GNN layer train_params dict, training hyperparameters (e.g., learning rate) feat_full np array of shape N x f, where N is the total num of nodes and f is the dimension for input node feature label_full np array, for single-class classification, the shape is N x 1 and for multi-class classification, the shape is N x c (where c = num_classes) cpu_eval bool, if True, will put the model on CPU. Outputs: None """ super(GraphSAINT, self).__init__() self.use_cuda = (args_global.gpu >= 0) if cpu_eval: self.use_cuda=False if "attention" in arch_gcn: if "gated_attention" in arch_gcn: if arch_gcn['gated_attention']: self.aggregator_cls = layers.GatedAttentionAggregator self.mulhead = int(arch_gcn['attention']) else: self.aggregator_cls = layers.AttentionAggregator self.mulhead = int(arch_gcn['attention']) else: self.aggregator_cls = layers.HighOrderAggregator self.mulhead = 1 self.num_layers = len(arch_gcn['arch'].split('-')) self.weight_decay = train_params['weight_decay'] self.dropout = train_params['dropout'] self.lr = train_params['lr'] self.arch_gcn = arch_gcn self.sigmoid_loss = (arch_gcn['loss'] == 'sigmoid') self.feat_full = torch.from_numpy(feat_full.astype(np.float32)) self.label_full = torch.from_numpy(label_full.astype(np.float32)) if self.use_cuda: self.feat_full = self.feat_full.cuda() self.label_full = self.label_full.cuda() if not self.sigmoid_loss: self.label_full_cat = torch.from_numpy(label_full.argmax(axis=1).astype(np.int64)) if self.use_cuda: self.label_full_cat = self.label_full_cat.cuda() self.num_classes = num_classes _dims, self.order_layer, self.act_layer, self.bias_layer, self.aggr_layer \ = parse_layer_yml(arch_gcn, self.feat_full.shape[1]) # get layer index for each conv layer, useful for jk net last layer aggregation self.set_idx_conv() self.set_dims(_dims) self.loss = 0 self.opt_op = None # build the model below self.num_params = 0 self.aggregators, num_param = self.get_aggregators() self.num_params += num_param self.conv_layers = nn.Sequential(*self.aggregators) self.classifier = layers.HighOrderAggregator(self.dims_feat[-1], self.num_classes,\ act='I', order=0, dropout=self.dropout, bias='bias') self.num_params += self.classifier.num_param self.optimizer = torch.optim.Adam(self.parameters(), lr=self.lr) def set_dims(self, dims): """ Set the feature dimension / weight dimension for each GNN or MLP layer. We will use the dimensions set here to initialize PyTorch layers. Inputs: dims list, length of node feature for each hidden layer Outputs: None """ self.dims_feat = [dims[0]] + [ ((self.aggr_layer[l]=='concat') * self.order_layer[l] + 1) * dims[l+1] for l in range(len(dims) - 1) ] self.dims_weight = [(self.dims_feat[l],dims[l+1]) for l in range(len(dims)-1)] def set_idx_conv(self): """ Set the index of GNN layers for the full neural net. For example, if the full NN is having 1-0-1-0 arch (1-hop graph conv, followed by 0-hop MLP, ...). Then the layer indices will be 0, 2. """ idx_conv = np.where(np.array(self.order_layer) >= 1)[0] idx_conv = list(idx_conv[1:] - 1) idx_conv.append(len(self.order_layer) - 1) _o_arr = np.array(self.order_layer)[idx_conv] if np.prod(np.ediff1d(_o_arr)) == 0: self.idx_conv = idx_conv else: self.idx_conv = list(np.where(np.array(self.order_layer) == 1)[0]) def forward(self, node_subgraph, adj_subgraph): feat_subg = self.feat_full[node_subgraph] label_subg = self.label_full[node_subgraph] label_subg_converted = label_subg if self.sigmoid_loss else self.label_full_cat[node_subgraph] _, emb_subg = self.conv_layers((adj_subgraph, feat_subg)) emb_subg_norm = F.normalize(emb_subg, p=2, dim=1) pred_subg = self.classifier((None, emb_subg_norm))[1] return pred_subg, label_subg, label_subg_converted def _loss(self, preds, labels, norm_loss): """ The predictor performs sigmoid (for multi-class) or softmax (for single-class) """ if self.sigmoid_loss: norm_loss = norm_loss.unsqueeze(1) return torch.nn.BCEWithLogitsLoss(weight=norm_loss,reduction='sum')(preds, labels) else: _ls = torch.nn.CrossEntropyLoss(reduction='none')(preds, labels) return (norm_loss*_ls).sum() def get_aggregators(self): """ Return a list of aggregator instances. to be used in self.build() """ num_param = 0 aggregators = [] for l in range(self.num_layers): aggr = self.aggregator_cls( *self.dims_weight[l], dropout=self.dropout, act=self.act_layer[l], order=self.order_layer[l], aggr=self.aggr_layer[l], bias=self.bias_layer[l], mulhead=self.mulhead, ) num_param += aggr.num_param aggregators.append(aggr) return aggregators, num_param def predict(self, preds): return nn.Sigmoid()(preds) if self.sigmoid_loss else F.softmax(preds, dim=1) def train_step(self, node_subgraph, adj_subgraph, norm_loss_subgraph): """ Forward and backward propagation """ self.train() self.optimizer.zero_grad() preds, labels, labels_converted = self(node_subgraph, adj_subgraph) loss = self._loss(preds, labels_converted, norm_loss_subgraph) # labels.squeeze()? loss.backward() torch.nn.utils.clip_grad_norm(self.parameters(), 5) self.optimizer.step() return loss, self.predict(preds), labels def eval_step(self, node_subgraph, adj_subgraph, norm_loss_subgraph): """ Forward propagation only """ self.eval() with torch.no_grad(): preds, labels, labels_converted = self(node_subgraph, adj_subgraph) loss = self._loss(preds, labels_converted, norm_loss_subgraph) return loss, self.predict(preds), labels

42.49162

103

0.58822

093c4535c1ce61b59f9d327f2b10e216bb19b0d4

5,319

py

Python

test/sagemaker_tests/mxnet/training/resources/mnist/mnist.py

Elizaaaaa/deep-learning-containers

6274ecb264645070d11b27e5c7e60d2e4110537d

[ "Apache-2.0" ]

383

2020-05-19T18:09:10.000Z

2022-03-29T22:41:05.000Z

test/sagemaker_tests/mxnet/training/resources/mnist/mnist.py

Elizaaaaa/deep-learning-containers

6274ecb264645070d11b27e5c7e60d2e4110537d

[ "Apache-2.0" ]

551

2020-05-27T17:25:50.000Z

2022-03-31T18:00:35.000Z

test/sagemaker_tests/mxnet/training/resources/mnist/mnist.py

Elizaaaaa/deep-learning-containers

6274ecb264645070d11b27e5c7e60d2e4110537d

[ "Apache-2.0" ]

263

2020-05-19T18:17:12.000Z

2022-03-29T22:41:10.000Z

# Copyright 2018-2020 Amazon.com, Inc. or its affiliates. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"). # You may not use this file except in compliance with the License. # A copy of the License is located at # # http://www.apache.org/licenses/LICENSE-2.0 # # or in the "license" file accompanying this file. This file is distributed # on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either # express or implied. See the License for the specific language governing # permissions and limitations under the License. import argparse import gzip import json import logging import os import struct import sys import mxnet as mx import numpy as np from sagemaker_mxnet_container.training_utils import scheduler_host def load_data(path): with gzip.open(find_file(path, 'labels.gz')) as flbl: struct.unpack('>II', flbl.read(8)) labels = np.fromstring(flbl.read(), dtype=np.int8) with gzip.open(find_file(path, 'images.gz')) as fimg: _, _, rows, cols = struct.unpack('>IIII', fimg.read(16)) images = np.fromstring(fimg.read(), dtype=np.uint8).reshape(len(labels), rows, cols) images = images.reshape(images.shape[0], 1, 28, 28).astype(np.float32) / 255 return labels, images def find_file(root_path, file_name): for root, dirs, files in os.walk(root_path): if file_name in files: return os.path.join(root, file_name) def build_graph(): data = mx.sym.var('data') data = mx.sym.flatten(data=data) fc1 = mx.sym.FullyConnected(data=data, num_hidden=128) act1 = mx.sym.Activation(data=fc1, act_type='relu') fc2 = mx.sym.FullyConnected(data=act1, num_hidden=64) act2 = mx.sym.Activation(data=fc2, act_type='relu') fc3 = mx.sym.FullyConnected(data=act2, num_hidden=10) return mx.sym.SoftmaxOutput(data=fc3, name='softmax') def get_training_context(num_gpus): if num_gpus: return [mx.gpu(i) for i in range(num_gpus)] else: return mx.cpu() def train(batch_size, epochs, learning_rate, num_gpus, training_channel, testing_channel, hosts, current_host, model_dir): (train_labels, train_images) = load_data(training_channel) (test_labels, test_images) = load_data(testing_channel) # Data parallel training - shard the data so each host # only trains on a subset of the total data. shard_size = len(train_images) // len(hosts) for i, host in enumerate(hosts): if host == current_host: start = shard_size * i end = start + shard_size break train_iter = mx.io.NDArrayIter(train_images[start:end], train_labels[start:end], batch_size, shuffle=True) val_iter = mx.io.NDArrayIter(test_images, test_labels, batch_size) logging.getLogger().setLevel(logging.DEBUG) kvstore = 'local' if len(hosts) == 1 else 'dist_sync' mlp_model = mx.mod.Module(symbol=build_graph(), context=get_training_context(num_gpus)) mlp_model.fit(train_iter, eval_data=val_iter, kvstore=kvstore, optimizer='sgd', optimizer_params={'learning_rate': learning_rate}, eval_metric='acc', batch_end_callback=mx.callback.Speedometer(batch_size, 100), num_epoch=epochs) if current_host == scheduler_host(hosts): save(model_dir, mlp_model) assert_can_track_sagemaker_experiments() def assert_can_track_sagemaker_experiments(): in_sagemaker_training = 'TRAINING_JOB_ARN' in os.environ in_python_three = sys.version_info[0] == 3 if in_sagemaker_training and in_python_three: import smexperiments.tracker with smexperiments.tracker.Tracker.load() as tracker: tracker.log_parameter('param', 1) tracker.log_metric('metric', 1.0) def save(model_dir, model): model.symbol.save(os.path.join(model_dir, 'model-symbol.json')) model.save_params(os.path.join(model_dir, 'model-0000.params')) signature = [{'name': data_desc.name, 'shape': [dim for dim in data_desc.shape]} for data_desc in model.data_shapes] with open(os.path.join(model_dir, 'model-shapes.json'), 'w') as f: json.dump(signature, f) def parse_args(): parser = argparse.ArgumentParser() parser.add_argument('--batch-size', type=int, default=100) parser.add_argument('--epochs', type=int, default=10) parser.add_argument('--learning-rate', type=float, default=0.1) parser.add_argument('--model-dir', type=str, default=os.environ['SM_MODEL_DIR']) parser.add_argument('--train', type=str, default=os.environ['SM_CHANNEL_TRAIN']) parser.add_argument('--test', type=str, default=os.environ['SM_CHANNEL_TEST']) parser.add_argument('--current-host', type=str, default=os.environ['SM_CURRENT_HOST']) parser.add_argument('--hosts', type=list, default=json.loads(os.environ['SM_HOSTS'])) return parser.parse_args() if __name__ == '__main__': args = parse_args() num_gpus = int(os.environ['SM_NUM_GPUS']) train(args.batch_size, args.epochs, args.learning_rate, num_gpus, args.train, args.test, args.hosts, args.current_host, args.model_dir)

36.682759

96

0.679263

4aad679f22b6819115723b88865258ef950cad8a

10,061

py

Python

stage/test_google_bigquery_destination.py

streamsets/datacollector-tests

6c3e908768e1d4a586e9183e2141096921ecd5be

[ "Apache-2.0" ]

14

2019-03-04T10:12:39.000Z

2021-11-24T16:17:09.000Z

stage/test_google_bigquery_destination.py

Pragatibs/datacollector-tests

aac53b2f0e056009ef0e437c8430651e3cf4d502

[ "Apache-2.0" ]

48

2019-03-08T14:59:06.000Z

2021-08-13T14:49:56.000Z

stage/test_google_bigquery_destination.py

Pragatibs/datacollector-tests

aac53b2f0e056009ef0e437c8430651e3cf4d502

[ "Apache-2.0" ]

23

2018-09-24T20:49:17.000Z

2021-11-24T16:17:11.000Z

# Copyright 2020 StreamSets Inc. # # 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 base64 import json import logging from string import ascii_letters import pytest from google.cloud.bigquery import Dataset, SchemaField, Table from streamsets.testframework.markers import gcp, sdc_min_version from streamsets.testframework.utils import get_random_string logger = logging.getLogger(__name__) name=DESTINATION_STAGE_NAME = 'com_streamsets_pipeline_stage_bigquery_destination_BigQueryDTarget' pytestmark = [pytest.mark.category('nonstandard')] ROWS_EXPECTED = [('Cristiano Ronaldo', 32), ('David Beckham', 32), ('Gerard Pique', 30), ('Lionel Messi', 30), ('Mario Gotze', 25), ('Neymar', 25), ('Pele', 76), ('Ronaldinho', 40), ('Ronaldo', 40), ('Zinedine Zidane', 42)] CSV_DATA_TO_INSERT = ['full_name,age'] + [','.join(str(element) for element in row) for row in ROWS_EXPECTED] @gcp @sdc_min_version('2.7.2.0') def test_google_bigquery_destination(sdc_builder, sdc_executor, gcp): """ Send data to Google BigQuery from Dev Raw Data Source and confirm that Google BigQuery destination successfully recieves them using Google BigQuery client. This is achieved by using a deduplicator which assures that there is only one ingest to Google BigQuery. The pipeline looks like: dev_raw_data_source >> record_deduplicator >> google_bigquery record_deduplicator >> trash """ pipeline_builder = sdc_builder.get_pipeline_builder() dev_raw_data_source = pipeline_builder.add_stage('Dev Raw Data Source') dev_raw_data_source.set_attributes(data_format='DELIMITED', header_line='WITH_HEADER', raw_data='\n'.join(CSV_DATA_TO_INSERT)) dataset_name = get_random_string(ascii_letters, 5) table_name = get_random_string(ascii_letters, 5) google_bigquery = pipeline_builder.add_stage(name=DESTINATION_STAGE_NAME, type='destination') google_bigquery.set_attributes(dataset=dataset_name, table_name=table_name) record_deduplicator = pipeline_builder.add_stage('Record Deduplicator') trash = pipeline_builder.add_stage('Trash') dev_raw_data_source >> record_deduplicator >> google_bigquery record_deduplicator >> trash pipeline = pipeline_builder.build(title='Google BigQuery Destination').configure_for_environment(gcp) sdc_executor.add_pipeline(pipeline) bigquery_client = gcp.bigquery_client schema = [SchemaField('full_name', 'STRING', mode='required'), SchemaField('age', 'INTEGER', mode='required')] dataset_ref = Dataset(bigquery_client.dataset(dataset_name)) try: logger.info('Creating dataset %s using Google BigQuery client ...', dataset_name) bigquery_client.create_dataset(dataset_ref) table = bigquery_client.create_table(Table(dataset_ref.table(table_name), schema=schema)) logger.info('Starting BigQuery Destination pipeline and waiting for it to produce records ...') sdc_executor.start_pipeline(pipeline).wait_for_pipeline_batch_count(1) logger.info('Stopping BigQuery Destination pipeline and getting the count of records produced in total ...') sdc_executor.stop_pipeline(pipeline) # Verify by reading records using Google BigQuery client data_from_bigquery = [tuple(row.values()) for row in bigquery_client.list_rows(table)] data_from_bigquery.sort() logger.debug('read_data = {}'.format(data_from_bigquery)) assert ROWS_EXPECTED == data_from_bigquery finally: bigquery_client.delete_dataset(dataset_ref, delete_contents=True) @gcp @sdc_min_version('2.7.2.0') def test_google_bigquery_destination_multiple_types(sdc_builder, sdc_executor, gcp): """Simple big query destination test with INSERT operation. The pipeline inserts 1000 records of multiple types. A type converter is included to transform decimal to float. The pipeline should look like: dev_data_generator >> field_type_converter >> [google_bigquery, wiretap.destination] """ pipeline_builder = sdc_builder.get_pipeline_builder() dev_data_generator = pipeline_builder.add_stage('Dev Data Generator') dev_data_generator.fields_to_generate = [ {'field': 'field1', 'type': 'STRING'}, {'field': 'field2', 'type': 'DATETIME'}, {'field': 'field3', 'type': 'INTEGER'}, {'field': 'field4', 'precision': 10, 'scale': 2, 'type': 'DECIMAL'}, {'field': 'field5', 'type': 'DOUBLE'} ] batch_size = 1000 dev_data_generator.set_attributes(delay_between_batches=1000, batch_size=batch_size) dataset_name = get_random_string(ascii_letters, 5) table_name = get_random_string(ascii_letters, 5) google_bigquery = pipeline_builder.add_stage(name=DESTINATION_STAGE_NAME, type='destination') google_bigquery.set_attributes(dataset=dataset_name, table_name=table_name, stage_on_record_error='TO_ERROR') # Create Field Type Converter conversions = [{'fields': ['/field4'], 'targetType': 'FLOAT'}] field_type_converter = pipeline_builder.add_stage('Field Type Converter') field_type_converter.set_attributes(conversion_method='BY_FIELD', field_type_converter_configs=conversions) wiretap = pipeline_builder.add_wiretap() dev_data_generator >> field_type_converter >> [google_bigquery, wiretap.destination] pipeline = pipeline_builder.build() sdc_executor.add_pipeline(pipeline.configure_for_environment(gcp)) # FLOAT64 is used because there is a bug with NUMERIC, in bigquery Client bigquery_client = gcp.bigquery_client schema = [SchemaField('field1', 'STRING', mode='required'), SchemaField('field2', 'DATETIME', mode='required'), SchemaField('field3', 'INTEGER', mode='required'), SchemaField('field4', 'FLOAT64', mode='required'), SchemaField('field5', 'FLOAT', mode='required') ] dataset_ref = Dataset(bigquery_client.dataset(dataset_name)) try: logger.info('Creating dataset %s using Google BigQuery client ...', dataset_name) bigquery_client.create_dataset(dataset_ref) table = bigquery_client.create_table(Table(dataset_ref.table(table_name), schema=schema)) logger.info('Starting BigQuery Destination pipeline and waiting for it to produce records ...') sdc_executor.start_pipeline(pipeline) sdc_executor.wait_for_pipeline_metric(pipeline, 'input_record_count', 1_000, timeout_sec=60) sdc_executor.stop_pipeline(pipeline) # Verify by reading records using Google BigQuery client data_from_bigquery = [{"field1" : row.values()[0], "field2" : row.values()[1].replace(microsecond = row.values()[1].microsecond * 1000), "field3" : row.values()[2], "field4" : row.values()[3], "field5" : row.values()[4]} for row in bigquery_client.list_rows(table)] data_from_wiretap = [rec.field for rec in wiretap.output_records] assert len(data_from_bigquery) >= batch_size assert len(wiretap.error_records) == 0 assert len(data_from_bigquery) == len(data_from_wiretap) assert all([element in data_from_bigquery for element in data_from_wiretap]) finally: logger.info('Dropping table %s in Google Big Query database ...', table_name) bigquery_client.delete_dataset(dataset_ref, delete_contents=True) @gcp @sdc_min_version('3.11.0') def test_google_bigquery_destination_empty_table_name_error(sdc_builder, sdc_executor, gcp): """Test that BigQuery API does not return a NullPointerException if asked for an empty table name Pipeline: dev_raw_data_source >> google_bigquery """ pipeline_builder = sdc_builder.get_pipeline_builder() json_data = {'table': ''} # Dev Raw Data Source dev_raw_data_source = pipeline_builder.add_stage('Dev Raw Data Source') dev_raw_data_source.set_attributes( data_format='JSON', raw_data=json.dumps(json_data), stop_after_first_batch=True ) # Google BigQuery Destination dataset_name = 'dont_care' table_name = '${record:value(\'/table\')}' google_bigquery = pipeline_builder.add_stage(name=DESTINATION_STAGE_NAME, type='destination') google_bigquery.set_attributes(dataset=dataset_name, table_name=table_name, stage_on_record_error='TO_ERROR') wiretap = pipeline_builder.add_wiretap() # Implement pipeline topology dev_raw_data_source >> [google_bigquery, wiretap.destination] pipeline = pipeline_builder.build() sdc_executor.add_pipeline(pipeline.configure_for_environment(gcp)) sdc_executor.start_pipeline(pipeline).wait_for_finished() # Verify that we have exactly one record assert len(wiretap.error_records) == 1 # Verify that the error is indeed a BIGQUERY_18 (table name is empty or expression evaluates to empty) assert wiretap.error_records[0].header['errorCode'] == 'BIGQUERY_18'

43.180258

116

0.688898

f01bdf47ea08dc3c9aac49fe707dbd4b07f65f71

12,939

py

Python

sympy/core/symbol.py

tesseralis/sympy

0c2f7e06b1a43d25ba93bac65e93f8d5f323be7a

[ "BSD-3-Clause" ]

1

2020-11-17T07:35:20.000Z

sympy/core/symbol.py

tesseralis/sympy

0c2f7e06b1a43d25ba93bac65e93f8d5f323be7a

[ "BSD-3-Clause" ]

null

sympy/core/symbol.py

tesseralis/sympy

0c2f7e06b1a43d25ba93bac65e93f8d5f323be7a

[ "BSD-3-Clause" ]

null

from sympy.core.assumptions import StdFactKB from basic import Basic from core import C from sympify import sympify from singleton import S from expr import Expr, AtomicExpr from cache import cacheit from function import FunctionClass from sympy.core.logic import fuzzy_bool from sympy.logic.boolalg import Boolean from sympy.utilities.exceptions import SymPyDeprecationWarning import re class Symbol(AtomicExpr, Boolean): """ Assumptions: commutative = True You can override the default assumptions in the constructor: >>> from sympy import symbols >>> A,B = symbols('A,B', commutative = False) >>> bool(A*B != B*A) True >>> bool(A*B*2 == 2*A*B) == True # multiplication by scalars is commutative True """ is_comparable = False __slots__ = ['name'] is_Symbol = True @property def _diff_wrt(self): """Allow derivatives wrt Symbols. Examples ======== >>> from sympy import Symbol >>> x = Symbol('x') >>> x._diff_wrt True """ return True def __new__(cls, name, **assumptions): """Symbols are identified by name and assumptions:: >>> from sympy import Symbol >>> Symbol("x") == Symbol("x") True >>> Symbol("x", real=True) == Symbol("x", real=False) False """ if 'dummy' in assumptions: SymPyDeprecationWarning( feature="Symbol('x', dummy=True)", useinstead="Dummy() or symbols(..., cls=Dummy)" ).warn() if assumptions.pop('dummy'): return Dummy(name, **assumptions) if assumptions.get('zero', False): return S.Zero is_commutative = fuzzy_bool(assumptions.get('commutative', True)) if is_commutative is None: raise ValueError( '''Symbol commutativity must be True or False.''') assumptions['commutative'] = is_commutative return Symbol.__xnew_cached_(cls, name, **assumptions) def __new_stage2__(cls, name, **assumptions): assert isinstance(name, str),repr(type(name)) obj = Expr.__new__(cls) obj.name = name obj._assumptions = StdFactKB(assumptions) return obj __xnew__ = staticmethod(__new_stage2__) # never cached (e.g. dummy) __xnew_cached_ = staticmethod(cacheit(__new_stage2__)) # symbols are always cached def __getnewargs__(self): return (self.name,) def __getstate__(self): return {'_assumptions': self._assumptions} def _hashable_content(self): return (self.name,) + tuple(sorted(self.assumptions0.iteritems())) @property def assumptions0(self): return dict((key, value) for key, value in self._assumptions.iteritems() if value is not None) @cacheit def sort_key(self, order=None): return self.class_key(), (1, (str(self),)), S.One.sort_key(), S.One def as_dummy(self): return Dummy(self.name, **self.assumptions0) def __call__(self, *args): from function import Function return Function(self.name)(*args) def as_real_imag(self, deep=True, **hints): if hints.get('ignore') == self: return None else: return (C.re(self), C.im(self)) def _eval_expand_complex(self, deep=True, **hints): re, im = self.as_real_imag() return re + im*S.ImaginaryUnit def _sage_(self): import sage.all as sage return sage.var(self.name) def is_constant(self, *wrt, **flags): if not wrt: return False return not self in wrt @property def is_number(self): return False @property def free_symbols(self): return set([self]) class Dummy(Symbol): """Dummy symbols are each unique, identified by an internal count index: >>> from sympy import Dummy >>> bool(Dummy("x") == Dummy("x")) == True False If a name is not supplied then a string value of the count index will be used. This is useful when a temporary variable is needed and the name of the variable used in the expression is not important. >>> Dummy._count = 0 # /!\ this should generally not be changed; it is being >>> Dummy() # used here to make sure that the doctest passes. _0 """ _count = 0 __slots__ = ['dummy_index'] is_Dummy = True def __new__(cls, name=None, **assumptions): if name is None: name = str(Dummy._count) is_commutative = fuzzy_bool(assumptions.get('commutative', True)) if is_commutative is None: raise ValueError( '''Dummy's commutativity must be True or False.''') assumptions['commutative'] = is_commutative obj = Symbol.__xnew__(cls, name, **assumptions) Dummy._count += 1 obj.dummy_index = Dummy._count return obj def __getstate__(self): return {'_assumptions': self._assumptions, 'dummy_index': self.dummy_index} def _hashable_content(self): return Symbol._hashable_content(self) + (self.dummy_index,) class Wild(Symbol): """ Wild() matches any expression but another Wild(). """ __slots__ = ['exclude', 'properties'] is_Wild = True def __new__(cls, name, exclude=(), properties=(), **assumptions): exclude = tuple([sympify(x) for x in exclude]) properties = tuple(properties) is_commutative = fuzzy_bool(assumptions.get('commutative', True)) if is_commutative is None: raise ValueError( '''Wild's commutativity must be True or False.''') assumptions['commutative'] = is_commutative return Wild.__xnew__(cls, name, exclude, properties, **assumptions) def __getnewargs__(self): return (self.name, self.exclude, self.properties) @staticmethod @cacheit def __xnew__(cls, name, exclude, properties, **assumptions): obj = Symbol.__xnew__(cls, name, **assumptions) obj.exclude = exclude obj.properties = properties return obj def _hashable_content(self): return super(Wild, self)._hashable_content() + (self.exclude, self.properties) # TODO add check against another Wild def matches(self, expr, repl_dict={}): if any(expr.has(x) for x in self.exclude): return None if any(not f(expr) for f in self.properties): return None repl_dict = repl_dict.copy() repl_dict[self] = expr return repl_dict def __call__(self, *args, **kwargs): raise TypeError("'%s' object is not callable" % type(self).__name__) _re_var_range = re.compile(r"^(.*?)(\d*):(\d+)$") _re_var_scope = re.compile(r"^(.):(.)$") _re_var_split = re.compile(r"\s*,\s*|\s+") def symbols(names, **args): """ Transform strings into instances of :class:`Symbol` class. :func:`symbols` function returns a sequence of symbols with names taken from ``names`` argument, which can be a comma or whitespace delimited string, or a sequence of strings:: >>> from sympy import symbols, Function >>> x, y, z = symbols('x,y,z') >>> a, b, c = symbols('a b c') The type of output is dependent on the properties of input arguments:: >>> symbols('x') x >>> symbols('x,') (x,) >>> symbols('x,y') (x, y) >>> symbols(('a', 'b', 'c')) (a, b, c) >>> symbols(['a', 'b', 'c']) [a, b, c] >>> symbols(set(['a', 'b', 'c'])) set([a, b, c]) If an iterable container is needed for a single symbol, set the ``seq`` argument to ``True`` or terminate the symbol name with a comma:: >>> symbols('x', seq=True) (x,) To reduce typing, range syntax is supported to create indexed symbols:: >>> symbols('x:10') (x0, x1, x2, x3, x4, x5, x6, x7, x8, x9) >>> symbols('x5:10') (x5, x6, x7, x8, x9) >>> symbols('x5:10,y:5') (x5, x6, x7, x8, x9, y0, y1, y2, y3, y4) >>> symbols(('x5:10', 'y:5')) ((x5, x6, x7, x8, x9), (y0, y1, y2, y3, y4)) To reduce typing even more, lexicographic range syntax is supported:: >>> symbols('x:z') (x, y, z) >>> symbols('a:d,x:z') (a, b, c, d, x, y, z) >>> symbols(('a:d', 'x:z')) ((a, b, c, d), (x, y, z)) All newly created symbols have assumptions set accordingly to ``args``:: >>> a = symbols('a', integer=True) >>> a.is_integer True >>> x, y, z = symbols('x,y,z', real=True) >>> x.is_real and y.is_real and z.is_real True Despite its name, :func:`symbols` can create symbol--like objects of other type, for example instances of Function or Wild classes. To achieve this, set ``cls`` keyword argument to the desired type:: >>> symbols('f,g,h', cls=Function) (f, g, h) >>> type(_[0]) <class 'sympy.core.function.UndefinedFunction'> """ result = [] if 'each_char' in args: SymPyDeprecationWarning( feature="each_char in the options to symbols() and var()", useinstead="spaces or commas between symbol names" ).warn() if isinstance(names, basestring): names = names.strip() as_seq= names.endswith(',') if as_seq: names = names[:-1].rstrip() if not names: raise ValueError('no symbols given') names = _re_var_split.split(names) if args.pop('each_char', False) and not as_seq and len(names) == 1: return symbols(tuple(names[0]), **args) cls = args.pop('cls', Symbol) seq = args.pop('seq', as_seq) for name in names: if not name: raise ValueError('missing symbol') if ':' not in name: symbol = cls(name, **args) result.append(symbol) continue match = _re_var_range.match(name) if match is not None: name, start, end = match.groups() if not start: start = 0 else: start = int(start) for i in xrange(start, int(end)): symbol = cls("%s%i" % (name, i), **args) result.append(symbol) seq = True continue match = _re_var_scope.match(name) if match is not None: start, end = match.groups() for name in xrange(ord(start), ord(end)+1): symbol = cls(chr(name), **args) result.append(symbol) seq = True continue raise ValueError("'%s' is not a valid symbol range specification" % name) if not seq and len(result) <= 1: if not result: raise ValueError('missing symbol') # should never happen return result[0] return tuple(result) else: for name in names: result.append(symbols(name, **args)) return type(names)(result) def var(names, **args): """ Create symbols and inject them into the global namespace. This calls :func:`symbols` with the same arguments and puts the results into the *global* namespace. It's recommended not to use :func:`var` in library code, where :func:`symbols` has to be used:: >>> from sympy import var >>> var('x') x >>> x x >>> var('a,ab,abc') (a, ab, abc) >>> abc abc >>> var('x,y', real=True) (x, y) >>> x.is_real and y.is_real True See :func:`symbol` documentation for more details on what kinds of arguments can be passed to :func:`var`. """ def traverse(symbols, frame): """Recursively inject symbols to the global namespace. """ for symbol in symbols: if isinstance(symbol, Basic): frame.f_globals[symbol.name] = symbol elif isinstance(symbol, FunctionClass): frame.f_globals[symbol.__name__] = symbol else: traverse(symbol, frame) from inspect import currentframe frame = currentframe().f_back try: syms = symbols(names, **args) if syms is not None: if isinstance(syms, Basic): frame.f_globals[syms.name] = syms elif isinstance(syms, FunctionClass): frame.f_globals[syms.__name__] = syms else: traverse(syms, frame) finally: del frame # break cyclic dependencies as stated in inspect docs return syms

29.141892

88

0.567277