paulo037/starcoder · Datasets at Hugging Face

max_stars_repo_path stringlengths 4 237	max_stars_repo_name stringlengths 6 117	max_stars_count int64 0 95.2k	id stringlengths 1 7	content stringlengths 12 593k	input_ids sequencelengths 7 549k
test/conftest.py	dmillington/lunchroulette	1	48583	import pytest from lunchroulette.app import app as _app from lunchroulette.app import db as _db from sqlalchemy import event from sqlalchemy.orm import sessionmaker @pytest.fixture(scope="session") def app(request): return _app @pytest.fixture(scope="function") def db(app, request): with app.app_context(): _db.drop_all() _db.create_all() @pytest.fixture(scope="function", autouse=True) def session(app, db, request): with app.app_context(): print(app.url_map) conn = _db.engine.connect() txn = conn.begin() options = dict(bind=conn, binds={}) sess = _db.create_scoped_session(options=options) sess.begin_nested() @event.listens_for(sess(), 'after_transaction_end') def restart_savepoint(sess2, trans): # Detecting whether this is indeed the nested transaction of the test if trans.nested and not trans._parent.nested: # The test should have normally called session.commit(), # but to be safe we explicitly expire the session sess2.expire_all() sess.begin_nested() _db.session = sess yield sess # Cleanup sess.remove() txn.rollback() conn.close() @pytest.fixture def client(app): return app.test_client()	[ 1, 1053, 11451, 1688, 13, 13, 3166, 301, 3322, 27537, 20200, 29889, 932, 1053, 623, 408, 903, 932, 13, 3166, 301, 3322, 27537, 20200, 29889, 932, 1053, 4833, 408, 903, 2585, 13, 3166, 4576, 284, 305, 6764, 1053, 1741, 13, 3166, 4576, 284, 305, 6764, 29889, 555, 1053, 4867, 28107, 13, 13, 29992, 2272, 1688, 29889, 7241, 15546, 29898, 6078, 543, 7924, 1159, 13, 1753, 623, 29898, 3827, 1125, 13, 1678, 736, 903, 932, 13, 13, 29992, 2272, 1688, 29889, 7241, 15546, 29898, 6078, 543, 2220, 1159, 13, 1753, 4833, 29898, 932, 29892, 2009, 1125, 13, 1678, 411, 623, 29889, 932, 29918, 4703, 7295, 13, 4706, 903, 2585, 29889, 8865, 29918, 497, 580, 13, 4706, 903, 2585, 29889, 3258, 29918, 497, 580, 13, 13, 29992, 2272, 1688, 29889, 7241, 15546, 29898, 6078, 543, 2220, 613, 1120, 1709, 29922, 5574, 29897, 13, 1753, 4867, 29898, 932, 29892, 4833, 29892, 2009, 1125, 13, 1678, 411, 623, 29889, 932, 29918, 4703, 7295, 13, 4706, 1596, 29898, 932, 29889, 2271, 29918, 1958, 29897, 13, 4706, 11009, 353, 903, 2585, 29889, 10599, 29889, 6915, 580, 13, 4706, 25568, 29876, 353, 11009, 29889, 463, 580, 13, 13, 4706, 3987, 353, 9657, 29898, 5355, 29922, 13082, 29892, 7868, 29879, 3790, 1800, 13, 4706, 27937, 353, 903, 2585, 29889, 3258, 29918, 21785, 287, 29918, 7924, 29898, 6768, 29922, 6768, 29897, 13, 13, 4706, 27937, 29889, 463, 29918, 27420, 580, 13, 13, 4706, 732, 3696, 29889, 1761, 575, 29918, 1454, 29898, 29879, 404, 3285, 525, 7045, 29918, 20736, 29918, 355, 1495, 13, 4706, 822, 10715, 29918, 7620, 3149, 29898, 29879, 404, 29906, 29892, 1301, 1125, 13, 9651, 396, 5953, 522, 292, 3692, 445, 338, 6200, 278, 9322, 10804, 310, 278, 1243, 13, 9651, 565, 1301, 29889, 27420, 322, 451, 1301, 3032, 3560, 29889, 27420, 29901, 13, 18884, 396, 450, 1243, 881, 505, 12891, 2000, 4867, 29889, 15060, 3285, 13, 18884, 396, 541, 304, 367, 9109, 591, 9479, 1518, 533, 278, 4867, 13, 18884, 27937, 29906, 29889, 4548, 533, 29918, 497, 580, 13, 18884, 27937, 29889, 463, 29918, 27420, 580, 13, 13, 4706, 903, 2585, 29889, 7924, 353, 27937, 13, 4706, 7709, 27937, 13, 13, 4706, 396, 315, 14044, 786, 13, 4706, 27937, 29889, 5992, 580, 13, 4706, 25568, 29876, 29889, 1245, 1627, 580, 13, 4706, 11009, 29889, 5358, 580, 13, 13, 29992, 2272, 1688, 29889, 7241, 15546, 13, 1753, 3132, 29898, 932, 1125, 13, 1678, 736, 623, 29889, 1688, 29918, 4645, 580, 2 ]
Notebooks/jhu/__init__.py	ProtoLife/covid-recovery	0	95246	import datetime import re from .get_data import get_data def Float(x): try: rtn = float(x) except: rtn = float('NaN') return rtn def get_jhu_ts(): stmp = __path__[0] # e.g. "../jhu" stmp = re.split('jhu',stmp)[0] # e.g. "../" base = stmp+'../covid-19-JH/csse_covid_19_data/csse_covid_19_time_series/' confirmed = get_data(base+'time_series_covid19_confirmed_global.csv') deaths = get_data(base+'time_series_covid19_deaths_global.csv') recovered = get_data(base+'time_series_covid19_recovered_global.csv') covid_ts = {'confirmed':confirmed,'deaths':deaths,'recovered':recovered} return covid_ts def get_jhu_countries(): dat = get_jhu_ts() confirmed = dat['confirmed'] countries = [(row[0],row[1]) for row in confirmed][1:] print("number of countries in jhu data = ",len(countries)) return countries def get_module_loc(): print(__path__) def get_ave_data(country_s,datatype='confirmed',dataaccum='daily_av_weekly', firstdate=None, lastdate=None): if dataaccum not in ['cum','daily','cum_av_weekly','daily_av_weekly']: print('data accumulation method not known',dataaccum) print('choose from:','cum','daily','cum_av_weekly','daily_av_weekly') return if datatype not in ['confirmed','deaths','recovered']: print('datatype not known: ',datatype) print('choose from: confirmed','deaths','recovered') return countries = [] if isinstance(country_s,list): for country in country_s: if isinstance(country,str): country = (country,'') countries.append(country) elif isinstance(country_s,str): countries = [(country_s,'')] else: # single ('country','reg') entry countries = [country_s] # get the jhu data dat = get_jhu_ts() popkeyed = dat[datatype] # accumulate the results res = {} # get x coord dates dates = popkeyed['dates'] fmt = '%m/%d/%y' xx = [datetime.datetime.strptime(dd,fmt) for dd in dates ] if firstdate: firstdate_d = datetime.datetime.strptime(firstdate,fmt) else: firstdate_d = datetime.datetime.strptime(dates[0],fmt) if lastdate: lastdate_d = datetime.datetime.strptime(lastdate,fmt) else: lastdate_d = datetime.datetime.strptime(dates[-1],fmt) daystart = (firstdate_d-xx[0]).days daystop = (lastdate_d-xx[-1]).days if daystart <0: xx0 = [xx[0]+datetime.timedelta(days=i) for i in range(daystart,0)] yy0 = [0.](-daystart) else: xx0 = [] yy0 = [] if daystop > 0: xx1 = [xx[-1]+datetime.timedelta(days=i) for i in range(daystop)] yy1 = [0.](daystop) else: xx1 = [] yy1 = [] xx = xx0 + xx + xx1 res['dates']=xx # get y coord for each country: i=0 j=0 for country in countries: try: yy = popkeyed[country] j = j+1 except: print('country not found',country) i = i + 1 continue yyf = [Float(y) for y in yy] yy = yy0 + yyf + yy1 # print('len yy',len(yy)) # ymax=np.max(np.array(yy)) yyf = [Float(y) for y in yy] if dataaccum == 'daily': yy = [0.]len(yy) yy[0] = yyf[0] for k in range(1,len(yy)): yy[k] = yyf[k]-yyf[k-1] elif dataaccum == 'cum_av_weekly': yy = [0.]len(yy) moving_av = 0. for k in range(len(yy)): if k-7 >= 0: moving_av = moving_av - yyf[k-7] moving_av = moving_av + yyf[k] yy[k] = moving_av/min(7.0,float(k+1)) elif dataaccum == 'daily_av_weekly': yy = [0.]len(yyf) yy[0] = yyf[0] for k in range(1,len(yy)): yy[k] = yyf[k]-yyf[k-1] yyf = [y for y in yy] yy = [0.]len(yy) moving_av = 0. for k in range(len(yy)): if k-7 >= 0: moving_av = moving_av - yyf[k-7] moving_av = moving_av + yyf[k] yy[k] = moving_av/min(7.0,float(k+1)) if country[1] != '': country = country[0]+'_'+country[1] else: country = country[0] res[country] = yy return res	[ 1, 1053, 12865, 13, 5215, 337, 13, 13, 13, 3166, 869, 657, 29918, 1272, 1053, 679, 29918, 1272, 13, 13, 1753, 27842, 29898, 29916, 1125, 13, 1678, 1018, 29901, 13, 4706, 364, 6277, 353, 5785, 29898, 29916, 29897, 13, 1678, 5174, 29901, 13, 4706, 364, 6277, 353, 5785, 877, 19377, 1495, 13, 1678, 736, 364, 6277, 29871, 13, 13, 1753, 679, 29918, 29926, 6905, 29918, 1372, 7295, 13, 1678, 380, 1526, 353, 4770, 2084, 1649, 29961, 29900, 29962, 396, 321, 29889, 29887, 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kubespawner/proxy.py	rccern/kubespawner	0	183285	import asyncio import functools import json import os import string import escapism from jupyterhub.proxy import Proxy from jupyterhub.utils import exponential_backoff from kubernetes_asyncio import client from traitlets import Unicode from .clients import load_config from .clients import shared_client from .objects import make_ingress from .reflector import ResourceReflector from .utils import generate_hashed_slug class IngressReflector(ResourceReflector): kind = 'ingresses' api_group_name = 'ExtensionsV1beta1Api' @property def ingresses(self): return self.resources class ServiceReflector(ResourceReflector): kind = 'services' @property def services(self): return self.resources class EndpointsReflector(ResourceReflector): kind = 'endpoints' @property def endpoints(self): return self.resources class KubeIngressProxy(Proxy): namespace = Unicode( config=True, help=""" Kubernetes namespace to spawn ingresses for single-user servers in. If running inside a kubernetes cluster with service accounts enabled, defaults to the current namespace. If not, defaults to 'default' """, ) def _namespace_default(self): """ Set namespace default to current namespace if running in a k8s cluster If not in a k8s cluster with service accounts enabled, default to 'default' """ ns_path = '/var/run/secrets/kubernetes.io/serviceaccount/namespace' if os.path.exists(ns_path): with open(ns_path) as f: return f.read().strip() return 'default' component_label = Unicode( 'singleuser-server', config=True, help=""" The component label used to tag the user pods. This can be used to override the spawner behavior when dealing with multiple hub instances in the same namespace. Usually helpful for CI workflows. """, ) k8s_api_ssl_ca_cert = Unicode( "", config=True, help=""" Location (absolute filepath) for CA certs of the k8s API server. Typically this is unnecessary, CA certs are picked up by config.load_incluster_config() or config.load_kube_config. In rare non-standard cases, such as using custom intermediate CA for your cluster, you may need to mount root CA's elsewhere in your Pod/Container and point this variable to that filepath """, ) k8s_api_host = Unicode( "", config=True, help=""" Full host name of the k8s API server ("https://hostname:port"). Typically this is unnecessary, the hostname is picked up by config.load_incluster_config() or config.load_kube_config. """, ) def __init__(self, args, kwargs): super().__init__(args, *kwargs) # Schedules async initialization logic that is to be awaited by async # functions by decorating them with @_await_async_init. self._async_init_future = asyncio.ensure_future(self._async_init()) async def _async_init(self): """ This method is scheduled to run from `__init__`, but not awaited there as `__init__` can't be marked as async. Since JupyterHub won't await this method, we ensure the async methods JupyterHub may call on this object will await this method before continuing. To do this, we decorate them with `_await_async_init`. But, how do we figure out the methods to decorate? Likely only those exposed by the base class that JupyterHub would know about. The base class is Proxy, as declared in proxy.py: https://github.com/jupyterhub/jupyterhub/blob/HEAD/jupyterhub/proxy.py. From the Proxy class docstring we can conclude that the following methods, if implemented, could be what we need to decorate with _await_async_init: - get_all_routes (implemented and decorated) - add_route (implemented and decorated) - delete_route (implemented and decorated) - start - stop - get_route """ await load_config(caller=self) self.core_api = shared_client('CoreV1Api') self.extension_api = shared_client('ExtensionsV1beta1Api') labels = { 'component': self.component_label, 'hub.jupyter.org/proxy-route': 'true', } self.ingress_reflector = IngressReflector( parent=self, namespace=self.namespace, labels=labels ) self.service_reflector = ServiceReflector( parent=self, namespace=self.namespace, labels=labels ) self.endpoint_reflector = EndpointsReflector( self, namespace=self.namespace, labels=labels ) await asyncio.gather( self.ingress_reflector.start(), self.service_reflector.start(), self.endpoint_reflector.start(), ) def _await_async_init(method): """A decorator to await the _async_init method after having been scheduled to run in the `__init__` method.""" @functools.wraps(method) async def async_method(self, args, *kwargs): if self._async_init_future is not None: await self._async_init_future self._async_init_future = None return await method(self, args, *kwargs) return async_method def safe_name_for_routespec(self, routespec): safe_chars = set(string.ascii_lowercase + string.digits) safe_name = generate_hashed_slug( 'jupyter-' + escapism.escape(routespec, safe=safe_chars, escape_char='-') + '-route' ) return safe_name async def delete_if_exists(self, kind, safe_name, future): try: await future self.log.info('Deleted %s/%s', kind, safe_name) except client.rest.ApiException as e: if e.status != 404: raise self.log.warn("Could not delete %s/%s: does not exist", kind, safe_name) @_await_async_init async def add_route(self, routespec, target, data): # Create a route with the name being escaped routespec # Use full routespec in label # 'data' is JSON encoded and put in an annotation - we don't need to query for it safe_name = self.safe_name_for_routespec(routespec).lower() labels = { 'heritage': 'jupyterhub', 'component': self.component_label, 'hub.jupyter.org/proxy-route': 'true', } endpoint, service, ingress = make_ingress( safe_name, routespec, target, labels, data ) async def ensure_object(create_func, patch_func, body, kind): try: resp = await create_func(namespace=self.namespace, body=body) self.log.info('Created %s/%s', kind, safe_name) except client.rest.ApiException as e: if e.status == 409: # This object already exists, we should patch it to make it be what we want self.log.warn( "Trying to patch %s/%s, it already exists", kind, safe_name ) resp = await patch_func( namespace=self.namespace, body=body, name=body.metadata.name, ) else: raise if endpoint is not None: await ensure_object( self.core_api.create_namespaced_endpoints, self.core_api.patch_namespaced_endpoints, body=endpoint, kind='endpoints', ) await exponential_backoff( lambda: f'{self.namespace}/{safe_name}' in self.endpoint_reflector.endpoints.keys(), 'Could not find endpoints/%s after creating it' % safe_name, ) else: delete_endpoint = await self.core_api.delete_namespaced_endpoints( name=safe_name, namespace=self.namespace, body=client.V1DeleteOptions(grace_period_seconds=0), ) await self.delete_if_exists('endpoint', safe_name, delete_endpoint) await ensure_object( self.core_api.create_namespaced_service, self.core_api.patch_namespaced_service, body=service, kind='service', ) await exponential_backoff( lambda: f'{self.namespace}/{safe_name}' in self.service_reflector.services.keys(), 'Could not find service/%s after creating it' % safe_name, ) await ensure_object( self.extension_api.create_namespaced_ingress, self.extension_api.patch_namespaced_ingress, body=ingress, kind='ingress', ) await exponential_backoff( lambda: f'{self.namespace}/{safe_name}' in self.ingress_reflector.ingresses.keys(), 'Could not find ingress/%s after creating it' % safe_name, ) @_await_async_init async def delete_route(self, routespec): # We just ensure that these objects are deleted. # This means if some of them are already deleted, we just let it # be. safe_name = self.safe_name_for_routespec(routespec).lower() delete_options = client.V1DeleteOptions(grace_period_seconds=0) delete_endpoint = await self.core_api.delete_namespaced_endpoints( name=safe_name, namespace=self.namespace, body=delete_options, ) delete_service = await self.core_api.delete_namespaced_service( name=safe_name, namespace=self.namespace, body=delete_options, ) delete_ingress = await self.extension_api.delete_namespaced_ingress( name=safe_name, namespace=self.namespace, body=delete_options, grace_period_seconds=0, ) # This seems like cleanest way to parallelize all three of these while # also making sure we only ignore the exception when it's a 404. # The order matters for endpoint & service - deleting the service deletes # the endpoint in the background. This can be racy however, so we do so # explicitly ourselves as well. In the future, we can probably try a # foreground cascading deletion (https://kubernetes.io/docs/concepts/workloads/controllers/garbage-collection/#foreground-cascading-deletion) # instead, but for now this works well enough. await asyncio.gather( self.delete_if_exists('endpoint', safe_name, delete_endpoint), self.delete_if_exists('service', safe_name, delete_service), self.delete_if_exists('ingress', safe_name, delete_ingress), ) @_await_async_init async def get_all_routes(self): # copy everything, because iterating over this directly is not threadsafe # FIXME: is this performance intensive? It could be! 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regolith/helpers/u_logurlhelper.py	sbillinge/regolith	1	129914	"""Helper for updating a projectum's log_url Log_urls are the google doc links to a projectum's Projectum Agenda Log """ from regolith.helpers.basehelper import DbHelperBase from regolith.fsclient import _id_key from regolith.tools import all_docs_from_collection, fragment_retrieval TARGET_COLL = "projecta" def subparser(subpi): subpi.add_argument("projectum_id", help="the ID or fragment of the ID of the " "Projectum to be updated, e.g., " "vl_m.", default=None) subpi.add_argument("log_url", help="Google Doc url link to project's Projectum Agenda Log") subpi.add_argument("-i", "--index", help="The index of the id you would like " "to update, from the returned list " "of projectum ids.") # Do not delete --database arg subpi.add_argument("-d", "--database", help="The database that will be updated. Defaults to " "first database in the regolithrc.json file." ) return subpi class LogUrlUpdaterHelper(DbHelperBase): """ Update a projectum's Log_url, will add a new Log_URL if one doesn't yet exist """ # btype must be the same as helper target in helper.py btype = "u_logurl" needed_dbs = [f'{TARGET_COLL}'] def construct_global_ctx(self): """Constructs the global context""" super().construct_global_ctx() gtx = self.gtx rc = self.rc rc.coll = f"{TARGET_COLL}" if not rc.database: rc.database = rc.databases[0]["name"] gtx[rc.coll] = sorted( all_docs_from_collection(rc.client, rc.coll), key=_id_key ) gtx["all_docs_from_collection"] = all_docs_from_collection gtx["float"] = float gtx["str"] = str gtx["zip"] = zip def db_updater(self): rc = self.rc key = rc.projectum_id filterid = {'_id': key} found_projectum = rc.client.find_one(rc.database, rc.coll, filterid) # id exists if found_projectum is not None: rc.client.update_one(rc.database, rc.coll, filterid, {'log_url': rc.log_url}) print(f"{rc.projectum_id} has been updated with a log_url of {rc.log_url}") else: # find all similar projectum ids pra = fragment_retrieval(self.gtx["projecta"], ["_id"], rc.projectum_id) # no matches to id fragment and id does not exist if len(pra) == 0: raise RuntimeError("Please input a valid projectum id or a valid fragment of a projectum id") # id fragment and no inputted number elif not rc.index: print("There does not seem to be a projectum with this exact name in this database.") print("However, there are projecta with similar names: ") for i in range(len(pra)): print(f"{i + 1}. {pra[i].get('_id')} current url: {pra[i].get('log_url')}") print("Please rerun the u_logurl helper with the same name as previously inputted, " "but with the addition of -i followed by a number corresponding to one of the above listed " "projectum ids that you would like to update.") # id fragment and inputted number else: if int(rc.index) < 1 or int(rc.index) > len(pra): raise RuntimeError("Sorry, you picked an invalid number.") else: filterid = {'_id': pra[int(rc.index) - 1].get('_id')} rc.client.update_one(rc.database, rc.coll, filterid, {'log_url': rc.log_url}) print(f"{pra[int(rc.index) - 1].get('_id')} has been updated with a log_url of {rc.log_url}") return	[ 1, 9995, 10739, 363, 13271, 263, 2060, 398, 29915, 29879, 1480, 29918, 2271, 13, 259, 4522, 29918, 26045, 526, 278, 5386, 1574, 2988, 304, 263, 2060, 398, 29915, 29879, 8010, 398, 4059, 8395, 4522, 13, 15945, 29908, 13, 3166, 1072, 324, 389, 29889, 3952, 6774, 29889, 3188, 20907, 1053, 24331, 10739, 5160, 13, 3166, 1072, 324, 389, 29889, 29888, 1557, 1593, 1053, 903, 333, 29918, 1989, 13, 3166, 1072, 324, 389, 29889, 8504, 1053, 599, 29918, 2640, 29918, 3166, 29918, 10855, 29892, 9376, 29918, 276, 509, 16837, 13, 13, 29911, 1718, 7194, 29918, 3217, 2208, 353, 376, 4836, 29874, 29908, 13, 13, 13, 1753, 1014, 16680, 29898, 1491, 1631, 1125, 13, 1678, 1014, 1631, 29889, 1202, 29918, 23516, 703, 4836, 398, 29918, 333, 613, 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lambda_freezer/__init__.py	klagrange/lambda-freezer	0	1615873	from collections import namedtuple import os import subprocess import re import boto3 import json STAGE_VARIABLE_ALIAS = "lambdaAlias" INTEGRATION_URI_APPENDER = ":${{stageVariables.{0}}}".format(STAGE_VARIABLE_ALIAS) _INTEGRATION = namedtuple( "INTEGRATION", [ "rest_api_id", "resource_id", "http_method", "path" ] ) _APIFNS = namedtuple( "APIFNS", [ "rest_api_id", "resource_id", "http_method", "uri", "path", "function_name", "source_arn", "aliases" ] ) STATEMENT_ID = "f6803b46-df32-4504-8c40-567a0390f549" CLIENT_GATEWAY = boto3.client('apigateway') CLIENT_LAMBDA = boto3.client('lambda') CLIENT_CLOUDFORMATION = boto3.client('cloudformation') SERVICE_FROM_PATH_PATTERN = re.compile(r"^(?:\\.\|[^/\\])/((?:\\.\|[^/\\]))/") class Mapper: """ This is a class that groups a collection of helper functions that help in gathering information of lambda functions that are integrated with an AWS api gateway """ @staticmethod def _map_fn(rest_api_id, region, http_method, path, uri): """ Extracts the underlying (lambda) function name and source arn of a particular resource attached to an api gateway. """ account_id = boto3.client('sts').get_caller_identity().get('Account') regex_template = "./arn:aws:lambda:{0}:{1}:function:(.)/invocations$" source_arn_template = "arn:aws:execute-api:{0}:{1}:{2}/*/{3}{4}" source_arn = source_arn_template.format( region, account_id, rest_api_id, http_method, path ) regex = regex_template.format(region, account_id) function_name = re.search(regex, uri).group(1) if function_name[-len(INTEGRATION_URI_APPENDER):] == INTEGRATION_URI_APPENDER: function_name = function_name[:-len(INTEGRATION_URI_APPENDER)] return function_name, source_arn @staticmethod def _map_aliases(func_name): """ Returns all aliases associated with a given (lambda) function. """ list_aliases_res = CLIENT_LAMBDA.list_aliases( FunctionName=func_name, ) aliases = list_aliases_res["Aliases"] return aliases @staticmethod def _get_integrations(rest_api_id): """ Gathers all resources for a given api gateway and returns its' integrations. In particular, the `resource id`, `resource method` and `resource path` """ # fetch api resources resources = CLIENT_GATEWAY.get_resources( restApiId=rest_api_id, ) integrations = [] # traverse all the resources for resource in resources['items']: # we are only interested in "resource methods" if "resourceMethods" not in resource.keys(): continue resource_methods = resource["resourceMethods"].keys() for resource_method in resource_methods: integrations.append( _INTEGRATION( rest_api_id, resource["id"], resource_method, resource["path"])) return integrations @staticmethod def _assemble(region, integrations): """ Helper function tha combines data. """ apifns = [] for integration in integrations: get_integration_res = CLIENT_GATEWAY.get_integration( restApiId=integration.rest_api_id, resourceId=integration.resource_id, httpMethod=integration.http_method ) # we are only interested at AWS_PROXY integrations as those # are integrations that are created by serverless framework if get_integration_res["type"] != "AWS_PROXY": continue function_name, source_arn = Mapper._map_fn( integration.rest_api_id, region, integration.http_method, integration.path, get_integration_res["uri"] ) aliases = Mapper._map_aliases(function_name) apifns.append( _APIFNS( integration.rest_api_id, integration.resource_id, integration.http_method, get_integration_res["uri"], integration.path, function_name, source_arn, aliases ) ) return apifns @staticmethod def run(rest_api_id, region): """ Gets the integrations of a given api gateway and appends information to them (_assemble) """ integrations = Mapper._get_integrations(rest_api_id) apifns = Mapper._assemble(region, integrations) return apifns def _add_permission(api_fn, stage_name): """ Add `lambda:InvokeFunction` permission to an (aliased) lambda function that is wired to an api gateway. """ try: CLIENT_LAMBDA.add_permission( FunctionName="{0}:{1}".format(api_fn.function_name, stage_name), StatementId=STATEMENT_ID, Action='lambda:InvokeFunction', Principal='apigateway.amazonaws.com', SourceArn=api_fn.source_arn, ) except CLIENT_LAMBDA.exceptions.ResourceConflictException as err: error_msg = { "exception": "CLIENT_LAMBDA.exceptions.ResourceConflictException", "fn": api_fn, "error": err } print(json.dumps(error_msg, indent=4)) except Exception as ex: print(ex) def _default_alias_is_added(fn_aliases, default_alias_name): """ Checks if the `default alias` is part of a list of aliases """ for alias in fn_aliases: if alias["Name"] == default_alias_name: return True return False def _create_alias(function_name, alias_name, function_version): """ Creates an alias and points to a specific version of a (lambda) function. """ CLIENT_LAMBDA.create_alias( FunctionName=function_name, Name=alias_name, FunctionVersion=function_version ) def _integration_uri_is_already_updated(uri): """ A typical uri would look like this: arn:aws:apigateway:{0}:lambda:path/2015-03-31/functions/arn:aws:lambda:{0}:{1}:function:{2}/invocations {0}: region {1}: account id {2}: function name Example of a function name: helloWorld We consider a uri integration to be `updated` when the function name is such: helloWorld:${stageVariables.lambdaAlias} This ensures that only a specific alias (therefore a specific locked version) can be called for a (lambda) function. """ invocation_str = "/invocations" uri_minus_invocation_str = uri[:-len(invocation_str)] is_already_updated = uri_minus_invocation_str[-len(INTEGRATION_URI_APPENDER):] == INTEGRATION_URI_APPENDER return is_already_updated, invocation_str, uri_minus_invocation_str def _get_service_from_path(path): """ Resource paths are general simple urls such as: /generalcalc/getHouseCostByCity /subscription/signUp /subscription/subscribeBasicPlan /country/countries /subscription/login /subscription/confirmSignUp /country/countries/{cca2} This simply extract the first piece of string that is between / / """ return re.search(SERVICE_FROM_PATH_PATTERN, path).group(1) def _tag_documentation_exists(rest_api_id, path, http_method): """ Checks if a specific resource path/method has a `tag` documentation. """ get_documentation_parts_res = CLIENT_GATEWAY.get_documentation_parts( restApiId=rest_api_id, type="METHOD", path=path, ) if not get_documentation_parts_res["items"]: return False for item in get_documentation_parts_res["items"]: if item["location"]["method"] == http_method: return True return False def _get_cloudformation_export(exportName): exports = CLIENT_CLOUDFORMATION.list_exports() if 'Exports' not in exports.keys(): exit() for export in exports['Exports']: if export['Name'] == exportName: return export['Value'] exit() def get_deployed_stages(rest_api_id): """ Returns all the stages that are deployed """ stages = [] get_stages_res = CLIENT_GATEWAY.get_stages( restApiId=rest_api_id, ) for get_stage_res in get_stages_res["item"]: stages.append(get_stage_res["stageName"]) return stages def create_alias_default(api_fns, default_alias_name): """ Creates the so called `default alias` which is just an alias that points to the latest version of (lambda) function(s). """ def create(function_name): return _create_alias(function_name, default_alias_name, "$LATEST") for api_fn in api_fns: aliases = api_fn.aliases function_name = api_fn.function_name if not aliases: create(function_name) else: default_is_added = _default_alias_is_added(aliases, default_alias_name) if not default_is_added: create(function_name) def update_integration_uri(api_fns, default_alias_name): """ For every (lambda) functions that are integrated with an api gateway - updated the uri integration such that the api gateway can only call a specific version of those (lambda) functions through an alias. """ for api_fn in api_fns: is_already_updated, invocation_str, uri_minus_invocation_str = _integration_uri_is_already_updated(api_fn.uri) if not is_already_updated: new_uri = "{0}{1}{2}".format( uri_minus_invocation_str, INTEGRATION_URI_APPENDER, invocation_str ) CLIENT_GATEWAY.update_integration( restApiId=api_fn.rest_api_id, resourceId=api_fn.resource_id, httpMethod=api_fn.http_method, patchOperations=[ { 'op': 'replace', 'path': '/uri', 'value': new_uri, }, ] ) _add_permission(api_fn, default_alias_name) def create_domain_mapping_default(rest_api_id, domain_name, default_alias_name): """ Creates the domain name mapping for the default stage. """ def create(): CLIENT_GATEWAY.create_base_path_mapping( domainName=domain_name, basePath=default_alias_name, restApiId=rest_api_id, stage=default_alias_name ) get_base_path_mappings_res = CLIENT_GATEWAY.get_base_path_mappings( domainName=domain_name, ) mappings = get_base_path_mappings_res['items'] if not mappings: create() else: already_mapped = False for mapping in mappings: if mapping["basePath"] == default_alias_name and mapping["stage"] == default_alias_name: already_mapped = True if not already_mapped: create() def default_stage_contains_staged_variable(rest_api_id, default_alias_name): """ Checks if the default stage contains the `stage variable alias`. """ get_stage_res = CLIENT_GATEWAY.get_stage( restApiId=rest_api_id, stageName=default_alias_name ) if "variables" not in get_stage_res.keys(): return False stage_variables = get_stage_res["variables"] if STAGE_VARIABLE_ALIAS not in stage_variables.keys(): return False if stage_variables[STAGE_VARIABLE_ALIAS] != default_alias_name: return False return True def create_tag_documentation(rest_api_id, api_fns): """ Creates tag documentation for a given list of functions. """ for api_fn in api_fns: path = api_fn.path http_method = api_fn.http_method already_exists = _tag_documentation_exists(rest_api_id, path, http_method) if not already_exists: service_name = _get_service_from_path(path) tag = { "tags": [ service_name ] } CLIENT_GATEWAY.create_documentation_part( restApiId=rest_api_id, location={ "type": "METHOD", "path": path, "method": http_method }, properties=json.dumps(tag), ) def run_after_default_deployment(rest_api_id, region, default_alias_name, domain_name=None): """ Typically what you would need to run after a `serverless deploy` deployment. This ensures that every function integrations' are such that the api gateway can only call a specific version of any lambda functions via an alias, using a stage variable. """ api_fns = Mapper.run(rest_api_id, region) create_alias_default(api_fns, default_alias_name) update_integration_uri(api_fns, default_alias_name) if domain_name is not None: create_domain_mapping_default(rest_api_id, domain_name, default_alias_name) # create_tag_documentation(rest_api_id, api_fns) contains_stage_var = default_stage_contains_staged_variable(rest_api_id, default_alias_name) if not contains_stage_var: print(''' PLEASE ADD THE FOLLOWING STAGE VARIABLE TO [STAGE: {0}]: {1} : {2} '''.format(default_alias_name, STAGE_VARIABLE_ALIAS, default_alias_name)) def freeze_functions(api_fns, stage_name): """ Creates an alias (that's the string for `stage_name`) for a given list of functions. """ for api_fn in api_fns: function_name = api_fn.function_name publish_version_res = CLIENT_LAMBDA.publish_version(FunctionName=function_name) try: CLIENT_LAMBDA.create_alias( FunctionName=function_name, Name=stage_name, FunctionVersion=publish_version_res["Version"] ) _add_permission(api_fn, stage_name) except Exception as ex: print("[WARNING] error while freezing function from source arn: {0}".format(api_fn.source_arn)) print("DETAILS: {0}".format(ex)) def deploy(rest_api_id, region, version, stage_description, domain_name=None): """ Deploys a stage to an api gateway. The definition of `deploy` here is: - take all resources of an api gateway (those are lambda functions at the core) - the integration of those lambda functions are assumed to have been modified such that only specific version - `freeze` those functions and create an alias that points to those frozen versions - creates a new stage under the api gateway and ensure that staged version calls only those previously frozen functions """ # ensure version follows semantic versioning standard if not re.match("\d+\.\d+\.\d+", version): exit("{0} DOES NOT FOLLOW SEMANTIC VERSIONING. FOLLOW SEMANTIC VERSIONING!".format(version)) # ensure that version was not already deployed stage_name = version.replace(".", "-") stages = get_deployed_stages(rest_api_id) if stage_name in stages: exit("YOU ALREADY DEPLOYED {0}".format(version)) # extracted functions' info and freeze them api_fns = Mapper.run(rest_api_id, region) freeze_functions(api_fns, stage_name) # deploy frozen functions CLIENT_GATEWAY.create_deployment( restApiId=rest_api_id, stageName=stage_name, stageDescription=stage_description, description=stage_description, variables={ "{0}".format(STAGE_VARIABLE_ALIAS): stage_name }, ) # map to domain if domain_name is not None: CLIENT_GATEWAY.create_base_path_mapping( domainName=domain_name, basePath=stage_name.replace("-", "."), restApiId=rest_api_id, stage=stage_name ) def re_deploy(rest_api_id, region, version, stage_description): """ Deploys a stage to an api gateway. 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data/Human36M/skeleton.py	GuillaumeRochette/HumanViewSynthesis	10	148374	JOINTS = { "HipCenter": 0, "RHip": 1, "RKnee": 2, "RFoot": 3, "LHip": 4, "LKnee": 5, "LFoot": 6, "Spine": 7, "Thorax": 8, "Neck/Nose": 9, "Head": 10, "LShoulder": 11, "LElbow": 12, "LWrist": 13, "RShoulder": 14, "RElbow": 15, "RWrist": 16, } EDGES = ( (0, 1), (1, 2), (2, 3), (0, 4), (4, 5), (5, 6), (0, 7), (7, 8), (8, 9), (9, 10), (8, 11), (11, 12), (12, 13), (8, 14), (14, 15), (15, 16), )	[ 1, 8780, 9375, 353, 426, 13, 1678, 376, 29950, 666, 13409, 1115, 29871, 29900, 29892, 13, 1678, 376, 29934, 29950, 666, 1115, 29871, 29896, 29892, 13, 1678, 376, 29934, 29968, 484, 29872, 1115, 29871, 29906, 29892, 13, 1678, 376, 29934, 13440, 1115, 29871, 29941, 29892, 13, 1678, 376, 29931, 29950, 666, 1115, 29871, 29946, 29892, 13, 1678, 376, 29931, 29968, 484, 29872, 1115, 29871, 29945, 29892, 13, 1678, 376, 29931, 13440, 1115, 29871, 29953, 29892, 13, 1678, 376, 5592, 457, 1115, 29871, 29955, 29892, 13, 1678, 376, 1349, 272, 1165, 1115, 29871, 29947, 29892, 13, 1678, 376, 8139, 384, 29914, 29940, 852, 1115, 29871, 29929, 29892, 13, 1678, 376, 5494, 1115, 29871, 29896, 29900, 29892, 13, 1678, 376, 29931, 26857, 261, 1115, 29871, 29896, 29896, 29892, 13, 1678, 376, 1307, 29880, 17729, 1115, 29871, 29896, 29906, 29892, 13, 1678, 376, 29931, 29956, 2021, 1115, 29871, 29896, 29941, 29892, 13, 1678, 376, 29934, 26857, 261, 1115, 29871, 29896, 29946, 29892, 13, 1678, 376, 1525, 29880, 17729, 1115, 29871, 29896, 29945, 29892, 13, 1678, 376, 29934, 29956, 2021, 1115, 29871, 29896, 29953, 29892, 13, 29913, 13, 13, 3352, 1692, 29903, 353, 313, 13, 1678, 313, 29900, 29892, 29871, 29896, 511, 13, 1678, 313, 29896, 29892, 29871, 29906, 511, 13, 1678, 313, 29906, 29892, 29871, 29941, 511, 13, 1678, 313, 29900, 29892, 29871, 29946, 511, 13, 1678, 313, 29946, 29892, 29871, 29945, 511, 13, 1678, 313, 29945, 29892, 29871, 29953, 511, 13, 1678, 313, 29900, 29892, 29871, 29955, 511, 13, 1678, 313, 29955, 29892, 29871, 29947, 511, 13, 1678, 313, 29947, 29892, 29871, 29929, 511, 13, 1678, 313, 29929, 29892, 29871, 29896, 29900, 511, 13, 1678, 313, 29947, 29892, 29871, 29896, 29896, 511, 13, 1678, 313, 29896, 29896, 29892, 29871, 29896, 29906, 511, 13, 1678, 313, 29896, 29906, 29892, 29871, 29896, 29941, 511, 13, 1678, 313, 29947, 29892, 29871, 29896, 29946, 511, 13, 1678, 313, 29896, 29946, 29892, 29871, 29896, 29945, 511, 13, 1678, 313, 29896, 29945, 29892, 29871, 29896, 29953, 511, 13, 29897, 13, 2 ]
img_main.py	liujiyuan13/MvDOCC-code	1	75573	import numpy as np import scipy.io as scio import scipy.sparse as scsp import h5py as hp from util import read_mymat73, read_mymat, build_img_dataset, process_ad_dataset, mv_dataset, mv_tabular_collate, AverageMeter, save_roc_pr_curve_data, get_all_labels, \ load_print_results, filter_nan_grad, read_dataset, build_vad_dataset, ss_dataset, ss_tabular_collate, simple_accuracy, get_large_class, ss_goad_dataset from models.encoder_decoder import mvae_ad, late_fusion, mvenc, mvae_fused, splitAE, mv_corrAE, classifier, tc_loss_func, mvae_ss, mvenc_fuse, mvae_tf, similarity_hinge_loss from models.DeepCCAModels import DeepCCA, cca_loss, gcca, cca from models.DBN import mv_DBN from torch.utils.data import DataLoader from torch.nn import MSELoss, CrossEntropyLoss from torch import optim from tensorboardX import SummaryWriter from tqdm import tqdm import torch import random from img_config import epochs_set, layer_size_set, \ batch_size_set_mvae, lr_set_mvae, wd_set_mvae, \ batch_size_set_dcca, lr_set_dcca, wd_set_dcca, alpha_set_dcca, \ batch_size_set_dsvdd, lr_set_dsvdd, wd_set_dsvdd, \ batch_size_set_fused, lr_set_fused, wd_set_fused, pt_epochs_set, \ batch_size_set_split, lr_set_split, wd_set_split, \ batch_size_set_tf, lr_set_tf, wd_set_tf, r_set_tf, \ batch_size_set_corr, lr_set_corr, wd_set_corr, alpha_set_corr, \ batch_size_set_sim, lr_set_sim, wd_set_sim, alpha_set_sim, m_set_sim, \ batch_size_set_dgcca, lr_set_dgcca, wd_set_dgcca, alpha_set_dgcca, \ batch_size_set_dbn, lr_set_dbn, wd_set_dbn import os from transformations import get_rp_num, trans_mv_data_new def get_radius(dist: torch.Tensor, nu: float): """Optimally solve for radius R via the (1-nu)-quantile of distances.""" return np.quantile(np.sqrt(dist.clone().data.cpu().numpy()), 1 - nu) # Baselines: def simple_mvae(dataset_name, X, Y, repeat_times, normal_train_ratio, results_path, device, qualified_Y, ADdataset_mode='minority'): torch.set_default_tensor_type(torch.DoubleTensor) roc_results = [[] for i in range(repeat_times)] pr_anom_results = [[] for i in range(repeat_times)] pr_norm_results = [[] for i in range(repeat_times)] tnr_results = [[] for i in range(repeat_times)] single_best_roc_results = [[] for i in range(repeat_times)] single_best_pr_anom_results = [[] for i in range(repeat_times)] single_best_pr_norm_results = [[] for i in range(repeat_times)] single_best_tnr_results = [[] for i in range(repeat_times)] # training config layer_size = layer_size_set[dataset_name] batch_size = batch_size_set_mvae[dataset_name] lr = lr_set_mvae[dataset_name] wd = wd_set_mvae[dataset_name] print('layer_size: {}, batch size: {}, lr: {}, wd:{}'.format(layer_size, batch_size, lr, wd)) loss_func = MSELoss() input_size_set = [x.shape[0] for x in X] for t in range(repeat_times): print('The {}-th run begins!'.format(t)) for i in range(min([10, len(qualified_Y)])): partition, Y_test = build_img_dataset(dataset_name, Y, neg_class_id=qualified_Y[i], mode=ADdataset_mode) X_train, X_test = process_ad_dataset(X, partition) # -----------------------------------------model training seed = 0 if torch.cuda.is_available(): torch.cuda.manual_seed_all(seed) torch.manual_seed(seed) torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = True model = mvae_ad(input_size_set=input_size_set, layer_sizes=layer_size).to(device).double() trainloader = DataLoader(dataset=mv_dataset(X_train), batch_size=batch_size, shuffle=True, num_workers=1, collate_fn=mv_tabular_collate) # specific training procedure epochs = epochs_set[dataset_name] print(max(epochs)) losses_set = [AverageMeter() for i in range(len(X_train))] optimizer = [optim.Adam(model.ae_set[i].parameters(), lr=lr, weight_decay=wd) for i in range(len(X_train))] # writer = SummaryWriter() model.train() cout = 0 for epoch in range(max(epochs)): trainloader = tqdm(trainloader) if ADdataset_mode is 'majority': trainloader.set_description('mvae, epoch{}, abnormal class{}'.format(epoch, qualified_Y[i])) else: trainloader.set_description('mvae, epoch{}, normal class{}'.format(epoch, qualified_Y[i])) for idx, batch in enumerate(trainloader): postfix = '' if batch[0].size(0) > 1: # for bn, batchsize > 1 batch = [batch[i].to(device) for i in range(len(batch))] outputs_set = model(batch) for view in range(len(X_train)): if epoch < epochs[view]: y = batch[view] loss = loss_func(outputs_set[view], y) optimizer[view].zero_grad() loss.backward() optimizer[view].step() losses_set[view].update(loss.item(), batch[0].size(0)) cur_postfix = ' view{}, rec_loss: {:.4f} '.format(view, losses_set[view].avg) # writer.add_scalar('runs/{}_loss_clasId_{}_view_{}'.format(dataset_name, y_set[i], view), loss.item(), cout) postfix += cur_postfix cout += 1 trainloader.set_postfix(log=postfix) # writer.close() # del writer # # ----------------------------------------model testing testloader = DataLoader(dataset=mv_dataset(X_test), batch_size=batch_size, shuffle=False, num_workers=1, collate_fn=mv_tabular_collate) scores = [[] for ss in range(len(X_test))] with torch.no_grad(): model.eval() for idx, batch in enumerate(tqdm(testloader)): batch = [batch[i].to(device) for i in range(len(batch))] cur_batch_scores = model.get_ad_scores(batch) for ss in range(len(X_test)): scores[ss].append(cur_batch_scores[ss]) scores = [torch.cat(scores[ss], dim=0) for ss in range(len(X_test))] # To find a single-best model scores_set = torch.cat(scores, dim=-1) single_best_roc = 0 single_best_pr_norm = 0 single_best_pr_anom = 0 for i in range(scores_set.shape[-1]): cur_view_scores = scores_set[:, i].cpu().detach().numpy() cur_roc, cur_pr_anom, cur_pr_norm, cur_tnr = save_roc_pr_curve_data(scores=cur_view_scores, labels=Y_test, file_path=None, verbose=False) if cur_roc > single_best_roc: single_best_roc = cur_roc single_best_pr_anom = cur_pr_anom single_best_pr_norm = cur_pr_norm single_best_tnr = cur_tnr single_best_roc_results[t].append(single_best_roc) single_best_pr_norm_results[t].append(single_best_pr_norm) single_best_pr_anom_results[t].append(single_best_pr_anom) single_best_tnr_results[t].append(single_best_tnr) scores = late_fusion(scores, merge='avg') # ----------------------------------------model eval roc, pr_anom, pr_norm, tnr = save_roc_pr_curve_data(scores=scores, labels=Y_test, file_path=None, verbose=False) roc_results[t].append(roc) pr_anom_results[t].append(pr_anom) pr_norm_results[t].append(pr_norm) tnr_results[t].append(tnr) del model # save the results file_name = results_path + dataset_name + '_mvae' np.savez(file=file_name, ROC=roc_results, PR_norm=pr_norm_results, PR_anom=pr_anom_results, tnr=tnr_results) load_print_results(file_name) file_name = results_path + dataset_name + '_mvae_single_best' np.savez(file=file_name, ROC=single_best_roc_results, PR_norm=single_best_pr_norm_results, PR_anom=single_best_pr_anom_results, tnr=single_best_tnr_results) load_print_results(file_name) print('dataset: {}, layer_size: {}, batch size: {}, lr: {}, wd:{}, eppchs: {}'.format(dataset_name, layer_size, batch_size, lr, wd, max(epochs))) def deepCCA(dataset_name, X, Y, repeat_times, normal_train_ratio, results_path, device, qualified_Y, ADdataset_mode='minority'): torch.set_default_tensor_type(torch.DoubleTensor) roc_results = [[] for i in range(repeat_times)] pr_anom_results = [[] for i in range(repeat_times)] pr_norm_results = [[] for i in range(repeat_times)] tnr_results = [[] for i in range(repeat_times)] # training config batch_size = batch_size_set_dcca[dataset_name] layer_size = layer_size_set[dataset_name] lr = lr_set_dcca[dataset_name] wd = wd_set_dcca[dataset_name] loss_func = MSELoss() cca_loss_func = cca(outdim_size=layer_size[-1]) input_size_set = [x.shape[0] for x in X] if results_path in ['./results/']: alpha_set = [alpha_set_dcca[dataset_name]] else: alpha_set = [0.01, 0.1, 0.5, 0.9, 0.99] for alpha in alpha_set: for t in range(repeat_times): print('The {}-th run begins!'.format(t)) for i in range(min([10, len(qualified_Y)])): partition, Y_test = build_img_dataset(dataset_name, Y, neg_class_id=qualified_Y[i], mode=ADdataset_mode) X_train, X_test = process_ad_dataset(X, partition) # -----------------------------------------model training seed = 0 if torch.cuda.is_available(): torch.cuda.manual_seed_all(seed) torch.manual_seed(seed) torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = True model = mv_corrAE(input_size_set=input_size_set, layer_sizes=layer_size).to(device).double() trainloader = DataLoader(dataset=mv_dataset(X_train), batch_size=batch_size, shuffle=True, num_workers=1, collate_fn=mv_tabular_collate) # specific training procedure epochs = epochs_set[dataset_name] cca_losses = AverageMeter() rec_losses = AverageMeter() optimizer = optim.Adam(model.parameters(), lr=lr, weight_decay=wd) model.train() cout = 0 for epoch in range(max(epochs)): trainloader = tqdm(trainloader) if ADdataset_mode is 'majority': trainloader.set_description('dcca, epoch{}, abnormal class{}'.format(epoch, qualified_Y[i])) else: trainloader.set_description('dcca, epoch{}, normal class{}'.format(epoch, qualified_Y[i])) for idx, batch in enumerate(trainloader): try: if batch[0].size(0) > 1: # for bn, batchsize > 1 batch = [batch[i].to(device) for i in range(len(batch))] latent_set, outputs_set = model(batch) recon_loss = torch.zeros(1).to(device) cca_loss_ = torch.zeros(1).to(device) for view in range(len(X_train)): if epoch < epochs[view]: y = batch[view] loss = loss_func(outputs_set[view], y) recon_loss += loss for v_idx in range(view + 1, len(X_train)): try: cur_cca_loss = cca_loss_func.loss(latent_set[view], latent_set[v_idx]) except: cur_cca_loss = torch.zeros(1).to(device) cca_loss_ += cur_cca_loss rec_losses.update(recon_loss.item(), batch[0].size(0)) cca_losses.update(cca_loss_.item(), batch[0].size(0)) tot_loss = (1 - alpha) * recon_loss + alpha * cca_loss_ postfix = ' rec_loss: {:.4f}, cca_loss: {:.4f} '.format(rec_losses.avg, cca_losses.avg) optimizer.zero_grad() tot_loss.backward() filter_nan_grad(optimizer) # if grad contains grad, the batch is not used to update the parameters optimizer.step() cout += 1 trainloader.set_postfix(log=postfix) except: print('The error idx is {}'.format(idx)) # # ----------------------------------------model testing testloader = DataLoader(dataset=mv_dataset(X_test), batch_size=batch_size, shuffle=False, num_workers=1, collate_fn=mv_tabular_collate) with torch.no_grad(): model.eval() scores = [[] for ss in range(len(X_test))] for idx, batch in enumerate(tqdm(testloader)): batch = [batch[i].to(device) for i in range(len(batch))] cur_batch_scores = model.get_ad_scores(batch) for ss in range(len(X_test)): scores[ss].append(cur_batch_scores[ss]) scores = [torch.cat(scores[ss], dim=0) for ss in range(len(X_test))] scores = late_fusion(scores, merge='avg') # ----------------------------------------model eval roc, pr_anom, pr_norm, tnr = save_roc_pr_curve_data(scores=scores, labels=Y_test, file_path=None, verbose=False) roc_results[t].append(roc) pr_anom_results[t].append(pr_anom) pr_norm_results[t].append(pr_norm) tnr_results[t].append(tnr) del model # save the results if results_path in ['./results/']: np.savez(file=results_path + dataset_name + '_dcca', ROC=roc_results, PR_norm=pr_norm_results, PR_anom=pr_anom_results, tnr=tnr_results) load_print_results(results_path + dataset_name + '_dcca') else: np.savez(file=results_path + dataset_name + '_dcca_alpha_{}'.format(alpha), ROC=roc_results, PR_norm=pr_norm_results, PR_anom=pr_anom_results, tnr=tnr_results) load_print_results(results_path + dataset_name + '_dcca_alpha_{}'.format(alpha)) def dgcca(dataset_name, X, Y, repeat_times, normal_train_ratio, results_path, device, qualified_Y, ADdataset_mode='minority'): torch.set_default_tensor_type(torch.DoubleTensor) roc_results = [[] for i in range(repeat_times)] pr_anom_results = [[] for i in range(repeat_times)] pr_norm_results = [[] for i in range(repeat_times)] tnr_results = [[] for i in range(repeat_times)] # training config batch_size = batch_size_set_dgcca[dataset_name] lr = lr_set_dgcca[dataset_name] wd = wd_set_dgcca[dataset_name] layer_size = layer_size_set[dataset_name] loss_func = MSELoss() gcca_loss_func = gcca(outdim_size=layer_size[-1]) input_size_set = [x.shape[0] for x in X] if results_path in ['./results/']: alpha_set = [alpha_set_dgcca[dataset_name]] else: alpha_set = [0.01, 0.1, 0.5, 0.9, 0.99] for alpha in alpha_set: for t in range(repeat_times): print('The {}-th run begins!'.format(t)) for i in range(min([10, len(qualified_Y)])): partition, Y_test = build_img_dataset(dataset_name, Y, neg_class_id=qualified_Y[i], mode=ADdataset_mode) X_train, X_test = process_ad_dataset(X, partition) # -----------------------------------------model training seed = 0 if torch.cuda.is_available(): torch.cuda.manual_seed_all(seed) torch.manual_seed(seed) torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = True model = mv_corrAE(input_size_set=input_size_set, layer_sizes=layer_size).to(device).double() trainloader = DataLoader(dataset=mv_dataset(X_train), batch_size=batch_size, shuffle=True, num_workers=1, collate_fn=mv_tabular_collate) # specific training procedure epochs = epochs_set[dataset_name] gcca_losses = AverageMeter() rec_losses = AverageMeter() optimizer = optim.Adam(model.parameters(), lr=lr, weight_decay=wd) model.train() cout = 0 for epoch in range(max(epochs)): trainloader = tqdm(trainloader) if ADdataset_mode is 'majority': trainloader.set_description('dgcca, epoch{}, abnormal class{}'.format(epoch, qualified_Y[i])) else: trainloader.set_description('dgcca, epoch{}, normal class{}'.format(epoch, qualified_Y[i])) for idx, batch in enumerate(trainloader): # try: if batch[0].size(0) > 1: # for bn, batchsize > 1 batch = [batch[i].to(device) for i in range(len(batch))] latent_set, outputs_set = model(batch) recon_loss = torch.zeros(1).to(device) try: gcca_loss_ = gcca_loss_func.loss(latent_set) except: gcca_loss_ = torch.zeros(1).to(device) for view in range(len(X_train)): if epoch < epochs[view]: y = batch[view] loss = loss_func(outputs_set[view], y) recon_loss += loss rec_losses.update(recon_loss.item(), batch[0].size(0)) gcca_losses.update(gcca_loss_.item(), batch[0].size(0)) tot_loss = (1 - alpha) * recon_loss + alpha * gcca_loss_ postfix = ' rec_loss: {:.4f}, gcca_loss: {:.4f} '.format(rec_losses.avg, gcca_losses.avg) optimizer.zero_grad() tot_loss.backward() filter_nan_grad(optimizer) # if grad contains grad, the batch is not used to update the parameters optimizer.step() cout += 1 trainloader.set_postfix(log=postfix) # # ----------------------------------------model testing testloader = DataLoader(dataset=mv_dataset(X_test), batch_size=batch_size, shuffle=False, num_workers=1, collate_fn=mv_tabular_collate) with torch.no_grad(): model.eval() scores = [[] for ss in range(len(X_test))] for idx, batch in enumerate(tqdm(testloader)): batch = [batch[i].to(device) for i in range(len(batch))] cur_batch_scores = model.get_ad_scores(batch) for ss in range(len(X_test)): scores[ss].append(cur_batch_scores[ss]) scores = [torch.cat(scores[ss], dim=0) for ss in range(len(X_test))] scores = late_fusion(scores, merge='avg') # ----------------------------------------model eval roc, pr_anom, pr_norm, tnr = save_roc_pr_curve_data(scores=scores, labels=Y_test, file_path=None, verbose=False) roc_results[t].append(roc) pr_anom_results[t].append(pr_anom) pr_norm_results[t].append(pr_norm) tnr_results[t].append(tnr) del model # save the results if results_path in ['./results/']: np.savez(file=results_path + dataset_name + '_dgcca', ROC=roc_results, PR_norm=pr_norm_results, PR_anom=pr_anom_results, tnr=tnr_results) load_print_results(results_path + dataset_name + '_dgcca') else: np.savez(file=results_path + dataset_name + '_dgcca_alpha_{}'.format(alpha), ROC=roc_results, PR_norm=pr_norm_results, PR_anom=pr_anom_results, tnr=tnr_results) load_print_results(results_path + dataset_name + '_dgcca_alpha_{}'.format(alpha)) def simple_mvDSVDD(dataset_name, X, Y, repeat_times, normal_train_ratio, results_path, device, qualified_Y, ADdataset_mode='minority'): torch.set_default_tensor_type(torch.DoubleTensor) roc_results = [[] for i in range(repeat_times)] pr_anom_results = [[] for i in range(repeat_times)] pr_norm_results = [[] for i in range(repeat_times)] tnr_results = [[] for i in range(repeat_times)] # training config batch_size = batch_size_set_dsvdd[dataset_name] layer_size = layer_size_set[dataset_name] lr = lr_set_dsvdd[dataset_name] wd = wd_set_dsvdd[dataset_name] loss_func = MSELoss() input_size_set = [x.shape[0] for x in X] eps = 1e-10 pretrain = True # indicate whether the DSVDD is pre-trained like AE mode = 'one_class' assert mode in ['one_class', 'soft_bound'] if mode is 'soft_bound': nu = 0.1 warm_epochs = 5 assert warm_epochs <= min(epochs_set[dataset_name]) for t in range(repeat_times): print('The {}-th run begins!'.format(t)) for i in range(min([10, len(qualified_Y)])): partition, Y_test = build_img_dataset(dataset_name, Y, neg_class_id=qualified_Y[i], mode=ADdataset_mode) X_train, X_test = process_ad_dataset(X, partition) # -----------------------------------------build dataloader under current config. trainloader = DataLoader(dataset=mv_dataset(X_train), batch_size=batch_size, shuffle=True, num_workers=1, collate_fn=mv_tabular_collate) testloader = DataLoader(dataset=mv_dataset(X_test), batch_size=batch_size, shuffle=False, num_workers=1, collate_fn=mv_tabular_collate) seed = 0 if torch.cuda.is_available(): torch.cuda.manual_seed_all(seed) torch.manual_seed(seed) torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = True # -----------------------------------------pre-training procedure if pretrain: model = mvae_ad(input_size_set=input_size_set, layer_sizes=layer_size).to(device) epochs = epochs_set[dataset_name] losses_set = [AverageMeter() for i in range(len(X_train))] optimizer = [optim.Adam(model.ae_set[i].parameters(), lr=lr, weight_decay=wd) for i in range(len(X_train))] model.train() cout = 0 for epoch in range(max(epochs)): trainloader = tqdm(trainloader) if ADdataset_mode is 'majority': trainloader.set_description('dsvdd, epoch{}, abnormal class{}'.format(epoch, qualified_Y[i])) else: trainloader.set_description('dsvdd, epoch{}, normal class{}'.format(epoch, qualified_Y[i])) for idx, batch in enumerate(trainloader): postfix = '' if batch[0].size(0) > 1: # for bn, batchsize > 1 batch = [batch[i].to(device) for i in range(len(batch))] outputs_set = model(batch) for view in range(len(X_train)): if epoch < epochs[view]: y = batch[view] loss = loss_func(outputs_set[view], y) optimizer[view].zero_grad() loss.backward() optimizer[view].step() losses_set[view].update(loss.item(), batch[0].size(0)) cur_postfix = ' view{}, rec_loss: {:.4f} '.format(view, losses_set[view].avg) postfix += cur_postfix cout += 1 trainloader.set_postfix(log=postfix) del trainloader # # ----------------------------------------set C for multi-view DSVDD trainloader = DataLoader(dataset=mv_dataset(X_train), batch_size=batch_size, shuffle=True, num_workers=1, collate_fn=mv_tabular_collate) mvDSVDD = mvenc(input_size_set=input_size_set, layer_sizes=layer_size).to(device) ae_dict = model.state_dict() dsvdd_dict = mvDSVDD.state_dict() ae_dict = {k: v for k, v in ae_dict.items() if k in dsvdd_dict} dsvdd_dict.update(ae_dict) mvDSVDD.load_state_dict(dsvdd_dict) mvDSVDD.eval() C_set = [[] for ss in range(len(X_train))] R_set = [torch.zeros(1).to(device) for ss in range(len(X_train))] with torch.no_grad(): for idx, batch in enumerate(tqdm(trainloader)): batch = [batch[i].to(device) for i in range(len(batch))] cur_batch_latent = mvDSVDD(batch) for ss in range(len(X_train)): C_set[ss].append(cur_batch_latent[ss].detach()) C_set = [torch.cat(C_set[cc], dim=0) for cc in range(len(X_train))] for cc in range(len(C_set)): tmp = torch.mean(C_set[cc], dim=0) tmp[(abs(tmp) < eps) & (tmp < 0)] = -eps tmp[(abs(tmp) < eps) & (tmp > 0)] = eps C_set[cc] = tmp # -------------------------------------train the DSVDD epochs = epochs_set[dataset_name] losses_set = [AverageMeter() for i in range(len(X_train))] optimizer = [optim.Adam(mvDSVDD.ae_set[i].parameters(), lr=lr, weight_decay=wd) for i in range(len(X_train))] mvDSVDD.train() cout = 0 for epoch in range(max(epochs)): trainloader = tqdm(trainloader) if ADdataset_mode is 'majority': trainloader.set_description('dsvdd, epoch{}, abnormal class{}'.format(epoch, qualified_Y[i])) else: trainloader.set_description('dsvdd, epoch{}, normal class{}'.format(epoch, qualified_Y[i])) for idx, batch in enumerate(trainloader): postfix = '' if batch[0].size(0) > 1: # for bn, batchsize > 1 batch = [batch[i].to(device) for i in range(len(batch))] outputs_set = mvDSVDD(batch) for view in range(len(X_train)): if epoch < epochs[view]: cur_c = C_set[view].to(device) cur_output = outputs_set[view] dist = torch.sum((cur_output - cur_c) 2, dim=1) if mode is 'soft_bound': scores = dist - R_set[view] 2 loss = R_set[view] ** 2 + (1 / nu) * torch.mean(torch.max(torch.zeros_like(scores), scores)) else: loss = torch.mean(dist) optimizer[view].zero_grad() loss.backward() optimizer[view].step() if mode is 'soft_bound' and epoch >= warm_epochs: R_set[view].data = torch.tensor(get_radius(dist, nu), device=device) losses_set[view].update(loss.item(), batch[0].size(0)) if mode is 'soft_bound': cur_postfix = ' view{}, dd_loss: {:.4f} R: {:.4f} '.format(view, losses_set[view].avg, R_set[view].item()) else: cur_postfix = ' view{}, dd_loss: {:.4f} '.format(view, losses_set[view].avg) postfix += cur_postfix cout += 1 trainloader.set_postfix(log=postfix) # ----------------------------------------model inferrence mvDSVDD.eval() scores_set = [[] for ss in range(len(X_test))] with torch.no_grad(): for idx, batch in enumerate(tqdm(testloader)): batch = [batch[i].to(device) for i in range(len(batch))] cur_batch_outputs = mvDSVDD(batch) for view in range(len(X_train)): scores = -1. * torch.sum((cur_batch_outputs[view] - C_set[view]) ** 2, dim=1, keepdim=True) scores_set[view].append(scores.detach()) scores_set = [torch.cat(scores_set[ss], dim=0) for ss in range(len(X_train))] scores = late_fusion(scores_set, merge='avg') # ----------------------------------------model eval roc, pr_anom, pr_norm, tnr = save_roc_pr_curve_data(scores=scores, labels=Y_test, file_path=None, verbose=False) roc_results[t].append(roc) pr_anom_results[t].append(pr_anom) pr_norm_results[t].append(pr_norm) tnr_results[t].append(tnr) del model # save the results np.savez(file=results_path + dataset_name + '_dsvdd', ROC=roc_results, PR_norm=pr_norm_results, PR_anom=pr_anom_results, tnr=tnr_results) load_print_results(results_path + dataset_name + '_dsvdd') def mv_corrae(dataset_name, X, Y, repeat_times, normal_train_ratio, results_path, device, qualified_Y, ADdataset_mode='minority'): torch.set_default_tensor_type(torch.DoubleTensor) roc_results = [[] for i in range(repeat_times)] pr_anom_results = [[] for i in range(repeat_times)] pr_norm_results = [[] for i in range(repeat_times)] tnr_results = [[] for i in range(repeat_times)] # training config batch_size = batch_size_set_corr[dataset_name] lr = lr_set_corr[dataset_name] wd = wd_set_corr[dataset_name] layer_size = layer_size_set[dataset_name] loss_func = MSELoss() input_size_set = [x.shape[0] for x in X] if results_path in ['./results/']: alpha_set = [alpha_set_corr[dataset_name]] else: alpha_set = [0.01, 0.1, 0.5, 0.9, 0.99] for alpha in alpha_set: for t in range(repeat_times): print('The {}-th run begins!'.format(t)) for i in range(min([10, len(qualified_Y)])): partition, Y_test = build_img_dataset(dataset_name, Y, neg_class_id=qualified_Y[i], mode=ADdataset_mode) X_train, X_test = process_ad_dataset(X, partition) # -----------------------------------------model training seed = 0 if torch.cuda.is_available(): torch.cuda.manual_seed_all(seed) torch.manual_seed(seed) torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = True model = mv_corrAE(input_size_set=input_size_set, layer_sizes=layer_size).to(device).double() trainloader = DataLoader(dataset=mv_dataset(X_train), batch_size=batch_size, shuffle=True, num_workers=1, collate_fn=mv_tabular_collate) # specific training procedure epochs = epochs_set[dataset_name] rec_losses = AverageMeter() corr_losses = AverageMeter() optimizer = optim.Adam(model.parameters(), lr=lr, weight_decay=wd) model.train() cout = 0 for epoch in range(max(epochs)): trainloader = tqdm(trainloader) if ADdataset_mode is 'majority': trainloader.set_description('corr, epoch{}, abnormal class{}'.format(epoch, qualified_Y[i])) else: trainloader.set_description('corr, epoch{}, normal class{}'.format(epoch, qualified_Y[i])) for idx, batch in enumerate(trainloader): if batch[0].size(0) > 1: # for bn, batchsize > 1 batch = [batch[i].to(device) for i in range(len(batch))] latent_set, outputs_set = model(batch) recon_loss = torch.zeros(1).to(device) corr_loss = torch.zeros(1).to(device) for view in range(len(X_train)): if epoch < epochs[view]: y = batch[view] loss = loss_func(outputs_set[view], y) recon_loss += loss for v_idx in range(view + 1, len(X_train)): cur_corr_loss = loss_func(latent_set[view], latent_set[v_idx]) corr_loss += cur_corr_loss rec_losses.update(recon_loss.item(), batch[0].size(0)) corr_losses.update(corr_loss.item(), batch[0].size(0)) postfix = ' rec_loss: {:.4f}, corr_loss: {:.4f} '.format(rec_losses.avg, corr_losses.avg) tot_loss = (1 - alpha) * recon_loss + alpha * corr_loss optimizer.zero_grad() tot_loss.backward() optimizer.step() cout += 1 trainloader.set_postfix(log=postfix) # # ----------------------------------------model testing testloader = DataLoader(dataset=mv_dataset(X_test), batch_size=batch_size, shuffle=False, num_workers=1, collate_fn=mv_tabular_collate) with torch.no_grad(): model.eval() scores = [[] for ss in range(len(X_test))] for idx, batch in enumerate(tqdm(testloader)): batch = [batch[i].to(device) for i in range(len(batch))] cur_batch_scores = model.get_ad_scores(batch) for ss in range(len(X_test)): scores[ss].append(cur_batch_scores[ss]) scores = [torch.cat(scores[ss], dim=0) for ss in range(len(X_test))] scores = late_fusion(scores, merge='avg') # ----------------------------------------model eval roc, pr_anom, pr_norm, tnr = save_roc_pr_curve_data(scores=scores, labels=Y_test, file_path=None, verbose=False) roc_results[t].append(roc) pr_anom_results[t].append(pr_anom) pr_norm_results[t].append(pr_norm) tnr_results[t].append(tnr) del model # save the results if results_path in ['./results/']: np.savez(file=results_path + dataset_name + '_corrAE', ROC=roc_results, PR_norm=pr_norm_results, PR_anom=pr_anom_results, tnr=tnr_results) load_print_results(results_path + dataset_name + '_corrAE') else: np.savez(file=results_path + dataset_name + '_corrAE_alpha_{}'.format(alpha), ROC=roc_results, PR_norm=pr_norm_results, PR_anom=pr_anom_results, tnr=tnr_results) load_print_results(results_path + dataset_name + '_corrAE_alpha_{}'.format(alpha)) def mv_sim(dataset_name, X, Y, repeat_times, normal_train_ratio, results_path, device, qualified_Y, ADdataset_mode='minority'): torch.set_default_tensor_type(torch.DoubleTensor) roc_results = [[] for i in range(repeat_times)] pr_anom_results = [[] for i in range(repeat_times)] pr_norm_results = [[] for i in range(repeat_times)] tnr_results = [[] for i in range(repeat_times)] # training config batch_size = batch_size_set_sim[dataset_name] lr = lr_set_sim[dataset_name] wd = wd_set_sim[dataset_name] layer_size = layer_size_set[dataset_name] loss_func = MSELoss() input_size_set = [x.shape[0] for x in X] if results_path in ['./results/']: alpha_set = [alpha_set_sim[dataset_name]] else: alpha_set = [0.01, 0.1, 0.5, 0.9, 0.99] if results_path in ['./results/']: m_set = [m_set_sim[dataset_name]] else: # m_set = [0, 1, 3, 5, 7] m_set = [0] for m in m_set: for alpha in alpha_set: for t in range(repeat_times): print('The {}-th run begins!'.format(t)) for i in range(min([10, len(qualified_Y)])): partition, Y_test = build_img_dataset(dataset_name, Y, neg_class_id=qualified_Y[i], mode=ADdataset_mode) X_train, X_test = process_ad_dataset(X, partition) # -----------------------------------------model training seed = 0 if torch.cuda.is_available(): torch.cuda.manual_seed_all(seed) torch.manual_seed(seed) torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = True model = mv_corrAE(input_size_set=input_size_set, layer_sizes=layer_size).to(device).double() trainloader = DataLoader(dataset=mv_dataset(X_train), batch_size=batch_size, shuffle=True, num_workers=1, collate_fn=mv_tabular_collate) # specific training procedure epochs = epochs_set[dataset_name] rec_losses = AverageMeter() sim_losses = AverageMeter() optimizer = optim.Adam(model.parameters(), lr=lr, weight_decay=wd) model.train() cout = 0 for epoch in range(max(epochs)): trainloader = tqdm(trainloader) if ADdataset_mode is 'majority': trainloader.set_description('sim, epoch{}, abnormal class{}'.format(epoch, qualified_Y[i])) else: trainloader.set_description('sim, epoch{}, normal class{}'.format(epoch, qualified_Y[i])) for idx, batch in enumerate(trainloader): if batch[0].size(0) > 1: # for bn, batchsize > 1 batch = [batch[i].to(device) for i in range(len(batch))] latent_set, outputs_set = model(batch) recon_loss = torch.zeros(1).to(device) sim_loss = torch.zeros(1).to(device) for view in range(len(X_train)): if epoch < epochs[view]: y = batch[view] loss = loss_func(outputs_set[view], y) recon_loss += loss for v_idx in range(view + 1, len(X_train)): cur_sim_loss = similarity_hinge_loss(latent_set[view], latent_set[v_idx], m) sim_loss += cur_sim_loss rec_losses.update(recon_loss.item(), batch[0].size(0)) sim_losses.update(sim_loss.item(), batch[0].size(0)) postfix = ' rec_loss: {:.4f}, sim_loss: {:.4f} '.format(rec_losses.avg, sim_losses.avg) tot_loss = (1 - alpha) * recon_loss + alpha * sim_loss optimizer.zero_grad() tot_loss.backward() optimizer.step() cout += 1 trainloader.set_postfix(log=postfix) # # ----------------------------------------model testing testloader = DataLoader(dataset=mv_dataset(X_test), batch_size=batch_size, shuffle=False, num_workers=1, collate_fn=mv_tabular_collate) with torch.no_grad(): model.eval() scores = [[] for ss in range(len(X_test))] for idx, batch in enumerate(tqdm(testloader)): batch = [batch[i].to(device) for i in range(len(batch))] cur_batch_scores = model.get_ad_scores(batch) for ss in range(len(X_test)): scores[ss].append(cur_batch_scores[ss]) scores = [torch.cat(scores[ss], dim=0) for ss in range(len(X_test))] scores = late_fusion(scores, merge='avg') # ----------------------------------------model eval roc, pr_anom, pr_norm, tnr = save_roc_pr_curve_data(scores=scores, labels=Y_test, file_path=None, verbose=False) roc_results[t].append(roc) pr_anom_results[t].append(pr_anom) pr_norm_results[t].append(pr_norm) tnr_results[t].append(tnr) del model # save the results if results_path in ['./results/']: np.savez(file=results_path + dataset_name + '_sim', ROC=roc_results, PR_norm=pr_norm_results, PR_anom=pr_anom_results, tnr=tnr_results) load_print_results(results_path + dataset_name + '_sim') else: np.savez(file=results_path + dataset_name + '_sim_alpha_{}_m_{}'.format(alpha, m), ROC=roc_results, PR_norm=pr_norm_results, PR_anom=pr_anom_results, tnr=tnr_results) load_print_results(results_path + dataset_name + '_sim_alpha_{}_m_{}'.format(alpha, m)) def mvae_fuse_latent(dataset_name, X, Y, repeat_times, normal_train_ratio, results_path, device, qualified_Y, ADdataset_mode='minority'): torch.set_default_tensor_type(torch.DoubleTensor) roc_results = [[] for i in range(repeat_times)] pr_anom_results = [[] for i in range(repeat_times)] pr_norm_results = [[] for i in range(repeat_times)] tnr_results = [[] for i in range(repeat_times)] # training config batch_size = batch_size_set_fused[dataset_name] layer_size = layer_size_set[dataset_name] lr = lr_set_fused[dataset_name] wd = wd_set_fused[dataset_name] fuse_dim = layer_size[-1] loss_func = MSELoss() input_size_set = [x.shape[0] for x in X] for t in range(repeat_times): print('The {}-th run begins!'.format(t)) for i in range(min([10, len(qualified_Y)])): partition, Y_test = build_img_dataset(dataset_name, Y, neg_class_id=qualified_Y[i], mode=ADdataset_mode) X_train, X_test = process_ad_dataset(X, partition) # -----------------------------------------model training seed = 0 if torch.cuda.is_available(): torch.cuda.manual_seed_all(seed) torch.manual_seed(seed) torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = True model = mvae_ad(input_size_set=input_size_set, layer_sizes=layer_size).to(device).double() trainloader = DataLoader(dataset=mv_dataset(X_train), batch_size=batch_size, shuffle=True, num_workers=1, collate_fn=mv_tabular_collate) # specific training procedure epochs = epochs_set[dataset_name] pt_epochs = pt_epochs_set[dataset_name] assert max(epochs) > max(pt_epochs) losses_set = [AverageMeter() for i in range(len(X_train))] optimizer = [optim.Adam(model.ae_set[i].parameters(), lr=lr, weight_decay=wd) for i in range(len(X_train))] # -----------------------------------------pretrain the MVAE model.train() cout = 0 for epoch in range(max(pt_epochs)): trainloader = tqdm(trainloader) if ADdataset_mode is 'majority': trainloader.set_description('fuse, epoch{}, abnormal class{}'.format(epoch, qualified_Y[i])) else: trainloader.set_description('fuse, epoch{}, normal class{}'.format(epoch, qualified_Y[i])) for idx, batch in enumerate(trainloader): postfix = '' if batch[0].size(0) > 1: # for bn, batchsize > 1 batch = [batch[i].to(device) for i in range(len(batch))] outputs_set = model(batch) for view in range(len(X_train)): if epoch < epochs[view]: y = batch[view] loss = loss_func(outputs_set[view], y) optimizer[view].zero_grad() loss.backward() optimizer[view].step() losses_set[view].update(loss.item(), batch[0].size(0)) cur_postfix = ' view{}, rec_loss: {:.4f} '.format(view, losses_set[view].avg) postfix += cur_postfix cout += 1 trainloader.set_postfix(log=postfix) del trainloader # -------------------------------load pretrained weights to MVAE_fused fused_model = mvae_fused(input_size_set=input_size_set, layer_sizes=layer_size, fuse_dim=fuse_dim).to(device).double() ae_dict = model.state_dict() ae_fused_dict = fused_model.state_dict() ae_dict = {k: v for k, v in ae_dict.items() if k in ae_fused_dict} ae_fused_dict.update(ae_dict) fused_model.load_state_dict(ae_fused_dict) fused_model.train() trainloader = DataLoader(dataset=mv_dataset(X_train), batch_size=batch_size, shuffle=True, num_workers=1, collate_fn=mv_tabular_collate) epochs = epochs_set[dataset_name] losses = AverageMeter() optimizer = optim.Adam(fused_model.parameters(), lr=lr, weight_decay=wd) cout = 0 for epoch in range(max(epochs) - max(pt_epochs)): trainloader = tqdm(trainloader) if ADdataset_mode is 'majority': trainloader.set_description('fuse, Epoch{}, abnormal class{}'.format(epoch, qualified_Y[i])) else: trainloader.set_description('fuse, Epoch{}, normal class{}'.format(epoch, qualified_Y[i])) for idx, batch in enumerate(trainloader): if batch[0].size(0) > 1: # for bn, batchsize > 1 batch = [batch[i].to(device) for i in range(len(batch))] outputs_set = fused_model(batch) loss = torch.zeros(1).to(device) for ll in range(len(batch)): loss += loss_func(outputs_set[ll], batch[ll]) optimizer.zero_grad() loss.backward() optimizer.step() losses.update(loss.item(), batch[0].size(0)) postfix = ' fused rec_loss: {:.4f} '.format(losses.avg) cout += 1 trainloader.set_postfix(log=postfix) # # ----------------------------------------model testing testloader = DataLoader(dataset=mv_dataset(X_test), batch_size=batch_size, shuffle=False, num_workers=1, collate_fn=mv_tabular_collate) with torch.no_grad(): fused_model.eval() scores = [[] for ss in range(len(X_test))] for idx, batch in enumerate(tqdm(testloader)): batch = [batch[i].to(device) for i in range(len(batch))] cur_batch_scores = fused_model.get_ad_scores(batch) for ss in range(len(X_test)): scores[ss].append(cur_batch_scores[ss]) scores = [torch.cat(scores[ss], dim=0) for ss in range(len(X_test))] scores = late_fusion(scores, merge='avg') # ----------------------------------------model eval roc, pr_anom, pr_norm, tnr = save_roc_pr_curve_data(scores=scores, labels=Y_test, file_path=None, verbose=False) roc_results[t].append(roc) pr_anom_results[t].append(pr_anom) pr_norm_results[t].append(pr_norm) tnr_results[t].append(tnr) del model # save the results np.savez(file=results_path + dataset_name + '_mvae_fused', ROC=roc_results, PR_norm=pr_norm_results, PR_anom=pr_anom_results, tnr=tnr_results) load_print_results(results_path + dataset_name + '_mvae_fused') def mv_dbn(dataset_name, X, Y, repeat_times, normal_train_ratio, results_path, device, qualified_Y, ADdataset_mode='minority'): torch.set_default_tensor_type(torch.DoubleTensor) roc_results = [[] for i in range(repeat_times)] pr_anom_results = [[] for i in range(repeat_times)] pr_norm_results = [[] for i in range(repeat_times)] tnr_results = [[] for i in range(repeat_times)] # training config layer_size = layer_size_set[dataset_name] batch_size = batch_size_set_dbn[dataset_name] lr = lr_set_dbn[dataset_name] wd = wd_set_dbn[dataset_name] print('layer_size: {}, batch size: {}, lr: {}, wd:{}'.format(layer_size, batch_size, lr, wd)) input_size_set = [x.shape[0] for x in X] fuse_dim = layer_size[-1] k = 3 epochs = epochs_set[dataset_name] for t in range(repeat_times): print('The {}-th run begins!'.format(t)) for i in range(min([10, len(qualified_Y)])): partition, Y_test = build_img_dataset(dataset_name, Y, neg_class_id=qualified_Y[i], mode=ADdataset_mode) X_train, X_test = process_ad_dataset(X, partition, normalization_range='01') # -----------------------------------------model training seed = 0 if torch.cuda.is_available(): torch.cuda.manual_seed_all(seed) torch.manual_seed(seed) torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = True # model = mvae_ad(input_size_set=input_size_set, layer_sizes=layer_size).to(device).double() model = mv_DBN(input_sizes=input_size_set, layers=layer_size, fuse_dim=fuse_dim, k=k) X_train = [torch.from_numpy(X_train[_]) for _ in range(len(X_train))] model.train_mv_DBN(X_train, epochs=max(epochs), lr=lr, batch_size=batch_size) # # ----------------------------------------model testing X_test = [torch.from_numpy(X_test[_]) for _ in range(len(X_test))] scores = model.get_ad_scores(X_test) scores = late_fusion(scores, merge='avg') # ----------------------------------------model eval roc, pr_anom, pr_norm, tnr = save_roc_pr_curve_data(scores=scores, labels=Y_test, file_path=None, verbose=False) roc_results[t].append(roc) pr_anom_results[t].append(pr_anom) pr_norm_results[t].append(pr_norm) tnr_results[t].append(tnr) del model # save the results file_name = results_path+dataset_name+'_dbn' np.savez(file=file_name, ROC=roc_results, PR_norm=pr_norm_results, PR_anom=pr_anom_results, tnr=tnr_results) load_print_results(file_name) def mv_splitae(dataset_name, X, Y, repeat_times, normal_train_ratio, results_path, device, qualified_Y, ADdataset_mode='minority'): torch.set_default_tensor_type(torch.DoubleTensor) roc_results = [[] for i in range(repeat_times)] pr_anom_results = [[] for i in range(repeat_times)] pr_norm_results = [[] for i in range(repeat_times)] tnr_results = [[] for i in range(repeat_times)] # training config batch_size = batch_size_set_split[dataset_name] lr = lr_set_split[dataset_name] wd = wd_set_split[dataset_name] layer_size = layer_size_set[dataset_name] loss_func = MSELoss() input_size_set = [x.shape[0] for x in X] dec_mode = 'fixed' for t in range(repeat_times): print('The {}-th run begins!'.format(t)) for i in range(min([10, len(qualified_Y)])): partition, Y_test = build_img_dataset(dataset_name, Y, neg_class_id=qualified_Y[i], mode=ADdataset_mode) X_train, X_test = process_ad_dataset(X, partition) # -----------------------------------------model training seed = 0 if torch.cuda.is_available(): torch.cuda.manual_seed_all(seed) torch.manual_seed(seed) torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = True model = splitAE(input_size_set=input_size_set, layer_sizes=layer_size, dec_mode=dec_mode).to(device) trainloader = DataLoader(dataset=mv_dataset(X_train), batch_size=batch_size, shuffle=True, num_workers=1, collate_fn=mv_tabular_collate) # specific training procedure epochs = epochs_set[dataset_name] losses_set = [AverageMeter() for i in range(len(X_train))] optimizer = optim.Adam(model.parameters(), lr=lr, weight_decay=wd) model.train() cout = 0 for epoch in range(max(epochs)): trainloader = tqdm(trainloader) if ADdataset_mode is 'majority': trainloader.set_description('split_fix, epoch{}, abnormal class{}'.format(epoch, qualified_Y[i])) else: trainloader.set_description('split_fix, epoch{}, normal class{}'.format(epoch, qualified_Y[i])) for idx, batch in enumerate(trainloader): postfix = '' if batch[0].size(0) > 1: # for bn, batchsize > 1 batch = [batch[i].to(device) for i in range(len(batch))] outputs_set = model(batch) loss = torch.zeros(1).to(device) cur_view_losses = [0. for cc in range(len(X_train))] for enc_ind in range(len(outputs_set)): cur_enc_recon = outputs_set[enc_ind] for view in range(len(X_train)): y = batch[view] cur_enc_view_loss = loss_func(cur_enc_recon[view], y) loss += cur_enc_view_loss cur_view_losses[view] += loss.item() optimizer.zero_grad() loss.backward() optimizer.step() for view in range(len(X_train)): losses_set[view].update(cur_view_losses[view], batch[0].size(0)) cur_postfix = ' view{}, rec_loss: {:.4f} '.format(view, losses_set[view].avg) postfix += cur_postfix cout += 1 trainloader.set_postfix(log=postfix) # # ----------------------------------------model testing testloader = DataLoader(dataset=mv_dataset(X_test), batch_size=batch_size, shuffle=False, num_workers=1, collate_fn=mv_tabular_collate) with torch.no_grad(): model.eval() scores = [[] for ss in range(len(X_test))] for idx, batch in enumerate(tqdm(testloader)): batch = [batch[i].to(device) for i in range(len(batch))] cur_batch_scores = model.get_ad_scores(batch) for ss in range(len(X_test)): scores[ss].append(cur_batch_scores[ss]) scores = [torch.cat(scores[ss], dim=0) for ss in range(len(X_test))] scores = late_fusion(scores, merge='avg') # ----------------------------------------model eval roc, pr_anom, pr_norm, tnr = save_roc_pr_curve_data(scores=scores, labels=Y_test, file_path=None, verbose=False) roc_results[t].append(roc) pr_anom_results[t].append(pr_anom) pr_norm_results[t].append(pr_norm) tnr_results[t].append(tnr) del model # save the results np.savez(file=results_path + dataset_name + '_splitAE_{}'.format(dec_mode), ROC=roc_results, PR_norm=pr_norm_results, PR_anom=pr_anom_results, tnr=tnr_results) load_print_results(results_path + dataset_name + '_splitAE_{}'.format(dec_mode)) def mv_ss(dataset_name, X, Y, repeat_times, normal_train_ratio, results_path, device, qualified_Y, ADdataset_mode='minority', mode='fuse', param_mode='fixed'): torch.set_default_tensor_type(torch.DoubleTensor) roc_results = [[] for i in range(repeat_times)] pr_anom_results = [[] for i in range(repeat_times)] pr_norm_results = [[] for i in range(repeat_times)] tnr_results = [[] for i in range(repeat_times)] # training config batch_size = batch_size_set_split[dataset_name] lr = lr_set_split[dataset_name] wd = wd_set_split[dataset_name] layer_size = layer_size_set[dataset_name] loss_func = MSELoss() input_size_set = [x.shape[0] for x in X] fuse_dim = layer_size[-1] mode = mode # fuse/pred/split if mode is 'fuse': if param_mode not in ['nn', 'sum', 'max']: raise NotImplementedError else: if param_mode not in ['fixed', 'non-fixed']: raise NotImplementedError for t in range(repeat_times): print('The {}-th run begins!'.format(t)) for i in range(min([10, len(qualified_Y)])): partition, Y_test = build_img_dataset(dataset_name, Y, neg_class_id=qualified_Y[i], mode=ADdataset_mode) X_train, X_test = process_ad_dataset(X, partition) # -----------------------------------------model training seed = 0 if torch.cuda.is_available(): torch.cuda.manual_seed_all(seed) torch.manual_seed(seed) torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = True model = mvae_ss(input_size_set=input_size_set, layer_sizes=layer_size, fuse_dim=fuse_dim, mode=mode, param_mode=param_mode).to(device) trainloader = DataLoader(dataset=mv_dataset(X_train), batch_size=batch_size, shuffle=True, num_workers=1, collate_fn=mv_tabular_collate) # specific training procedure epochs = epochs_set[dataset_name] losses_set = [AverageMeter() for i in range(len(X_train))] optimizer = optim.Adam(model.parameters(), lr=lr, weight_decay=wd) model.train() for epoch in range(max(epochs)): trainloader = tqdm(trainloader) if ADdataset_mode is 'majority': trainloader.set_description('ss_{}_{}, epoch{}, abnormal class{}'.format(mode, param_mode, epoch, qualified_Y[i])) else: trainloader.set_description('ss_{}_{}, epoch{}, normal class{}'.format(mode, param_mode, epoch, qualified_Y[i])) for idx, batch in enumerate(trainloader): postfix = '' if batch[0].size(0) > 1: # for bn, batchsize > 1 batch = [batch[i].to(device) for i in range(len(batch))] outputs_set = model(batch) if mode is 'fuse' or mode is 'pred': loss = torch.zeros(1).to(device) for ll in range(len(batch)): cur_view_loss = loss_func(outputs_set[ll], batch[ll]) losses_set[ll].update(cur_view_loss, batch[0].size(0)) cur_postfix = ' view{}, rec_loss: {:.4f} '.format(ll, losses_set[ll].avg) postfix += cur_postfix loss += cur_view_loss optimizer.zero_grad() loss.backward() optimizer.step() elif mode is 'split': loss = torch.zeros(1).to(device) cur_view_losses = [0. for cc in range(len(X_train))] for enc_ind in range(len(outputs_set)): cur_enc_recon = outputs_set[enc_ind] for view in range(len(X_train)): y = batch[view] cur_enc_view_loss = loss_func(cur_enc_recon[view], y) loss += cur_enc_view_loss cur_view_losses[view] += loss.item() optimizer.zero_grad() loss.backward() optimizer.step() for view in range(len(X_train)): losses_set[view].update(cur_view_losses[view], batch[0].size(0)) cur_postfix = ' view{}, rec_loss: {:.4f} '.format(view, losses_set[view].avg) postfix += cur_postfix else: raise NotImplementedError trainloader.set_postfix(log=postfix) # # ----------------------------------------model testing testloader = DataLoader(dataset=mv_dataset(X_test), batch_size=batch_size, shuffle=False, num_workers=1, collate_fn=mv_tabular_collate) with torch.no_grad(): model.eval() scores = [[] for ss in range(len(X_test))] for idx, batch in enumerate(tqdm(testloader)): batch = [batch[i].to(device) for i in range(len(batch))] cur_batch_scores = model.get_ad_scores(batch) for ss in range(len(X_test)): scores[ss].append(cur_batch_scores[ss]) scores = [torch.cat(scores[ss], dim=0) for ss in range(len(X_test))] scores = late_fusion(scores, merge='avg') # ----------------------------------------model eval roc, pr_anom, pr_norm, tnr = save_roc_pr_curve_data(scores=scores, labels=Y_test, file_path=None, verbose=False) roc_results[t].append(roc) pr_anom_results[t].append(pr_anom) pr_norm_results[t].append(pr_norm) tnr_results[t].append(tnr) del model # save the results np.savez(file=results_path + dataset_name + '_ss_{}_{}'.format(mode, param_mode), ROC=roc_results, PR_norm=pr_norm_results, PR_anom=pr_anom_results, tnr=tnr_results) load_print_results(results_path + dataset_name + '_ss_{}_{}'.format(mode, param_mode)) def mvDSVDD_fused(dataset_name, X, Y, repeat_times, normal_train_ratio, results_path, device, qualified_Y, ADdataset_mode='minority'): torch.set_default_tensor_type(torch.DoubleTensor) roc_results = [[] for i in range(repeat_times)] pr_anom_results = [[] for i in range(repeat_times)] pr_norm_results = [[] for i in range(repeat_times)] tnr_results = [[] for i in range(repeat_times)] # training config batch_size = batch_size_set_dsvdd[dataset_name] layer_size = layer_size_set[dataset_name] lr = lr_set_dsvdd[dataset_name] wd = wd_set_dsvdd[dataset_name] loss_func = MSELoss() input_size_set = [x.shape[0] for x in X] eps = 1e-10 pretrain = True # indicate whether the DSVDD is pre-trained like AE mode = 'one_class' assert mode in ['one_class', 'soft_bound'] if mode is 'soft_bound': nu = 0.1 warm_epochs = 5 assert warm_epochs <= min(epochs_set[dataset_name]) for t in range(repeat_times): print('The {}-th run begins!'.format(t)) for i in range(min([10, len(qualified_Y)])): partition, Y_test = build_img_dataset(dataset_name, Y, neg_class_id=qualified_Y[i], mode=ADdataset_mode) X_train, X_test = process_ad_dataset(X, partition) # -----------------------------------------build dataloader under current config. trainloader = DataLoader(dataset=mv_dataset(X_train), batch_size=batch_size, shuffle=True, num_workers=1, collate_fn=mv_tabular_collate) testloader = DataLoader(dataset=mv_dataset(X_test), batch_size=batch_size, shuffle=False, num_workers=1, collate_fn=mv_tabular_collate) seed = 0 if torch.cuda.is_available(): torch.cuda.manual_seed_all(seed) torch.manual_seed(seed) torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = True # -----------------------------------------pre-training procedure if pretrain: model = mvae_ad(input_size_set=input_size_set, layer_sizes=layer_size).to(device) epochs = epochs_set[dataset_name] losses_set = [AverageMeter() for i in range(len(X_train))] optimizer = [optim.Adam(model.ae_set[i].parameters(), lr=lr, weight_decay=wd) for i in range(len(X_train))] model.train() cout = 0 for epoch in range(max(epochs)): trainloader = tqdm(trainloader) if ADdataset_mode is 'majority': trainloader.set_description('dsvdd_fuse, epoch{}, abnormal class{}'.format(epoch, qualified_Y[i])) else: trainloader.set_description('dsvdd_fuse, epoch{}, normal class{}'.format(epoch, qualified_Y[i])) for idx, batch in enumerate(trainloader): postfix = '' if batch[0].size(0) > 1: # for bn, batchsize > 1 batch = [batch[i].to(device) for i in range(len(batch))] outputs_set = model(batch) for view in range(len(X_train)): if epoch < epochs[view]: y = batch[view] loss = loss_func(outputs_set[view], y) optimizer[view].zero_grad() loss.backward() optimizer[view].step() losses_set[view].update(loss.item(), batch[0].size(0)) cur_postfix = ' view{}, rec_loss: {:.4f} '.format(view, losses_set[view].avg) postfix += cur_postfix cout += 1 trainloader.set_postfix(log=postfix) del trainloader # # ----------------------------------------set C for multi-view DSVDD trainloader = DataLoader(dataset=mv_dataset(X_train), batch_size=batch_size, shuffle=True, num_workers=1, collate_fn=mv_tabular_collate) mvDSVDD = mvenc_fuse(input_size_set=input_size_set, layer_sizes=layer_size).to(device) ae_dict = model.state_dict() dsvdd_dict = mvDSVDD.state_dict() ae_dict = {k: v for k, v in ae_dict.items() if k in dsvdd_dict} dsvdd_dict.update(ae_dict) mvDSVDD.load_state_dict(dsvdd_dict) mvDSVDD.eval() C_set = [] R = torch.zeros(1).to(device) with torch.no_grad(): for idx, batch in enumerate(tqdm(trainloader)): batch = [batch[i].to(device) for i in range(len(batch))] cur_batch_latent = mvDSVDD(batch) C_set.append(cur_batch_latent.detach()) C_set = torch.cat(C_set, dim=0) tmp = torch.mean(C_set, dim=0) tmp[(abs(tmp) < eps) & (tmp < 0)] = -eps tmp[(abs(tmp) < eps) & (tmp > 0)] = eps C = tmp # -------------------------------------train the DSVDD epochs = epochs_set[dataset_name] losses = AverageMeter() optimizer = optim.Adam(mvDSVDD.parameters(), lr=lr, weight_decay=wd) mvDSVDD.train() cout = 0 for epoch in range(max(epochs)): trainloader = tqdm(trainloader) if ADdataset_mode is 'majority': trainloader.set_description('dsvdd_fuse, epoch{}, abnormal class{}'.format(epoch, qualified_Y[i])) else: trainloader.set_description('dsvdd_fuse, epoch{}, normal class{}'.format(epoch, qualified_Y[i])) for idx, batch in enumerate(trainloader): postfix = '' if batch[0].size(0) > 1: # for bn, batchsize > 1 batch = [batch[i].to(device) for i in range(len(batch))] outputs = mvDSVDD(batch) C = C.to(device) dist = torch.sum((outputs - C) 2, dim=1) if mode is 'soft_bound': scores = dist - R 2 loss = R ** 2 + (1 / nu) * torch.mean(torch.max(torch.zeros_like(scores), scores)) else: loss = torch.mean(dist) optimizer.zero_grad() loss.backward() optimizer.step() losses.update(loss.item(), batch[0].size(0)) if mode is 'soft_bound': postfix += ' dd_loss: {:.4f} R: {:.4f} '.format(losses.avg, R.item()) else: postfix += ' dd_loss: {:.4f} '.format(losses.avg) if mode is 'soft_bound' and epoch >= warm_epochs: R.data = torch.tensor(get_radius(dist, nu), device=device) cout += 1 trainloader.set_postfix(log=postfix) # ----------------------------------------model inferrence mvDSVDD.eval() scores_set = [] with torch.no_grad(): for idx, batch in enumerate(tqdm(testloader)): batch = [batch[i].to(device) for i in range(len(batch))] cur_batch_outputs = mvDSVDD(batch) scores = -1. * torch.sum((cur_batch_outputs - C) ** 2, dim=1) scores_set.append(scores.detach()) scores_set = torch.cat(scores_set, dim=0) scores = scores_set.cpu().detach().numpy() # ----------------------------------------model eval roc, pr_anom, pr_norm, tnr = save_roc_pr_curve_data(scores=scores, labels=Y_test, file_path=None, verbose=False) roc_results[t].append(roc) pr_anom_results[t].append(pr_anom) pr_norm_results[t].append(pr_norm) tnr_results[t].append(tnr) del model # save the results np.savez(file=results_path + dataset_name + '_dsvdd_fused', ROC=roc_results, PR_norm=pr_norm_results, PR_anom=pr_anom_results, tnr=tnr_results) load_print_results(results_path + dataset_name + '_dsvdd_fused') def mv_tf(dataset_name, X, Y, repeat_times, normal_train_ratio, results_path, device, qualified_Y, ADdataset_mode='minority'): torch.set_default_tensor_type(torch.DoubleTensor) roc_results = [[] for i in range(repeat_times)] pr_anom_results = [[] for i in range(repeat_times)] pr_norm_results = [[] for i in range(repeat_times)] tnr_results = [[] for i in range(repeat_times)] # training config batch_size = batch_size_set_tf[dataset_name] lr = lr_set_tf[dataset_name] wd = wd_set_tf[dataset_name] if results_path in ['./results/']: r_set = [r_set_tf[dataset_name]] else: r_set = [4, 8, 16, 32, 64] layer_size = layer_size_set[dataset_name] loss_func = MSELoss() input_size_set = [x.shape[0] for x in X] for r in r_set: for t in range(repeat_times): print('The {}-th run begins!'.format(t)) for i in range(min([10, len(qualified_Y)])): partition, Y_test = build_img_dataset(dataset_name, Y, neg_class_id=qualified_Y[i], mode=ADdataset_mode) X_train, X_test = process_ad_dataset(X, partition) # -----------------------------------------model training seed = 0 if torch.cuda.is_available(): torch.cuda.manual_seed_all(seed) torch.manual_seed(seed) torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = True model = mvae_tf(input_size_set=input_size_set, layer_sizes=layer_size, rank=r).to(device) trainloader = DataLoader(dataset=mv_dataset(X_train), batch_size=batch_size, shuffle=True, num_workers=1, collate_fn=mv_tabular_collate) # specific training procedure epochs = epochs_set[dataset_name] losses_set = [AverageMeter() for i in range(len(X_train))] optimizer = optim.Adam(model.parameters(), lr=lr, weight_decay=wd) model.train() for epoch in range(max(epochs)): trainloader = tqdm(trainloader) if ADdataset_mode is 'majority': trainloader.set_description('mvtf, epoch{}, abnormal class{}'.format(epoch, qualified_Y[i])) else: trainloader.set_description('mvtf, epoch{}, normal class{}'.format(epoch, qualified_Y[i])) for idx, batch in enumerate(trainloader): postfix = '' if batch[0].size(0) > 1: # for bn, batchsize > 1 batch = [batch[i].to(device) for i in range(len(batch))] outputs_set = model(batch) loss = torch.zeros(1).to(device) for ll in range(len(batch)): cur_view_loss = loss_func(outputs_set[ll], batch[ll]) losses_set[ll].update(cur_view_loss, batch[0].size(0)) cur_postfix = ' view{}, rec_loss: {:.4f} '.format(ll, losses_set[ll].avg) postfix += cur_postfix loss += cur_view_loss optimizer.zero_grad() loss.backward() optimizer.step() trainloader.set_postfix(log=postfix) # # ----------------------------------------model testing testloader = DataLoader(dataset=mv_dataset(X_test), batch_size=batch_size, shuffle=False, num_workers=1, collate_fn=mv_tabular_collate) with torch.no_grad(): model.eval() scores = [[] for ss in range(len(X_test))] for idx, batch in enumerate(tqdm(testloader)): batch = [batch[i].to(device) for i in range(len(batch))] cur_batch_scores = model.get_ad_scores(batch) for ss in range(len(X_test)): scores[ss].append(cur_batch_scores[ss]) scores = [torch.cat(scores[ss], dim=0) for ss in range(len(X_test))] scores = late_fusion(scores, merge='avg') # ----------------------------------------model eval roc, pr_anom, pr_norm, tnr = save_roc_pr_curve_data(scores=scores, labels=Y_test, file_path=None, verbose=False) roc_results[t].append(roc) pr_anom_results[t].append(pr_anom) pr_norm_results[t].append(pr_norm) tnr_results[t].append(tnr) del model # save the results if results_path in ['./results/']: np.savez(file=results_path + dataset_name + '_tf', ROC=roc_results, PR_norm=pr_norm_results, PR_anom=pr_anom_results, tnr=tnr_results) load_print_results(results_path + dataset_name + '_tf') else: np.savez(file=results_path + dataset_name + '_tf_r_{}'.format(r), ROC=roc_results, PR_norm=pr_norm_results, PR_anom=pr_anom_results, tnr=tnr_results) load_print_results(results_path + dataset_name + '_tf_r_{}'.format(r)) if __name__ == '__main__': os.environ["CUDA_VISIBLE_DEVICES"] = "0" device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') # ----------------------------------------prepare the dataset # dataset_set = ['oct', 'breast', 'retina', 'axial', 'coronal', 'sagittal', 'path', 'derma', 'pneumonia'] # dataset_set = ['mvtec_bottle', # 'mvtec_cable', 'mvtec_capsule', 'mvtec_carpet', 'mvtec_grid', 'mvtec_hazelnut', 'mvtec_leather', 'mvtec_metal_nut', 'mvtec_pill', # 'mvtec_screw', 'mvtec_tile', 'mvtec_toothbrush', 'mvtec_transistor', 'mvtec_wood', 'mvtec_zipper'] # dataset_set = ['cifar10', 'cifar100', 'mnist', 'fmnist', 'svhn'] # dataset_set = ['ytface'] dataset_set = ['ytface'] # -----------------------------------------methods for dataset_name in dataset_set: X, Y = read_dataset('./data/', dataset_name) # for i in range(len(X)): # cur_X = X[i] # idx = np.isnan(cur_X).sum() # if idx > 0: # print('dataset: {}, view: {} has NaN!'.format(dataset_name, i)) min_class_num = 300 normal_train_ratio = 0.7 if dataset_name[:5] in ['mvtec']: qualified_Y = get_large_class(Y, min_class_num=min_class_num, semantics=True) else: qualified_Y = get_large_class(Y, min_class_num=min_class_num) num_qualified_class = len(qualified_Y) repeat_times = 1 # train-test split is fixed ADdataset_mode = 'minority' if num_qualified_class > 0: print('Training dataset name: {}, qualified class number: {}/{}'.format(dataset_name, num_qualified_class, len(get_all_labels(Y)))) # --------------------------------Baselines----------------------------------- simple_mvae(dataset_name, X, Y, repeat_times, normal_train_ratio, results_path='./results/', device=device, qualified_Y=qualified_Y, ADdataset_mode=ADdataset_mode) # --------------------------------Multi-view fusion based methods------------- # simple fusion: mv_ss(dataset_name, X, Y, repeat_times, normal_train_ratio, results_path='./results/', device=device, qualified_Y=qualified_Y, ADdataset_mode=ADdataset_mode, mode='fuse', param_mode='max') mv_ss(dataset_name, X, Y, repeat_times, normal_train_ratio, results_path='./results/', device=device, qualified_Y=qualified_Y, ADdataset_mode=ADdataset_mode, mode='fuse', param_mode='sum') mv_ss(dataset_name, X, Y, repeat_times, normal_train_ratio, results_path='./results/', device=device, qualified_Y=qualified_Y, ADdataset_mode=ADdataset_mode, mode='fuse', param_mode='nn') # pre-trained AE based fusion mvae_fuse_latent(dataset_name, X, Y, repeat_times, normal_train_ratio, results_path='./results/', device=device, qualified_Y=qualified_Y, ADdataset_mode=ADdataset_mode) # DBN based try: mv_dbn(dataset_name, X, Y, repeat_times, normal_train_ratio, results_path='./results/', device=device, qualified_Y=qualified_Y, ADdataset_mode=ADdataset_mode) except: print('dataset: {} dbn error'.format(dataset_name)) pass # Tensor fusion network based methods mv_tf(dataset_name, X, Y, repeat_times, normal_train_ratio, results_path='./results/', device=device, qualified_Y=qualified_Y, ADdataset_mode=ADdataset_mode) # --------------------------------Multi-view alignment based methods---------- mv_corrae(dataset_name, X, Y, repeat_times, normal_train_ratio, results_path='./results/', device=device, qualified_Y=qualified_Y, ADdataset_mode=ADdataset_mode) mv_sim(dataset_name, X, Y, repeat_times, normal_train_ratio, results_path='./results/', device=device, qualified_Y=qualified_Y, ADdataset_mode=ADdataset_mode) try: deepCCA(dataset_name, X, Y, repeat_times, normal_train_ratio, results_path='./results/', device=device, qualified_Y=qualified_Y, ADdataset_mode=ADdataset_mode) except: print('dataset: {} dcca error'.format(dataset_name)) pass try: dgcca(dataset_name, X, Y, repeat_times, normal_train_ratio, results_path='./results/', device=device, qualified_Y=qualified_Y, ADdataset_mode=ADdataset_mode) except: print('dataset: {} dgcca error'.format(dataset_name)) pass # -------------------------------Deep one-class learning based methods tailored for multi-view case----- simple_mvDSVDD(dataset_name, X, Y, repeat_times, normal_train_ratio, results_path='./results/', device=device, qualified_Y=qualified_Y, ADdataset_mode=ADdataset_mode) mvDSVDD_fused(dataset_name, X, Y, repeat_times, normal_train_ratio, results_path='./results/', device=device, qualified_Y=qualified_Y, ADdataset_mode=ADdataset_mode) # -------------------------------Self-supervision based methods--------------------------------------------- mv_ss(dataset_name, X, Y, repeat_times, normal_train_ratio, results_path='./results/', device=device, qualified_Y=qualified_Y, ADdataset_mode=ADdataset_mode, mode='pred', param_mode='fixed') mv_ss(dataset_name, X, Y, repeat_times, normal_train_ratio, results_path='./results/', device=device, qualified_Y=qualified_Y, ADdataset_mode=ADdataset_mode, mode='split', param_mode='fixed') # # -------------------------For hyperparameter analysis------------------------------- # # mv_sim(dataset_name, X, Y, repeat_times, normal_train_ratio, results_path='./analysis/', device=device, qualified_Y=qualified_Y, ADdataset_mode=ADdataset_mode) # # mv_corrae(dataset_name, X, Y, repeat_times, normal_train_ratio, results_path='./analysis/', device=device, qualified_Y=qualified_Y, ADdataset_mode=ADdataset_mode) # # try: # deepCCA(dataset_name, X, Y, repeat_times, normal_train_ratio, results_path='./analysis/', device=device, qualified_Y=qualified_Y, ADdataset_mode=ADdataset_mode) # except: # print('dataset: {} dcca error'.format(dataset_name)) # pass # # try: # dgcca(dataset_name, X, Y, repeat_times, normal_train_ratio, results_path='./analysis/', device=device, qualified_Y=qualified_Y, ADdataset_mode=ADdataset_mode) # except: # print('dataset: {} dgcca error'.format(dataset_name)) # pass # # mv_tf(dataset_name, X, Y, repeat_times, normal_train_ratio, results_path='./analysis/', device=device, qualified_Y=qualified_Y, ADdataset_mode=ADdataset_mode) # else: # print('No qualified class in this dataset!')	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remote-scripts/ConfigureDnsServer.py	xian123/azure-freebsd-automation	1	46533	<reponame>xian123/azure-freebsd-automation #!/usr/bin/python import argparse import sys from azuremodules import * import paramiko parser = argparse.ArgumentParser() parser.add_argument('-D', '--vnetDomain_db_filepath', help='VNET Domain db filepath', required=True) parser.add_argument('-r', '--vnetDomain_rev_filepath', help='VNET rev filepath',required=True) parser.add_argument('-v', '--HostnameDIP', help='hosts filepath',required = True) args = parser.parse_args() vnetDomain_db_filepath = str(args.vnetDomain_db_filepath) vnetDomain_rev_filepath = str(args.vnetDomain_rev_filepath) HostnameDIP=str(args.HostnameDIP) vnetDomain=(vnetDomain_db_filepath.split("/"))[len((vnetDomain_db_filepath.split("/")))-1].replace(".db","") #SAMPLE INPUT FOR --vms #HostnameDIP = 'ICA-VNETVM-Ubuntu1210PL-4-16-2013-1-2-0-role-0:192.168.4.196^ICA-VNETVM-Ubuntu1210PL-4-16-2013-1-2-0-role-1:192.168.4.132^ICA-VNETVM-Ubuntu1210PL-4-16-2013-1-2-1-role-0:192.168.4.133^ICA-VNETVM-Ubuntu1210PL-4-16-2013-1-2-1-role-1:192.168.4.197' #SETTING THE GLOBAL PARAMS.. #SetVnetGlobalParameters() #CONFIGURIG DNS SERVER CONFIGURATIONS FILES.. DNSServerStatus = AddICAVMsToDnsServer(HostnameDIP,vnetDomain_db_filepath,vnetDomain_rev_filepath) #RESTARTING BIND9 SERVICE.. output = JustRun('service bind9 restart') if DNSServerStatus == 0: print("CONFIGURATION_SUCCESSFUL") else: print("CONFIGURATION_FAILED")	[ 1, 529, 276, 1112, 420, 29958, 29916, 713, 29896, 29906, 29941, 29914, 17688, 29899, 10745, 774, 4928, 29899, 17405, 362, 13, 29937, 14708, 4855, 29914, 2109, 29914, 4691, 13, 13, 5215, 1852, 5510, 13, 5215, 10876, 13, 3166, 15699, 7576, 1053, 334, 13, 5215, 1828, 10349, 13, 16680, 353, 1852, 5510, 29889, 15730, 11726, 580, 13, 16680, 29889, 1202, 29918, 23516, 877, 29899, 29928, 742, 525, 489, 29894, 1212, 15951, 29918, 2585, 29918, 1445, 2084, 742, 1371, 2433, 29963, 6006, 28460, 4833, 934, 2084, 742, 3734, 29922, 5574, 29897, 13, 16680, 29889, 1202, 29918, 23516, 877, 29899, 29878, 742, 525, 489, 29894, 1212, 15951, 29918, 13478, 29918, 1445, 2084, 742, 1371, 2433, 29963, 6006, 6664, 934, 2084, 742, 12403, 29922, 5574, 29897, 13, 16680, 29889, 1202, 29918, 23516, 877, 29899, 29894, 742, 525, 489, 8514, 978, 4571, 29925, 742, 1371, 2433, 23525, 934, 2084, 742, 12403, 353, 5852, 29897, 13, 5085, 353, 13812, 29889, 5510, 29918, 5085, 580, 13, 29894, 1212, 15951, 29918, 2585, 29918, 1445, 2084, 353, 29871, 851, 29898, 5085, 29889, 29894, 1212, 15951, 29918, 2585, 29918, 1445, 2084, 29897, 13, 29894, 1212, 15951, 29918, 13478, 29918, 1445, 2084, 353, 851, 29898, 5085, 29889, 29894, 1212, 15951, 29918, 13478, 29918, 1445, 2084, 29897, 13, 8514, 978, 4571, 29925, 29922, 710, 29898, 5085, 29889, 8514, 978, 4571, 29925, 29897, 13, 29894, 1212, 15951, 7607, 29894, 1212, 15951, 29918, 2585, 29918, 1445, 2084, 29889, 5451, 11974, 5783, 29961, 2435, 3552, 29894, 1212, 15951, 29918, 2585, 29918, 1445, 2084, 29889, 5451, 11974, 29908, 4961, 29899, 29896, 1822, 6506, 17350, 2585, 3284, 1159, 13, 29937, 8132, 3580, 1307, 2672, 12336, 15842, 1192, 29894, 1516, 13, 29937, 8514, 978, 4571, 29925, 353, 525, 2965, 29909, 29899, 29963, 6006, 9219, 29899, 29965, 6037, 29896, 29906, 29896, 29900, 7390, 29899, 29946, 29899, 29896, 29953, 29899, 29906, 29900, 29896, 29941, 29899, 29896, 29899, 29906, 29899, 29900, 29899, 12154, 29899, 29900, 29901, 29896, 29929, 29906, 29889, 29896, 29953, 29947, 29889, 29946, 29889, 29896, 29929, 29953, 29985, 2965, 29909, 29899, 29963, 6006, 9219, 29899, 29965, 6037, 29896, 29906, 29896, 29900, 7390, 29899, 29946, 29899, 29896, 29953, 29899, 29906, 29900, 29896, 29941, 29899, 29896, 29899, 29906, 29899, 29900, 29899, 12154, 29899, 29896, 29901, 29896, 29929, 29906, 29889, 29896, 29953, 29947, 29889, 29946, 29889, 29896, 29941, 29906, 29985, 2965, 29909, 29899, 29963, 6006, 9219, 29899, 29965, 6037, 29896, 29906, 29896, 29900, 7390, 29899, 29946, 29899, 29896, 29953, 29899, 29906, 29900, 29896, 29941, 29899, 29896, 29899, 29906, 29899, 29896, 29899, 12154, 29899, 29900, 29901, 29896, 29929, 29906, 29889, 29896, 29953, 29947, 29889, 29946, 29889, 29896, 29941, 29941, 29985, 2965, 29909, 29899, 29963, 6006, 9219, 29899, 29965, 6037, 29896, 29906, 29896, 29900, 7390, 29899, 29946, 29899, 29896, 29953, 29899, 29906, 29900, 29896, 29941, 29899, 29896, 29899, 29906, 29899, 29896, 29899, 12154, 29899, 29896, 29901, 29896, 29929, 29906, 29889, 29896, 29953, 29947, 29889, 29946, 29889, 29896, 29929, 29955, 29915, 13, 29937, 10490, 29911, 4214, 6093, 402, 28902, 1964, 349, 1718, 29909, 4345, 636, 13, 29937, 2697, 29963, 1212, 12756, 11507, 580, 13, 29937, 25903, 15551, 29954, 16332, 26996, 5348, 8707, 18667, 4574, 8098, 29903, 9338, 17101, 636, 13, 29928, 3059, 6004, 5709, 353, 3462, 2965, 7520, 29924, 29879, 1762, 29928, 1983, 6004, 29898, 8514, 978, 4571, 29925, 29892, 29894, 1212, 15951, 29918, 2585, 29918, 1445, 2084, 29892, 29894, 1212, 15951, 29918, 13478, 29918, 1445, 2084, 29897, 13, 29937, 1525, 25826, 4214, 350, 22255, 29929, 26996, 19059, 636, 13, 4905, 353, 3387, 6558, 877, 5509, 7868, 29929, 10715, 1495, 13, 361, 16332, 6004, 5709, 1275, 29871, 29900, 29901, 13, 4706, 1596, 703, 25903, 4574, 8098, 29918, 14605, 26925, 29943, 13309, 1159, 13, 2870, 29901, 13, 4706, 1596, 703, 25903, 4574, 8098, 29918, 4519, 29902, 20566, 1159, 2 ]
uq_benchmark_2019/imagenet/end_to_end_test.py	deepneuralmachine/google-research	23,901	20175	<gh_stars>1000+ # coding=utf-8 # Copyright 2021 The Google Research Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # Lint as: python2, python3 """End-to-end test for ImageNet. Tests for imagenet.resnet50_train, run_predict, run_temp_scaling, and run_metrics. Real data doesn't work under blaze, so execute the test binary directly. """ from __future__ import absolute_import from __future__ import division from __future__ import print_function import os import tempfile from absl import flags from absl.testing import absltest from absl.testing import parameterized import tensorflow.compat.v2 as tf from uq_benchmark_2019.imagenet import resnet50_train # pylint: disable=line-too-long from uq_benchmark_2019.imagenet import run_metrics from uq_benchmark_2019.imagenet import run_predict from uq_benchmark_2019.imagenet import run_temp_scaling gfile = tf.io.gfile flags.DEFINE_bool('fake_data', True, 'Use dummy random data.') flags.DEFINE_bool('fake_training', True, 'Train with trivial number of steps.') DATA_NAMES = ['train', 'test', 'corrupt-static-gaussian_noise-2', 'celeb_a'] METHODS = ['vanilla', 'll_dropout', 'll_svi', 'dropout'] class EndToEndTest(parameterized.TestCase): @parameterized.parameters([(d, m) for d in DATA_NAMES for m in METHODS]) # pylint: disable=g-complex-comprehension def test_end_to_end_train(self, data_name, method): with tempfile.TemporaryDirectory() as model_dir: metrics = ['sparse_categorical_crossentropy'] if flags.FLAGS.fake_data and (data_name != 'test'): pass else: temp_model_dir = os.path.join(model_dir, data_name, method) resnet50_train.run( method, temp_model_dir, task_number=0, use_tpu=False, tpu=None, metrics=metrics, fake_data=flags.FLAGS.fake_data, fake_training=flags.FLAGS.fake_training) run_predict.run( data_name, temp_model_dir, batch_size=8, predictions_per_example=4, max_examples=44, output_dir=temp_model_dir, fake_data=flags.FLAGS.fake_data) tmpl = os.path.join(temp_model_dir, '_small_*') glob_results = gfile.glob(tmpl) path = glob_results[0] if data_name == 'valid': run_temp_scaling(path) run_metrics.run(path, path, model_dir_ensemble=None, use_temp_scaling=False) if __name__ == '__main__': absltest.main()	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29896, 29900, 29900, 29900, 29974, 13, 29937, 14137, 29922, 9420, 29899, 29947, 13, 29937, 14187, 1266, 29871, 29906, 29900, 29906, 29896, 450, 5087, 10550, 13189, 943, 29889, 13, 29937, 13, 29937, 10413, 21144, 1090, 278, 13380, 19245, 29892, 10079, 29871, 29906, 29889, 29900, 313, 1552, 376, 29931, 293, 1947, 1496, 13, 29937, 366, 1122, 451, 671, 445, 934, 5174, 297, 752, 13036, 411, 278, 19245, 29889, 13, 29937, 887, 1122, 4017, 263, 3509, 310, 278, 19245, 472, 13, 29937, 13, 29937, 268, 1732, 597, 1636, 29889, 4288, 29889, 990, 29914, 506, 11259, 29914, 27888, 1430, 1660, 29899, 29906, 29889, 29900, 13, 29937, 13, 29937, 25870, 3734, 491, 22903, 4307, 470, 15502, 304, 297, 5007, 29892, 7047, 13, 29937, 13235, 1090, 278, 19245, 338, 13235, 373, 385, 376, 3289, 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src/NetworkHandler.py	BlankShrimp/MarkDownald-Server	0	56532	from socket import * import json import JsonParser import DBHelper conn = socket(AF_INET, SOCK_STREAM) conn.bind(('',4687)) conn.listen(40) while True: new_conn, addr = conn.accept() msg = new_conn.recv(1024).decode() obj = json.loads(msg) if obj['instruction'] == 'reg': result = DBHelper.registry(obj['userid'], obj['passwd'], obj['nickname']) new_conn.send(result.encode()) elif obj['instruction'] == 'add_note': result = DBHelper.insert_note(obj['userid'], obj['passwd'], obj['noteid'], obj['title'], obj['folderid'], obj['value']) new_conn.send(result.encode()) elif obj['instruction'] == 'del_note': result = DBHelper.delete_note(obj['userid'], obj['passwd'], obj['noteid']) new_conn.send(result.encode()) elif obj['instruction'] == 'sel_note': result = DBHelper.select_single_note(obj['userid'], obj['passwd'], obj['noteid']) if isinstance(result, str): new_conn.send(result.encode()) else: new_conn.send(JsonParser.dump_single_note(result).encode()) elif obj['instruction'] == 'up_note': result = DBHelper.update_note(obj['userid'], obj['passwd'], obj['noteid'], obj['title'], obj['folderid'], obj['value']) new_conn.send(result.encode()) elif obj['instruction'] == 'add_folder': result = DBHelper.insert_folder(obj['userid'], obj['passwd'], obj['folderid'], obj['foldername'], obj['parentid']) new_conn.send(result.encode()) elif obj['instruction'] == 'del_folder': result = DBHelper.delete_folder(obj['userid'], obj['passwd'], obj['folderid']) new_conn.send(result.encode()) elif obj['instruction'] == 'up_folder': result = DBHelper.update_folder(obj['userid'], obj['passwd'], obj['folderid'], obj['foldername'], obj['parentid']) new_conn.send(result.encode()) elif obj['instruction'] == 'sel_notes': result = DBHelper.select_all_notes(obj['userid'], obj['passwd']) if isinstance(result, str): new_conn.send(result.encode()) else: new_conn.send(JsonParser.dump_all_notes(result).encode()) elif obj['instruction'] == 'sel_folders': result = DBHelper.select_all_folders(obj['userid'], obj['passwd']) if isinstance(result, str): new_conn.send(result.encode()) else: new_conn.send(JsonParser.dump_all_folder(result).encode()) new_conn.close()	[ 1, 515, 9909, 1053, 334, 13, 5215, 4390, 13, 5215, 14355, 11726, 13, 5215, 6535, 10739, 13, 13, 13082, 353, 9909, 29898, 5098, 29918, 1177, 2544, 29892, 7791, 7077, 29918, 1254, 1525, 5194, 29897, 13, 13082, 29889, 5355, 29898, 877, 742, 29946, 29953, 29947, 29955, 876, 13, 13082, 29889, 20631, 29898, 29946, 29900, 29897, 13, 13, 8000, 5852, 29901, 13, 1678, 716, 29918, 13082, 29892, 28915, 353, 11009, 29889, 16044, 580, 13, 1678, 10191, 353, 716, 29918, 13082, 29889, 3757, 29894, 29898, 29896, 29900, 29906, 29946, 467, 13808, 580, 13, 13, 1678, 5446, 353, 4390, 29889, 18132, 29898, 7645, 29897, 13, 1678, 565, 5446, 1839, 2611, 4080, 2033, 1275, 525, 1727, 2396, 13, 4706, 1121, 353, 6535, 10739, 29889, 1727, 6020, 29898, 5415, 1839, 1792, 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src/util_walk_sftp.py	Chrisebell24/walk_sftp	0	34925	import paramiko class _FastTransport(paramiko.Transport): def __init__(self, sock): super(_FastTransport, self).__init__(sock) self.window_size = 2147483647 self.packetizer.REKEY_BYTES = pow(2, 40) self.packetizer.REKEY_PACKETS = pow(2, 40)	[ 1, 1053, 1828, 10349, 13, 13, 1990, 903, 29943, 579, 27395, 29898, 3207, 10349, 29889, 27395, 1125, 13, 1678, 822, 4770, 2344, 12035, 1311, 29892, 577, 384, 1125, 13, 4706, 2428, 7373, 29943, 579, 27395, 29892, 1583, 467, 1649, 2344, 12035, 21852, 29897, 13, 4706, 1583, 29889, 7165, 29918, 2311, 353, 29871, 29906, 29896, 29946, 29955, 29946, 29947, 29941, 29953, 29946, 29955, 13, 4706, 1583, 29889, 4058, 300, 3950, 29889, 1525, 10818, 29918, 22716, 29911, 2890, 353, 4764, 29898, 29906, 29892, 29871, 29946, 29900, 29897, 13, 4706, 1583, 29889, 4058, 300, 3950, 29889, 1525, 10818, 29918, 29925, 11375, 2544, 29903, 353, 4764, 29898, 29906, 29892, 29871, 29946, 29900, 29897, 2 ]
tools/esp_prov/security/__init__.py	fbucafusco/esp-idf	0	189785	<reponame>fbucafusco/esp-idf # SPDX-FileCopyrightText: 2018-2022 Espressif Systems (Shanghai) CO LTD # SPDX-License-Identifier: Apache-2.0 # from .security0 import * # noqa: F403, F401 from .security1 import * # noqa: F403, F401 from .security2 import * # noqa: F403, F401	[ 1, 529, 276, 1112, 420, 29958, 29888, 2423, 1113, 29888, 375, 1111, 29914, 9983, 29899, 333, 29888, 13, 29937, 10937, 29928, 29990, 29899, 2283, 11882, 1266, 1626, 29901, 29871, 29906, 29900, 29896, 29947, 29899, 29906, 29900, 29906, 29906, 3423, 2139, 361, 23985, 313, 2713, 574, 23535, 29897, 4810, 365, 24495, 13, 29937, 10937, 29928, 29990, 29899, 29931, 293, 1947, 29899, 12889, 29901, 13380, 29899, 29906, 29889, 29900, 13, 29937, 13, 13, 3166, 869, 8926, 29900, 1053, 334, 29871, 396, 694, 25621, 29901, 383, 29946, 29900, 29941, 29892, 383, 29946, 29900, 29896, 13, 3166, 869, 8926, 29896, 1053, 334, 29871, 396, 694, 25621, 29901, 383, 29946, 29900, 29941, 29892, 383, 29946, 29900, 29896, 13, 3166, 869, 8926, 29906, 1053, 334, 29871, 396, 694, 25621, 29901, 383, 29946, 29900, 29941, 29892, 383, 29946, 29900, 29896, 13, 2 ]
python/ovs/db/idl.py	homework/openvswitch	9	87504	<gh_stars>1-10 # Copyright (c) 2009, 2010, 2011 Nicira Networks # # 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 logging import ovs.jsonrpc import ovs.db.parser import ovs.db.schema from ovs.db import error import ovs.ovsuuid class Idl: """Open vSwitch Database Interface Definition Language (OVSDB IDL). The OVSDB IDL maintains an in-memory replica of a database. It issues RPC requests to an OVSDB database server and parses the responses, converting raw JSON into data structures that are easier for clients to digest. The IDL also assists with issuing database transactions. The client creates a transaction, manipulates the IDL data structures, and commits or aborts the transaction. The IDL then composes and issues the necessary JSON-RPC requests and reports to the client whether the transaction completed successfully. If 'schema_cb' is provided, it should be a callback function that accepts an ovs.db.schema.DbSchema as its argument. It should determine whether the schema is acceptable and raise an ovs.db.error.Error if it is not. It may also delete any tables or columns from the schema that the client has no interest in monitoring, to save time and bandwidth during monitoring. Its return value is ignored.""" def __init__(self, remote, db_name, schema_cb=None): """Creates and returns a connection to the database named 'db_name' on 'remote', which should be in a form acceptable to ovs.jsonrpc.session.open(). The connection will maintain an in-memory replica of the remote database.""" self.remote = remote self.session = ovs.jsonrpc.Session.open(remote) self.db_name = db_name self.last_seqno = None self.schema = None self.state = None self.change_seqno = 0 self.data = {} self.schema_cb = schema_cb def close(self): self.session.close() def run(self): """Processes a batch of messages from the database server. Returns True if the database as seen through the IDL changed, False if it did not change. The initial fetch of the entire contents of the remote database is considered to be one kind of change. This function can return occasional false positives, that is, report that the database changed even though it didn't. This happens if the connection to the database drops and reconnects, which causes the database contents to be reloaded even if they didn't change. (It could also happen if the database server sends out a "change" that reflects what we already thought was in the database, but the database server is not supposed to do that.) As an alternative to checking the return value, the client may check for changes in the value returned by self.get_seqno().""" initial_change_seqno = self.change_seqno self.session.run() if self.session.is_connected(): seqno = self.session.get_seqno() if seqno != self.last_seqno: self.last_seqno = seqno self.state = (self.__send_schema_request, None) if self.state: self.state[0]() return initial_change_seqno != self.change_seqno def wait(self, poller): """Arranges for poller.block() to wake up when self.run() has something to do or when activity occurs on a transaction on 'self'.""" self.session.wait(poller) if self.state and self.state[1]: self.state[1](poller) def get_seqno(self): """Returns a number that represents the IDL's state. When the IDL updated (by self.run()), the return value changes.""" return self.change_seqno def __send_schema_request(self): msg = ovs.jsonrpc.Message.create_request("get_schema", [self.db_name]) self.session.send(msg) self.state = (lambda: self.__recv_schema(msg.id), self.__recv_wait) def __recv_schema(self, id): msg = self.session.recv() if msg and msg.type == ovs.jsonrpc.Message.T_REPLY and msg.id == id: try: self.schema = ovs.db.schema.DbSchema.from_json(msg.result) except error.Error, e: logging.error("%s: parse error in received schema: %s" % (self.remote, e)) self.__error() return if self.schema_cb: try: self.schema_cb(self.schema) except error.Error, e: logging.error("%s: error validating schema: %s" % (self.remote, e)) self.__error() return self.__send_monitor_request() elif msg: logging.error("%s: unexpected message expecting schema: %s" % (self.remote, msg)) self.__error() def __recv_wait(self, poller): self.session.recv_wait(poller) def __send_monitor_request(self): monitor_requests = {} for table in self.schema.tables.itervalues(): monitor_requests[table.name] = {"columns": table.columns.keys()} msg = ovs.jsonrpc.Message.create_request( "monitor", [self.db_name, None, monitor_requests]) self.session.send(msg) self.state = (lambda: self.__recv_monitor_reply(msg.id), self.__recv_wait) def __recv_monitor_reply(self, id): msg = self.session.recv() if msg and msg.type == ovs.jsonrpc.Message.T_REPLY and msg.id == id: try: self.change_seqno += 1 self.state = (self.__recv_update, self.__recv_wait) self.__clear() self.__parse_update(msg.result) except error.Error, e: logging.error("%s: parse error in received schema: %s" % (self.remote, e)) self.__error() elif msg: logging.error("%s: unexpected message expecting schema: %s" % (self.remote, msg)) self.__error() def __recv_update(self): msg = self.session.recv() if (msg and msg.type == ovs.jsonrpc.Message.T_NOTIFY and type(msg.params) == list and len(msg.params) == 2 and msg.params[0] is None): self.__parse_update(msg.params[1]) elif msg: logging.error("%s: unexpected message expecting update: %s" % (self.remote, msg)) self.__error() def __error(self): self.session.force_reconnect() def __parse_update(self, update): try: self.__do_parse_update(update) except error.Error, e: logging.error("%s: error parsing update: %s" % (self.remote, e)) def __do_parse_update(self, table_updates): if type(table_updates) != dict: raise error.Error("<table-updates> is not an object", table_updates) for table_name, table_update in table_updates.iteritems(): table = self.schema.tables.get(table_name) if not table: raise error.Error('<table-updates> includes unknown ' 'table "%s"' % table_name) if type(table_update) != dict: raise error.Error('<table-update> for table "%s" is not ' 'an object' % table_name, table_update) for uuid_string, row_update in table_update.iteritems(): if not ovs.ovsuuid.UUID.is_valid_string(uuid_string): raise error.Error('<table-update> for table "%s" ' 'contains bad UUID "%s" as member ' 'name' % (table_name, uuid_string), table_update) uuid = ovs.ovsuuid.UUID.from_string(uuid_string) if type(row_update) != dict: raise error.Error('<table-update> for table "%s" ' 'contains <row-update> for %s that ' 'is not an object' % (table_name, uuid_string)) parser = ovs.db.parser.Parser(row_update, "row-update") old = parser.get_optional("old", [dict]) new = parser.get_optional("new", [dict]) parser.finish() if not old and not new: raise error.Error('<row-update> missing "old" and ' '"new" members', row_update) if self.__parse_row_update(table, uuid, old, new): self.change_seqno += 1 def __parse_row_update(self, table, uuid, old, new): """Returns True if a column changed, False otherwise.""" row = self.data[table.name].get(uuid) changed = False if not new: # Delete row. if row: del self.data[table.name][uuid] changed = True else: # XXX rate-limit logging.warning("cannot delete missing row %s from table %s" % (uuid, table.name)) elif not old: # Insert row. if not row: row = self.__create_row(table, uuid) changed = True else: # XXX rate-limit logging.warning("cannot add existing row %s to table %s" % (uuid, table.name)) if self.__modify_row(table, row, new): changed = True else: if not row: row = self.__create_row(table, uuid) changed = True # XXX rate-limit logging.warning("cannot modify missing row %s in table %s" % (uuid, table.name)) if self.__modify_row(table, row, new): changed = True return changed def __modify_row(self, table, row, row_json): changed = False for column_name, datum_json in row_json.iteritems(): column = table.columns.get(column_name) if not column: # XXX rate-limit logging.warning("unknown column %s updating table %s" % (column_name, table.name)) continue try: datum = ovs.db.data.Datum.from_json(column.type, datum_json) except error.Error, e: # XXX rate-limit logging.warning("error parsing column %s in table %s: %s" % (column_name, table.name, e)) continue if datum != getattr(row, column_name): setattr(row, column_name, datum) changed = True else: # Didn't really change but the OVSDB monitor protocol always # includes every value in a row. pass return changed def __clear(self): if self.data != {}: for table_name in self.schema.tables: if self.data[table_name] != {}: self.change_seqno += 1 break self.data = {} for table_name in self.schema.tables: self.data[table_name] = {} def __create_row(self, table, uuid): row = self.data[table.name][uuid] = Row() for column in table.columns.itervalues(): setattr(row, column.name, ovs.db.data.Datum.default(column.type)) return row def force_reconnect(self): """Forces the IDL to drop its connection to the database and reconnect. In the meantime, the contents of the IDL will not change.""" self.session.force_reconnect() class Row(object): """A row within an Idl. Data for each column is stored as an attribute with the same name as the column and using an ovs.db.data.Datum as the value.""" pass	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29896, 29899, 29896, 29900, 13, 29937, 14187, 1266, 313, 29883, 29897, 29871, 29906, 29900, 29900, 29929, 29892, 29871, 29906, 29900, 29896, 29900, 29892, 29871, 29906, 29900, 29896, 29896, 405, 1654, 336, 8527, 29879, 13, 29937, 13, 29937, 10413, 21144, 1090, 278, 13380, 19245, 29892, 10079, 29871, 29906, 29889, 29900, 313, 1552, 376, 29931, 293, 1947, 1496, 13, 29937, 366, 1122, 451, 671, 445, 934, 5174, 297, 752, 13036, 411, 278, 19245, 29889, 13, 29937, 887, 1122, 4017, 263, 3509, 310, 278, 19245, 472, 29901, 13, 29937, 13, 29937, 268, 1732, 597, 1636, 29889, 4288, 29889, 990, 29914, 506, 11259, 29914, 27888, 1430, 1660, 29899, 29906, 29889, 29900, 13, 29937, 13, 29937, 25870, 3734, 491, 22903, 4307, 470, 15502, 304, 297, 5007, 29892, 7047, 13, 29937, 13235, 1090, 278, 19245, 338, 13235, 373, 385, 376, 3289, 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Validation/HGCalValidation/test/python/runHGCHitAnalyzer_cfg.py	SWuchterl/cmssw	1	1610351	<reponame>SWuchterl/cmssw import FWCore.ParameterSet.Config as cms #from Configuration.Eras.Era_Phase2C4_cff import Phase2C4 #process = cms.Process('HGCGeomAnalysis',Phase2C4) #process.load('Configuration.Geometry.GeometryExtended2026D35_cff') #process.load('Configuration.Geometry.GeometryExtended2026D35Reco_cff') #from Configuration.Eras.Era_Phase2C8_cff import Phase2C8 #process = cms.Process('HGCGeomAnalysis',Phase2C8) #process.load('Configuration.Geometry.GeometryExtended2026D41_cff') #process.load('Configuration.Geometry.GeometryExtended2026D41Reco_cff') #from Configuration.Eras.Era_Phase2C9_cff import Phase2C9 #process = cms.Process('HGCGeomAnalysis',Phase2C9) #process.load('Configuration.Geometry.GeometryExtended2026D49_cff') #process.load('Configuration.Geometry.GeometryExtended2026D49Reco_cff') #from Configuration.Eras.Era_Phase2C12_cff import Phase2C12 #process = cms.Process('HGCGeomAnalysis',Phase2C12) #process.load('Configuration.Geometry.GeometryExtended2026D58_cff') #process.load('Configuration.Geometry.GeometryExtended2026D58Reco_cff') #from Configuration.Eras.Era_Phase2C11_cff import Phase2C11 #process = cms.Process('HGCGeomAnalysis',Phase2C11) #process.load('Configuration.Geometry.GeometryExtended2026D59_cff') #process.load('Configuration.Geometry.GeometryExtended2026D59Reco_cff') from Configuration.Eras.Era_Phase2C11_cff import Phase2C11 process = cms.Process('HGCGeomAnalysis',Phase2C11) process.load('Configuration.Geometry.GeometryExtended2026D62_cff') process.load('Configuration.Geometry.GeometryExtended2026D62Reco_cff') process.load('Configuration.StandardSequences.Services_cff') process.load('SimGeneral.HepPDTESSource.pythiapdt_cfi') process.load('FWCore.MessageService.MessageLogger_cfi') process.load('Configuration.EventContent.EventContent_cff') process.load('Configuration.StandardSequences.MagneticField_cff') process.load('Configuration.StandardSequences.FrontierConditions_GlobalTag_cff') from Configuration.AlCa.GlobalTag import GlobalTag ##Global Tag used for production in process.GlobalTag = GlobalTag(process.GlobalTag, 'auto:phase2_realistic', '') #process.MessageLogger.cerr.FwkReport.reportEvery = 100 #if 'MessageLogger' in process.__dict__: # process.MessageLogger.categories.append('HGCalValid') process.source = cms.Source("PoolSource", fileNames = cms.untracked.vstring( 'file:step3.root', #'root://cms-xrd-global.cern.ch//store/relval/CMSSW_9_0_0_pre1/RelValZEE_14/GEN-SIM-RECO/90X_upgrade2023_realistic_v0_2023D4-v1/10000/085AD7B1-8ABA-E611-A7DA-0CC47A4C8EB6.root', #'/store/relval/CMSSW_8_1_0_pre8/RelValTTbar_14TeV/GEN-SIM-RECO/81X_mcRun2_asymptotic_v1_2023LReco-v1/10000/08E719D4-DE3D-E611-9092-003048FFD722.root', ) ) process.load('Validation.HGCalValidation.hgcHitValidation_cfi') process.TFileService = cms.Service("TFileService", fileName = cms.string('relValTTbarD62.root'), closeFileFast = cms.untracked.bool(True) ) SimpleMemoryCheck = cms.Service("SimpleMemoryCheck",ignoreTotal = cms.untracked.int32(1) ) #process.hgcHitAnalysis.ietaExcludeBH = [16,92,93,94,95,96,97,98,99,100] #process.hgcHitAnalysis.ietaExcludeBH = [16, 32, 33] process.p = cms.Path(process.hgcHitAnalysis)	[ 1, 529, 276, 1112, 420, 29958, 23066, 987, 357, 29880, 29914, 4912, 893, 29893, 13, 5215, 383, 29956, 9203, 29889, 9329, 2697, 29889, 3991, 408, 274, 1516, 13, 13, 29937, 3166, 20999, 29889, 2110, 294, 29889, 29923, 336, 29918, 4819, 559, 29906, 29907, 29946, 29918, 29883, 600, 1053, 1963, 559, 29906, 29907, 29946, 13, 29937, 5014, 353, 274, 1516, 29889, 7032, 877, 29950, 8766, 7999, 290, 21067, 4848, 742, 4819, 559, 29906, 29907, 29946, 29897, 13, 29937, 5014, 29889, 1359, 877, 8614, 29889, 7999, 7843, 29889, 7999, 7843, 5647, 2760, 29906, 29900, 29906, 29953, 29928, 29941, 29945, 29918, 29883, 600, 1495, 13, 29937, 5014, 29889, 1359, 877, 8614, 29889, 7999, 7843, 29889, 7999, 7843, 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src/applications/Application.py	BMW-Group-Quantum/QUARK	12	1613296	# Copyright 2021 The QUARK 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 abc import ABC, abstractmethod class Application(ABC): """ The application component defines the workload, comprising a dataset of increasing complexity, a validation, and an evaluation function. """ def __init__(self, application_name): """ Constructor method """ self.application_name = application_name self.application = None self.mapping_options = [] super().__init__() def get_application(self) -> any: """ Getter that returns the application :return: self.application :rtype: any """ return self.application @abstractmethod def get_solution_quality_unit(self) -> str: """ Method to return the unit of the evaluation which is used to make the plots nicer. :return: String with the unit :rtype: str """ @abstractmethod def get_parameter_options(self) -> dict: """ Method to return the parameters needed to create a concrete problem of an application. Should always be in this format: .. code-block:: json { "parameter_name":{ "values":[1, 2, 3], "description":"How many nodes do you need?" }, "parameter_name_2":{ "values":["x", "y"], "description":"Which type of problem do you want?" } } :return: Available application settings for this application :rtype: dict """ pass @abstractmethod def generate_problem(self, config) -> any: """ Depending on the config this method creates a concrete problem and returns it. :param config: :type config: dict :return: :rtype: any """ pass def process_solution(self, solution) -> (any, float): """ Most of the time the solution has to be processed before it can be validated and evaluated This might not be necessary in all cases, so the default is to return the original solution. :param solution: :type solution: any :return: Processed solution and the execution time to process it :rtype: tuple(any, float) """ return solution, 0 @abstractmethod def validate(self, solution) -> (bool, float): """ Check if the solution is a valid solution. :return: bool and the time it took to create it :param solution: :type solution: any :rtype: tuple(bool, float) """ pass @abstractmethod def evaluate(self, solution: any) -> (float, float): """ Checks how good the solution is to allow comparison to other solutions. :param solution: :type solution: any :return: Evaluation and the time it took to create it :rtype: tuple(any, float) """ pass @abstractmethod def save(self, path) -> None: """ Function to save the concrete problem. :param path: path of the experiment directory for this run :type path: str :return: :rtype: None """ pass @abstractmethod def get_mapping(self, mapping_option: str) -> any: """ Return a mapping for an application. :param mapping_option: String with the option :rtype: str :return: instance of a mapping class :rtype: any """ pass def get_available_mapping_options(self) -> list: """ Get list of available mapping options. :return: list of mapping options :rtype: list """ return 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Reading Data/lesson-18-get-fifa-players-from-the-web/tests/test_fifa_1.py	danielgarm/Data-Science-and-Machine-Learning	0	197720	<reponame>danielgarm/Data-Science-and-Machine-Learning def test_fifa_1(): assert fifa_df.shape == (30, 5)	[ 1, 529, 276, 1112, 420, 29958, 18386, 709, 29887, 2817, 29914, 1469, 29899, 29903, 15277, 29899, 392, 29899, 29076, 29899, 29931, 799, 1076, 13, 1753, 1243, 29918, 29888, 26056, 29918, 29896, 7295, 30004, 13, 1678, 4974, 8461, 29874, 29918, 2176, 29889, 12181, 1275, 313, 29941, 29900, 29892, 29871, 29945, 8443, 13, 2 ]
gui/dataset/budgetary.py	vishalbelsare/prest	6	197810	import os import dataset import util.tree_model import uic.view_dataset import numpy as np import dataset.budgetary_consistency from core import Core from dataset import Dataset, Analysis, ExportVariant, DatasetHeaderC from dataset.budgetary_consistency import BudgetaryConsistency from typing import Sequence, NamedTuple, List, Dict, Tuple, Iterator, Union, Optional, cast from gui.progress import Worker, Cancelled from util.codec import FileOut, FileIn, namedtupleC, strC, numpyC, listC from util.codec_progress import CodecProgress, listCP, oneCP from PyQt5.QtWidgets import QDialog, QTreeWidgetItem, QHeaderView class Subject(NamedTuple): name : str prices : np.ndarray amounts : np.ndarray SubjectC = namedtupleC(Subject, strC, numpyC(np.float32), numpyC(np.float32)) class RowNode(util.tree_model.Node): def __init__(self, parent_node, row: int, prices: np.ndarray, amounts: np.ndarray) -> None: util.tree_model.Node.__init__( self, parent_node, row, fields=[''] \ + ['%.2f' % p for p in prices] \ + ['%g' % x for x in amounts] \ + ['%.2f' % np.dot(prices, amounts)] ) class SubjectNode(util.tree_model.Node): def __init__(self, parent_node, row: int, subject: Subject) -> None: self.subject = subject util.tree_model.Node.__init__( self, parent_node, row, fields=(subject.name,), child_count=subject.prices.shape[0], ) def create_child(self, row: int) -> RowNode: return RowNode(self, row, self.subject.prices[row], self.subject.amounts[row]) class RootNode(util.tree_model.RootNode): def __init__(self, subjects : List[Subject]) -> None: util.tree_model.RootNode.__init__(self, len(subjects)) self.subjects = subjects def create_child(self, row: int) -> SubjectNode: return SubjectNode(self, row, self.subjects[row]) class Budgetary(Dataset): class ViewDialog(QDialog, uic.view_dataset.Ui_ViewDataset): def __init__(self, ds : 'Budgetary') -> None: QDialog.__init__(self) self.setupUi(self) self.ds = ds self.model = util.tree_model.TreeModel( RootNode(ds.subjects), headers= \ ['Subject'] \ + [f'Price {a}' for a in ds.alternatives] \ + [f'Quantity {a}' for a in ds.alternatives] \ + ['Expenditure'] ) self.twRows.setModel(self.model) self.twRows.header().setSectionResizeMode(QHeaderView.ResizeToContents) self.twRows.header().setStretchLastSection(False) def __init__(self, name : str, alternatives : Sequence[str]) -> None: Dataset.__init__(self, name, alternatives) self.subjects : List[Subject] = [] self.nr_observations = 0 def label_size(self): return f'{len(self.subjects)} subjects, {self.nr_observations} observations' def analysis_consistency(self, worker : Worker, _config : None) -> BudgetaryConsistency: with Core() as core: worker.interrupt = lambda: core.shutdown() # interrupt hook rows = [] worker.set_work_size(len(self.subjects)) for i, subject in enumerate(self.subjects): response = core.call( 'budgetary-consistency', SubjectC, dataset.budgetary_consistency.SubjectC, subject ) rows.append(response) worker.set_progress(i+1) ds = BudgetaryConsistency( self.name + ' (consistency)', self.alternatives, rows, ) return ds def get_analyses(self) -> Sequence[Analysis]: return ( Analysis( name='Consistency analysis', config=None, run=self.analysis_consistency, ), ) @staticmethod def get_codec_progress() -> CodecProgress: DatasetHeaderC_encode, DatasetHeaderC_decode = DatasetHeaderC subjects_get_size, subjects_encode, subjects_decode = listCP(oneCP(SubjectC)) def get_size(x : 'Budgetary') -> int: return cast(int, subjects_get_size(x.subjects)) def encode(worker : Worker, f : FileOut, x : 'Budgetary') -> None: DatasetHeaderC_encode(f, (x.name, x.alternatives)) subjects_encode(worker, f, x.subjects) def decode(worker : Worker, f : FileIn) -> 'Budgetary': ds = Budgetary(DatasetHeaderC_decode(f)) ds.subjects = subjects_decode(worker, f) ds.update_nr_observations() return ds return CodecProgress(get_size, encode, decode) def update_nr_observations(self) -> None: self.nr_observations = sum(len(s.prices) for s in self.subjects) def get_export_variants(self) -> Sequence[ExportVariant]: if not self.subjects: return [] _n_observ, n_alts = self.subjects[0].prices.shape return [ ExportVariant( name='Detailed', column_names=\ ['subject'] \ + [f'price{i+1}' for i in range(n_alts)] \ + [f'quantity{i+1}' for i in range(n_alts)], get_rows=self.export_detailed, size=len(self.subjects), ), ] def export_detailed(self) -> Iterator[Optional[Tuple[Union[str, float], ...]]]: for subject in self.subjects: # subject.prices and subject.amounts are matrices for prices, amounts in zip(subject.prices, subject.amounts): row : List[Union[str, float]] = [subject.name] row += list(prices) row += list(amounts) yield tuple(row) yield None # bump progress class BudgetaryError(Exception): pass def load_from_csv(fname : str) -> Budgetary: lines = dataset.load_raw_csv(fname) if not lines: raise BudgetaryError("the CSV file is empty") header, rows = lines if (len(header)-1) % 2 != 0: raise BudgetaryError("budgetary datasets should have an even number of numeric columns") n_alts = (len(header)-1) // 2 alternatives = [f'{i+1}' for i in range(n_alts)] subjects : Dict[str,Tuple[List[np.ndarray],List[np.ndarray]]] = dict() # ordered for line_no, row in enumerate(rows, start=2): if len(row) != len(header): raise BudgetaryError(f'{fname}, line {line_no}: incorrect number of columns') subj_name, *cols = row if subj_name not in subjects: subjects[subj_name] = ([], []) prices, amounts = subjects[subj_name] prices.append(np.array([float(x) for x in cols[:n_alts]], dtype=np.float32)) amounts.append(np.array([float(x) for x in cols[n_alts:]], dtype=np.float32)) ds = Budgetary(os.path.basename(fname), alternatives) ds.subjects = [ Subject(name=n, prices=np.vstack(ps), amounts=np.vstack(ams)) for (n,(ps,ams)) in subjects.items() ] ds.update_nr_observations() return ds	[ 1, 1053, 2897, 13, 5215, 8783, 13, 5215, 3667, 29889, 8336, 29918, 4299, 13, 5215, 318, 293, 29889, 1493, 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test/integration/test_command.py	jmgao/bfg9000	0	169339	<filename>test/integration/test_command.py import os.path import re from six import assertRegex from . import * class TestCommand(IntegrationTest): def __init__(self, args, kwargs): IntegrationTest.__init__( self, os.path.join(examples_dir, '07_commands'), args, *kwargs ) def test_hello(self): assertRegex(self, self.build('hello'), re.compile(r'^\shello$', re.MULTILINE)) def test_world(self): assertRegex(self, self.build('world'), re.compile(r'^\sworld$', re.MULTILINE)) def test_script(self): assertRegex(self, self.build('script'), re.compile(r'^\shello, world!$', re.MULTILINE)) self.assertExists(output_file('file')) def test_alias(self): output = self.build('hello-world') assertRegex(self, output, re.compile(r'^\shello$', re.MULTILINE)) assertRegex(self, output, re.compile(r'^\sworld$', re.MULTILINE)) @skip_if_backend('msbuild') class TestRunExecutable(IntegrationTest): def __init__(self, args, kwargs): IntegrationTest.__init__(self, 'run_executable', args, *kwargs) def test_env_run(self): self.assertExists(output_file('file.txt')) def test_cxx(self): assertRegex(self, self.build('cxx'), re.compile(r'^\shello from c\+\+!$', re.MULTILINE)) def test_java(self): assertRegex(self, self.build('java'), re.compile(r'^\shello from java!$', re.MULTILINE)) def test_java_classlist(self): assertRegex(self, self.build('java-classlist'), re.compile(r'^\shello from java!$', re.MULTILINE)) def test_python(self): assertRegex(self, self.build('python'), re.compile(r'^\s*hello from python!$', re.MULTILINE))	[ 1, 529, 9507, 29958, 1688, 29914, 27925, 29914, 1688, 29918, 6519, 29889, 2272, 13, 5215, 2897, 29889, 2084, 13, 5215, 337, 13, 3166, 4832, 1053, 4974, 4597, 735, 13, 13, 3166, 869, 1053, 334, 13, 13, 13, 1990, 4321, 6255, 29898, 23573, 362, 3057, 1125, 13, 1678, 822, 4770, 2344, 12035, 1311, 29892, 334, 5085, 29892, 3579, 19290, 1125, 13, 4706, 17100, 362, 3057, 17255, 2344, 12035, 13, 9651, 1583, 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mask_rcnn_2go/code/model_utils.py	dreiss/maskrcnn-nnapi	1	164389	from __future__ import absolute_import, division, print_function, unicode_literals import os import subprocess from caffe2.proto import caffe2_pb2 from caffe2.python import core, workspace def create_blobs_if_not_existed(blob_names): existd_names = set(workspace.Blobs()) for xx in blob_names: if xx not in existd_names: workspace.CreateBlob(str(xx)) def load_model_pb(net_file, init_file): subprocess.check_call(["adb", "push", net_file, "/data/local/tmp/predict_net.pb"]) subprocess.check_call(["adb", "push", init_file, "/data/local/tmp/init_net.pb"]) net = caffe2_pb2.NetDef() if net_file is not None: net.ParseFromString(open(net_file, "rb").read()) return (net, None)	[ 1, 515, 4770, 29888, 9130, 1649, 1053, 8380, 29918, 5215, 29892, 8542, 29892, 1596, 29918, 2220, 29892, 29104, 29918, 20889, 1338, 13, 13, 5215, 2897, 13, 5215, 1014, 5014, 13, 3166, 274, 3470, 29872, 29906, 29889, 17529, 1053, 274, 3470, 29872, 29906, 29918, 24381, 29906, 13, 3166, 274, 3470, 29872, 29906, 29889, 4691, 1053, 7136, 29892, 664, 3493, 13, 13, 13, 1753, 1653, 29918, 10054, 29879, 29918, 361, 29918, 1333, 29918, 735, 12652, 29898, 10054, 29918, 7039, 1125, 13, 1678, 1863, 29881, 29918, 7039, 353, 731, 29898, 1287, 3493, 29889, 29933, 2127, 29879, 3101, 13, 1678, 363, 15473, 297, 23755, 29918, 7039, 29901, 13, 4706, 565, 15473, 451, 297, 1863, 29881, 29918, 7039, 29901, 13, 9651, 664, 3493, 29889, 4391, 29933, 2127, 29898, 710, 29898, 4419, 876, 13, 13, 13, 1753, 2254, 29918, 4299, 29918, 24381, 29898, 1212, 29918, 1445, 29892, 2069, 29918, 1445, 1125, 13, 1678, 1014, 5014, 29889, 3198, 29918, 4804, 29898, 3366, 328, 29890, 613, 376, 5910, 613, 7787, 29918, 1445, 29892, 5591, 1272, 29914, 2997, 29914, 7050, 29914, 27711, 29918, 1212, 29889, 24381, 20068, 13, 1678, 1014, 5014, 29889, 3198, 29918, 4804, 29898, 3366, 328, 29890, 613, 376, 5910, 613, 2069, 29918, 1445, 29892, 5591, 1272, 29914, 2997, 29914, 7050, 29914, 2344, 29918, 1212, 29889, 24381, 20068, 13, 13, 1678, 7787, 353, 274, 3470, 29872, 29906, 29918, 24381, 29906, 29889, 6779, 3206, 580, 13, 1678, 565, 7787, 29918, 1445, 338, 451, 6213, 29901, 13, 4706, 7787, 29889, 12914, 4591, 1231, 29898, 3150, 29898, 1212, 29918, 1445, 29892, 376, 6050, 2564, 949, 3101, 13, 1678, 736, 313, 1212, 29892, 6213, 29897, 13, 2 ]
Digit_Recognizer/digit_recognizer.py	williamyao66/Projects	0	175285	<reponame>williamyao66/Projects import pandas as pd import os from tensorflow.keras.models import load_model tr = pd.read_csv('./Digit-Recognizer/train.csv') te = pd.read_csv('./Digit-Recognizer/test.csv') x_te = te.values.copy().astype('float64') final_model = load_model(os.getcwd() + '/model/') predictions = final_model.predict(x_te) test_predictions = np.argmax(predictions, axis=1)	[ 1, 529, 276, 1112, 420, 29958, 14043, 2829, 3761, 29877, 29953, 29953, 29914, 25119, 13, 5215, 11701, 408, 10518, 29871, 13, 5215, 2897, 29871, 13, 13, 3166, 26110, 29889, 3946, 294, 29889, 9794, 1053, 2254, 29918, 4299, 13, 13, 13, 509, 353, 10518, 29889, 949, 29918, 7638, 877, 6904, 14991, 277, 29899, 27475, 29914, 14968, 29889, 7638, 1495, 13, 371, 353, 10518, 29889, 949, 29918, 7638, 877, 6904, 14991, 277, 29899, 27475, 29914, 1688, 29889, 7638, 1495, 13, 29916, 29918, 371, 353, 734, 29889, 5975, 29889, 8552, 2141, 579, 668, 877, 7411, 29953, 29946, 1495, 13, 13, 8394, 29918, 4299, 353, 2254, 29918, 4299, 29898, 359, 29889, 657, 29883, 9970, 580, 718, 8207, 4299, 29914, 1495, 13, 27711, 1080, 353, 2186, 29918, 4299, 29889, 27711, 29898, 29916, 29918, 371, 29897, 13, 1688, 29918, 27711, 1080, 353, 7442, 29889, 1191, 3317, 29898, 27711, 1080, 29892, 9685, 29922, 29896, 29897, 2 ]
freeCodeCamp/01-scientific-computing-with-python/src/12-objects_inheritance.py	aysedemirel/python-journey	1	130624	<gh_stars>1-10 class PartyAnimal: x = 0 name = "" def __init__(self, nameCons): self.name = nameCons print("name: ", self.name) def party(self): self.x = self.x + 1 print(self.name," - party count: ", self.x) class FootballFan(PartyAnimal): points = 0 def touchdown(self): self.points = self.points + 7 self.party() print(self.name, "points", self.points) s = FootballFan("Sally") s.party() j = FootballFan("Jim") j.party() j.touchdown()	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29896, 29899, 29896, 29900, 13, 1990, 9173, 2744, 3039, 29901, 13, 1678, 921, 353, 29871, 29900, 13, 1678, 1024, 353, 5124, 13, 1678, 822, 4770, 2344, 12035, 1311, 29892, 1024, 13696, 1125, 13, 4706, 1583, 29889, 978, 353, 1024, 13696, 13, 4706, 1596, 703, 978, 29901, 9162, 1583, 29889, 978, 29897, 13, 268, 13, 1678, 822, 6263, 29898, 1311, 1125, 13, 4706, 1583, 29889, 29916, 353, 1583, 29889, 29916, 718, 29871, 29896, 13, 4706, 1596, 29898, 1311, 29889, 978, 1699, 448, 6263, 2302, 29901, 9162, 1583, 29889, 29916, 29897, 13, 13, 13, 1990, 8914, 29943, 273, 29898, 7439, 29891, 2744, 3039, 1125, 13, 1678, 3291, 353, 29871, 29900, 13, 1678, 822, 6023, 3204, 29898, 1311, 1125, 13, 4706, 1583, 29889, 9748, 353, 1583, 29889, 9748, 718, 29871, 29955, 13, 4706, 1583, 29889, 22633, 580, 13, 4706, 1596, 29898, 1311, 29889, 978, 29892, 376, 9748, 613, 1583, 29889, 9748, 29897, 13, 308, 13, 13, 29879, 353, 8914, 29943, 273, 703, 29903, 635, 1159, 13, 29879, 29889, 22633, 580, 13, 13, 29926, 353, 8914, 29943, 273, 703, 29967, 326, 1159, 13, 29926, 29889, 22633, 580, 13, 29926, 29889, 16747, 3204, 580, 2 ]
python/reachy_sdk_api/kinematics_pb2.py	marjoriePaillet/reachy-sdk-api	1	184949	# -- coding: utf-8 -- # Generated by the protocol buffer compiler. DO NOT EDIT! # source: kinematics.proto """Generated protocol buffer code.""" from google.protobuf import descriptor as _descriptor from google.protobuf import message as _message from google.protobuf import reflection as _reflection from google.protobuf import symbol_database as _symbol_database # @@protoc_insertion_point(imports) _sym_db = _symbol_database.Default() import joint_pb2 as joint__pb2 DESCRIPTOR = _descriptor.FileDescriptor( name='kinematics.proto', package='reachy.sdk.kinematics', syntax='proto3', serialized_options=None, create_key=_descriptor._internal_create_key, serialized_pb=b'\n\x10kinematics.proto\x12\x15reachy.sdk.kinematics\x1a\x0bjoint.proto\"J\n\rJointPosition\x12&\n\x03ids\x18\x01 \x03(\x0b\x32\x19.reachy.sdk.joint.JointId\x12\x11\n\tpositions\x18\x02 \x03(\x01\"\x19\n\tMatrix4x4\x12\x0c\n\x04\x64\x61ta\x18\x01 \x03(\x01\"8\n\nQuaternion\x12\t\n\x01w\x18\x01 \x01(\x01\x12\t\n\x01x\x18\x02 \x01(\x01\x12\t\n\x01y\x18\x03 \x01(\x01\x12\t\n\x01z\x18\x04 \x01(\x01\x62\x06proto3' , dependencies=[joint__pb2.DESCRIPTOR,]) _JOINTPOSITION = _descriptor.Descriptor( name='JointPosition', full_name='reachy.sdk.kinematics.JointPosition', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='ids', full_name='reachy.sdk.kinematics.JointPosition.ids', index=0, number=1, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='positions', full_name='reachy.sdk.kinematics.JointPosition.positions', index=1, number=2, type=1, cpp_type=5, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=56, serialized_end=130, ) _MATRIX4X4 = _descriptor.Descriptor( name='Matrix4x4', full_name='reachy.sdk.kinematics.Matrix4x4', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='data', full_name='reachy.sdk.kinematics.Matrix4x4.data', index=0, number=1, type=1, cpp_type=5, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=132, serialized_end=157, ) _QUATERNION = _descriptor.Descriptor( name='Quaternion', full_name='reachy.sdk.kinematics.Quaternion', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='w', full_name='reachy.sdk.kinematics.Quaternion.w', index=0, number=1, type=1, cpp_type=5, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='x', full_name='reachy.sdk.kinematics.Quaternion.x', index=1, number=2, type=1, cpp_type=5, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='y', full_name='reachy.sdk.kinematics.Quaternion.y', index=2, number=3, type=1, cpp_type=5, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='z', full_name='reachy.sdk.kinematics.Quaternion.z', index=3, number=4, type=1, cpp_type=5, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=159, serialized_end=215, ) _JOINTPOSITION.fields_by_name['ids'].message_type = joint__pb2._JOINTID DESCRIPTOR.message_types_by_name['JointPosition'] = _JOINTPOSITION DESCRIPTOR.message_types_by_name['Matrix4x4'] = _MATRIX4X4 DESCRIPTOR.message_types_by_name['Quaternion'] = _QUATERNION _sym_db.RegisterFileDescriptor(DESCRIPTOR) JointPosition = _reflection.GeneratedProtocolMessageType('JointPosition', (_message.Message,), { 'DESCRIPTOR' : _JOINTPOSITION, '__module__' : 'kinematics_pb2' # @@protoc_insertion_point(class_scope:reachy.sdk.kinematics.JointPosition) }) _sym_db.RegisterMessage(JointPosition) Matrix4x4 = _reflection.GeneratedProtocolMessageType('Matrix4x4', (_message.Message,), { 'DESCRIPTOR' : _MATRIX4X4, '__module__' : 'kinematics_pb2' # @@protoc_insertion_point(class_scope:reachy.sdk.kinematics.Matrix4x4) }) _sym_db.RegisterMessage(Matrix4x4) Quaternion = _reflection.GeneratedProtocolMessageType('Quaternion', (_message.Message,), { 'DESCRIPTOR' : _QUATERNION, '__module__' : 'kinematics_pb2' # 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Statistics/variance_of_sample_proportion.py	mykolatyniv/database_NJIT601	0	198459	<reponame>mykolatyniv/database_NJIT601 def variance_of_sample_proportion(a,b,c,d,e,f,g,h,j,k): try: a = int(a) b = int(b) c = int(c) d = int(d) e = int(e) f = int(f) g = int(g) h = int(h) j = int(j) k = int(k) sample = [a,b,c,d,e,f,g,h,j,k] # Count how many people are over the age 80 i = 0 occurrence = 0 while i < len(sample): if (sample[i])> 80: occurrence = occurrence + 1 else: i = i + 1 i = i + 1 # n is population size n = len(sample) # p is probability p = float(occurrence/n) var_samp_propor = float((p * (1-p))/n) print("variance_of_sample_proportion:",var_samp_propor ) return var_samp_propor except ZeroDivisionError: print("Error: Dividing by Zero is not valid!!") except ValueError: print ("Error: Only Numeric Values are valid!!")	[ 1, 529, 276, 1112, 420, 29958, 1357, 10028, 271, 948, 440, 29914, 9803, 29918, 29940, 29967, 1806, 29953, 29900, 29896, 13, 13, 1753, 20162, 29918, 974, 29918, 11249, 29918, 771, 637, 291, 29898, 29874, 29892, 29890, 29892, 29883, 29892, 29881, 29892, 29872, 29892, 29888, 29892, 29887, 29892, 29882, 29892, 29926, 29892, 29895, 1125, 13, 1678, 1018, 29901, 13, 4706, 263, 353, 938, 29898, 29874, 29897, 13, 4706, 289, 353, 938, 29898, 29890, 29897, 13, 4706, 274, 353, 938, 29898, 29883, 29897, 13, 4706, 270, 353, 938, 29898, 29881, 29897, 13, 4706, 321, 353, 938, 29898, 29872, 29897, 13, 4706, 285, 353, 938, 29898, 29888, 29897, 13, 4706, 330, 353, 938, 29898, 29887, 29897, 13, 4706, 298, 353, 938, 29898, 29882, 29897, 13, 4706, 432, 353, 938, 29898, 29926, 29897, 13, 4706, 413, 353, 938, 29898, 29895, 29897, 13, 4706, 4559, 353, 518, 29874, 29892, 29890, 29892, 29883, 29892, 29881, 29892, 29872, 29892, 29888, 29892, 29887, 29892, 29882, 29892, 29926, 29892, 29895, 29962, 13, 4706, 396, 3917, 920, 1784, 2305, 526, 975, 278, 5046, 29871, 29947, 29900, 13, 4706, 474, 353, 29871, 29900, 13, 4706, 27170, 353, 29871, 29900, 13, 4706, 1550, 474, 529, 7431, 29898, 11249, 1125, 13, 9651, 565, 313, 11249, 29961, 29875, 2314, 29958, 29871, 29947, 29900, 29901, 13, 18884, 27170, 353, 27170, 718, 29871, 29896, 13, 9651, 1683, 29901, 13, 18884, 474, 353, 474, 718, 29871, 29896, 13, 9651, 474, 353, 474, 718, 29871, 29896, 13, 4706, 396, 302, 338, 4665, 2159, 13, 4706, 302, 353, 7431, 29898, 11249, 29897, 13, 4706, 396, 282, 338, 6976, 13, 4706, 282, 353, 5785, 29898, 15693, 26841, 29914, 29876, 29897, 13, 4706, 722, 29918, 29879, 1160, 29918, 771, 1971, 353, 5785, 3552, 29886, 334, 313, 29896, 29899, 29886, 876, 29914, 29876, 29897, 13, 4706, 1596, 703, 1707, 8837, 29918, 974, 29918, 11249, 29918, 771, 637, 291, 29901, 613, 1707, 29918, 29879, 1160, 29918, 771, 1971, 1723, 13, 4706, 736, 722, 29918, 29879, 1160, 29918, 771, 1971, 13, 1678, 5174, 28933, 12596, 2459, 2392, 29901, 13, 4706, 1596, 703, 2392, 29901, 360, 3640, 292, 491, 28933, 338, 451, 2854, 6824, 1159, 13, 1678, 5174, 7865, 2392, 29901, 13, 4706, 1596, 4852, 2392, 29901, 9333, 405, 25099, 2630, 1041, 526, 2854, 6824, 1159, 2 ]
workflow/migrations/0017_organization_logo.py	meetdatastory/Activity-CE	0	177025	# -- coding: utf-8 -- # Generated by Django 1.11.2 on 2018-11-14 00:31 from __future__ import unicode_literals from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('workflow', '0016_auto_20170623_1306'), ] operations = [ migrations.AddField( model_name='organization', name='logo', field=models.FileField(blank=True, null=True, upload_to='static/img/', verbose_name='Your Organization Logo'), ), ]	[ 1, 396, 448, 29930, 29899, 14137, 29901, 23616, 29899, 29947, 448, 29930, 29899, 13, 29937, 3251, 630, 491, 15337, 29871, 29896, 29889, 29896, 29896, 29889, 29906, 373, 29871, 29906, 29900, 29896, 29947, 29899, 29896, 29896, 29899, 29896, 29946, 29871, 29900, 29900, 29901, 29941, 29896, 13, 3166, 4770, 29888, 9130, 1649, 1053, 29104, 29918, 20889, 1338, 13, 13, 3166, 9557, 29889, 2585, 1053, 9725, 800, 29892, 4733, 13, 13, 13, 1990, 341, 16783, 29898, 26983, 800, 29889, 29924, 16783, 1125, 13, 13, 1678, 9962, 353, 518, 13, 4706, 6702, 1287, 1731, 742, 525, 29900, 29900, 29896, 29953, 29918, 6921, 29918, 29906, 29900, 29896, 29955, 29900, 29953, 29906, 29941, 29918, 29896, 29941, 29900, 29953, 5477, 13, 1678, 4514, 13, 13, 1678, 6931, 353, 518, 13, 4706, 9725, 800, 29889, 2528, 3073, 29898, 13, 9651, 1904, 29918, 978, 2433, 6388, 2133, 742, 13, 9651, 1024, 2433, 14569, 742, 13, 9651, 1746, 29922, 9794, 29889, 2283, 3073, 29898, 19465, 29922, 5574, 29892, 1870, 29922, 5574, 29892, 6441, 29918, 517, 2433, 7959, 29914, 2492, 29914, 742, 26952, 29918, 978, 2433, 10858, 9205, 2133, 4522, 29877, 5477, 13, 4706, 10353, 13, 1678, 4514, 13, 2 ]
get_neowise_data.py	Aparctias/lightcurve_fft	0	1604646	#!/usr/bin/python # Get NEOWISE data import os import time import json import requests MPC_SPIN_PROC = "mpc_spins_proc" NEOWISE_DIR = "neowise_stat" NEOWISE_SKIP = "neowise_skip_objs" data = [] with open(MPC_SPIN_PROC, 'r') as f: data = json.load(f) url = 'http://irsa.ipac.caltech.edu/cgi-bin/Gator/nph-query?searchForm=MO' \ '&spatial=cone&catalog=allsky_4band_p1bs_psd&mobj=smo&outfmt=1' \ '&mobjstr=%s' def save_to_file(obj_name, stat): with open(NEOWISE_DIR + '/' + obj_name, 'w') as f: f.write(stat) def execute_request(request_url): retries = 1 i = 0 text = '' try: while (i < retries): r = requests.get(request_url) # Very bad if r.text.startswith("[struct stat=\"ERROR\""): print "Retry: ", request_url time.sleep(1) else: text = r.text break i += 1 except: text = '' return text def stat_file_exists(obj_name): return os.path.exists(NEOWISE_DIR + '/' + obj_name) def get_objs_to_skip(): if os.path.exists(NEOWISE_SKIP): with open(NEOWISE_SKIP, 'r') as f: return f.read().split() else: return [] def add_obj_to_skip(obj_name): with open(NEOWISE_SKIP, 'a') as f: f.write(obj_name + ' ') if not os.path.exists(NEOWISE_DIR): os.mkdir(NEOWISE_DIR) print "Total: ", len(data) skip_objs = get_objs_to_skip() print "Not uploaded objs: ", skip_objs for i in range(0, len(data)): mobjstr = '' for n in ('name', 'number', 'designation'): if data[i].get(n): mobjstr = data[i].get(n) if not mobjstr or stat_file_exists(str(mobjstr)) or\ str(mobjstr) in skip_objs: continue mobjstr = str(mobjstr) print "Processing: %s - %s" % (i, mobjstr) stat = execute_request(url%mobjstr) if stat: save_to_file(mobjstr, stat) else: add_obj_to_skip(mobjstr)	[ 1, 18787, 4855, 29914, 2109, 29914, 4691, 13, 13, 29937, 3617, 14693, 9806, 29902, 1660, 848, 13, 13, 5215, 2897, 13, 5215, 931, 13, 5215, 4390, 13, 5215, 7274, 13, 13, 3580, 29907, 29918, 5550, 1177, 29918, 8618, 29907, 353, 376, 1526, 29883, 29918, 1028, 1144, 29918, 15439, 29908, 13, 8186, 9806, 29902, 1660, 29918, 9464, 353, 376, 484, 340, 895, 29918, 6112, 29908, 13, 8186, 9806, 29902, 1660, 29918, 16033, 5690, 353, 376, 484, 340, 895, 29918, 11014, 29918, 711, 1315, 29908, 13, 13, 1272, 353, 5159, 13, 2541, 1722, 29898, 3580, 29907, 29918, 5550, 1177, 29918, 8618, 29907, 29892, 525, 29878, 1495, 408, 285, 29901, 13, 1678, 848, 353, 4390, 29889, 1359, 29898, 29888, 29897, 13, 13, 2271, 353, 525, 1124, 597, 381, 4977, 29889, 666, 562, 29889, 1052, 11345, 29889, 6085, 29914, 20006, 29899, 2109, 29914, 29954, 1061, 29914, 29876, 561, 29899, 1972, 29973, 4478, 2500, 29922, 6720, 29915, 320, 13, 418, 525, 29987, 1028, 15238, 29922, 535, 29872, 29987, 28045, 29922, 497, 7912, 29918, 29946, 4980, 29918, 29886, 29896, 5824, 29918, 567, 29881, 29987, 29885, 5415, 29922, 3844, 29877, 29987, 449, 23479, 29922, 29896, 29915, 320, 13, 418, 525, 29987, 29885, 5415, 710, 16328, 29879, 29915, 13, 13, 1753, 4078, 29918, 517, 29918, 1445, 29898, 5415, 29918, 978, 29892, 1002, 1125, 13, 1678, 411, 1722, 29898, 8186, 9806, 29902, 1660, 29918, 9464, 718, 8207, 29915, 718, 5446, 29918, 978, 29892, 525, 29893, 1495, 408, 285, 29901, 13, 4706, 285, 29889, 3539, 29898, 6112, 29897, 13, 13, 1753, 6222, 29918, 3827, 29898, 3827, 29918, 2271, 1125, 13, 1678, 3240, 2722, 353, 29871, 29896, 13, 1678, 474, 353, 29871, 29900, 13, 1678, 1426, 353, 6629, 13, 1678, 1018, 29901, 13, 4706, 1550, 313, 29875, 529, 3240, 2722, 1125, 13, 9651, 364, 353, 7274, 29889, 657, 29898, 3827, 29918, 2271, 29897, 13, 9651, 396, 18064, 4319, 13, 9651, 565, 364, 29889, 726, 29889, 27382, 2541, 703, 29961, 4984, 1002, 14672, 11432, 5931, 29908, 1125, 13, 18884, 1596, 376, 8015, 719, 29901, 9162, 2009, 29918, 2271, 13, 18884, 931, 29889, 17059, 29898, 29896, 29897, 13, 9651, 1683, 29901, 13, 18884, 1426, 353, 364, 29889, 726, 13, 18884, 2867, 13, 9651, 474, 4619, 29871, 29896, 13, 1678, 5174, 29901, 13, 4706, 1426, 353, 6629, 13, 1678, 736, 1426, 13, 13, 1753, 1002, 29918, 1445, 29918, 9933, 29898, 5415, 29918, 978, 1125, 13, 1678, 736, 2897, 29889, 2084, 29889, 9933, 29898, 8186, 9806, 29902, 1660, 29918, 9464, 718, 8207, 29915, 718, 5446, 29918, 978, 29897, 13, 13, 1753, 679, 29918, 711, 1315, 29918, 517, 29918, 11014, 7295, 13, 1678, 565, 2897, 29889, 2084, 29889, 9933, 29898, 8186, 9806, 29902, 1660, 29918, 16033, 5690, 1125, 13, 4706, 411, 1722, 29898, 8186, 9806, 29902, 1660, 29918, 16033, 5690, 29892, 525, 29878, 1495, 408, 285, 29901, 13, 9651, 736, 285, 29889, 949, 2141, 5451, 580, 13, 1678, 1683, 29901, 13, 4706, 736, 5159, 13, 13, 1753, 788, 29918, 5415, 29918, 517, 29918, 11014, 29898, 5415, 29918, 978, 1125, 13, 1678, 411, 1722, 29898, 8186, 9806, 29902, 1660, 29918, 16033, 5690, 29892, 525, 29874, 1495, 408, 285, 29901, 13, 4706, 285, 29889, 3539, 29898, 5415, 29918, 978, 718, 525, 25710, 13, 13, 361, 451, 2897, 29889, 2084, 29889, 9933, 29898, 8186, 9806, 29902, 1660, 29918, 9464, 1125, 13, 1678, 2897, 29889, 11256, 3972, 29898, 8186, 9806, 29902, 1660, 29918, 9464, 29897, 13, 13, 2158, 376, 11536, 29901, 9162, 7431, 29898, 1272, 29897, 13, 11014, 29918, 711, 1315, 353, 679, 29918, 711, 1315, 29918, 517, 29918, 11014, 580, 13, 2158, 376, 3664, 20373, 704, 1315, 29901, 9162, 14383, 29918, 711, 1315, 13, 1454, 474, 297, 3464, 29898, 29900, 29892, 7431, 29898, 1272, 22164, 13, 1678, 286, 5415, 710, 353, 6629, 13, 1678, 363, 302, 297, 6702, 978, 742, 525, 4537, 742, 525, 13892, 362, 29374, 13, 4706, 565, 848, 29961, 29875, 1822, 657, 29898, 29876, 1125, 13, 9651, 286, 5415, 710, 353, 848, 29961, 29875, 1822, 657, 29898, 29876, 29897, 13, 1678, 565, 451, 286, 5415, 710, 470, 1002, 29918, 1445, 29918, 9933, 29898, 710, 29898, 29885, 5415, 710, 876, 470, 29905, 13, 462, 418, 851, 29898, 29885, 5415, 710, 29897, 297, 14383, 29918, 711, 1315, 29901, 13, 4706, 6773, 13, 1678, 286, 5415, 710, 353, 851, 29898, 29885, 5415, 710, 29897, 13, 1678, 1596, 376, 7032, 292, 29901, 1273, 29879, 448, 1273, 29879, 29908, 1273, 313, 29875, 29892, 286, 5415, 710, 29897, 13, 1678, 1002, 353, 6222, 29918, 3827, 29898, 2271, 29995, 29885, 5415, 710, 29897, 13, 1678, 565, 1002, 29901, 13, 4706, 4078, 29918, 517, 29918, 1445, 29898, 29885, 5415, 710, 29892, 1002, 29897, 13, 1678, 1683, 29901, 13, 4706, 788, 29918, 5415, 29918, 517, 29918, 11014, 29898, 29885, 5415, 710, 29897, 13, 13, 2 ]
common.py	sleclair0/spacy-youtube-material	93	66175	<filename>common.py<gh_stars>10-100 from spacy.matcher import Matcher def create_versioned(name): return [ [{'LOWER': name}], [{'LOWER': {'REGEX': f'({name}\d+\.?\d.?\d)'}}], [{'LOWER': name}, {'TEXT': {'REGEX': '(\d+\.?\d.?\d)'}}], ] def create_patterns(): versioned_languages = ['ruby', 'php', 'python', 'perl', 'java', 'haskell', 'scala', 'c', 'cpp', 'matlab', 'bash', 'delphi'] flatten = lambda l: [item for sublist in l for item in sublist] versioned_patterns = flatten([create_versioned(lang) for lang in versioned_languages]) lang_patterns = [ [{'LOWER': 'objective'}, {'IS_PUNCT': True, 'OP': '?'},{'LOWER': 'c'}], [{'LOWER': 'objectivec'}], [{'LOWER': 'c'}, {'LOWER': '#'}], [{'LOWER': 'c'}, {'LOWER': 'sharp'}], [{'LOWER': 'c#'}], [{'LOWER': 'f'}, {'LOWER': '#'}], [{'LOWER': 'f'}, {'LOWER': 'sharp'}], [{'LOWER': 'f#'}], [{'LOWER': 'lisp'}], [{'LOWER': 'common'}, {'LOWER': 'lisp'}], [{'LOWER': 'go', 'POS': {'NOT_IN': ['VERB']}}], [{'LOWER': 'golang'}], [{'LOWER': 'html'}], [{'LOWER': 'css'}], [{'LOWER': 'sql'}], [{'LOWER': {'IN': ['js', 'javascript']}}], [{'LOWER': 'c++'}], ] return versioned_patterns + lang_patterns	[ 1, 529, 9507, 29958, 9435, 29889, 2272, 29966, 12443, 29918, 303, 1503, 29958, 29896, 29900, 29899, 29896, 29900, 29900, 13, 3166, 805, 4135, 29889, 4352, 261, 1053, 14514, 261, 13, 13, 1753, 1653, 29918, 3259, 287, 29898, 978, 1125, 13, 1678, 736, 518, 13, 4706, 518, 10998, 27998, 1001, 2396, 1024, 29913, 1402, 29871, 13, 4706, 518, 10998, 27998, 1001, 2396, 11117, 1525, 1692, 29990, 2396, 285, 29915, 3319, 978, 1012, 29881, 3124, 29889, 29973, 29905, 29881, 10521, 29973, 29905, 29881, 7528, 29915, 930, 1402, 29871, 13, 4706, 518, 10998, 27998, 1001, 2396, 1024, 1118, 11117, 16975, 2396, 11117, 1525, 1692, 29990, 2396, 525, 1194, 29881, 3124, 29889, 29973, 29905, 29881, 10521, 29973, 29905, 29881, 7528, 29915, 930, 1402, 13, 1678, 4514, 13, 13, 13, 1753, 1653, 29918, 11037, 29879, 7295, 13, 1678, 1873, 287, 29918, 29880, 8737, 353, 6024, 9754, 742, 525, 1961, 742, 525, 4691, 742, 525, 22032, 742, 525, 1645, 742, 525, 29882, 21918, 742, 29871, 13, 462, 965, 525, 15820, 742, 525, 29883, 742, 525, 8223, 742, 525, 2922, 8205, 742, 525, 13067, 742, 525, 6144, 2876, 2033, 13, 1678, 1652, 8606, 353, 14013, 301, 29901, 518, 667, 363, 1014, 1761, 297, 301, 363, 2944, 297, 1014, 1761, 29962, 13, 1678, 1873, 287, 29918, 11037, 29879, 353, 1652, 8606, 4197, 3258, 29918, 3259, 287, 29898, 3893, 29897, 363, 6361, 297, 1873, 287, 29918, 29880, 8737, 2314, 13, 13, 1678, 6361, 29918, 11037, 29879, 353, 518, 13, 4706, 518, 10998, 27998, 1001, 2396, 525, 3318, 573, 16675, 11117, 3235, 29918, 29925, 3904, 1783, 2396, 5852, 29892, 525, 4590, 2396, 525, 29973, 16675, 10998, 27998, 1001, 2396, 525, 29883, 10827, 1402, 13, 4706, 518, 10998, 27998, 1001, 2396, 525, 3318, 573, 29883, 10827, 1402, 13, 4706, 518, 10998, 27998, 1001, 2396, 525, 29883, 16675, 11117, 27998, 1001, 2396, 16321, 10827, 1402, 13, 4706, 518, 10998, 27998, 1001, 2396, 525, 29883, 16675, 11117, 27998, 1001, 2396, 525, 22064, 10827, 1402, 13, 4706, 518, 10998, 27998, 1001, 2396, 525, 29883, 29937, 10827, 1402, 13, 4706, 518, 10998, 27998, 1001, 2396, 525, 29888, 16675, 11117, 27998, 1001, 2396, 16321, 10827, 1402, 13, 4706, 518, 10998, 27998, 1001, 2396, 525, 29888, 16675, 11117, 27998, 1001, 2396, 525, 22064, 10827, 1402, 13, 4706, 518, 10998, 27998, 1001, 2396, 525, 29888, 29937, 10827, 1402, 13, 4706, 518, 10998, 27998, 1001, 2396, 525, 29880, 11936, 10827, 1402, 13, 4706, 518, 10998, 27998, 1001, 2396, 525, 9435, 16675, 11117, 27998, 1001, 2396, 525, 29880, 11936, 10827, 1402, 13, 4706, 518, 10998, 27998, 1001, 2396, 525, 1484, 742, 525, 24815, 2396, 11117, 12256, 29918, 1177, 2396, 6024, 5348, 29933, 2033, 930, 1402, 13, 4706, 518, 10998, 27998, 1001, 2396, 525, 29887, 324, 574, 10827, 1402, 13, 4706, 518, 10998, 27998, 1001, 2396, 525, 1420, 10827, 1402, 13, 4706, 518, 10998, 27998, 1001, 2396, 525, 4268, 10827, 1402, 13, 4706, 518, 10998, 27998, 1001, 2396, 525, 2850, 10827, 1402, 13, 4706, 518, 10998, 27998, 1001, 2396, 11117, 1177, 2396, 6024, 1315, 742, 525, 7729, 2033, 930, 1402, 13, 4706, 518, 10998, 27998, 1001, 2396, 525, 29883, 1817, 10827, 1402, 13, 1678, 4514, 13, 13, 1678, 736, 1873, 287, 29918, 11037, 29879, 718, 6361, 29918, 11037, 29879, 13, 2 ]
src/dials/algorithms/profile_model/ellipsoid/__init__.py	dials-src/dials	1	146677	<reponame>dials-src/dials from __future__ import annotations from math import pi, sqrt from dials.array_family import flex # noqa: F401; from dials_algorithms_profile_model_ellipsoid_ext import * # noqa: F401, F403; def mosaicity_from_eigen_decomposition(eigen_values): return ( sqrt(eigen_values[0]) * 180.0 / pi, sqrt(eigen_values[1]) * 180.0 / pi, sqrt(eigen_values[2]) * 180.0 / pi, )	[ 1, 529, 276, 1112, 420, 29958, 29881, 616, 29879, 29899, 4351, 29914, 29881, 616, 29879, 13, 3166, 4770, 29888, 9130, 1649, 1053, 25495, 13, 13, 3166, 5844, 1053, 2930, 29892, 18074, 2273, 13, 13, 3166, 270, 616, 29879, 29889, 2378, 29918, 11922, 1053, 8525, 29871, 396, 694, 25621, 29901, 383, 29946, 29900, 29896, 29936, 13, 3166, 270, 616, 29879, 29918, 9564, 12404, 29918, 10185, 29918, 4299, 29918, 5481, 567, 3398, 29918, 1062, 1053, 334, 29871, 396, 694, 25621, 29901, 383, 29946, 29900, 29896, 29892, 383, 29946, 29900, 29941, 29936, 13, 13, 13, 1753, 286, 3628, 24798, 29918, 3166, 29918, 29872, 2101, 29918, 311, 510, 3283, 29898, 29872, 2101, 29918, 5975, 1125, 13, 1678, 736, 313, 13, 4706, 18074, 2273, 29898, 29872, 2101, 29918, 5975, 29961, 29900, 2314, 334, 29871, 29896, 29947, 29900, 29889, 29900, 847, 2930, 29892, 13, 4706, 18074, 2273, 29898, 29872, 2101, 29918, 5975, 29961, 29896, 2314, 334, 29871, 29896, 29947, 29900, 29889, 29900, 847, 2930, 29892, 13, 4706, 18074, 2273, 29898, 29872, 2101, 29918, 5975, 29961, 29906, 2314, 334, 29871, 29896, 29947, 29900, 29889, 29900, 847, 2930, 29892, 13, 1678, 1723, 13, 2 ]
osisoft/pidevclub/piwebapi/models/pi_data_server_license.py	jugillar/PI-Web-API-Client-Python	30	12808	# coding: utf-8 """ Copyright 2018 OSIsoft, LLC Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at <http://www.apache.org/licenses/LICENSE-2.0> Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. """ from pprint import pformat from six import iteritems class PIDataServerLicense(object): swagger_types = { 'amount_left': 'str', 'amount_used': 'str', 'name': 'str', 'total_amount': 'str', 'links': 'PIDataServerLicenseLinks', 'web_exception': 'PIWebException', } attribute_map = { 'amount_left': 'AmountLeft', 'amount_used': 'AmountUsed', 'name': 'Name', 'total_amount': 'TotalAmount', 'links': 'Links', 'web_exception': 'WebException', } def __init__(self, amount_left=None, amount_used=None, name=None, total_amount=None, links=None, web_exception=None): self._amount_left = None self._amount_used = None self._name = None self._total_amount = None self._links = None self._web_exception = None if amount_left is not None: self.amount_left = amount_left if amount_used is not None: self.amount_used = amount_used if name is not None: self.name = name if total_amount is not None: self.total_amount = total_amount if links is not None: self.links = links if web_exception is not None: self.web_exception = web_exception @property def amount_left(self): return self._amount_left @amount_left.setter def amount_left(self, amount_left): self._amount_left = amount_left @property def amount_used(self): return self._amount_used @amount_used.setter def amount_used(self, amount_used): self._amount_used = amount_used @property def name(self): return self._name @name.setter def name(self, name): self._name = name @property def total_amount(self): return self._total_amount @total_amount.setter def total_amount(self, total_amount): self._total_amount = total_amount @property def links(self): return self._links @links.setter def links(self, links): self._links = links @property def web_exception(self): return self._web_exception @web_exception.setter def web_exception(self, web_exception): self._web_exception = web_exception def to_dict(self): result = {} for attr, _ in 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): return pformat(self.to_dict()) def __repr__(self): return self.to_str() def __ne__(self, other): return not self == other def __eq__(self, other): if not isinstance(other, PIDataServerLicense): return False return self.__dict__ == other.__dict__	[ 1, 396, 14137, 29901, 23616, 29899, 29947, 13, 13, 15945, 29908, 13, 12, 11882, 1266, 29871, 29906, 29900, 29896, 29947, 438, 5425, 2695, 29892, 365, 12182, 13, 12, 29931, 293, 21144, 1090, 278, 13380, 19245, 29892, 10079, 29871, 29906, 29889, 29900, 313, 1552, 376, 29931, 293, 1947, 1496, 13, 12, 6293, 1122, 451, 671, 445, 934, 5174, 297, 752, 13036, 411, 278, 19245, 29889, 13, 12, 3492, 1122, 4017, 263, 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diplomacy/src/scripts/yamlizer.py	MaxStrange/nlp	1	37174	<filename>diplomacy/src/scripts/yamlizer.py """ This script takes a single message gathered from playdiplomacy.com as an input file and outputs a YAML version of it so that it can be used as an input file into the program. Usage: python3 yamlizer.py msg.txt (You can also feed it a list of files). NOTE: This script is not designed to handle multiple recipient messages. So if you want to run this script on a message that has a CC line like: CC: ENGLAND, FRANCE, ITALY You must alter this to be: CC: ENGLAND or CC: FRANCE or CC: ITALY But there is nothing stopping you from running this script on the file multiple times - one for each recipient. Example: [Contents of msg.txt]: ===================================================== From: ITALY Date: May 07 2017 15:04 (GMT-8) Spring 1903 CC: FRANCE Re:Movement Awesome. About Bohemia... You should have Burgundy support Bohemia to Munich. Meanwhile, attack Ruhr with Belgium. Unless you want me to contact Russia about what I should do with Bohemia. That would be fine too. Up to you. ====================================================== When fed through this script, this will output as msg.yml: ====================================================== year: 1903 season: Spring a_to_b: from_player: Italy from_country: Italy to_player: France to_country: France messages: - > "Re:Movement Awesome. About Bohemia... You should have Burgundy support Bohemia to Munich. Meanwhile, attack Ruhr with Belgium. Unless you want me to contact Russia about what I should do with Bohemia. That would be fine too. Up to you." ====================================================== You are free to do what you want with the from_player and to_player fields. Those are not really used - they were included because I didn't know if they would be necessary or not - turns out not. You can also add more messages to the end of that and also add b_to_a messages as well. See examples. """ import os import sys class Message: def __init__(self, txt): from_line = next((line for line in txt if line.lower().startswith("from:"))) date_line = next((line for line in txt if line.lower().startswith("date:"))) to_line = next((line for line in txt if line.lower().startswith("cc:"))) to_line_index = [i for i, line in enumerate(txt) if line.lower().startswith("cc:")][0] msg_body = os.linesep.join([line for line in txt[(to_line_index + 1):]]) self.from_ = from_line.split("From:")[1].strip() self.date = date_line.split("Date:")[1].strip() self.to = to_line.split("CC:")[1].strip() self.message = msg_body.strip() def __str__(self): s = "FROM: " + str(self.from_) + os.linesep s += "DATE: " + str(self.date) + os.linesep s += "TO: " + str(self.to) + os.linesep s += "Message: " + self.message return s def yamlize(txt): """ Converts the message text from playdiplomacy raw text to the YAML format that this program requires. """ message = Message(txt) year = message.date.split(" ")[-1] season = message.date.split(" ")[-2] from_player = from_country = message.from_.lower().title() to_player = to_country = message.to.lower().title() msg = message.message.replace("\"", "\\\"") msg = "\"" + msg + "\"" msg = msg.replace(os.linesep, os.linesep + " ") yaml = "year: " + year + os.linesep yaml += "season: " + season + os.linesep yaml += "a_to_b:" + os.linesep yaml += " from_player: " + from_player + os.linesep yaml += " from_country: " + from_country + os.linesep yaml += " to_player: " + to_player + os.linesep yaml += " to_country: " + to_country + os.linesep yaml += " messages:" + os.linesep yaml += " - >" + os.linesep yaml += msg yaml += os.linesep return yaml if __name__ == "__main__": if len(sys.argv) < 2: print("Need at least one file path.") exit() for file_path in sys.argv[1:]: try: with open(file_path) as f: text = [line for line in f] yaml = yamlize(text) with open(os.path.splitext(file_path)[0] + ".yml", 'w') as f: f.write(yaml) except FileNotFoundError: print("File", file_path, "does not exist.")	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lib/googlecloudsdk/command_lib/privateca/storage.py	google-cloud-sdk-unofficial/google-cloud-sdk	2	198493	# Lint as: python3 # -- coding: utf-8 -- # # Copyright 2020 Google LLC. 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. """Helpers for dealing with storage buckets.""" from __future__ import absolute_import from __future__ import division from __future__ import unicode_literals from googlecloudsdk.api_lib.storage import storage_api from googlecloudsdk.api_lib.storage import storage_util from googlecloudsdk.calliope import exceptions from googlecloudsdk.core import log def _BucketAllowsPublicObjectReads(bucket): return any([acl.entity.lower() == 'allusers' and acl.role.lower() == 'reader' for acl in bucket.defaultObjectAcl]) def ValidateBucketForCertificateAuthority(bucket_name): """Validates that a user-specified bucket can be used with a Private CA. Args: bucket_name: The name of the GCS bucket to validate. Returns: A BucketReference wrapping the given bucket name. Raises: InvalidArgumentException: when the given bucket can't be used with a CA. """ messages = storage_util.GetMessages() client = storage_api.StorageClient(messages=messages) try: bucket = client.GetBucket( bucket_name, messages.StorageBucketsGetRequest.ProjectionValueValuesEnum.full) if not _BucketAllowsPublicObjectReads(bucket): # Show a warning but don't fail, since this could be intentional. log.warning( 'The specified bucket does not publicly expose new objects by ' 'default, so some clients may not be able to access the CA ' 'certificate or CRLs. For more details, see ' 'https://cloud.google.com/storage/docs/access-control/making-data-public' ) return storage_util.BucketReference(bucket_name) except storage_api.BucketNotFoundError: raise exceptions.InvalidArgumentException( 'gcs-bucket', 'The given bucket does not exist.')	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src/ui/ui_send_payout_dlg.py	muteio/ghostnode-tool	1	35312	<gh_stars>1-10 # -- coding: utf-8 -- # Form implementation generated from reading ui file 'ui_send_payout_dlg.ui' # # Created by: PyQt5 UI code generator 5.9.2 # # WARNING! All changes made in this file will be lost! from PyQt5 import QtCore, QtGui, QtWidgets class Ui_SendPayoutDlg(object): def setupUi(self, SendPayoutDlg): SendPayoutDlg.setObjectName("SendPayoutDlg") SendPayoutDlg.resize(832, 507) sizePolicy = QtWidgets.QSizePolicy(QtWidgets.QSizePolicy.Preferred, QtWidgets.QSizePolicy.Preferred) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth(SendPayoutDlg.sizePolicy().hasHeightForWidth()) SendPayoutDlg.setSizePolicy(sizePolicy) SendPayoutDlg.setSizeGripEnabled(True) SendPayoutDlg.setModal(True) self.verticalLayout = QtWidgets.QVBoxLayout(SendPayoutDlg) self.verticalLayout.setObjectName("verticalLayout") self.pnl_input = QtWidgets.QWidget(SendPayoutDlg) self.pnl_input.setObjectName("pnl_input") self.verticalLayout_4 = QtWidgets.QVBoxLayout(self.pnl_input) self.verticalLayout_4.setContentsMargins(0, 0, 0, 0) self.verticalLayout_4.setSpacing(0) self.verticalLayout_4.setObjectName("verticalLayout_4") self.lay_input = QtWidgets.QHBoxLayout() self.lay_input.setSpacing(8) self.lay_input.setObjectName("lay_input") self.label_3 = QtWidgets.QLabel(self.pnl_input) self.label_3.setObjectName("label_3") self.lay_input.addWidget(self.label_3) self.cbo_address_source_mode = QtWidgets.QComboBox(self.pnl_input) sizePolicy = QtWidgets.QSizePolicy(QtWidgets.QSizePolicy.Fixed, QtWidgets.QSizePolicy.Fixed) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth(self.cbo_address_source_mode.sizePolicy().hasHeightForWidth()) self.cbo_address_source_mode.setSizePolicy(sizePolicy) self.cbo_address_source_mode.setMinimumSize(QtCore.QSize(0, 0)) self.cbo_address_source_mode.setMaximumSize(QtCore.QSize(160, 16777215)) self.cbo_address_source_mode.setObjectName("cbo_address_source_mode") self.cbo_address_source_mode.addItem("") self.cbo_address_source_mode.addItem("") self.cbo_address_source_mode.addItem("") self.lay_input.addWidget(self.cbo_address_source_mode) self.sw_address_source = QtWidgets.QStackedWidget(self.pnl_input) sizePolicy = QtWidgets.QSizePolicy(QtWidgets.QSizePolicy.Maximum, QtWidgets.QSizePolicy.Maximum) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth(self.sw_address_source.sizePolicy().hasHeightForWidth()) self.sw_address_source.setSizePolicy(sizePolicy) self.sw_address_source.setObjectName("sw_address_source") self.wdg_address_source_1 = QtWidgets.QWidget() sizePolicy = QtWidgets.QSizePolicy(QtWidgets.QSizePolicy.Maximum, QtWidgets.QSizePolicy.Preferred) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth(self.wdg_address_source_1.sizePolicy().hasHeightForWidth()) self.wdg_address_source_1.setSizePolicy(sizePolicy) self.wdg_address_source_1.setObjectName("wdg_address_source_1") self.horizontalLayout_6 = QtWidgets.QHBoxLayout(self.wdg_address_source_1) self.horizontalLayout_6.setContentsMargins(0, 0, 0, 0) self.horizontalLayout_6.setSpacing(1) self.horizontalLayout_6.setObjectName("horizontalLayout_6") self.lbl_account = QtWidgets.QLabel(self.wdg_address_source_1) self.lbl_account.setObjectName("lbl_account") self.horizontalLayout_6.addWidget(self.lbl_account) self.cbo_hw_account_nr = QtWidgets.QComboBox(self.wdg_address_source_1) self.cbo_hw_account_nr.setObjectName("cbo_hw_account_nr") self.horizontalLayout_6.addWidget(self.cbo_hw_account_nr) self.btn_add_hw_account_nr = QtWidgets.QToolButton(self.wdg_address_source_1) self.btn_add_hw_account_nr.setObjectName("btn_add_hw_account_nr") self.horizontalLayout_6.addWidget(self.btn_add_hw_account_nr) spacerItem = QtWidgets.QSpacerItem(40, 20, QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Minimum) self.horizontalLayout_6.addItem(spacerItem) self.lbl_hw_account_base_path = QtWidgets.QLabel(self.wdg_address_source_1) self.lbl_hw_account_base_path.setObjectName("lbl_hw_account_base_path") self.horizontalLayout_6.addWidget(self.lbl_hw_account_base_path) self.sw_address_source.addWidget(self.wdg_address_source_1) self.wdg_address_source_2 = QtWidgets.QWidget() sizePolicy = QtWidgets.QSizePolicy(QtWidgets.QSizePolicy.Maximum, QtWidgets.QSizePolicy.Preferred) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth(self.wdg_address_source_2.sizePolicy().hasHeightForWidth()) self.wdg_address_source_2.setSizePolicy(sizePolicy) self.wdg_address_source_2.setObjectName("wdg_address_source_2") self.horizontalLayout_2 = QtWidgets.QHBoxLayout(self.wdg_address_source_2) self.horizontalLayout_2.setContentsMargins(0, 0, 0, 0) self.horizontalLayout_2.setObjectName("horizontalLayout_2") self.lblSourceBip32Path = QtWidgets.QLabel(self.wdg_address_source_2) self.lblSourceBip32Path.setObjectName("lblSourceBip32Path") self.horizontalLayout_2.addWidget(self.lblSourceBip32Path) self.edt_src_bip32_path = QtWidgets.QLineEdit(self.wdg_address_source_2) sizePolicy = QtWidgets.QSizePolicy(QtWidgets.QSizePolicy.Fixed, QtWidgets.QSizePolicy.Fixed) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth(self.edt_src_bip32_path.sizePolicy().hasHeightForWidth()) self.edt_src_bip32_path.setSizePolicy(sizePolicy) self.edt_src_bip32_path.setMaximumSize(QtCore.QSize(100, 16777215)) self.edt_src_bip32_path.setStyleSheet("background-color: lightgray;") self.edt_src_bip32_path.setReadOnly(True) self.edt_src_bip32_path.setObjectName("edt_src_bip32_path") self.horizontalLayout_2.addWidget(self.edt_src_bip32_path) self.btn_src_bip32_path = QtWidgets.QToolButton(self.wdg_address_source_2) self.btn_src_bip32_path.setObjectName("btn_src_bip32_path") self.horizontalLayout_2.addWidget(self.btn_src_bip32_path) spacerItem1 = QtWidgets.QSpacerItem(40, 20, QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Minimum) self.horizontalLayout_2.addItem(spacerItem1) self.sw_address_source.addWidget(self.wdg_address_source_2) self.wdg_address_source_3 = QtWidgets.QWidget() self.wdg_address_source_3.setObjectName("wdg_address_source_3") self.horizontalLayout = QtWidgets.QHBoxLayout(self.wdg_address_source_3) self.horizontalLayout.setContentsMargins(0, 0, 0, 0) self.horizontalLayout.setObjectName("horizontalLayout") self.lbl_src_masternode = QtWidgets.QLabel(self.wdg_address_source_3) self.lbl_src_masternode.setObjectName("lbl_src_masternode") self.horizontalLayout.addWidget(self.lbl_src_masternode) self.cbo_src_masternodes = QtWidgets.QComboBox(self.wdg_address_source_3) self.cbo_src_masternodes.setObjectName("cbo_src_masternodes") self.horizontalLayout.addWidget(self.cbo_src_masternodes) spacerItem2 = QtWidgets.QSpacerItem(40, 20, QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Minimum) self.horizontalLayout.addItem(spacerItem2) self.sw_address_source.addWidget(self.wdg_address_source_3) self.lay_input.addWidget(self.sw_address_source) self.btnLoadTransactions = QtWidgets.QPushButton(self.pnl_input) self.btnLoadTransactions.setAutoDefault(False) self.btnLoadTransactions.setObjectName("btnLoadTransactions") self.lay_input.addWidget(self.btnLoadTransactions) spacerItem3 = QtWidgets.QSpacerItem(40, 20, QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Minimum) self.lay_input.addItem(spacerItem3) self.verticalLayout_4.addLayout(self.lay_input) self.verticalLayout.addWidget(self.pnl_input) self.splitter = QtWidgets.QSplitter(SendPayoutDlg) self.splitter.setOrientation(QtCore.Qt.Vertical) self.splitter.setObjectName("splitter") self.main_widget = QtWidgets.QWidget(self.splitter) self.main_widget.setObjectName("main_widget") self.verticalLayout_2 = QtWidgets.QVBoxLayout(self.main_widget) self.verticalLayout_2.setContentsMargins(0, 0, 0, 0) self.verticalLayout_2.setSpacing(2) self.verticalLayout_2.setObjectName("verticalLayout_2") self.lbl_message_2 = QtWidgets.QLabel(self.main_widget) self.lbl_message_2.setText("") self.lbl_message_2.setOpenExternalLinks(True) self.lbl_message_2.setTextInteractionFlags(QtCore.Qt.LinksAccessibleByMouse\|QtCore.Qt.TextSelectableByMouse) self.lbl_message_2.setObjectName("lbl_message_2") self.verticalLayout_2.addWidget(self.lbl_message_2) self.horizontalLayout_4 = QtWidgets.QHBoxLayout() self.horizontalLayout_4.setContentsMargins(-1, 8, -1, -1) self.horizontalLayout_4.setSpacing(6) self.horizontalLayout_4.setObjectName("horizontalLayout_4") self.btnCheckAll = QtWidgets.QToolButton(self.main_widget) self.btnCheckAll.setToolTip("") self.btnCheckAll.setIconSize(QtCore.QSize(12, 12)) self.btnCheckAll.setToolButtonStyle(QtCore.Qt.ToolButtonTextBesideIcon) self.btnCheckAll.setObjectName("btnCheckAll") self.horizontalLayout_4.addWidget(self.btnCheckAll) self.btnUncheckAll = QtWidgets.QToolButton(self.main_widget) self.btnUncheckAll.setToolTip("") self.btnUncheckAll.setIconSize(QtCore.QSize(12, 12)) self.btnUncheckAll.setToolButtonStyle(QtCore.Qt.ToolButtonTextBesideIcon) self.btnUncheckAll.setObjectName("btnUncheckAll") self.horizontalLayout_4.addWidget(self.btnUncheckAll) self.chbHideCollateralTx = QtWidgets.QCheckBox(self.main_widget) self.chbHideCollateralTx.setStyleSheet("") self.chbHideCollateralTx.setObjectName("chbHideCollateralTx") self.horizontalLayout_4.addWidget(self.chbHideCollateralTx) self.lbl_message = QtWidgets.QLabel(self.main_widget) self.lbl_message.setStyleSheet("margin-left:20px;\n" "font-size:11px;\n" "background-color: rgb(56, 181, 255);\n" "color: rgb(255, 255, 255);") self.lbl_message.setWordWrap(False) self.lbl_message.setOpenExternalLinks(True) self.lbl_message.setTextInteractionFlags(QtCore.Qt.LinksAccessibleByMouse\|QtCore.Qt.TextSelectableByMouse) self.lbl_message.setObjectName("lbl_message") self.horizontalLayout_4.addWidget(self.lbl_message) spacerItem4 = QtWidgets.QSpacerItem(40, 20, QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Minimum) self.horizontalLayout_4.addItem(spacerItem4) self.verticalLayout_2.addLayout(self.horizontalLayout_4) self.tableView = QtWidgets.QTableView(self.main_widget) self.tableView.setSizeAdjustPolicy(QtWidgets.QAbstractScrollArea.AdjustToContentsOnFirstShow) self.tableView.setSelectionBehavior(QtWidgets.QAbstractItemView.SelectRows) self.tableView.setShowGrid(True) self.tableView.setSortingEnabled(False) self.tableView.setObjectName("tableView") self.tableView.verticalHeader().setVisible(False) self.tableView.verticalHeader().setCascadingSectionResizes(True) self.tableView.verticalHeader().setHighlightSections(False) self.verticalLayout_2.addWidget(self.tableView) self.dest_widget1 = QtWidgets.QWidget(self.splitter) sizePolicy = QtWidgets.QSizePolicy(QtWidgets.QSizePolicy.Preferred, QtWidgets.QSizePolicy.Expanding) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth(self.dest_widget1.sizePolicy().hasHeightForWidth()) self.dest_widget1.setSizePolicy(sizePolicy) self.dest_widget1.setObjectName("dest_widget1") self.verticalLayout_3 = QtWidgets.QVBoxLayout(self.dest_widget1) self.verticalLayout_3.setContentsMargins(0, 0, 0, 0) self.verticalLayout_3.setObjectName("verticalLayout_3") self.dest_widget = QtWidgets.QFrame(self.dest_widget1) self.dest_widget.setFrameShape(QtWidgets.QFrame.StyledPanel) self.dest_widget.setObjectName("dest_widget") self.verticalLayout_3.addWidget(self.dest_widget) self.verticalLayout.addWidget(self.splitter) self.horizontalLayout_3 = QtWidgets.QHBoxLayout() self.horizontalLayout_3.setObjectName("horizontalLayout_3") spacerItem5 = QtWidgets.QSpacerItem(40, 20, QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Minimum) self.horizontalLayout_3.addItem(spacerItem5) self.btnSend = QtWidgets.QPushButton(SendPayoutDlg) sizePolicy = QtWidgets.QSizePolicy(QtWidgets.QSizePolicy.Minimum, QtWidgets.QSizePolicy.Minimum) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth(self.btnSend.sizePolicy().hasHeightForWidth()) self.btnSend.setSizePolicy(sizePolicy) self.btnSend.setMinimumSize(QtCore.QSize(200, 0)) self.btnSend.setMaximumSize(QtCore.QSize(200, 16777215)) self.btnSend.setAutoDefault(False) self.btnSend.setObjectName("btnSend") self.horizontalLayout_3.addWidget(self.btnSend) spacerItem6 = QtWidgets.QSpacerItem(40, 20, QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Minimum) self.horizontalLayout_3.addItem(spacerItem6) self.btnClose = QtWidgets.QPushButton(SendPayoutDlg) sizePolicy = QtWidgets.QSizePolicy(QtWidgets.QSizePolicy.Maximum, QtWidgets.QSizePolicy.Fixed) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth(self.btnClose.sizePolicy().hasHeightForWidth()) self.btnClose.setSizePolicy(sizePolicy) self.btnClose.setMinimumSize(QtCore.QSize(0, 0)) self.btnClose.setLayoutDirection(QtCore.Qt.LeftToRight) self.btnClose.setAutoDefault(False) self.btnClose.setObjectName("btnClose") self.horizontalLayout_3.addWidget(self.btnClose, 0, QtCore.Qt.AlignRight) self.verticalLayout.addLayout(self.horizontalLayout_3) self.retranslateUi(SendPayoutDlg) self.sw_address_source.setCurrentIndex(2) QtCore.QMetaObject.connectSlotsByName(SendPayoutDlg) def retranslateUi(self, SendPayoutDlg): _translate = QtCore.QCoreApplication.translate SendPayoutDlg.setWindowTitle(_translate("SendPayoutDlg", "Dialog")) self.label_3.setText(_translate("SendPayoutDlg", "View as")) self.cbo_address_source_mode.setItemText(0, _translate("SendPayoutDlg", "Wallet Account")) self.cbo_address_source_mode.setItemText(1, _translate("SendPayoutDlg", "BIP32 Path")) self.cbo_address_source_mode.setItemText(2, _translate("SendPayoutDlg", "Ghostnode Address")) self.lbl_account.setText(_translate("SendPayoutDlg", "Account ")) self.btn_add_hw_account_nr.setToolTip(_translate("SendPayoutDlg", "Add new account number")) self.btn_add_hw_account_nr.setText(_translate("SendPayoutDlg", ".")) self.lbl_hw_account_base_path.setText(_translate("SendPayoutDlg", "...")) self.lblSourceBip32Path.setText(_translate("SendPayoutDlg", "BIP32 path")) self.btn_src_bip32_path.setToolTip(_translate("SendPayoutDlg", "Change BIP32 path")) self.btn_src_bip32_path.setText(_translate("SendPayoutDlg", "...")) self.lbl_src_masternode.setText(_translate("SendPayoutDlg", "Ghostnode")) self.btnLoadTransactions.setText(_translate("SendPayoutDlg", "Reload")) self.btnCheckAll.setText(_translate("SendPayoutDlg", "Select All")) self.btnUncheckAll.setText(_translate("SendPayoutDlg", "Unselect All")) self.chbHideCollateralTx.setText(_translate("SendPayoutDlg", "Hide collateral utxos")) self.lbl_message.setText(_translate("SendPayoutDlg", "....")) self.btnSend.setText(_translate("SendPayoutDlg", "Prepare Transaction")) self.btnClose.setText(_translate("SendPayoutDlg", "Close")) if __name__ == "__main__": import sys app = QtWidgets.QApplication(sys.argv) SendPayoutDlg = QtWidgets.QDialog() ui = Ui_SendPayoutDlg() ui.setupUi(SendPayoutDlg) SendPayoutDlg.show() sys.exit(app.exec_())	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29896, 29899, 29896, 29900, 13, 29937, 448, 29930, 29899, 14137, 29901, 23616, 29899, 29947, 448, 29930, 29899, 13, 13, 29937, 3812, 5314, 5759, 515, 5183, 14313, 934, 525, 1481, 29918, 6717, 29918, 29886, 1534, 29918, 11671, 29887, 29889, 1481, 29915, 13, 29937, 13, 29937, 6760, 630, 491, 29901, 10772, 17303, 29945, 3740, 775, 15299, 29871, 29945, 29889, 29929, 29889, 29906, 13, 29937, 13, 29937, 399, 25614, 29991, 2178, 3620, 1754, 297, 445, 934, 674, 367, 5714, 29991, 13, 13, 3166, 10772, 17303, 29945, 1053, 14705, 9203, 29892, 14705, 28707, 29892, 14705, 8801, 29879, 13, 13, 1990, 501, 29875, 29918, 12600, 29925, 1534, 29928, 19920, 29898, 3318, 1125, 13, 1678, 822, 6230, 29965, 29875, 29898, 1311, 29892, 15076, 29925, 1534, 29928, 19920, 1125, 13, 4706, 15076, 29925, 1534, 29928, 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telethon/tl/functions/stickers.py	polisitni1/DogeClickBot	0	2388	"""File generated by TLObjects' generator. All changes will be ERASED""" from ...tl.tlobject import TLRequest from typing import Optional, List, Union, TYPE_CHECKING import os import struct if TYPE_CHECKING: from ...tl.types import TypeInputStickerSet, TypeInputUser, TypeInputStickerSetItem, TypeInputDocument class AddStickerToSetRequest(TLRequest): CONSTRUCTOR_ID = 0x8653febe SUBCLASS_OF_ID = 0x9b704a5a def __init__(self, stickerset, sticker): """ :param TypeInputStickerSet stickerset: :param TypeInputStickerSetItem sticker: :returns messages.StickerSet: Instance of StickerSet. """ self.stickerset = stickerset # type: TypeInputStickerSet self.sticker = sticker # type: TypeInputStickerSetItem def to_dict(self): return { '_': 'AddStickerToSetRequest', 'stickerset': None if self.stickerset is None else self.stickerset.to_dict(), 'sticker': None if self.sticker is None else self.sticker.to_dict() } def __bytes__(self): return b''.join(( b'\xbe\xfeS\x86', bytes(self.stickerset), bytes(self.sticker), )) @classmethod def from_reader(cls, reader): _stickerset = reader.tgread_object() _sticker = reader.tgread_object() return cls(stickerset=_stickerset, sticker=_sticker) class ChangeStickerPositionRequest(TLRequest): CONSTRUCTOR_ID = 0xffb6d4ca SUBCLASS_OF_ID = 0x9b704a5a def __init__(self, sticker, position): """ :param TypeInputDocument sticker: :param int position: :returns messages.StickerSet: Instance of StickerSet. """ self.sticker = sticker # type: TypeInputDocument self.position = position # type: int def to_dict(self): return { '_': 'ChangeStickerPositionRequest', 'sticker': None if self.sticker is None else self.sticker.to_dict(), 'position': self.position } def __bytes__(self): return b''.join(( b'\xca\xd4\xb6\xff', bytes(self.sticker), struct.pack('<i', self.position), )) @classmethod def from_reader(cls, reader): _sticker = reader.tgread_object() _position = reader.read_int() return cls(sticker=_sticker, position=_position) class CreateStickerSetRequest(TLRequest): CONSTRUCTOR_ID = 0x9bd86e6a SUBCLASS_OF_ID = 0x9b704a5a def __init__(self, user_id, title, short_name, stickers, masks=None): """ :param TypeInputUser user_id: :param str title: :param str short_name: :param List[TypeInputStickerSetItem] stickers: :param Optional[bool] masks: :returns messages.StickerSet: Instance of StickerSet. """ self.user_id = user_id # type: TypeInputUser self.title = title # type: str self.short_name = short_name # type: str self.stickers = stickers # type: List[TypeInputStickerSetItem] self.masks = masks # type: Optional[bool] async def resolve(self, client, utils): self.user_id = utils.get_input_user(await client.get_input_entity(self.user_id)) def to_dict(self): return { '_': 'CreateStickerSetRequest', 'user_id': None if self.user_id is None else self.user_id.to_dict(), 'title': self.title, 'short_name': self.short_name, 'stickers': [] if self.stickers is None else [None if x is None else x.to_dict() for x in self.stickers], 'masks': self.masks } def __bytes__(self): return b''.join(( b'jn\xd8\x9b', struct.pack('<I', (0 if self.masks is None or self.masks is False else 1)), bytes(self.user_id), self.serialize_bytes(self.title), self.serialize_bytes(self.short_name), b'\x15\xc4\xb5\x1c',struct.pack('<i', len(self.stickers)),b''.join(bytes(x) for x in self.stickers), )) @classmethod def from_reader(cls, reader): flags = reader.read_int() _masks = bool(flags & 1) _user_id = reader.tgread_object() _title = reader.tgread_string() _short_name = reader.tgread_string() reader.read_int() _stickers = [] for _ in range(reader.read_int()): _x = reader.tgread_object() _stickers.append(_x) return cls(user_id=_user_id, title=_title, short_name=_short_name, stickers=_stickers, masks=_masks) class RemoveStickerFromSetRequest(TLRequest): CONSTRUCTOR_ID = 0xf7760f51 SUBCLASS_OF_ID = 0x9b704a5a def __init__(self, sticker): """ :param TypeInputDocument sticker: :returns messages.StickerSet: Instance of StickerSet. 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PCI_o_B/SharedFunctions.py	MatteoMilani95/PCI_o_Bpy	1	159935	import numpy as np import matplotlib.pyplot as plt import math from pynverse import inversefunc from IPython import get_ipython get_ipython().magic('reset -sf') import pandas as pd from scipy.optimize import leastsq, least_squares, curve_fit import os from scipy import interpolate import scipy.integrate as integrate def theta1_func(H_value,R,n1,n2): if n1>n2: tc=np.arcsin(n2/n1) H=lambda theta1 :Rnp.sin(theta1)/np.cos(np.arcsin(n1/n2np.sin(theta1))-theta1)1/(1-np.tan(np.arcsin(n1/n2np.sin(theta1))-theta1)/np.tan(np.arcsin(n1/n2np.sin(theta1)))) theta=inversefunc(H,y_values=H_value,domain=[-tc, tc]) if H_value>=0: h=Rnp.sin(theta)/np.cos(np.arcsin(n1/n2np.sin(theta))-theta) theta_scattering=np.arcsin(Rnp.sin(theta)/h) else: h=Rnp.sin(theta)/np.cos(np.arcsin(n1/n2np.sin(theta))-theta) theta_scattering=math.pi-np.arcsin(Rnp.sin(theta)/h) else: tc=np.arcsin(n1/n2) H=lambda theta1 :Rnp.sin(theta1)/np.cos(np.arcsin(n1/n2np.sin(theta1))-theta1)1/(1+np.tan(np.arcsin(n1/n2np.sin(theta1))-theta1)/np.tan(np.arcsin(n1/n2np.sin(theta1)))) theta=inversefunc(H,y_values=H_value,domain=[-tc, tc]) if H_value<=0: h=Rnp.sin(theta)/np.cos(np.arcsin(n1/n2np.sin(theta))-theta) theta_scattering=np.arcsin(Rnp.sin(theta)/h) else: h=Rnp.sin(theta)/np.cos(np.arcsin(n1/n2np.sin(theta))-theta) theta_scattering=math.pi-np.arcsin(Rnp.sin(theta)/h) return h,theta_scattering def SingExp(x, amp, decay, baseline ): """Model a decaying sine wave and subtract data.""" model = (amp * np.exp(-x/decay))*2 + baseline return model def DoubleExp(x, amp1, decay1, amp2, decay2, baseline ): """Model a decaying sine wave and subtract data.""" model = (amp1 np.exp(-x/decay1) + amp2 * np.exp(-x/decay2))*2 + baseline return model def DoubleStretchExp(x, amp1, decay1, amp2, decay2, baseline, beta,gamma ): """Model a decaying sine wave and subtract data.""" model = (amp1 np.exp(-x/decay1))*(2beta) + (amp2 * np.exp(-x/decay2))*(2gamma) + baseline return model def SingleStretchExp(x, amp1, decay1, baseline, beta): """Model a decaying sine wave and subtract data.""" model = amp1 * np.exp(-(x/decay1)) *beta + baseline return model def TripleExp(x, amp1, decay1, amp2, decay2, amp3, decay3, baseline ): """Model a decaying sine wave and subtract data.""" model = (amp1 np.exp(-x/decay1) + amp2 * np.exp(-x/decay2) + amp3 * np.exp(-x/decay3) )*2 + baseline return model def StretchExp(x, amp, decay, baseline, beta ): """Model a decaying sine wave and subtract data.""" model = (amp np.exp(-x / decay))*(2beta) + baseline return model def FromTautoTheta(tau,tau_err,T,R_particle,wavelength,nu,n): kb=1.3806485210-23 D = kbT/(6math.pinu(R_particle10*-9)) theta = 2 np.arcsin( (1/(Dtau))0.5wavelength/(4math.pin) ) 360/(2math.pi) theta_err = 2 * 1 / ( 1- ( wavelength / (4 * n * math.pi * 1 / ( D * tau )0.5 ) )2 )*0.5 wavelength / (8 * n * math.pi) * 1 / D*0.5 tau*-1.5 tau_err 360/(2math.pi) return D, theta, theta_err def SFinterpolation(x,y): f = interpolate.interp1d(x, y) return f def SFintegration(x,y,x0,xmax): f = interpolate.interp1d(x, y) I = integrate.quad(f , x0, xmax) return I def AsymmetryCalculator(func): ass = [] for i in range(int(len(func)/2)): ass.append( ( np.abs(func[i] - func[-i]) / 2 ) / np.mean(np.asarray(func)) ) asymmerty = np.sum(np.asarray(ass)) return asymmerty def truncate(n, decimals=0): multiplier = 10 ** decimals return int(n * multiplier) / multiplier	[ 1, 6756, 13, 5215, 12655, 408, 7442, 30004, 13, 5215, 22889, 29889, 2272, 5317, 408, 14770, 30004, 13, 5215, 5844, 30004, 13, 3166, 282, 948, 3901, 1053, 16402, 9891, 30004, 13, 3166, 5641, 1656, 1053, 679, 29918, 666, 1656, 30004, 13, 657, 29918, 666, 1656, 2141, 11082, 293, 877, 12071, 448, 4668, 1495, 30004, 13, 5215, 11701, 408, 10518, 30004, 13, 3166, 4560, 2272, 29889, 20640, 675, 1053, 3203, 3044, 29892, 3203, 29918, 26613, 5114, 29892, 11672, 29918, 9202, 30004, 13, 5215, 2897, 30004, 13, 3166, 4560, 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test/pynrn/test_units.py	shhong/nrn	1	146837	<gh_stars>1-10 from neuron import h def switch_units(legacy): try: h.nrnunit_use_legacy(legacy) except: pass def test_mod_legacy(): s = h.Section() ut = h.UnitsTest(s(.5)) h.ion_style("na_ion", 1, 2, 1, 1, 0, sec=s) switch_units(1) h.finitialize() names = ["mole", "e", "faraday", "planck", "hbar", "gasconst"] legacy_hex_values = ["0x1.fe18fef60659ap+78", "0x1.7a4e7164efbbcp-63", "0x1.78e54cccccccdp+16", "0x1.b85f8c5445f02p-111", "0x1.18779454e3d48p-113", "0x1.0a10624dd2f1bp+3"] for i, n in enumerate(names): val = eval("ut." + n) print ("%s = %s (%s)" %(n, str(val.hex()), str(val))) legacy_value = float.fromhex(legacy_hex_values[i]) assert (val == legacy_value) assert (ut.avogadro == float.fromhex(legacy_hex_values[0])) ghk_std = h.ghk(-50, .001, 10, 2) erev_std = h.nernst(s(.5).nai, s(.5).nao, 1) assert (ut.ghk == ghk_std) assert (ut.erev == erev_std) switch_units(0) h.finitialize() ghk_std = h.ghk(-50, .001, 10, 2) erev_std = h.nernst(s(.5).nai, s(.5).nao, 1) assert (ut.mole != float.fromhex(legacy_hex_values[0])) assert (ut.eut.avogadro == ut.faraday) assert (abs(ut.faraday - h.FARADAY) < 1e-10) assert (ut.gasconst == h.R) assert (ut.kut.avogadro == ut.gasconst) assert (abs(ut.planck - ut.hbar2.0h.PI) < 1e-49) assert (ut.avogadro == h.Avogadro_constant) assert (ut.ghk == h.ghk(-50, .001, 10, 2)) assert (ut.erev == h.nernst(s(.5).nai, s(.5).nao, 1)) def test_hoc_legacy(): switch_units(1) # legacy print ("R = %s" % str(h.R)) print ("FARADAY = %s" % str(h.FARADAY)) celsius = 6.3 ghk = h.ghk(30, .01, 10, 1) # nernst requires a Section to get voltage print ("ghk = %s" % str(ghk)) assert(h.R == 8.31441) assert(h.FARADAY == 96485.309) assert(ghk == -483.7914803097116) switch_units(0) # Modern print ("R = %s" % str(h.R)) print ("FARADAY = %s" % str(h.FARADAY)) ghk = h.ghk(30, .01, 10, 1) print ("ghk = %s" % str(ghk)) assert(h.R == 8.31446261815324) assert(ghk == -483.8380971405879) def test_env_legacy(): for i in [0,1]: a = None try: # test NRNUNIT_USE_LEGACY not whether we can successfully run a subprocess import sys, subprocess a = "NRNUNIT_USE_LEGACY=%d %s -c 'from neuron import h; print (h.nrnunit_use_legacy())'"%(i, sys.executable) a = subprocess.check_output(a, shell=True) a = int(float(a.decode().split()[0])) except: pass assert(a == i) if __name__ == "__main__": test_mod_legacy() test_hoc_legacy() test_env_legacy()	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29896, 29899, 29896, 29900, 13, 3166, 26808, 265, 1053, 298, 13, 13, 1753, 4607, 29918, 348, 1169, 29898, 1397, 4135, 1125, 13, 29871, 1018, 29901, 13, 1678, 298, 29889, 22230, 29876, 5441, 29918, 1509, 29918, 1397, 4135, 29898, 1397, 4135, 29897, 13, 29871, 5174, 29901, 13, 1678, 1209, 13, 13, 1753, 1243, 29918, 1545, 29918, 1397, 4135, 7295, 13, 29871, 269, 353, 298, 29889, 13438, 580, 13, 29871, 3477, 353, 298, 29889, 2525, 1169, 3057, 29898, 29879, 11891, 29945, 876, 13, 29871, 298, 29889, 291, 29918, 3293, 703, 1056, 29918, 291, 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knet/views/__init__.py	aethersoft/metaview	1	1610602	from typing import Text from flask import Blueprint, render_template from knet.config import CONFIG bp = Blueprint('views', __name__) @bp.route('/') def home(): return render_template('index.html', title=CONFIG['APP_TITLE'], page='Home') @bp.route('/<page>') def pages(page: Text): return render_template('index.html', title=CONFIG['APP_TITLE'], page=page.capitalize())	[ 1, 515, 19229, 1053, 3992, 13, 13, 3166, 29784, 1053, 10924, 2158, 29892, 4050, 29918, 6886, 13, 13, 3166, 889, 300, 29889, 2917, 1053, 8707, 18667, 13, 13, 25288, 353, 10924, 2158, 877, 7406, 742, 4770, 978, 1649, 29897, 13, 13, 13, 29992, 25288, 29889, 13134, 11219, 1495, 13, 1753, 3271, 7295, 13, 1678, 736, 4050, 29918, 6886, 877, 2248, 29889, 1420, 742, 3611, 29922, 25903, 1839, 20576, 29918, 29911, 1806, 1307, 7464, 1813, 2433, 11184, 1495, 13, 13, 13, 29992, 25288, 29889, 13134, 11219, 29966, 3488, 29958, 1495, 13, 1753, 6515, 29898, 3488, 29901, 3992, 1125, 13, 1678, 736, 4050, 29918, 6886, 877, 2248, 29889, 1420, 742, 3611, 29922, 25903, 1839, 20576, 29918, 29911, 1806, 1307, 7464, 1813, 29922, 3488, 29889, 5030, 2410, 675, 3101, 13, 2 ]
api/login_request.py	dyphen12/full-stack-login	0	1605627	import database as datb #### REQUESTS ##### def login(user,passw): auth, ssid = datb.user_validation(user,passw) return auth, ssid	[ 1, 1053, 2566, 408, 1418, 29890, 13, 13, 4136, 5195, 14130, 29903, 16101, 13, 13, 1753, 6464, 29898, 1792, 29892, 3364, 29893, 1125, 13, 13, 1678, 4817, 29892, 17971, 333, 353, 1418, 29890, 29889, 1792, 29918, 18157, 29898, 1792, 29892, 3364, 29893, 29897, 13, 13, 1678, 736, 4817, 29892, 17971, 333, 13, 2 ]
setup.py	cr3/lookuper	0	189537	<filename>setup.py from setuptools import ( setup, find_packages, ) setup( name='lookuper', use_scm_version=True, description='Lookup nested data structures', long_description=open('README.rst').read(), url='https://github.com/cr3/lookuper', author='<NAME>', author_email='<EMAIL>', setup_requires=['setuptools_scm'], packages=find_packages(where='src'), package_dir={'': 'src'}, license='MIT', keywords='lookup nested', classifiers=[ 'Development Status :: 4 - Beta', 'Intended Audience :: Developers', 'License :: OSI Approved :: MIT License', 'Operating System :: OS Independent', 'Programming Language :: Python', 'Programming Language :: Python :: 3', 'Programming Language :: Python :: 3.5', 'Programming Language :: Python :: 3.6', 'Programming Language :: Python :: 3.7', 'Programming Language :: Python :: 3.8', 'Programming Language :: Python :: Implementation :: CPython', 'Programming Language :: Python :: Implementation :: PyPy', 'Topic :: Software Development :: Libraries :: Python Modules', ], )	[ 1, 529, 9507, 29958, 14669, 29889, 2272, 13, 3166, 731, 21245, 8789, 1053, 313, 13, 1678, 6230, 29892, 13, 1678, 1284, 29918, 8318, 29892, 13, 29897, 13, 13, 13, 14669, 29898, 13, 1678, 1024, 2433, 6914, 14805, 742, 13, 1678, 671, 29918, 1557, 29885, 29918, 3259, 29922, 5574, 29892, 13, 1678, 6139, 2433, 14959, 786, 9322, 848, 12286, 742, 13, 1678, 1472, 29918, 8216, 29922, 3150, 877, 16310, 2303, 29889, 29878, 303, 2824, 949, 3285, 13, 1678, 3142, 2433, 991, 597, 3292, 29889, 510, 29914, 7283, 29941, 29914, 6914, 14805, 742, 13, 1678, 4148, 2433, 29966, 5813, 29958, 742, 13, 1678, 4148, 29918, 5269, 2433, 29966, 26862, 6227, 29958, 742, 13, 1678, 6230, 29918, 276, 339, 2658, 29922, 1839, 842, 21245, 8789, 29918, 1557, 29885, 7464, 13, 1678, 9741, 29922, 2886, 29918, 8318, 29898, 3062, 2433, 4351, 5477, 13, 1678, 3577, 29918, 3972, 3790, 29915, 2396, 525, 4351, 16675, 13, 1678, 19405, 2433, 26349, 742, 13, 1678, 29361, 2433, 20401, 9322, 742, 13, 1678, 770, 14903, 11759, 13, 4706, 525, 21956, 358, 16034, 4761, 29871, 29946, 448, 350, 1187, 742, 13, 4706, 525, 2928, 2760, 319, 4749, 663, 4761, 10682, 414, 742, 13, 4706, 525, 29931, 293, 1947, 4761, 438, 5425, 28268, 1490, 4761, 341, 1806, 19245, 742, 13, 4706, 525, 7094, 1218, 2184, 4761, 6570, 25266, 742, 13, 4706, 525, 9283, 4056, 17088, 4761, 5132, 742, 13, 4706, 525, 9283, 4056, 17088, 4761, 5132, 4761, 29871, 29941, 742, 13, 4706, 525, 9283, 4056, 17088, 4761, 5132, 4761, 29871, 29941, 29889, 29945, 742, 13, 4706, 525, 9283, 4056, 17088, 4761, 5132, 4761, 29871, 29941, 29889, 29953, 742, 13, 4706, 525, 9283, 4056, 17088, 4761, 5132, 4761, 29871, 29941, 29889, 29955, 742, 13, 4706, 525, 9283, 4056, 17088, 4761, 5132, 4761, 29871, 29941, 29889, 29947, 742, 13, 4706, 525, 9283, 4056, 17088, 4761, 5132, 4761, 1954, 14607, 4761, 315, 11980, 742, 13, 4706, 525, 9283, 4056, 17088, 4761, 5132, 4761, 1954, 14607, 4761, 10772, 19737, 742, 13, 4706, 525, 7031, 293, 4761, 18540, 14650, 4761, 365, 4626, 4314, 4761, 5132, 3382, 2540, 742, 13, 1678, 21251, 13, 29897, 13, 2 ]
client/base_client.py	renatopp/liac-chess	6	138575	<gh_stars>1-10 # ============================================================================= # Federal University of Rio Grande do Sul (UFRGS) # Connectionist Artificial Intelligence Laboratory (LIAC) # <NAME> - <EMAIL> # ============================================================================= # Copyright (c) 2011 <NAME>, renato.ppontes at gmail dot com # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in # all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. # ============================================================================= '''This is the base client for LIAC CHESS. Use `LiacBot` as your base class to create a new bot. ''' import sys import json import time import socket import random class LiacBot(object): '''LiacBot implements a basic client for LIAC CHESS. LiacBot encapsulates the basic features to communicate with the LIAC CHESS server, such as serialization and deserialization of json messages, connection handshaking, etc. Use this class as a base implementation for your bots. ''' name = '' ip = '127.0.0.1' port = 50100 def __init__(self): '''Constructor.''' self._socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM) if not self.name: self.name = random.choice([ 'Liacnator', '<NAME>', 'Liaczors', 'Liaco' ]) # INTERNAL METHODS ======================================================== def _connect(self): '''(INTERNAL) Connects to the server.''' self._socket.connect((self.ip, self.port)) def _send_data(self, data): '''(INTERNAL) Serialize a ``data`` object and sends it to the server. :param data: a Python object. ''' d = json.dumps(data) self._socket.sendall(d) def _receive_data(self): '''(INTERNAL) Receives a message from server and deserialize it. :return: a Python object. ''' data = self._socket.recv(2**12) return json.loads(data) def _send_name(self): '''(INTERNAL) Sends the bot's name to the server as part of the handshaking procedure. ''' self._send_data({ 'name':self.name }) def _receive_state(self): '''(INTERNAL) Handle a state message.''' state = self._receive_data() if state['winner'] != 0 or state['draw']: self.on_game_over(state) else: self.on_move(state) # ========================================================================= # INTERFACE =============================================================== def send_move(self, from_, to_): '''Sends a movement to the server. :param from_: a 2-tuple with the piece-to-move position. :param to_: a 2-tuple with the target position. ''' self._send_data({ 'from': from_, 'to': to_ }) def on_move(self, state): '''Receives the state from server, when the server asks for a movement. Consult the documentation see which information comes within the `state` object. :param state: a state object. ''' pass def on_game_over(self, state): '''Receives the state from server, when the server acknowledges a winner for the game. Consult the documentation see which information comes within the `state` object. :param state: a state object. ''' pass def start(self): '''Starts the bot.''' self._connect() self._send_name() while True: self._receive_state() # ========================================================================= if __name__ == '__main__': bot = LiacBot() bot.start()	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29896, 29899, 29896, 29900, 13, 29937, 1275, 9166, 9166, 9166, 9166, 4936, 25512, 13, 29937, 14879, 3014, 310, 11611, 12848, 437, 13358, 313, 29965, 15860, 10749, 29897, 13, 29937, 15160, 391, 3012, 928, 616, 3159, 28286, 16715, 7606, 313, 5265, 2477, 29897, 13, 29937, 529, 5813, 29958, 448, 529, 26862, 6227, 29958, 13, 29937, 1275, 9166, 9166, 9166, 9166, 4936, 25512, 13, 29937, 14187, 1266, 313, 29883, 29897, 29871, 29906, 29900, 29896, 29896, 529, 5813, 10202, 4325, 1219, 29889, 407, 609, 267, 472, 330, 2549, 8329, 419, 13, 29937, 29871, 13, 29937, 20894, 2333, 338, 1244, 1609, 16896, 29892, 3889, 310, 8323, 29892, 304, 738, 2022, 4017, 292, 263, 3509, 29871, 13, 29937, 310, 445, 7047, 322, 6942, 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nodevectors/evaluation/__init__.py	tboser/nodevectors	0	61551	<reponame>tboser/nodevectors from nodevectors.evaluation.graph_eval import * from nodevectors.evaluation.link_pred import *	[ 1, 529, 276, 1112, 420, 29958, 22625, 22969, 29914, 3177, 345, 14359, 13, 3166, 2943, 345, 14359, 29889, 24219, 362, 29889, 4262, 29918, 14513, 1053, 334, 13, 3166, 2943, 345, 14359, 29889, 24219, 362, 29889, 2324, 29918, 11965, 1053, 334, 13, 13, 2 ]
reg_analysis.py	prakharchoudhary/Tale-O-Regression	2	162941	""" Train the regressor and plot the results """ import os import numpy as np from matplotlib import pyplot as plt from sklearn.preprocessing import StandardScaler from linear_model import LinearRegressionGD def train_reg(df): X = df[['RM']].values y = df['MEDV'].values sc_x = StandardScaler() sc_y = StandardScaler() X_std = sc_x.fit_transform(X) y_std = sc_y.fit_transform(y[:, np.newaxis]).flatten() lr = LinearRegressionGD() lr.fit(X_std, y_std) return lr def cost_against_epochs(lr): plt.plot(range(1, lr.n_iter+1), lr.cost_) plt.ylabel('SSE') plt.xlabel('Epoch') # plt.show() plt.savefig('./figures/cost-against-epoch.png') plt.gcf().clear() def lin_regplot(df, model): X = df[['RM']].values y = df['MEDV'].values sc_x = StandardScaler() sc_y = StandardScaler() X_std = sc_x.fit_transform(X) y_std = sc_y.fit_transform(y[:, np.newaxis]).flatten() plt.scatter(X_std, y_std, c='blue') plt.plot(X_std, model.predict(X_std), color='red') plt.xlabel('Average number of rooms [RM] (standardized)') plt.ylabel('Price in $1000\'s [MEDV] (standardized)') # plt.show() if not os.path.exists(os.path.join(os.getcwd(), 'figures')): os.mkdir('figures') plt.savefig('./figures/plotting-linear-reg.png')	[ 1, 9995, 13, 5323, 262, 278, 337, 3663, 272, 322, 6492, 278, 2582, 13, 15945, 29908, 13, 5215, 2897, 13, 5215, 12655, 408, 7442, 13, 3166, 22889, 1053, 11451, 5317, 408, 14770, 13, 3166, 2071, 19668, 29889, 1457, 19170, 1053, 10117, 29636, 261, 13, 3166, 5608, 29918, 4299, 1053, 22985, 4597, 23881, 29954, 29928, 13, 13, 1753, 7945, 29918, 1727, 29898, 2176, 1125, 13, 12, 29990, 353, 4489, 29961, 1839, 29934, 29924, 2033, 1822, 5975, 13, 12, 29891, 353, 4489, 1839, 2303, 29928, 29963, 13359, 5975, 13, 12, 1557, 29918, 29916, 353, 10117, 29636, 261, 580, 13, 12, 1557, 29918, 29891, 353, 10117, 29636, 261, 580, 13, 12, 29990, 29918, 4172, 353, 885, 29918, 29916, 29889, 9202, 29918, 9067, 29898, 29990, 29897, 13, 12, 29891, 29918, 4172, 353, 885, 29918, 29891, 29889, 9202, 29918, 9067, 29898, 29891, 7503, 29892, 7442, 29889, 1482, 8990, 14664, 1579, 8606, 580, 13, 12, 29212, 353, 22985, 4597, 23881, 29954, 29928, 580, 13, 12, 29212, 29889, 9202, 29898, 29990, 29918, 4172, 29892, 343, 29918, 4172, 29897, 13, 12, 2457, 301, 29878, 13, 13, 1753, 3438, 29918, 351, 475, 303, 29918, 1022, 2878, 29879, 29898, 29212, 1125, 13, 12, 572, 29873, 29889, 5317, 29898, 3881, 29898, 29896, 29892, 301, 29878, 29889, 29876, 29918, 1524, 29974, 29896, 511, 301, 29878, 29889, 18253, 19925, 13, 12, 572, 29873, 29889, 29891, 1643, 877, 29903, 1660, 1495, 13, 12, 572, 29873, 29889, 29916, 1643, 877, 29923, 1129, 305, 1495, 13, 12, 29937, 14770, 29889, 4294, 580, 13, 12, 572, 29873, 29889, 7620, 1003, 877, 6904, 1003, 1973, 29914, 18253, 29899, 351, 475, 303, 29899, 1022, 2878, 29889, 2732, 1495, 13, 12, 572, 29873, 29889, 29887, 6854, 2141, 8551, 580, 13, 13, 1753, 6276, 29918, 1727, 5317, 29898, 2176, 29892, 1904, 1125, 13, 12, 29990, 353, 4489, 29961, 1839, 29934, 29924, 2033, 1822, 5975, 13, 12, 29891, 353, 4489, 1839, 2303, 29928, 29963, 13359, 5975, 13, 12, 1557, 29918, 29916, 353, 10117, 29636, 261, 580, 13, 12, 1557, 29918, 29891, 353, 10117, 29636, 261, 580, 13, 12, 29990, 29918, 4172, 353, 885, 29918, 29916, 29889, 9202, 29918, 9067, 29898, 29990, 29897, 13, 12, 29891, 29918, 4172, 353, 885, 29918, 29891, 29889, 9202, 29918, 9067, 29898, 29891, 7503, 29892, 7442, 29889, 1482, 8990, 14664, 1579, 8606, 580, 13, 13, 12, 572, 29873, 29889, 1557, 2620, 29898, 29990, 29918, 4172, 29892, 343, 29918, 4172, 29892, 274, 2433, 9539, 1495, 13, 12, 572, 29873, 29889, 5317, 29898, 29990, 29918, 4172, 29892, 1904, 29889, 27711, 29898, 29990, 29918, 4172, 511, 2927, 2433, 1127, 1495, 13, 12, 572, 29873, 29889, 29916, 1643, 877, 29909, 19698, 1353, 310, 19600, 518, 29934, 29924, 29962, 313, 15770, 1891, 29897, 1495, 13, 12, 572, 29873, 29889, 29891, 1643, 877, 13026, 297, 395, 29896, 29900, 29900, 29900, 20333, 29879, 518, 2303, 29928, 29963, 29962, 313, 15770, 1891, 29897, 1495, 13, 12, 29937, 14770, 29889, 4294, 580, 13, 13, 12, 361, 451, 2897, 29889, 2084, 29889, 9933, 29898, 359, 29889, 2084, 29889, 7122, 29898, 359, 29889, 657, 29883, 9970, 3285, 525, 1003, 1973, 8785, 29901, 13, 12, 12, 359, 29889, 11256, 3972, 877, 1003, 1973, 1495, 13, 13, 12, 572, 29873, 29889, 7620, 1003, 877, 6904, 1003, 1973, 29914, 5317, 1259, 29899, 10660, 29899, 1727, 29889, 2732, 1495, 2 ]
main_app/migrations/0006_auto_20210905_0013.py	boyoon-c/readuck-api	0	1600682	# Generated by Django 3.2.6 on 2021-09-05 00:13 from django.conf import settings from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ migrations.swappable_dependency(settings.AUTH_USER_MODEL), ('main_app', '0005_rename_article_group_articles'), ] operations = [ migrations.AlterField( model_name='group', name='articles', field=models.ManyToManyField(blank=True, to='main_app.Article'), ), migrations.AlterField( model_name='group', name='participants', field=models.ManyToManyField(blank=True, to=settings.AUTH_USER_MODEL), ), ]	[ 1, 396, 3251, 630, 491, 15337, 29871, 29941, 29889, 29906, 29889, 29953, 373, 29871, 29906, 29900, 29906, 29896, 29899, 29900, 29929, 29899, 29900, 29945, 29871, 29900, 29900, 29901, 29896, 29941, 13, 13, 3166, 9557, 29889, 5527, 1053, 6055, 13, 3166, 9557, 29889, 2585, 1053, 9725, 800, 29892, 4733, 13, 13, 13, 1990, 341, 16783, 29898, 26983, 800, 29889, 29924, 16783, 1125, 13, 13, 1678, 9962, 353, 518, 13, 4706, 9725, 800, 29889, 2774, 932, 519, 29918, 10836, 29898, 11027, 29889, 20656, 29950, 29918, 11889, 29918, 20387, 29931, 511, 13, 4706, 6702, 3396, 29918, 932, 742, 525, 29900, 29900, 29900, 29945, 29918, 1267, 420, 29918, 7914, 29918, 2972, 29918, 18569, 5477, 13, 1678, 4514, 13, 13, 1678, 6931, 353, 518, 13, 4706, 9725, 800, 29889, 2499, 357, 3073, 29898, 13, 9651, 1904, 29918, 978, 2433, 2972, 742, 13, 9651, 1024, 2433, 18569, 742, 13, 9651, 1746, 29922, 9794, 29889, 14804, 1762, 14804, 3073, 29898, 19465, 29922, 5574, 29892, 304, 2433, 3396, 29918, 932, 29889, 9986, 2512, 5477, 13, 4706, 10353, 13, 4706, 9725, 800, 29889, 2499, 357, 3073, 29898, 13, 9651, 1904, 29918, 978, 2433, 2972, 742, 13, 9651, 1024, 2433, 1595, 12654, 1934, 742, 13, 9651, 1746, 29922, 9794, 29889, 14804, 1762, 14804, 3073, 29898, 19465, 29922, 5574, 29892, 304, 29922, 11027, 29889, 20656, 29950, 29918, 11889, 29918, 20387, 29931, 511, 13, 4706, 10353, 13, 1678, 4514, 13, 2 ]
beer_carousel/migrations/0003_auto_20180610_1258.py	GeniALE/SiteWebGeniALE	4	180081	# -- coding: utf-8 -- # Generated by Django 1.11 on 2018-06-10 16:58 from __future__ import unicode_literals from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ('beer_carousel', '0002_auto_20180605_1010'), ] operations = [ migrations.CreateModel( name='BeerContainer', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('name', models.CharField(max_length=100)), ('css_class', models.CharField(max_length=100)), ], ), migrations.AddField( model_name='beermodel', name='beer_container', field=models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, to='beer_carousel.BeerContainer'), ), ]	[ 1, 396, 448, 29930, 29899, 14137, 29901, 23616, 29899, 29947, 448, 29930, 29899, 13, 29937, 3251, 630, 491, 15337, 29871, 29896, 29889, 29896, 29896, 373, 29871, 29906, 29900, 29896, 29947, 29899, 29900, 29953, 29899, 29896, 29900, 29871, 29896, 29953, 29901, 29945, 29947, 13, 3166, 4770, 29888, 9130, 1649, 1053, 29104, 29918, 20889, 1338, 13, 13, 3166, 9557, 29889, 2585, 1053, 9725, 800, 29892, 4733, 13, 5215, 9557, 29889, 2585, 29889, 9794, 29889, 311, 1026, 291, 13, 13, 13, 1990, 341, 16783, 29898, 26983, 800, 29889, 29924, 16783, 1125, 13, 29871, 9962, 353, 518, 13, 1678, 6702, 915, 261, 29918, 4287, 21299, 742, 525, 29900, 29900, 29900, 29906, 29918, 6921, 29918, 29906, 29900, 29896, 29947, 29900, 29953, 29900, 29945, 29918, 29896, 29900, 29896, 29900, 5477, 13, 29871, 4514, 13, 13, 29871, 6931, 353, 518, 13, 1678, 9725, 800, 29889, 4391, 3195, 29898, 13, 418, 1024, 2433, 3629, 261, 7895, 742, 13, 418, 4235, 11759, 13, 4706, 6702, 333, 742, 4733, 29889, 12300, 3073, 29898, 6921, 29918, 11600, 29922, 5574, 29892, 7601, 29918, 1989, 29922, 5574, 29892, 28755, 29922, 8824, 29892, 26952, 29918, 978, 2433, 1367, 1495, 511, 13, 4706, 6702, 978, 742, 4733, 29889, 27890, 29898, 3317, 29918, 2848, 29922, 29896, 29900, 29900, 8243, 13, 4706, 6702, 4268, 29918, 1990, 742, 4733, 29889, 27890, 29898, 3317, 29918, 2848, 29922, 29896, 29900, 29900, 8243, 13, 418, 21251, 13, 1678, 10353, 13, 1678, 9725, 800, 29889, 2528, 3073, 29898, 13, 418, 1904, 29918, 978, 2433, 915, 837, 27224, 742, 13, 418, 1024, 2433, 915, 261, 29918, 7611, 742, 13, 418, 1746, 29922, 9794, 29889, 27755, 2558, 29898, 4304, 29922, 5574, 29892, 373, 29918, 8143, 29922, 14095, 29889, 2585, 29889, 9794, 29889, 311, 1026, 291, 29889, 10490, 29918, 10074, 29892, 13, 462, 795, 304, 2433, 915, 261, 29918, 4287, 21299, 29889, 3629, 261, 7895, 5477, 13, 1678, 10353, 13, 29871, 4514, 13, 2 ]
kite-python/kite_ml/kite/name_encoder/scope_encoder.py	kiteco/kiteco-public	17	36213	<gh_stars>10-100 from typing import Dict, Any import tensorflow as tf from ..utils.segmented_data import SegmentedIndices, SegmentedIndicesFeed from ..graph_encoder.embeddings import NodeEmbeddings class Encoder(object): def __init__(self, nodes: NodeEmbeddings): self._nodes = nodes self._build() def _build(self): self._build_placeholders() self._build_scope_state() def _build_placeholders(self): with tf.name_scope('placeholders'): # shape [number of variables in batch] # sample_ids[i] = s means that means that variable i is part of sample s in the batch self._variable_node_ids = SegmentedIndices('variable_node_ids') def _build_scope_state(self): with tf.name_scope('build_scope_state'): # [num variable nodes in batch] self._variable_nodes_embedded: tf.Tensor = tf.gather( self._nodes.embeddings, self._variable_node_ids.indices, name='scope_nodes_embedded', ) # reduce across variable nodes in each graph in the batch # shape [batch size, graph embedding depth] self._scope_state: tf.Tensor = tf.segment_max( self._variable_nodes_embedded, self._variable_node_ids.sample_ids, name='scope_state', ) def feed_dict(self, feed: SegmentedIndicesFeed) -> Dict[tf.Tensor, Any]: return self._variable_node_ids.feed_dict(feed) def placeholders_dict(self) -> Dict[str, tf.Tensor]: return self._variable_node_ids.dict() def scope_state(self) -> tf.Tensor: """ :return: representation of all the variables in scope, shape [batch size, graph embedding depth] """ return self._scope_state	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29896, 29900, 29899, 29896, 29900, 29900, 13, 3166, 19229, 1053, 360, 919, 29892, 3139, 13, 13, 5215, 26110, 408, 15886, 13, 13, 3166, 6317, 13239, 29889, 28192, 287, 29918, 1272, 1053, 6667, 358, 287, 2568, 1575, 29892, 6667, 358, 287, 2568, 1575, 29737, 13, 13, 3166, 6317, 4262, 29918, 3977, 6119, 29889, 17987, 29881, 886, 1053, 9071, 6026, 2580, 29881, 886, 13, 13, 13, 1990, 11346, 6119, 29898, 3318, 1125, 13, 1678, 822, 4770, 2344, 12035, 1311, 29892, 7573, 29901, 9071, 6026, 2580, 29881, 886, 1125, 13, 4706, 1583, 3032, 18010, 353, 7573, 13, 4706, 1583, 3032, 4282, 580, 13, 13, 1678, 822, 903, 4282, 29898, 1311, 1125, 13, 4706, 1583, 3032, 4282, 29918, 6689, 8948, 414, 580, 13, 4706, 1583, 3032, 4282, 29918, 6078, 29918, 3859, 580, 13, 13, 1678, 822, 903, 4282, 29918, 6689, 8948, 414, 29898, 1311, 1125, 13, 4706, 411, 15886, 29889, 978, 29918, 6078, 877, 6689, 8948, 414, 29374, 13, 9651, 396, 8267, 518, 4537, 310, 3651, 297, 9853, 29962, 13, 9651, 396, 4559, 29918, 4841, 29961, 29875, 29962, 353, 269, 2794, 393, 2794, 393, 2286, 474, 338, 760, 310, 4559, 269, 297, 278, 9853, 13, 9651, 1583, 3032, 11918, 29918, 3177, 29918, 4841, 353, 6667, 358, 287, 2568, 1575, 877, 11918, 29918, 3177, 29918, 4841, 1495, 13, 13, 1678, 822, 903, 4282, 29918, 6078, 29918, 3859, 29898, 1311, 1125, 13, 4706, 411, 15886, 29889, 978, 29918, 6078, 877, 4282, 29918, 6078, 29918, 3859, 29374, 13, 9651, 396, 518, 1949, 2286, 7573, 297, 9853, 29962, 13, 9651, 1583, 3032, 11918, 29918, 18010, 29918, 17987, 7176, 29901, 15886, 29889, 29911, 6073, 353, 15886, 29889, 29887, 1624, 29898, 13, 18884, 1583, 3032, 18010, 29889, 17987, 29881, 886, 29892, 13, 18884, 1583, 3032, 11918, 29918, 3177, 29918, 4841, 29889, 513, 1575, 29892, 13, 18884, 1024, 2433, 6078, 29918, 18010, 29918, 17987, 7176, 742, 13, 9651, 1723, 13, 13, 9651, 396, 10032, 4822, 2286, 7573, 297, 1269, 3983, 297, 278, 9853, 13, 9651, 396, 8267, 518, 16175, 2159, 29892, 3983, 23655, 10809, 29962, 13, 9651, 1583, 3032, 6078, 29918, 3859, 29901, 15886, 29889, 29911, 6073, 353, 15886, 29889, 28192, 29918, 3317, 29898, 13, 18884, 1583, 3032, 11918, 29918, 18010, 29918, 17987, 7176, 29892, 13, 18884, 1583, 3032, 11918, 29918, 3177, 29918, 4841, 29889, 11249, 29918, 4841, 29892, 13, 18884, 1024, 2433, 6078, 29918, 3859, 742, 13, 9651, 1723, 13, 13, 1678, 822, 8343, 29918, 8977, 29898, 1311, 29892, 8343, 29901, 6667, 358, 287, 2568, 1575, 29737, 29897, 1599, 360, 919, 29961, 13264, 29889, 29911, 6073, 29892, 3139, 5387, 13, 4706, 736, 1583, 3032, 11918, 29918, 3177, 29918, 4841, 29889, 18798, 29918, 8977, 29898, 18798, 29897, 13, 13, 1678, 822, 2058, 8948, 414, 29918, 8977, 29898, 1311, 29897, 1599, 360, 919, 29961, 710, 29892, 15886, 29889, 29911, 6073, 5387, 13, 4706, 736, 1583, 3032, 11918, 29918, 3177, 29918, 4841, 29889, 8977, 580, 13, 13, 1678, 822, 6874, 29918, 3859, 29898, 1311, 29897, 1599, 15886, 29889, 29911, 6073, 29901, 13, 4706, 9995, 13, 4706, 584, 2457, 29901, 8954, 310, 599, 278, 3651, 297, 6874, 29892, 8267, 518, 16175, 2159, 29892, 3983, 23655, 10809, 29962, 13, 4706, 9995, 13, 4706, 736, 1583, 3032, 6078, 29918, 3859, 13, 2 ]
reverseWord.py	lovefov/Python	0	3324	#!/usr/bin/env python3 #-- coding:utf-8 -- #Author:贾江超 def spin_words(sentence): list1=sentence.split() l=len(list1) for i in range(l): relen = len(sentence.split()[i:][0]) if relen > 5: list1[i]=list1[i][::-1] return ' '.join(list1) ''' 注意在2.x版本可以用len()得到list的长度 3.x版本就不行了优化版本 def spin_words(sentence): # Your code goes here return " ".join([x[::-1] if len(x) >= 5 else x for x in sentence.split(" ")]) 在这里倒序字符串用切片很方便 str[::-1] 就ok了 '''	[ 1, 18787, 4855, 29914, 2109, 29914, 6272, 3017, 29941, 13, 29937, 29899, 29930, 29899, 14137, 29901, 9420, 29899, 29947, 448, 29930, 29899, 13, 29937, 13720, 29901, 235, 183, 193, 30775, 31480, 13, 13, 1753, 10917, 29918, 9303, 29898, 18616, 663, 1125, 13, 1678, 1051, 29896, 29922, 18616, 663, 29889, 5451, 580, 13, 1678, 301, 29922, 2435, 29898, 1761, 29896, 29897, 13, 1678, 363, 474, 297, 3464, 29898, 29880, 1125, 13, 4706, 1104, 264, 353, 7431, 29898, 18616, 663, 29889, 5451, 580, 29961, 29875, 29901, 3816, 29900, 2314, 13, 4706, 565, 1104, 264, 1405, 29871, 29945, 29901, 13, 965, 1051, 29896, 29961, 29875, 13192, 1761, 29896, 29961, 29875, 3816, 1057, 29899, 29896, 29962, 13, 1678, 736, 525, 15300, 7122, 29898, 1761, 29896, 29897, 13, 13, 12008, 13, 31368, 31474, 29871, 30505, 29906, 29889, 29916, 30845, 30346, 30682, 30651, 30406, 2435, 580, 31050, 30780, 1761, 30210, 31143, 30898, 29871, 29941, 29889, 29916, 30845, 30346, 31238, 30413, 30448, 30743, 13, 13, 231, 191, 155, 30705, 30845, 30346, 259, 13, 13, 1753, 10917, 29918, 9303, 29898, 18616, 663, 1125, 13, 1678, 396, 3575, 775, 5771, 1244, 13, 1678, 736, 376, 11393, 7122, 4197, 29916, 29961, 1057, 29899, 29896, 29962, 565, 7431, 29898, 29916, 29897, 6736, 29871, 29945, 1683, 921, 363, 921, 297, 10541, 29889, 5451, 703, 16521, 2314, 13, 268, 13, 268, 30505, 30810, 30755, 232, 131, 149, 31463, 30578, 31277, 31767, 30406, 31757, 31122, 232, 193, 139, 30525, 231, 193, 194, 851, 29961, 1057, 29899, 29896, 29962, 29871, 31238, 554, 30743, 13, 12008, 13, 13, 2 ]
applications/python/mqtt-lcp/lib/node_base_i2c.py	rphughespa/mqtt-lcp	4	65409	<filename>applications/python/mqtt-lcp/lib/node_base_i2c.py<gh_stars>1-10 # node_base_i2c.py - base class for mqtt nodes """ node_base_i2c - Base class for nodes that use i2c. Derived from node_base_mqtt. The MIT License (MIT) Copyright 2020 <NAME> Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. """ import sys import os.path if sys.platform.startswith("esp32_LoBo"): from smbus2_go_lobo import SMBus from queue_esp32 import Queue import ujson elif sys.platform.startswith("esp32"): from smbus2_esp32 import SMBus from queue_esp32 import Queue import ujson else: from smbus2 import SMBus from queue_py import Queue import json import time from global_constants import Global from node_base_mqtt import NodeBaseMqtt from i2c_client_thread import I2cClientThread from io_data import IoData from i2c_mux import I2cMux from i2c_rfid import I2cRfid from i2c_rotary import I2cRotary from i2c_servo import I2cServo from i2c_servo_hat import I2cServoHat class NodeBaseI2c(NodeBaseMqtt): """ Base class for node programs usig I2c IO Manages IO via threads """ def __init__(self): super().__init__() self.config_servos = None self.servos_params = None self.i2c_client = None self.i2c_devices = [] self.mqtt_devices = {} self.i2c_in_queue = Queue(100) self.i2c_out_queue = Queue(100) self.i2c_display_imported = False if os.path.isfile("servos.json"): with open('servos.json') as json_file: if sys.platform.startswith("esp32"): self.config_servos = ujson.load(json_file) else: self.config_servos = json.load(json_file) if self.config_servos is not None: self.parse_servo_params(self.config_servos) def start_i2c(self): """Start I2C IO""" self.log_queue.add_message("info", 'start i2c client thread') #if sys.platform.startswith('esp32'): # with SMBus(1) as i2cbus: # print("I2C Scan: "+str(i2cbus.scan())) if Global.CONFIG in self.config: if Global.IO in self.config[Global.CONFIG]: if Global.I2C in self.config[Global.CONFIG][Global.IO]: self.parse_config_data(self.config[Global.CONFIG][Global.IO][Global.I2C]) self.i2c_client = I2cClientThread(self.log_queue, self.i2c_devices, self.mqtt_devices, self.i2c_in_queue, self.i2c_out_queue, self.display_queue, "I2cClient", self.config[Global.CONFIG][Global.IO][Global.I2C]) self.i2c_client.start() time.sleep(1) def received_from_i2c(self, io_data): """ call back to process received subsctibed messages. override in derived class""" pass def get_incomming_message_from_queue(self): """ retrieve a receieved message from the queue """ message = None if not self.i2c_in_queue.empty(): message = self.i2c_in_queue.get() return message def process_input(self): """ cprocess input from all source input queues""" # process all input of a given type in order of importance: keyboard, serial, mqtt self.write_log_messages() if self.i2c_client is not None: self.i2c_client.check_msg() i2c_input = self.get_incomming_i2c_message_from_queue() if i2c_input is not None: self.received_from_i2c(i2c_input) super().process_input() def send_to_i2c(self, message): """ send message to an I2C device""" if self.i2c_client is not None: self.i2c_out_queue.put(message) def get_incomming_i2c_message_from_queue(self): """ retrieve a receieved message from the queue """ message = None if not self.i2c_in_queue.empty(): message = self.i2c_in_queue.get() return message def initialize_threads(self): """ initialize all IO threads""" # load config file # do not enable keyboard for apps meant to be run in background super().initialize_threads() if Global.CONFIG in self.config: if Global.IO in self.config[Global.CONFIG]: io_config = self.config[Global.CONFIG][Global.IO] if Global.I2C in io_config: self.start_i2c() def loop(self): """ loop through IO. override in dericed class""" while not self.shutdown_app: try: time.sleep(0.01) self.write_log_messages() self.process_input() except KeyboardInterrupt: self.shutdown_app = True def shutdown_threads(self): """ shutdown all IO threads""" if self.i2c_client is not None: # print("shutdown serial") self.i2c_client.shutdown() super().shutdown_threads() def parse_servo_params(self, servo_config_data): """ parse parameters for servos """ # print("servo params") if Global.CONFIG in servo_config_data: if Global.SERVOS in servo_config_data[Global.CONFIG]: for i2c_servo_mux in servo_config_data[Global.CONFIG][Global.SERVOS]: address = i2c_servo_mux[Global.ADDRESS] if isinstance(address, str): # assume hex format "0x77" address = int(address, 16) if Global.PORTS in i2c_servo_mux: for i2c_servo in i2c_servo_mux[Global.PORTS]: sub_address = i2c_servo[Global.SUB_ADDRESS] key = str(address)+":"+str(sub_address) if self.servos_params is None: self.servos_params = {} self.servos_params[key] = i2c_servo #print("%%%"+key+"..."+str(i2c_servo)) def parse_config_data(self, config_data): """ Parse out I2C items from config json file""" self.log_queue.add_message("debug", "parse i2c config") for i2c_node in config_data: self.parse_device_config(i2c_node, i2c_bus=1) def parse_device_config(self, i2c_node, i2c_bus=None, i2c_mux_device=None): """ parse configuration """ i2c_type = None i2c_device = None i2c_mux = i2c_mux_device mqtt_port = None mqtt_type = None mqtt_send_sensor_msg = False mux_type = None address = None if Global.BUS in i2c_node: i2c_bus = i2c_node[Global.BUS] if Global.ADDRESS in i2c_node: address = i2c_node[Global.ADDRESS] if isinstance(address, str): # assume hex format "0x77" address = int(address, 16) # self.log_queue.add_message("info", "I2C Device config: "+str(address)) if Global.DEVICE_TYPE in i2c_node: # a block of i2c device, either mux or gpio or unitary (single device per address) i2c_type = i2c_node[Global.DEVICE_TYPE] if i2c_type == Global.MUX: mux_type = i2c_node[Global.MUX+"-"+Global.TYPE] # print("### mux type: "+str(mux_type)) if mux_type == Global.SERVO_HAT: i2c_mux = I2cServoHat(self.log_queue, address, mux_type=mux_type, input_queue=self.i2c_in_queue) elif mux_type == Global.I2C: i2c_mux = I2cMux(self.log_queue, address, mux_type) else: self.i2c_device_not_connected_error(i2c_bus, address, Global.MUX) if not self.is_device_connected(i2c_bus, address): self.i2c_device_not_connected_error(i2c_bus, address, i2c_type) ports = i2c_node[Global.PORTS] for port in ports: self.parse_device_config(port, i2c_bus=i2c_bus, i2c_mux_device=i2c_mux) i2c_device = i2c_mux else: i2c_sub_address = None if i2c_mux is not None: if Global.SUB_ADDRESS in i2c_node: i2c_sub_address = i2c_node[Global.SUB_ADDRESS] device_type = None if Global.MQTT+"-"+Global.PORT in i2c_node: mqtt_port = i2c_node[Global.MQTT+"-"+Global.PORT] if Global.MQTT+"-"+Global.TYPE in i2c_node: mqtt_type = i2c_node[Global.MQTT+"-"+Global.TYPE] if Global.MQTT+"-"+Global.SENSOR in i2c_node: sensor = i2c_node[Global.MQTT+"-"+Global.SENSOR] mqtt_send_sensor_msg = bool(sensor == Global.TRUE) if Global.DEVICE_TYPE in i2c_node: device_type = i2c_node[Global.DEVICE_TYPE] if i2c_type == Global.RFID: self.log_queue.add_message("info", "I2C Device config: "+str(address)+"..."+str(i2c_type)+ "..."+str(mqtt_port)+"..."+str(mqtt_type)) # print("%%%% new rfid: "+str(address)+" .. "+str(i2c_sub_address)+" .. "+str(i2c_mux)) i2c_device = I2cRfid(self.log_queue, address, input_queue=self.i2c_in_queue, mqtt_port=mqtt_port, mqtt_type=mqtt_type, i2c_device_type=device_type, i2c_bus_number=i2c_bus, i2c_mux=i2c_mux, i2c_sub_address=i2c_sub_address) elif i2c_type == Global.SERVO: # print("!!!Servo") center_degrees = 45 # defaults for 90 degree servo throw_degrees = 90 close_degrees = 0 if self.servos_params is not None: key = str(i2c_mux_device.i2c_address)+":"+str(i2c_sub_address) #print("@@@key2: "+str(key)) if key in self.servos_params: servo_params = self.servos_params[key] center_degrees = servo_params[Global.CENTER] throw_degrees = servo_params[Global.THROW] close_degrees = servo_params[Global.CLOSE] # print("config degrees: "+str(center_degrees)+ # ".."+str(throw_degrees)+".."+str(close_degrees)) else: self.log_queue.add_message("error", "No Config for Servo: "+ str(key)) self.log_queue.add_message("info", "I2C Servo config: "+str(address)+"..."+str(i2c_type)+ "..."+str(mqtt_port)+"..."+str(mqtt_type)+ "..."+str(center_degrees)+"..."+str(throw_degrees)+"..."+str(close_degrees)) i2c_device = I2cServo(self.log_queue, address, input_queue=self.i2c_in_queue, mqtt_port=mqtt_port, mqtt_type=mqtt_type, mqtt_send_sensor_msg=mqtt_send_sensor_msg, i2c_device_type=device_type, i2c_bus_number=i2c_bus, i2c_mux=i2c_mux_device, i2c_sub_address=i2c_sub_address) i2c_device.center_degrees = center_degrees i2c_device.throw_degrees = throw_degrees i2c_device.close_degrees = close_degrees elif i2c_type == Global.ROTARY: self.log_queue.add_message("info", "I2C Device config: "+str(address)+"..."+str(i2c_type)+ "..."+str(mqtt_port)+"..."+str(mqtt_type)) i2c_device = I2cRotary(self.log_queue, address, input_queue=self.i2c_in_queue, mqtt_port=mqtt_port, mqtt_type=mqtt_type, i2c_device_type=device_type, i2c_bus_number=i2c_bus, i2c_mux=i2c_mux_device, i2c_sub_address=i2c_sub_address) elif i2c_type == Global.DISPLAY: display_size = None display_type = None if Global.DISPLAY+"-"+Global.SIZE in i2c_node: display_size = i2c_node[Global.DISPLAY+"-"+Global.SIZE] if Global.DISPLAY+"-"+Global.TYPE in i2c_node: display_type = i2c_node[Global.DISPLAY+"-"+Global.TYPE] # not everyone needs disply, import only if needed if not self.i2c_display_imported: from i2c_display import I2cDisplay self.i2c_display_imported = True self.log_queue.add_message("info", "I2C Device config: "+str(address)+"..."+i2c_type) i2c_device = I2cDisplay(self.log_queue, address, display_size=display_size, display_type=display_type, i2c_bus_number=i2c_bus, i2c_mux=i2c_mux_device, i2c_sub_address=i2c_sub_address) elif i2c_type == Global.GPIO: pass if i2c_type is not None and i2c_device is not None: if i2c_device.i2c_sub_address is None: if not self.is_device_connected(i2c_device.i2c_bus_number, i2c_device.i2c_address): self.i2c_device_not_connected_error(i2c_device.i2c_bus_number, i2c_device.i2c_address, i2c_device.i2c_device_type) # print("@@@ "+str(i2c_type)) self.i2c_devices.append({'type': i2c_type, 'mqtt-port': mqtt_port, 'dev': i2c_device}) if mqtt_port is not None: mqtt_key = mqtt_port+":"+mqtt_type self.mqtt_devices[mqtt_key] = i2c_device def i2c_device_not_connected_error(self, bus, address, dtype): """ raise Exception(Global.MSG_I2C_NOT_CONNECTED+" "+Global.DEVICE_TYPE+": "+str(type)+", "+ Global.BUS+": "+str(bus)+", "+Global.ADDRESS+": "+str(address)) """ self.log_queue.add_message("error", Global.MSG_I2C_NOT_CONNECTED+" "+Global.DEVICE_TYPE+ ": "+str(dtype)+", "+ Global.BUS+": "+str(bus)+", "+Global.ADDRESS+": "+str(address)) # modified routine from sparkfun qwiic_i2c def is_device_connected(self, smbus, address): """ is i2c device connect to i2c bus """ device_connected = False with SMBus(smbus) as bus: try: # Try to write a byte to the device, command 0x0 # If it throws an I/O error - the device isn't connected bus.write_byte(address, 0x0) device_connected = True except Exception as ee: print("Error connecting to Device: "+str(address)+" .. 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stubs.min/System/ComponentModel/__init___parts/LookupBindingPropertiesAttribute.py	ricardyn/ironpython-stubs	1	172571	<reponame>ricardyn/ironpython-stubs<gh_stars>1-10 class LookupBindingPropertiesAttribute(Attribute,_Attribute): """ Specifies the properties that support lookup-based binding. This class cannot be inherited. LookupBindingPropertiesAttribute() LookupBindingPropertiesAttribute(dataSource: str,displayMember: str,valueMember: str,lookupMember: str) """ def Equals(self,obj): """ Equals(self: LookupBindingPropertiesAttribute,obj: object) -> bool Determines whether the specified System.Object is equal to the current System.ComponentModel.LookupBindingPropertiesAttribute instance. obj: The System.Object to compare with the current System.ComponentModel.LookupBindingPropertiesAttribute instance Returns: true if the object is equal to the current instance; otherwise,false, indicating they are not equal. """ pass def GetHashCode(self): """ GetHashCode(self: LookupBindingPropertiesAttribute) -> int Returns the hash code for this instance. Returns: A hash code for the current System.ComponentModel.LookupBindingPropertiesAttribute. """ pass def __eq__(self,args): """ x.__eq__(y) <==> x==y """ pass def __init__(self,args): """ x.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signature """ pass @staticmethod def __new__(self,dataSource=None,displayMember=None,valueMember=None,lookupMember=None): """ __new__(cls: type) __new__(cls: type,dataSource: str,displayMember: str,valueMember: str,lookupMember: str) """ pass def __ne__(self,*args): pass DataSource=property(lambda self: object(),lambda self,v: None,lambda self: None) """Gets the name of the data source property for the component to which the System.ComponentModel.LookupBindingPropertiesAttribute is bound. Get: DataSource(self: LookupBindingPropertiesAttribute) -> str """ DisplayMember=property(lambda self: object(),lambda self,v: None,lambda self: None) """Gets the name of the display member property for the component to which the System.ComponentModel.LookupBindingPropertiesAttribute is bound. Get: DisplayMember(self: LookupBindingPropertiesAttribute) -> str """ LookupMember=property(lambda self: object(),lambda self,v: None,lambda self: None) """Gets the name of the lookup member for the component to which this attribute is bound. Get: LookupMember(self: LookupBindingPropertiesAttribute) -> str """ ValueMember=property(lambda self: object(),lambda self,v: None,lambda self: None) """Gets the name of the value member property for the component to which the System.ComponentModel.LookupBindingPropertiesAttribute is bound. 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camerartc/camerartc.gyp	Teaonly/yacamera	20	52789	<gh_stars>10-100 # # Building script for ipcamera application # { 'includes': ['build/common.gypi'], 'targets' : [ { 'target_name': 'camerartc', 'type': 'executable', 'include_dirs': [ '../third_party/webrtc/modules/interface', ], 'dependencies': [ 'libjingle.gyp:libjingle_peerconnection', '<(DEPTH)/third_party/jsoncpp/jsoncpp.gyp:jsoncpp', ], 'sources': [ 'camerartc/main.cpp', 'camerartc/peer.h', 'camerartc/peer.cpp', 'camerartc/camerartc.h', 'camerartc/camerartc.cpp', 'camerartc/rtcstream.h', 'camerartc/rtcstream.cpp', 'camerartc/attendee.h', 'camerartc/attendee.cpp', 'camerartc/mediabuffer.h', 'camerartc/mediabuffer.cpp', 'camerartc/raptor/numberdb.h', 'camerartc/raptor/numberdb.cpp', 'camerartc/raptor/rprandom.h', 'camerartc/raptor/rprandom.cpp', 'camerartc/raptor/raptor.h', 'camerartc/raptor/aaptor.cpp', 'camerartc/g72x/g726_32.c', 'camerartc/g72x/g711.c', 'camerartc/g72x/g72x.c', ], }, ], }	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29896, 29900, 29899, 29896, 29900, 29900, 13, 29937, 13, 29937, 259, 17166, 2471, 363, 474, 6739, 314, 1572, 2280, 13, 29937, 13, 29912, 13, 1678, 525, 24572, 2396, 6024, 4282, 29914, 9435, 29889, 29887, 1478, 29875, 7464, 13, 1678, 525, 5182, 29879, 29915, 584, 518, 13, 1678, 426, 13, 4706, 525, 5182, 29918, 978, 2396, 525, 29883, 4183, 442, 29883, 742, 13, 4706, 525, 1853, 2396, 525, 4258, 9246, 742, 13, 4706, 525, 2856, 29918, 3972, 29879, 2396, 518, 29871, 13, 9651, 525, 6995, 22585, 29918, 22633, 29914, 705, 1182, 14246, 29914, 7576, 29914, 13248, 742, 13, 4706, 21251, 29871, 13, 4706, 525, 22594, 2396, 518, 13, 9651, 525, 1982, 29926, 292, 280, 29889, 29887, 1478, 29901, 1982, 29926, 292, 280, 29918, 412, 261, 9965, 742, 13, 9651, 12801, 29898, 2287, 29925, 4690, 6802, 22585, 29918, 22633, 29914, 3126, 8223, 29914, 3126, 8223, 29889, 29887, 1478, 29901, 3126, 8223, 742, 13, 4706, 21251, 13, 4706, 525, 29879, 2863, 2396, 518, 13, 9651, 525, 29883, 4183, 442, 29883, 29914, 3396, 29889, 8223, 742, 13, 9651, 525, 29883, 4183, 442, 29883, 29914, 412, 261, 29889, 29882, 742, 13, 9651, 525, 29883, 4183, 442, 29883, 29914, 412, 261, 29889, 8223, 742, 13, 9651, 525, 29883, 4183, 442, 29883, 29914, 29883, 4183, 442, 29883, 29889, 29882, 742, 13, 9651, 525, 29883, 4183, 442, 29883, 29914, 29883, 4183, 442, 29883, 29889, 8223, 742, 13, 9651, 525, 29883, 4183, 442, 29883, 29914, 2273, 29883, 5461, 29889, 29882, 742, 13, 9651, 525, 29883, 4183, 442, 29883, 29914, 2273, 29883, 5461, 29889, 8223, 742, 13, 9651, 525, 29883, 4183, 442, 29883, 29914, 8606, 311, 29872, 29889, 29882, 742, 13, 9651, 525, 29883, 4183, 442, 29883, 29914, 8606, 311, 29872, 29889, 8223, 742, 13, 9651, 525, 29883, 4183, 442, 29883, 29914, 4210, 370, 3043, 29889, 29882, 742, 13, 9651, 525, 29883, 4183, 442, 29883, 29914, 4210, 370, 3043, 29889, 8223, 742, 13, 9651, 525, 29883, 4183, 442, 29883, 29914, 336, 415, 272, 29914, 4537, 2585, 29889, 29882, 742, 13, 9651, 525, 29883, 4183, 442, 29883, 29914, 336, 415, 272, 29914, 4537, 2585, 29889, 8223, 742, 13, 9651, 525, 29883, 4183, 442, 29883, 29914, 336, 415, 272, 29914, 29878, 558, 2685, 29889, 29882, 742, 13, 9651, 525, 29883, 4183, 442, 29883, 29914, 336, 415, 272, 29914, 29878, 558, 2685, 29889, 8223, 742, 29871, 13, 9651, 525, 29883, 4183, 442, 29883, 29914, 336, 415, 272, 29914, 336, 415, 272, 29889, 29882, 742, 13, 9651, 525, 29883, 4183, 442, 29883, 29914, 336, 415, 272, 29914, 29874, 2156, 272, 29889, 8223, 742, 13, 9651, 525, 29883, 4183, 442, 29883, 29914, 29887, 29955, 29906, 29916, 29914, 29887, 29955, 29906, 29953, 29918, 29941, 29906, 29889, 29883, 742, 13, 9651, 525, 29883, 4183, 442, 29883, 29914, 29887, 29955, 29906, 29916, 29914, 29887, 29955, 29896, 29896, 29889, 29883, 742, 13, 9651, 525, 29883, 4183, 442, 29883, 29914, 29887, 29955, 29906, 29916, 29914, 29887, 29955, 29906, 29916, 29889, 29883, 742, 13, 9651, 21251, 13, 1678, 2981, 13, 1678, 21251, 13, 29913, 13, 2 ]
Experiment Processing/experiment1/calculate_mutual_tracks.py	Austaon/GroupRecommendationThesis	0	109905	import statistics from database.user import SessionUser import matplotlib.pyplot as plt def calculate_mutual_tracks(key="real", plot_data=True): """ Finds the number of tracks that are in both the selected data and the given dataset in each time range. The average and standard deviation are printed and a boxplot is generated. :param key: The dataset to use: "real", "recommended", or "random" :param plot_data: :return: """ types = ["tracks_short_term", "tracks_medium_term", "tracks_long_term"] result = { "tracks_short_term": [], "tracks_medium_term": [], "tracks_long_term": [] } count = 0 for user in SessionUser.objects: count += 1 print(f"User {count} out of 88") chosen_tracks = user.tracks for category in types: top_tracks = user.survey[key][category] result[category].append(len([t for t in top_tracks if t in chosen_tracks])) for category in types: print( f"{category:18s}: mean {statistics.mean(result[category]):.2f}, stdev: {statistics.stdev(result[category]):.2f}") if plot_data: fig, ax = plt.subplots() boxplot_data = [ result["tracks_short_term"], result["tracks_medium_term"], result["tracks_long_term"] ] labels = ["Short Term", "Medium Term", "Long Term"] ax.boxplot(boxplot_data, labels=labels, boxprops=dict(linestyle='-', linewidth=1.5), medianprops=dict(linestyle='-', linewidth=2), whiskerprops=dict(linestyle='-', linewidth=1.5), capprops=dict(linestyle='-', linewidth=1.5), showfliers=True ) ax.set_xticklabels(labels) ax.yaxis.grid(True, linestyle='-', which='major', color='lightgrey', alpha=0.5) ax.set_ylim((0, 10)) ax.set_ylabel("Matching Items") fig.tight_layout() plt.show()	[ 1, 1053, 13964, 13, 13, 3166, 2566, 29889, 1792, 1053, 16441, 2659, 13, 13, 5215, 22889, 29889, 2272, 5317, 408, 14770, 13, 13, 13, 1753, 8147, 29918, 6149, 950, 29918, 3018, 4684, 29898, 1989, 543, 6370, 613, 6492, 29918, 1272, 29922, 5574, 1125, 13, 1678, 9995, 13, 1678, 10987, 29879, 278, 1353, 310, 16257, 393, 526, 297, 1716, 278, 4629, 848, 322, 278, 2183, 8783, 297, 1269, 931, 3464, 29889, 13, 1678, 450, 6588, 322, 3918, 29522, 526, 13350, 322, 263, 3800, 5317, 338, 5759, 29889, 13, 1678, 584, 3207, 1820, 29901, 450, 8783, 304, 671, 29901, 376, 6370, 613, 376, 276, 2055, 2760, 613, 470, 376, 8172, 29908, 13, 1678, 584, 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democracy_club/apps/everyelection/migrations/0001_initial.py	chris48s/Website	0	96120	<filename>democracy_club/apps/everyelection/migrations/0001_initial.py<gh_stars>0 # -- coding: utf-8 -- from __future__ import unicode_literals from django.db import migrations, models import django.utils.timezone from django.conf import settings import django_extensions.db.fields class Migration(migrations.Migration): dependencies = [ ('authorities', '0012_mapitarea'), migrations.swappable_dependency(settings.AUTH_USER_MODEL), ] operations = [ migrations.CreateModel( name='AuthorityElection', fields=[ ('id', models.AutoField(primary_key=True, verbose_name='ID', auto_created=True, serialize=False)), ('election_date', models.DateField()), ('percent_posts', models.IntegerField()), ('authority', models.ForeignKey(to='authorities.Authority')), ], ), migrations.CreateModel( name='AuthorityElectionPositions', fields=[ ('id', models.AutoField(primary_key=True, verbose_name='ID', auto_created=True, serialize=False)), ('created', django_extensions.db.fields.CreationDateTimeField(editable=False, blank=True, default=django.utils.timezone.now, verbose_name='created')), ('modified', django_extensions.db.fields.ModificationDateTimeField(editable=False, blank=True, default=django.utils.timezone.now, verbose_name='modified')), ('area', models.ForeignKey(to='authorities.MapitArea')), ('authority_election', models.ForeignKey(to='everyelection.AuthorityElection')), ('user', models.ForeignKey(to=settings.AUTH_USER_MODEL)), ], options={ 'abstract': False, 'ordering': ('-modified', '-created'), 'get_latest_by': 'modified', }, ), ]	[ 1, 529, 9507, 29958, 2310, 25804, 29918, 29066, 29914, 13371, 29914, 17991, 29872, 1464, 29914, 26983, 800, 29914, 29900, 29900, 29900, 29896, 29918, 11228, 29889, 2272, 29966, 12443, 29918, 303, 1503, 29958, 29900, 13, 29937, 448, 29930, 29899, 14137, 29901, 23616, 29899, 29947, 448, 29930, 29899, 13, 3166, 4770, 29888, 9130, 1649, 1053, 29104, 29918, 20889, 1338, 13, 13, 3166, 9557, 29889, 2585, 1053, 9725, 800, 29892, 4733, 13, 5215, 9557, 29889, 13239, 29889, 2230, 8028, 13, 3166, 9557, 29889, 5527, 1053, 6055, 13, 5215, 9557, 29918, 24299, 29889, 2585, 29889, 9621, 13, 13, 13, 1990, 341, 16783, 29898, 26983, 800, 29889, 29924, 16783, 1125, 13, 13, 1678, 9962, 353, 518, 13, 4706, 6702, 8921, 1907, 742, 525, 29900, 29900, 29896, 29906, 29918, 1958, 277, 6203, 5477, 13, 4706, 9725, 800, 29889, 2774, 932, 519, 29918, 10836, 29898, 11027, 29889, 20656, 29950, 29918, 11889, 29918, 20387, 29931, 511, 13, 1678, 4514, 13, 13, 1678, 6931, 353, 518, 13, 4706, 9725, 800, 29889, 4391, 3195, 29898, 13, 9651, 1024, 2433, 13720, 537, 29923, 1464, 742, 13, 9651, 4235, 11759, 13, 18884, 6702, 333, 742, 4733, 29889, 12300, 3073, 29898, 16072, 29918, 1989, 29922, 5574, 29892, 26952, 29918, 978, 2433, 1367, 742, 4469, 29918, 11600, 29922, 5574, 29892, 28755, 29922, 8824, 8243, 13, 18884, 6702, 29872, 1464, 29918, 1256, 742, 4733, 29889, 2539, 3073, 25739, 13, 18884, 6702, 25376, 29918, 14080, 742, 4733, 29889, 7798, 3073, 25739, 13, 18884, 6702, 8921, 537, 742, 4733, 29889, 27755, 2558, 29898, 517, 2433, 8921, 1907, 29889, 13720, 537, 1495, 511, 13, 9651, 21251, 13, 4706, 10353, 13, 4706, 9725, 800, 29889, 4391, 3195, 29898, 13, 9651, 1024, 2433, 13720, 537, 29923, 1464, 9135, 2187, 742, 13, 9651, 4235, 11759, 13, 18884, 6702, 333, 742, 4733, 29889, 12300, 3073, 29898, 16072, 29918, 1989, 29922, 5574, 29892, 26952, 29918, 978, 2433, 1367, 742, 4469, 29918, 11600, 29922, 5574, 29892, 28755, 29922, 8824, 8243, 13, 18884, 6702, 11600, 742, 9557, 29918, 24299, 29889, 2585, 29889, 9621, 29889, 9832, 362, 11384, 3073, 29898, 5628, 519, 29922, 8824, 29892, 9654, 29922, 5574, 29892, 2322, 29922, 14095, 29889, 13239, 29889, 2230, 8028, 29889, 3707, 29892, 26952, 29918, 978, 2433, 11600, 1495, 511, 13, 18884, 6702, 1545, 2164, 742, 9557, 29918, 24299, 29889, 2585, 29889, 9621, 29889, 2111, 2450, 11384, 3073, 29898, 5628, 519, 29922, 8824, 29892, 9654, 29922, 5574, 29892, 2322, 29922, 14095, 29889, 13239, 29889, 2230, 8028, 29889, 3707, 29892, 26952, 29918, 978, 2433, 1545, 2164, 1495, 511, 13, 18884, 6702, 6203, 742, 4733, 29889, 27755, 2558, 29898, 517, 2433, 8921, 1907, 29889, 3388, 277, 13799, 1495, 511, 13, 18884, 6702, 8921, 537, 29918, 29872, 1464, 742, 4733, 29889, 27755, 2558, 29898, 517, 2433, 17991, 29872, 1464, 29889, 13720, 537, 29923, 1464, 1495, 511, 13, 18884, 6702, 1792, 742, 4733, 29889, 27755, 2558, 29898, 517, 29922, 11027, 29889, 20656, 29950, 29918, 11889, 29918, 20387, 29931, 8243, 13, 9651, 21251, 13, 9651, 3987, 3790, 13, 18884, 525, 16595, 2396, 7700, 29892, 13, 18884, 525, 2098, 292, 2396, 6702, 29899, 1545, 2164, 742, 17411, 11600, 5477, 13, 18884, 525, 657, 29918, 12333, 29918, 1609, 2396, 525, 1545, 2164, 742, 13, 9651, 2981, 13, 4706, 10353, 13, 1678, 4514, 13, 2 ]
SelectUniqueTaxid.py	BonizzoniLab/Refine_EVEs_annotation	1	147160	import argparse def read_blast_table(blastx_file): '''Read blastx table''' totalList=[] for line in blastx_file: line=line.rstrip() if line.startswith('ID'): continue else: parts=line.split('\t') try: taxid=parts[int(opts.position)].split(';') except: taxid='' for t in taxid: if t != '.': totalList.append(t) for t in sorted(set(totalList)): print(t) def main(): parser = argparse.ArgumentParser('Select unique taxid') parser.add_argument('-i_Blastx', '--fileBlast', help="blastx output table") parser.add_argument('-position', '--position', help="column position of taxid") global opts opts = parser.parse_args() in_fileBlastx = open(opts.fileBlast) read_blast_table(in_fileBlastx) main()	[ 1, 1053, 1852, 5510, 13, 268, 13, 1753, 1303, 29918, 23190, 29918, 2371, 29898, 23190, 29916, 29918, 1445, 1125, 13, 1678, 14550, 6359, 1999, 579, 29916, 1591, 12008, 13, 1678, 3001, 1293, 29922, 2636, 13, 13, 1678, 363, 1196, 297, 1999, 579, 29916, 29918, 1445, 29901, 13, 308, 13, 4706, 1196, 29922, 1220, 29889, 29878, 17010, 580, 13, 4706, 565, 1196, 29889, 27382, 2541, 877, 1367, 29374, 13, 9651, 6773, 13, 4706, 1683, 29901, 13, 9651, 5633, 29922, 1220, 29889, 5451, 28909, 29873, 1495, 13, 13, 9651, 1018, 29901, 13, 18884, 8818, 333, 29922, 20895, 29961, 524, 29898, 25707, 29889, 3283, 29897, 1822, 5451, 877, 29936, 1495, 13, 9651, 5174, 29901, 13, 18884, 8818, 333, 2433, 29915, 13, 632, 13, 9651, 363, 260, 297, 8818, 333, 29901, 13, 18884, 565, 260, 2804, 15300, 2396, 13, 462, 1678, 3001, 1293, 29889, 4397, 29898, 29873, 29897, 13, 13, 1678, 363, 260, 297, 12705, 29898, 842, 29898, 7827, 1293, 22164, 13, 4706, 1596, 29898, 29873, 29897, 13, 13, 13, 1753, 1667, 7295, 13, 1678, 13812, 353, 1852, 5510, 29889, 15730, 11726, 877, 3549, 5412, 8818, 333, 1495, 13, 1678, 13812, 29889, 1202, 29918, 23516, 877, 29899, 29875, 29918, 29933, 4230, 29916, 742, 525, 489, 1445, 29933, 4230, 742, 1371, 543, 23190, 29916, 1962, 1591, 1159, 13, 1678, 13812, 29889, 1202, 29918, 23516, 877, 29899, 3283, 742, 525, 489, 3283, 742, 1371, 543, 4914, 2602, 310, 8818, 333, 1159, 13, 13, 1678, 5534, 29111, 13, 1678, 29111, 353, 13812, 29889, 5510, 29918, 5085, 580, 13, 13, 1678, 297, 29918, 1445, 29933, 4230, 29916, 353, 1722, 29898, 25707, 29889, 1445, 29933, 4230, 29897, 13, 1678, 1303, 29918, 23190, 29918, 2371, 29898, 262, 29918, 1445, 29933, 4230, 29916, 29897, 13, 13, 3396, 580, 2 ]
tests/syntax/async_def_missing_parens.py	matan-h/friendly	287	133925	<filename>tests/syntax/async_def_missing_parens.py async def name: pass	[ 1, 529, 9507, 29958, 21150, 29914, 29562, 29914, 12674, 29918, 1753, 29918, 27259, 29918, 862, 575, 29889, 2272, 13, 12674, 822, 1024, 29901, 13, 1678, 1209, 13, 2 ]
utils/tinyimages_80mn_loader.py	jfc43/robust-ood-detection	55	196882	import numpy as np import torch from bisect import bisect_left class TinyImages(torch.utils.data.Dataset): def __init__(self, transform=None, exclude_cifar=True): data_file = open('datasets/unlabeled_datasets/80M_Tiny_Images/tiny_images.bin', "rb") def load_image(idx): data_file.seek(idx * 3072) data = data_file.read(3072) return np.fromstring(data, dtype='uint8').reshape(32, 32, 3, order="F") self.load_image = load_image self.offset = 0 # offset index self.transform = transform self.exclude_cifar = exclude_cifar if exclude_cifar: self.cifar_idxs = [] with open('datasets/unlabeled_datasets/80M_Tiny_Images/80mn_cifar_idxs.txt', 'r') as idxs: for idx in idxs: # indices in file take the 80mn database to start at 1, hence "- 1" self.cifar_idxs.append(int(idx) - 1) # hash table option self.cifar_idxs = set(self.cifar_idxs) self.in_cifar = lambda x: x in self.cifar_idxs # bisection search option # self.cifar_idxs = tuple(sorted(self.cifar_idxs)) # # def binary_search(x, hi=len(self.cifar_idxs)): # pos = bisect_left(self.cifar_idxs, x, 0, hi) # find insertion position # return True if pos != hi and self.cifar_idxs[pos] == x else False # # self.in_cifar = binary_search def __getitem__(self, index): index = (index + self.offset) % 79302016 if self.exclude_cifar: while self.in_cifar(index): index = np.random.randint(79302017) img = self.load_image(index) if self.transform is not None: img = self.transform(img) return img, 0 # 0 is the class def __len__(self): return 79302017	[ 1, 1053, 12655, 408, 7442, 13, 5215, 4842, 305, 13, 3166, 2652, 522, 1053, 2652, 522, 29918, 1563, 13, 13, 1990, 323, 4901, 20163, 29898, 7345, 305, 29889, 13239, 29889, 1272, 29889, 16390, 24541, 1125, 13, 13, 1678, 822, 4770, 2344, 12035, 1311, 29892, 4327, 29922, 8516, 29892, 19060, 29918, 29883, 361, 279, 29922, 5574, 1125, 13, 13, 4706, 848, 29918, 1445, 353, 1722, 877, 14538, 1691, 29914, 348, 29880, 24025, 29918, 14538, 1691, 29914, 29947, 29900, 29924, 29918, 29911, 4901, 29918, 20163, 29914, 25649, 29918, 8346, 29889, 2109, 742, 376, 6050, 1159, 13, 13, 4706, 822, 2254, 29918, 3027, 29898, 13140, 1125, 13, 9651, 848, 29918, 1445, 29889, 344, 1416, 29898, 13140, 334, 29871, 29941, 29900, 29955, 29906, 29897, 13, 9651, 848, 353, 848, 29918, 1445, 29889, 949, 29898, 29941, 29900, 29955, 29906, 29897, 13, 9651, 736, 7442, 29889, 3166, 1807, 29898, 1272, 29892, 26688, 2433, 13470, 29947, 2824, 690, 14443, 29898, 29941, 29906, 29892, 29871, 29941, 29906, 29892, 29871, 29941, 29892, 1797, 543, 29943, 1159, 13, 13, 4706, 1583, 29889, 1359, 29918, 3027, 353, 2254, 29918, 3027, 13, 4706, 1583, 29889, 10289, 353, 29871, 29900, 268, 396, 9210, 2380, 13, 13, 4706, 1583, 29889, 9067, 353, 4327, 13, 4706, 1583, 29889, 735, 2325, 29918, 29883, 361, 279, 353, 19060, 29918, 29883, 361, 279, 13, 13, 4706, 565, 19060, 29918, 29883, 361, 279, 29901, 13, 9651, 1583, 29889, 29883, 361, 279, 29918, 333, 10351, 353, 5159, 13, 9651, 411, 1722, 877, 14538, 1691, 29914, 348, 29880, 24025, 29918, 14538, 1691, 29914, 29947, 29900, 29924, 29918, 29911, 4901, 29918, 20163, 29914, 29947, 29900, 23521, 29918, 29883, 361, 279, 29918, 333, 10351, 29889, 3945, 742, 525, 29878, 1495, 408, 1178, 10351, 29901, 13, 18884, 363, 22645, 297, 1178, 10351, 29901, 13, 462, 1678, 396, 16285, 297, 934, 2125, 278, 29871, 29947, 29900, 23521, 2566, 304, 1369, 472, 29871, 29896, 29892, 8151, 11663, 29871, 29896, 29908, 13, 462, 1678, 1583, 29889, 29883, 361, 279, 29918, 333, 10351, 29889, 4397, 29898, 524, 29898, 13140, 29897, 448, 29871, 29896, 29897, 13, 13, 9651, 396, 6608, 1591, 2984, 13, 9651, 1583, 29889, 29883, 361, 279, 29918, 333, 10351, 353, 731, 29898, 1311, 29889, 29883, 361, 279, 29918, 333, 10351, 29897, 13, 9651, 1583, 29889, 262, 29918, 29883, 361, 279, 353, 14013, 921, 29901, 921, 297, 1583, 29889, 29883, 361, 279, 29918, 333, 10351, 13, 13, 9651, 396, 289, 895, 428, 2740, 2984, 13, 9651, 396, 1583, 29889, 29883, 361, 279, 29918, 333, 10351, 353, 18761, 29898, 24582, 29898, 1311, 29889, 29883, 361, 279, 29918, 333, 10351, 876, 13, 9651, 396, 13, 9651, 396, 822, 7581, 29918, 4478, 29898, 29916, 29892, 7251, 29922, 2435, 29898, 1311, 29889, 29883, 361, 279, 29918, 333, 10351, 22164, 13, 9651, 396, 268, 926, 353, 2652, 522, 29918, 1563, 29898, 1311, 29889, 29883, 361, 279, 29918, 333, 10351, 29892, 921, 29892, 29871, 29900, 29892, 7251, 29897, 29871, 396, 1284, 4635, 291, 2602, 13, 9651, 396, 268, 736, 5852, 565, 926, 2804, 7251, 322, 1583, 29889, 29883, 361, 279, 29918, 333, 10351, 29961, 1066, 29962, 1275, 921, 1683, 7700, 13, 9651, 396, 13, 9651, 396, 1583, 29889, 262, 29918, 29883, 361, 279, 353, 7581, 29918, 4478, 13, 13, 1678, 822, 4770, 657, 667, 12035, 1311, 29892, 2380, 1125, 13, 4706, 2380, 353, 313, 2248, 718, 1583, 29889, 10289, 29897, 1273, 29871, 29955, 29929, 29941, 29900, 29906, 29900, 29896, 29953, 13, 13, 4706, 565, 1583, 29889, 735, 2325, 29918, 29883, 361, 279, 29901, 13, 9651, 1550, 1583, 29889, 262, 29918, 29883, 361, 279, 29898, 2248, 1125, 13, 18884, 2380, 353, 7442, 29889, 8172, 29889, 9502, 524, 29898, 29955, 29929, 29941, 29900, 29906, 29900, 29896, 29955, 29897, 13, 13, 4706, 10153, 353, 1583, 29889, 1359, 29918, 3027, 29898, 2248, 29897, 13, 4706, 565, 1583, 29889, 9067, 338, 451, 6213, 29901, 13, 9651, 10153, 353, 1583, 29889, 9067, 29898, 2492, 29897, 13, 13, 4706, 736, 10153, 29892, 29871, 29900, 29871, 396, 29871, 29900, 338, 278, 770, 13, 13, 1678, 822, 4770, 2435, 12035, 1311, 1125, 13, 4706, 736, 29871, 29955, 29929, 29941, 29900, 29906, 29900, 29896, 29955, 13, 2 ]
5.Operators/2.membership_operator.py	Tazri/Python	0	194730	<filename>5.Operators/2.membership_operator.py list = ['Apple','Orange','Benana','Mango']; name = "<NAME>"; print('"Apple" in list : ',"Apple" in list); print('"Kiwi" in list : ',"Kiwi" in list); print('"Water" not in list : ',"Water" not in list); print("'Md' in name : ",'Md' in name); print("'Tazri' not in name : ",'Tazri' not in name);	[ 1, 529, 9507, 29958, 29945, 29889, 7094, 4097, 29914, 29906, 29889, 29885, 1590, 10475, 29918, 6891, 29889, 2272, 13, 1761, 353, 6024, 2052, 280, 3788, 29949, 3881, 3788, 20841, 1648, 3788, 29924, 4524, 8219, 13, 978, 353, 9872, 5813, 16871, 13, 13, 2158, 877, 29908, 2052, 280, 29908, 297, 1051, 584, 525, 1699, 2052, 280, 29908, 297, 1051, 416, 13, 2158, 877, 29908, 29968, 29875, 4353, 29908, 297, 1051, 584, 525, 1699, 29968, 29875, 4353, 29908, 297, 1051, 416, 13, 2158, 877, 29908, 29956, 1008, 29908, 451, 297, 1051, 584, 525, 1699, 29956, 1008, 29908, 451, 297, 1051, 416, 13, 2158, 703, 29915, 29924, 29881, 29915, 297, 1024, 584, 376, 5501, 29924, 29881, 29915, 297, 1024, 416, 13, 2158, 703, 29915, 29911, 834, 374, 29915, 451, 297, 1024, 584, 376, 5501, 29911, 834, 374, 29915, 451, 297, 1024, 416, 2 ]
photonpy/tests/psf_g2d_sigma.py	qnano/photonpy	5	7095	import matplotlib.pyplot as plt import numpy as np from photonpy.cpp.context import Context import photonpy.cpp.gaussian as gaussian from photonpy.smlm.util import imshow_hstack from photonpy.cpp.estimator import Estimator def CheckDeriv(psf:Estimator, theta): nderiv,ev=psf.NumDeriv(theta,eps=1e-6) deriv,ev=psf.Derivatives(theta) maxerr = np.max( np.abs(deriv-nderiv), (-1,-2) ) print(f"PSF {psf.ParamFormat()}, max {np.max(deriv)}, min: {np.min(deriv)}: Deriv-NumDeriv: {maxerr}") plt.figure() imshow_hstack(deriv[0] - nderiv[0]) with Context() as ctx: g = gaussian.Gaussian(ctx) for cuda in [False]: print(f"CUDA = {cuda}") sigma=2 roisize=12 psf = g.CreatePSF_XYIBg(roisize, sigma, cuda) theta = [[4, 4, 1000, 3]] img = psf.ExpectedValue(theta) plt.figure() plt.set_cmap('inferno') smp = np.random.poisson(img) plt.imshow(smp[0]) psf_sigma = g.CreatePSF_XYIBgSigma(roisize, sigma, cuda) theta_s = [[4,4,1000,3,sigma]] img2 = psf_sigma.ExpectedValue(theta_s) CheckDeriv(psf, theta) # CheckDeriv(psf_sigma) print(f"PSF Sigma crlb: {psf_sigma.CRLB(theta_s)}") theta = psf_sigma.Estimate(smp)[0] print(theta)	[ 1, 1053, 22889, 29889, 2272, 5317, 408, 14770, 13, 5215, 12655, 408, 7442, 13, 13, 3166, 6731, 265, 2272, 29889, 8223, 29889, 4703, 1053, 15228, 13, 5215, 6731, 265, 2272, 29889, 8223, 29889, 29887, 17019, 408, 330, 17019, 13, 3166, 6731, 265, 2272, 29889, 29879, 828, 29885, 29889, 4422, 1053, 527, 4294, 29918, 29882, 1429, 13, 3166, 6731, 265, 2272, 29889, 8223, 29889, 342, 326, 1061, 1053, 2661, 326, 1061, 13, 13, 1753, 5399, 15383, 440, 29898, 567, 29888, 29901, 12787, 326, 1061, 29892, 278, 941, 1125, 13, 268, 5740, 440, 29892, 5750, 29922, 567, 29888, 29889, 8009, 15383, 440, 29898, 3416, 29892, 8961, 29922, 29896, 29872, 29899, 29953, 29897, 13, 1678, 7750, 29892, 5750, 29922, 567, 29888, 29889, 15383, 440, 5056, 29898, 3416, 29897, 13, 13, 1678, 4236, 3127, 353, 7442, 29889, 3317, 29898, 7442, 29889, 6897, 29898, 672, 440, 29899, 5740, 440, 511, 8521, 29896, 6653, 29906, 29897, 1723, 13, 1678, 1596, 29898, 29888, 29908, 7024, 29943, 426, 567, 29888, 29889, 4736, 5809, 580, 1118, 4236, 426, 9302, 29889, 3317, 29898, 672, 440, 19230, 1375, 29901, 426, 9302, 29889, 1195, 29898, 672, 440, 2915, 29901, 2452, 440, 29899, 8009, 15383, 440, 29901, 426, 3317, 3127, 27195, 13, 13, 1678, 14770, 29889, 4532, 580, 13, 1678, 527, 4294, 29918, 29882, 1429, 29898, 672, 440, 29961, 29900, 29962, 448, 29871, 5740, 440, 29961, 29900, 2314, 13, 13, 13, 2541, 15228, 580, 408, 12893, 29901, 13, 1678, 330, 353, 330, 17019, 29889, 29954, 17019, 29898, 13073, 29897, 13, 13, 1678, 363, 274, 6191, 297, 518, 8824, 5387, 13, 4706, 1596, 29898, 29888, 29908, 29907, 29965, 7698, 353, 426, 29883, 6191, 27195, 13, 4706, 269, 2934, 29922, 29906, 13, 4706, 696, 275, 675, 29922, 29896, 29906, 13, 13, 4706, 282, 4668, 353, 330, 29889, 4391, 7024, 29943, 29918, 18454, 8979, 29887, 29898, 307, 275, 675, 29892, 269, 2934, 29892, 274, 6191, 29897, 13, 308, 13, 4706, 278, 941, 353, 5519, 29946, 29892, 29871, 29946, 29892, 29871, 29896, 29900, 29900, 29900, 29892, 29871, 29941, 5262, 13, 4706, 10153, 353, 282, 4668, 29889, 1252, 6021, 1917, 29898, 3416, 29897, 13, 4706, 14770, 29889, 4532, 580, 13, 4706, 14770, 29889, 842, 29918, 29883, 1958, 877, 262, 571, 1217, 1495, 13, 13, 4706, 269, 1526, 353, 7442, 29889, 8172, 29889, 1129, 17387, 29898, 2492, 29897, 13, 4706, 14770, 29889, 326, 4294, 29898, 29879, 1526, 29961, 29900, 2314, 13, 308, 13, 4706, 282, 4668, 29918, 3754, 353, 330, 29889, 4391, 7024, 29943, 29918, 18454, 8979, 29887, 10142, 29898, 307, 275, 675, 29892, 269, 2934, 29892, 274, 6191, 29897, 13, 462, 268, 13, 4706, 278, 941, 29918, 29879, 353, 5519, 29946, 29892, 29946, 29892, 29896, 29900, 29900, 29900, 29892, 29941, 29892, 3754, 5262, 13, 4706, 10153, 29906, 353, 282, 4668, 29918, 3754, 29889, 1252, 6021, 1917, 29898, 3416, 29918, 29879, 29897, 13, 13, 4706, 5399, 15383, 440, 29898, 567, 29888, 29892, 278, 941, 29897, 13, 29937, 9651, 5399, 15383, 440, 29898, 567, 29888, 29918, 3754, 29897, 13, 308, 13, 308, 13, 4706, 1596, 29898, 29888, 29908, 7024, 29943, 317, 2934, 274, 2096, 29890, 29901, 426, 567, 29888, 29918, 3754, 29889, 29907, 2241, 29933, 29898, 3416, 29918, 29879, 2915, 1159, 13, 13, 4706, 278, 941, 353, 282, 4668, 29918, 3754, 29889, 12787, 6490, 29898, 29879, 1526, 9601, 29900, 29962, 13, 4706, 1596, 29898, 3416, 29897, 13, 308, 2 ]
src/pataka/models/__init__.py	astrogewgaw/pataka	6	167218	<reponame>astrogewgaw/pataka from .furby import Furby from .pulsar import Pulsar __all__ = ["Furby", "Pulsar"]	[ 1, 529, 276, 1112, 420, 29958, 23364, 9396, 29887, 1450, 29914, 29886, 532, 1335, 13, 3166, 869, 22613, 1609, 1053, 7509, 1609, 13, 3166, 869, 29886, 7273, 279, 1053, 349, 7273, 279, 13, 13, 1649, 497, 1649, 353, 6796, 29943, 332, 1609, 613, 376, 29925, 7273, 279, 3108, 13, 2 ]
main.py	neurosity/notion-pylsl-starter	5	72044	<gh_stars>1-10 """Example program to show how to read a multi-channel time series from LSL.""" from pylsl import StreamInlet, resolve_stream try: # first resolve an EEG stream on the lab network print("looking for an EEG stream...") streams = resolve_stream('type', 'EEG') # create a new inlet to read from the stream inlet = StreamInlet(streams[0]) while True: # get a new sample (you can also omit the timestamp part if you're not # interested in it) sample, timestamp = inlet.pull_sample() print(timestamp, sample) except KeyboardInterrupt as e: print("Ending program") raise e	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29896, 29899, 29896, 29900, 13, 15945, 29908, 14023, 1824, 304, 1510, 920, 304, 1303, 263, 2473, 29899, 12719, 931, 3652, 515, 365, 12750, 1213, 15945, 13, 13, 3166, 282, 2904, 2536, 1053, 13763, 797, 1026, 29892, 8814, 29918, 5461, 13, 13, 2202, 29901, 13, 1678, 396, 937, 8814, 385, 382, 11787, 4840, 373, 278, 9775, 3564, 13, 1678, 1596, 703, 23261, 363, 385, 382, 11787, 4840, 856, 1159, 13, 1678, 20873, 353, 8814, 29918, 5461, 877, 1853, 742, 525, 29923, 11787, 1495, 13, 13, 1678, 396, 1653, 263, 716, 297, 1026, 304, 1303, 515, 278, 4840, 13, 1678, 297, 1026, 353, 13763, 797, 1026, 29898, 5461, 29879, 29961, 29900, 2314, 13, 1678, 1550, 5852, 29901, 13, 4706, 396, 679, 263, 716, 4559, 313, 6293, 508, 884, 288, 2415, 278, 14334, 760, 565, 366, 29915, 276, 451, 13, 4706, 396, 8852, 297, 372, 29897, 13, 4706, 4559, 29892, 14334, 353, 297, 1026, 29889, 26746, 29918, 11249, 580, 13, 4706, 1596, 29898, 16394, 29892, 4559, 29897, 13, 19499, 7670, 3377, 4074, 6685, 408, 321, 29901, 13, 1678, 1596, 703, 5044, 292, 1824, 1159, 13, 1678, 12020, 321, 13, 2 ]
python/048-self-powers.py	timschlechter/project-euler	0	172004	<reponame>timschlechter/project-euler from functools import reduce powers = map(lambda x: x**x, range(1, 1001)) summation = reduce(lambda x, y: x + y, powers) last10 = str(summation)[-10:] print(last10)	[ 1, 529, 276, 1112, 420, 29958, 9346, 816, 280, 8255, 29914, 4836, 29899, 29872, 8584, 13, 3166, 2090, 312, 8789, 1053, 10032, 13, 13, 12248, 414, 353, 2910, 29898, 2892, 921, 29901, 921, 1068, 29916, 29892, 3464, 29898, 29896, 29892, 29871, 29896, 29900, 29900, 29896, 876, 13, 2083, 29885, 362, 353, 10032, 29898, 2892, 921, 29892, 343, 29901, 921, 718, 343, 29892, 10801, 29897, 13, 4230, 29896, 29900, 353, 851, 29898, 2083, 29885, 362, 9601, 29899, 29896, 29900, 17531, 13, 13, 2158, 29898, 4230, 29896, 29900, 29897, 13, 2 ]
tests/util_test.py	yyang08/swagger-spec-compatibility	18	117864	# -- coding: utf-8 -- from __future__ import absolute_import from __future__ import print_function from __future__ import unicode_literals import mock import pytest from swagger_spec_compatibility.util import EntityMapping from swagger_spec_compatibility.util import is_path_in_top_level_paths from swagger_spec_compatibility.util import wrap @pytest.mark.parametrize( 'input_string, width, expected_result', [ ('this is a string', 50, 'this is a string'), # No wrapping needed ('this is a string', 5, 'this\nis a\nstring'), # The lines are stripped ('this is a string', 5, 'this\nis a\nstring'), # Multiple spaces are condensed ('this_is_a_string', 5, 'this_is_a_string'), # Words (characters with no spaces) will not be split ], ) def test_wrap_provides_the_expected_string(input_string, width, expected_result): assert wrap(input_string, width=width) == expected_result def test_EntityMapping_equality_and_hash(): entity_mappint_1 = EntityMapping(old=1, new=2) entity_mappint_2 = EntityMapping(old=1, new=2) entity_mappint_3 = EntityMapping(old=1, new=3) assert hash(entity_mappint_1) == hash(entity_mappint_2) assert entity_mappint_1 == entity_mappint_2 assert hash(entity_mappint_1) != hash(entity_mappint_3) assert entity_mappint_1 != entity_mappint_3 def test_EntityMapping_upacking_works(): entity_mappint = EntityMapping(old=mock.sentinel.OLD, new=mock.sentinel.NEW) old, new = entity_mappint assert old == mock.sentinel.OLD assert new == mock.sentinel.NEW @pytest.mark.parametrize( 'top_level_paths, path, expected_result', [ ([tuple()], ('path_item',), True), ([('top',)], ('path_item',), False), ([('top',), ('path_item',)], ('path_item',), True), ([('top', 'inner')], ('top', 'different_inner'), False), ([('top', 'inner')], ('top', 'inner'), True), ([('top', 'inner')], ('top', 'inner', 'inner_inner'), True), ], ) def test_is_path_in_top_level_paths(top_level_paths, path, expected_result): assert 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src/synthesize_predictions.py	bluetyson/concept-tagging-training	10	30322	import argparse import logging from pathlib import Path import dask import h5py import joblib import numpy as np import pandas as pd from dask.diagnostics import ProgressBar from tqdm import tqdm from dsconcept.get_metrics import ( get_cat_inds, get_synth_preds, load_category_models, load_concept_models, HierarchicalClassifier, get_mets, ) logging.basicConfig(level=logging.INFO) LOG = logging.getLogger(__name__) LOG.setLevel(logging.INFO) def main( experiment_name, synth_strat, in_cat_preds, out_store, synth_batch_size, t, out_synth_scores, limit=None, con_limit=None, ): test_inds = np.load(f"data/interim/{experiment_name}/test_inds.npy") feature_matrix = joblib.load(f"data/interim/{experiment_name}/feature_matrix.jbl") in_cat_models = Path(f"models/{experiment_name}/categories/models/") in_kwd_models = Path(f"models/{experiment_name}/keywords/models/") cat_preds = np.load(in_cat_preds) # based on experiment or explicit path? cat_clfs = load_category_models(in_cat_models) cd = load_concept_models(in_kwd_models) clf = HierarchicalClassifier(cat_clfs, cd) if limit is not None: LOG.info(f"Limiting to {limit} test records.") feature_matrix_test = feature_matrix.tocsc()[test_inds[0:limit], :] cat_preds = cat_preds[0:limit, :] # TODO: How does this affect indices? else: feature_matrix_test = feature_matrix.tocsc()[test_inds, :] LOG.info(f'Synthesizing predictions with strategy "{synth_strat}".') all_cat_inds = get_cat_inds(clf.categories, cat_preds, t=t) if con_limit is not None: conwc = clf.concepts_with_classifiers[0:con_limit] else: conwc = clf.concepts_with_classifiers shape = (feature_matrix_test.shape[0], len(conwc)) with tqdm(total=shape[0]) as pbar: get_synth_preds( out_store, shape, all_cat_inds, clf.categories, synth_batch_size, only_cat=False, synth_strat=synth_strat, con_limit=con_limit, limit=limit, pbar=pbar, ) LOG.info("Obtaining metrics.") with h5py.File(out_store, "r") as f0: if limit is not None: target_values = f0["ground_truth"][0:limit, :] else: target_values = f0["ground_truth"].value with h5py.File(out_store, "r") as f0: synth_preds = f0["synthesis"].value jobs = [] mets_pbar = tqdm( range(len(conwc)), total=len(conwc), ) for i in mets_pbar: job = dask.delayed(get_mets)( i, synth_preds, target_values, conwc, mets_pbar ) jobs.append(job) records = dask.compute(jobs) new_recs_df = pd.DataFrame(records[0]) LOG.info(f"Saving results to {out_synth_scores}.") new_recs_df.to_csv(out_synth_scores) if __name__ == "__main__": parser = argparse.ArgumentParser(description="Say hello") parser.add_argument("--experiment_name", help="input txt file") parser.add_argument("--synth_strat", help="input txt file") parser.add_argument("--in_cat_preds", help="input txt file") parser.add_argument("--store", help="input txt file") parser.add_argument("--synth_batch_size", help="input txt file", type=int) parser.add_argument("--threshold", help="input txt file", type=float) parser.add_argument("--out_synth_scores", help="input txt file") parser.add_argument( "--limit", help="size for sample to test synthesis", type=int, default=None ) parser.add_argument( "--con_limit", help="size for concept sample", type=int, default=None ) args = parser.parse_args() main( args.experiment_name, args.synth_strat, args.in_cat_preds, args.store, args.synth_batch_size, args.threshold, args.out_synth_scores, args.limit, args.con_limit, )	[ 1, 1053, 1852, 5510, 13, 5215, 12183, 13, 3166, 2224, 1982, 1053, 10802, 13, 13, 5215, 270, 1278, 13, 5215, 298, 29945, 2272, 13, 5215, 4982, 1982, 13, 5215, 12655, 408, 7442, 13, 5215, 11701, 408, 10518, 13, 3166, 270, 1278, 29889, 6051, 20921, 1053, 20018, 4297, 13, 3166, 260, 29939, 18933, 1053, 260, 29939, 18933, 13, 13, 3166, 18031, 535, 1547, 29889, 657, 29918, 2527, 10817, 1053, 313, 13, 1678, 679, 29918, 4117, 29918, 12772, 29892, 13, 1678, 679, 29918, 19274, 386, 29918, 11965, 29879, 29892, 13, 1678, 2254, 29918, 7320, 29918, 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13400, 613, 1371, 543, 2311, 363, 4559, 304, 1243, 14710, 6656, 613, 1134, 29922, 524, 29892, 2322, 29922, 8516, 13, 1678, 1723, 13, 1678, 13812, 29889, 1202, 29918, 23516, 29898, 13, 4706, 376, 489, 535, 29918, 13400, 613, 1371, 543, 2311, 363, 6964, 4559, 613, 1134, 29922, 524, 29892, 2322, 29922, 8516, 13, 1678, 1723, 13, 1678, 6389, 353, 13812, 29889, 5510, 29918, 5085, 580, 13, 1678, 1667, 29898, 13, 4706, 6389, 29889, 735, 15362, 29918, 978, 29892, 13, 4706, 6389, 29889, 19274, 386, 29918, 710, 271, 29892, 13, 4706, 6389, 29889, 262, 29918, 4117, 29918, 11965, 29879, 29892, 13, 4706, 6389, 29889, 8899, 29892, 13, 4706, 6389, 29889, 19274, 386, 29918, 16175, 29918, 2311, 29892, 13, 4706, 6389, 29889, 386, 12268, 29892, 13, 4706, 6389, 29889, 449, 29918, 19274, 386, 29918, 1557, 2361, 29892, 13, 4706, 6389, 29889, 13400, 29892, 13, 4706, 6389, 29889, 535, 29918, 13400, 29892, 13, 1678, 1723, 13, 2 ]
djnic/cambios/apps.py	avdata99/nic	8	41802	<filename>djnic/cambios/apps.py from django.apps import AppConfig class CambiosConfig(AppConfig): name = 'cambios'	[ 1, 529, 9507, 29958, 19776, 7823, 29914, 29883, 1117, 2363, 29914, 13371, 29889, 2272, 13, 3166, 9557, 29889, 13371, 1053, 2401, 3991, 13, 13, 13, 1990, 9287, 2363, 3991, 29898, 2052, 3991, 1125, 13, 1678, 1024, 353, 525, 29883, 1117, 2363, 29915, 13, 2 ]
venv/Lib/site-packages/PyOpenGL-3.0.1/OpenGL/GL/EXT/draw_buffers2.py	temelkirci/Motion_Editor	1	8208	<reponame>temelkirci/Motion_Editor '''OpenGL extension EXT.draw_buffers2 This module customises the behaviour of the OpenGL.raw.GL.EXT.draw_buffers2 to provide a more Python-friendly API Overview (from the spec) This extension builds upon the ARB_draw_buffers extension and provides separate blend enables and color write masks for each color output. In ARB_draw_buffers (part of OpenGL 2.0), separate values can be written to each color buffer, but the blend enable and color write mask are global and apply to all color outputs. While this extension does provide separate blend enables, it does not provide separate blend functions or blend equations per color output. The official definition of this extension is available here: http://www.opengl.org/registry/specs/EXT/draw_buffers2.txt ''' from OpenGL import platform, constants, constant, arrays from OpenGL import extensions, wrapper from OpenGL.GL import glget import ctypes from OpenGL.raw.GL.EXT.draw_buffers2 import * ### END AUTOGENERATED SECTION	[ 1, 529, 276, 1112, 420, 29958, 1356, 295, 14166, 455, 29914, 29924, 8194, 29918, 15280, 13, 12008, 6585, 7239, 6081, 8528, 29911, 29889, 4012, 29918, 28040, 414, 29906, 13, 13, 4013, 3883, 2888, 4637, 278, 10468, 310, 278, 29871, 13, 6585, 7239, 29889, 1610, 29889, 7239, 29889, 12194, 29889, 4012, 29918, 28040, 414, 29906, 304, 3867, 263, 901, 29871, 13, 11980, 29899, 18326, 368, 3450, 13, 13, 3563, 1493, 313, 3166, 278, 1580, 29897, 13, 12, 13, 12, 4013, 6081, 23315, 2501, 278, 9033, 29933, 29918, 4012, 29918, 28040, 414, 6081, 322, 8128, 13, 12, 25048, 403, 1999, 355, 28936, 322, 2927, 2436, 11105, 29879, 363, 1269, 2927, 1962, 29889, 29871, 512, 13, 12, 1718, 29933, 29918, 4012, 29918, 28040, 414, 313, 1595, 310, 29508, 29871, 29906, 29889, 29900, 511, 5004, 1819, 508, 367, 3971, 304, 13, 12, 4204, 2927, 6835, 29892, 541, 278, 1999, 355, 9025, 322, 2927, 2436, 11105, 526, 5534, 13, 12, 392, 3394, 304, 599, 2927, 14391, 29889, 13, 12, 13, 12, 8809, 488, 445, 6081, 947, 3867, 5004, 1999, 355, 28936, 29892, 372, 947, 451, 13, 12, 16123, 680, 5004, 1999, 355, 3168, 470, 1999, 355, 10693, 639, 2927, 1962, 29889, 13, 12, 13, 13, 1576, 6221, 5023, 310, 445, 6081, 338, 3625, 1244, 29901, 13, 1124, 597, 1636, 29889, 459, 6180, 29889, 990, 29914, 1727, 6020, 29914, 5965, 2395, 29914, 12194, 29914, 4012, 29918, 28040, 414, 29906, 29889, 3945, 13, 12008, 13, 3166, 29508, 1053, 7481, 29892, 17727, 29892, 4868, 29892, 7049, 13, 3166, 29508, 1053, 17752, 29892, 14476, 13, 3166, 29508, 29889, 7239, 1053, 3144, 657, 13, 5215, 274, 8768, 13, 3166, 29508, 29889, 1610, 29889, 7239, 29889, 12194, 29889, 4012, 29918, 28040, 414, 29906, 1053, 334, 13, 2277, 29937, 11056, 26524, 29949, 24647, 1001, 3040, 29928, 3725, 9838, 2 ]
misc/wapiti-2.3.0/wapitiCore/report/xmlreportgenerator.py	sapientgov/openfda-travel	377	179028	<reponame>sapientgov/openfda-travel<filename>misc/wapiti-2.3.0/wapitiCore/report/xmlreportgenerator.py #!/usr/bin/env python # -- coding: utf-8 -- # This file is part of the Wapiti project (http://wapiti.sourceforge.net) # Copyright (C) 2008-2013 <NAME> # # Original authors : # <NAME> # <NAME> # Copyright (C) 2008 Informatica Gesfor # ICT Romulus (http://www.ict-romulus.eu) # # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program; if not, write to the Free Software # Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA from xml.dom.minidom import Document from wapitiCore.report.reportgenerator import ReportGenerator class XMLReportGenerator(ReportGenerator): """ This class generates a report with the method printToFile(fileName) which contains the information of all the vulnerabilities notified to this object through the method logVulnerability(vulnerabilityTypeName,level,url,parameter,info). The format of the file is XML and it has the following structure: <report type="security"> <generatedBy id="Wapiti 2.3.0"/> <vulnerabilityTypeList> <vulnerabilityType name="SQL Injection"> <vulnerabilityTypeList> <vulnerabilityType name="SQL Injection"> <vulnerabilityList> <vulnerability level="3"> <url>http://www.a.com</url> <parameters>id=23</parameters> <info>SQL Injection</info> </vulnerability> </vulnerabilityList> </vulnerablityType> </vulnerabilityTypeList> </report> """ __xmlDoc = None __infos = {} __flawTypes = {} __vulns = {} __anomalies = {} __infos = {} def __init__(self): self.__xmlDoc = Document() def setReportInfo(self, target, scope=None, date_string="", version=""): self.__infos["target"] = target self.__infos["date"] = date_string self.__infos["version"] = version if scope: self.__infos["scope"] = scope # Vulnerabilities def addVulnerabilityType(self, name, description="", solution="", references={}): if name not in self.__flawTypes: self.__flawTypes[name] = {'desc': description, 'sol': solution, 'ref': references} if name not in self.__vulns: self.__vulns[name] = [] def logVulnerability(self, category=None, level=0, request=None, parameter="", info=""): """ Store the information about the vulnerability to be printed later. The method printToFile(fileName) can be used to save in a file the vulnerabilities notified through the current method. """ vuln_dict = {"method": request.method, "path": request.file_path, "info": info, "level": level, "parameter": parameter, "http_request": request.http_repr, "curl_command": request.curl_repr, } if category not in self.__vulns: self.__vulns[category] = [] self.__vulns[category].append(vuln_dict) # Anomalies def addAnomalyType(self, name, description="", solution="", references={}): if name not in self.__flawTypes: self.__flawTypes[name] = {'desc': description, 'sol': solution, 'ref': references} if name not in self.__anomalies: self.__anomalies[name] = [] def logAnomaly(self, category=None, level=0, request=None, parameter="", info=""): """ Store the information about the vulnerability to be printed later. The method printToFile(fileName) can be used to save in a file the vulnerabilities notified through the current method. """ anom_dict = {"method": request.method, "path": request.file_path, "info": info, "level": level, "parameter": parameter, "http_request": request.http_repr, "curl_command": request.curl_repr, } if category not in self.__anomalies: self.__anomalies[category] = [] self.__anomalies[category].append(anom_dict) def generateReport(self, fileName): """ Create a xml file with a report of the vulnerabilities which have been logged with the method logVulnerability(vulnerabilityTypeName,level,url,parameter,info) """ report = self.__xmlDoc.createElement("report") report.setAttribute("type", "security") self.__xmlDoc.appendChild(report) # Add report infos report_infos = self.__xmlDoc.createElement("report_infos") generatorName = self.__xmlDoc.createElement("info") generatorName.setAttribute("name", "generatorName") generatorName.appendChild(self.__xmlDoc.createTextNode("wapiti")) report_infos.appendChild(generatorName) generatorVersion = self.__xmlDoc.createElement("info") generatorVersion.setAttribute("name", "generatorVersion") generatorVersion.appendChild(self.__xmlDoc.createTextNode(self.__infos["version"])) report_infos.appendChild(generatorVersion) scope = self.__xmlDoc.createElement("info") scope.setAttribute("name", "scope") scope.appendChild(self.__xmlDoc.createTextNode(self.__infos["scope"])) report_infos.appendChild(scope) dateOfScan = self.__xmlDoc.createElement("info") dateOfScan.setAttribute("name", "dateOfScan") dateOfScan.appendChild(self.__xmlDoc.createTextNode(self.__infos["date"])) report_infos.appendChild(dateOfScan) report.appendChild(report_infos) vulnerabilities = self.__xmlDoc.createElement("vulnerabilities") anomalies = self.__xmlDoc.createElement("anomalies") # Loop on each flaw classification for flawType in self.__flawTypes: container = None classification = "" flaw_dict = {} if flawType in self.__vulns: container = vulnerabilities classification = "vulnerability" flaw_dict = self.__vulns elif flawType in self.__anomalies: container = anomalies classification = "anomaly" flaw_dict = self.__anomalies # Child nodes with a description of the flaw type flawTypeNode = self.__xmlDoc.createElement(classification) flawTypeNode.setAttribute("name", flawType) flawTypeDesc = self.__xmlDoc.createElement("description") flawTypeDesc.appendChild(self.__xmlDoc.createCDATASection(self.__flawTypes[flawType]['desc'])) flawTypeNode.appendChild(flawTypeDesc) flawTypeSolution = self.__xmlDoc.createElement("solution") flawTypeSolution.appendChild(self.__xmlDoc.createCDATASection(self.__flawTypes[flawType]['sol'])) flawTypeNode.appendChild(flawTypeSolution) flawTypeReferences = self.__xmlDoc.createElement("references") for ref in self.__flawTypes[flawType]['ref']: referenceNode = self.__xmlDoc.createElement("reference") titleNode = self.__xmlDoc.createElement("title") urlNode = self.__xmlDoc.createElement("url") titleNode.appendChild(self.__xmlDoc.createTextNode(ref)) url = self.__flawTypes[flawType]['ref'][ref] urlNode.appendChild(self.__xmlDoc.createTextNode(url)) referenceNode.appendChild(titleNode) referenceNode.appendChild(urlNode) flawTypeReferences.appendChild(referenceNode) flawTypeNode.appendChild(flawTypeReferences) # And child nodes with each flaw of the current type entriesNode = self.__xmlDoc.createElement("entries") for flaw in flaw_dict[flawType]: entryNode = self.__xmlDoc.createElement("entry") methodNode = self.__xmlDoc.createElement("method") methodNode.appendChild(self.__xmlDoc.createTextNode(flaw["method"])) entryNode.appendChild(methodNode) pathNode = self.__xmlDoc.createElement("path") pathNode.appendChild(self.__xmlDoc.createTextNode(flaw["path"])) entryNode.appendChild(pathNode) levelNode = self.__xmlDoc.createElement("level") levelNode.appendChild(self.__xmlDoc.createTextNode(str(flaw["level"]))) entryNode.appendChild(levelNode) parameterNode = self.__xmlDoc.createElement("parameter") parameterNode.appendChild(self.__xmlDoc.createTextNode(flaw["parameter"])) entryNode.appendChild(parameterNode) infoNode = self.__xmlDoc.createElement("info") infoNode.appendChild(self.__xmlDoc.createTextNode(flaw["info"])) entryNode.appendChild(infoNode) httpRequestNode = self.__xmlDoc.createElement("http_request") httpRequestNode.appendChild(self.__xmlDoc.createCDATASection(flaw["http_request"])) entryNode.appendChild(httpRequestNode) curlCommandNode = self.__xmlDoc.createElement("curl_command") curlCommandNode.appendChild(self.__xmlDoc.createCDATASection(flaw["curl_command"])) entryNode.appendChild(curlCommandNode) entriesNode.appendChild(entryNode) flawTypeNode.appendChild(entriesNode) container.appendChild(flawTypeNode) report.appendChild(vulnerabilities) report.appendChild(anomalies) f = open(fileName, "w") try: f.write(self.__xmlDoc.toprettyxml(indent=" ", encoding="UTF-8")) finally: f.close() if __name__ == "__main__": SQL_INJECTION = "Sql Injection" FILE_HANDLING = "File Handling" XSS = "Cross Site Scripting" CRLF = "CRLF Injection" EXEC = "Commands execution" try: xmlGen = XMLReportGenerator() xmlGen.addVulnerabilityType(SQL_INJECTION) xmlGen.addVulnerabilityType(FILE_HANDLING) xmlGen.addVulnerabilityType(XSS) xmlGen.addVulnerabilityType(CRLF) xmlGen.addVulnerabilityType(EXEC) xmlGen.logVulnerability("SQL Inyection", "1", "url1", "parameter1", "info1") xmlGen.logVulnerability("SQL Inyection", "2", "url2", "parameter2", "info2") xmlGen.logVulnerability("SQL Inyection", "2", "url3", "parameter3", "info3") xmlGen.logVulnerability("SQL Inyection", "3", "url4", "parameter4", "info4") xmlGen.logVulnerability("Cross Site Scripting", "3", "url5", "parameter5", "info5") xmlGen.logVulnerability("Cross Site Scripting", "3", "url6", "parameter6", "info6") xmlGen.logVulnerability("Cross Site Scripting", "2", "url7", "parameter7", "info7") xmlGen.logVulnerability("Cross Site Scripting", "1", "url8", "parameter8", "info8") xmlGen.logVulnerability("Google Hacking", "2", "url9", "parameter9", "info9") xmlGen.printToFile("sampleReport.xml") except SystemExit: pass	[ 1, 529, 276, 1112, 420, 29958, 29879, 481, 993, 13513, 29914, 3150, 29888, 1388, 29899, 3018, 955, 29966, 9507, 29958, 29885, 10669, 29914, 29893, 481, 4812, 29899, 29906, 29889, 29941, 29889, 29900, 29914, 29893, 481, 4812, 9203, 29914, 12276, 29914, 3134, 12276, 27959, 29889, 2272, 13, 29937, 14708, 4855, 29914, 2109, 29914, 6272, 3017, 13, 29937, 448, 29930, 29899, 14137, 29901, 23616, 29899, 29947, 448, 29930, 29899, 13, 29937, 910, 934, 338, 760, 310, 278, 399, 481, 4812, 2060, 313, 1124, 597, 29893, 481, 4812, 29889, 28315, 29889, 1212, 29897, 13, 29937, 14187, 1266, 313, 29907, 29897, 29871, 29906, 29900, 29900, 29947, 29899, 29906, 29900, 29896, 29941, 529, 5813, 29958, 13, 29937, 13, 29937, 8533, 15717, 584, 13, 29937, 529, 5813, 29958, 13, 29937, 529, 5813, 29958, 13, 29937, 14187, 1266, 313, 29907, 29897, 29871, 29906, 29900, 29900, 29947, 512, 4830, 983, 6328, 1454, 13, 29937, 306, 1783, 6033, 14999, 313, 1124, 597, 1636, 29889, 919, 29899, 456, 14999, 29889, 12932, 29897, 13, 29937, 13, 29937, 910, 1824, 338, 3889, 7047, 29936, 366, 508, 2654, 391, 2666, 372, 322, 29914, 272, 6623, 13, 29937, 372, 1090, 278, 4958, 310, 278, 15143, 4593, 5236, 19245, 408, 6369, 491, 13, 29937, 278, 12362, 18540, 10606, 29936, 2845, 1873, 29871, 29906, 310, 278, 19245, 29892, 470, 13, 29937, 313, 271, 596, 2984, 29897, 738, 2678, 1873, 29889, 13, 29937, 13, 29937, 910, 1824, 338, 13235, 297, 278, 4966, 393, 372, 674, 367, 5407, 29892, 13, 29937, 541, 399, 1806, 8187, 2692, 13764, 29979, 399, 1718, 29934, 13566, 29979, 29936, 1728, 1584, 278, 2411, 2957, 1370, 21867, 29891, 310, 13, 29937, 341, 1001, 3210, 13566, 2882, 6227, 11937, 470, 383, 1806, 8186, 1799, 15842, 319, 349, 8322, 2965, 13309, 1718, 349, 4574, 13152, 1660, 29889, 29871, 2823, 278, 13, 29937, 15143, 4593, 5236, 19245, 363, 901, 4902, 29889, 13, 29937, 13, 29937, 887, 881, 505, 4520, 263, 3509, 310, 278, 15143, 4593, 5236, 19245, 13, 29937, 3412, 411, 445, 1824, 29936, 565, 451, 29892, 2436, 304, 278, 12362, 18540, 13, 29937, 10606, 29892, 9266, 1696, 29871, 29945, 29896, 21504, 624, 29892, 29008, 386, 383, 10102, 29892, 12115, 29892, 14861, 259, 29900, 29906, 29896, 29896, 29900, 29899, 29896, 29941, 29900, 29896, 29871, 8278, 13, 3166, 4903, 29889, 3129, 29889, 1195, 333, 290, 1053, 10854, 13, 3166, 281, 481, 4812, 9203, 29889, 12276, 29889, 12276, 27959, 1053, 13969, 21575, 13, 13, 13, 1990, 6560, 13020, 21575, 29898, 13020, 21575, 1125, 13, 1678, 9995, 13, 1678, 910, 770, 16785, 263, 3461, 411, 278, 1158, 1596, 1762, 2283, 29898, 28926, 29897, 607, 3743, 13, 1678, 278, 2472, 310, 599, 278, 23180, 11614, 451, 2164, 304, 445, 1203, 1549, 278, 13, 1678, 1158, 1480, 29963, 352, 1089, 3097, 29898, 29894, 352, 1089, 3097, 1542, 1170, 29892, 5563, 29892, 2271, 29892, 15501, 29892, 3888, 467, 13, 1678, 450, 3402, 310, 278, 934, 338, 6560, 322, 372, 756, 278, 1494, 3829, 29901, 13, 1678, 529, 12276, 1134, 543, 8926, 1013, 13, 4706, 529, 13525, 2059, 1178, 543, 29956, 481, 4812, 29871, 29906, 29889, 29941, 29889, 29900, 4681, 13, 4706, 529, 29894, 352, 1089, 3097, 1542, 1293, 29958, 13, 9651, 529, 29894, 352, 1089, 3097, 1542, 1024, 543, 4176, 512, 6929, 1013, 13, 13, 4706, 529, 29894, 352, 1089, 3097, 1542, 1293, 29958, 13, 9651, 529, 29894, 352, 1089, 3097, 1542, 1024, 543, 4176, 512, 6929, 1013, 13, 18884, 529, 29894, 352, 1089, 3097, 1293, 29958, 13, 462, 1678, 529, 29894, 352, 1089, 3097, 3233, 543, 29941, 1013, 13, 462, 4706, 529, 2271, 29958, 1124, 597, 1636, 29889, 29874, 29889, 510, 829, 2271, 29958, 13, 462, 4706, 529, 16744, 29958, 333, 29922, 29906, 29941, 829, 16744, 29958, 13, 462, 4706, 529, 3888, 29958, 4176, 512, 6929, 829, 3888, 29958, 13, 462, 1678, 1533, 29894, 352, 1089, 3097, 29958, 13, 18884, 1533, 29894, 352, 1089, 3097, 1293, 29958, 13, 9651, 1533, 29894, 352, 1089, 12478, 537, 1542, 29958, 13, 4706, 1533, 29894, 352, 1089, 3097, 1542, 1293, 29958, 13, 1678, 1533, 12276, 29958, 13, 1678, 9995, 13, 13, 1678, 4770, 3134, 14526, 353, 6213, 13, 1678, 4770, 7192, 359, 353, 6571, 13, 1678, 4770, 29888, 10653, 10562, 353, 6571, 13, 13, 1678, 4770, 29894, 352, 1983, 353, 6571, 13, 1678, 4770, 273, 290, 284, 583, 353, 6571, 13, 13, 1678, 4770, 7192, 359, 353, 6571, 13, 13, 1678, 822, 4770, 2344, 12035, 1311, 1125, 13, 4706, 1583, 17255, 3134, 14526, 353, 10854, 580, 13, 13, 1678, 822, 731, 13020, 3401, 29898, 1311, 29892, 3646, 29892, 6874, 29922, 8516, 29892, 2635, 29918, 1807, 543, 613, 1873, 13776, 1125, 13, 4706, 1583, 17255, 7192, 359, 3366, 5182, 3108, 353, 3646, 13, 4706, 1583, 17255, 7192, 359, 3366, 1256, 3108, 353, 2635, 29918, 1807, 13, 4706, 1583, 17255, 7192, 359, 3366, 3259, 3108, 353, 1873, 13, 4706, 565, 6874, 29901, 13, 9651, 1583, 17255, 7192, 359, 3366, 6078, 3108, 353, 6874, 13, 13, 1678, 396, 478, 352, 1089, 11614, 13, 1678, 822, 788, 29963, 352, 1089, 3097, 1542, 29898, 1311, 29892, 1024, 29892, 13, 462, 632, 6139, 543, 613, 13, 462, 632, 1650, 543, 613, 13, 462, 632, 9282, 3790, 29913, 1125, 13, 4706, 565, 1024, 451, 297, 1583, 17255, 29888, 10653, 10562, 29901, 13, 9651, 1583, 17255, 29888, 10653, 10562, 29961, 978, 29962, 353, 11117, 14273, 2396, 6139, 29892, 13, 462, 462, 418, 525, 2929, 2396, 1650, 29892, 13, 462, 462, 418, 525, 999, 2396, 9282, 29913, 13, 4706, 565, 1024, 451, 297, 1583, 17255, 29894, 352, 1983, 29901, 13, 9651, 1583, 17255, 29894, 352, 1983, 29961, 978, 29962, 353, 5159, 13, 13, 1678, 822, 1480, 29963, 352, 1089, 3097, 29898, 1311, 29892, 13, 462, 308, 7663, 29922, 8516, 29892, 13, 462, 308, 3233, 29922, 29900, 29892, 13, 462, 308, 2009, 29922, 8516, 29892, 13, 462, 308, 3443, 543, 613, 13, 462, 308, 5235, 13776, 1125, 13, 4706, 9995, 13, 4706, 14491, 278, 2472, 1048, 278, 23180, 3097, 304, 367, 13350, 2678, 29889, 13, 4706, 450, 1158, 1596, 1762, 2283, 29898, 28926, 29897, 508, 367, 1304, 304, 4078, 297, 263, 934, 278, 13, 4706, 23180, 11614, 451, 2164, 1549, 278, 1857, 1158, 29889, 13, 4706, 9995, 13, 13, 4706, 12799, 29876, 29918, 8977, 353, 8853, 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rpython/translator/jvm/test/test_unicode.py	kantai/passe-pypy-taint-tracking	2	162321	<reponame>kantai/passe-pypy-taint-tracking import py from rpython.translator.jvm.test.runtest import JvmTest from rpython.rtyper.test.test_runicode import BaseTestRUnicode # ====> ../../../rpython/test/test_runicode.py class TestJvmUnicode(JvmTest, BaseTestRUnicode): EMPTY_STRING_HASH = 0 def test_unichar_const(self): def fn(): return u'\u03b1' assert self.interpret(fn, []) == u'\u03b1' def test_unichar_eq(self): py.test.skip("JVM doesn't support unicode for command line arguments") test_unichar_ord = test_unichar_eq test_unichar_hash = test_unichar_eq test_char_unichar_eq = test_unichar_eq test_char_unichar_eq_2 = test_unichar_eq def test_getitem_exc(self): py.test.skip('fixme!') def test_unicode_constant(self): const = u''.join(map(unichr, range(0, 256))) const = const + u'\ufffd' def fn(): return const res = self.interpret(fn, []) assert res == const def test_strformat_unicode_arg(self): py.test.skip('fixme!')	[ 1, 529, 276, 1112, 420, 29958, 29895, 424, 1794, 29914, 29886, 5793, 29899, 29886, 1478, 29891, 29899, 29873, 2365, 29899, 11294, 292, 13, 5215, 11451, 13, 3166, 364, 4691, 29889, 3286, 29880, 1061, 29889, 29926, 6925, 29889, 1688, 29889, 29878, 1657, 342, 1053, 435, 6925, 3057, 13, 3166, 364, 4691, 29889, 29878, 1017, 546, 29889, 1688, 29889, 1688, 29918, 29878, 2523, 356, 1053, 7399, 3057, 29934, 2525, 12858, 13, 13, 29937, 1275, 1360, 29958, 29772, 21546, 29878, 4691, 29914, 1688, 29914, 1688, 29918, 29878, 2523, 356, 29889, 2272, 13, 13, 1990, 4321, 29967, 6925, 2525, 12858, 29898, 29967, 6925, 3057, 29892, 7399, 3057, 29934, 2525, 12858, 1125, 13, 13, 1678, 382, 3580, 15631, 29918, 20785, 29918, 29950, 24943, 353, 29871, 29900, 13, 13, 1678, 822, 1243, 29918, 348, 436, 279, 29918, 3075, 29898, 1311, 1125, 13, 4706, 822, 7876, 7295, 13, 9651, 736, 318, 12764, 29884, 29900, 29941, 29890, 29896, 29915, 13, 4706, 4974, 1583, 29889, 1639, 19819, 29898, 9144, 29892, 518, 2314, 1275, 318, 12764, 29884, 29900, 29941, 29890, 29896, 29915, 13, 13, 1678, 822, 1243, 29918, 348, 436, 279, 29918, 1837, 29898, 1311, 1125, 13, 4706, 11451, 29889, 1688, 29889, 11014, 703, 29967, 9219, 1838, 29915, 29873, 2304, 29104, 363, 1899, 1196, 6273, 1159, 13, 1678, 1243, 29918, 348, 436, 279, 29918, 536, 353, 1243, 29918, 348, 436, 279, 29918, 1837, 13, 1678, 1243, 29918, 348, 436, 279, 29918, 8568, 353, 1243, 29918, 348, 436, 279, 29918, 1837, 13, 1678, 1243, 29918, 3090, 29918, 348, 436, 279, 29918, 1837, 353, 1243, 29918, 348, 436, 279, 29918, 1837, 13, 1678, 1243, 29918, 3090, 29918, 348, 436, 279, 29918, 1837, 29918, 29906, 353, 1243, 29918, 348, 436, 279, 29918, 1837, 13, 13, 1678, 822, 1243, 29918, 657, 667, 29918, 735, 29883, 29898, 1311, 1125, 13, 4706, 11451, 29889, 1688, 29889, 11014, 877, 5878, 1004, 29991, 1495, 13, 13, 1678, 822, 1243, 29918, 2523, 356, 29918, 23362, 29898, 1311, 1125, 13, 4706, 1040, 353, 318, 29915, 4286, 7122, 29898, 1958, 29898, 348, 436, 29878, 29892, 3464, 29898, 29900, 29892, 29871, 29906, 29945, 29953, 4961, 13, 4706, 1040, 353, 1040, 718, 318, 12764, 3096, 11512, 29915, 13, 4706, 822, 7876, 7295, 13, 9651, 736, 1040, 13, 4706, 620, 353, 1583, 29889, 1639, 19819, 29898, 9144, 29892, 518, 2314, 13, 4706, 4974, 620, 1275, 1040, 13, 13, 1678, 822, 1243, 29918, 710, 4830, 29918, 2523, 356, 29918, 1191, 29898, 1311, 1125, 13, 4706, 11451, 29889, 1688, 29889, 11014, 877, 5878, 1004, 29991, 1495, 13, 2 ]
ros/devel/lib/python2.7/dist-packages/darknet_ros_msgs/msg/_CheckForObjectsAction.py	wutianze/ComP	3	34043	<gh_stars>1-10 # This Python file uses the following encoding: utf-8 """autogenerated by genpy from darknet_ros_msgs/CheckForObjectsAction.msg. Do not edit.""" import sys python3 = True if sys.hexversion > 0x03000000 else False import genpy import struct import darknet_ros_msgs.msg import sensor_msgs.msg import genpy import actionlib_msgs.msg import std_msgs.msg class CheckForObjectsAction(genpy.Message): _md5sum = "98095af4078a4c5df88f8e6a4db52e32" _type = "darknet_ros_msgs/CheckForObjectsAction" _has_header = False #flag to mark the presence of a Header object _full_text = """# ====== DO NOT MODIFY! AUTOGENERATED FROM AN ACTION DEFINITION ====== CheckForObjectsActionGoal action_goal CheckForObjectsActionResult action_result CheckForObjectsActionFeedback action_feedback ================================================================================ MSG: darknet_ros_msgs/CheckForObjectsActionGoal # ====== DO NOT MODIFY! AUTOGENERATED FROM AN ACTION DEFINITION ====== Header header actionlib_msgs/GoalID goal_id CheckForObjectsGoal goal ================================================================================ MSG: std_msgs/Header # Standard metadata for higher-level stamped data types. # This is generally used to communicate timestamped data # in a particular coordinate frame. # # sequence ID: consecutively increasing ID uint32 seq #Two-integer timestamp that is expressed as: # * stamp.sec: seconds (stamp_secs) since epoch (in Python the variable is called 'secs') # * stamp.nsec: nanoseconds since stamp_secs (in Python the variable is called 'nsecs') # time-handling sugar is provided by the client library time stamp #Frame this data is associated with string frame_id ================================================================================ MSG: actionlib_msgs/GoalID # The stamp should store the time at which this goal was requested. # It is used by an action server when it tries to preempt all # goals that were requested before a certain time time stamp # The id provides a way to associate feedback and # result message with specific goal requests. The id # specified must be unique. string id ================================================================================ MSG: darknet_ros_msgs/CheckForObjectsGoal # ====== DO NOT MODIFY! AUTOGENERATED FROM AN ACTION DEFINITION ====== # Check if objects in image # Goal definition int16 id sensor_msgs/Image image ================================================================================ MSG: sensor_msgs/Image # This message contains an uncompressed image # (0, 0) is at top-left corner of image # Header header # Header timestamp should be acquisition time of image # Header frame_id should be optical frame of camera # origin of frame should be optical center of camera # +x should point to the right in the image # +y should point down in the image # +z should point into to plane of the image # If the frame_id here and the frame_id of the CameraInfo # message associated with the image conflict # the behavior is undefined uint32 height # image height, that is, number of rows uint32 width # image width, that is, number of columns # The legal values for encoding are in file src/image_encodings.cpp # If you want to standardize a new string format, join # <EMAIL> and send an email proposing a new encoding. string encoding # Encoding of pixels -- channel meaning, ordering, size # taken from the list of strings in include/sensor_msgs/image_encodings.h uint8 is_bigendian # is this data bigendian? uint32 step # Full row length in bytes uint8[] data # actual matrix data, size is (step * rows) ================================================================================ MSG: darknet_ros_msgs/CheckForObjectsActionResult # ====== DO NOT MODIFY! AUTOGENERATED FROM AN ACTION DEFINITION ====== Header header actionlib_msgs/GoalStatus status CheckForObjectsResult result ================================================================================ MSG: actionlib_msgs/GoalStatus GoalID goal_id uint8 status uint8 PENDING = 0 # The goal has yet to be processed by the action server uint8 ACTIVE = 1 # The goal is currently being processed by the action server uint8 PREEMPTED = 2 # The goal received a cancel request after it started executing # and has since completed its execution (Terminal State) uint8 SUCCEEDED = 3 # The goal was achieved successfully by the action server (Terminal State) uint8 ABORTED = 4 # The goal was aborted during execution by the action server due # to some failure (Terminal State) uint8 REJECTED = 5 # The goal was rejected by the action server without being processed, # because the goal was unattainable or invalid (Terminal State) uint8 PREEMPTING = 6 # The goal received a cancel request after it started executing # and has not yet completed execution uint8 RECALLING = 7 # The goal received a cancel request before it started executing, # but the action server has not yet confirmed that the goal is canceled uint8 RECALLED = 8 # The goal received a cancel request before it started executing # and was successfully cancelled (Terminal State) uint8 LOST = 9 # An action client can determine that a goal is LOST. This should not be # sent over the wire by an action server #Allow for the user to associate a string with GoalStatus for debugging string text ================================================================================ MSG: darknet_ros_msgs/CheckForObjectsResult # ====== DO NOT MODIFY! AUTOGENERATED FROM AN ACTION DEFINITION ====== # Result definition int16 id darknet_ros_msgs/BoundingBoxes bounding_boxes ================================================================================ MSG: darknet_ros_msgs/BoundingBoxes Header header Header image_header BoundingBox[] bounding_boxes ================================================================================ MSG: darknet_ros_msgs/BoundingBox float64 probability int64 xmin int64 ymin int64 xmax int64 ymax int16 id string Class ================================================================================ MSG: darknet_ros_msgs/CheckForObjectsActionFeedback # ====== DO NOT MODIFY! AUTOGENERATED FROM AN ACTION DEFINITION ====== Header header actionlib_msgs/GoalStatus status CheckForObjectsFeedback feedback ================================================================================ MSG: darknet_ros_msgs/CheckForObjectsFeedback # ====== DO NOT MODIFY! AUTOGENERATED FROM AN ACTION DEFINITION ====== # Feedback definition """ __slots__ = ['action_goal','action_result','action_feedback'] _slot_types = ['darknet_ros_msgs/CheckForObjectsActionGoal','darknet_ros_msgs/CheckForObjectsActionResult','darknet_ros_msgs/CheckForObjectsActionFeedback'] def __init__(self, args, kwds): """ Constructor. Any message fields that are implicitly/explicitly set to None will be assigned a default value. The recommend use is keyword arguments as this is more robust to future message changes. You cannot mix in-order arguments and keyword arguments. The available fields are: action_goal,action_result,action_feedback :param args: complete set of field values, in .msg order :param kwds: use keyword arguments corresponding to message field names to set specific fields. """ if args or kwds: super(CheckForObjectsAction, self).__init__(args, *kwds) #message fields cannot be None, assign default values for those that are if self.action_goal is None: self.action_goal = darknet_ros_msgs.msg.CheckForObjectsActionGoal() if self.action_result is None: self.action_result = darknet_ros_msgs.msg.CheckForObjectsActionResult() if self.action_feedback is None: self.action_feedback = darknet_ros_msgs.msg.CheckForObjectsActionFeedback() else: self.action_goal = darknet_ros_msgs.msg.CheckForObjectsActionGoal() self.action_result = darknet_ros_msgs.msg.CheckForObjectsActionResult() self.action_feedback = darknet_ros_msgs.msg.CheckForObjectsActionFeedback() def _get_types(self): """ internal API method """ return self._slot_types def serialize(self, buff): """ serialize message into buffer :param buff: buffer, ``StringIO`` """ try: _x = self buff.write(_get_struct_3I().pack(_x.action_goal.header.seq, _x.action_goal.header.stamp.secs, _x.action_goal.header.stamp.nsecs)) _x = self.action_goal.header.frame_id length = len(_x) if python3 or type(_x) == unicode: _x = _x.encode('utf-8') length = len(_x) buff.write(struct.pack('<I%ss'%length, length, _x)) _x = self buff.write(_get_struct_2I().pack(_x.action_goal.goal_id.stamp.secs, _x.action_goal.goal_id.stamp.nsecs)) _x = self.action_goal.goal_id.id length = len(_x) if python3 or type(_x) == unicode: _x = _x.encode('utf-8') length = len(_x) buff.write(struct.pack('<I%ss'%length, length, _x)) _x = self buff.write(_get_struct_h3I().pack(_x.action_goal.goal.id, _x.action_goal.goal.image.header.seq, _x.action_goal.goal.image.header.stamp.secs, _x.action_goal.goal.image.header.stamp.nsecs)) _x = self.action_goal.goal.image.header.frame_id length = len(_x) if python3 or type(_x) == unicode: _x = _x.encode('utf-8') length = len(_x) buff.write(struct.pack('<I%ss'%length, length, _x)) _x = self buff.write(_get_struct_2I().pack(_x.action_goal.goal.image.height, _x.action_goal.goal.image.width)) _x = self.action_goal.goal.image.encoding length = len(_x) if python3 or type(_x) == unicode: _x = _x.encode('utf-8') length = len(_x) buff.write(struct.pack('<I%ss'%length, length, _x)) _x = self buff.write(_get_struct_BI().pack(_x.action_goal.goal.image.is_bigendian, _x.action_goal.goal.image.step)) _x = self.action_goal.goal.image.data length = len(_x) # - if encoded as a list instead, serialize as bytes instead of string if type(_x) in [list, tuple]: buff.write(struct.pack('<I%sB'%length, length, _x)) else: buff.write(struct.pack('<I%ss'%length, length, _x)) _x = self buff.write(_get_struct_3I().pack(_x.action_result.header.seq, _x.action_result.header.stamp.secs, _x.action_result.header.stamp.nsecs)) _x = self.action_result.header.frame_id length = len(_x) if python3 or type(_x) == unicode: _x = _x.encode('utf-8') length = len(_x) buff.write(struct.pack('<I%ss'%length, length, _x)) _x = self buff.write(_get_struct_2I().pack(_x.action_result.status.goal_id.stamp.secs, _x.action_result.status.goal_id.stamp.nsecs)) _x = self.action_result.status.goal_id.id length = len(_x) if python3 or type(_x) == unicode: _x = _x.encode('utf-8') length = len(_x) buff.write(struct.pack('<I%ss'%length, length, _x)) buff.write(_get_struct_B().pack(self.action_result.status.status)) _x = self.action_result.status.text length = len(_x) if python3 or type(_x) == unicode: _x = _x.encode('utf-8') length = len(_x) buff.write(struct.pack('<I%ss'%length, length, _x)) _x = self buff.write(_get_struct_h3I().pack(_x.action_result.result.id, _x.action_result.result.bounding_boxes.header.seq, _x.action_result.result.bounding_boxes.header.stamp.secs, _x.action_result.result.bounding_boxes.header.stamp.nsecs)) _x = self.action_result.result.bounding_boxes.header.frame_id length = len(_x) if python3 or type(_x) == unicode: _x = _x.encode('utf-8') length = len(_x) buff.write(struct.pack('<I%ss'%length, length, _x)) _x = self buff.write(_get_struct_3I().pack(_x.action_result.result.bounding_boxes.image_header.seq, _x.action_result.result.bounding_boxes.image_header.stamp.secs, _x.action_result.result.bounding_boxes.image_header.stamp.nsecs)) _x = self.action_result.result.bounding_boxes.image_header.frame_id length = len(_x) if python3 or type(_x) == unicode: _x = _x.encode('utf-8') length = len(_x) buff.write(struct.pack('<I%ss'%length, length, _x)) length = len(self.action_result.result.bounding_boxes.bounding_boxes) buff.write(_struct_I.pack(length)) for val1 in self.action_result.result.bounding_boxes.bounding_boxes: _x = val1 buff.write(_get_struct_d4qh().pack(_x.probability, _x.xmin, _x.ymin, _x.xmax, _x.ymax, _x.id)) _x = val1.Class length = len(_x) if python3 or type(_x) == unicode: _x = _x.encode('utf-8') length = len(_x) buff.write(struct.pack('<I%ss'%length, length, _x)) _x = self buff.write(_get_struct_3I().pack(_x.action_feedback.header.seq, _x.action_feedback.header.stamp.secs, _x.action_feedback.header.stamp.nsecs)) _x = self.action_feedback.header.frame_id length = len(_x) if python3 or type(_x) == unicode: _x = _x.encode('utf-8') length = len(_x) buff.write(struct.pack('<I%ss'%length, length, _x)) _x = self buff.write(_get_struct_2I().pack(_x.action_feedback.status.goal_id.stamp.secs, _x.action_feedback.status.goal_id.stamp.nsecs)) _x = self.action_feedback.status.goal_id.id length = len(_x) if python3 or type(_x) == unicode: _x = _x.encode('utf-8') length = len(_x) buff.write(struct.pack('<I%ss'%length, length, _x)) buff.write(_get_struct_B().pack(self.action_feedback.status.status)) _x = self.action_feedback.status.text length = len(_x) if python3 or type(_x) == unicode: _x = _x.encode('utf-8') length = len(_x) buff.write(struct.pack('<I%ss'%length, length, _x)) except struct.error as se: self._check_types(struct.error("%s: '%s' when writing '%s'" % (type(se), str(se), str(locals().get('_x', self))))) except TypeError as te: self._check_types(ValueError("%s: '%s' when writing '%s'" % (type(te), str(te), str(locals().get('_x', self))))) def deserialize(self, str): """ unpack serialized message in str into this message instance :param str: byte array of serialized message, ``str`` """ try: if self.action_goal is None: self.action_goal = darknet_ros_msgs.msg.CheckForObjectsActionGoal() if self.action_result is None: self.action_result = darknet_ros_msgs.msg.CheckForObjectsActionResult() if self.action_feedback is None: self.action_feedback = darknet_ros_msgs.msg.CheckForObjectsActionFeedback() end = 0 _x = self start = end end += 12 (_x.action_goal.header.seq, _x.action_goal.header.stamp.secs, _x.action_goal.header.stamp.nsecs,) = _get_struct_3I().unpack(str[start:end]) start = end end += 4 (length,) = _struct_I.unpack(str[start:end]) start = end end += length if python3: self.action_goal.header.frame_id = str[start:end].decode('utf-8') else: self.action_goal.header.frame_id = str[start:end] _x = self start = end end += 8 (_x.action_goal.goal_id.stamp.secs, _x.action_goal.goal_id.stamp.nsecs,) = _get_struct_2I().unpack(str[start:end]) start = end end += 4 (length,) = _struct_I.unpack(str[start:end]) start = end end += length if python3: self.action_goal.goal_id.id = str[start:end].decode('utf-8') else: self.action_goal.goal_id.id = str[start:end] _x = self start = end end += 14 (_x.action_goal.goal.id, _x.action_goal.goal.image.header.seq, _x.action_goal.goal.image.header.stamp.secs, _x.action_goal.goal.image.header.stamp.nsecs,) = _get_struct_h3I().unpack(str[start:end]) start = end end += 4 (length,) = _struct_I.unpack(str[start:end]) start = end end += length if python3: self.action_goal.goal.image.header.frame_id = str[start:end].decode('utf-8') else: self.action_goal.goal.image.header.frame_id = str[start:end] _x = self start = end end += 8 (_x.action_goal.goal.image.height, _x.action_goal.goal.image.width,) = _get_struct_2I().unpack(str[start:end]) start = end end += 4 (length,) = _struct_I.unpack(str[start:end]) start = end end += length if python3: self.action_goal.goal.image.encoding = str[start:end].decode('utf-8') else: self.action_goal.goal.image.encoding = str[start:end] _x = self start = end end += 5 (_x.action_goal.goal.image.is_bigendian, _x.action_goal.goal.image.step,) = _get_struct_BI().unpack(str[start:end]) start = end end += 4 (length,) = _struct_I.unpack(str[start:end]) start = end end += length self.action_goal.goal.image.data = str[start:end] _x = self start = end end += 12 (_x.action_result.header.seq, _x.action_result.header.stamp.secs, _x.action_result.header.stamp.nsecs,) = _get_struct_3I().unpack(str[start:end]) start = end end += 4 (length,) = _struct_I.unpack(str[start:end]) start = end end += length if python3: self.action_result.header.frame_id = str[start:end].decode('utf-8') else: self.action_result.header.frame_id = str[start:end] _x = self start = end end += 8 (_x.action_result.status.goal_id.stamp.secs, _x.action_result.status.goal_id.stamp.nsecs,) = _get_struct_2I().unpack(str[start:end]) start = end end += 4 (length,) = _struct_I.unpack(str[start:end]) start = end end += length if python3: self.action_result.status.goal_id.id = str[start:end].decode('utf-8') else: self.action_result.status.goal_id.id = str[start:end] start = end end += 1 (self.action_result.status.status,) = _get_struct_B().unpack(str[start:end]) start = end end += 4 (length,) = _struct_I.unpack(str[start:end]) start = end end += length if python3: self.action_result.status.text = str[start:end].decode('utf-8') else: self.action_result.status.text = str[start:end] _x = self start = end end += 14 (_x.action_result.result.id, _x.action_result.result.bounding_boxes.header.seq, _x.action_result.result.bounding_boxes.header.stamp.secs, _x.action_result.result.bounding_boxes.header.stamp.nsecs,) = _get_struct_h3I().unpack(str[start:end]) start = end end += 4 (length,) = _struct_I.unpack(str[start:end]) start = end end += length if python3: self.action_result.result.bounding_boxes.header.frame_id = str[start:end].decode('utf-8') else: self.action_result.result.bounding_boxes.header.frame_id = str[start:end] _x = self start = end end += 12 (_x.action_result.result.bounding_boxes.image_header.seq, _x.action_result.result.bounding_boxes.image_header.stamp.secs, _x.action_result.result.bounding_boxes.image_header.stamp.nsecs,) = _get_struct_3I().unpack(str[start:end]) start = end end += 4 (length,) = _struct_I.unpack(str[start:end]) start = end end += length if python3: self.action_result.result.bounding_boxes.image_header.frame_id = str[start:end].decode('utf-8') else: self.action_result.result.bounding_boxes.image_header.frame_id = str[start:end] start = end end += 4 (length,) = _struct_I.unpack(str[start:end]) self.action_result.result.bounding_boxes.bounding_boxes = [] for i in range(0, length): val1 = darknet_ros_msgs.msg.BoundingBox() _x = val1 start = end end += 42 (_x.probability, _x.xmin, _x.ymin, _x.xmax, _x.ymax, _x.id,) = _get_struct_d4qh().unpack(str[start:end]) start = end end += 4 (length,) = _struct_I.unpack(str[start:end]) start = end end += length if python3: val1.Class = str[start:end].decode('utf-8') else: val1.Class = str[start:end] self.action_result.result.bounding_boxes.bounding_boxes.append(val1) _x = self start = end end += 12 (_x.action_feedback.header.seq, _x.action_feedback.header.stamp.secs, _x.action_feedback.header.stamp.nsecs,) = _get_struct_3I().unpack(str[start:end]) start = end end += 4 (length,) = _struct_I.unpack(str[start:end]) start = end end += length if python3: self.action_feedback.header.frame_id = str[start:end].decode('utf-8') else: self.action_feedback.header.frame_id = str[start:end] _x = self start = end end += 8 (_x.action_feedback.status.goal_id.stamp.secs, _x.action_feedback.status.goal_id.stamp.nsecs,) = _get_struct_2I().unpack(str[start:end]) start = end end += 4 (length,) = _struct_I.unpack(str[start:end]) start = end end += length if python3: self.action_feedback.status.goal_id.id = str[start:end].decode('utf-8') else: self.action_feedback.status.goal_id.id = str[start:end] start = end end += 1 (self.action_feedback.status.status,) = _get_struct_B().unpack(str[start:end]) start = end end += 4 (length,) = _struct_I.unpack(str[start:end]) start = end end += length if python3: self.action_feedback.status.text = str[start:end].decode('utf-8') else: self.action_feedback.status.text = str[start:end] return self except struct.error as e: raise genpy.DeserializationError(e) #most likely buffer underfill def serialize_numpy(self, buff, numpy): """ serialize message with numpy array types into buffer :param buff: buffer, ``StringIO`` :param numpy: numpy python module """ try: _x = self buff.write(_get_struct_3I().pack(_x.action_goal.header.seq, _x.action_goal.header.stamp.secs, _x.action_goal.header.stamp.nsecs)) _x = self.action_goal.header.frame_id length = len(_x) if python3 or type(_x) == unicode: _x = _x.encode('utf-8') length = len(_x) buff.write(struct.pack('<I%ss'%length, length, _x)) _x = self buff.write(_get_struct_2I().pack(_x.action_goal.goal_id.stamp.secs, _x.action_goal.goal_id.stamp.nsecs)) _x = self.action_goal.goal_id.id length = len(_x) if python3 or type(_x) == unicode: _x = _x.encode('utf-8') length = len(_x) buff.write(struct.pack('<I%ss'%length, length, _x)) _x = self buff.write(_get_struct_h3I().pack(_x.action_goal.goal.id, _x.action_goal.goal.image.header.seq, _x.action_goal.goal.image.header.stamp.secs, _x.action_goal.goal.image.header.stamp.nsecs)) _x = self.action_goal.goal.image.header.frame_id length = len(_x) if python3 or type(_x) == unicode: _x = _x.encode('utf-8') length = len(_x) buff.write(struct.pack('<I%ss'%length, length, _x)) _x = self buff.write(_get_struct_2I().pack(_x.action_goal.goal.image.height, _x.action_goal.goal.image.width)) _x = self.action_goal.goal.image.encoding length = len(_x) if python3 or type(_x) == unicode: _x = _x.encode('utf-8') length = len(_x) buff.write(struct.pack('<I%ss'%length, length, _x)) _x = self buff.write(_get_struct_BI().pack(_x.action_goal.goal.image.is_bigendian, _x.action_goal.goal.image.step)) _x = self.action_goal.goal.image.data length = len(_x) # - if encoded as a list instead, serialize as bytes instead of string if type(_x) in [list, tuple]: buff.write(struct.pack('<I%sB'%length, length, *_x)) else: buff.write(struct.pack('<I%ss'%length, length, _x)) _x = self buff.write(_get_struct_3I().pack(_x.action_result.header.seq, _x.action_result.header.stamp.secs, _x.action_result.header.stamp.nsecs)) _x = self.action_result.header.frame_id length = len(_x) if python3 or type(_x) == unicode: _x = _x.encode('utf-8') length = len(_x) buff.write(struct.pack('<I%ss'%length, length, _x)) _x = self buff.write(_get_struct_2I().pack(_x.action_result.status.goal_id.stamp.secs, _x.action_result.status.goal_id.stamp.nsecs)) _x = self.action_result.status.goal_id.id length = len(_x) if python3 or type(_x) == unicode: _x = _x.encode('utf-8') length = len(_x) buff.write(struct.pack('<I%ss'%length, length, _x)) buff.write(_get_struct_B().pack(self.action_result.status.status)) _x = self.action_result.status.text length = len(_x) if python3 or type(_x) == unicode: _x = _x.encode('utf-8') length = len(_x) buff.write(struct.pack('<I%ss'%length, length, _x)) _x = self buff.write(_get_struct_h3I().pack(_x.action_result.result.id, _x.action_result.result.bounding_boxes.header.seq, _x.action_result.result.bounding_boxes.header.stamp.secs, _x.action_result.result.bounding_boxes.header.stamp.nsecs)) _x = self.action_result.result.bounding_boxes.header.frame_id length = len(_x) if python3 or type(_x) == unicode: _x = _x.encode('utf-8') length = len(_x) buff.write(struct.pack('<I%ss'%length, length, _x)) _x = self buff.write(_get_struct_3I().pack(_x.action_result.result.bounding_boxes.image_header.seq, _x.action_result.result.bounding_boxes.image_header.stamp.secs, _x.action_result.result.bounding_boxes.image_header.stamp.nsecs)) _x = self.action_result.result.bounding_boxes.image_header.frame_id length = len(_x) if python3 or type(_x) == unicode: _x = _x.encode('utf-8') length = len(_x) buff.write(struct.pack('<I%ss'%length, length, _x)) length = len(self.action_result.result.bounding_boxes.bounding_boxes) buff.write(_struct_I.pack(length)) for val1 in self.action_result.result.bounding_boxes.bounding_boxes: _x = val1 buff.write(_get_struct_d4qh().pack(_x.probability, _x.xmin, _x.ymin, _x.xmax, _x.ymax, _x.id)) _x = val1.Class length = len(_x) if python3 or type(_x) == unicode: _x = _x.encode('utf-8') length = len(_x) buff.write(struct.pack('<I%ss'%length, length, _x)) _x = self buff.write(_get_struct_3I().pack(_x.action_feedback.header.seq, _x.action_feedback.header.stamp.secs, _x.action_feedback.header.stamp.nsecs)) _x = self.action_feedback.header.frame_id length = len(_x) if python3 or type(_x) == unicode: _x = _x.encode('utf-8') length = len(_x) buff.write(struct.pack('<I%ss'%length, length, _x)) _x = self buff.write(_get_struct_2I().pack(_x.action_feedback.status.goal_id.stamp.secs, _x.action_feedback.status.goal_id.stamp.nsecs)) _x = self.action_feedback.status.goal_id.id length = len(_x) if python3 or type(_x) == unicode: _x = _x.encode('utf-8') length = len(_x) buff.write(struct.pack('<I%ss'%length, length, _x)) buff.write(_get_struct_B().pack(self.action_feedback.status.status)) _x = self.action_feedback.status.text length = len(_x) if python3 or type(_x) == unicode: _x = _x.encode('utf-8') length = len(_x) buff.write(struct.pack('<I%ss'%length, length, _x)) except struct.error as se: self._check_types(struct.error("%s: '%s' when writing '%s'" % (type(se), str(se), str(locals().get('_x', self))))) except TypeError as te: self._check_types(ValueError("%s: '%s' when writing '%s'" % (type(te), str(te), str(locals().get('_x', self))))) def deserialize_numpy(self, str, numpy): """ unpack serialized message in str into this message instance using numpy for array types :param str: byte array of serialized message, ``str`` :param numpy: numpy python module """ try: if self.action_goal is None: self.action_goal = darknet_ros_msgs.msg.CheckForObjectsActionGoal() if self.action_result is None: self.action_result = darknet_ros_msgs.msg.CheckForObjectsActionResult() if self.action_feedback is None: self.action_feedback = darknet_ros_msgs.msg.CheckForObjectsActionFeedback() end = 0 _x = self start = end end += 12 (_x.action_goal.header.seq, _x.action_goal.header.stamp.secs, _x.action_goal.header.stamp.nsecs,) = _get_struct_3I().unpack(str[start:end]) start = end end += 4 (length,) = _struct_I.unpack(str[start:end]) start = end end += length if python3: self.action_goal.header.frame_id = str[start:end].decode('utf-8') else: self.action_goal.header.frame_id = str[start:end] _x = self start = end end += 8 (_x.action_goal.goal_id.stamp.secs, _x.action_goal.goal_id.stamp.nsecs,) = _get_struct_2I().unpack(str[start:end]) start = end end += 4 (length,) = _struct_I.unpack(str[start:end]) start = end end += length if python3: self.action_goal.goal_id.id = str[start:end].decode('utf-8') else: self.action_goal.goal_id.id = str[start:end] _x = self start = end end += 14 (_x.action_goal.goal.id, _x.action_goal.goal.image.header.seq, _x.action_goal.goal.image.header.stamp.secs, _x.action_goal.goal.image.header.stamp.nsecs,) = _get_struct_h3I().unpack(str[start:end]) start = end end += 4 (length,) = _struct_I.unpack(str[start:end]) start = end end += length if python3: self.action_goal.goal.image.header.frame_id = str[start:end].decode('utf-8') else: self.action_goal.goal.image.header.frame_id = str[start:end] _x = self start = end end += 8 (_x.action_goal.goal.image.height, _x.action_goal.goal.image.width,) = _get_struct_2I().unpack(str[start:end]) start = end end += 4 (length,) = _struct_I.unpack(str[start:end]) start = end end += length if python3: self.action_goal.goal.image.encoding = str[start:end].decode('utf-8') else: self.action_goal.goal.image.encoding = str[start:end] _x = self start = end end += 5 (_x.action_goal.goal.image.is_bigendian, _x.action_goal.goal.image.step,) = _get_struct_BI().unpack(str[start:end]) start = end end += 4 (length,) = _struct_I.unpack(str[start:end]) start = end end += length self.action_goal.goal.image.data = str[start:end] _x = self start = end end += 12 (_x.action_result.header.seq, _x.action_result.header.stamp.secs, _x.action_result.header.stamp.nsecs,) = _get_struct_3I().unpack(str[start:end]) start = end end += 4 (length,) = _struct_I.unpack(str[start:end]) start = end end += length if python3: self.action_result.header.frame_id = str[start:end].decode('utf-8') else: self.action_result.header.frame_id = str[start:end] _x = self start = end end += 8 (_x.action_result.status.goal_id.stamp.secs, _x.action_result.status.goal_id.stamp.nsecs,) = _get_struct_2I().unpack(str[start:end]) start = end end += 4 (length,) = _struct_I.unpack(str[start:end]) start = end end += length if python3: self.action_result.status.goal_id.id = str[start:end].decode('utf-8') else: self.action_result.status.goal_id.id = str[start:end] start = end end += 1 (self.action_result.status.status,) = _get_struct_B().unpack(str[start:end]) start = end end += 4 (length,) = _struct_I.unpack(str[start:end]) start = end end += length if python3: self.action_result.status.text = str[start:end].decode('utf-8') else: self.action_result.status.text = str[start:end] _x = self start = end end += 14 (_x.action_result.result.id, _x.action_result.result.bounding_boxes.header.seq, _x.action_result.result.bounding_boxes.header.stamp.secs, _x.action_result.result.bounding_boxes.header.stamp.nsecs,) = _get_struct_h3I().unpack(str[start:end]) start = end end += 4 (length,) = _struct_I.unpack(str[start:end]) start = end end += length if python3: self.action_result.result.bounding_boxes.header.frame_id = str[start:end].decode('utf-8') else: self.action_result.result.bounding_boxes.header.frame_id = str[start:end] _x = self start = end end += 12 (_x.action_result.result.bounding_boxes.image_header.seq, _x.action_result.result.bounding_boxes.image_header.stamp.secs, _x.action_result.result.bounding_boxes.image_header.stamp.nsecs,) = _get_struct_3I().unpack(str[start:end]) start = end end += 4 (length,) = _struct_I.unpack(str[start:end]) start = end end += length if python3: self.action_result.result.bounding_boxes.image_header.frame_id = str[start:end].decode('utf-8') else: self.action_result.result.bounding_boxes.image_header.frame_id = str[start:end] start = end end += 4 (length,) = _struct_I.unpack(str[start:end]) self.action_result.result.bounding_boxes.bounding_boxes = [] for i in range(0, length): val1 = darknet_ros_msgs.msg.BoundingBox() _x = val1 start = end end += 42 (_x.probability, _x.xmin, _x.ymin, _x.xmax, _x.ymax, _x.id,) = _get_struct_d4qh().unpack(str[start:end]) start = end end += 4 (length,) = _struct_I.unpack(str[start:end]) start = end end += length if python3: val1.Class = str[start:end].decode('utf-8') else: val1.Class = str[start:end] self.action_result.result.bounding_boxes.bounding_boxes.append(val1) _x = self start = end end += 12 (_x.action_feedback.header.seq, _x.action_feedback.header.stamp.secs, _x.action_feedback.header.stamp.nsecs,) = _get_struct_3I().unpack(str[start:end]) start = end end += 4 (length,) = _struct_I.unpack(str[start:end]) start = end end += length if python3: self.action_feedback.header.frame_id = str[start:end].decode('utf-8') else: self.action_feedback.header.frame_id = str[start:end] _x = self start = end end += 8 (_x.action_feedback.status.goal_id.stamp.secs, _x.action_feedback.status.goal_id.stamp.nsecs,) = _get_struct_2I().unpack(str[start:end]) start = end end += 4 (length,) = _struct_I.unpack(str[start:end]) start = end end += length if python3: self.action_feedback.status.goal_id.id = str[start:end].decode('utf-8') else: self.action_feedback.status.goal_id.id = str[start:end] start = end end += 1 (self.action_feedback.status.status,) = _get_struct_B().unpack(str[start:end]) start = end end += 4 (length,) = _struct_I.unpack(str[start:end]) start = end end += length if python3: self.action_feedback.status.text = str[start:end].decode('utf-8') else: self.action_feedback.status.text = str[start:end] return self except struct.error as e: raise genpy.DeserializationError(e) #most likely buffer underfill _struct_I = genpy.struct_I def _get_struct_I(): global _struct_I return _struct_I _struct_B = None def _get_struct_B(): global _struct_B if _struct_B is None: _struct_B = struct.Struct("<B") return _struct_B _struct_d4qh = None def _get_struct_d4qh(): global _struct_d4qh if _struct_d4qh is None: _struct_d4qh = struct.Struct("<d4qh") return _struct_d4qh _struct_h3I = None def _get_struct_h3I(): global _struct_h3I if _struct_h3I is None: _struct_h3I = struct.Struct("<h3I") return _struct_h3I _struct_BI = None def _get_struct_BI(): global _struct_BI if _struct_BI is None: _struct_BI = struct.Struct("<BI") return _struct_BI _struct_3I = None def _get_struct_3I(): global _struct_3I if _struct_3I is None: _struct_3I = struct.Struct("<3I") return _struct_3I _struct_2I = None def _get_struct_2I(): global _struct_2I if _struct_2I is None: _struct_2I = struct.Struct("<2I") return _struct_2I	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29896, 29899, 29896, 29900, 13, 29937, 910, 5132, 934, 3913, 278, 1494, 8025, 29901, 23616, 29899, 29947, 13, 15945, 29908, 1300, 468, 759, 630, 491, 2531, 2272, 515, 6501, 1212, 29918, 1883, 29918, 1516, 3174, 29914, 5596, 2831, 12724, 4276, 29889, 7645, 29889, 1938, 451, 3863, 1213, 15945, 13, 5215, 10876, 13, 4691, 29941, 353, 5852, 565, 10876, 29889, 20970, 3259, 1405, 29871, 29900, 29916, 29900, 29941, 29900, 29900, 29900, 29900, 29900, 29900, 1683, 7700, 13, 5215, 2531, 2272, 13, 5215, 2281, 13, 13, 5215, 6501, 1212, 29918, 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examples/similarity_conf.py	allenye0119/Amaze	0	161744	<filename>examples/similarity_conf.py """ This module gives an example of how to configure similarity measures computation. """ from __future__ import (absolute_import, division, print_function, unicode_literals) from amaze import KNNBasic from amaze import Dataset from amaze.model_selection import cross_validate # Load the movielens-100k dataset. data = Dataset.load_builtin('ml-100k') # Example using cosine similarity sim_options = {'name': 'cosine', 'user_based': False # compute similarities between items } algo = KNNBasic(sim_options=sim_options) cross_validate(algo, data, verbose=True) # Example using pearson_baseline similarity sim_options = {'name': 'pearson_baseline', 'shrinkage': 0 # no shrinkage } algo = KNNBasic(sim_options=sim_options) cross_validate(algo, data, verbose=True)	[ 1, 529, 9507, 29958, 19057, 29914, 29764, 537, 29918, 5527, 29889, 2272, 13, 15945, 29908, 13, 4013, 3883, 4076, 385, 1342, 310, 920, 304, 10822, 29501, 15366, 13, 12097, 362, 29889, 13, 15945, 29908, 13, 13, 3166, 4770, 29888, 9130, 1649, 1053, 313, 23552, 29918, 5215, 29892, 8542, 29892, 1596, 29918, 2220, 29892, 13, 462, 4706, 29104, 29918, 20889, 1338, 29897, 13, 13, 3166, 21863, 29872, 1053, 476, 10262, 16616, 13, 3166, 21863, 29872, 1053, 13373, 24541, 13, 3166, 21863, 29872, 29889, 4299, 29918, 21731, 1053, 4891, 29918, 15480, 13, 13, 13, 29937, 16012, 278, 2351, 709, 575, 29899, 29896, 29900, 29900, 29895, 8783, 29889, 13, 1272, 353, 13373, 24541, 29889, 1359, 29918, 16145, 262, 877, 828, 29899, 29896, 29900, 29900, 29895, 1495, 13, 13, 29937, 8741, 773, 6776, 457, 29501, 13, 3601, 29918, 6768, 353, 11117, 978, 2396, 525, 3944, 457, 742, 13, 1669, 525, 1792, 29918, 6707, 2396, 7700, 29871, 396, 10272, 29871, 2788, 1907, 1546, 4452, 13, 1669, 500, 13, 284, 1484, 353, 476, 10262, 16616, 29898, 3601, 29918, 6768, 29922, 3601, 29918, 6768, 29897, 13, 13, 19128, 29918, 15480, 29898, 284, 1484, 29892, 848, 29892, 26952, 29922, 5574, 29897, 13, 13, 29937, 8741, 773, 282, 799, 1100, 29918, 6500, 5570, 29501, 13, 3601, 29918, 6768, 353, 11117, 978, 2396, 525, 412, 279, 1100, 29918, 6500, 5570, 742, 13, 1669, 525, 845, 29878, 682, 482, 2396, 29871, 29900, 29871, 396, 694, 14653, 682, 482, 13, 1669, 500, 13, 284, 1484, 353, 476, 10262, 16616, 29898, 3601, 29918, 6768, 29922, 3601, 29918, 6768, 29897, 13, 13, 19128, 29918, 15480, 29898, 284, 1484, 29892, 848, 29892, 26952, 29922, 5574, 29897, 13, 2 ]
tests/contrib/test_cached_dataset.py	mmathys/bagua	635	61948	from bagua.torch_api.contrib.cached_dataset import CachedDataset from torch.utils.data.dataset import Dataset import numpy as np import logging import unittest from tests import skip_if_cuda_available logging.basicConfig(level=logging.DEBUG) class MyDataset(Dataset): def __init__(self, size): self.size = size self.dataset = [(np.random.rand(5, 2), np.random.rand(1)) for _ in range(size)] def __getitem__(self, item): return self.dataset[item] def __len__(self): return self.size class TestCacheDataset(unittest.TestCase): def check_dataset(self, dataset, cache_dataset): for _ in range(10): for _, _ in enumerate(cache_dataset): pass for i in range(len(dataset)): self.assertTrue((dataset[i][0] == cache_dataset[i][0]).all()) self.assertTrue((dataset[i][1] == cache_dataset[i][1]).all()) @skip_if_cuda_available() def test_redis(self): dataset1 = MyDataset(102) dataset2 = MyDataset(102) cache_dataset1 = CachedDataset( dataset1, backend="redis", dataset_name="d1", ) cache_dataset2 = CachedDataset( dataset2, backend="redis", dataset_name="d2", ) cache_dataset1.cache_loader.store.clear() self.check_dataset(dataset1, cache_dataset1) self.assertEqual(cache_dataset1.cache_loader.num_keys(), len(dataset1)) self.check_dataset(dataset2, cache_dataset2) self.assertEqual( cache_dataset2.cache_loader.num_keys(), len(dataset1) + len(dataset2) ) if __name__ == "__main__": unittest.main()	[ 1, 515, 19548, 3357, 29889, 7345, 305, 29918, 2754, 29889, 21570, 29889, 29883, 3791, 29918, 24713, 1053, 315, 3791, 16390, 24541, 13, 3166, 4842, 305, 29889, 13239, 29889, 1272, 29889, 24713, 1053, 13373, 24541, 13, 5215, 12655, 408, 7442, 13, 5215, 12183, 13, 5215, 443, 27958, 13, 3166, 6987, 1053, 14383, 29918, 361, 29918, 29883, 6191, 29918, 16515, 13, 13, 21027, 29889, 16121, 3991, 29898, 5563, 29922, 21027, 29889, 18525, 29897, 13, 13, 13, 1990, 1619, 16390, 24541, 29898, 16390, 24541, 1125, 13, 1678, 822, 4770, 2344, 12035, 1311, 29892, 2159, 1125, 13, 4706, 1583, 29889, 2311, 353, 2159, 13, 4706, 1583, 29889, 24713, 353, 17288, 9302, 29889, 8172, 29889, 9502, 29898, 29945, 29892, 29871, 29906, 511, 7442, 29889, 8172, 29889, 9502, 29898, 29896, 876, 363, 903, 297, 3464, 29898, 2311, 4638, 13, 13, 1678, 822, 4770, 657, 667, 12035, 1311, 29892, 2944, 1125, 13, 4706, 736, 1583, 29889, 24713, 29961, 667, 29962, 13, 13, 1678, 822, 4770, 2435, 12035, 1311, 1125, 13, 4706, 736, 1583, 29889, 2311, 13, 13, 13, 1990, 4321, 10408, 16390, 24541, 29898, 348, 27958, 29889, 3057, 8259, 1125, 13, 1678, 822, 1423, 29918, 24713, 29898, 1311, 29892, 8783, 29892, 7090, 29918, 24713, 1125, 13, 4706, 363, 903, 297, 3464, 29898, 29896, 29900, 1125, 13, 9651, 363, 17117, 903, 297, 26985, 29898, 8173, 29918, 24713, 1125, 13, 18884, 1209, 13, 13, 4706, 363, 474, 297, 3464, 29898, 2435, 29898, 24713, 22164, 13, 9651, 1583, 29889, 9294, 5574, 3552, 24713, 29961, 29875, 3816, 29900, 29962, 1275, 7090, 29918, 24713, 29961, 29875, 3816, 29900, 14664, 497, 3101, 13, 9651, 1583, 29889, 9294, 5574, 3552, 24713, 29961, 29875, 3816, 29896, 29962, 1275, 7090, 29918, 24713, 29961, 29875, 3816, 29896, 14664, 497, 3101, 13, 13, 1678, 732, 11014, 29918, 361, 29918, 29883, 6191, 29918, 16515, 580, 13, 1678, 822, 1243, 29918, 1127, 275, 29898, 1311, 1125, 13, 4706, 8783, 29896, 353, 1619, 16390, 24541, 29898, 29896, 29900, 29906, 29897, 13, 4706, 8783, 29906, 353, 1619, 16390, 24541, 29898, 29896, 29900, 29906, 29897, 13, 4706, 7090, 29918, 24713, 29896, 353, 315, 3791, 16390, 24541, 29898, 13, 9651, 8783, 29896, 29892, 13, 9651, 14998, 543, 1127, 275, 613, 13, 9651, 8783, 29918, 978, 543, 29881, 29896, 613, 13, 4706, 1723, 13, 4706, 7090, 29918, 24713, 29906, 353, 315, 3791, 16390, 24541, 29898, 13, 9651, 8783, 29906, 29892, 13, 9651, 14998, 543, 1127, 275, 613, 13, 9651, 8783, 29918, 978, 543, 29881, 29906, 613, 13, 4706, 1723, 13, 13, 4706, 7090, 29918, 24713, 29896, 29889, 8173, 29918, 12657, 29889, 8899, 29889, 8551, 580, 13, 13, 4706, 1583, 29889, 3198, 29918, 24713, 29898, 24713, 29896, 29892, 7090, 29918, 24713, 29896, 29897, 13, 4706, 1583, 29889, 9294, 9843, 29898, 8173, 29918, 24713, 29896, 29889, 8173, 29918, 12657, 29889, 1949, 29918, 8149, 3285, 7431, 29898, 24713, 29896, 876, 13, 13, 4706, 1583, 29889, 3198, 29918, 24713, 29898, 24713, 29906, 29892, 7090, 29918, 24713, 29906, 29897, 13, 4706, 1583, 29889, 9294, 9843, 29898, 13, 9651, 7090, 29918, 24713, 29906, 29889, 8173, 29918, 12657, 29889, 1949, 29918, 8149, 3285, 7431, 29898, 24713, 29896, 29897, 718, 7431, 29898, 24713, 29906, 29897, 13, 4706, 1723, 13, 13, 13, 361, 4770, 978, 1649, 1275, 376, 1649, 3396, 1649, 1115, 13, 1678, 443, 27958, 29889, 3396, 580, 13, 2 ]
ImageDeleter.py	warifp/InstagramPostAndDelete	4	114591	import os def delete_png(): list = os.listdir() images = [x for x in list if ".png" in x] for x in images: os.remove(x)	[ 1, 1053, 2897, 13, 1753, 5217, 29918, 2732, 7295, 13, 1678, 1051, 353, 2897, 29889, 1761, 3972, 580, 13, 1678, 4558, 353, 518, 29916, 363, 921, 297, 1051, 565, 11393, 2732, 29908, 297, 921, 29962, 13, 1678, 363, 921, 297, 4558, 29901, 13, 4706, 2897, 29889, 5992, 29898, 29916, 29897, 13, 2 ]
viscyc/detector/__init__.py	csachs/viscyc	1	1601104	import argparse import importlib import importlib.util import json import socket import sys import time from pathlib import Path from urllib.error import HTTPError, URLError from urllib.request import Request, urlopen import cv2 class RawSender: def __init__(self, host, port): self.host, self.port = host, port def send(self, kwargs): message_data = json.dumps(kwargs).encode() try: s = socket.socket(socket.AF_INET, socket.SOCK_STREAM) s.connect((self.host, self.port)) s.send(message_data) s.close() except ConnectionError: pass class HttpSender: def __init__(self, url): self.url = url def send(self, kwargs): json_data = json.dumps(kwargs).encode() try: urlopen( Request(self.url, headers={'Content-type': 'application/json'}), data=json_data, ) except (URLError, HTTPError, ConnectionError): pass def create_argparser(): parser = argparse.ArgumentParser( description="watch cross-trainer revolutions via computer vision" ) parser.add_argument('--capture-device', type=int, default=0) parser.add_argument('--capture-width', type=int, default=640) parser.add_argument('--capture-height', type=int, default=480) parser.add_argument('--roi-width', type=int, default=64) parser.add_argument('--roi-height', type=int, default=64) parser.add_argument('--roi-placement-horizontal', type=int, default=-1) parser.add_argument('--roi-placement-vertical', type=int, default=-1) parser.add_argument('--threshold', type=int, default=100) parser.add_argument('--quiet', default=False, action='store_true') parser.add_argument('--no-display', default=False, action='store_true') parser.add_argument('--single-jpeg-preview', default=False, action='store_true') parser.add_argument('--power-expression', default='1.0rpm+0') parser.add_argument('--load', action='append', default=[]) parser.add_argument('--target', type=str, default='127.0.0.1:7654') parser.add_argument( '--http-target', type=str, default='http://127.0.0.1:8901/cadence' ) return parser def load_file_as_module(file_path): name = '_' + Path(file_path).stem spec = importlib.util.spec_from_file_location(name, file_path) module = importlib.util.module_from_spec(spec) sys.modules[name] = module spec.loader.exec_module(module) return module def find_power_function(hooks, default): calculate_watt = default for hook in hooks: if getattr(hook, 'calculate_watt', None): calculate_watt = getattr(hook, 'calculate_watt') print( "Using %s.%s as power calculation function." % ( calculate_watt.__module__, calculate_watt.__name__, ) ) return calculate_watt def create_power_expression_function(power_expression): frag = f''' def power_expression(rpm): return ({power_expression}) ''' l, g = {}, {} exec(frag, g, l) return l['power_expression'] def get_sizes(args): size = (args.capture_width, args.capture_height) position = [size[0] // 2, size[1] // 2] if args.roi_placement_horizontal != -1: position[0] = args.roi_placement_horizontal if args.roi_placement_vertical != -1: position[1] = args.roi_placement_vertical roi = args.roi_width, args.roi_height return size, position, roi def prepare_hooks(args): hooks = hooks_load(args.load) hooks += [RawSender(args.target.split(':')[0], int(args.target.split(':')[1]))] if args.http_target: hooks += [HttpSender(args.http_target)] return hooks def hooks_load(load): hooks = [load_file_as_module(file_name) for file_name in load] return hooks def hooks_call(hooks, what, args, *kwargs): for hook in hooks: func = getattr(hook, what, None) if func: try: func(args, *kwargs) except BaseException as e: print("Exception occurred while calling %r" % func) print(e) def prepare_preview(display_frame, color, text, position, roi): cv2.rectangle( display_frame, (position[0] - roi[0] // 2, position[1] - roi[1] // 2), (position[0] + roi[0] // 2, position[1] + roi[1] // 2), color, ) cv2.putText( display_frame, text, org=(0, 24), fontFace=cv2.FONT_HERSHEY_SIMPLEX, fontScale=1.0, color=(0, 255, 0), ) return display_frame COLOR_INSIDE = (0, 0, 255) COLOR_OUTSIDE = (0, 255, 0) def main_loop( capture_function, position, roi, threshold, hook_send, quiet, preview, return_jpg=False, ): last_timepoint = None last_acquisition = None inside = False last_rpm = None rev_count = 0 while True: frame_input = capture_function() timepoint = time.time() frame_grayscale = cv2.cvtColor(frame_input, cv2.COLOR_BGR2GRAY) _, frame_binary = cv2.threshold( frame_grayscale, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU ) frame_roi = frame_binary[ position[1] - roi[1] // 2 : position[1] + roi[1] // 2, position[0] - roi[0] // 2 : position[0] + roi[0] // 2, ] mean_value = frame_roi.mean() color = COLOR_OUTSIDE text = "RPM: % 2.2f Revs: %04d" % ( last_rpm if last_rpm else 0.0, rev_count, ) if last_acquisition: text += " FPS: %2.1f" % (1.0 / (timepoint - last_acquisition)) last_acquisition = timepoint if mean_value < threshold: color = COLOR_INSIDE if not inside and last_timepoint: delta = timepoint - last_timepoint rpm = (1.0 / delta) 60.0 rev_count += 1 last_rpm = rpm hook_send(rev_count=rev_count, rpm=rpm) if not quiet: print(text) inside = True last_timepoint = timepoint else: inside = False if preview: display_frame = prepare_preview( frame_input.copy(), color, text, position, roi ) if return_jpg: _, result = cv2.imencode('.jpg', display_frame) return result.tostring() else: cv2.imshow("Viscyc Detector", display_frame) if cv2.waitKey(1) & 0xff == ord('q'): break def main(args=None, return_instead_print=False): if args is None: args = sys.argv[1:] parser = create_argparser() args = parser.parse_args(args) power_expression = args.power_expression calculate_watt = create_power_expression_function(power_expression) hooks = prepare_hooks(args) calculate_watt = find_power_function(hooks, default=calculate_watt) size, position, roi = get_sizes(args) capture = cv2.VideoCapture(args.capture_device) capture.set(cv2.CAP_PROP_FRAME_WIDTH, size[0]) capture.set(cv2.CAP_PROP_FRAME_HEIGHT, size[1]) def capture_function(): return capture.read()[1] def hook_send(rev_count=0, rpm=0): return hooks_call( hooks, 'send', watts=calculate_watt(rpm), rev_count=rev_count, rpm=rpm ) try: hooks_call(hooks, 'start') hook_send() print("Beginning processing") result = main_loop( capture_function, position, roi, args.threshold, hook_send, args.quiet, preview=not args.no_display or args.single_jpeg_preview, return_jpg=args.single_jpeg_preview, ) if result: if return_instead_print: return result else: sys.stdout.buffer.write(result) finally: capture.release() cv2.destroyAllWindows() hook_send() hooks_call(hooks, 'stop') print("Halted processing")	[ 1, 1053, 1852, 5510, 13, 5215, 1053, 1982, 13, 5215, 1053, 1982, 29889, 4422, 13, 5215, 4390, 13, 5215, 9909, 13, 5215, 10876, 13, 5215, 931, 13, 3166, 2224, 1982, 1053, 10802, 13, 3166, 3142, 1982, 29889, 2704, 1053, 7331, 2392, 29892, 501, 29934, 1307, 24616, 13, 3166, 3142, 1982, 29889, 3827, 1053, 10729, 29892, 5065, 417, 2238, 13, 13, 5215, 13850, 29906, 13, 13, 13, 1990, 22038, 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scraper/twitter.py	AgentHH/SBCFireDispatch	2	1612302	#!/usr/bin/python import oauth2 import psycopg2 import simplejson import urllib2 from config import * token = oauth2.Token(key=access_token_key, secret=access_token_secret) consumer = oauth2.Consumer(key=consumer_key, secret=consumer_secret) signature_method = oauth2.SignatureMethod_HMAC_SHA1() def get(url, **args): method = 'GET' parameters = args request = oauth2.Request.from_consumer_and_token(consumer, token=token, http_method=method, http_url=url, parameters=parameters) request.sign_request(signature_method, consumer, token) if method == "POST": post = request.to_postdata() else: post = None url = request.to_url() try: file = urllib2.urlopen(url, post) data = file.read() except urllib2.HTTPError, e: print e.read() raise e data = simplejson.loads(data) return data	[ 1, 18787, 4855, 29914, 2109, 29914, 4691, 13, 13, 5215, 288, 5150, 29906, 13, 5215, 6529, 29891, 9708, 29887, 29906, 13, 5215, 2560, 3126, 13, 5215, 3142, 1982, 29906, 13, 13, 3166, 2295, 1053, 334, 13, 13, 6979, 353, 288, 5150, 29906, 29889, 6066, 29898, 1989, 29922, 5943, 29918, 6979, 29918, 1989, 29892, 7035, 29922, 5943, 29918, 6979, 29918, 19024, 29897, 13, 25978, 261, 353, 288, 5150, 29906, 29889, 13696, 4680, 29898, 1989, 29922, 25978, 261, 29918, 1989, 29892, 7035, 29922, 25978, 261, 29918, 19024, 29897, 13, 4530, 1535, 29918, 5696, 353, 288, 5150, 29906, 29889, 10140, 1535, 4062, 29918, 29950, 1529, 29907, 29918, 23498, 29896, 580, 13, 13, 1753, 679, 29898, 2271, 29892, 3579, 5085, 1125, 13, 1678, 1158, 353, 525, 7194, 29915, 13, 13, 1678, 4128, 353, 6389, 13, 13, 1678, 2009, 353, 288, 5150, 29906, 29889, 3089, 29889, 3166, 29918, 25978, 261, 29918, 392, 29918, 6979, 29898, 25978, 261, 29892, 5993, 29922, 6979, 29892, 1732, 29918, 5696, 29922, 5696, 29892, 1732, 29918, 2271, 29922, 2271, 29892, 4128, 29922, 16744, 29897, 13, 1678, 2009, 29889, 4530, 29918, 3827, 29898, 4530, 1535, 29918, 5696, 29892, 21691, 29892, 5993, 29897, 13, 13, 1678, 565, 1158, 1275, 376, 5438, 1115, 13, 4706, 1400, 353, 2009, 29889, 517, 29918, 2490, 1272, 580, 13, 1678, 1683, 29901, 13, 4706, 1400, 353, 6213, 13, 4706, 3142, 353, 2009, 29889, 517, 29918, 2271, 580, 13, 13, 1678, 1018, 29901, 13, 4706, 934, 353, 3142, 1982, 29906, 29889, 332, 417, 2238, 29898, 2271, 29892, 1400, 29897, 13, 4706, 848, 353, 934, 29889, 949, 580, 13, 1678, 5174, 3142, 1982, 29906, 29889, 10493, 2392, 29892, 321, 29901, 13, 4706, 1596, 321, 29889, 949, 580, 13, 4706, 12020, 321, 13, 13, 1678, 848, 353, 2560, 3126, 29889, 18132, 29898, 1272, 29897, 13, 1678, 736, 848, 13, 2 ]
attack.py	RishavMz/strategic_siege	0	145729	import pygame from data.troops import * from data.defenses import * from data.attack_levels import * import math WIDTH = 800 HEIGHT = 600 state = 0 level = 0 totalDamageDone = 0 totalDamageTaken = 0 class Table: def __init__(self,width,height): self.width=width self.height=height self.canvas = canvas = pygame.display.set_mode((self.width,self.height)) def draw(self, st): self.canvas = pygame.display.set_mode((self.width,self.height)) pygame.display.set_caption(st) self.canvas.fill((0,0,0)) pygame.draw.rect(self.canvas, (255,255,255), (30,90, self.width - 60, self.height-90),2) pygame.draw.rect(self.canvas, (255,255,255), (500,90, 300, self.height-90),1) class Cards: def __init__(self,table,posx,state,number): self.table = table self.posx = posx self.posy = 10 self.height = 60 self.width = 50 self.state = state self.number = number def checkmouse(self,posx): global state if(posx >= self.posx) and (posx <= (self.posx+self.width)): state = self.state def draw(self): pygame.draw.rect(self.table.canvas, (255,255,255), (self.posx,self.posy, self.width, self.height)) def assemble(): for i in levels[level].showcards(): cards.append(Cards(table1,i[0],i[1],i[2])) for i in levels[level].showdefenses(): if(i[0]==1): defence.append(Cannon(table1,i[1],i[2])) elif(i[0]==2): defence.append(Tower(table1,i[1],i[2])) elif(i[0]==3): defence.append(Bunker(table1,i[1],i[2])) table1 = Table(WIDTH, HEIGHT) Table_name="Battle_Machine" run = True army = [] cards = [] defence = [] assemble() cardinfantryImage = pygame.image.load( 'images/cardinfantry.png') cardarcherImage = pygame.image.load( 'images/cardarcher.png') cardcavalryImage = pygame.image.load( 'images/cardcavalry.png') cardheavycavalryImage = pygame.image.load('images/cardheavycavalry.png') cannonImage = pygame.image.load( 'images/cannon.png') towerImage = pygame.image.load( 'images/tower.png') bunkerImage = pygame.image.load( 'images/bunker.png') infantryImage = pygame.image.load( 'images/infantry.png') archerImage = pygame.image.load( 'images/archer.png') cavalryImage = pygame.image.load( 'images/cavalry.png') heavycavalryImage = pygame.image.load( 'images/heavycavalry.png') displaySpace = pygame.display.set_mode((600,700)) def distanceCalc(body1, body2): return math.sqrt((body1.posx - body2.posx)2 + (body1.posy - body2.posy)2) play = 0 while run: table1.draw(Table_name) for i in range(len(cards)): if(state == (i+1)): for j in range(cards[i].number): pygame.draw.rect(table1.canvas, (255,255,255), (cards[i].posx + (j*4),75,2, 5)) if(len(army)>0): play = 1 if(play==1 and len(defence)==0): army=[] cards = [] level += 1 if(level == len(levels)): run = False print("\nYou won\n") print("Total damage done : ",totalDamageDone) print("Total damage taken: ",totalDamageTaken) print("\n") break assemble() fallen = [] for i in range(len(army)): army[i].draw() if(army[i].health<=0): fallen.append(i) for i in fallen: army.pop(i) for i in defence: i.draw() for i in army: if(len(defence)>0): nearest , distance = None , 1000000 for j in range(len(defence)): dist = distanceCalc(i,defence[j]) if(dist<distance): nearest = j distance = dist if(nearest is not None): if(abs(i.posx - defence[nearest].posx)<=i.range): if(abs(i.posy - defence[nearest].posy)<=i.range): defence[nearest].damage(i.strength) totalDamageDone += i.strength pygame.draw.line(table1.canvas,(68,85,90),(defence[nearest].posx-10,defence[nearest].posy-10),(i.posx,i.posy)) if(defence[nearest].health<=0): defence.pop(nearest) break elif(i.posx > defence[j].posx): i.movex(i.posx-i.speed) elif(i.posx < defence[j].posx): i.movex(i.posx+i.speed) if(abs(i.posy - defence[nearest].posy)<=i.range): pass elif(i.posy > defence[j].posy): i.movey(i.posy-i.speed) elif(i.posy < defence[j].posy): i.movey(i.posy+i.speed) if(len(army)>0): for i in range(len(defence)): damagedata = defence[i].attack(army) if(damagedata is not None): pygame.draw.line(table1.canvas,(195,115,119),(defence[i].posx-10,defence[i].posy-10),(army[damagedata].posx,army[damagedata].posy)) army[damagedata].damage(defence[i].power) totalDamageTaken += defence[i].power cardsrem = 0 for i in cards: cardsrem += i.number if(len(army)==0 and len(defence)>0 and cardsrem == 0): run = False print("\n GAME OVER\n") print("Total damage done : ",totalDamageDone) print("Total damage taken: ",totalDamageTaken) print("\n") pygame.time.delay(10) for event in pygame.event.get(): if event.type == pygame.QUIT: run = False if event.type == pygame.MOUSEBUTTONUP: pos = pygame.mouse.get_pos() if(pos[1]<=60): for i in cards: i.checkmouse(pos[0]) elif(pos[1]>=100 and pos[0]<500): if(state==1): if(cards[0].number>0): army.append(Infantry(table1,pos[0],pos[1])) cards[0].number -= 1 elif(state==2): if(cards[1].number>0): army.append(Archer(table1,pos[0],pos[1])) cards[1].number -= 1 elif(state==3): if(cards[2].number>0): army.append(Cavalry(table1,pos[0],pos[1])) cards[2].number -= 1 elif(state==4): if(cards[3].number>0): army.append(HeavyCavalry(table1,pos[0],pos[1])) cards[3].number -= 1 if event.type == pygame.KEYDOWN: if event.key == pygame.K_LEFT: if(state>1): state -= 1 if event.key == pygame.K_RIGHT: if(state<=3): state += 1 displaySpace.blit(cardinfantryImage,(cards[0].posx,cards[0].posy)) displaySpace.blit(cardarcherImage,(cards[1].posx,cards[1].posy)) displaySpace.blit(cardcavalryImage,(cards[2].posx,cards[2].posy)) displaySpace.blit(cardheavycavalryImage,(cards[3].posx,cards[3].posy)) for i in defence: if(i.type=="cannon"): displaySpace.blit(cannonImage,(i.posx-22,i.posy-22)) elif(i.type=="tower"): displaySpace.blit(towerImage,(i.posx-15,i.posy-15)) elif(i.type=="bunker"): displaySpace.blit(bunkerImage,(i.posx-22,i.posy-22)) for i in army: if(i.type=="infantry"): displaySpace.blit(infantryImage,(i.posx-10,i.posy-10)) elif(i.type=="archer"): displaySpace.blit(archerImage,(i.posx-8,i.posy-8)) elif(i.type=="cavalry"): 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hmc/applications/banana/banana.py	JamesBrofos/Thresholds-in-Hamiltonian-Monte-Carlo	1	10740	from typing import Callable, Tuple import numpy as np def posterior_factory(y: np.ndarray, sigma_y: float, sigma_theta: float) -> Tuple[Callable]: """The banana distribution is a distribution that exhibits a characteristic banana-shaped ridge that resembles the posterior that can emerge from models that are not identifiable. The distribution is the posterior of the following generative model. y ~ Normal(theta[0] + theta[1]*2, sigma_sq_y) theta[i] ~ Normal(0, sigma_sq_theta) Args: y: Observations of the banana model. sigma_y: Standard deviation of the observations. sigma_theta: Standard deviation of prior over linear coefficients. Returns: log_posterior: Function to compute the log-posterior. metric: Function to compute the Fisher information metric. euclidean_auxiliaries: Function to compute the log-posterior and its gradient. riemannian_auxiliaries: Function to compute the log-posterior, the gradient of the log-posterior, the Fisher information metric, and the derivatives of the Fisher information metric. """ sigma_sq_y = np.square(sigma_y) sigma_sq_theta = np.square(sigma_theta) def log_posterior(theta: np.ndarray) -> float: """The banana-shaped distribution posterior. Args: theta: Linear coefficients. Returns: out: The log-posterior of the banana-shaped distribution. """ p = theta[0] + np.square(theta[1]) ll = -0.5 / sigma_sq_y np.square(y - p).sum() lp = -0.5 / sigma_sq_theta * np.square(theta).sum() return ll + lp def grad_log_posterior(theta: np.ndarray) -> np.ndarray: """Gradient of the banana-shaped distribution with respect to the linear coefficients. Args: theta: Linear coefficients. Returns: out: The gradient of the log-posterior of the banana-shaped distribution with respect to the linear coefficients. """ p = theta[0] + np.square(theta[1]) d = np.sum(y - p) ga = d / sigma_sq_y - theta[0] / sigma_sq_theta gb = 2.0d / sigma_sq_y theta[1] - theta[1] / sigma_sq_theta return np.hstack((ga, gb)) def metric(theta: np.ndarray) -> np.ndarray: """The Fisher information is the negative expected outer product of the gradient of the posterior. Args: theta: Linear coefficients. Returns: G: The Fisher information metric of the banana-shaped distribution. """ n = y.size s = 2.0ntheta[1] / sigma_sq_y G = np.array([[n / sigma_sq_y + 1.0 / sigma_sq_theta, s], [s, 4.0nnp.square(theta[1]) / sigma_sq_y + 1.0 / sigma_sq_theta]]) return G def grad_metric(theta: np.ndarray) -> np.ndarray: """The gradient of the Fisher information metric with respect to the linear coefficients. Args: theta: Linear coefficients. Returns: dG: The gradient of the Fisher information metric with respect to the linear coefficients. """ n = y.size dG = np.array([ [[0.0, 0.0], [0.0, 2.0n / sigma_sq_y]], [[0.0, 2.0n / sigma_sq_y], [0.0, 8.0ntheta[1] / sigma_sq_y]] ]) return dG def euclidean_auxiliaries(theta: np.ndarray) -> Tuple[np.ndarray]: """Function to compute the log-posterior and the gradient of the log-posterior. Args: theta: Linear coefficients. Returns: lp: The log-posterior of the banana-shaped distribution. glp: The gradient of the log-posterior of the banana-shaped distribution with respect to the linear coefficients. """ lp = log_posterior(theta) glp = grad_log_posterior(theta) return lp, glp def riemannnian_auxiliaries(theta: np.ndarray) -> Tuple[np.ndarray]: """Function to compute the log-posterior, the gradient of the log-posterior, the Fisher information metric and the derivatives of the Fisher information metric. Args: theta: Linear coefficients. Returns: lp: The log-posterior of the banana-shaped distribution. glp: The gradient of the log-posterior of the banana-shaped distribution with respect to the linear coefficients. G: The Fisher information metric of the banana-shaped distribution. dG: The gradient of the Fisher information metric with respect to the linear coefficients. """ lp = log_posterior(theta) glp = grad_log_posterior(theta) G = metric(theta) dG = grad_metric(theta) return lp, glp, G, dG def log_posterior_and_metric(theta: np.ndarray) -> Tuple[np.ndarray]: lp = log_posterior(theta) G = metric(theta) return lp, G return log_posterior, metric, log_posterior_and_metric, euclidean_auxiliaries, riemannnian_auxiliaries def generate_data(t: float, sigma_y: float, sigma_theta: float, num_obs: int) -> np.ndarray: """Generate data from the banana-shaped posterior distribution. Args: t: Free-parameter determining the thetas. sigma_y: Noise standard deviation. sigma_theta: Prior standard deviation over the thetas. num_obs: Number of observations to generate. Returns: theta: Linear coefficients of the banana-shaped distribution. y: Observations from the unidentifiable model. 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tests/test_cfloader.py	shachibista/mloader	0	52926	<filename>tests/test_cfloader.py #!/usr/bin/env python """Tests for `cfloader` package.""" import json from pathlib import Path import pytest import cfloader from cfloader.loader import Loader from cfloader.readers import ConfigFileNotFoundError def test_cfloader_can_open_path_objects(): config_filepath = Path("tests/examples/config.json") loader = cfloader.open(config_filepath) def test_cfloader_can_open_str_paths(): config_filepath = "tests/examples/config.json" loader = cfloader.open(config_filepath) def test_cfloader_can_open_dicts(): config_dict = {} loader = cfloader.open(config_dict) assert isinstance(loader, Loader) assert loader.config == config_dict def test_cfloader_open_returns_loader(): config_filepath = Path("tests/examples/config.json") loader = cfloader.open(config_filepath) assert isinstance(loader, Loader) assert loader.config == json.loads(config_filepath.read_text()) def test_cfloader_can_open_zip_files(): archive_path = "tests/examples/archive.zip" loader = cfloader.open(cfloader.Archive(archive_path)) assert isinstance(loader, Loader) import zipfile with zipfile.ZipFile(archive_path, "r") as archive_fs: with archive_fs.open("config.json", "r") as config_file: assert loader.config == json.load(config_file) def test_cfloader_can_open_tar_files(): archive_path = "tests/examples/archive.tar" loader = cfloader.open(cfloader.Archive(archive_path)) assert isinstance(loader, Loader) import tarfile with tarfile.open(archive_path, "r") as archive_fs: config_fileinfo = archive_fs.getmember("config.json") config_file = archive_fs.extractfile(config_fileinfo) assert loader.config == json.loads(config_file.read()) def test_cfloader_can_open_tar_gz_files(): archive_path = "tests/examples/archive.tar.gz" loader = cfloader.open(cfloader.Archive(archive_path)) assert isinstance(loader, Loader) import tarfile with tarfile.open(archive_path, "r:gz") as archive_fs: config_fileinfo = archive_fs.getmember("config.json") config_file = archive_fs.extractfile(config_fileinfo) assert loader.config == json.loads(config_file.read()) # disabled because extractfile returns a <ExFileObject name=None> # def test_cfloader_can_open_tar_bz_files(): # archive_path = "tests/examples/archive.tar.bz" # loader = cfloader.open(cfloader.Archive(archive_path)) # assert isinstance(loader, Loader) # import tarfile # with tarfile.open(archive_path, "r:bz2") as archive_fs: # config_fileinfo = archive_fs.getmember("config.json") # config_file = archive_fs.extractfile(config_fileinfo) # breakpoint() # assert loader.config == json.loads(config_file.read()) def test_cfloader_can_open_tar_xz_files(): archive_path = "tests/examples/archive.tar.xz" loader = cfloader.open(cfloader.Archive(archive_path)) assert isinstance(loader, Loader) import tarfile with tarfile.open(archive_path, "r:xz") as archive_fs: config_fileinfo = archive_fs.getmember("config.json") config_file = archive_fs.extractfile(config_fileinfo) assert loader.config == json.loads(config_file.read()) def test_cfloader_complains_if_config_not_found_in_zip_archive(): archive_path = "tests/examples/archive.zip" with pytest.raises(ConfigFileNotFoundError): loader = cfloader.open(cfloader.Archive(archive_path, "cfg.json")) def test_cfloader_complains_if_config_not_found_in_tar_archive(): archive_path = "tests/examples/archive.tar.xz" with pytest.raises(ConfigFileNotFoundError): loader = cfloader.open(cfloader.Archive(archive_path, "cfg.json"))	[ 1, 529, 9507, 29958, 21150, 29914, 1688, 29918, 6854, 12657, 29889, 2272, 13, 29937, 14708, 4855, 29914, 2109, 29914, 6272, 3017, 13, 13, 15945, 29908, 24376, 363, 421, 6854, 12657, 29952, 3577, 1213, 15945, 13, 13, 5215, 4390, 13, 3166, 2224, 1982, 1053, 10802, 13, 13, 5215, 11451, 1688, 13, 13, 5215, 274, 29888, 12657, 13, 3166, 274, 29888, 12657, 29889, 12657, 1053, 4309, 1664, 13, 3166, 274, 29888, 12657, 29889, 949, 414, 1053, 12782, 2283, 17413, 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tests/python/gaia-ui-tests/gaiatest/tests/accessibility/phone/test_a11y_callscreen_visibility.py	gregarndt/gaia	1	155498	<filename>tests/python/gaia-ui-tests/gaiatest/tests/accessibility/phone/test_a11y_callscreen_visibility.py # This Source Code Form is subject to the terms of the Mozilla Public # License, v. 2.0. If a copy of the MPL was not distributed with this # file, You can obtain one at http://mozilla.org/MPL/2.0/. from gaiatest import GaiaTestCase from gaiatest.mocks.mock_contact import MockContact from gaiatest.apps.phone.app import Phone class TestAccessibilityCallscreenVisibility(GaiaTestCase): def setUp(self): GaiaTestCase.setUp(self) self.contact = MockContact() self.phone = Phone(self.marionette) self.phone.launch() def test_a11y_callscreen_visibility(self): test_phone_number = self.contact['tel']['value'] # Make a call self.phone.a11y_make_call(test_phone_number) # Check that the keyboard view is hidden by default self.assertTrue(self.accessibility.is_hidden(self.marionette.find_element( self.phone.call_screen._views_locator))) # Check that the incoming container is hidden by default self.assertTrue(self.accessibility.is_hidden(self.marionette.find_element( self.phone.call_screen._incoming_container_locator))) # Check that the handled call hangup button is hidden by default self.assertTrue(self.accessibility.is_hidden(self.marionette.find_element( *self.phone.call_screen._hangup_button_locator))) # Hang up self.phone.a11y_hang_up()	[ 1, 529, 9507, 29958, 21150, 29914, 4691, 29914, 3249, 423, 29899, 1481, 29899, 21150, 29914, 29887, 1794, 271, 342, 29914, 21150, 29914, 5943, 4127, 29914, 6710, 29914, 1688, 29918, 29874, 29896, 29896, 29891, 29918, 29883, 4293, 5576, 29918, 28814, 29889, 2272, 13, 29937, 910, 7562, 5920, 3812, 338, 4967, 304, 278, 4958, 310, 278, 18129, 2911, 5236, 13, 29937, 19245, 29892, 325, 29889, 29871, 29906, 29889, 29900, 29889, 960, 263, 3509, 310, 278, 341, 7390, 471, 451, 13235, 411, 445, 13, 29937, 934, 29892, 887, 508, 4017, 697, 472, 1732, 597, 13025, 29889, 990, 29914, 3580, 29931, 29914, 29906, 29889, 29900, 6294, 13, 13, 3166, 330, 1794, 271, 342, 1053, 10415, 423, 3057, 8259, 13, 3166, 330, 1794, 271, 342, 29889, 17640, 29879, 29889, 17640, 29918, 12346, 1053, 26297, 13443, 13, 3166, 330, 1794, 271, 342, 29889, 13371, 29889, 6710, 29889, 932, 1053, 24323, 13, 13, 13, 1990, 4321, 6638, 4127, 29907, 4293, 5576, 23318, 29898, 29954, 29874, 423, 3057, 8259, 1125, 13, 13, 1678, 822, 731, 3373, 29898, 1311, 1125, 13, 4706, 10415, 423, 3057, 8259, 29889, 842, 3373, 29898, 1311, 29897, 13, 4706, 1583, 29889, 12346, 353, 26297, 13443, 580, 13, 13, 4706, 1583, 29889, 6710, 353, 24323, 29898, 1311, 29889, 3034, 291, 2353, 29897, 13, 4706, 1583, 29889, 6710, 29889, 15343, 580, 13, 13, 1678, 822, 1243, 29918, 29874, 29896, 29896, 29891, 29918, 29883, 4293, 5576, 29918, 28814, 29898, 1311, 1125, 13, 13, 4706, 1243, 29918, 6710, 29918, 4537, 353, 1583, 29889, 12346, 1839, 28497, 16215, 1767, 2033, 13, 13, 4706, 396, 8561, 263, 1246, 13, 4706, 1583, 29889, 6710, 29889, 29874, 29896, 29896, 29891, 29918, 5675, 29918, 4804, 29898, 1688, 29918, 6710, 29918, 4537, 29897, 13, 13, 4706, 396, 5399, 393, 278, 12247, 1776, 338, 7934, 491, 2322, 13, 4706, 1583, 29889, 9294, 5574, 29898, 1311, 29889, 5943, 4127, 29889, 275, 29918, 10892, 29898, 1311, 29889, 3034, 291, 2353, 29889, 2886, 29918, 5029, 29898, 13, 9651, 334, 1311, 29889, 6710, 29889, 4804, 29918, 10525, 3032, 7406, 29918, 2029, 1061, 4961, 13, 13, 4706, 396, 5399, 393, 278, 23235, 5639, 338, 7934, 491, 2322, 13, 4706, 1583, 29889, 9294, 5574, 29898, 1311, 29889, 5943, 4127, 29889, 275, 29918, 10892, 29898, 1311, 29889, 3034, 291, 2353, 29889, 2886, 29918, 5029, 29898, 13, 9651, 334, 1311, 29889, 6710, 29889, 4804, 29918, 10525, 3032, 262, 11506, 29918, 7611, 29918, 2029, 1061, 4961, 13, 13, 4706, 396, 5399, 393, 278, 16459, 1246, 13958, 786, 2826, 338, 7934, 491, 2322, 13, 4706, 1583, 29889, 9294, 5574, 29898, 1311, 29889, 5943, 4127, 29889, 275, 29918, 10892, 29898, 1311, 29889, 3034, 291, 2353, 29889, 2886, 29918, 5029, 29898, 13, 9651, 334, 1311, 29889, 6710, 29889, 4804, 29918, 10525, 3032, 11895, 786, 29918, 3092, 29918, 2029, 1061, 4961, 13, 13, 4706, 396, 379, 574, 701, 13, 4706, 1583, 29889, 6710, 29889, 29874, 29896, 29896, 29891, 29918, 11895, 29918, 786, 580, 13, 2 ]
Chapter04/simple_examples/functional_example.py	arifmudi/Machine-Learning-Engineering-with-Python	67	1612700	import pandas as pd import numpy as np data = [ ['The', 'Business', 'Centre', '15', 'Stevenson', 'Lane'], ['6', 'Mossvale', 'Road'], ['Studio', '7', 'Tottenham', 'Court', 'Road'] ] def len_strings_in_list(data_list): return list(map(lambda x: len(x), data_list)) def list_of_list_func_results(list_func, list_of_lists): return list(map(lambda x: list_func(x), list_of_lists))	[ 1, 1053, 11701, 408, 10518, 13, 5215, 12655, 408, 7442, 13, 13, 1272, 353, 518, 13, 1678, 6024, 1576, 742, 525, 16890, 3335, 742, 525, 29907, 14056, 742, 525, 29896, 29945, 742, 525, 7789, 9852, 265, 742, 525, 29931, 1662, 7464, 13, 1678, 6024, 29953, 742, 525, 29924, 2209, 29894, 744, 742, 525, 9588, 328, 7464, 13, 1678, 6024, 28867, 742, 525, 29955, 742, 525, 29911, 327, 841, 3391, 742, 525, 29907, 14316, 742, 525, 9588, 328, 2033, 13, 29962, 13, 13, 1753, 7431, 29918, 19651, 29918, 262, 29918, 1761, 29898, 1272, 29918, 1761, 1125, 13, 1678, 736, 1051, 29898, 1958, 29898, 2892, 921, 29901, 7431, 29898, 29916, 511, 848, 29918, 1761, 876, 13, 13, 1753, 1051, 29918, 974, 29918, 1761, 29918, 9891, 29918, 9902, 29898, 1761, 29918, 9891, 29892, 1051, 29918, 974, 29918, 21513, 1125, 13, 1678, 736, 1051, 29898, 1958, 29898, 2892, 921, 29901, 1051, 29918, 9891, 29898, 29916, 511, 1051, 29918, 974, 29918, 21513, 876, 2 ]
one_page/customapp/templatetags/testtag.py	aptuz/mezzanine_onepage_theme	0	56669	from __future__ import unicode_literals from future.builtins import int from collections import defaultdict from django.core.urlresolvers import reverse from django.template.defaultfilters import linebreaksbr, urlize from mezzanine import template from mezzanine.conf import settings from mezzanine.generic.forms import ThreadedCommentForm from mezzanine.generic.models import ThreadedComment from mezzanine.utils.importing import import_dotted_path from mezzanine.pages.models import Page, RichTextPage register = template.Library() @register.assignment_tag def allpages(): page_fields = [ 'content', 'created', 'description', 'expiry_date', 'gen_description', u'id', 'keywords', u'keywords_string', 'publish_date', 'short_url', 'slug', 'status', 'title', 'titles', 'updated'] output = [] # import pdb;pdb.set_trace() AllPages = RichTextPage.objects.all() for item in AllPages: temp = {} for fld in page_fields: temp[fld] = getattr(item, fld) output.append(temp) return { 'pages': output } @register.filter() def remove_slash(value): return '#' + value[1:-1] @register.filter() def lower(value): # import pdb;pdb.set_trace() return value.lower()	[ 1, 515, 4770, 29888, 9130, 1649, 1053, 29104, 29918, 20889, 1338, 13, 3166, 5434, 29889, 16145, 1144, 1053, 938, 13, 13, 3166, 16250, 1053, 2322, 8977, 13, 13, 3166, 9557, 29889, 3221, 29889, 2271, 9778, 874, 1053, 11837, 13, 3166, 9557, 29889, 6886, 29889, 4381, 26705, 1053, 1196, 8690, 29879, 1182, 29892, 3142, 675, 13, 13, 3166, 592, 5617, 273, 457, 1053, 4472, 13, 3166, 592, 5617, 273, 457, 29889, 5527, 1053, 6055, 13, 3166, 592, 5617, 273, 457, 29889, 19206, 29889, 9514, 1053, 10480, 287, 20001, 2500, 13, 3166, 592, 5617, 273, 457, 29889, 19206, 29889, 9794, 1053, 10480, 287, 20001, 13, 3166, 592, 5617, 273, 457, 29889, 13239, 29889, 5215, 292, 1053, 1053, 29918, 29881, 15048, 29918, 2084, 13, 3166, 592, 5617, 273, 457, 29889, 12292, 29889, 9794, 1053, 9305, 29892, 4385, 1626, 5074, 13, 13, 9573, 353, 4472, 29889, 12284, 580, 13, 13, 29992, 9573, 29889, 465, 10194, 29918, 4039, 13, 1753, 599, 12292, 7295, 13, 1678, 1813, 29918, 9621, 353, 518, 525, 3051, 742, 525, 11600, 742, 525, 8216, 742, 525, 4548, 16129, 29918, 1256, 742, 525, 1885, 29918, 8216, 742, 318, 29915, 333, 742, 29871, 525, 1989, 9303, 742, 318, 29915, 1989, 9303, 29918, 1807, 742, 29871, 525, 23679, 29918, 1256, 742, 525, 12759, 29918, 2271, 742, 525, 29517, 742, 525, 4882, 742, 525, 3257, 742, 525, 23545, 793, 742, 525, 21402, 2033, 13, 1678, 1962, 353, 5159, 13, 1678, 396, 1053, 282, 2585, 29936, 29886, 2585, 29889, 842, 29918, 15003, 580, 13, 1678, 2178, 27514, 353, 4385, 1626, 5074, 29889, 12650, 29889, 497, 580, 13, 1678, 363, 2944, 297, 2178, 27514, 29901, 13, 4706, 5694, 353, 6571, 13, 4706, 363, 285, 430, 297, 1813, 29918, 9621, 29901, 13, 9651, 5694, 29961, 29888, 430, 29962, 353, 679, 5552, 29898, 667, 29892, 285, 430, 29897, 13, 4706, 1962, 29889, 4397, 29898, 7382, 29897, 13, 1678, 736, 426, 13, 4706, 525, 12292, 2396, 1962, 13, 1678, 500, 13, 13, 29992, 9573, 29889, 4572, 580, 13, 1753, 3349, 29918, 17057, 29898, 1767, 1125, 13, 1678, 736, 16321, 29915, 718, 995, 29961, 29896, 13018, 29896, 29962, 13, 13, 13, 29992, 9573, 29889, 4572, 580, 13, 1753, 5224, 29898, 1767, 1125, 13, 1678, 396, 1053, 282, 2585, 29936, 29886, 2585, 29889, 842, 29918, 15003, 580, 13, 1678, 736, 995, 29889, 13609, 580, 2 ]
h5parm/datapack.py	Joshuaalbert/h5parm	0	76104	<reponame>Joshuaalbert/h5parm<filename>h5parm/datapack.py import tables as tb import os import numpy as np import astropy.units as au import astropy.time as at import astropy.coordinates as ac import sys import time import itertools import re import logging logger = logging.getLogger(__name__) from h5parm.maintenance import deprecated def _load_array_file(array_file): '''Loads a csv where each row is x,y,z in geocentric ITRS coords of the antennas''' types = np.dtype({'names': ['station', 'X_ITRS', 'Y_ITRS', 'Z_ITRS'], 'formats': ['S16', np.double, np.double, np.double, np.double]}) d = np.genfromtxt(array_file, comments='#', delimiter=',', dtype=types) labels = np.array(d['station'].astype(str)) locs = ac.SkyCoord(x=d['X_ITRS'] * au.m, y=d['Y_ITRS'] * au.m, z=d['Z_ITRS'] * au.m, frame='itrs') Nantenna = int(np.size(d['X_ITRS'])) diameters = None return np.array(labels).astype(np.str_), locs.cartesian.xyz.to(au.m).value.transpose() def update_h5parm(old_h5parm, new_h5parm): """ Clones an old H5parm typically created with LoSoTO, that only has readonly access. :param old_h5parm: :param new_h5parm: :return: """ logger.info("Updating {}".format(old_h5parm)) select = dict(ant=None, time=None, dir=None, freq=None, pol=None) old = DataPack(old_h5parm, readonly=True) new = DataPack(new_h5parm, readonly=False) logger.info("Created {}".format(new_h5parm)) solsets = old.solsets for solset in solsets: old.current_solset = solset old.select(*select) antenna_labels, antennas = old.antennas patch_names, directions = old.directions # Sometimes the antennas are not set properly in the original datapack if solset in new.solsets: new.delete_solset(solset) if np.sum(antennas) == 0.: new.add_solset(solset, array_file=DataPack.lofar_array, directions=directions, patch_names=patch_names) else: new.add_solset(solset, antenna_labels=antenna_labels, antennas=antennas, directions=directions, patch_names=patch_names) new.current_solset = solset soltabs = old.soltabs for soltab in soltabs: if soltab in new.soltabs: new.delete_soltab(soltab) axes = {k: v for (v, k) in zip(old.soltab_axes(soltab))} antenna_labels, antennas = old.get_antennas(axes['ant']) patch_names, directions = old.get_directions(axes['dir']) timestamps, times = old.get_times(axes['time']) pol_labels, pols = old.get_pols(axes['pol']) vals, _ = old.get_soltab(soltab, weight=False) weight_vals, _ = old.get_soltab(soltab, weight=True) if 'freq' in axes.keys(): freq_labels, freqs = old.get_freqs(axes['freq']) new.add_soltab(soltab, values=vals, weights=weight_vals, weightDtype='f16', time=times.mjd * 86400., pol=pol_labels, ant=antenna_labels, dir=patch_names, freq=freqs) else: new.add_soltab(soltab, values=vals, weights=weight_vals, weightDtype='f16', time=times.mjd * 86400., pol=pol_labels, ant=antenna_labels, dir=patch_names) class DataPack(object): # _H: tb.File _arrays = os.path.dirname(sys.modules["h5parm"].__file__) lofar_array = os.path.join(_arrays, 'arrays/lofar.antenna.cfg') lofar_array_hba = os.path.join(_arrays, 'arrays/lofar.hba.antenna.cfg') gmrt_array = os.path.join(_arrays, 'arrays/gmrtPos.cfg') def __init__(self, filename, readonly=False): if isinstance(filename, DataPack): filename = filename.filename self.filename = os.path.abspath(filename) if not os.path.isfile(self.filename) and readonly: raise IOError("File {} doesn't exist, and in readonly mode".format(self.filename)) self.readonly = readonly self._H = None self._contexts_open = 0 self._selection = None self._current_solset = None self.axes_order = ['pol', 'dir', 'ant', 'freq', 'time'] self.axes_atoms = {'pol': (np.str_, tb.StringAtom(16)), 'dir': (np.str_, tb.StringAtom(128)), 'ant': (np.str_, tb.StringAtom(16)), 'freq': (np.float64, tb.Float64Atom()), 'time': (np.float64, tb.Float64Atom())} if len(self.solsets) > 0: self.current_solset = self.solsets[0] @property def axes_order(self): return self._axes_order @axes_order.setter def axes_order(self, axes): if not isinstance(axes, (tuple, list)): raise ValueError("axes should be a list or tuple. {}".format(type(axes))) order = [] for axis in axes: if axis not in ['ant', 'dir', 'freq', 'pol', 'time']: raise ValueError("Axis {} not a valid axis.".format(axis)) if axis in order: raise ValueError("Found duplicate in ordering. {}".format(axes)) order.append(axis) self._axes_order = order @property def readonly(self): return self._readonly @readonly.setter def readonly(self, value): if not isinstance(value, bool): raise ValueError("Readonly must be a bool.") self._readonly = value def __enter__(self): if self._contexts_open == 0: self._H = tb.open_file(self.filename, mode='r' if self.readonly else 'a') self._contexts_open += 1 return self def __exit__(self, exc_type, exc_val, exc_tb): if self._contexts_open == 1: self._H.close() self._H = None self._contexts_open -= 1 @property def current_solset(self): return self._current_solset @current_solset.setter def current_solset(self, solset): if solset not in self.solsets: raise ValueError("Solset {} does not exist.".format(solset)) self._current_solset = solset logger.info("Set current solset to: {}".format(self._current_solset)) def set_current_solset(self, solset): if solset not in self.solsets: raise ValueError("Solset {} does not exist.".format(solset)) self._current_solset = solset logger.info("Set current solset to: {}".format(self._current_solset)) @property def solsets(self): with self: return [k for k, v in self._H.root._v_groups.items()] @property def soltabs(self): with self: if self.current_solset is None: raise ValueError("Current solset is None.") solset_group = self._H.root._v_groups[self.current_solset] return [k for k, v in solset_group._v_groups.items()] @deprecated("Use current_solset and add_solset") def switch_solset(self, solset, antenna_labels=None, antennas=None, array_file=None, directions=None, patch_names=None): self.add_solset(solset, antenna_labels, antennas, array_file, directions, patch_names) def add_solset(self, solset, antenna_labels=None, antennas=None, array_file=None, patch_names=None, directions=None): """ Create a solset. :param solset: str Name of solset :param antenna_labels, antennas: see set_antennas :param array_file: str array file to load, lofar array if None :params patch_names, directions: see set_directions :param directions: :return: """ if solset in self.solsets: logger.warning("Solset {} already exists.".format(solset)) self.current_solset = solset return with self: self._H.create_group(self._H.root, solset, title='Solset: {}'.format(solset)) self.current_solset = solset self.add_antenna_table() if antennas is None: antenna_labels, antennas = _load_array_file(self.lofar_array_hba if array_file is None else array_file) self.set_antennas(antenna_labels, antennas) self.add_directions_table() if directions is not None: self.set_directions(patch_names, directions) logger.info("Created solset {}.".format(solset)) def add_soltab(self, soltab, values=None, weights=None, weightDtype='f16', *axes): if soltab in self.soltabs: logger.warning('Soltab {} already exists.'.format(soltab)) return with self: if self.current_solset is None: raise ValueError("Current solset is None.") solset_group = self._H.root._v_groups[self.current_solset] self._H.create_group(solset_group, soltab, "Soltab: {}".format(soltab)) soltab_group = solset_group._v_groups[soltab] soltab_group._v_attrs['parmdb_type'] = "" shape = [] ordered_axes = [] for axis_name in self.axes_order: if axis_name not in axes.keys(): logger.info("Soltab missing axis {}".format(axis_name)) continue shape.append(len(axes[axis_name])) ordered_axes.append(axis_name) self._H.create_array(soltab_group, axis_name, obj=np.array(axes[axis_name]), title='Axis: {}'.format(axis_name)) # ,atom=self.axes_atoms[axis_name][1]) if values is None: values = np.zeros(shape) self._H.create_array(soltab_group, 'val', obj=values.astype(np.float64), atom=tb.Float64Atom()) val_leaf = soltab_group._v_leaves['val'] val_leaf.attrs['AXES'] = ','.join(ordered_axes) if weightDtype not in ['f16', 'f32', 'f64']: raise ValueError("Allowed weight dtypes are 'f16','f32', 'f64'") if weights is None: weights = np.ones(shape) if weightDtype == 'f16': self._H.create_array(soltab_group, 'weight', obj=weights.astype(np.float16), title='Weights', atom=tb.Float16Atom()) elif weightDtype == 'f32': self._H.create_array(soltab_group, 'weight', obj=weights.astype(np.float32), title='Weights', atom=tb.Float32Atom()) elif weightDtype == 'f64': self._H.create_array(soltab_group, 'weight', obj=weights.astype(np.float64), title='Weights', atom=tb.Float64Atom()) weight_leaf = soltab_group._v_leaves['weight'] weight_leaf.attrs['AXES'] = ','.join(ordered_axes) logger.info("Created soltab {}/{}".format(self.current_solset, soltab)) def delete_soltab(self, soltab): if soltab not in self.soltabs: raise ValueError("Soltab {} not in solset {}.".format(soltab, self.current_solset)) with self: solset_group = self._H.root._v_groups[self.current_solset] soltab_group = solset_group._v_groups[soltab] soltab_group._f_remove(recursive=True) def delete_solset(self, solset): if solset not in self.solsets: raise ValueError("Solset {} appears not to exist.".format(solset)) with self: solset_group = self._H.root._v_groups[solset] solset_group._f_remove(recursive=True) if solset == self.current_solset: logger.warning("Setting current solset to None because you deleted it.") self._current_solset = None logger.info("Deleted solset {}.".format(solset)) def add_antenna_table(self): with self: if self.current_solset is None: raise ValueError("Current solset is None.") solset_group = self._H.root._v_groups[self.current_solset] class Antenna(tb.IsDescription): name = tb.StringCol(16, pos=1) position = tb.Float64Col(shape=3, dflt=0.0, pos=2) # tb.Col(np.float64,3, np.zeros(3, dtype=np.float64),pos=2) # descriptor = np.dtype([('name', np.str_, 16), ('position', np.float64, 3)]) self._H.create_table(solset_group, 'antenna', Antenna, title='Antenna names and positions', expectedrows=62) logger.info("Created antenna table.") def add_directions_table(self): with self: if self.current_solset is None: raise ValueError("Current solset is None.") solset_group = self._H.root._v_groups[self.current_solset] class Direction(tb.IsDescription): name = tb.StringCol(128, pos=1) dir = tb.Float64Col(shape=2, dflt=0.0, pos=2) # tb.Col(np.float64, 2, np.zeros(2, dtype=np.float64), pos=2) # descriptor = np.dtype([('name', np.str_, 16), ('position', np.float64, 3)]) self._H.create_table(solset_group, 'source', Direction, title='Direction names and directions', expectedrows=35) logger.info("Created direction table.") def set_antennas(self, antenna_labels, antennas): with self: if self.current_solset is None: raise ValueError("Current solset is None.") solset_group = self._H.root._v_groups[self.current_solset] if 'antenna' not in solset_group._v_leaves: logger.info("antenna not in leaves. Adding.") self.add_antenna_table() antenna_table = solset_group._v_leaves['antenna'] antenna_table.remove_rows(0) antenna_table.append(list(zip(antenna_labels, antennas))) logger.info("Set the antenna table.") def set_directions(self, patch_names, directions): if patch_names is None: patch_names = ["patch_{:03d}".format(i) for i in range(len(directions))] with self: if self.current_solset is None: raise ValueError("Current solset is None.") solset_group = self._H.root._v_groups[self.current_solset] if 'source' not in solset_group._v_leaves: self.add_directions_table() direction_table = solset_group._v_leaves['source'] direction_table.remove_rows(0) direction_table.append(list(zip(patch_names, directions))) logger.info("Set the direction table.") def save_array_file(self, array_file): with self: ants = self._solset.getAnt() labels = [] locs = [] for label, pos in ants.items(): labels.append(label) locs.append(pos) Na = len(labels) with open(array_file, 'w') as f: f.write('# Created on {0} by <NAME>\n'.format(time.strftime("%a %c", time.localtime()))) f.write('# ITRS(m)\n') f.write('# X\tY\tZ\tlabels\n') i = 0 while i < Na: f.write( '{0:1.9e}\t{1:1.9e}\t{2:1.9e}\t{4}'.format(locs[i][0], locs[i][1], locs[i][2], labels[i])) if i < Na - 1: f.write('\n') i += 1 @property def antennas(self): with self: if self.current_solset is None: raise ValueError("Current solset is None.") solset_group = self._H.root._v_groups[self.current_solset] if 'antenna' not in solset_group._v_leaves: self.add_antenna_table() antenna_table = solset_group._v_leaves['antenna'] return antenna_table.col('name'), antenna_table.col('position') @property def directions(self): with self: if self.current_solset is None: raise ValueError("Current solset is None.") solset_group = self._H.root._v_groups[self.current_solset] if 'source' not in solset_group._v_leaves: self.add_directions_table() direction_table = solset_group._v_leaves['source'] return direction_table.col('name'), direction_table.col('dir') def __repr__(self): def grouper(n, iterable, fillvalue=None): "grouper(3, 'ABCDEFG', 'x') --> ABC DEF Gxx" args = [iter(iterable)] n return itertools.zip_longest(args, fillvalue=fillvalue) _temp_solset = self.current_solset info = "==== DataPack: {} ====\n".format(os.path.abspath(self.filename)) for solset in self.solsets: self.current_solset = solset info += "=== solset: {} ===\n".format(solset) info += "Directions: \n" for i, src_name1 in enumerate(zip(self.directions)): info += "{} -> {}\t{}\n".format(i, src_name1[0], list(src_name1[1])) info += "\nStations: \n" for i, ant1 in enumerate(zip(self.antennas)): info += "{} -> {}\t{}\n".format(i, ant1[0], list(ant1[1])) for soltab in self.soltabs: info += "== soltab: {} ==\n".format(soltab) shape = [len(axis_vals) for axis_vals, axis in zip(self.soltab_axes(soltab))] axes = [axis for axis_vals, axis in zip(self.soltab_axes(soltab))] info += "shape: {}\n".format(shape) info += "axes: {}\n".format(axes) # for axis_vals, axis, size, dtype in zip(self.soltab_axes(soltab)): # info += "Axis: {} {} {}\n{}\n".format(axis,size, dtype,list(axis_vals)) self.current_solset = _temp_solset return info def select(self, *axes): self._selection = {} for axis_name in self.axes_order: if axis_name not in axes.keys(): continue if isinstance(axes[axis_name], int): self._selection[axis_name] = [axes[axis_name]] else: self._selection[axis_name] = axes[axis_name] def select_all(self): self._selection = None @property def allowed_soltab_prefixes(self): # return [soltab.replace("000","") for soltab in self.soltabs] return ['phase', 'amplitude', 'tec', 'clock', 'const'] def soltab_axes(self, soltab): with self: if soltab not in self.soltabs: logger.warning('Soltab {} does not exist.'.format(soltab)) return with self: if self.current_solset is None: raise ValueError("Current solset is None.") solset_group = self._H.root._v_groups[self.current_solset] soltab_group = solset_group._v_groups[soltab] val_leaf = soltab_group._v_leaves['val'] try: axes = val_leaf.attrs['AXES'].split(',') except TypeError: axes = [s.decode().lower() for s in val_leaf.attrs['AXES'].split(b',')] shape = [] type = [] vals = [] for axis in axes: axis_vals = soltab_group._v_leaves[axis].read() vals.append(axis_vals) shape.append(len(axis_vals)) type.append(np.array(axis_vals).dtype) return vals, axes def get_selection(self, soltab): if self._selection is None: self._selection = {} if soltab not in self.soltabs: raise ValueError('Soltab {} does not exist.'.format(soltab)) if self.current_solset is None: raise ValueError("Current solset is None.") selection = [] # goal is to reduce everything to slices if possible for efficient usage of pytables for axis_val, axis in zip(self.soltab_axes(soltab)): axis_selection = self._selection.get(axis, None) if axis_selection is None: selection.append(slice(None, None, None)) elif isinstance(axis_selection, slice): selection.append(axis_selection) elif isinstance(axis_selection, (tuple, list)): list_select = [] for element in axis_selection: if isinstance(element, int): if element >= len(axis_val): raise ValueError( "Selecting index greater than length of axis {} {}".format(element, axis_val)) list_select.append(element) else: idx = np.where(axis_val.astype(type(element)) == element)[0] if len(idx) == 0: raise ValueError("Element not in axis {} {}".format(element, axis_val)) list_select.append(idx[0]) selection.append(list_select) elif isinstance(axis_selection, str): axis_val = np.asarray(axis_val) is_pattern = [] for idx, element in enumerate(axis_val.astype(type(axis_selection))): if re.search(axis_selection, element) is not None: is_pattern.append(idx) selection.append(is_pattern) else: raise ValueError("Unable to parse {}".format(axis_selection)) # replace all lists with slices if possible: limitation of only one list per indexing corrected_selection = [] for sel in selection: _sel = sel if isinstance(sel, list): if sel[0] != np.min(sel) or sel[-1] != np.max(sel): break if len(sel) == 1: _sel = slice(sel[0], sel[0] + 1, 1) else: try_slice = slice(sel[0], sel[-1] + 1, (sel[-1] - sel[0]) // (len(sel) - 1)) comp_list = list(range(sel[0], sel[-1] + 1, (sel[-1] - sel[0]) // (len(sel) - 1))) if comp_list == sel: _sel = try_slice corrected_selection.append(_sel) num_lists = sum([1 if isinstance(sel, list) else 0 for sel in corrected_selection]) if num_lists > 1: raise IndexError("Due to a limitation, only one fancy indexing can be applied per pytables getattr.") return tuple(corrected_selection) def get_axes_perm(self, actual_axes, want_axes): """ Get the permutation that changes the actual stored axes to prefered axes order stored in self.axes_order. :param actual_axes: list of str The order of axes in an array :return: tuple of int """ if not isinstance(actual_axes, (list, tuple)): raise TypeError("actual axes must be a list or tuple of str") actual_axes = list(actual_axes) if not isinstance(want_axes, (list, tuple)): raise TypeError("want axes must be a list or tuple of str") want_axes = list(want_axes) for a in want_axes: if a not in actual_axes: raise ValueError("Missing {} in {}.".format(a, actual_axes)) return tuple([actual_axes.index(a) for a in want_axes]) def get_soltab(self, soltab, weight=False): with self: selection = self.get_selection(soltab) soltab_axes_vals, soltab_axes = self.soltab_axes(soltab) ### # assumes the desired axes are in self.ordered_axes want_axes = [a for a in self.axes_order if a in soltab_axes] actual_axes = soltab_axes perm = self.get_axes_perm(actual_axes, want_axes) solset_group = self._H.root._v_groups[self.current_solset] soltab_group = solset_group._v_groups[soltab] if weight: leaf = soltab_group._v_leaves['weight'] else: leaf = soltab_group._v_leaves['val'] out_axes = {name: np.array(vals)[selection[i]] for i, (vals, name) in enumerate(zip(soltab_axes_vals, soltab_axes))} out_vals = leaf.__getitem__(selection).transpose(perm) return out_vals, out_axes def set_soltab(self, soltab, value, weight=False): """ Sets the values of soltab, according to the current selection. :param soltab: str :param value: np.array or array like Should have shape of self.axes_order :param weight: bool Whether you are setting weights or not :return: """ with self: selection = self.get_selection(soltab) solset_group = self._H.root._v_groups[self.current_solset] soltab_group = solset_group._v_groups[soltab] _, soltab_axes = self.soltab_axes(soltab) ### # want_axes = soltab_axes actual_axes = [a for a in self.axes_order if a in soltab_axes] perm = self.get_axes_perm(actual_axes, want_axes) if weight: leaf = soltab_group._v_leaves['weight'] else: leaf = soltab_group._v_leaves['val'] leaf.__setitem__(selection, value.transpose(perm)) def __getattr__(self, tab): """ Get a value in allowed soltabs or pass on to underlying. :param tab: :return: np.array Shape as determined by self.axes_order """ # with self: # tabs = self._solset.getSoltabNames() tabs = self.allowed_soltab_prefixes tabs = ["weights_{}".format(t) for t in tabs] + ["axes_{}".format(t) for t in tabs] + tabs weight = False axes = False if any([tab.startswith(t) for t in tabs]): if tab.startswith("weights_"): tab = "".join(tab.split('weights_')[1:]) weight = True if tab.startswith("axes_"): tab = "".join(tab.split('axes_')[1:]) axes = True with self: soltab = "{}000".format(tab) selection = self.get_selection(soltab) soltab_axes_vals, soltab_axes = self.soltab_axes(soltab) ### # assumes the desired axes are in self.ordered_axes want_axes = [a for a in self.axes_order if a in soltab_axes] actual_axes = soltab_axes perm = self.get_axes_perm(actual_axes, want_axes) if not axes: solset_group = self._H.root._v_groups[self.current_solset] soltab_group = solset_group._v_groups[soltab] if weight: leaf = soltab_group._v_leaves['weight'] else: leaf = soltab_group._v_leaves['val'] out_axes = {name: np.array(vals)[selection[i]] for i, (vals, name) in enumerate(zip(soltab_axes_vals, soltab_axes))} out_vals = leaf.__getitem__(selection).transpose(perm) return out_vals, out_axes else: out_axes = {name: np.array(vals)[selection[i]] for i, (vals, name) in enumerate(zip(soltab_axes_vals, soltab_axes))} return out_axes else: return object.__getattribute__(self, tab) def __setattr__(self, tab, value): """ Links any attribute with an "axis name" to getValuesAxis("axis name") also links val and weight to the relative arrays. Parameter ---------- axis : str The axis name. value : array The array of the right shape for selection. Assumes the values to set are in self.axes_order """ # with self: # tabs = self._solset.getSoltabNames() tabs = self.allowed_soltab_prefixes tabs = ["weights_{}".format(t) for t in tabs] + ["axes_{}".format(t) for t in tabs] + tabs weight = False axes = False if any([tab.startswith(t) for t in tabs]): if tab.startswith("weights_"): tab = "".join(tab.split('weights_')[1:]) weight = True if tab.startswith("axes_"): tab = "".join(tab.split('axes_')[1:]) axes = True with self: soltab = "{}000".format(tab) selection = self.get_selection(soltab) solset_group = self._H.root._v_groups[self.current_solset] soltab_group = solset_group._v_groups[soltab] _, soltab_axes = self.soltab_axes(soltab) ### # want_axes = soltab_axes actual_axes = [a for a in self.axes_order if a in soltab_axes] perm = self.get_axes_perm(actual_axes, want_axes) if not axes: if weight: leaf = soltab_group._v_leaves['weight'] else: leaf = soltab_group._v_leaves['val'] leaf.__setitem__(selection, value.transpose(perm)) else: if not isinstance(value, dict): raise ("Axes must come in dict of 'name':vals") for i, (k, v) in enumerate(value.items()): axis_vals = soltab_group._v_leaves[k] axis_vals[selection[i]] = v else: object.__setattr__(self, tab, value) @property def ref_ant(self): with self: antenna_labels, antennas = self.antennas return antenna_labels[0] @property def array_center(self): with self: _, antennas = self.get_antennas(None) center = antennas.cartesian.xyz[:, 0] center = ac.SkyCoord(x=center[0], y=center[1], z=center[2], frame='itrs') return center @property def mean_array_loc(self): with self: _, antennas = self.get_antennas(None) center = np.mean(antennas.cartesian.xyz, axis=1) center = ac.SkyCoord(x=center[0], y=center[1], z=center[2], frame='itrs') return center def get_antennas(self, ants): with self: antenna_labels, antennas = self.antennas if ants is None: lookup = slice(None, None, None) else: ants = np.array(ants).astype(antenna_labels.dtype) lookup = [] for a in ants: if a not in antenna_labels: raise ValueError("Antenna not found in solset {} {}".format(a, antenna_labels)) lookup.append(np.where(a == antenna_labels)[0][0]) antennas = antennas[lookup, :] return antenna_labels[lookup], ac.SkyCoord(antennas[:, 0] * au.m, antennas[:, 1] * au.m, antennas[:, 2] * au.m, frame='itrs') @property def pointing_center(self): with self: _, directions = self.get_directions(None) ra_mean = np.mean(directions.transform_to('icrs').ra) dec_mean = np.mean(directions.transform_to('icrs').dec) dir = ac.SkyCoord(ra_mean, dec_mean, frame='icrs') return dir def get_directions(self, dirs): with self: patch_names, directions = self.directions if dirs is None: lookup = slice(None, None, None) else: dirs = np.array(dirs).astype(patch_names.dtype) lookup = [] for a in dirs: if a not in patch_names: raise ValueError("Direction not found in solset {} {}".format(a, patch_names)) lookup.append(np.where(a == patch_names)[0][0]) directions = directions[lookup, :] return patch_names[lookup], ac.SkyCoord(directions[:, 0] * au.rad, directions[:, 1] * au.rad, frame='icrs') def get_times(self, times): """ times are stored as mjs """ times = at.Time(times / 86400., format='mjd') return times.isot, times def get_freqs(self, freqs): labs = ['{:.1f}MHz'.format(f / 1e6) for f in freqs] return np.array(labs), freqs*au.Hz def get_pols(self, pols): with self: return pols, np.arange(len(pols), dtype=np.int32)	[ 1, 529, 276, 1112, 420, 29958, 29967, 10578, 3357, 284, 2151, 29914, 29882, 29945, 862, 29885, 29966, 9507, 29958, 29882, 29945, 862, 29885, 29914, 4130, 481, 547, 29889, 2272, 13, 5215, 6131, 408, 260, 29890, 13, 5215, 2897, 13, 5215, 12655, 408, 7442, 13, 5215, 8717, 14441, 29889, 348, 1169, 408, 782, 13, 5215, 8717, 14441, 29889, 2230, 408, 472, 13, 5215, 8717, 14441, 29889, 1111, 24266, 408, 1274, 13, 5215, 10876, 13, 5215, 931, 13, 5215, 4256, 8504, 13, 5215, 337, 13, 5215, 12183, 13, 13, 21707, 353, 12183, 29889, 657, 16363, 22168, 978, 1649, 29897, 13, 13, 3166, 298, 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examples/para.py	nagareproject/renderers-rml	0	1609041	<gh_stars>0 # Encoding: UTF-8 # -- # Copyright (c) 2008-2021 Net-ng. # All rights reserved. # # This software is licensed under the BSD License, as described in # the file LICENSE.txt, which you should have received as part of # this distribution. # -- from nagare.renderers import rml def sample(output): r = rml.Renderer() with r.document(invariant=1): with r.template(pageSize='letter', leftMargin=72, showBoundary=1): with r.pageTemplate(id='main', pageSize='letter portrait'): with r.pageGraphics: r << r.setFont(name='Helvetica-BoldOblique', size=18) r << r.drawRightString('RML2PDF Test Suite', x=523, y=800) with r.textAnnotation: r << r.param('0,0,1,1', name='Rect') r << r.param(3, name='F') r << r.param(6, name='escape') r << '''X::PDF PX(S) MT(PINK) ''' r << r.frame(id='first', x1='1in', y1='1in', width='6.27in', height='9.69in') with r.stylesheet: with r.initialize: r << r.alias(id='style.normal', value='style.Normal') r << r.paraStyle(name='h1', fontName='Helvetica-BoldOblique', fontSize=32, leading=36) r << r.paraStyle(name='normal', fontName='Helvetica', fontSize=10, leading=12) r << r.paraStyle(name='spaced', fontName='Helvetica', fontSize=10, leading=12, spaceBefore=12, spaceAfter=12) with r.story: with r.para(style='normal'): r << u'Il était là. Hello World. This is a normal paragraph. Blah ' r << r.font('IPO ', color='red') r << 'blah blah blah blah growth forecast blah ' r << 'blah blah forecast blah.Blah blah blah blah blah blah blah blah blah blah blah profit blah blah blah blah blah ' r << 'blah blah blah blah blah IPO.Blah blah blah blah blah blah blah reengineering blah growth blah blah blah ' r << 'proactive direction strategic blah blah blah forward-thinking blah.Blah blah doubletalk blah blah blah blah ' r << 'blah profit blah blah growth blah blah blah blah blah profit.Blah blah blah blah venture capital blah blah blah ' r << 'blah blah forward-thinking blah.' with r.para(style='normal'): r << 'This is another normal paragraph. Blah IPO blah blah blah blah growth forecast blah ' r << 'blah blah forecast blah.Blah blah blah blah blah blah blah blah blah blah blah profit blah blah blah blah blah ' r << 'blah blah blah blah blah IPO.Blah blah blah blah blah blah blah reengineering blah growth blah blah blah ' r << 'proactive direction strategic blah blah blah forward-thinking blah.Blah blah doubletalk blah blah blah blah ' r << 'blah profit blah blah growth blah blah blah blah blah profit.Blah blah blah blah venture capital blah blah blah ' r << 'blah blah forward-thinking blah.' r << r.para('I should NOT have a tiny leading space in front of me!', style='normal') r << r.para('This is spaced. There should be 12 points before and after.', style='spaced') with r.para(style='normal'): r << 'Hello World. This is a normal paragraph. Blah IPO blah blah blah blah growth forecast blah ' r << 'blah blah forecast blah.Blah blah blah blah blah blah blah blah blah blah blah profit blah blah blah blah blah ' r << 'blah blah blah blah blah IPO.Blah blah blah blah blah blah blah reengineering blah growth blah blah blah ' r << 'proactive direction strategic blah blah blah forward-thinking blah.Blah blah doubletalk blah blah blah blah ' r << 'blah profit blah blah growth blah blah blah blah blah profit.Blah blah blah blah venture capital blah blah blah ' r << 'blah blah forward-thinking blah.' return r.root.topdffile(output) if __name__ == '__main__': sample('/tmp/sample.pdf')	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29900, 13, 29937, 11346, 3689, 29901, 18351, 29899, 29947, 13, 29937, 1192, 13, 29937, 14187, 1266, 313, 29883, 29897, 29871, 29906, 29900, 29900, 29947, 29899, 29906, 29900, 29906, 29896, 12670, 29899, 865, 29889, 13, 29937, 2178, 10462, 21676, 29889, 13, 29937, 13, 29937, 910, 7047, 338, 7794, 21144, 1090, 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04_datacamp/solutions/05_solutions.py	HirahTang/datascience_starter_course	3	189091	df[(df['Sex'] == 'male') & (df['Age'] > 60)].shape[0]	[ 1, 4489, 15625, 2176, 1839, 29903, 735, 2033, 1275, 525, 19202, 1495, 669, 313, 2176, 1839, 22406, 2033, 1405, 29871, 29953, 29900, 29897, 1822, 12181, 29961, 29900, 29962, 13, 2 ]
gist_magic/extensions/gist.py	timbr-io/gist-magic	3	50698	<gh_stars>1-10 from IPython.core.magic import (Magics, magics_class, line_magic, cell_magic, line_cell_magic) from IPython import get_ipython import argparse from pygithub3 import Github from pygithub3.resources.gists import Gist import os import shlex from itertools import chain from IPython.display import publish_display_data from .pretty import PrettyGist, PrettyGistList, build_display_data from IPython.core.formatters import DisplayFormatter def publish_to_display(obj): output, _ = DisplayFormatter().format(obj) publish_display_data(output) # The class MUST call this class decorator at creation time @magics_class class GistMagics(Magics): def __init__(self, shell): super(GistMagics, self).__init__(shell) self._token = os.environ.get("GITHUB_ACCESS_TOKEN") self.gh = Github(token=self._token) self.preset_id = None self._parser = self.generate_parser() def generate_parser(self): parser = argparse.ArgumentParser(prog="gist") subparsers = parser.add_subparsers() token_parser = subparsers.add_parser("token", help="Register a Github token for authentication") token_parser.add_argument("github_token", help="Github access token") token_parser.set_defaults(fn=self.token) list_parser = subparsers.add_parser("list", help="List current user's gists (or recent public gists if token is not set)") list_parser.add_argument("-l", "--limit", help="number of gist to list", default=-1) list_parser.set_defaults(fn=self.list) delete_parser = subparsers.add_parser("delete", help="Delete gist specified by id") delete_parser.add_argument("gist_id", help="ID of gist to delete") delete_parser.set_defaults(fn=self.delete) preset_parser = subparsers.add_parser("preset", help="Create or register a preset gist as active") preset_parser.add_argument("preset_gist_id", help="ID of gist preset to select", default=None, nargs="?") preset_parser.add_argument("--no-display", action="store_false", dest="display") preset_parser.set_defaults(fn=self.preset) insert_parser = subparsers.add_parser("insert", help="Insert snippet code into this cell") insert_parser.add_argument("gist_id", help="ID of gist to insert") insert_parser.add_argument("-f", "--filename", help="name of the gist file to insert", default="snippet.py") insert_parser.add_argument("-a", "--append", help="Insert a new cell rather than replacing the content of the current one", action="store_false", dest="replace") insert_parser.set_defaults(fn=self.insert) show_parser = subparsers.add_parser("show", help="Show (or update) a gist") show_parser.add_argument("gist_id", help="ID of gist to load/update", nargs="?") show_parser.add_argument("--no-display", action="store_false", dest="display") show_parser.add_argument("-e", "--evaluate", action="store_true") show_parser.add_argument("-f", "--filename", help="Name of the gist file to create / update") show_parser.add_argument("-d", "--description", help="a description for the gist", default="") show_parser.set_defaults(fn=self.show_or_update) return parser @line_cell_magic def gist(self, line, cell=None): try: input_args = shlex.split(line) if len(input_args) == 0 or input_args[0] not in ["token", "list", "delete", "preset", "insert"]: input_args.insert(0, "show") args, extra = self._parser.parse_known_args(input_args) return args.fn(cell=cell, vars(args)) except SystemExit, se: pass def preset(self, preset_gist_id=None, cell=None, display=True, kwargs): if cell is None: if preset_gist_id is None: # create an empty gist and output the id self.create("%%gist preset\n# gist ids\n", filename="preset.txt") # -> prints the id else: self.preset_id = preset_gist_id pretty_gist = self.show(preset_gist_id, evaluate=True) if display == True: return pretty_gist else: return None else: # execute as a cell magic for line in cell.splitlines(): try: gist_id = line.split("#", 2)[0] if len(gist_id) > 0: self.show(gist_id, display=False, evaluate=True) except Exception as e: print("Unable to load snippet with id: %s" % gist_id) print(str(e)) def token(self, github_token, kwargs): self._token = github_token self.gh = Github(token=self._token) def list(self, limit=-1, kwargs): try: assert self._token is not None gists = self.gh.gists.list() gists_list = PrettyGistList(list(gists.iterator())[:int(limit)]) return gists_list except AssertionError: print("No token defined. Unable to list gists") def show_or_update(self, gist_id=None, cell=None, display=True, evaluate=True, filename="snippet.py", description='', kwargs): if cell is not None: if gist_id is None: return self.create(cell, filename=filename, description=description) else: return self.update(gist_id, cell, filename=filename) else: if gist_id is not None: return self.show(gist_id, display, evaluate) else: return self.list() def show(self, gist_id, display=True, evaluate=False, kwargs): gist = self.gh.gists.get(gist_id) pretty_gist = PrettyGist(gist, display=display) if evaluate: get_ipython().run_cell(pretty_gist.content) return pretty_gist def insert(self, gist_id, filename="snippet.py", replace=True, kwargs): gist = self.gh.gists.get(gist_id) if filename in gist.files: get_ipython().set_next_input(gist.files[filename].content, replace=replace) else: print("{} file not found in gist with id {}".format(filename, gist_id)) def create(self, cell, filename="snippet.py", description=''): try: assert cell is not None assert self._token is not None config = dict(description=description, public=False, files={}) config['files'][filename or 'snippet.py'] = {'content': cell} gist = self.gh.gists.create(config) self.add_to_preset(gist.id) print("gist id: %s" % gist.id) return gist.id except AssertionError: print("No token defined. Unable to create gists.") def delete(self, gist_id, kwargs): try: self.gh.gists.delete(gist_id) print("Deleted gist %s" % gist_id) self.remove_from_preset(gist_id) except Exception as e: print("Could not delete gist %s" % gist_id) def update(self, gist_id, cell, filename="snippet.py"): try: assert cell is not None assert self._token is not None gist = self.gh.gists.get(gist_id) config = { "description": gist.description, "public": gist.public, "files": {} } config["files"][filename or 'snippet.py'] = {"content": cell} self.gh.gists.update(gist_id, config) except AssertionError: print("No token defined. 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savu/plugins/ptychography/base_ptycho.py	malte-storm/Savu	1	89560	<gh_stars>1-10 # Copyright 2014 Diamond Light Source Ltd. # # 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. """ .. module:: base_ptycho :platform: Unix :synopsis: A base class for all ptychographic analysis methods .. moduleauthor:: <NAME> <<EMAIL>> """ from savu.plugins.plugin import Plugin from savu.plugins.driver.cpu_plugin import CpuPlugin import logging import numpy as np class BasePtycho(Plugin, CpuPlugin): # also make one for gpu """ A base plugin for doing ptychography. Other ptychography plugins should \ inherit from this. :param in_datasets: A list of the dataset(s) to process. Default: []. :param out_datasets: A list of the dataset(s) to \ process. Default: ['probe', 'object_transmission', 'positions']. """ def __init__(self, name): super(BasePtycho, self).__init__(name) def setup(self): self.exp.log(self.name + " Setting up the ptycho") in_dataset, out_dataset = self.get_datasets() in_meta_data = in_dataset[0].meta_data# grab the positions from the metadata logging.debug('getting the positions...') self.positions = in_meta_data.get('xy') # get the positions and bind them # lets set up the axis labels for output datasets position_labels, probe_labels, object_labels, self.sh = self.setup_axis_labels(in_dataset) # print "probe labels are:"+str(probe_labels) # print "object labels are:"+str(object_labels) # print "position labels are:"+str(position_labels) # Now create the datasets and work out the patterns ### PROBE ### probe = out_dataset[0] # probe_shape = in_dataset[0].get_shape()[-2:] + (self.get_num_probe_modes(),) self.set_size_probe(in_dataset[0].get_shape()[-2:]) logging.debug("##### PROBE #####") #print("probe shape is:%s",str(self.get_size_probe())) probe.create_dataset(axis_labels=probe_labels, shape=self.get_size_probe()) # create the dataset self.probe_pattern_setup(probe_labels, probe) ### OBJECT #### self.set_size_object(in_dataset[0], self.get_positions(), self.get_pixel_size()) object_trans = out_dataset[1] object_shape = self.sh + self.get_size_object() logging.debug("##### OBJECT #####") #print("object shape is:%s",str(object_shape)) # print object_labels object_trans.create_dataset(axis_labels=object_labels, shape=object_shape) # create the dataset self.object_pattern_setup(object_labels, object_trans) ### POSITIONS ### logging.debug('##### POSITIONS #####') positions = out_dataset[2] #print self.sh, self.get_positions().shape positions_shape = self.sh + self.get_positions().shape[-2:] logging.debug('positions shape is:%s',str(positions_shape)) #print "positions shape",positions_shape positions.create_dataset(axis_labels=position_labels, shape=positions_shape) rest_pos = range(len(position_labels)) pos_md = \ {'core_dims':tuple(set(rest_pos) - set([0])), 'slice_dims':(0,)} positions.add_pattern("CHANNEL", pos_md) ''' now we need to tell the setup what we want as input shapes, output shapes, and the number of each of them in one go. ''' in_pData, out_pData = self.get_plugin_datasets() in_pData[0].plugin_data_setup(self.get_plugin_pattern(), self.get_max_frames()) out_pData[0].plugin_data_setup("PROJECTION", self.get_num_probe_modes()) out_pData[1].plugin_data_setup("PROJECTION", self.get_num_object_modes()) out_pData[2].plugin_data_setup("CHANNEL", self.get_max_frames()) self.exp.log(self.name + " End") ''' The below methods influence the set-up and can be over-ridden depending on which software package we are using ''' def get_plugin_pattern(self): ''' sets the pattern to work in. In this case we consider a ptycho scan to be a 4D_SCAN. ''' return "4D_SCAN" def nInput_datasets(self): return 1 def nOutput_datasets(self): return 3 def get_num_probe_modes(self): return 1 def get_num_object_modes(self): return 1 def get_positions(self): return self.positions def get_pixel_size(self): return 30e-9 def set_size_object(self, dataset,positions, pobj=33e-9): ''' returns tuple ''' # print "positions is "+str(self.get_positions().shape) x,y = self.get_positions()[0],self.get_positions()[1] probe_size = self.get_size_probe() x_fov = np.max(x)-np.min(x) y_fov = np.max(y)-np.min(y) xsize = int(x_fov//pobj) + probe_size[0] ysize = int(y_fov//pobj) + probe_size[1] self.obj_shape = xsize,ysize,self.get_num_object_modes() def get_size_object(self): return self.obj_shape def set_size_probe(self,val): self.probe_size = (1,)+val + (self.get_num_probe_modes(),) def get_size_probe(self): ''' returns tuple ''' return self.probe_size def get_max_frames(self): return 'single' def get_output_axis_units(self): return 'nm' def probe_pattern_setup(self, probe_labels, probe): ''' This is where we set up the patterns, we need to add, PROJECTIONS, SINOGRAMS, TIMESERIES and SPECTRA I've created the TIMESERIES because we could in theory have a time series of spectra probe_patterns: PROJECTION, TIMESERIES (for each projection), SPECTRUM (for each energy) object_patterns: PROJECTION, SINOGRAM, SPECTRUM (for each energy) position_patterns: 1D_METADATA ''' probe_dims = len(probe_labels) # the number of dimensions from the axis labels rest_probe = range(probe_dims) # all the dimensions we have self.set_projection_pattern(probe, rest_probe) self.set_probe_rotation_patterns(probe, rest_probe) self.set_probe_energy_patterns(probe, rest_probe) def object_pattern_setup(self, object_labels, object_trans): ''' This is where we set up the patterns, we need to add, PROJECTIONS, SINOGRAMS, TIMESERIES and SPECTRA I've created the TIMESERIES because we could in theory have a time series of spectra probe_patterns: PROJECTION, TIMESERIES (for each projection), SPECTRUM (for each energy) object_patterns: PROJECTION, SINOGRAM, SPECTRUM (for each energy) position_patterns: 1D_METADATA ''' obj_dims = len(object_labels) # the number of dimensions from the axis labels # print "object has "+str(obj_dims)+"dimensions" rest_obj = range(obj_dims) # all the dimensions we have self.set_projection_pattern(object_trans, rest_obj) self.set_object_rotation_patterns(object_trans, rest_obj) self.set_object_energy_patterns(object_trans, rest_obj) def setup_axis_labels(self, in_dataset): ''' This is where we set up the axis labels the 4D scan will contain labels that are: 'xy', 'detectorX', 'detectorY', but the data itself may be scanned in energy or rotation or something else. We want to remove all the above, and amend them to be the following (preferably with additional scan axes at the front): probe: 'x','y','mode_idx' object: 'x','y','mode_idx' positions: 'xy' ''' PATTERN_LABELS = ['xy', 'detectorX', 'detectorY'] in_labels = in_dataset[0].data_info.get('axis_labels') # this is a list of dictionarys existing_labels = [d.keys()[0] for d in in_labels] # this just gets the axes names logging.debug('The existing labels are:%s, we will remove:%s' % (existing_labels, PATTERN_LABELS)) logging.debug('removing these labels from the list') core_labels_raw = [l for l in existing_labels if l not in PATTERN_LABELS] # removes them from the list core_labels = [l + '.' + in_labels[0][l] for l in core_labels_raw] # add the units in for the ones we are keeping # now we just have to add the new ones to this. trans_units = self.get_output_axis_units() probe_labels = list(core_labels) # take a copy probe_labels.extend(['mode_idx.number','x.' + trans_units, 'y.' + trans_units, ]) logging.debug('the labels for the probe are:%s' % str(probe_labels)) object_labels = list(core_labels) object_labels.extend(['mode_idx.number','x.' + trans_units, 'y.' + trans_units]) logging.debug('the labels for the object are:%s' % str(object_labels)) position_labels = list(core_labels) position_labels.extend(['xy.m','idx']) logging.debug('the labels for the positions are:%s' % str(position_labels)) # now we also need this part of the shape of the data so... md = in_dataset[0].meta_data sh = tuple([len(md.get(l)) for l in core_labels_raw]) return position_labels, probe_labels, object_labels, sh def set_probe_rotation_patterns(self, probe, rest_probe): try: rot_axis = probe.get_data_dimension_by_axis_label('rotation_angle', contains=True) # get the rotation axis except Exception as e: logging.warn(str(e) + 'we were looking for "rotation_angle"') logging.debug('This is not a tomography, so no time series for the probe') else: # print('the rotation axis is:%s' % str(rot_axis)) probe_ts = {'core_dims':(rot_axis,), 'slice_dims':tuple(set(rest_probe) - set([rot_axis]))} probe.add_pattern("TIMESERIES", probe_ts) # so we can FT the wiggles etc... # print('This is a tomography so I have added a TIMESERIES pattern to the probe') # the probe oscillates in time for each projection, set this as a time series pattern def set_probe_energy_patterns(self, probe, rest_probe): try: energy_axis = probe.get_data_dimension_by_axis_label('energy', contains=True) # get an energy axis except Exception as e: logging.warn(str(e) + 'we were looking for "energy"') logging.debug('This is not spectro-microscopy, so no spectrum/timeseries for the probe') else: probe_spec = {'core_dims':tuple(energy_axis), 'slice_dims':tuple(set(rest_probe) - set([energy_axis]))} probe.add_pattern("SPECTRUM", probe_spec) probe.add_pattern("TIMESERIES", probe_spec) logging.debug('This is probably spectro-microscopy so I have added a SPECTRUM pattern to the probe') logging.debug('I have also added a TIMESERIES pattern on the same axis, but be careful with what this means!') # the probe oscillates in time for each projection, set this as a time series pattern def set_projection_pattern(self, probe, rest_probe): probe_proj_core = tuple([rest_probe[idx] for idx in (-3, -2)]) # hard coded since we set them just above probe_slice = tuple(set(rest_probe) - set(probe_proj_core)) probe_proj = {'core_dims':probe_proj_core, 'slice_dims':probe_slice} probe.add_pattern("PROJECTION", probe_proj) logging.debug('have added a PROJECTION pattern') def set_object_energy_patterns(self, object_trans, rest_obj): try: energy_axis = object_trans.get_data_dimension_by_axis_label('energy', contains=True) # get an energy axis except Exception as e: logging.warn(str(e) + 'we were looking for "energy"') logging.debug('This is not spectro-microscopy, so no spectrum for the object') else: obj_spec = {'core_dims':tuple(energy_axis), 'slice_dims':tuple(set(rest_obj) - set([energy_axis]))} object_trans.add_pattern("SPECTRUM", obj_spec) logging.debug('This is probably spectro-microscopy so I have added a SPECTRUM pattern to the object') # the probe oscillates in time for each projection, set this as a time series pattern def set_object_rotation_patterns(self, object_trans, rest_obj): try: rot_axis = object_trans.get_data_dimension_by_axis_label('rotation_angle', contains=True) # get the rotation axis except Exception as e: logging.warn(str(e) + 'we were looking for "rotation_angle"') logging.debug('This is not a tomography, so no sinograms for the object transmission') else: x_axis = object_trans.get_data_dimension_by_axis_label('x', contains=True) # get the x axis obj_sino = {'core_dims':(rot_axis, x_axis), 'slice_dims':tuple(set(rest_obj) - set((rot_axis, x_axis)))} object_trans.add_pattern("SINOGRAM", **obj_sino) # for the tomography logging.debug('This is a tomography so I have added a SINOGRAM pattern to the object transmission') # the probe oscillates in time for 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wal_e/worker/gs/__init__.py	paalkr/wal-e	2,739	146868	<filename>wal_e/worker/gs/__init__.py from wal_e.worker.gs.gs_deleter import Deleter from wal_e.worker.gs.gs_worker import BackupFetcher from wal_e.worker.gs.gs_worker import BackupList from wal_e.worker.gs.gs_worker import DeleteFromContext from wal_e.worker.gs.gs_worker import TarPartitionLister __all__ = [ 'Deleter', 'TarPartitionLister', 'BackupFetcher', 'BackupList', 'DeleteFromContext', ]	[ 1, 529, 9507, 29958, 14625, 29918, 29872, 29914, 24602, 29914, 3174, 29914, 1649, 2344, 26914, 2272, 13, 3166, 17042, 29918, 29872, 29889, 24602, 29889, 3174, 29889, 3174, 29918, 311, 280, 357, 1053, 897, 280, 357, 13, 3166, 17042, 29918, 29872, 29889, 24602, 29889, 3174, 29889, 3174, 29918, 24602, 1053, 7437, 786, 20927, 261, 13, 3166, 17042, 29918, 29872, 29889, 24602, 29889, 3174, 29889, 3174, 29918, 24602, 1053, 7437, 786, 1293, 13, 3166, 17042, 29918, 29872, 29889, 24602, 29889, 3174, 29889, 3174, 29918, 24602, 1053, 21267, 4591, 2677, 13, 3166, 17042, 29918, 29872, 29889, 24602, 29889, 3174, 29889, 3174, 29918, 24602, 1053, 11740, 7439, 654, 29931, 1531, 13, 13, 1649, 497, 1649, 353, 518, 13, 1678, 525, 2772, 280, 357, 742, 13, 1678, 525, 29911, 279, 7439, 654, 29931, 1531, 742, 13, 1678, 525, 5841, 786, 20927, 261, 742, 13, 1678, 525, 5841, 786, 1293, 742, 13, 1678, 525, 12498, 4591, 2677, 742, 13, 29962, 13, 2 ]
test31.py	cieczpj/spider	0	61483	<filename>test31.py from lxml import etree import requests import urllib.parse import pymongo headers = { 'User-Agent':'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_14_2) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/71.0.3578.98 Safari/537.36', } mylist = [] for j in range(50): if j == 1: reponse= requests.get('http://v.7192.com/movie_0_0_0_0_1',headers=headers) else: reponse = requests.get('http://v.7192.com/movie_0_0_0_0_{}'.format(j),headers=headers) reponse.encoding="gbk" tree = etree.HTML(reponse.text) res = tree.xpath('//div[@class="result_right_list"]/ul/li/div/a') #print(res) #mylist = [] for a in res: href1_ = a.xpath('./@href')[0] href_ = ('http://v.7192.com'+href1_) title_ = a.xpath('./@title')[0] src_ = a.xpath('./img/@data-original')[0] print(src_) print(href_,title_) #text_=href_+title_+src_ reponse1 = requests.get('http://v.7192.com'+href1_,headers=headers) reponse1.encoding="gbk" tree1 = etree.HTML(reponse1.text) res2 = tree1.xpath('//div[@class="show_information"]/table[@id="mv_play_list"]/tr/th[@width="180px"]/a') #res2 = tree1.xpath('//div[@class="result_right_list"]/ul/li/div/a') #mylist = [] for b in res2: href2= b.xpath('./@rel')[0] print(href2) mylist.append({"src_":src_,"href_":href_,"title_":title_,"href2":href2}) #lock.acquire() # 锁住mongo myclient = pymongo.MongoClient("mongodb://localhost:27017/") mydb = myclient["wemovie"] mycol = mydb["movie"] mycol.insert_many(mylist) myclient.close() #!!!!!!!!!!! #lock.release() # 打开mongo	[ 1, 529, 9507, 29958, 1688, 29941, 29896, 29889, 2272, 13, 3166, 301, 3134, 1053, 634, 929, 13, 5215, 7274, 13, 5215, 3142, 1982, 29889, 5510, 13, 5215, 282, 962, 7443, 13, 13662, 353, 426, 13, 1678, 525, 2659, 29899, 19661, 22099, 29924, 2112, 2911, 29914, 29945, 29889, 29900, 313, 15735, 524, 10578, 29936, 18555, 4326, 6570, 1060, 29871, 29896, 29900, 29918, 29896, 29946, 29918, 29906, 29897, 12113, 3609, 13117, 29914, 29945, 29941, 29955, 29889, 29941, 29953, 313, 29968, 7020, 29892, 763, 1879, 27604, 29897, 10228, 29914, 29955, 29896, 29889, 29900, 29889, 29941, 29945, 29955, 29947, 29889, 29929, 29947, 24544, 29914, 29945, 29941, 29955, 29889, 29941, 29953, 742, 13, 29913, 13, 1357, 1761, 353, 5159, 13, 1454, 432, 297, 3464, 29898, 29945, 29900, 1125, 13, 1678, 565, 432, 1275, 29871, 29896, 29901, 13, 4706, 337, 1713, 29922, 7274, 29889, 657, 877, 1124, 597, 29894, 29889, 29955, 29896, 29929, 29906, 29889, 510, 29914, 27362, 29918, 29900, 29918, 29900, 29918, 29900, 29918, 29900, 29918, 29896, 742, 13662, 29922, 13662, 29897, 13, 1678, 1683, 29901, 13, 4706, 337, 1713, 353, 7274, 29889, 657, 877, 1124, 597, 29894, 29889, 29955, 29896, 29929, 29906, 29889, 510, 29914, 27362, 29918, 29900, 29918, 29900, 29918, 29900, 29918, 29900, 648, 29913, 4286, 4830, 29898, 29926, 511, 13662, 29922, 13662, 29897, 13, 1678, 337, 1713, 29889, 22331, 543, 26300, 29895, 29908, 13, 1678, 5447, 353, 634, 929, 29889, 7020, 29898, 276, 1713, 29889, 726, 29897, 13, 1678, 620, 353, 5447, 29889, 23635, 877, 458, 4563, 17548, 1990, 543, 2914, 29918, 1266, 29918, 1761, 3108, 29914, 352, 29914, 492, 29914, 4563, 29914, 29874, 1495, 13, 1678, 396, 2158, 29898, 690, 29897, 13, 1678, 396, 1357, 1761, 353, 5159, 13, 1678, 363, 263, 297, 620, 29901, 13, 4706, 2822, 29896, 29918, 353, 263, 29889, 23635, 877, 6904, 29992, 12653, 29861, 29900, 29962, 13, 4706, 2822, 29918, 353, 6702, 1124, 597, 29894, 29889, 29955, 29896, 29929, 29906, 29889, 510, 18717, 12653, 29896, 19925, 13, 4706, 3611, 29918, 353, 263, 29889, 23635, 877, 6904, 29992, 3257, 29861, 29900, 29962, 13, 4706, 4765, 29918, 353, 263, 29889, 23635, 877, 6904, 2492, 29368, 1272, 29899, 13492, 29861, 29900, 29962, 13, 4706, 1596, 29898, 4351, 19925, 13, 4706, 1596, 29898, 12653, 3383, 3257, 19925, 13, 4706, 396, 726, 29918, 29922, 12653, 28842, 3257, 28842, 4351, 29918, 13, 4706, 337, 1713, 29896, 353, 7274, 29889, 657, 877, 1124, 597, 29894, 29889, 29955, 29896, 29929, 29906, 29889, 510, 18717, 12653, 29896, 3383, 13662, 29922, 13662, 29897, 13, 4706, 337, 1713, 29896, 29889, 22331, 543, 26300, 29895, 29908, 13, 4706, 5447, 29896, 353, 634, 929, 29889, 7020, 29898, 276, 1713, 29896, 29889, 726, 29897, 13, 4706, 620, 29906, 353, 5447, 29896, 29889, 23635, 877, 458, 4563, 17548, 1990, 543, 4294, 29918, 19678, 3108, 29914, 2371, 17548, 333, 543, 29324, 29918, 1456, 29918, 1761, 3108, 29914, 509, 29914, 386, 17548, 2103, 543, 29896, 29947, 29900, 1756, 3108, 29914, 29874, 1495, 13, 4706, 396, 690, 29906, 353, 5447, 29896, 29889, 23635, 877, 458, 4563, 17548, 1990, 543, 2914, 29918, 1266, 29918, 1761, 3108, 29914, 352, 29914, 492, 29914, 4563, 29914, 29874, 1495, 13, 4706, 396, 1357, 1761, 353, 5159, 13, 4706, 363, 289, 297, 620, 29906, 29901, 13, 9651, 2822, 29906, 29922, 289, 29889, 23635, 877, 6904, 29992, 2674, 29861, 29900, 29962, 13, 9651, 1596, 29898, 12653, 29906, 29897, 13, 9651, 590, 1761, 29889, 4397, 3319, 29908, 4351, 29918, 1115, 4351, 29918, 1699, 12653, 29918, 1115, 12653, 29918, 1699, 3257, 29918, 1115, 3257, 29918, 1699, 12653, 29906, 1115, 12653, 29906, 1800, 13, 29937, 908, 29889, 562, 1548, 580, 29871, 396, 29871, 236, 151, 132, 231, 192, 146, 29885, 7443, 13, 1357, 4645, 353, 282, 962, 7443, 29889, 29924, 7443, 4032, 703, 23264, 597, 7640, 29901, 29906, 29955, 29900, 29896, 29955, 29914, 1159, 13, 1357, 2585, 353, 590, 4645, 3366, 29893, 331, 5672, 3108, 13, 1357, 1054, 353, 590, 2585, 3366, 27362, 3108, 13, 1357, 1054, 29889, 7851, 29918, 13011, 29898, 1357, 1761, 29897, 13, 1357, 4645, 29889, 5358, 580, 396, 6824, 6824, 6824, 6824, 21004, 13, 29937, 908, 29889, 14096, 580, 396, 29871, 31656, 31026, 29885, 7443, 13, 13, 13, 268, 13, 268, 13, 13, 2 ]
orders/views.py	DobromirZlatkov/anteya	0	8699	<filename>orders/views.py from django.core.urlresolvers import reverse_lazy from django.views import generic from django.shortcuts import redirect, render from django.http import HttpResponseRedirect from django.core.urlresolvers import reverse from . import forms from . import models from custommixins import mixins class OrderView(generic.View): template_name = 'orders/order_create.html' def get(self, request): qs = models.Product.objects.none() formset = forms.ProductFormSet(queryset=qs, prefix='formset') order_form = forms.OrderForm(prefix='order_form') return render(request, self.template_name, {'formset': formset, 'order_form': order_form}) def post(self, request): formset = forms.ProductFormSet(request.POST, prefix='formset') order_form = forms.OrderForm(request.POST, prefix='order_form') if formset.is_valid(): order = order_form.save() for form in formset.forms: product = form.save(commit=False) order.products.add(product) order.save() return HttpResponseRedirect(reverse('order_details', args=(order.id,))) else: return render(request, self.template_name, {'formset': formset, 'order_form': order_form}) class OrderDetails(generic.DetailView): model = models.Order template_name_suffix = '_details' class OrderList(mixins.LoginRequiredMixin, mixins.AdminRequiredMixin, generic.ListView): model = models.Order class OrderEdit(generic.View): template_name = 'orders/order_edit.html' def get(self, request, pk): order = models.Order.objects.get(pk=pk) formset = forms.ProductFormSet(queryset=order.products.all(), prefix='formset') order_form = forms.OrderForm(prefix='order_form', instance=order) return render(request, self.template_name, {'formset': formset, 'order_form': order_form}) def post(self, request, pk): order = models.Order.objects.get(pk=pk) formset = forms.ProductFormSet(request.POST, prefix='formset') order_form = forms.OrderForm(request.POST, prefix='order_form') if formset.is_valid(): order = order_form.save() for form in formset.forms: product = form.save(commit=False) order.products.add(product) order.save() return HttpResponseRedirect(reverse('order_details', args=(order.id,))) else: return render(request, self.template_name, {'formset': formset, 'order_form': order_form})	[ 1, 529, 9507, 29958, 20488, 29914, 7406, 29889, 2272, 13, 3166, 9557, 29889, 3221, 29889, 2271, 9778, 874, 1053, 11837, 29918, 433, 1537, 13, 3166, 9557, 29889, 7406, 1053, 10035, 13, 3166, 9557, 29889, 12759, 7582, 29879, 1053, 6684, 29892, 4050, 13, 3166, 9557, 29889, 1124, 1053, 9056, 5103, 24735, 13, 3166, 9557, 29889, 3221, 29889, 2271, 9778, 874, 1053, 11837, 13, 13, 3166, 869, 1053, 7190, 13, 3166, 869, 1053, 4733, 13, 3166, 2888, 28084, 1144, 1053, 6837, 1144, 13, 13, 13, 1990, 8170, 1043, 29898, 19206, 29889, 1043, 1125, 13, 1678, 4472, 29918, 978, 353, 525, 20488, 29914, 2098, 29918, 3258, 29889, 1420, 29915, 13, 13, 1678, 822, 679, 29898, 1311, 29892, 2009, 1125, 13, 4706, 3855, 29879, 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docs/10.level3_demo_streaming/pc_server/server.py	FaBoPlatform/RobotCarAI	10	5706	<reponame>FaBoPlatform/RobotCarAI<gh_stars>1-10 #!/usr/bin/env python # -- coding: utf-8 -- # ClientからOpenCV画像データを受け取り、ライン検出して制御命令を送る # Server: Jetson TX2 # Client: Jetson TX2/Raspberry Pi3 Docker # 1. FFMPEG UDP StreamingをClientで実行する。AWS向け10FPS,Jetson TX2向け1FPS # 2. Serverを起動する # 3. Clientを起動する # コード修正 # lib/camera.py: vid = cv2.VideoCapture()を環境に合わせて修正する必要がある # lib/object_detection.py: /home/ubuntu/notebooks/github/SSD-Tensorflow/ を環境に合わせて修正する必要がある ''' Python 3.6 送信するmessageは.encode('ascii')や.encode('utf-8')等でエンコードする必要がるここではClientから送られてくるOpenCV BGR画像データが'ascii'に変換されているので'ascii'で統一している ''' print("wait. launching...") import socket, select import time import cv2 import numpy as np import time import os import sys import logging import threading import numpy as np from lib.functions import * from lib.object_detection import ObjectDetection from lib.opencv_lane_detection import LaneDetection from lib.webcam import WebcamVideoStream # ログ設定 logging.basicConfig(level=logging.DEBUG, format='[%(levelname)s] time:%(created).8f pid:%(process)d pn:%(processName)-10s tid:%(thread)d tn:%(threadName)-10s fn:%(funcName)-10s %(message)s', ) # 解析、送信スレッド動作フラグ is_analyze_running = False sock = None out = None # IPM変換後の画像におけるx,yメートル(黒い部分も含む) X_METER=1.5 Y_METER=1 # ライン検出クラス ld = None # 物体検出クラス od = None def do_analyze(): global is_analyze_running global sock global out global X_METER global Y_METER global ld global od # 映像を保存するかどうか IS_SAVE = True OUTPUT_DIR ='./' OUTPUT_FILENAME = 'received.avi' HANDLE_ANGLE = 42 frame_counter = 0 fourcc = None control = None roi_vertices = None ipm_vertices = None speed = None # 映像準備 camera = WebcamVideoStream() cols,rows,fps,fourcc = camera.init_webcam() camera.start() fps = 1 if IS_SAVE: out = cv2.VideoWriter(os.path.join(OUTPUT_DIR, OUTPUT_FILENAME), int(fourcc), fps, (int(cols), int(rows))) ######################################## # ライン検出準備 ######################################## ld = LaneDetection(X_METER,Y_METER,cols=cols,rows=rows) while is_analyze_running: frame_start_time = time.time() #time.sleep(0.2) ######################################## # 映像取得 ######################################## cv_bgr = camera.read() frame_counter += 1 ######################################## # 物体認識 ######################################## # avi動画に保存する if IS_SAVE: out.write(cv_bgr) rclasses,rscores,rbboxes = od.get_detection(cv_bgr) print(rclasses,rscores,rbboxes) if len(rclasses) > 0: prediction_class = np.min(rclasses) if prediction_class == 1: # 止まれを検出した is_need_header_receive = True control='0,0,' sock.sendall(("CONTROL,"+ control).encode('ascii')) continue elif prediction_class == 2: # 10を検出した speed = 40 elif prediction_class == 3: # 20を検出した speed = 50 elif prediction_class == 4: # 30を検出した speed = 60 else: # 物体検出無し if speed is None: speed = 40 handle_angle = 0 ######################################## # ライン検出 ######################################## ld.cv_bgr = cv_bgr # ラインを検出する try: tilt1_deg,tilt2_deg,angle1_deg,angle2_deg,curve1_r,curve2_r, \ meters_from_center = ld.lane_detection() except: # ライン検出失敗 is_need_header_receive = True control='0,0,' sock.sendall(("CONTROL,"+ control).encode('ascii')) continue ######################################## # 速度調整を行う ######################################## #if np.abs(angle2_deg) > np.abs(angle1_deg): # speed = 50 #else: # speed = 60 ''' 左右について tilt_deg: -が右、+が左 angle_deg: +が右、-が左 meters_from_center: -が右にいる、+が左にいる handle_angle: +が右、-が左 ''' ######################################## # ハンドル角調整を行う ######################################## handle_angle = -1tilt1_deg if meters_from_center >= 0: # 左にいる if np.abs(meters_from_center)100 > 20: # とても離れて左にいる：右に全開で曲がる handle_angle=HANDLE_ANGLE elif np.abs(meters_from_center)100 > 10: if tilt2_deg > 0 : # 離れて左いる、奥は左カーブ：右に少し曲がる handle_angle=HANDLE_ANGLE/2 else: # 離れて左いる、奥は右カーブ：右に全開で曲がる handle_angle=HANDLE_ANGLE else: # 右にいる if np.abs(meters_from_center)100 > 20: # とても離れて右にいる：左に全開で曲がる handle_angle=-1HANDLE_ANGLE elif np.abs(meters_from_center)100 > 10: if tilt2_deg < 0 : # 離れて右いる、奥は右カーブ：左に少し曲がる handle_angle=-1HANDLE_ANGLE/2 else: # 離れて右いる、奥は左カーブ、左に全開で曲がる handle_angle=-1HANDLE_ANGLE # 動作可能な角度内に調整する if handle_angle > HANDLE_ANGLE: handle_angle = HANDLE_ANGLE if handle_angle < -1HANDLE_ANGLE: handle_angle = -1HANDLE_ANGLE # 車両制御送信 control=str(speed)+','+str(handle_angle)+',' print("speed={},handle_angle={},CONTROL,{}".format(speed,handle_angle,control)) sock.sendall(("CONTROL,"+ control).encode('ascii')) frame_end_time = time.time() print("FPS={}".format(round(1/(frame_end_time-frame_start_time),2))) def main(): global is_analyze_running global sock global out global ld global od # 通信設定 HOST = '192.168.0.77' # Server IP Address PORT = 6666 # Server TCP Port #HOST = 'a32158c3da9f' # AWS Docker #PORT = 8091 # AWS TCP Port #HOST = '2204f9b0e871' # PC Docker #PORT = 8091 # PC TCP Port ######################################## # 通信準備 ######################################## connected_clients_sockets = [] server_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM) server_socket.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1) server_socket.bind((HOST, PORT)) server_socket.listen(10) connected_clients_sockets.append(server_socket) # Headerの受信が必要かどうか。Headerを受信したら、encode('ascii')を通さずに受信データを解析する is_need_header_receive = True ######################################## # 物体認識準備 ######################################## od = ObjectDetection() print("Server start") try: while True: ######################################## # 受信待ち ######################################## read_sockets, write_sockets, error_sockets = select.select(connected_clients_sockets, [], []) for sock in read_sockets: if sock == server_socket: sockfd, client_address = server_socket.accept() connected_clients_sockets.append(sockfd) else: # ClientがServerにHeaderを送る時は4096 Byte以下にすること packet = sock.recv(4096) print(type(packet)) # if is_need_header_receive: print('header') packet = packet.decode('ascii') txt = str(packet) if packet: print('packet True') if packet == 'START': is_analyze_running = True t = threading.Thread(target=do_analyze) t.start() elif packet.startswith('BYE'): print('got BYE') is_need_header_receive = True is_analyze_running = False sock.shutdown(socket.SHUT_RDWR) sock.close() connected_clients_sockets.remove(sock) if out is not None: out.release() else: print('client disconnect') is_need_header_receive = True is_analyze_running = False sock.shutdown(socket.SHUT_RDWR) sock.close() connected_clients_sockets.remove(sock) if out is not None: out.release() if not is_need_header_receive: # ここには来ない print('body') if packet: print('packet True') is_need_header_receive = True else: print('data finished') is_need_header_receive = True is_analyze_running = False sock.shutdown(socket.SHUT_RDWR) sock.close() connected_clients_sockets.remove(sock) if out is not None: out.release() except: import traceback traceback.print_exc() finally: is_need_header_receive = True is_analyze_running = False sock.shutdown(socket.SHUT_RDWR) sock.close() connected_clients_sockets.remove(sock) server_socket.close() if out is not None: out.release() if __name__ == '__main__': main() print("end server")	[ 1, 529, 276, 1112, 420, 29958, 14206, 8431, 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mom/itertools.py	ands904/ands904-tinypyclone	16	32257	<filename>mom/itertools.py #!/usr/bin/env python # -- coding: utf-8 -- """:synopsis: Implements :mod:`itertools` for older versions of Python. :module: mom.itertools :copyright: 2010-2011 by <NAME> :license: BSD, PSF Borrowed from brownie.itools. """ from __future__ import absolute_import import itertools from mom import builtins try: # Python 2.x from itertools import izip except ImportError: # Python 3.x izip = zip __author__ = "<EMAIL> (<NAME>)" __all__ = [ "chain", "combinations_with_replacement", "compress", "count", "flatten", "grouped", "izip_longest", "permutations", "product", "starmap", "unique", ] if getattr(itertools.chain, "from_iterable", None): chain = itertools.chain else: class chain(object): """An iterator which yields elements from the given `iterables` until each iterable is exhausted. .. versionadded:: 0.2 """ @classmethod def from_iterable(cls, iterable): """Alternative constructor which takes an `iterable` yielding iterators, this can be used to chain an infinite number of iterators. """ rv = object.__new__(cls) rv._init(iterable) return rv def __init__(self, iterables): self._init(iterables) def _init(self, iterables): self.iterables = iter(iterables) self.current_iterable = iter([]) def __iter__(self): return self def next(self): try: return self.current_iterable.next() except StopIteration: self.current_iterable = iter(self.iterables.next()) return self.current_iterable.next() def izip_longest(iterables, *kwargs): """Make an iterator that aggregates elements from each of the iterables. If the iterables are of uneven length, missing values are filled-in with `fillvalue`. Iteration continues until the longest iterable is exhausted. If one of the iterables is potentially infinite, then the :func:`izip_longest` function should be wrapped with something that limits the number of calls (for example :func:`itertools.islice` or :func:`itertools.takewhile`.) If not specified, `fillvalue` defaults to ``None``. .. note:: Software and documentation for this function are taken from CPython, :ref:`license details <psf-license>`. """ fillvalue = kwargs.get("fillvalue") def sentinel(counter=([fillvalue] (len(iterables) - 1)).pop): yield counter() fillers = itertools.repeat(fillvalue) iters = [chain(it, sentinel(), fillers) for it in iterables] try: for tup in izip(iters): yield tup except IndexError: pass def permutations(iterable, r=None): """Return successive `r` length permutations of elements in the `iterable`. If `r` is not specified or is ``None``, then `r` defaults to the length of the `iterable` and all possible full-length permutations are generated. Permutations are emitted in lexicographic sort order. So, if the input `iterable` is sorted, the permutation tuples will be produced in sorted order. Elements are treated as unique based on their position, not on their value. So if the input elements are unique, there will be no repeating value in each permutation. The number of items returned is ``n! / (n - r)!`` when ``0 <= r <= n`` or zero when `r > n`. .. note:: Software and documentation for this function are taken from CPython, :ref:`license details <psf-license>`. """ pool = tuple(iterable) pool_length = len(pool) r = pool_length if r is None else r for indices in product(builtins.range(pool_length), repeat=r): if len(set(indices)) == r: yield tuple(pool[i] for i in indices) def product(iterables, *kwargs): """Cartesian product of input iterables. Equivalent to nested for-loops in a generator expression. For example, ``product(A, B)`` returns the same as ``((x, y) for x in A for y in B)``. The nested loops cycle like an odometer with the rightmost element advancing on every iteration. The pattern creates a lexicographic ordering so that if the input's iterables are sorted, the product tuples are emitted in sorted order. To compute the product of an iterable with itself, specify the number of repetitions with the optional `repeat` keyword argument. For example, ``product(A, repeat=4)`` means the same as ``product(A, A, A, A)``. .. note:: Software and documentation for this function are taken from CPython, :ref:`license details <psf-license>`. """ pools = map(tuple, iterables) kwargs.get("repeat", 1) result = [[]] for pool in pools: result = [x + [y] for x in result for y in pool] for prod in result: yield tuple(prod) try: from itertools import starmap starmap = starmap except ImportError: def starmap(function, iterable): """Make an iterator that computes the function using arguments obtained from the iterable. Used instead of :func:`itertools.imap` when an argument parameters are already grouped in tuples from a single iterable (the data has been "pre-zipped"). The difference between :func:`itertools.imap` and :func:`starmap` parallels the distinction between ``function(a, b)`` and ``function(c)``. .. note:: Software and documentation for this function are taken from CPython, :ref:`license details <psf-license>`. """ for args in iterable: yield function(args) def combinations_with_replacement(iterable, r): """Return `r` length sub-sequences of elements from the `iterable` allowing individual elements to be replaced more than once. Combinations are emitted in lexicographic sort order. So, if the input `iterable` is sorted, the combinations tuples will be produced in sorted order. Elements are treated as unique based on their position, not on their value. So if the input elements are unique, the generated combinations will also be unique. The number of items returned is ``(n + r - 1)! / r! / (n - 1)!`` when ``n > 0``. .. note:: Software and documentation for this function are taken from CPython, :ref:`license details <psf-license>`. """ pool = tuple(iterable) n = len(pool) for indices in product(builtins.range(n), repeat=r): if sorted(indices) == list(indices): yield tuple(pool[i] for i in indices) def compress(data, selectors): """Make an iterator that filters elements from the `data` returning only those that have a corresponding element in `selectors` that evaluates to ``True``. Stops when either the `data` or `selectors` iterables have been exhausted. .. note:: Software and documentation for this function are taken from CPython, :ref:`license details <psf-license>`. """ return (d for d, s in izip(data, selectors) if s) def count(start=0, step=1): """Make an iterator that returns evenly spaced values starting with `start`. Often used as an argument to :func:`imap` to generate consecutive data points. Also, used with :func:`izip` to add sequence numbers. When counting with floating point numbers, better accuracy can sometimes be achieved by substituting multiplicative code such as: ``(start + step * i for i in count())``. .. note:: Software and documentation for this function are taken from CPython, :ref:`license details <psf-license>`. """ n = start while True: yield n n += step def grouped(n, iterable, fillvalue=None): """Groups the items in the given `iterable` to tuples of size `n`. In order for groups to always be of the size `n` the `fillvalue` is used for padding. """ return izip_longest(fillvalue=fillvalue, ([iter(iterable)] n)) def unique(iterable, seen=None): """Yields items from the given `iterable` of (hashable) items, once seen an item is not yielded again. :param seen: An iterable specifying already "seen" items which will be excluded from the result. .. versionadded:: 0.5 .. versionchanged:: 0.5 Items don't have to be hashable any more. 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zkay/transaction/crypto/ecdh_aes.py	nibau/zkay	0	146893	from typing import Tuple, List, Any from Crypto.Cipher import AES from zkay.config import cfg from zkay.transaction.crypto.ecdh_base import EcdhBase class EcdhAesCrypto(EcdhBase): def _enc(self, plain: int, my_sk: int, target_pk: int) -> Tuple[List[int], None]: key = self._ecdh_sha256(target_pk, my_sk) plain_bytes = plain.to_bytes(32, byteorder='big') # Encrypt and extract iv cipher = AES.new(key, AES.MODE_CBC) cipher_bytes = cipher.encrypt(plain_bytes) iv = cipher.iv # Pack iv and cipher iv_cipher = b''.join([iv, cipher_bytes]) return self.pack_byte_array(iv_cipher, cfg.cipher_chunk_size), None def _dec(self, cipher: Tuple[int, ...], my_sk: Any) -> Tuple[int, None]: # Extract sender address from cipher metadata and request corresponding public key sender_pk = cipher[-1] cipher = cipher[:-1] assert len(cipher) == cfg.cipher_payload_len # Compute shared key key = self._ecdh_sha256(sender_pk, my_sk) # Unpack iv and cipher iv_cipher = self.unpack_to_byte_array(cipher, cfg.cipher_chunk_size, cfg.cipher_bytes_payload) iv, cipher_bytes = iv_cipher[:16], iv_cipher[16:] # Decrypt cipher = AES.new(key, AES.MODE_CBC, iv=iv) plain_bytes = cipher.decrypt(cipher_bytes) plain = int.from_bytes(plain_bytes, byteorder='big') return plain, None	[ 1, 515, 19229, 1053, 12603, 552, 29892, 2391, 29892, 3139, 13, 13, 3166, 315, 17929, 29889, 29907, 29875, 8096, 1053, 319, 2890, 13, 13, 3166, 503, 29895, 388, 29889, 2917, 1053, 274, 16434, 13, 3166, 503, 29895, 388, 29889, 20736, 29889, 29883, 17929, 29889, 687, 12744, 29918, 3188, 1053, 382, 2252, 29882, 5160, 13, 13, 13, 1990, 382, 2252, 29882, 29909, 267, 29907, 17929, 29898, 29923, 2252, 29882, 5160, 1125, 13, 1678, 822, 903, 3977, 29898, 1311, 29892, 8656, 29901, 938, 29892, 590, 29918, 808, 29901, 938, 29892, 3646, 29918, 20571, 29901, 938, 29897, 1599, 12603, 552, 29961, 1293, 29961, 524, 1402, 6213, 5387, 13, 4706, 1820, 353, 1583, 3032, 687, 12744, 29918, 17051, 29906, 29945, 29953, 29898, 5182, 29918, 20571, 29892, 590, 29918, 808, 29897, 13, 4706, 8656, 29918, 13193, 353, 8656, 29889, 517, 29918, 13193, 29898, 29941, 29906, 29892, 7023, 2098, 2433, 3752, 1495, 13, 13, 4706, 396, 11346, 4641, 322, 6597, 20444, 13, 4706, 4583, 8096, 353, 319, 2890, 29889, 1482, 29898, 1989, 29892, 319, 2890, 29889, 20387, 29918, 29907, 5371, 29897, 13, 4706, 4583, 8096, 29918, 13193, 353, 4583, 8096, 29889, 3977, 4641, 29898, 24595, 29918, 13193, 29897, 13, 4706, 20444, 353, 4583, 8096, 29889, 440, 13, 13, 4706, 396, 18744, 20444, 322, 4583, 8096, 13, 4706, 20444, 29918, 455, 8096, 353, 289, 29915, 4286, 7122, 4197, 440, 29892, 4583, 8096, 29918, 13193, 2314, 13, 13, 4706, 736, 1583, 29889, 4058, 29918, 10389, 29918, 2378, 29898, 440, 29918, 455, 8096, 29892, 274, 16434, 29889, 455, 8096, 29918, 29812, 29918, 2311, 511, 6213, 13, 13, 1678, 822, 903, 7099, 29898, 1311, 29892, 4583, 8096, 29901, 12603, 552, 29961, 524, 29892, 2023, 1402, 590, 29918, 808, 29901, 3139, 29897, 1599, 12603, 552, 29961, 524, 29892, 6213, 5387, 13, 4706, 396, 7338, 1461, 10004, 3211, 515, 4583, 8096, 15562, 322, 2009, 6590, 970, 1820, 13, 4706, 10004, 29918, 20571, 353, 4583, 8096, 14352, 29896, 29962, 13, 4706, 4583, 8096, 353, 4583, 8096, 7503, 29899, 29896, 29962, 13, 4706, 4974, 7431, 29898, 455, 8096, 29897, 1275, 274, 16434, 29889, 455, 8096, 29918, 23813, 29918, 2435, 13, 13, 4706, 396, 11796, 29872, 7258, 1820, 13, 4706, 1820, 353, 1583, 3032, 687, 12744, 29918, 17051, 29906, 29945, 29953, 29898, 15452, 29918, 20571, 29892, 590, 29918, 808, 29897, 13, 13, 4706, 396, 853, 4058, 20444, 322, 4583, 8096, 13, 4706, 20444, 29918, 455, 8096, 353, 1583, 29889, 348, 4058, 29918, 517, 29918, 10389, 29918, 2378, 29898, 455, 8096, 29892, 274, 16434, 29889, 455, 8096, 29918, 29812, 29918, 2311, 29892, 274, 16434, 29889, 455, 8096, 29918, 13193, 29918, 23813, 29897, 13, 4706, 20444, 29892, 4583, 8096, 29918, 13193, 353, 20444, 29918, 455, 8096, 7503, 29896, 29953, 1402, 20444, 29918, 455, 8096, 29961, 29896, 29953, 17531, 13, 13, 4706, 396, 3826, 4641, 13, 4706, 4583, 8096, 353, 319, 2890, 29889, 1482, 29898, 1989, 29892, 319, 2890, 29889, 20387, 29918, 29907, 5371, 29892, 20444, 29922, 440, 29897, 13, 4706, 8656, 29918, 13193, 353, 4583, 8096, 29889, 7099, 4641, 29898, 455, 8096, 29918, 13193, 29897, 13, 13, 4706, 8656, 353, 938, 29889, 3166, 29918, 13193, 29898, 24595, 29918, 13193, 29892, 7023, 2098, 2433, 3752, 1495, 13, 13, 4706, 736, 8656, 29892, 6213, 13, 2 ]
data/train/python/51ac8632d350c422dea2ba0ea797fbc604f88f75__init__.py	harshp8l/deep-learning-lang-detection	84	174271	from DCWorkflowGraph import getGraph from Products.DCWorkflow.DCWorkflow import DCWorkflowDefinition from Products.PageTemplates.PageTemplateFile import PageTemplateFile import os # Import "MessageFactory" to create messages in the DCWorkflowGraph domain from zope.i18nmessageid import MessageFactory _ = MessageFactory('DCWorkflowGraph') manage_workflowGraph = PageTemplateFile(os.path.join('www','manage_workflowGraph'), globals()) manage_workflowGraph.__name__ = 'manage_workflowGraph' manage_workflowGraph._need__name__ = 0 DCWorkflowDefinition.getGraph=getGraph DCWorkflowDefinition.manage_workflowGraph=manage_workflowGraph DCWorkflowDefinition.manage_options=tuple(DCWorkflowDefinition.manage_options)+({'label': _(u'Graph'), 'action': 'manage_workflowGraph'},)	[ 1, 515, 13681, 5531, 1731, 9527, 1053, 679, 9527, 13, 3166, 10969, 29879, 29889, 12696, 5531, 1731, 29889, 12696, 5531, 1731, 1053, 13681, 5531, 1731, 14683, 13, 3166, 10969, 29879, 29889, 5074, 5776, 9884, 29889, 5074, 6733, 2283, 1053, 9305, 6733, 2283, 13, 5215, 2897, 13, 13, 29937, 16032, 376, 3728, 5126, 29908, 304, 1653, 7191, 297, 278, 13681, 5531, 1731, 9527, 5354, 13, 3166, 503, 2300, 29889, 29875, 29896, 29947, 29876, 4906, 333, 1053, 7777, 5126, 13, 29918, 353, 7777, 5126, 877, 12696, 5531, 1731, 9527, 1495, 13, 13, 1171, 482, 29918, 1287, 1731, 9527, 353, 9305, 6733, 2283, 29898, 359, 29889, 2084, 29889, 7122, 877, 1636, 3788, 1171, 482, 29918, 1287, 1731, 9527, 5477, 13149, 1338, 3101, 13, 1171, 482, 29918, 1287, 1731, 9527, 17255, 978, 1649, 353, 525, 1171, 482, 29918, 1287, 1731, 9527, 29915, 13, 1171, 482, 29918, 1287, 1731, 9527, 3032, 26180, 1649, 978, 1649, 353, 29871, 29900, 13, 13, 12696, 5531, 1731, 14683, 29889, 657, 9527, 29922, 657, 9527, 13, 12696, 5531, 1731, 14683, 29889, 1171, 482, 29918, 1287, 1731, 9527, 29922, 1171, 482, 29918, 1287, 1731, 9527, 13, 12696, 5531, 1731, 14683, 29889, 1171, 482, 29918, 6768, 29922, 23583, 29898, 12696, 5531, 1731, 14683, 29889, 1171, 482, 29918, 6768, 7240, 3319, 29915, 1643, 2396, 903, 29898, 29884, 29915, 9527, 5477, 525, 2467, 2396, 525, 1171, 482, 29918, 1287, 1731, 9527, 16675, 29897, 13, 2 ]
GA/ba-code/main/mycode/rep/individual2b.py	CN-UPB/fcapp	0	63850	<gh_stars>0 from __future__ import (absolute_import, division, print_function) from array import array import copy import random from mycode import mydeap from mycode.rep.fitness import Fitness from mycode.settings import config # print("using REPR-C = reprb + fperm order") from mycode.rep.individual_base import IndividualBase class Individual2b(IndividualBase): """ REPR C: Represents an individual. has a fitness. each individual specifies which crcs and clcs are to be used and fperm for flow processing order evaluation tries in that order to fulfil all requirements by using shortest possible paths. assignments crcs to clcs are made first, then clc-bs, then flows. within each of the first two groups, shortest satisfaction is done first. """ def __init__(self, cn): super(Individual2b, self).__init__() self.cn = cn self.fitness = Fitness() self.crcs = array('B', (False for _ in self.cn.C)) self.clcs = array('B', (False for _ in self.cn.C)) self.fperm = [i for i in xrange(len(self.cn.F))] def __str__(self): return "{}, {}, {}".format(self.fitness, str(self.crcs.tolist()), str(self.clcs.tolist())) def short_str(self): return str(self) def __deepcopy__(self, memo): """Replace the basic deepcopy function with a faster one.""" cls = self.__class__ result = cls.__new__(cls) result.cn = self.cn result.fitness = copy.deepcopy(self.fitness, memo) result.crcs = copy.deepcopy(self.crcs, memo) result.clcs = copy.deepcopy(self.clcs, memo) result.fperm = copy.deepcopy(self.fperm, memo) return result @classmethod def create_random(cls, cn): self = cls(cn) for i in xrange(len(self.crcs)): self.crcs[i] = random.random() < .2 for i in xrange(len(self.clcs)): self.clcs[i] = random.random() < .5 random.shuffle(self.fperm) use_least_demanding_flow_first = random.random() < .5 self.fperm.sort(reverse=not use_least_demanding_flow_first, key=lambda it: self.cn.flows[it].p_flow) if random.random() < .6: self.fperm.sort(key=lambda it: -len(self.cn.flows[it].nodes)) return self def set_fitness_values(self, values): self.fitness.values = values def _get_controller_id_set(self, c_array): ids = set() for i in xrange(len(c_array)): if c_array[i]: ids.add(self.cn.C_list[i]) return ids def _evaluate(self): self.cn.restore_backup() crc_set, clc_set = map(self._get_controller_id_set, (self.crcs, self.clcs)) self.cn.route_crc_controls(crc_set, clc_set) self.cn.route_clc_controls(clc_set) self.cn.route_flow_satisfactions(clc_set, self.fperm) self.set_fitness_values(self.cn.compute_fitness_values()) @staticmethod def mate(ind1, ind2): 'copies uniformly randomly half of the genes from one parent, half of the other parent' child = Individual2b(ind1.cn) if random.random() < .5: for i in xrange(len(child.crcs)): child.crcs[i] = ind1.crcs[i] or ind2.crcs[i] for i in xrange(len(child.clcs)): child.clcs[i] = ind1.clcs[i] or ind2.clcs[i] else: for i in xrange(len(child.crcs)): child.crcs[i] = ind1.crcs[i] and ind2.crcs[i] for i in xrange(len(child.clcs)): child.clcs[i] = ind1.clcs[i] and ind2.clcs[i] child.fperm = mydeap.cx_alternating_position(ind1.fperm, ind2.fperm) return child @staticmethod def mutate(ind): child = copy.deepcopy(ind) num_unsatisfied_flows = child.fitness.unsatisfied_flows child.fitness.invalidate() mydeap.mutShuffleIndexes(child.fperm, config.MUTR) attr = child.crcs if random.random() < .9: attr = child.clcs if num_unsatisfied_flows > 0: if sum(attr) != len(attr): index = random.choice([i for i, v in enumerate(attr) if not v]) attr[index] = not attr[index] return child if sum(attr) <= 1: return child n_max = min(sum(attr), sum(1 - it for it in attr) + 1, 5) n = random.choice(range(1, n_max + 1)) flips = random.sample([i for i, v in enumerate(attr) if v], n) 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gdsfactory/simulation/simphony/components/coupler.py	jorgepadilla19/gdsfactory	0	158675	<reponame>jorgepadilla19/gdsfactory from SiPANN.scee import Standard from SiPANN.scee_int import SimphonyWrapper def coupler( width: float = 0.5, thickness: float = 0.22, gap: float = 0.22, length: float = 10.0, sw_angle: float = 90.0, dx: float = 1.5, dy: float = 5.0, *kwargs, ): r"""Return simphony Directional coupler model. Args: width: Width of the straight in um (Valid for 0.4-0.6) thickness: Thickness of straight in um (Valid for 0.18-0.24) gap: Minimum distance between the two straights edge in um. (Must be > 0.1) length: float or ndarray Length of the straight portion of both straights in um. dx: Horizontal distance between end of coupler until straight portion in nm. dy: Vertical distance between end of coupler until straight portion in um. sw_angle: Sidewall angle from horizontal in degrees This is what most people think of when they think directional coupler. Ports are named as .. code:: H dx dx \|------\| \|------\| o2 ________ _______o3 _ _ \ / \| \| \ length / \| _\|_V ======================= gap \| dy / \ \| ________/ \_______ \| o1 o4 .. plot:: :include-source: import gdsfactory as gf c = gf.components.coupler(gap=0.2, length=10) c.plot() .. plot:: :include-source: import gdsfactory.simulation.simphony.components as gc import gdsfactory.simulation simphony as gs c = gc.coupler() gs.plot_model(c) """ # SiPANN units are in nm width = width 1e3 thickness = thickness * 1e3 gap = gap * 1e3 length = length * 1e3 H = dx * 1e3 V = dy * 1e3 / 2 s = Standard( width=width, thickness=thickness, gap=gap, length=length, H=H, V=V, sw_angle=sw_angle, ) model = SimphonyWrapper(s) model.pins = ("o1", "o2", "o4", "o3") model.sipann = s return model if __name__ == "__main__": import matplotlib.pyplot as plt from gdsfactory.simulation.simphony.plot_model import plot_model c = coupler() print(c) plot_model(c) plt.show()	[ 1, 529, 276, 1112, 420, 29958, 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aula#10/desafio035.py	daramariabs/exercicios-python	0	198209	""" Desenvolva um programa que leia o comprimento de três retas e diga ao usuário se elas podem ou não formar um triângulo.""" a = float(input('Reta A:')) b = float(input('Reta B:')) c = float(input('Reta C:')) if a < b + c and b < a + c and c < a + b: print('Forma um triangulo') else: print('Não forma um triangulo')	[ 1, 9995, 2726, 264, 1555, 1564, 1922, 16914, 712, 454, 423, 288, 7199, 6174, 316, 26299, 3240, 294, 13, 29872, 270, 4324, 5017, 502, 29884, 12288, 409, 560, 294, 13279, 29885, 2123, 8145, 883, 279, 1922, 3367, 30057, 865, 7207, 1213, 15945, 13, 29874, 353, 5785, 29898, 2080, 877, 29934, 1187, 319, 29901, 8785, 13, 29890, 353, 5785, 29898, 2080, 877, 29934, 1187, 350, 29901, 8785, 13, 29883, 353, 5785, 29898, 2080, 877, 29934, 1187, 315, 29901, 8785, 13, 361, 263, 529, 289, 718, 274, 322, 289, 529, 263, 718, 274, 322, 274, 529, 263, 718, 289, 29901, 13, 1678, 1596, 877, 2500, 29874, 1922, 3367, 574, 7207, 1495, 13, 2870, 29901, 13, 1678, 1596, 877, 29940, 1368, 5954, 1922, 3367, 574, 7207, 1495, 2 ]
src/napari_threedee/lighting/lighting_control.py	alisterburt/napari-threedee	5	55290	<filename>src/napari_threedee/lighting/lighting_control.py from typing import List import numpy as np import napari from ..base import ThreeDeeModel from ..utils.napari_utils import add_mouse_callback_safe, get_napari_visual, remove_mouse_callback_safe class LightingControl(ThreeDeeModel): def __init__(self, viewer: napari.Viewer): self._viewer = viewer self._selected_layers = [] self._selected_layer_visuals = [] self.enabled = False def set_layers(self, layers: List[napari.layers.Surface]): self.selected_layers = layers self._on_camera_change() def _on_enable(self): self._connect_events() self._enabled = True self._on_camera_change() for layer in self.selected_layers: layer.events.shading() def _on_disable(self): self._disconnect_events() @property def selected_layers(self) -> List[napari.layers.Surface]: return self._selected_layers @selected_layers.setter def selected_layers(self, layers: List[napari.layers.Surface]): if isinstance(layers, napari.layers.base.Layer): layers = [layers] self._selected_layer_visuals = [ get_napari_visual(viewer=self._viewer, layer=layer) for layer in layers ] self._selected_layers = layers @property def selected_layer_visuals(self): return self._selected_layer_visuals def _on_camera_change(self, event=None): if self.enabled is False: # only update lighting direction if enabled return view_direction = np.asarray(self._viewer.camera.view_direction) for layer, visual in zip(self.selected_layers, self.selected_layer_visuals): dims_displayed = layer._dims_displayed layer_view_direction = np.asarray(layer._world_to_data_ray(view_direction))[dims_displayed] visual.node.shading_filter.light_dir = layer_view_direction[::-1] def _connect_events(self): self._viewer.camera.events.angles.connect(self._on_camera_change) def _disconnect_events(self): self._viewer.camera.events.angles.disconnect(self._on_camera_change)	[ 1, 529, 9507, 29958, 4351, 29914, 8971, 1306, 29918, 386, 276, 2742, 29872, 29914, 4366, 292, 29914, 4366, 292, 29918, 6451, 29889, 2272, 13, 3166, 19229, 1053, 2391, 13, 13, 5215, 12655, 408, 7442, 13, 5215, 9709, 1306, 13, 13, 3166, 6317, 3188, 1053, 12753, 2772, 29872, 3195, 13, 3166, 6317, 13239, 29889, 8971, 1306, 29918, 13239, 1053, 788, 29918, 15769, 29918, 14035, 29918, 11177, 29892, 679, 29918, 8971, 1306, 29918, 20119, 29892, 3349, 29918, 15769, 29918, 14035, 29918, 11177, 13, 13, 13, 1990, 12790, 292, 4809, 29898, 28575, 2772, 29872, 3195, 1125, 13, 1678, 822, 4770, 2344, 12035, 1311, 29892, 6316, 556, 29901, 9709, 1306, 29889, 29963, 15580, 1125, 13, 4706, 1583, 3032, 29894, 15580, 353, 6316, 556, 13, 4706, 1583, 3032, 8391, 29918, 29277, 353, 5159, 13, 4706, 1583, 3032, 8391, 29918, 13148, 29918, 20119, 29879, 353, 5159, 13, 4706, 1583, 29889, 17590, 353, 7700, 13, 13, 1678, 822, 731, 29918, 29277, 29898, 1311, 29892, 15359, 29901, 2391, 29961, 8971, 1306, 29889, 29277, 29889, 18498, 2161, 29962, 1125, 13, 4706, 1583, 29889, 8391, 29918, 29277, 353, 15359, 13, 4706, 1583, 3032, 265, 29918, 26065, 29918, 3167, 580, 13, 13, 1678, 822, 903, 265, 29918, 12007, 29898, 1311, 1125, 13, 4706, 1583, 3032, 6915, 29918, 13604, 580, 13, 4706, 1583, 3032, 17590, 353, 5852, 13, 4706, 1583, 3032, 265, 29918, 26065, 29918, 3167, 580, 13, 4706, 363, 7546, 297, 1583, 29889, 8391, 29918, 29277, 29901, 13, 9651, 7546, 29889, 13604, 29889, 845, 9382, 580, 13, 13, 1678, 822, 903, 265, 29918, 20472, 29898, 1311, 1125, 13, 4706, 1583, 3032, 2218, 6915, 29918, 13604, 580, 13, 13, 1678, 732, 6799, 13, 1678, 822, 4629, 29918, 29277, 29898, 1311, 29897, 1599, 2391, 29961, 8971, 1306, 29889, 29277, 29889, 18498, 2161, 5387, 13, 4706, 736, 1583, 3032, 8391, 29918, 29277, 13, 13, 1678, 732, 8391, 29918, 29277, 29889, 842, 357, 13, 1678, 822, 4629, 29918, 29277, 29898, 1311, 29892, 15359, 29901, 2391, 29961, 8971, 1306, 29889, 29277, 29889, 18498, 2161, 29962, 1125, 13, 4706, 565, 338, 8758, 29898, 29277, 29892, 9709, 1306, 29889, 29277, 29889, 3188, 29889, 14420, 1125, 13, 9651, 15359, 353, 518, 29277, 29962, 13, 4706, 1583, 3032, 8391, 29918, 13148, 29918, 20119, 29879, 353, 518, 13, 9651, 679, 29918, 8971, 1306, 29918, 20119, 29898, 29894, 15580, 29922, 1311, 3032, 29894, 15580, 29892, 7546, 29922, 13148, 29897, 363, 7546, 297, 15359, 13, 4706, 4514, 13, 4706, 1583, 3032, 8391, 29918, 29277, 353, 15359, 13, 13, 1678, 732, 6799, 13, 1678, 822, 4629, 29918, 13148, 29918, 20119, 29879, 29898, 1311, 1125, 13, 4706, 736, 1583, 3032, 8391, 29918, 13148, 29918, 20119, 29879, 13, 13, 1678, 822, 903, 265, 29918, 26065, 29918, 3167, 29898, 1311, 29892, 1741, 29922, 8516, 1125, 13, 4706, 565, 1583, 29889, 17590, 338, 7700, 29901, 13, 9651, 396, 871, 2767, 3578, 292, 5305, 565, 9615, 13, 9651, 736, 13, 4706, 1776, 29918, 20845, 353, 7442, 29889, 294, 2378, 29898, 1311, 3032, 29894, 15580, 29889, 26065, 29889, 1493, 29918, 20845, 29897, 13, 13, 4706, 363, 7546, 29892, 7604, 297, 14319, 29898, 1311, 29889, 8391, 29918, 29277, 29892, 1583, 29889, 8391, 29918, 13148, 29918, 20119, 29879, 1125, 13, 9651, 3964, 29879, 29918, 4990, 287, 353, 7546, 3032, 6229, 29879, 29918, 4990, 287, 13, 9651, 7546, 29918, 1493, 29918, 20845, 353, 7442, 29889, 294, 2378, 29898, 13148, 3032, 11526, 29918, 517, 29918, 1272, 29918, 764, 29898, 1493, 29918, 20845, 876, 29961, 6229, 29879, 29918, 4990, 287, 29962, 13, 9651, 7604, 29889, 3177, 29889, 845, 9382, 29918, 4572, 29889, 4366, 29918, 3972, 353, 7546, 29918, 1493, 29918, 20845, 29961, 1057, 29899, 29896, 29962, 13, 13, 1678, 822, 903, 6915, 29918, 13604, 29898, 1311, 1125, 13, 4706, 1583, 3032, 29894, 15580, 29889, 26065, 29889, 13604, 29889, 19536, 29889, 6915, 29898, 1311, 3032, 265, 29918, 26065, 29918, 3167, 29897, 13, 13, 1678, 822, 903, 2218, 6915, 29918, 13604, 29898, 1311, 1125, 13, 4706, 1583, 3032, 29894, 15580, 29889, 26065, 29889, 13604, 29889, 19536, 29889, 2218, 6915, 29898, 1311, 3032, 265, 29918, 26065, 29918, 3167, 29897, 13, 2 ]
tests/test_cli.py	kids-first/kf-model-omop	0	61428	<gh_stars>0 import os from sqlalchemy import inspect, create_engine from click.testing import CliRunner from kf_model_omop import cli from kf_model_omop.config import config as config_dict, APP_CONFIG_ENV_VAR from kf_model_omop.factory.utils import auto_gen_models env = {APP_CONFIG_ENV_VAR: 'testing'} def test_create_omop(): """ Test create omop db """ runner = CliRunner() result = runner.invoke(cli.create_omop, [], env=env) assert result.exit_code == 0 config = config_dict.get('testing') engine = create_engine(config.SQLALCHEMY_DATABASE_URI) inspector = inspect(engine) assert len(inspector.get_table_names()) == 37 def test_code_template(tmpdir): """ Test cli command code-template """ # Create temp file fn = tmpdir.mkdir("data") expected_file = os.path.join(str(fn), 'loader.py') # Gen code template runner = CliRunner() result = runner.invoke(cli.generate_code_template, ['-o', str(fn)], env=env) assert result.exit_code == 0 assert os.path.isfile(expected_file) # Update file with open(expected_file, 'w') as f: f.write('print("hello world!")') # Try to generate file again result = runner.invoke(cli.generate_code_template, ['-o', str(fn)], env=env) assert 'already exists' in result.stdout assert result.exit_code == 0 # Check file with open(expected_file, 'r') as f: assert 'hello world!' in f.read() def test_list_tables(): """ Test list tables """ runner = CliRunner() result = runner.invoke(cli.list_tables, [], env=env) assert 'concept' in result.stdout assert result.exit_code == 0 def test_auto_gen_models(tmpdir_factory): """ Test cli command code-template """ # Create temp file fn = tmpdir_factory.mktemp("data").join('models.py') expected_file = str(fn) # Autogen models auto_gen_models(config_name='testing', refresh_schema=True, model_filepath=expected_file) assert os.path.isfile(expected_file) # Check content with open(expected_file, 'r') as f: content = f.read() # assert 'ModelMixins' in content assert 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tilemap.py	AI0702/Among-Us-clone	0	20956	<filename>tilemap.py import pygame as pg from settings import * import pytmx class Map: def __init__(self, filename): self.data = [] with open(filename, 'rt') as f: for line in f: self.data.append(line.strip()) self.tilewidth = len(self.data[0]) self.tileheight = len(self.data) self.width = self.tilewidth * TILESIZE self.height = self.tileheight * TILESIZE class TiledMap: def __init__(self, filename): tm = pytmx.load_pygame(filename, pixelalpha=True) self.width = tm.width * tm.tilewidth self.height = tm.height * tm.tileheight self.tmxdata = tm def render(self, surface): # tile data ti = self.tmxdata.get_tile_image_by_gid # For each layer look for each tile and draw on surface for layer in self.tmxdata.visible_layers: if isinstance(layer, pytmx.TiledTileLayer): for x, y, gid, in layer: tile = ti(gid) if tile: surface.blit(tile, (x * self.tmxdata.tilewidth, y * self.tmxdata.tileheight)) def make_map(self): temp_surface = pg.Surface((self.width, self.height)) self.render(temp_surface) return temp_surface class Camera: def __init__(self, width, height): self.camera = pg.Rect(0, 0, width, height) self.width = width self.height = height def apply(self, entity): # Move rectangle according to camera coordinates are return entity.rect.move(self.camera.topleft) def apply_rect(self, rect): return rect.move(self.camera.topleft) def update(self, player_sprite): # center player sprite on screen x = -player_sprite.rect.x + int(WIDTH / 2) y = -player_sprite.rect.y + int(HEIGHT / 2) # Limit the map scrolling when sprite reaches end point of map # For left boundary x = min(0, x) # For right boundary x = max(-(self.width - 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kick/device2/series3/actions/webserver/__init__.py	CiscoDevNet/firepower-kickstart	2	56137	"""Library to access the webserver.""" import re import time import logging logger = logging.getLogger(__name__) from unicon.eal.expect import Spawn, TimeoutError from unicon.utils import AttributeDict from unicon.eal.dialogs import Dialog MAX_RETRY_COUNT = 3 class Webserver: def __init__(self, hostname='AST-INSTALL', login_username='', login_password='', sudo_password=''): """Constructor for Webserver. :param hostname: hostname for the device :param login_password: device login password for a user :param sudo_password: device sudo password for 'root' :return: None """ # set hostname and enable_password from .constants import WebserverConstants WebserverConstants.hostname = hostname WebserverConstants.username = login_username WebserverConstants.login_password = <PASSWORD> WebserverConstants.sudo_password = <PASSWORD> # now that FtdConstants is initialized, create the state machine # that contains the proper variables from .statemachine import WebserverStatemachine self.sm = WebserverStatemachine() def ssh_vty(self, ip, port='22'): """Setup ssh connection to the server. :param ip: ssh host/ip :param port: ssh port :return: ssh_line """ from .constants import WebserverConstants spawn = Spawn('ssh -o UserKnownHostsFile=/dev/null -o StrictHostKeyChecking=no ' '-l {} -p {} {} \n'.format(WebserverConstants.username, port, ip)) d1 = Dialog([ ['continue connecting (yes/no)?', 'sendline(yes)', None, True, False], ['(p\|P)assword:', 'sendline({})'.format(WebserverConstants.login_password), None, False, False], ]) try: d1.process(spawn, timeout=60) spawn.sendline() except TimeoutError: pass ssh_line = WebserverLine(spawn, self.sm, 'ssh_ftd') return ssh_line class WebserverLine: def __init__(self, spawn, sm, type): """Constructor for ssh line. Attributes that are None will be set in rommon_to_new_image() :param spawn: spawn of the line :param sm: object of statemachine :param type: line type, e.g. 'ssh_vty' :return: object of the line """ self.spawn = spawn self.sm = sm self.type = type sm.go_to('any', spawn) def go_to(self, state): """Go to specified state. :param state: string of the state to go to :return: None """ self.sm.go_to(state, self.spawn) def execute(self, cmd, timeout=10): """Run a command, get the console output. Then exclude the command echo, and the new prompt to return the content in between. :param cmd: command to be executed :param timeout: timeout for the command to be executed, defaulted to 10 seconds :return: output of the command executed """ current_state = self.sm.current_state current_prompt = self.sm.get_state(current_state).pattern self.spawn.sendline(cmd) # expect and trim cmd # commented for long command # output_cmd = self.spawn.expect(cmd, timeout=timeout).last_match.string # expect and trim current_prompt output = self.spawn.expect(current_prompt, timeout=timeout).last_match.string logger.debug("before trimming in execute(): {}".format(output)) r = re.search(current_prompt, output) end = r.span()[0] return_data = output[:end].strip() return return_data def execute_lines(self, cmd_lines, timeout=10): """When given a string of multiple lines, run them one by one and return the result for the last line. :param cmd_lines: commands to be executed line by line :param timeout: timeout for individual command to be executed, defaulted to 10 seconds :return: output of the last command executed """ for cmd in cmd_lines.split('\n'): cmd = cmd.strip() if cmd == "": continue # empty line return_data = self.execute(cmd, timeout=timeout) return return_data def disconnect(self): """Disconnect the session. :return: None """ if self.type == 'ssh_vty': self.sm.go_to('user_state', self.spawn) self.spawn.sendline('exit') self.spawn.expect('closed.') logger.info('ssh to ftd connection disconnected')	[ 1, 9995, 12284, 304, 2130, 278, 1856, 2974, 1213, 15945, 13, 5215, 337, 13, 5215, 931, 13, 5215, 12183, 13, 21707, 353, 12183, 29889, 657, 16363, 22168, 978, 1649, 29897, 13, 13, 3166, 443, 4144, 29889, 29872, 284, 29889, 17854, 1053, 1706, 18101, 29892, 5974, 449, 2392, 13, 3166, 443, 4144, 29889, 13239, 1053, 23833, 21533, 13, 3166, 443, 4144, 29889, 29872, 284, 29889, 15901, 29879, 1053, 18878, 13, 13, 12648, 29918, 1525, 5659, 29979, 29918, 18736, 353, 29871, 29941, 13, 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expenseManager/app/UtilGp.py	PacktPublishing/Python-Programming	0	1604864	<filename>expenseManager/app/UtilGp.py from datetime import datetime,timedelta from os import system, name from time import sleep import re import random class UtilGp: @staticmethod def clear(): if name == 'nt': #windows _ = system('cls') else: #mac,linux _ = system('clear') @staticmethod def dateToStr(valueDate,formatStr = '%d-%m-%Y'): strDate = valueDate.strftime(formatStr) return strDate @staticmethod def strToDate(strDate,formatStr = '%d-%m-%Y %H:%M:%S'): valueDate = datetime.strptime(strDate,formatStr) return valueDate @staticmethod def now(): valueNow = datetime.strptime(datetime.now().strftime("%d-%m-%Y %H:%M:%S"),"%d-%m-%Y %H:%M:%S") return valueNow @staticmethod def dateAddDays(valueDate,valueDays): resDate = valueDate + timedelta(days=valueDays) return resDate @staticmethod def age(dob): today = UtilGp.now() years = today.year - dob.year if today.month < dob.month or (today.month == dob.month and today.day < dob.day): years -= 1 return years @staticmethod def isValidDate(strDate,formatStr='%d-%m-%Y'): try: valueDate = UtilGp.strToDate(strDate,formatStr) return True except: return False @staticmethod def isValidDigit(strNo,isExactLen=True,noOfDigits=10): return str(strNo).isdigit() and \ ((not isExactLen) or (isExactLen and len(strNo) == noOfDigits)) @staticmethod def numberInput(prompt,isExactLen=False,noOfDigits=5): strNo = input(prompt) if not UtilGp.isValidDigit(strNo,isExactLen,noOfDigits): return(False,0) return (True, int(strNo)) @staticmethod def sleep(sec): sleep(sec-1) pass @staticmethod def randNo(startNo, endNo, stepValue, isExcluded=False): endNo=endNo/stepValue startNo=startNo/stepValue if startNo==0: startNo=1 if isExcluded: startNo+=1 #startNo+1 to endNo-1 else: endNo+=1 #startNo to endNo return random.randint(startNo,endNo)stepValue # Generate randNo here @staticmethod def parseItemMsg(valueItem,passCode,creditBal): # :price :tranMonYear :priceInput(msg) :item :tranNextDate :tranDate #print(valueItem.category,valueItem.subCategory,valueItem.itemName) isTranSuccess=True SuccessCode=0 newPrice = 0 if valueItem.itemType==1: nos = re.split('-\|,', valueItem.priceRange) #print(result) #print(nos) newPrice = UtilGp.randNo(int(nos[0]),int(nos[1]),int(nos[2]),True) isNewPrice = True printPrice = newPrice else: printPrice = valueItem.price isNewPrice=False msg = valueItem.msg msgLines = msg.split("\n") nowValue = UtilGp.now() nowValuePlOne = UtilGp.dateAddDays(nowValue,1) for line in msgLines: line = str(line).replace(":price","Rs.%.2f"%(printPrice,)) line = str(line).replace(":item", valueItem.itemName) line = str(line).replace(":tranMonYear", UtilGp.dateToStr(nowValue,'%b-%Y')) line = str(line).replace(":tranNextDate", UtilGp.dateToStr(nowValuePlOne, '%d-%m-%Y')) line = str(line).replace(":tranDate", UtilGp.dateToStr(nowValue, '%d-%m-%Y')) if line.startswith(":inputPrice"): newPrice = float(input("Enter Amount:")) printPrice=newPrice isNewPrice=True elif line.startswith(":pinConfirm"): if printPrice>creditBal: isTranSuccess = False SuccessCode = 1 else: from getpass import getpass resPassCode = getpass("Confirm Your PIN:") print() if passCode!=resPassCode: isTranSuccess=False SuccessCode = 2 break else: print(line) ret = (isTranSuccess,SuccessCode,valueItem,isNewPrice,printPrice) #print(ret[0]) return ret @staticmethod def mainMenu(): UtilGp.sleep(2) UtilGp.clear() from frmPageHome import Home from frmPageApplyCard import ApplyCard from frmPageShop import Shop from frmPageReport import Report choice=Home.show() if choice== 1: ApplyCard.show() elif choice == 2: Shop.show() elif choice == 3: Report.show() @staticmethod def startApp(): UtilGp.clear(); from dBase import ndb ndb.loadItemCard() UtilGp.mainMenu() @staticmethod def Login(fnToCall,tranLoader,pageTitle='Login'): from dBase import ndb ndb.loadItemCard() UtilGp.clear();UtilGp.title(pageTitle) from CardData import DbCardData from dBase import ndb from frmPageShop import Shop cardNo = input("Enter your card number:") vRet = DbCardData.getCardObject(ndb.dbCard, cardNo) # print(vRet) if vRet[0]: valueCard = vRet[1] from getpass import getpass passCode = getpass("Enter your PIN:") # print("%s %s" % (cardNo, passCode)) if valueCard.passCode == passCode: ndb.IsLoggedIn = True; ndb.logInCard = valueCard; tranLoader(valueCard, True) fnToCall() #Shop.showCat() else: print("Incorrect PIN") Shop.showFailure() else: print("\nCard does not exits.\n") Shop.showFailure() @staticmethod def centerText(text): text=str(text) l=len(text) sp=(79-len(text))//2 return text.ljust(sp+l,' ').rjust(sp2+l,' ') @staticmethod def linePrint(text='-',spaces=79): print(text.ljust(spaces,text)) @staticmethod def title(screenName): titleCaption = "Expense Manager" + '-' + screenName UtilGp.linePrint() print("%s" % (UtilGp.centerText(titleCaption))) UtilGp.linePrint() #UtilGp.printCaptionData(('A','B'),('1000','Welcome'),20) @staticmethod def priceToStr(price): return 'Rs.%7.2f' % (price,) @staticmethod def creditLine(): from dBase import ndb vCard = ndb.logInCard print("%s" % (UtilGp.centerText("Credit Limit Used:%s\tCredit Limit Available:%s"%(UtilGp.priceToStr(vCard.creditUsed),UtilGp.priceToStr(vCard.creditBal()))))) UtilGp.linePrint() #UtilGp.printCaptionData(('A','B'),('1000','Welcome'),20) @staticmethod def printCaptionData(caption, data, colonAt=40): sp1 = colonAt-2 sp2 = 80-colonAt-4 print() UtilGp.linePrint('') for i in range(0,len(caption)): format ='# %-' + str(sp1) + 's:%-' + str(sp2) + 's #' print(format%(caption[i],data[i])) UtilGp.linePrint('') print()	[ 1, 529, 9507, 29958, 4548, 1947, 3260, 29914, 932, 29914, 7270, 29954, 29886, 29889, 2272, 13, 3166, 12865, 1053, 12865, 29892, 9346, 287, 2554, 13, 3166, 2897, 1053, 1788, 29892, 1024, 13, 3166, 931, 1053, 8709, 13, 5215, 337, 13, 5215, 4036, 13, 13, 1990, 22310, 29954, 29886, 29901, 13, 1678, 732, 7959, 5696, 13, 1678, 822, 2821, 7295, 13, 4706, 565, 1024, 1275, 525, 593, 2396, 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python/edl/utils/data_server_client.py	WEARE0/edl	90	62421	<reponame>WEARE0/edl # Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import grpc import threading from edl.utils import data_server_pb2 from edl.utils import data_server_pb2_grpc from edl.utils import error_utils from edl.utils import exceptions from edl.utils import pb_utils from edl.utils.log_utils import logger class Conn(object): def __init__(self, channel, stub): self.channel = channel self.stub = stub self.lock = threading.Lock() # FIXME(gongwb): fix protocal with # https://medium.com/kuranda-labs-engineering/gracefully-handling-grpc-errors-in-a-go-server-python-client-setup-9805a5464692 class Client(object): def __init__(self): self._conn = {} # endpoint=>(channel, stub) @error_utils.handle_errors_until_timeout def _connect(self, endpoint, timeout=30): if endpoint not in self._conn: c = grpc.insecure_channel(endpoint) s = data_server_pb2_grpc.DataServerStub(c) self._conn[endpoint] = Conn(c, s) return self._conn[endpoint] @error_utils.handle_errors_until_timeout def get_file_list( self, reader_leader_endpoint, reader_name, pod_id, file_list, timeout=60 ): """ Return list[(idx,path)...] for pod """ conn = self._connect(reader_leader_endpoint, timeout=30) req = data_server_pb2.FileListRequest() req.pod_id = pod_id req.reader_name = reader_name for idx, path in enumerate(file_list): ele = data_server_pb2.FileListElement() ele.idx = idx ele.path = path req.file_list.append(ele) with conn.lock: res = conn.stub.GetFileList(req) if res.status.type != "": exceptions.deserialize(res.status) logger.debug("pod client get file_list:{}".format(res.file_list)) return res.file_list @error_utils.handle_errors_until_timeout def report_batch_data_meta( self, reader_leader_endpoint, reader_name, pod_id, dataserver_endpoint, batch_data_ids, timeout=60, ): conn = self._connect(reader_leader_endpoint, timeout=30) req = data_server_pb2.ReportBatchDataMetaRequest() req.reader_name = reader_name req.pod_id = pod_id req.data_server_endpoint = dataserver_endpoint for batch_data_id in batch_data_ids: req.batch_data_ids.append(batch_data_id) with conn.lock: res = conn.stub.ReportBatchDataMeta(req) if res.status.type != "": exceptions.deserialize(res.status) @error_utils.handle_errors_until_timeout def reach_data_end(self, reader_leader_endpoint, reader_name, pod_id, timeout=60): conn = self.connect(reader_leader_endpoint, timeout=30) req = data_server_pb2.ReachDataEndRequest() req.reader_name = reader_name req.pod_id = pod_id with conn.lock: res = conn.stub.ReachDataEnd(req) if res.status.type != "": exceptions.deserialize(res.status) @error_utils.handle_errors_until_timeout def get_batch_data_meta( self, reader_leader_endpoint, reader_name, pod_id, timeout=60 ): conn = self._connect(reader_leader_endpoint, timeout=30) req = data_server_pb2.GetBatchDataMetaRequest() req.reader_name = reader_name req.pod_id = pod_id with conn.lock: res = conn.stub.GetBatchDataMeta(req) if res.status.type != "": exceptions.deserialize(res.status) logger.debug( "pod client get_balanced_batch_data meta:{}".format( pb_utils.batch_data_meta_response_to_string(res) ) ) return res.data @error_utils.handle_errors_until_timeout def get_batch_data(self, reader_leader_endpoint, req, timeout=60): """ return BatchDataResponse """ conn = self._connect(reader_leader_endpoint, timeout=30) with conn.lock: res = conn.stub.GetBatchData(req) if res.status.type != "": exceptions.deserialize(res.status) logger.debug( "pod client get batch_data meta:{}".format( pb_utils.batch_data_response_to_string(res) ) ) return res.data	[ 1, 529, 276, 1112, 420, 29958, 8851, 29909, 1525, 29900, 29914, 287, 29880, 13, 29937, 14187, 1266, 313, 29883, 29897, 29871, 29906, 29900, 29906, 29900, 349, 22352, 29925, 22352, 13189, 943, 29889, 2178, 26863, 2538, 9841, 29889, 13, 29937, 13, 29937, 10413, 21144, 1090, 278, 13380, 19245, 29892, 10079, 29871, 29906, 29889, 29900, 313, 1552, 376, 29931, 293, 1947, 1496, 13, 29937, 366, 1122, 451, 671, 445, 934, 5174, 297, 752, 13036, 411, 278, 19245, 29889, 13, 29937, 887, 1122, 4017, 263, 3509, 310, 278, 19245, 472, 13, 29937, 13, 29937, 268, 1732, 597, 1636, 29889, 4288, 29889, 990, 29914, 506, 11259, 29914, 27888, 1430, 1660, 29899, 29906, 29889, 29900, 13, 29937, 13, 29937, 25870, 3734, 491, 22903, 4307, 470, 15502, 304, 297, 5007, 29892, 7047, 13, 29937, 13235, 1090, 278, 19245, 338, 13235, 373, 385, 376, 3289, 8519, 29908, 350, 3289, 3235, 29892, 13, 29937, 399, 1806, 8187, 2692, 399, 1718, 29934, 13566, 29059, 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nova/tests/unit/virt/libvirt/volume/test_glusterfs.py	bopopescu/nova-token	0	189290	begin_unit comment\|'# Licensed under the Apache License, Version 2.0 (the "License"); you may' nl\|'\n' comment\|'# not use this file except in compliance with the License. You may obtain' nl\|'\n' comment\|'# a copy of the License at' nl\|'\n' comment\|'#' nl\|'\n' comment\|'# http://www.apache.org/licenses/LICENSE-2.0' nl\|'\n' comment\|'#' nl\|'\n' comment\|'# Unless required by applicable law or agreed to in writing, software' nl\|'\n' comment\|'# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT' nl\|'\n' comment\|'# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the' nl\|'\n' comment\|'# License for the specific language governing permissions and limitations' nl\|'\n' comment\|'# under the License.' nl\|'\n' nl\|'\n' name\|'import' name\|'os' newline\|'\n' nl\|'\n' name\|'import' name\|'mock' newline\|'\n' name\|'from' name\|'oslo_concurrency' name\|'import' name\|'processutils' newline\|'\n' nl\|'\n' name\|'from' name\|'nova' op\|'.' name\|'tests' op\|'.' name\|'unit' op\|'.' name\|'virt' op\|'.' name\|'libvirt' op\|'.' name\|'volume' name\|'import' name\|'test_volume' newline\|'\n' name\|'from' name\|'nova' name\|'import' name\|'utils' newline\|'\n' name\|'from' name\|'nova' op\|'.' name\|'virt' op\|'.' name\|'libvirt' name\|'import' name\|'utils' name\|'as' name\|'libvirt_utils' newline\|'\n' name\|'from' name\|'nova' op\|'.' name\|'virt' op\|'.' name\|'libvirt' op\|'.' name\|'volume' name\|'import' name\|'glusterfs' newline\|'\n' nl\|'\n' nl\|'\n' name\|'class' name\|'LibvirtGlusterfsVolumeDriverTestCase' op\|'(' nl\|'\n' DECL\|class\|LibvirtGlusterfsVolumeDriverTestCase name\|'test_volume' op\|'.' name\|'LibvirtVolumeBaseTestCase' op\|')' op\|':' newline\|'\n' nl\|'\n' DECL\|member\|test_libvirt_glusterfs_driver indent\|' ' name\|'def' name\|'test_libvirt_glusterfs_driver' op\|'(' name\|'self' op\|')' op\|':' newline\|'\n' indent\|' ' name\|'mnt_base' op\|'=' string\|"'/mnt'" newline\|'\n' name\|'self' op\|'.' name\|'flags' op\|'(' name\|'glusterfs_mount_point_base' op\|'=' name\|'mnt_base' op\|',' name\|'group' op\|'=' string\|"'libvirt'" op\|')' newline\|'\n' nl\|'\n' name\|'libvirt_driver' op\|'=' name\|'glusterfs' op\|'.' name\|'LibvirtGlusterfsVolumeDriver' op\|'(' name\|'self' op\|'.' name\|'fake_conn' op\|')' newline\|'\n' name\|'self' op\|'.' name\|'stubs' op\|'.' name\|'Set' op\|'(' name\|'libvirt_utils' op\|',' string\|"'is_mounted'" op\|',' name\|'lambda' name\|'x' op\|',' name\|'d' op\|':' name\|'False' op\|')' newline\|'\n' name\|'export_string' op\|'=' string\|"'192.168.1.1:/volume-00001'" newline\|'\n' name\|'export_mnt_base' op\|'=' name\|'os' op\|'.' name\|'path' op\|'.' name\|'join' op\|'(' name\|'mnt_base' op\|',' nl\|'\n' name\|'utils' op\|'.' name\|'get_hash_str' op\|'(' name\|'export_string' op\|')' op\|')' newline\|'\n' nl\|'\n' name\|'connection_info' op\|'=' op\|'{' string\|"'data'" op\|':' op\|'{' string\|"'export'" op\|':' name\|'export_string' op\|',' nl\|'\n' string\|"'name'" op\|':' name\|'self' op\|'.' name\|'name' op\|'}' op\|'}' newline\|'\n' name\|'libvirt_driver' op\|'.' name\|'connect_volume' op\|'(' name\|'connection_info' op\|',' name\|'self' op\|'.' name\|'disk_info' op\|')' newline\|'\n' name\|'libvirt_driver' op\|'.' name\|'disconnect_volume' op\|'(' name\|'connection_info' op\|',' string\|'"vde"' op\|')' newline\|'\n' nl\|'\n' name\|'device_path' op\|'=' name\|'os' op\|'.' name\|'path' op\|'.' name\|'join' op\|'(' name\|'export_mnt_base' op\|',' nl\|'\n' name\|'connection_info' op\|'[' string\|"'data'" op\|']' op\|'[' string\|"'name'" op\|']' op\|')' newline\|'\n' name\|'self' op\|'.' name\|'assertEqual' op\|'(' name\|'connection_info' op\|'[' string\|"'data'" op\|']' op\|'[' string\|"'device_path'" op\|']' op\|',' name\|'device_path' op\|')' newline\|'\n' name\|'expected_commands' op\|'=' op\|'[' nl\|'\n' op\|'(' string\|"'mkdir'" op\|',' string\|"'-p'" op\|',' name\|'export_mnt_base' op\|')' op\|',' nl\|'\n' op\|'(' string\|"'mount'" op\|',' string\|"'-t'" op\|',' string\|"'glusterfs'" op\|',' name\|'export_string' op\|',' name\|'export_mnt_base' op\|')' op\|',' nl\|'\n' op\|'(' string\|"'umount'" op\|',' name\|'export_mnt_base' op\|')' op\|']' newline\|'\n' name\|'self' op\|'.' name\|'assertEqual' op\|'(' name\|'expected_commands' op\|',' name\|'self' op\|'.' name\|'executes' op\|')' newline\|'\n' nl\|'\n' DECL\|member\|test_libvirt_glusterfs_driver_get_config dedent\|'' name\|'def' name\|'test_libvirt_glusterfs_driver_get_config' op\|'(' name\|'self' op\|')' op\|':' newline\|'\n' indent\|' ' name\|'mnt_base' op\|'=' string\|"'/mnt'" newline\|'\n' name\|'self' op\|'.' name\|'flags' op\|'(' name\|'glusterfs_mount_point_base' op\|'=' name\|'mnt_base' op\|',' name\|'group' op\|'=' string\|"'libvirt'" op\|')' newline\|'\n' nl\|'\n' name\|'libvirt_driver' op\|'=' name\|'glusterfs' op\|'.' name\|'LibvirtGlusterfsVolumeDriver' op\|'(' name\|'self' op\|'.' name\|'fake_conn' op\|')' newline\|'\n' name\|'export_string' op\|'=' string\|"'192.168.1.1:/volume-00001'" newline\|'\n' name\|'export_mnt_base' op\|'=' name\|'os' op\|'.' name\|'path' op\|'.' name\|'join' op\|'(' name\|'mnt_base' op\|',' nl\|'\n' name\|'utils' op\|'.' name\|'get_hash_str' op\|'(' name\|'export_string' op\|')' op\|')' newline\|'\n' name\|'file_path' op\|'=' name\|'os' op\|'.' name\|'path' op\|'.' name\|'join' op\|'(' name\|'export_mnt_base' op\|',' name\|'self' op\|'.' name\|'name' op\|')' newline\|'\n' nl\|'\n' comment\|'# Test default format - raw' nl\|'\n' name\|'connection_info' op\|'=' op\|'{' string\|"'data'" op\|':' op\|'{' string\|"'export'" op\|':' name\|'export_string' op\|',' nl\|'\n' string\|"'name'" op\|':' name\|'self' op\|'.' name\|'name' op\|',' nl\|'\n' string\|"'device_path'" op\|':' name\|'file_path' op\|'}' op\|'}' newline\|'\n' name\|'conf' op\|'=' name\|'libvirt_driver' op\|'.' name\|'get_config' op\|'(' name\|'connection_info' op\|',' name\|'self' op\|'.' name\|'disk_info' op\|')' newline\|'\n' name\|'tree' op\|'=' name\|'conf' op\|'.' name\|'format_dom' op\|'(' op\|')' newline\|'\n' name\|'self' op\|'.' name\|'_assertFileTypeEquals' op\|'(' name\|'tree' op\|',' name\|'file_path' op\|')' newline\|'\n' name\|'self' op\|'.' name\|'assertEqual' op\|'(' string\|"'raw'" op\|',' name\|'tree' op\|'.' name\|'find' op\|'(' string\|"'./driver'" op\|')' op\|'.' name\|'get' op\|'(' string\|"'type'" op\|')' op\|')' newline\|'\n' nl\|'\n' comment\|'# Test specified format - qcow2' nl\|'\n' name\|'connection_info' op\|'=' op\|'{' string\|"'data'" op\|':' op\|'{' string\|"'export'" op\|':' name\|'export_string' op\|',' nl\|'\n' string\|"'name'" op\|':' name\|'self' op\|'.' name\|'name' op\|',' nl\|'\n' string\|"'device_path'" op\|':' name\|'file_path' op\|',' nl\|'\n' string\|"'format'" op\|':' string\|"'qcow2'" op\|'}' op\|'}' newline\|'\n' name\|'conf' op\|'=' name\|'libvirt_driver' op\|'.' name\|'get_config' op\|'(' name\|'connection_info' op\|',' name\|'self' op\|'.' name\|'disk_info' op\|')' newline\|'\n' name\|'tree' op\|'=' name\|'conf' op\|'.' name\|'format_dom' op\|'(' op\|')' newline\|'\n' name\|'self' op\|'.' name\|'_assertFileTypeEquals' op\|'(' name\|'tree' op\|',' name\|'file_path' op\|')' newline\|'\n' name\|'self' op\|'.' name\|'assertEqual' op\|'(' string\|"'qcow2'" op\|',' name\|'tree' op\|'.' name\|'find' op\|'(' string\|"'./driver'" op\|')' op\|'.' name\|'get' op\|'(' string\|"'type'" op\|')' op\|')' newline\|'\n' nl\|'\n' DECL\|member\|test_libvirt_glusterfs_driver_already_mounted dedent\|'' name\|'def' name\|'test_libvirt_glusterfs_driver_already_mounted' op\|'(' name\|'self' op\|')' op\|':' newline\|'\n' indent\|' ' name\|'mnt_base' op\|'=' string\|"'/mnt'" newline\|'\n' name\|'self' op\|'.' name\|'flags' op\|'(' name\|'glusterfs_mount_point_base' op\|'=' name\|'mnt_base' op\|',' name\|'group' op\|'=' string\|"'libvirt'" op\|')' newline\|'\n' nl\|'\n' name\|'libvirt_driver' op\|'=' name\|'glusterfs' op\|'.' name\|'LibvirtGlusterfsVolumeDriver' op\|'(' name\|'self' op\|'.' name\|'fake_conn' op\|')' newline\|'\n' name\|'export_string' op\|'=' string\|"'192.168.1.1:/volume-00001'" newline\|'\n' name\|'export_mnt_base' op\|'=' name\|'os' op\|'.' name\|'path' op\|'.' name\|'join' op\|'(' name\|'mnt_base' op\|',' nl\|'\n' name\|'utils' op\|'.' name\|'get_hash_str' op\|'(' name\|'export_string' op\|')' op\|')' newline\|'\n' nl\|'\n' name\|'connection_info' op\|'=' op\|'{' string\|"'data'" op\|':' op\|'{' string\|"'export'" op\|':' name\|'export_string' op\|',' nl\|'\n' string\|"'name'" op\|':' name\|'self' op\|'.' name\|'name' op\|'}' op\|'}' newline\|'\n' name\|'libvirt_driver' op\|'.' name\|'connect_volume' op\|'(' name\|'connection_info' op\|',' name\|'self' op\|'.' name\|'disk_info' op\|')' newline\|'\n' name\|'libvirt_driver' op\|'.' name\|'disconnect_volume' op\|'(' name\|'connection_info' op\|',' string\|'"vde"' op\|')' newline\|'\n' nl\|'\n' name\|'expected_commands' op\|'=' op\|'[' nl\|'\n' op\|'(' string\|"'findmnt'" op\|',' string\|"'--target'" op\|',' name\|'export_mnt_base' op\|',' nl\|'\n' string\|"'--source'" op\|',' name\|'export_string' op\|')' op\|',' nl\|'\n' op\|'(' string\|"'umount'" op\|',' name\|'export_mnt_base' op\|')' op\|']' newline\|'\n' name\|'self' op\|'.' name\|'assertEqual' op\|'(' name\|'expected_commands' op\|',' name\|'self' op\|'.' name\|'executes' op\|')' newline\|'\n' nl\|'\n' dedent\|'' op\|'@' name\|'mock' op\|'.' name\|'patch' op\|'.' name\|'object' op\|'(' name\|'glusterfs' op\|'.' name\|'utils' op\|',' string\|"'execute'" op\|')' newline\|'\n' op\|'@' name\|'mock' op\|'.' name\|'patch' op\|'.' name\|'object' op\|'(' name\|'glusterfs' op\|'.' name\|'LOG' op\|',' string\|"'debug'" op\|')' newline\|'\n' op\|'@' name\|'mock' op\|'.' name\|'patch' op\|'.' name\|'object' op\|'(' name\|'glusterfs' op\|'.' name\|'LOG' op\|',' string\|"'exception'" op\|')' newline\|'\n' DECL\|member\|test_libvirt_glusterfs_driver_umount_error name\|'def' name\|'test_libvirt_glusterfs_driver_umount_error' op\|'(' name\|'self' op\|',' name\|'mock_LOG_exception' op\|',' nl\|'\n' name\|'mock_LOG_debug' op\|',' name\|'mock_utils_exe' op\|')' op\|':' newline\|'\n' indent\|' ' name\|'export_string' op\|'=' string\|"'192.168.1.1:/volume-00001'" newline\|'\n' name\|'connection_info' op\|'=' op\|'{' string\|"'data'" op\|':' op\|'{' string\|"'export'" op\|':' name\|'export_string' op\|',' nl\|'\n' string\|"'name'" op\|':' name\|'self' op\|'.' name\|'name' op\|'}' op\|'}' newline\|'\n' name\|'libvirt_driver' op\|'=' name\|'glusterfs' op\|'.' name\|'LibvirtGlusterfsVolumeDriver' op\|'(' name\|'self' op\|'.' name\|'fake_conn' op\|')' newline\|'\n' name\|'mock_utils_exe' op\|'.' name\|'side_effect' op\|'=' name\|'processutils' op\|'.' name\|'ProcessExecutionError' op\|'(' nl\|'\n' name\|'None' op\|',' name\|'None' op\|',' name\|'None' op\|',' string\|"'umount'" op\|',' string\|"'umount: target is busy.'" op\|')' newline\|'\n' name\|'libvirt_driver' op\|'.' name\|'disconnect_volume' op\|'(' name\|'connection_info' op\|',' string\|'"vde"' op\|')' newline\|'\n' name\|'self' op\|'.' name\|'assertTrue' op\|'(' name\|'mock_LOG_debug' op\|'.' name\|'called' op\|')' newline\|'\n' nl\|'\n' DECL\|member\|test_libvirt_glusterfs_driver_with_opts dedent\|'' name\|'def' name\|'test_libvirt_glusterfs_driver_with_opts' op\|'(' name\|'self' op\|')' op\|':' newline\|'\n' indent\|' ' name\|'mnt_base' op\|'=' string\|"'/mnt'" newline\|'\n' name\|'self' op\|'.' name\|'flags' op\|'(' name\|'glusterfs_mount_point_base' op\|'=' name\|'mnt_base' op\|',' name\|'group' op\|'=' string\|"'libvirt'" op\|')' newline\|'\n' nl\|'\n' name\|'libvirt_driver' op\|'=' name\|'glusterfs' op\|'.' name\|'LibvirtGlusterfsVolumeDriver' op\|'(' name\|'self' op\|'.' name\|'fake_conn' op\|')' newline\|'\n' name\|'self' op\|'.' name\|'stubs' op\|'.' name\|'Set' op\|'(' name\|'libvirt_utils' op\|',' string\|"'is_mounted'" op\|',' name\|'lambda' name\|'x' op\|',' name\|'d' op\|':' name\|'False' op\|')' newline\|'\n' name\|'export_string' op\|'=' string\|"'192.168.1.1:/volume-00001'" newline\|'\n' name\|'options' op\|'=' string\|"'-o backupvolfile-server=192.168.1.2'" newline\|'\n' name\|'export_mnt_base' op\|'=' name\|'os' op\|'.' name\|'path' op\|'.' name\|'join' op\|'(' name\|'mnt_base' op\|',' nl\|'\n' name\|'utils' op\|'.' name\|'get_hash_str' op\|'(' name\|'export_string' op\|')' op\|')' newline\|'\n' nl\|'\n' name\|'connection_info' op\|'=' op\|'{' string\|"'data'" op\|':' op\|'{' string\|"'export'" op\|':' name\|'export_string' op\|',' nl\|'\n' string\|"'name'" op\|':' name\|'self' op\|'.' name\|'name' op\|',' nl\|'\n' string\|"'options'" op\|':' name\|'options' op\|'}' op\|'}' newline\|'\n' name\|'libvirt_driver' op\|'.' name\|'connect_volume' op\|'(' name\|'connection_info' op\|',' name\|'self' op\|'.' name\|'disk_info' op\|')' newline\|'\n' name\|'libvirt_driver' op\|'.' name\|'disconnect_volume' op\|'(' name\|'connection_info' op\|',' string\|'"vde"' op\|')' newline\|'\n' nl\|'\n' name\|'expected_commands' op\|'=' op\|'[' nl\|'\n' op\|'(' string\|"'mkdir'" op\|',' string\|"'-p'" op\|',' name\|'export_mnt_base' op\|')' op\|',' nl\|'\n' op\|'(' string\|"'mount'" op\|',' string\|"'-t'" op\|',' string\|"'glusterfs'" op\|',' nl\|'\n' string\|"'-o'" op\|',' string\|"'backupvolfile-server=192.168.1.2'" op\|',' nl\|'\n' name\|'export_string' op\|',' name\|'export_mnt_base' op\|')' op\|',' nl\|'\n' op\|'(' string\|"'umount'" op\|',' name\|'export_mnt_base' op\|')' op\|',' nl\|'\n' op\|']' newline\|'\n' name\|'self' op\|'.' name\|'assertEqual' op\|'(' name\|'expected_commands' op\|',' name\|'self' op\|'.' name\|'executes' op\|')' newline\|'\n' nl\|'\n' DECL\|member\|test_libvirt_glusterfs_libgfapi dedent\|'' name\|'def' name\|'test_libvirt_glusterfs_libgfapi' op\|'(' name\|'self' op\|')' op\|':' newline\|'\n' indent\|' ' name\|'self' op\|'.' name\|'flags' op\|'(' name\|'qemu_allowed_storage_drivers' op\|'=' op\|'[' string\|"'gluster'" op\|']' op\|',' name\|'group' op\|'=' string\|"'libvirt'" op\|')' newline\|'\n' name\|'libvirt_driver' op\|'=' name\|'glusterfs' op\|'.' name\|'LibvirtGlusterfsVolumeDriver' op\|'(' name\|'self' op\|'.' name\|'fake_conn' op\|')' newline\|'\n' name\|'self' op\|'.' name\|'stubs' op\|'.' name\|'Set' op\|'(' name\|'libvirt_utils' op\|',' string\|"'is_mounted'" op\|',' name\|'lambda' name\|'x' op\|',' name\|'d' op\|':' name\|'False' op\|')' newline\|'\n' name\|'export_string' op\|'=' string\|"'192.168.1.1:/volume-00001'" newline\|'\n' name\|'name' op\|'=' string\|"'volume-00001'" newline\|'\n' nl\|'\n' name\|'connection_info' op\|'=' op\|'{' string\|"'data'" op\|':' op\|'{' string\|"'export'" op\|':' name\|'export_string' op\|',' string\|"'name'" op\|':' name\|'name' op\|'}' op\|'}' newline\|'\n' nl\|'\n' name\|'disk_info' op\|'=' op\|'{' nl\|'\n' string\|'"dev"' op\|':' string\|'"vde"' op\|',' nl\|'\n' string\|'"type"' op\|':' string\|'"disk"' op\|',' nl\|'\n' string\|'"bus"' op\|':' string\|'"virtio"' op\|',' nl\|'\n' op\|'}' newline\|'\n' nl\|'\n' name\|'libvirt_driver' op\|'.' name\|'connect_volume' op\|'(' name\|'connection_info' op\|',' name\|'disk_info' op\|')' newline\|'\n' name\|'conf' op\|'=' name\|'libvirt_driver' op\|'.' name\|'get_config' op\|'(' name\|'connection_info' op\|',' name\|'disk_info' op\|')' newline\|'\n' name\|'tree' op\|'=' name\|'conf' op\|'.' name\|'format_dom' op\|'(' op\|')' newline\|'\n' name\|'self' op\|'.' name\|'assertEqual' op\|'(' string\|"'network'" op\|',' name\|'tree' op\|'.' name\|'get' op\|'(' string\|"'type'" op\|')' op\|')' newline\|'\n' name\|'self' op\|'.' name\|'assertEqual' op\|'(' string\|"'raw'" op\|',' name\|'tree' op\|'.' name\|'find' op\|'(' string\|"'./driver'" op\|')' op\|'.' name\|'get' op\|'(' string\|"'type'" op\|')' op\|')' newline\|'\n' nl\|'\n' name\|'source' op\|'=' name\|'tree' op\|'.' name\|'find' op\|'(' string\|"'./source'" op\|')' newline\|'\n' name\|'self' op\|'.' name\|'assertEqual' op\|'(' string\|"'gluster'" op\|',' name\|'source' op\|'.' name\|'get' op\|'(' string\|"'protocol'" op\|')' op\|')' newline\|'\n' name\|'self' op\|'.' name\|'assertEqual' op\|'(' string\|"'volume-00001/volume-00001'" op\|',' name\|'source' op\|'.' name\|'get' op\|'(' string\|"'name'" op\|')' op\|')' newline\|'\n' name\|'self' op\|'.' name\|'assertEqual' op\|'(' string\|"'192.168.1.1'" op\|',' name\|'source' op\|'.' name\|'find' op\|'(' string\|"'./host'" op\|')' op\|'.' name\|'get' op\|'(' string\|"'name'" op\|')' op\|')' newline\|'\n' name\|'self' op\|'.' name\|'assertEqual' op\|'(' string\|"'24007'" op\|',' name\|'source' op\|'.' name\|'find' op\|'(' string\|"'./host'" op\|')' op\|'.' name\|'get' op\|'(' string\|"'port'" op\|')' op\|')' newline\|'\n' nl\|'\n' name\|'libvirt_driver' op\|'.' name\|'disconnect_volume' op\|'(' name\|'connection_info' op\|',' string\|'"vde"' op\|')' newline\|'\n' dedent\|'' dedent\|'' endmarker\|'' end_unit	[ 1, 3380, 29918, 5441, 13, 9342, 29989, 29915, 29937, 1678, 10413, 21144, 1090, 278, 13380, 19245, 29892, 10079, 29871, 29906, 29889, 29900, 313, 1552, 376, 29931, 293, 1947, 1496, 366, 1122, 29915, 13, 12938, 29989, 12764, 29876, 29915, 13, 9342, 29989, 29915, 29937, 1678, 451, 671, 445, 934, 5174, 297, 752, 13036, 411, 278, 19245, 29889, 887, 1122, 4017, 29915, 13, 12938, 29989, 12764, 29876, 29915, 13, 9342, 29989, 29915, 29937, 1678, 263, 3509, 310, 278, 19245, 472, 29915, 13, 12938, 29989, 12764, 29876, 29915, 13, 9342, 29989, 29915, 29937, 29915, 13, 12938, 29989, 12764, 29876, 29915, 13, 9342, 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misc/eulerhelper.py	Hashi4/vmdgadgets	0	39658	import sys sys.path.append('../vmdgadgets') import vmdutil import functools def alt_dot_v(v1, v2): return functools.reduce( lambda i, j: i + j, [x * y for x, y in zip(v1, v2)]) vmdutil.vmdutil.dot_v = alt_dot_v vmdutil.dot_v = alt_dot_v class strexp(): def __init__(self, val): self.val = val def __mul__(self, other): if other.val == '0' or self.val == '0': return strexp('0') elif other.val == '1': return self elif self.val == '1': return other else: return strexp('(' + self.val + ')*(' + other.val + ')') def __add__(self, other): if other.val == '0': return self elif self.val == '0': return other else: return strexp(self.val + '+' + other.val) def __sub__(self, other): if other.val == '0': return self elif self.val == '0': return strexp('-' + other.val) else: return strexp(self.val + '-' + other.val) def __neg__(self): return strexp('-' + self.val) def __repr__(self): return self.val if __name__ == '__main__': zero = strexp('0') one = strexp('1') p = [strexp('sp'), zero, zero, strexp('cp')] # q(1, 0, 0, wx) y = [zero, strexp('sy'), zero, strexp('cy')] r = [zero, zero, strexp('sr'), strexp('cr')] o = vmdutil.multiply_quaternion( vmdutil.multiply_quaternion(r, p), y) print('euler2quaternion of z-x-y(global)') print(o) print() # rotx = [[1, 0, 0], [0, cx, -sx], [0, sx, cx]] # roty = [[cy, 0, sy], [0, 1, 0], [-sy, 0, cy]] # rotz = [[cz, -sz, 0], [sz, cz, 0], [0, 0, 1]] cx = strexp('cx') cy = strexp('cy') cz = strexp('cz') sx = strexp('sx') sy = strexp('sy') sz = strexp('sz') print('euler2matrix of z-x-y(global)') o = vmdutil.dot_m( vmdutil.dot_m( [[cz, -sz, zero], [sz, cz, zero], [zero, zero, one]], [[one, zero, zero], [zero, cx, -sx], [zero, sx, cx]]), [[cy, zero, sy], [zero, one, zero], [-sy, zero, cy]]) for r in o: print(r)	[ 1, 1053, 10876, 13, 9675, 29889, 2084, 29889, 4397, 877, 6995, 29894, 3487, 29887, 328, 20078, 1495, 13, 5215, 325, 3487, 4422, 13, 13, 5215, 2090, 312, 8789, 13, 1753, 5272, 29918, 6333, 29918, 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13, 9651, 736, 12243, 26330, 877, 877, 718, 1583, 29889, 791, 718, 525, 11877, 877, 718, 916, 29889, 791, 718, 25710, 1495, 13, 13, 1678, 822, 4770, 1202, 12035, 1311, 29892, 916, 1125, 13, 4706, 565, 916, 29889, 791, 1275, 525, 29900, 2396, 13, 9651, 736, 1583, 13, 4706, 25342, 1583, 29889, 791, 1275, 525, 29900, 2396, 13, 9651, 736, 916, 13, 4706, 1683, 29901, 13, 9651, 736, 12243, 26330, 29898, 1311, 29889, 791, 718, 525, 23097, 718, 916, 29889, 791, 29897, 13, 13, 1678, 822, 4770, 1491, 12035, 1311, 29892, 916, 1125, 13, 4706, 565, 916, 29889, 791, 1275, 525, 29900, 2396, 13, 9651, 736, 1583, 13, 4706, 25342, 1583, 29889, 791, 1275, 525, 29900, 2396, 13, 9651, 736, 12243, 26330, 877, 29899, 29915, 718, 916, 29889, 791, 29897, 13, 4706, 1683, 29901, 13, 9651, 736, 12243, 26330, 29898, 1311, 29889, 791, 718, 17411, 29915, 718, 916, 29889, 791, 29897, 13, 13, 1678, 822, 4770, 10052, 12035, 1311, 1125, 13, 4706, 736, 12243, 26330, 877, 29899, 29915, 718, 1583, 29889, 791, 29897, 13, 1678, 822, 4770, 276, 558, 12035, 1311, 1125, 13, 4706, 736, 1583, 29889, 791, 13, 13, 13, 361, 4770, 978, 1649, 1275, 525, 1649, 3396, 1649, 2396, 13, 1678, 5225, 353, 12243, 26330, 877, 29900, 1495, 13, 1678, 697, 353, 12243, 26330, 877, 29896, 1495, 13, 1678, 282, 353, 518, 13045, 26330, 877, 1028, 5477, 5225, 29892, 5225, 29892, 12243, 26330, 877, 6814, 1495, 29962, 396, 3855, 29898, 29896, 29892, 29871, 29900, 29892, 29871, 29900, 29892, 26437, 29897, 13, 1678, 343, 353, 518, 9171, 29892, 12243, 26330, 877, 29879, 29891, 5477, 5225, 29892, 12243, 26330, 877, 1270, 1495, 29962, 13, 1678, 364, 353, 518, 9171, 29892, 5225, 29892, 12243, 26330, 877, 21935, 5477, 12243, 26330, 877, 7283, 1495, 29962, 13, 1678, 288, 353, 325, 3487, 4422, 29889, 18056, 368, 29918, 339, 25744, 291, 29898, 13, 9651, 325, 3487, 4422, 29889, 18056, 368, 29918, 339, 25744, 291, 29898, 29878, 29892, 282, 511, 343, 29897, 13, 1678, 1596, 877, 29872, 8584, 29906, 339, 25744, 291, 310, 503, 29899, 29916, 29899, 29891, 29898, 10945, 29897, 1495, 13, 1678, 1596, 29898, 29877, 29897, 13, 1678, 1596, 580, 13, 13, 13, 1678, 396, 5731, 29916, 353, 5519, 29896, 29892, 29871, 29900, 29892, 29871, 29900, 1402, 518, 29900, 29892, 28232, 29892, 448, 29879, 29916, 1402, 518, 29900, 29892, 269, 29916, 29892, 28232, 5262, 13, 1678, 396, 696, 1017, 353, 5519, 1270, 29892, 29871, 29900, 29892, 9878, 1402, 518, 29900, 29892, 29871, 29896, 29892, 29871, 29900, 1402, 21069, 29879, 29891, 29892, 29871, 29900, 29892, 5094, 5262, 13, 1678, 396, 5731, 29920, 353, 5519, 2067, 29892, 448, 3616, 29892, 29871, 29900, 1402, 518, 3616, 29892, 5316, 29892, 29871, 29900, 1402, 518, 29900, 29892, 29871, 29900, 29892, 29871, 29896, 5262, 13, 1678, 28232, 353, 12243, 26330, 877, 18904, 1495, 13, 1678, 5094, 353, 12243, 26330, 877, 1270, 1495, 13, 1678, 5316, 353, 12243, 26330, 877, 2067, 1495, 13, 1678, 269, 29916, 353, 12243, 26330, 877, 29879, 29916, 1495, 13, 1678, 9878, 353, 12243, 26330, 877, 29879, 29891, 1495, 13, 1678, 2268, 353, 12243, 26330, 877, 3616, 1495, 13, 1678, 1596, 877, 29872, 8584, 29906, 5344, 310, 503, 29899, 29916, 29899, 29891, 29898, 10945, 29897, 1495, 13, 1678, 288, 353, 325, 3487, 4422, 29889, 6333, 29918, 29885, 29898, 13, 9651, 325, 3487, 4422, 29889, 6333, 29918, 29885, 29898, 13, 18884, 5519, 2067, 29892, 448, 3616, 29892, 5225, 1402, 518, 3616, 29892, 5316, 29892, 5225, 1402, 518, 9171, 29892, 5225, 29892, 697, 20526, 13, 18884, 5519, 650, 29892, 5225, 29892, 5225, 1402, 518, 9171, 29892, 28232, 29892, 448, 29879, 29916, 1402, 518, 9171, 29892, 269, 29916, 29892, 28232, 5262, 511, 13, 9651, 5519, 1270, 29892, 5225, 29892, 9878, 1402, 518, 9171, 29892, 697, 29892, 5225, 1402, 21069, 29879, 29891, 29892, 5225, 29892, 5094, 24960, 13, 1678, 363, 364, 297, 288, 29901, 13, 4706, 1596, 29898, 29878, 29897, 13, 2 ]
src/tdparse.py	bluemonk482/tdparse	27	1615292	""" For generating libsvm-formated feature vectors. Many functions here are borrowed from Duy-Tin Vo's implementation """ import sys sys.path.insert(0, r'../') import os import re import numpy as np import gensim from collections import defaultdict import argparse from argparse import ArgumentParser from utilities import readConll, writevec, writey, traversaltree from data.dataprocessing import streamtw, streamtwElec import warnings warnings.simplefilter("error") #------------------------------------------------------------------------------ #------------------------------------------------------------------------------ class lexicon(object): def __init__(self,Wfile='../resources/lexicons/wilson/subjclueslen1-HLTEMNLP05.tff', Bfiles=['../resources/lexicons/binhliu/negative-words.txt','../resources/lexicons/binhliu/positive-words.txt'], Mfile='../resources/lexicons/mohammad/NRC-emotion-lexicon-wordlevel-alphabetized-v0.92.txt',size=100): self.pos,self.neg,self.neu=self.Wilson(Wfile) self.updateBinhliu(Bfiles) self.intersect() def extract(self,word): return word.split('=')[-1] def Wilson(self,filename): neg=[] pos=[] neu=[] with open(filename,'r') as f: for line in f: wtype,wlen,word1,pos1,stemmed1,priorpolarity=line.strip().split(' ') if self.extract(priorpolarity)=='negative': neg.append(self.extract(word1)) elif self.extract(priorpolarity)=='positive': pos.append(self.extract(word1)) else: neu.append(self.extract(word1)) pos=set(pos) neg=set(neg) neu=set(neu) return pos,neg,neu def Mohammad(self,filename): neg=[] pos=[] with open(filename,'r') as f: for line in f: wd,senti,flag=line.strip().split('\t') if senti=='negative' and flag=='1': neg.append(wd) elif senti=='positive' and flag=='1': pos.append(wd) else: pass pos=set(pos) neg=set(neg) return pos,neg def Binhliu(self,filename): neg=np.loadtxt(filename[0],skiprows=1,dtype='string',comments=';') pos=np.loadtxt(filename[1],skiprows=1,dtype='string',comments=';') pos=set(pos) neg=set(neg) return pos,neg def updateBinhliu(self,Bfiles): lexBinh=self.Binhliu(Bfiles) self.pos.update(lexBinh[0]) self.neg.update(lexBinh[1]) def updateMohammad(self,Mfile): lexMoh=self.Mohammad(Mfile) self.pos.update(lexMoh[0]) self.neg.update(lexMoh[1]) def intersect(self): temp1=self.pos.intersection(self.neg) temp2=self.pos.intersection(self.neu) temp3=self.neg.intersection(self.neu) self.pos.difference_update(temp1) self.pos.difference_update(temp2) self.neg.difference_update(temp1) self.neg.difference_update(temp3) self.neu.difference_update(temp2) self.neu.difference_update(temp3) def intersect2(self): temp1=self.pos.intersection(self.neg) self.pos.difference_update(temp1) self.neg.difference_update(temp1) #------------------------------------------------------------------------------ #------------------------------------------------------------------------------ class streamdata(object): def __init__(self, dirname): self.dirname = dirname def __iter__(self): for fname in os.listdir(self.dirname): for line in open(os.path.join(self.dirname, fname)): yield line.rstrip().split('\t') def readTang(fname='../resources/wordemb/sswe'): embs=streamdata(fname) embedmodel={} for tw2vec in embs: wd=tw2vec[0] value=[float(i) for i in tw2vec[1:]] embedmodel[wd]=np.array(value) return embedmodel #------------------------------------------------------------------------------ #------------------------------------------------------------------------------ class targettw(object): def __init__(self,w2vf='../resources/wordemb/w2v/c10_w3_s100', sswef='../resources/wordemb/sswe'): self.w2v=gensim.models.Word2Vec.load(w2vf) self.sswe=readTang(sswef) self.lexicons=lexicon() def emdsswe(self,i,loc,uni,target): f=np.array([]) l=np.array([]) r=np.array([]) t=np.array([]) ls=np.array([]) rs=np.array([]) #embeddings of fulltw features f=self.sswe.get(uni,self.sswe['<unk>']) #embeddings of left context features if i<loc: l=self.sswe.get(uni,self.sswe['<unk>']) if self.lexicons.pos.issuperset(set([uni])): try: ls=self.sswe[uni] except: pass if self.lexicons.neg.issuperset(set([uni])): try: ls=self.sswe[uni] except: pass #embbeddings of target features elif(i==loc): t=self.sswe.get(target.replace('_',''),self.sswe['<unk>']) target2=target.split('_') for wd in target2: ti=self.sswe.get(wd,self.sswe['<unk>']) t=np.concatenate([t,ti]) #embbeddings of right context features else: r=self.sswe.get(uni,self.sswe['<unk>']) if self.lexicons.pos.issuperset(set([uni])): try: rs=self.sswe[uni] except: pass if self.lexicons.neg.issuperset(set([uni])): try: rs=self.sswe[uni] except: pass return [f,l,t,r,ls,rs] def emdw2v(self,i,loc,uni,target): f=np.array([]) l=np.array([]) r=np.array([]) t=np.array([]) ls=np.array([]) rs=np.array([]) try: f=self.w2v[uni] except: pass #embbeddings of left context features if i<loc: try: l=self.w2v[uni] except: pass try: if self.lexicons.pos.issuperset(set([uni])): ls=self.w2v[uni] except: pass try: if self.lexicons.neg.issuperset(set([uni])): ls=self.w2v[uni] except: pass #embbeddings of target feature elif(i==loc): try: t=self.w2v[target.replace('_','')] except: pass target2=target.split('_') for wd in target2: try: ti=self.w2v[wd] t=np.concatenate([t,ti]) except: pass #embbeddings of right context features else: try: r=self.w2v[uni] except: pass try: if self.lexicons.pos.issuperset(set([uni])): rs=self.w2v[uni] except: pass try: if self.lexicons.neg.issuperset(set([uni])): rs=self.w2v[uni] except: pass return [f,l,t,r,ls,rs] def concattw(self,feature,size,tws,etype,locs,target,emp,emode): """ Concatenation of different features """ fullf=np.array([]) leftf=np.array([]) rightf=np.array([]) tarf=np.array([]) fulltw_maxs=np.array([]) fulltw_mins=np.array([]) fulltw_means=np.array([]) fulltw_prods=np.array([]) left_maxs=np.array([]) left_mins=np.array([]) left_means=np.array([]) left_prods=np.array([]) right_maxs=np.array([]) right_mins=np.array([]) right_means=np.array([]) right_prods=np.array([]) tar_maxs=np.array([]) tar_mins=np.array([]) tar_means=np.array([]) tar_prods=np.array([]) leftsenti_max=np.array([]) rightsenti_max=np.array([]) leftsenti_sum=np.array([]) rightsenti_sum=np.array([]) fulltws=np.array([]) lefts=np.array([]) rights=np.array([]) tars=np.array([]) leftsentis=np.array([]) rightsentis=np.array([]) for a in range(len(locs)): if emode=='full': tw = tws else: tw = tws[a] loc = locs[a] fulltw=np.array([]) left=np.array([]) right=np.array([]) tar=np.array([]) leftsenti=np.array([]) rightsenti=np.array([]) for i,uni in enumerate(tw): if emode =='parse': uni = uni[0] if etype=='w2v': f,l,t,r,ls,rs=self.emdw2v(i,loc,uni,target) if etype=='sswe': f,l,t,r,ls,rs=self.emdsswe(i,loc,uni,target) fulltw=np.concatenate([fulltw,f]) left=np.concatenate([left,l]) tar=np.concatenate([tar,t]) right=np.concatenate([right,r]) leftsenti=np.concatenate([leftsenti,ls]) rightsenti=np.concatenate([rightsenti,rs]) #padding if list(left)==[]: left=np.zeros((2size,)) if list(right)==[]: right=np.zeros((2size,)) if list(fulltw)==[]: fulltw=np.zeros((2size,)) if list(tar)==[]: tar=np.zeros((2size,)) if len(left)<=size: left=np.concatenate([left,np.zeros((size,))]) if len(right)<=size: right=np.concatenate([right,np.zeros((size,))]) if len(fulltw)<=size: fulltw=np.concatenate([fulltw,np.zeros((size,))]) if len(tar)<=size: tar=np.concatenate([tar,np.zeros((size,))]) if list(leftsenti)==[]: leftsenti=np.zeros((size,)) if list(rightsenti)==[]: rightsenti=np.zeros((size,)) fullf=np.append(fullf,fulltw, axis=0) leftf=np.append(leftf,left, axis=0) rightf=np.append(rightf,right, axis=0) tarf=np.append(tarf,tar, axis=0) fulltw=fulltw.reshape(len(fulltw)/size,size) left=left.reshape(len(left)/size,size) right=right.reshape(len(right)/size,size) tar=tar.reshape(len(tar)/size,size) leftsenti=leftsenti.reshape(len(leftsenti)/size,size) rightsenti=rightsenti.reshape(len(rightsenti)/size,size) fulltw_maxs=np.append(fulltw_maxs,fulltw.max(axis=0), axis=0) fulltw_mins=np.append(fulltw_mins,fulltw.min(axis=0), axis=0) fulltw_means=np.append(fulltw_means,fulltw.mean(axis=0), axis=0) fulltw_prods=np.append(fulltw_prods,fulltw.prod(axis=0), axis=0) left_maxs=np.append(left_maxs,left.max(axis=0), axis=0) left_mins=np.append(left_mins,left.min(axis=0), axis=0) left_means=np.append(left_means,left.mean(axis=0), axis=0) if not np.count_nonzero(left): left_prods=np.append(left_prods,left.prod(axis=0), axis=0) else: left_prods=np.append(left_prods,left[~np.all(left == 0, axis=1)].prod(axis=0), axis=0) right_maxs=np.append(right_maxs,right.max(axis=0), axis=0) right_mins=np.append(right_mins,right.min(axis=0), axis=0) right_means=np.append(right_means,right.mean(axis=0), axis=0) if not np.count_nonzero(right): right_prods=np.append(right_prods,right.prod(axis=0), axis=0) else: right_prods=np.append(right_prods,right[~np.all(right == 0, axis=1)].prod(axis=0), axis=0) tar_maxs=np.append(tar_maxs,tar.max(axis=0), axis=0) tar_mins=np.append(tar_mins,tar.min(axis=0), axis=0) tar_means=np.append(tar_means,tar.mean(axis=0), axis=0) tar_prods=np.append(tar_prods,tar.prod(axis=0), axis=0) leftsenti_max=np.append(leftsenti_max,leftsenti.max(axis=0), axis=0) rightsenti_max=np.append(rightsenti_max,rightsenti.max(axis=0), axis=0) leftsenti_sum=np.append(leftsenti_sum,leftsenti.sum(axis=0), axis=0) rightsenti_sum=np.append(rightsenti_sum,rightsenti.sum(axis=0), axis=0) fullf=fullf.reshape(len(fullf)/size,size) leftf=leftf.reshape(len(leftf)/size,size) rightf=rightf.reshape(len(rightf)/size,size) tarf=tarf.reshape(len(tarf)/size,size) fulltw_maxs=fulltw_maxs.reshape(len(fulltw_maxs)/size,size) fulltw_mins=fulltw_mins.reshape(len(fulltw_mins)/size,size) fulltw_means=fulltw_means.reshape(len(fulltw_means)/size,size) fulltw_prods=fulltw_prods.reshape(len(fulltw_prods)/size,size) left_maxs=left_maxs.reshape(len(left_maxs)/size,size) left_mins=left_mins.reshape(len(left_mins)/size,size) left_means=left_means.reshape(len(left_means)/size,size) left_prods=left_prods.reshape(len(left_prods)/size,size) right_maxs=right_maxs.reshape(len(right_maxs)/size,size) right_mins=right_mins.reshape(len(right_mins)/size,size) right_means=right_means.reshape(len(right_means)/size,size) right_prods=right_prods.reshape(len(right_prods)/size,size) tar_maxs=tar_maxs.reshape(len(tar_maxs)/size,size) tar_mins=tar_mins.reshape(len(tar_mins)/size,size) tar_means=tar_means.reshape(len(tar_means)/size,size) tar_prods=tar_prods.reshape(len(tar_prods)/size,size) leftsenti_max=leftsenti_max.reshape(len(leftsenti_max)/size,size) rightsenti_max=rightsenti_max.reshape(len(rightsenti_max)/size,size) leftsenti_sum=leftsenti_sum.reshape(len(leftsenti_sum)/size,size) rightsenti_sum=rightsenti_sum.reshape(len(rightsenti_sum)/size,size) fulltws=np.concatenate([np.median(fulltw_maxs, axis=0), np.median(fulltw_mins, axis=0), np.median(fulltw_means, axis=0), np.std(fullf, axis=0), np.median(fulltw_prods, axis=0) ]) lefts=np.concatenate([np.median(left_maxs, axis=0), np.median(left_mins, axis=0), np.median(left_means, axis=0), np.std(leftf, axis=0), np.median(left_prods, axis=0) ]) rights=np.concatenate([np.median(right_maxs, axis=0), np.median(right_mins, axis=0), np.median(right_means, axis=0), np.std(rightf, axis=0), np.median(right_prods, axis=0) ]) tars=np.concatenate([np.median(tar_maxs, axis=0), np.median(tar_mins, axis=0), np.median(tar_means, axis=0), np.std(tarf, axis=0), np.median(tar_prods, axis=0) ]) leftsentis=np.concatenate([np.median(leftsenti_max, axis=0), np.median(leftsenti_sum, axis=0)]) rightsentis=np.concatenate([np.median(rightsenti_max, axis=0), np.median(rightsenti_sum, axis=0)]) if emode == 'full': if emp: # Adds full tweet feature vectors feature=np.concatenate([feature,fulltws]) else: feature=np.concatenate([feature,lefts]) feature=np.concatenate([feature,rights]) feature=np.concatenate([feature,tars]) # Adds features from lexicon filtered words # (not used on the election data) feature=np.concatenate([feature,leftsentis]) feature=np.concatenate([feature,rightsentis]) elif emode == 'parse': feature=np.concatenate([feature,fulltws]) return feature def lidongfeat(self, dataf, conllpath): """ Main function for (Dong et al., 2014) data feature extraction. """ size1=len(self.w2v['the']) size2=len(self.sswe['the']) conll = readConll(conllpath, 'cmu') data = streamtw(dataf) y=[] x=np.array([]) count=0 for a, d in enumerate(data): feature=np.array([]) emp=False tw = [t[1].lower() for t in conll[a]] target = d[1] y.append(d[2]) try: loc=[i for i, j in enumerate(tw) if j == target] except Exception as e: print "Couldn't find the tokenised target!" print target, tw subtw, target_position, emp = traversaltree(conll[a],target,emp,'cmu') #returns relevant words to the target emp = False if emp: count+=1 feature=self.concattw(feature,size1,tw,'w2v',loc,target,emp,'full') feature=self.concattw(feature,size2,tw,'sswe',loc,target,emp,'full') else: feature=self.concattw(feature,size1,subtw,'w2v',target_position,target,False,'parse') feature=self.concattw(feature,size2,subtw,'sswe',target_position,target,False,'parse') feature=self.concattw(feature,size1,tw,'w2v',loc,target,False,'full') feature=self.concattw(feature,size2,tw,'sswe',loc,target,False,'full') x=np.concatenate([x,feature]) x=x.reshape((len(y),len(x)/len(y))) print x.shape print count return(x,y) def elecfeat(self, dataf, conllpath): """ Main function for election data feature extraction. """ size1=len(self.w2v['the']) size2=len(self.sswe['the']) conll = readConll(conllpath, 'cmu') data = streamtwElec(dataf) y=[] x=np.array([]) id = [] count=0 for a, d in enumerate(data): feature=np.array([]) emp=False tw=[t[1] for t in conll[a]] # tw=d[0] target=d[1] if target=='"long_term_economic"_plans': target='long_term_economic' y.append(d[2]) id.append(d[3]) whichone = d[4] locations = [i for i, j in enumerate(tw) if j == target] if (whichone != 'nan') and (len(locations)> 1): if whichone >= len(locations): loc = locations[-1] else: loc = locations[whichone] else: loc = tw.index(target) subtw, target_position, emp = traversaltree(conll[a],target,emp,'cmu') emp = False if emp: count+=1 feature=self.concattw(feature,size1,tw,'w2v',[loc],target,emp,'full') feature=self.concattw(feature,size2,tw,'sswe',[loc],target,emp,'full') else: feature=self.concattw(feature,size1,subtw,'w2v',target_position,target,False,'parse') feature=self.concattw(feature,size2,subtw,'sswe',target_position,target,False,'parse') feature=self.concattw(feature,size1,tw,'w2v',[loc],target,False,'full') feature=self.concattw(feature,size2,tw,'sswe',[loc],target,False,'full') x=np.concatenate([x,feature]) x=x.reshape((len(y),len(x)/len(y))) print x.shape print count return(x,y,id) def main(d, train_conllpath, test_conllpath): features=targettw() print "extracting features for training" if (d == 'lidong') or (d == 'semeval'): x_train,y_train=features.lidongfeat('../data/'+d+'/training/', train_conllpath) print 'Parse source: ', train_conllpath elif d == 'election': print 'election training data' x_train,y_train,id_train=features.elecfeat('../data/'+d+'/training/', train_conllpath) writey('../data/'+d+'/output/id_train',id_train) print 'Parse source: ', train_conllpath writevec('../data/'+d+'/output/training',x_train,y_train) print "extracting features for testing" if (d == 'lidong') or (d == 'semeval'): x_test,y_test=features.lidongfeat('../data/'+d+'/testing/', test_conllpath) print 'Parse source: ', test_conllpath elif d == 'election': print 'election testing data' x_test,y_test,id_test=features.elecfeat('../data/'+d+'/testing/', test_conllpath) print 'Parse source: ', test_conllpath writevec('../data/'+d+'/output/testing',x_test,y_test) writey('../data/'+d+'/output/y_test',y_test) if __name__ == "__main__": parser = ArgumentParser() parser.add_argument("--data", dest="d", help="dataset", default='data') parser.add_argument("--trainparse", dest="conll1", help="train parse conll filename", default='../data/election/parses/election.train.conll') parser.add_argument("--testparse", dest="conll2", help="test parse conll filename", default='../data/election/parses/election.test.conll') args = parser.parse_args() main(args.d, args.conll1, args.conll2)	[ 1, 9995, 29871, 13, 2831, 14655, 4303, 4501, 29885, 29899, 689, 630, 4682, 12047, 29889, 13, 14804, 3168, 1244, 526, 27942, 287, 515, 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app/main/forms.py	MichelAtieno/Personal-Blog	0	69661	<gh_stars>0 from flask_wtf import FlaskForm from wtforms import StringField, SubmitField, TextAreaField from wtforms.validators import Required, DataRequired, Length class SubscriberForm(FlaskForm): email = StringField('Email', validators=[Required()]) subscribe = SubmitField('Subscribe') class BlogPostForm(FlaskForm): title = StringField('Title', validators=[DataRequired(),Length(min=1, max=1000)]) photo_url = StringField('Photo URL', validators=[Required()]) blog_content= TextAreaField('Post your blog', validators=[DataRequired(), Length(min=1)]) submit = SubmitField('Submit') class CommentForm(FlaskForm): name = StringField('Name',validators=[Required()]) comment_content = TextAreaField('Enter Comment', validators=[Required()]) submit = SubmitField('submit Comment')	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29900, 13, 3166, 29784, 29918, 29893, 13264, 1053, 2379, 1278, 2500, 13, 3166, 281, 29873, 9514, 1053, 1714, 3073, 29892, 3323, 2415, 3073, 29892, 3992, 13799, 3073, 13, 3166, 281, 29873, 9514, 29889, 3084, 4097, 1053, 830, 5958, 29892, 3630, 19347, 29892, 365, 1477, 13, 13, 1990, 3323, 7588, 495, 2500, 29898, 8754, 1278, 2500, 1125, 13, 1678, 4876, 353, 1714, 3073, 877, 9823, 742, 2854, 4097, 11759, 19347, 580, 2314, 13, 1678, 1014, 13086, 353, 3323, 2415, 3073, 877, 4035, 13086, 1495, 13, 13, 1990, 350, 1188, 6747, 2500, 29898, 8754, 1278, 2500, 1125, 13, 1678, 3611, 353, 1714, 3073, 877, 7030, 742, 2854, 4097, 11759, 1469, 19347, 3285, 6513, 29898, 1195, 29922, 29896, 29892, 4236, 29922, 29896, 29900, 29900, 29900, 29897, 2314, 13, 1678, 15373, 29918, 2271, 353, 1714, 3073, 877, 25971, 3988, 742, 2854, 4097, 11759, 19347, 580, 2314, 13, 1678, 12618, 29918, 3051, 29922, 3992, 13799, 3073, 877, 6747, 596, 12618, 742, 2854, 4097, 11759, 1469, 19347, 3285, 365, 1477, 29898, 1195, 29922, 29896, 29897, 2314, 13, 1678, 9752, 353, 3323, 2415, 3073, 877, 16228, 1495, 13, 13, 1990, 461, 2500, 29898, 8754, 1278, 2500, 1125, 13, 1678, 1024, 353, 1714, 3073, 877, 1170, 742, 3084, 4097, 11759, 19347, 580, 2314, 13, 1678, 3440, 29918, 3051, 353, 3992, 13799, 3073, 877, 10399, 461, 742, 2854, 4097, 11759, 19347, 580, 2314, 13, 1678, 9752, 353, 3323, 2415, 3073, 877, 7892, 461, 1495, 13, 2 ]

test/conftest.py

dmillington/lunchroulette

1

48583

import pytest from lunchroulette.app import app as _app from lunchroulette.app import db as _db from sqlalchemy import event from sqlalchemy.orm import sessionmaker @pytest.fixture(scope="session") def app(request): return _app @pytest.fixture(scope="function") def db(app, request): with app.app_context(): _db.drop_all() _db.create_all() @pytest.fixture(scope="function", autouse=True) def session(app, db, request): with app.app_context(): print(app.url_map) conn = _db.engine.connect() txn = conn.begin() options = dict(bind=conn, binds={}) sess = _db.create_scoped_session(options=options) sess.begin_nested() @event.listens_for(sess(), 'after_transaction_end') def restart_savepoint(sess2, trans): # Detecting whether this is indeed the nested transaction of the test if trans.nested and not trans._parent.nested: # The test should have normally called session.commit(), # but to be safe we explicitly expire the session sess2.expire_all() sess.begin_nested() _db.session = sess yield sess # Cleanup sess.remove() txn.rollback() conn.close() @pytest.fixture def client(app): return app.test_client()

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Notebooks/jhu/__init__.py

ProtoLife/covid-recovery

0

95246

import datetime import re from .get_data import get_data def Float(x): try: rtn = float(x) except: rtn = float('NaN') return rtn def get_jhu_ts(): stmp = __path__[0] # e.g. "../jhu" stmp = re.split('jhu',stmp)[0] # e.g. "../" base = stmp+'../covid-19-JH/csse_covid_19_data/csse_covid_19_time_series/' confirmed = get_data(base+'time_series_covid19_confirmed_global.csv') deaths = get_data(base+'time_series_covid19_deaths_global.csv') recovered = get_data(base+'time_series_covid19_recovered_global.csv') covid_ts = {'confirmed':confirmed,'deaths':deaths,'recovered':recovered} return covid_ts def get_jhu_countries(): dat = get_jhu_ts() confirmed = dat['confirmed'] countries = [(row[0],row[1]) for row in confirmed][1:] print("number of countries in jhu data = ",len(countries)) return countries def get_module_loc(): print(__path__) def get_ave_data(country_s,datatype='confirmed',dataaccum='daily_av_weekly', firstdate=None, lastdate=None): if dataaccum not in ['cum','daily','cum_av_weekly','daily_av_weekly']: print('data accumulation method not known',dataaccum) print('choose from:','cum','daily','cum_av_weekly','daily_av_weekly') return if datatype not in ['confirmed','deaths','recovered']: print('datatype not known: ',datatype) print('choose from: confirmed','deaths','recovered') return countries = [] if isinstance(country_s,list): for country in country_s: if isinstance(country,str): country = (country,'') countries.append(country) elif isinstance(country_s,str): countries = [(country_s,'')] else: # single ('country','reg') entry countries = [country_s] # get the jhu data dat = get_jhu_ts() popkeyed = dat[datatype] # accumulate the results res = {} # get x coord dates dates = popkeyed['dates'] fmt = '%m/%d/%y' xx = [datetime.datetime.strptime(dd,fmt) for dd in dates ] if firstdate: firstdate_d = datetime.datetime.strptime(firstdate,fmt) else: firstdate_d = datetime.datetime.strptime(dates[0],fmt) if lastdate: lastdate_d = datetime.datetime.strptime(lastdate,fmt) else: lastdate_d = datetime.datetime.strptime(dates[-1],fmt) daystart = (firstdate_d-xx[0]).days daystop = (lastdate_d-xx[-1]).days if daystart <0: xx0 = [xx[0]+datetime.timedelta(days=i) for i in range(daystart,0)] yy0 = [0.]*(-daystart) else: xx0 = [] yy0 = [] if daystop > 0: xx1 = [xx[-1]+datetime.timedelta(days=i) for i in range(daystop)] yy1 = [0.]*(daystop) else: xx1 = [] yy1 = [] xx = xx0 + xx + xx1 res['dates']=xx # get y coord for each country: i=0 j=0 for country in countries: try: yy = popkeyed[country] j = j+1 except: print('country not found',country) i = i + 1 continue yyf = [Float(y) for y in yy] yy = yy0 + yyf + yy1 # print('len yy',len(yy)) # ymax=np.max(np.array(yy)) yyf = [Float(y) for y in yy] if dataaccum == 'daily': yy = [0.]*len(yy) yy[0] = yyf[0] for k in range(1,len(yy)): yy[k] = yyf[k]-yyf[k-1] elif dataaccum == 'cum_av_weekly': yy = [0.]*len(yy) moving_av = 0. for k in range(len(yy)): if k-7 >= 0: moving_av = moving_av - yyf[k-7] moving_av = moving_av + yyf[k] yy[k] = moving_av/min(7.0,float(k+1)) elif dataaccum == 'daily_av_weekly': yy = [0.]*len(yyf) yy[0] = yyf[0] for k in range(1,len(yy)): yy[k] = yyf[k]-yyf[k-1] yyf = [y for y in yy] yy = [0.]*len(yy) moving_av = 0. for k in range(len(yy)): if k-7 >= 0: moving_av = moving_av - yyf[k-7] moving_av = moving_av + yyf[k] yy[k] = moving_av/min(7.0,float(k+1)) if country[1] != '': country = country[0]+'_'+country[1] else: country = country[0] res[country] = yy return res

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kubespawner/proxy.py

rccern/kubespawner

0

183285

import asyncio import functools import json import os import string import escapism from jupyterhub.proxy import Proxy from jupyterhub.utils import exponential_backoff from kubernetes_asyncio import client from traitlets import Unicode from .clients import load_config from .clients import shared_client from .objects import make_ingress from .reflector import ResourceReflector from .utils import generate_hashed_slug class IngressReflector(ResourceReflector): kind = 'ingresses' api_group_name = 'ExtensionsV1beta1Api' @property def ingresses(self): return self.resources class ServiceReflector(ResourceReflector): kind = 'services' @property def services(self): return self.resources class EndpointsReflector(ResourceReflector): kind = 'endpoints' @property def endpoints(self): return self.resources class KubeIngressProxy(Proxy): namespace = Unicode( config=True, help=""" Kubernetes namespace to spawn ingresses for single-user servers in. If running inside a kubernetes cluster with service accounts enabled, defaults to the current namespace. If not, defaults to 'default' """, ) def _namespace_default(self): """ Set namespace default to current namespace if running in a k8s cluster If not in a k8s cluster with service accounts enabled, default to 'default' """ ns_path = '/var/run/secrets/kubernetes.io/serviceaccount/namespace' if os.path.exists(ns_path): with open(ns_path) as f: return f.read().strip() return 'default' component_label = Unicode( 'singleuser-server', config=True, help=""" The component label used to tag the user pods. This can be used to override the spawner behavior when dealing with multiple hub instances in the same namespace. Usually helpful for CI workflows. """, ) k8s_api_ssl_ca_cert = Unicode( "", config=True, help=""" Location (absolute filepath) for CA certs of the k8s API server. Typically this is unnecessary, CA certs are picked up by config.load_incluster_config() or config.load_kube_config. In rare non-standard cases, such as using custom intermediate CA for your cluster, you may need to mount root CA's elsewhere in your Pod/Container and point this variable to that filepath """, ) k8s_api_host = Unicode( "", config=True, help=""" Full host name of the k8s API server ("https://hostname:port"). Typically this is unnecessary, the hostname is picked up by config.load_incluster_config() or config.load_kube_config. """, ) def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) # Schedules async initialization logic that is to be awaited by async # functions by decorating them with @_await_async_init. self._async_init_future = asyncio.ensure_future(self._async_init()) async def _async_init(self): """ This method is scheduled to run from `__init__`, but not awaited there as `__init__` can't be marked as async. Since JupyterHub won't await this method, we ensure the async methods JupyterHub may call on this object will await this method before continuing. To do this, we decorate them with `_await_async_init`. But, how do we figure out the methods to decorate? Likely only those exposed by the base class that JupyterHub would know about. The base class is Proxy, as declared in proxy.py: https://github.com/jupyterhub/jupyterhub/blob/HEAD/jupyterhub/proxy.py. From the Proxy class docstring we can conclude that the following methods, if implemented, could be what we need to decorate with _await_async_init: - get_all_routes (implemented and decorated) - add_route (implemented and decorated) - delete_route (implemented and decorated) - start - stop - get_route """ await load_config(caller=self) self.core_api = shared_client('CoreV1Api') self.extension_api = shared_client('ExtensionsV1beta1Api') labels = { 'component': self.component_label, 'hub.jupyter.org/proxy-route': 'true', } self.ingress_reflector = IngressReflector( parent=self, namespace=self.namespace, labels=labels ) self.service_reflector = ServiceReflector( parent=self, namespace=self.namespace, labels=labels ) self.endpoint_reflector = EndpointsReflector( self, namespace=self.namespace, labels=labels ) await asyncio.gather( self.ingress_reflector.start(), self.service_reflector.start(), self.endpoint_reflector.start(), ) def _await_async_init(method): """A decorator to await the _async_init method after having been scheduled to run in the `__init__` method.""" @functools.wraps(method) async def async_method(self, *args, **kwargs): if self._async_init_future is not None: await self._async_init_future self._async_init_future = None return await method(self, *args, **kwargs) return async_method def safe_name_for_routespec(self, routespec): safe_chars = set(string.ascii_lowercase + string.digits) safe_name = generate_hashed_slug( 'jupyter-' + escapism.escape(routespec, safe=safe_chars, escape_char='-') + '-route' ) return safe_name async def delete_if_exists(self, kind, safe_name, future): try: await future self.log.info('Deleted %s/%s', kind, safe_name) except client.rest.ApiException as e: if e.status != 404: raise self.log.warn("Could not delete %s/%s: does not exist", kind, safe_name) @_await_async_init async def add_route(self, routespec, target, data): # Create a route with the name being escaped routespec # Use full routespec in label # 'data' is JSON encoded and put in an annotation - we don't need to query for it safe_name = self.safe_name_for_routespec(routespec).lower() labels = { 'heritage': 'jupyterhub', 'component': self.component_label, 'hub.jupyter.org/proxy-route': 'true', } endpoint, service, ingress = make_ingress( safe_name, routespec, target, labels, data ) async def ensure_object(create_func, patch_func, body, kind): try: resp = await create_func(namespace=self.namespace, body=body) self.log.info('Created %s/%s', kind, safe_name) except client.rest.ApiException as e: if e.status == 409: # This object already exists, we should patch it to make it be what we want self.log.warn( "Trying to patch %s/%s, it already exists", kind, safe_name ) resp = await patch_func( namespace=self.namespace, body=body, name=body.metadata.name, ) else: raise if endpoint is not None: await ensure_object( self.core_api.create_namespaced_endpoints, self.core_api.patch_namespaced_endpoints, body=endpoint, kind='endpoints', ) await exponential_backoff( lambda: f'{self.namespace}/{safe_name}' in self.endpoint_reflector.endpoints.keys(), 'Could not find endpoints/%s after creating it' % safe_name, ) else: delete_endpoint = await self.core_api.delete_namespaced_endpoints( name=safe_name, namespace=self.namespace, body=client.V1DeleteOptions(grace_period_seconds=0), ) await self.delete_if_exists('endpoint', safe_name, delete_endpoint) await ensure_object( self.core_api.create_namespaced_service, self.core_api.patch_namespaced_service, body=service, kind='service', ) await exponential_backoff( lambda: f'{self.namespace}/{safe_name}' in self.service_reflector.services.keys(), 'Could not find service/%s after creating it' % safe_name, ) await ensure_object( self.extension_api.create_namespaced_ingress, self.extension_api.patch_namespaced_ingress, body=ingress, kind='ingress', ) await exponential_backoff( lambda: f'{self.namespace}/{safe_name}' in self.ingress_reflector.ingresses.keys(), 'Could not find ingress/%s after creating it' % safe_name, ) @_await_async_init async def delete_route(self, routespec): # We just ensure that these objects are deleted. # This means if some of them are already deleted, we just let it # be. safe_name = self.safe_name_for_routespec(routespec).lower() delete_options = client.V1DeleteOptions(grace_period_seconds=0) delete_endpoint = await self.core_api.delete_namespaced_endpoints( name=safe_name, namespace=self.namespace, body=delete_options, ) delete_service = await self.core_api.delete_namespaced_service( name=safe_name, namespace=self.namespace, body=delete_options, ) delete_ingress = await self.extension_api.delete_namespaced_ingress( name=safe_name, namespace=self.namespace, body=delete_options, grace_period_seconds=0, ) # This seems like cleanest way to parallelize all three of these while # also making sure we only ignore the exception when it's a 404. # The order matters for endpoint & service - deleting the service deletes # the endpoint in the background. This can be racy however, so we do so # explicitly ourselves as well. In the future, we can probably try a # foreground cascading deletion (https://kubernetes.io/docs/concepts/workloads/controllers/garbage-collection/#foreground-cascading-deletion) # instead, but for now this works well enough. await asyncio.gather( self.delete_if_exists('endpoint', safe_name, delete_endpoint), self.delete_if_exists('service', safe_name, delete_service), self.delete_if_exists('ingress', safe_name, delete_ingress), ) @_await_async_init async def get_all_routes(self): # copy everything, because iterating over this directly is not threadsafe # FIXME: is this performance intensive? It could be! Measure? # FIXME: Validate that this shallow copy *is* thread safe ingress_copy = dict(self.ingress_reflector.ingresses) routes = { ingress["metadata"]["annotations"]['hub.jupyter.org/proxy-routespec']: { 'routespec': ingress["metadata"]["annotations"][ 'hub.jupyter.org/proxy-routespec' ], 'target': ingress["metadata"]["annotations"][ 'hub.jupyter.org/proxy-target' ], 'data': json.loads( ingress["metadata"]["annotations"]['hub.jupyter.org/proxy-data'] ), } for ingress in ingress_copy.values() } return routes

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regolith/helpers/u_logurlhelper.py

sbillinge/regolith

1

129914

"""Helper for updating a projectum's log_url Log_urls are the google doc links to a projectum's Projectum Agenda Log """ from regolith.helpers.basehelper import DbHelperBase from regolith.fsclient import _id_key from regolith.tools import all_docs_from_collection, fragment_retrieval TARGET_COLL = "projecta" def subparser(subpi): subpi.add_argument("projectum_id", help="the ID or fragment of the ID of the " "Projectum to be updated, e.g., " "vl_m.", default=None) subpi.add_argument("log_url", help="Google Doc url link to project's Projectum Agenda Log") subpi.add_argument("-i", "--index", help="The index of the id you would like " "to update, from the returned list " "of projectum ids.") # Do not delete --database arg subpi.add_argument("-d", "--database", help="The database that will be updated. Defaults to " "first database in the regolithrc.json file." ) return subpi class LogUrlUpdaterHelper(DbHelperBase): """ Update a projectum's Log_url, will add a new Log_URL if one doesn't yet exist """ # btype must be the same as helper target in helper.py btype = "u_logurl" needed_dbs = [f'{TARGET_COLL}'] def construct_global_ctx(self): """Constructs the global context""" super().construct_global_ctx() gtx = self.gtx rc = self.rc rc.coll = f"{TARGET_COLL}" if not rc.database: rc.database = rc.databases[0]["name"] gtx[rc.coll] = sorted( all_docs_from_collection(rc.client, rc.coll), key=_id_key ) gtx["all_docs_from_collection"] = all_docs_from_collection gtx["float"] = float gtx["str"] = str gtx["zip"] = zip def db_updater(self): rc = self.rc key = rc.projectum_id filterid = {'_id': key} found_projectum = rc.client.find_one(rc.database, rc.coll, filterid) # id exists if found_projectum is not None: rc.client.update_one(rc.database, rc.coll, filterid, {'log_url': rc.log_url}) print(f"{rc.projectum_id} has been updated with a log_url of {rc.log_url}") else: # find all similar projectum ids pra = fragment_retrieval(self.gtx["projecta"], ["_id"], rc.projectum_id) # no matches to id fragment and id does not exist if len(pra) == 0: raise RuntimeError("Please input a valid projectum id or a valid fragment of a projectum id") # id fragment and no inputted number elif not rc.index: print("There does not seem to be a projectum with this exact name in this database.") print("However, there are projecta with similar names: ") for i in range(len(pra)): print(f"{i + 1}. {pra[i].get('_id')} current url: {pra[i].get('log_url')}") print("Please rerun the u_logurl helper with the same name as previously inputted, " "but with the addition of -i followed by a number corresponding to one of the above listed " "projectum ids that you would like to update.") # id fragment and inputted number else: if int(rc.index) < 1 or int(rc.index) > len(pra): raise RuntimeError("Sorry, you picked an invalid number.") else: filterid = {'_id': pra[int(rc.index) - 1].get('_id')} rc.client.update_one(rc.database, rc.coll, filterid, {'log_url': rc.log_url}) print(f"{pra[int(rc.index) - 1].get('_id')} has been updated with a log_url of {rc.log_url}") return

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lambda_freezer/__init__.py

klagrange/lambda-freezer

0

1615873

from collections import namedtuple import os import subprocess import re import boto3 import json STAGE_VARIABLE_ALIAS = "lambdaAlias" INTEGRATION_URI_APPENDER = ":${{stageVariables.{0}}}".format(STAGE_VARIABLE_ALIAS) _INTEGRATION = namedtuple( "INTEGRATION", [ "rest_api_id", "resource_id", "http_method", "path" ] ) _APIFNS = namedtuple( "APIFNS", [ "rest_api_id", "resource_id", "http_method", "uri", "path", "function_name", "source_arn", "aliases" ] ) STATEMENT_ID = "f6803b46-df32-4504-8c40-567a0390f549" CLIENT_GATEWAY = boto3.client('apigateway') CLIENT_LAMBDA = boto3.client('lambda') CLIENT_CLOUDFORMATION = boto3.client('cloudformation') SERVICE_FROM_PATH_PATTERN = re.compile(r"^(?:\\.|[^/\\])*/((?:\\.|[^/\\])*)/") class Mapper: """ This is a class that groups a collection of helper functions that help in gathering information of lambda functions that are integrated with an AWS api gateway """ @staticmethod def _map_fn(rest_api_id, region, http_method, path, uri): """ Extracts the underlying (lambda) function name and source arn of a particular resource attached to an api gateway. """ account_id = boto3.client('sts').get_caller_identity().get('Account') regex_template = ".*/arn:aws:lambda:{0}:{1}:function:(.*)/invocations$" source_arn_template = "arn:aws:execute-api:{0}:{1}:{2}/*/{3}{4}" source_arn = source_arn_template.format( region, account_id, rest_api_id, http_method, path ) regex = regex_template.format(region, account_id) function_name = re.search(regex, uri).group(1) if function_name[-len(INTEGRATION_URI_APPENDER):] == INTEGRATION_URI_APPENDER: function_name = function_name[:-len(INTEGRATION_URI_APPENDER)] return function_name, source_arn @staticmethod def _map_aliases(func_name): """ Returns all aliases associated with a given (lambda) function. """ list_aliases_res = CLIENT_LAMBDA.list_aliases( FunctionName=func_name, ) aliases = list_aliases_res["Aliases"] return aliases @staticmethod def _get_integrations(rest_api_id): """ Gathers all resources for a given api gateway and returns its' integrations. In particular, the `resource id`, `resource method` and `resource path` """ # fetch api resources resources = CLIENT_GATEWAY.get_resources( restApiId=rest_api_id, ) integrations = [] # traverse all the resources for resource in resources['items']: # we are only interested in "resource methods" if "resourceMethods" not in resource.keys(): continue resource_methods = resource["resourceMethods"].keys() for resource_method in resource_methods: integrations.append( _INTEGRATION( rest_api_id, resource["id"], resource_method, resource["path"])) return integrations @staticmethod def _assemble(region, integrations): """ Helper function tha combines data. """ apifns = [] for integration in integrations: get_integration_res = CLIENT_GATEWAY.get_integration( restApiId=integration.rest_api_id, resourceId=integration.resource_id, httpMethod=integration.http_method ) # we are only interested at AWS_PROXY integrations as those # are integrations that are created by serverless framework if get_integration_res["type"] != "AWS_PROXY": continue function_name, source_arn = Mapper._map_fn( integration.rest_api_id, region, integration.http_method, integration.path, get_integration_res["uri"] ) aliases = Mapper._map_aliases(function_name) apifns.append( _APIFNS( integration.rest_api_id, integration.resource_id, integration.http_method, get_integration_res["uri"], integration.path, function_name, source_arn, aliases ) ) return apifns @staticmethod def run(rest_api_id, region): """ Gets the integrations of a given api gateway and appends information to them (_assemble) """ integrations = Mapper._get_integrations(rest_api_id) apifns = Mapper._assemble(region, integrations) return apifns def _add_permission(api_fn, stage_name): """ Add `lambda:InvokeFunction` permission to an (aliased) lambda function that is wired to an api gateway. """ try: CLIENT_LAMBDA.add_permission( FunctionName="{0}:{1}".format(api_fn.function_name, stage_name), StatementId=STATEMENT_ID, Action='lambda:InvokeFunction', Principal='apigateway.amazonaws.com', SourceArn=api_fn.source_arn, ) except CLIENT_LAMBDA.exceptions.ResourceConflictException as err: error_msg = { "exception": "CLIENT_LAMBDA.exceptions.ResourceConflictException", "fn": api_fn, "error": err } print(json.dumps(error_msg, indent=4)) except Exception as ex: print(ex) def _default_alias_is_added(fn_aliases, default_alias_name): """ Checks if the `default alias` is part of a list of aliases """ for alias in fn_aliases: if alias["Name"] == default_alias_name: return True return False def _create_alias(function_name, alias_name, function_version): """ Creates an alias and points to a specific version of a (lambda) function. """ CLIENT_LAMBDA.create_alias( FunctionName=function_name, Name=alias_name, FunctionVersion=function_version ) def _integration_uri_is_already_updated(uri): """ A typical uri would look like this: arn:aws:apigateway:{0}:lambda:path/2015-03-31/functions/arn:aws:lambda:{0}:{1}:function:{2}/invocations {0}: region {1}: account id {2}: function name Example of a function name: helloWorld We consider a uri integration to be `updated` when the function name is such: helloWorld:${stageVariables.lambdaAlias} This ensures that only a specific alias (therefore a specific locked version) can be called for a (lambda) function. """ invocation_str = "/invocations" uri_minus_invocation_str = uri[:-len(invocation_str)] is_already_updated = uri_minus_invocation_str[-len(INTEGRATION_URI_APPENDER):] == INTEGRATION_URI_APPENDER return is_already_updated, invocation_str, uri_minus_invocation_str def _get_service_from_path(path): """ Resource paths are general simple urls such as: /generalcalc/getHouseCostByCity /subscription/signUp /subscription/subscribeBasicPlan /country/countries /subscription/login /subscription/confirmSignUp /country/countries/{cca2} This simply extract the first piece of string that is between / / """ return re.search(SERVICE_FROM_PATH_PATTERN, path).group(1) def _tag_documentation_exists(rest_api_id, path, http_method): """ Checks if a specific resource path/method has a `tag` documentation. """ get_documentation_parts_res = CLIENT_GATEWAY.get_documentation_parts( restApiId=rest_api_id, type="METHOD", path=path, ) if not get_documentation_parts_res["items"]: return False for item in get_documentation_parts_res["items"]: if item["location"]["method"] == http_method: return True return False def _get_cloudformation_export(exportName): exports = CLIENT_CLOUDFORMATION.list_exports() if 'Exports' not in exports.keys(): exit() for export in exports['Exports']: if export['Name'] == exportName: return export['Value'] exit() def get_deployed_stages(rest_api_id): """ Returns all the stages that are deployed """ stages = [] get_stages_res = CLIENT_GATEWAY.get_stages( restApiId=rest_api_id, ) for get_stage_res in get_stages_res["item"]: stages.append(get_stage_res["stageName"]) return stages def create_alias_default(api_fns, default_alias_name): """ Creates the so called `default alias` which is just an alias that points to the latest version of (lambda) function(s). """ def create(function_name): return _create_alias(function_name, default_alias_name, "$LATEST") for api_fn in api_fns: aliases = api_fn.aliases function_name = api_fn.function_name if not aliases: create(function_name) else: default_is_added = _default_alias_is_added(aliases, default_alias_name) if not default_is_added: create(function_name) def update_integration_uri(api_fns, default_alias_name): """ For every (lambda) functions that are integrated with an api gateway - updated the uri integration such that the api gateway can only call a specific version of those (lambda) functions through an alias. """ for api_fn in api_fns: is_already_updated, invocation_str, uri_minus_invocation_str = _integration_uri_is_already_updated(api_fn.uri) if not is_already_updated: new_uri = "{0}{1}{2}".format( uri_minus_invocation_str, INTEGRATION_URI_APPENDER, invocation_str ) CLIENT_GATEWAY.update_integration( restApiId=api_fn.rest_api_id, resourceId=api_fn.resource_id, httpMethod=api_fn.http_method, patchOperations=[ { 'op': 'replace', 'path': '/uri', 'value': new_uri, }, ] ) _add_permission(api_fn, default_alias_name) def create_domain_mapping_default(rest_api_id, domain_name, default_alias_name): """ Creates the domain name mapping for the default stage. """ def create(): CLIENT_GATEWAY.create_base_path_mapping( domainName=domain_name, basePath=default_alias_name, restApiId=rest_api_id, stage=default_alias_name ) get_base_path_mappings_res = CLIENT_GATEWAY.get_base_path_mappings( domainName=domain_name, ) mappings = get_base_path_mappings_res['items'] if not mappings: create() else: already_mapped = False for mapping in mappings: if mapping["basePath"] == default_alias_name and mapping["stage"] == default_alias_name: already_mapped = True if not already_mapped: create() def default_stage_contains_staged_variable(rest_api_id, default_alias_name): """ Checks if the default stage contains the `stage variable alias`. """ get_stage_res = CLIENT_GATEWAY.get_stage( restApiId=rest_api_id, stageName=default_alias_name ) if "variables" not in get_stage_res.keys(): return False stage_variables = get_stage_res["variables"] if STAGE_VARIABLE_ALIAS not in stage_variables.keys(): return False if stage_variables[STAGE_VARIABLE_ALIAS] != default_alias_name: return False return True def create_tag_documentation(rest_api_id, api_fns): """ Creates tag documentation for a given list of functions. """ for api_fn in api_fns: path = api_fn.path http_method = api_fn.http_method already_exists = _tag_documentation_exists(rest_api_id, path, http_method) if not already_exists: service_name = _get_service_from_path(path) tag = { "tags": [ service_name ] } CLIENT_GATEWAY.create_documentation_part( restApiId=rest_api_id, location={ "type": "METHOD", "path": path, "method": http_method }, properties=json.dumps(tag), ) def run_after_default_deployment(rest_api_id, region, default_alias_name, domain_name=None): """ Typically what you would need to run after a `serverless deploy` deployment. This ensures that every function integrations' are such that the api gateway can only call a specific version of any lambda functions via an alias, using a stage variable. """ api_fns = Mapper.run(rest_api_id, region) create_alias_default(api_fns, default_alias_name) update_integration_uri(api_fns, default_alias_name) if domain_name is not None: create_domain_mapping_default(rest_api_id, domain_name, default_alias_name) # create_tag_documentation(rest_api_id, api_fns) contains_stage_var = default_stage_contains_staged_variable(rest_api_id, default_alias_name) if not contains_stage_var: print(''' PLEASE ADD THE FOLLOWING STAGE VARIABLE TO [STAGE: {0}]: {1} : {2} '''.format(default_alias_name, STAGE_VARIABLE_ALIAS, default_alias_name)) def freeze_functions(api_fns, stage_name): """ Creates an alias (that's the string for `stage_name`) for a given list of functions. """ for api_fn in api_fns: function_name = api_fn.function_name publish_version_res = CLIENT_LAMBDA.publish_version(FunctionName=function_name) try: CLIENT_LAMBDA.create_alias( FunctionName=function_name, Name=stage_name, FunctionVersion=publish_version_res["Version"] ) _add_permission(api_fn, stage_name) except Exception as ex: print("[WARNING] error while freezing function from source arn: {0}".format(api_fn.source_arn)) print("DETAILS: {0}".format(ex)) def deploy(rest_api_id, region, version, stage_description, domain_name=None): """ Deploys a stage to an api gateway. The definition of `deploy` here is: - take all resources of an api gateway (those are lambda functions at the core) - the integration of those lambda functions are assumed to have been modified such that only specific version - `freeze` those functions and create an alias that points to those frozen versions - creates a new stage under the api gateway and ensure that staged version calls only those previously frozen functions """ # ensure version follows semantic versioning standard if not re.match("\d+\.\d+\.\d+", version): exit("{0} DOES NOT FOLLOW SEMANTIC VERSIONING. FOLLOW SEMANTIC VERSIONING!".format(version)) # ensure that version was not already deployed stage_name = version.replace(".", "-") stages = get_deployed_stages(rest_api_id) if stage_name in stages: exit("YOU ALREADY DEPLOYED {0}".format(version)) # extracted functions' info and freeze them api_fns = Mapper.run(rest_api_id, region) freeze_functions(api_fns, stage_name) # deploy frozen functions CLIENT_GATEWAY.create_deployment( restApiId=rest_api_id, stageName=stage_name, stageDescription=stage_description, description=stage_description, variables={ "{0}".format(STAGE_VARIABLE_ALIAS): stage_name }, ) # map to domain if domain_name is not None: CLIENT_GATEWAY.create_base_path_mapping( domainName=domain_name, basePath=stage_name.replace("-", "."), restApiId=rest_api_id, stage=stage_name ) def re_deploy(rest_api_id, region, version, stage_description): """ Deploys a stage to an api gateway. The definition of `deploy` here is: - take all resources of an api gateway (those are lambda functions at the core) - the integration of those lambda functions are assumed to have been modified such that only specific version - `freeze` those functions and create an alias that points to those frozen versions - creates a new stage under the api gateway and ensure that staged version calls only those previously frozen functions """ # # ensure that version was not already deployed stage_name = version.replace(".", "-") stages = get_deployed_stages(rest_api_id) if stage_name not in stages: exit("YOU HAVE NEVER DEPLOYED {0}".format(version)) # deploy frozen functions CLIENT_GATEWAY.create_deployment( restApiId=rest_api_id, stageName=stage_name, stageDescription=stage_description, description=stage_description, variables={ "{0}".format(STAGE_VARIABLE_ALIAS): stage_name }, )

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data/Human36M/skeleton.py

GuillaumeRochette/HumanViewSynthesis

10

148374

JOINTS = { "HipCenter": 0, "RHip": 1, "RKnee": 2, "RFoot": 3, "LHip": 4, "LKnee": 5, "LFoot": 6, "Spine": 7, "Thorax": 8, "Neck/Nose": 9, "Head": 10, "LShoulder": 11, "LElbow": 12, "LWrist": 13, "RShoulder": 14, "RElbow": 15, "RWrist": 16, } EDGES = ( (0, 1), (1, 2), (2, 3), (0, 4), (4, 5), (5, 6), (0, 7), (7, 8), (8, 9), (9, 10), (8, 11), (11, 12), (12, 13), (8, 14), (14, 15), (15, 16), )

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img_main.py

liujiyuan13/MvDOCC-code

1

75573

import numpy as np import scipy.io as scio import scipy.sparse as scsp import h5py as hp from util import read_mymat73, read_mymat, build_img_dataset, process_ad_dataset, mv_dataset, mv_tabular_collate, AverageMeter, save_roc_pr_curve_data, get_all_labels, \ load_print_results, filter_nan_grad, read_dataset, build_vad_dataset, ss_dataset, ss_tabular_collate, simple_accuracy, get_large_class, ss_goad_dataset from models.encoder_decoder import mvae_ad, late_fusion, mvenc, mvae_fused, splitAE, mv_corrAE, classifier, tc_loss_func, mvae_ss, mvenc_fuse, mvae_tf, similarity_hinge_loss from models.DeepCCAModels import DeepCCA, cca_loss, gcca, cca from models.DBN import mv_DBN from torch.utils.data import DataLoader from torch.nn import MSELoss, CrossEntropyLoss from torch import optim from tensorboardX import SummaryWriter from tqdm import tqdm import torch import random from img_config import epochs_set, layer_size_set, \ batch_size_set_mvae, lr_set_mvae, wd_set_mvae, \ batch_size_set_dcca, lr_set_dcca, wd_set_dcca, alpha_set_dcca, \ batch_size_set_dsvdd, lr_set_dsvdd, wd_set_dsvdd, \ batch_size_set_fused, lr_set_fused, wd_set_fused, pt_epochs_set, \ batch_size_set_split, lr_set_split, wd_set_split, \ batch_size_set_tf, lr_set_tf, wd_set_tf, r_set_tf, \ batch_size_set_corr, lr_set_corr, wd_set_corr, alpha_set_corr, \ batch_size_set_sim, lr_set_sim, wd_set_sim, alpha_set_sim, m_set_sim, \ batch_size_set_dgcca, lr_set_dgcca, wd_set_dgcca, alpha_set_dgcca, \ batch_size_set_dbn, lr_set_dbn, wd_set_dbn import os from transformations import get_rp_num, trans_mv_data_new def get_radius(dist: torch.Tensor, nu: float): """Optimally solve for radius R via the (1-nu)-quantile of distances.""" return np.quantile(np.sqrt(dist.clone().data.cpu().numpy()), 1 - nu) # Baselines: def simple_mvae(dataset_name, X, Y, repeat_times, normal_train_ratio, results_path, device, qualified_Y, ADdataset_mode='minority'): torch.set_default_tensor_type(torch.DoubleTensor) roc_results = [[] for i in range(repeat_times)] pr_anom_results = [[] for i in range(repeat_times)] pr_norm_results = [[] for i in range(repeat_times)] tnr_results = [[] for i in range(repeat_times)] single_best_roc_results = [[] for i in range(repeat_times)] single_best_pr_anom_results = [[] for i in range(repeat_times)] single_best_pr_norm_results = [[] for i in range(repeat_times)] single_best_tnr_results = [[] for i in range(repeat_times)] # training config layer_size = layer_size_set[dataset_name] batch_size = batch_size_set_mvae[dataset_name] lr = lr_set_mvae[dataset_name] wd = wd_set_mvae[dataset_name] print('layer_size: {}, batch size: {}, lr: {}, wd:{}'.format(layer_size, batch_size, lr, wd)) loss_func = MSELoss() input_size_set = [x.shape[0] for x in X] for t in range(repeat_times): print('The {}-th run begins!'.format(t)) for i in range(min([10, len(qualified_Y)])): partition, Y_test = build_img_dataset(dataset_name, Y, neg_class_id=qualified_Y[i], mode=ADdataset_mode) X_train, X_test = process_ad_dataset(X, partition) # -----------------------------------------model training seed = 0 if torch.cuda.is_available(): torch.cuda.manual_seed_all(seed) torch.manual_seed(seed) torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = True model = mvae_ad(input_size_set=input_size_set, layer_sizes=layer_size).to(device).double() trainloader = DataLoader(dataset=mv_dataset(X_train), batch_size=batch_size, shuffle=True, num_workers=1, collate_fn=mv_tabular_collate) # specific training procedure epochs = epochs_set[dataset_name] print(max(epochs)) losses_set = [AverageMeter() for i in range(len(X_train))] optimizer = [optim.Adam(model.ae_set[i].parameters(), lr=lr, weight_decay=wd) for i in range(len(X_train))] # writer = SummaryWriter() model.train() cout = 0 for epoch in range(max(epochs)): trainloader = tqdm(trainloader) if ADdataset_mode is 'majority': trainloader.set_description('mvae, epoch{}, abnormal class{}'.format(epoch, qualified_Y[i])) else: trainloader.set_description('mvae, epoch{}, normal class{}'.format(epoch, qualified_Y[i])) for idx, batch in enumerate(trainloader): postfix = '' if batch[0].size(0) > 1: # for bn, batchsize > 1 batch = [batch[i].to(device) for i in range(len(batch))] outputs_set = model(batch) for view in range(len(X_train)): if epoch < epochs[view]: y = batch[view] loss = loss_func(outputs_set[view], y) optimizer[view].zero_grad() loss.backward() optimizer[view].step() losses_set[view].update(loss.item(), batch[0].size(0)) cur_postfix = ' view{}, rec_loss: {:.4f} '.format(view, losses_set[view].avg) # writer.add_scalar('runs/{}_loss_clasId_{}_view_{}'.format(dataset_name, y_set[i], view), loss.item(), cout) postfix += cur_postfix cout += 1 trainloader.set_postfix(log=postfix) # writer.close() # del writer # # ----------------------------------------model testing testloader = DataLoader(dataset=mv_dataset(X_test), batch_size=batch_size, shuffle=False, num_workers=1, collate_fn=mv_tabular_collate) scores = [[] for ss in range(len(X_test))] with torch.no_grad(): model.eval() for idx, batch in enumerate(tqdm(testloader)): batch = [batch[i].to(device) for i in range(len(batch))] cur_batch_scores = model.get_ad_scores(batch) for ss in range(len(X_test)): scores[ss].append(cur_batch_scores[ss]) scores = [torch.cat(scores[ss], dim=0) for ss in range(len(X_test))] # To find a single-best model scores_set = torch.cat(scores, dim=-1) single_best_roc = 0 single_best_pr_norm = 0 single_best_pr_anom = 0 for i in range(scores_set.shape[-1]): cur_view_scores = scores_set[:, i].cpu().detach().numpy() cur_roc, cur_pr_anom, cur_pr_norm, cur_tnr = save_roc_pr_curve_data(scores=cur_view_scores, labels=Y_test, file_path=None, verbose=False) if cur_roc > single_best_roc: single_best_roc = cur_roc single_best_pr_anom = cur_pr_anom single_best_pr_norm = cur_pr_norm single_best_tnr = cur_tnr single_best_roc_results[t].append(single_best_roc) single_best_pr_norm_results[t].append(single_best_pr_norm) single_best_pr_anom_results[t].append(single_best_pr_anom) single_best_tnr_results[t].append(single_best_tnr) scores = late_fusion(scores, merge='avg') # ----------------------------------------model eval roc, pr_anom, pr_norm, tnr = save_roc_pr_curve_data(scores=scores, labels=Y_test, file_path=None, verbose=False) roc_results[t].append(roc) pr_anom_results[t].append(pr_anom) pr_norm_results[t].append(pr_norm) tnr_results[t].append(tnr) del model # save the results file_name = results_path + dataset_name + '_mvae' np.savez(file=file_name, ROC=roc_results, PR_norm=pr_norm_results, PR_anom=pr_anom_results, tnr=tnr_results) load_print_results(file_name) file_name = results_path + dataset_name + '_mvae_single_best' np.savez(file=file_name, ROC=single_best_roc_results, PR_norm=single_best_pr_norm_results, PR_anom=single_best_pr_anom_results, tnr=single_best_tnr_results) load_print_results(file_name) print('dataset: {}, layer_size: {}, batch size: {}, lr: {}, wd:{}, eppchs: {}'.format(dataset_name, layer_size, batch_size, lr, wd, max(epochs))) def deepCCA(dataset_name, X, Y, repeat_times, normal_train_ratio, results_path, device, qualified_Y, ADdataset_mode='minority'): torch.set_default_tensor_type(torch.DoubleTensor) roc_results = [[] for i in range(repeat_times)] pr_anom_results = [[] for i in range(repeat_times)] pr_norm_results = [[] for i in range(repeat_times)] tnr_results = [[] for i in range(repeat_times)] # training config batch_size = batch_size_set_dcca[dataset_name] layer_size = layer_size_set[dataset_name] lr = lr_set_dcca[dataset_name] wd = wd_set_dcca[dataset_name] loss_func = MSELoss() cca_loss_func = cca(outdim_size=layer_size[-1]) input_size_set = [x.shape[0] for x in X] if results_path in ['./results/']: alpha_set = [alpha_set_dcca[dataset_name]] else: alpha_set = [0.01, 0.1, 0.5, 0.9, 0.99] for alpha in alpha_set: for t in range(repeat_times): print('The {}-th run begins!'.format(t)) for i in range(min([10, len(qualified_Y)])): partition, Y_test = build_img_dataset(dataset_name, Y, neg_class_id=qualified_Y[i], mode=ADdataset_mode) X_train, X_test = process_ad_dataset(X, partition) # -----------------------------------------model training seed = 0 if torch.cuda.is_available(): torch.cuda.manual_seed_all(seed) torch.manual_seed(seed) torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = True model = mv_corrAE(input_size_set=input_size_set, layer_sizes=layer_size).to(device).double() trainloader = DataLoader(dataset=mv_dataset(X_train), batch_size=batch_size, shuffle=True, num_workers=1, collate_fn=mv_tabular_collate) # specific training procedure epochs = epochs_set[dataset_name] cca_losses = AverageMeter() rec_losses = AverageMeter() optimizer = optim.Adam(model.parameters(), lr=lr, weight_decay=wd) model.train() cout = 0 for epoch in range(max(epochs)): trainloader = tqdm(trainloader) if ADdataset_mode is 'majority': trainloader.set_description('dcca, epoch{}, abnormal class{}'.format(epoch, qualified_Y[i])) else: trainloader.set_description('dcca, epoch{}, normal class{}'.format(epoch, qualified_Y[i])) for idx, batch in enumerate(trainloader): try: if batch[0].size(0) > 1: # for bn, batchsize > 1 batch = [batch[i].to(device) for i in range(len(batch))] latent_set, outputs_set = model(batch) recon_loss = torch.zeros(1).to(device) cca_loss_ = torch.zeros(1).to(device) for view in range(len(X_train)): if epoch < epochs[view]: y = batch[view] loss = loss_func(outputs_set[view], y) recon_loss += loss for v_idx in range(view + 1, len(X_train)): try: cur_cca_loss = cca_loss_func.loss(latent_set[view], latent_set[v_idx]) except: cur_cca_loss = torch.zeros(1).to(device) cca_loss_ += cur_cca_loss rec_losses.update(recon_loss.item(), batch[0].size(0)) cca_losses.update(cca_loss_.item(), batch[0].size(0)) tot_loss = (1 - alpha) * recon_loss + alpha * cca_loss_ postfix = ' rec_loss: {:.4f}, cca_loss: {:.4f} '.format(rec_losses.avg, cca_losses.avg) optimizer.zero_grad() tot_loss.backward() filter_nan_grad(optimizer) # if grad contains grad, the batch is not used to update the parameters optimizer.step() cout += 1 trainloader.set_postfix(log=postfix) except: print('The error idx is {}'.format(idx)) # # ----------------------------------------model testing testloader = DataLoader(dataset=mv_dataset(X_test), batch_size=batch_size, shuffle=False, num_workers=1, collate_fn=mv_tabular_collate) with torch.no_grad(): model.eval() scores = [[] for ss in range(len(X_test))] for idx, batch in enumerate(tqdm(testloader)): batch = [batch[i].to(device) for i in range(len(batch))] cur_batch_scores = model.get_ad_scores(batch) for ss in range(len(X_test)): scores[ss].append(cur_batch_scores[ss]) scores = [torch.cat(scores[ss], dim=0) for ss in range(len(X_test))] scores = late_fusion(scores, merge='avg') # ----------------------------------------model eval roc, pr_anom, pr_norm, tnr = save_roc_pr_curve_data(scores=scores, labels=Y_test, file_path=None, verbose=False) roc_results[t].append(roc) pr_anom_results[t].append(pr_anom) pr_norm_results[t].append(pr_norm) tnr_results[t].append(tnr) del model # save the results if results_path in ['./results/']: np.savez(file=results_path + dataset_name + '_dcca', ROC=roc_results, PR_norm=pr_norm_results, PR_anom=pr_anom_results, tnr=tnr_results) load_print_results(results_path + dataset_name + '_dcca') else: np.savez(file=results_path + dataset_name + '_dcca_alpha_{}'.format(alpha), ROC=roc_results, PR_norm=pr_norm_results, PR_anom=pr_anom_results, tnr=tnr_results) load_print_results(results_path + dataset_name + '_dcca_alpha_{}'.format(alpha)) def dgcca(dataset_name, X, Y, repeat_times, normal_train_ratio, results_path, device, qualified_Y, ADdataset_mode='minority'): torch.set_default_tensor_type(torch.DoubleTensor) roc_results = [[] for i in range(repeat_times)] pr_anom_results = [[] for i in range(repeat_times)] pr_norm_results = [[] for i in range(repeat_times)] tnr_results = [[] for i in range(repeat_times)] # training config batch_size = batch_size_set_dgcca[dataset_name] lr = lr_set_dgcca[dataset_name] wd = wd_set_dgcca[dataset_name] layer_size = layer_size_set[dataset_name] loss_func = MSELoss() gcca_loss_func = gcca(outdim_size=layer_size[-1]) input_size_set = [x.shape[0] for x in X] if results_path in ['./results/']: alpha_set = [alpha_set_dgcca[dataset_name]] else: alpha_set = [0.01, 0.1, 0.5, 0.9, 0.99] for alpha in alpha_set: for t in range(repeat_times): print('The {}-th run begins!'.format(t)) for i in range(min([10, len(qualified_Y)])): partition, Y_test = build_img_dataset(dataset_name, Y, neg_class_id=qualified_Y[i], mode=ADdataset_mode) X_train, X_test = process_ad_dataset(X, partition) # -----------------------------------------model training seed = 0 if torch.cuda.is_available(): torch.cuda.manual_seed_all(seed) torch.manual_seed(seed) torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = True model = mv_corrAE(input_size_set=input_size_set, layer_sizes=layer_size).to(device).double() trainloader = DataLoader(dataset=mv_dataset(X_train), batch_size=batch_size, shuffle=True, num_workers=1, collate_fn=mv_tabular_collate) # specific training procedure epochs = epochs_set[dataset_name] gcca_losses = AverageMeter() rec_losses = AverageMeter() optimizer = optim.Adam(model.parameters(), lr=lr, weight_decay=wd) model.train() cout = 0 for epoch in range(max(epochs)): trainloader = tqdm(trainloader) if ADdataset_mode is 'majority': trainloader.set_description('dgcca, epoch{}, abnormal class{}'.format(epoch, qualified_Y[i])) else: trainloader.set_description('dgcca, epoch{}, normal class{}'.format(epoch, qualified_Y[i])) for idx, batch in enumerate(trainloader): # try: if batch[0].size(0) > 1: # for bn, batchsize > 1 batch = [batch[i].to(device) for i in range(len(batch))] latent_set, outputs_set = model(batch) recon_loss = torch.zeros(1).to(device) try: gcca_loss_ = gcca_loss_func.loss(latent_set) except: gcca_loss_ = torch.zeros(1).to(device) for view in range(len(X_train)): if epoch < epochs[view]: y = batch[view] loss = loss_func(outputs_set[view], y) recon_loss += loss rec_losses.update(recon_loss.item(), batch[0].size(0)) gcca_losses.update(gcca_loss_.item(), batch[0].size(0)) tot_loss = (1 - alpha) * recon_loss + alpha * gcca_loss_ postfix = ' rec_loss: {:.4f}, gcca_loss: {:.4f} '.format(rec_losses.avg, gcca_losses.avg) optimizer.zero_grad() tot_loss.backward() filter_nan_grad(optimizer) # if grad contains grad, the batch is not used to update the parameters optimizer.step() cout += 1 trainloader.set_postfix(log=postfix) # # ----------------------------------------model testing testloader = DataLoader(dataset=mv_dataset(X_test), batch_size=batch_size, shuffle=False, num_workers=1, collate_fn=mv_tabular_collate) with torch.no_grad(): model.eval() scores = [[] for ss in range(len(X_test))] for idx, batch in enumerate(tqdm(testloader)): batch = [batch[i].to(device) for i in range(len(batch))] cur_batch_scores = model.get_ad_scores(batch) for ss in range(len(X_test)): scores[ss].append(cur_batch_scores[ss]) scores = [torch.cat(scores[ss], dim=0) for ss in range(len(X_test))] scores = late_fusion(scores, merge='avg') # ----------------------------------------model eval roc, pr_anom, pr_norm, tnr = save_roc_pr_curve_data(scores=scores, labels=Y_test, file_path=None, verbose=False) roc_results[t].append(roc) pr_anom_results[t].append(pr_anom) pr_norm_results[t].append(pr_norm) tnr_results[t].append(tnr) del model # save the results if results_path in ['./results/']: np.savez(file=results_path + dataset_name + '_dgcca', ROC=roc_results, PR_norm=pr_norm_results, PR_anom=pr_anom_results, tnr=tnr_results) load_print_results(results_path + dataset_name + '_dgcca') else: np.savez(file=results_path + dataset_name + '_dgcca_alpha_{}'.format(alpha), ROC=roc_results, PR_norm=pr_norm_results, PR_anom=pr_anom_results, tnr=tnr_results) load_print_results(results_path + dataset_name + '_dgcca_alpha_{}'.format(alpha)) def simple_mvDSVDD(dataset_name, X, Y, repeat_times, normal_train_ratio, results_path, device, qualified_Y, ADdataset_mode='minority'): torch.set_default_tensor_type(torch.DoubleTensor) roc_results = [[] for i in range(repeat_times)] pr_anom_results = [[] for i in range(repeat_times)] pr_norm_results = [[] for i in range(repeat_times)] tnr_results = [[] for i in range(repeat_times)] # training config batch_size = batch_size_set_dsvdd[dataset_name] layer_size = layer_size_set[dataset_name] lr = lr_set_dsvdd[dataset_name] wd = wd_set_dsvdd[dataset_name] loss_func = MSELoss() input_size_set = [x.shape[0] for x in X] eps = 1e-10 pretrain = True # indicate whether the DSVDD is pre-trained like AE mode = 'one_class' assert mode in ['one_class', 'soft_bound'] if mode is 'soft_bound': nu = 0.1 warm_epochs = 5 assert warm_epochs <= min(epochs_set[dataset_name]) for t in range(repeat_times): print('The {}-th run begins!'.format(t)) for i in range(min([10, len(qualified_Y)])): partition, Y_test = build_img_dataset(dataset_name, Y, neg_class_id=qualified_Y[i], mode=ADdataset_mode) X_train, X_test = process_ad_dataset(X, partition) # -----------------------------------------build dataloader under current config. trainloader = DataLoader(dataset=mv_dataset(X_train), batch_size=batch_size, shuffle=True, num_workers=1, collate_fn=mv_tabular_collate) testloader = DataLoader(dataset=mv_dataset(X_test), batch_size=batch_size, shuffle=False, num_workers=1, collate_fn=mv_tabular_collate) seed = 0 if torch.cuda.is_available(): torch.cuda.manual_seed_all(seed) torch.manual_seed(seed) torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = True # -----------------------------------------pre-training procedure if pretrain: model = mvae_ad(input_size_set=input_size_set, layer_sizes=layer_size).to(device) epochs = epochs_set[dataset_name] losses_set = [AverageMeter() for i in range(len(X_train))] optimizer = [optim.Adam(model.ae_set[i].parameters(), lr=lr, weight_decay=wd) for i in range(len(X_train))] model.train() cout = 0 for epoch in range(max(epochs)): trainloader = tqdm(trainloader) if ADdataset_mode is 'majority': trainloader.set_description('dsvdd, epoch{}, abnormal class{}'.format(epoch, qualified_Y[i])) else: trainloader.set_description('dsvdd, epoch{}, normal class{}'.format(epoch, qualified_Y[i])) for idx, batch in enumerate(trainloader): postfix = '' if batch[0].size(0) > 1: # for bn, batchsize > 1 batch = [batch[i].to(device) for i in range(len(batch))] outputs_set = model(batch) for view in range(len(X_train)): if epoch < epochs[view]: y = batch[view] loss = loss_func(outputs_set[view], y) optimizer[view].zero_grad() loss.backward() optimizer[view].step() losses_set[view].update(loss.item(), batch[0].size(0)) cur_postfix = ' view{}, rec_loss: {:.4f} '.format(view, losses_set[view].avg) postfix += cur_postfix cout += 1 trainloader.set_postfix(log=postfix) del trainloader # # ----------------------------------------set C for multi-view DSVDD trainloader = DataLoader(dataset=mv_dataset(X_train), batch_size=batch_size, shuffle=True, num_workers=1, collate_fn=mv_tabular_collate) mvDSVDD = mvenc(input_size_set=input_size_set, layer_sizes=layer_size).to(device) ae_dict = model.state_dict() dsvdd_dict = mvDSVDD.state_dict() ae_dict = {k: v for k, v in ae_dict.items() if k in dsvdd_dict} dsvdd_dict.update(ae_dict) mvDSVDD.load_state_dict(dsvdd_dict) mvDSVDD.eval() C_set = [[] for ss in range(len(X_train))] R_set = [torch.zeros(1).to(device) for ss in range(len(X_train))] with torch.no_grad(): for idx, batch in enumerate(tqdm(trainloader)): batch = [batch[i].to(device) for i in range(len(batch))] cur_batch_latent = mvDSVDD(batch) for ss in range(len(X_train)): C_set[ss].append(cur_batch_latent[ss].detach()) C_set = [torch.cat(C_set[cc], dim=0) for cc in range(len(X_train))] for cc in range(len(C_set)): tmp = torch.mean(C_set[cc], dim=0) tmp[(abs(tmp) < eps) & (tmp < 0)] = -eps tmp[(abs(tmp) < eps) & (tmp > 0)] = eps C_set[cc] = tmp # -------------------------------------train the DSVDD epochs = epochs_set[dataset_name] losses_set = [AverageMeter() for i in range(len(X_train))] optimizer = [optim.Adam(mvDSVDD.ae_set[i].parameters(), lr=lr, weight_decay=wd) for i in range(len(X_train))] mvDSVDD.train() cout = 0 for epoch in range(max(epochs)): trainloader = tqdm(trainloader) if ADdataset_mode is 'majority': trainloader.set_description('dsvdd, epoch{}, abnormal class{}'.format(epoch, qualified_Y[i])) else: trainloader.set_description('dsvdd, epoch{}, normal class{}'.format(epoch, qualified_Y[i])) for idx, batch in enumerate(trainloader): postfix = '' if batch[0].size(0) > 1: # for bn, batchsize > 1 batch = [batch[i].to(device) for i in range(len(batch))] outputs_set = mvDSVDD(batch) for view in range(len(X_train)): if epoch < epochs[view]: cur_c = C_set[view].to(device) cur_output = outputs_set[view] dist = torch.sum((cur_output - cur_c) ** 2, dim=1) if mode is 'soft_bound': scores = dist - R_set[view] ** 2 loss = R_set[view] ** 2 + (1 / nu) * torch.mean(torch.max(torch.zeros_like(scores), scores)) else: loss = torch.mean(dist) optimizer[view].zero_grad() loss.backward() optimizer[view].step() if mode is 'soft_bound' and epoch >= warm_epochs: R_set[view].data = torch.tensor(get_radius(dist, nu), device=device) losses_set[view].update(loss.item(), batch[0].size(0)) if mode is 'soft_bound': cur_postfix = ' view{}, dd_loss: {:.4f} R: {:.4f} '.format(view, losses_set[view].avg, R_set[view].item()) else: cur_postfix = ' view{}, dd_loss: {:.4f} '.format(view, losses_set[view].avg) postfix += cur_postfix cout += 1 trainloader.set_postfix(log=postfix) # ----------------------------------------model inferrence mvDSVDD.eval() scores_set = [[] for ss in range(len(X_test))] with torch.no_grad(): for idx, batch in enumerate(tqdm(testloader)): batch = [batch[i].to(device) for i in range(len(batch))] cur_batch_outputs = mvDSVDD(batch) for view in range(len(X_train)): scores = -1. * torch.sum((cur_batch_outputs[view] - C_set[view]) ** 2, dim=1, keepdim=True) scores_set[view].append(scores.detach()) scores_set = [torch.cat(scores_set[ss], dim=0) for ss in range(len(X_train))] scores = late_fusion(scores_set, merge='avg') # ----------------------------------------model eval roc, pr_anom, pr_norm, tnr = save_roc_pr_curve_data(scores=scores, labels=Y_test, file_path=None, verbose=False) roc_results[t].append(roc) pr_anom_results[t].append(pr_anom) pr_norm_results[t].append(pr_norm) tnr_results[t].append(tnr) del model # save the results np.savez(file=results_path + dataset_name + '_dsvdd', ROC=roc_results, PR_norm=pr_norm_results, PR_anom=pr_anom_results, tnr=tnr_results) load_print_results(results_path + dataset_name + '_dsvdd') def mv_corrae(dataset_name, X, Y, repeat_times, normal_train_ratio, results_path, device, qualified_Y, ADdataset_mode='minority'): torch.set_default_tensor_type(torch.DoubleTensor) roc_results = [[] for i in range(repeat_times)] pr_anom_results = [[] for i in range(repeat_times)] pr_norm_results = [[] for i in range(repeat_times)] tnr_results = [[] for i in range(repeat_times)] # training config batch_size = batch_size_set_corr[dataset_name] lr = lr_set_corr[dataset_name] wd = wd_set_corr[dataset_name] layer_size = layer_size_set[dataset_name] loss_func = MSELoss() input_size_set = [x.shape[0] for x in X] if results_path in ['./results/']: alpha_set = [alpha_set_corr[dataset_name]] else: alpha_set = [0.01, 0.1, 0.5, 0.9, 0.99] for alpha in alpha_set: for t in range(repeat_times): print('The {}-th run begins!'.format(t)) for i in range(min([10, len(qualified_Y)])): partition, Y_test = build_img_dataset(dataset_name, Y, neg_class_id=qualified_Y[i], mode=ADdataset_mode) X_train, X_test = process_ad_dataset(X, partition) # -----------------------------------------model training seed = 0 if torch.cuda.is_available(): torch.cuda.manual_seed_all(seed) torch.manual_seed(seed) torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = True model = mv_corrAE(input_size_set=input_size_set, layer_sizes=layer_size).to(device).double() trainloader = DataLoader(dataset=mv_dataset(X_train), batch_size=batch_size, shuffle=True, num_workers=1, collate_fn=mv_tabular_collate) # specific training procedure epochs = epochs_set[dataset_name] rec_losses = AverageMeter() corr_losses = AverageMeter() optimizer = optim.Adam(model.parameters(), lr=lr, weight_decay=wd) model.train() cout = 0 for epoch in range(max(epochs)): trainloader = tqdm(trainloader) if ADdataset_mode is 'majority': trainloader.set_description('corr, epoch{}, abnormal class{}'.format(epoch, qualified_Y[i])) else: trainloader.set_description('corr, epoch{}, normal class{}'.format(epoch, qualified_Y[i])) for idx, batch in enumerate(trainloader): if batch[0].size(0) > 1: # for bn, batchsize > 1 batch = [batch[i].to(device) for i in range(len(batch))] latent_set, outputs_set = model(batch) recon_loss = torch.zeros(1).to(device) corr_loss = torch.zeros(1).to(device) for view in range(len(X_train)): if epoch < epochs[view]: y = batch[view] loss = loss_func(outputs_set[view], y) recon_loss += loss for v_idx in range(view + 1, len(X_train)): cur_corr_loss = loss_func(latent_set[view], latent_set[v_idx]) corr_loss += cur_corr_loss rec_losses.update(recon_loss.item(), batch[0].size(0)) corr_losses.update(corr_loss.item(), batch[0].size(0)) postfix = ' rec_loss: {:.4f}, corr_loss: {:.4f} '.format(rec_losses.avg, corr_losses.avg) tot_loss = (1 - alpha) * recon_loss + alpha * corr_loss optimizer.zero_grad() tot_loss.backward() optimizer.step() cout += 1 trainloader.set_postfix(log=postfix) # # ----------------------------------------model testing testloader = DataLoader(dataset=mv_dataset(X_test), batch_size=batch_size, shuffle=False, num_workers=1, collate_fn=mv_tabular_collate) with torch.no_grad(): model.eval() scores = [[] for ss in range(len(X_test))] for idx, batch in enumerate(tqdm(testloader)): batch = [batch[i].to(device) for i in range(len(batch))] cur_batch_scores = model.get_ad_scores(batch) for ss in range(len(X_test)): scores[ss].append(cur_batch_scores[ss]) scores = [torch.cat(scores[ss], dim=0) for ss in range(len(X_test))] scores = late_fusion(scores, merge='avg') # ----------------------------------------model eval roc, pr_anom, pr_norm, tnr = save_roc_pr_curve_data(scores=scores, labels=Y_test, file_path=None, verbose=False) roc_results[t].append(roc) pr_anom_results[t].append(pr_anom) pr_norm_results[t].append(pr_norm) tnr_results[t].append(tnr) del model # save the results if results_path in ['./results/']: np.savez(file=results_path + dataset_name + '_corrAE', ROC=roc_results, PR_norm=pr_norm_results, PR_anom=pr_anom_results, tnr=tnr_results) load_print_results(results_path + dataset_name + '_corrAE') else: np.savez(file=results_path + dataset_name + '_corrAE_alpha_{}'.format(alpha), ROC=roc_results, PR_norm=pr_norm_results, PR_anom=pr_anom_results, tnr=tnr_results) load_print_results(results_path + dataset_name + '_corrAE_alpha_{}'.format(alpha)) def mv_sim(dataset_name, X, Y, repeat_times, normal_train_ratio, results_path, device, qualified_Y, ADdataset_mode='minority'): torch.set_default_tensor_type(torch.DoubleTensor) roc_results = [[] for i in range(repeat_times)] pr_anom_results = [[] for i in range(repeat_times)] pr_norm_results = [[] for i in range(repeat_times)] tnr_results = [[] for i in range(repeat_times)] # training config batch_size = batch_size_set_sim[dataset_name] lr = lr_set_sim[dataset_name] wd = wd_set_sim[dataset_name] layer_size = layer_size_set[dataset_name] loss_func = MSELoss() input_size_set = [x.shape[0] for x in X] if results_path in ['./results/']: alpha_set = [alpha_set_sim[dataset_name]] else: alpha_set = [0.01, 0.1, 0.5, 0.9, 0.99] if results_path in ['./results/']: m_set = [m_set_sim[dataset_name]] else: # m_set = [0, 1, 3, 5, 7] m_set = [0] for m in m_set: for alpha in alpha_set: for t in range(repeat_times): print('The {}-th run begins!'.format(t)) for i in range(min([10, len(qualified_Y)])): partition, Y_test = build_img_dataset(dataset_name, Y, neg_class_id=qualified_Y[i], mode=ADdataset_mode) X_train, X_test = process_ad_dataset(X, partition) # -----------------------------------------model training seed = 0 if torch.cuda.is_available(): torch.cuda.manual_seed_all(seed) torch.manual_seed(seed) torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = True model = mv_corrAE(input_size_set=input_size_set, layer_sizes=layer_size).to(device).double() trainloader = DataLoader(dataset=mv_dataset(X_train), batch_size=batch_size, shuffle=True, num_workers=1, collate_fn=mv_tabular_collate) # specific training procedure epochs = epochs_set[dataset_name] rec_losses = AverageMeter() sim_losses = AverageMeter() optimizer = optim.Adam(model.parameters(), lr=lr, weight_decay=wd) model.train() cout = 0 for epoch in range(max(epochs)): trainloader = tqdm(trainloader) if ADdataset_mode is 'majority': trainloader.set_description('sim, epoch{}, abnormal class{}'.format(epoch, qualified_Y[i])) else: trainloader.set_description('sim, epoch{}, normal class{}'.format(epoch, qualified_Y[i])) for idx, batch in enumerate(trainloader): if batch[0].size(0) > 1: # for bn, batchsize > 1 batch = [batch[i].to(device) for i in range(len(batch))] latent_set, outputs_set = model(batch) recon_loss = torch.zeros(1).to(device) sim_loss = torch.zeros(1).to(device) for view in range(len(X_train)): if epoch < epochs[view]: y = batch[view] loss = loss_func(outputs_set[view], y) recon_loss += loss for v_idx in range(view + 1, len(X_train)): cur_sim_loss = similarity_hinge_loss(latent_set[view], latent_set[v_idx], m) sim_loss += cur_sim_loss rec_losses.update(recon_loss.item(), batch[0].size(0)) sim_losses.update(sim_loss.item(), batch[0].size(0)) postfix = ' rec_loss: {:.4f}, sim_loss: {:.4f} '.format(rec_losses.avg, sim_losses.avg) tot_loss = (1 - alpha) * recon_loss + alpha * sim_loss optimizer.zero_grad() tot_loss.backward() optimizer.step() cout += 1 trainloader.set_postfix(log=postfix) # # ----------------------------------------model testing testloader = DataLoader(dataset=mv_dataset(X_test), batch_size=batch_size, shuffle=False, num_workers=1, collate_fn=mv_tabular_collate) with torch.no_grad(): model.eval() scores = [[] for ss in range(len(X_test))] for idx, batch in enumerate(tqdm(testloader)): batch = [batch[i].to(device) for i in range(len(batch))] cur_batch_scores = model.get_ad_scores(batch) for ss in range(len(X_test)): scores[ss].append(cur_batch_scores[ss]) scores = [torch.cat(scores[ss], dim=0) for ss in range(len(X_test))] scores = late_fusion(scores, merge='avg') # ----------------------------------------model eval roc, pr_anom, pr_norm, tnr = save_roc_pr_curve_data(scores=scores, labels=Y_test, file_path=None, verbose=False) roc_results[t].append(roc) pr_anom_results[t].append(pr_anom) pr_norm_results[t].append(pr_norm) tnr_results[t].append(tnr) del model # save the results if results_path in ['./results/']: np.savez(file=results_path + dataset_name + '_sim', ROC=roc_results, PR_norm=pr_norm_results, PR_anom=pr_anom_results, tnr=tnr_results) load_print_results(results_path + dataset_name + '_sim') else: np.savez(file=results_path + dataset_name + '_sim_alpha_{}_m_{}'.format(alpha, m), ROC=roc_results, PR_norm=pr_norm_results, PR_anom=pr_anom_results, tnr=tnr_results) load_print_results(results_path + dataset_name + '_sim_alpha_{}_m_{}'.format(alpha, m)) def mvae_fuse_latent(dataset_name, X, Y, repeat_times, normal_train_ratio, results_path, device, qualified_Y, ADdataset_mode='minority'): torch.set_default_tensor_type(torch.DoubleTensor) roc_results = [[] for i in range(repeat_times)] pr_anom_results = [[] for i in range(repeat_times)] pr_norm_results = [[] for i in range(repeat_times)] tnr_results = [[] for i in range(repeat_times)] # training config batch_size = batch_size_set_fused[dataset_name] layer_size = layer_size_set[dataset_name] lr = lr_set_fused[dataset_name] wd = wd_set_fused[dataset_name] fuse_dim = layer_size[-1] loss_func = MSELoss() input_size_set = [x.shape[0] for x in X] for t in range(repeat_times): print('The {}-th run begins!'.format(t)) for i in range(min([10, len(qualified_Y)])): partition, Y_test = build_img_dataset(dataset_name, Y, neg_class_id=qualified_Y[i], mode=ADdataset_mode) X_train, X_test = process_ad_dataset(X, partition) # -----------------------------------------model training seed = 0 if torch.cuda.is_available(): torch.cuda.manual_seed_all(seed) torch.manual_seed(seed) torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = True model = mvae_ad(input_size_set=input_size_set, layer_sizes=layer_size).to(device).double() trainloader = DataLoader(dataset=mv_dataset(X_train), batch_size=batch_size, shuffle=True, num_workers=1, collate_fn=mv_tabular_collate) # specific training procedure epochs = epochs_set[dataset_name] pt_epochs = pt_epochs_set[dataset_name] assert max(epochs) > max(pt_epochs) losses_set = [AverageMeter() for i in range(len(X_train))] optimizer = [optim.Adam(model.ae_set[i].parameters(), lr=lr, weight_decay=wd) for i in range(len(X_train))] # -----------------------------------------pretrain the MVAE model.train() cout = 0 for epoch in range(max(pt_epochs)): trainloader = tqdm(trainloader) if ADdataset_mode is 'majority': trainloader.set_description('fuse, epoch{}, abnormal class{}'.format(epoch, qualified_Y[i])) else: trainloader.set_description('fuse, epoch{}, normal class{}'.format(epoch, qualified_Y[i])) for idx, batch in enumerate(trainloader): postfix = '' if batch[0].size(0) > 1: # for bn, batchsize > 1 batch = [batch[i].to(device) for i in range(len(batch))] outputs_set = model(batch) for view in range(len(X_train)): if epoch < epochs[view]: y = batch[view] loss = loss_func(outputs_set[view], y) optimizer[view].zero_grad() loss.backward() optimizer[view].step() losses_set[view].update(loss.item(), batch[0].size(0)) cur_postfix = ' view{}, rec_loss: {:.4f} '.format(view, losses_set[view].avg) postfix += cur_postfix cout += 1 trainloader.set_postfix(log=postfix) del trainloader # -------------------------------load pretrained weights to MVAE_fused fused_model = mvae_fused(input_size_set=input_size_set, layer_sizes=layer_size, fuse_dim=fuse_dim).to(device).double() ae_dict = model.state_dict() ae_fused_dict = fused_model.state_dict() ae_dict = {k: v for k, v in ae_dict.items() if k in ae_fused_dict} ae_fused_dict.update(ae_dict) fused_model.load_state_dict(ae_fused_dict) fused_model.train() trainloader = DataLoader(dataset=mv_dataset(X_train), batch_size=batch_size, shuffle=True, num_workers=1, collate_fn=mv_tabular_collate) epochs = epochs_set[dataset_name] losses = AverageMeter() optimizer = optim.Adam(fused_model.parameters(), lr=lr, weight_decay=wd) cout = 0 for epoch in range(max(epochs) - max(pt_epochs)): trainloader = tqdm(trainloader) if ADdataset_mode is 'majority': trainloader.set_description('fuse, Epoch{}, abnormal class{}'.format(epoch, qualified_Y[i])) else: trainloader.set_description('fuse, Epoch{}, normal class{}'.format(epoch, qualified_Y[i])) for idx, batch in enumerate(trainloader): if batch[0].size(0) > 1: # for bn, batchsize > 1 batch = [batch[i].to(device) for i in range(len(batch))] outputs_set = fused_model(batch) loss = torch.zeros(1).to(device) for ll in range(len(batch)): loss += loss_func(outputs_set[ll], batch[ll]) optimizer.zero_grad() loss.backward() optimizer.step() losses.update(loss.item(), batch[0].size(0)) postfix = ' fused rec_loss: {:.4f} '.format(losses.avg) cout += 1 trainloader.set_postfix(log=postfix) # # ----------------------------------------model testing testloader = DataLoader(dataset=mv_dataset(X_test), batch_size=batch_size, shuffle=False, num_workers=1, collate_fn=mv_tabular_collate) with torch.no_grad(): fused_model.eval() scores = [[] for ss in range(len(X_test))] for idx, batch in enumerate(tqdm(testloader)): batch = [batch[i].to(device) for i in range(len(batch))] cur_batch_scores = fused_model.get_ad_scores(batch) for ss in range(len(X_test)): scores[ss].append(cur_batch_scores[ss]) scores = [torch.cat(scores[ss], dim=0) for ss in range(len(X_test))] scores = late_fusion(scores, merge='avg') # ----------------------------------------model eval roc, pr_anom, pr_norm, tnr = save_roc_pr_curve_data(scores=scores, labels=Y_test, file_path=None, verbose=False) roc_results[t].append(roc) pr_anom_results[t].append(pr_anom) pr_norm_results[t].append(pr_norm) tnr_results[t].append(tnr) del model # save the results np.savez(file=results_path + dataset_name + '_mvae_fused', ROC=roc_results, PR_norm=pr_norm_results, PR_anom=pr_anom_results, tnr=tnr_results) load_print_results(results_path + dataset_name + '_mvae_fused') def mv_dbn(dataset_name, X, Y, repeat_times, normal_train_ratio, results_path, device, qualified_Y, ADdataset_mode='minority'): torch.set_default_tensor_type(torch.DoubleTensor) roc_results = [[] for i in range(repeat_times)] pr_anom_results = [[] for i in range(repeat_times)] pr_norm_results = [[] for i in range(repeat_times)] tnr_results = [[] for i in range(repeat_times)] # training config layer_size = layer_size_set[dataset_name] batch_size = batch_size_set_dbn[dataset_name] lr = lr_set_dbn[dataset_name] wd = wd_set_dbn[dataset_name] print('layer_size: {}, batch size: {}, lr: {}, wd:{}'.format(layer_size, batch_size, lr, wd)) input_size_set = [x.shape[0] for x in X] fuse_dim = layer_size[-1] k = 3 epochs = epochs_set[dataset_name] for t in range(repeat_times): print('The {}-th run begins!'.format(t)) for i in range(min([10, len(qualified_Y)])): partition, Y_test = build_img_dataset(dataset_name, Y, neg_class_id=qualified_Y[i], mode=ADdataset_mode) X_train, X_test = process_ad_dataset(X, partition, normalization_range='01') # -----------------------------------------model training seed = 0 if torch.cuda.is_available(): torch.cuda.manual_seed_all(seed) torch.manual_seed(seed) torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = True # model = mvae_ad(input_size_set=input_size_set, layer_sizes=layer_size).to(device).double() model = mv_DBN(input_sizes=input_size_set, layers=layer_size, fuse_dim=fuse_dim, k=k) X_train = [torch.from_numpy(X_train[_]) for _ in range(len(X_train))] model.train_mv_DBN(X_train, epochs=max(epochs), lr=lr, batch_size=batch_size) # # ----------------------------------------model testing X_test = [torch.from_numpy(X_test[_]) for _ in range(len(X_test))] scores = model.get_ad_scores(X_test) scores = late_fusion(scores, merge='avg') # ----------------------------------------model eval roc, pr_anom, pr_norm, tnr = save_roc_pr_curve_data(scores=scores, labels=Y_test, file_path=None, verbose=False) roc_results[t].append(roc) pr_anom_results[t].append(pr_anom) pr_norm_results[t].append(pr_norm) tnr_results[t].append(tnr) del model # save the results file_name = results_path+dataset_name+'_dbn' np.savez(file=file_name, ROC=roc_results, PR_norm=pr_norm_results, PR_anom=pr_anom_results, tnr=tnr_results) load_print_results(file_name) def mv_splitae(dataset_name, X, Y, repeat_times, normal_train_ratio, results_path, device, qualified_Y, ADdataset_mode='minority'): torch.set_default_tensor_type(torch.DoubleTensor) roc_results = [[] for i in range(repeat_times)] pr_anom_results = [[] for i in range(repeat_times)] pr_norm_results = [[] for i in range(repeat_times)] tnr_results = [[] for i in range(repeat_times)] # training config batch_size = batch_size_set_split[dataset_name] lr = lr_set_split[dataset_name] wd = wd_set_split[dataset_name] layer_size = layer_size_set[dataset_name] loss_func = MSELoss() input_size_set = [x.shape[0] for x in X] dec_mode = 'fixed' for t in range(repeat_times): print('The {}-th run begins!'.format(t)) for i in range(min([10, len(qualified_Y)])): partition, Y_test = build_img_dataset(dataset_name, Y, neg_class_id=qualified_Y[i], mode=ADdataset_mode) X_train, X_test = process_ad_dataset(X, partition) # -----------------------------------------model training seed = 0 if torch.cuda.is_available(): torch.cuda.manual_seed_all(seed) torch.manual_seed(seed) torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = True model = splitAE(input_size_set=input_size_set, layer_sizes=layer_size, dec_mode=dec_mode).to(device) trainloader = DataLoader(dataset=mv_dataset(X_train), batch_size=batch_size, shuffle=True, num_workers=1, collate_fn=mv_tabular_collate) # specific training procedure epochs = epochs_set[dataset_name] losses_set = [AverageMeter() for i in range(len(X_train))] optimizer = optim.Adam(model.parameters(), lr=lr, weight_decay=wd) model.train() cout = 0 for epoch in range(max(epochs)): trainloader = tqdm(trainloader) if ADdataset_mode is 'majority': trainloader.set_description('split_fix, epoch{}, abnormal class{}'.format(epoch, qualified_Y[i])) else: trainloader.set_description('split_fix, epoch{}, normal class{}'.format(epoch, qualified_Y[i])) for idx, batch in enumerate(trainloader): postfix = '' if batch[0].size(0) > 1: # for bn, batchsize > 1 batch = [batch[i].to(device) for i in range(len(batch))] outputs_set = model(batch) loss = torch.zeros(1).to(device) cur_view_losses = [0. for cc in range(len(X_train))] for enc_ind in range(len(outputs_set)): cur_enc_recon = outputs_set[enc_ind] for view in range(len(X_train)): y = batch[view] cur_enc_view_loss = loss_func(cur_enc_recon[view], y) loss += cur_enc_view_loss cur_view_losses[view] += loss.item() optimizer.zero_grad() loss.backward() optimizer.step() for view in range(len(X_train)): losses_set[view].update(cur_view_losses[view], batch[0].size(0)) cur_postfix = ' view{}, rec_loss: {:.4f} '.format(view, losses_set[view].avg) postfix += cur_postfix cout += 1 trainloader.set_postfix(log=postfix) # # ----------------------------------------model testing testloader = DataLoader(dataset=mv_dataset(X_test), batch_size=batch_size, shuffle=False, num_workers=1, collate_fn=mv_tabular_collate) with torch.no_grad(): model.eval() scores = [[] for ss in range(len(X_test))] for idx, batch in enumerate(tqdm(testloader)): batch = [batch[i].to(device) for i in range(len(batch))] cur_batch_scores = model.get_ad_scores(batch) for ss in range(len(X_test)): scores[ss].append(cur_batch_scores[ss]) scores = [torch.cat(scores[ss], dim=0) for ss in range(len(X_test))] scores = late_fusion(scores, merge='avg') # ----------------------------------------model eval roc, pr_anom, pr_norm, tnr = save_roc_pr_curve_data(scores=scores, labels=Y_test, file_path=None, verbose=False) roc_results[t].append(roc) pr_anom_results[t].append(pr_anom) pr_norm_results[t].append(pr_norm) tnr_results[t].append(tnr) del model # save the results np.savez(file=results_path + dataset_name + '_splitAE_{}'.format(dec_mode), ROC=roc_results, PR_norm=pr_norm_results, PR_anom=pr_anom_results, tnr=tnr_results) load_print_results(results_path + dataset_name + '_splitAE_{}'.format(dec_mode)) def mv_ss(dataset_name, X, Y, repeat_times, normal_train_ratio, results_path, device, qualified_Y, ADdataset_mode='minority', mode='fuse', param_mode='fixed'): torch.set_default_tensor_type(torch.DoubleTensor) roc_results = [[] for i in range(repeat_times)] pr_anom_results = [[] for i in range(repeat_times)] pr_norm_results = [[] for i in range(repeat_times)] tnr_results = [[] for i in range(repeat_times)] # training config batch_size = batch_size_set_split[dataset_name] lr = lr_set_split[dataset_name] wd = wd_set_split[dataset_name] layer_size = layer_size_set[dataset_name] loss_func = MSELoss() input_size_set = [x.shape[0] for x in X] fuse_dim = layer_size[-1] mode = mode # fuse/pred/split if mode is 'fuse': if param_mode not in ['nn', 'sum', 'max']: raise NotImplementedError else: if param_mode not in ['fixed', 'non-fixed']: raise NotImplementedError for t in range(repeat_times): print('The {}-th run begins!'.format(t)) for i in range(min([10, len(qualified_Y)])): partition, Y_test = build_img_dataset(dataset_name, Y, neg_class_id=qualified_Y[i], mode=ADdataset_mode) X_train, X_test = process_ad_dataset(X, partition) # -----------------------------------------model training seed = 0 if torch.cuda.is_available(): torch.cuda.manual_seed_all(seed) torch.manual_seed(seed) torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = True model = mvae_ss(input_size_set=input_size_set, layer_sizes=layer_size, fuse_dim=fuse_dim, mode=mode, param_mode=param_mode).to(device) trainloader = DataLoader(dataset=mv_dataset(X_train), batch_size=batch_size, shuffle=True, num_workers=1, collate_fn=mv_tabular_collate) # specific training procedure epochs = epochs_set[dataset_name] losses_set = [AverageMeter() for i in range(len(X_train))] optimizer = optim.Adam(model.parameters(), lr=lr, weight_decay=wd) model.train() for epoch in range(max(epochs)): trainloader = tqdm(trainloader) if ADdataset_mode is 'majority': trainloader.set_description('ss_{}_{}, epoch{}, abnormal class{}'.format(mode, param_mode, epoch, qualified_Y[i])) else: trainloader.set_description('ss_{}_{}, epoch{}, normal class{}'.format(mode, param_mode, epoch, qualified_Y[i])) for idx, batch in enumerate(trainloader): postfix = '' if batch[0].size(0) > 1: # for bn, batchsize > 1 batch = [batch[i].to(device) for i in range(len(batch))] outputs_set = model(batch) if mode is 'fuse' or mode is 'pred': loss = torch.zeros(1).to(device) for ll in range(len(batch)): cur_view_loss = loss_func(outputs_set[ll], batch[ll]) losses_set[ll].update(cur_view_loss, batch[0].size(0)) cur_postfix = ' view{}, rec_loss: {:.4f} '.format(ll, losses_set[ll].avg) postfix += cur_postfix loss += cur_view_loss optimizer.zero_grad() loss.backward() optimizer.step() elif mode is 'split': loss = torch.zeros(1).to(device) cur_view_losses = [0. for cc in range(len(X_train))] for enc_ind in range(len(outputs_set)): cur_enc_recon = outputs_set[enc_ind] for view in range(len(X_train)): y = batch[view] cur_enc_view_loss = loss_func(cur_enc_recon[view], y) loss += cur_enc_view_loss cur_view_losses[view] += loss.item() optimizer.zero_grad() loss.backward() optimizer.step() for view in range(len(X_train)): losses_set[view].update(cur_view_losses[view], batch[0].size(0)) cur_postfix = ' view{}, rec_loss: {:.4f} '.format(view, losses_set[view].avg) postfix += cur_postfix else: raise NotImplementedError trainloader.set_postfix(log=postfix) # # ----------------------------------------model testing testloader = DataLoader(dataset=mv_dataset(X_test), batch_size=batch_size, shuffle=False, num_workers=1, collate_fn=mv_tabular_collate) with torch.no_grad(): model.eval() scores = [[] for ss in range(len(X_test))] for idx, batch in enumerate(tqdm(testloader)): batch = [batch[i].to(device) for i in range(len(batch))] cur_batch_scores = model.get_ad_scores(batch) for ss in range(len(X_test)): scores[ss].append(cur_batch_scores[ss]) scores = [torch.cat(scores[ss], dim=0) for ss in range(len(X_test))] scores = late_fusion(scores, merge='avg') # ----------------------------------------model eval roc, pr_anom, pr_norm, tnr = save_roc_pr_curve_data(scores=scores, labels=Y_test, file_path=None, verbose=False) roc_results[t].append(roc) pr_anom_results[t].append(pr_anom) pr_norm_results[t].append(pr_norm) tnr_results[t].append(tnr) del model # save the results np.savez(file=results_path + dataset_name + '_ss_{}_{}'.format(mode, param_mode), ROC=roc_results, PR_norm=pr_norm_results, PR_anom=pr_anom_results, tnr=tnr_results) load_print_results(results_path + dataset_name + '_ss_{}_{}'.format(mode, param_mode)) def mvDSVDD_fused(dataset_name, X, Y, repeat_times, normal_train_ratio, results_path, device, qualified_Y, ADdataset_mode='minority'): torch.set_default_tensor_type(torch.DoubleTensor) roc_results = [[] for i in range(repeat_times)] pr_anom_results = [[] for i in range(repeat_times)] pr_norm_results = [[] for i in range(repeat_times)] tnr_results = [[] for i in range(repeat_times)] # training config batch_size = batch_size_set_dsvdd[dataset_name] layer_size = layer_size_set[dataset_name] lr = lr_set_dsvdd[dataset_name] wd = wd_set_dsvdd[dataset_name] loss_func = MSELoss() input_size_set = [x.shape[0] for x in X] eps = 1e-10 pretrain = True # indicate whether the DSVDD is pre-trained like AE mode = 'one_class' assert mode in ['one_class', 'soft_bound'] if mode is 'soft_bound': nu = 0.1 warm_epochs = 5 assert warm_epochs <= min(epochs_set[dataset_name]) for t in range(repeat_times): print('The {}-th run begins!'.format(t)) for i in range(min([10, len(qualified_Y)])): partition, Y_test = build_img_dataset(dataset_name, Y, neg_class_id=qualified_Y[i], mode=ADdataset_mode) X_train, X_test = process_ad_dataset(X, partition) # -----------------------------------------build dataloader under current config. trainloader = DataLoader(dataset=mv_dataset(X_train), batch_size=batch_size, shuffle=True, num_workers=1, collate_fn=mv_tabular_collate) testloader = DataLoader(dataset=mv_dataset(X_test), batch_size=batch_size, shuffle=False, num_workers=1, collate_fn=mv_tabular_collate) seed = 0 if torch.cuda.is_available(): torch.cuda.manual_seed_all(seed) torch.manual_seed(seed) torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = True # -----------------------------------------pre-training procedure if pretrain: model = mvae_ad(input_size_set=input_size_set, layer_sizes=layer_size).to(device) epochs = epochs_set[dataset_name] losses_set = [AverageMeter() for i in range(len(X_train))] optimizer = [optim.Adam(model.ae_set[i].parameters(), lr=lr, weight_decay=wd) for i in range(len(X_train))] model.train() cout = 0 for epoch in range(max(epochs)): trainloader = tqdm(trainloader) if ADdataset_mode is 'majority': trainloader.set_description('dsvdd_fuse, epoch{}, abnormal class{}'.format(epoch, qualified_Y[i])) else: trainloader.set_description('dsvdd_fuse, epoch{}, normal class{}'.format(epoch, qualified_Y[i])) for idx, batch in enumerate(trainloader): postfix = '' if batch[0].size(0) > 1: # for bn, batchsize > 1 batch = [batch[i].to(device) for i in range(len(batch))] outputs_set = model(batch) for view in range(len(X_train)): if epoch < epochs[view]: y = batch[view] loss = loss_func(outputs_set[view], y) optimizer[view].zero_grad() loss.backward() optimizer[view].step() losses_set[view].update(loss.item(), batch[0].size(0)) cur_postfix = ' view{}, rec_loss: {:.4f} '.format(view, losses_set[view].avg) postfix += cur_postfix cout += 1 trainloader.set_postfix(log=postfix) del trainloader # # ----------------------------------------set C for multi-view DSVDD trainloader = DataLoader(dataset=mv_dataset(X_train), batch_size=batch_size, shuffle=True, num_workers=1, collate_fn=mv_tabular_collate) mvDSVDD = mvenc_fuse(input_size_set=input_size_set, layer_sizes=layer_size).to(device) ae_dict = model.state_dict() dsvdd_dict = mvDSVDD.state_dict() ae_dict = {k: v for k, v in ae_dict.items() if k in dsvdd_dict} dsvdd_dict.update(ae_dict) mvDSVDD.load_state_dict(dsvdd_dict) mvDSVDD.eval() C_set = [] R = torch.zeros(1).to(device) with torch.no_grad(): for idx, batch in enumerate(tqdm(trainloader)): batch = [batch[i].to(device) for i in range(len(batch))] cur_batch_latent = mvDSVDD(batch) C_set.append(cur_batch_latent.detach()) C_set = torch.cat(C_set, dim=0) tmp = torch.mean(C_set, dim=0) tmp[(abs(tmp) < eps) & (tmp < 0)] = -eps tmp[(abs(tmp) < eps) & (tmp > 0)] = eps C = tmp # -------------------------------------train the DSVDD epochs = epochs_set[dataset_name] losses = AverageMeter() optimizer = optim.Adam(mvDSVDD.parameters(), lr=lr, weight_decay=wd) mvDSVDD.train() cout = 0 for epoch in range(max(epochs)): trainloader = tqdm(trainloader) if ADdataset_mode is 'majority': trainloader.set_description('dsvdd_fuse, epoch{}, abnormal class{}'.format(epoch, qualified_Y[i])) else: trainloader.set_description('dsvdd_fuse, epoch{}, normal class{}'.format(epoch, qualified_Y[i])) for idx, batch in enumerate(trainloader): postfix = '' if batch[0].size(0) > 1: # for bn, batchsize > 1 batch = [batch[i].to(device) for i in range(len(batch))] outputs = mvDSVDD(batch) C = C.to(device) dist = torch.sum((outputs - C) ** 2, dim=1) if mode is 'soft_bound': scores = dist - R ** 2 loss = R ** 2 + (1 / nu) * torch.mean(torch.max(torch.zeros_like(scores), scores)) else: loss = torch.mean(dist) optimizer.zero_grad() loss.backward() optimizer.step() losses.update(loss.item(), batch[0].size(0)) if mode is 'soft_bound': postfix += ' dd_loss: {:.4f} R: {:.4f} '.format(losses.avg, R.item()) else: postfix += ' dd_loss: {:.4f} '.format(losses.avg) if mode is 'soft_bound' and epoch >= warm_epochs: R.data = torch.tensor(get_radius(dist, nu), device=device) cout += 1 trainloader.set_postfix(log=postfix) # ----------------------------------------model inferrence mvDSVDD.eval() scores_set = [] with torch.no_grad(): for idx, batch in enumerate(tqdm(testloader)): batch = [batch[i].to(device) for i in range(len(batch))] cur_batch_outputs = mvDSVDD(batch) scores = -1. * torch.sum((cur_batch_outputs - C) ** 2, dim=1) scores_set.append(scores.detach()) scores_set = torch.cat(scores_set, dim=0) scores = scores_set.cpu().detach().numpy() # ----------------------------------------model eval roc, pr_anom, pr_norm, tnr = save_roc_pr_curve_data(scores=scores, labels=Y_test, file_path=None, verbose=False) roc_results[t].append(roc) pr_anom_results[t].append(pr_anom) pr_norm_results[t].append(pr_norm) tnr_results[t].append(tnr) del model # save the results np.savez(file=results_path + dataset_name + '_dsvdd_fused', ROC=roc_results, PR_norm=pr_norm_results, PR_anom=pr_anom_results, tnr=tnr_results) load_print_results(results_path + dataset_name + '_dsvdd_fused') def mv_tf(dataset_name, X, Y, repeat_times, normal_train_ratio, results_path, device, qualified_Y, ADdataset_mode='minority'): torch.set_default_tensor_type(torch.DoubleTensor) roc_results = [[] for i in range(repeat_times)] pr_anom_results = [[] for i in range(repeat_times)] pr_norm_results = [[] for i in range(repeat_times)] tnr_results = [[] for i in range(repeat_times)] # training config batch_size = batch_size_set_tf[dataset_name] lr = lr_set_tf[dataset_name] wd = wd_set_tf[dataset_name] if results_path in ['./results/']: r_set = [r_set_tf[dataset_name]] else: r_set = [4, 8, 16, 32, 64] layer_size = layer_size_set[dataset_name] loss_func = MSELoss() input_size_set = [x.shape[0] for x in X] for r in r_set: for t in range(repeat_times): print('The {}-th run begins!'.format(t)) for i in range(min([10, len(qualified_Y)])): partition, Y_test = build_img_dataset(dataset_name, Y, neg_class_id=qualified_Y[i], mode=ADdataset_mode) X_train, X_test = process_ad_dataset(X, partition) # -----------------------------------------model training seed = 0 if torch.cuda.is_available(): torch.cuda.manual_seed_all(seed) torch.manual_seed(seed) torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = True model = mvae_tf(input_size_set=input_size_set, layer_sizes=layer_size, rank=r).to(device) trainloader = DataLoader(dataset=mv_dataset(X_train), batch_size=batch_size, shuffle=True, num_workers=1, collate_fn=mv_tabular_collate) # specific training procedure epochs = epochs_set[dataset_name] losses_set = [AverageMeter() for i in range(len(X_train))] optimizer = optim.Adam(model.parameters(), lr=lr, weight_decay=wd) model.train() for epoch in range(max(epochs)): trainloader = tqdm(trainloader) if ADdataset_mode is 'majority': trainloader.set_description('mvtf, epoch{}, abnormal class{}'.format(epoch, qualified_Y[i])) else: trainloader.set_description('mvtf, epoch{}, normal class{}'.format(epoch, qualified_Y[i])) for idx, batch in enumerate(trainloader): postfix = '' if batch[0].size(0) > 1: # for bn, batchsize > 1 batch = [batch[i].to(device) for i in range(len(batch))] outputs_set = model(batch) loss = torch.zeros(1).to(device) for ll in range(len(batch)): cur_view_loss = loss_func(outputs_set[ll], batch[ll]) losses_set[ll].update(cur_view_loss, batch[0].size(0)) cur_postfix = ' view{}, rec_loss: {:.4f} '.format(ll, losses_set[ll].avg) postfix += cur_postfix loss += cur_view_loss optimizer.zero_grad() loss.backward() optimizer.step() trainloader.set_postfix(log=postfix) # # ----------------------------------------model testing testloader = DataLoader(dataset=mv_dataset(X_test), batch_size=batch_size, shuffle=False, num_workers=1, collate_fn=mv_tabular_collate) with torch.no_grad(): model.eval() scores = [[] for ss in range(len(X_test))] for idx, batch in enumerate(tqdm(testloader)): batch = [batch[i].to(device) for i in range(len(batch))] cur_batch_scores = model.get_ad_scores(batch) for ss in range(len(X_test)): scores[ss].append(cur_batch_scores[ss]) scores = [torch.cat(scores[ss], dim=0) for ss in range(len(X_test))] scores = late_fusion(scores, merge='avg') # ----------------------------------------model eval roc, pr_anom, pr_norm, tnr = save_roc_pr_curve_data(scores=scores, labels=Y_test, file_path=None, verbose=False) roc_results[t].append(roc) pr_anom_results[t].append(pr_anom) pr_norm_results[t].append(pr_norm) tnr_results[t].append(tnr) del model # save the results if results_path in ['./results/']: np.savez(file=results_path + dataset_name + '_tf', ROC=roc_results, PR_norm=pr_norm_results, PR_anom=pr_anom_results, tnr=tnr_results) load_print_results(results_path + dataset_name + '_tf') else: np.savez(file=results_path + dataset_name + '_tf_r_{}'.format(r), ROC=roc_results, PR_norm=pr_norm_results, PR_anom=pr_anom_results, tnr=tnr_results) load_print_results(results_path + dataset_name + '_tf_r_{}'.format(r)) if __name__ == '__main__': os.environ["CUDA_VISIBLE_DEVICES"] = "0" device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') # ----------------------------------------prepare the dataset # dataset_set = ['oct', 'breast', 'retina', 'axial', 'coronal', 'sagittal', 'path', 'derma', 'pneumonia'] # dataset_set = ['mvtec_bottle', # 'mvtec_cable', 'mvtec_capsule', 'mvtec_carpet', 'mvtec_grid', 'mvtec_hazelnut', 'mvtec_leather', 'mvtec_metal_nut', 'mvtec_pill', # 'mvtec_screw', 'mvtec_tile', 'mvtec_toothbrush', 'mvtec_transistor', 'mvtec_wood', 'mvtec_zipper'] # dataset_set = ['cifar10', 'cifar100', 'mnist', 'fmnist', 'svhn'] # dataset_set = ['ytface'] dataset_set = ['ytface'] # -----------------------------------------methods for dataset_name in dataset_set: X, Y = read_dataset('./data/', dataset_name) # for i in range(len(X)): # cur_X = X[i] # idx = np.isnan(cur_X).sum() # if idx > 0: # print('dataset: {}, view: {} has NaN!'.format(dataset_name, i)) min_class_num = 300 normal_train_ratio = 0.7 if dataset_name[:5] in ['mvtec']: qualified_Y = get_large_class(Y, min_class_num=min_class_num, semantics=True) else: qualified_Y = get_large_class(Y, min_class_num=min_class_num) num_qualified_class = len(qualified_Y) repeat_times = 1 # train-test split is fixed ADdataset_mode = 'minority' if num_qualified_class > 0: print('Training dataset name: {}, qualified class number: {}/{}'.format(dataset_name, num_qualified_class, len(get_all_labels(Y)))) # --------------------------------Baselines----------------------------------- simple_mvae(dataset_name, X, Y, repeat_times, normal_train_ratio, results_path='./results/', device=device, qualified_Y=qualified_Y, ADdataset_mode=ADdataset_mode) # --------------------------------Multi-view fusion based methods------------- # simple fusion: mv_ss(dataset_name, X, Y, repeat_times, normal_train_ratio, results_path='./results/', device=device, qualified_Y=qualified_Y, ADdataset_mode=ADdataset_mode, mode='fuse', param_mode='max') mv_ss(dataset_name, X, Y, repeat_times, normal_train_ratio, results_path='./results/', device=device, qualified_Y=qualified_Y, ADdataset_mode=ADdataset_mode, mode='fuse', param_mode='sum') mv_ss(dataset_name, X, Y, repeat_times, normal_train_ratio, results_path='./results/', device=device, qualified_Y=qualified_Y, ADdataset_mode=ADdataset_mode, mode='fuse', param_mode='nn') # pre-trained AE based fusion mvae_fuse_latent(dataset_name, X, Y, repeat_times, normal_train_ratio, results_path='./results/', device=device, qualified_Y=qualified_Y, ADdataset_mode=ADdataset_mode) # DBN based try: mv_dbn(dataset_name, X, Y, repeat_times, normal_train_ratio, results_path='./results/', device=device, qualified_Y=qualified_Y, ADdataset_mode=ADdataset_mode) except: print('dataset: {} dbn error'.format(dataset_name)) pass # Tensor fusion network based methods mv_tf(dataset_name, X, Y, repeat_times, normal_train_ratio, results_path='./results/', device=device, qualified_Y=qualified_Y, ADdataset_mode=ADdataset_mode) # --------------------------------Multi-view alignment based methods---------- mv_corrae(dataset_name, X, Y, repeat_times, normal_train_ratio, results_path='./results/', device=device, qualified_Y=qualified_Y, ADdataset_mode=ADdataset_mode) mv_sim(dataset_name, X, Y, repeat_times, normal_train_ratio, results_path='./results/', device=device, qualified_Y=qualified_Y, ADdataset_mode=ADdataset_mode) try: deepCCA(dataset_name, X, Y, repeat_times, normal_train_ratio, results_path='./results/', device=device, qualified_Y=qualified_Y, ADdataset_mode=ADdataset_mode) except: print('dataset: {} dcca error'.format(dataset_name)) pass try: dgcca(dataset_name, X, Y, repeat_times, normal_train_ratio, results_path='./results/', device=device, qualified_Y=qualified_Y, ADdataset_mode=ADdataset_mode) except: print('dataset: {} dgcca error'.format(dataset_name)) pass # -------------------------------Deep one-class learning based methods tailored for multi-view case----- simple_mvDSVDD(dataset_name, X, Y, repeat_times, normal_train_ratio, results_path='./results/', device=device, qualified_Y=qualified_Y, ADdataset_mode=ADdataset_mode) mvDSVDD_fused(dataset_name, X, Y, repeat_times, normal_train_ratio, results_path='./results/', device=device, qualified_Y=qualified_Y, ADdataset_mode=ADdataset_mode) # -------------------------------Self-supervision based methods--------------------------------------------- mv_ss(dataset_name, X, Y, repeat_times, normal_train_ratio, results_path='./results/', device=device, qualified_Y=qualified_Y, ADdataset_mode=ADdataset_mode, mode='pred', param_mode='fixed') mv_ss(dataset_name, X, Y, repeat_times, normal_train_ratio, results_path='./results/', device=device, qualified_Y=qualified_Y, ADdataset_mode=ADdataset_mode, mode='split', param_mode='fixed') # # -------------------------For hyperparameter analysis------------------------------- # # mv_sim(dataset_name, X, Y, repeat_times, normal_train_ratio, results_path='./analysis/', device=device, qualified_Y=qualified_Y, ADdataset_mode=ADdataset_mode) # # mv_corrae(dataset_name, X, Y, repeat_times, normal_train_ratio, results_path='./analysis/', device=device, qualified_Y=qualified_Y, ADdataset_mode=ADdataset_mode) # # try: # deepCCA(dataset_name, X, Y, repeat_times, normal_train_ratio, results_path='./analysis/', device=device, qualified_Y=qualified_Y, ADdataset_mode=ADdataset_mode) # except: # print('dataset: {} dcca error'.format(dataset_name)) # pass # # try: # dgcca(dataset_name, X, Y, repeat_times, normal_train_ratio, results_path='./analysis/', device=device, qualified_Y=qualified_Y, ADdataset_mode=ADdataset_mode) # except: # print('dataset: {} dgcca error'.format(dataset_name)) # pass # # mv_tf(dataset_name, X, Y, repeat_times, normal_train_ratio, results_path='./analysis/', device=device, qualified_Y=qualified_Y, ADdataset_mode=ADdataset_mode) # else: # print('No qualified class in this dataset!')

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remote-scripts/ConfigureDnsServer.py

xian123/azure-freebsd-automation

1

46533

<reponame>xian123/azure-freebsd-automation #!/usr/bin/python import argparse import sys from azuremodules import * import paramiko parser = argparse.ArgumentParser() parser.add_argument('-D', '--vnetDomain_db_filepath', help='VNET Domain db filepath', required=True) parser.add_argument('-r', '--vnetDomain_rev_filepath', help='VNET rev filepath',required=True) parser.add_argument('-v', '--HostnameDIP', help='hosts filepath',required = True) args = parser.parse_args() vnetDomain_db_filepath = str(args.vnetDomain_db_filepath) vnetDomain_rev_filepath = str(args.vnetDomain_rev_filepath) HostnameDIP=str(args.HostnameDIP) vnetDomain=(vnetDomain_db_filepath.split("/"))[len((vnetDomain_db_filepath.split("/")))-1].replace(".db","") #SAMPLE INPUT FOR --vms #HostnameDIP = 'ICA-VNETVM-Ubuntu1210PL-4-16-2013-1-2-0-role-0:192.168.4.196^ICA-VNETVM-Ubuntu1210PL-4-16-2013-1-2-0-role-1:192.168.4.132^ICA-VNETVM-Ubuntu1210PL-4-16-2013-1-2-1-role-0:192.168.4.133^ICA-VNETVM-Ubuntu1210PL-4-16-2013-1-2-1-role-1:192.168.4.197' #SETTING THE GLOBAL PARAMS.. #SetVnetGlobalParameters() #CONFIGURIG DNS SERVER CONFIGURATIONS FILES.. DNSServerStatus = AddICAVMsToDnsServer(HostnameDIP,vnetDomain_db_filepath,vnetDomain_rev_filepath) #RESTARTING BIND9 SERVICE.. output = JustRun('service bind9 restart') if DNSServerStatus == 0: print("CONFIGURATION_SUCCESSFUL") else: print("CONFIGURATION_FAILED")

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uq_benchmark_2019/imagenet/end_to_end_test.py

deepneuralmachine/google-research

23,901

20175

<gh_stars>1000+ # coding=utf-8 # Copyright 2021 The Google Research Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # Lint as: python2, python3 """End-to-end test for ImageNet. Tests for imagenet.resnet50_train, run_predict, run_temp_scaling, and run_metrics. Real data doesn't work under blaze, so execute the test binary directly. """ from __future__ import absolute_import from __future__ import division from __future__ import print_function import os import tempfile from absl import flags from absl.testing import absltest from absl.testing import parameterized import tensorflow.compat.v2 as tf from uq_benchmark_2019.imagenet import resnet50_train # pylint: disable=line-too-long from uq_benchmark_2019.imagenet import run_metrics from uq_benchmark_2019.imagenet import run_predict from uq_benchmark_2019.imagenet import run_temp_scaling gfile = tf.io.gfile flags.DEFINE_bool('fake_data', True, 'Use dummy random data.') flags.DEFINE_bool('fake_training', True, 'Train with trivial number of steps.') DATA_NAMES = ['train', 'test', 'corrupt-static-gaussian_noise-2', 'celeb_a'] METHODS = ['vanilla', 'll_dropout', 'll_svi', 'dropout'] class EndToEndTest(parameterized.TestCase): @parameterized.parameters(*[(d, m) for d in DATA_NAMES for m in METHODS]) # pylint: disable=g-complex-comprehension def test_end_to_end_train(self, data_name, method): with tempfile.TemporaryDirectory() as model_dir: metrics = ['sparse_categorical_crossentropy'] if flags.FLAGS.fake_data and (data_name != 'test'): pass else: temp_model_dir = os.path.join(model_dir, data_name, method) resnet50_train.run( method, temp_model_dir, task_number=0, use_tpu=False, tpu=None, metrics=metrics, fake_data=flags.FLAGS.fake_data, fake_training=flags.FLAGS.fake_training) run_predict.run( data_name, temp_model_dir, batch_size=8, predictions_per_example=4, max_examples=44, output_dir=temp_model_dir, fake_data=flags.FLAGS.fake_data) tmpl = os.path.join(temp_model_dir, '*_small_*') glob_results = gfile.glob(tmpl) path = glob_results[0] if data_name == 'valid': run_temp_scaling(path) run_metrics.run(path, path, model_dir_ensemble=None, use_temp_scaling=False) if __name__ == '__main__': absltest.main()

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src/NetworkHandler.py

BlankShrimp/MarkDownald-Server

0

56532

from socket import * import json import JsonParser import DBHelper conn = socket(AF_INET, SOCK_STREAM) conn.bind(('',4687)) conn.listen(40) while True: new_conn, addr = conn.accept() msg = new_conn.recv(1024).decode() obj = json.loads(msg) if obj['instruction'] == 'reg': result = DBHelper.registry(obj['userid'], obj['passwd'], obj['nickname']) new_conn.send(result.encode()) elif obj['instruction'] == 'add_note': result = DBHelper.insert_note(obj['userid'], obj['passwd'], obj['noteid'], obj['title'], obj['folderid'], obj['value']) new_conn.send(result.encode()) elif obj['instruction'] == 'del_note': result = DBHelper.delete_note(obj['userid'], obj['passwd'], obj['noteid']) new_conn.send(result.encode()) elif obj['instruction'] == 'sel_note': result = DBHelper.select_single_note(obj['userid'], obj['passwd'], obj['noteid']) if isinstance(result, str): new_conn.send(result.encode()) else: new_conn.send(JsonParser.dump_single_note(result).encode()) elif obj['instruction'] == 'up_note': result = DBHelper.update_note(obj['userid'], obj['passwd'], obj['noteid'], obj['title'], obj['folderid'], obj['value']) new_conn.send(result.encode()) elif obj['instruction'] == 'add_folder': result = DBHelper.insert_folder(obj['userid'], obj['passwd'], obj['folderid'], obj['foldername'], obj['parentid']) new_conn.send(result.encode()) elif obj['instruction'] == 'del_folder': result = DBHelper.delete_folder(obj['userid'], obj['passwd'], obj['folderid']) new_conn.send(result.encode()) elif obj['instruction'] == 'up_folder': result = DBHelper.update_folder(obj['userid'], obj['passwd'], obj['folderid'], obj['foldername'], obj['parentid']) new_conn.send(result.encode()) elif obj['instruction'] == 'sel_notes': result = DBHelper.select_all_notes(obj['userid'], obj['passwd']) if isinstance(result, str): new_conn.send(result.encode()) else: new_conn.send(JsonParser.dump_all_notes(result).encode()) elif obj['instruction'] == 'sel_folders': result = DBHelper.select_all_folders(obj['userid'], obj['passwd']) if isinstance(result, str): new_conn.send(result.encode()) else: new_conn.send(JsonParser.dump_all_folder(result).encode()) new_conn.close()

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src/util_walk_sftp.py

Chrisebell24/walk_sftp

0

34925

import paramiko class _FastTransport(paramiko.Transport): def __init__(self, sock): super(_FastTransport, self).__init__(sock) self.window_size = 2147483647 self.packetizer.REKEY_BYTES = pow(2, 40) self.packetizer.REKEY_PACKETS = pow(2, 40)

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tools/esp_prov/security/__init__.py

fbucafusco/esp-idf

0

189785

<reponame>fbucafusco/esp-idf # SPDX-FileCopyrightText: 2018-2022 Espressif Systems (Shanghai) CO LTD # SPDX-License-Identifier: Apache-2.0 # from .security0 import * # noqa: F403, F401 from .security1 import * # noqa: F403, F401 from .security2 import * # noqa: F403, F401

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python/ovs/db/idl.py

homework/openvswitch

9

87504

<gh_stars>1-10 # Copyright (c) 2009, 2010, 2011 Nicira Networks # # 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 logging import ovs.jsonrpc import ovs.db.parser import ovs.db.schema from ovs.db import error import ovs.ovsuuid class Idl: """Open vSwitch Database Interface Definition Language (OVSDB IDL). The OVSDB IDL maintains an in-memory replica of a database. It issues RPC requests to an OVSDB database server and parses the responses, converting raw JSON into data structures that are easier for clients to digest. The IDL also assists with issuing database transactions. The client creates a transaction, manipulates the IDL data structures, and commits or aborts the transaction. The IDL then composes and issues the necessary JSON-RPC requests and reports to the client whether the transaction completed successfully. If 'schema_cb' is provided, it should be a callback function that accepts an ovs.db.schema.DbSchema as its argument. It should determine whether the schema is acceptable and raise an ovs.db.error.Error if it is not. It may also delete any tables or columns from the schema that the client has no interest in monitoring, to save time and bandwidth during monitoring. Its return value is ignored.""" def __init__(self, remote, db_name, schema_cb=None): """Creates and returns a connection to the database named 'db_name' on 'remote', which should be in a form acceptable to ovs.jsonrpc.session.open(). The connection will maintain an in-memory replica of the remote database.""" self.remote = remote self.session = ovs.jsonrpc.Session.open(remote) self.db_name = db_name self.last_seqno = None self.schema = None self.state = None self.change_seqno = 0 self.data = {} self.schema_cb = schema_cb def close(self): self.session.close() def run(self): """Processes a batch of messages from the database server. Returns True if the database as seen through the IDL changed, False if it did not change. The initial fetch of the entire contents of the remote database is considered to be one kind of change. This function can return occasional false positives, that is, report that the database changed even though it didn't. This happens if the connection to the database drops and reconnects, which causes the database contents to be reloaded even if they didn't change. (It could also happen if the database server sends out a "change" that reflects what we already thought was in the database, but the database server is not supposed to do that.) As an alternative to checking the return value, the client may check for changes in the value returned by self.get_seqno().""" initial_change_seqno = self.change_seqno self.session.run() if self.session.is_connected(): seqno = self.session.get_seqno() if seqno != self.last_seqno: self.last_seqno = seqno self.state = (self.__send_schema_request, None) if self.state: self.state[0]() return initial_change_seqno != self.change_seqno def wait(self, poller): """Arranges for poller.block() to wake up when self.run() has something to do or when activity occurs on a transaction on 'self'.""" self.session.wait(poller) if self.state and self.state[1]: self.state[1](poller) def get_seqno(self): """Returns a number that represents the IDL's state. When the IDL updated (by self.run()), the return value changes.""" return self.change_seqno def __send_schema_request(self): msg = ovs.jsonrpc.Message.create_request("get_schema", [self.db_name]) self.session.send(msg) self.state = (lambda: self.__recv_schema(msg.id), self.__recv_wait) def __recv_schema(self, id): msg = self.session.recv() if msg and msg.type == ovs.jsonrpc.Message.T_REPLY and msg.id == id: try: self.schema = ovs.db.schema.DbSchema.from_json(msg.result) except error.Error, e: logging.error("%s: parse error in received schema: %s" % (self.remote, e)) self.__error() return if self.schema_cb: try: self.schema_cb(self.schema) except error.Error, e: logging.error("%s: error validating schema: %s" % (self.remote, e)) self.__error() return self.__send_monitor_request() elif msg: logging.error("%s: unexpected message expecting schema: %s" % (self.remote, msg)) self.__error() def __recv_wait(self, poller): self.session.recv_wait(poller) def __send_monitor_request(self): monitor_requests = {} for table in self.schema.tables.itervalues(): monitor_requests[table.name] = {"columns": table.columns.keys()} msg = ovs.jsonrpc.Message.create_request( "monitor", [self.db_name, None, monitor_requests]) self.session.send(msg) self.state = (lambda: self.__recv_monitor_reply(msg.id), self.__recv_wait) def __recv_monitor_reply(self, id): msg = self.session.recv() if msg and msg.type == ovs.jsonrpc.Message.T_REPLY and msg.id == id: try: self.change_seqno += 1 self.state = (self.__recv_update, self.__recv_wait) self.__clear() self.__parse_update(msg.result) except error.Error, e: logging.error("%s: parse error in received schema: %s" % (self.remote, e)) self.__error() elif msg: logging.error("%s: unexpected message expecting schema: %s" % (self.remote, msg)) self.__error() def __recv_update(self): msg = self.session.recv() if (msg and msg.type == ovs.jsonrpc.Message.T_NOTIFY and type(msg.params) == list and len(msg.params) == 2 and msg.params[0] is None): self.__parse_update(msg.params[1]) elif msg: logging.error("%s: unexpected message expecting update: %s" % (self.remote, msg)) self.__error() def __error(self): self.session.force_reconnect() def __parse_update(self, update): try: self.__do_parse_update(update) except error.Error, e: logging.error("%s: error parsing update: %s" % (self.remote, e)) def __do_parse_update(self, table_updates): if type(table_updates) != dict: raise error.Error("<table-updates> is not an object", table_updates) for table_name, table_update in table_updates.iteritems(): table = self.schema.tables.get(table_name) if not table: raise error.Error('<table-updates> includes unknown ' 'table "%s"' % table_name) if type(table_update) != dict: raise error.Error('<table-update> for table "%s" is not ' 'an object' % table_name, table_update) for uuid_string, row_update in table_update.iteritems(): if not ovs.ovsuuid.UUID.is_valid_string(uuid_string): raise error.Error('<table-update> for table "%s" ' 'contains bad UUID "%s" as member ' 'name' % (table_name, uuid_string), table_update) uuid = ovs.ovsuuid.UUID.from_string(uuid_string) if type(row_update) != dict: raise error.Error('<table-update> for table "%s" ' 'contains <row-update> for %s that ' 'is not an object' % (table_name, uuid_string)) parser = ovs.db.parser.Parser(row_update, "row-update") old = parser.get_optional("old", [dict]) new = parser.get_optional("new", [dict]) parser.finish() if not old and not new: raise error.Error('<row-update> missing "old" and ' '"new" members', row_update) if self.__parse_row_update(table, uuid, old, new): self.change_seqno += 1 def __parse_row_update(self, table, uuid, old, new): """Returns True if a column changed, False otherwise.""" row = self.data[table.name].get(uuid) changed = False if not new: # Delete row. if row: del self.data[table.name][uuid] changed = True else: # XXX rate-limit logging.warning("cannot delete missing row %s from table %s" % (uuid, table.name)) elif not old: # Insert row. if not row: row = self.__create_row(table, uuid) changed = True else: # XXX rate-limit logging.warning("cannot add existing row %s to table %s" % (uuid, table.name)) if self.__modify_row(table, row, new): changed = True else: if not row: row = self.__create_row(table, uuid) changed = True # XXX rate-limit logging.warning("cannot modify missing row %s in table %s" % (uuid, table.name)) if self.__modify_row(table, row, new): changed = True return changed def __modify_row(self, table, row, row_json): changed = False for column_name, datum_json in row_json.iteritems(): column = table.columns.get(column_name) if not column: # XXX rate-limit logging.warning("unknown column %s updating table %s" % (column_name, table.name)) continue try: datum = ovs.db.data.Datum.from_json(column.type, datum_json) except error.Error, e: # XXX rate-limit logging.warning("error parsing column %s in table %s: %s" % (column_name, table.name, e)) continue if datum != getattr(row, column_name): setattr(row, column_name, datum) changed = True else: # Didn't really change but the OVSDB monitor protocol always # includes every value in a row. pass return changed def __clear(self): if self.data != {}: for table_name in self.schema.tables: if self.data[table_name] != {}: self.change_seqno += 1 break self.data = {} for table_name in self.schema.tables: self.data[table_name] = {} def __create_row(self, table, uuid): row = self.data[table.name][uuid] = Row() for column in table.columns.itervalues(): setattr(row, column.name, ovs.db.data.Datum.default(column.type)) return row def force_reconnect(self): """Forces the IDL to drop its connection to the database and reconnect. In the meantime, the contents of the IDL will not change.""" self.session.force_reconnect() class Row(object): """A row within an Idl. Data for each column is stored as an attribute with the same name as the column and using an ovs.db.data.Datum as the value.""" pass

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Validation/HGCalValidation/test/python/runHGCHitAnalyzer_cfg.py

SWuchterl/cmssw

1

1610351

<reponame>SWuchterl/cmssw import FWCore.ParameterSet.Config as cms #from Configuration.Eras.Era_Phase2C4_cff import Phase2C4 #process = cms.Process('HGCGeomAnalysis',Phase2C4) #process.load('Configuration.Geometry.GeometryExtended2026D35_cff') #process.load('Configuration.Geometry.GeometryExtended2026D35Reco_cff') #from Configuration.Eras.Era_Phase2C8_cff import Phase2C8 #process = cms.Process('HGCGeomAnalysis',Phase2C8) #process.load('Configuration.Geometry.GeometryExtended2026D41_cff') #process.load('Configuration.Geometry.GeometryExtended2026D41Reco_cff') #from Configuration.Eras.Era_Phase2C9_cff import Phase2C9 #process = cms.Process('HGCGeomAnalysis',Phase2C9) #process.load('Configuration.Geometry.GeometryExtended2026D49_cff') #process.load('Configuration.Geometry.GeometryExtended2026D49Reco_cff') #from Configuration.Eras.Era_Phase2C12_cff import Phase2C12 #process = cms.Process('HGCGeomAnalysis',Phase2C12) #process.load('Configuration.Geometry.GeometryExtended2026D58_cff') #process.load('Configuration.Geometry.GeometryExtended2026D58Reco_cff') #from Configuration.Eras.Era_Phase2C11_cff import Phase2C11 #process = cms.Process('HGCGeomAnalysis',Phase2C11) #process.load('Configuration.Geometry.GeometryExtended2026D59_cff') #process.load('Configuration.Geometry.GeometryExtended2026D59Reco_cff') from Configuration.Eras.Era_Phase2C11_cff import Phase2C11 process = cms.Process('HGCGeomAnalysis',Phase2C11) process.load('Configuration.Geometry.GeometryExtended2026D62_cff') process.load('Configuration.Geometry.GeometryExtended2026D62Reco_cff') process.load('Configuration.StandardSequences.Services_cff') process.load('SimGeneral.HepPDTESSource.pythiapdt_cfi') process.load('FWCore.MessageService.MessageLogger_cfi') process.load('Configuration.EventContent.EventContent_cff') process.load('Configuration.StandardSequences.MagneticField_cff') process.load('Configuration.StandardSequences.FrontierConditions_GlobalTag_cff') from Configuration.AlCa.GlobalTag import GlobalTag ##Global Tag used for production in process.GlobalTag = GlobalTag(process.GlobalTag, 'auto:phase2_realistic', '') #process.MessageLogger.cerr.FwkReport.reportEvery = 100 #if 'MessageLogger' in process.__dict__: # process.MessageLogger.categories.append('HGCalValid') process.source = cms.Source("PoolSource", fileNames = cms.untracked.vstring( 'file:step3.root', #'root://cms-xrd-global.cern.ch//store/relval/CMSSW_9_0_0_pre1/RelValZEE_14/GEN-SIM-RECO/90X_upgrade2023_realistic_v0_2023D4-v1/10000/085AD7B1-8ABA-E611-A7DA-0CC47A4C8EB6.root', #'/store/relval/CMSSW_8_1_0_pre8/RelValTTbar_14TeV/GEN-SIM-RECO/81X_mcRun2_asymptotic_v1_2023LReco-v1/10000/08E719D4-DE3D-E611-9092-003048FFD722.root', ) ) process.load('Validation.HGCalValidation.hgcHitValidation_cfi') process.TFileService = cms.Service("TFileService", fileName = cms.string('relValTTbarD62.root'), closeFileFast = cms.untracked.bool(True) ) SimpleMemoryCheck = cms.Service("SimpleMemoryCheck",ignoreTotal = cms.untracked.int32(1) ) #process.hgcHitAnalysis.ietaExcludeBH = [16,92,93,94,95,96,97,98,99,100] #process.hgcHitAnalysis.ietaExcludeBH = [16, 32, 33] process.p = cms.Path(process.hgcHitAnalysis)

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src/applications/Application.py

BMW-Group-Quantum/QUARK

12

1613296

# Copyright 2021 The QUARK 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 abc import ABC, abstractmethod class Application(ABC): """ The application component defines the workload, comprising a dataset of increasing complexity, a validation, and an evaluation function. """ def __init__(self, application_name): """ Constructor method """ self.application_name = application_name self.application = None self.mapping_options = [] super().__init__() def get_application(self) -> any: """ Getter that returns the application :return: self.application :rtype: any """ return self.application @abstractmethod def get_solution_quality_unit(self) -> str: """ Method to return the unit of the evaluation which is used to make the plots nicer. :return: String with the unit :rtype: str """ @abstractmethod def get_parameter_options(self) -> dict: """ Method to return the parameters needed to create a concrete problem of an application. Should always be in this format: .. code-block:: json { "parameter_name":{ "values":[1, 2, 3], "description":"How many nodes do you need?" }, "parameter_name_2":{ "values":["x", "y"], "description":"Which type of problem do you want?" } } :return: Available application settings for this application :rtype: dict """ pass @abstractmethod def generate_problem(self, config) -> any: """ Depending on the config this method creates a concrete problem and returns it. :param config: :type config: dict :return: :rtype: any """ pass def process_solution(self, solution) -> (any, float): """ Most of the time the solution has to be processed before it can be validated and evaluated This might not be necessary in all cases, so the default is to return the original solution. :param solution: :type solution: any :return: Processed solution and the execution time to process it :rtype: tuple(any, float) """ return solution, 0 @abstractmethod def validate(self, solution) -> (bool, float): """ Check if the solution is a valid solution. :return: bool and the time it took to create it :param solution: :type solution: any :rtype: tuple(bool, float) """ pass @abstractmethod def evaluate(self, solution: any) -> (float, float): """ Checks how good the solution is to allow comparison to other solutions. :param solution: :type solution: any :return: Evaluation and the time it took to create it :rtype: tuple(any, float) """ pass @abstractmethod def save(self, path) -> None: """ Function to save the concrete problem. :param path: path of the experiment directory for this run :type path: str :return: :rtype: None """ pass @abstractmethod def get_mapping(self, mapping_option: str) -> any: """ Return a mapping for an application. :param mapping_option: String with the option :rtype: str :return: instance of a mapping class :rtype: any """ pass def get_available_mapping_options(self) -> list: """ Get list of available mapping options. :return: list of mapping options :rtype: list """ return self.mapping_options

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Reading Data/lesson-18-get-fifa-players-from-the-web/tests/test_fifa_1.py

danielgarm/Data-Science-and-Machine-Learning

0

197720

<reponame>danielgarm/Data-Science-and-Machine-Learning def test_fifa_1(): assert fifa_df.shape == (30, 5)

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gui/dataset/budgetary.py

vishalbelsare/prest

6

197810

import os import dataset import util.tree_model import uic.view_dataset import numpy as np import dataset.budgetary_consistency from core import Core from dataset import Dataset, Analysis, ExportVariant, DatasetHeaderC from dataset.budgetary_consistency import BudgetaryConsistency from typing import Sequence, NamedTuple, List, Dict, Tuple, Iterator, Union, Optional, cast from gui.progress import Worker, Cancelled from util.codec import FileOut, FileIn, namedtupleC, strC, numpyC, listC from util.codec_progress import CodecProgress, listCP, oneCP from PyQt5.QtWidgets import QDialog, QTreeWidgetItem, QHeaderView class Subject(NamedTuple): name : str prices : np.ndarray amounts : np.ndarray SubjectC = namedtupleC(Subject, strC, numpyC(np.float32), numpyC(np.float32)) class RowNode(util.tree_model.Node): def __init__(self, parent_node, row: int, prices: np.ndarray, amounts: np.ndarray) -> None: util.tree_model.Node.__init__( self, parent_node, row, fields=[''] \ + ['%.2f' % p for p in prices] \ + ['%g' % x for x in amounts] \ + ['%.2f' % np.dot(prices, amounts)] ) class SubjectNode(util.tree_model.Node): def __init__(self, parent_node, row: int, subject: Subject) -> None: self.subject = subject util.tree_model.Node.__init__( self, parent_node, row, fields=(subject.name,), child_count=subject.prices.shape[0], ) def create_child(self, row: int) -> RowNode: return RowNode(self, row, self.subject.prices[row], self.subject.amounts[row]) class RootNode(util.tree_model.RootNode): def __init__(self, subjects : List[Subject]) -> None: util.tree_model.RootNode.__init__(self, len(subjects)) self.subjects = subjects def create_child(self, row: int) -> SubjectNode: return SubjectNode(self, row, self.subjects[row]) class Budgetary(Dataset): class ViewDialog(QDialog, uic.view_dataset.Ui_ViewDataset): def __init__(self, ds : 'Budgetary') -> None: QDialog.__init__(self) self.setupUi(self) self.ds = ds self.model = util.tree_model.TreeModel( RootNode(ds.subjects), headers= \ ['Subject'] \ + [f'Price {a}' for a in ds.alternatives] \ + [f'Quantity {a}' for a in ds.alternatives] \ + ['Expenditure'] ) self.twRows.setModel(self.model) self.twRows.header().setSectionResizeMode(QHeaderView.ResizeToContents) self.twRows.header().setStretchLastSection(False) def __init__(self, name : str, alternatives : Sequence[str]) -> None: Dataset.__init__(self, name, alternatives) self.subjects : List[Subject] = [] self.nr_observations = 0 def label_size(self): return f'{len(self.subjects)} subjects, {self.nr_observations} observations' def analysis_consistency(self, worker : Worker, _config : None) -> BudgetaryConsistency: with Core() as core: worker.interrupt = lambda: core.shutdown() # interrupt hook rows = [] worker.set_work_size(len(self.subjects)) for i, subject in enumerate(self.subjects): response = core.call( 'budgetary-consistency', SubjectC, dataset.budgetary_consistency.SubjectC, subject ) rows.append(response) worker.set_progress(i+1) ds = BudgetaryConsistency( self.name + ' (consistency)', self.alternatives, rows, ) return ds def get_analyses(self) -> Sequence[Analysis]: return ( Analysis( name='Consistency analysis', config=None, run=self.analysis_consistency, ), ) @staticmethod def get_codec_progress() -> CodecProgress: DatasetHeaderC_encode, DatasetHeaderC_decode = DatasetHeaderC subjects_get_size, subjects_encode, subjects_decode = listCP(oneCP(SubjectC)) def get_size(x : 'Budgetary') -> int: return cast(int, subjects_get_size(x.subjects)) def encode(worker : Worker, f : FileOut, x : 'Budgetary') -> None: DatasetHeaderC_encode(f, (x.name, x.alternatives)) subjects_encode(worker, f, x.subjects) def decode(worker : Worker, f : FileIn) -> 'Budgetary': ds = Budgetary(*DatasetHeaderC_decode(f)) ds.subjects = subjects_decode(worker, f) ds.update_nr_observations() return ds return CodecProgress(get_size, encode, decode) def update_nr_observations(self) -> None: self.nr_observations = sum(len(s.prices) for s in self.subjects) def get_export_variants(self) -> Sequence[ExportVariant]: if not self.subjects: return [] _n_observ, n_alts = self.subjects[0].prices.shape return [ ExportVariant( name='Detailed', column_names=\ ['subject'] \ + [f'price{i+1}' for i in range(n_alts)] \ + [f'quantity{i+1}' for i in range(n_alts)], get_rows=self.export_detailed, size=len(self.subjects), ), ] def export_detailed(self) -> Iterator[Optional[Tuple[Union[str, float], ...]]]: for subject in self.subjects: # subject.prices and subject.amounts are matrices for prices, amounts in zip(subject.prices, subject.amounts): row : List[Union[str, float]] = [subject.name] row += list(prices) row += list(amounts) yield tuple(row) yield None # bump progress class BudgetaryError(Exception): pass def load_from_csv(fname : str) -> Budgetary: lines = dataset.load_raw_csv(fname) if not lines: raise BudgetaryError("the CSV file is empty") header, *rows = lines if (len(header)-1) % 2 != 0: raise BudgetaryError("budgetary datasets should have an even number of numeric columns") n_alts = (len(header)-1) // 2 alternatives = [f'{i+1}' for i in range(n_alts)] subjects : Dict[str,Tuple[List[np.ndarray],List[np.ndarray]]] = dict() # ordered for line_no, row in enumerate(rows, start=2): if len(row) != len(header): raise BudgetaryError(f'{fname}, line {line_no}: incorrect number of columns') subj_name, *cols = row if subj_name not in subjects: subjects[subj_name] = ([], []) prices, amounts = subjects[subj_name] prices.append(np.array([float(x) for x in cols[:n_alts]], dtype=np.float32)) amounts.append(np.array([float(x) for x in cols[n_alts:]], dtype=np.float32)) ds = Budgetary(os.path.basename(fname), alternatives) ds.subjects = [ Subject(name=n, prices=np.vstack(ps), amounts=np.vstack(ams)) for (n,(ps,ams)) in subjects.items() ] ds.update_nr_observations() return ds

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test/integration/test_command.py

jmgao/bfg9000

0

169339

<filename>test/integration/test_command.py import os.path import re from six import assertRegex from . import * class TestCommand(IntegrationTest): def __init__(self, *args, **kwargs): IntegrationTest.__init__( self, os.path.join(examples_dir, '07_commands'), *args, **kwargs ) def test_hello(self): assertRegex(self, self.build('hello'), re.compile(r'^\s*hello$', re.MULTILINE)) def test_world(self): assertRegex(self, self.build('world'), re.compile(r'^\s*world$', re.MULTILINE)) def test_script(self): assertRegex(self, self.build('script'), re.compile(r'^\s*hello, world!$', re.MULTILINE)) self.assertExists(output_file('file')) def test_alias(self): output = self.build('hello-world') assertRegex(self, output, re.compile(r'^\s*hello$', re.MULTILINE)) assertRegex(self, output, re.compile(r'^\s*world$', re.MULTILINE)) @skip_if_backend('msbuild') class TestRunExecutable(IntegrationTest): def __init__(self, *args, **kwargs): IntegrationTest.__init__(self, 'run_executable', *args, **kwargs) def test_env_run(self): self.assertExists(output_file('file.txt')) def test_cxx(self): assertRegex(self, self.build('cxx'), re.compile(r'^\s*hello from c\+\+!$', re.MULTILINE)) def test_java(self): assertRegex(self, self.build('java'), re.compile(r'^\s*hello from java!$', re.MULTILINE)) def test_java_classlist(self): assertRegex(self, self.build('java-classlist'), re.compile(r'^\s*hello from java!$', re.MULTILINE)) def test_python(self): assertRegex(self, self.build('python'), re.compile(r'^\s*hello from python!$', re.MULTILINE))

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mask_rcnn_2go/code/model_utils.py

dreiss/maskrcnn-nnapi

1

164389

from __future__ import absolute_import, division, print_function, unicode_literals import os import subprocess from caffe2.proto import caffe2_pb2 from caffe2.python import core, workspace def create_blobs_if_not_existed(blob_names): existd_names = set(workspace.Blobs()) for xx in blob_names: if xx not in existd_names: workspace.CreateBlob(str(xx)) def load_model_pb(net_file, init_file): subprocess.check_call(["adb", "push", net_file, "/data/local/tmp/predict_net.pb"]) subprocess.check_call(["adb", "push", init_file, "/data/local/tmp/init_net.pb"]) net = caffe2_pb2.NetDef() if net_file is not None: net.ParseFromString(open(net_file, "rb").read()) return (net, None)

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Digit_Recognizer/digit_recognizer.py

williamyao66/Projects

0

175285

<reponame>williamyao66/Projects import pandas as pd import os from tensorflow.keras.models import load_model tr = pd.read_csv('./Digit-Recognizer/train.csv') te = pd.read_csv('./Digit-Recognizer/test.csv') x_te = te.values.copy().astype('float64') final_model = load_model(os.getcwd() + '/model/') predictions = final_model.predict(x_te) test_predictions = np.argmax(predictions, axis=1)

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freeCodeCamp/01-scientific-computing-with-python/src/12-objects_inheritance.py

aysedemirel/python-journey

1

130624

<gh_stars>1-10 class PartyAnimal: x = 0 name = "" def __init__(self, nameCons): self.name = nameCons print("name: ", self.name) def party(self): self.x = self.x + 1 print(self.name," - party count: ", self.x) class FootballFan(PartyAnimal): points = 0 def touchdown(self): self.points = self.points + 7 self.party() print(self.name, "points", self.points) s = FootballFan("Sally") s.party() j = FootballFan("Jim") j.party() j.touchdown()

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python/reachy_sdk_api/kinematics_pb2.py

marjoriePaillet/reachy-sdk-api

1

184949

# -*- coding: utf-8 -*- # Generated by the protocol buffer compiler. DO NOT EDIT! # source: kinematics.proto """Generated protocol buffer code.""" from google.protobuf import descriptor as _descriptor from google.protobuf import message as _message from google.protobuf import reflection as _reflection from google.protobuf import symbol_database as _symbol_database # @@protoc_insertion_point(imports) _sym_db = _symbol_database.Default() import joint_pb2 as joint__pb2 DESCRIPTOR = _descriptor.FileDescriptor( name='kinematics.proto', package='reachy.sdk.kinematics', syntax='proto3', serialized_options=None, create_key=_descriptor._internal_create_key, serialized_pb=b'\n\x10kinematics.proto\x12\x15reachy.sdk.kinematics\x1a\x0bjoint.proto\"J\n\rJointPosition\x12&\n\x03ids\x18\x01 \x03(\x0b\x32\x19.reachy.sdk.joint.JointId\x12\x11\n\tpositions\x18\x02 \x03(\x01\"\x19\n\tMatrix4x4\x12\x0c\n\x04\x64\x61ta\x18\x01 \x03(\x01\"8\n\nQuaternion\x12\t\n\x01w\x18\x01 \x01(\x01\x12\t\n\x01x\x18\x02 \x01(\x01\x12\t\n\x01y\x18\x03 \x01(\x01\x12\t\n\x01z\x18\x04 \x01(\x01\x62\x06proto3' , dependencies=[joint__pb2.DESCRIPTOR,]) _JOINTPOSITION = _descriptor.Descriptor( name='JointPosition', full_name='reachy.sdk.kinematics.JointPosition', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='ids', full_name='reachy.sdk.kinematics.JointPosition.ids', index=0, number=1, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='positions', full_name='reachy.sdk.kinematics.JointPosition.positions', index=1, number=2, type=1, cpp_type=5, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=56, serialized_end=130, ) _MATRIX4X4 = _descriptor.Descriptor( name='Matrix4x4', full_name='reachy.sdk.kinematics.Matrix4x4', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='data', full_name='reachy.sdk.kinematics.Matrix4x4.data', index=0, number=1, type=1, cpp_type=5, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=132, serialized_end=157, ) _QUATERNION = _descriptor.Descriptor( name='Quaternion', full_name='reachy.sdk.kinematics.Quaternion', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='w', full_name='reachy.sdk.kinematics.Quaternion.w', index=0, number=1, type=1, cpp_type=5, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='x', full_name='reachy.sdk.kinematics.Quaternion.x', index=1, number=2, type=1, cpp_type=5, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='y', full_name='reachy.sdk.kinematics.Quaternion.y', index=2, number=3, type=1, cpp_type=5, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='z', full_name='reachy.sdk.kinematics.Quaternion.z', index=3, number=4, type=1, cpp_type=5, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=159, serialized_end=215, ) _JOINTPOSITION.fields_by_name['ids'].message_type = joint__pb2._JOINTID DESCRIPTOR.message_types_by_name['JointPosition'] = _JOINTPOSITION DESCRIPTOR.message_types_by_name['Matrix4x4'] = _MATRIX4X4 DESCRIPTOR.message_types_by_name['Quaternion'] = _QUATERNION _sym_db.RegisterFileDescriptor(DESCRIPTOR) JointPosition = _reflection.GeneratedProtocolMessageType('JointPosition', (_message.Message,), { 'DESCRIPTOR' : _JOINTPOSITION, '__module__' : 'kinematics_pb2' # @@protoc_insertion_point(class_scope:reachy.sdk.kinematics.JointPosition) }) _sym_db.RegisterMessage(JointPosition) Matrix4x4 = _reflection.GeneratedProtocolMessageType('Matrix4x4', (_message.Message,), { 'DESCRIPTOR' : _MATRIX4X4, '__module__' : 'kinematics_pb2' # @@protoc_insertion_point(class_scope:reachy.sdk.kinematics.Matrix4x4) }) _sym_db.RegisterMessage(Matrix4x4) Quaternion = _reflection.GeneratedProtocolMessageType('Quaternion', (_message.Message,), { 'DESCRIPTOR' : _QUATERNION, '__module__' : 'kinematics_pb2' # @@protoc_insertion_point(class_scope:reachy.sdk.kinematics.Quaternion) }) _sym_db.RegisterMessage(Quaternion) # @@protoc_insertion_point(module_scope)

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Statistics/variance_of_sample_proportion.py

mykolatyniv/database_NJIT601

0

198459

<reponame>mykolatyniv/database_NJIT601 def variance_of_sample_proportion(a,b,c,d,e,f,g,h,j,k): try: a = int(a) b = int(b) c = int(c) d = int(d) e = int(e) f = int(f) g = int(g) h = int(h) j = int(j) k = int(k) sample = [a,b,c,d,e,f,g,h,j,k] # Count how many people are over the age 80 i = 0 occurrence = 0 while i < len(sample): if (sample[i])> 80: occurrence = occurrence + 1 else: i = i + 1 i = i + 1 # n is population size n = len(sample) # p is probability p = float(occurrence/n) var_samp_propor = float((p * (1-p))/n) print("variance_of_sample_proportion:",var_samp_propor ) return var_samp_propor except ZeroDivisionError: print("Error: Dividing by Zero is not valid!!") except ValueError: print ("Error: Only Numeric Values are valid!!")

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workflow/migrations/0017_organization_logo.py

meetdatastory/Activity-CE

0

177025

# -*- coding: utf-8 -*- # Generated by Django 1.11.2 on 2018-11-14 00:31 from __future__ import unicode_literals from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('workflow', '0016_auto_20170623_1306'), ] operations = [ migrations.AddField( model_name='organization', name='logo', field=models.FileField(blank=True, null=True, upload_to='static/img/', verbose_name='Your Organization Logo'), ), ]

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get_neowise_data.py

Aparctias/lightcurve_fft

0

1604646

#!/usr/bin/python # Get NEOWISE data import os import time import json import requests MPC_SPIN_PROC = "mpc_spins_proc" NEOWISE_DIR = "neowise_stat" NEOWISE_SKIP = "neowise_skip_objs" data = [] with open(MPC_SPIN_PROC, 'r') as f: data = json.load(f) url = 'http://irsa.ipac.caltech.edu/cgi-bin/Gator/nph-query?searchForm=MO' \ '&spatial=cone&catalog=allsky_4band_p1bs_psd&mobj=smo&outfmt=1' \ '&mobjstr=%s' def save_to_file(obj_name, stat): with open(NEOWISE_DIR + '/' + obj_name, 'w') as f: f.write(stat) def execute_request(request_url): retries = 1 i = 0 text = '' try: while (i < retries): r = requests.get(request_url) # Very bad if r.text.startswith("[struct stat=\"ERROR\""): print "Retry: ", request_url time.sleep(1) else: text = r.text break i += 1 except: text = '' return text def stat_file_exists(obj_name): return os.path.exists(NEOWISE_DIR + '/' + obj_name) def get_objs_to_skip(): if os.path.exists(NEOWISE_SKIP): with open(NEOWISE_SKIP, 'r') as f: return f.read().split() else: return [] def add_obj_to_skip(obj_name): with open(NEOWISE_SKIP, 'a') as f: f.write(obj_name + ' ') if not os.path.exists(NEOWISE_DIR): os.mkdir(NEOWISE_DIR) print "Total: ", len(data) skip_objs = get_objs_to_skip() print "Not uploaded objs: ", skip_objs for i in range(0, len(data)): mobjstr = '' for n in ('name', 'number', 'designation'): if data[i].get(n): mobjstr = data[i].get(n) if not mobjstr or stat_file_exists(str(mobjstr)) or\ str(mobjstr) in skip_objs: continue mobjstr = str(mobjstr) print "Processing: %s - %s" % (i, mobjstr) stat = execute_request(url%mobjstr) if stat: save_to_file(mobjstr, stat) else: add_obj_to_skip(mobjstr)

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common.py

sleclair0/spacy-youtube-material

93

66175

<filename>common.py<gh_stars>10-100 from spacy.matcher import Matcher def create_versioned(name): return [ [{'LOWER': name}], [{'LOWER': {'REGEX': f'({name}\d+\.?\d*.?\d*)'}}], [{'LOWER': name}, {'TEXT': {'REGEX': '(\d+\.?\d*.?\d*)'}}], ] def create_patterns(): versioned_languages = ['ruby', 'php', 'python', 'perl', 'java', 'haskell', 'scala', 'c', 'cpp', 'matlab', 'bash', 'delphi'] flatten = lambda l: [item for sublist in l for item in sublist] versioned_patterns = flatten([create_versioned(lang) for lang in versioned_languages]) lang_patterns = [ [{'LOWER': 'objective'}, {'IS_PUNCT': True, 'OP': '?'},{'LOWER': 'c'}], [{'LOWER': 'objectivec'}], [{'LOWER': 'c'}, {'LOWER': '#'}], [{'LOWER': 'c'}, {'LOWER': 'sharp'}], [{'LOWER': 'c#'}], [{'LOWER': 'f'}, {'LOWER': '#'}], [{'LOWER': 'f'}, {'LOWER': 'sharp'}], [{'LOWER': 'f#'}], [{'LOWER': 'lisp'}], [{'LOWER': 'common'}, {'LOWER': 'lisp'}], [{'LOWER': 'go', 'POS': {'NOT_IN': ['VERB']}}], [{'LOWER': 'golang'}], [{'LOWER': 'html'}], [{'LOWER': 'css'}], [{'LOWER': 'sql'}], [{'LOWER': {'IN': ['js', 'javascript']}}], [{'LOWER': 'c++'}], ] return versioned_patterns + lang_patterns

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src/dials/algorithms/profile_model/ellipsoid/__init__.py

dials-src/dials

1

146677

<reponame>dials-src/dials from __future__ import annotations from math import pi, sqrt from dials.array_family import flex # noqa: F401; from dials_algorithms_profile_model_ellipsoid_ext import * # noqa: F401, F403; def mosaicity_from_eigen_decomposition(eigen_values): return ( sqrt(eigen_values[0]) * 180.0 / pi, sqrt(eigen_values[1]) * 180.0 / pi, sqrt(eigen_values[2]) * 180.0 / pi, )

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osisoft/pidevclub/piwebapi/models/pi_data_server_license.py

jugillar/PI-Web-API-Client-Python

30

12808

# coding: utf-8 """ Copyright 2018 OSIsoft, LLC Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at <http://www.apache.org/licenses/LICENSE-2.0> Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. """ from pprint import pformat from six import iteritems class PIDataServerLicense(object): swagger_types = { 'amount_left': 'str', 'amount_used': 'str', 'name': 'str', 'total_amount': 'str', 'links': 'PIDataServerLicenseLinks', 'web_exception': 'PIWebException', } attribute_map = { 'amount_left': 'AmountLeft', 'amount_used': 'AmountUsed', 'name': 'Name', 'total_amount': 'TotalAmount', 'links': 'Links', 'web_exception': 'WebException', } def __init__(self, amount_left=None, amount_used=None, name=None, total_amount=None, links=None, web_exception=None): self._amount_left = None self._amount_used = None self._name = None self._total_amount = None self._links = None self._web_exception = None if amount_left is not None: self.amount_left = amount_left if amount_used is not None: self.amount_used = amount_used if name is not None: self.name = name if total_amount is not None: self.total_amount = total_amount if links is not None: self.links = links if web_exception is not None: self.web_exception = web_exception @property def amount_left(self): return self._amount_left @amount_left.setter def amount_left(self, amount_left): self._amount_left = amount_left @property def amount_used(self): return self._amount_used @amount_used.setter def amount_used(self, amount_used): self._amount_used = amount_used @property def name(self): return self._name @name.setter def name(self, name): self._name = name @property def total_amount(self): return self._total_amount @total_amount.setter def total_amount(self, total_amount): self._total_amount = total_amount @property def links(self): return self._links @links.setter def links(self, links): self._links = links @property def web_exception(self): return self._web_exception @web_exception.setter def web_exception(self, web_exception): self._web_exception = web_exception def to_dict(self): result = {} for attr, _ in 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): return pformat(self.to_dict()) def __repr__(self): return self.to_str() def __ne__(self, other): return not self == other def __eq__(self, other): if not isinstance(other, PIDataServerLicense): return False return self.__dict__ == other.__dict__

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diplomacy/src/scripts/yamlizer.py

MaxStrange/nlp

1

37174

<filename>diplomacy/src/scripts/yamlizer.py """ This script takes a single message gathered from playdiplomacy.com as an input file and outputs a YAML version of it so that it can be used as an input file into the program. Usage: python3 yamlizer.py msg.txt (You can also feed it a list of files). NOTE: This script is not designed to handle multiple recipient messages. So if you want to run this script on a message that has a CC line like: CC: ENGLAND, FRANCE, ITALY You must alter this to be: CC: ENGLAND or CC: FRANCE or CC: ITALY But there is nothing stopping you from running this script on the file multiple times - one for each recipient. Example: [Contents of msg.txt]: ===================================================== From: ITALY Date: May 07 2017 15:04 (GMT-8) Spring 1903 CC: FRANCE Re:Movement Awesome. About Bohemia... You should have Burgundy support Bohemia to Munich. Meanwhile, attack Ruhr with Belgium. Unless you want me to contact Russia about what I should do with Bohemia. That would be fine too. Up to you. ====================================================== When fed through this script, this will output as msg.yml: ====================================================== year: 1903 season: Spring a_to_b: from_player: Italy from_country: Italy to_player: France to_country: France messages: - > "Re:Movement Awesome. About Bohemia... You should have Burgundy support Bohemia to Munich. Meanwhile, attack Ruhr with Belgium. Unless you want me to contact Russia about what I should do with Bohemia. That would be fine too. Up to you." ====================================================== You are free to do what you want with the from_player and to_player fields. Those are not really used - they were included because I didn't know if they would be necessary or not - turns out not. You can also add more messages to the end of that and also add b_to_a messages as well. See examples. """ import os import sys class Message: def __init__(self, txt): from_line = next((line for line in txt if line.lower().startswith("from:"))) date_line = next((line for line in txt if line.lower().startswith("date:"))) to_line = next((line for line in txt if line.lower().startswith("cc:"))) to_line_index = [i for i, line in enumerate(txt) if line.lower().startswith("cc:")][0] msg_body = os.linesep.join([line for line in txt[(to_line_index + 1):]]) self.from_ = from_line.split("From:")[1].strip() self.date = date_line.split("Date:")[1].strip() self.to = to_line.split("CC:")[1].strip() self.message = msg_body.strip() def __str__(self): s = "FROM: " + str(self.from_) + os.linesep s += "DATE: " + str(self.date) + os.linesep s += "TO: " + str(self.to) + os.linesep s += "Message: " + self.message return s def yamlize(txt): """ Converts the message text from playdiplomacy raw text to the YAML format that this program requires. """ message = Message(txt) year = message.date.split(" ")[-1] season = message.date.split(" ")[-2] from_player = from_country = message.from_.lower().title() to_player = to_country = message.to.lower().title() msg = message.message.replace("\"", "\\\"") msg = "\"" + msg + "\"" msg = msg.replace(os.linesep, os.linesep + " ") yaml = "year: " + year + os.linesep yaml += "season: " + season + os.linesep yaml += "a_to_b:" + os.linesep yaml += " from_player: " + from_player + os.linesep yaml += " from_country: " + from_country + os.linesep yaml += " to_player: " + to_player + os.linesep yaml += " to_country: " + to_country + os.linesep yaml += " messages:" + os.linesep yaml += " - >" + os.linesep yaml += msg yaml += os.linesep return yaml if __name__ == "__main__": if len(sys.argv) < 2: print("Need at least one file path.") exit() for file_path in sys.argv[1:]: try: with open(file_path) as f: text = [line for line in f] yaml = yamlize(text) with open(os.path.splitext(file_path)[0] + ".yml", 'w') as f: f.write(yaml) except FileNotFoundError: print("File", file_path, "does not exist.")

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lib/googlecloudsdk/command_lib/privateca/storage.py

google-cloud-sdk-unofficial/google-cloud-sdk

2

198493

# Lint as: python3 # -*- coding: utf-8 -*- # # Copyright 2020 Google LLC. 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. """Helpers for dealing with storage buckets.""" from __future__ import absolute_import from __future__ import division from __future__ import unicode_literals from googlecloudsdk.api_lib.storage import storage_api from googlecloudsdk.api_lib.storage import storage_util from googlecloudsdk.calliope import exceptions from googlecloudsdk.core import log def _BucketAllowsPublicObjectReads(bucket): return any([acl.entity.lower() == 'allusers' and acl.role.lower() == 'reader' for acl in bucket.defaultObjectAcl]) def ValidateBucketForCertificateAuthority(bucket_name): """Validates that a user-specified bucket can be used with a Private CA. Args: bucket_name: The name of the GCS bucket to validate. Returns: A BucketReference wrapping the given bucket name. Raises: InvalidArgumentException: when the given bucket can't be used with a CA. """ messages = storage_util.GetMessages() client = storage_api.StorageClient(messages=messages) try: bucket = client.GetBucket( bucket_name, messages.StorageBucketsGetRequest.ProjectionValueValuesEnum.full) if not _BucketAllowsPublicObjectReads(bucket): # Show a warning but don't fail, since this could be intentional. log.warning( 'The specified bucket does not publicly expose new objects by ' 'default, so some clients may not be able to access the CA ' 'certificate or CRLs. For more details, see ' 'https://cloud.google.com/storage/docs/access-control/making-data-public' ) return storage_util.BucketReference(bucket_name) except storage_api.BucketNotFoundError: raise exceptions.InvalidArgumentException( 'gcs-bucket', 'The given bucket does not exist.')

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src/ui/ui_send_payout_dlg.py

muteio/ghostnode-tool

1

35312

<gh_stars>1-10 # -*- coding: utf-8 -*- # Form implementation generated from reading ui file 'ui_send_payout_dlg.ui' # # Created by: PyQt5 UI code generator 5.9.2 # # WARNING! All changes made in this file will be lost! from PyQt5 import QtCore, QtGui, QtWidgets class Ui_SendPayoutDlg(object): def setupUi(self, SendPayoutDlg): SendPayoutDlg.setObjectName("SendPayoutDlg") SendPayoutDlg.resize(832, 507) sizePolicy = QtWidgets.QSizePolicy(QtWidgets.QSizePolicy.Preferred, QtWidgets.QSizePolicy.Preferred) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth(SendPayoutDlg.sizePolicy().hasHeightForWidth()) SendPayoutDlg.setSizePolicy(sizePolicy) SendPayoutDlg.setSizeGripEnabled(True) SendPayoutDlg.setModal(True) self.verticalLayout = QtWidgets.QVBoxLayout(SendPayoutDlg) self.verticalLayout.setObjectName("verticalLayout") self.pnl_input = QtWidgets.QWidget(SendPayoutDlg) self.pnl_input.setObjectName("pnl_input") self.verticalLayout_4 = QtWidgets.QVBoxLayout(self.pnl_input) self.verticalLayout_4.setContentsMargins(0, 0, 0, 0) self.verticalLayout_4.setSpacing(0) self.verticalLayout_4.setObjectName("verticalLayout_4") self.lay_input = QtWidgets.QHBoxLayout() self.lay_input.setSpacing(8) self.lay_input.setObjectName("lay_input") self.label_3 = QtWidgets.QLabel(self.pnl_input) self.label_3.setObjectName("label_3") self.lay_input.addWidget(self.label_3) self.cbo_address_source_mode = QtWidgets.QComboBox(self.pnl_input) sizePolicy = QtWidgets.QSizePolicy(QtWidgets.QSizePolicy.Fixed, QtWidgets.QSizePolicy.Fixed) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth(self.cbo_address_source_mode.sizePolicy().hasHeightForWidth()) self.cbo_address_source_mode.setSizePolicy(sizePolicy) self.cbo_address_source_mode.setMinimumSize(QtCore.QSize(0, 0)) self.cbo_address_source_mode.setMaximumSize(QtCore.QSize(160, 16777215)) self.cbo_address_source_mode.setObjectName("cbo_address_source_mode") self.cbo_address_source_mode.addItem("") self.cbo_address_source_mode.addItem("") self.cbo_address_source_mode.addItem("") self.lay_input.addWidget(self.cbo_address_source_mode) self.sw_address_source = QtWidgets.QStackedWidget(self.pnl_input) sizePolicy = QtWidgets.QSizePolicy(QtWidgets.QSizePolicy.Maximum, QtWidgets.QSizePolicy.Maximum) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth(self.sw_address_source.sizePolicy().hasHeightForWidth()) self.sw_address_source.setSizePolicy(sizePolicy) self.sw_address_source.setObjectName("sw_address_source") self.wdg_address_source_1 = QtWidgets.QWidget() sizePolicy = QtWidgets.QSizePolicy(QtWidgets.QSizePolicy.Maximum, QtWidgets.QSizePolicy.Preferred) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth(self.wdg_address_source_1.sizePolicy().hasHeightForWidth()) self.wdg_address_source_1.setSizePolicy(sizePolicy) self.wdg_address_source_1.setObjectName("wdg_address_source_1") self.horizontalLayout_6 = QtWidgets.QHBoxLayout(self.wdg_address_source_1) self.horizontalLayout_6.setContentsMargins(0, 0, 0, 0) self.horizontalLayout_6.setSpacing(1) self.horizontalLayout_6.setObjectName("horizontalLayout_6") self.lbl_account = QtWidgets.QLabel(self.wdg_address_source_1) self.lbl_account.setObjectName("lbl_account") self.horizontalLayout_6.addWidget(self.lbl_account) self.cbo_hw_account_nr = QtWidgets.QComboBox(self.wdg_address_source_1) self.cbo_hw_account_nr.setObjectName("cbo_hw_account_nr") self.horizontalLayout_6.addWidget(self.cbo_hw_account_nr) self.btn_add_hw_account_nr = QtWidgets.QToolButton(self.wdg_address_source_1) self.btn_add_hw_account_nr.setObjectName("btn_add_hw_account_nr") self.horizontalLayout_6.addWidget(self.btn_add_hw_account_nr) spacerItem = QtWidgets.QSpacerItem(40, 20, QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Minimum) self.horizontalLayout_6.addItem(spacerItem) self.lbl_hw_account_base_path = QtWidgets.QLabel(self.wdg_address_source_1) self.lbl_hw_account_base_path.setObjectName("lbl_hw_account_base_path") self.horizontalLayout_6.addWidget(self.lbl_hw_account_base_path) self.sw_address_source.addWidget(self.wdg_address_source_1) self.wdg_address_source_2 = QtWidgets.QWidget() sizePolicy = QtWidgets.QSizePolicy(QtWidgets.QSizePolicy.Maximum, QtWidgets.QSizePolicy.Preferred) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth(self.wdg_address_source_2.sizePolicy().hasHeightForWidth()) self.wdg_address_source_2.setSizePolicy(sizePolicy) self.wdg_address_source_2.setObjectName("wdg_address_source_2") self.horizontalLayout_2 = QtWidgets.QHBoxLayout(self.wdg_address_source_2) self.horizontalLayout_2.setContentsMargins(0, 0, 0, 0) self.horizontalLayout_2.setObjectName("horizontalLayout_2") self.lblSourceBip32Path = QtWidgets.QLabel(self.wdg_address_source_2) self.lblSourceBip32Path.setObjectName("lblSourceBip32Path") self.horizontalLayout_2.addWidget(self.lblSourceBip32Path) self.edt_src_bip32_path = QtWidgets.QLineEdit(self.wdg_address_source_2) sizePolicy = QtWidgets.QSizePolicy(QtWidgets.QSizePolicy.Fixed, QtWidgets.QSizePolicy.Fixed) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth(self.edt_src_bip32_path.sizePolicy().hasHeightForWidth()) self.edt_src_bip32_path.setSizePolicy(sizePolicy) self.edt_src_bip32_path.setMaximumSize(QtCore.QSize(100, 16777215)) self.edt_src_bip32_path.setStyleSheet("background-color: lightgray;") self.edt_src_bip32_path.setReadOnly(True) self.edt_src_bip32_path.setObjectName("edt_src_bip32_path") self.horizontalLayout_2.addWidget(self.edt_src_bip32_path) self.btn_src_bip32_path = QtWidgets.QToolButton(self.wdg_address_source_2) self.btn_src_bip32_path.setObjectName("btn_src_bip32_path") self.horizontalLayout_2.addWidget(self.btn_src_bip32_path) spacerItem1 = QtWidgets.QSpacerItem(40, 20, QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Minimum) self.horizontalLayout_2.addItem(spacerItem1) self.sw_address_source.addWidget(self.wdg_address_source_2) self.wdg_address_source_3 = QtWidgets.QWidget() self.wdg_address_source_3.setObjectName("wdg_address_source_3") self.horizontalLayout = QtWidgets.QHBoxLayout(self.wdg_address_source_3) self.horizontalLayout.setContentsMargins(0, 0, 0, 0) self.horizontalLayout.setObjectName("horizontalLayout") self.lbl_src_masternode = QtWidgets.QLabel(self.wdg_address_source_3) self.lbl_src_masternode.setObjectName("lbl_src_masternode") self.horizontalLayout.addWidget(self.lbl_src_masternode) self.cbo_src_masternodes = QtWidgets.QComboBox(self.wdg_address_source_3) self.cbo_src_masternodes.setObjectName("cbo_src_masternodes") self.horizontalLayout.addWidget(self.cbo_src_masternodes) spacerItem2 = QtWidgets.QSpacerItem(40, 20, QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Minimum) self.horizontalLayout.addItem(spacerItem2) self.sw_address_source.addWidget(self.wdg_address_source_3) self.lay_input.addWidget(self.sw_address_source) self.btnLoadTransactions = QtWidgets.QPushButton(self.pnl_input) self.btnLoadTransactions.setAutoDefault(False) self.btnLoadTransactions.setObjectName("btnLoadTransactions") self.lay_input.addWidget(self.btnLoadTransactions) spacerItem3 = QtWidgets.QSpacerItem(40, 20, QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Minimum) self.lay_input.addItem(spacerItem3) self.verticalLayout_4.addLayout(self.lay_input) self.verticalLayout.addWidget(self.pnl_input) self.splitter = QtWidgets.QSplitter(SendPayoutDlg) self.splitter.setOrientation(QtCore.Qt.Vertical) self.splitter.setObjectName("splitter") self.main_widget = QtWidgets.QWidget(self.splitter) self.main_widget.setObjectName("main_widget") self.verticalLayout_2 = QtWidgets.QVBoxLayout(self.main_widget) self.verticalLayout_2.setContentsMargins(0, 0, 0, 0) self.verticalLayout_2.setSpacing(2) self.verticalLayout_2.setObjectName("verticalLayout_2") self.lbl_message_2 = QtWidgets.QLabel(self.main_widget) self.lbl_message_2.setText("") self.lbl_message_2.setOpenExternalLinks(True) self.lbl_message_2.setTextInteractionFlags(QtCore.Qt.LinksAccessibleByMouse|QtCore.Qt.TextSelectableByMouse) self.lbl_message_2.setObjectName("lbl_message_2") self.verticalLayout_2.addWidget(self.lbl_message_2) self.horizontalLayout_4 = QtWidgets.QHBoxLayout() self.horizontalLayout_4.setContentsMargins(-1, 8, -1, -1) self.horizontalLayout_4.setSpacing(6) self.horizontalLayout_4.setObjectName("horizontalLayout_4") self.btnCheckAll = QtWidgets.QToolButton(self.main_widget) self.btnCheckAll.setToolTip("") self.btnCheckAll.setIconSize(QtCore.QSize(12, 12)) self.btnCheckAll.setToolButtonStyle(QtCore.Qt.ToolButtonTextBesideIcon) self.btnCheckAll.setObjectName("btnCheckAll") self.horizontalLayout_4.addWidget(self.btnCheckAll) self.btnUncheckAll = QtWidgets.QToolButton(self.main_widget) self.btnUncheckAll.setToolTip("") self.btnUncheckAll.setIconSize(QtCore.QSize(12, 12)) self.btnUncheckAll.setToolButtonStyle(QtCore.Qt.ToolButtonTextBesideIcon) self.btnUncheckAll.setObjectName("btnUncheckAll") self.horizontalLayout_4.addWidget(self.btnUncheckAll) self.chbHideCollateralTx = QtWidgets.QCheckBox(self.main_widget) self.chbHideCollateralTx.setStyleSheet("") self.chbHideCollateralTx.setObjectName("chbHideCollateralTx") self.horizontalLayout_4.addWidget(self.chbHideCollateralTx) self.lbl_message = QtWidgets.QLabel(self.main_widget) self.lbl_message.setStyleSheet("margin-left:20px;\n" "font-size:11px;\n" "background-color: rgb(56, 181, 255);\n" "color: rgb(255, 255, 255);") self.lbl_message.setWordWrap(False) self.lbl_message.setOpenExternalLinks(True) self.lbl_message.setTextInteractionFlags(QtCore.Qt.LinksAccessibleByMouse|QtCore.Qt.TextSelectableByMouse) self.lbl_message.setObjectName("lbl_message") self.horizontalLayout_4.addWidget(self.lbl_message) spacerItem4 = QtWidgets.QSpacerItem(40, 20, QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Minimum) self.horizontalLayout_4.addItem(spacerItem4) self.verticalLayout_2.addLayout(self.horizontalLayout_4) self.tableView = QtWidgets.QTableView(self.main_widget) self.tableView.setSizeAdjustPolicy(QtWidgets.QAbstractScrollArea.AdjustToContentsOnFirstShow) self.tableView.setSelectionBehavior(QtWidgets.QAbstractItemView.SelectRows) self.tableView.setShowGrid(True) self.tableView.setSortingEnabled(False) self.tableView.setObjectName("tableView") self.tableView.verticalHeader().setVisible(False) self.tableView.verticalHeader().setCascadingSectionResizes(True) self.tableView.verticalHeader().setHighlightSections(False) self.verticalLayout_2.addWidget(self.tableView) self.dest_widget1 = QtWidgets.QWidget(self.splitter) sizePolicy = QtWidgets.QSizePolicy(QtWidgets.QSizePolicy.Preferred, QtWidgets.QSizePolicy.Expanding) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth(self.dest_widget1.sizePolicy().hasHeightForWidth()) self.dest_widget1.setSizePolicy(sizePolicy) self.dest_widget1.setObjectName("dest_widget1") self.verticalLayout_3 = QtWidgets.QVBoxLayout(self.dest_widget1) self.verticalLayout_3.setContentsMargins(0, 0, 0, 0) self.verticalLayout_3.setObjectName("verticalLayout_3") self.dest_widget = QtWidgets.QFrame(self.dest_widget1) self.dest_widget.setFrameShape(QtWidgets.QFrame.StyledPanel) self.dest_widget.setObjectName("dest_widget") self.verticalLayout_3.addWidget(self.dest_widget) self.verticalLayout.addWidget(self.splitter) self.horizontalLayout_3 = QtWidgets.QHBoxLayout() self.horizontalLayout_3.setObjectName("horizontalLayout_3") spacerItem5 = QtWidgets.QSpacerItem(40, 20, QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Minimum) self.horizontalLayout_3.addItem(spacerItem5) self.btnSend = QtWidgets.QPushButton(SendPayoutDlg) sizePolicy = QtWidgets.QSizePolicy(QtWidgets.QSizePolicy.Minimum, QtWidgets.QSizePolicy.Minimum) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth(self.btnSend.sizePolicy().hasHeightForWidth()) self.btnSend.setSizePolicy(sizePolicy) self.btnSend.setMinimumSize(QtCore.QSize(200, 0)) self.btnSend.setMaximumSize(QtCore.QSize(200, 16777215)) self.btnSend.setAutoDefault(False) self.btnSend.setObjectName("btnSend") self.horizontalLayout_3.addWidget(self.btnSend) spacerItem6 = QtWidgets.QSpacerItem(40, 20, QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Minimum) self.horizontalLayout_3.addItem(spacerItem6) self.btnClose = QtWidgets.QPushButton(SendPayoutDlg) sizePolicy = QtWidgets.QSizePolicy(QtWidgets.QSizePolicy.Maximum, QtWidgets.QSizePolicy.Fixed) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth(self.btnClose.sizePolicy().hasHeightForWidth()) self.btnClose.setSizePolicy(sizePolicy) self.btnClose.setMinimumSize(QtCore.QSize(0, 0)) self.btnClose.setLayoutDirection(QtCore.Qt.LeftToRight) self.btnClose.setAutoDefault(False) self.btnClose.setObjectName("btnClose") self.horizontalLayout_3.addWidget(self.btnClose, 0, QtCore.Qt.AlignRight) self.verticalLayout.addLayout(self.horizontalLayout_3) self.retranslateUi(SendPayoutDlg) self.sw_address_source.setCurrentIndex(2) QtCore.QMetaObject.connectSlotsByName(SendPayoutDlg) def retranslateUi(self, SendPayoutDlg): _translate = QtCore.QCoreApplication.translate SendPayoutDlg.setWindowTitle(_translate("SendPayoutDlg", "Dialog")) self.label_3.setText(_translate("SendPayoutDlg", "View as")) self.cbo_address_source_mode.setItemText(0, _translate("SendPayoutDlg", "Wallet Account")) self.cbo_address_source_mode.setItemText(1, _translate("SendPayoutDlg", "BIP32 Path")) self.cbo_address_source_mode.setItemText(2, _translate("SendPayoutDlg", "Ghostnode Address")) self.lbl_account.setText(_translate("SendPayoutDlg", "Account ")) self.btn_add_hw_account_nr.setToolTip(_translate("SendPayoutDlg", "Add new account number")) self.btn_add_hw_account_nr.setText(_translate("SendPayoutDlg", ".")) self.lbl_hw_account_base_path.setText(_translate("SendPayoutDlg", "...")) self.lblSourceBip32Path.setText(_translate("SendPayoutDlg", "BIP32 path")) self.btn_src_bip32_path.setToolTip(_translate("SendPayoutDlg", "Change BIP32 path")) self.btn_src_bip32_path.setText(_translate("SendPayoutDlg", "...")) self.lbl_src_masternode.setText(_translate("SendPayoutDlg", "Ghostnode")) self.btnLoadTransactions.setText(_translate("SendPayoutDlg", "Reload")) self.btnCheckAll.setText(_translate("SendPayoutDlg", "Select All")) self.btnUncheckAll.setText(_translate("SendPayoutDlg", "Unselect All")) self.chbHideCollateralTx.setText(_translate("SendPayoutDlg", "Hide collateral utxos")) self.lbl_message.setText(_translate("SendPayoutDlg", "....")) self.btnSend.setText(_translate("SendPayoutDlg", "Prepare Transaction")) self.btnClose.setText(_translate("SendPayoutDlg", "Close")) if __name__ == "__main__": import sys app = QtWidgets.QApplication(sys.argv) SendPayoutDlg = QtWidgets.QDialog() ui = Ui_SendPayoutDlg() ui.setupUi(SendPayoutDlg) SendPayoutDlg.show() sys.exit(app.exec_())

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telethon/tl/functions/stickers.py

polisitni1/DogeClickBot

0

2388

"""File generated by TLObjects' generator. All changes will be ERASED""" from ...tl.tlobject import TLRequest from typing import Optional, List, Union, TYPE_CHECKING import os import struct if TYPE_CHECKING: from ...tl.types import TypeInputStickerSet, TypeInputUser, TypeInputStickerSetItem, TypeInputDocument class AddStickerToSetRequest(TLRequest): CONSTRUCTOR_ID = 0x8653febe SUBCLASS_OF_ID = 0x9b704a5a def __init__(self, stickerset, sticker): """ :param TypeInputStickerSet stickerset: :param TypeInputStickerSetItem sticker: :returns messages.StickerSet: Instance of StickerSet. """ self.stickerset = stickerset # type: TypeInputStickerSet self.sticker = sticker # type: TypeInputStickerSetItem def to_dict(self): return { '_': 'AddStickerToSetRequest', 'stickerset': None if self.stickerset is None else self.stickerset.to_dict(), 'sticker': None if self.sticker is None else self.sticker.to_dict() } def __bytes__(self): return b''.join(( b'\xbe\xfeS\x86', bytes(self.stickerset), bytes(self.sticker), )) @classmethod def from_reader(cls, reader): _stickerset = reader.tgread_object() _sticker = reader.tgread_object() return cls(stickerset=_stickerset, sticker=_sticker) class ChangeStickerPositionRequest(TLRequest): CONSTRUCTOR_ID = 0xffb6d4ca SUBCLASS_OF_ID = 0x9b704a5a def __init__(self, sticker, position): """ :param TypeInputDocument sticker: :param int position: :returns messages.StickerSet: Instance of StickerSet. """ self.sticker = sticker # type: TypeInputDocument self.position = position # type: int def to_dict(self): return { '_': 'ChangeStickerPositionRequest', 'sticker': None if self.sticker is None else self.sticker.to_dict(), 'position': self.position } def __bytes__(self): return b''.join(( b'\xca\xd4\xb6\xff', bytes(self.sticker), struct.pack('<i', self.position), )) @classmethod def from_reader(cls, reader): _sticker = reader.tgread_object() _position = reader.read_int() return cls(sticker=_sticker, position=_position) class CreateStickerSetRequest(TLRequest): CONSTRUCTOR_ID = 0x9bd86e6a SUBCLASS_OF_ID = 0x9b704a5a def __init__(self, user_id, title, short_name, stickers, masks=None): """ :param TypeInputUser user_id: :param str title: :param str short_name: :param List[TypeInputStickerSetItem] stickers: :param Optional[bool] masks: :returns messages.StickerSet: Instance of StickerSet. """ self.user_id = user_id # type: TypeInputUser self.title = title # type: str self.short_name = short_name # type: str self.stickers = stickers # type: List[TypeInputStickerSetItem] self.masks = masks # type: Optional[bool] async def resolve(self, client, utils): self.user_id = utils.get_input_user(await client.get_input_entity(self.user_id)) def to_dict(self): return { '_': 'CreateStickerSetRequest', 'user_id': None if self.user_id is None else self.user_id.to_dict(), 'title': self.title, 'short_name': self.short_name, 'stickers': [] if self.stickers is None else [None if x is None else x.to_dict() for x in self.stickers], 'masks': self.masks } def __bytes__(self): return b''.join(( b'jn\xd8\x9b', struct.pack('<I', (0 if self.masks is None or self.masks is False else 1)), bytes(self.user_id), self.serialize_bytes(self.title), self.serialize_bytes(self.short_name), b'\x15\xc4\xb5\x1c',struct.pack('<i', len(self.stickers)),b''.join(bytes(x) for x in self.stickers), )) @classmethod def from_reader(cls, reader): flags = reader.read_int() _masks = bool(flags & 1) _user_id = reader.tgread_object() _title = reader.tgread_string() _short_name = reader.tgread_string() reader.read_int() _stickers = [] for _ in range(reader.read_int()): _x = reader.tgread_object() _stickers.append(_x) return cls(user_id=_user_id, title=_title, short_name=_short_name, stickers=_stickers, masks=_masks) class RemoveStickerFromSetRequest(TLRequest): CONSTRUCTOR_ID = 0xf7760f51 SUBCLASS_OF_ID = 0x9b704a5a def __init__(self, sticker): """ :param TypeInputDocument sticker: :returns messages.StickerSet: Instance of StickerSet. """ self.sticker = sticker # type: TypeInputDocument def to_dict(self): return { '_': 'RemoveStickerFromSetRequest', 'sticker': None if self.sticker is None else self.sticker.to_dict() } def __bytes__(self): return b''.join(( b'Q\x0fv\xf7', bytes(self.sticker), )) @classmethod def from_reader(cls, reader): _sticker = reader.tgread_object() return cls(sticker=_sticker)

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PCI_o_B/SharedFunctions.py

MatteoMilani95/PCI_o_Bpy

1

159935

import numpy as np import matplotlib.pyplot as plt import math from pynverse import inversefunc from IPython import get_ipython get_ipython().magic('reset -sf') import pandas as pd from scipy.optimize import leastsq, least_squares, curve_fit import os from scipy import interpolate import scipy.integrate as integrate def theta1_func(H_value,R,n1,n2): if n1>n2: tc=np.arcsin(n2/n1) H=lambda theta1 :R*np.sin(theta1)/np.cos(np.arcsin(n1/n2*np.sin(theta1))-theta1)*1/(1-np.tan(np.arcsin(n1/n2*np.sin(theta1))-theta1)/np.tan(np.arcsin(n1/n2*np.sin(theta1)))) theta=inversefunc(H,y_values=H_value,domain=[-tc, tc]) if H_value>=0: h=R*np.sin(theta)/np.cos(np.arcsin(n1/n2*np.sin(theta))-theta) theta_scattering=np.arcsin(R*np.sin(theta)/h) else: h=R*np.sin(theta)/np.cos(np.arcsin(n1/n2*np.sin(theta))-theta) theta_scattering=math.pi-np.arcsin(R*np.sin(theta)/h) else: tc=np.arcsin(n1/n2) H=lambda theta1 :R*np.sin(theta1)/np.cos(np.arcsin(n1/n2*np.sin(theta1))-theta1)*1/(1+np.tan(np.arcsin(n1/n2*np.sin(theta1))-theta1)/np.tan(np.arcsin(n1/n2*np.sin(theta1)))) theta=inversefunc(H,y_values=H_value,domain=[-tc, tc]) if H_value<=0: h=R*np.sin(theta)/np.cos(np.arcsin(n1/n2*np.sin(theta))-theta) theta_scattering=np.arcsin(R*np.sin(theta)/h) else: h=R*np.sin(theta)/np.cos(np.arcsin(n1/n2*np.sin(theta))-theta) theta_scattering=math.pi-np.arcsin(R*np.sin(theta)/h) return h,theta_scattering def SingExp(x, amp, decay, baseline ): """Model a decaying sine wave and subtract data.""" model = (amp * np.exp(-x/decay))**2 + baseline return model def DoubleExp(x, amp1, decay1, amp2, decay2, baseline ): """Model a decaying sine wave and subtract data.""" model = (amp1 * np.exp(-x/decay1) + amp2 * np.exp(-x/decay2))**2 + baseline return model def DoubleStretchExp(x, amp1, decay1, amp2, decay2, baseline, beta,gamma ): """Model a decaying sine wave and subtract data.""" model = (amp1 * np.exp(-x/decay1))**(2*beta) + (amp2 * np.exp(-x/decay2))**(2*gamma) + baseline return model def SingleStretchExp(x, amp1, decay1, baseline, beta): """Model a decaying sine wave and subtract data.""" model = amp1 * np.exp(-(x/decay1)) **beta + baseline return model def TripleExp(x, amp1, decay1, amp2, decay2, amp3, decay3, baseline ): """Model a decaying sine wave and subtract data.""" model = (amp1 * np.exp(-x/decay1) + amp2 * np.exp(-x/decay2) + amp3 * np.exp(-x/decay3) )**2 + baseline return model def StretchExp(x, amp, decay, baseline, beta ): """Model a decaying sine wave and subtract data.""" model = (amp * np.exp(-x / decay))**(2*beta) + baseline return model def FromTautoTheta(tau,tau_err,T,R_particle,wavelength,nu,n): kb=1.38064852*10**-23 D = kb*T/(6*math.pi*nu*(R_particle*10**-9)) theta = 2* np.arcsin( (1/(D*tau))**0.5*wavelength/(4*math.pi*n) ) *360/(2*math.pi) theta_err = 2 * 1 / ( 1- ( wavelength / (4 * n * math.pi * 1 / ( D * tau )**0.5 ) )**2 )**0.5 * wavelength / (8 * n * math.pi) * 1 / D**0.5 * tau**-1.5 * tau_err *360/(2*math.pi) return D, theta, theta_err def SFinterpolation(x,y): f = interpolate.interp1d(x, y) return f def SFintegration(x,y,x0,xmax): f = interpolate.interp1d(x, y) I = integrate.quad(f , x0, xmax) return I def AsymmetryCalculator(func): ass = [] for i in range(int(len(func)/2)): ass.append( ( np.abs(func[i] - func[-i]) / 2 ) / np.mean(np.asarray(func)) ) asymmerty = np.sum(np.asarray(ass)) return asymmerty def truncate(n, decimals=0): multiplier = 10 ** decimals return int(n * multiplier) / multiplier

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test/pynrn/test_units.py

shhong/nrn

1

146837

<gh_stars>1-10 from neuron import h def switch_units(legacy): try: h.nrnunit_use_legacy(legacy) except: pass def test_mod_legacy(): s = h.Section() ut = h.UnitsTest(s(.5)) h.ion_style("na_ion", 1, 2, 1, 1, 0, sec=s) switch_units(1) h.finitialize() names = ["mole", "e", "faraday", "planck", "hbar", "gasconst"] legacy_hex_values = ["0x1.fe18fef60659ap+78", "0x1.7a4e7164efbbcp-63", "0x1.78e54cccccccdp+16", "0x1.b85f8c5445f02p-111", "0x1.18779454e3d48p-113", "0x1.0a10624dd2f1bp+3"] for i, n in enumerate(names): val = eval("ut." + n) print ("%s = %s (%s)" %(n, str(val.hex()), str(val))) legacy_value = float.fromhex(legacy_hex_values[i]) assert (val == legacy_value) assert (ut.avogadro == float.fromhex(legacy_hex_values[0])) ghk_std = h.ghk(-50, .001, 10, 2) erev_std = h.nernst(s(.5).nai, s(.5).nao, 1) assert (ut.ghk == ghk_std) assert (ut.erev == erev_std) switch_units(0) h.finitialize() ghk_std = h.ghk(-50, .001, 10, 2) erev_std = h.nernst(s(.5).nai, s(.5).nao, 1) assert (ut.mole != float.fromhex(legacy_hex_values[0])) assert (ut.e*ut.avogadro == ut.faraday) assert (abs(ut.faraday - h.FARADAY) < 1e-10) assert (ut.gasconst == h.R) assert (ut.k*ut.avogadro == ut.gasconst) assert (abs(ut.planck - ut.hbar*2.0*h.PI) < 1e-49) assert (ut.avogadro == h.Avogadro_constant) assert (ut.ghk == h.ghk(-50, .001, 10, 2)) assert (ut.erev == h.nernst(s(.5).nai, s(.5).nao, 1)) def test_hoc_legacy(): switch_units(1) # legacy print ("R = %s" % str(h.R)) print ("FARADAY = %s" % str(h.FARADAY)) celsius = 6.3 ghk = h.ghk(30, .01, 10, 1) # nernst requires a Section to get voltage print ("ghk = %s" % str(ghk)) assert(h.R == 8.31441) assert(h.FARADAY == 96485.309) assert(ghk == -483.7914803097116) switch_units(0) # Modern print ("R = %s" % str(h.R)) print ("FARADAY = %s" % str(h.FARADAY)) ghk = h.ghk(30, .01, 10, 1) print ("ghk = %s" % str(ghk)) assert(h.R == 8.31446261815324) assert(ghk == -483.8380971405879) def test_env_legacy(): for i in [0,1]: a = None try: # test NRNUNIT_USE_LEGACY not whether we can successfully run a subprocess import sys, subprocess a = "NRNUNIT_USE_LEGACY=%d %s -c 'from neuron import h; print (h.nrnunit_use_legacy())'"%(i, sys.executable) a = subprocess.check_output(a, shell=True) a = int(float(a.decode().split()[0])) except: pass assert(a == i) if __name__ == "__main__": test_mod_legacy() test_hoc_legacy() test_env_legacy()

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knet/views/__init__.py

aethersoft/metaview

1

1610602

from typing import Text from flask import Blueprint, render_template from knet.config import CONFIG bp = Blueprint('views', __name__) @bp.route('/') def home(): return render_template('index.html', title=CONFIG['APP_TITLE'], page='Home') @bp.route('/<page>') def pages(page: Text): return render_template('index.html', title=CONFIG['APP_TITLE'], page=page.capitalize())

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api/login_request.py

dyphen12/full-stack-login

0

1605627

import database as datb #### REQUESTS ##### def login(user,passw): auth, ssid = datb.user_validation(user,passw) return auth, ssid

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setup.py

cr3/lookuper

0

189537

<filename>setup.py from setuptools import ( setup, find_packages, ) setup( name='lookuper', use_scm_version=True, description='Lookup nested data structures', long_description=open('README.rst').read(), url='https://github.com/cr3/lookuper', author='<NAME>', author_email='<EMAIL>', setup_requires=['setuptools_scm'], packages=find_packages(where='src'), package_dir={'': 'src'}, license='MIT', keywords='lookup nested', classifiers=[ 'Development Status :: 4 - Beta', 'Intended Audience :: Developers', 'License :: OSI Approved :: MIT License', 'Operating System :: OS Independent', 'Programming Language :: Python', 'Programming Language :: Python :: 3', 'Programming Language :: Python :: 3.5', 'Programming Language :: Python :: 3.6', 'Programming Language :: Python :: 3.7', 'Programming Language :: Python :: 3.8', 'Programming Language :: Python :: Implementation :: CPython', 'Programming Language :: Python :: Implementation :: PyPy', 'Topic :: Software Development :: Libraries :: Python Modules', ], )

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client/base_client.py

renatopp/liac-chess

6

138575

<gh_stars>1-10 # ============================================================================= # Federal University of Rio Grande do Sul (UFRGS) # Connectionist Artificial Intelligence Laboratory (LIAC) # <NAME> - <EMAIL> # ============================================================================= # Copyright (c) 2011 <NAME>, renato.ppontes at gmail dot com # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in # all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. # ============================================================================= '''This is the base client for LIAC CHESS. Use `LiacBot` as your base class to create a new bot. ''' import sys import json import time import socket import random class LiacBot(object): '''LiacBot implements a basic client for LIAC CHESS. LiacBot encapsulates the basic features to communicate with the LIAC CHESS server, such as serialization and deserialization of json messages, connection handshaking, etc. Use this class as a base implementation for your bots. ''' name = '' ip = '127.0.0.1' port = 50100 def __init__(self): '''Constructor.''' self._socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM) if not self.name: self.name = random.choice([ 'Liacnator', '<NAME>', 'Liaczors', 'Liaco' ]) # INTERNAL METHODS ======================================================== def _connect(self): '''(INTERNAL) Connects to the server.''' self._socket.connect((self.ip, self.port)) def _send_data(self, data): '''(INTERNAL) Serialize a ``data`` object and sends it to the server. :param data: a Python object. ''' d = json.dumps(data) self._socket.sendall(d) def _receive_data(self): '''(INTERNAL) Receives a message from server and deserialize it. :return: a Python object. ''' data = self._socket.recv(2**12) return json.loads(data) def _send_name(self): '''(INTERNAL) Sends the bot's name to the server as part of the handshaking procedure. ''' self._send_data({ 'name':self.name }) def _receive_state(self): '''(INTERNAL) Handle a state message.''' state = self._receive_data() if state['winner'] != 0 or state['draw']: self.on_game_over(state) else: self.on_move(state) # ========================================================================= # INTERFACE =============================================================== def send_move(self, from_, to_): '''Sends a movement to the server. :param from_: a 2-tuple with the piece-to-move position. :param to_: a 2-tuple with the target position. ''' self._send_data({ 'from': from_, 'to': to_ }) def on_move(self, state): '''Receives the state from server, when the server asks for a movement. Consult the documentation see which information comes within the `state` object. :param state: a state object. ''' pass def on_game_over(self, state): '''Receives the state from server, when the server acknowledges a winner for the game. Consult the documentation see which information comes within the `state` object. :param state: a state object. ''' pass def start(self): '''Starts the bot.''' self._connect() self._send_name() while True: self._receive_state() # ========================================================================= if __name__ == '__main__': bot = LiacBot() bot.start()

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nodevectors/evaluation/__init__.py

tboser/nodevectors

0

61551

<reponame>tboser/nodevectors from nodevectors.evaluation.graph_eval import * from nodevectors.evaluation.link_pred import *

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reg_analysis.py

prakharchoudhary/Tale-O-Regression

2

162941

""" Train the regressor and plot the results """ import os import numpy as np from matplotlib import pyplot as plt from sklearn.preprocessing import StandardScaler from linear_model import LinearRegressionGD def train_reg(df): X = df[['RM']].values y = df['MEDV'].values sc_x = StandardScaler() sc_y = StandardScaler() X_std = sc_x.fit_transform(X) y_std = sc_y.fit_transform(y[:, np.newaxis]).flatten() lr = LinearRegressionGD() lr.fit(X_std, y_std) return lr def cost_against_epochs(lr): plt.plot(range(1, lr.n_iter+1), lr.cost_) plt.ylabel('SSE') plt.xlabel('Epoch') # plt.show() plt.savefig('./figures/cost-against-epoch.png') plt.gcf().clear() def lin_regplot(df, model): X = df[['RM']].values y = df['MEDV'].values sc_x = StandardScaler() sc_y = StandardScaler() X_std = sc_x.fit_transform(X) y_std = sc_y.fit_transform(y[:, np.newaxis]).flatten() plt.scatter(X_std, y_std, c='blue') plt.plot(X_std, model.predict(X_std), color='red') plt.xlabel('Average number of rooms [RM] (standardized)') plt.ylabel('Price in $1000\'s [MEDV] (standardized)') # plt.show() if not os.path.exists(os.path.join(os.getcwd(), 'figures')): os.mkdir('figures') plt.savefig('./figures/plotting-linear-reg.png')

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main_app/migrations/0006_auto_20210905_0013.py

boyoon-c/readuck-api

0

1600682

# Generated by Django 3.2.6 on 2021-09-05 00:13 from django.conf import settings from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ migrations.swappable_dependency(settings.AUTH_USER_MODEL), ('main_app', '0005_rename_article_group_articles'), ] operations = [ migrations.AlterField( model_name='group', name='articles', field=models.ManyToManyField(blank=True, to='main_app.Article'), ), migrations.AlterField( model_name='group', name='participants', field=models.ManyToManyField(blank=True, to=settings.AUTH_USER_MODEL), ), ]

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beer_carousel/migrations/0003_auto_20180610_1258.py

GeniALE/SiteWebGeniALE

4

180081

# -*- coding: utf-8 -*- # Generated by Django 1.11 on 2018-06-10 16:58 from __future__ import unicode_literals from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ('beer_carousel', '0002_auto_20180605_1010'), ] operations = [ migrations.CreateModel( name='BeerContainer', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('name', models.CharField(max_length=100)), ('css_class', models.CharField(max_length=100)), ], ), migrations.AddField( model_name='beermodel', name='beer_container', field=models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, to='beer_carousel.BeerContainer'), ), ]

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kite-python/kite_ml/kite/name_encoder/scope_encoder.py

kiteco/kiteco-public

17

36213

<gh_stars>10-100 from typing import Dict, Any import tensorflow as tf from ..utils.segmented_data import SegmentedIndices, SegmentedIndicesFeed from ..graph_encoder.embeddings import NodeEmbeddings class Encoder(object): def __init__(self, nodes: NodeEmbeddings): self._nodes = nodes self._build() def _build(self): self._build_placeholders() self._build_scope_state() def _build_placeholders(self): with tf.name_scope('placeholders'): # shape [number of variables in batch] # sample_ids[i] = s means that means that variable i is part of sample s in the batch self._variable_node_ids = SegmentedIndices('variable_node_ids') def _build_scope_state(self): with tf.name_scope('build_scope_state'): # [num variable nodes in batch] self._variable_nodes_embedded: tf.Tensor = tf.gather( self._nodes.embeddings, self._variable_node_ids.indices, name='scope_nodes_embedded', ) # reduce across variable nodes in each graph in the batch # shape [batch size, graph embedding depth] self._scope_state: tf.Tensor = tf.segment_max( self._variable_nodes_embedded, self._variable_node_ids.sample_ids, name='scope_state', ) def feed_dict(self, feed: SegmentedIndicesFeed) -> Dict[tf.Tensor, Any]: return self._variable_node_ids.feed_dict(feed) def placeholders_dict(self) -> Dict[str, tf.Tensor]: return self._variable_node_ids.dict() def scope_state(self) -> tf.Tensor: """ :return: representation of all the variables in scope, shape [batch size, graph embedding depth] """ return self._scope_state

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reverseWord.py

lovefov/Python

0

3324

#!/usr/bin/env python3 #-*- coding:utf-8 -*- #Author:贾江超 def spin_words(sentence): list1=sentence.split() l=len(list1) for i in range(l): relen = len(sentence.split()[i:][0]) if relen > 5: list1[i]=list1[i][::-1] return ' '.join(list1) ''' 注意在2.x版本可以用len()得到list的长度 3.x版本就不行了优化版本 def spin_words(sentence): # Your code goes here return " ".join([x[::-1] if len(x) >= 5 else x for x in sentence.split(" ")]) 在这里倒序字符串用切片很方便 str[::-1] 就ok了 '''

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applications/python/mqtt-lcp/lib/node_base_i2c.py

rphughespa/mqtt-lcp

4

65409

<filename>applications/python/mqtt-lcp/lib/node_base_i2c.py<gh_stars>1-10 # node_base_i2c.py - base class for mqtt nodes """ node_base_i2c - Base class for nodes that use i2c. Derived from node_base_mqtt. The MIT License (MIT) Copyright 2020 <NAME> Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. """ import sys import os.path if sys.platform.startswith("esp32_LoBo"): from smbus2_go_lobo import SMBus from queue_esp32 import Queue import ujson elif sys.platform.startswith("esp32"): from smbus2_esp32 import SMBus from queue_esp32 import Queue import ujson else: from smbus2 import SMBus from queue_py import Queue import json import time from global_constants import Global from node_base_mqtt import NodeBaseMqtt from i2c_client_thread import I2cClientThread from io_data import IoData from i2c_mux import I2cMux from i2c_rfid import I2cRfid from i2c_rotary import I2cRotary from i2c_servo import I2cServo from i2c_servo_hat import I2cServoHat class NodeBaseI2c(NodeBaseMqtt): """ Base class for node programs usig I2c IO Manages IO via threads """ def __init__(self): super().__init__() self.config_servos = None self.servos_params = None self.i2c_client = None self.i2c_devices = [] self.mqtt_devices = {} self.i2c_in_queue = Queue(100) self.i2c_out_queue = Queue(100) self.i2c_display_imported = False if os.path.isfile("servos.json"): with open('servos.json') as json_file: if sys.platform.startswith("esp32"): self.config_servos = ujson.load(json_file) else: self.config_servos = json.load(json_file) if self.config_servos is not None: self.parse_servo_params(self.config_servos) def start_i2c(self): """Start I2C IO""" self.log_queue.add_message("info", 'start i2c client thread') #if sys.platform.startswith('esp32'): # with SMBus(1) as i2cbus: # print("I2C Scan: "+str(i2cbus.scan())) if Global.CONFIG in self.config: if Global.IO in self.config[Global.CONFIG]: if Global.I2C in self.config[Global.CONFIG][Global.IO]: self.parse_config_data(self.config[Global.CONFIG][Global.IO][Global.I2C]) self.i2c_client = I2cClientThread(self.log_queue, self.i2c_devices, self.mqtt_devices, self.i2c_in_queue, self.i2c_out_queue, self.display_queue, "I2cClient", self.config[Global.CONFIG][Global.IO][Global.I2C]) self.i2c_client.start() time.sleep(1) def received_from_i2c(self, io_data): """ call back to process received subsctibed messages. override in derived class""" pass def get_incomming_message_from_queue(self): """ retrieve a receieved message from the queue """ message = None if not self.i2c_in_queue.empty(): message = self.i2c_in_queue.get() return message def process_input(self): """ cprocess input from all source input queues""" # process all input of a given type in order of importance: keyboard, serial, mqtt self.write_log_messages() if self.i2c_client is not None: self.i2c_client.check_msg() i2c_input = self.get_incomming_i2c_message_from_queue() if i2c_input is not None: self.received_from_i2c(i2c_input) super().process_input() def send_to_i2c(self, message): """ send message to an I2C device""" if self.i2c_client is not None: self.i2c_out_queue.put(message) def get_incomming_i2c_message_from_queue(self): """ retrieve a receieved message from the queue """ message = None if not self.i2c_in_queue.empty(): message = self.i2c_in_queue.get() return message def initialize_threads(self): """ initialize all IO threads""" # load config file # do not enable keyboard for apps meant to be run in background super().initialize_threads() if Global.CONFIG in self.config: if Global.IO in self.config[Global.CONFIG]: io_config = self.config[Global.CONFIG][Global.IO] if Global.I2C in io_config: self.start_i2c() def loop(self): """ loop through IO. override in dericed class""" while not self.shutdown_app: try: time.sleep(0.01) self.write_log_messages() self.process_input() except KeyboardInterrupt: self.shutdown_app = True def shutdown_threads(self): """ shutdown all IO threads""" if self.i2c_client is not None: # print("shutdown serial") self.i2c_client.shutdown() super().shutdown_threads() def parse_servo_params(self, servo_config_data): """ parse parameters for servos """ # print("servo params") if Global.CONFIG in servo_config_data: if Global.SERVOS in servo_config_data[Global.CONFIG]: for i2c_servo_mux in servo_config_data[Global.CONFIG][Global.SERVOS]: address = i2c_servo_mux[Global.ADDRESS] if isinstance(address, str): # assume hex format "0x77" address = int(address, 16) if Global.PORTS in i2c_servo_mux: for i2c_servo in i2c_servo_mux[Global.PORTS]: sub_address = i2c_servo[Global.SUB_ADDRESS] key = str(address)+":"+str(sub_address) if self.servos_params is None: self.servos_params = {} self.servos_params[key] = i2c_servo #print("%%%"+key+"..."+str(i2c_servo)) def parse_config_data(self, config_data): """ Parse out I2C items from config json file""" self.log_queue.add_message("debug", "parse i2c config") for i2c_node in config_data: self.parse_device_config(i2c_node, i2c_bus=1) def parse_device_config(self, i2c_node, i2c_bus=None, i2c_mux_device=None): """ parse configuration """ i2c_type = None i2c_device = None i2c_mux = i2c_mux_device mqtt_port = None mqtt_type = None mqtt_send_sensor_msg = False mux_type = None address = None if Global.BUS in i2c_node: i2c_bus = i2c_node[Global.BUS] if Global.ADDRESS in i2c_node: address = i2c_node[Global.ADDRESS] if isinstance(address, str): # assume hex format "0x77" address = int(address, 16) # self.log_queue.add_message("info", "I2C Device config: "+str(address)) if Global.DEVICE_TYPE in i2c_node: # a block of i2c device, either mux or gpio or unitary (single device per address) i2c_type = i2c_node[Global.DEVICE_TYPE] if i2c_type == Global.MUX: mux_type = i2c_node[Global.MUX+"-"+Global.TYPE] # print("### mux type: "+str(mux_type)) if mux_type == Global.SERVO_HAT: i2c_mux = I2cServoHat(self.log_queue, address, mux_type=mux_type, input_queue=self.i2c_in_queue) elif mux_type == Global.I2C: i2c_mux = I2cMux(self.log_queue, address, mux_type) else: self.i2c_device_not_connected_error(i2c_bus, address, Global.MUX) if not self.is_device_connected(i2c_bus, address): self.i2c_device_not_connected_error(i2c_bus, address, i2c_type) ports = i2c_node[Global.PORTS] for port in ports: self.parse_device_config(port, i2c_bus=i2c_bus, i2c_mux_device=i2c_mux) i2c_device = i2c_mux else: i2c_sub_address = None if i2c_mux is not None: if Global.SUB_ADDRESS in i2c_node: i2c_sub_address = i2c_node[Global.SUB_ADDRESS] device_type = None if Global.MQTT+"-"+Global.PORT in i2c_node: mqtt_port = i2c_node[Global.MQTT+"-"+Global.PORT] if Global.MQTT+"-"+Global.TYPE in i2c_node: mqtt_type = i2c_node[Global.MQTT+"-"+Global.TYPE] if Global.MQTT+"-"+Global.SENSOR in i2c_node: sensor = i2c_node[Global.MQTT+"-"+Global.SENSOR] mqtt_send_sensor_msg = bool(sensor == Global.TRUE) if Global.DEVICE_TYPE in i2c_node: device_type = i2c_node[Global.DEVICE_TYPE] if i2c_type == Global.RFID: self.log_queue.add_message("info", "I2C Device config: "+str(address)+"..."+str(i2c_type)+ "..."+str(mqtt_port)+"..."+str(mqtt_type)) # print("%%%% new rfid: "+str(address)+" .. "+str(i2c_sub_address)+" .. "+str(i2c_mux)) i2c_device = I2cRfid(self.log_queue, address, input_queue=self.i2c_in_queue, mqtt_port=mqtt_port, mqtt_type=mqtt_type, i2c_device_type=device_type, i2c_bus_number=i2c_bus, i2c_mux=i2c_mux, i2c_sub_address=i2c_sub_address) elif i2c_type == Global.SERVO: # print("!!!Servo") center_degrees = 45 # defaults for 90 degree servo throw_degrees = 90 close_degrees = 0 if self.servos_params is not None: key = str(i2c_mux_device.i2c_address)+":"+str(i2c_sub_address) #print("@@@key2: "+str(key)) if key in self.servos_params: servo_params = self.servos_params[key] center_degrees = servo_params[Global.CENTER] throw_degrees = servo_params[Global.THROW] close_degrees = servo_params[Global.CLOSE] # print("config degrees: "+str(center_degrees)+ # ".."+str(throw_degrees)+".."+str(close_degrees)) else: self.log_queue.add_message("error", "No Config for Servo: "+ str(key)) self.log_queue.add_message("info", "I2C Servo config: "+str(address)+"..."+str(i2c_type)+ "..."+str(mqtt_port)+"..."+str(mqtt_type)+ "..."+str(center_degrees)+"..."+str(throw_degrees)+"..."+str(close_degrees)) i2c_device = I2cServo(self.log_queue, address, input_queue=self.i2c_in_queue, mqtt_port=mqtt_port, mqtt_type=mqtt_type, mqtt_send_sensor_msg=mqtt_send_sensor_msg, i2c_device_type=device_type, i2c_bus_number=i2c_bus, i2c_mux=i2c_mux_device, i2c_sub_address=i2c_sub_address) i2c_device.center_degrees = center_degrees i2c_device.throw_degrees = throw_degrees i2c_device.close_degrees = close_degrees elif i2c_type == Global.ROTARY: self.log_queue.add_message("info", "I2C Device config: "+str(address)+"..."+str(i2c_type)+ "..."+str(mqtt_port)+"..."+str(mqtt_type)) i2c_device = I2cRotary(self.log_queue, address, input_queue=self.i2c_in_queue, mqtt_port=mqtt_port, mqtt_type=mqtt_type, i2c_device_type=device_type, i2c_bus_number=i2c_bus, i2c_mux=i2c_mux_device, i2c_sub_address=i2c_sub_address) elif i2c_type == Global.DISPLAY: display_size = None display_type = None if Global.DISPLAY+"-"+Global.SIZE in i2c_node: display_size = i2c_node[Global.DISPLAY+"-"+Global.SIZE] if Global.DISPLAY+"-"+Global.TYPE in i2c_node: display_type = i2c_node[Global.DISPLAY+"-"+Global.TYPE] # not everyone needs disply, import only if needed if not self.i2c_display_imported: from i2c_display import I2cDisplay self.i2c_display_imported = True self.log_queue.add_message("info", "I2C Device config: "+str(address)+"..."+i2c_type) i2c_device = I2cDisplay(self.log_queue, address, display_size=display_size, display_type=display_type, i2c_bus_number=i2c_bus, i2c_mux=i2c_mux_device, i2c_sub_address=i2c_sub_address) elif i2c_type == Global.GPIO: pass if i2c_type is not None and i2c_device is not None: if i2c_device.i2c_sub_address is None: if not self.is_device_connected(i2c_device.i2c_bus_number, i2c_device.i2c_address): self.i2c_device_not_connected_error(i2c_device.i2c_bus_number, i2c_device.i2c_address, i2c_device.i2c_device_type) # print("@@@ "+str(i2c_type)) self.i2c_devices.append({'type': i2c_type, 'mqtt-port': mqtt_port, 'dev': i2c_device}) if mqtt_port is not None: mqtt_key = mqtt_port+":"+mqtt_type self.mqtt_devices[mqtt_key] = i2c_device def i2c_device_not_connected_error(self, bus, address, dtype): """ raise Exception(Global.MSG_I2C_NOT_CONNECTED+" "+Global.DEVICE_TYPE+": "+str(type)+", "+ Global.BUS+": "+str(bus)+", "+Global.ADDRESS+": "+str(address)) """ self.log_queue.add_message("error", Global.MSG_I2C_NOT_CONNECTED+" "+Global.DEVICE_TYPE+ ": "+str(dtype)+", "+ Global.BUS+": "+str(bus)+", "+Global.ADDRESS+": "+str(address)) # modified routine from sparkfun qwiic_i2c def is_device_connected(self, smbus, address): """ is i2c device connect to i2c bus """ device_connected = False with SMBus(smbus) as bus: try: # Try to write a byte to the device, command 0x0 # If it throws an I/O error - the device isn't connected bus.write_byte(address, 0x0) device_connected = True except Exception as ee: print("Error connecting to Device: "+str(address)+" .. "+str(ee)) return device_connected

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stubs.min/System/ComponentModel/__init___parts/LookupBindingPropertiesAttribute.py

ricardyn/ironpython-stubs

1

172571

<reponame>ricardyn/ironpython-stubs<gh_stars>1-10 class LookupBindingPropertiesAttribute(Attribute,_Attribute): """ Specifies the properties that support lookup-based binding. This class cannot be inherited. LookupBindingPropertiesAttribute() LookupBindingPropertiesAttribute(dataSource: str,displayMember: str,valueMember: str,lookupMember: str) """ def Equals(self,obj): """ Equals(self: LookupBindingPropertiesAttribute,obj: object) -> bool Determines whether the specified System.Object is equal to the current System.ComponentModel.LookupBindingPropertiesAttribute instance. obj: The System.Object to compare with the current System.ComponentModel.LookupBindingPropertiesAttribute instance Returns: true if the object is equal to the current instance; otherwise,false, indicating they are not equal. """ pass def GetHashCode(self): """ GetHashCode(self: LookupBindingPropertiesAttribute) -> int Returns the hash code for this instance. Returns: A hash code for the current System.ComponentModel.LookupBindingPropertiesAttribute. """ pass def __eq__(self,*args): """ x.__eq__(y) <==> x==y """ pass def __init__(self,*args): """ x.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signature """ pass @staticmethod def __new__(self,dataSource=None,displayMember=None,valueMember=None,lookupMember=None): """ __new__(cls: type) __new__(cls: type,dataSource: str,displayMember: str,valueMember: str,lookupMember: str) """ pass def __ne__(self,*args): pass DataSource=property(lambda self: object(),lambda self,v: None,lambda self: None) """Gets the name of the data source property for the component to which the System.ComponentModel.LookupBindingPropertiesAttribute is bound. Get: DataSource(self: LookupBindingPropertiesAttribute) -> str """ DisplayMember=property(lambda self: object(),lambda self,v: None,lambda self: None) """Gets the name of the display member property for the component to which the System.ComponentModel.LookupBindingPropertiesAttribute is bound. Get: DisplayMember(self: LookupBindingPropertiesAttribute) -> str """ LookupMember=property(lambda self: object(),lambda self,v: None,lambda self: None) """Gets the name of the lookup member for the component to which this attribute is bound. Get: LookupMember(self: LookupBindingPropertiesAttribute) -> str """ ValueMember=property(lambda self: object(),lambda self,v: None,lambda self: None) """Gets the name of the value member property for the component to which the System.ComponentModel.LookupBindingPropertiesAttribute is bound. Get: ValueMember(self: LookupBindingPropertiesAttribute) -> str """ Default=None

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camerartc/camerartc.gyp

Teaonly/yacamera

20

52789

<gh_stars>10-100 # # Building script for ipcamera application # { 'includes': ['build/common.gypi'], 'targets' : [ { 'target_name': 'camerartc', 'type': 'executable', 'include_dirs': [ '../third_party/webrtc/modules/interface', ], 'dependencies': [ 'libjingle.gyp:libjingle_peerconnection', '<(DEPTH)/third_party/jsoncpp/jsoncpp.gyp:jsoncpp', ], 'sources': [ 'camerartc/main.cpp', 'camerartc/peer.h', 'camerartc/peer.cpp', 'camerartc/camerartc.h', 'camerartc/camerartc.cpp', 'camerartc/rtcstream.h', 'camerartc/rtcstream.cpp', 'camerartc/attendee.h', 'camerartc/attendee.cpp', 'camerartc/mediabuffer.h', 'camerartc/mediabuffer.cpp', 'camerartc/raptor/numberdb.h', 'camerartc/raptor/numberdb.cpp', 'camerartc/raptor/rprandom.h', 'camerartc/raptor/rprandom.cpp', 'camerartc/raptor/raptor.h', 'camerartc/raptor/aaptor.cpp', 'camerartc/g72x/g726_32.c', 'camerartc/g72x/g711.c', 'camerartc/g72x/g72x.c', ], }, ], }

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Experiment Processing/experiment1/calculate_mutual_tracks.py

Austaon/GroupRecommendationThesis

0

109905

import statistics from database.user import SessionUser import matplotlib.pyplot as plt def calculate_mutual_tracks(key="real", plot_data=True): """ Finds the number of tracks that are in both the selected data and the given dataset in each time range. The average and standard deviation are printed and a boxplot is generated. :param key: The dataset to use: "real", "recommended", or "random" :param plot_data: :return: """ types = ["tracks_short_term", "tracks_medium_term", "tracks_long_term"] result = { "tracks_short_term": [], "tracks_medium_term": [], "tracks_long_term": [] } count = 0 for user in SessionUser.objects: count += 1 print(f"User {count} out of 88") chosen_tracks = user.tracks for category in types: top_tracks = user.survey[key][category] result[category].append(len([t for t in top_tracks if t in chosen_tracks])) for category in types: print( f"{category:18s}: mean {statistics.mean(result[category]):.2f}, stdev: {statistics.stdev(result[category]):.2f}") if plot_data: fig, ax = plt.subplots() boxplot_data = [ result["tracks_short_term"], result["tracks_medium_term"], result["tracks_long_term"] ] labels = ["Short Term", "Medium Term", "Long Term"] ax.boxplot(boxplot_data, labels=labels, boxprops=dict(linestyle='-', linewidth=1.5), medianprops=dict(linestyle='-', linewidth=2), whiskerprops=dict(linestyle='-', linewidth=1.5), capprops=dict(linestyle='-', linewidth=1.5), showfliers=True ) ax.set_xticklabels(labels) ax.yaxis.grid(True, linestyle='-', which='major', color='lightgrey', alpha=0.5) ax.set_ylim((0, 10)) ax.set_ylabel("Matching Items") fig.tight_layout() plt.show()

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democracy_club/apps/everyelection/migrations/0001_initial.py

chris48s/Website

0

96120

<filename>democracy_club/apps/everyelection/migrations/0001_initial.py<gh_stars>0 # -*- coding: utf-8 -*- from __future__ import unicode_literals from django.db import migrations, models import django.utils.timezone from django.conf import settings import django_extensions.db.fields class Migration(migrations.Migration): dependencies = [ ('authorities', '0012_mapitarea'), migrations.swappable_dependency(settings.AUTH_USER_MODEL), ] operations = [ migrations.CreateModel( name='AuthorityElection', fields=[ ('id', models.AutoField(primary_key=True, verbose_name='ID', auto_created=True, serialize=False)), ('election_date', models.DateField()), ('percent_posts', models.IntegerField()), ('authority', models.ForeignKey(to='authorities.Authority')), ], ), migrations.CreateModel( name='AuthorityElectionPositions', fields=[ ('id', models.AutoField(primary_key=True, verbose_name='ID', auto_created=True, serialize=False)), ('created', django_extensions.db.fields.CreationDateTimeField(editable=False, blank=True, default=django.utils.timezone.now, verbose_name='created')), ('modified', django_extensions.db.fields.ModificationDateTimeField(editable=False, blank=True, default=django.utils.timezone.now, verbose_name='modified')), ('area', models.ForeignKey(to='authorities.MapitArea')), ('authority_election', models.ForeignKey(to='everyelection.AuthorityElection')), ('user', models.ForeignKey(to=settings.AUTH_USER_MODEL)), ], options={ 'abstract': False, 'ordering': ('-modified', '-created'), 'get_latest_by': 'modified', }, ), ]

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SelectUniqueTaxid.py

BonizzoniLab/Refine_EVEs_annotation

1

147160

import argparse def read_blast_table(blastx_file): '''Read blastx table''' totalList=[] for line in blastx_file: line=line.rstrip() if line.startswith('ID'): continue else: parts=line.split('\t') try: taxid=parts[int(opts.position)].split(';') except: taxid='' for t in taxid: if t != '.': totalList.append(t) for t in sorted(set(totalList)): print(t) def main(): parser = argparse.ArgumentParser('Select unique taxid') parser.add_argument('-i_Blastx', '--fileBlast', help="blastx output table") parser.add_argument('-position', '--position', help="column position of taxid") global opts opts = parser.parse_args() in_fileBlastx = open(opts.fileBlast) read_blast_table(in_fileBlastx) main()

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tests/syntax/async_def_missing_parens.py

matan-h/friendly

287

133925

<filename>tests/syntax/async_def_missing_parens.py async def name: pass

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utils/tinyimages_80mn_loader.py

jfc43/robust-ood-detection

55

196882

import numpy as np import torch from bisect import bisect_left class TinyImages(torch.utils.data.Dataset): def __init__(self, transform=None, exclude_cifar=True): data_file = open('datasets/unlabeled_datasets/80M_Tiny_Images/tiny_images.bin', "rb") def load_image(idx): data_file.seek(idx * 3072) data = data_file.read(3072) return np.fromstring(data, dtype='uint8').reshape(32, 32, 3, order="F") self.load_image = load_image self.offset = 0 # offset index self.transform = transform self.exclude_cifar = exclude_cifar if exclude_cifar: self.cifar_idxs = [] with open('datasets/unlabeled_datasets/80M_Tiny_Images/80mn_cifar_idxs.txt', 'r') as idxs: for idx in idxs: # indices in file take the 80mn database to start at 1, hence "- 1" self.cifar_idxs.append(int(idx) - 1) # hash table option self.cifar_idxs = set(self.cifar_idxs) self.in_cifar = lambda x: x in self.cifar_idxs # bisection search option # self.cifar_idxs = tuple(sorted(self.cifar_idxs)) # # def binary_search(x, hi=len(self.cifar_idxs)): # pos = bisect_left(self.cifar_idxs, x, 0, hi) # find insertion position # return True if pos != hi and self.cifar_idxs[pos] == x else False # # self.in_cifar = binary_search def __getitem__(self, index): index = (index + self.offset) % 79302016 if self.exclude_cifar: while self.in_cifar(index): index = np.random.randint(79302017) img = self.load_image(index) if self.transform is not None: img = self.transform(img) return img, 0 # 0 is the class def __len__(self): return 79302017

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5.Operators/2.membership_operator.py

Tazri/Python

0

194730

<filename>5.Operators/2.membership_operator.py list = ['Apple','Orange','Benana','Mango']; name = "<NAME>"; print('"Apple" in list : ',"Apple" in list); print('"Kiwi" in list : ',"Kiwi" in list); print('"Water" not in list : ',"Water" not in list); print("'Md' in name : ",'Md' in name); print("'Tazri' not in name : ",'Tazri' not in name);

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photonpy/tests/psf_g2d_sigma.py

qnano/photonpy

5

7095

import matplotlib.pyplot as plt import numpy as np from photonpy.cpp.context import Context import photonpy.cpp.gaussian as gaussian from photonpy.smlm.util import imshow_hstack from photonpy.cpp.estimator import Estimator def CheckDeriv(psf:Estimator, theta): nderiv,ev=psf.NumDeriv(theta,eps=1e-6) deriv,ev=psf.Derivatives(theta) maxerr = np.max( np.abs(deriv-nderiv), (-1,-2) ) print(f"PSF {psf.ParamFormat()}, max {np.max(deriv)}, min: {np.min(deriv)}: Deriv-NumDeriv: {maxerr}") plt.figure() imshow_hstack(deriv[0] - nderiv[0]) with Context() as ctx: g = gaussian.Gaussian(ctx) for cuda in [False]: print(f"CUDA = {cuda}") sigma=2 roisize=12 psf = g.CreatePSF_XYIBg(roisize, sigma, cuda) theta = [[4, 4, 1000, 3]] img = psf.ExpectedValue(theta) plt.figure() plt.set_cmap('inferno') smp = np.random.poisson(img) plt.imshow(smp[0]) psf_sigma = g.CreatePSF_XYIBgSigma(roisize, sigma, cuda) theta_s = [[4,4,1000,3,sigma]] img2 = psf_sigma.ExpectedValue(theta_s) CheckDeriv(psf, theta) # CheckDeriv(psf_sigma) print(f"PSF Sigma crlb: {psf_sigma.CRLB(theta_s)}") theta = psf_sigma.Estimate(smp)[0] print(theta)

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src/pataka/models/__init__.py

astrogewgaw/pataka

6

167218

<reponame>astrogewgaw/pataka from .furby import Furby from .pulsar import Pulsar __all__ = ["Furby", "Pulsar"]

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main.py

neurosity/notion-pylsl-starter

5

72044

<gh_stars>1-10 """Example program to show how to read a multi-channel time series from LSL.""" from pylsl import StreamInlet, resolve_stream try: # first resolve an EEG stream on the lab network print("looking for an EEG stream...") streams = resolve_stream('type', 'EEG') # create a new inlet to read from the stream inlet = StreamInlet(streams[0]) while True: # get a new sample (you can also omit the timestamp part if you're not # interested in it) sample, timestamp = inlet.pull_sample() print(timestamp, sample) except KeyboardInterrupt as e: print("Ending program") raise e

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python/048-self-powers.py

timschlechter/project-euler

0

172004

<reponame>timschlechter/project-euler from functools import reduce powers = map(lambda x: x**x, range(1, 1001)) summation = reduce(lambda x, y: x + y, powers) last10 = str(summation)[-10:] print(last10)

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tests/util_test.py

yyang08/swagger-spec-compatibility

18

117864

# -*- coding: utf-8 -*- from __future__ import absolute_import from __future__ import print_function from __future__ import unicode_literals import mock import pytest from swagger_spec_compatibility.util import EntityMapping from swagger_spec_compatibility.util import is_path_in_top_level_paths from swagger_spec_compatibility.util import wrap @pytest.mark.parametrize( 'input_string, width, expected_result', [ ('this is a string', 50, 'this is a string'), # No wrapping needed ('this is a string', 5, 'this\nis a\nstring'), # The lines are stripped ('this is a string', 5, 'this\nis a\nstring'), # Multiple spaces are condensed ('this_is_a_string', 5, 'this_is_a_string'), # Words (characters with no spaces) will not be split ], ) def test_wrap_provides_the_expected_string(input_string, width, expected_result): assert wrap(input_string, width=width) == expected_result def test_EntityMapping_equality_and_hash(): entity_mappint_1 = EntityMapping(old=1, new=2) entity_mappint_2 = EntityMapping(old=1, new=2) entity_mappint_3 = EntityMapping(old=1, new=3) assert hash(entity_mappint_1) == hash(entity_mappint_2) assert entity_mappint_1 == entity_mappint_2 assert hash(entity_mappint_1) != hash(entity_mappint_3) assert entity_mappint_1 != entity_mappint_3 def test_EntityMapping_upacking_works(): entity_mappint = EntityMapping(old=mock.sentinel.OLD, new=mock.sentinel.NEW) old, new = entity_mappint assert old == mock.sentinel.OLD assert new == mock.sentinel.NEW @pytest.mark.parametrize( 'top_level_paths, path, expected_result', [ ([tuple()], ('path_item',), True), ([('top',)], ('path_item',), False), ([('top',), ('path_item',)], ('path_item',), True), ([('top', 'inner')], ('top', 'different_inner'), False), ([('top', 'inner')], ('top', 'inner'), True), ([('top', 'inner')], ('top', 'inner', 'inner_inner'), True), ], ) def test_is_path_in_top_level_paths(top_level_paths, path, expected_result): assert is_path_in_top_level_paths(top_level_paths, path) is expected_result

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src/synthesize_predictions.py

bluetyson/concept-tagging-training

10

30322

import argparse import logging from pathlib import Path import dask import h5py import joblib import numpy as np import pandas as pd from dask.diagnostics import ProgressBar from tqdm import tqdm from dsconcept.get_metrics import ( get_cat_inds, get_synth_preds, load_category_models, load_concept_models, HierarchicalClassifier, get_mets, ) logging.basicConfig(level=logging.INFO) LOG = logging.getLogger(__name__) LOG.setLevel(logging.INFO) def main( experiment_name, synth_strat, in_cat_preds, out_store, synth_batch_size, t, out_synth_scores, limit=None, con_limit=None, ): test_inds = np.load(f"data/interim/{experiment_name}/test_inds.npy") feature_matrix = joblib.load(f"data/interim/{experiment_name}/feature_matrix.jbl") in_cat_models = Path(f"models/{experiment_name}/categories/models/") in_kwd_models = Path(f"models/{experiment_name}/keywords/models/") cat_preds = np.load(in_cat_preds) # based on experiment or explicit path? cat_clfs = load_category_models(in_cat_models) cd = load_concept_models(in_kwd_models) clf = HierarchicalClassifier(cat_clfs, cd) if limit is not None: LOG.info(f"Limiting to {limit} test records.") feature_matrix_test = feature_matrix.tocsc()[test_inds[0:limit], :] cat_preds = cat_preds[0:limit, :] # TODO: How does this affect indices? else: feature_matrix_test = feature_matrix.tocsc()[test_inds, :] LOG.info(f'Synthesizing predictions with strategy "{synth_strat}".') all_cat_inds = get_cat_inds(clf.categories, cat_preds, t=t) if con_limit is not None: conwc = clf.concepts_with_classifiers[0:con_limit] else: conwc = clf.concepts_with_classifiers shape = (feature_matrix_test.shape[0], len(conwc)) with tqdm(total=shape[0]) as pbar: get_synth_preds( out_store, shape, all_cat_inds, clf.categories, synth_batch_size, only_cat=False, synth_strat=synth_strat, con_limit=con_limit, limit=limit, pbar=pbar, ) LOG.info("Obtaining metrics.") with h5py.File(out_store, "r") as f0: if limit is not None: target_values = f0["ground_truth"][0:limit, :] else: target_values = f0["ground_truth"].value with h5py.File(out_store, "r") as f0: synth_preds = f0["synthesis"].value jobs = [] mets_pbar = tqdm( range(len(conwc)), total=len(conwc), ) for i in mets_pbar: job = dask.delayed(get_mets)( i, synth_preds, target_values, conwc, mets_pbar ) jobs.append(job) records = dask.compute(jobs) new_recs_df = pd.DataFrame(records[0]) LOG.info(f"Saving results to {out_synth_scores}.") new_recs_df.to_csv(out_synth_scores) if __name__ == "__main__": parser = argparse.ArgumentParser(description="Say hello") parser.add_argument("--experiment_name", help="input txt file") parser.add_argument("--synth_strat", help="input txt file") parser.add_argument("--in_cat_preds", help="input txt file") parser.add_argument("--store", help="input txt file") parser.add_argument("--synth_batch_size", help="input txt file", type=int) parser.add_argument("--threshold", help="input txt file", type=float) parser.add_argument("--out_synth_scores", help="input txt file") parser.add_argument( "--limit", help="size for sample to test synthesis", type=int, default=None ) parser.add_argument( "--con_limit", help="size for concept sample", type=int, default=None ) args = parser.parse_args() main( args.experiment_name, args.synth_strat, args.in_cat_preds, args.store, args.synth_batch_size, args.threshold, args.out_synth_scores, args.limit, args.con_limit, )

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djnic/cambios/apps.py

avdata99/nic

8

41802

<filename>djnic/cambios/apps.py from django.apps import AppConfig class CambiosConfig(AppConfig): name = 'cambios'

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venv/Lib/site-packages/PyOpenGL-3.0.1/OpenGL/GL/EXT/draw_buffers2.py

temelkirci/Motion_Editor

1

8208

<reponame>temelkirci/Motion_Editor '''OpenGL extension EXT.draw_buffers2 This module customises the behaviour of the OpenGL.raw.GL.EXT.draw_buffers2 to provide a more Python-friendly API Overview (from the spec) This extension builds upon the ARB_draw_buffers extension and provides separate blend enables and color write masks for each color output. In ARB_draw_buffers (part of OpenGL 2.0), separate values can be written to each color buffer, but the blend enable and color write mask are global and apply to all color outputs. While this extension does provide separate blend enables, it does not provide separate blend functions or blend equations per color output. The official definition of this extension is available here: http://www.opengl.org/registry/specs/EXT/draw_buffers2.txt ''' from OpenGL import platform, constants, constant, arrays from OpenGL import extensions, wrapper from OpenGL.GL import glget import ctypes from OpenGL.raw.GL.EXT.draw_buffers2 import * ### END AUTOGENERATED SECTION

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misc/wapiti-2.3.0/wapitiCore/report/xmlreportgenerator.py

sapientgov/openfda-travel

377

179028

<reponame>sapientgov/openfda-travel<filename>misc/wapiti-2.3.0/wapitiCore/report/xmlreportgenerator.py #!/usr/bin/env python # -*- coding: utf-8 -*- # This file is part of the Wapiti project (http://wapiti.sourceforge.net) # Copyright (C) 2008-2013 <NAME> # # Original authors : # <NAME> # <NAME> # Copyright (C) 2008 Informatica Gesfor # ICT Romulus (http://www.ict-romulus.eu) # # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program; if not, write to the Free Software # Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA from xml.dom.minidom import Document from wapitiCore.report.reportgenerator import ReportGenerator class XMLReportGenerator(ReportGenerator): """ This class generates a report with the method printToFile(fileName) which contains the information of all the vulnerabilities notified to this object through the method logVulnerability(vulnerabilityTypeName,level,url,parameter,info). The format of the file is XML and it has the following structure: <report type="security"> <generatedBy id="Wapiti 2.3.0"/> <vulnerabilityTypeList> <vulnerabilityType name="SQL Injection"> <vulnerabilityTypeList> <vulnerabilityType name="SQL Injection"> <vulnerabilityList> <vulnerability level="3"> <url>http://www.a.com</url> <parameters>id=23</parameters> <info>SQL Injection</info> </vulnerability> </vulnerabilityList> </vulnerablityType> </vulnerabilityTypeList> </report> """ __xmlDoc = None __infos = {} __flawTypes = {} __vulns = {} __anomalies = {} __infos = {} def __init__(self): self.__xmlDoc = Document() def setReportInfo(self, target, scope=None, date_string="", version=""): self.__infos["target"] = target self.__infos["date"] = date_string self.__infos["version"] = version if scope: self.__infos["scope"] = scope # Vulnerabilities def addVulnerabilityType(self, name, description="", solution="", references={}): if name not in self.__flawTypes: self.__flawTypes[name] = {'desc': description, 'sol': solution, 'ref': references} if name not in self.__vulns: self.__vulns[name] = [] def logVulnerability(self, category=None, level=0, request=None, parameter="", info=""): """ Store the information about the vulnerability to be printed later. The method printToFile(fileName) can be used to save in a file the vulnerabilities notified through the current method. """ vuln_dict = {"method": request.method, "path": request.file_path, "info": info, "level": level, "parameter": parameter, "http_request": request.http_repr, "curl_command": request.curl_repr, } if category not in self.__vulns: self.__vulns[category] = [] self.__vulns[category].append(vuln_dict) # Anomalies def addAnomalyType(self, name, description="", solution="", references={}): if name not in self.__flawTypes: self.__flawTypes[name] = {'desc': description, 'sol': solution, 'ref': references} if name not in self.__anomalies: self.__anomalies[name] = [] def logAnomaly(self, category=None, level=0, request=None, parameter="", info=""): """ Store the information about the vulnerability to be printed later. The method printToFile(fileName) can be used to save in a file the vulnerabilities notified through the current method. """ anom_dict = {"method": request.method, "path": request.file_path, "info": info, "level": level, "parameter": parameter, "http_request": request.http_repr, "curl_command": request.curl_repr, } if category not in self.__anomalies: self.__anomalies[category] = [] self.__anomalies[category].append(anom_dict) def generateReport(self, fileName): """ Create a xml file with a report of the vulnerabilities which have been logged with the method logVulnerability(vulnerabilityTypeName,level,url,parameter,info) """ report = self.__xmlDoc.createElement("report") report.setAttribute("type", "security") self.__xmlDoc.appendChild(report) # Add report infos report_infos = self.__xmlDoc.createElement("report_infos") generatorName = self.__xmlDoc.createElement("info") generatorName.setAttribute("name", "generatorName") generatorName.appendChild(self.__xmlDoc.createTextNode("wapiti")) report_infos.appendChild(generatorName) generatorVersion = self.__xmlDoc.createElement("info") generatorVersion.setAttribute("name", "generatorVersion") generatorVersion.appendChild(self.__xmlDoc.createTextNode(self.__infos["version"])) report_infos.appendChild(generatorVersion) scope = self.__xmlDoc.createElement("info") scope.setAttribute("name", "scope") scope.appendChild(self.__xmlDoc.createTextNode(self.__infos["scope"])) report_infos.appendChild(scope) dateOfScan = self.__xmlDoc.createElement("info") dateOfScan.setAttribute("name", "dateOfScan") dateOfScan.appendChild(self.__xmlDoc.createTextNode(self.__infos["date"])) report_infos.appendChild(dateOfScan) report.appendChild(report_infos) vulnerabilities = self.__xmlDoc.createElement("vulnerabilities") anomalies = self.__xmlDoc.createElement("anomalies") # Loop on each flaw classification for flawType in self.__flawTypes: container = None classification = "" flaw_dict = {} if flawType in self.__vulns: container = vulnerabilities classification = "vulnerability" flaw_dict = self.__vulns elif flawType in self.__anomalies: container = anomalies classification = "anomaly" flaw_dict = self.__anomalies # Child nodes with a description of the flaw type flawTypeNode = self.__xmlDoc.createElement(classification) flawTypeNode.setAttribute("name", flawType) flawTypeDesc = self.__xmlDoc.createElement("description") flawTypeDesc.appendChild(self.__xmlDoc.createCDATASection(self.__flawTypes[flawType]['desc'])) flawTypeNode.appendChild(flawTypeDesc) flawTypeSolution = self.__xmlDoc.createElement("solution") flawTypeSolution.appendChild(self.__xmlDoc.createCDATASection(self.__flawTypes[flawType]['sol'])) flawTypeNode.appendChild(flawTypeSolution) flawTypeReferences = self.__xmlDoc.createElement("references") for ref in self.__flawTypes[flawType]['ref']: referenceNode = self.__xmlDoc.createElement("reference") titleNode = self.__xmlDoc.createElement("title") urlNode = self.__xmlDoc.createElement("url") titleNode.appendChild(self.__xmlDoc.createTextNode(ref)) url = self.__flawTypes[flawType]['ref'][ref] urlNode.appendChild(self.__xmlDoc.createTextNode(url)) referenceNode.appendChild(titleNode) referenceNode.appendChild(urlNode) flawTypeReferences.appendChild(referenceNode) flawTypeNode.appendChild(flawTypeReferences) # And child nodes with each flaw of the current type entriesNode = self.__xmlDoc.createElement("entries") for flaw in flaw_dict[flawType]: entryNode = self.__xmlDoc.createElement("entry") methodNode = self.__xmlDoc.createElement("method") methodNode.appendChild(self.__xmlDoc.createTextNode(flaw["method"])) entryNode.appendChild(methodNode) pathNode = self.__xmlDoc.createElement("path") pathNode.appendChild(self.__xmlDoc.createTextNode(flaw["path"])) entryNode.appendChild(pathNode) levelNode = self.__xmlDoc.createElement("level") levelNode.appendChild(self.__xmlDoc.createTextNode(str(flaw["level"]))) entryNode.appendChild(levelNode) parameterNode = self.__xmlDoc.createElement("parameter") parameterNode.appendChild(self.__xmlDoc.createTextNode(flaw["parameter"])) entryNode.appendChild(parameterNode) infoNode = self.__xmlDoc.createElement("info") infoNode.appendChild(self.__xmlDoc.createTextNode(flaw["info"])) entryNode.appendChild(infoNode) httpRequestNode = self.__xmlDoc.createElement("http_request") httpRequestNode.appendChild(self.__xmlDoc.createCDATASection(flaw["http_request"])) entryNode.appendChild(httpRequestNode) curlCommandNode = self.__xmlDoc.createElement("curl_command") curlCommandNode.appendChild(self.__xmlDoc.createCDATASection(flaw["curl_command"])) entryNode.appendChild(curlCommandNode) entriesNode.appendChild(entryNode) flawTypeNode.appendChild(entriesNode) container.appendChild(flawTypeNode) report.appendChild(vulnerabilities) report.appendChild(anomalies) f = open(fileName, "w") try: f.write(self.__xmlDoc.toprettyxml(indent=" ", encoding="UTF-8")) finally: f.close() if __name__ == "__main__": SQL_INJECTION = "Sql Injection" FILE_HANDLING = "File Handling" XSS = "Cross Site Scripting" CRLF = "CRLF Injection" EXEC = "Commands execution" try: xmlGen = XMLReportGenerator() xmlGen.addVulnerabilityType(SQL_INJECTION) xmlGen.addVulnerabilityType(FILE_HANDLING) xmlGen.addVulnerabilityType(XSS) xmlGen.addVulnerabilityType(CRLF) xmlGen.addVulnerabilityType(EXEC) xmlGen.logVulnerability("SQL Inyection", "1", "url1", "parameter1", "info1") xmlGen.logVulnerability("SQL Inyection", "2", "url2", "parameter2", "info2") xmlGen.logVulnerability("SQL Inyection", "2", "url3", "parameter3", "info3") xmlGen.logVulnerability("SQL Inyection", "3", "url4", "parameter4", "info4") xmlGen.logVulnerability("Cross Site Scripting", "3", "url5", "parameter5", "info5") xmlGen.logVulnerability("Cross Site Scripting", "3", "url6", "parameter6", "info6") xmlGen.logVulnerability("Cross Site Scripting", "2", "url7", "parameter7", "info7") xmlGen.logVulnerability("Cross Site Scripting", "1", "url8", "parameter8", "info8") xmlGen.logVulnerability("Google Hacking", "2", "url9", "parameter9", "info9") xmlGen.printToFile("sampleReport.xml") except SystemExit: pass

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rpython/translator/jvm/test/test_unicode.py

kantai/passe-pypy-taint-tracking

2

162321

<reponame>kantai/passe-pypy-taint-tracking import py from rpython.translator.jvm.test.runtest import JvmTest from rpython.rtyper.test.test_runicode import BaseTestRUnicode # ====> ../../../rpython/test/test_runicode.py class TestJvmUnicode(JvmTest, BaseTestRUnicode): EMPTY_STRING_HASH = 0 def test_unichar_const(self): def fn(): return u'\u03b1' assert self.interpret(fn, []) == u'\u03b1' def test_unichar_eq(self): py.test.skip("JVM doesn't support unicode for command line arguments") test_unichar_ord = test_unichar_eq test_unichar_hash = test_unichar_eq test_char_unichar_eq = test_unichar_eq test_char_unichar_eq_2 = test_unichar_eq def test_getitem_exc(self): py.test.skip('fixme!') def test_unicode_constant(self): const = u''.join(map(unichr, range(0, 256))) const = const + u'\ufffd' def fn(): return const res = self.interpret(fn, []) assert res == const def test_strformat_unicode_arg(self): py.test.skip('fixme!')

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ros/devel/lib/python2.7/dist-packages/darknet_ros_msgs/msg/_CheckForObjectsAction.py

wutianze/ComP

3

34043

<gh_stars>1-10 # This Python file uses the following encoding: utf-8 """autogenerated by genpy from darknet_ros_msgs/CheckForObjectsAction.msg. Do not edit.""" import sys python3 = True if sys.hexversion > 0x03000000 else False import genpy import struct import darknet_ros_msgs.msg import sensor_msgs.msg import genpy import actionlib_msgs.msg import std_msgs.msg class CheckForObjectsAction(genpy.Message): _md5sum = "98095af4078a4c5df88f8e6a4db52e32" _type = "darknet_ros_msgs/CheckForObjectsAction" _has_header = False #flag to mark the presence of a Header object _full_text = """# ====== DO NOT MODIFY! AUTOGENERATED FROM AN ACTION DEFINITION ====== CheckForObjectsActionGoal action_goal CheckForObjectsActionResult action_result CheckForObjectsActionFeedback action_feedback ================================================================================ MSG: darknet_ros_msgs/CheckForObjectsActionGoal # ====== DO NOT MODIFY! AUTOGENERATED FROM AN ACTION DEFINITION ====== Header header actionlib_msgs/GoalID goal_id CheckForObjectsGoal goal ================================================================================ MSG: std_msgs/Header # Standard metadata for higher-level stamped data types. # This is generally used to communicate timestamped data # in a particular coordinate frame. # # sequence ID: consecutively increasing ID uint32 seq #Two-integer timestamp that is expressed as: # * stamp.sec: seconds (stamp_secs) since epoch (in Python the variable is called 'secs') # * stamp.nsec: nanoseconds since stamp_secs (in Python the variable is called 'nsecs') # time-handling sugar is provided by the client library time stamp #Frame this data is associated with string frame_id ================================================================================ MSG: actionlib_msgs/GoalID # The stamp should store the time at which this goal was requested. # It is used by an action server when it tries to preempt all # goals that were requested before a certain time time stamp # The id provides a way to associate feedback and # result message with specific goal requests. The id # specified must be unique. string id ================================================================================ MSG: darknet_ros_msgs/CheckForObjectsGoal # ====== DO NOT MODIFY! AUTOGENERATED FROM AN ACTION DEFINITION ====== # Check if objects in image # Goal definition int16 id sensor_msgs/Image image ================================================================================ MSG: sensor_msgs/Image # This message contains an uncompressed image # (0, 0) is at top-left corner of image # Header header # Header timestamp should be acquisition time of image # Header frame_id should be optical frame of camera # origin of frame should be optical center of camera # +x should point to the right in the image # +y should point down in the image # +z should point into to plane of the image # If the frame_id here and the frame_id of the CameraInfo # message associated with the image conflict # the behavior is undefined uint32 height # image height, that is, number of rows uint32 width # image width, that is, number of columns # The legal values for encoding are in file src/image_encodings.cpp # If you want to standardize a new string format, join # <EMAIL> and send an email proposing a new encoding. string encoding # Encoding of pixels -- channel meaning, ordering, size # taken from the list of strings in include/sensor_msgs/image_encodings.h uint8 is_bigendian # is this data bigendian? uint32 step # Full row length in bytes uint8[] data # actual matrix data, size is (step * rows) ================================================================================ MSG: darknet_ros_msgs/CheckForObjectsActionResult # ====== DO NOT MODIFY! AUTOGENERATED FROM AN ACTION DEFINITION ====== Header header actionlib_msgs/GoalStatus status CheckForObjectsResult result ================================================================================ MSG: actionlib_msgs/GoalStatus GoalID goal_id uint8 status uint8 PENDING = 0 # The goal has yet to be processed by the action server uint8 ACTIVE = 1 # The goal is currently being processed by the action server uint8 PREEMPTED = 2 # The goal received a cancel request after it started executing # and has since completed its execution (Terminal State) uint8 SUCCEEDED = 3 # The goal was achieved successfully by the action server (Terminal State) uint8 ABORTED = 4 # The goal was aborted during execution by the action server due # to some failure (Terminal State) uint8 REJECTED = 5 # The goal was rejected by the action server without being processed, # because the goal was unattainable or invalid (Terminal State) uint8 PREEMPTING = 6 # The goal received a cancel request after it started executing # and has not yet completed execution uint8 RECALLING = 7 # The goal received a cancel request before it started executing, # but the action server has not yet confirmed that the goal is canceled uint8 RECALLED = 8 # The goal received a cancel request before it started executing # and was successfully cancelled (Terminal State) uint8 LOST = 9 # An action client can determine that a goal is LOST. This should not be # sent over the wire by an action server #Allow for the user to associate a string with GoalStatus for debugging string text ================================================================================ MSG: darknet_ros_msgs/CheckForObjectsResult # ====== DO NOT MODIFY! AUTOGENERATED FROM AN ACTION DEFINITION ====== # Result definition int16 id darknet_ros_msgs/BoundingBoxes bounding_boxes ================================================================================ MSG: darknet_ros_msgs/BoundingBoxes Header header Header image_header BoundingBox[] bounding_boxes ================================================================================ MSG: darknet_ros_msgs/BoundingBox float64 probability int64 xmin int64 ymin int64 xmax int64 ymax int16 id string Class ================================================================================ MSG: darknet_ros_msgs/CheckForObjectsActionFeedback # ====== DO NOT MODIFY! AUTOGENERATED FROM AN ACTION DEFINITION ====== Header header actionlib_msgs/GoalStatus status CheckForObjectsFeedback feedback ================================================================================ MSG: darknet_ros_msgs/CheckForObjectsFeedback # ====== DO NOT MODIFY! AUTOGENERATED FROM AN ACTION DEFINITION ====== # Feedback definition """ __slots__ = ['action_goal','action_result','action_feedback'] _slot_types = ['darknet_ros_msgs/CheckForObjectsActionGoal','darknet_ros_msgs/CheckForObjectsActionResult','darknet_ros_msgs/CheckForObjectsActionFeedback'] def __init__(self, *args, **kwds): """ Constructor. Any message fields that are implicitly/explicitly set to None will be assigned a default value. The recommend use is keyword arguments as this is more robust to future message changes. You cannot mix in-order arguments and keyword arguments. The available fields are: action_goal,action_result,action_feedback :param args: complete set of field values, in .msg order :param kwds: use keyword arguments corresponding to message field names to set specific fields. """ if args or kwds: super(CheckForObjectsAction, self).__init__(*args, **kwds) #message fields cannot be None, assign default values for those that are if self.action_goal is None: self.action_goal = darknet_ros_msgs.msg.CheckForObjectsActionGoal() if self.action_result is None: self.action_result = darknet_ros_msgs.msg.CheckForObjectsActionResult() if self.action_feedback is None: self.action_feedback = darknet_ros_msgs.msg.CheckForObjectsActionFeedback() else: self.action_goal = darknet_ros_msgs.msg.CheckForObjectsActionGoal() self.action_result = darknet_ros_msgs.msg.CheckForObjectsActionResult() self.action_feedback = darknet_ros_msgs.msg.CheckForObjectsActionFeedback() def _get_types(self): """ internal API method """ return self._slot_types def serialize(self, buff): """ serialize message into buffer :param buff: buffer, ``StringIO`` """ try: _x = self buff.write(_get_struct_3I().pack(_x.action_goal.header.seq, _x.action_goal.header.stamp.secs, _x.action_goal.header.stamp.nsecs)) _x = self.action_goal.header.frame_id length = len(_x) if python3 or type(_x) == unicode: _x = _x.encode('utf-8') length = len(_x) buff.write(struct.pack('<I%ss'%length, length, _x)) _x = self buff.write(_get_struct_2I().pack(_x.action_goal.goal_id.stamp.secs, _x.action_goal.goal_id.stamp.nsecs)) _x = self.action_goal.goal_id.id length = len(_x) if python3 or type(_x) == unicode: _x = _x.encode('utf-8') length = len(_x) buff.write(struct.pack('<I%ss'%length, length, _x)) _x = self buff.write(_get_struct_h3I().pack(_x.action_goal.goal.id, _x.action_goal.goal.image.header.seq, _x.action_goal.goal.image.header.stamp.secs, _x.action_goal.goal.image.header.stamp.nsecs)) _x = self.action_goal.goal.image.header.frame_id length = len(_x) if python3 or type(_x) == unicode: _x = _x.encode('utf-8') length = len(_x) buff.write(struct.pack('<I%ss'%length, length, _x)) _x = self buff.write(_get_struct_2I().pack(_x.action_goal.goal.image.height, _x.action_goal.goal.image.width)) _x = self.action_goal.goal.image.encoding length = len(_x) if python3 or type(_x) == unicode: _x = _x.encode('utf-8') length = len(_x) buff.write(struct.pack('<I%ss'%length, length, _x)) _x = self buff.write(_get_struct_BI().pack(_x.action_goal.goal.image.is_bigendian, _x.action_goal.goal.image.step)) _x = self.action_goal.goal.image.data length = len(_x) # - if encoded as a list instead, serialize as bytes instead of string if type(_x) in [list, tuple]: buff.write(struct.pack('<I%sB'%length, length, *_x)) else: buff.write(struct.pack('<I%ss'%length, length, _x)) _x = self buff.write(_get_struct_3I().pack(_x.action_result.header.seq, _x.action_result.header.stamp.secs, _x.action_result.header.stamp.nsecs)) _x = self.action_result.header.frame_id length = len(_x) if python3 or type(_x) == unicode: _x = _x.encode('utf-8') length = len(_x) buff.write(struct.pack('<I%ss'%length, length, _x)) _x = self buff.write(_get_struct_2I().pack(_x.action_result.status.goal_id.stamp.secs, _x.action_result.status.goal_id.stamp.nsecs)) _x = self.action_result.status.goal_id.id length = len(_x) if python3 or type(_x) == unicode: _x = _x.encode('utf-8') length = len(_x) buff.write(struct.pack('<I%ss'%length, length, _x)) buff.write(_get_struct_B().pack(self.action_result.status.status)) _x = self.action_result.status.text length = len(_x) if python3 or type(_x) == unicode: _x = _x.encode('utf-8') length = len(_x) buff.write(struct.pack('<I%ss'%length, length, _x)) _x = self buff.write(_get_struct_h3I().pack(_x.action_result.result.id, _x.action_result.result.bounding_boxes.header.seq, _x.action_result.result.bounding_boxes.header.stamp.secs, _x.action_result.result.bounding_boxes.header.stamp.nsecs)) _x = self.action_result.result.bounding_boxes.header.frame_id length = len(_x) if python3 or type(_x) == unicode: _x = _x.encode('utf-8') length = len(_x) buff.write(struct.pack('<I%ss'%length, length, _x)) _x = self buff.write(_get_struct_3I().pack(_x.action_result.result.bounding_boxes.image_header.seq, _x.action_result.result.bounding_boxes.image_header.stamp.secs, _x.action_result.result.bounding_boxes.image_header.stamp.nsecs)) _x = self.action_result.result.bounding_boxes.image_header.frame_id length = len(_x) if python3 or type(_x) == unicode: _x = _x.encode('utf-8') length = len(_x) buff.write(struct.pack('<I%ss'%length, length, _x)) length = len(self.action_result.result.bounding_boxes.bounding_boxes) buff.write(_struct_I.pack(length)) for val1 in self.action_result.result.bounding_boxes.bounding_boxes: _x = val1 buff.write(_get_struct_d4qh().pack(_x.probability, _x.xmin, _x.ymin, _x.xmax, _x.ymax, _x.id)) _x = val1.Class length = len(_x) if python3 or type(_x) == unicode: _x = _x.encode('utf-8') length = len(_x) buff.write(struct.pack('<I%ss'%length, length, _x)) _x = self buff.write(_get_struct_3I().pack(_x.action_feedback.header.seq, _x.action_feedback.header.stamp.secs, _x.action_feedback.header.stamp.nsecs)) _x = self.action_feedback.header.frame_id length = len(_x) if python3 or type(_x) == unicode: _x = _x.encode('utf-8') length = len(_x) buff.write(struct.pack('<I%ss'%length, length, _x)) _x = self buff.write(_get_struct_2I().pack(_x.action_feedback.status.goal_id.stamp.secs, _x.action_feedback.status.goal_id.stamp.nsecs)) _x = self.action_feedback.status.goal_id.id length = len(_x) if python3 or type(_x) == unicode: _x = _x.encode('utf-8') length = len(_x) buff.write(struct.pack('<I%ss'%length, length, _x)) buff.write(_get_struct_B().pack(self.action_feedback.status.status)) _x = self.action_feedback.status.text length = len(_x) if python3 or type(_x) == unicode: _x = _x.encode('utf-8') length = len(_x) buff.write(struct.pack('<I%ss'%length, length, _x)) except struct.error as se: self._check_types(struct.error("%s: '%s' when writing '%s'" % (type(se), str(se), str(locals().get('_x', self))))) except TypeError as te: self._check_types(ValueError("%s: '%s' when writing '%s'" % (type(te), str(te), str(locals().get('_x', self))))) def deserialize(self, str): """ unpack serialized message in str into this message instance :param str: byte array of serialized message, ``str`` """ try: if self.action_goal is None: self.action_goal = darknet_ros_msgs.msg.CheckForObjectsActionGoal() if self.action_result is None: self.action_result = darknet_ros_msgs.msg.CheckForObjectsActionResult() if self.action_feedback is None: self.action_feedback = darknet_ros_msgs.msg.CheckForObjectsActionFeedback() end = 0 _x = self start = end end += 12 (_x.action_goal.header.seq, _x.action_goal.header.stamp.secs, _x.action_goal.header.stamp.nsecs,) = _get_struct_3I().unpack(str[start:end]) start = end end += 4 (length,) = _struct_I.unpack(str[start:end]) start = end end += length if python3: self.action_goal.header.frame_id = str[start:end].decode('utf-8') else: self.action_goal.header.frame_id = str[start:end] _x = self start = end end += 8 (_x.action_goal.goal_id.stamp.secs, _x.action_goal.goal_id.stamp.nsecs,) = _get_struct_2I().unpack(str[start:end]) start = end end += 4 (length,) = _struct_I.unpack(str[start:end]) start = end end += length if python3: self.action_goal.goal_id.id = str[start:end].decode('utf-8') else: self.action_goal.goal_id.id = str[start:end] _x = self start = end end += 14 (_x.action_goal.goal.id, _x.action_goal.goal.image.header.seq, _x.action_goal.goal.image.header.stamp.secs, _x.action_goal.goal.image.header.stamp.nsecs,) = _get_struct_h3I().unpack(str[start:end]) start = end end += 4 (length,) = _struct_I.unpack(str[start:end]) start = end end += length if python3: self.action_goal.goal.image.header.frame_id = str[start:end].decode('utf-8') else: self.action_goal.goal.image.header.frame_id = str[start:end] _x = self start = end end += 8 (_x.action_goal.goal.image.height, _x.action_goal.goal.image.width,) = _get_struct_2I().unpack(str[start:end]) start = end end += 4 (length,) = _struct_I.unpack(str[start:end]) start = end end += length if python3: self.action_goal.goal.image.encoding = str[start:end].decode('utf-8') else: self.action_goal.goal.image.encoding = str[start:end] _x = self start = end end += 5 (_x.action_goal.goal.image.is_bigendian, _x.action_goal.goal.image.step,) = _get_struct_BI().unpack(str[start:end]) start = end end += 4 (length,) = _struct_I.unpack(str[start:end]) start = end end += length self.action_goal.goal.image.data = str[start:end] _x = self start = end end += 12 (_x.action_result.header.seq, _x.action_result.header.stamp.secs, _x.action_result.header.stamp.nsecs,) = _get_struct_3I().unpack(str[start:end]) start = end end += 4 (length,) = _struct_I.unpack(str[start:end]) start = end end += length if python3: self.action_result.header.frame_id = str[start:end].decode('utf-8') else: self.action_result.header.frame_id = str[start:end] _x = self start = end end += 8 (_x.action_result.status.goal_id.stamp.secs, _x.action_result.status.goal_id.stamp.nsecs,) = _get_struct_2I().unpack(str[start:end]) start = end end += 4 (length,) = _struct_I.unpack(str[start:end]) start = end end += length if python3: self.action_result.status.goal_id.id = str[start:end].decode('utf-8') else: self.action_result.status.goal_id.id = str[start:end] start = end end += 1 (self.action_result.status.status,) = _get_struct_B().unpack(str[start:end]) start = end end += 4 (length,) = _struct_I.unpack(str[start:end]) start = end end += length if python3: self.action_result.status.text = str[start:end].decode('utf-8') else: self.action_result.status.text = str[start:end] _x = self start = end end += 14 (_x.action_result.result.id, _x.action_result.result.bounding_boxes.header.seq, _x.action_result.result.bounding_boxes.header.stamp.secs, _x.action_result.result.bounding_boxes.header.stamp.nsecs,) = _get_struct_h3I().unpack(str[start:end]) start = end end += 4 (length,) = _struct_I.unpack(str[start:end]) start = end end += length if python3: self.action_result.result.bounding_boxes.header.frame_id = str[start:end].decode('utf-8') else: self.action_result.result.bounding_boxes.header.frame_id = str[start:end] _x = self start = end end += 12 (_x.action_result.result.bounding_boxes.image_header.seq, _x.action_result.result.bounding_boxes.image_header.stamp.secs, _x.action_result.result.bounding_boxes.image_header.stamp.nsecs,) = _get_struct_3I().unpack(str[start:end]) start = end end += 4 (length,) = _struct_I.unpack(str[start:end]) start = end end += length if python3: self.action_result.result.bounding_boxes.image_header.frame_id = str[start:end].decode('utf-8') else: self.action_result.result.bounding_boxes.image_header.frame_id = str[start:end] start = end end += 4 (length,) = _struct_I.unpack(str[start:end]) self.action_result.result.bounding_boxes.bounding_boxes = [] for i in range(0, length): val1 = darknet_ros_msgs.msg.BoundingBox() _x = val1 start = end end += 42 (_x.probability, _x.xmin, _x.ymin, _x.xmax, _x.ymax, _x.id,) = _get_struct_d4qh().unpack(str[start:end]) start = end end += 4 (length,) = _struct_I.unpack(str[start:end]) start = end end += length if python3: val1.Class = str[start:end].decode('utf-8') else: val1.Class = str[start:end] self.action_result.result.bounding_boxes.bounding_boxes.append(val1) _x = self start = end end += 12 (_x.action_feedback.header.seq, _x.action_feedback.header.stamp.secs, _x.action_feedback.header.stamp.nsecs,) = _get_struct_3I().unpack(str[start:end]) start = end end += 4 (length,) = _struct_I.unpack(str[start:end]) start = end end += length if python3: self.action_feedback.header.frame_id = str[start:end].decode('utf-8') else: self.action_feedback.header.frame_id = str[start:end] _x = self start = end end += 8 (_x.action_feedback.status.goal_id.stamp.secs, _x.action_feedback.status.goal_id.stamp.nsecs,) = _get_struct_2I().unpack(str[start:end]) start = end end += 4 (length,) = _struct_I.unpack(str[start:end]) start = end end += length if python3: self.action_feedback.status.goal_id.id = str[start:end].decode('utf-8') else: self.action_feedback.status.goal_id.id = str[start:end] start = end end += 1 (self.action_feedback.status.status,) = _get_struct_B().unpack(str[start:end]) start = end end += 4 (length,) = _struct_I.unpack(str[start:end]) start = end end += length if python3: self.action_feedback.status.text = str[start:end].decode('utf-8') else: self.action_feedback.status.text = str[start:end] return self except struct.error as e: raise genpy.DeserializationError(e) #most likely buffer underfill def serialize_numpy(self, buff, numpy): """ serialize message with numpy array types into buffer :param buff: buffer, ``StringIO`` :param numpy: numpy python module """ try: _x = self buff.write(_get_struct_3I().pack(_x.action_goal.header.seq, _x.action_goal.header.stamp.secs, _x.action_goal.header.stamp.nsecs)) _x = self.action_goal.header.frame_id length = len(_x) if python3 or type(_x) == unicode: _x = _x.encode('utf-8') length = len(_x) buff.write(struct.pack('<I%ss'%length, length, _x)) _x = self buff.write(_get_struct_2I().pack(_x.action_goal.goal_id.stamp.secs, _x.action_goal.goal_id.stamp.nsecs)) _x = self.action_goal.goal_id.id length = len(_x) if python3 or type(_x) == unicode: _x = _x.encode('utf-8') length = len(_x) buff.write(struct.pack('<I%ss'%length, length, _x)) _x = self buff.write(_get_struct_h3I().pack(_x.action_goal.goal.id, _x.action_goal.goal.image.header.seq, _x.action_goal.goal.image.header.stamp.secs, _x.action_goal.goal.image.header.stamp.nsecs)) _x = self.action_goal.goal.image.header.frame_id length = len(_x) if python3 or type(_x) == unicode: _x = _x.encode('utf-8') length = len(_x) buff.write(struct.pack('<I%ss'%length, length, _x)) _x = self buff.write(_get_struct_2I().pack(_x.action_goal.goal.image.height, _x.action_goal.goal.image.width)) _x = self.action_goal.goal.image.encoding length = len(_x) if python3 or type(_x) == unicode: _x = _x.encode('utf-8') length = len(_x) buff.write(struct.pack('<I%ss'%length, length, _x)) _x = self buff.write(_get_struct_BI().pack(_x.action_goal.goal.image.is_bigendian, _x.action_goal.goal.image.step)) _x = self.action_goal.goal.image.data length = len(_x) # - if encoded as a list instead, serialize as bytes instead of string if type(_x) in [list, tuple]: buff.write(struct.pack('<I%sB'%length, length, *_x)) else: buff.write(struct.pack('<I%ss'%length, length, _x)) _x = self buff.write(_get_struct_3I().pack(_x.action_result.header.seq, _x.action_result.header.stamp.secs, _x.action_result.header.stamp.nsecs)) _x = self.action_result.header.frame_id length = len(_x) if python3 or type(_x) == unicode: _x = _x.encode('utf-8') length = len(_x) buff.write(struct.pack('<I%ss'%length, length, _x)) _x = self buff.write(_get_struct_2I().pack(_x.action_result.status.goal_id.stamp.secs, _x.action_result.status.goal_id.stamp.nsecs)) _x = self.action_result.status.goal_id.id length = len(_x) if python3 or type(_x) == unicode: _x = _x.encode('utf-8') length = len(_x) buff.write(struct.pack('<I%ss'%length, length, _x)) buff.write(_get_struct_B().pack(self.action_result.status.status)) _x = self.action_result.status.text length = len(_x) if python3 or type(_x) == unicode: _x = _x.encode('utf-8') length = len(_x) buff.write(struct.pack('<I%ss'%length, length, _x)) _x = self buff.write(_get_struct_h3I().pack(_x.action_result.result.id, _x.action_result.result.bounding_boxes.header.seq, _x.action_result.result.bounding_boxes.header.stamp.secs, _x.action_result.result.bounding_boxes.header.stamp.nsecs)) _x = self.action_result.result.bounding_boxes.header.frame_id length = len(_x) if python3 or type(_x) == unicode: _x = _x.encode('utf-8') length = len(_x) buff.write(struct.pack('<I%ss'%length, length, _x)) _x = self buff.write(_get_struct_3I().pack(_x.action_result.result.bounding_boxes.image_header.seq, _x.action_result.result.bounding_boxes.image_header.stamp.secs, _x.action_result.result.bounding_boxes.image_header.stamp.nsecs)) _x = self.action_result.result.bounding_boxes.image_header.frame_id length = len(_x) if python3 or type(_x) == unicode: _x = _x.encode('utf-8') length = len(_x) buff.write(struct.pack('<I%ss'%length, length, _x)) length = len(self.action_result.result.bounding_boxes.bounding_boxes) buff.write(_struct_I.pack(length)) for val1 in self.action_result.result.bounding_boxes.bounding_boxes: _x = val1 buff.write(_get_struct_d4qh().pack(_x.probability, _x.xmin, _x.ymin, _x.xmax, _x.ymax, _x.id)) _x = val1.Class length = len(_x) if python3 or type(_x) == unicode: _x = _x.encode('utf-8') length = len(_x) buff.write(struct.pack('<I%ss'%length, length, _x)) _x = self buff.write(_get_struct_3I().pack(_x.action_feedback.header.seq, _x.action_feedback.header.stamp.secs, _x.action_feedback.header.stamp.nsecs)) _x = self.action_feedback.header.frame_id length = len(_x) if python3 or type(_x) == unicode: _x = _x.encode('utf-8') length = len(_x) buff.write(struct.pack('<I%ss'%length, length, _x)) _x = self buff.write(_get_struct_2I().pack(_x.action_feedback.status.goal_id.stamp.secs, _x.action_feedback.status.goal_id.stamp.nsecs)) _x = self.action_feedback.status.goal_id.id length = len(_x) if python3 or type(_x) == unicode: _x = _x.encode('utf-8') length = len(_x) buff.write(struct.pack('<I%ss'%length, length, _x)) buff.write(_get_struct_B().pack(self.action_feedback.status.status)) _x = self.action_feedback.status.text length = len(_x) if python3 or type(_x) == unicode: _x = _x.encode('utf-8') length = len(_x) buff.write(struct.pack('<I%ss'%length, length, _x)) except struct.error as se: self._check_types(struct.error("%s: '%s' when writing '%s'" % (type(se), str(se), str(locals().get('_x', self))))) except TypeError as te: self._check_types(ValueError("%s: '%s' when writing '%s'" % (type(te), str(te), str(locals().get('_x', self))))) def deserialize_numpy(self, str, numpy): """ unpack serialized message in str into this message instance using numpy for array types :param str: byte array of serialized message, ``str`` :param numpy: numpy python module """ try: if self.action_goal is None: self.action_goal = darknet_ros_msgs.msg.CheckForObjectsActionGoal() if self.action_result is None: self.action_result = darknet_ros_msgs.msg.CheckForObjectsActionResult() if self.action_feedback is None: self.action_feedback = darknet_ros_msgs.msg.CheckForObjectsActionFeedback() end = 0 _x = self start = end end += 12 (_x.action_goal.header.seq, _x.action_goal.header.stamp.secs, _x.action_goal.header.stamp.nsecs,) = _get_struct_3I().unpack(str[start:end]) start = end end += 4 (length,) = _struct_I.unpack(str[start:end]) start = end end += length if python3: self.action_goal.header.frame_id = str[start:end].decode('utf-8') else: self.action_goal.header.frame_id = str[start:end] _x = self start = end end += 8 (_x.action_goal.goal_id.stamp.secs, _x.action_goal.goal_id.stamp.nsecs,) = _get_struct_2I().unpack(str[start:end]) start = end end += 4 (length,) = _struct_I.unpack(str[start:end]) start = end end += length if python3: self.action_goal.goal_id.id = str[start:end].decode('utf-8') else: self.action_goal.goal_id.id = str[start:end] _x = self start = end end += 14 (_x.action_goal.goal.id, _x.action_goal.goal.image.header.seq, _x.action_goal.goal.image.header.stamp.secs, _x.action_goal.goal.image.header.stamp.nsecs,) = _get_struct_h3I().unpack(str[start:end]) start = end end += 4 (length,) = _struct_I.unpack(str[start:end]) start = end end += length if python3: self.action_goal.goal.image.header.frame_id = str[start:end].decode('utf-8') else: self.action_goal.goal.image.header.frame_id = str[start:end] _x = self start = end end += 8 (_x.action_goal.goal.image.height, _x.action_goal.goal.image.width,) = _get_struct_2I().unpack(str[start:end]) start = end end += 4 (length,) = _struct_I.unpack(str[start:end]) start = end end += length if python3: self.action_goal.goal.image.encoding = str[start:end].decode('utf-8') else: self.action_goal.goal.image.encoding = str[start:end] _x = self start = end end += 5 (_x.action_goal.goal.image.is_bigendian, _x.action_goal.goal.image.step,) = _get_struct_BI().unpack(str[start:end]) start = end end += 4 (length,) = _struct_I.unpack(str[start:end]) start = end end += length self.action_goal.goal.image.data = str[start:end] _x = self start = end end += 12 (_x.action_result.header.seq, _x.action_result.header.stamp.secs, _x.action_result.header.stamp.nsecs,) = _get_struct_3I().unpack(str[start:end]) start = end end += 4 (length,) = _struct_I.unpack(str[start:end]) start = end end += length if python3: self.action_result.header.frame_id = str[start:end].decode('utf-8') else: self.action_result.header.frame_id = str[start:end] _x = self start = end end += 8 (_x.action_result.status.goal_id.stamp.secs, _x.action_result.status.goal_id.stamp.nsecs,) = _get_struct_2I().unpack(str[start:end]) start = end end += 4 (length,) = _struct_I.unpack(str[start:end]) start = end end += length if python3: self.action_result.status.goal_id.id = str[start:end].decode('utf-8') else: self.action_result.status.goal_id.id = str[start:end] start = end end += 1 (self.action_result.status.status,) = _get_struct_B().unpack(str[start:end]) start = end end += 4 (length,) = _struct_I.unpack(str[start:end]) start = end end += length if python3: self.action_result.status.text = str[start:end].decode('utf-8') else: self.action_result.status.text = str[start:end] _x = self start = end end += 14 (_x.action_result.result.id, _x.action_result.result.bounding_boxes.header.seq, _x.action_result.result.bounding_boxes.header.stamp.secs, _x.action_result.result.bounding_boxes.header.stamp.nsecs,) = _get_struct_h3I().unpack(str[start:end]) start = end end += 4 (length,) = _struct_I.unpack(str[start:end]) start = end end += length if python3: self.action_result.result.bounding_boxes.header.frame_id = str[start:end].decode('utf-8') else: self.action_result.result.bounding_boxes.header.frame_id = str[start:end] _x = self start = end end += 12 (_x.action_result.result.bounding_boxes.image_header.seq, _x.action_result.result.bounding_boxes.image_header.stamp.secs, _x.action_result.result.bounding_boxes.image_header.stamp.nsecs,) = _get_struct_3I().unpack(str[start:end]) start = end end += 4 (length,) = _struct_I.unpack(str[start:end]) start = end end += length if python3: self.action_result.result.bounding_boxes.image_header.frame_id = str[start:end].decode('utf-8') else: self.action_result.result.bounding_boxes.image_header.frame_id = str[start:end] start = end end += 4 (length,) = _struct_I.unpack(str[start:end]) self.action_result.result.bounding_boxes.bounding_boxes = [] for i in range(0, length): val1 = darknet_ros_msgs.msg.BoundingBox() _x = val1 start = end end += 42 (_x.probability, _x.xmin, _x.ymin, _x.xmax, _x.ymax, _x.id,) = _get_struct_d4qh().unpack(str[start:end]) start = end end += 4 (length,) = _struct_I.unpack(str[start:end]) start = end end += length if python3: val1.Class = str[start:end].decode('utf-8') else: val1.Class = str[start:end] self.action_result.result.bounding_boxes.bounding_boxes.append(val1) _x = self start = end end += 12 (_x.action_feedback.header.seq, _x.action_feedback.header.stamp.secs, _x.action_feedback.header.stamp.nsecs,) = _get_struct_3I().unpack(str[start:end]) start = end end += 4 (length,) = _struct_I.unpack(str[start:end]) start = end end += length if python3: self.action_feedback.header.frame_id = str[start:end].decode('utf-8') else: self.action_feedback.header.frame_id = str[start:end] _x = self start = end end += 8 (_x.action_feedback.status.goal_id.stamp.secs, _x.action_feedback.status.goal_id.stamp.nsecs,) = _get_struct_2I().unpack(str[start:end]) start = end end += 4 (length,) = _struct_I.unpack(str[start:end]) start = end end += length if python3: self.action_feedback.status.goal_id.id = str[start:end].decode('utf-8') else: self.action_feedback.status.goal_id.id = str[start:end] start = end end += 1 (self.action_feedback.status.status,) = _get_struct_B().unpack(str[start:end]) start = end end += 4 (length,) = _struct_I.unpack(str[start:end]) start = end end += length if python3: self.action_feedback.status.text = str[start:end].decode('utf-8') else: self.action_feedback.status.text = str[start:end] return self except struct.error as e: raise genpy.DeserializationError(e) #most likely buffer underfill _struct_I = genpy.struct_I def _get_struct_I(): global _struct_I return _struct_I _struct_B = None def _get_struct_B(): global _struct_B if _struct_B is None: _struct_B = struct.Struct("<B") return _struct_B _struct_d4qh = None def _get_struct_d4qh(): global _struct_d4qh if _struct_d4qh is None: _struct_d4qh = struct.Struct("<d4qh") return _struct_d4qh _struct_h3I = None def _get_struct_h3I(): global _struct_h3I if _struct_h3I is None: _struct_h3I = struct.Struct("<h3I") return _struct_h3I _struct_BI = None def _get_struct_BI(): global _struct_BI if _struct_BI is None: _struct_BI = struct.Struct("<BI") return _struct_BI _struct_3I = None def _get_struct_3I(): global _struct_3I if _struct_3I is None: _struct_3I = struct.Struct("<3I") return _struct_3I _struct_2I = None def _get_struct_2I(): global _struct_2I if _struct_2I is None: _struct_2I = struct.Struct("<2I") return _struct_2I

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examples/similarity_conf.py

allenye0119/Amaze

0

161744

<filename>examples/similarity_conf.py """ This module gives an example of how to configure similarity measures computation. """ from __future__ import (absolute_import, division, print_function, unicode_literals) from amaze import KNNBasic from amaze import Dataset from amaze.model_selection import cross_validate # Load the movielens-100k dataset. data = Dataset.load_builtin('ml-100k') # Example using cosine similarity sim_options = {'name': 'cosine', 'user_based': False # compute similarities between items } algo = KNNBasic(sim_options=sim_options) cross_validate(algo, data, verbose=True) # Example using pearson_baseline similarity sim_options = {'name': 'pearson_baseline', 'shrinkage': 0 # no shrinkage } algo = KNNBasic(sim_options=sim_options) cross_validate(algo, data, verbose=True)

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tests/contrib/test_cached_dataset.py

mmathys/bagua

635

61948

from bagua.torch_api.contrib.cached_dataset import CachedDataset from torch.utils.data.dataset import Dataset import numpy as np import logging import unittest from tests import skip_if_cuda_available logging.basicConfig(level=logging.DEBUG) class MyDataset(Dataset): def __init__(self, size): self.size = size self.dataset = [(np.random.rand(5, 2), np.random.rand(1)) for _ in range(size)] def __getitem__(self, item): return self.dataset[item] def __len__(self): return self.size class TestCacheDataset(unittest.TestCase): def check_dataset(self, dataset, cache_dataset): for _ in range(10): for _, _ in enumerate(cache_dataset): pass for i in range(len(dataset)): self.assertTrue((dataset[i][0] == cache_dataset[i][0]).all()) self.assertTrue((dataset[i][1] == cache_dataset[i][1]).all()) @skip_if_cuda_available() def test_redis(self): dataset1 = MyDataset(102) dataset2 = MyDataset(102) cache_dataset1 = CachedDataset( dataset1, backend="redis", dataset_name="d1", ) cache_dataset2 = CachedDataset( dataset2, backend="redis", dataset_name="d2", ) cache_dataset1.cache_loader.store.clear() self.check_dataset(dataset1, cache_dataset1) self.assertEqual(cache_dataset1.cache_loader.num_keys(), len(dataset1)) self.check_dataset(dataset2, cache_dataset2) self.assertEqual( cache_dataset2.cache_loader.num_keys(), len(dataset1) + len(dataset2) ) if __name__ == "__main__": unittest.main()

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ImageDeleter.py

warifp/InstagramPostAndDelete

4

114591

import os def delete_png(): list = os.listdir() images = [x for x in list if ".png" in x] for x in images: os.remove(x)

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viscyc/detector/__init__.py

csachs/viscyc

1

1601104

import argparse import importlib import importlib.util import json import socket import sys import time from pathlib import Path from urllib.error import HTTPError, URLError from urllib.request import Request, urlopen import cv2 class RawSender: def __init__(self, host, port): self.host, self.port = host, port def send(self, **kwargs): message_data = json.dumps(kwargs).encode() try: s = socket.socket(socket.AF_INET, socket.SOCK_STREAM) s.connect((self.host, self.port)) s.send(message_data) s.close() except ConnectionError: pass class HttpSender: def __init__(self, url): self.url = url def send(self, **kwargs): json_data = json.dumps(kwargs).encode() try: urlopen( Request(self.url, headers={'Content-type': 'application/json'}), data=json_data, ) except (URLError, HTTPError, ConnectionError): pass def create_argparser(): parser = argparse.ArgumentParser( description="watch cross-trainer revolutions via computer vision" ) parser.add_argument('--capture-device', type=int, default=0) parser.add_argument('--capture-width', type=int, default=640) parser.add_argument('--capture-height', type=int, default=480) parser.add_argument('--roi-width', type=int, default=64) parser.add_argument('--roi-height', type=int, default=64) parser.add_argument('--roi-placement-horizontal', type=int, default=-1) parser.add_argument('--roi-placement-vertical', type=int, default=-1) parser.add_argument('--threshold', type=int, default=100) parser.add_argument('--quiet', default=False, action='store_true') parser.add_argument('--no-display', default=False, action='store_true') parser.add_argument('--single-jpeg-preview', default=False, action='store_true') parser.add_argument('--power-expression', default='1.0*rpm+0') parser.add_argument('--load', action='append', default=[]) parser.add_argument('--target', type=str, default='127.0.0.1:7654') parser.add_argument( '--http-target', type=str, default='http://127.0.0.1:8901/cadence' ) return parser def load_file_as_module(file_path): name = '_' + Path(file_path).stem spec = importlib.util.spec_from_file_location(name, file_path) module = importlib.util.module_from_spec(spec) sys.modules[name] = module spec.loader.exec_module(module) return module def find_power_function(hooks, default): calculate_watt = default for hook in hooks: if getattr(hook, 'calculate_watt', None): calculate_watt = getattr(hook, 'calculate_watt') print( "Using %s.%s as power calculation function." % ( calculate_watt.__module__, calculate_watt.__name__, ) ) return calculate_watt def create_power_expression_function(power_expression): frag = f''' def power_expression(rpm): return ({power_expression}) ''' l, g = {}, {} exec(frag, g, l) return l['power_expression'] def get_sizes(args): size = (args.capture_width, args.capture_height) position = [size[0] // 2, size[1] // 2] if args.roi_placement_horizontal != -1: position[0] = args.roi_placement_horizontal if args.roi_placement_vertical != -1: position[1] = args.roi_placement_vertical roi = args.roi_width, args.roi_height return size, position, roi def prepare_hooks(args): hooks = hooks_load(args.load) hooks += [RawSender(args.target.split(':')[0], int(args.target.split(':')[1]))] if args.http_target: hooks += [HttpSender(args.http_target)] return hooks def hooks_load(load): hooks = [load_file_as_module(file_name) for file_name in load] return hooks def hooks_call(hooks, what, *args, **kwargs): for hook in hooks: func = getattr(hook, what, None) if func: try: func(*args, **kwargs) except BaseException as e: print("Exception occurred while calling %r" % func) print(e) def prepare_preview(display_frame, color, text, position, roi): cv2.rectangle( display_frame, (position[0] - roi[0] // 2, position[1] - roi[1] // 2), (position[0] + roi[0] // 2, position[1] + roi[1] // 2), color, ) cv2.putText( display_frame, text, org=(0, 24), fontFace=cv2.FONT_HERSHEY_SIMPLEX, fontScale=1.0, color=(0, 255, 0), ) return display_frame COLOR_INSIDE = (0, 0, 255) COLOR_OUTSIDE = (0, 255, 0) def main_loop( capture_function, position, roi, threshold, hook_send, quiet, preview, return_jpg=False, ): last_timepoint = None last_acquisition = None inside = False last_rpm = None rev_count = 0 while True: frame_input = capture_function() timepoint = time.time() frame_grayscale = cv2.cvtColor(frame_input, cv2.COLOR_BGR2GRAY) _, frame_binary = cv2.threshold( frame_grayscale, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU ) frame_roi = frame_binary[ position[1] - roi[1] // 2 : position[1] + roi[1] // 2, position[0] - roi[0] // 2 : position[0] + roi[0] // 2, ] mean_value = frame_roi.mean() color = COLOR_OUTSIDE text = "RPM: % 2.2f Revs: %04d" % ( last_rpm if last_rpm else 0.0, rev_count, ) if last_acquisition: text += " FPS: %2.1f" % (1.0 / (timepoint - last_acquisition)) last_acquisition = timepoint if mean_value < threshold: color = COLOR_INSIDE if not inside and last_timepoint: delta = timepoint - last_timepoint rpm = (1.0 / delta) * 60.0 rev_count += 1 last_rpm = rpm hook_send(rev_count=rev_count, rpm=rpm) if not quiet: print(text) inside = True last_timepoint = timepoint else: inside = False if preview: display_frame = prepare_preview( frame_input.copy(), color, text, position, roi ) if return_jpg: _, result = cv2.imencode('.jpg', display_frame) return result.tostring() else: cv2.imshow("Viscyc Detector", display_frame) if cv2.waitKey(1) & 0xff == ord('q'): break def main(args=None, return_instead_print=False): if args is None: args = sys.argv[1:] parser = create_argparser() args = parser.parse_args(args) power_expression = args.power_expression calculate_watt = create_power_expression_function(power_expression) hooks = prepare_hooks(args) calculate_watt = find_power_function(hooks, default=calculate_watt) size, position, roi = get_sizes(args) capture = cv2.VideoCapture(args.capture_device) capture.set(cv2.CAP_PROP_FRAME_WIDTH, size[0]) capture.set(cv2.CAP_PROP_FRAME_HEIGHT, size[1]) def capture_function(): return capture.read()[1] def hook_send(rev_count=0, rpm=0): return hooks_call( hooks, 'send', watts=calculate_watt(rpm), rev_count=rev_count, rpm=rpm ) try: hooks_call(hooks, 'start') hook_send() print("Beginning processing") result = main_loop( capture_function, position, roi, args.threshold, hook_send, args.quiet, preview=not args.no_display or args.single_jpeg_preview, return_jpg=args.single_jpeg_preview, ) if result: if return_instead_print: return result else: sys.stdout.buffer.write(result) finally: capture.release() cv2.destroyAllWindows() hook_send() hooks_call(hooks, 'stop') print("Halted processing")

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scraper/twitter.py

AgentHH/SBCFireDispatch

2

1612302

#!/usr/bin/python import oauth2 import psycopg2 import simplejson import urllib2 from config import * token = oauth2.Token(key=access_token_key, secret=access_token_secret) consumer = oauth2.Consumer(key=consumer_key, secret=consumer_secret) signature_method = oauth2.SignatureMethod_HMAC_SHA1() def get(url, **args): method = 'GET' parameters = args request = oauth2.Request.from_consumer_and_token(consumer, token=token, http_method=method, http_url=url, parameters=parameters) request.sign_request(signature_method, consumer, token) if method == "POST": post = request.to_postdata() else: post = None url = request.to_url() try: file = urllib2.urlopen(url, post) data = file.read() except urllib2.HTTPError, e: print e.read() raise e data = simplejson.loads(data) return data

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attack.py

RishavMz/strategic_siege

0

145729

import pygame from data.troops import * from data.defenses import * from data.attack_levels import * import math WIDTH = 800 HEIGHT = 600 state = 0 level = 0 totalDamageDone = 0 totalDamageTaken = 0 class Table: def __init__(self,width,height): self.width=width self.height=height self.canvas = canvas = pygame.display.set_mode((self.width,self.height)) def draw(self, st): self.canvas = pygame.display.set_mode((self.width,self.height)) pygame.display.set_caption(st) self.canvas.fill((0,0,0)) pygame.draw.rect(self.canvas, (255,255,255), (30,90, self.width - 60, self.height-90),2) pygame.draw.rect(self.canvas, (255,255,255), (500,90, 300, self.height-90),1) class Cards: def __init__(self,table,posx,state,number): self.table = table self.posx = posx self.posy = 10 self.height = 60 self.width = 50 self.state = state self.number = number def checkmouse(self,posx): global state if(posx >= self.posx) and (posx <= (self.posx+self.width)): state = self.state def draw(self): pygame.draw.rect(self.table.canvas, (255,255,255), (self.posx,self.posy, self.width, self.height)) def assemble(): for i in levels[level].showcards(): cards.append(Cards(table1,i[0],i[1],i[2])) for i in levels[level].showdefenses(): if(i[0]==1): defence.append(Cannon(table1,i[1],i[2])) elif(i[0]==2): defence.append(Tower(table1,i[1],i[2])) elif(i[0]==3): defence.append(Bunker(table1,i[1],i[2])) table1 = Table(WIDTH, HEIGHT) Table_name="Battle_Machine" run = True army = [] cards = [] defence = [] assemble() cardinfantryImage = pygame.image.load( 'images/cardinfantry.png') cardarcherImage = pygame.image.load( 'images/cardarcher.png') cardcavalryImage = pygame.image.load( 'images/cardcavalry.png') cardheavycavalryImage = pygame.image.load('images/cardheavycavalry.png') cannonImage = pygame.image.load( 'images/cannon.png') towerImage = pygame.image.load( 'images/tower.png') bunkerImage = pygame.image.load( 'images/bunker.png') infantryImage = pygame.image.load( 'images/infantry.png') archerImage = pygame.image.load( 'images/archer.png') cavalryImage = pygame.image.load( 'images/cavalry.png') heavycavalryImage = pygame.image.load( 'images/heavycavalry.png') displaySpace = pygame.display.set_mode((600,700)) def distanceCalc(body1, body2): return math.sqrt((body1.posx - body2.posx)**2 + (body1.posy - body2.posy)**2) play = 0 while run: table1.draw(Table_name) for i in range(len(cards)): if(state == (i+1)): for j in range(cards[i].number): pygame.draw.rect(table1.canvas, (255,255,255), (cards[i].posx + (j*4),75,2, 5)) if(len(army)>0): play = 1 if(play==1 and len(defence)==0): army=[] cards = [] level += 1 if(level == len(levels)): run = False print("\nYou won\n") print("Total damage done : ",totalDamageDone) print("Total damage taken: ",totalDamageTaken) print("\n") break assemble() fallen = [] for i in range(len(army)): army[i].draw() if(army[i].health<=0): fallen.append(i) for i in fallen: army.pop(i) for i in defence: i.draw() for i in army: if(len(defence)>0): nearest , distance = None , 1000000 for j in range(len(defence)): dist = distanceCalc(i,defence[j]) if(dist<distance): nearest = j distance = dist if(nearest is not None): if(abs(i.posx - defence[nearest].posx)<=i.range): if(abs(i.posy - defence[nearest].posy)<=i.range): defence[nearest].damage(i.strength) totalDamageDone += i.strength pygame.draw.line(table1.canvas,(68,85,90),(defence[nearest].posx-10,defence[nearest].posy-10),(i.posx,i.posy)) if(defence[nearest].health<=0): defence.pop(nearest) break elif(i.posx > defence[j].posx): i.movex(i.posx-i.speed) elif(i.posx < defence[j].posx): i.movex(i.posx+i.speed) if(abs(i.posy - defence[nearest].posy)<=i.range): pass elif(i.posy > defence[j].posy): i.movey(i.posy-i.speed) elif(i.posy < defence[j].posy): i.movey(i.posy+i.speed) if(len(army)>0): for i in range(len(defence)): damagedata = defence[i].attack(army) if(damagedata is not None): pygame.draw.line(table1.canvas,(195,115,119),(defence[i].posx-10,defence[i].posy-10),(army[damagedata].posx,army[damagedata].posy)) army[damagedata].damage(defence[i].power) totalDamageTaken += defence[i].power cardsrem = 0 for i in cards: cardsrem += i.number if(len(army)==0 and len(defence)>0 and cardsrem == 0): run = False print("\n GAME OVER\n") print("Total damage done : ",totalDamageDone) print("Total damage taken: ",totalDamageTaken) print("\n") pygame.time.delay(10) for event in pygame.event.get(): if event.type == pygame.QUIT: run = False if event.type == pygame.MOUSEBUTTONUP: pos = pygame.mouse.get_pos() if(pos[1]<=60): for i in cards: i.checkmouse(pos[0]) elif(pos[1]>=100 and pos[0]<500): if(state==1): if(cards[0].number>0): army.append(Infantry(table1,pos[0],pos[1])) cards[0].number -= 1 elif(state==2): if(cards[1].number>0): army.append(Archer(table1,pos[0],pos[1])) cards[1].number -= 1 elif(state==3): if(cards[2].number>0): army.append(Cavalry(table1,pos[0],pos[1])) cards[2].number -= 1 elif(state==4): if(cards[3].number>0): army.append(HeavyCavalry(table1,pos[0],pos[1])) cards[3].number -= 1 if event.type == pygame.KEYDOWN: if event.key == pygame.K_LEFT: if(state>1): state -= 1 if event.key == pygame.K_RIGHT: if(state<=3): state += 1 displaySpace.blit(cardinfantryImage,(cards[0].posx,cards[0].posy)) displaySpace.blit(cardarcherImage,(cards[1].posx,cards[1].posy)) displaySpace.blit(cardcavalryImage,(cards[2].posx,cards[2].posy)) displaySpace.blit(cardheavycavalryImage,(cards[3].posx,cards[3].posy)) for i in defence: if(i.type=="cannon"): displaySpace.blit(cannonImage,(i.posx-22,i.posy-22)) elif(i.type=="tower"): displaySpace.blit(towerImage,(i.posx-15,i.posy-15)) elif(i.type=="bunker"): displaySpace.blit(bunkerImage,(i.posx-22,i.posy-22)) for i in army: if(i.type=="infantry"): displaySpace.blit(infantryImage,(i.posx-10,i.posy-10)) elif(i.type=="archer"): displaySpace.blit(archerImage,(i.posx-8,i.posy-8)) elif(i.type=="cavalry"): displaySpace.blit(cavalryImage,(i.posx-6,i.posy-6)) elif(i.type=="heavycavalry"): displaySpace.blit(heavycavalryImage,(i.posx-15,i.posy-15)) pygame.display.update() pygame.quit()

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hmc/applications/banana/banana.py

JamesBrofos/Thresholds-in-Hamiltonian-Monte-Carlo

1

10740

from typing import Callable, Tuple import numpy as np def posterior_factory(y: np.ndarray, sigma_y: float, sigma_theta: float) -> Tuple[Callable]: """The banana distribution is a distribution that exhibits a characteristic banana-shaped ridge that resembles the posterior that can emerge from models that are not identifiable. The distribution is the posterior of the following generative model. y ~ Normal(theta[0] + theta[1]**2, sigma_sq_y) theta[i] ~ Normal(0, sigma_sq_theta) Args: y: Observations of the banana model. sigma_y: Standard deviation of the observations. sigma_theta: Standard deviation of prior over linear coefficients. Returns: log_posterior: Function to compute the log-posterior. metric: Function to compute the Fisher information metric. euclidean_auxiliaries: Function to compute the log-posterior and its gradient. riemannian_auxiliaries: Function to compute the log-posterior, the gradient of the log-posterior, the Fisher information metric, and the derivatives of the Fisher information metric. """ sigma_sq_y = np.square(sigma_y) sigma_sq_theta = np.square(sigma_theta) def log_posterior(theta: np.ndarray) -> float: """The banana-shaped distribution posterior. Args: theta: Linear coefficients. Returns: out: The log-posterior of the banana-shaped distribution. """ p = theta[0] + np.square(theta[1]) ll = -0.5 / sigma_sq_y * np.square(y - p).sum() lp = -0.5 / sigma_sq_theta * np.square(theta).sum() return ll + lp def grad_log_posterior(theta: np.ndarray) -> np.ndarray: """Gradient of the banana-shaped distribution with respect to the linear coefficients. Args: theta: Linear coefficients. Returns: out: The gradient of the log-posterior of the banana-shaped distribution with respect to the linear coefficients. """ p = theta[0] + np.square(theta[1]) d = np.sum(y - p) ga = d / sigma_sq_y - theta[0] / sigma_sq_theta gb = 2.0*d / sigma_sq_y * theta[1] - theta[1] / sigma_sq_theta return np.hstack((ga, gb)) def metric(theta: np.ndarray) -> np.ndarray: """The Fisher information is the negative expected outer product of the gradient of the posterior. Args: theta: Linear coefficients. Returns: G: The Fisher information metric of the banana-shaped distribution. """ n = y.size s = 2.0*n*theta[1] / sigma_sq_y G = np.array([[n / sigma_sq_y + 1.0 / sigma_sq_theta, s], [s, 4.0*n*np.square(theta[1]) / sigma_sq_y + 1.0 / sigma_sq_theta]]) return G def grad_metric(theta: np.ndarray) -> np.ndarray: """The gradient of the Fisher information metric with respect to the linear coefficients. Args: theta: Linear coefficients. Returns: dG: The gradient of the Fisher information metric with respect to the linear coefficients. """ n = y.size dG = np.array([ [[0.0, 0.0], [0.0, 2.0*n / sigma_sq_y]], [[0.0, 2.0*n / sigma_sq_y], [0.0, 8.0*n*theta[1] / sigma_sq_y]] ]) return dG def euclidean_auxiliaries(theta: np.ndarray) -> Tuple[np.ndarray]: """Function to compute the log-posterior and the gradient of the log-posterior. Args: theta: Linear coefficients. Returns: lp: The log-posterior of the banana-shaped distribution. glp: The gradient of the log-posterior of the banana-shaped distribution with respect to the linear coefficients. """ lp = log_posterior(theta) glp = grad_log_posterior(theta) return lp, glp def riemannnian_auxiliaries(theta: np.ndarray) -> Tuple[np.ndarray]: """Function to compute the log-posterior, the gradient of the log-posterior, the Fisher information metric and the derivatives of the Fisher information metric. Args: theta: Linear coefficients. Returns: lp: The log-posterior of the banana-shaped distribution. glp: The gradient of the log-posterior of the banana-shaped distribution with respect to the linear coefficients. G: The Fisher information metric of the banana-shaped distribution. dG: The gradient of the Fisher information metric with respect to the linear coefficients. """ lp = log_posterior(theta) glp = grad_log_posterior(theta) G = metric(theta) dG = grad_metric(theta) return lp, glp, G, dG def log_posterior_and_metric(theta: np.ndarray) -> Tuple[np.ndarray]: lp = log_posterior(theta) G = metric(theta) return lp, G return log_posterior, metric, log_posterior_and_metric, euclidean_auxiliaries, riemannnian_auxiliaries def generate_data(t: float, sigma_y: float, sigma_theta: float, num_obs: int) -> np.ndarray: """Generate data from the banana-shaped posterior distribution. Args: t: Free-parameter determining the thetas. sigma_y: Noise standard deviation. sigma_theta: Prior standard deviation over the thetas. num_obs: Number of observations to generate. Returns: theta: Linear coefficients of the banana-shaped distribution. y: Observations from the unidentifiable model. """ theta = np.array([t, np.sqrt(1.0 - t)]) y = theta[0] + np.square(theta[1]) + sigma_y * np.random.normal(size=(num_obs, )) return theta, y

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tests/test_cfloader.py

shachibista/mloader

0

52926

<filename>tests/test_cfloader.py #!/usr/bin/env python """Tests for `cfloader` package.""" import json from pathlib import Path import pytest import cfloader from cfloader.loader import Loader from cfloader.readers import ConfigFileNotFoundError def test_cfloader_can_open_path_objects(): config_filepath = Path("tests/examples/config.json") loader = cfloader.open(config_filepath) def test_cfloader_can_open_str_paths(): config_filepath = "tests/examples/config.json" loader = cfloader.open(config_filepath) def test_cfloader_can_open_dicts(): config_dict = {} loader = cfloader.open(config_dict) assert isinstance(loader, Loader) assert loader.config == config_dict def test_cfloader_open_returns_loader(): config_filepath = Path("tests/examples/config.json") loader = cfloader.open(config_filepath) assert isinstance(loader, Loader) assert loader.config == json.loads(config_filepath.read_text()) def test_cfloader_can_open_zip_files(): archive_path = "tests/examples/archive.zip" loader = cfloader.open(cfloader.Archive(archive_path)) assert isinstance(loader, Loader) import zipfile with zipfile.ZipFile(archive_path, "r") as archive_fs: with archive_fs.open("config.json", "r") as config_file: assert loader.config == json.load(config_file) def test_cfloader_can_open_tar_files(): archive_path = "tests/examples/archive.tar" loader = cfloader.open(cfloader.Archive(archive_path)) assert isinstance(loader, Loader) import tarfile with tarfile.open(archive_path, "r") as archive_fs: config_fileinfo = archive_fs.getmember("config.json") config_file = archive_fs.extractfile(config_fileinfo) assert loader.config == json.loads(config_file.read()) def test_cfloader_can_open_tar_gz_files(): archive_path = "tests/examples/archive.tar.gz" loader = cfloader.open(cfloader.Archive(archive_path)) assert isinstance(loader, Loader) import tarfile with tarfile.open(archive_path, "r:gz") as archive_fs: config_fileinfo = archive_fs.getmember("config.json") config_file = archive_fs.extractfile(config_fileinfo) assert loader.config == json.loads(config_file.read()) # disabled because extractfile returns a <ExFileObject name=None> # def test_cfloader_can_open_tar_bz_files(): # archive_path = "tests/examples/archive.tar.bz" # loader = cfloader.open(cfloader.Archive(archive_path)) # assert isinstance(loader, Loader) # import tarfile # with tarfile.open(archive_path, "r:bz2") as archive_fs: # config_fileinfo = archive_fs.getmember("config.json") # config_file = archive_fs.extractfile(config_fileinfo) # breakpoint() # assert loader.config == json.loads(config_file.read()) def test_cfloader_can_open_tar_xz_files(): archive_path = "tests/examples/archive.tar.xz" loader = cfloader.open(cfloader.Archive(archive_path)) assert isinstance(loader, Loader) import tarfile with tarfile.open(archive_path, "r:xz") as archive_fs: config_fileinfo = archive_fs.getmember("config.json") config_file = archive_fs.extractfile(config_fileinfo) assert loader.config == json.loads(config_file.read()) def test_cfloader_complains_if_config_not_found_in_zip_archive(): archive_path = "tests/examples/archive.zip" with pytest.raises(ConfigFileNotFoundError): loader = cfloader.open(cfloader.Archive(archive_path, "cfg.json")) def test_cfloader_complains_if_config_not_found_in_tar_archive(): archive_path = "tests/examples/archive.tar.xz" with pytest.raises(ConfigFileNotFoundError): loader = cfloader.open(cfloader.Archive(archive_path, "cfg.json"))

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tests/python/gaia-ui-tests/gaiatest/tests/accessibility/phone/test_a11y_callscreen_visibility.py

gregarndt/gaia

1

155498

<filename>tests/python/gaia-ui-tests/gaiatest/tests/accessibility/phone/test_a11y_callscreen_visibility.py # This Source Code Form is subject to the terms of the Mozilla Public # License, v. 2.0. If a copy of the MPL was not distributed with this # file, You can obtain one at http://mozilla.org/MPL/2.0/. from gaiatest import GaiaTestCase from gaiatest.mocks.mock_contact import MockContact from gaiatest.apps.phone.app import Phone class TestAccessibilityCallscreenVisibility(GaiaTestCase): def setUp(self): GaiaTestCase.setUp(self) self.contact = MockContact() self.phone = Phone(self.marionette) self.phone.launch() def test_a11y_callscreen_visibility(self): test_phone_number = self.contact['tel']['value'] # Make a call self.phone.a11y_make_call(test_phone_number) # Check that the keyboard view is hidden by default self.assertTrue(self.accessibility.is_hidden(self.marionette.find_element( *self.phone.call_screen._views_locator))) # Check that the incoming container is hidden by default self.assertTrue(self.accessibility.is_hidden(self.marionette.find_element( *self.phone.call_screen._incoming_container_locator))) # Check that the handled call hangup button is hidden by default self.assertTrue(self.accessibility.is_hidden(self.marionette.find_element( *self.phone.call_screen._hangup_button_locator))) # Hang up self.phone.a11y_hang_up()

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Chapter04/simple_examples/functional_example.py

arifmudi/Machine-Learning-Engineering-with-Python

67

1612700

import pandas as pd import numpy as np data = [ ['The', 'Business', 'Centre', '15', 'Stevenson', 'Lane'], ['6', 'Mossvale', 'Road'], ['Studio', '7', 'Tottenham', 'Court', 'Road'] ] def len_strings_in_list(data_list): return list(map(lambda x: len(x), data_list)) def list_of_list_func_results(list_func, list_of_lists): return list(map(lambda x: list_func(x), list_of_lists))

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one_page/customapp/templatetags/testtag.py

aptuz/mezzanine_onepage_theme

0

56669

from __future__ import unicode_literals from future.builtins import int from collections import defaultdict from django.core.urlresolvers import reverse from django.template.defaultfilters import linebreaksbr, urlize from mezzanine import template from mezzanine.conf import settings from mezzanine.generic.forms import ThreadedCommentForm from mezzanine.generic.models import ThreadedComment from mezzanine.utils.importing import import_dotted_path from mezzanine.pages.models import Page, RichTextPage register = template.Library() @register.assignment_tag def allpages(): page_fields = [ 'content', 'created', 'description', 'expiry_date', 'gen_description', u'id', 'keywords', u'keywords_string', 'publish_date', 'short_url', 'slug', 'status', 'title', 'titles', 'updated'] output = [] # import pdb;pdb.set_trace() AllPages = RichTextPage.objects.all() for item in AllPages: temp = {} for fld in page_fields: temp[fld] = getattr(item, fld) output.append(temp) return { 'pages': output } @register.filter() def remove_slash(value): return '#' + value[1:-1] @register.filter() def lower(value): # import pdb;pdb.set_trace() return value.lower()

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h5parm/datapack.py

Joshuaalbert/h5parm

0

76104

<reponame>Joshuaalbert/h5parm<filename>h5parm/datapack.py import tables as tb import os import numpy as np import astropy.units as au import astropy.time as at import astropy.coordinates as ac import sys import time import itertools import re import logging logger = logging.getLogger(__name__) from h5parm.maintenance import deprecated def _load_array_file(array_file): '''Loads a csv where each row is x,y,z in geocentric ITRS coords of the antennas''' types = np.dtype({'names': ['station', 'X_ITRS', 'Y_ITRS', 'Z_ITRS'], 'formats': ['S16', np.double, np.double, np.double, np.double]}) d = np.genfromtxt(array_file, comments='#', delimiter=',', dtype=types) labels = np.array(d['station'].astype(str)) locs = ac.SkyCoord(x=d['X_ITRS'] * au.m, y=d['Y_ITRS'] * au.m, z=d['Z_ITRS'] * au.m, frame='itrs') Nantenna = int(np.size(d['X_ITRS'])) diameters = None return np.array(labels).astype(np.str_), locs.cartesian.xyz.to(au.m).value.transpose() def update_h5parm(old_h5parm, new_h5parm): """ Clones an old H5parm typically created with LoSoTO, that only has readonly access. :param old_h5parm: :param new_h5parm: :return: """ logger.info("Updating {}".format(old_h5parm)) select = dict(ant=None, time=None, dir=None, freq=None, pol=None) old = DataPack(old_h5parm, readonly=True) new = DataPack(new_h5parm, readonly=False) logger.info("Created {}".format(new_h5parm)) solsets = old.solsets for solset in solsets: old.current_solset = solset old.select(**select) antenna_labels, antennas = old.antennas patch_names, directions = old.directions # Sometimes the antennas are not set properly in the original datapack if solset in new.solsets: new.delete_solset(solset) if np.sum(antennas) == 0.: new.add_solset(solset, array_file=DataPack.lofar_array, directions=directions, patch_names=patch_names) else: new.add_solset(solset, antenna_labels=antenna_labels, antennas=antennas, directions=directions, patch_names=patch_names) new.current_solset = solset soltabs = old.soltabs for soltab in soltabs: if soltab in new.soltabs: new.delete_soltab(soltab) axes = {k: v for (v, k) in zip(*old.soltab_axes(soltab))} antenna_labels, antennas = old.get_antennas(axes['ant']) patch_names, directions = old.get_directions(axes['dir']) timestamps, times = old.get_times(axes['time']) pol_labels, pols = old.get_pols(axes['pol']) vals, _ = old.get_soltab(soltab, weight=False) weight_vals, _ = old.get_soltab(soltab, weight=True) if 'freq' in axes.keys(): freq_labels, freqs = old.get_freqs(axes['freq']) new.add_soltab(soltab, values=vals, weights=weight_vals, weightDtype='f16', time=times.mjd * 86400., pol=pol_labels, ant=antenna_labels, dir=patch_names, freq=freqs) else: new.add_soltab(soltab, values=vals, weights=weight_vals, weightDtype='f16', time=times.mjd * 86400., pol=pol_labels, ant=antenna_labels, dir=patch_names) class DataPack(object): # _H: tb.File _arrays = os.path.dirname(sys.modules["h5parm"].__file__) lofar_array = os.path.join(_arrays, 'arrays/lofar.antenna.cfg') lofar_array_hba = os.path.join(_arrays, 'arrays/lofar.hba.antenna.cfg') gmrt_array = os.path.join(_arrays, 'arrays/gmrtPos.cfg') def __init__(self, filename, readonly=False): if isinstance(filename, DataPack): filename = filename.filename self.filename = os.path.abspath(filename) if not os.path.isfile(self.filename) and readonly: raise IOError("File {} doesn't exist, and in readonly mode".format(self.filename)) self.readonly = readonly self._H = None self._contexts_open = 0 self._selection = None self._current_solset = None self.axes_order = ['pol', 'dir', 'ant', 'freq', 'time'] self.axes_atoms = {'pol': (np.str_, tb.StringAtom(16)), 'dir': (np.str_, tb.StringAtom(128)), 'ant': (np.str_, tb.StringAtom(16)), 'freq': (np.float64, tb.Float64Atom()), 'time': (np.float64, tb.Float64Atom())} if len(self.solsets) > 0: self.current_solset = self.solsets[0] @property def axes_order(self): return self._axes_order @axes_order.setter def axes_order(self, axes): if not isinstance(axes, (tuple, list)): raise ValueError("axes should be a list or tuple. {}".format(type(axes))) order = [] for axis in axes: if axis not in ['ant', 'dir', 'freq', 'pol', 'time']: raise ValueError("Axis {} not a valid axis.".format(axis)) if axis in order: raise ValueError("Found duplicate in ordering. {}".format(axes)) order.append(axis) self._axes_order = order @property def readonly(self): return self._readonly @readonly.setter def readonly(self, value): if not isinstance(value, bool): raise ValueError("Readonly must be a bool.") self._readonly = value def __enter__(self): if self._contexts_open == 0: self._H = tb.open_file(self.filename, mode='r' if self.readonly else 'a') self._contexts_open += 1 return self def __exit__(self, exc_type, exc_val, exc_tb): if self._contexts_open == 1: self._H.close() self._H = None self._contexts_open -= 1 @property def current_solset(self): return self._current_solset @current_solset.setter def current_solset(self, solset): if solset not in self.solsets: raise ValueError("Solset {} does not exist.".format(solset)) self._current_solset = solset logger.info("Set current solset to: {}".format(self._current_solset)) def set_current_solset(self, solset): if solset not in self.solsets: raise ValueError("Solset {} does not exist.".format(solset)) self._current_solset = solset logger.info("Set current solset to: {}".format(self._current_solset)) @property def solsets(self): with self: return [k for k, v in self._H.root._v_groups.items()] @property def soltabs(self): with self: if self.current_solset is None: raise ValueError("Current solset is None.") solset_group = self._H.root._v_groups[self.current_solset] return [k for k, v in solset_group._v_groups.items()] @deprecated("Use current_solset and add_solset") def switch_solset(self, solset, antenna_labels=None, antennas=None, array_file=None, directions=None, patch_names=None): self.add_solset(solset, antenna_labels, antennas, array_file, directions, patch_names) def add_solset(self, solset, antenna_labels=None, antennas=None, array_file=None, patch_names=None, directions=None): """ Create a solset. :param solset: str Name of solset :param antenna_labels, antennas: see set_antennas :param array_file: str array file to load, lofar array if None :params patch_names, directions: see set_directions :param directions: :return: """ if solset in self.solsets: logger.warning("Solset {} already exists.".format(solset)) self.current_solset = solset return with self: self._H.create_group(self._H.root, solset, title='Solset: {}'.format(solset)) self.current_solset = solset self.add_antenna_table() if antennas is None: antenna_labels, antennas = _load_array_file(self.lofar_array_hba if array_file is None else array_file) self.set_antennas(antenna_labels, antennas) self.add_directions_table() if directions is not None: self.set_directions(patch_names, directions) logger.info("Created solset {}.".format(solset)) def add_soltab(self, soltab, values=None, weights=None, weightDtype='f16', **axes): if soltab in self.soltabs: logger.warning('Soltab {} already exists.'.format(soltab)) return with self: if self.current_solset is None: raise ValueError("Current solset is None.") solset_group = self._H.root._v_groups[self.current_solset] self._H.create_group(solset_group, soltab, "Soltab: {}".format(soltab)) soltab_group = solset_group._v_groups[soltab] soltab_group._v_attrs['parmdb_type'] = "" shape = [] ordered_axes = [] for axis_name in self.axes_order: if axis_name not in axes.keys(): logger.info("Soltab missing axis {}".format(axis_name)) continue shape.append(len(axes[axis_name])) ordered_axes.append(axis_name) self._H.create_array(soltab_group, axis_name, obj=np.array(axes[axis_name]), title='Axis: {}'.format(axis_name)) # ,atom=self.axes_atoms[axis_name][1]) if values is None: values = np.zeros(shape) self._H.create_array(soltab_group, 'val', obj=values.astype(np.float64), atom=tb.Float64Atom()) val_leaf = soltab_group._v_leaves['val'] val_leaf.attrs['AXES'] = ','.join(ordered_axes) if weightDtype not in ['f16', 'f32', 'f64']: raise ValueError("Allowed weight dtypes are 'f16','f32', 'f64'") if weights is None: weights = np.ones(shape) if weightDtype == 'f16': self._H.create_array(soltab_group, 'weight', obj=weights.astype(np.float16), title='Weights', atom=tb.Float16Atom()) elif weightDtype == 'f32': self._H.create_array(soltab_group, 'weight', obj=weights.astype(np.float32), title='Weights', atom=tb.Float32Atom()) elif weightDtype == 'f64': self._H.create_array(soltab_group, 'weight', obj=weights.astype(np.float64), title='Weights', atom=tb.Float64Atom()) weight_leaf = soltab_group._v_leaves['weight'] weight_leaf.attrs['AXES'] = ','.join(ordered_axes) logger.info("Created soltab {}/{}".format(self.current_solset, soltab)) def delete_soltab(self, soltab): if soltab not in self.soltabs: raise ValueError("Soltab {} not in solset {}.".format(soltab, self.current_solset)) with self: solset_group = self._H.root._v_groups[self.current_solset] soltab_group = solset_group._v_groups[soltab] soltab_group._f_remove(recursive=True) def delete_solset(self, solset): if solset not in self.solsets: raise ValueError("Solset {} appears not to exist.".format(solset)) with self: solset_group = self._H.root._v_groups[solset] solset_group._f_remove(recursive=True) if solset == self.current_solset: logger.warning("Setting current solset to None because you deleted it.") self._current_solset = None logger.info("Deleted solset {}.".format(solset)) def add_antenna_table(self): with self: if self.current_solset is None: raise ValueError("Current solset is None.") solset_group = self._H.root._v_groups[self.current_solset] class Antenna(tb.IsDescription): name = tb.StringCol(16, pos=1) position = tb.Float64Col(shape=3, dflt=0.0, pos=2) # tb.Col(np.float64,3, np.zeros(3, dtype=np.float64),pos=2) # descriptor = np.dtype([('name', np.str_, 16), ('position', np.float64, 3)]) self._H.create_table(solset_group, 'antenna', Antenna, title='Antenna names and positions', expectedrows=62) logger.info("Created antenna table.") def add_directions_table(self): with self: if self.current_solset is None: raise ValueError("Current solset is None.") solset_group = self._H.root._v_groups[self.current_solset] class Direction(tb.IsDescription): name = tb.StringCol(128, pos=1) dir = tb.Float64Col(shape=2, dflt=0.0, pos=2) # tb.Col(np.float64, 2, np.zeros(2, dtype=np.float64), pos=2) # descriptor = np.dtype([('name', np.str_, 16), ('position', np.float64, 3)]) self._H.create_table(solset_group, 'source', Direction, title='Direction names and directions', expectedrows=35) logger.info("Created direction table.") def set_antennas(self, antenna_labels, antennas): with self: if self.current_solset is None: raise ValueError("Current solset is None.") solset_group = self._H.root._v_groups[self.current_solset] if 'antenna' not in solset_group._v_leaves: logger.info("antenna not in leaves. Adding.") self.add_antenna_table() antenna_table = solset_group._v_leaves['antenna'] antenna_table.remove_rows(0) antenna_table.append(list(zip(antenna_labels, antennas))) logger.info("Set the antenna table.") def set_directions(self, patch_names, directions): if patch_names is None: patch_names = ["patch_{:03d}".format(i) for i in range(len(directions))] with self: if self.current_solset is None: raise ValueError("Current solset is None.") solset_group = self._H.root._v_groups[self.current_solset] if 'source' not in solset_group._v_leaves: self.add_directions_table() direction_table = solset_group._v_leaves['source'] direction_table.remove_rows(0) direction_table.append(list(zip(patch_names, directions))) logger.info("Set the direction table.") def save_array_file(self, array_file): with self: ants = self._solset.getAnt() labels = [] locs = [] for label, pos in ants.items(): labels.append(label) locs.append(pos) Na = len(labels) with open(array_file, 'w') as f: f.write('# Created on {0} by <NAME>\n'.format(time.strftime("%a %c", time.localtime()))) f.write('# ITRS(m)\n') f.write('# X\tY\tZ\tlabels\n') i = 0 while i < Na: f.write( '{0:1.9e}\t{1:1.9e}\t{2:1.9e}\t{4}'.format(locs[i][0], locs[i][1], locs[i][2], labels[i])) if i < Na - 1: f.write('\n') i += 1 @property def antennas(self): with self: if self.current_solset is None: raise ValueError("Current solset is None.") solset_group = self._H.root._v_groups[self.current_solset] if 'antenna' not in solset_group._v_leaves: self.add_antenna_table() antenna_table = solset_group._v_leaves['antenna'] return antenna_table.col('name'), antenna_table.col('position') @property def directions(self): with self: if self.current_solset is None: raise ValueError("Current solset is None.") solset_group = self._H.root._v_groups[self.current_solset] if 'source' not in solset_group._v_leaves: self.add_directions_table() direction_table = solset_group._v_leaves['source'] return direction_table.col('name'), direction_table.col('dir') def __repr__(self): def grouper(n, iterable, fillvalue=None): "grouper(3, 'ABCDEFG', 'x') --> ABC DEF Gxx" args = [iter(iterable)] * n return itertools.zip_longest(*args, fillvalue=fillvalue) _temp_solset = self.current_solset info = "==== DataPack: {} ====\n".format(os.path.abspath(self.filename)) for solset in self.solsets: self.current_solset = solset info += "=== solset: {} ===\n".format(solset) info += "Directions: \n" for i, src_name1 in enumerate(zip(*self.directions)): info += "{} -> {}\t{}\n".format(i, src_name1[0], list(src_name1[1])) info += "\nStations: \n" for i, ant1 in enumerate(zip(*self.antennas)): info += "{} -> {}\t{}\n".format(i, ant1[0], list(ant1[1])) for soltab in self.soltabs: info += "== soltab: {} ==\n".format(soltab) shape = [len(axis_vals) for axis_vals, axis in zip(*self.soltab_axes(soltab))] axes = [axis for axis_vals, axis in zip(*self.soltab_axes(soltab))] info += "shape: {}\n".format(shape) info += "axes: {}\n".format(axes) # for axis_vals, axis, size, dtype in zip(*self.soltab_axes(soltab)): # info += "Axis: {} {} {}\n{}\n".format(axis,size, dtype,list(axis_vals)) self.current_solset = _temp_solset return info def select(self, **axes): self._selection = {} for axis_name in self.axes_order: if axis_name not in axes.keys(): continue if isinstance(axes[axis_name], int): self._selection[axis_name] = [axes[axis_name]] else: self._selection[axis_name] = axes[axis_name] def select_all(self): self._selection = None @property def allowed_soltab_prefixes(self): # return [soltab.replace("000","") for soltab in self.soltabs] return ['phase', 'amplitude', 'tec', 'clock', 'const'] def soltab_axes(self, soltab): with self: if soltab not in self.soltabs: logger.warning('Soltab {} does not exist.'.format(soltab)) return with self: if self.current_solset is None: raise ValueError("Current solset is None.") solset_group = self._H.root._v_groups[self.current_solset] soltab_group = solset_group._v_groups[soltab] val_leaf = soltab_group._v_leaves['val'] try: axes = val_leaf.attrs['AXES'].split(',') except TypeError: axes = [s.decode().lower() for s in val_leaf.attrs['AXES'].split(b',')] shape = [] type = [] vals = [] for axis in axes: axis_vals = soltab_group._v_leaves[axis].read() vals.append(axis_vals) shape.append(len(axis_vals)) type.append(np.array(axis_vals).dtype) return vals, axes def get_selection(self, soltab): if self._selection is None: self._selection = {} if soltab not in self.soltabs: raise ValueError('Soltab {} does not exist.'.format(soltab)) if self.current_solset is None: raise ValueError("Current solset is None.") selection = [] # goal is to reduce everything to slices if possible for efficient usage of pytables for axis_val, axis in zip(*self.soltab_axes(soltab)): axis_selection = self._selection.get(axis, None) if axis_selection is None: selection.append(slice(None, None, None)) elif isinstance(axis_selection, slice): selection.append(axis_selection) elif isinstance(axis_selection, (tuple, list)): list_select = [] for element in axis_selection: if isinstance(element, int): if element >= len(axis_val): raise ValueError( "Selecting index greater than length of axis {} {}".format(element, axis_val)) list_select.append(element) else: idx = np.where(axis_val.astype(type(element)) == element)[0] if len(idx) == 0: raise ValueError("Element not in axis {} {}".format(element, axis_val)) list_select.append(idx[0]) selection.append(list_select) elif isinstance(axis_selection, str): axis_val = np.asarray(axis_val) is_pattern = [] for idx, element in enumerate(axis_val.astype(type(axis_selection))): if re.search(axis_selection, element) is not None: is_pattern.append(idx) selection.append(is_pattern) else: raise ValueError("Unable to parse {}".format(axis_selection)) # replace all lists with slices if possible: limitation of only one list per indexing corrected_selection = [] for sel in selection: _sel = sel if isinstance(sel, list): if sel[0] != np.min(sel) or sel[-1] != np.max(sel): break if len(sel) == 1: _sel = slice(sel[0], sel[0] + 1, 1) else: try_slice = slice(sel[0], sel[-1] + 1, (sel[-1] - sel[0]) // (len(sel) - 1)) comp_list = list(range(sel[0], sel[-1] + 1, (sel[-1] - sel[0]) // (len(sel) - 1))) if comp_list == sel: _sel = try_slice corrected_selection.append(_sel) num_lists = sum([1 if isinstance(sel, list) else 0 for sel in corrected_selection]) if num_lists > 1: raise IndexError("Due to a limitation, only one fancy indexing can be applied per pytables getattr.") return tuple(corrected_selection) def get_axes_perm(self, actual_axes, want_axes): """ Get the permutation that changes the actual stored axes to prefered axes order stored in self.axes_order. :param actual_axes: list of str The order of axes in an array :return: tuple of int """ if not isinstance(actual_axes, (list, tuple)): raise TypeError("actual axes must be a list or tuple of str") actual_axes = list(actual_axes) if not isinstance(want_axes, (list, tuple)): raise TypeError("want axes must be a list or tuple of str") want_axes = list(want_axes) for a in want_axes: if a not in actual_axes: raise ValueError("Missing {} in {}.".format(a, actual_axes)) return tuple([actual_axes.index(a) for a in want_axes]) def get_soltab(self, soltab, weight=False): with self: selection = self.get_selection(soltab) soltab_axes_vals, soltab_axes = self.soltab_axes(soltab) ### # assumes the desired axes are in self.ordered_axes want_axes = [a for a in self.axes_order if a in soltab_axes] actual_axes = soltab_axes perm = self.get_axes_perm(actual_axes, want_axes) solset_group = self._H.root._v_groups[self.current_solset] soltab_group = solset_group._v_groups[soltab] if weight: leaf = soltab_group._v_leaves['weight'] else: leaf = soltab_group._v_leaves['val'] out_axes = {name: np.array(vals)[selection[i]] for i, (vals, name) in enumerate(zip(soltab_axes_vals, soltab_axes))} out_vals = leaf.__getitem__(selection).transpose(perm) return out_vals, out_axes def set_soltab(self, soltab, value, weight=False): """ Sets the values of soltab, according to the current selection. :param soltab: str :param value: np.array or array like Should have shape of self.axes_order :param weight: bool Whether you are setting weights or not :return: """ with self: selection = self.get_selection(soltab) solset_group = self._H.root._v_groups[self.current_solset] soltab_group = solset_group._v_groups[soltab] _, soltab_axes = self.soltab_axes(soltab) ### # want_axes = soltab_axes actual_axes = [a for a in self.axes_order if a in soltab_axes] perm = self.get_axes_perm(actual_axes, want_axes) if weight: leaf = soltab_group._v_leaves['weight'] else: leaf = soltab_group._v_leaves['val'] leaf.__setitem__(selection, value.transpose(perm)) def __getattr__(self, tab): """ Get a value in allowed soltabs or pass on to underlying. :param tab: :return: np.array Shape as determined by self.axes_order """ # with self: # tabs = self._solset.getSoltabNames() tabs = self.allowed_soltab_prefixes tabs = ["weights_{}".format(t) for t in tabs] + ["axes_{}".format(t) for t in tabs] + tabs weight = False axes = False if any([tab.startswith(t) for t in tabs]): if tab.startswith("weights_"): tab = "".join(tab.split('weights_')[1:]) weight = True if tab.startswith("axes_"): tab = "".join(tab.split('axes_')[1:]) axes = True with self: soltab = "{}000".format(tab) selection = self.get_selection(soltab) soltab_axes_vals, soltab_axes = self.soltab_axes(soltab) ### # assumes the desired axes are in self.ordered_axes want_axes = [a for a in self.axes_order if a in soltab_axes] actual_axes = soltab_axes perm = self.get_axes_perm(actual_axes, want_axes) if not axes: solset_group = self._H.root._v_groups[self.current_solset] soltab_group = solset_group._v_groups[soltab] if weight: leaf = soltab_group._v_leaves['weight'] else: leaf = soltab_group._v_leaves['val'] out_axes = {name: np.array(vals)[selection[i]] for i, (vals, name) in enumerate(zip(soltab_axes_vals, soltab_axes))} out_vals = leaf.__getitem__(selection).transpose(perm) return out_vals, out_axes else: out_axes = {name: np.array(vals)[selection[i]] for i, (vals, name) in enumerate(zip(soltab_axes_vals, soltab_axes))} return out_axes else: return object.__getattribute__(self, tab) def __setattr__(self, tab, value): """ Links any attribute with an "axis name" to getValuesAxis("axis name") also links val and weight to the relative arrays. Parameter ---------- axis : str The axis name. value : array The array of the right shape for selection. Assumes the values to set are in self.axes_order """ # with self: # tabs = self._solset.getSoltabNames() tabs = self.allowed_soltab_prefixes tabs = ["weights_{}".format(t) for t in tabs] + ["axes_{}".format(t) for t in tabs] + tabs weight = False axes = False if any([tab.startswith(t) for t in tabs]): if tab.startswith("weights_"): tab = "".join(tab.split('weights_')[1:]) weight = True if tab.startswith("axes_"): tab = "".join(tab.split('axes_')[1:]) axes = True with self: soltab = "{}000".format(tab) selection = self.get_selection(soltab) solset_group = self._H.root._v_groups[self.current_solset] soltab_group = solset_group._v_groups[soltab] _, soltab_axes = self.soltab_axes(soltab) ### # want_axes = soltab_axes actual_axes = [a for a in self.axes_order if a in soltab_axes] perm = self.get_axes_perm(actual_axes, want_axes) if not axes: if weight: leaf = soltab_group._v_leaves['weight'] else: leaf = soltab_group._v_leaves['val'] leaf.__setitem__(selection, value.transpose(perm)) else: if not isinstance(value, dict): raise ("Axes must come in dict of 'name':vals") for i, (k, v) in enumerate(value.items()): axis_vals = soltab_group._v_leaves[k] axis_vals[selection[i]] = v else: object.__setattr__(self, tab, value) @property def ref_ant(self): with self: antenna_labels, antennas = self.antennas return antenna_labels[0] @property def array_center(self): with self: _, antennas = self.get_antennas(None) center = antennas.cartesian.xyz[:, 0] center = ac.SkyCoord(x=center[0], y=center[1], z=center[2], frame='itrs') return center @property def mean_array_loc(self): with self: _, antennas = self.get_antennas(None) center = np.mean(antennas.cartesian.xyz, axis=1) center = ac.SkyCoord(x=center[0], y=center[1], z=center[2], frame='itrs') return center def get_antennas(self, ants): with self: antenna_labels, antennas = self.antennas if ants is None: lookup = slice(None, None, None) else: ants = np.array(ants).astype(antenna_labels.dtype) lookup = [] for a in ants: if a not in antenna_labels: raise ValueError("Antenna not found in solset {} {}".format(a, antenna_labels)) lookup.append(np.where(a == antenna_labels)[0][0]) antennas = antennas[lookup, :] return antenna_labels[lookup], ac.SkyCoord(antennas[:, 0] * au.m, antennas[:, 1] * au.m, antennas[:, 2] * au.m, frame='itrs') @property def pointing_center(self): with self: _, directions = self.get_directions(None) ra_mean = np.mean(directions.transform_to('icrs').ra) dec_mean = np.mean(directions.transform_to('icrs').dec) dir = ac.SkyCoord(ra_mean, dec_mean, frame='icrs') return dir def get_directions(self, dirs): with self: patch_names, directions = self.directions if dirs is None: lookup = slice(None, None, None) else: dirs = np.array(dirs).astype(patch_names.dtype) lookup = [] for a in dirs: if a not in patch_names: raise ValueError("Direction not found in solset {} {}".format(a, patch_names)) lookup.append(np.where(a == patch_names)[0][0]) directions = directions[lookup, :] return patch_names[lookup], ac.SkyCoord(directions[:, 0] * au.rad, directions[:, 1] * au.rad, frame='icrs') def get_times(self, times): """ times are stored as mjs """ times = at.Time(times / 86400., format='mjd') return times.isot, times def get_freqs(self, freqs): labs = ['{:.1f}MHz'.format(f / 1e6) for f in freqs] return np.array(labs), freqs*au.Hz def get_pols(self, pols): with self: return pols, np.arange(len(pols), dtype=np.int32)

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examples/para.py

nagareproject/renderers-rml

0

1609041

<gh_stars>0 # Encoding: UTF-8 # -- # Copyright (c) 2008-2021 Net-ng. # All rights reserved. # # This software is licensed under the BSD License, as described in # the file LICENSE.txt, which you should have received as part of # this distribution. # -- from nagare.renderers import rml def sample(output): r = rml.Renderer() with r.document(invariant=1): with r.template(pageSize='letter', leftMargin=72, showBoundary=1): with r.pageTemplate(id='main', pageSize='letter portrait'): with r.pageGraphics: r << r.setFont(name='Helvetica-BoldOblique', size=18) r << r.drawRightString('RML2PDF Test Suite', x=523, y=800) with r.textAnnotation: r << r.param('0,0,1,1', name='Rect') r << r.param(3, name='F') r << r.param(6, name='escape') r << '''X::PDF PX(S) MT(PINK) ''' r << r.frame(id='first', x1='1in', y1='1in', width='6.27in', height='9.69in') with r.stylesheet: with r.initialize: r << r.alias(id='style.normal', value='style.Normal') r << r.paraStyle(name='h1', fontName='Helvetica-BoldOblique', fontSize=32, leading=36) r << r.paraStyle(name='normal', fontName='Helvetica', fontSize=10, leading=12) r << r.paraStyle(name='spaced', fontName='Helvetica', fontSize=10, leading=12, spaceBefore=12, spaceAfter=12) with r.story: with r.para(style='normal'): r << u'Il était là. Hello World. This is a normal paragraph. Blah ' r << r.font('IPO ', color='red') r << 'blah blah blah blah growth forecast blah ' r << 'blah blah forecast blah.Blah blah blah blah blah blah blah blah blah blah blah profit blah blah blah blah blah ' r << 'blah blah blah blah blah IPO.Blah blah blah blah blah blah blah reengineering blah growth blah blah blah ' r << 'proactive direction strategic blah blah blah forward-thinking blah.Blah blah doubletalk blah blah blah blah ' r << 'blah profit blah blah growth blah blah blah blah blah profit.Blah blah blah blah venture capital blah blah blah ' r << 'blah blah forward-thinking blah.' with r.para(style='normal'): r << 'This is another normal paragraph. Blah IPO blah blah blah blah growth forecast blah ' r << 'blah blah forecast blah.Blah blah blah blah blah blah blah blah blah blah blah profit blah blah blah blah blah ' r << 'blah blah blah blah blah IPO.Blah blah blah blah blah blah blah reengineering blah growth blah blah blah ' r << 'proactive direction strategic blah blah blah forward-thinking blah.Blah blah doubletalk blah blah blah blah ' r << 'blah profit blah blah growth blah blah blah blah blah profit.Blah blah blah blah venture capital blah blah blah ' r << 'blah blah forward-thinking blah.' r << r.para('I should NOT have a tiny leading space in front of me!', style='normal') r << r.para('This is spaced. There should be 12 points before and after.', style='spaced') with r.para(style='normal'): r << 'Hello World. This is a normal paragraph. Blah IPO blah blah blah blah growth forecast blah ' r << 'blah blah forecast blah.Blah blah blah blah blah blah blah blah blah blah blah profit blah blah blah blah blah ' r << 'blah blah blah blah blah IPO.Blah blah blah blah blah blah blah reengineering blah growth blah blah blah ' r << 'proactive direction strategic blah blah blah forward-thinking blah.Blah blah doubletalk blah blah blah blah ' r << 'blah profit blah blah growth blah blah blah blah blah profit.Blah blah blah blah venture capital blah blah blah ' r << 'blah blah forward-thinking blah.' return r.root.topdffile(output) if __name__ == '__main__': sample('/tmp/sample.pdf')

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04_datacamp/solutions/05_solutions.py

HirahTang/datascience_starter_course

3

189091

df[(df['Sex'] == 'male') & (df['Age'] > 60)].shape[0]

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gist_magic/extensions/gist.py

timbr-io/gist-magic

3

50698

<gh_stars>1-10 from IPython.core.magic import (Magics, magics_class, line_magic, cell_magic, line_cell_magic) from IPython import get_ipython import argparse from pygithub3 import Github from pygithub3.resources.gists import Gist import os import shlex from itertools import chain from IPython.display import publish_display_data from .pretty import PrettyGist, PrettyGistList, build_display_data from IPython.core.formatters import DisplayFormatter def publish_to_display(obj): output, _ = DisplayFormatter().format(obj) publish_display_data(output) # The class MUST call this class decorator at creation time @magics_class class GistMagics(Magics): def __init__(self, shell): super(GistMagics, self).__init__(shell) self._token = os.environ.get("GITHUB_ACCESS_TOKEN") self.gh = Github(token=self._token) self.preset_id = None self._parser = self.generate_parser() def generate_parser(self): parser = argparse.ArgumentParser(prog="gist") subparsers = parser.add_subparsers() token_parser = subparsers.add_parser("token", help="Register a Github token for authentication") token_parser.add_argument("github_token", help="Github access token") token_parser.set_defaults(fn=self.token) list_parser = subparsers.add_parser("list", help="List current user's gists (or recent public gists if token is not set)") list_parser.add_argument("-l", "--limit", help="number of gist to list", default=-1) list_parser.set_defaults(fn=self.list) delete_parser = subparsers.add_parser("delete", help="Delete gist specified by id") delete_parser.add_argument("gist_id", help="ID of gist to delete") delete_parser.set_defaults(fn=self.delete) preset_parser = subparsers.add_parser("preset", help="Create or register a preset gist as active") preset_parser.add_argument("preset_gist_id", help="ID of gist preset to select", default=None, nargs="?") preset_parser.add_argument("--no-display", action="store_false", dest="display") preset_parser.set_defaults(fn=self.preset) insert_parser = subparsers.add_parser("insert", help="Insert snippet code into this cell") insert_parser.add_argument("gist_id", help="ID of gist to insert") insert_parser.add_argument("-f", "--filename", help="name of the gist file to insert", default="snippet.py") insert_parser.add_argument("-a", "--append", help="Insert a new cell rather than replacing the content of the current one", action="store_false", dest="replace") insert_parser.set_defaults(fn=self.insert) show_parser = subparsers.add_parser("show", help="Show (or update) a gist") show_parser.add_argument("gist_id", help="ID of gist to load/update", nargs="?") show_parser.add_argument("--no-display", action="store_false", dest="display") show_parser.add_argument("-e", "--evaluate", action="store_true") show_parser.add_argument("-f", "--filename", help="Name of the gist file to create / update") show_parser.add_argument("-d", "--description", help="a description for the gist", default="") show_parser.set_defaults(fn=self.show_or_update) return parser @line_cell_magic def gist(self, line, cell=None): try: input_args = shlex.split(line) if len(input_args) == 0 or input_args[0] not in ["token", "list", "delete", "preset", "insert"]: input_args.insert(0, "show") args, extra = self._parser.parse_known_args(input_args) return args.fn(cell=cell, **vars(args)) except SystemExit, se: pass def preset(self, preset_gist_id=None, cell=None, display=True, **kwargs): if cell is None: if preset_gist_id is None: # create an empty gist and output the id self.create("%%gist preset\n# gist ids\n", filename="preset.txt") # -> prints the id else: self.preset_id = preset_gist_id pretty_gist = self.show(preset_gist_id, evaluate=True) if display == True: return pretty_gist else: return None else: # execute as a cell magic for line in cell.splitlines(): try: gist_id = line.split("#", 2)[0] if len(gist_id) > 0: self.show(gist_id, display=False, evaluate=True) except Exception as e: print("Unable to load snippet with id: %s" % gist_id) print(str(e)) def token(self, github_token, **kwargs): self._token = github_token self.gh = Github(token=self._token) def list(self, limit=-1, **kwargs): try: assert self._token is not None gists = self.gh.gists.list() gists_list = PrettyGistList(list(gists.iterator())[:int(limit)]) return gists_list except AssertionError: print("No token defined. Unable to list gists") def show_or_update(self, gist_id=None, cell=None, display=True, evaluate=True, filename="snippet.py", description='', **kwargs): if cell is not None: if gist_id is None: return self.create(cell, filename=filename, description=description) else: return self.update(gist_id, cell, filename=filename) else: if gist_id is not None: return self.show(gist_id, display, evaluate) else: return self.list() def show(self, gist_id, display=True, evaluate=False, **kwargs): gist = self.gh.gists.get(gist_id) pretty_gist = PrettyGist(gist, display=display) if evaluate: get_ipython().run_cell(pretty_gist.content) return pretty_gist def insert(self, gist_id, filename="snippet.py", replace=True, **kwargs): gist = self.gh.gists.get(gist_id) if filename in gist.files: get_ipython().set_next_input(gist.files[filename].content, replace=replace) else: print("{} file not found in gist with id {}".format(filename, gist_id)) def create(self, cell, filename="snippet.py", description=''): try: assert cell is not None assert self._token is not None config = dict(description=description, public=False, files={}) config['files'][filename or 'snippet.py'] = {'content': cell} gist = self.gh.gists.create(config) self.add_to_preset(gist.id) print("gist id: %s" % gist.id) return gist.id except AssertionError: print("No token defined. Unable to create gists.") def delete(self, gist_id, **kwargs): try: self.gh.gists.delete(gist_id) print("Deleted gist %s" % gist_id) self.remove_from_preset(gist_id) except Exception as e: print("Could not delete gist %s" % gist_id) def update(self, gist_id, cell, filename="snippet.py"): try: assert cell is not None assert self._token is not None gist = self.gh.gists.get(gist_id) config = { "description": gist.description, "public": gist.public, "files": {} } config["files"][filename or 'snippet.py'] = {"content": cell} self.gh.gists.update(gist_id, config) except AssertionError: print("No token defined. Unable to update gists or add them to presets") def add_to_preset(self, gist_id): if self.preset_id is not None: preset_gist = self.gh.gists.get(self.preset_id) preset_content = preset_gist.files["preset.txt"].content + "\n{}".format(gist_id) self.update(preset_gist.id, preset_content, filename="preset.txt") def remove_from_preset(self, gist_id): if self.preset_id is not None: preset_gist = self.gh.gists.get(self.preset_id) preset_content = preset_gist.files["preset.txt"].content + "\n{}".format(gist_id) preset_lines = [line for line in preset_content.splitlines() if not line.startswith(gist_id)] new_preset_content = "\n".join(preset_lines) if new_preset_content != preset_content: self.update(preset_gist.id, new_preset_content, filename="preset.txt")

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savu/plugins/ptychography/base_ptycho.py

malte-storm/Savu

1

89560

<gh_stars>1-10 # Copyright 2014 Diamond Light Source Ltd. # # 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. """ .. module:: base_ptycho :platform: Unix :synopsis: A base class for all ptychographic analysis methods .. moduleauthor:: <NAME> <<EMAIL>> """ from savu.plugins.plugin import Plugin from savu.plugins.driver.cpu_plugin import CpuPlugin import logging import numpy as np class BasePtycho(Plugin, CpuPlugin): # also make one for gpu """ A base plugin for doing ptychography. Other ptychography plugins should \ inherit from this. :param in_datasets: A list of the dataset(s) to process. Default: []. :param out_datasets: A list of the dataset(s) to \ process. Default: ['probe', 'object_transmission', 'positions']. """ def __init__(self, name): super(BasePtycho, self).__init__(name) def setup(self): self.exp.log(self.name + " Setting up the ptycho") in_dataset, out_dataset = self.get_datasets() in_meta_data = in_dataset[0].meta_data# grab the positions from the metadata logging.debug('getting the positions...') self.positions = in_meta_data.get('xy') # get the positions and bind them # lets set up the axis labels for output datasets position_labels, probe_labels, object_labels, self.sh = self.setup_axis_labels(in_dataset) # print "probe labels are:"+str(probe_labels) # print "object labels are:"+str(object_labels) # print "position labels are:"+str(position_labels) # Now create the datasets and work out the patterns ### PROBE ### probe = out_dataset[0] # probe_shape = in_dataset[0].get_shape()[-2:] + (self.get_num_probe_modes(),) self.set_size_probe(in_dataset[0].get_shape()[-2:]) logging.debug("##### PROBE #####") #print("probe shape is:%s",str(self.get_size_probe())) probe.create_dataset(axis_labels=probe_labels, shape=self.get_size_probe()) # create the dataset self.probe_pattern_setup(probe_labels, probe) ### OBJECT #### self.set_size_object(in_dataset[0], self.get_positions(), self.get_pixel_size()) object_trans = out_dataset[1] object_shape = self.sh + self.get_size_object() logging.debug("##### OBJECT #####") #print("object shape is:%s",str(object_shape)) # print object_labels object_trans.create_dataset(axis_labels=object_labels, shape=object_shape) # create the dataset self.object_pattern_setup(object_labels, object_trans) ### POSITIONS ### logging.debug('##### POSITIONS #####') positions = out_dataset[2] #print self.sh, self.get_positions().shape positions_shape = self.sh + self.get_positions().shape[-2:] logging.debug('positions shape is:%s',str(positions_shape)) #print "positions shape",positions_shape positions.create_dataset(axis_labels=position_labels, shape=positions_shape) rest_pos = range(len(position_labels)) pos_md = \ {'core_dims':tuple(set(rest_pos) - set([0])), 'slice_dims':(0,)} positions.add_pattern("CHANNEL", **pos_md) ''' now we need to tell the setup what we want as input shapes, output shapes, and the number of each of them in one go. ''' in_pData, out_pData = self.get_plugin_datasets() in_pData[0].plugin_data_setup(self.get_plugin_pattern(), self.get_max_frames()) out_pData[0].plugin_data_setup("PROJECTION", self.get_num_probe_modes()) out_pData[1].plugin_data_setup("PROJECTION", self.get_num_object_modes()) out_pData[2].plugin_data_setup("CHANNEL", self.get_max_frames()) self.exp.log(self.name + " End") ''' The below methods influence the set-up and can be over-ridden depending on which software package we are using ''' def get_plugin_pattern(self): ''' sets the pattern to work in. In this case we consider a ptycho scan to be a 4D_SCAN. ''' return "4D_SCAN" def nInput_datasets(self): return 1 def nOutput_datasets(self): return 3 def get_num_probe_modes(self): return 1 def get_num_object_modes(self): return 1 def get_positions(self): return self.positions def get_pixel_size(self): return 30e-9 def set_size_object(self, dataset,positions, pobj=33e-9): ''' returns tuple ''' # print "positions is "+str(self.get_positions().shape) x,y = self.get_positions()[0],self.get_positions()[1] probe_size = self.get_size_probe() x_fov = np.max(x)-np.min(x) y_fov = np.max(y)-np.min(y) xsize = int(x_fov//pobj) + probe_size[0] ysize = int(y_fov//pobj) + probe_size[1] self.obj_shape = xsize,ysize,self.get_num_object_modes() def get_size_object(self): return self.obj_shape def set_size_probe(self,val): self.probe_size = (1,)+val + (self.get_num_probe_modes(),) def get_size_probe(self): ''' returns tuple ''' return self.probe_size def get_max_frames(self): return 'single' def get_output_axis_units(self): return 'nm' def probe_pattern_setup(self, probe_labels, probe): ''' This is where we set up the patterns, we need to add, PROJECTIONS, SINOGRAMS, TIMESERIES and SPECTRA I've created the TIMESERIES because we could in theory have a time series of spectra probe_patterns: PROJECTION, TIMESERIES (for each projection), SPECTRUM (for each energy) object_patterns: PROJECTION, SINOGRAM, SPECTRUM (for each energy) position_patterns: 1D_METADATA ''' probe_dims = len(probe_labels) # the number of dimensions from the axis labels rest_probe = range(probe_dims) # all the dimensions we have self.set_projection_pattern(probe, rest_probe) self.set_probe_rotation_patterns(probe, rest_probe) self.set_probe_energy_patterns(probe, rest_probe) def object_pattern_setup(self, object_labels, object_trans): ''' This is where we set up the patterns, we need to add, PROJECTIONS, SINOGRAMS, TIMESERIES and SPECTRA I've created the TIMESERIES because we could in theory have a time series of spectra probe_patterns: PROJECTION, TIMESERIES (for each projection), SPECTRUM (for each energy) object_patterns: PROJECTION, SINOGRAM, SPECTRUM (for each energy) position_patterns: 1D_METADATA ''' obj_dims = len(object_labels) # the number of dimensions from the axis labels # print "object has "+str(obj_dims)+"dimensions" rest_obj = range(obj_dims) # all the dimensions we have self.set_projection_pattern(object_trans, rest_obj) self.set_object_rotation_patterns(object_trans, rest_obj) self.set_object_energy_patterns(object_trans, rest_obj) def setup_axis_labels(self, in_dataset): ''' This is where we set up the axis labels the 4D scan will contain labels that are: 'xy', 'detectorX', 'detectorY', but the data itself may be scanned in energy or rotation or something else. We want to remove all the above, and amend them to be the following (preferably with additional scan axes at the front): probe: 'x','y','mode_idx' object: 'x','y','mode_idx' positions: 'xy' ''' PATTERN_LABELS = ['xy', 'detectorX', 'detectorY'] in_labels = in_dataset[0].data_info.get('axis_labels') # this is a list of dictionarys existing_labels = [d.keys()[0] for d in in_labels] # this just gets the axes names logging.debug('The existing labels are:%s, we will remove:%s' % (existing_labels, PATTERN_LABELS)) logging.debug('removing these labels from the list') core_labels_raw = [l for l in existing_labels if l not in PATTERN_LABELS] # removes them from the list core_labels = [l + '.' + in_labels[0][l] for l in core_labels_raw] # add the units in for the ones we are keeping # now we just have to add the new ones to this. trans_units = self.get_output_axis_units() probe_labels = list(core_labels) # take a copy probe_labels.extend(['mode_idx.number','x.' + trans_units, 'y.' + trans_units, ]) logging.debug('the labels for the probe are:%s' % str(probe_labels)) object_labels = list(core_labels) object_labels.extend(['mode_idx.number','x.' + trans_units, 'y.' + trans_units]) logging.debug('the labels for the object are:%s' % str(object_labels)) position_labels = list(core_labels) position_labels.extend(['xy.m','idx']) logging.debug('the labels for the positions are:%s' % str(position_labels)) # now we also need this part of the shape of the data so... md = in_dataset[0].meta_data sh = tuple([len(md.get(l)) for l in core_labels_raw]) return position_labels, probe_labels, object_labels, sh def set_probe_rotation_patterns(self, probe, rest_probe): try: rot_axis = probe.get_data_dimension_by_axis_label('rotation_angle', contains=True) # get the rotation axis except Exception as e: logging.warn(str(e) + 'we were looking for "rotation_angle"') logging.debug('This is not a tomography, so no time series for the probe') else: # print('the rotation axis is:%s' % str(rot_axis)) probe_ts = {'core_dims':(rot_axis,), 'slice_dims':tuple(set(rest_probe) - set([rot_axis]))} probe.add_pattern("TIMESERIES", **probe_ts) # so we can FT the wiggles etc... # print('This is a tomography so I have added a TIMESERIES pattern to the probe') # the probe oscillates in time for each projection, set this as a time series pattern def set_probe_energy_patterns(self, probe, rest_probe): try: energy_axis = probe.get_data_dimension_by_axis_label('energy', contains=True) # get an energy axis except Exception as e: logging.warn(str(e) + 'we were looking for "energy"') logging.debug('This is not spectro-microscopy, so no spectrum/timeseries for the probe') else: probe_spec = {'core_dims':tuple(energy_axis), 'slice_dims':tuple(set(rest_probe) - set([energy_axis]))} probe.add_pattern("SPECTRUM", **probe_spec) probe.add_pattern("TIMESERIES", **probe_spec) logging.debug('This is probably spectro-microscopy so I have added a SPECTRUM pattern to the probe') logging.debug('I have also added a TIMESERIES pattern on the same axis, but be careful with what this means!') # the probe oscillates in time for each projection, set this as a time series pattern def set_projection_pattern(self, probe, rest_probe): probe_proj_core = tuple([rest_probe[idx] for idx in (-3, -2)]) # hard coded since we set them just above probe_slice = tuple(set(rest_probe) - set(probe_proj_core)) probe_proj = {'core_dims':probe_proj_core, 'slice_dims':probe_slice} probe.add_pattern("PROJECTION", **probe_proj) logging.debug('have added a PROJECTION pattern') def set_object_energy_patterns(self, object_trans, rest_obj): try: energy_axis = object_trans.get_data_dimension_by_axis_label('energy', contains=True) # get an energy axis except Exception as e: logging.warn(str(e) + 'we were looking for "energy"') logging.debug('This is not spectro-microscopy, so no spectrum for the object') else: obj_spec = {'core_dims':tuple(energy_axis), 'slice_dims':tuple(set(rest_obj) - set([energy_axis]))} object_trans.add_pattern("SPECTRUM", **obj_spec) logging.debug('This is probably spectro-microscopy so I have added a SPECTRUM pattern to the object') # the probe oscillates in time for each projection, set this as a time series pattern def set_object_rotation_patterns(self, object_trans, rest_obj): try: rot_axis = object_trans.get_data_dimension_by_axis_label('rotation_angle', contains=True) # get the rotation axis except Exception as e: logging.warn(str(e) + 'we were looking for "rotation_angle"') logging.debug('This is not a tomography, so no sinograms for the object transmission') else: x_axis = object_trans.get_data_dimension_by_axis_label('x', contains=True) # get the x axis obj_sino = {'core_dims':(rot_axis, x_axis), 'slice_dims':tuple(set(rest_obj) - set((rot_axis, x_axis)))} object_trans.add_pattern("SINOGRAM", **obj_sino) # for the tomography logging.debug('This is a tomography so I have added a SINOGRAM pattern to the object transmission') # the probe oscillates in time for each projection, set this as a time series pattern

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wal_e/worker/gs/__init__.py

paalkr/wal-e

2,739

146868

<filename>wal_e/worker/gs/__init__.py from wal_e.worker.gs.gs_deleter import Deleter from wal_e.worker.gs.gs_worker import BackupFetcher from wal_e.worker.gs.gs_worker import BackupList from wal_e.worker.gs.gs_worker import DeleteFromContext from wal_e.worker.gs.gs_worker import TarPartitionLister __all__ = [ 'Deleter', 'TarPartitionLister', 'BackupFetcher', 'BackupList', 'DeleteFromContext', ]

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test31.py

cieczpj/spider

0

61483

<filename>test31.py from lxml import etree import requests import urllib.parse import pymongo headers = { 'User-Agent':'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_14_2) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/71.0.3578.98 Safari/537.36', } mylist = [] for j in range(50): if j == 1: reponse= requests.get('http://v.7192.com/movie_0_0_0_0_1',headers=headers) else: reponse = requests.get('http://v.7192.com/movie_0_0_0_0_{}'.format(j),headers=headers) reponse.encoding="gbk" tree = etree.HTML(reponse.text) res = tree.xpath('//div[@class="result_right_list"]/ul/li/div/a') #print(res) #mylist = [] for a in res: href1_ = a.xpath('./@href')[0] href_ = ('http://v.7192.com'+href1_) title_ = a.xpath('./@title')[0] src_ = a.xpath('./img/@data-original')[0] print(src_) print(href_,title_) #text_=href_+title_+src_ reponse1 = requests.get('http://v.7192.com'+href1_,headers=headers) reponse1.encoding="gbk" tree1 = etree.HTML(reponse1.text) res2 = tree1.xpath('//div[@class="show_information"]/table[@id="mv_play_list"]/tr/th[@width="180px"]/a') #res2 = tree1.xpath('//div[@class="result_right_list"]/ul/li/div/a') #mylist = [] for b in res2: href2= b.xpath('./@rel')[0] print(href2) mylist.append({"src_":src_,"href_":href_,"title_":title_,"href2":href2}) #lock.acquire() # 锁住mongo myclient = pymongo.MongoClient("mongodb://localhost:27017/") mydb = myclient["wemovie"] mycol = mydb["movie"] mycol.insert_many(mylist) myclient.close() #!!!!!!!!!!! #lock.release() # 打开mongo

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orders/views.py

DobromirZlatkov/anteya

0

8699

<filename>orders/views.py from django.core.urlresolvers import reverse_lazy from django.views import generic from django.shortcuts import redirect, render from django.http import HttpResponseRedirect from django.core.urlresolvers import reverse from . import forms from . import models from custommixins import mixins class OrderView(generic.View): template_name = 'orders/order_create.html' def get(self, request): qs = models.Product.objects.none() formset = forms.ProductFormSet(queryset=qs, prefix='formset') order_form = forms.OrderForm(prefix='order_form') return render(request, self.template_name, {'formset': formset, 'order_form': order_form}) def post(self, request): formset = forms.ProductFormSet(request.POST, prefix='formset') order_form = forms.OrderForm(request.POST, prefix='order_form') if formset.is_valid(): order = order_form.save() for form in formset.forms: product = form.save(commit=False) order.products.add(product) order.save() return HttpResponseRedirect(reverse('order_details', args=(order.id,))) else: return render(request, self.template_name, {'formset': formset, 'order_form': order_form}) class OrderDetails(generic.DetailView): model = models.Order template_name_suffix = '_details' class OrderList(mixins.LoginRequiredMixin, mixins.AdminRequiredMixin, generic.ListView): model = models.Order class OrderEdit(generic.View): template_name = 'orders/order_edit.html' def get(self, request, pk): order = models.Order.objects.get(pk=pk) formset = forms.ProductFormSet(queryset=order.products.all(), prefix='formset') order_form = forms.OrderForm(prefix='order_form', instance=order) return render(request, self.template_name, {'formset': formset, 'order_form': order_form}) def post(self, request, pk): order = models.Order.objects.get(pk=pk) formset = forms.ProductFormSet(request.POST, prefix='formset') order_form = forms.OrderForm(request.POST, prefix='order_form') if formset.is_valid(): order = order_form.save() for form in formset.forms: product = form.save(commit=False) order.products.add(product) order.save() return HttpResponseRedirect(reverse('order_details', args=(order.id,))) else: return render(request, self.template_name, {'formset': formset, 'order_form': order_form})

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docs/10.level3_demo_streaming/pc_server/server.py

FaBoPlatform/RobotCarAI

10

5706

<reponame>FaBoPlatform/RobotCarAI<gh_stars>1-10 #!/usr/bin/env python # -*- coding: utf-8 -*- # ClientからOpenCV画像データを受け取り、ライン検出して制御命令を送る # Server: Jetson TX2 # Client: Jetson TX2/Raspberry Pi3 Docker # 1. FFMPEG UDP StreamingをClientで実行する。AWS向け10FPS,Jetson TX2向け1FPS # 2. Serverを起動する # 3. Clientを起動する # コード修正 # lib/camera.py: vid = cv2.VideoCapture()を環境に合わせて修正する必要がある # lib/object_detection.py: /home/ubuntu/notebooks/github/SSD-Tensorflow/ を環境に合わせて修正する必要がある ''' Python 3.6 送信するmessageは.encode('ascii')や.encode('utf-8')等でエンコードする必要がるここではClientから送られてくるOpenCV BGR画像データが'ascii'に変換されているので'ascii'で統一している ''' print("wait. launching...") import socket, select import time import cv2 import numpy as np import time import os import sys import logging import threading import numpy as np from lib.functions import * from lib.object_detection import ObjectDetection from lib.opencv_lane_detection import LaneDetection from lib.webcam import WebcamVideoStream # ログ設定 logging.basicConfig(level=logging.DEBUG, format='[%(levelname)s] time:%(created).8f pid:%(process)d pn:%(processName)-10s tid:%(thread)d tn:%(threadName)-10s fn:%(funcName)-10s %(message)s', ) # 解析、送信スレッド動作フラグ is_analyze_running = False sock = None out = None # IPM変換後の画像におけるx,yメートル(黒い部分も含む) X_METER=1.5 Y_METER=1 # ライン検出クラス ld = None # 物体検出クラス od = None def do_analyze(): global is_analyze_running global sock global out global X_METER global Y_METER global ld global od # 映像を保存するかどうか IS_SAVE = True OUTPUT_DIR ='./' OUTPUT_FILENAME = 'received.avi' HANDLE_ANGLE = 42 frame_counter = 0 fourcc = None control = None roi_vertices = None ipm_vertices = None speed = None # 映像準備 camera = WebcamVideoStream() cols,rows,fps,fourcc = camera.init_webcam() camera.start() fps = 1 if IS_SAVE: out = cv2.VideoWriter(os.path.join(OUTPUT_DIR, OUTPUT_FILENAME), int(fourcc), fps, (int(cols), int(rows))) ######################################## # ライン検出準備 ######################################## ld = LaneDetection(X_METER,Y_METER,cols=cols,rows=rows) while is_analyze_running: frame_start_time = time.time() #time.sleep(0.2) ######################################## # 映像取得 ######################################## cv_bgr = camera.read() frame_counter += 1 ######################################## # 物体認識 ######################################## # avi動画に保存する if IS_SAVE: out.write(cv_bgr) rclasses,rscores,rbboxes = od.get_detection(cv_bgr) print(rclasses,rscores,rbboxes) if len(rclasses) > 0: prediction_class = np.min(rclasses) if prediction_class == 1: # 止まれを検出した is_need_header_receive = True control='0,0,' sock.sendall(("CONTROL,"+ control).encode('ascii')) continue elif prediction_class == 2: # 10を検出した speed = 40 elif prediction_class == 3: # 20を検出した speed = 50 elif prediction_class == 4: # 30を検出した speed = 60 else: # 物体検出無し if speed is None: speed = 40 handle_angle = 0 ######################################## # ライン検出 ######################################## ld.cv_bgr = cv_bgr # ラインを検出する try: tilt1_deg,tilt2_deg,angle1_deg,angle2_deg,curve1_r,curve2_r, \ meters_from_center = ld.lane_detection() except: # ライン検出失敗 is_need_header_receive = True control='0,0,' sock.sendall(("CONTROL,"+ control).encode('ascii')) continue ######################################## # 速度調整を行う ######################################## #if np.abs(angle2_deg) > np.abs(angle1_deg): # speed = 50 #else: # speed = 60 ''' 左右について tilt_deg: -が右、+が左 angle_deg: +が右、-が左 meters_from_center: -が右にいる、+が左にいる handle_angle: +が右、-が左 ''' ######################################## # ハンドル角調整を行う ######################################## handle_angle = -1*tilt1_deg if meters_from_center >= 0: # 左にいる if np.abs(meters_from_center)*100 > 20: # とても離れて左にいる：右に全開で曲がる handle_angle=HANDLE_ANGLE elif np.abs(meters_from_center)*100 > 10: if tilt2_deg > 0 : # 離れて左いる、奥は左カーブ：右に少し曲がる handle_angle=HANDLE_ANGLE/2 else: # 離れて左いる、奥は右カーブ：右に全開で曲がる handle_angle=HANDLE_ANGLE else: # 右にいる if np.abs(meters_from_center)*100 > 20: # とても離れて右にいる：左に全開で曲がる handle_angle=-1*HANDLE_ANGLE elif np.abs(meters_from_center)*100 > 10: if tilt2_deg < 0 : # 離れて右いる、奥は右カーブ：左に少し曲がる handle_angle=-1*HANDLE_ANGLE/2 else: # 離れて右いる、奥は左カーブ、左に全開で曲がる handle_angle=-1*HANDLE_ANGLE # 動作可能な角度内に調整する if handle_angle > HANDLE_ANGLE: handle_angle = HANDLE_ANGLE if handle_angle < -1*HANDLE_ANGLE: handle_angle = -1*HANDLE_ANGLE # 車両制御送信 control=str(speed)+','+str(handle_angle)+',' print("speed={},handle_angle={},CONTROL,{}".format(speed,handle_angle,control)) sock.sendall(("CONTROL,"+ control).encode('ascii')) frame_end_time = time.time() print("FPS={}".format(round(1/(frame_end_time-frame_start_time),2))) def main(): global is_analyze_running global sock global out global ld global od # 通信設定 HOST = '192.168.0.77' # Server IP Address PORT = 6666 # Server TCP Port #HOST = 'a32158c3da9f' # AWS Docker #PORT = 8091 # AWS TCP Port #HOST = '2204f9b0e871' # PC Docker #PORT = 8091 # PC TCP Port ######################################## # 通信準備 ######################################## connected_clients_sockets = [] server_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM) server_socket.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1) server_socket.bind((HOST, PORT)) server_socket.listen(10) connected_clients_sockets.append(server_socket) # Headerの受信が必要かどうか。Headerを受信したら、encode('ascii')を通さずに受信データを解析する is_need_header_receive = True ######################################## # 物体認識準備 ######################################## od = ObjectDetection() print("Server start") try: while True: ######################################## # 受信待ち ######################################## read_sockets, write_sockets, error_sockets = select.select(connected_clients_sockets, [], []) for sock in read_sockets: if sock == server_socket: sockfd, client_address = server_socket.accept() connected_clients_sockets.append(sockfd) else: # ClientがServerにHeaderを送る時は4096 Byte以下にすること packet = sock.recv(4096) print(type(packet)) # if is_need_header_receive: print('header') packet = packet.decode('ascii') txt = str(packet) if packet: print('packet True') if packet == 'START': is_analyze_running = True t = threading.Thread(target=do_analyze) t.start() elif packet.startswith('BYE'): print('got BYE') is_need_header_receive = True is_analyze_running = False sock.shutdown(socket.SHUT_RDWR) sock.close() connected_clients_sockets.remove(sock) if out is not None: out.release() else: print('client disconnect') is_need_header_receive = True is_analyze_running = False sock.shutdown(socket.SHUT_RDWR) sock.close() connected_clients_sockets.remove(sock) if out is not None: out.release() if not is_need_header_receive: # ここには来ない print('body') if packet: print('packet True') is_need_header_receive = True else: print('data finished') is_need_header_receive = True is_analyze_running = False sock.shutdown(socket.SHUT_RDWR) sock.close() connected_clients_sockets.remove(sock) if out is not None: out.release() except: import traceback traceback.print_exc() finally: is_need_header_receive = True is_analyze_running = False sock.shutdown(socket.SHUT_RDWR) sock.close() connected_clients_sockets.remove(sock) server_socket.close() if out is not None: out.release() if __name__ == '__main__': main() print("end server")

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mom/itertools.py

ands904/ands904-tinypyclone

16

32257

<filename>mom/itertools.py #!/usr/bin/env python # -*- coding: utf-8 -*- """:synopsis: Implements :mod:`itertools` for older versions of Python. :module: mom.itertools :copyright: 2010-2011 by <NAME> :license: BSD, PSF Borrowed from brownie.itools. """ from __future__ import absolute_import import itertools from mom import builtins try: # Python 2.x from itertools import izip except ImportError: # Python 3.x izip = zip __author__ = "<EMAIL> (<NAME>)" __all__ = [ "chain", "combinations_with_replacement", "compress", "count", "flatten", "grouped", "izip_longest", "permutations", "product", "starmap", "unique", ] if getattr(itertools.chain, "from_iterable", None): chain = itertools.chain else: class chain(object): """An iterator which yields elements from the given `iterables` until each iterable is exhausted. .. versionadded:: 0.2 """ @classmethod def from_iterable(cls, iterable): """Alternative constructor which takes an `iterable` yielding iterators, this can be used to chain an infinite number of iterators. """ rv = object.__new__(cls) rv._init(iterable) return rv def __init__(self, *iterables): self._init(iterables) def _init(self, iterables): self.iterables = iter(iterables) self.current_iterable = iter([]) def __iter__(self): return self def next(self): try: return self.current_iterable.next() except StopIteration: self.current_iterable = iter(self.iterables.next()) return self.current_iterable.next() def izip_longest(*iterables, **kwargs): """Make an iterator that aggregates elements from each of the iterables. If the iterables are of uneven length, missing values are filled-in with `fillvalue`. Iteration continues until the longest iterable is exhausted. If one of the iterables is potentially infinite, then the :func:`izip_longest` function should be wrapped with something that limits the number of calls (for example :func:`itertools.islice` or :func:`itertools.takewhile`.) If not specified, `fillvalue` defaults to ``None``. .. note:: Software and documentation for this function are taken from CPython, :ref:`license details <psf-license>`. """ fillvalue = kwargs.get("fillvalue") def sentinel(counter=([fillvalue] * (len(iterables) - 1)).pop): yield counter() fillers = itertools.repeat(fillvalue) iters = [chain(it, sentinel(), fillers) for it in iterables] try: for tup in izip(*iters): yield tup except IndexError: pass def permutations(iterable, r=None): """Return successive `r` length permutations of elements in the `iterable`. If `r` is not specified or is ``None``, then `r` defaults to the length of the `iterable` and all possible full-length permutations are generated. Permutations are emitted in lexicographic sort order. So, if the input `iterable` is sorted, the permutation tuples will be produced in sorted order. Elements are treated as unique based on their position, not on their value. So if the input elements are unique, there will be no repeating value in each permutation. The number of items returned is ``n! / (n - r)!`` when ``0 <= r <= n`` or zero when `r > n`. .. note:: Software and documentation for this function are taken from CPython, :ref:`license details <psf-license>`. """ pool = tuple(iterable) pool_length = len(pool) r = pool_length if r is None else r for indices in product(builtins.range(pool_length), repeat=r): if len(set(indices)) == r: yield tuple(pool[i] for i in indices) def product(*iterables, **kwargs): """Cartesian product of input iterables. Equivalent to nested for-loops in a generator expression. For example, ``product(A, B)`` returns the same as ``((x, y) for x in A for y in B)``. The nested loops cycle like an odometer with the rightmost element advancing on every iteration. The pattern creates a lexicographic ordering so that if the input's iterables are sorted, the product tuples are emitted in sorted order. To compute the product of an iterable with itself, specify the number of repetitions with the optional `repeat` keyword argument. For example, ``product(A, repeat=4)`` means the same as ``product(A, A, A, A)``. .. note:: Software and documentation for this function are taken from CPython, :ref:`license details <psf-license>`. """ pools = map(tuple, iterables) * kwargs.get("repeat", 1) result = [[]] for pool in pools: result = [x + [y] for x in result for y in pool] for prod in result: yield tuple(prod) try: from itertools import starmap starmap = starmap except ImportError: def starmap(function, iterable): """Make an iterator that computes the function using arguments obtained from the iterable. Used instead of :func:`itertools.imap` when an argument parameters are already grouped in tuples from a single iterable (the data has been "pre-zipped"). The difference between :func:`itertools.imap` and :func:`starmap` parallels the distinction between ``function(a, b)`` and ``function(*c)``. .. note:: Software and documentation for this function are taken from CPython, :ref:`license details <psf-license>`. """ for args in iterable: yield function(*args) def combinations_with_replacement(iterable, r): """Return `r` length sub-sequences of elements from the `iterable` allowing individual elements to be replaced more than once. Combinations are emitted in lexicographic sort order. So, if the input `iterable` is sorted, the combinations tuples will be produced in sorted order. Elements are treated as unique based on their position, not on their value. So if the input elements are unique, the generated combinations will also be unique. The number of items returned is ``(n + r - 1)! / r! / (n - 1)!`` when ``n > 0``. .. note:: Software and documentation for this function are taken from CPython, :ref:`license details <psf-license>`. """ pool = tuple(iterable) n = len(pool) for indices in product(builtins.range(n), repeat=r): if sorted(indices) == list(indices): yield tuple(pool[i] for i in indices) def compress(data, selectors): """Make an iterator that filters elements from the `data` returning only those that have a corresponding element in `selectors` that evaluates to ``True``. Stops when either the `data` or `selectors` iterables have been exhausted. .. note:: Software and documentation for this function are taken from CPython, :ref:`license details <psf-license>`. """ return (d for d, s in izip(data, selectors) if s) def count(start=0, step=1): """Make an iterator that returns evenly spaced values starting with `start`. Often used as an argument to :func:`imap` to generate consecutive data points. Also, used with :func:`izip` to add sequence numbers. When counting with floating point numbers, better accuracy can sometimes be achieved by substituting multiplicative code such as: ``(start + step * i for i in count())``. .. note:: Software and documentation for this function are taken from CPython, :ref:`license details <psf-license>`. """ n = start while True: yield n n += step def grouped(n, iterable, fillvalue=None): """Groups the items in the given `iterable` to tuples of size `n`. In order for groups to always be of the size `n` the `fillvalue` is used for padding. """ return izip_longest(fillvalue=fillvalue, *([iter(iterable)] * n)) def unique(iterable, seen=None): """Yields items from the given `iterable` of (hashable) items, once seen an item is not yielded again. :param seen: An iterable specifying already "seen" items which will be excluded from the result. .. versionadded:: 0.5 .. versionchanged:: 0.5 Items don't have to be hashable any more. """ seen = set() if seen is None else set(seen) seen_unhashable = [] for item in iterable: try: if item not in seen: seen.add(item) yield item except TypeError: if item not in seen_unhashable: seen_unhashable.append(item) yield item def flatten(iterable, ignore=None): """Flattens a nested `iterable`. :param ignore: Types of iterable objects which should be yielded as-is. .. versionadded:: 0.5 """ stack = [iter(iterable)] while stack: try: item = stack[-1].next() if ignore and isinstance(item, ignore): yield item elif builtins.is_bytes_or_unicode(item) and len(item) == 1: yield item else: try: stack.append(iter(item)) except TypeError: yield item except StopIteration: stack.pop()

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zkay/transaction/crypto/ecdh_aes.py

nibau/zkay

0

146893

from typing import Tuple, List, Any from Crypto.Cipher import AES from zkay.config import cfg from zkay.transaction.crypto.ecdh_base import EcdhBase class EcdhAesCrypto(EcdhBase): def _enc(self, plain: int, my_sk: int, target_pk: int) -> Tuple[List[int], None]: key = self._ecdh_sha256(target_pk, my_sk) plain_bytes = plain.to_bytes(32, byteorder='big') # Encrypt and extract iv cipher = AES.new(key, AES.MODE_CBC) cipher_bytes = cipher.encrypt(plain_bytes) iv = cipher.iv # Pack iv and cipher iv_cipher = b''.join([iv, cipher_bytes]) return self.pack_byte_array(iv_cipher, cfg.cipher_chunk_size), None def _dec(self, cipher: Tuple[int, ...], my_sk: Any) -> Tuple[int, None]: # Extract sender address from cipher metadata and request corresponding public key sender_pk = cipher[-1] cipher = cipher[:-1] assert len(cipher) == cfg.cipher_payload_len # Compute shared key key = self._ecdh_sha256(sender_pk, my_sk) # Unpack iv and cipher iv_cipher = self.unpack_to_byte_array(cipher, cfg.cipher_chunk_size, cfg.cipher_bytes_payload) iv, cipher_bytes = iv_cipher[:16], iv_cipher[16:] # Decrypt cipher = AES.new(key, AES.MODE_CBC, iv=iv) plain_bytes = cipher.decrypt(cipher_bytes) plain = int.from_bytes(plain_bytes, byteorder='big') return plain, None

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data/train/python/51ac8632d350c422dea2ba0ea797fbc604f88f75__init__.py

harshp8l/deep-learning-lang-detection

84

174271

from DCWorkflowGraph import getGraph from Products.DCWorkflow.DCWorkflow import DCWorkflowDefinition from Products.PageTemplates.PageTemplateFile import PageTemplateFile import os # Import "MessageFactory" to create messages in the DCWorkflowGraph domain from zope.i18nmessageid import MessageFactory _ = MessageFactory('DCWorkflowGraph') manage_workflowGraph = PageTemplateFile(os.path.join('www','manage_workflowGraph'), globals()) manage_workflowGraph.__name__ = 'manage_workflowGraph' manage_workflowGraph._need__name__ = 0 DCWorkflowDefinition.getGraph=getGraph DCWorkflowDefinition.manage_workflowGraph=manage_workflowGraph DCWorkflowDefinition.manage_options=tuple(DCWorkflowDefinition.manage_options)+({'label': _(u'Graph'), 'action': 'manage_workflowGraph'},)

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GA/ba-code/main/mycode/rep/individual2b.py

CN-UPB/fcapp

0

63850

<gh_stars>0 from __future__ import (absolute_import, division, print_function) from array import array import copy import random from mycode import mydeap from mycode.rep.fitness import Fitness from mycode.settings import config # print("using REPR-C = reprb + fperm order") from mycode.rep.individual_base import IndividualBase class Individual2b(IndividualBase): """ REPR C: Represents an individual. has a fitness. each individual specifies which crcs and clcs are to be used and fperm for flow processing order evaluation tries in that order to fulfil all requirements by using shortest possible paths. assignments crcs to clcs are made first, then clc-bs, then flows. within each of the first two groups, shortest satisfaction is done first. """ def __init__(self, cn): super(Individual2b, self).__init__() self.cn = cn self.fitness = Fitness() self.crcs = array('B', (False for _ in self.cn.C)) self.clcs = array('B', (False for _ in self.cn.C)) self.fperm = [i for i in xrange(len(self.cn.F))] def __str__(self): return "{}, {}, {}".format(self.fitness, str(self.crcs.tolist()), str(self.clcs.tolist())) def short_str(self): return str(self) def __deepcopy__(self, memo): """Replace the basic deepcopy function with a faster one.""" cls = self.__class__ result = cls.__new__(cls) result.cn = self.cn result.fitness = copy.deepcopy(self.fitness, memo) result.crcs = copy.deepcopy(self.crcs, memo) result.clcs = copy.deepcopy(self.clcs, memo) result.fperm = copy.deepcopy(self.fperm, memo) return result @classmethod def create_random(cls, cn): self = cls(cn) for i in xrange(len(self.crcs)): self.crcs[i] = random.random() < .2 for i in xrange(len(self.clcs)): self.clcs[i] = random.random() < .5 random.shuffle(self.fperm) use_least_demanding_flow_first = random.random() < .5 self.fperm.sort(reverse=not use_least_demanding_flow_first, key=lambda it: self.cn.flows[it].p_flow) if random.random() < .6: self.fperm.sort(key=lambda it: -len(self.cn.flows[it].nodes)) return self def set_fitness_values(self, values): self.fitness.values = values def _get_controller_id_set(self, c_array): ids = set() for i in xrange(len(c_array)): if c_array[i]: ids.add(self.cn.C_list[i]) return ids def _evaluate(self): self.cn.restore_backup() crc_set, clc_set = map(self._get_controller_id_set, (self.crcs, self.clcs)) self.cn.route_crc_controls(crc_set, clc_set) self.cn.route_clc_controls(clc_set) self.cn.route_flow_satisfactions(clc_set, self.fperm) self.set_fitness_values(self.cn.compute_fitness_values()) @staticmethod def mate(ind1, ind2): 'copies uniformly randomly half of the genes from one parent, half of the other parent' child = Individual2b(ind1.cn) if random.random() < .5: for i in xrange(len(child.crcs)): child.crcs[i] = ind1.crcs[i] or ind2.crcs[i] for i in xrange(len(child.clcs)): child.clcs[i] = ind1.clcs[i] or ind2.clcs[i] else: for i in xrange(len(child.crcs)): child.crcs[i] = ind1.crcs[i] and ind2.crcs[i] for i in xrange(len(child.clcs)): child.clcs[i] = ind1.clcs[i] and ind2.clcs[i] child.fperm = mydeap.cx_alternating_position(ind1.fperm, ind2.fperm) return child @staticmethod def mutate(ind): child = copy.deepcopy(ind) num_unsatisfied_flows = child.fitness.unsatisfied_flows child.fitness.invalidate() mydeap.mutShuffleIndexes(child.fperm, config.MUTR) attr = child.crcs if random.random() < .9: attr = child.clcs if num_unsatisfied_flows > 0: if sum(attr) != len(attr): index = random.choice([i for i, v in enumerate(attr) if not v]) attr[index] = not attr[index] return child if sum(attr) <= 1: return child n_max = min(sum(attr), sum(1 - it for it in attr) + 1, 5) n = random.choice(range(1, n_max + 1)) flips = random.sample([i for i, v in enumerate(attr) if v], n) flips.extend(random.sample([i for i, v in enumerate(attr) if not v], n - 1)) for i in flips: attr[i] = not attr[i] return child def __ne__(self, other): return not self == other def __eq__(self, other): return self.clcs == other.clcs and self.crcs == other.crcs

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gdsfactory/simulation/simphony/components/coupler.py

jorgepadilla19/gdsfactory

0

158675

<reponame>jorgepadilla19/gdsfactory from SiPANN.scee import Standard from SiPANN.scee_int import SimphonyWrapper def coupler( width: float = 0.5, thickness: float = 0.22, gap: float = 0.22, length: float = 10.0, sw_angle: float = 90.0, dx: float = 1.5, dy: float = 5.0, **kwargs, ): r"""Return simphony Directional coupler model. Args: width: Width of the straight in um (Valid for 0.4-0.6) thickness: Thickness of straight in um (Valid for 0.18-0.24) gap: Minimum distance between the two straights edge in um. (Must be > 0.1) length: float or ndarray Length of the straight portion of both straights in um. dx: Horizontal distance between end of coupler until straight portion in nm. dy: Vertical distance between end of coupler until straight portion in um. sw_angle: Sidewall angle from horizontal in degrees This is what most people think of when they think directional coupler. Ports are named as .. code:: H dx dx |------| |------| o2 ________ _______o3 _ _ \ / | | \ length / | _|_V ======================= gap | dy / \ | ________/ \_______ | o1 o4 .. plot:: :include-source: import gdsfactory as gf c = gf.components.coupler(gap=0.2, length=10) c.plot() .. plot:: :include-source: import gdsfactory.simulation.simphony.components as gc import gdsfactory.simulation simphony as gs c = gc.coupler() gs.plot_model(c) """ # SiPANN units are in nm width = width * 1e3 thickness = thickness * 1e3 gap = gap * 1e3 length = length * 1e3 H = dx * 1e3 V = dy * 1e3 / 2 s = Standard( width=width, thickness=thickness, gap=gap, length=length, H=H, V=V, sw_angle=sw_angle, ) model = SimphonyWrapper(s) model.pins = ("o1", "o2", "o4", "o3") model.sipann = s return model if __name__ == "__main__": import matplotlib.pyplot as plt from gdsfactory.simulation.simphony.plot_model import plot_model c = coupler() print(c) plot_model(c) plt.show()

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aula#10/desafio035.py

daramariabs/exercicios-python

0

198209

""" Desenvolva um programa que leia o comprimento de três retas e diga ao usuário se elas podem ou não formar um triângulo.""" a = float(input('Reta A:')) b = float(input('Reta B:')) c = float(input('Reta C:')) if a < b + c and b < a + c and c < a + b: print('Forma um triangulo') else: print('Não forma um triangulo')

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src/napari_threedee/lighting/lighting_control.py

alisterburt/napari-threedee

5

55290

<filename>src/napari_threedee/lighting/lighting_control.py from typing import List import numpy as np import napari from ..base import ThreeDeeModel from ..utils.napari_utils import add_mouse_callback_safe, get_napari_visual, remove_mouse_callback_safe class LightingControl(ThreeDeeModel): def __init__(self, viewer: napari.Viewer): self._viewer = viewer self._selected_layers = [] self._selected_layer_visuals = [] self.enabled = False def set_layers(self, layers: List[napari.layers.Surface]): self.selected_layers = layers self._on_camera_change() def _on_enable(self): self._connect_events() self._enabled = True self._on_camera_change() for layer in self.selected_layers: layer.events.shading() def _on_disable(self): self._disconnect_events() @property def selected_layers(self) -> List[napari.layers.Surface]: return self._selected_layers @selected_layers.setter def selected_layers(self, layers: List[napari.layers.Surface]): if isinstance(layers, napari.layers.base.Layer): layers = [layers] self._selected_layer_visuals = [ get_napari_visual(viewer=self._viewer, layer=layer) for layer in layers ] self._selected_layers = layers @property def selected_layer_visuals(self): return self._selected_layer_visuals def _on_camera_change(self, event=None): if self.enabled is False: # only update lighting direction if enabled return view_direction = np.asarray(self._viewer.camera.view_direction) for layer, visual in zip(self.selected_layers, self.selected_layer_visuals): dims_displayed = layer._dims_displayed layer_view_direction = np.asarray(layer._world_to_data_ray(view_direction))[dims_displayed] visual.node.shading_filter.light_dir = layer_view_direction[::-1] def _connect_events(self): self._viewer.camera.events.angles.connect(self._on_camera_change) def _disconnect_events(self): self._viewer.camera.events.angles.disconnect(self._on_camera_change)

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tests/test_cli.py

kids-first/kf-model-omop

0

61428

<gh_stars>0 import os from sqlalchemy import inspect, create_engine from click.testing import CliRunner from kf_model_omop import cli from kf_model_omop.config import config as config_dict, APP_CONFIG_ENV_VAR from kf_model_omop.factory.utils import auto_gen_models env = {APP_CONFIG_ENV_VAR: 'testing'} def test_create_omop(): """ Test create omop db """ runner = CliRunner() result = runner.invoke(cli.create_omop, [], env=env) assert result.exit_code == 0 config = config_dict.get('testing') engine = create_engine(config.SQLALCHEMY_DATABASE_URI) inspector = inspect(engine) assert len(inspector.get_table_names()) == 37 def test_code_template(tmpdir): """ Test cli command code-template """ # Create temp file fn = tmpdir.mkdir("data") expected_file = os.path.join(str(fn), 'loader.py') # Gen code template runner = CliRunner() result = runner.invoke(cli.generate_code_template, ['-o', str(fn)], env=env) assert result.exit_code == 0 assert os.path.isfile(expected_file) # Update file with open(expected_file, 'w') as f: f.write('print("hello world!")') # Try to generate file again result = runner.invoke(cli.generate_code_template, ['-o', str(fn)], env=env) assert 'already exists' in result.stdout assert result.exit_code == 0 # Check file with open(expected_file, 'r') as f: assert 'hello world!' in f.read() def test_list_tables(): """ Test list tables """ runner = CliRunner() result = runner.invoke(cli.list_tables, [], env=env) assert 'concept' in result.stdout assert result.exit_code == 0 def test_auto_gen_models(tmpdir_factory): """ Test cli command code-template """ # Create temp file fn = tmpdir_factory.mktemp("data").join('models.py') expected_file = str(fn) # Autogen models auto_gen_models(config_name='testing', refresh_schema=True, model_filepath=expected_file) assert os.path.isfile(expected_file) # Check content with open(expected_file, 'r') as f: content = f.read() # assert 'ModelMixins' in content assert 'metadata' in content

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tilemap.py

AI0702/Among-Us-clone

0

20956

<filename>tilemap.py import pygame as pg from settings import * import pytmx class Map: def __init__(self, filename): self.data = [] with open(filename, 'rt') as f: for line in f: self.data.append(line.strip()) self.tilewidth = len(self.data[0]) self.tileheight = len(self.data) self.width = self.tilewidth * TILESIZE self.height = self.tileheight * TILESIZE class TiledMap: def __init__(self, filename): tm = pytmx.load_pygame(filename, pixelalpha=True) self.width = tm.width * tm.tilewidth self.height = tm.height * tm.tileheight self.tmxdata = tm def render(self, surface): # tile data ti = self.tmxdata.get_tile_image_by_gid # For each layer look for each tile and draw on surface for layer in self.tmxdata.visible_layers: if isinstance(layer, pytmx.TiledTileLayer): for x, y, gid, in layer: tile = ti(gid) if tile: surface.blit(tile, (x * self.tmxdata.tilewidth, y * self.tmxdata.tileheight)) def make_map(self): temp_surface = pg.Surface((self.width, self.height)) self.render(temp_surface) return temp_surface class Camera: def __init__(self, width, height): self.camera = pg.Rect(0, 0, width, height) self.width = width self.height = height def apply(self, entity): # Move rectangle according to camera coordinates are return entity.rect.move(self.camera.topleft) def apply_rect(self, rect): return rect.move(self.camera.topleft) def update(self, player_sprite): # center player sprite on screen x = -player_sprite.rect.x + int(WIDTH / 2) y = -player_sprite.rect.y + int(HEIGHT / 2) # Limit the map scrolling when sprite reaches end point of map # For left boundary x = min(0, x) # For right boundary x = max(-(self.width - WIDTH), x) # For top boundary y = min(0, y) # checks if y < 0 # For bottom boundary y = max(-(self.height - HEIGHT), y) # Adjust camera rectangle self.camera = pg.Rect(x, y, self.width, self.height)

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kick/device2/series3/actions/webserver/__init__.py

CiscoDevNet/firepower-kickstart

2

56137

"""Library to access the webserver.""" import re import time import logging logger = logging.getLogger(__name__) from unicon.eal.expect import Spawn, TimeoutError from unicon.utils import AttributeDict from unicon.eal.dialogs import Dialog MAX_RETRY_COUNT = 3 class Webserver: def __init__(self, hostname='AST-INSTALL', login_username='', login_password='', sudo_password=''): """Constructor for Webserver. :param hostname: hostname for the device :param login_password: device login password for a user :param sudo_password: device sudo password for 'root' :return: None """ # set hostname and enable_password from .constants import WebserverConstants WebserverConstants.hostname = hostname WebserverConstants.username = login_username WebserverConstants.login_password = <PASSWORD> WebserverConstants.sudo_password = <PASSWORD> # now that FtdConstants is initialized, create the state machine # that contains the proper variables from .statemachine import WebserverStatemachine self.sm = WebserverStatemachine() def ssh_vty(self, ip, port='22'): """Setup ssh connection to the server. :param ip: ssh host/ip :param port: ssh port :return: ssh_line """ from .constants import WebserverConstants spawn = Spawn('ssh -o UserKnownHostsFile=/dev/null -o StrictHostKeyChecking=no ' '-l {} -p {} {} \n'.format(WebserverConstants.username, port, ip)) d1 = Dialog([ ['continue connecting (yes/no)?', 'sendline(yes)', None, True, False], ['(p|P)assword:', 'sendline({})'.format(WebserverConstants.login_password), None, False, False], ]) try: d1.process(spawn, timeout=60) spawn.sendline() except TimeoutError: pass ssh_line = WebserverLine(spawn, self.sm, 'ssh_ftd') return ssh_line class WebserverLine: def __init__(self, spawn, sm, type): """Constructor for ssh line. Attributes that are None will be set in rommon_to_new_image() :param spawn: spawn of the line :param sm: object of statemachine :param type: line type, e.g. 'ssh_vty' :return: object of the line """ self.spawn = spawn self.sm = sm self.type = type sm.go_to('any', spawn) def go_to(self, state): """Go to specified state. :param state: string of the state to go to :return: None """ self.sm.go_to(state, self.spawn) def execute(self, cmd, timeout=10): """Run a command, get the console output. Then exclude the command echo, and the new prompt to return the content in between. :param cmd: command to be executed :param timeout: timeout for the command to be executed, defaulted to 10 seconds :return: output of the command executed """ current_state = self.sm.current_state current_prompt = self.sm.get_state(current_state).pattern self.spawn.sendline(cmd) # expect and trim cmd # commented for long command # output_cmd = self.spawn.expect(cmd, timeout=timeout).last_match.string # expect and trim current_prompt output = self.spawn.expect(current_prompt, timeout=timeout).last_match.string logger.debug("before trimming in execute(): {}".format(output)) r = re.search(current_prompt, output) end = r.span()[0] return_data = output[:end].strip() return return_data def execute_lines(self, cmd_lines, timeout=10): """When given a string of multiple lines, run them one by one and return the result for the last line. :param cmd_lines: commands to be executed line by line :param timeout: timeout for individual command to be executed, defaulted to 10 seconds :return: output of the last command executed """ for cmd in cmd_lines.split('\n'): cmd = cmd.strip() if cmd == "": continue # empty line return_data = self.execute(cmd, timeout=timeout) return return_data def disconnect(self): """Disconnect the session. :return: None """ if self.type == 'ssh_vty': self.sm.go_to('user_state', self.spawn) self.spawn.sendline('exit') self.spawn.expect('closed.') logger.info('ssh to ftd connection disconnected')

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expenseManager/app/UtilGp.py

PacktPublishing/Python-Programming

0

1604864

<filename>expenseManager/app/UtilGp.py from datetime import datetime,timedelta from os import system, name from time import sleep import re import random class UtilGp: @staticmethod def clear(): if name == 'nt': #windows _ = system('cls') else: #mac,linux _ = system('clear') @staticmethod def dateToStr(valueDate,formatStr = '%d-%m-%Y'): strDate = valueDate.strftime(formatStr) return strDate @staticmethod def strToDate(strDate,formatStr = '%d-%m-%Y %H:%M:%S'): valueDate = datetime.strptime(strDate,formatStr) return valueDate @staticmethod def now(): valueNow = datetime.strptime(datetime.now().strftime("%d-%m-%Y %H:%M:%S"),"%d-%m-%Y %H:%M:%S") return valueNow @staticmethod def dateAddDays(valueDate,valueDays): resDate = valueDate + timedelta(days=valueDays) return resDate @staticmethod def age(dob): today = UtilGp.now() years = today.year - dob.year if today.month < dob.month or (today.month == dob.month and today.day < dob.day): years -= 1 return years @staticmethod def isValidDate(strDate,formatStr='%d-%m-%Y'): try: valueDate = UtilGp.strToDate(strDate,formatStr) return True except: return False @staticmethod def isValidDigit(strNo,isExactLen=True,noOfDigits=10): return str(strNo).isdigit() and \ ((not isExactLen) or (isExactLen and len(strNo) == noOfDigits)) @staticmethod def numberInput(prompt,isExactLen=False,noOfDigits=5): strNo = input(prompt) if not UtilGp.isValidDigit(strNo,isExactLen,noOfDigits): return(False,0) return (True, int(strNo)) @staticmethod def sleep(sec): sleep(sec-1) pass @staticmethod def randNo(startNo, endNo, stepValue, isExcluded=False): endNo=endNo/stepValue startNo=startNo/stepValue if startNo==0: startNo=1 if isExcluded: startNo+=1 #startNo+1 to endNo-1 else: endNo+=1 #startNo to endNo return random.randint(startNo,endNo)*stepValue # Generate randNo here @staticmethod def parseItemMsg(valueItem,passCode,creditBal): # :price :tranMonYear :priceInput(msg) :item :tranNextDate :tranDate #print(valueItem.category,valueItem.subCategory,valueItem.itemName) isTranSuccess=True SuccessCode=0 newPrice = 0 if valueItem.itemType==1: nos = re.split('-|,', valueItem.priceRange) #print(result) #print(nos) newPrice = UtilGp.randNo(int(nos[0]),int(nos[1]),int(nos[2]),True) isNewPrice = True printPrice = newPrice else: printPrice = valueItem.price isNewPrice=False msg = valueItem.msg msgLines = msg.split("\n") nowValue = UtilGp.now() nowValuePlOne = UtilGp.dateAddDays(nowValue,1) for line in msgLines: line = str(line).replace(":price","Rs.%.2f"%(printPrice,)) line = str(line).replace(":item", valueItem.itemName) line = str(line).replace(":tranMonYear", UtilGp.dateToStr(nowValue,'%b-%Y')) line = str(line).replace(":tranNextDate", UtilGp.dateToStr(nowValuePlOne, '%d-%m-%Y')) line = str(line).replace(":tranDate", UtilGp.dateToStr(nowValue, '%d-%m-%Y')) if line.startswith(":inputPrice"): newPrice = float(input("Enter Amount:")) printPrice=newPrice isNewPrice=True elif line.startswith(":pinConfirm"): if printPrice>creditBal: isTranSuccess = False SuccessCode = 1 else: from getpass import getpass resPassCode = getpass("Confirm Your PIN:") print() if passCode!=resPassCode: isTranSuccess=False SuccessCode = 2 break else: print(line) ret = (isTranSuccess,SuccessCode,valueItem,isNewPrice,printPrice) #print(ret[0]) return ret @staticmethod def mainMenu(): UtilGp.sleep(2) UtilGp.clear() from frmPageHome import Home from frmPageApplyCard import ApplyCard from frmPageShop import Shop from frmPageReport import Report choice=Home.show() if choice== 1: ApplyCard.show() elif choice == 2: Shop.show() elif choice == 3: Report.show() @staticmethod def startApp(): UtilGp.clear(); from dBase import ndb ndb.loadItemCard() UtilGp.mainMenu() @staticmethod def Login(fnToCall,tranLoader,pageTitle='Login'): from dBase import ndb ndb.loadItemCard() UtilGp.clear();UtilGp.title(pageTitle) from CardData import DbCardData from dBase import ndb from frmPageShop import Shop cardNo = input("Enter your card number:") vRet = DbCardData.getCardObject(ndb.dbCard, cardNo) # print(vRet) if vRet[0]: valueCard = vRet[1] from getpass import getpass passCode = getpass("Enter your PIN:") # print("%s %s" % (cardNo, passCode)) if valueCard.passCode == passCode: ndb.IsLoggedIn = True; ndb.logInCard = valueCard; tranLoader(valueCard, True) fnToCall() #Shop.showCat() else: print("Incorrect PIN") Shop.showFailure() else: print("\nCard does not exits.\n") Shop.showFailure() @staticmethod def centerText(text): text=str(text) l=len(text) sp=(79-len(text))//2 return text.ljust(sp+l,' ').rjust(sp*2+l,' ') @staticmethod def linePrint(text='-',spaces=79): print(text.ljust(spaces,text)) @staticmethod def title(screenName): titleCaption = "Expense Manager" + '-' + screenName UtilGp.linePrint() print("%s" % (UtilGp.centerText(titleCaption))) UtilGp.linePrint() #UtilGp.printCaptionData(('A','B'),('1000','Welcome'),20) @staticmethod def priceToStr(price): return 'Rs.%7.2f' % (price,) @staticmethod def creditLine(): from dBase import ndb vCard = ndb.logInCard print("%s" % (UtilGp.centerText("Credit Limit Used:%s\tCredit Limit Available:%s"%(UtilGp.priceToStr(vCard.creditUsed),UtilGp.priceToStr(vCard.creditBal()))))) UtilGp.linePrint() #UtilGp.printCaptionData(('A','B'),('1000','Welcome'),20) @staticmethod def printCaptionData(caption, data, colonAt=40): sp1 = colonAt-2 sp2 = 80-colonAt-4 print() UtilGp.linePrint('*') for i in range(0,len(caption)): format ='# %-' + str(sp1) + 's:%-' + str(sp2) + 's #' print(format%(caption[i],data[i])) UtilGp.linePrint('*') print()

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python/edl/utils/data_server_client.py

WEARE0/edl

90

62421

<reponame>WEARE0/edl # Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import grpc import threading from edl.utils import data_server_pb2 from edl.utils import data_server_pb2_grpc from edl.utils import error_utils from edl.utils import exceptions from edl.utils import pb_utils from edl.utils.log_utils import logger class Conn(object): def __init__(self, channel, stub): self.channel = channel self.stub = stub self.lock = threading.Lock() # FIXME(gongwb): fix protocal with # https://medium.com/kuranda-labs-engineering/gracefully-handling-grpc-errors-in-a-go-server-python-client-setup-9805a5464692 class Client(object): def __init__(self): self._conn = {} # endpoint=>(channel, stub) @error_utils.handle_errors_until_timeout def _connect(self, endpoint, timeout=30): if endpoint not in self._conn: c = grpc.insecure_channel(endpoint) s = data_server_pb2_grpc.DataServerStub(c) self._conn[endpoint] = Conn(c, s) return self._conn[endpoint] @error_utils.handle_errors_until_timeout def get_file_list( self, reader_leader_endpoint, reader_name, pod_id, file_list, timeout=60 ): """ Return list[(idx,path)...] for pod """ conn = self._connect(reader_leader_endpoint, timeout=30) req = data_server_pb2.FileListRequest() req.pod_id = pod_id req.reader_name = reader_name for idx, path in enumerate(file_list): ele = data_server_pb2.FileListElement() ele.idx = idx ele.path = path req.file_list.append(ele) with conn.lock: res = conn.stub.GetFileList(req) if res.status.type != "": exceptions.deserialize(res.status) logger.debug("pod client get file_list:{}".format(res.file_list)) return res.file_list @error_utils.handle_errors_until_timeout def report_batch_data_meta( self, reader_leader_endpoint, reader_name, pod_id, dataserver_endpoint, batch_data_ids, timeout=60, ): conn = self._connect(reader_leader_endpoint, timeout=30) req = data_server_pb2.ReportBatchDataMetaRequest() req.reader_name = reader_name req.pod_id = pod_id req.data_server_endpoint = dataserver_endpoint for batch_data_id in batch_data_ids: req.batch_data_ids.append(batch_data_id) with conn.lock: res = conn.stub.ReportBatchDataMeta(req) if res.status.type != "": exceptions.deserialize(res.status) @error_utils.handle_errors_until_timeout def reach_data_end(self, reader_leader_endpoint, reader_name, pod_id, timeout=60): conn = self.connect(reader_leader_endpoint, timeout=30) req = data_server_pb2.ReachDataEndRequest() req.reader_name = reader_name req.pod_id = pod_id with conn.lock: res = conn.stub.ReachDataEnd(req) if res.status.type != "": exceptions.deserialize(res.status) @error_utils.handle_errors_until_timeout def get_batch_data_meta( self, reader_leader_endpoint, reader_name, pod_id, timeout=60 ): conn = self._connect(reader_leader_endpoint, timeout=30) req = data_server_pb2.GetBatchDataMetaRequest() req.reader_name = reader_name req.pod_id = pod_id with conn.lock: res = conn.stub.GetBatchDataMeta(req) if res.status.type != "": exceptions.deserialize(res.status) logger.debug( "pod client get_balanced_batch_data meta:{}".format( pb_utils.batch_data_meta_response_to_string(res) ) ) return res.data @error_utils.handle_errors_until_timeout def get_batch_data(self, reader_leader_endpoint, req, timeout=60): """ return BatchDataResponse """ conn = self._connect(reader_leader_endpoint, timeout=30) with conn.lock: res = conn.stub.GetBatchData(req) if res.status.type != "": exceptions.deserialize(res.status) logger.debug( "pod client get batch_data meta:{}".format( pb_utils.batch_data_response_to_string(res) ) ) return res.data

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nova/tests/unit/virt/libvirt/volume/test_glusterfs.py

bopopescu/nova-token

0

189290

begin_unit comment|'# Licensed under the Apache License, Version 2.0 (the "License"); you may' nl|'\n' comment|'# not use this file except in compliance with the License. You may obtain' nl|'\n' comment|'# a copy of the License at' nl|'\n' comment|'#' nl|'\n' comment|'# http://www.apache.org/licenses/LICENSE-2.0' nl|'\n' comment|'#' nl|'\n' comment|'# Unless required by applicable law or agreed to in writing, software' nl|'\n' comment|'# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT' nl|'\n' comment|'# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the' nl|'\n' comment|'# License for the specific language governing permissions and limitations' nl|'\n' comment|'# under the License.' nl|'\n' nl|'\n' name|'import' name|'os' newline|'\n' nl|'\n' name|'import' name|'mock' newline|'\n' name|'from' name|'oslo_concurrency' name|'import' name|'processutils' newline|'\n' nl|'\n' name|'from' name|'nova' op|'.' name|'tests' op|'.' name|'unit' op|'.' name|'virt' op|'.' name|'libvirt' op|'.' name|'volume' name|'import' name|'test_volume' newline|'\n' name|'from' name|'nova' name|'import' name|'utils' newline|'\n' name|'from' name|'nova' op|'.' name|'virt' op|'.' name|'libvirt' name|'import' name|'utils' name|'as' name|'libvirt_utils' newline|'\n' name|'from' name|'nova' op|'.' name|'virt' op|'.' name|'libvirt' op|'.' name|'volume' name|'import' name|'glusterfs' newline|'\n' nl|'\n' nl|'\n' name|'class' name|'LibvirtGlusterfsVolumeDriverTestCase' op|'(' nl|'\n' DECL|class|LibvirtGlusterfsVolumeDriverTestCase name|'test_volume' op|'.' name|'LibvirtVolumeBaseTestCase' op|')' op|':' newline|'\n' nl|'\n' DECL|member|test_libvirt_glusterfs_driver indent|' ' name|'def' name|'test_libvirt_glusterfs_driver' op|'(' name|'self' op|')' op|':' newline|'\n' indent|' ' name|'mnt_base' op|'=' string|"'/mnt'" newline|'\n' name|'self' op|'.' name|'flags' op|'(' name|'glusterfs_mount_point_base' op|'=' name|'mnt_base' op|',' name|'group' op|'=' string|"'libvirt'" op|')' newline|'\n' nl|'\n' name|'libvirt_driver' op|'=' name|'glusterfs' op|'.' name|'LibvirtGlusterfsVolumeDriver' op|'(' name|'self' op|'.' name|'fake_conn' op|')' newline|'\n' name|'self' op|'.' name|'stubs' op|'.' name|'Set' op|'(' name|'libvirt_utils' op|',' string|"'is_mounted'" op|',' name|'lambda' name|'x' op|',' name|'d' op|':' name|'False' op|')' newline|'\n' name|'export_string' op|'=' string|"'192.168.1.1:/volume-00001'" newline|'\n' name|'export_mnt_base' op|'=' name|'os' op|'.' name|'path' op|'.' name|'join' op|'(' name|'mnt_base' op|',' nl|'\n' name|'utils' op|'.' name|'get_hash_str' op|'(' name|'export_string' op|')' op|')' newline|'\n' nl|'\n' name|'connection_info' op|'=' op|'{' string|"'data'" op|':' op|'{' string|"'export'" op|':' name|'export_string' op|',' nl|'\n' string|"'name'" op|':' name|'self' op|'.' name|'name' op|'}' op|'}' newline|'\n' name|'libvirt_driver' op|'.' name|'connect_volume' op|'(' name|'connection_info' op|',' name|'self' op|'.' name|'disk_info' op|')' newline|'\n' name|'libvirt_driver' op|'.' name|'disconnect_volume' op|'(' name|'connection_info' op|',' string|'"vde"' op|')' newline|'\n' nl|'\n' name|'device_path' op|'=' name|'os' op|'.' name|'path' op|'.' name|'join' op|'(' name|'export_mnt_base' op|',' nl|'\n' name|'connection_info' op|'[' string|"'data'" op|']' op|'[' string|"'name'" op|']' op|')' newline|'\n' name|'self' op|'.' name|'assertEqual' op|'(' name|'connection_info' op|'[' string|"'data'" op|']' op|'[' string|"'device_path'" op|']' op|',' name|'device_path' op|')' newline|'\n' name|'expected_commands' op|'=' op|'[' nl|'\n' op|'(' string|"'mkdir'" op|',' string|"'-p'" op|',' name|'export_mnt_base' op|')' op|',' nl|'\n' op|'(' string|"'mount'" op|',' string|"'-t'" op|',' string|"'glusterfs'" op|',' name|'export_string' op|',' name|'export_mnt_base' op|')' op|',' nl|'\n' op|'(' string|"'umount'" op|',' name|'export_mnt_base' op|')' op|']' newline|'\n' name|'self' op|'.' name|'assertEqual' op|'(' name|'expected_commands' op|',' name|'self' op|'.' name|'executes' op|')' newline|'\n' nl|'\n' DECL|member|test_libvirt_glusterfs_driver_get_config dedent|'' name|'def' name|'test_libvirt_glusterfs_driver_get_config' op|'(' name|'self' op|')' op|':' newline|'\n' indent|' ' name|'mnt_base' op|'=' string|"'/mnt'" newline|'\n' name|'self' op|'.' name|'flags' op|'(' name|'glusterfs_mount_point_base' op|'=' name|'mnt_base' op|',' name|'group' op|'=' string|"'libvirt'" op|')' newline|'\n' nl|'\n' name|'libvirt_driver' op|'=' name|'glusterfs' op|'.' name|'LibvirtGlusterfsVolumeDriver' op|'(' name|'self' op|'.' name|'fake_conn' op|')' newline|'\n' name|'export_string' op|'=' string|"'192.168.1.1:/volume-00001'" newline|'\n' name|'export_mnt_base' op|'=' name|'os' op|'.' name|'path' op|'.' name|'join' op|'(' name|'mnt_base' op|',' nl|'\n' name|'utils' op|'.' name|'get_hash_str' op|'(' name|'export_string' op|')' op|')' newline|'\n' name|'file_path' op|'=' name|'os' op|'.' name|'path' op|'.' name|'join' op|'(' name|'export_mnt_base' op|',' name|'self' op|'.' name|'name' op|')' newline|'\n' nl|'\n' comment|'# Test default format - raw' nl|'\n' name|'connection_info' op|'=' op|'{' string|"'data'" op|':' op|'{' string|"'export'" op|':' name|'export_string' op|',' nl|'\n' string|"'name'" op|':' name|'self' op|'.' name|'name' op|',' nl|'\n' string|"'device_path'" op|':' name|'file_path' op|'}' op|'}' newline|'\n' name|'conf' op|'=' name|'libvirt_driver' op|'.' name|'get_config' op|'(' name|'connection_info' op|',' name|'self' op|'.' name|'disk_info' op|')' newline|'\n' name|'tree' op|'=' name|'conf' op|'.' name|'format_dom' op|'(' op|')' newline|'\n' name|'self' op|'.' name|'_assertFileTypeEquals' op|'(' name|'tree' op|',' name|'file_path' op|')' newline|'\n' name|'self' op|'.' name|'assertEqual' op|'(' string|"'raw'" op|',' name|'tree' op|'.' name|'find' op|'(' string|"'./driver'" op|')' op|'.' name|'get' op|'(' string|"'type'" op|')' op|')' newline|'\n' nl|'\n' comment|'# Test specified format - qcow2' nl|'\n' name|'connection_info' op|'=' op|'{' string|"'data'" op|':' op|'{' string|"'export'" op|':' name|'export_string' op|',' nl|'\n' string|"'name'" op|':' name|'self' op|'.' name|'name' op|',' nl|'\n' string|"'device_path'" op|':' name|'file_path' op|',' nl|'\n' string|"'format'" op|':' string|"'qcow2'" op|'}' op|'}' newline|'\n' name|'conf' op|'=' name|'libvirt_driver' op|'.' name|'get_config' op|'(' name|'connection_info' op|',' name|'self' op|'.' name|'disk_info' op|')' newline|'\n' name|'tree' op|'=' name|'conf' op|'.' name|'format_dom' op|'(' op|')' newline|'\n' name|'self' op|'.' name|'_assertFileTypeEquals' op|'(' name|'tree' op|',' name|'file_path' op|')' newline|'\n' name|'self' op|'.' name|'assertEqual' op|'(' string|"'qcow2'" op|',' name|'tree' op|'.' name|'find' op|'(' string|"'./driver'" op|')' op|'.' name|'get' op|'(' string|"'type'" op|')' op|')' newline|'\n' nl|'\n' DECL|member|test_libvirt_glusterfs_driver_already_mounted dedent|'' name|'def' name|'test_libvirt_glusterfs_driver_already_mounted' op|'(' name|'self' op|')' op|':' newline|'\n' indent|' ' name|'mnt_base' op|'=' string|"'/mnt'" newline|'\n' name|'self' op|'.' name|'flags' op|'(' name|'glusterfs_mount_point_base' op|'=' name|'mnt_base' op|',' name|'group' op|'=' string|"'libvirt'" op|')' newline|'\n' nl|'\n' name|'libvirt_driver' op|'=' name|'glusterfs' op|'.' name|'LibvirtGlusterfsVolumeDriver' op|'(' name|'self' op|'.' name|'fake_conn' op|')' newline|'\n' name|'export_string' op|'=' string|"'192.168.1.1:/volume-00001'" newline|'\n' name|'export_mnt_base' op|'=' name|'os' op|'.' name|'path' op|'.' name|'join' op|'(' name|'mnt_base' op|',' nl|'\n' name|'utils' op|'.' name|'get_hash_str' op|'(' name|'export_string' op|')' op|')' newline|'\n' nl|'\n' name|'connection_info' op|'=' op|'{' string|"'data'" op|':' op|'{' string|"'export'" op|':' name|'export_string' op|',' nl|'\n' string|"'name'" op|':' name|'self' op|'.' name|'name' op|'}' op|'}' newline|'\n' name|'libvirt_driver' op|'.' name|'connect_volume' op|'(' name|'connection_info' op|',' name|'self' op|'.' name|'disk_info' op|')' newline|'\n' name|'libvirt_driver' op|'.' name|'disconnect_volume' op|'(' name|'connection_info' op|',' string|'"vde"' op|')' newline|'\n' nl|'\n' name|'expected_commands' op|'=' op|'[' nl|'\n' op|'(' string|"'findmnt'" op|',' string|"'--target'" op|',' name|'export_mnt_base' op|',' nl|'\n' string|"'--source'" op|',' name|'export_string' op|')' op|',' nl|'\n' op|'(' string|"'umount'" op|',' name|'export_mnt_base' op|')' op|']' newline|'\n' name|'self' op|'.' name|'assertEqual' op|'(' name|'expected_commands' op|',' name|'self' op|'.' name|'executes' op|')' newline|'\n' nl|'\n' dedent|'' op|'@' name|'mock' op|'.' name|'patch' op|'.' name|'object' op|'(' name|'glusterfs' op|'.' name|'utils' op|',' string|"'execute'" op|')' newline|'\n' op|'@' name|'mock' op|'.' name|'patch' op|'.' name|'object' op|'(' name|'glusterfs' op|'.' name|'LOG' op|',' string|"'debug'" op|')' newline|'\n' op|'@' name|'mock' op|'.' name|'patch' op|'.' name|'object' op|'(' name|'glusterfs' op|'.' name|'LOG' op|',' string|"'exception'" op|')' newline|'\n' DECL|member|test_libvirt_glusterfs_driver_umount_error name|'def' name|'test_libvirt_glusterfs_driver_umount_error' op|'(' name|'self' op|',' name|'mock_LOG_exception' op|',' nl|'\n' name|'mock_LOG_debug' op|',' name|'mock_utils_exe' op|')' op|':' newline|'\n' indent|' ' name|'export_string' op|'=' string|"'192.168.1.1:/volume-00001'" newline|'\n' name|'connection_info' op|'=' op|'{' string|"'data'" op|':' op|'{' string|"'export'" op|':' name|'export_string' op|',' nl|'\n' string|"'name'" op|':' name|'self' op|'.' name|'name' op|'}' op|'}' newline|'\n' name|'libvirt_driver' op|'=' name|'glusterfs' op|'.' name|'LibvirtGlusterfsVolumeDriver' op|'(' name|'self' op|'.' name|'fake_conn' op|')' newline|'\n' name|'mock_utils_exe' op|'.' name|'side_effect' op|'=' name|'processutils' op|'.' name|'ProcessExecutionError' op|'(' nl|'\n' name|'None' op|',' name|'None' op|',' name|'None' op|',' string|"'umount'" op|',' string|"'umount: target is busy.'" op|')' newline|'\n' name|'libvirt_driver' op|'.' name|'disconnect_volume' op|'(' name|'connection_info' op|',' string|'"vde"' op|')' newline|'\n' name|'self' op|'.' name|'assertTrue' op|'(' name|'mock_LOG_debug' op|'.' name|'called' op|')' newline|'\n' nl|'\n' DECL|member|test_libvirt_glusterfs_driver_with_opts dedent|'' name|'def' name|'test_libvirt_glusterfs_driver_with_opts' op|'(' name|'self' op|')' op|':' newline|'\n' indent|' ' name|'mnt_base' op|'=' string|"'/mnt'" newline|'\n' name|'self' op|'.' name|'flags' op|'(' name|'glusterfs_mount_point_base' op|'=' name|'mnt_base' op|',' name|'group' op|'=' string|"'libvirt'" op|')' newline|'\n' nl|'\n' name|'libvirt_driver' op|'=' name|'glusterfs' op|'.' name|'LibvirtGlusterfsVolumeDriver' op|'(' name|'self' op|'.' name|'fake_conn' op|')' newline|'\n' name|'self' op|'.' name|'stubs' op|'.' name|'Set' op|'(' name|'libvirt_utils' op|',' string|"'is_mounted'" op|',' name|'lambda' name|'x' op|',' name|'d' op|':' name|'False' op|')' newline|'\n' name|'export_string' op|'=' string|"'192.168.1.1:/volume-00001'" newline|'\n' name|'options' op|'=' string|"'-o backupvolfile-server=192.168.1.2'" newline|'\n' name|'export_mnt_base' op|'=' name|'os' op|'.' name|'path' op|'.' name|'join' op|'(' name|'mnt_base' op|',' nl|'\n' name|'utils' op|'.' name|'get_hash_str' op|'(' name|'export_string' op|')' op|')' newline|'\n' nl|'\n' name|'connection_info' op|'=' op|'{' string|"'data'" op|':' op|'{' string|"'export'" op|':' name|'export_string' op|',' nl|'\n' string|"'name'" op|':' name|'self' op|'.' name|'name' op|',' nl|'\n' string|"'options'" op|':' name|'options' op|'}' op|'}' newline|'\n' name|'libvirt_driver' op|'.' name|'connect_volume' op|'(' name|'connection_info' op|',' name|'self' op|'.' name|'disk_info' op|')' newline|'\n' name|'libvirt_driver' op|'.' name|'disconnect_volume' op|'(' name|'connection_info' op|',' string|'"vde"' op|')' newline|'\n' nl|'\n' name|'expected_commands' op|'=' op|'[' nl|'\n' op|'(' string|"'mkdir'" op|',' string|"'-p'" op|',' name|'export_mnt_base' op|')' op|',' nl|'\n' op|'(' string|"'mount'" op|',' string|"'-t'" op|',' string|"'glusterfs'" op|',' nl|'\n' string|"'-o'" op|',' string|"'backupvolfile-server=192.168.1.2'" op|',' nl|'\n' name|'export_string' op|',' name|'export_mnt_base' op|')' op|',' nl|'\n' op|'(' string|"'umount'" op|',' name|'export_mnt_base' op|')' op|',' nl|'\n' op|']' newline|'\n' name|'self' op|'.' name|'assertEqual' op|'(' name|'expected_commands' op|',' name|'self' op|'.' name|'executes' op|')' newline|'\n' nl|'\n' DECL|member|test_libvirt_glusterfs_libgfapi dedent|'' name|'def' name|'test_libvirt_glusterfs_libgfapi' op|'(' name|'self' op|')' op|':' newline|'\n' indent|' ' name|'self' op|'.' name|'flags' op|'(' name|'qemu_allowed_storage_drivers' op|'=' op|'[' string|"'gluster'" op|']' op|',' name|'group' op|'=' string|"'libvirt'" op|')' newline|'\n' name|'libvirt_driver' op|'=' name|'glusterfs' op|'.' name|'LibvirtGlusterfsVolumeDriver' op|'(' name|'self' op|'.' name|'fake_conn' op|')' newline|'\n' name|'self' op|'.' name|'stubs' op|'.' name|'Set' op|'(' name|'libvirt_utils' op|',' string|"'is_mounted'" op|',' name|'lambda' name|'x' op|',' name|'d' op|':' name|'False' op|')' newline|'\n' name|'export_string' op|'=' string|"'192.168.1.1:/volume-00001'" newline|'\n' name|'name' op|'=' string|"'volume-00001'" newline|'\n' nl|'\n' name|'connection_info' op|'=' op|'{' string|"'data'" op|':' op|'{' string|"'export'" op|':' name|'export_string' op|',' string|"'name'" op|':' name|'name' op|'}' op|'}' newline|'\n' nl|'\n' name|'disk_info' op|'=' op|'{' nl|'\n' string|'"dev"' op|':' string|'"vde"' op|',' nl|'\n' string|'"type"' op|':' string|'"disk"' op|',' nl|'\n' string|'"bus"' op|':' string|'"virtio"' op|',' nl|'\n' op|'}' newline|'\n' nl|'\n' name|'libvirt_driver' op|'.' name|'connect_volume' op|'(' name|'connection_info' op|',' name|'disk_info' op|')' newline|'\n' name|'conf' op|'=' name|'libvirt_driver' op|'.' name|'get_config' op|'(' name|'connection_info' op|',' name|'disk_info' op|')' newline|'\n' name|'tree' op|'=' name|'conf' op|'.' name|'format_dom' op|'(' op|')' newline|'\n' name|'self' op|'.' name|'assertEqual' op|'(' string|"'network'" op|',' name|'tree' op|'.' name|'get' op|'(' string|"'type'" op|')' op|')' newline|'\n' name|'self' op|'.' name|'assertEqual' op|'(' string|"'raw'" op|',' name|'tree' op|'.' name|'find' op|'(' string|"'./driver'" op|')' op|'.' name|'get' op|'(' string|"'type'" op|')' op|')' newline|'\n' nl|'\n' name|'source' op|'=' name|'tree' op|'.' name|'find' op|'(' string|"'./source'" op|')' newline|'\n' name|'self' op|'.' name|'assertEqual' op|'(' string|"'gluster'" op|',' name|'source' op|'.' name|'get' op|'(' string|"'protocol'" op|')' op|')' newline|'\n' name|'self' op|'.' name|'assertEqual' op|'(' string|"'volume-00001/volume-00001'" op|',' name|'source' op|'.' name|'get' op|'(' string|"'name'" op|')' op|')' newline|'\n' name|'self' op|'.' name|'assertEqual' op|'(' string|"'192.168.1.1'" op|',' name|'source' op|'.' name|'find' op|'(' string|"'./host'" op|')' op|'.' name|'get' op|'(' string|"'name'" op|')' op|')' newline|'\n' name|'self' op|'.' name|'assertEqual' op|'(' string|"'24007'" op|',' name|'source' op|'.' name|'find' op|'(' string|"'./host'" op|')' op|'.' name|'get' op|'(' string|"'port'" op|')' op|')' newline|'\n' nl|'\n' name|'libvirt_driver' op|'.' name|'disconnect_volume' op|'(' name|'connection_info' op|',' string|'"vde"' op|')' newline|'\n' dedent|'' dedent|'' endmarker|'' end_unit

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2 ]

misc/eulerhelper.py

Hashi4/vmdgadgets

0

39658

import sys sys.path.append('../vmdgadgets') import vmdutil import functools def alt_dot_v(v1, v2): return functools.reduce( lambda i, j: i + j, [x * y for x, y in zip(v1, v2)]) vmdutil.vmdutil.dot_v = alt_dot_v vmdutil.dot_v = alt_dot_v class strexp(): def __init__(self, val): self.val = val def __mul__(self, other): if other.val == '0' or self.val == '0': return strexp('0') elif other.val == '1': return self elif self.val == '1': return other else: return strexp('(' + self.val + ')*(' + other.val + ')') def __add__(self, other): if other.val == '0': return self elif self.val == '0': return other else: return strexp(self.val + '+' + other.val) def __sub__(self, other): if other.val == '0': return self elif self.val == '0': return strexp('-' + other.val) else: return strexp(self.val + '-' + other.val) def __neg__(self): return strexp('-' + self.val) def __repr__(self): return self.val if __name__ == '__main__': zero = strexp('0') one = strexp('1') p = [strexp('sp'), zero, zero, strexp('cp')] # q(1, 0, 0, wx) y = [zero, strexp('sy'), zero, strexp('cy')] r = [zero, zero, strexp('sr'), strexp('cr')] o = vmdutil.multiply_quaternion( vmdutil.multiply_quaternion(r, p), y) print('euler2quaternion of z-x-y(global)') print(o) print() # rotx = [[1, 0, 0], [0, cx, -sx], [0, sx, cx]] # roty = [[cy, 0, sy], [0, 1, 0], [-sy, 0, cy]] # rotz = [[cz, -sz, 0], [sz, cz, 0], [0, 0, 1]] cx = strexp('cx') cy = strexp('cy') cz = strexp('cz') sx = strexp('sx') sy = strexp('sy') sz = strexp('sz') print('euler2matrix of z-x-y(global)') o = vmdutil.dot_m( vmdutil.dot_m( [[cz, -sz, zero], [sz, cz, zero], [zero, zero, one]], [[one, zero, zero], [zero, cx, -sx], [zero, sx, cx]]), [[cy, zero, sy], [zero, one, zero], [-sy, zero, cy]]) for r in o: print(r)

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src/tdparse.py

bluemonk482/tdparse

27

1615292

""" For generating libsvm-formated feature vectors. Many functions here are borrowed from Duy-Tin Vo's implementation """ import sys sys.path.insert(0, r'../') import os import re import numpy as np import gensim from collections import defaultdict import argparse from argparse import ArgumentParser from utilities import readConll, writevec, writey, traversaltree from data.dataprocessing import streamtw, streamtwElec import warnings warnings.simplefilter("error") #------------------------------------------------------------------------------ #------------------------------------------------------------------------------ class lexicon(object): def __init__(self,Wfile='../resources/lexicons/wilson/subjclueslen1-HLTEMNLP05.tff', Bfiles=['../resources/lexicons/binhliu/negative-words.txt','../resources/lexicons/binhliu/positive-words.txt'], Mfile='../resources/lexicons/mohammad/NRC-emotion-lexicon-wordlevel-alphabetized-v0.92.txt',size=100): self.pos,self.neg,self.neu=self.Wilson(Wfile) self.updateBinhliu(Bfiles) self.intersect() def extract(self,word): return word.split('=')[-1] def Wilson(self,filename): neg=[] pos=[] neu=[] with open(filename,'r') as f: for line in f: wtype,wlen,word1,pos1,stemmed1,priorpolarity=line.strip().split(' ') if self.extract(priorpolarity)=='negative': neg.append(self.extract(word1)) elif self.extract(priorpolarity)=='positive': pos.append(self.extract(word1)) else: neu.append(self.extract(word1)) pos=set(pos) neg=set(neg) neu=set(neu) return pos,neg,neu def Mohammad(self,filename): neg=[] pos=[] with open(filename,'r') as f: for line in f: wd,senti,flag=line.strip().split('\t') if senti=='negative' and flag=='1': neg.append(wd) elif senti=='positive' and flag=='1': pos.append(wd) else: pass pos=set(pos) neg=set(neg) return pos,neg def Binhliu(self,filename): neg=np.loadtxt(filename[0],skiprows=1,dtype='string',comments=';') pos=np.loadtxt(filename[1],skiprows=1,dtype='string',comments=';') pos=set(pos) neg=set(neg) return pos,neg def updateBinhliu(self,Bfiles): lexBinh=self.Binhliu(Bfiles) self.pos.update(lexBinh[0]) self.neg.update(lexBinh[1]) def updateMohammad(self,Mfile): lexMoh=self.Mohammad(Mfile) self.pos.update(lexMoh[0]) self.neg.update(lexMoh[1]) def intersect(self): temp1=self.pos.intersection(self.neg) temp2=self.pos.intersection(self.neu) temp3=self.neg.intersection(self.neu) self.pos.difference_update(temp1) self.pos.difference_update(temp2) self.neg.difference_update(temp1) self.neg.difference_update(temp3) self.neu.difference_update(temp2) self.neu.difference_update(temp3) def intersect2(self): temp1=self.pos.intersection(self.neg) self.pos.difference_update(temp1) self.neg.difference_update(temp1) #------------------------------------------------------------------------------ #------------------------------------------------------------------------------ class streamdata(object): def __init__(self, dirname): self.dirname = dirname def __iter__(self): for fname in os.listdir(self.dirname): for line in open(os.path.join(self.dirname, fname)): yield line.rstrip().split('\t') def readTang(fname='../resources/wordemb/sswe'): embs=streamdata(fname) embedmodel={} for tw2vec in embs: wd=tw2vec[0] value=[float(i) for i in tw2vec[1:]] embedmodel[wd]=np.array(value) return embedmodel #------------------------------------------------------------------------------ #------------------------------------------------------------------------------ class targettw(object): def __init__(self,w2vf='../resources/wordemb/w2v/c10_w3_s100', sswef='../resources/wordemb/sswe'): self.w2v=gensim.models.Word2Vec.load(w2vf) self.sswe=readTang(sswef) self.lexicons=lexicon() def emdsswe(self,i,loc,uni,target): f=np.array([]) l=np.array([]) r=np.array([]) t=np.array([]) ls=np.array([]) rs=np.array([]) #embeddings of fulltw features f=self.sswe.get(uni,self.sswe['<unk>']) #embeddings of left context features if i<loc: l=self.sswe.get(uni,self.sswe['<unk>']) if self.lexicons.pos.issuperset(set([uni])): try: ls=self.sswe[uni] except: pass if self.lexicons.neg.issuperset(set([uni])): try: ls=self.sswe[uni] except: pass #embbeddings of target features elif(i==loc): t=self.sswe.get(target.replace('_',''),self.sswe['<unk>']) target2=target.split('_') for wd in target2: ti=self.sswe.get(wd,self.sswe['<unk>']) t=np.concatenate([t,ti]) #embbeddings of right context features else: r=self.sswe.get(uni,self.sswe['<unk>']) if self.lexicons.pos.issuperset(set([uni])): try: rs=self.sswe[uni] except: pass if self.lexicons.neg.issuperset(set([uni])): try: rs=self.sswe[uni] except: pass return [f,l,t,r,ls,rs] def emdw2v(self,i,loc,uni,target): f=np.array([]) l=np.array([]) r=np.array([]) t=np.array([]) ls=np.array([]) rs=np.array([]) try: f=self.w2v[uni] except: pass #embbeddings of left context features if i<loc: try: l=self.w2v[uni] except: pass try: if self.lexicons.pos.issuperset(set([uni])): ls=self.w2v[uni] except: pass try: if self.lexicons.neg.issuperset(set([uni])): ls=self.w2v[uni] except: pass #embbeddings of target feature elif(i==loc): try: t=self.w2v[target.replace('_','')] except: pass target2=target.split('_') for wd in target2: try: ti=self.w2v[wd] t=np.concatenate([t,ti]) except: pass #embbeddings of right context features else: try: r=self.w2v[uni] except: pass try: if self.lexicons.pos.issuperset(set([uni])): rs=self.w2v[uni] except: pass try: if self.lexicons.neg.issuperset(set([uni])): rs=self.w2v[uni] except: pass return [f,l,t,r,ls,rs] def concattw(self,feature,size,tws,etype,locs,target,emp,emode): """ Concatenation of different features """ fullf=np.array([]) leftf=np.array([]) rightf=np.array([]) tarf=np.array([]) fulltw_maxs=np.array([]) fulltw_mins=np.array([]) fulltw_means=np.array([]) fulltw_prods=np.array([]) left_maxs=np.array([]) left_mins=np.array([]) left_means=np.array([]) left_prods=np.array([]) right_maxs=np.array([]) right_mins=np.array([]) right_means=np.array([]) right_prods=np.array([]) tar_maxs=np.array([]) tar_mins=np.array([]) tar_means=np.array([]) tar_prods=np.array([]) leftsenti_max=np.array([]) rightsenti_max=np.array([]) leftsenti_sum=np.array([]) rightsenti_sum=np.array([]) fulltws=np.array([]) lefts=np.array([]) rights=np.array([]) tars=np.array([]) leftsentis=np.array([]) rightsentis=np.array([]) for a in range(len(locs)): if emode=='full': tw = tws else: tw = tws[a] loc = locs[a] fulltw=np.array([]) left=np.array([]) right=np.array([]) tar=np.array([]) leftsenti=np.array([]) rightsenti=np.array([]) for i,uni in enumerate(tw): if emode =='parse': uni = uni[0] if etype=='w2v': f,l,t,r,ls,rs=self.emdw2v(i,loc,uni,target) if etype=='sswe': f,l,t,r,ls,rs=self.emdsswe(i,loc,uni,target) fulltw=np.concatenate([fulltw,f]) left=np.concatenate([left,l]) tar=np.concatenate([tar,t]) right=np.concatenate([right,r]) leftsenti=np.concatenate([leftsenti,ls]) rightsenti=np.concatenate([rightsenti,rs]) #padding if list(left)==[]: left=np.zeros((2*size,)) if list(right)==[]: right=np.zeros((2*size,)) if list(fulltw)==[]: fulltw=np.zeros((2*size,)) if list(tar)==[]: tar=np.zeros((2*size,)) if len(left)<=size: left=np.concatenate([left,np.zeros((size,))]) if len(right)<=size: right=np.concatenate([right,np.zeros((size,))]) if len(fulltw)<=size: fulltw=np.concatenate([fulltw,np.zeros((size,))]) if len(tar)<=size: tar=np.concatenate([tar,np.zeros((size,))]) if list(leftsenti)==[]: leftsenti=np.zeros((size,)) if list(rightsenti)==[]: rightsenti=np.zeros((size,)) fullf=np.append(fullf,fulltw, axis=0) leftf=np.append(leftf,left, axis=0) rightf=np.append(rightf,right, axis=0) tarf=np.append(tarf,tar, axis=0) fulltw=fulltw.reshape(len(fulltw)/size,size) left=left.reshape(len(left)/size,size) right=right.reshape(len(right)/size,size) tar=tar.reshape(len(tar)/size,size) leftsenti=leftsenti.reshape(len(leftsenti)/size,size) rightsenti=rightsenti.reshape(len(rightsenti)/size,size) fulltw_maxs=np.append(fulltw_maxs,fulltw.max(axis=0), axis=0) fulltw_mins=np.append(fulltw_mins,fulltw.min(axis=0), axis=0) fulltw_means=np.append(fulltw_means,fulltw.mean(axis=0), axis=0) fulltw_prods=np.append(fulltw_prods,fulltw.prod(axis=0), axis=0) left_maxs=np.append(left_maxs,left.max(axis=0), axis=0) left_mins=np.append(left_mins,left.min(axis=0), axis=0) left_means=np.append(left_means,left.mean(axis=0), axis=0) if not np.count_nonzero(left): left_prods=np.append(left_prods,left.prod(axis=0), axis=0) else: left_prods=np.append(left_prods,left[~np.all(left == 0, axis=1)].prod(axis=0), axis=0) right_maxs=np.append(right_maxs,right.max(axis=0), axis=0) right_mins=np.append(right_mins,right.min(axis=0), axis=0) right_means=np.append(right_means,right.mean(axis=0), axis=0) if not np.count_nonzero(right): right_prods=np.append(right_prods,right.prod(axis=0), axis=0) else: right_prods=np.append(right_prods,right[~np.all(right == 0, axis=1)].prod(axis=0), axis=0) tar_maxs=np.append(tar_maxs,tar.max(axis=0), axis=0) tar_mins=np.append(tar_mins,tar.min(axis=0), axis=0) tar_means=np.append(tar_means,tar.mean(axis=0), axis=0) tar_prods=np.append(tar_prods,tar.prod(axis=0), axis=0) leftsenti_max=np.append(leftsenti_max,leftsenti.max(axis=0), axis=0) rightsenti_max=np.append(rightsenti_max,rightsenti.max(axis=0), axis=0) leftsenti_sum=np.append(leftsenti_sum,leftsenti.sum(axis=0), axis=0) rightsenti_sum=np.append(rightsenti_sum,rightsenti.sum(axis=0), axis=0) fullf=fullf.reshape(len(fullf)/size,size) leftf=leftf.reshape(len(leftf)/size,size) rightf=rightf.reshape(len(rightf)/size,size) tarf=tarf.reshape(len(tarf)/size,size) fulltw_maxs=fulltw_maxs.reshape(len(fulltw_maxs)/size,size) fulltw_mins=fulltw_mins.reshape(len(fulltw_mins)/size,size) fulltw_means=fulltw_means.reshape(len(fulltw_means)/size,size) fulltw_prods=fulltw_prods.reshape(len(fulltw_prods)/size,size) left_maxs=left_maxs.reshape(len(left_maxs)/size,size) left_mins=left_mins.reshape(len(left_mins)/size,size) left_means=left_means.reshape(len(left_means)/size,size) left_prods=left_prods.reshape(len(left_prods)/size,size) right_maxs=right_maxs.reshape(len(right_maxs)/size,size) right_mins=right_mins.reshape(len(right_mins)/size,size) right_means=right_means.reshape(len(right_means)/size,size) right_prods=right_prods.reshape(len(right_prods)/size,size) tar_maxs=tar_maxs.reshape(len(tar_maxs)/size,size) tar_mins=tar_mins.reshape(len(tar_mins)/size,size) tar_means=tar_means.reshape(len(tar_means)/size,size) tar_prods=tar_prods.reshape(len(tar_prods)/size,size) leftsenti_max=leftsenti_max.reshape(len(leftsenti_max)/size,size) rightsenti_max=rightsenti_max.reshape(len(rightsenti_max)/size,size) leftsenti_sum=leftsenti_sum.reshape(len(leftsenti_sum)/size,size) rightsenti_sum=rightsenti_sum.reshape(len(rightsenti_sum)/size,size) fulltws=np.concatenate([np.median(fulltw_maxs, axis=0), np.median(fulltw_mins, axis=0), np.median(fulltw_means, axis=0), np.std(fullf, axis=0), np.median(fulltw_prods, axis=0) ]) lefts=np.concatenate([np.median(left_maxs, axis=0), np.median(left_mins, axis=0), np.median(left_means, axis=0), np.std(leftf, axis=0), np.median(left_prods, axis=0) ]) rights=np.concatenate([np.median(right_maxs, axis=0), np.median(right_mins, axis=0), np.median(right_means, axis=0), np.std(rightf, axis=0), np.median(right_prods, axis=0) ]) tars=np.concatenate([np.median(tar_maxs, axis=0), np.median(tar_mins, axis=0), np.median(tar_means, axis=0), np.std(tarf, axis=0), np.median(tar_prods, axis=0) ]) leftsentis=np.concatenate([np.median(leftsenti_max, axis=0), np.median(leftsenti_sum, axis=0)]) rightsentis=np.concatenate([np.median(rightsenti_max, axis=0), np.median(rightsenti_sum, axis=0)]) if emode == 'full': if emp: # Adds full tweet feature vectors feature=np.concatenate([feature,fulltws]) else: feature=np.concatenate([feature,lefts]) feature=np.concatenate([feature,rights]) feature=np.concatenate([feature,tars]) # Adds features from lexicon filtered words # (not used on the election data) feature=np.concatenate([feature,leftsentis]) feature=np.concatenate([feature,rightsentis]) elif emode == 'parse': feature=np.concatenate([feature,fulltws]) return feature def lidongfeat(self, dataf, conllpath): """ Main function for (Dong et al., 2014) data feature extraction. """ size1=len(self.w2v['the']) size2=len(self.sswe['the']) conll = readConll(conllpath, 'cmu') data = streamtw(dataf) y=[] x=np.array([]) count=0 for a, d in enumerate(data): feature=np.array([]) emp=False tw = [t[1].lower() for t in conll[a]] target = d[1] y.append(d[2]) try: loc=[i for i, j in enumerate(tw) if j == target] except Exception as e: print "Couldn't find the tokenised target!" print target, tw subtw, target_position, emp = traversaltree(conll[a],target,emp,'cmu') #returns relevant words to the target emp = False if emp: count+=1 feature=self.concattw(feature,size1,tw,'w2v',loc,target,emp,'full') feature=self.concattw(feature,size2,tw,'sswe',loc,target,emp,'full') else: feature=self.concattw(feature,size1,subtw,'w2v',target_position,target,False,'parse') feature=self.concattw(feature,size2,subtw,'sswe',target_position,target,False,'parse') feature=self.concattw(feature,size1,tw,'w2v',loc,target,False,'full') feature=self.concattw(feature,size2,tw,'sswe',loc,target,False,'full') x=np.concatenate([x,feature]) x=x.reshape((len(y),len(x)/len(y))) print x.shape print count return(x,y) def elecfeat(self, dataf, conllpath): """ Main function for election data feature extraction. """ size1=len(self.w2v['the']) size2=len(self.sswe['the']) conll = readConll(conllpath, 'cmu') data = streamtwElec(dataf) y=[] x=np.array([]) id = [] count=0 for a, d in enumerate(data): feature=np.array([]) emp=False tw=[t[1] for t in conll[a]] # tw=d[0] target=d[1] if target=='"long_term_economic"_plans': target='long_term_economic' y.append(d[2]) id.append(d[3]) whichone = d[4] locations = [i for i, j in enumerate(tw) if j == target] if (whichone != 'nan') and (len(locations)> 1): if whichone >= len(locations): loc = locations[-1] else: loc = locations[whichone] else: loc = tw.index(target) subtw, target_position, emp = traversaltree(conll[a],target,emp,'cmu') emp = False if emp: count+=1 feature=self.concattw(feature,size1,tw,'w2v',[loc],target,emp,'full') feature=self.concattw(feature,size2,tw,'sswe',[loc],target,emp,'full') else: feature=self.concattw(feature,size1,subtw,'w2v',target_position,target,False,'parse') feature=self.concattw(feature,size2,subtw,'sswe',target_position,target,False,'parse') feature=self.concattw(feature,size1,tw,'w2v',[loc],target,False,'full') feature=self.concattw(feature,size2,tw,'sswe',[loc],target,False,'full') x=np.concatenate([x,feature]) x=x.reshape((len(y),len(x)/len(y))) print x.shape print count return(x,y,id) def main(d, train_conllpath, test_conllpath): features=targettw() print "extracting features for training" if (d == 'lidong') or (d == 'semeval'): x_train,y_train=features.lidongfeat('../data/'+d+'/training/', train_conllpath) print 'Parse source: ', train_conllpath elif d == 'election': print 'election training data' x_train,y_train,id_train=features.elecfeat('../data/'+d+'/training/', train_conllpath) writey('../data/'+d+'/output/id_train',id_train) print 'Parse source: ', train_conllpath writevec('../data/'+d+'/output/training',x_train,y_train) print "extracting features for testing" if (d == 'lidong') or (d == 'semeval'): x_test,y_test=features.lidongfeat('../data/'+d+'/testing/', test_conllpath) print 'Parse source: ', test_conllpath elif d == 'election': print 'election testing data' x_test,y_test,id_test=features.elecfeat('../data/'+d+'/testing/', test_conllpath) print 'Parse source: ', test_conllpath writevec('../data/'+d+'/output/testing',x_test,y_test) writey('../data/'+d+'/output/y_test',y_test) if __name__ == "__main__": parser = ArgumentParser() parser.add_argument("--data", dest="d", help="dataset", default='data') parser.add_argument("--trainparse", dest="conll1", help="train parse conll filename", default='../data/election/parses/election.train.conll') parser.add_argument("--testparse", dest="conll2", help="test parse conll filename", default='../data/election/parses/election.test.conll') args = parser.parse_args() main(args.d, args.conll1, args.conll2)

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app/main/forms.py

MichelAtieno/Personal-Blog

0

69661

<gh_stars>0 from flask_wtf import FlaskForm from wtforms import StringField, SubmitField, TextAreaField from wtforms.validators import Required, DataRequired, Length class SubscriberForm(FlaskForm): email = StringField('Email', validators=[Required()]) subscribe = SubmitField('Subscribe') class BlogPostForm(FlaskForm): title = StringField('Title', validators=[DataRequired(),Length(min=1, max=1000)]) photo_url = StringField('Photo URL', validators=[Required()]) blog_content= TextAreaField('Post your blog', validators=[DataRequired(), Length(min=1)]) submit = SubmitField('Submit') class CommentForm(FlaskForm): name = StringField('Name',validators=[Required()]) comment_content = TextAreaField('Enter Comment', validators=[Required()]) submit = SubmitField('submit Comment')

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