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

e62035eb55279994b4c05dd9b254069dd128fe17

2,583

py

Python

rolling/hash.py

ajcr/rolling

7bbac93809a0ad1cd6e128cfd64b70a99d26ea8c

[ "MIT" ]

189

2018-03-12T00:31:19.000Z

2022-03-26T00:17:38.000Z

rolling/hash.py

ajcr/rolling

7bbac93809a0ad1cd6e128cfd64b70a99d26ea8c

[ "MIT" ]

23

2017-12-31T17:50:48.000Z

2021-11-27T15:31:54.000Z

rolling/hash.py

ajcr/rolling

7bbac93809a0ad1cd6e128cfd64b70a99d26ea8c

[ "MIT" ]

7

2019-01-28T02:53:49.000Z

2021-11-11T18:34:45.000Z

from collections import Counter, deque from itertools import islice from .base import RollingObject DEF_BASE = 719 DEF_MOD = 2 ** 61 - 1 def polynomial_hash_sequence(seq, base=DEF_BASE, mod=DEF_MOD): """ Compute the polynomial hash of a sequence. """ return sum(hash(c) * pow(base, k, mod) for k, c in enumerate(reversed(seq))) % mod class PolynomialHash(RollingObject): """ Rolling polynomial hash. Computes a hash of a window of size k as: hash(w_0) * base ** (k - 1) + hash(w_1) * base ** (k - 2) + ... + hash(w_(k-2)) * base + hash(w_(k-1)) % mod where `base` and `mod` are constant integers. To minimise collisions, it is recommended that you adjust these values to meet your specific use case. See: wikipedia.org/wiki/Rolling_hash#Polynomial_rolling_hash Parameters ---------- iterable : iterable of hashable objects window_size : integer, the size of the rolling window moving over the iterable window_type : str, "fixed" or "variable" base : integer, polynomial base mod : integer, all hashes are modulus this value Complexity ---------- Update time: O(1) Memory usage: O(k) where k is the size of the rolling window Examples -------- >>> import rolling >>> r_hash = rolling.PolynomialHash("abcxyabc", window_size=3, ... base=31, mod=9967) >>> list(r_hash) [4984, 900, 5072, 771, 8757, 4984] """ def __init__( self, iterable, window_size, window_type="fixed", base=DEF_BASE, mod=DEF_MOD ): self._hash = 0 self._base = base self._mod = mod super().__init__(iterable, window_size, window_type) def _init_fixed(self, *args, **kwargs): self._buffer = deque([0]) for val in islice(self._iterator, self.window_size - 1): self._add_new(val) def _init_variable(self, *args, **kwargs): self._buffer = deque() def _add_new(self, new): self._hash *= self._base self._hash += hash(new) self._hash %= self._mod self._buffer.append(new) def _remove_old(self): old = self._buffer.popleft() self._hash -= hash(old) * pow(self._base, self._obs, self._mod) self._hash %= self._mod def _update_window(self, new): self._remove_old() self._add_new(new) @property def current_value(self): return self._hash @property def _obs(self): return len(self._buffer)

24.6

86

0.603175

8f69c335bd08d12466ea7908b7bcec825f79053c

4,012

py

Python

mydataset.py

dreamakeo/catsvsdogs

4113e60f348ca882aebd63315526116df2e550c4

[ "MIT" ]

null

mydataset.py

dreamakeo/catsvsdogs

4113e60f348ca882aebd63315526116df2e550c4

[ "MIT" ]

null

mydataset.py

dreamakeo/catsvsdogs

4113e60f348ca882aebd63315526116df2e550c4

[ "MIT" ]

null

#对原始数据进行划分，得到标记文件 import os import shutil import collections import math import csv import random root_dir = '/home/ding/DATA/dataset/' valid_ratio = 0.1 batch_size = 4 data_train_dir = os.path.join(root_dir,'train') #随机字典 def random_dic(dicts): dict_key_ls = list(dicts.keys()) random.shuffle(dict_key_ls) new_dic = {} for key in dict_key_ls: new_dic[key] = dicts.get(key) return new_dic #建立CSV标记文件 def create_csv_labels(data_train_dir,csv_dir = os.path.join(data_train_dir,'../trainLabels.csv')): for root,dir,files in os.walk(data_train_dir): tokens = [name.rstrip().split('.') for name in files] data_label_list = [label for label,id,suffix in tokens] data_id_list = [(label+'.'+ id) for label,id,suffix in tokens] csv_dict = dict(zip(data_id_list,data_label_list)) csv_dict = random_dic(csv_dict) #print(csv_dict.items()) with open(csv_dir,'w') as f: csv_write = csv.writer(f) csv_head = ["id","label"] csv_write.writerow(csv_head) for id,label in csv_dict.items(): #print(id,label) csv_write.writerow([id,label]) # for name in files: # print(name.rstrip().split('.',1)) labels_map = { 0: "cat", 1: "dog", } #建立CSV标记文件V2 def create_csv_labels_v2(data_train_dir,csv_dir = os.path.join(data_train_dir,'../trainLabels1.csv')): for root,dir,files in os.walk(data_train_dir): tokens = [name.rstrip().split('.') for name in files] data_label_list = [label for label,id,suffix in tokens] data_id_list = [(label+'.'+ id+'.'+suffix) for label,id,suffix in tokens] csv_dict = dict(zip(data_id_list,data_label_list)) csv_dict = random_dic(csv_dict) #print(csv_dict.items()) with open(csv_dir,'w') as f: csv_write = csv.writer(f) csv_head = ["id","label"] csv_write.writerow(csv_head) for id,label in csv_dict.items(): #print(id,label) csv_write.writerow([id,label]) # for name in files: # print(name.rstrip().split('.',1)) #读取标记字典 def read_csv_labels(fname): with open(fname, 'r') as f: lines = f.readlines()[1:] tokens = [l.rstrip().split(',') for l in lines] return dict(((name, label) for name, label in tokens)) def copyfile(filename,target_dir): os.makedirs(target_dir,exist_ok = True) shutil.copy(filename,target_dir) #划分训练校验集 def reorg_train_valid(data_dir, labels, valid_ratio): n = collections.Counter(labels.values()).most_common()[-1][1] n_valid_per_label = max(1, math.floor(n * valid_ratio)) label_count = {} for train_file in os.listdir(os.path.join(data_dir, 'train')): l = train_file.split('.') label = labels[l[0]+'.'+l[1]] fname = os.path.join(data_dir, 'train', train_file) copyfile( fname, os.path.join(data_dir, 'train_valid_test', 'train_valid', label)) if label not in label_count or label_count[label] < n_valid_per_label: copyfile( fname, os.path.join(data_dir, 'train_valid_test', 'valid', label)) label_count[label] = label_count.get(label, 0) + 1 else: copyfile( fname, os.path.join(data_dir, 'train_valid_test', 'train', label)) return n_valid_per_label #划分测试集 def reorg_test(data_dir): for test_file in os.listdir(os.path.join(data_dir, 'test')): copyfile( os.path.join(data_dir, 'test', test_file), os.path.join(data_dir, 'train_valid_test', 'test', 'unknown')) def reorg_catsanddog_data(data_dir, valid_ratio): labels = read_csv_labels(os.path.join(data_dir, 'trainLabels.csv')) reorg_train_valid(data_dir, labels, valid_ratio) reorg_test(data_dir) valid_ratio = 0.1 #create_csv_labels_v2(data_train_dir) #reorg_catsanddog_data(root_dir,valid_ratio)

33.714286

102

0.629611

88d7e0baffc611d60cbeb788fae0f16b5ee4937f

6,960

py

Python

tests/django_init.py

tbaschak/peeringdb

20d89d53d8e1d807383fa84d74601e37ba4dc9d4

[ "BSD-2-Clause" ]

null

tests/django_init.py

tbaschak/peeringdb

20d89d53d8e1d807383fa84d74601e37ba4dc9d4

[ "BSD-2-Clause" ]

null

tests/django_init.py

tbaschak/peeringdb

20d89d53d8e1d807383fa84d74601e37ba4dc9d4

[ "BSD-2-Clause" ]

null

import os from django.conf import settings # lazy init for translations _ = lambda s: s # from django.utils.translation import ugettext_lazy as _ settings.configure( PACKAGE_VERSION="dev", RELEASE_ENV="dev", MIGRATION_MODULES={"django_peeringdb": None}, INSTALLED_APPS=[ "django.contrib.auth", "django.contrib.contenttypes", "django_otp", "django_otp.plugins.otp_static", "django_otp.plugins.otp_totp", "django_otp.plugins.otp_email", "two_factor", "grappelli", "django.contrib.admin", "django.contrib.sessions", "django.contrib.sites", "django_inet", "django_peeringdb", "django_namespace_perms", "django_countries", "oauth2_provider", "peeringdb_server", "allauth", "allauth.account", "reversion", "rest_framework", "dal", "dal_select2", "corsheaders", "captcha", ], CACHES={ "default": { "BACKEND": "django.core.cache.backends.db.DatabaseCache", "LOCATION": "django_cache", } }, TEMPLATES=[ { "BACKEND": "django.template.backends.django.DjangoTemplates", "APP_DIRS": True, "DIRS": ( os.path.join( os.path.dirname(__file__), "..", "peeringdb_server", "templates" ), ), "OPTIONS": { "context_processors": [ "django.contrib.auth.context_processors.auth", "django.template.context_processors.debug", "django.template.context_processors.request", "django.template.context_processors.i18n", "django.template.context_processors.media", "django.template.context_processors.static", "django.template.context_processors.tz", "django.contrib.messages.context_processors.messages", ], }, } ], LANGUAGE_CODE="en-us", LANGUAGES=[("en", _("English")), ("pt", _("Portuguese")),], USE_L10N=True, USE_I18N=True, MEDIA_URL="/m/", STATIC_URL="/s/", MIDDLEWARE=( "corsheaders.middleware.CorsMiddleware", "django.contrib.sessions.middleware.SessionMiddleware", "django.middleware.locale.LocaleMiddleware", "django.middleware.common.CommonMiddleware", "django.middleware.csrf.CsrfViewMiddleware", "django.contrib.auth.middleware.AuthenticationMiddleware", "django.contrib.messages.middleware.MessageMiddleware", "peeringdb_server.maintenance.Middleware", ), SOUTH_TESTS_MIGRATE=False, SOUTH_SKIP_TESTS=True, AUTH_USER_MODEL="peeringdb_server.User", TABLE_PREFIX="peeringdb_", PEERINGDB_ABSTRACT_ONLY=True, COUNTRIES_OVERRIDE={"XK": _("Kosovo")}, CLIENT_COMPAT={ "client": {"min": (0, 6), "max": (0, 6, 5)}, "backends": {"django_peeringdb": {"min": (0, 6), "max": (0, 6, 5)}}, }, DATABASE_ENGINE="django.db.backends.sqlite3", DATABASES={ "default": {"ENGINE": "django.db.backends.sqlite3", "NAME": ":memory:",}, # TODO - this is supposed to work to mimic replication # during tests, but doesnt. So instead we use the # peeringdb_server.db_router.TestRouter class instead # which just always used the default db for read and writes #'read' : { # 'ENGINE': 'django.db.backends.sqlite3', # 'NAME': ':memory:', # 'TEST' : { 'MIRROR' : 'default' } # } }, # TODO - change to peeringdb_server.db_router.DatabaseRouter # if repliation mimicing (see above) gets fixed DATABASE_ROUTERS=["peeringdb_server.db_router.TestRouter"], DEBUG=False, GUEST_GROUP_ID=1, USER_GROUP_ID=2, TEMPLATE_DEBUG=False, BASE_URL="https://localhost", PASSWORD_RESET_URL="localhost", API_CACHE_ROOT="tests/api-cache", API_CACHE_ENABLED=False, SUGGEST_ENTITY_ORG=1234, API_URL="localhost", REST_FRAMEWORK={ "DEFAULT_AUTHENTICATION_CLASSES": ( "rest_framework.authentication.BasicAuthentication", "rest_framework.authentication.SessionAuthentication", ), "DEFAULT_MODEL_SERIALIZER_CLASS": "rest_framework.serializers.HyperlinkedModelSerializer", "DEFAULT_PERMISSION_CLASSES": [ "rest_framework.permissions.DjangoModelPermissionsOrAnonReadOnly", "django_namespace_perms.rest.BasePermission", ], "DEFAULT_RENDERER_CLASSES": ("peeringdb_server.renderers.MetaJSONRenderer",), "DEFAULT_SCHEMA_CLASS": "rest_framework.schemas.coreapi.AutoSchema", }, NSP_MODE="crud", NSP_GUEST_GROUP="guest", DEBUG_EMAIL=True, TIME_ZONE="UTC", USE_TZ=True, POC_DELETION_PERIOD=30, PROTECTED_OBJECT_NOTIFICATION_PERIOD=1, AUTHENTICATION_BACKENDS=("django_namespace_perms.auth.backends.NSPBackend",), ROOT_URLCONF="mainsite.urls", LOGGING={ "version": 1, "disable_existing_loggers": True, "handlers": {"stderr": {"level": "DEBUG", "class": "logging.StreamHandler",},}, "loggers": { "": {"handlers": ["stderr"], "level": "DEBUG", "propagate": False}, }, }, API_DOC_STR={ "retrieve": "retrieve object", "list": "list objects", "update": "update object", "delete": "delete object", "create": "create object", }, LOGIN_URL="/account/login", LOGIN_REDIRECT_URL="/", OAUTH_ENABLED=False, RECAPTCHA_PUBLIC_KEY="", EMAIL_SUBJECT_PREFIX="[test]", CORS_ORIGIN_WHITELIST=[], CORS_ALLOW_METHODS=["GET", "OPTIONS"], CORS_ALLOW_CREDENTIALS=False, DATA_QUALITY_MAX_PREFIX_V4_LIMIT=500000, DATA_QUALITY_MAX_PREFIX_V6_LIMIT=500000, DATA_QUALITY_MIN_PREFIXLEN_V4=18, DATA_QUALITY_MAX_PREFIXLEN_V4=28, DATA_QUALITY_MIN_PREFIXLEN_V6=64, DATA_QUALITY_MAX_PREFIXLEN_V6=116, DATA_QUALITY_MAX_IRR_DEPTH=3, TUTORIAL_MODE=False, CAPTCHA_TEST_MODE=True, SITE_ID=1, IXF_POSTMORTEM_LIMIT=250, IXF_NOTIFY_IX_ON_CONFLICT=True, IXF_NOTIFY_NET_ON_CONFLICT=True, IXF_TICKET_ON_CONFLICT=True, ABSTRACT_ONLY=True, GOOGLE_GEOLOC_API_KEY="AIzatest", RATELIMITS={ "view_affiliate_to_org_POST": "100/m", "resend_confirmation_mail": "2/m", "view_request_ownership_GET": "3/m", "view_username_retrieve_initiate": "2/m", "view_request_ownership_POST": "3/m", "request_login_POST": "10/m", "view_verify_POST": "2/m", "request_translation": "10/m", "view_import_ixlan_ixf_preview": "1/m", "view_import_net_ixf_postmortem": "1/m", }, MAX_USER_AFFILIATION_REQUESTS=10, MAIL_DEBUG=True, IXF_PARSE_ERROR_NOTIFICATION_PERIOD=36, )

35.151515

98

0.624569

6b3d480afd6a58ab60f5551cbcee669d966fc73b

1,807

py

Python

OpdrachtC2.py

kalkoen/informatica-olympiade-2019

0b6e810d906b105ad7a1a1a6800e5710517c3527

[ "MIT" ]

null

OpdrachtC2.py

kalkoen/informatica-olympiade-2019

0b6e810d906b105ad7a1a1a6800e5710517c3527

[ "MIT" ]

null

OpdrachtC2.py

kalkoen/informatica-olympiade-2019

0b6e810d906b105ad7a1a1a6800e5710517c3527

[ "MIT" ]

null

grid_size = 8 start = "@" jewel = "+" wall = "#" bomb = "*" stop = "O" free = "-" directions = [(0, 1), (1, 1), (1, 0), (1, -1), (0, -1), (-1, -1), (-1, 0), (-1, 1)] class Tile: def __init__(self, x, y, value): self.value = value self.x = x self.y = y self.paths = [] class Path: def __init__(self): passing_tiles = [] def print_grid(): print() for y in range(grid_size): for x in range(grid_size): print(grid[x][y].value, end="") print() print() #Initalize and fill grid #grid= [[]] * 8 only creates one list instance. Good to know grid = [[] for i in range(grid_size)] for y in range(grid_size): line = input() for x in range(len(line)): grid[x].append(Tile(x, y, line[x])) #Define possible paths for each tile def path(start_tile, direction): path = [] v = directions[direction] condition = 1 while condition: next_tile = tile(start_tile.x + v[0], start_tile.y + v[1]) print("Test") if not next_tile: break condition = next_tile.value != wall if next_tile.value == bomb: return path.append(next_tile) start_tile = next_tile return path def tile(x, y): if x < 0 or x >= grid_size or y < 0 or y >= grid_size: return 0 return grid[x][y] def add_paths(): print() for y in range(grid_size): for x in range(grid_size): for d in range(8): grid[x][y].paths.append(path(grid[x][y], d)) print(x, y, "has", len(grid[x][y].paths), "paths") print() print() x = 1 y = 6 for d in range(8): grid[x][y].paths.append(path(grid[x][y], d)) print(x, y, "has", len(grid[x][y].paths), "paths") print_grid()

19.021053

83

0.53016

136886429ff0d9fe219b886015a04ca006a5a494

39,295

py

Python

frappe/__init__.py

olderp/frappe

92700852a8d182e3ec6c987123428c323eaa8844

[ "MIT" ]

null

frappe/__init__.py

olderp/frappe

92700852a8d182e3ec6c987123428c323eaa8844

[ "MIT" ]

null

frappe/__init__.py

olderp/frappe

92700852a8d182e3ec6c987123428c323eaa8844

[ "MIT" ]

5

2016-06-20T08:48:11.000Z

2018-12-12T09:42:31.000Z

# Copyright (c) 2015, Frappe Technologies Pvt. Ltd. and Contributors # MIT License. See license.txt """ globals attached to frappe module + some utility functions that should probably be moved """ from __future__ import unicode_literals from werkzeug.local import Local, release_local import os, sys, importlib, inspect, json # public from .exceptions import * from .utils.jinja import get_jenv, get_template, render_template __version__ = "7.0.30" local = Local() class _dict(dict): """dict like object that exposes keys as attributes""" def __getattr__(self, key): ret = self.get(key) if not ret and key.startswith("__"): raise AttributeError() return ret def __setattr__(self, key, value): self[key] = value def __getstate__(self): return self def __setstate__(self, d): self.update(d) def update(self, d): """update and return self -- the missing dict feature in python""" super(_dict, self).update(d) return self def copy(self): return _dict(dict(self).copy()) def _(msg, lang=None): """Returns translated string in current lang, if exists.""" from frappe.translate import get_full_dict from frappe.utils import cstr if not lang: lang = local.lang # msg should always be unicode msg = cstr(msg) return get_full_dict(local.lang).get(msg) or msg def get_lang_dict(fortype, name=None): """Returns the translated language dict for the given type and name. :param fortype: must be one of `doctype`, `page`, `report`, `include`, `jsfile`, `boot` :param name: name of the document for which assets are to be returned.""" if local.lang=="en": return {} from frappe.translate import get_dict return get_dict(fortype, name) def set_user_lang(user, user_language=None): """Guess and set user language for the session. `frappe.local.lang`""" from frappe.translate import get_user_lang local.lang = get_user_lang(user) # local-globals db = local("db") conf = local("conf") form = form_dict = local("form_dict") request = local("request") response = local("response") session = local("session") user = local("user") flags = local("flags") error_log = local("error_log") debug_log = local("debug_log") message_log = local("message_log") lang = local("lang") def init(site, sites_path=None, new_site=False): """Initialize frappe for the current site. Reset thread locals `frappe.local`""" if getattr(local, "initialised", None): return if not sites_path: sites_path = '.' local.error_log = [] local.message_log = [] local.debug_log = [] local.realtime_log = [] local.flags = _dict({ "ran_schedulers": [], "currently_saving": [], "redirect_location": "", "in_install_db": False, "in_install_app": False, "in_import": False, "in_test": False, "mute_messages": False, "ignore_links": False, "mute_emails": False, "has_dataurl": False, "new_site": new_site }) local.rollback_observers = [] local.test_objects = {} local.site = site local.sites_path = sites_path local.site_path = os.path.join(sites_path, site) local.request_ip = None local.response = _dict({"docs":[]}) local.task_id = None local.conf = _dict(get_site_config()) local.lang = local.conf.lang or "en" local.lang_full_dict = None local.module_app = None local.app_modules = None local.system_settings = None local.user = None local.user_perms = None local.session = None local.role_permissions = {} local.valid_columns = {} local.new_doc_templates = {} local.jenv = None local.jloader =None local.cache = {} setup_module_map() local.initialised = True def connect(site=None, db_name=None): """Connect to site database instance. :param site: If site is given, calls `frappe.init`. :param db_name: Optional. Will use from `site_config.json`.""" from database import Database if site: init(site) local.db = Database(user=db_name or local.conf.db_name) local.form_dict = _dict() local.session = _dict() set_user("Administrator") def get_site_config(sites_path=None, site_path=None): """Returns `site_config.json` combined with `sites/common_site_config.json`. `site_config` is a set of site wide settings like database name, password, email etc.""" config = {} sites_path = sites_path or getattr(local, "sites_path", None) site_path = site_path or getattr(local, "site_path", None) if sites_path: common_site_config = os.path.join(sites_path, "common_site_config.json") if os.path.exists(common_site_config): config.update(get_file_json(common_site_config)) if site_path: site_config = os.path.join(site_path, "site_config.json") if os.path.exists(site_config): config.update(get_file_json(site_config)) elif local.site and not local.flags.new_site: raise IncorrectSitePath, "{0} does not exist".format(site_config) return _dict(config) def get_conf(site=None): if hasattr(local, 'conf'): return local.conf else: # if no site, get from common_site_config.json with init_site(site): return local.conf class init_site: def __init__(self, site=None): '''If site==None, initialize it for empty site ('') to load common_site_config.json''' self.site = site or '' def __enter__(self): init(self.site) return local def __exit__(self, type, value, traceback): destroy() def destroy(): """Closes connection and releases werkzeug local.""" if db: db.close() release_local(local) # memcache redis_server = None def cache(): """Returns memcache connection.""" global redis_server if not redis_server: from frappe.utils.redis_wrapper import RedisWrapper redis_server = RedisWrapper.from_url(conf.get('redis_cache') or "redis://localhost:11311") return redis_server def get_traceback(): """Returns error traceback.""" import utils return utils.get_traceback() def errprint(msg): """Log error. This is sent back as `exc` in response. :param msg: Message.""" from utils import cstr if not request or (not "cmd" in local.form_dict): print cstr(msg) error_log.append(cstr(msg)) def log(msg): """Add to `debug_log`. :param msg: Message.""" if not request: if conf.get("logging") or False: print repr(msg) from utils import cstr debug_log.append(cstr(msg)) def msgprint(msg, title=None, raise_exception=0, as_table=False, indicator=None, alert=False): """Print a message to the user (via HTTP response). Messages are sent in the `__server_messages` property in the response JSON and shown in a pop-up / modal. :param msg: Message. :param title: [optional] Message title. :param raise_exception: [optional] Raise given exception and show message. :param as_table: [optional] If `msg` is a list of lists, render as HTML table. """ from utils import encode out = _dict(message=msg) def _raise_exception(): if raise_exception: if flags.rollback_on_exception: db.rollback() import inspect if inspect.isclass(raise_exception) and issubclass(raise_exception, Exception): raise raise_exception, encode(msg) else: raise ValidationError, encode(msg) if flags.mute_messages: _raise_exception() return if as_table and type(msg) in (list, tuple): out.msg = '<table border="1px" style="border-collapse: collapse" cellpadding="2px">' + ''.join(['<tr>'+''.join(['<td>%s</td>' % c for c in r])+'</tr>' for r in msg]) + '</table>' if flags.print_messages: print "Message: " + repr(out.msg).encode("utf-8") if title: out.title = title if not indicator and raise_exception: indicator = 'red' if indicator: out.indicator = indicator if alert: out.alert = 1 message_log.append(json.dumps(out)) _raise_exception() def throw(msg, exc=ValidationError, title=None): """Throw execption and show message (`msgprint`). :param msg: Message. :param exc: Exception class. Default `frappe.ValidationError`""" msgprint(msg, raise_exception=exc, title=title, indicator='red') def emit_js(js, user=False, **kwargs): from frappe.async import publish_realtime if user == False: user = session.user publish_realtime('eval_js', js, user=user, **kwargs) def create_folder(path, with_init=False): """Create a folder in the given path and add an `__init__.py` file (optional). :param path: Folder path. :param with_init: Create `__init__.py` in the new folder.""" from frappe.utils import touch_file if not os.path.exists(path): os.makedirs(path) if with_init: touch_file(os.path.join(path, "__init__.py")) def set_user(username): """Set current user. :param username: **User** name to set as current user.""" local.session.user = username local.session.sid = username local.cache = {} local.form_dict = _dict() local.jenv = None local.session.data = _dict() local.role_permissions = {} local.new_doc_templates = {} local.user_perms = None def get_user(): from frappe.utils.user import UserPermissions if not local.user_perms: local.user_perms = UserPermissions(local.session.user) return local.user_perms def get_roles(username=None): """Returns roles of current user.""" if not local.session: return ["Guest"] if username: import frappe.utils.user return frappe.utils.user.get_roles(username) else: return get_user().get_roles() def get_request_header(key, default=None): """Return HTTP request header. :param key: HTTP header key. :param default: Default value.""" return request.headers.get(key, default) def sendmail(recipients=(), sender="", subject="No Subject", message="No Message", as_markdown=False, delayed=True, reference_doctype=None, reference_name=None, unsubscribe_method=None, unsubscribe_params=None, unsubscribe_message=None, attachments=None, content=None, doctype=None, name=None, reply_to=None, cc=(), show_as_cc=(), message_id=None, in_reply_to=None, send_after=None, expose_recipients=False, send_priority=1, communication=None): """Send email using user's default **Email Account** or global default **Email Account**. :param recipients: List of recipients. :param sender: Email sender. Default is current user. :param subject: Email Subject. :param message: (or `content`) Email Content. :param as_markdown: Convert content markdown to HTML. :param delayed: Send via scheduled email sender **Email Queue**. Don't send immediately. Default is true :param send_priority: Priority for Email Queue, default 1. :param reference_doctype: (or `doctype`) Append as communication to this DocType. :param reference_name: (or `name`) Append as communication to this document name. :param unsubscribe_method: Unsubscribe url with options email, doctype, name. e.g. `/api/method/unsubscribe` :param unsubscribe_params: Unsubscribe paramaters to be loaded on the unsubscribe_method [optional] (dict). :param attachments: List of attachments. :param reply_to: Reply-To email id. :param message_id: Used for threading. If a reply is received to this email, Message-Id is sent back as In-Reply-To in received email. :param in_reply_to: Used to send the Message-Id of a received email back as In-Reply-To. :param send_after: Send after the given datetime. :param expose_recipients: Display all recipients in the footer message - "This email was sent to" :param communication: Communication link to be set in Email Queue record """ if delayed: import frappe.email.queue frappe.email.queue.send(recipients=recipients, sender=sender, subject=subject, message=content or message, reference_doctype = doctype or reference_doctype, reference_name = name or reference_name, unsubscribe_method=unsubscribe_method, unsubscribe_params=unsubscribe_params, unsubscribe_message=unsubscribe_message, attachments=attachments, reply_to=reply_to, cc=cc, show_as_cc=show_as_cc, message_id=message_id, in_reply_to=in_reply_to, send_after=send_after, expose_recipients=expose_recipients, send_priority=send_priority, communication=communication) else: import frappe.email if as_markdown: frappe.email.sendmail_md(recipients, sender=sender, subject=subject, msg=content or message, attachments=attachments, reply_to=reply_to, cc=cc, message_id=message_id, in_reply_to=in_reply_to) else: frappe.email.sendmail(recipients, sender=sender, subject=subject, msg=content or message, attachments=attachments, reply_to=reply_to, cc=cc, message_id=message_id, in_reply_to=in_reply_to) whitelisted = [] guest_methods = [] xss_safe_methods = [] def whitelist(allow_guest=False, xss_safe=False): """ Decorator for whitelisting a function and making it accessible via HTTP. Standard request will be `/api/method/[path.to.method]` :param allow_guest: Allow non logged-in user to access this method. Use as: @frappe.whitelist() def myfunc(param1, param2): pass """ def innerfn(fn): global whitelisted, guest_methods, xss_safe_methods whitelisted.append(fn) if allow_guest: guest_methods.append(fn) if xss_safe: xss_safe_methods.append(fn) return fn return innerfn def only_for(roles): """Raise `frappe.PermissionError` if the user does not have any of the given **Roles**. :param roles: List of roles to check.""" if not isinstance(roles, (tuple, list)): roles = (roles,) roles = set(roles) myroles = set(get_roles()) if not roles.intersection(myroles): raise PermissionError def clear_cache(user=None, doctype=None): """Clear **User**, **DocType** or global cache. :param user: If user is given, only user cache is cleared. :param doctype: If doctype is given, only DocType cache is cleared.""" import frappe.sessions if doctype: import frappe.model.meta frappe.model.meta.clear_cache(doctype) reset_metadata_version() elif user: frappe.sessions.clear_cache(user) else: # everything import translate frappe.sessions.clear_cache() translate.clear_cache() reset_metadata_version() local.cache = {} local.new_doc_templates = {} for fn in get_hooks("clear_cache"): get_attr(fn)() local.role_permissions = {} def has_permission(doctype=None, ptype="read", doc=None, user=None, verbose=False, throw=False): """Raises `frappe.PermissionError` if not permitted. :param doctype: DocType for which permission is to be check. :param ptype: Permission type (`read`, `write`, `create`, `submit`, `cancel`, `amend`). Default: `read`. :param doc: [optional] Checks User permissions for given doc. :param user: [optional] Check for given user. Default: current user.""" if not doctype and doc: doctype = doc.doctype import frappe.permissions out = frappe.permissions.has_permission(doctype, ptype, doc=doc, verbose=verbose, user=user) if throw and not out: if doc: frappe.throw(_("No permission for {0}").format(doc.doctype + " " + doc.name)) else: frappe.throw(_("No permission for {0}").format(doctype)) return out def has_website_permission(doctype, ptype="read", doc=None, user=None, verbose=False): """Raises `frappe.PermissionError` if not permitted. :param doctype: DocType for which permission is to be check. :param ptype: Permission type (`read`, `write`, `create`, `submit`, `cancel`, `amend`). Default: `read`. :param doc: Checks User permissions for given doc. :param user: [optional] Check for given user. Default: current user.""" if not user: user = session.user hooks = (get_hooks("has_website_permission") or {}).get(doctype, []) if hooks: if isinstance(doc, basestring): doc = get_doc(doctype, doc) for method in hooks: result = call(method, doc=doc, ptype=ptype, user=user, verbose=verbose) # if even a single permission check is Falsy if not result: return False # else it is Truthy return True else: return False def is_table(doctype): """Returns True if `istable` property (indicating child Table) is set for given DocType.""" def get_tables(): return db.sql_list("select name from tabDocType where istable=1") tables = cache().get_value("is_table", get_tables) return doctype in tables def get_precision(doctype, fieldname, currency=None, doc=None): """Get precision for a given field""" from frappe.model.meta import get_field_precision return get_field_precision(get_meta(doctype).get_field(fieldname), doc, currency) def generate_hash(txt=None, length=None): """Generates random hash for given text + current timestamp + random string.""" import hashlib, time from .utils import random_string digest = hashlib.sha224((txt or "") + repr(time.time()) + repr(random_string(8))).hexdigest() if length: digest = digest[:length] return digest def reset_metadata_version(): """Reset `metadata_version` (Client (Javascript) build ID) hash.""" v = generate_hash() cache().set_value("metadata_version", v) return v def new_doc(doctype, parent_doc=None, parentfield=None, as_dict=False): """Returns a new document of the given DocType with defaults set. :param doctype: DocType of the new document. :param parent_doc: [optional] add to parent document. :param parentfield: [optional] add against this `parentfield`.""" from frappe.model.create_new import get_new_doc return get_new_doc(doctype, parent_doc, parentfield, as_dict=as_dict) def set_value(doctype, docname, fieldname, value): """Set document value. Calls `frappe.client.set_value`""" import frappe.client return frappe.client.set_value(doctype, docname, fieldname, value) def get_doc(arg1, arg2=None): """Return a `frappe.model.document.Document` object of the given type and name. :param arg1: DocType name as string **or** document JSON. :param arg2: [optional] Document name as string. Examples: # insert a new document todo = frappe.get_doc({"doctype":"ToDo", "description": "test"}) tood.insert() # open an existing document todo = frappe.get_doc("ToDo", "TD0001") """ import frappe.model.document return frappe.model.document.get_doc(arg1, arg2) def get_last_doc(doctype): """Get last created document of this type.""" d = get_all(doctype, ["name"], order_by="creation desc", limit_page_length=1) if d: return get_doc(doctype, d[0].name) else: raise DoesNotExistError def get_single(doctype): """Return a `frappe.model.document.Document` object of the given Single doctype.""" return get_doc(doctype, doctype) def get_meta(doctype, cached=True): """Get `frappe.model.meta.Meta` instance of given doctype name.""" import frappe.model.meta return frappe.model.meta.get_meta(doctype, cached=cached) def get_meta_module(doctype): import frappe.modules return frappe.modules.load_doctype_module(doctype) def delete_doc(doctype=None, name=None, force=0, ignore_doctypes=None, for_reload=False, ignore_permissions=False, flags=None): """Delete a document. Calls `frappe.model.delete_doc.delete_doc`. :param doctype: DocType of document to be delete. :param name: Name of document to be delete. :param force: Allow even if document is linked. Warning: This may lead to data integrity errors. :param ignore_doctypes: Ignore if child table is one of these. :param for_reload: Call `before_reload` trigger before deleting. :param ignore_permissions: Ignore user permissions.""" import frappe.model.delete_doc frappe.model.delete_doc.delete_doc(doctype, name, force, ignore_doctypes, for_reload, ignore_permissions, flags) def delete_doc_if_exists(doctype, name, force=0): """Delete document if exists.""" if db.exists(doctype, name): delete_doc(doctype, name, force=force) def reload_doctype(doctype, force=False): """Reload DocType from model (`[module]/[doctype]/[name]/[name].json`) files.""" reload_doc(scrub(db.get_value("DocType", doctype, "module")), "doctype", scrub(doctype), force=force) def reload_doc(module, dt=None, dn=None, force=False): """Reload Document from model (`[module]/[doctype]/[name]/[name].json`) files. :param module: Module name. :param dt: DocType name. :param dn: Document name. :param force: Reload even if `modified` timestamp matches. """ import frappe.modules return frappe.modules.reload_doc(module, dt, dn, force=force) def rename_doc(doctype, old, new, debug=0, force=False, merge=False, ignore_permissions=False): """Rename a document. Calls `frappe.model.rename_doc.rename_doc`""" from frappe.model.rename_doc import rename_doc return rename_doc(doctype, old, new, force=force, merge=merge, ignore_permissions=ignore_permissions) def get_module(modulename): """Returns a module object for given Python module name using `importlib.import_module`.""" return importlib.import_module(modulename) def scrub(txt): """Returns sluggified string. e.g. `Sales Order` becomes `sales_order`.""" return txt.replace(' ','_').replace('-', '_').lower() def unscrub(txt): """Returns titlified string. e.g. `sales_order` becomes `Sales Order`.""" return txt.replace('_',' ').replace('-', ' ').title() def get_module_path(module, *joins): """Get the path of the given module name. :param module: Module name. :param *joins: Join additional path elements using `os.path.join`.""" module = scrub(module) return get_pymodule_path(local.module_app[module] + "." + module, *joins) def get_app_path(app_name, *joins): """Return path of given app. :param app: App name. :param *joins: Join additional path elements using `os.path.join`.""" return get_pymodule_path(app_name, *joins) def get_site_path(*joins): """Return path of current site. :param *joins: Join additional path elements using `os.path.join`.""" return os.path.join(local.site_path, *joins) def get_pymodule_path(modulename, *joins): """Return path of given Python module name. :param modulename: Python module name. :param *joins: Join additional path elements using `os.path.join`.""" if not "public" in joins: joins = [scrub(part) for part in joins] return os.path.join(os.path.dirname(get_module(scrub(modulename)).__file__), *joins) def get_module_list(app_name): """Get list of modules for given all via `app/modules.txt`.""" return get_file_items(os.path.join(os.path.dirname(get_module(app_name).__file__), "modules.txt")) def get_all_apps(with_internal_apps=True, sites_path=None): """Get list of all apps via `sites/apps.txt`.""" if not sites_path: sites_path = local.sites_path apps = get_file_items(os.path.join(sites_path, "apps.txt"), raise_not_found=True) if with_internal_apps: for app in get_file_items(os.path.join(local.site_path, "apps.txt")): if app not in apps: apps.append(app) if "frappe" in apps: apps.remove("frappe") apps.insert(0, 'frappe') return apps def get_installed_apps(sort=False, frappe_last=False): """Get list of installed apps in current site.""" if getattr(flags, "in_install_db", True): return [] if not db: connect() installed = json.loads(db.get_global("installed_apps") or "[]") if sort: installed = [app for app in get_all_apps(True) if app in installed] if frappe_last: if 'frappe' in installed: installed.remove('frappe') installed.append('frappe') return installed def get_doc_hooks(): '''Returns hooked methods for given doc. It will expand the dict tuple if required.''' if not hasattr(local, 'doc_events_hooks'): hooks = get_hooks('doc_events', {}) out = {} for key, value in hooks.iteritems(): if isinstance(key, tuple): for doctype in key: append_hook(out, doctype, value) else: append_hook(out, key, value) local.doc_events_hooks = out return local.doc_events_hooks def get_hooks(hook=None, default=None, app_name=None): """Get hooks via `app/hooks.py` :param hook: Name of the hook. Will gather all hooks for this name and return as a list. :param default: Default if no hook found. :param app_name: Filter by app.""" def load_app_hooks(app_name=None): hooks = {} for app in [app_name] if app_name else get_installed_apps(sort=True): app = "frappe" if app=="webnotes" else app try: app_hooks = get_module(app + ".hooks") except ImportError: if local.flags.in_install_app: # if app is not installed while restoring # ignore it pass print 'Could not find app "{0}"'.format(app_name) if not request: sys.exit(1) raise for key in dir(app_hooks): if not key.startswith("_"): append_hook(hooks, key, getattr(app_hooks, key)) return hooks if app_name: hooks = _dict(load_app_hooks(app_name)) else: hooks = _dict(cache().get_value("app_hooks", load_app_hooks)) if hook: return hooks.get(hook) or (default if default is not None else []) else: return hooks def append_hook(target, key, value): '''appends a hook to the the target dict. If the hook key, exists, it will make it a key. If the hook value is a dict, like doc_events, it will listify the values against the key. ''' if isinstance(value, dict): # dict? make a list of values against each key target.setdefault(key, {}) for inkey in value: append_hook(target[key], inkey, value[inkey]) else: # make a list target.setdefault(key, []) if not isinstance(value, list): value = [value] target[key].extend(value) def setup_module_map(): """Rebuild map of all modules (internal).""" _cache = cache() if conf.db_name: local.app_modules = _cache.get_value("app_modules") local.module_app = _cache.get_value("module_app") if not (local.app_modules and local.module_app): local.module_app, local.app_modules = {}, {} for app in get_all_apps(True): if app=="webnotes": app="frappe" local.app_modules.setdefault(app, []) for module in get_module_list(app): module = scrub(module) local.module_app[module] = app local.app_modules[app].append(module) if conf.db_name: _cache.set_value("app_modules", local.app_modules) _cache.set_value("module_app", local.module_app) def get_file_items(path, raise_not_found=False, ignore_empty_lines=True): """Returns items from text file as a list. Ignores empty lines.""" import frappe.utils content = read_file(path, raise_not_found=raise_not_found) if content: content = frappe.utils.strip(content) return [p.strip() for p in content.splitlines() if (not ignore_empty_lines) or (p.strip() and not p.startswith("#"))] else: return [] def get_file_json(path): """Read a file and return parsed JSON object.""" with open(path, 'r') as f: return json.load(f) def read_file(path, raise_not_found=False): """Open a file and return its content as Unicode.""" from frappe.utils import cstr if isinstance(path, unicode): path = path.encode("utf-8") if os.path.exists(path): with open(path, "r") as f: return cstr(f.read()) elif raise_not_found: raise IOError("{} Not Found".format(path)) else: return None def get_attr(method_string): """Get python method object from its name.""" app_name = method_string.split(".")[0] if not local.flags.in_install and app_name not in get_installed_apps(): throw(_("App {0} is not installed").format(app_name), AppNotInstalledError) modulename = '.'.join(method_string.split('.')[:-1]) methodname = method_string.split('.')[-1] return getattr(get_module(modulename), methodname) def call(fn, *args, **kwargs): """Call a function and match arguments.""" if isinstance(fn, basestring): fn = get_attr(fn) if hasattr(fn, 'fnargs'): fnargs = fn.fnargs else: fnargs, varargs, varkw, defaults = inspect.getargspec(fn) newargs = {} for a in kwargs: if (a in fnargs) or varkw: newargs[a] = kwargs.get(a) if "flags" in newargs: del newargs["flags"] return fn(*args, **newargs) def make_property_setter(args, ignore_validate=False, validate_fields_for_doctype=True): """Create a new **Property Setter** (for overriding DocType and DocField properties). If doctype is not specified, it will create a property setter for all fields with the given fieldname""" args = _dict(args) if not args.doctype_or_field: args.doctype_or_field = 'DocField' if not args.property_type: args.property_type = db.get_value('DocField', {'parent': 'DocField', 'fieldname': args.property}, 'fieldtype') or 'Data' if not args.doctype: doctype_list = db.sql_list('select distinct parent from tabDocField where fieldname=%s', args.fieldname) else: doctype_list = [args.doctype] for doctype in doctype_list: if not args.property_type: args.property_type = db.get_value('DocField', {'parent': doctype, 'fieldname': args.fieldname}, 'fieldtype') or 'Data' ps = get_doc({ 'doctype': "Property Setter", 'doctype_or_field': args.doctype_or_field, 'doc_type': doctype, 'field_name': args.fieldname, 'property': args.property, 'value': args.value, 'property_type': args.property_type or "Data", '__islocal': 1 }) ps.flags.ignore_validate = ignore_validate ps.flags.validate_fields_for_doctype = validate_fields_for_doctype ps.insert() def import_doc(path, ignore_links=False, ignore_insert=False, insert=False): """Import a file using Data Import Tool.""" from frappe.core.page.data_import_tool import data_import_tool data_import_tool.import_doc(path, ignore_links=ignore_links, ignore_insert=ignore_insert, insert=insert) def copy_doc(doc, ignore_no_copy=True): """ No_copy fields also get copied.""" import copy def remove_no_copy_fields(d): for df in d.meta.get("fields", {"no_copy": 1}): if hasattr(d, df.fieldname): d.set(df.fieldname, None) fields_to_clear = ['name', 'owner', 'creation', 'modified', 'modified_by'] if not local.flags.in_test: fields_to_clear.append("docstatus") if not isinstance(doc, dict): d = doc.as_dict() else: d = doc newdoc = get_doc(copy.deepcopy(d)) newdoc.set("__islocal", 1) for fieldname in (fields_to_clear + ['amended_from', 'amendment_date']): newdoc.set(fieldname, None) if not ignore_no_copy: remove_no_copy_fields(newdoc) for i, d in enumerate(newdoc.get_all_children()): d.set("__islocal", 1) for fieldname in fields_to_clear: d.set(fieldname, None) if not ignore_no_copy: remove_no_copy_fields(d) return newdoc def compare(val1, condition, val2): """Compare two values using `frappe.utils.compare` `condition` could be: - "^" - "in" - "not in" - "=" - "!=" - ">" - "<" - ">=" - "<=" - "not None" - "None" """ import frappe.utils return frappe.utils.compare(val1, condition, val2) def respond_as_web_page(title, html, success=None, http_status_code=None, context=None): """Send response as a web page with a message rather than JSON. Used to show permission errors etc. :param title: Page title and heading. :param message: Message to be shown. :param success: Alert message. :param http_status_code: HTTP status code.""" local.message_title = title local.message = html local.message_success = success local.response['type'] = 'page' local.response['route'] = 'message' if http_status_code: local.response['http_status_code'] = http_status_code if context: local.response['context'] = context def redirect_to_message(title, html, http_status_code=None, context=None): """Redirects to /message?id=random Similar to respond_as_web_page, but used to 'redirect' and show message pages like success, failure, etc. with a detailed message :param title: Page title and heading. :param message: Message to be shown. :param http_status_code: HTTP status code. Example Usage: frappe.redirect_to_message(_('Thank you'), "<div><p>You will receive an email at test@example.com</p></div>") """ message_id = generate_hash(length=8) message = { 'context': context or {}, 'http_status_code': http_status_code or 200 } message['context'].update({ 'header': title, 'title': title, 'message': html }) cache().set_value("message_id:{0}".format(message_id), message, expires_in_sec=60) location = '/message?id={0}'.format(message_id) if not getattr(local, 'is_ajax', False): local.response["type"] = "redirect" local.response["location"] = location else: return location def build_match_conditions(doctype, as_condition=True): """Return match (User permissions) for given doctype as list or SQL.""" import frappe.desk.reportview return frappe.desk.reportview.build_match_conditions(doctype, as_condition) def get_list(doctype, *args, **kwargs): """List database query via `frappe.model.db_query`. Will also check for permissions. :param doctype: DocType on which query is to be made. :param fields: List of fields or `*`. :param filters: List of filters (see example). :param order_by: Order By e.g. `modified desc`. :param limit_page_start: Start results at record #. Default 0. :param limit_poge_length: No of records in the page. Default 20. Example usage: # simple dict filter frappe.get_list("ToDo", fields=["name", "description"], filters = {"owner":"test@example.com"}) # filter as a list of lists frappe.get_list("ToDo", fields="*", filters = [["modified", ">", "2014-01-01"]]) # filter as a list of dicts frappe.get_list("ToDo", fields="*", filters = {"description": ("like", "test%")}) """ import frappe.model.db_query return frappe.model.db_query.DatabaseQuery(doctype).execute(None, *args, **kwargs) def get_all(doctype, *args, **kwargs): """List database query via `frappe.model.db_query`. Will **not** check for conditions. Parameters are same as `frappe.get_list` :param doctype: DocType on which query is to be made. :param fields: List of fields or `*`. Default is: `["name"]`. :param filters: List of filters (see example). :param order_by: Order By e.g. `modified desc`. :param limit_page_start: Start results at record #. Default 0. :param limit_poge_length: No of records in the page. Default 20. Example usage: # simple dict filter frappe.get_all("ToDo", fields=["name", "description"], filters = {"owner":"test@example.com"}) # filter as a list of lists frappe.get_all("ToDo", fields=["*"], filters = [["modified", ">", "2014-01-01"]]) # filter as a list of dicts frappe.get_all("ToDo", fields=["*"], filters = {"description": ("like", "test%")}) """ kwargs["ignore_permissions"] = True if not "limit_page_length" in kwargs: kwargs["limit_page_length"] = 0 return get_list(doctype, *args, **kwargs) def get_value(*args, **kwargs): """Returns a document property or list of properties. Alias for `frappe.db.get_value` :param doctype: DocType name. :param filters: Filters like `{"x":"y"}` or name of the document. `None` if Single DocType. :param fieldname: Column name. :param ignore: Don't raise exception if table, column is missing. :param as_dict: Return values as dict. :param debug: Print query in error log. """ return db.get_value(*args, **kwargs) def add_version(doc): """Insert a new **Version** of the given document. A **Version** is a JSON dump of the current document state.""" get_doc({ "doctype": "Version", "ref_doctype": doc.doctype, "docname": doc.name, "doclist_json": as_json(doc.as_dict()) }).insert(ignore_permissions=True) def as_json(obj, indent=1): from frappe.utils.response import json_handler return json.dumps(obj, indent=indent, sort_keys=True, default=json_handler) def are_emails_muted(): return flags.mute_emails or conf.get("mute_emails") or False def get_test_records(doctype): """Returns list of objects from `test_records.json` in the given doctype's folder.""" from frappe.modules import get_doctype_module, get_module_path path = os.path.join(get_module_path(get_doctype_module(doctype)), "doctype", scrub(doctype), "test_records.json") if os.path.exists(path): with open(path, "r") as f: return json.loads(f.read()) else: return [] def format_value(value, df, doc=None, currency=None): """Format value with given field properties. :param value: Value to be formatted. :param df: DocField object with properties `fieldtype`, `options` etc.""" import frappe.utils.formatters return frappe.utils.formatters.format_value(value, df, doc, currency=currency) def get_print(doctype=None, name=None, print_format=None, style=None, html=None, as_pdf=False, doc=None): """Get Print Format for given document. :param doctype: DocType of document. :param name: Name of document. :param print_format: Print Format name. Default 'Standard', :param style: Print Format style. :param as_pdf: Return as PDF. Default False.""" from frappe.website.render import build_page from frappe.utils.pdf import get_pdf local.form_dict.doctype = doctype local.form_dict.name = name local.form_dict.format = print_format local.form_dict.style = style local.form_dict.doc = doc if not html: html = build_page("print") if as_pdf: return get_pdf(html) else: return html def attach_print(doctype, name, file_name=None, print_format=None, style=None, html=None, doc=None): from frappe.utils import scrub_urls if not file_name: file_name = name file_name = file_name.replace(' ','').replace('/','-') print_settings = db.get_singles_dict("Print Settings") local.flags.ignore_print_permissions = True if int(print_settings.send_print_as_pdf or 0): out = { "fname": file_name + ".pdf", "fcontent": get_print(doctype, name, print_format=print_format, style=style, html=html, as_pdf=True, doc=doc) } else: out = { "fname": file_name + ".html", "fcontent": scrub_urls(get_print(doctype, name, print_format=print_format, style=style, html=html, doc=doc)).encode("utf-8") } local.flags.ignore_print_permissions = False return out def publish_progress(*args, **kwargs): """Show the user progress for a long request :param percent: Percent progress :param title: Title :param doctype: Optional, for DocType :param name: Optional, for Document name """ import frappe.async return frappe.async.publish_progress(*args, **kwargs) def publish_realtime(*args, **kwargs): """Publish real-time updates :param event: Event name, like `task_progress` etc. :param message: JSON message object. For async must contain `task_id` :param room: Room in which to publish update (default entire site) :param user: Transmit to user :param doctype: Transmit to doctype, docname :param docname: Transmit to doctype, docname :param after_commit: (default False) will emit after current transaction is committed """ import frappe.async return frappe.async.publish_realtime(*args, **kwargs) def local_cache(namespace, key, generator, regenerate_if_none=False): """A key value store for caching within a request :param namespace: frappe.local.cache[namespace] :param key: frappe.local.cache[namespace][key] used to retrieve value :param generator: method to generate a value if not found in store """ if namespace not in local.cache: local.cache[namespace] = {} if key not in local.cache[namespace]: local.cache[namespace][key] = generator() elif local.cache[namespace][key]==None and regenerate_if_none: # if key exists but the previous result was None local.cache[namespace][key] = generator() return local.cache[namespace][key] def get_doctype_app(doctype): def _get_doctype_app(): doctype_module = local.db.get_value("DocType", doctype, "module") return local.module_app[scrub(doctype_module)] return local_cache("doctype_app", doctype, generator=_get_doctype_app) loggers = {} log_level = None def logger(module=None, with_more_info=True): '''Returns a python logger that uses StreamHandler''' from frappe.utils.logger import get_logger return get_logger(module or __name__, with_more_info=with_more_info) def get_desk_link(doctype, name): return '<a href="#Form/{0}/{1}" style="font-weight: bold;">{2} {1}</a>'.format(doctype, name, _(doctype)) def bold(text): return '<b>{0}</b>'.format(text)

31.410871

180

0.728897

00fef0f8f05c031dacdaf59d4ececb299cc35aa7

4,890

py

Python

src/v5.3/resources/swagger_client/models/ed_fi_survey_response_survey_level.py

xmarcosx/edfi-notebook

0564ebdf1d0f45a9d25056e7e61369f0a837534d

[ "Apache-2.0" ]

2

2021-04-27T17:18:17.000Z

2021-04-27T19:14:39.000Z

src/v5.1/resources/swagger_client/models/ed_fi_survey_response_survey_level.py

xmarcosx/edfi-notebook

0564ebdf1d0f45a9d25056e7e61369f0a837534d

[ "Apache-2.0" ]

null

src/v5.1/resources/swagger_client/models/ed_fi_survey_response_survey_level.py

xmarcosx/edfi-notebook

0564ebdf1d0f45a9d25056e7e61369f0a837534d

[ "Apache-2.0" ]

1

2022-01-06T09:43:11.000Z

# coding: utf-8 """ Ed-Fi Operational Data Store API The Ed-Fi ODS / API enables applications to read and write education data stored in an Ed-Fi ODS through a secure REST interface. *** > *Note: Consumers of ODS / API information should sanitize all data for display and storage. The ODS / API provides reasonable safeguards against cross-site scripting attacks and other malicious content, but the platform does not and cannot guarantee that the data it contains is free of all potentially harmful content.* *** # noqa: E501 OpenAPI spec version: 3 Generated by: https://github.com/swagger-api/swagger-codegen.git """ import pprint import re # noqa: F401 import six from swagger_client.configuration import Configuration class EdFiSurveyResponseSurveyLevel(object): """NOTE: This class is auto generated by the swagger code generator program. Do not edit the class manually. """ """ Attributes: swagger_types (dict): The key is attribute name and the value is attribute type. attribute_map (dict): The key is attribute name and the value is json key in definition. """ swagger_types = { 'survey_level_descriptor': 'str' } attribute_map = { 'survey_level_descriptor': 'surveyLevelDescriptor' } def __init__(self, survey_level_descriptor=None, _configuration=None): # noqa: E501 """EdFiSurveyResponseSurveyLevel - a model defined in Swagger""" # noqa: E501 if _configuration is None: _configuration = Configuration() self._configuration = _configuration self._survey_level_descriptor = None self.discriminator = None self.survey_level_descriptor = survey_level_descriptor @property def survey_level_descriptor(self): """Gets the survey_level_descriptor of this EdFiSurveyResponseSurveyLevel. # noqa: E501 Provides information about the respondents of a survey and how they can be grouped together. # noqa: E501 :return: The survey_level_descriptor of this EdFiSurveyResponseSurveyLevel. # noqa: E501 :rtype: str """ return self._survey_level_descriptor @survey_level_descriptor.setter def survey_level_descriptor(self, survey_level_descriptor): """Sets the survey_level_descriptor of this EdFiSurveyResponseSurveyLevel. Provides information about the respondents of a survey and how they can be grouped together. # noqa: E501 :param survey_level_descriptor: The survey_level_descriptor of this EdFiSurveyResponseSurveyLevel. # noqa: E501 :type: str """ if self._configuration.client_side_validation and survey_level_descriptor is None: raise ValueError("Invalid value for `survey_level_descriptor`, must not be `None`") # noqa: E501 if (self._configuration.client_side_validation and survey_level_descriptor is not None and len(survey_level_descriptor) > 306): raise ValueError("Invalid value for `survey_level_descriptor`, length must be less than or equal to `306`") # noqa: E501 self._survey_level_descriptor = survey_level_descriptor def to_dict(self): """Returns the model properties as a dict""" result = {} for attr, _ in six.iteritems(self.swagger_types): value = getattr(self, attr) if isinstance(value, list): result[attr] = list(map( lambda x: x.to_dict() if hasattr(x, "to_dict") else x, value )) elif hasattr(value, "to_dict"): result[attr] = value.to_dict() elif isinstance(value, dict): result[attr] = dict(map( lambda item: (item[0], item[1].to_dict()) if hasattr(item[1], "to_dict") else item, value.items() )) else: result[attr] = value if issubclass(EdFiSurveyResponseSurveyLevel, dict): for key, value in self.items(): result[key] = value return result def to_str(self): """Returns the string representation of the model""" return pprint.pformat(self.to_dict()) def __repr__(self): """For `print` and `pprint`""" return self.to_str() def __eq__(self, other): """Returns true if both objects are equal""" if not isinstance(other, EdFiSurveyResponseSurveyLevel): return False return self.to_dict() == other.to_dict() def __ne__(self, other): """Returns true if both objects are not equal""" if not isinstance(other, EdFiSurveyResponseSurveyLevel): return True return self.to_dict() != other.to_dict()

37.615385

482

0.646012

8bad285ffd8c126e1ff2dea69bb3c438f06b28f0

6,454

py

Python

airflow/contrib/operators/ecs_operator.py

dossett/incubator-airflow

60583a3c6d1c4b5bbecaad6cd195301107530de9

[ "Apache-2.0" ]

1

2019-06-04T06:09:55.000Z

airflow/contrib/operators/ecs_operator.py

berniechiu/incubator-airflow

b85210d8596c1e05fbf3da2941aaca8170ae871e

[ "Apache-2.0", "BSD-2-Clause", "MIT", "ECL-2.0", "BSD-3-Clause" ]

4

2018-01-31T19:28:31.000Z

2019-03-07T15:56:45.000Z

airflow/contrib/operators/ecs_operator.py

berniechiu/incubator-airflow

b85210d8596c1e05fbf3da2941aaca8170ae871e

[ "Apache-2.0", "BSD-2-Clause", "MIT", "ECL-2.0", "BSD-3-Clause" ]

2

2021-05-14T09:19:52.000Z

2022-01-12T19:08:31.000Z

# -*- coding: utf-8 -*- # # Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you under the Apache License, Version 2.0 (the # "License"); you may not use this file except in compliance # with the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND, either express or implied. See the License for the # specific language governing permissions and limitations # under the License. import sys from airflow.exceptions import AirflowException from airflow.models import BaseOperator from airflow.utils.decorators import apply_defaults from airflow.contrib.hooks.aws_hook import AwsHook class ECSOperator(BaseOperator): """ Execute a task on AWS EC2 Container Service :param task_definition: the task definition name on EC2 Container Service :type task_definition: str :param cluster: the cluster name on EC2 Container Service :type cluster: str :param overrides: the same parameter that boto3 will receive (templated): http://boto3.readthedocs.org/en/latest/reference/services/ecs.html#ECS.Client.run_task :type overrides: dict :param aws_conn_id: connection id of AWS credentials / region name. If None, credential boto3 strategy will be used (http://boto3.readthedocs.io/en/latest/guide/configuration.html). :type aws_conn_id: str :param region_name: region name to use in AWS Hook. Override the region_name in connection (if provided) :type region_name: str :param launch_type: the launch type on which to run your task ('EC2' or 'FARGATE') :type launch_type: str :param group: the name of the task group associated with the task :type group: str :param placement_constraints: an array of placement constraint objects to use for the task :type placement_constraints: list :param platform_version: the platform version on which your task is running :type platform_version: str :param network_configuration: the network configuration for the task :type network_configuration: dict """ ui_color = '#f0ede4' client = None arn = None template_fields = ('overrides',) @apply_defaults def __init__(self, task_definition, cluster, overrides, aws_conn_id=None, region_name=None, launch_type='EC2', group=None, placement_constraints=None, platform_version='LATEST', network_configuration=None, **kwargs): super(ECSOperator, self).__init__(**kwargs) self.aws_conn_id = aws_conn_id self.region_name = region_name self.task_definition = task_definition self.cluster = cluster self.overrides = overrides self.launch_type = launch_type self.group = group self.placement_constraints = placement_constraints self.platform_version = platform_version self.network_configuration = network_configuration self.hook = self.get_hook() def execute(self, context): self.log.info( 'Running ECS Task - Task definition: %s - on cluster %s', self.task_definition, self.cluster ) self.log.info('ECSOperator overrides: %s', self.overrides) self.client = self.hook.get_client_type( 'ecs', region_name=self.region_name ) run_opts = { 'cluster': self.cluster, 'taskDefinition': self.task_definition, 'overrides': self.overrides, 'startedBy': self.owner, 'launchType': self.launch_type, 'platformVersion': self.platform_version, } if self.group is not None: run_opts['group'] = self.group if self.placement_constraints is not None: run_opts['placementConstraints'] = self.placement_constraints if self.network_configuration is not None: run_opts['networkConfiguration'] = self.network_configuration response = self.client.run_task(**run_opts) failures = response['failures'] if len(failures) > 0: raise AirflowException(response) self.log.info('ECS Task started: %s', response) self.arn = response['tasks'][0]['taskArn'] self._wait_for_task_ended() self._check_success_task() self.log.info('ECS Task has been successfully executed: %s', response) def _wait_for_task_ended(self): waiter = self.client.get_waiter('tasks_stopped') waiter.config.max_attempts = sys.maxsize # timeout is managed by airflow waiter.wait( cluster=self.cluster, tasks=[self.arn] ) def _check_success_task(self): response = self.client.describe_tasks( cluster=self.cluster, tasks=[self.arn] ) self.log.info('ECS Task stopped, check status: %s', response) if len(response.get('failures', [])) > 0: raise AirflowException(response) for task in response['tasks']: containers = task['containers'] for container in containers: if container.get('lastStatus') == 'STOPPED' and \ container['exitCode'] != 0: raise AirflowException( 'This task is not in success state {}'.format(task)) elif container.get('lastStatus') == 'PENDING': raise AirflowException('This task is still pending {}'.format(task)) elif 'error' in container.get('reason', '').lower(): raise AirflowException( 'This containers encounter an error during launching : {}'. format(container.get('reason', '').lower())) def get_hook(self): return AwsHook( aws_conn_id=self.aws_conn_id ) def on_kill(self): response = self.client.stop_task( cluster=self.cluster, task=self.arn, reason='Task killed by the user') self.log.info(response)

38.879518

94

0.650449

f3eb3e6ca23b840bd6bf1b2944473cf9520b9b96

2,691

py

Python

binarySearch/binarySearchSimulation.py

trthanhquang/wayback-data-collector

304172bd16135321d997dc13970feb7c8047c3be

[ "Apache-2.0" ]

1

2015-11-20T17:22:57.000Z

binarySearch/binarySearchSimulation.py

trthanhquang/wayback-data-collector

304172bd16135321d997dc13970feb7c8047c3be

[ "Apache-2.0" ]

null

binarySearch/binarySearchSimulation.py

trthanhquang/wayback-data-collector

304172bd16135321d997dc13970feb7c8047c3be

[ "Apache-2.0" ]

null

from bisect import bisect_left from Queue import * import random randomSeed = 123456789 # randomSeed = random.random()*123456789 noVersion = 10 listSize = 200 itemList = None #url list crawledlist = None #downloaded page downloadCount = None def init(): global itemList,crawledlist, downloadCount newList = [] crawledlist = [] downloadCount = 0 random.seed(randomSeed) for i in range(listSize): newItem = int(random.random()*noVersion) newList.append(newItem) crawledlist.append(0) itemList = sorted(newList) def download(dlist): global downloadCount download = False for i in dlist: if(crawledlist[i]==0): crawledlist[i]=1 download = True if download: downloadCount = downloadCount+1 def getDivideIndex(lo,hi,level): indexList = [] if level == 0: return indexList mid = lo + (hi-lo)/2 indexList.append(mid) indexList.extend(getDivideIndex(lo,mid,level-1)) indexList.extend(getDivideIndex(mid,hi,level-1)) return set(indexList) def binarySearchDiff(lo, hi): digLevel = 6 download(getDivideIndex(lo,hi,digLevel)) searchItem = itemList[lo] if(itemList[hi]==searchItem): return -1 while(lo<=hi): if(digLevel == 0): print 'additional dig at (%s,%s)'%(lo,hi) digLevel = 6 download(getDivideIndex(lo,hi,digLevel)) mid = lo+(hi-lo)/2 # print 'lo= %s, hi=%s, mid=%s'%(lo,hi,mid) if(itemList[mid]!=searchItem): hi = mid -1 else: lo = mid +1 digLevel = digLevel -1 return lo def refineSearchRange(lo,hi): searchItem = itemList[lo] for i in range(lo,hi,1): if(crawledlist[i]==1): if(itemList[i]==searchItem): lo = i else: hi = i break # print 'update range from %s to %s'%(lo,hi) return (lo,hi) def normalSearch(): init() # print itemList lo = 0 endIndex = len(itemList)-1 while(lo!=-1): hi = endIndex # (lo,hi)= refineSearchRange(lo,hi) lo = binarySearchDiff(lo,hi) # print 'index=%s, item=%s'%(lo,itemList[lo]) visited = sum(crawledlist) percentage = 100.0*visited/len(itemList) print 'normalSearch: download {2} times ({0} snapshots downloaded, {1:.3g}%)'.format(visited,percentage,downloadCount) def advanceSearch(): init() # print itemList lo = 0 endIndex = len(itemList)-1 while(lo!=-1): hi = endIndex (lo,hi)= refineSearchRange(lo,hi) lo= binarySearchDiff(lo,hi) print 'index=%s, item=%s'%(lo,itemList[lo]) visited = sum(crawledlist) percentage = 100.0*visited/len(itemList) print 'advanceSearch: download {2} times ({0} snapshots downloaded, {1:.3g}%)'.format(visited,percentage,downloadCount) def main(): global noVersion,listSize noVersion = 20 listSize = 2000 # normalSearch() advanceSearch() if __name__ == '__main__': main()

20.860465

120

0.687477

99340da8591505b6addfc4e3e26d99de41be9b27

786

py

Python

renku/service/views/v1_0/__init__.py

lokijuhy/renku-python

0bfceafa4e6b4750439ab0ed20c61b0a6ba03a1f

[ "Apache-2.0" ]

null

renku/service/views/v1_0/__init__.py

lokijuhy/renku-python

0bfceafa4e6b4750439ab0ed20c61b0a6ba03a1f

[ "Apache-2.0" ]

null

renku/service/views/v1_0/__init__.py

lokijuhy/renku-python

0bfceafa4e6b4750439ab0ed20c61b0a6ba03a1f

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- # # Copyright 2020 - Swiss Data Science Center (SDSC) # A partnership between École Polytechnique Fédérale de Lausanne (EPFL) and # Eidgenössische Technische Hochschule Zürich (ETHZ). # # 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. """Renku service v1.0 views."""

41.368421

75

0.751908

bd81db15d34fbfd3ba18a575531c439585e5236b

1,251

py

Python

app/models/place.py

hashtrip/api

1a968f47dbd5c9d24fb45f3a6360505009f6c4d8

[ "MIT" ]

1

2021-01-30T08:38:15.000Z

app/models/place.py

hashtrip/backend

1a968f47dbd5c9d24fb45f3a6360505009f6c4d8

[ "MIT" ]

null

app/models/place.py

hashtrip/backend

1a968f47dbd5c9d24fb45f3a6360505009f6c4d8

[ "MIT" ]

null

from typing import List, Optional from pydantic import Field from .dbmodel import DateTimeModelMixin, DBModelMixin from .util import GeoJson, Time from .profile import Profile from .rwmodel import RWModel class PlaceFilterParams(RWModel): tag: str = "" author: str = "" favorited: str = "" limit: int = 20 offset: int = 0 class PlaceBase(RWModel): title: str description: str location: GeoJson body: str capacity: int time_start: Optional[Time] = Field(None, alias="timeStart") time_end: Optional[Time] = Field(None, alias="timeEnd") tag_list: List[str] = Field([], alias="tagList") class Place(DateTimeModelMixin, PlaceBase): slug: str author: Profile favorited: bool favorites_count: int = Field(..., alias="favoritesCount") class PlaceInDB(DBModelMixin, Place): pass class PlaceInResponse(RWModel): place: Place class ManyPlacesInResponse(RWModel): places: List[Place] places_count: int = Field(..., alias="placesCount") class PlaceInCreate(PlaceBase): pass class PlaceInUpdate(RWModel): title: Optional[str] = None description: Optional[str] = None body: Optional[str] = None tag_list: List[str] = Field([], alias="tagList")

21.20339

63

0.688249

c8e0607aaf509219bea9178014e95b68c1fe7da3

298

py

Python

generate/common.py

xx-li/CustomTemplates

aa0a1ebc6788fe58babd0324190bae7ca00d2351

[ "MIT" ]

1

2021-07-15T01:08:28.000Z

generate/common.py

xx-li/CustomTemplates

aa0a1ebc6788fe58babd0324190bae7ca00d2351

[ "MIT" ]

null

generate/common.py

xx-li/CustomTemplates

aa0a1ebc6788fe58babd0324190bae7ca00d2351

[ "MIT" ]

null

# -*- coding: utf-8 -*- from enum import Enum class TemplateType(Enum): Blank = "BlankCode" BlankXib = "BlankXib" List = "ListCode" ListXib = "ListXib" ListEdit = "ListEditCode" ListEditXib = "ListEditXib" MultiPage= "MultiPageCode" MultiPageXib= "MultiPageXib"

18.625

32

0.647651

32e31857ffdcd390a4920b6946df4f889464e411

12,367

py

Python

pytorch_tutorials/cifar10_tutorial.py

adam-dziedzic/time-series-ml

81aaa27f1dd9ea3d7d62b661dac40cac6c1ef77a

[ "Apache-2.0" ]

1

2018-03-25T13:19:46.000Z

pytorch_tutorials/cifar10_tutorial.py

adam-dziedzic/time-series-ml

81aaa27f1dd9ea3d7d62b661dac40cac6c1ef77a

[ "Apache-2.0" ]

null

pytorch_tutorials/cifar10_tutorial.py

adam-dziedzic/time-series-ml

81aaa27f1dd9ea3d7d62b661dac40cac6c1ef77a

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- """ Training a classifier ===================== This is it. You have seen how to define neural networks, compute loss and make updates to the weights of the network. Now you might be thinking, What about data? ---------------- Generally, when you have to deal with image, text, audio or video data, you can use standard python packages that load data into a numpy array. Then you can convert this array into a ``torch.*Tensor``. - For images, packages such as Pillow, OpenCV are useful - For audio, packages such as scipy and librosa - For text, either raw Python or Cython based loading, or NLTK and SpaCy are useful Specifically for vision, we have created a package called ``torchvision``, that has data loaders for common datasets such as Imagenet, CIFAR10, MNIST, etc. and data transformers for images, viz., ``torchvision.datasets`` and ``torch.track_utils.data.DataLoader``. This provides a huge convenience and avoids writing boilerplate code. For this tutorial, we will use the CIFAR10 dataset. It has the classes: ‘airplane’, ‘automobile’, ‘bird’, ‘cat’, ‘deer’, ‘dog’, ‘frog’, ‘horse’, ‘ship’, ‘truck’. The images in CIFAR-10 are of size 3x32x32, i.e. 3-channel color images of 32x32 pixels in size. .. figure:: /_static/img/cifar10.png :alt: cifar10 cifar10 Training an image classifier ---------------------------- We will do the following steps in order: 1. Load and normalizing the CIFAR10 training and test datasets using ``torchvision`` 2. Define a Convolution Neural Network 3. Define a loss function 4. Train the network on the training data 5. ExperimentSpectralSpatial the network on the test data 1. Loading and normalizing CIFAR10 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Using ``torchvision``, it’s extremely easy to load CIFAR10. """ import torch import torchvision import torchvision.transforms as transforms ######################################################################## # The output of torchvision datasets are PILImage images of range [0, 1]. # We transform them to Tensors of normalized range [-1, 1]. transform = transforms.Compose( [transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]) trainset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform) trainloader = torch.utils.data.DataLoader(trainset, batch_size=4, shuffle=True, num_workers=2) testset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transform) testloader = torch.utils.data.DataLoader(testset, batch_size=4, shuffle=False, num_workers=2) classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck') ######################################################################## # Let us show some of the training images, for fun. import matplotlib.pyplot as plt import numpy as np # functions to show an image def imshow(img): img = img / 2 + 0.5 # unnormalize npimg = img.numpy() plt.imshow(np.transpose(npimg, (1, 2, 0))) # get some random training images dataiter = iter(trainloader) images, labels = dataiter.next() # show images imshow(torchvision.utils.make_grid(images)) # print labels print(' '.join('%5s' % classes[labels[j]] for j in range(4))) ######################################################################## # 2. Define a Convolution Neural Network # ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ # Copy the neural network from the Neural Networks section before and modify it to # take 3-channel images (instead of 1-channel images as it was defined). import torch.nn as nn import torch.nn.functional as F class Net(nn.Module): def __init__(self): super(Net, self).__init__() self.conv1 = nn.Conv2d(3, 6, 5) self.pool = nn.MaxPool2d(2, 2) self.conv2 = nn.Conv2d(6, 16, 5) self.fc1 = nn.Linear(16 * 5 * 5, 120) self.fc2 = nn.Linear(120, 84) self.fc3 = nn.Linear(84, 10) def forward(self, x): x = self.pool(F.relu(self.conv1(x))) x = self.pool(F.relu(self.conv2(x))) x = x.view(-1, 16 * 5 * 5) x = F.relu(self.fc1(x)) x = F.relu(self.fc2(x)) x = self.fc3(x) return x net = Net() ######################################################################## # 3. Define a Loss function and optimizer # ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ # Let's use a Classification Cross-Entropy loss and SGD with momentum. import torch.optim as optim criterion = nn.CrossEntropyLoss() optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9) ######################################################################## # 4. Train the network # ^^^^^^^^^^^^^^^^^^^^ # # This is when things start to get interesting. # We simply have to loop over our data iterator, and feed the inputs to the # network and optimize. for epoch in range(2): # loop over the dataset multiple times running_loss = 0.0 for i, data in enumerate(trainloader, 0): # get the inputs inputs, labels = data # zero the parameter gradients optimizer.zero_grad() # forward + backward + optimize outputs = net(inputs) loss = criterion(outputs, labels) loss.backward() optimizer.step() # print statistics running_loss += loss.item() if i % 2000 == 1999: # print every 2000 mini-batches print('[%d, %5d] loss: %.3f' % (epoch + 1, i + 1, running_loss / 2000)) running_loss = 0.0 print('Finished Training') ######################################################################## # 5. ExperimentSpectralSpatial the network on the test data # ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ # # We have trained the network for 2 passes over the training dataset. # But we need to check if the network has learnt anything at all. # # We will check this by predicting the class label that the neural network # outputs, and checking it against the ground-truth. If the prediction is # correct, we add the sample to the list of correct predictions. # # Okay, first step. Let us display an image from the test set to get familiar. dataiter = iter(testloader) images, labels = dataiter.next() # print images imshow(torchvision.utils.make_grid(images)) print('GroundTruth: ', ' '.join('%5s' % classes[labels[j]] for j in range(4))) ######################################################################## # Okay, now let us see what the neural network thinks these examples above are: outputs = net(images) ######################################################################## # The outputs are energies for the 10 classes. # Higher the energy for a class, the more the network # thinks that the image is of the particular class. # So, let's get the index of the highest energy: _, predicted = torch.max(outputs, 1) print('Predicted: ', ' '.join('%5s' % classes[predicted[j]] for j in range(4))) ######################################################################## # The results seem pretty good. # # Let us look at how the network performs on the whole dataset. correct = 0 total = 0 with torch.no_grad(): for data in testloader: images, labels = data outputs = net(images) _, predicted = torch.max(outputs.data, 1) total += labels.size(0) correct += (predicted == labels).sum().item() print('Accuracy of the network on the 10000 test images: %d %%' % (100 * correct / total)) ######################################################################## # That looks waaay better than chance, which is 10% accuracy (randomly picking # a class out of 10 classes). # Seems like the network learnt something. # # Hmmm, what are the classes that performed well, and the classes that did # not perform well: class_correct = list(0. for i in range(10)) class_total = list(0. for i in range(10)) with torch.no_grad(): for data in testloader: images, labels = data outputs = net(images) _, predicted = torch.max(outputs, 1) c = (predicted == labels).squeeze() for i in range(4): label = labels[i] class_correct[label] += c[i].item() class_total[label] += 1 for i in range(10): print('Accuracy of %5s : %2d %%' % ( classes[i], 100 * class_correct[i] / class_total[i])) ######################################################################## # Okay, so what next? # # How do we run these neural networks on the GPU? # # Training on GPU # ---------------- # Just like how you transfer a Tensor on to the GPU, you transfer the neural # net onto the GPU. # # Let's first define our device as the first visible conv1D_cuda device if we have # CUDA available: device = torch.device("conv1D_cuda:0" if torch.cuda.is_available() else "cpu") # Assume that we are on a CUDA machine, then this should print a CUDA device: print(device) ######################################################################## # The rest of this section assumes that `device` is a CUDA device. # # Then these methods will recursively go over all modules and convert their # parameters and buffers to CUDA tensors: # # .. code:: python # # net.to(device) # # # Remember that you will have to send the inputs and targets at every step # to the GPU too: # # .. code:: python # # inputs, labels = inputs.to(device), labels.to(device) # # Why dont I notice MASSIVE speedup compared to CPU? Because your network # is realllly small. # # **Exercise:** Try increasing the width of your network (argument 2 of # the first ``nn.Conv2d``, and argument 1 of the second ``nn.Conv2d`` – # they need to be the same number), see what kind of speedup you get. # # **Goals achieved**: # # - Understanding PyTorch's Tensor library and neural networks at a high level. # - Train a small neural network to classify images # # Training on multiple GPUs # ------------------------- # If you want to see even more MASSIVE speedup using all of your GPUs, # please check out :doc:`data_parallel_tutorial`. # # Where do I go next? # ------------------- # # - :doc:`Train neural nets to play video games </intermediate/reinforcement_q_learning>` # - `Train a state-of-the-art ResNet network on imagenet`_ # - `Train a face generator using Generative Adversarial Networks`_ # - `Train a word-level language model using Recurrent LSTM networks`_ # - `More examples`_ # - `More tutorials`_ # - `Discuss PyTorch on the Forums`_ # - `Chat with other users on Slack`_ # # .. _Train a state-of-the-art ResNet network on imagenet: https://github.com/pytorch/examples/tree/master/imagenet # .. _Train a face generator using Generative Adversarial Networks: https://github.com/pytorch/examples/tree/master/dcgan # .. _Train a word-level language model using Recurrent LSTM networks: https://github.com/pytorch/examples/tree/master/word_language_model # .. _More examples: https://github.com/pytorch/examples # .. _More tutorials: https://github.com/pytorch/tutorials # .. _Discuss PyTorch on the Forums: https://discuss.pytorch.org/ # .. _Chat with other users on Slack: http://pytorch.slack.com/messages/beginner/ """ Full output: /home/adam/anaconda3/bin/python /home/adam/code/time-series-ml/cnns/pytorch_tutorials/cifar10_tutorial.py Downloading http://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz Files already downloaded and verified cat dog frog plane [1, 2000] loss: 2.208 [1, 4000] loss: 1.882 [1, 6000] loss: 1.706 [1, 8000] loss: 1.600 [1, 10000] loss: 1.529 [1, 12000] loss: 1.486 [2, 2000] loss: 1.416 [2, 4000] loss: 1.368 [2, 6000] loss: 1.341 [2, 8000] loss: 1.328 [2, 10000] loss: 1.297 [2, 12000] loss: 1.277 Finished Training GroundTruth: cat ship ship plane Predicted: dog car ship ship Accuracy of the network on the 10000 test images: 53 % Accuracy of plane : 53 % Accuracy of car : 83 % Accuracy of bird : 48 % Accuracy of cat : 10 % Accuracy of deer : 31 % Accuracy of dog : 58 % Accuracy of frog : 71 % Accuracy of horse : 66 % Accuracy of ship : 66 % Accuracy of truck : 47 % cpu Process finished with exit code 0 """

33.334232

138

0.61656

b917540d8e25a1a24b074791de2b51eff10e98c9

8,726

py

Python

tests/test_domain_py.py

balabit-deps/balabit-os-7-sphinx

4e18ca37f4ddddf346c0b30835a544db20887259

[ "BSD-2-Clause" ]

null

tests/test_domain_py.py

balabit-deps/balabit-os-7-sphinx

4e18ca37f4ddddf346c0b30835a544db20887259

[ "BSD-2-Clause" ]

null

tests/test_domain_py.py

balabit-deps/balabit-os-7-sphinx

4e18ca37f4ddddf346c0b30835a544db20887259

[ "BSD-2-Clause" ]

null

# -*- coding: utf-8 -*- """ test_domain_py ~~~~~~~~~~~~~~ Tests the Python Domain :copyright: Copyright 2007-2018 by the Sphinx team, see AUTHORS. :license: BSD, see LICENSE for details. """ import pytest from mock import Mock from six import text_type from docutils import nodes from sphinx import addnodes from sphinx.domains.python import py_sig_re, _pseudo_parse_arglist, PythonDomain from sphinx.testing.util import assert_node def parse(sig): m = py_sig_re.match(sig) if m is None: raise ValueError name_prefix, name, arglist, retann = m.groups() signode = addnodes.desc_signature(sig, '') _pseudo_parse_arglist(signode, arglist) return signode.astext() def test_function_signatures(): rv = parse('func(a=1) -> int object') assert text_type(rv) == u'a=1' rv = parse('func(a=1, [b=None])') assert text_type(rv) == u'a=1, [b=None]' rv = parse('func(a=1[, b=None])') assert text_type(rv) == u'a=1, [b=None]' rv = parse("compile(source : string, filename, symbol='file')") assert text_type(rv) == u"source : string, filename, symbol='file'" rv = parse('func(a=[], [b=None])') assert text_type(rv) == u'a=[], [b=None]' rv = parse('func(a=[][, b=None])') assert text_type(rv) == u'a=[], [b=None]' @pytest.mark.sphinx('dummy', testroot='domain-py') def test_domain_py_xrefs(app, status, warning): """Domain objects have correct prefixes when looking up xrefs""" app.builder.build_all() def assert_refnode(node, module_name, class_name, target, reftype=None, domain='py'): attributes = { 'refdomain': domain, 'reftarget': target, } if reftype is not None: attributes['reftype'] = reftype if module_name is not False: attributes['py:module'] = module_name if class_name is not False: attributes['py:class'] = class_name assert_node(node, **attributes) doctree = app.env.get_doctree('roles') refnodes = list(doctree.traverse(addnodes.pending_xref)) assert_refnode(refnodes[0], None, None, u'TopLevel', u'class') assert_refnode(refnodes[1], None, None, u'top_level', u'meth') assert_refnode(refnodes[2], None, u'NestedParentA', u'child_1', u'meth') assert_refnode(refnodes[3], None, u'NestedParentA', u'NestedChildA.subchild_2', u'meth') assert_refnode(refnodes[4], None, u'NestedParentA', u'child_2', u'meth') assert_refnode(refnodes[5], False, u'NestedParentA', u'any_child', domain='') assert_refnode(refnodes[6], None, u'NestedParentA', u'NestedChildA', u'class') assert_refnode(refnodes[7], None, u'NestedParentA.NestedChildA', u'subchild_2', u'meth') assert_refnode(refnodes[8], None, u'NestedParentA.NestedChildA', u'NestedParentA.child_1', u'meth') assert_refnode(refnodes[9], None, u'NestedParentA', u'NestedChildA.subchild_1', u'meth') assert_refnode(refnodes[10], None, u'NestedParentB', u'child_1', u'meth') assert_refnode(refnodes[11], None, u'NestedParentB', u'NestedParentB', u'class') assert_refnode(refnodes[12], None, None, u'NestedParentA.NestedChildA', u'class') assert len(refnodes) == 13 doctree = app.env.get_doctree('module') refnodes = list(doctree.traverse(addnodes.pending_xref)) assert_refnode(refnodes[0], 'module_a.submodule', None, 'ModTopLevel', 'class') assert_refnode(refnodes[1], 'module_a.submodule', 'ModTopLevel', 'mod_child_1', 'meth') assert_refnode(refnodes[2], 'module_a.submodule', 'ModTopLevel', 'ModTopLevel.mod_child_1', 'meth') assert_refnode(refnodes[3], 'module_a.submodule', 'ModTopLevel', 'mod_child_2', 'meth') assert_refnode(refnodes[4], 'module_a.submodule', 'ModTopLevel', 'module_a.submodule.ModTopLevel.mod_child_1', 'meth') assert_refnode(refnodes[5], 'module_b.submodule', None, 'ModTopLevel', 'class') assert_refnode(refnodes[6], 'module_b.submodule', 'ModTopLevel', 'ModNoModule', 'class') assert_refnode(refnodes[7], False, False, 'int', 'obj') assert_refnode(refnodes[8], False, False, 'tuple', 'obj') assert_refnode(refnodes[9], False, False, 'str', 'obj') assert_refnode(refnodes[10], False, False, 'float', 'obj') assert len(refnodes) == 11 doctree = app.env.get_doctree('module_option') refnodes = list(doctree.traverse(addnodes.pending_xref)) print(refnodes) print(refnodes[0]) print(refnodes[1]) assert_refnode(refnodes[0], 'test.extra', 'B', 'foo', 'meth') assert_refnode(refnodes[1], 'test.extra', 'B', 'foo', 'meth') assert len(refnodes) == 2 @pytest.mark.sphinx('dummy', testroot='domain-py') def test_domain_py_objects(app, status, warning): app.builder.build_all() modules = app.env.domains['py'].data['modules'] objects = app.env.domains['py'].data['objects'] assert 'module_a.submodule' in modules assert 'module_a.submodule' in objects assert 'module_b.submodule' in modules assert 'module_b.submodule' in objects assert objects['module_a.submodule.ModTopLevel'] == ('module', 'class') assert objects['module_a.submodule.ModTopLevel.mod_child_1'] == ('module', 'method') assert objects['module_a.submodule.ModTopLevel.mod_child_2'] == ('module', 'method') assert 'ModTopLevel.ModNoModule' not in objects assert objects['ModNoModule'] == ('module', 'class') assert objects['module_b.submodule.ModTopLevel'] == ('module', 'class') assert objects['TopLevel'] == ('roles', 'class') assert objects['top_level'] == ('roles', 'method') assert objects['NestedParentA'] == ('roles', 'class') assert objects['NestedParentA.child_1'] == ('roles', 'method') assert objects['NestedParentA.any_child'] == ('roles', 'method') assert objects['NestedParentA.NestedChildA'] == ('roles', 'class') assert objects['NestedParentA.NestedChildA.subchild_1'] == ('roles', 'method') assert objects['NestedParentA.NestedChildA.subchild_2'] == ('roles', 'method') assert objects['NestedParentA.child_2'] == ('roles', 'method') assert objects['NestedParentB'] == ('roles', 'class') assert objects['NestedParentB.child_1'] == ('roles', 'method') @pytest.mark.sphinx('dummy', testroot='domain-py') def test_domain_py_find_obj(app, status, warning): def find_obj(modname, prefix, obj_name, obj_type, searchmode=0): return app.env.domains['py'].find_obj( app.env, modname, prefix, obj_name, obj_type, searchmode) app.builder.build_all() assert (find_obj(None, None, u'NONEXISTANT', u'class') == []) assert (find_obj(None, None, u'NestedParentA', u'class') == [(u'NestedParentA', (u'roles', u'class'))]) assert (find_obj(None, None, u'NestedParentA.NestedChildA', u'class') == [(u'NestedParentA.NestedChildA', (u'roles', u'class'))]) assert (find_obj(None, 'NestedParentA', u'NestedChildA', u'class') == [(u'NestedParentA.NestedChildA', (u'roles', u'class'))]) assert (find_obj(None, None, u'NestedParentA.NestedChildA.subchild_1', u'meth') == [(u'NestedParentA.NestedChildA.subchild_1', (u'roles', u'method'))]) assert (find_obj(None, u'NestedParentA', u'NestedChildA.subchild_1', u'meth') == [(u'NestedParentA.NestedChildA.subchild_1', (u'roles', u'method'))]) assert (find_obj(None, u'NestedParentA.NestedChildA', u'subchild_1', u'meth') == [(u'NestedParentA.NestedChildA.subchild_1', (u'roles', u'method'))]) def test_get_full_qualified_name(): env = Mock(domaindata={}) domain = PythonDomain(env) # non-python references node = nodes.reference() assert domain.get_full_qualified_name(node) is None # simple reference node = nodes.reference(reftarget='func') assert domain.get_full_qualified_name(node) == 'func' # with py:module context kwargs = {'py:module': 'module1'} node = nodes.reference(reftarget='func', **kwargs) assert domain.get_full_qualified_name(node) == 'module1.func' # with py:class context kwargs = {'py:class': 'Class'} node = nodes.reference(reftarget='func', **kwargs) assert domain.get_full_qualified_name(node) == 'Class.func' # with both py:module and py:class context kwargs = {'py:module': 'module1', 'py:class': 'Class'} node = nodes.reference(reftarget='func', **kwargs) assert domain.get_full_qualified_name(node) == 'module1.Class.func'

41.35545

88

0.649897

ffdb43a1df2671f5ace274edff96d15196e2dfaf

1,071

py

Python

msmart/const.py

rokam/midea-msmart

41f957e330b9d679cbdedb4972efa88184e8c63f

[ "MIT" ]

1

2021-11-26T09:19:05.000Z

msmart/const.py

rokam/midea-msmart

41f957e330b9d679cbdedb4972efa88184e8c63f

[ "MIT" ]

null

msmart/const.py

rokam/midea-msmart

41f957e330b9d679cbdedb4972efa88184e8c63f

[ "MIT" ]

null

VERSION = '0.1.33' BROADCAST_MSG = bytearray([ 0x5a, 0x5a, 0x01, 0x11, 0x48, 0x00, 0x92, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x7f, 0x75, 0xbd, 0x6b, 0x3e, 0x4f, 0x8b, 0x76, 0x2e, 0x84, 0x9c, 0x6e, 0x57, 0x8d, 0x65, 0x90, 0x03, 0x6e, 0x9d, 0x43, 0x42, 0xa5, 0x0f, 0x1f, 0x56, 0x9e, 0xb8, 0xec, 0x91, 0x8e, 0x92, 0xe5 ]) DEVICE_INFO_MSG = bytearray([ 0x5a, 0x5a, 0x15, 0x00, 0x00, 0x38, 0x00, 0x04, 0x00, 0x00, 0x00, 0x00, 0x00, 0x27, 0x33, 0x05, 0x13, 0x06, 0x14, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x03, 0xe8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xca, 0x8d, 0x9b, 0xf9, 0xa0, 0x30, 0x1a, 0xe3, 0xb7, 0xe4, 0x2d, 0x53, 0x49, 0x47, 0x62, 0xbe ]) MSGTYPE_HANDSHAKE_REQUEST = 0x0 MSGTYPE_HANDSHAKE_RESPONSE = 0x1 MSGTYPE_ENCRYPTED_RESPONSE = 0x3 MSGTYPE_ENCRYPTED_REQUEST = 0x6 MSGTYPE_TRANSPARENT = 0xf

36.931034

51

0.658263

1e9581e034bf7359333bd0ed25edff6997802ad5

2,182

py

Python

challange_accepted.py

tborisova/hackfmi4

5e9fd056c09d4e36e24a04a3eb70d480aa6a664d

[ "MIT" ]

1

2017-01-29T21:39:50.000Z

challange_accepted.py

tborisova/hackfmi4

5e9fd056c09d4e36e24a04a3eb70d480aa6a664d

[ "MIT" ]

null

challange_accepted.py

tborisova/hackfmi4

5e9fd056c09d4e36e24a04a3eb70d480aa6a664d

[ "MIT" ]

null

import socket import sys import pygame pygame.init() from hardcoded import Layout import images from client import * from parent_server import GameServer FPS = 60 SCREEN_WIDTH = 800 SCREEN_HEIGHT = 600 def check_for_internet_conection(): s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM) try: s.connect(('8.8.8.8', 0)) return s.getsockname()[0] except OSError: return None def init_graphics(): logo = pygame.transform.scale(images.images["logo"], (32, 32)) pygame.display.set_icon(logo) pygame.display.set_caption("Challenge Accepted", "Challenge Accepted") # if __name__ == "__main__": # # s = GameServer(localaddr=('10.0.201.111', 22022)) # s.current_game = kick_ball.Game(5) # s.Launch() # host = '10.0.201.111' # port = 22022 # c = Client(host, int(port)) ingame = False LAYOUTS = Layout.load_layouts() current_left_layout = "start" current_right_layout = None def start_conntrol(): if LAYOUTS[current_left_layout].connect_to_server_button.clicked: globals()["current_right_layout"] = "connect_to_server" if LAYOUTS[current_left_layout].connect_to_server_button.clicked: globals()["current_right_layout"] = "create_server" if LAYOUTS[current_left_layout].connect_to_server_button.clicked: sys.exit() def start_server_controll(): if LAYOUTS[current_left_layout].connect_to_server_button.clicked: globals()["current_left_layout"] = "game" globals()["current_right_layout"] = None if __name__ == "__main__": init_graphics() screen = pygame.display.set_mode((SCREEN_WIDTH, SCREEN_HEIGHT)) while True: events = pygame.event.get() if ingame: continue else: screen.fill((55, 155, 255)) if current_left_layout is not None: LAYOUTS[current_left_layout].update_elements(events) LAYOUTS[current_left_layout].draw(screen) if current_right_layout is not None: LAYOUTS[current_right_layout].update_elements(events) LAYOUTS[current_right_layout].draw(srceen) pygame.display.update() # c.Loop() # sleep(0.01)

28.337662

74

0.682401

92adb85a4eb3efaf7dd6076d356a071ec17c7036

26,816

py

Python

deepmars/models/train_model_sys.py

utplanets/deepmars

ba306aa9b25b654636b61cf952af2791b7ed0e56

[ "MIT" ]

2

2021-08-08T03:06:58.000Z

2021-11-25T04:06:00.000Z

deepmars/models/train_model_sys.py

utplanets/deepmars

ba306aa9b25b654636b61cf952af2791b7ed0e56

[ "MIT" ]

null

deepmars/models/train_model_sys.py

utplanets/deepmars

ba306aa9b25b654636b61cf952af2791b7ed0e56

[ "MIT" ]

2

2020-11-23T09:38:26.000Z

2021-02-26T01:14:28.000Z

#!/usr/bin/env python """Convolutional Neural Network Training Functions Functions for building and training a (UNET) Convolutional Neural Network on images of the Mars and binary ring targets. """ from __future__ import absolute_import, division, print_function import numpy as np import pandas as pd import h5py from keras.models import Model from keras.layers.core import Dropout, Reshape from keras.regularizers import l2 from keras.optimizers import Adam from keras.callbacks import EarlyStopping from keras import backend as K import deepmars.features.template_match_target as tmt import deepmars.utils.processing as proc import click import logging from pathlib import Path from dotenv import find_dotenv, load_dotenv import os from joblib import Parallel, delayed from tqdm import tqdm, trange # Check Keras version - code will switch API if needed. from keras import __version__ as keras_version K.set_image_dim_ordering('tf') k2 = True if keras_version[0] == '2' else False # If Keras is v2.x.x, create Keras 1-syntax wrappers. if not k2: from keras.models import load_model from keras.layers import merge, Input from keras.layers.convolutional import (Convolution2D, MaxPooling2D, UpSampling2D) else: from keras.models import load_model from keras.layers import Concatenate, Input from keras.layers.convolutional import (Conv2D, MaxPooling2D, UpSampling2D) def merge(layers, mode=None, concat_axis=None): """Wrapper for Keras 2's Concatenate class (`mode` is discarded).""" return Concatenate(axis=concat_axis)(list(layers)) def Convolution2D(n_filters, FL, FLredundant, activation=None, init=None, W_regularizer=None, border_mode=None): """Wrapper for Keras 2's Conv2D class.""" return Conv2D(n_filters, FL, activation=activation, kernel_initializer=init, kernel_regularizer=W_regularizer, padding=border_mode) minrad_ = 5 maxrad_ = 40 longlat_thresh2_ = 1.8 rad_thresh_ = 1.0 template_thresh_ = 0.5 target_thresh_ = 0.1 @click.group() def dl(): log_fmt = '%(asctime)s - %(name)s - %(levelname)s - %(message)s' logging.basicConfig(level=logging.INFO, format=log_fmt) # not used in this stub but often useful for finding various files project_dir = Path(__file__).resolve().parents[2] # find .env automagically by walking up directories until it's found, then # load up the .env entries as environment variables load_dotenv(find_dotenv()) import sys sys.path.append(os.getenv("DM_ROOTDIR")) pass ######################## def get_param_i(param, i): """Gets correct parameter for iteration i. Parameters ---------- param : list List of model hyperparameters to be iterated over. i : integer Hyperparameter iteration. Returns ------- Correct hyperparameter for iteration i. """ if len(param) > i: return param[i] else: return param[0] ######################## def custom_image_generator(data, target, batch_size=32): """Custom image generator that manipulates image/target pairs to prevent overfitting in the Convolutional Neural Network. Parameters ---------- data : array Input images. target : array Target images. batch_size : int, optional Batch size for image manipulation. Yields ------ Manipulated images and targets. """ D, L, W = data.shape[0], data[0].shape[0], data[0].shape[1] while True: shuffle_index = np.arange(D) # only shuffle once each loop through the data np.random.shuffle(shuffle_index) for i in np.arange(0, len(data), batch_size): index = shuffle_index[i:i + batch_size] d, t = data[index].copy(), target[index].copy() # Random color inversion # for j in np.where(np.random.randint(0, 2, batch_size) == 1)[0]: # d[j][d[j] > 0.] = 1. - d[j][d[j] > 0.] # Horizontal/vertical flips for j in np.where(np.random.randint(0, 2, batch_size) == 1)[0]: d[j], t[j] = np.fliplr(d[j]), np.fliplr(t[j]) # left/right for j in np.where(np.random.randint(0, 2, batch_size) == 1)[0]: d[j], t[j] = np.flipud(d[j]), np.flipud(t[j]) # up/down # Random up/down & left/right pixel shifts, 90 degree rotations npix = 15 # Horizontal shift h = np.random.randint(-npix, npix + 1, batch_size) # Vertical shift v = np.random.randint(-npix, npix + 1, batch_size) # 90 degree rotations r = np.random.randint(0, 4, batch_size) for j in range(batch_size): d[j] = np.pad(d[j], ((npix, npix), (npix, npix), (0, 0)), mode='constant')[npix + h[j]:L + h[j] + npix, npix + v[j]:W + v[j] + npix, :] sh, sv = slice(npix + h[j], L + h[j] + npix),\ slice(npix + v[j], W + v[j] + npix) t[j] = np.pad(t[j], (npix,), mode='constant')[sh, sv] d[j], t[j] = np.rot90(d[j], r[j]), np.rot90(t[j], r[j]) yield (d, t) def t2c(pred, csv, i, minrad=minrad_, maxrad=maxrad_, longlat_thresh2=longlat_thresh2_, rad_thresh=rad_thresh_, template_thresh=template_thresh_, target_thresh=target_thresh_): return np.hstack([i, tmt.template_match_t2c(pred, csv, minrad=minrad, maxrad=maxrad longlat_thresh2=longlat_thresh2, rad_thresh=rad_thresh, template_thresh=template_thresh, target_thresh=target_thresh)]) def diagnostic(res, beta): """Calculate the metrics from the predictions compared to the CSV. Parameters ------------ res: list of results containing: image number, number of matched, number of existing craters, number of detected craters, maximum radius detected, mean error in longitude, mean error in latitude, mean error in radius, fraction of duplicates in detections. beta : int Beta value when calculating F-beta score. Returns ------- dictionary : metrics stored in a dictionary """ counter, N_match, N_csv, N_detect,\ mrad, err_lo, err_la, err_r, frac_duplicates = np.array(res).T w = np.where(N_match == 0) w = np.where(N_match > 0) counter, N_match, N_csv, N_detect,\ mrad, err_lo, err_la, errr_, frac_dupes =\ counter[w], N_match[w], N_csv[w], N_detect[w],\ mrad[w], err_lo[w], err_la[w], err_r[w], frac_duplicates[w] precision = N_match / (N_match + (N_detect - N_match)) recall = N_match / N_csv fscore = (1 + beta**2) * (recall * precision) / \ (precision * beta**2 + recall) diff = N_detect - N_match frac_new = diff / (N_detect + diff) frac_new2 = diff / (N_csv + diff) frac_duplicates = frac_dupes return dict(precision=precision, recall=recall, fscore=fscore, frac_new=frac_new, frac_new2=frac_new2, err_lo=err_lo, err_la=err_la, err_r=err_r, frac_duplicates=frac_duplicates, maxrad=mrad, counter=counter, N_match=N_match, N_csv=N_csv) def get_metrics(data, craters_images, dim, model, name, beta=1, offset=0, minrad=minrad_, maxrad=maxrad_, longlat_thresh2=longlat_thresh2_, rad_thresh=rad_thresh_, template_thresh=template_thresh_, target_thresh=target_thresh_, rmv_oor_csvs=0): """Function that prints pertinent metrics at the end of each epoch. Parameters ---------- data : hdf5 Input images. craters : hdf5 Pandas arrays of human-counted crater data. dim : int Dimension of input images (assumes square). model : keras model object Keras model beta : int, optional Beta value when calculating F-beta score. Defaults to 1. """ X, Y = data[0], data[1] craters, images = craters_images # Get csvs of human-counted craters csvs = [] # minrad, maxrad = 3, 50 cutrad, n_csvs = 0.8, len(X) diam = 'Diameter (pix)' for i in range(len(X)): imname = images[i] # name = "img_{0:05d}".format(i) found = False for crat in craters: if imname in crat: csv = crat[imname] found = True if not found: csvs.append([-2]) continue # remove small/large/half craters csv = csv[(csv[diam] < 2 * maxrad) & (csv[diam] > 2 * minrad)] csv = csv[(csv['x'] + cutrad * csv[diam] / 2 <= dim)] csv = csv[(csv['y'] + cutrad * csv[diam] / 2 <= dim)] csv = csv[(csv['x'] - cutrad * csv[diam] / 2 > 0)] csv = csv[(csv['y'] - cutrad * csv[diam] / 2 > 0)] if len(csv) < 3: # Exclude csvs with few craters csvs.append([-1]) else: csv_coords = np.asarray((csv['x'], csv['y'], csv[diam] / 2)).T csvs.append(csv_coords) # Calculate custom metrics print("csvs: {}".format(len(csvs))) print("") print("*********Custom Loss*********") recall, precision, fscore = [], [], [] frac_new, frac_new2, mrad = [], [], [] err_lo, err_la, err_r = [], [], [] frac_duplicates = [] if isinstance(model, Model): preds = None # print(X[6].min(),X[6].max(),X.dtype,np.percentile(X[6],99)) preds = model.predict(X, verbose=1) # save h5f = h5py.File("predictions.hdf5", 'w') h5f.create_dataset(name, data=preds) print("Successfully generated and saved model predictions.") else: preds = model # print(csvs) countme = [i for i in range(n_csvs) if len(csvs[i]) >= 3] print("Processing {} fields".format(len(countme))) # preds contains a large number of predictions, # so we run the template code in parallel. res = Parallel(n_jobs=24, verbose=5)(delayed(t2c)(preds[i], csvs[i], i, minrad=minrad, maxrad=maxrad, longlat_thresh2=longlat_thresh2, rad_thresh=rad_thresh, template_thresh=template_thresh, target_thresh=target_thresh) for i in range(n_csvs) if len(csvs[i]) >= 3) if len(res) == 0: print("No valid results: ", res) return None # At this point we've processed the predictions with the template matching # algorithm, now calculate the metrics from the data. diag = diagnostic(res, beta) print(len(diag["recall"])) # print("binary XE score = %f" % model.evaluate(X, Y)) if len(diag["recall"]) > 3: metric_data = [("N_match/N_csv (recall)", diag["recall"]), ("N_match/(N_match + (N_detect-N_match)) (precision)", diag["precision"]), ("F_{} score".format(beta), diag["fscore"]), ("(N_detect - N_match)/N_detect" + "(fraction of craters that are new)", diag["frac_new"]), ("(N_detect - N_match)/N_csv (fraction" + "of craters that are new, 2)", diag["frac_new2"])] for fname, data in metric_data: print("mean and std of %s = %f, %f" % (fname, np.mean(data), np.std(data))) for fname, data in [("fractional longitude diff", diag["err_lo"]), ("fractional latitude diff", diag["err_la"]), ("fractional radius diff", diag["err_r"]), ]: print("median and IQR %s = %f, 25:%f, 75:%f" % (fname, np.median(data), np.percentile(data, 25), np.percentile(data, 75))) print("""mean and std of maximum detected pixel radius in an image = %f, %f""" % (np.mean(diag["maxrad"]), np.std(diag["maxrad"]))) print("""absolute maximum detected pixel radius over all images = %f""" % np.max(diag["maxrad"])) print("") return diag ######################## def build_model(dim, learn_rate, lmbda, drop, FL, init, n_filters): """Function that builds the (UNET) convolutional neural network. Parameters ---------- dim : int Dimension of input images (assumes square). learn_rate : float Learning rate. lmbda : float Convolution2D regularization parameter. drop : float Dropout fraction. FL : int Filter length. init : string Weight initialization type. n_filters : int Number of filters in each layer. Returns ------- model : keras model object Constructed Keras model. """ print('Making UNET model...') img_input = Input(batch_shape=(None, dim, dim, 1)) a1 = Convolution2D(n_filters, FL, FL, activation='relu', init=init, W_regularizer=l2(lmbda), border_mode='same')(img_input) a1 = Convolution2D(n_filters, FL, FL, activation='relu', init=init, W_regularizer=l2(lmbda), border_mode='same')(a1) a1P = MaxPooling2D((2, 2), strides=(2, 2))(a1) a2 = Convolution2D(n_filters * 2, FL, FL, activation='relu', init=init, W_regularizer=l2(lmbda), border_mode='same')(a1P) a2 = Convolution2D(n_filters * 2, FL, FL, activation='relu', init=init, W_regularizer=l2(lmbda), border_mode='same')(a2) a2P = MaxPooling2D((2, 2), strides=(2, 2))(a2) a3 = Convolution2D(n_filters * 4, FL, FL, activation='relu', init=init, W_regularizer=l2(lmbda), border_mode='same')(a2P) a3 = Convolution2D(n_filters * 4, FL, FL, activation='relu', init=init, W_regularizer=l2(lmbda), border_mode='same')(a3) a3P = MaxPooling2D((2, 2), strides=(2, 2),)(a3) u = Convolution2D(n_filters * 4, FL, FL, activation='relu', init=init, W_regularizer=l2(lmbda), border_mode='same')(a3P) u = Convolution2D(n_filters * 4, FL, FL, activation='relu', init=init, W_regularizer=l2(lmbda), border_mode='same')(u) u = UpSampling2D((2, 2))(u) u = merge((a3, u), mode='concat', concat_axis=3) u = Dropout(drop)(u) u = Convolution2D(n_filters * 2, FL, FL, activation='relu', init=init, W_regularizer=l2(lmbda), border_mode='same')(u) u = Convolution2D(n_filters * 2, FL, FL, activation='relu', init=init, W_regularizer=l2(lmbda), border_mode='same')(u) u = UpSampling2D((2, 2))(u) u = merge((a2, u), mode='concat', concat_axis=3) u = Dropout(drop)(u) u = Convolution2D(n_filters, FL, FL, activation='relu', init=init, W_regularizer=l2(lmbda), border_mode='same')(u) u = Convolution2D(n_filters, FL, FL, activation='relu', init=init, W_regularizer=l2(lmbda), border_mode='same')(u) u = UpSampling2D((2, 2))(u) u = merge((a1, u), mode='concat', concat_axis=3) u = Dropout(drop)(u) u = Convolution2D(n_filters, FL, FL, activation='relu', init=init, W_regularizer=l2(lmbda), border_mode='same')(u) u = Convolution2D(n_filters, FL, FL, activation='relu', init=init, W_regularizer=l2(lmbda), border_mode='same')(u) # Final output final_activation = 'sigmoid' u = Convolution2D(1, 1, 1, activation=final_activation, init=init, W_regularizer=l2(lmbda), border_mode='same')(u) u = Reshape((dim, dim))(u) if k2: model = Model(inputs=img_input, outputs=u) else: model = Model(input=img_input, output=u) optimizer = Adam(lr=learn_rate) model.compile(loss='binary_crossentropy', optimizer=optimizer) print(model.summary()) return model ######################## def test_model(Data, Craters, MP, i_MP): # Static params dim, nb_epoch, bs = MP['dim'], MP['epochs'], MP['bs'] # Iterating params FL = get_param_i(MP['filter_length'], i_MP) learn_rate = get_param_i(MP['lr'], i_MP) n_filters = get_param_i(MP['n_filters'], i_MP) init = get_param_i(MP['init'], i_MP) lmbda = get_param_i(MP['lambda'], i_MP) drop = get_param_i(MP['dropout'], i_MP) model = load_model(MP["model"]) get_metrics(Data[MP["test_dataset"]], Craters[MP["test_dataset"]], dim, model, MP["test_dataset"]) def train_and_test_model(Data, Craters, MP, i_MP): """Function that trains, tests and saves the model, printing out metrics after each model. Parameters ---------- Data : dict Inputs and Target Moon data. Craters : dict Human-counted crater data. MP : dict Contains all relevant parameters. i_MP : int Iteration number (when iterating over hypers). """ # Static params dim, nb_epoch, bs = MP['dim'], MP['epochs'], MP['bs'] # Iterating params FL = get_param_i(MP['filter_length'], i_MP) learn_rate = get_param_i(MP['lr'], i_MP) n_filters = get_param_i(MP['n_filters'], i_MP) init = get_param_i(MP['init'], i_MP) lmbda = get_param_i(MP['lambda'], i_MP) drop = get_param_i(MP['dropout'], i_MP) # Build model if MP["model"] is not None: model = load_model(MP["model"]) else: model = build_model(dim, learn_rate, lmbda, drop, FL, init, n_filters) # Main loop n_samples = MP['n_train'] for nb in range(nb_epoch): if k2: model.fit_generator( custom_image_generator(Data['train'][0], Data['train'][1], batch_size=bs), steps_per_epoch=n_samples / bs, epochs=1, verbose=1, # validation_data=(Data['dev'][0],Data['dev'][1]), #no gen validation_data=custom_image_generator(Data['dev'][0], Data['dev'][1], batch_size=bs), validation_steps=MP['n_dev'] / bs, callbacks=[ EarlyStopping(monitor='val_loss', patience=3, verbose=0)]) else: model.fit_generator( custom_image_generator(Data['train'][0], Data['train'][1], batch_size=bs), samples_per_epoch=n_samples, nb_epoch=1, verbose=1, # validation_data=(Data['dev'][0],Data['dev'][1]), #no gen validation_data=custom_image_generator(Data['dev'][0], Data['dev'][1], batch_size=bs), nb_val_samples=n_samples, callbacks=[ EarlyStopping(monitor='val_loss', patience=3, verbose=0)]) suffix = "{}_{}_{}_{}_{}_{}_{}.hdf5".format(learn_rate, n_filters, init, lmbda, drop, nb, nb_epoch) model_save_name = os.path.join(MP["save_dir"], "model_".format(suffix)) if MP['save_models']: model.save(model_save_name) if MP["calculate_custom_loss"]: get_metrics(Data['dev'], Craters['dev'], dim, model, "dev") if MP["save_models"] == 1: model.save(os.path.join(MP["save_dir"], MP["final_save_name"])) print("###################################") print("##########END_OF_RUN_INFO##########") print("""learning_rate=%e, batch_size=%d, filter_length=%e, n_epoch=%d n_train=%d, img_dimensions=%d, init=%s, n_filters=%d, lambda=%e dropout=%f""" % (learn_rate, bs, FL, nb_epoch, MP['n_train'], MP['dim'], init, n_filters, lmbda, drop)) if MP["calculate_custom_loss"]: get_metrics(Data['test'], Craters['test'], dim, model, "test") print("###################################") print("###################################") ######################## def get_models(MP): """Top-level function that loads data files and calls train_and_test_model. Parameters ---------- MP : dict Model Parameters. """ dir = MP['dir'] n_train, n_dev, n_test = MP['n_train'], MP['n_dev'], MP['n_test'] # Load data def load_files(numbers, test, this_dataset): res0 = [] res1 = [] files = [] craters = [] images = [] npic = 0 if not test or (test and this_dataset): for n in tqdm(numbers): files.append(h5py.File(os.path.join( dir, "sys_images_{0:05d}.hdf5".format(n)), 'r')) images.extend(["img_{0:05d}".format(a) for a in np.arange(n, n + 1000)]) res0.append(files[-1]["input_images"][:].astype('float32')) npic = npic + len(res0[-1]) res1.append(files[-1]["target_masks"][:].astype('float32')) files[-1].close() craters.append(pd.HDFStore(os.path.join( dir, "sys_craters_{0:05d}.hdf5".format(n)), 'r')) res0 = np.vstack(res0) res1 = np.vstack(res1) return files, res0, res1, npic, craters, images train_files,\ train0,\ train1,\ Ntrain,\ train_craters,\ train_images = load_files(MP["train_indices"], MP["test"], MP["test_dataset"] == "train") print(Ntrain, n_train) dev_files,\ dev0,\ dev1,\ Ndev,\ dev_craters,\ dev_images = load_files(MP["dev_indices"], MP["test"], MP["test_dataset"] == "dev") print(Ndev, n_dev) test_files,\ test0,\ test1,\ Ntest,\ test_craters,\ test_images = load_files(MP["test_indices"], MP["test"], MP["test_dataset"] == "test") print(Ntest, n_test) Data = { "train": [train0, train1], "dev": [dev0, dev1], "test": [test0[:n_test], test1[:n_test]] } # Rescale, normalize, add extra dim proc.preprocess(Data) # Load ground-truth craters Craters = { 'train': [train_craters, train_images], 'dev': [dev_craters, dev_images], 'test': [test_craters, test_images] } # Iterate over parameters if MP["test"]: test_model(Data, Craters, MP, 0) return else: for i in range(MP['N_runs']): train_and_test_model(Data, Craters, MP, i) @dl.command() @click.option("--test", is_flag=True, default=False) @click.option("--test_dataset", default="dev") @click.option("--model", default=None) def train_model(test, test_dataset, model): """Run Convolutional Neural Network Training Execute the training of a (UNET) Convolutional Neural Network on images of the Moon and binary ring targets. """ # Model Parameters MP = {} # Directory of train/dev/test image and crater hdf5 files. MP['dir'] = os.path.join(os.getenv("DM_ROOTDIR"), 'data/processed/') # Image width/height, assuming square images. MP['dim'] = 256 # Batch size: smaller values = less memory, less accurate gradient estimate MP['bs'] = 10 # Number of training epochs. MP['epochs'] = 30 # Number of train/valid/test samples, needs to be a multiple of batch size. # sample every even numbered image file to use in the training, # half of the odd number for testing. # half of the odd numbers for validataion. MP['train_indices'] = list(np.arange(162000, 208000, 2000)) MP['dev_indices'] = list(np.arange(161000, 206000, 4000)) MP['test_indices'] = list(np.arange(163000, 206000, 4000)) # MP['test_indices'] = 90000#list(np.arange(10000,184000,8000)) MP['n_train'] = len(MP["train_indices"]) * 1000 MP['n_dev'] = len(MP["dev_indices"]) * 1000 MP['n_test'] = len(MP["test_indices"]) * 1000 print(MP["n_train"], MP["n_dev"], MP["n_test"]) # Save model (binary flag) and directory. MP['save_models'] = 1 MP["calculate_custom_loss"] = False MP['save_dir'] = 'models' MP['final_save_name'] = 'model.h5' # initial model MP["model"] = model # testing only MP["test"] = test MP["test_dataset"] = test_dataset # Model Parameters (to potentially iterate over, keep in lists). # runs.csv looks like # filter_length,lr,n_filters,init,lambda,dropout # 3,0.0001,112,he_normal,1e-6,0.15 # # each line is a new run. df = pd.read_csv("runs.csv") for na, ty in [("filter_length", int), ("lr", float), ("n_filters", int), ("init", str), ("lambda", float), ("dropout", float)]: MP[na] = df[na].astype(ty).values MP['N_runs'] = len(MP['lambda']) # Number of runs MP['filter_length'] = [3] # Filter length # MP['lr'] = [0.0001] # Learning rate # MP['n_filters'] = [112] # Number of filters # MP['init'] = ['he_normal'] # Weight initialization # MP['lambda'] = [1e-6] # Weight regularization # MP['dropout'] = [0.15] # Dropout fraction # Iterating over parameters example. # MP['N_runs'] = 2 # MP['lambda']=[1e-4,1e-4] print(MP) get_models(MP) if __name__ == '__main__': dl()

36.237838

79

0.545085

43a210fbe85ce85f3fa711f9696ca943b58d01dd

3,667

py

Python

dendropy/test/support/dendropytest.py

EnjoyLifeFund/macHighSierra-py36-pkgs

5668b5785296b314ea1321057420bcd077dba9ea

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

null

dendropy/test/support/dendropytest.py

EnjoyLifeFund/macHighSierra-py36-pkgs

5668b5785296b314ea1321057420bcd077dba9ea

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

null

dendropy/test/support/dendropytest.py

EnjoyLifeFund/macHighSierra-py36-pkgs

5668b5785296b314ea1321057420bcd077dba9ea

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

null

#! /usr/bin/env python ############################################################################## ## DendroPy Phylogenetic Computing Library. ## ## Copyright 2010-2015 Jeet Sukumaran and Mark T. Holder. ## All rights reserved. ## ## See "LICENSE.rst" for terms and conditions of usage. ## ## If you use this work or any portion thereof in published work, ## please cite it as: ## ## Sukumaran, J. and M. T. Holder. 2010. DendroPy: a Python library ## for phylogenetic computing. Bioinformatics 26: 1569-1571. ## ############################################################################## """ Extension to the basic unittest TestCase. """ import collections import sys import re import os import unittest from distutils.util import strtobool from dendropy.utility import metavar from dendropy.utility import messaging # Defining this here means that unittest will exclude all lines from this # module in the traceback report when an assertion fails, allowing # for the starting point of the traceback to be the point where the assertion # was made, rather than the point where an exception was raised because # the assertion was false. __unittest = True def discover_test_module_paths(filter_patterns=None): """ Discovers test modules. If ``filter_patterns`` is |None|, then all files in *immediate* directory that begin with 'test' will be added to the set returned. If ``filter_patterns`` is not |None|, then it should be a list of regular expression patterns, and only files that match at least one of the patterns will be returned. """ test_module_pattern = re.compile("^test.*\.py$", re.IGNORECASE) if filter_patterns: filter_pattern = re.compile("(" + r"\|".join(filter_patterns) + ")") else: filter_pattern = None path = os.path.dirname(os.path.dirname(__file__)) filenames = os.listdir(path) test_modules = [] for filename in filenames: if test_module_pattern.match(filename): if filter_pattern is None or filter_pattern.match(filename): # test_modules.append("" + os.path.splitext(filename)[0]) test_modules.append("dendropy.test." + os.path.splitext(filename)[0]) return test_modules def get_test_suite(test_names=None): """ If ``test_names`` is not |None|, creates a test suite out of those modules. Otherwise, creates a test suite from all of the modules in ``dendropy.test`` using the discovery. """ if test_names is None: test_names = discover_test_module_paths() tests = unittest.defaultTestLoader.loadTestsFromNames(test_names) return unittest.TestSuite(tests) class ExtendedTestCase(unittest.TestCase): """ Extends unittest.TestCase with various new assertion tests. """ def _get_logger(self): if not hasattr(self, "_logger") or self._logger is None: self._logger = messaging.get_logger(self.__class__.__name__) return self._logger def _set_logger(self, logger): self._logger = logger logger = property(_get_logger, _set_logger) def assertCountEqual(self, *args, **kwargs): if sys.hexversion >= 0x03020000: super(ExtendedTestCase, self).assertCountEqual(*args, **kwargs) else: self.assertEqual(collections.Counter(args[0]), collections.Counter(args[1])) def fail_incomplete_tests(self): return bool(strtobool(os.environ.get(metavar.FAIL_INCOMPLETE_TESTS_ENVAR, "0"))) def assertEqualUnorderedSequences(self, x1, x2): c1 = collections.Counter(x1) c2 = collections.Counter(x2) return self.assertEqual(c1, c2)

37.040404

88

0.669757

9c2d102be62aa973cdae90e3136547104dd7fd91

2,427

py

Python

setup.py

virtuald/header2whatever

a838cbf3e232b41ea387274730251778a84d831f

[ "Apache-2.0" ]

3

2018-07-04T12:36:56.000Z

2021-04-14T17:42:43.000Z

setup.py

virtuald/header2whatever

a838cbf3e232b41ea387274730251778a84d831f

[ "Apache-2.0" ]

null

setup.py

virtuald/header2whatever

a838cbf3e232b41ea387274730251778a84d831f

[ "Apache-2.0" ]

1

2021-12-31T07:23:18.000Z

from __future__ import print_function from os.path import dirname, exists, join import sys, subprocess from setuptools import setup setup_dir = dirname(__file__) git_dir = join(setup_dir, '.git') base_package = 'header2whatever' version_file = join(setup_dir, base_package, 'version.py') # Automatically generate a version.py based on the git version if exists(git_dir): p = subprocess.Popen(["git", "describe", "--tags", "--long", "--dirty=-dirty"], stdout=subprocess.PIPE, stderr=subprocess.PIPE) out, err = p.communicate() # Make sure the git version has at least one tag if err: print("Error: You need to create a tag for this repo to use the builder") sys.exit(1) # Convert git version to PEP440 compliant version # - Older versions of pip choke on local identifiers, so we can't include the git commit v, commits, local = out.decode('utf-8').rstrip().split('-', 2) if commits != '0' or '-dirty' in local: v = '%s.post0.dev%s' % (v, commits) # Create the version.py file with open(version_file, 'w') as fp: fp.write("# Autogenerated by setup.py\n__version__ = '{0}'".format(v)) with open(version_file, 'r') as fp: exec(fp.read(), globals()) with open(join(setup_dir, 'README.rst'), 'r') as readme_file: long_description = readme_file.read() setup( name='header2whatever', version=__version__, description='Generate files from C/C++ headers using jinja2 templates', long_description=long_description, author='Dustin Spicuzza', author_email='dustin@virtualroadside.com', url='https://github.com/virtualroadside/header2whatever', keywords='c++ cpp codegen generator header jinja2 template', packages=[ base_package, base_package + "/_pcpp", base_package + "/_pcpp/ply/ply", ], install_requires=[ 'robotpy-cppheaderparser>=5.0.0', 'jinja2', 'pyyaml', 'schematics>=2.1.1', ], license='Apache 2.0', classifiers=[ 'Operating System :: OS Independent', 'Environment :: Console', 'Programming Language :: Python', 'License :: OSI Approved :: Apache Software License' ], entry_points = { 'console_scripts': [ 'h2w = header2whatever.parse:main', 'h2w-batch = header2whatever.parse:batch' ] } )

32.797297

92

0.634116

cf6a4a88ff55f9e5dbd64e7ad1a40288f4ec8eb7

1,672

py

Python

config/wsgi.py

devnelmar/cornershop

f3af70577f28577fbd91e762df7388ff9aba54ee

[ "MIT" ]

null

config/wsgi.py

devnelmar/cornershop

f3af70577f28577fbd91e762df7388ff9aba54ee

[ "MIT" ]

null

config/wsgi.py

devnelmar/cornershop

f3af70577f28577fbd91e762df7388ff9aba54ee

[ "MIT" ]

null

""" WSGI config for cornershoop project. This module contains the WSGI application used by Django's development server and any production WSGI deployments. It should expose a module-level variable named ``application``. Django's ``runserver`` and ``runfcgi`` commands discover this application via the ``WSGI_APPLICATION`` setting. Usually you will have the standard Django WSGI application here, but it also might make sense to replace the whole Django WSGI application with a custom one that later delegates to the Django one. For example, you could introduce WSGI middleware here, or combine a Django application with an application of another framework. """ import os import sys from pathlib import Path from django.core.wsgi import get_wsgi_application # This allows easy placement of apps within the interior # cornershoop directory. ROOT_DIR = Path(__file__).resolve(strict=True).parent.parent sys.path.append(str(ROOT_DIR / "cornershoop")) # We defer to a DJANGO_SETTINGS_MODULE already in the environment. This breaks # if running multiple sites in the same mod_wsgi process. To fix this, use # mod_wsgi daemon mode with each site in its own daemon process, or use # os.environ["DJANGO_SETTINGS_MODULE"] = "config.settings.production" os.environ.setdefault("DJANGO_SETTINGS_MODULE", "config.settings.production") # This application object is used by any WSGI server configured to use this # file. This includes Django's development server, if the WSGI_APPLICATION # setting points here. application = get_wsgi_application() # Apply WSGI middleware here. # from helloworld.wsgi import HelloWorldApplication # application = HelloWorldApplication(application)

42.871795

79

0.803828

35d67388ca65cdac5160d1f38b1d5565881975b1

2,774

py

Python

python_tutorial/settings.py

sunayaakula/outlookapp

84ad1c4293cae1d557ed79b105399221ab7a372c

[ "MIT" ]

null

python_tutorial/settings.py

sunayaakula/outlookapp

84ad1c4293cae1d557ed79b105399221ab7a372c

[ "MIT" ]

4

2020-06-06T01:27:26.000Z

2021-06-10T22:38:58.000Z

python_tutorial/settings.py

sunayaakula/outlookapp

84ad1c4293cae1d557ed79b105399221ab7a372c

[ "MIT" ]

null

""" Django settings for python_tutorial project. Generated by 'django-admin startproject' using Django 1.10. For more information on this file, see https://docs.djangoproject.com/en/1.10/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/1.10/ref/settings/ """ import django_heroku import os # Build paths inside the project like this: os.path.join(BASE_DIR, ...) BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/1.10/howto/deployment/checklist/ # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = 'v@*)_*6xx)#t@4np043msmvg%^ez2p46ke#2*dtsf_bnvgxjuj' # SECURITY WARNING: don't run with debug turned on in production! DEBUG = True TEMPLATE_DEBUG = True ALLOWED_HOSTS = ['*'] # Application definition INSTALLED_APPS = ( 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', 'tutorial', ) MIDDLEWARE = ( 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ) ROOT_URLCONF = 'python_tutorial.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] WSGI_APPLICATION = 'python_tutorial.wsgi.application' # Database # https://docs.djangoproject.com/en/1.7/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.sqlite3', 'NAME': os.path.join(BASE_DIR, 'db.sqlite3'), } } # Internationalization # https://docs.djangoproject.com/en/1.7/topics/i18n/ LANGUAGE_CODE = 'en-us' TIME_ZONE = 'UTC' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/1.7/howto/static-files/ STATIC_URL = '/static/' STATIC_ROOT = os.path.join(BASE_DIR, 'static') MEDIA_URL = '/assets/' MEDIA_ROOT = os.path.join(BASE_DIR, 'assets') # Activate Django-Heroku. django_heroku.settings(locals())

25.218182

72

0.702596

de192dd4b0ba4fcd927703f57e392efa24796a81

572

py

Python

tests/rpc/python/exn_test.py

omnisci3nce/ocaml-rpc

982f950ee7b6d792bd4ae097393d9bed545bef90

[ "ISC" ]

76

2015-01-22T09:40:14.000Z

2022-03-30T19:44:53.000Z

tests/rpc/python/exn_test.py

omnisci3nce/ocaml-rpc

982f950ee7b6d792bd4ae097393d9bed545bef90

[ "ISC" ]

106

2015-05-15T14:20:17.000Z

2022-03-09T11:36:53.000Z

tests/rpc/python/exn_test.py

omnisci3nce/ocaml-rpc

982f950ee7b6d792bd4ae097393d9bed545bef90

[ "ISC" ]

26

2015-06-09T15:55:29.000Z

2022-02-04T12:29:32.000Z

""" Tests that the exceptions are correctly generated. """ import bindings if __name__ == "__main__": try: raise bindings.Error1("test") except bindings.Error1 as exn: pass try: raise bindings.Error2((4, True)) except bindings.Error2 as exn: pass try: raise bindings.Error3((4, True, "error")) except bindings.Error3 as exn: pass try: raise bindings.Error2(("4", "True")) except bindings.TypeError: pass except _: raise Exception("Should have raised TypeError")

22

55

0.606643

b18680e12c75c6e6712b2b03cc985614e8283b20

10,814

py

Python

python/ray/tune/suggest/suggestion.py

noahshpak/ray

edd783bc327760a4892ab89222ee551e42df15b9

[ "Apache-2.0" ]

1

2019-06-21T10:06:41.000Z

python/ray/tune/suggest/suggestion.py

noahshpak/ray

edd783bc327760a4892ab89222ee551e42df15b9

[ "Apache-2.0" ]

null

python/ray/tune/suggest/suggestion.py

noahshpak/ray

edd783bc327760a4892ab89222ee551e42df15b9

[ "Apache-2.0" ]

null

import copy import glob import logging import os from ray.util.debug import log_once logger = logging.getLogger(__name__) class Searcher: """Abstract class for wrapping suggesting algorithms. Custom algorithms can extend this class easily by overriding the `suggest` method provide generated parameters for the trials. Any subclass that implements ``__init__`` must also call the constructor of this class: ``super(Subclass, self).__init__(...)``. To track suggestions and their corresponding evaluations, the method `suggest` will be passed a trial_id, which will be used in subsequent notifications. Args: metric (str): The training result objective value attribute. mode (str): One of {min, max}. Determines whether objective is minimizing or maximizing the metric attribute. .. code-block:: python class ExampleSearch(Searcher): def __init__(self, metric="mean_loss", mode="min", **kwargs): super(ExampleSearch, self).__init__( metric=metric, mode=mode, **kwargs) self.optimizer = Optimizer() self.configurations = {} def suggest(self, trial_id): configuration = self.optimizer.query() self.configurations[trial_id] = configuration def on_trial_complete(self, trial_id, result, **kwargs): configuration = self.configurations[trial_id] if result and self.metric in result: self.optimizer.update(configuration, result[self.metric]) tune.run(trainable_function, search_alg=ExampleSearch()) """ FINISHED = "FINISHED" CKPT_FILE_TMPL = "searcher-state-{}.pkl" def __init__(self, metric="episode_reward_mean", mode="max", max_concurrent=None, use_early_stopped_trials=None): if use_early_stopped_trials is False: raise DeprecationWarning( "Early stopped trials are now always used. If this is a " "problem, file an issue: https://github.com/ray-project/ray.") if max_concurrent is not None: logger.warning( "DeprecationWarning: `max_concurrent` is deprecated for this " "search algorithm. Use tune.suggest.ConcurrencyLimiter() " "instead. This will raise an error in future versions of Ray.") assert mode in ["min", "max"], "`mode` must be 'min' or 'max'!" self._metric = metric self._mode = mode def on_trial_result(self, trial_id, result): """Optional notification for result during training. Note that by default, the result dict may include NaNs or may not include the optimization metric. It is up to the subclass implementation to preprocess the result to avoid breaking the optimization process. Args: trial_id (str): A unique string ID for the trial. result (dict): Dictionary of metrics for current training progress. Note that the result dict may include NaNs or may not include the optimization metric. It is up to the subclass implementation to preprocess the result to avoid breaking the optimization process. """ pass def on_trial_complete(self, trial_id, result=None, error=False): """Notification for the completion of trial. Typically, this method is used for notifying the underlying optimizer of the result. Args: trial_id (str): A unique string ID for the trial. result (dict): Dictionary of metrics for current training progress. Note that the result dict may include NaNs or may not include the optimization metric. It is up to the subclass implementation to preprocess the result to avoid breaking the optimization process. Upon errors, this may also be None. error (bool): True if the training process raised an error. """ raise NotImplementedError def suggest(self, trial_id): """Queries the algorithm to retrieve the next set of parameters. Arguments: trial_id (str): Trial ID used for subsequent notifications. Returns: dict | FINISHED | None: Configuration for a trial, if possible. If FINISHED is returned, Tune will be notified that no more suggestions/configurations will be provided. If None is returned, Tune will skip the querying of the searcher for this step. """ raise NotImplementedError def save(self, checkpoint_path): """Save state to path for this search algorithm. Args: checkpoint_path (str): File where the search algorithm state is saved. This path should be used later when restoring from file. Example: .. code-block:: python search_alg = Searcher(...) analysis = tune.run( cost, num_samples=5, search_alg=search_alg, name=self.experiment_name, local_dir=self.tmpdir) search_alg.save("./my_favorite_path.pkl") .. versionchanged:: 0.8.7 Save is automatically called by `tune.run`. You can use `restore_from_dir` to restore from an experiment directory such as `~/ray_results/trainable`. """ raise NotImplementedError def restore(self, checkpoint_path): """Restore state for this search algorithm Args: checkpoint_path (str): File where the search algorithm state is saved. This path should be the same as the one provided to "save". Example: .. code-block:: python search_alg.save("./my_favorite_path.pkl") search_alg2 = Searcher(...) search_alg2 = ConcurrencyLimiter(search_alg2, 1) search_alg2.restore(checkpoint_path) tune.run(cost, num_samples=5, search_alg=search_alg2) """ raise NotImplementedError def get_state(self): raise NotImplementedError def set_state(self, state): raise NotImplementedError def save_to_dir(self, checkpoint_dir, session_str="default"): """Automatically saves the given searcher to the checkpoint_dir. This is automatically used by tune.run during a Tune job. Args: checkpoint_dir (str): Filepath to experiment dir. session_str (str): Unique identifier of the current run session. """ tmp_search_ckpt_path = os.path.join(checkpoint_dir, ".tmp_searcher_ckpt") success = True try: self.save(tmp_search_ckpt_path) except NotImplementedError: if log_once("suggest:save_to_dir"): logger.warning( "save not implemented for Searcher. Skipping save.") success = False if success and os.path.exists(tmp_search_ckpt_path): os.rename( tmp_search_ckpt_path, os.path.join(checkpoint_dir, self.CKPT_FILE_TMPL.format(session_str))) def restore_from_dir(self, checkpoint_dir): """Restores the state of a searcher from a given checkpoint_dir. Typically, you should use this function to restore from an experiment directory such as `~/ray_results/trainable`. .. code-block:: python experiment_1 = tune.run( cost, num_samples=5, search_alg=search_alg, verbose=0, name=self.experiment_name, local_dir="~/my_results") search_alg2 = Searcher() search_alg2.restore_from_dir( os.path.join("~/my_results", self.experiment_name) """ pattern = self.CKPT_FILE_TMPL.format("*") full_paths = glob.glob(os.path.join(checkpoint_dir, pattern)) if not full_paths: raise RuntimeError( "Searcher unable to find checkpoint in {}".format( checkpoint_dir)) # TODO most_recent_checkpoint = max(full_paths) self.restore(most_recent_checkpoint) @property def metric(self): """The training result objective value attribute.""" return self._metric @property def mode(self): """Specifies if minimizing or maximizing the metric.""" return self._mode class ConcurrencyLimiter(Searcher): """A wrapper algorithm for limiting the number of concurrent trials. Args: searcher (Searcher): Searcher object that the ConcurrencyLimiter will manage. Example: .. code-block:: python from ray.tune.suggest import ConcurrencyLimiter search_alg = HyperOptSearch(metric="accuracy") search_alg = ConcurrencyLimiter(search_alg, max_concurrent=2) tune.run(trainable, search_alg=search_alg) """ def __init__(self, searcher, max_concurrent): assert type(max_concurrent) is int and max_concurrent > 0 self.searcher = searcher self.max_concurrent = max_concurrent self.live_trials = set() super(ConcurrencyLimiter, self).__init__( metric=self.searcher.metric, mode=self.searcher.mode) def suggest(self, trial_id): assert trial_id not in self.live_trials, ( f"Trial ID {trial_id} must be unique: already found in set.") if len(self.live_trials) >= self.max_concurrent: logger.debug( f"Not providing a suggestion for {trial_id} due to " "concurrency limit: %s/%s.", len(self.live_trials), self.max_concurrent) return suggestion = self.searcher.suggest(trial_id) if suggestion not in (None, Searcher.FINISHED): self.live_trials.add(trial_id) return suggestion def on_trial_complete(self, trial_id, result=None, error=False): if trial_id not in self.live_trials: return else: self.searcher.on_trial_complete( trial_id, result=result, error=error) self.live_trials.remove(trial_id) def get_state(self): state = self.__dict__.copy() del state["searcher"] return copy.deepcopy(state) def set_state(self, state): self.__dict__.update(state)

35.224756

79

0.610875

b143b5a013fb3c60db822057b92655667079a542

1,591

py

Python

mopidy/http/__init__.py

stekern/mopidy

94509cf70f2bd35ebb13e746dde135bfbe35ce0e

[ "Apache-2.0" ]

2

2019-02-13T15:16:55.000Z

2019-02-18T08:47:29.000Z

mopidy/http/__init__.py

stekern/mopidy

94509cf70f2bd35ebb13e746dde135bfbe35ce0e

[ "Apache-2.0" ]

40

2019-02-13T09:33:00.000Z

2019-02-19T13:21:12.000Z

mopidy/http/__init__.py

stekern/mopidy

94509cf70f2bd35ebb13e746dde135bfbe35ce0e

[ "Apache-2.0" ]

null

from __future__ import absolute_import, unicode_literals import logging import os import mopidy from mopidy import config as config_lib, exceptions, ext logger = logging.getLogger(__name__) class Extension(ext.Extension): dist_name = 'Mopidy-HTTP' ext_name = 'http' version = mopidy.__version__ def get_default_config(self): conf_file = os.path.join(os.path.dirname(__file__), 'ext.conf') return config_lib.read(conf_file) def get_config_schema(self): schema = super(Extension, self).get_config_schema() schema['hostname'] = config_lib.Hostname() schema['port'] = config_lib.Port() schema['static_dir'] = config_lib.Deprecated() schema['zeroconf'] = config_lib.String(optional=True) schema['allowed_origins'] = config_lib.List(optional=True) schema['csrf_protection'] = config_lib.Boolean(optional=True) return schema def validate_environment(self): try: import tornado.web # noqa except ImportError as e: raise exceptions.ExtensionError('tornado library not found', e) def setup(self, registry): from .actor import HttpFrontend from .handlers import make_mopidy_app_factory HttpFrontend.apps = registry['http:app'] HttpFrontend.statics = registry['http:static'] registry.add('frontend', HttpFrontend) registry.add('http:app', { 'name': 'mopidy', 'factory': make_mopidy_app_factory( registry['http:app'], registry['http:static']), })

31.196078

75

0.660591

68ee01bc4c247867a691d991d3173ff429fac1d0

583

py

Python

lightly/data/__init__.py

umami-ware/lightly

5d70b34df7f784af249f9e9a6bfd6256756a877f

[ "MIT" ]

1,515

2020-10-05T13:04:17.000Z

2022-03-31T16:14:55.000Z

lightly/data/__init__.py

umami-ware/lightly

5d70b34df7f784af249f9e9a6bfd6256756a877f

[ "MIT" ]

628

2020-10-14T11:38:51.000Z

2022-03-31T14:40:54.000Z

lightly/data/__init__.py

umami-ware/lightly

5d70b34df7f784af249f9e9a6bfd6256756a877f

[ "MIT" ]

108

2020-10-17T08:31:06.000Z

2022-03-20T16:44:22.000Z

"""The lightly.data module provides a dataset wrapper and collate functions. """ # Copyright (c) 2020. Lightly AG and its affiliates. # All Rights Reserved from lightly.data.dataset import LightlyDataset from lightly.data.collate import BaseCollateFunction from lightly.data.collate import ImageCollateFunction from lightly.data.collate import SimCLRCollateFunction from lightly.data.collate import MoCoCollateFunction from lightly.data.collate import MultiCropCollateFunction from lightly.data.collate import SwaVCollateFunction from lightly.data.collate import imagenet_normalize

44.846154

80

0.850772

6ba65583c641ff148200ec76f98807a15f261116

7,140

py

Python

cryptoapis/model/add_tokens_to_existing_from_address_response_item_token_data_ethereum_erc20_token.py

xan187/Crypto_APIs_2.0_SDK_Python

a56c75df54ef037b39be1315ed6e54de35bed55b

[ "MIT" ]

null

cryptoapis/model/add_tokens_to_existing_from_address_response_item_token_data_ethereum_erc20_token.py

xan187/Crypto_APIs_2.0_SDK_Python

a56c75df54ef037b39be1315ed6e54de35bed55b

[ "MIT" ]

null

cryptoapis/model/add_tokens_to_existing_from_address_response_item_token_data_ethereum_erc20_token.py

xan187/Crypto_APIs_2.0_SDK_Python

a56c75df54ef037b39be1315ed6e54de35bed55b

[ "MIT" ]

1

2021-07-21T03:35:18.000Z

""" CryptoAPIs Crypto APIs 2.0 is a complex and innovative infrastructure layer that radically simplifies the development of any Blockchain and Crypto related applications. Organized around REST, Crypto APIs 2.0 can assist both novice Bitcoin/Ethereum enthusiasts and crypto experts with the development of their blockchain applications. Crypto APIs 2.0 provides unified endpoints and data, raw data, automatic tokens and coins forwardings, callback functionalities, and much more. # noqa: E501 The version of the OpenAPI document: 2.0.0 Contact: developers@cryptoapis.io Generated by: https://openapi-generator.tech """ import re # noqa: F401 import sys # noqa: F401 from cryptoapis.model_utils import ( # noqa: F401 ApiTypeError, ModelComposed, ModelNormal, ModelSimple, cached_property, change_keys_js_to_python, convert_js_args_to_python_args, date, datetime, file_type, none_type, validate_get_composed_info, ) class AddTokensToExistingFromAddressResponseItemTokenDataEthereumErc20Token(ModelNormal): """NOTE: This class is auto generated by OpenAPI Generator. Ref: https://openapi-generator.tech Do not edit the class manually. Attributes: allowed_values (dict): The key is the tuple path to the attribute and the for var_name this is (var_name,). The value is a dict with a capitalized key describing the allowed value and an allowed value. These dicts store the allowed enum values. attribute_map (dict): The key is attribute name and the value is json key in definition. discriminator_value_class_map (dict): A dict to go from the discriminator variable value to the discriminator class name. validations (dict): The key is the tuple path to the attribute and the for var_name this is (var_name,). The value is a dict that stores validations for max_length, min_length, max_items, min_items, exclusive_maximum, inclusive_maximum, exclusive_minimum, inclusive_minimum, and regex. additional_properties_type (tuple): A tuple of classes accepted as additional properties values. """ allowed_values = { } validations = { } additional_properties_type = None _nullable = False @cached_property def openapi_types(): """ This must be a method because a model may have properties that are of type self, this must run after the class is loaded Returns openapi_types (dict): The key is attribute name and the value is attribute type. """ return { 'contract_address': (str,), # noqa: E501 } @cached_property def discriminator(): return None attribute_map = { 'contract_address': 'contractAddress', # noqa: E501 } _composed_schemas = {} required_properties = set([ '_data_store', '_check_type', '_spec_property_naming', '_path_to_item', '_configuration', '_visited_composed_classes', ]) @convert_js_args_to_python_args def __init__(self, contract_address, *args, **kwargs): # noqa: E501 """AddTokensToExistingFromAddressResponseItemTokenDataEthereumErc20Token - a model defined in OpenAPI Args: contract_address (str): Token contract address to be transferred Keyword Args: _check_type (bool): if True, values for parameters in openapi_types will be type checked and a TypeError will be raised if the wrong type is input. Defaults to True _path_to_item (tuple/list): This is a list of keys or values to drill down to the model in received_data when deserializing a response _spec_property_naming (bool): True if the variable names in the input data are serialized names, as specified in the OpenAPI document. False if the variable names in the input data are pythonic names, e.g. snake case (default) _configuration (Configuration): the instance to use when deserializing a file_type parameter. If passed, type conversion is attempted If omitted no type conversion is done. _visited_composed_classes (tuple): This stores a tuple of classes that we have traveled through so that if we see that class again we will not use its discriminator again. When traveling through a discriminator, the composed schema that is is traveled through is added to this set. For example if Animal has a discriminator petType and we pass in "Dog", and the class Dog allOf includes Animal, we move through Animal once using the discriminator, and pick Dog. Then in Dog, we will make an instance of the Animal class but this time we won't travel through its discriminator because we passed in _visited_composed_classes = (Animal,) """ _check_type = kwargs.pop('_check_type', True) _spec_property_naming = kwargs.pop('_spec_property_naming', False) _path_to_item = kwargs.pop('_path_to_item', ()) _configuration = kwargs.pop('_configuration', None) _visited_composed_classes = kwargs.pop('_visited_composed_classes', ()) if args: raise ApiTypeError( "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." % ( args, self.__class__.__name__, ), path_to_item=_path_to_item, valid_classes=(self.__class__,), ) self._data_store = {} self._check_type = _check_type self._spec_property_naming = _spec_property_naming self._path_to_item = _path_to_item self._configuration = _configuration self._visited_composed_classes = _visited_composed_classes + (self.__class__,) self.contract_address = contract_address for var_name, var_value in kwargs.items(): if var_name not in self.attribute_map and \ self._configuration is not None and \ self._configuration.discard_unknown_keys and \ self.additional_properties_type is None: # discard variable. continue setattr(self, var_name, var_value)

41.754386

484

0.608543

1ecd80d34344c3419b94e557c344f05202b12dc0

586

py

Python

agent/src/agent/pipeline/config/stages/jdbc_offset.py

eacherkan-aternity/daria

7c77a2f52c09c852017b16949a848fa51f0fb579

[ "Apache-2.0" ]

null

agent/src/agent/pipeline/config/stages/jdbc_offset.py

eacherkan-aternity/daria

7c77a2f52c09c852017b16949a848fa51f0fb579

[ "Apache-2.0" ]

null

agent/src/agent/pipeline/config/stages/jdbc_offset.py

eacherkan-aternity/daria

7c77a2f52c09c852017b16949a848fa51f0fb579

[ "Apache-2.0" ]

null

from .base import Stage class JDBCScript(Stage): JYTHON_SCRIPT = 'jdbc.py' def _get_config(self) -> dict: with open(self.get_jython_file_path()) as f: return { 'scriptConf.params': [ {'key': 'INITIAL_OFFSET', 'value': self.get_initial_timestamp().strftime('%d/%m/%Y %H:%M')}, {'key': 'INTERVAL_IN_SECONDS', 'value': str(self.pipeline.interval)}, {'key': 'DELAY_IN_SECONDS', 'value': str(self.pipeline.delay)}, ], 'script': f.read(), }

34.470588

112

0.515358

11c44c7c4b71277486e40cd1790d44b511563ff3

4,403

py

Python

pkg/Python27/Lib/ctypes/test/test_arrays.py

jkolokotronis/ds_mod_tools

d9fd4def34f6adfd0e2b176d0a9bf2a3dfd43f93

[ "MIT" ]

1

2021-05-19T16:14:23.000Z

pkg/Python27/Lib/ctypes/test/test_arrays.py

jkolokotronis/ds_mod_tools

d9fd4def34f6adfd0e2b176d0a9bf2a3dfd43f93

[ "MIT" ]

null

pkg/Python27/Lib/ctypes/test/test_arrays.py

jkolokotronis/ds_mod_tools

d9fd4def34f6adfd0e2b176d0a9bf2a3dfd43f93

[ "MIT" ]

2

2020-04-30T17:34:06.000Z

2020-09-03T23:54:39.000Z

import unittest from ctypes import * formats = "bBhHiIlLqQfd" formats = c_byte, c_ubyte, c_short, c_ushort, c_int, c_uint, \ c_long, c_ulonglong, c_float, c_double, c_longdouble class ArrayTestCase(unittest.TestCase): def test_simple(self): # create classes holding simple numeric types, and check # various properties. init = range(15, 25) for fmt in formats: alen = len(init) int_array = ARRAY(fmt, alen) ia = int_array(*init) # length of instance ok? self.assertEqual(len(ia), alen) # slot values ok? values = [ia[i] for i in range(len(init))] self.assertEqual(values, init) # change the items from operator import setitem new_values = range(42, 42+alen) [setitem(ia, n, new_values[n]) for n in range(alen)] values = [ia[i] for i in range(len(init))] self.assertEqual(values, new_values) # are the items initialized to 0? ia = int_array() values = [ia[i] for i in range(len(init))] self.assertEqual(values, [0] * len(init)) # Too many initializers should be caught self.assertRaises(IndexError, int_array, *range(alen*2)) CharArray = ARRAY(c_char, 3) ca = CharArray("a", "b", "c") # Should this work? It doesn't: # CharArray("abc") self.assertRaises(TypeError, CharArray, "abc") self.assertEqual(ca[0], "a") self.assertEqual(ca[1], "b") self.assertEqual(ca[2], "c") self.assertEqual(ca[-3], "a") self.assertEqual(ca[-2], "b") self.assertEqual(ca[-1], "c") self.assertEqual(len(ca), 3) # slicing is now supported, but not extended slicing (3-argument)! from operator import getslice, delitem self.assertRaises(TypeError, getslice, ca, 0, 1, -1) # cannot delete items self.assertRaises(TypeError, delitem, ca, 0) def test_numeric_arrays(self): alen = 5 numarray = ARRAY(c_int, alen) na = numarray() values = [na[i] for i in range(alen)] self.assertEqual(values, [0] * alen) na = numarray(*[c_int()] * alen) values = [na[i] for i in range(alen)] self.assertEqual(values, [0]*alen) na = numarray(1, 2, 3, 4, 5) values = [i for i in na] self.assertEqual(values, [1, 2, 3, 4, 5]) na = numarray(*map(c_int, (1, 2, 3, 4, 5))) values = [i for i in na] self.assertEqual(values, [1, 2, 3, 4, 5]) def test_classcache(self): self.assertTrue(not ARRAY(c_int, 3) is ARRAY(c_int, 4)) self.assertTrue(ARRAY(c_int, 3) is ARRAY(c_int, 3)) def test_from_address(self): # Failed with 0.9.8, reported by JUrner p = create_string_buffer("foo") sz = (c_char * 3).from_address(addressof(p)) self.assertEqual(sz[:], "foo") self.assertEqual(sz[::], "foo") self.assertEqual(sz[::-1], "oof") self.assertEqual(sz[::3], "f") self.assertEqual(sz[1:4:2], "o") self.assertEqual(sz.value, "foo") try: create_unicode_buffer except NameError: pass else: def test_from_addressW(self): p = create_unicode_buffer("foo") sz = (c_wchar * 3).from_address(addressof(p)) self.assertEqual(sz[:], "foo") self.assertEqual(sz[::], "foo") self.assertEqual(sz[::-1], "oof") self.assertEqual(sz[::3], "f") self.assertEqual(sz[1:4:2], "o") self.assertEqual(sz.value, "foo") def test_cache(self): # Array types are cached internally in the _ctypes extension, # in a WeakValueDictionary. Make sure the array type is # removed from the cache when the itemtype goes away. This # test will not fail, but will show a leak in the testsuite. # Create a new type: class my_int(c_int): pass # Create a new array type based on it: t1 = my_int * 1 t2 = my_int * 1 self.assertTrue(t1 is t2) if __name__ == '__main__': unittest.main()

32.614815

75

0.545083

9358c4fd07111f7adfbf60241727215f978b2a36

10,428

py

Python

vendor/github.com/tensorflow/tensorflow/tensorflow/python/eager/function_argument_naming_test.py

owennewo/kfserving

89f73c87525b8e06ea799f69f2979c4ad272fcb3

[ "Apache-2.0" ]

5

2019-01-13T16:15:25.000Z

2019-07-07T16:17:32.000Z

vendor/github.com/tensorflow/tensorflow/tensorflow/python/eager/function_argument_naming_test.py

owennewo/kfserving

89f73c87525b8e06ea799f69f2979c4ad272fcb3

[ "Apache-2.0" ]

13

2020-11-13T18:53:29.000Z

2022-03-12T00:33:00.000Z

vendor/github.com/tensorflow/tensorflow/tensorflow/python/eager/function_argument_naming_test.py

owennewo/kfserving

89f73c87525b8e06ea799f69f2979c4ad272fcb3

[ "Apache-2.0" ]

2

2019-12-17T09:27:07.000Z

2020-05-24T13:09:49.000Z

# Copyright 2017 The TensorFlow 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 __future__ import absolute_import from __future__ import division from __future__ import print_function from absl.testing import parameterized from tensorflow.core.protobuf import config_pb2 from tensorflow.python.eager import def_function from tensorflow.python.eager import function from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_spec from tensorflow.python.ops import array_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import variables from tensorflow.python.platform import test @parameterized.named_parameters( dict(testcase_name='Defun', function_decorator=function.defun), dict(testcase_name='DefFunction', function_decorator=def_function.function)) class ArgumentNamingTests(test.TestCase, parameterized.TestCase): """Tests for recognizable export signatures from concrete functions.""" def testBasic(self, function_decorator): @function_decorator def fn(a, b): return a + b, a * b # Call the function to make def_function happy fn(array_ops.ones([]), array_ops.ones([])) fn_op = fn.get_concrete_function( tensor_spec.TensorSpec(shape=(None,), dtype=dtypes.float32), tensor_spec.TensorSpec(shape=(), dtype=dtypes.float32)) self.assertEqual( ['a', 'b'], [inp.op.name for inp in fn_op.inputs]) self.assertEqual( [b'a', b'b'], [inp.op.get_attr('_user_specified_name') for inp in fn_op.inputs]) self.assertEqual(2, len(fn_op.graph.structured_outputs)) self.assertAllClose( [3., 2.], fn_op(constant_op.constant(1.), constant_op.constant(2.))) self.assertAllClose( [3., 2.], fn_op(a=constant_op.constant(1.), b=constant_op.constant(2.))) def testVariable(self, function_decorator): @function_decorator def fn(a, b): return a + b, a * b # Call the function to make def_function happy fn(array_ops.ones([]), array_ops.ones([])) fn_op = fn.get_concrete_function( tensor_spec.TensorSpec(shape=(None,), dtype=dtypes.float32), variables.Variable(1.)) self.assertEqual( ['a', 'b'], [inp.op.name for inp in fn_op.inputs]) self.assertEqual( [b'a', b'b'], [inp.op.get_attr('_user_specified_name') for inp in fn_op.inputs]) self.assertEqual(2, len(fn_op.graph.structured_outputs)) def testDictReturned(self, function_decorator): @function_decorator def fn(x, z=(1., 2.), y=3.): z1, z2 = z return {'alpha': x + y + z1, 'beta': x * y + z2} # Call the function to make def_function happy fn(array_ops.ones([])) fn_op = fn.get_concrete_function( x=tensor_spec.TensorSpec(shape=(None,), dtype=dtypes.float32), y=tensor_spec.TensorSpec(shape=(), dtype=dtypes.float32)) self.assertEqual( ['x', 'y'], [inp.op.name for inp in fn_op.inputs]) self.assertEqual( [b'x', b'y'], [inp.op.get_attr('_user_specified_name') for inp in fn_op.inputs]) self.assertEqual({'alpha', 'beta'}, set(fn_op.graph.structured_outputs.keys())) with self.assertRaisesRegexp(ValueError, "two arguments named 'z'"): fn.get_concrete_function( z=(tensor_spec.TensorSpec(shape=(None,), dtype=dtypes.float32), tensor_spec.TensorSpec(shape=(), dtype=dtypes.float32)), y=tensor_spec.TensorSpec(shape=(), dtype=dtypes.float32, name='custom'), x=4.) fn_op2 = fn.get_concrete_function( z=(tensor_spec.TensorSpec(shape=(None,), dtype=dtypes.float32, name='z_first'), tensor_spec.TensorSpec(shape=(), dtype=dtypes.float32, name='z_second')), y=tensor_spec.TensorSpec(shape=(), dtype=dtypes.float32, name='custom'), x=4.) self.assertEqual( ['z_first', 'z_second', 'custom'], [inp.op.name for inp in fn_op2.inputs]) self.assertEqual( [b'z_first', b'z_second', b'custom'], [inp.op.get_attr('_user_specified_name') for inp in fn_op2.inputs]) fn_op3 = fn.get_concrete_function( tensor_spec.TensorSpec(shape=(), dtype=dtypes.float32, name='custom'), z=(tensor_spec.TensorSpec(shape=(None,), dtype=dtypes.float32, name='z1'), tensor_spec.TensorSpec(shape=(), dtype=dtypes.float32, name='z2')), y=tensor_spec.TensorSpec(shape=(), dtype=dtypes.float32)) self.assertEqual( ['custom', 'z1', 'z2', 'y'], [inp.op.name for inp in fn_op3.inputs]) self.assertEqual( [b'custom', b'z1', b'z2', b'y'], [inp.op.get_attr('_user_specified_name') for inp in fn_op3.inputs]) def testMethod(self, function_decorator): class HasMethod(object): @function_decorator def method(self, x): return x has_method = HasMethod() # Call the function to make def_function happy HasMethod.method(has_method, array_ops.ones([])) class_op = HasMethod.method.get_concrete_function( has_method, tensor_spec.TensorSpec(shape=(), dtype=dtypes.float32)) self.assertEqual( ['x'], [inp.op.name for inp in class_op.inputs]) self.assertEqual( [b'x'], [inp.op.get_attr('_user_specified_name') for inp in class_op.inputs]) # Call the function to make def_function happy has_method.method(array_ops.ones([])) method_op = has_method.method.get_concrete_function( tensor_spec.TensorSpec(shape=(), dtype=dtypes.float32)) self.assertEqual( ['x'], [inp.op.name for inp in method_op.inputs]) self.assertEqual( [b'x'], [inp.op.get_attr('_user_specified_name') for inp in method_op.inputs]) # TODO(allenl): It should be possible to override names when exporting. Do # TensorSpec names need to go in cache keys? Or maybe get_concrete_function # should always retrace? self.skipTest('Not working') method_op = has_method.method.get_concrete_function( tensor_spec.TensorSpec(shape=(), dtype=dtypes.float32, name='y')) self.assertEqual( ['y'], [inp.op.name for inp in method_op.inputs]) self.assertEqual( [b'y'], [inp.op.get_attr('_user_specified_name') for inp in method_op.inputs]) def testMethodSignature(self, function_decorator): class HasMethod(object): @function_decorator( input_signature=(tensor_spec.TensorSpec( shape=None, dtype=dtypes.float64, name='y'),)) def method(self, x): hash(self) # No weak proxies passed as `self` return x has_method = HasMethod() # Call the function to make def_function happy has_method.method(array_ops.ones([], dtype=dtypes.float64)) method_op = has_method.method.get_concrete_function() self.assertEqual( ['y'], [inp.op.name for inp in method_op.inputs]) self.assertEqual( [b'y'], [inp.op.get_attr('_user_specified_name') for inp in method_op.inputs]) method_op2 = has_method.method.get_concrete_function() self.assertEqual( ['y'], [inp.op.name for inp in method_op2.inputs]) self.assertEqual( [b'y'], [inp.op.get_attr('_user_specified_name') for inp in method_op2.inputs]) def testVariadic(self, function_decorator): @function_decorator def variadic_fn(x, *args, **kwargs): return x + math_ops.add_n(list(args) + list(kwargs.values())) # Call the function to make def_function happy variadic_fn(array_ops.ones([]), array_ops.ones([])) variadic_op = variadic_fn.get_concrete_function( tensor_spec.TensorSpec(shape=(), dtype=dtypes.float32), tensor_spec.TensorSpec(shape=None, dtype=dtypes.float32, name='y'), tensor_spec.TensorSpec(shape=(), dtype=dtypes.float32), tensor_spec.TensorSpec(shape=(), dtype=dtypes.float32, name='second_variadic'), z=tensor_spec.TensorSpec(shape=(), dtype=dtypes.float32), zz=tensor_spec.TensorSpec(shape=(), dtype=dtypes.float32, name='cust')) self.assertEqual( ['x', 'y', 'args', 'second_variadic', 'z', 'cust'], [inp.op.name for inp in variadic_op.inputs]) self.assertEqual( [b'x', b'y', b'args', b'second_variadic', b'z', b'cust'], [inp.op.get_attr('_user_specified_name') for inp in variadic_op.inputs]) def testVariadicInputSignature(self, function_decorator): @function_decorator( input_signature=( tensor_spec.TensorSpec(shape=None, dtype=dtypes.float32), tensor_spec.TensorSpec(shape=None, dtype=dtypes.float32, name='y'), tensor_spec.TensorSpec(shape=(), dtype=dtypes.float32), tensor_spec.TensorSpec(shape=(), dtype=dtypes.float32, name='z'), )) def variadic_fn(x, *args): return x + math_ops.add_n(list(args)) # Call the function to make def_function happy variadic_fn(array_ops.ones([]), array_ops.ones([]), array_ops.ones([]), array_ops.ones([])) variadic_op = variadic_fn.get_concrete_function() self.assertIn(b'variadic_fn', variadic_op.name) self.assertEqual( ['x', 'y', 'args', 'z'], [inp.op.name for inp in variadic_op.inputs]) self.assertEqual( [b'x', b'y', b'args', b'z'], [inp.op.get_attr('_user_specified_name') for inp in variadic_op.inputs]) if __name__ == '__main__': ops.enable_eager_execution( config=config_pb2.ConfigProto(device_count={'CPU': 4})) test.main()

40.262548

80

0.654776

e7c08a5b8148b4852ce28accb4d7f6cea0b47e2d

1,212

py

Python

sample/1.number.py

Hyper-Devil/learn-python3

c097158c48b9f6804c8c46aebf00025249089bf6

[ "MIT" ]

null

sample/1.number.py

Hyper-Devil/learn-python3

c097158c48b9f6804c8c46aebf00025249089bf6

[ "MIT" ]

null

sample/1.number.py

Hyper-Devil/learn-python3

c097158c48b9f6804c8c46aebf00025249089bf6

[ "MIT" ]

null

a = 1 b = 1.0 c = 10000 # python3中长整数不用再加L print('你好世界') # pyhon3 print函数格式：print() print('hello world!') print(a) print(b) print(c) str = 'hello world' # 字符串 print('字符串') print(str) print(str[0]) # 切片数字从0开始 print(str[2:5]) # 左闭右开 print(str[2:6]) # 此时末尾输出了一个空格 print(str[2:]) # 输出从第3个字符开始的字符串 print(str * 2) # 输出字符串两次 print(str * 2) print(str + 'TEST') # 输出字符串连接字符串 list_a = ["str", 1, ["a", "b", "c"], 4] ''' 列表一个有序可变集合的容器。不同的数据结构也可以放在同一个列表中，没有统一类型的限制。使用[]方括号 ''' list_b = ["hello"] print('列表') print(list_a[0]) print(list_a[1:3]) # 输出含第二个和第三个元素的列表 print(list_a[1:]) # 输出含从第二个开始的元素的列表 print(list_b * 2) # 输出含两个list_b元素的列表 print(list_a + list_b) # 输出含list_a和list_b的列表 tuple_a = ("str", 1, ["a", "b", "c"], 4) # 元组赋值后不可变的列表使用()圆括号 tuple_b = ("hello", ) # 只有1个元素的tuple定义时必须加一个逗号, 来消除歧义 print('元组') print(tuple_a[0]) print(tuple_a[1:3]) print(tuple_a[1:]) print(tuple_b * 2) print(tuple_a + tuple_b) dict_a = {"name": "Alan", "age": 24, 1: "level_1"} print('字典') print(dict_a["name"]) # 查找 print(dict_a["age"]) print(dict_a[1]) print("name" in dict_a) # 判断key是否存在 print("xxx" in dict_a) print(dict_a.keys()) print(dict_a.values()) print(dict_a.items()) dict_a[2] = 'level_2' # 放入新数据，可覆盖 print(dict_a[2])

16.16

50

0.660891

1b6abae3e9d18ab3b79a4d7e0f830ee28e21e24f

67,227

py

Python

src/hapPyTango/CosNotifyChannelAdmin_skel/__init__.py

mguijarr/hapPyTango

2506c8e83d93fbd2c0a0115983489d59c74caa2f

[ "MIT" ]

1

2020-10-28T16:57:36.000Z

src/hapPyTango/CosNotifyChannelAdmin_skel/__init__.py

mguijarr/hapPyTango

2506c8e83d93fbd2c0a0115983489d59c74caa2f

[ "MIT" ]

null

src/hapPyTango/CosNotifyChannelAdmin_skel/__init__.py

mguijarr/hapPyTango

2506c8e83d93fbd2c0a0115983489d59c74caa2f

[ "MIT" ]

null

""" Module: IDL:omg.org/CosNotifyChannelAdmin:1.0 Automagically generated by:- The ORB called Fnorb v1.1.Return.of.Fnorb """ _FNORB_ID = "IDL:omg.org/CosNotifyChannelAdmin:1.0" # Fnorb modules. import Fnorb.orb.CORBA import Fnorb.orb.TypeManager import Fnorb.orb.Util # Import base interface packages. import CosNotification_skel import CosNotifyFilter_skel class ProxyConsumer_skel(Fnorb.orb.CORBA.Object_skel, CosNotification_skel.QoSAdmin_skel, CosNotifyFilter_skel.FilterAdmin_skel): """ Interface: IDL:omg.org/CosNotifyChannelAdmin/ProxyConsumer:1.0 """ _FNORB_ID = "IDL:omg.org/CosNotifyChannelAdmin/ProxyConsumer:1.0" def _skel__get_MyType(self, server_request): """ Attribute: IDL:omg.org/CosNotifyChannelAdmin/ProxyConsumer/MyType:1.0 """ # Typecode for the attribute value. outputs = [] outputs.append(Fnorb.orb.CORBA.typecode("IDL:omg.org/CosNotifyChannelAdmin/ProxyType:1.0")) # Initialise the server request object. server_request.initialise([], outputs, []) # Invoke the implementation. results = self._get_MyType() # Create the reply. server_request.results(results) return def _skel__get_MyAdmin(self, server_request): """ Attribute: IDL:omg.org/CosNotifyChannelAdmin/ProxyConsumer/MyAdmin:1.0 """ # Typecode for the attribute value. outputs = [] outputs.append(Fnorb.orb.CORBA.TC_Object) # Initialise the server request object. server_request.initialise([], outputs, []) # Invoke the implementation. results = self._get_MyAdmin() # Create the reply. server_request.results(results) return def _skel_obtain_subscription_types(self, server_request): """ Operation: IDL:omg.org/CosNotifyChannelAdmin/ProxyConsumer/obtain_subscription_types:1.0 """ # Typecodes for 'in' and 'inout' parameters. inputs = [] inputs.append(Fnorb.orb.CORBA.typecode("IDL:omg.org/CosNotifyChannelAdmin/ObtainInfoMode:1.0")) # Typecodes for the result, 'inout' and 'out' parameters. outputs = [] outputs.append(Fnorb.orb.CORBA.typecode("IDL:omg.org/CosNotification/EventTypeSeq:1.0")) # Typecodes for user exceptions. exceptions = [] # Initialise the server request object. server_request.initialise(inputs, outputs, exceptions) # Unmarshal the arguments to the request. arguments = server_request.arguments() # Invoke the implementation. results = apply(self.obtain_subscription_types, arguments) # Create the reply. server_request.results(results) return def _skel_validate_event_qos(self, server_request): """ Operation: IDL:omg.org/CosNotifyChannelAdmin/ProxyConsumer/validate_event_qos:1.0 """ # Typecodes for 'in' and 'inout' parameters. inputs = [] inputs.append(Fnorb.orb.CORBA.typecode("IDL:omg.org/CosNotification/QoSProperties:1.0")) # Typecodes for the result, 'inout' and 'out' parameters. outputs = [] outputs.append(Fnorb.orb.CORBA.typecode("IDL:omg.org/CosNotification/NamedPropertyRangeSeq:1.0")) # Typecodes for user exceptions. exceptions = [] exceptions.append(Fnorb.orb.CORBA.typecode("IDL:omg.org/CosNotification/UnsupportedQoS:1.0")) # Initialise the server request object. server_request.initialise(inputs, outputs, exceptions) # Unmarshal the arguments to the request. arguments = server_request.arguments() # Invoke the implementation. results = apply(self.validate_event_qos, arguments) # Create the reply. server_request.results(results) return # Import base interface packages. import CosNotification_skel import CosNotifyFilter_skel class ProxySupplier_skel(Fnorb.orb.CORBA.Object_skel, CosNotification_skel.QoSAdmin_skel, CosNotifyFilter_skel.FilterAdmin_skel): """ Interface: IDL:omg.org/CosNotifyChannelAdmin/ProxySupplier:1.0 """ _FNORB_ID = "IDL:omg.org/CosNotifyChannelAdmin/ProxySupplier:1.0" def _skel__get_MyType(self, server_request): """ Attribute: IDL:omg.org/CosNotifyChannelAdmin/ProxySupplier/MyType:1.0 """ # Typecode for the attribute value. outputs = [] outputs.append(Fnorb.orb.CORBA.typecode("IDL:omg.org/CosNotifyChannelAdmin/ProxyType:1.0")) # Initialise the server request object. server_request.initialise([], outputs, []) # Invoke the implementation. results = self._get_MyType() # Create the reply. server_request.results(results) return def _skel__get_MyAdmin(self, server_request): """ Attribute: IDL:omg.org/CosNotifyChannelAdmin/ProxySupplier/MyAdmin:1.0 """ # Typecode for the attribute value. outputs = [] outputs.append(Fnorb.orb.CORBA.TC_Object) # Initialise the server request object. server_request.initialise([], outputs, []) # Invoke the implementation. results = self._get_MyAdmin() # Create the reply. server_request.results(results) return def _skel__get_priority_filter(self, server_request): """ Attribute: IDL:omg.org/CosNotifyChannelAdmin/ProxySupplier/priority_filter:1.0 """ # Typecode for the attribute value. outputs = [] outputs.append(Fnorb.orb.CORBA.TC_Object) # Initialise the server request object. server_request.initialise([], outputs, []) # Invoke the implementation. results = self._get_priority_filter() # Create the reply. server_request.results(results) return def _skel__set_priority_filter(self, server_request): """ Attribute: IDL:omg.org/CosNotifyChannelAdmin/ProxySupplier/priority_filter:1.0 """ # Typecode for the attribute value. inputs = [] inputs.append(Fnorb.orb.CORBA.TC_Object) # Initialise the server request object. server_request.initialise(inputs, [], []) # Unmarshal the attribute value. value = server_request.arguments()[0] # Invoke the implementation. results = self._set_priority_filter(value) # Create the reply. server_request.results(results) return def _skel__get_lifetime_filter(self, server_request): """ Attribute: IDL:omg.org/CosNotifyChannelAdmin/ProxySupplier/lifetime_filter:1.0 """ # Typecode for the attribute value. outputs = [] outputs.append(Fnorb.orb.CORBA.TC_Object) # Initialise the server request object. server_request.initialise([], outputs, []) # Invoke the implementation. results = self._get_lifetime_filter() # Create the reply. server_request.results(results) return def _skel__set_lifetime_filter(self, server_request): """ Attribute: IDL:omg.org/CosNotifyChannelAdmin/ProxySupplier/lifetime_filter:1.0 """ # Typecode for the attribute value. inputs = [] inputs.append(Fnorb.orb.CORBA.TC_Object) # Initialise the server request object. server_request.initialise(inputs, [], []) # Unmarshal the attribute value. value = server_request.arguments()[0] # Invoke the implementation. results = self._set_lifetime_filter(value) # Create the reply. server_request.results(results) return def _skel_obtain_offered_types(self, server_request): """ Operation: IDL:omg.org/CosNotifyChannelAdmin/ProxySupplier/obtain_offered_types:1.0 """ # Typecodes for 'in' and 'inout' parameters. inputs = [] inputs.append(Fnorb.orb.CORBA.typecode("IDL:omg.org/CosNotifyChannelAdmin/ObtainInfoMode:1.0")) # Typecodes for the result, 'inout' and 'out' parameters. outputs = [] outputs.append(Fnorb.orb.CORBA.typecode("IDL:omg.org/CosNotification/EventTypeSeq:1.0")) # Typecodes for user exceptions. exceptions = [] # Initialise the server request object. server_request.initialise(inputs, outputs, exceptions) # Unmarshal the arguments to the request. arguments = server_request.arguments() # Invoke the implementation. results = apply(self.obtain_offered_types, arguments) # Create the reply. server_request.results(results) return def _skel_validate_event_qos(self, server_request): """ Operation: IDL:omg.org/CosNotifyChannelAdmin/ProxySupplier/validate_event_qos:1.0 """ # Typecodes for 'in' and 'inout' parameters. inputs = [] inputs.append(Fnorb.orb.CORBA.typecode("IDL:omg.org/CosNotification/QoSProperties:1.0")) # Typecodes for the result, 'inout' and 'out' parameters. outputs = [] outputs.append(Fnorb.orb.CORBA.typecode("IDL:omg.org/CosNotification/NamedPropertyRangeSeq:1.0")) # Typecodes for user exceptions. exceptions = [] exceptions.append(Fnorb.orb.CORBA.typecode("IDL:omg.org/CosNotification/UnsupportedQoS:1.0")) # Initialise the server request object. server_request.initialise(inputs, outputs, exceptions) # Unmarshal the arguments to the request. arguments = server_request.arguments() # Invoke the implementation. results = apply(self.validate_event_qos, arguments) # Create the reply. server_request.results(results) return # Import base interface packages. import CosNotifyComm_skel class ProxyPushConsumer_skel(Fnorb.orb.CORBA.Object_skel, ProxyConsumer_skel, CosNotifyComm_skel.PushConsumer_skel): """ Interface: IDL:omg.org/CosNotifyChannelAdmin/ProxyPushConsumer:1.0 """ _FNORB_ID = "IDL:omg.org/CosNotifyChannelAdmin/ProxyPushConsumer:1.0" def _skel_connect_any_push_supplier(self, server_request): """ Operation: IDL:omg.org/CosNotifyChannelAdmin/ProxyPushConsumer/connect_any_push_supplier:1.0 """ # Typecodes for 'in' and 'inout' parameters. inputs = [] inputs.append(Fnorb.orb.CORBA.TC_Object) # Typecodes for the result, 'inout' and 'out' parameters. outputs = [] # Typecodes for user exceptions. exceptions = [] exceptions.append(Fnorb.orb.CORBA.typecode("IDL:omg.org/CosEventChannelAdmin/AlreadyConnected:1.0")) # Initialise the server request object. server_request.initialise(inputs, outputs, exceptions) # Unmarshal the arguments to the request. arguments = server_request.arguments() # Invoke the implementation. results = apply(self.connect_any_push_supplier, arguments) # Create the reply. server_request.results(results) return # Import base interface packages. import CosNotifyComm_skel class StructuredProxyPushConsumer_skel(Fnorb.orb.CORBA.Object_skel, ProxyConsumer_skel, CosNotifyComm_skel.StructuredPushConsumer_skel): """ Interface: IDL:omg.org/CosNotifyChannelAdmin/StructuredProxyPushConsumer:1.0 """ _FNORB_ID = "IDL:omg.org/CosNotifyChannelAdmin/StructuredProxyPushConsumer:1.0" def _skel_connect_structured_push_supplier(self, server_request): """ Operation: IDL:omg.org/CosNotifyChannelAdmin/StructuredProxyPushConsumer/connect_structured_push_supplier:1.0 """ # Typecodes for 'in' and 'inout' parameters. inputs = [] inputs.append(Fnorb.orb.CORBA.TC_Object) # Typecodes for the result, 'inout' and 'out' parameters. outputs = [] # Typecodes for user exceptions. exceptions = [] exceptions.append(Fnorb.orb.CORBA.typecode("IDL:omg.org/CosEventChannelAdmin/AlreadyConnected:1.0")) # Initialise the server request object. server_request.initialise(inputs, outputs, exceptions) # Unmarshal the arguments to the request. arguments = server_request.arguments() # Invoke the implementation. results = apply(self.connect_structured_push_supplier, arguments) # Create the reply. server_request.results(results) return # Import base interface packages. import CosNotifyComm_skel class SequenceProxyPushConsumer_skel(Fnorb.orb.CORBA.Object_skel, ProxyConsumer_skel, CosNotifyComm_skel.SequencePushConsumer_skel): """ Interface: IDL:omg.org/CosNotifyChannelAdmin/SequenceProxyPushConsumer:1.0 """ _FNORB_ID = "IDL:omg.org/CosNotifyChannelAdmin/SequenceProxyPushConsumer:1.0" def _skel_connect_sequence_push_supplier(self, server_request): """ Operation: IDL:omg.org/CosNotifyChannelAdmin/SequenceProxyPushConsumer/connect_sequence_push_supplier:1.0 """ # Typecodes for 'in' and 'inout' parameters. inputs = [] inputs.append(Fnorb.orb.CORBA.TC_Object) # Typecodes for the result, 'inout' and 'out' parameters. outputs = [] # Typecodes for user exceptions. exceptions = [] exceptions.append(Fnorb.orb.CORBA.typecode("IDL:omg.org/CosEventChannelAdmin/AlreadyConnected:1.0")) # Initialise the server request object. server_request.initialise(inputs, outputs, exceptions) # Unmarshal the arguments to the request. arguments = server_request.arguments() # Invoke the implementation. results = apply(self.connect_sequence_push_supplier, arguments) # Create the reply. server_request.results(results) return # Import base interface packages. import CosNotifyComm_skel class ProxyPullSupplier_skel(Fnorb.orb.CORBA.Object_skel, ProxySupplier_skel, CosNotifyComm_skel.PullSupplier_skel): """ Interface: IDL:omg.org/CosNotifyChannelAdmin/ProxyPullSupplier:1.0 """ _FNORB_ID = "IDL:omg.org/CosNotifyChannelAdmin/ProxyPullSupplier:1.0" def _skel_connect_any_pull_consumer(self, server_request): """ Operation: IDL:omg.org/CosNotifyChannelAdmin/ProxyPullSupplier/connect_any_pull_consumer:1.0 """ # Typecodes for 'in' and 'inout' parameters. inputs = [] inputs.append(Fnorb.orb.CORBA.TC_Object) # Typecodes for the result, 'inout' and 'out' parameters. outputs = [] # Typecodes for user exceptions. exceptions = [] exceptions.append(Fnorb.orb.CORBA.typecode("IDL:omg.org/CosEventChannelAdmin/AlreadyConnected:1.0")) # Initialise the server request object. server_request.initialise(inputs, outputs, exceptions) # Unmarshal the arguments to the request. arguments = server_request.arguments() # Invoke the implementation. results = apply(self.connect_any_pull_consumer, arguments) # Create the reply. server_request.results(results) return # Import base interface packages. import CosNotifyComm_skel class StructuredProxyPullSupplier_skel(Fnorb.orb.CORBA.Object_skel, ProxySupplier_skel, CosNotifyComm_skel.StructuredPullSupplier_skel): """ Interface: IDL:omg.org/CosNotifyChannelAdmin/StructuredProxyPullSupplier:1.0 """ _FNORB_ID = "IDL:omg.org/CosNotifyChannelAdmin/StructuredProxyPullSupplier:1.0" def _skel_connect_structured_pull_consumer(self, server_request): """ Operation: IDL:omg.org/CosNotifyChannelAdmin/StructuredProxyPullSupplier/connect_structured_pull_consumer:1.0 """ # Typecodes for 'in' and 'inout' parameters. inputs = [] inputs.append(Fnorb.orb.CORBA.TC_Object) # Typecodes for the result, 'inout' and 'out' parameters. outputs = [] # Typecodes for user exceptions. exceptions = [] exceptions.append(Fnorb.orb.CORBA.typecode("IDL:omg.org/CosEventChannelAdmin/AlreadyConnected:1.0")) # Initialise the server request object. server_request.initialise(inputs, outputs, exceptions) # Unmarshal the arguments to the request. arguments = server_request.arguments() # Invoke the implementation. results = apply(self.connect_structured_pull_consumer, arguments) # Create the reply. server_request.results(results) return # Import base interface packages. import CosNotifyComm_skel class SequenceProxyPullSupplier_skel(Fnorb.orb.CORBA.Object_skel, ProxySupplier_skel, CosNotifyComm_skel.SequencePullSupplier_skel): """ Interface: IDL:omg.org/CosNotifyChannelAdmin/SequenceProxyPullSupplier:1.0 """ _FNORB_ID = "IDL:omg.org/CosNotifyChannelAdmin/SequenceProxyPullSupplier:1.0" def _skel_connect_sequence_pull_consumer(self, server_request): """ Operation: IDL:omg.org/CosNotifyChannelAdmin/SequenceProxyPullSupplier/connect_sequence_pull_consumer:1.0 """ # Typecodes for 'in' and 'inout' parameters. inputs = [] inputs.append(Fnorb.orb.CORBA.TC_Object) # Typecodes for the result, 'inout' and 'out' parameters. outputs = [] # Typecodes for user exceptions. exceptions = [] exceptions.append(Fnorb.orb.CORBA.typecode("IDL:omg.org/CosEventChannelAdmin/AlreadyConnected:1.0")) # Initialise the server request object. server_request.initialise(inputs, outputs, exceptions) # Unmarshal the arguments to the request. arguments = server_request.arguments() # Invoke the implementation. results = apply(self.connect_sequence_pull_consumer, arguments) # Create the reply. server_request.results(results) return # Import base interface packages. import CosNotifyComm_skel class ProxyPullConsumer_skel(Fnorb.orb.CORBA.Object_skel, ProxyConsumer_skel, CosNotifyComm_skel.PullConsumer_skel): """ Interface: IDL:omg.org/CosNotifyChannelAdmin/ProxyPullConsumer:1.0 """ _FNORB_ID = "IDL:omg.org/CosNotifyChannelAdmin/ProxyPullConsumer:1.0" def _skel_connect_any_pull_supplier(self, server_request): """ Operation: IDL:omg.org/CosNotifyChannelAdmin/ProxyPullConsumer/connect_any_pull_supplier:1.0 """ # Typecodes for 'in' and 'inout' parameters. inputs = [] inputs.append(Fnorb.orb.CORBA.TC_Object) # Typecodes for the result, 'inout' and 'out' parameters. outputs = [] # Typecodes for user exceptions. exceptions = [] exceptions.append(Fnorb.orb.CORBA.typecode("IDL:omg.org/CosEventChannelAdmin/AlreadyConnected:1.0")) exceptions.append(Fnorb.orb.CORBA.typecode("IDL:omg.org/CosEventChannelAdmin/TypeError:1.0")) # Initialise the server request object. server_request.initialise(inputs, outputs, exceptions) # Unmarshal the arguments to the request. arguments = server_request.arguments() # Invoke the implementation. results = apply(self.connect_any_pull_supplier, arguments) # Create the reply. server_request.results(results) return def _skel_suspend_connection(self, server_request): """ Operation: IDL:omg.org/CosNotifyChannelAdmin/ProxyPullConsumer/suspend_connection:1.0 """ # Typecodes for 'in' and 'inout' parameters. inputs = [] # Typecodes for the result, 'inout' and 'out' parameters. outputs = [] # Typecodes for user exceptions. exceptions = [] exceptions.append(Fnorb.orb.CORBA.typecode("IDL:omg.org/CosNotifyChannelAdmin/ConnectionAlreadyInactive:1.0")) exceptions.append(Fnorb.orb.CORBA.typecode("IDL:omg.org/CosNotifyChannelAdmin/NotConnected:1.0")) # Initialise the server request object. server_request.initialise(inputs, outputs, exceptions) # This operation has no arguments. arguments = () # Invoke the implementation. results = apply(self.suspend_connection, arguments) # Create the reply. server_request.results(results) return def _skel_resume_connection(self, server_request): """ Operation: IDL:omg.org/CosNotifyChannelAdmin/ProxyPullConsumer/resume_connection:1.0 """ # Typecodes for 'in' and 'inout' parameters. inputs = [] # Typecodes for the result, 'inout' and 'out' parameters. outputs = [] # Typecodes for user exceptions. exceptions = [] exceptions.append(Fnorb.orb.CORBA.typecode("IDL:omg.org/CosNotifyChannelAdmin/ConnectionAlreadyActive:1.0")) exceptions.append(Fnorb.orb.CORBA.typecode("IDL:omg.org/CosNotifyChannelAdmin/NotConnected:1.0")) # Initialise the server request object. server_request.initialise(inputs, outputs, exceptions) # This operation has no arguments. arguments = () # Invoke the implementation. results = apply(self.resume_connection, arguments) # Create the reply. server_request.results(results) return # Import base interface packages. import CosNotifyComm_skel class StructuredProxyPullConsumer_skel(Fnorb.orb.CORBA.Object_skel, ProxyConsumer_skel, CosNotifyComm_skel.StructuredPullConsumer_skel): """ Interface: IDL:omg.org/CosNotifyChannelAdmin/StructuredProxyPullConsumer:1.0 """ _FNORB_ID = "IDL:omg.org/CosNotifyChannelAdmin/StructuredProxyPullConsumer:1.0" def _skel_connect_structured_pull_supplier(self, server_request): """ Operation: IDL:omg.org/CosNotifyChannelAdmin/StructuredProxyPullConsumer/connect_structured_pull_supplier:1.0 """ # Typecodes for 'in' and 'inout' parameters. inputs = [] inputs.append(Fnorb.orb.CORBA.TC_Object) # Typecodes for the result, 'inout' and 'out' parameters. outputs = [] # Typecodes for user exceptions. exceptions = [] exceptions.append(Fnorb.orb.CORBA.typecode("IDL:omg.org/CosEventChannelAdmin/AlreadyConnected:1.0")) exceptions.append(Fnorb.orb.CORBA.typecode("IDL:omg.org/CosEventChannelAdmin/TypeError:1.0")) # Initialise the server request object. server_request.initialise(inputs, outputs, exceptions) # Unmarshal the arguments to the request. arguments = server_request.arguments() # Invoke the implementation. results = apply(self.connect_structured_pull_supplier, arguments) # Create the reply. server_request.results(results) return def _skel_suspend_connection(self, server_request): """ Operation: IDL:omg.org/CosNotifyChannelAdmin/StructuredProxyPullConsumer/suspend_connection:1.0 """ # Typecodes for 'in' and 'inout' parameters. inputs = [] # Typecodes for the result, 'inout' and 'out' parameters. outputs = [] # Typecodes for user exceptions. exceptions = [] exceptions.append(Fnorb.orb.CORBA.typecode("IDL:omg.org/CosNotifyChannelAdmin/ConnectionAlreadyInactive:1.0")) exceptions.append(Fnorb.orb.CORBA.typecode("IDL:omg.org/CosNotifyChannelAdmin/NotConnected:1.0")) # Initialise the server request object. server_request.initialise(inputs, outputs, exceptions) # This operation has no arguments. arguments = () # Invoke the implementation. results = apply(self.suspend_connection, arguments) # Create the reply. server_request.results(results) return def _skel_resume_connection(self, server_request): """ Operation: IDL:omg.org/CosNotifyChannelAdmin/StructuredProxyPullConsumer/resume_connection:1.0 """ # Typecodes for 'in' and 'inout' parameters. inputs = [] # Typecodes for the result, 'inout' and 'out' parameters. outputs = [] # Typecodes for user exceptions. exceptions = [] exceptions.append(Fnorb.orb.CORBA.typecode("IDL:omg.org/CosNotifyChannelAdmin/ConnectionAlreadyActive:1.0")) exceptions.append(Fnorb.orb.CORBA.typecode("IDL:omg.org/CosNotifyChannelAdmin/NotConnected:1.0")) # Initialise the server request object. server_request.initialise(inputs, outputs, exceptions) # This operation has no arguments. arguments = () # Invoke the implementation. results = apply(self.resume_connection, arguments) # Create the reply. server_request.results(results) return # Import base interface packages. import CosNotifyComm_skel class SequenceProxyPullConsumer_skel(Fnorb.orb.CORBA.Object_skel, ProxyConsumer_skel, CosNotifyComm_skel.SequencePullConsumer_skel): """ Interface: IDL:omg.org/CosNotifyChannelAdmin/SequenceProxyPullConsumer:1.0 """ _FNORB_ID = "IDL:omg.org/CosNotifyChannelAdmin/SequenceProxyPullConsumer:1.0" def _skel_connect_sequence_pull_supplier(self, server_request): """ Operation: IDL:omg.org/CosNotifyChannelAdmin/SequenceProxyPullConsumer/connect_sequence_pull_supplier:1.0 """ # Typecodes for 'in' and 'inout' parameters. inputs = [] inputs.append(Fnorb.orb.CORBA.TC_Object) # Typecodes for the result, 'inout' and 'out' parameters. outputs = [] # Typecodes for user exceptions. exceptions = [] exceptions.append(Fnorb.orb.CORBA.typecode("IDL:omg.org/CosEventChannelAdmin/AlreadyConnected:1.0")) exceptions.append(Fnorb.orb.CORBA.typecode("IDL:omg.org/CosEventChannelAdmin/TypeError:1.0")) # Initialise the server request object. server_request.initialise(inputs, outputs, exceptions) # Unmarshal the arguments to the request. arguments = server_request.arguments() # Invoke the implementation. results = apply(self.connect_sequence_pull_supplier, arguments) # Create the reply. server_request.results(results) return def _skel_suspend_connection(self, server_request): """ Operation: IDL:omg.org/CosNotifyChannelAdmin/SequenceProxyPullConsumer/suspend_connection:1.0 """ # Typecodes for 'in' and 'inout' parameters. inputs = [] # Typecodes for the result, 'inout' and 'out' parameters. outputs = [] # Typecodes for user exceptions. exceptions = [] exceptions.append(Fnorb.orb.CORBA.typecode("IDL:omg.org/CosNotifyChannelAdmin/ConnectionAlreadyInactive:1.0")) exceptions.append(Fnorb.orb.CORBA.typecode("IDL:omg.org/CosNotifyChannelAdmin/NotConnected:1.0")) # Initialise the server request object. server_request.initialise(inputs, outputs, exceptions) # This operation has no arguments. arguments = () # Invoke the implementation. results = apply(self.suspend_connection, arguments) # Create the reply. server_request.results(results) return def _skel_resume_connection(self, server_request): """ Operation: IDL:omg.org/CosNotifyChannelAdmin/SequenceProxyPullConsumer/resume_connection:1.0 """ # Typecodes for 'in' and 'inout' parameters. inputs = [] # Typecodes for the result, 'inout' and 'out' parameters. outputs = [] # Typecodes for user exceptions. exceptions = [] exceptions.append(Fnorb.orb.CORBA.typecode("IDL:omg.org/CosNotifyChannelAdmin/ConnectionAlreadyActive:1.0")) exceptions.append(Fnorb.orb.CORBA.typecode("IDL:omg.org/CosNotifyChannelAdmin/NotConnected:1.0")) # Initialise the server request object. server_request.initialise(inputs, outputs, exceptions) # This operation has no arguments. arguments = () # Invoke the implementation. results = apply(self.resume_connection, arguments) # Create the reply. server_request.results(results) return # Import base interface packages. import CosNotifyComm_skel class ProxyPushSupplier_skel(Fnorb.orb.CORBA.Object_skel, ProxySupplier_skel, CosNotifyComm_skel.PushSupplier_skel): """ Interface: IDL:omg.org/CosNotifyChannelAdmin/ProxyPushSupplier:1.0 """ _FNORB_ID = "IDL:omg.org/CosNotifyChannelAdmin/ProxyPushSupplier:1.0" def _skel_connect_any_push_consumer(self, server_request): """ Operation: IDL:omg.org/CosNotifyChannelAdmin/ProxyPushSupplier/connect_any_push_consumer:1.0 """ # Typecodes for 'in' and 'inout' parameters. inputs = [] inputs.append(Fnorb.orb.CORBA.TC_Object) # Typecodes for the result, 'inout' and 'out' parameters. outputs = [] # Typecodes for user exceptions. exceptions = [] exceptions.append(Fnorb.orb.CORBA.typecode("IDL:omg.org/CosEventChannelAdmin/AlreadyConnected:1.0")) exceptions.append(Fnorb.orb.CORBA.typecode("IDL:omg.org/CosEventChannelAdmin/TypeError:1.0")) # Initialise the server request object. server_request.initialise(inputs, outputs, exceptions) # Unmarshal the arguments to the request. arguments = server_request.arguments() # Invoke the implementation. results = apply(self.connect_any_push_consumer, arguments) # Create the reply. server_request.results(results) return def _skel_suspend_connection(self, server_request): """ Operation: IDL:omg.org/CosNotifyChannelAdmin/ProxyPushSupplier/suspend_connection:1.0 """ # Typecodes for 'in' and 'inout' parameters. inputs = [] # Typecodes for the result, 'inout' and 'out' parameters. outputs = [] # Typecodes for user exceptions. exceptions = [] exceptions.append(Fnorb.orb.CORBA.typecode("IDL:omg.org/CosNotifyChannelAdmin/ConnectionAlreadyInactive:1.0")) exceptions.append(Fnorb.orb.CORBA.typecode("IDL:omg.org/CosNotifyChannelAdmin/NotConnected:1.0")) # Initialise the server request object. server_request.initialise(inputs, outputs, exceptions) # This operation has no arguments. arguments = () # Invoke the implementation. results = apply(self.suspend_connection, arguments) # Create the reply. server_request.results(results) return def _skel_resume_connection(self, server_request): """ Operation: IDL:omg.org/CosNotifyChannelAdmin/ProxyPushSupplier/resume_connection:1.0 """ # Typecodes for 'in' and 'inout' parameters. inputs = [] # Typecodes for the result, 'inout' and 'out' parameters. outputs = [] # Typecodes for user exceptions. exceptions = [] exceptions.append(Fnorb.orb.CORBA.typecode("IDL:omg.org/CosNotifyChannelAdmin/ConnectionAlreadyActive:1.0")) exceptions.append(Fnorb.orb.CORBA.typecode("IDL:omg.org/CosNotifyChannelAdmin/NotConnected:1.0")) # Initialise the server request object. server_request.initialise(inputs, outputs, exceptions) # This operation has no arguments. arguments = () # Invoke the implementation. results = apply(self.resume_connection, arguments) # Create the reply. server_request.results(results) return # Import base interface packages. import CosNotifyComm_skel class StructuredProxyPushSupplier_skel(Fnorb.orb.CORBA.Object_skel, ProxySupplier_skel, CosNotifyComm_skel.StructuredPushSupplier_skel): """ Interface: IDL:omg.org/CosNotifyChannelAdmin/StructuredProxyPushSupplier:1.0 """ _FNORB_ID = "IDL:omg.org/CosNotifyChannelAdmin/StructuredProxyPushSupplier:1.0" def _skel_connect_structured_push_consumer(self, server_request): """ Operation: IDL:omg.org/CosNotifyChannelAdmin/StructuredProxyPushSupplier/connect_structured_push_consumer:1.0 """ # Typecodes for 'in' and 'inout' parameters. inputs = [] inputs.append(Fnorb.orb.CORBA.TC_Object) # Typecodes for the result, 'inout' and 'out' parameters. outputs = [] # Typecodes for user exceptions. exceptions = [] exceptions.append(Fnorb.orb.CORBA.typecode("IDL:omg.org/CosEventChannelAdmin/AlreadyConnected:1.0")) exceptions.append(Fnorb.orb.CORBA.typecode("IDL:omg.org/CosEventChannelAdmin/TypeError:1.0")) # Initialise the server request object. server_request.initialise(inputs, outputs, exceptions) # Unmarshal the arguments to the request. arguments = server_request.arguments() # Invoke the implementation. results = apply(self.connect_structured_push_consumer, arguments) # Create the reply. server_request.results(results) return def _skel_suspend_connection(self, server_request): """ Operation: IDL:omg.org/CosNotifyChannelAdmin/StructuredProxyPushSupplier/suspend_connection:1.0 """ # Typecodes for 'in' and 'inout' parameters. inputs = [] # Typecodes for the result, 'inout' and 'out' parameters. outputs = [] # Typecodes for user exceptions. exceptions = [] exceptions.append(Fnorb.orb.CORBA.typecode("IDL:omg.org/CosNotifyChannelAdmin/ConnectionAlreadyInactive:1.0")) exceptions.append(Fnorb.orb.CORBA.typecode("IDL:omg.org/CosNotifyChannelAdmin/NotConnected:1.0")) # Initialise the server request object. server_request.initialise(inputs, outputs, exceptions) # This operation has no arguments. arguments = () # Invoke the implementation. results = apply(self.suspend_connection, arguments) # Create the reply. server_request.results(results) return def _skel_resume_connection(self, server_request): """ Operation: IDL:omg.org/CosNotifyChannelAdmin/StructuredProxyPushSupplier/resume_connection:1.0 """ # Typecodes for 'in' and 'inout' parameters. inputs = [] # Typecodes for the result, 'inout' and 'out' parameters. outputs = [] # Typecodes for user exceptions. exceptions = [] exceptions.append(Fnorb.orb.CORBA.typecode("IDL:omg.org/CosNotifyChannelAdmin/ConnectionAlreadyActive:1.0")) exceptions.append(Fnorb.orb.CORBA.typecode("IDL:omg.org/CosNotifyChannelAdmin/NotConnected:1.0")) # Initialise the server request object. server_request.initialise(inputs, outputs, exceptions) # This operation has no arguments. arguments = () # Invoke the implementation. results = apply(self.resume_connection, arguments) # Create the reply. server_request.results(results) return # Import base interface packages. import CosNotifyComm_skel class SequenceProxyPushSupplier_skel(Fnorb.orb.CORBA.Object_skel, ProxySupplier_skel, CosNotifyComm_skel.SequencePushSupplier_skel): """ Interface: IDL:omg.org/CosNotifyChannelAdmin/SequenceProxyPushSupplier:1.0 """ _FNORB_ID = "IDL:omg.org/CosNotifyChannelAdmin/SequenceProxyPushSupplier:1.0" def _skel_connect_sequence_push_consumer(self, server_request): """ Operation: IDL:omg.org/CosNotifyChannelAdmin/SequenceProxyPushSupplier/connect_sequence_push_consumer:1.0 """ # Typecodes for 'in' and 'inout' parameters. inputs = [] inputs.append(Fnorb.orb.CORBA.TC_Object) # Typecodes for the result, 'inout' and 'out' parameters. outputs = [] # Typecodes for user exceptions. exceptions = [] exceptions.append(Fnorb.orb.CORBA.typecode("IDL:omg.org/CosEventChannelAdmin/AlreadyConnected:1.0")) exceptions.append(Fnorb.orb.CORBA.typecode("IDL:omg.org/CosEventChannelAdmin/TypeError:1.0")) # Initialise the server request object. server_request.initialise(inputs, outputs, exceptions) # Unmarshal the arguments to the request. arguments = server_request.arguments() # Invoke the implementation. results = apply(self.connect_sequence_push_consumer, arguments) # Create the reply. server_request.results(results) return def _skel_suspend_connection(self, server_request): """ Operation: IDL:omg.org/CosNotifyChannelAdmin/SequenceProxyPushSupplier/suspend_connection:1.0 """ # Typecodes for 'in' and 'inout' parameters. inputs = [] # Typecodes for the result, 'inout' and 'out' parameters. outputs = [] # Typecodes for user exceptions. exceptions = [] exceptions.append(Fnorb.orb.CORBA.typecode("IDL:omg.org/CosNotifyChannelAdmin/ConnectionAlreadyInactive:1.0")) exceptions.append(Fnorb.orb.CORBA.typecode("IDL:omg.org/CosNotifyChannelAdmin/NotConnected:1.0")) # Initialise the server request object. server_request.initialise(inputs, outputs, exceptions) # This operation has no arguments. arguments = () # Invoke the implementation. results = apply(self.suspend_connection, arguments) # Create the reply. server_request.results(results) return def _skel_resume_connection(self, server_request): """ Operation: IDL:omg.org/CosNotifyChannelAdmin/SequenceProxyPushSupplier/resume_connection:1.0 """ # Typecodes for 'in' and 'inout' parameters. inputs = [] # Typecodes for the result, 'inout' and 'out' parameters. outputs = [] # Typecodes for user exceptions. exceptions = [] exceptions.append(Fnorb.orb.CORBA.typecode("IDL:omg.org/CosNotifyChannelAdmin/ConnectionAlreadyActive:1.0")) exceptions.append(Fnorb.orb.CORBA.typecode("IDL:omg.org/CosNotifyChannelAdmin/NotConnected:1.0")) # Initialise the server request object. server_request.initialise(inputs, outputs, exceptions) # This operation has no arguments. arguments = () # Invoke the implementation. results = apply(self.resume_connection, arguments) # Create the reply. server_request.results(results) return # Import base interface packages. import CosNotification_skel import CosNotifyComm_skel import CosNotifyFilter_skel import CosEventChannelAdmin_skel class ConsumerAdmin_skel(Fnorb.orb.CORBA.Object_skel, CosNotification_skel.QoSAdmin_skel, CosNotifyComm_skel.NotifySubscribe_skel, CosNotifyFilter_skel.FilterAdmin_skel, CosEventChannelAdmin_skel.ConsumerAdmin_skel): """ Interface: IDL:omg.org/CosNotifyChannelAdmin/ConsumerAdmin:1.0 """ _FNORB_ID = "IDL:omg.org/CosNotifyChannelAdmin/ConsumerAdmin:1.0" def _skel__get_MyID(self, server_request): """ Attribute: IDL:omg.org/CosNotifyChannelAdmin/ConsumerAdmin/MyID:1.0 """ # Typecode for the attribute value. outputs = [] outputs.append(Fnorb.orb.CORBA.typecode("IDL:omg.org/CosNotifyChannelAdmin/AdminID:1.0")) # Initialise the server request object. server_request.initialise([], outputs, []) # Invoke the implementation. results = self._get_MyID() # Create the reply. server_request.results(results) return def _skel__get_MyChannel(self, server_request): """ Attribute: IDL:omg.org/CosNotifyChannelAdmin/ConsumerAdmin/MyChannel:1.0 """ # Typecode for the attribute value. outputs = [] outputs.append(Fnorb.orb.CORBA.TC_Object) # Initialise the server request object. server_request.initialise([], outputs, []) # Invoke the implementation. results = self._get_MyChannel() # Create the reply. server_request.results(results) return def _skel__get_MyOperator(self, server_request): """ Attribute: IDL:omg.org/CosNotifyChannelAdmin/ConsumerAdmin/MyOperator:1.0 """ # Typecode for the attribute value. outputs = [] outputs.append(Fnorb.orb.CORBA.typecode("IDL:omg.org/CosNotifyChannelAdmin/InterFilterGroupOperator:1.0")) # Initialise the server request object. server_request.initialise([], outputs, []) # Invoke the implementation. results = self._get_MyOperator() # Create the reply. server_request.results(results) return def _skel__get_priority_filter(self, server_request): """ Attribute: IDL:omg.org/CosNotifyChannelAdmin/ConsumerAdmin/priority_filter:1.0 """ # Typecode for the attribute value. outputs = [] outputs.append(Fnorb.orb.CORBA.TC_Object) # Initialise the server request object. server_request.initialise([], outputs, []) # Invoke the implementation. results = self._get_priority_filter() # Create the reply. server_request.results(results) return def _skel__set_priority_filter(self, server_request): """ Attribute: IDL:omg.org/CosNotifyChannelAdmin/ConsumerAdmin/priority_filter:1.0 """ # Typecode for the attribute value. inputs = [] inputs.append(Fnorb.orb.CORBA.TC_Object) # Initialise the server request object. server_request.initialise(inputs, [], []) # Unmarshal the attribute value. value = server_request.arguments()[0] # Invoke the implementation. results = self._set_priority_filter(value) # Create the reply. server_request.results(results) return def _skel__get_lifetime_filter(self, server_request): """ Attribute: IDL:omg.org/CosNotifyChannelAdmin/ConsumerAdmin/lifetime_filter:1.0 """ # Typecode for the attribute value. outputs = [] outputs.append(Fnorb.orb.CORBA.TC_Object) # Initialise the server request object. server_request.initialise([], outputs, []) # Invoke the implementation. results = self._get_lifetime_filter() # Create the reply. server_request.results(results) return def _skel__set_lifetime_filter(self, server_request): """ Attribute: IDL:omg.org/CosNotifyChannelAdmin/ConsumerAdmin/lifetime_filter:1.0 """ # Typecode for the attribute value. inputs = [] inputs.append(Fnorb.orb.CORBA.TC_Object) # Initialise the server request object. server_request.initialise(inputs, [], []) # Unmarshal the attribute value. value = server_request.arguments()[0] # Invoke the implementation. results = self._set_lifetime_filter(value) # Create the reply. server_request.results(results) return def _skel__get_pull_suppliers(self, server_request): """ Attribute: IDL:omg.org/CosNotifyChannelAdmin/ConsumerAdmin/pull_suppliers:1.0 """ # Typecode for the attribute value. outputs = [] outputs.append(Fnorb.orb.CORBA.typecode("IDL:omg.org/CosNotifyChannelAdmin/ProxyIDSeq:1.0")) # Initialise the server request object. server_request.initialise([], outputs, []) # Invoke the implementation. results = self._get_pull_suppliers() # Create the reply. server_request.results(results) return def _skel__get_push_suppliers(self, server_request): """ Attribute: IDL:omg.org/CosNotifyChannelAdmin/ConsumerAdmin/push_suppliers:1.0 """ # Typecode for the attribute value. outputs = [] outputs.append(Fnorb.orb.CORBA.typecode("IDL:omg.org/CosNotifyChannelAdmin/ProxyIDSeq:1.0")) # Initialise the server request object. server_request.initialise([], outputs, []) # Invoke the implementation. results = self._get_push_suppliers() # Create the reply. server_request.results(results) return def _skel_get_proxy_supplier(self, server_request): """ Operation: IDL:omg.org/CosNotifyChannelAdmin/ConsumerAdmin/get_proxy_supplier:1.0 """ # Typecodes for 'in' and 'inout' parameters. inputs = [] inputs.append(Fnorb.orb.CORBA.typecode("IDL:omg.org/CosNotifyChannelAdmin/ProxyID:1.0")) # Typecodes for the result, 'inout' and 'out' parameters. outputs = [] outputs.append(Fnorb.orb.CORBA.TC_Object) # Typecodes for user exceptions. exceptions = [] exceptions.append(Fnorb.orb.CORBA.typecode("IDL:omg.org/CosNotifyChannelAdmin/ProxyNotFound:1.0")) # Initialise the server request object. server_request.initialise(inputs, outputs, exceptions) # Unmarshal the arguments to the request. arguments = server_request.arguments() # Invoke the implementation. results = apply(self.get_proxy_supplier, arguments) # Create the reply. server_request.results(results) return def _skel_obtain_notification_pull_supplier(self, server_request): """ Operation: IDL:omg.org/CosNotifyChannelAdmin/ConsumerAdmin/obtain_notification_pull_supplier:1.0 """ # Typecodes for 'in' and 'inout' parameters. inputs = [] inputs.append(Fnorb.orb.CORBA.typecode("IDL:omg.org/CosNotifyChannelAdmin/ClientType:1.0")) # Typecodes for the result, 'inout' and 'out' parameters. outputs = [] outputs.append(Fnorb.orb.CORBA.TC_Object) outputs.append(Fnorb.orb.CORBA.typecode("IDL:omg.org/CosNotifyChannelAdmin/ProxyID:1.0")) # Typecodes for user exceptions. exceptions = [] exceptions.append(Fnorb.orb.CORBA.typecode("IDL:omg.org/CosNotifyChannelAdmin/AdminLimitExceeded:1.0")) # Initialise the server request object. server_request.initialise(inputs, outputs, exceptions) # Unmarshal the arguments to the request. arguments = server_request.arguments() # Invoke the implementation. results = apply(self.obtain_notification_pull_supplier, arguments) # Create the reply. server_request.results(results) return def _skel_obtain_notification_push_supplier(self, server_request): """ Operation: IDL:omg.org/CosNotifyChannelAdmin/ConsumerAdmin/obtain_notification_push_supplier:1.0 """ # Typecodes for 'in' and 'inout' parameters. inputs = [] inputs.append(Fnorb.orb.CORBA.typecode("IDL:omg.org/CosNotifyChannelAdmin/ClientType:1.0")) # Typecodes for the result, 'inout' and 'out' parameters. outputs = [] outputs.append(Fnorb.orb.CORBA.TC_Object) outputs.append(Fnorb.orb.CORBA.typecode("IDL:omg.org/CosNotifyChannelAdmin/ProxyID:1.0")) # Typecodes for user exceptions. exceptions = [] exceptions.append(Fnorb.orb.CORBA.typecode("IDL:omg.org/CosNotifyChannelAdmin/AdminLimitExceeded:1.0")) # Initialise the server request object. server_request.initialise(inputs, outputs, exceptions) # Unmarshal the arguments to the request. arguments = server_request.arguments() # Invoke the implementation. results = apply(self.obtain_notification_push_supplier, arguments) # Create the reply. server_request.results(results) return def _skel_destroy(self, server_request): """ Operation: IDL:omg.org/CosNotifyChannelAdmin/ConsumerAdmin/destroy:1.0 """ # Typecodes for 'in' and 'inout' parameters. inputs = [] # Typecodes for the result, 'inout' and 'out' parameters. outputs = [] # Typecodes for user exceptions. exceptions = [] # Initialise the server request object. server_request.initialise(inputs, outputs, exceptions) # This operation has no arguments. arguments = () # Invoke the implementation. results = apply(self.destroy, arguments) # Create the reply. server_request.results(results) return # Import base interface packages. import CosNotification_skel import CosNotifyComm_skel import CosNotifyFilter_skel import CosEventChannelAdmin_skel class SupplierAdmin_skel(Fnorb.orb.CORBA.Object_skel, CosNotification_skel.QoSAdmin_skel, CosNotifyComm_skel.NotifyPublish_skel, CosNotifyFilter_skel.FilterAdmin_skel, CosEventChannelAdmin_skel.SupplierAdmin_skel): """ Interface: IDL:omg.org/CosNotifyChannelAdmin/SupplierAdmin:1.0 """ _FNORB_ID = "IDL:omg.org/CosNotifyChannelAdmin/SupplierAdmin:1.0" def _skel__get_MyID(self, server_request): """ Attribute: IDL:omg.org/CosNotifyChannelAdmin/SupplierAdmin/MyID:1.0 """ # Typecode for the attribute value. outputs = [] outputs.append(Fnorb.orb.CORBA.typecode("IDL:omg.org/CosNotifyChannelAdmin/AdminID:1.0")) # Initialise the server request object. server_request.initialise([], outputs, []) # Invoke the implementation. results = self._get_MyID() # Create the reply. server_request.results(results) return def _skel__get_MyChannel(self, server_request): """ Attribute: IDL:omg.org/CosNotifyChannelAdmin/SupplierAdmin/MyChannel:1.0 """ # Typecode for the attribute value. outputs = [] outputs.append(Fnorb.orb.CORBA.TC_Object) # Initialise the server request object. server_request.initialise([], outputs, []) # Invoke the implementation. results = self._get_MyChannel() # Create the reply. server_request.results(results) return def _skel__get_MyOperator(self, server_request): """ Attribute: IDL:omg.org/CosNotifyChannelAdmin/SupplierAdmin/MyOperator:1.0 """ # Typecode for the attribute value. outputs = [] outputs.append(Fnorb.orb.CORBA.typecode("IDL:omg.org/CosNotifyChannelAdmin/InterFilterGroupOperator:1.0")) # Initialise the server request object. server_request.initialise([], outputs, []) # Invoke the implementation. results = self._get_MyOperator() # Create the reply. server_request.results(results) return def _skel__get_pull_consumers(self, server_request): """ Attribute: IDL:omg.org/CosNotifyChannelAdmin/SupplierAdmin/pull_consumers:1.0 """ # Typecode for the attribute value. outputs = [] outputs.append(Fnorb.orb.CORBA.typecode("IDL:omg.org/CosNotifyChannelAdmin/ProxyIDSeq:1.0")) # Initialise the server request object. server_request.initialise([], outputs, []) # Invoke the implementation. results = self._get_pull_consumers() # Create the reply. server_request.results(results) return def _skel__get_push_consumers(self, server_request): """ Attribute: IDL:omg.org/CosNotifyChannelAdmin/SupplierAdmin/push_consumers:1.0 """ # Typecode for the attribute value. outputs = [] outputs.append(Fnorb.orb.CORBA.typecode("IDL:omg.org/CosNotifyChannelAdmin/ProxyIDSeq:1.0")) # Initialise the server request object. server_request.initialise([], outputs, []) # Invoke the implementation. results = self._get_push_consumers() # Create the reply. server_request.results(results) return def _skel_get_proxy_consumer(self, server_request): """ Operation: IDL:omg.org/CosNotifyChannelAdmin/SupplierAdmin/get_proxy_consumer:1.0 """ # Typecodes for 'in' and 'inout' parameters. inputs = [] inputs.append(Fnorb.orb.CORBA.typecode("IDL:omg.org/CosNotifyChannelAdmin/ProxyID:1.0")) # Typecodes for the result, 'inout' and 'out' parameters. outputs = [] outputs.append(Fnorb.orb.CORBA.TC_Object) # Typecodes for user exceptions. exceptions = [] exceptions.append(Fnorb.orb.CORBA.typecode("IDL:omg.org/CosNotifyChannelAdmin/ProxyNotFound:1.0")) # Initialise the server request object. server_request.initialise(inputs, outputs, exceptions) # Unmarshal the arguments to the request. arguments = server_request.arguments() # Invoke the implementation. results = apply(self.get_proxy_consumer, arguments) # Create the reply. server_request.results(results) return def _skel_obtain_notification_pull_consumer(self, server_request): """ Operation: IDL:omg.org/CosNotifyChannelAdmin/SupplierAdmin/obtain_notification_pull_consumer:1.0 """ # Typecodes for 'in' and 'inout' parameters. inputs = [] inputs.append(Fnorb.orb.CORBA.typecode("IDL:omg.org/CosNotifyChannelAdmin/ClientType:1.0")) # Typecodes for the result, 'inout' and 'out' parameters. outputs = [] outputs.append(Fnorb.orb.CORBA.TC_Object) outputs.append(Fnorb.orb.CORBA.typecode("IDL:omg.org/CosNotifyChannelAdmin/ProxyID:1.0")) # Typecodes for user exceptions. exceptions = [] exceptions.append(Fnorb.orb.CORBA.typecode("IDL:omg.org/CosNotifyChannelAdmin/AdminLimitExceeded:1.0")) # Initialise the server request object. server_request.initialise(inputs, outputs, exceptions) # Unmarshal the arguments to the request. arguments = server_request.arguments() # Invoke the implementation. results = apply(self.obtain_notification_pull_consumer, arguments) # Create the reply. server_request.results(results) return def _skel_obtain_notification_push_consumer(self, server_request): """ Operation: IDL:omg.org/CosNotifyChannelAdmin/SupplierAdmin/obtain_notification_push_consumer:1.0 """ # Typecodes for 'in' and 'inout' parameters. inputs = [] inputs.append(Fnorb.orb.CORBA.typecode("IDL:omg.org/CosNotifyChannelAdmin/ClientType:1.0")) # Typecodes for the result, 'inout' and 'out' parameters. outputs = [] outputs.append(Fnorb.orb.CORBA.TC_Object) outputs.append(Fnorb.orb.CORBA.typecode("IDL:omg.org/CosNotifyChannelAdmin/ProxyID:1.0")) # Typecodes for user exceptions. exceptions = [] exceptions.append(Fnorb.orb.CORBA.typecode("IDL:omg.org/CosNotifyChannelAdmin/AdminLimitExceeded:1.0")) # Initialise the server request object. server_request.initialise(inputs, outputs, exceptions) # Unmarshal the arguments to the request. arguments = server_request.arguments() # Invoke the implementation. results = apply(self.obtain_notification_push_consumer, arguments) # Create the reply. server_request.results(results) return def _skel_destroy(self, server_request): """ Operation: IDL:omg.org/CosNotifyChannelAdmin/SupplierAdmin/destroy:1.0 """ # Typecodes for 'in' and 'inout' parameters. inputs = [] # Typecodes for the result, 'inout' and 'out' parameters. outputs = [] # Typecodes for user exceptions. exceptions = [] # Initialise the server request object. server_request.initialise(inputs, outputs, exceptions) # This operation has no arguments. arguments = () # Invoke the implementation. results = apply(self.destroy, arguments) # Create the reply. server_request.results(results) return # Import base interface packages. import CosNotification_skel import CosNotification_skel import CosEventChannelAdmin_skel class EventChannel_skel(Fnorb.orb.CORBA.Object_skel, CosNotification_skel.QoSAdmin_skel, CosNotification_skel.AdminPropertiesAdmin_skel, CosEventChannelAdmin_skel.EventChannel_skel): """ Interface: IDL:omg.org/CosNotifyChannelAdmin/EventChannel:1.0 """ _FNORB_ID = "IDL:omg.org/CosNotifyChannelAdmin/EventChannel:1.0" def _skel__get_MyFactory(self, server_request): """ Attribute: IDL:omg.org/CosNotifyChannelAdmin/EventChannel/MyFactory:1.0 """ # Typecode for the attribute value. outputs = [] outputs.append(Fnorb.orb.CORBA.TC_Object) # Initialise the server request object. server_request.initialise([], outputs, []) # Invoke the implementation. results = self._get_MyFactory() # Create the reply. server_request.results(results) return def _skel__get_default_consumer_admin(self, server_request): """ Attribute: IDL:omg.org/CosNotifyChannelAdmin/EventChannel/default_consumer_admin:1.0 """ # Typecode for the attribute value. outputs = [] outputs.append(Fnorb.orb.CORBA.TC_Object) # Initialise the server request object. server_request.initialise([], outputs, []) # Invoke the implementation. results = self._get_default_consumer_admin() # Create the reply. server_request.results(results) return def _skel__get_default_supplier_admin(self, server_request): """ Attribute: IDL:omg.org/CosNotifyChannelAdmin/EventChannel/default_supplier_admin:1.0 """ # Typecode for the attribute value. outputs = [] outputs.append(Fnorb.orb.CORBA.TC_Object) # Initialise the server request object. server_request.initialise([], outputs, []) # Invoke the implementation. results = self._get_default_supplier_admin() # Create the reply. server_request.results(results) return def _skel__get_default_filter_factory(self, server_request): """ Attribute: IDL:omg.org/CosNotifyChannelAdmin/EventChannel/default_filter_factory:1.0 """ # Typecode for the attribute value. outputs = [] outputs.append(Fnorb.orb.CORBA.TC_Object) # Initialise the server request object. server_request.initialise([], outputs, []) # Invoke the implementation. results = self._get_default_filter_factory() # Create the reply. server_request.results(results) return def _skel_new_for_consumers(self, server_request): """ Operation: IDL:omg.org/CosNotifyChannelAdmin/EventChannel/new_for_consumers:1.0 """ # Typecodes for 'in' and 'inout' parameters. inputs = [] inputs.append(Fnorb.orb.CORBA.typecode("IDL:omg.org/CosNotifyChannelAdmin/InterFilterGroupOperator:1.0")) # Typecodes for the result, 'inout' and 'out' parameters. outputs = [] outputs.append(Fnorb.orb.CORBA.TC_Object) outputs.append(Fnorb.orb.CORBA.typecode("IDL:omg.org/CosNotifyChannelAdmin/AdminID:1.0")) # Typecodes for user exceptions. exceptions = [] # Initialise the server request object. server_request.initialise(inputs, outputs, exceptions) # Unmarshal the arguments to the request. arguments = server_request.arguments() # Invoke the implementation. results = apply(self.new_for_consumers, arguments) # Create the reply. server_request.results(results) return def _skel_new_for_suppliers(self, server_request): """ Operation: IDL:omg.org/CosNotifyChannelAdmin/EventChannel/new_for_suppliers:1.0 """ # Typecodes for 'in' and 'inout' parameters. inputs = [] inputs.append(Fnorb.orb.CORBA.typecode("IDL:omg.org/CosNotifyChannelAdmin/InterFilterGroupOperator:1.0")) # Typecodes for the result, 'inout' and 'out' parameters. outputs = [] outputs.append(Fnorb.orb.CORBA.TC_Object) outputs.append(Fnorb.orb.CORBA.typecode("IDL:omg.org/CosNotifyChannelAdmin/AdminID:1.0")) # Typecodes for user exceptions. exceptions = [] # Initialise the server request object. server_request.initialise(inputs, outputs, exceptions) # Unmarshal the arguments to the request. arguments = server_request.arguments() # Invoke the implementation. results = apply(self.new_for_suppliers, arguments) # Create the reply. server_request.results(results) return def _skel_get_consumeradmin(self, server_request): """ Operation: IDL:omg.org/CosNotifyChannelAdmin/EventChannel/get_consumeradmin:1.0 """ # Typecodes for 'in' and 'inout' parameters. inputs = [] inputs.append(Fnorb.orb.CORBA.typecode("IDL:omg.org/CosNotifyChannelAdmin/AdminID:1.0")) # Typecodes for the result, 'inout' and 'out' parameters. outputs = [] outputs.append(Fnorb.orb.CORBA.TC_Object) # Typecodes for user exceptions. exceptions = [] exceptions.append(Fnorb.orb.CORBA.typecode("IDL:omg.org/CosNotifyChannelAdmin/AdminNotFound:1.0")) # Initialise the server request object. server_request.initialise(inputs, outputs, exceptions) # Unmarshal the arguments to the request. arguments = server_request.arguments() # Invoke the implementation. results = apply(self.get_consumeradmin, arguments) # Create the reply. server_request.results(results) return def _skel_get_supplieradmin(self, server_request): """ Operation: IDL:omg.org/CosNotifyChannelAdmin/EventChannel/get_supplieradmin:1.0 """ # Typecodes for 'in' and 'inout' parameters. inputs = [] inputs.append(Fnorb.orb.CORBA.typecode("IDL:omg.org/CosNotifyChannelAdmin/AdminID:1.0")) # Typecodes for the result, 'inout' and 'out' parameters. outputs = [] outputs.append(Fnorb.orb.CORBA.TC_Object) # Typecodes for user exceptions. exceptions = [] exceptions.append(Fnorb.orb.CORBA.typecode("IDL:omg.org/CosNotifyChannelAdmin/AdminNotFound:1.0")) # Initialise the server request object. server_request.initialise(inputs, outputs, exceptions) # Unmarshal the arguments to the request. arguments = server_request.arguments() # Invoke the implementation. results = apply(self.get_supplieradmin, arguments) # Create the reply. server_request.results(results) return def _skel_get_all_consumeradmins(self, server_request): """ Operation: IDL:omg.org/CosNotifyChannelAdmin/EventChannel/get_all_consumeradmins:1.0 """ # Typecodes for 'in' and 'inout' parameters. inputs = [] # Typecodes for the result, 'inout' and 'out' parameters. outputs = [] outputs.append(Fnorb.orb.CORBA.typecode("IDL:omg.org/CosNotifyChannelAdmin/AdminIDSeq:1.0")) # Typecodes for user exceptions. exceptions = [] # Initialise the server request object. server_request.initialise(inputs, outputs, exceptions) # This operation has no arguments. arguments = () # Invoke the implementation. results = apply(self.get_all_consumeradmins, arguments) # Create the reply. server_request.results(results) return def _skel_get_all_supplieradmins(self, server_request): """ Operation: IDL:omg.org/CosNotifyChannelAdmin/EventChannel/get_all_supplieradmins:1.0 """ # Typecodes for 'in' and 'inout' parameters. inputs = [] # Typecodes for the result, 'inout' and 'out' parameters. outputs = [] outputs.append(Fnorb.orb.CORBA.typecode("IDL:omg.org/CosNotifyChannelAdmin/AdminIDSeq:1.0")) # Typecodes for user exceptions. exceptions = [] # Initialise the server request object. server_request.initialise(inputs, outputs, exceptions) # This operation has no arguments. arguments = () # Invoke the implementation. results = apply(self.get_all_supplieradmins, arguments) # Create the reply. server_request.results(results) return class EventChannelFactory_skel(Fnorb.orb.CORBA.Object_skel): """ Interface: IDL:omg.org/CosNotifyChannelAdmin/EventChannelFactory:1.0 """ _FNORB_ID = "IDL:omg.org/CosNotifyChannelAdmin/EventChannelFactory:1.0" def _skel_create_channel(self, server_request): """ Operation: IDL:omg.org/CosNotifyChannelAdmin/EventChannelFactory/create_channel:1.0 """ # Typecodes for 'in' and 'inout' parameters. inputs = [] inputs.append(Fnorb.orb.CORBA.typecode("IDL:omg.org/CosNotification/QoSProperties:1.0")) inputs.append(Fnorb.orb.CORBA.typecode("IDL:omg.org/CosNotification/AdminProperties:1.0")) # Typecodes for the result, 'inout' and 'out' parameters. outputs = [] outputs.append(Fnorb.orb.CORBA.TC_Object) outputs.append(Fnorb.orb.CORBA.typecode("IDL:omg.org/CosNotifyChannelAdmin/ChannelID:1.0")) # Typecodes for user exceptions. exceptions = [] exceptions.append(Fnorb.orb.CORBA.typecode("IDL:omg.org/CosNotification/UnsupportedQoS:1.0")) exceptions.append(Fnorb.orb.CORBA.typecode("IDL:omg.org/CosNotification/UnsupportedAdmin:1.0")) # Initialise the server request object. server_request.initialise(inputs, outputs, exceptions) # Unmarshal the arguments to the request. arguments = server_request.arguments() # Invoke the implementation. results = apply(self.create_channel, arguments) # Create the reply. server_request.results(results) return def _skel_get_all_channels(self, server_request): """ Operation: IDL:omg.org/CosNotifyChannelAdmin/EventChannelFactory/get_all_channels:1.0 """ # Typecodes for 'in' and 'inout' parameters. inputs = [] # Typecodes for the result, 'inout' and 'out' parameters. outputs = [] outputs.append(Fnorb.orb.CORBA.typecode("IDL:omg.org/CosNotifyChannelAdmin/ChannelIDSeq:1.0")) # Typecodes for user exceptions. exceptions = [] # Initialise the server request object. server_request.initialise(inputs, outputs, exceptions) # This operation has no arguments. arguments = () # Invoke the implementation. results = apply(self.get_all_channels, arguments) # Create the reply. server_request.results(results) return def _skel_get_event_channel(self, server_request): """ Operation: IDL:omg.org/CosNotifyChannelAdmin/EventChannelFactory/get_event_channel:1.0 """ # Typecodes for 'in' and 'inout' parameters. inputs = [] inputs.append(Fnorb.orb.CORBA.typecode("IDL:omg.org/CosNotifyChannelAdmin/ChannelID:1.0")) # Typecodes for the result, 'inout' and 'out' parameters. outputs = [] outputs.append(Fnorb.orb.CORBA.TC_Object) # Typecodes for user exceptions. exceptions = [] exceptions.append(Fnorb.orb.CORBA.typecode("IDL:omg.org/CosNotifyChannelAdmin/ChannelNotFound:1.0")) # Initialise the server request object. server_request.initialise(inputs, outputs, exceptions) # Unmarshal the arguments to the request. arguments = server_request.arguments() # Invoke the implementation. results = apply(self.get_event_channel, arguments) # Create the reply. server_request.results(results) return #############################################################################

34.868776

216

0.681839

2ff55050e42ae030c060c932064218690c231573

931

py

Python

database/invitrodb/tox21_antag_summary.py

mshobair/invitro_cheminformatics

17201496c73453accd440646a1ee81726119a59c

[ "MIT" ]

null

database/invitrodb/tox21_antag_summary.py

mshobair/invitro_cheminformatics

17201496c73453accd440646a1ee81726119a59c

[ "MIT" ]

null

database/invitrodb/tox21_antag_summary.py

mshobair/invitro_cheminformatics

17201496c73453accd440646a1ee81726119a59c

[ "MIT" ]

null

import datetime from database.database_schemas import Schemas from sqlalchemy import Column, ForeignKey, Integer, String, DateTime from sqlalchemy.dialects.mysql import BIGINT, SMALLINT, DOUBLE, TIMESTAMP, TINYINT from database.base import Base class Tox21AntagSummary(Base): """Maps to tox21_antag_summary table in invitrodb databases.""" __tablename__ = 'tox21_antag_summary' __table_args__ = {'schema': Schemas.invitrodb_schema} Assay_Name = Column(String) Protocol_Name = Column(String) Name_of_control_compound = Column(String) Concentration_used_in_antagonist_mode = Column(String) Hill_Coef = Column(String) Calculating_ECXX = Column(String) Online_Validation_EC50 = Column(String) Online_Screening_EC50 = Column(String) Validation_sample_size = Column(String) Screening_sample_size = Column(String) aid = Column(BIGINT) Antag_assay_name = Column(String)

30.032258

82

0.769066

f4005934a82afad03466fcf5b747c48c61e5f8a2

22,702

py

Python

test/test_cmake_parser.py

eurogroep/catkin_lint

987d8e1378b4963e4d36032b9410e13c7bbab8f3

[ "BSD-3-Clause" ]

null

test/test_cmake_parser.py

eurogroep/catkin_lint

987d8e1378b4963e4d36032b9410e13c7bbab8f3

[ "BSD-3-Clause" ]

null

test/test_cmake_parser.py

eurogroep/catkin_lint

987d8e1378b4963e4d36032b9410e13c7bbab8f3

[ "BSD-3-Clause" ]

null

# coding=utf-8 # # catkin_lint # Copyright (c) 2013-2020 Fraunhofer FKIE # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions # are met: # # * Redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer. # * Redistributions in binary form must reproduce the above copyright # notice, this list of conditions and the following disclaimer in the # documentation and/or other materials provided with the distribution. # * Neither the name of the Fraunhofer organization nor the names of its # contributors may be used to endorse or promote products derived from # this software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS # IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED # TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A # PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT # HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, # SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED # TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR # PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF # LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING # NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS # SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. import unittest import catkin_lint.cmake as cmake class CMakeParserTest(unittest.TestCase): def parse_all(self, s, var=None, env_var=None, location=None): result = [] ctxt = cmake.ParserContext() for cmd, args, arg_tokens, (fname, line, column) in ctxt.parse(s, var=var, env_var=env_var): if cmd == "#catkin_lint" and args and args[0] == "skip": ctxt.skip_block() continue if location is None: result.append((cmd, args)) elif location == 1: result.append((cmd, args, line)) elif location == 2: result.append((cmd, args, line, column)) return result def test_empty(self): """Test CMake parser with empty file""" self.assertEqual( self.parse_all(""), [] ) def test_generator_expressions(self): """Test CMake parser generator expressions""" self.assertEqual( self.parse_all("command($<0:ignore_me>)"), [("command", [])] ) def test_command(self): """Test CMake parser command parsing""" self.assertEqual( self.parse_all("command()"), [("command", [])] ) self.assertEqual( self.parse_all("MiXeDCaSe()"), [("MiXeDCaSe", [])] ) self.assertRaises(cmake.CMakeSyntaxError, self.parse_all, "unbalanced(") self.assertRaises(cmake.CMakeSyntaxError, self.parse_all, "invalid%=characters$()") self.assertRaises(cmake.CMakeSyntaxError, self.parse_all, "()") self.assertRaises(cmake.CMakeSyntaxError, self.parse_all, "missing_braces") self.assertRaises(cmake.CMakeSyntaxError, self.parse_all, "cmd();") self.assertRaises(cmake.CMakeSyntaxError, self.parse_all, "cmd cmd()") def test_string(self): """Test CMake parser string parsing""" self.assertEqual( self.parse_all('cmd("simple string")'), [("cmd", ["simple string"])] ) self.assertEqual( self.parse_all('cmd("string with \\"quote\\"")'), [("cmd", ['string with "quote"'])] ) self.assertEqual( self.parse_all('cmd("string that spans\nmultiple lines")'), [("cmd", ['string that spans\nmultiple lines'])] ) self.assertEqual( self.parse_all('cmd("\\\\"\\")'), [("cmd", ['\\', '"'])] ) def test_macro(self): """Test CMake parser macro expansion""" self.assertEqual( self.parse_all("macro(test) cmd() endmacro() test()"), [("macro", ["test"]), ("endmacro", []), ("cmd", [])] ) self.assertEqual( self.parse_all("macro(test) cmd() test() endmacro() test()"), [("macro", ["test"]), ("endmacro", []), ("cmd", [])] ) self.assertEqual( self.parse_all("macro(test) cmd(${global}) test() endmacro() test()", {"global": "value"}), [("macro", ["test"]), ("endmacro", []), ("cmd", ["value"])] ) self.assertEqual( self.parse_all("macro(test arg) cmd(${arg}) endmacro() test(fun)"), [("macro", ["test", "arg"]), ("endmacro", []), ("cmd", ["fun"])] ) self.assertEqual( self.parse_all("macro(test arg) cmd(${arg}) endmacro() test(local) cmd(${arg})", {"arg": "global"}), [("macro", ["test", "arg"]), ("endmacro", []), ("cmd", ["local"]), ("cmd", ["global"])] ) self.assertEqual( self.parse_all('macro(test arg) cmd(${arg}) endmacro() test("one;two;three")'), [("macro", ["test", "arg"]), ("endmacro", []), ("cmd", ["one", "two", "three"])] ) self.assertEqual( self.parse_all('macro(test arg) cmd(${arg}) cmd(${ARGN}) endmacro() test(one;two;three)'), [("macro", ["test", "arg"]), ("endmacro", []), ("cmd", ["one"]), ("cmd", ["two", "three"])] ) self.assertEqual( self.parse_all('macro(test arg1 arg2) cmd("${arg2}") cmd(${ARGN}) endmacro() test(one)'), [("macro", ["test", "arg1", "arg2"]), ("endmacro", []), ("cmd", [""]), ("cmd", [])] ) self.assertEqual( self.parse_all('macro(test arg) cmd("${arg}") endmacro() test("one;two;three")'), [("macro", ["test", "arg"]), ("endmacro", []), ("cmd", ["one;two;three"])] ) self.assertEqual( self.parse_all('macro(test arg) cmd(${arg} ${ARGN}) endmacro() test(arg extra stuff)'), [("macro", ["test", "arg"]), ("endmacro", []), ("cmd", ["arg", "extra", "stuff"])] ) self.assertEqual( self.parse_all('macro(TEST arg) cmd(${arg}) endmacro() test(value)'), [("macro", ["TEST", "arg"]), ("endmacro", []), ("cmd", ["value"])] ) self.assertRaises(cmake.CMakeSyntaxError, self.parse_all, "macro() endmacro()") self.assertRaises(cmake.CMakeSyntaxError, self.parse_all, "macro(fun)") def test_function(self): """Test CMake parser function definitions""" self.assertEqual( self.parse_all("function(test) cmd() endfunction() test()"), [("function", ["test"]), ("endfunction", []), ("test", [])] ) self.assertRaises(cmake.CMakeSyntaxError, self.parse_all, "function() endfunction()") self.assertRaises(cmake.CMakeSyntaxError, self.parse_all, "function(fun)") def test_foreach(self): """Test CMake parser foreach() loop handling""" self.assertEqual( self.parse_all('foreach(arg RANGE 2) cmd(${arg}) endforeach()'), [("foreach", ["arg", "RANGE", "2"]), ("cmd", ["0"]), ("cmd", ["1"]), ("cmd", ["2"]), ("endforeach", [])] ) self.assertEqual( self.parse_all('foreach(arg RANGE 1 3) cmd(${arg}) endforeach()'), [("foreach", ["arg", "RANGE", "1", "3"]), ("cmd", ["1"]), ("cmd", ["2"]), ("cmd", ["3"]), ("endforeach", [])] ) self.assertEqual( self.parse_all('foreach(arg RANGE 1 5 2) cmd(${arg}) endforeach()'), [("foreach", ["arg", "RANGE", "1", "5", "2"]), ("cmd", ["1"]), ("cmd", ["3"]), ("cmd", ["5"]), ("endforeach", [])] ) self.assertEqual( self.parse_all('foreach(arg 1 2 3 4 5) endforeach()'), [("foreach", ["arg", "1", "2", "3", "4", "5"]), ("endforeach", [])] ) self.assertEqual( self.parse_all('foreach(arg one) cmd(${global}) endforeach()', {"global": "value"}), [("foreach", ["arg", "one"]), ("cmd", ["value"]), ("endforeach", [])] ) self.assertEqual( self.parse_all('foreach(arg IN LISTS dummy) cmd(${arg}) endforeach()', {"dummy": "one;two;three"}), [("foreach", ["arg", "IN", "LISTS", "dummy"]), ("cmd", ["one"]), ("cmd", ["two"]), ("cmd", ["three"]), ("endforeach", [])] ) self.assertEqual( self.parse_all('foreach(arg IN ITEMS ${dummy}) cmd(${arg}) endforeach()', {"dummy": "one;two;three"}), [("foreach", ["arg", "IN", "ITEMS", "one", "two", "three"]), ("cmd", ["one"]), ("cmd", ["two"]), ("cmd", ["three"]), ("endforeach", [])] ) self.assertEqual( self.parse_all('foreach(arg ${dummy}) cmd(${arg}) endforeach()', {"dummy": "one;two;three"}), [("foreach", ["arg", "one", "two", "three"]), ("cmd", ["one"]), ("cmd", ["two"]), ("cmd", ["three"]), ("endforeach", [])] ) self.assertEqual( self.parse_all('foreach(arg) cmd(${arg}) endforeach()'), [("foreach", ["arg"]), ("endforeach", [])] ) self.assertEqual( self.parse_all('foreach(a 1 2) foreach(b 3 4) cmd(${a} ${b}) endforeach() endforeach()'), [("foreach", ["a", "1", "2"]), ("foreach", ["b", "3", "4"]), ("cmd", ["1", "3"]), ("cmd", ["1", "4"]), ("endforeach", []), ("foreach", ["b", "3", "4"]), ("cmd", ["2", "3"]), ("cmd", ["2", "4"]), ("endforeach", []), ("endforeach", [])] ) self.assertEqual( self.parse_all('FOREACH(a 1 2) FOREACH(b 3 4) cmd(${a} ${b}) ENDFOREACH() ENDFOREACH()'), [("FOREACH", ["a", "1", "2"]), ("FOREACH", ["b", "3", "4"]), ("cmd", ["1", "3"]), ("cmd", ["1", "4"]), ("ENDFOREACH", []), ("FOREACH", ["b", "3", "4"]), ("cmd", ["2", "3"]), ("cmd", ["2", "4"]), ("ENDFOREACH", []), ("ENDFOREACH", [])] ) self.assertRaises(cmake.CMakeSyntaxError, self.parse_all, "foreach(arg)") self.assertRaises(cmake.CMakeSyntaxError, self.parse_all, "foreach(arg RANGE bla) endforeach()") self.assertRaises(cmake.CMakeSyntaxError, self.parse_all, "foreach(arg RANGE 1 5 2 0) endforeach()") self.assertRaises(cmake.CMakeSyntaxError, self.parse_all, "foreach() endforeach()") def test_arguments(self): """Test CMake parser argument parsing""" self.assertEqual( self.parse_all("cmd(one two three)"), [("cmd", ["one", "two", "three"])] ) self.assertEqual( self.parse_all("cmd(one two;three)"), [("cmd", ["one", "two", "three"])] ) self.assertEqual( self.parse_all("cmd(one;two;three)"), [("cmd", ["one", "two", "three"])] ) self.assertEqual( self.parse_all("cmd(one;two three)"), [("cmd", ["one", "two", "three"])] ) self.assertEqual( self.parse_all('cmd("one;two" three)'), [("cmd", ["one;two", "three"])] ) self.assertEqual( self.parse_all('cmd("one;two";three)'), [("cmd", ["one;two", "three"])] ) self.assertEqual( self.parse_all('cmd(one;"two;three")'), [("cmd", ["one", "two;three"])] ) self.assertEqual( self.parse_all('if(NOT (A OR B)) endif()'), [("if", ["NOT", "(", "A", "OR", "B", ")"]), ("endif", [])] ) self.assertEqual( self.parse_all('cmd("(")'), [("cmd", ["("])] ) self.assertEqual( self.parse_all('cmd(")")'), [("cmd", [")"])] ) self.assertEqual( self.parse_all('cmd("\\"")'), [("cmd", ['"'])] ) self.assertEqual( self.parse_all('cmd(\\")'), [("cmd", ['"'])] ) self.assertEqual( self.parse_all('cmd(a\\ b)'), [("cmd", ['a b'])] ) self.assertEqual( self.parse_all("cmd(ENV{PATH})"), [("cmd", ["ENV{PATH}"])] ) self.assertRaises(cmake.CMakeSyntaxError, self.parse_all, 'cmd("unclosed string)') def test_substitution(self): """Test CMake parser variable substitution semantics""" self.assertEqual( self.parse_all("cmd(${args})", var={"args": "one;two;three"}), [("cmd", ["one", "two", "three"])] ) self.assertEqual( self.parse_all("cmd(${missing})"), [("cmd", [])] ) self.assertEqual( self.parse_all('cmd("${missing}")'), [("cmd", [""])] ) self.assertEqual( self.parse_all("${fun}()", var={"fun": "cmd"}), [("cmd", [])] ) self.assertEqual( self.parse_all("cmd(${args})", var={"args": "one two three"}), [("cmd", ["one two three"])] ) self.assertEqual( self.parse_all('cmd("${args}")', var={"args": "one;two;three"}), [("cmd", ["one;two;three"])] ) self.assertEqual( self.parse_all('cmd("\\${args}")', var={"args": "fail"}), [("cmd", ["${args}"])] ) self.assertEqual( self.parse_all('cmd(\\${args})', var={"args": "fail"}), [("cmd", ["${args}"])] ) self.assertEqual( self.parse_all('cmd(${args})', var={"args": "\\\\"}), [("cmd", ["\\\\"])] ) self.assertEqual( self.parse_all('cmd(${args})', var={"args": "${looks_like_a_variable}"}), [("cmd", ["${looks_like_a_variable}"])] ) self.assertEqual( self.parse_all('cmd(${args})', var={"args": ")"}), [("cmd", [")"])] ) self.assertEqual( self.parse_all('cmd(fun ${args})', var={"args": "stuff"}), [("cmd", ["fun", "stuff"])] ) self.assertEqual( self.parse_all("cmd($ENV{PATH})"), [("cmd", ["$ENV{PATH}"])] ) self.assertEqual( self.parse_all("cmd($env{test})", env_var={"test": "foo"}), [("cmd", ["$env{test}"])] ) self.assertEqual( self.parse_all("cmd($ENV{test})", env_var={"test": "foo"}), [("cmd", ["foo"])] ) self.assertEqual( self.parse_all("cmd($ENV{Test})", env_var={"test": "foo"}), [("cmd", ["$ENV{Test}"])] ) def test_pragma(self): """Test CMake parser catkin_lint pragmas""" self.assertEqual( self.parse_all("# catkin_lint: extra space\n#catkin_lint:\n#catkin_lint: \n#catkin_lint: one two three \n#catkin_lint :\n"), [("#catkin_lint", []), ("#catkin_lint", []), ("#catkin_lint", ["one", "two", "three"])] ) def test_skip_block(self): """Test CMaker parser skip block""" self.assertEqual( self.parse_all("""\ #catkin_lint: skip cmd() """), [("cmd", [])] ) self.assertEqual( self.parse_all("""\ if() endif() #catkin_lint: skip cmd() """), [("if", []), ("endif", []), ("cmd", [])] ) self.assertEqual( self.parse_all("""\ #catkin_lint: skip if(test) endif() """), [("if", ["test"]), ("endif", [])] ) self.assertEqual( self.parse_all("""\ if(condition) #catkin_lint: skip cmd() endif() """), [("if", ["condition"]), ("endif", [])] ) self.assertEqual( self.parse_all("""\ if(condition) #catkin_lint: skip cmd1() else() cmd2() endif() """), [("if", ["condition"]), ("else", []), ("cmd2", []), ("endif", [])] ) self.assertEqual( self.parse_all("""\ if(condition) cmd1() else() #catkin_lint: skip cmd2() endif() """), [("if", ["condition"]), ("cmd1", []), ("else", []), ("endif", [])] ) self.assertEqual( self.parse_all("""\ foreach(arg 1 2) cmd(${arg}) #catkin_lint: skip do_not_parse_this() endforeach() """), [("foreach", ["arg", "1", "2"]), ("cmd", ["1"]), ("cmd", ["2"]), ("endforeach", [])] ) self.assertEqual( self.parse_all("""\ macro(test) cmd() #catkin_lint: skip do_not_parse_this() endmacro() test() """), [("macro", ["test"]), ("endmacro", []), ("cmd", [])] ) self.assertEqual( self.parse_all("""\ if(first) #catkin_lint: skip if(second) endif(second) do_not_parse_this() endif() """), [("if", ["first"]), ("endif", [])] ) self.assertEqual( self.parse_all("""\ if(first) #catkin_lint: skip foreach(second 1 2) endforeach() do_not_parse_this() endif() """), [("if", ["first"]), ("endif", [])] ) self.assertEqual( self.parse_all("""\ macro(test) do_not_parse_this() endmacro() if(first) #catkin_lint: skip test() do_not_parse_this() endif() """), [("macro", ["test"]), ("endmacro", []), ("if", ["first"]), ("endif", [])] ) def test_comments(self): """Test CMake parser comment handling""" self.assertEqual( self.parse_all("""\ # initial comment cmd(one # first argument comment two # second argument comment three# third argument comment without space )## closing comment # commented-out command # cmd() """), [("cmd", ["one", "two", "three"])] ) def test_line_numbering(self): """Test CMake parser line numbering""" self.assertEqual( self.parse_all("""\ cmd1() cmd2( ) # Comment cmd3() """, location=1), [("cmd1", [], 1), ("cmd2", [], 2), ("cmd3", [], 5)] ) self.assertEqual( self.parse_all("cmd1()\rcmd2()\rcmd3()\r", location=1), [("cmd1", [], 1), ("cmd2", [], 2), ("cmd3", [], 3)] ) self.assertEqual( self.parse_all("cmd1()\ncmd2()\ncmd3()\n", location=1), [("cmd1", [], 1), ("cmd2", [], 2), ("cmd3", [], 3)] ) self.assertEqual( self.parse_all("cmd1()\r\ncmd2()\r\ncmd3()\r\n", location=1), [("cmd1", [], 1), ("cmd2", [], 2), ("cmd3", [], 3)] ) def test_line_columns(self): """Test CMake parser column numbering""" self.assertEqual( self.parse_all("cmd1()\n cmd2()\n cmd3()\n", location=2), [("cmd1", [], 1, 1), ("cmd2", [], 2, 2), ("cmd3", [], 3, 3)] ) def test_argparse(self): """Test CMake parser argparse utility function""" self.assertRaises(RuntimeError, cmake.argparse, [], {"TEST": "xxx"}) opts, args = cmake.argparse([], {}) self.assertEqual({}, opts) self.assertEqual([], args) opts, args = cmake.argparse([], {"TEST": "-"}) self.assertEqual({"TEST": False}, opts) self.assertEqual([], args) opts, args = cmake.argparse([], {"TEST": "?"}) self.assertEqual({"TEST": None}, opts) self.assertEqual([], args) self.assertRaises(cmake.CMakeSyntaxError, cmake.argparse, [], {"TEST": "!"}) opts, args = cmake.argparse([], {"TEST": "*"}) self.assertEqual({"TEST": []}, opts) self.assertEqual([], args) self.assertRaises(cmake.CMakeSyntaxError, cmake.argparse, [], {"TEST": "+"}) opts, args = cmake.argparse([], {"TEST": "p"}) self.assertEqual({"TEST": {}}, opts) self.assertEqual([], args) opts, args = cmake.argparse(["argument", "BOOL"], {"BOOL": "-"}) self.assertEqual({"BOOL": True}, opts) self.assertEqual(["argument"], args) opts, args = cmake.argparse(["argument", "KEY", "value"], {"KEY": "?"}) self.assertEqual({"KEY": "value"}, opts) self.assertEqual(["argument"], args) opts, args = cmake.argparse(["argument", "KEY", "value"], {"KEY": "!"}) self.assertEqual({"KEY": "value"}, opts) self.assertEqual(["argument"], args) opts, args = cmake.argparse(["argument", "LIST", "value1", "value2"], {"LIST": "*"}) self.assertEqual({"LIST": ["value1", "value2"]}, opts) self.assertEqual(["argument"], args) opts, args = cmake.argparse(["argument", "LIST", "value1", "value2"], {"LIST": "+"}) self.assertEqual({"LIST": ["value1", "value2"]}, opts) self.assertEqual(["argument"], args) opts, args = cmake.argparse(["argument", "PROPERTIES", "key1", "value1", "key2", "value2"], {"PROPERTIES": "p"}) self.assertEqual({"PROPERTIES": {"key1": "value1", "key2": "value2"}}, opts) self.assertEqual(["argument"], args) opts, args = cmake.argparse(["PROPERTIES", "key1", "value1", "key2"], {"PROPERTIES": "p"}) self.assertEqual({"PROPERTIES": {"key1": "value1", "key2": ""}}, opts) opts, args = cmake.argparse(["DOUBLE", "DOUBLE", "ARGUMENT", "ARGUMENT"], {"DOUBLE ARGUMENT": "?"}) self.assertEqual({"DOUBLE ARGUMENT": "ARGUMENT"}, opts) self.assertEqual(["DOUBLE"], args) opts, args = cmake.argparse(["BOOL", "argument"], {"BOOL": "-"}) self.assertEqual({"BOOL": True}, opts) self.assertEqual(["argument"], args)

39.07401

148

0.481632

362991897db1fd56e898613756c2f16c0f64c89d

9,134

py

Python

scripts/igvSnapshots.py

skchronicles/ChIPSeqPeakCalling

24a8ad6736c7c9e3a29b20da1a092693f9c1c926

[ "MIT" ]

3

2019-05-24T05:58:19.000Z

2021-12-20T11:52:29.000Z

scripts/igvSnapshots.py

skchronicles/PeakCalling

24a8ad6736c7c9e3a29b20da1a092693f9c1c926

[ "MIT" ]

null

scripts/igvSnapshots.py

skchronicles/PeakCalling

24a8ad6736c7c9e3a29b20da1a092693f9c1c926

[ "MIT" ]

4

2021-07-14T15:13:29.000Z

2021-12-20T11:52:33.000Z

########################################################################################################## # ChIP-Seq Pipeline: PeakCaller -> IGV # Author: Skyler Kuhn (NIH/NCI) [C] # CCR Collaborative Bioinformatics Resource # Version 1.0.3 # See readme.txt for more information # USAGE: # python igvSnapshots.py # --n=5 # --narrowPeak_file=CHIP_Thpok_Biotin_vs_Input_Thpok_peaks.narrowPeak # --treatmentBW_file=CHIP_Thpok_Biotin.R1.trim.not_blacklist_plus.sorted.mapq_gt_3.normalized.bw # --inputBW_file=Input_Thpok.R1.trim.not_blacklist_plus.sorted.mapq_gt_3.normalized.bw # --output_folder=SNAPTEST # --genome=mm10 ########################################################################################################## # Imports from __future__ import print_function, division import subprocess as sp import sys import time import os def check_args(all_args): """ :param all_args: # (this is a list of all provided command-line arguements) :return: arg_dict # if 6 or 11arguments are not provided an Exception is raised TLDR: This function checks the provided command-line arguments and them uses regex to check to see if they are valid, if they are not an Exception is raised! """ def parse_args(args): # maybe look into using a decorator here, the more python-ic way """ Generator that returns, :param args: (the list of args provided by sys.argv) :return: """ for i in range(1, len(args)-1, 2): # the zero-th index is the program name j = i + 1 yield args[i], args[j] def invalid_usage_message(): """ :return: docstring error message TDLR: An error Message is returned if the wrong number of command-line args are provided """ return """Failed to Provide Required Input Arguments: --n --narrowPeak_file --treatmentBW_file --inputBW_file --output_folder --genome\n* Invalid Input Arguments provided * \nUsage:\npython igvSnapshots.py --n=5 --narrowPeak_file=narrow.narrowPeak --treatmentBW_file=treatment.bw --inputBW_file=inputfile.bw --output_folder=FolderName --genome=mm10 """ if len(all_args) != 7 and len(all_args) != 13: # maybe use an assert statement here raise Exception(invalid_usage_message()) arg_dict = {} # k:switch (ex. --n), v:arguement (ex. 5) if len(all_args) == 11: arg_dict = {switch.lstrip("-"): argument for switch, argument in parse_args(args=all_args)} else: # 6 args formatted like this: python ChiPSeqhelper.py --n=1 --narrowfile=narrowFH.txt ...(etc.) for arg in all_args[1:]: stripped_args_list = arg.lstrip("-").split("=") arg_dict[stripped_args_list[0]] = stripped_args_list[1] return arg_dict def benchmarker(any_function): """ :param any_function: (function to be timed) :return: String giving timing info for a given function TDLR: Decorator that takes a function as a parameter and outputs its timing info (benchmarking) """ def timing_wrapper(*args, **kwargs): t1 = time.time() any_function(*args, **kwargs) t2 = time.time() return "Time it took to run {} function:{}\n".format(any_function.__name__, str(t2 - t1)) return timing_wrapper class ChipSeqPipeline(object): """ Takes dictionary of validated arguments from check_args, sorts & selects MAC CLT output, and then calls IGV CLT """ def __init__(self, args_dict): # Attributes self.args_dict = args_dict self.n = args_dict['n'] self.genome = args_dict['genome'] self.narrow_peaks = args_dict['narrowPeak_file'] self.sortedNnarrowpeaks = self.narrow_peaks.split(".")[0] + "_SORTEDqValue_TOP50.narrowPeaks" # Output Name self.treatment_bw = args_dict['treatmentBW_file'] self.input_bw = args_dict['inputBW_file'] self.output_folder = args_dict['output_folder'] # Methods #self.validate() # insert check args into this section self.run() def validate(self): # insert check_args code into here """ takes a list of __init__ attributes :return: boolean """ pass def createIGVscript(self, inputBWfile, treatBWfile, sortedNarrowPeaksfile, genome, maxPanelheight=500, padding=500, snapdirectory="snapstest"): """ :param inputBWfile: (ex. input.bw) :param treatBWfile: (ex. treatment.bw) :param genome: (ex. hg19, mm10, etc.) :param sortedNarrowPeaksfile: # Used to grab chr# and start/stop positions :param maxPanelheight: (default: 500px) # maximum screen shot panel size :param padding: (default: -/+ 500bp) # left and right padding adjustments added to start/stop positions :param snapdirectory: (default:IGVsnaps) # Name of directory where the files will be stored ------------------------------------ Example IGV scrpt: new snapshotDirectory IGV_Snapshots load test_alignments.bam genome hg19 maxPanelHeight 500 goto chr1:713167-714758 snapshot chr1_713167_714758_h500.png goto chr1:713500-714900 snapshot chr1_713500_714900_h500.png exit :return: (igv_batch_script.txt) """ def get_workingDir(): """ Used for finding absolute paths :return: returns the current working directory """ return os.getcwd() working_directory = get_workingDir() narrowFH = open(sortedNarrowPeaksfile, "r") outFH = open("igv_batch_script.txt", "w") # Writing out to the batch script file outFH.write("new\n") outFH.write("snapshotDirectory {}\n".format(snapdirectory)) outFH.write("load {}\n".format(os.path.join(working_directory, treatBWfile))) outFH.write("load {}\n".format(os.path.join(working_directory, inputBWfile))) outFH.write("genome {}\n".format(genome)) outFH.write("maxPanelHeight {}\n".format(maxPanelheight)) for line in narrowFH: linelist = line.split() chr = linelist[0] # chromosome number start = int(linelist[1]) - int(padding) # start_position + some left padding stop = int(linelist[2]) + int(padding) # stop_postion + some right padding outFH.write("goto {}:{}-{}\n".format(chr, start, stop)) outFH.write("snapshot {}_{}_h{}.png\n".format(chr, start, stop, maxPanelheight)) outFH.write("exit") outFH.close() narrowFH.close() def __run(self, command_list, pipe): """ Private method used to run commands in shell When running pipe="yes", it runs: sort -k9nr,9 CHIP_Thpok_Biotin_vs_Input_Thpok_peaks.narrowPeak | head -50 > outputfile.narrowfile :param command_list: :pipe specify whether you want to pipe commands :return: """ if pipe == "yes": p1 = sp.Popen(command_list, stdout=sp.PIPE) p2 = sp.Popen("head -{}".format(self.n).split(), stdin=p1.stdout, stdout=sp.PIPE) p1.stdout.close() # Allow p1 to receive a SIGPIPE if p2 exits. output = p2.communicate()[0] fh = open(self.sortedNnarrowpeaks, "w") fh.write(output) fh.close() elif pipe == "no": sp.Popen(command_list).wait() def run(self): """ Sorts the Output of MACS (by decreasing q-value) & selects tops 'n' results calls IGV CLT, loops through results -> saves screenshots to an Output folder :return: Output folder with IGv N-peaks results """ self.validate() self.__run("mkdir --p {}".format(self.output_folder).split(), "no") self.__run("sort -k9nr,9 {}".format(self.narrow_peaks).split(), "yes") self.__run("echo module load igv".split(), "no") self.createIGVscript(self.input_bw, self.treatment_bw, self.sortedNnarrowpeaks, genome=self.genome, maxPanelheight=500, padding=500, snapdirectory=self.output_folder) self.__run("igv -m 40g -b igv_batch_script.txt".split(), "no") #self.__run("echo Clean up the directory as needed-- rm any un-needed files!".split()) def __str__(self): return "Parameters: {}".format(self.args_dict) def main(): """ Pseudo-main method :return: """ # Checking the Arguments pass through CL arg_list = sys.argv print(arg_list) args_dict = check_args(all_args=arg_list) print(args_dict) # Start working with interfacing into the Pipeline #for command in run_shell_commands(parsed_args=args_dict): # sp.Popen("echo {}".format(command).split()).wait() # Popen takes a list as parameter useChIPSeq = ChipSeqPipeline(args_dict) print(useChIPSeq) if __name__ == "__main__": main()

39.541126

187

0.614408

0bbf603a743155dd872aaec0166eee3b125a5c33

2,443

py

Python

test/net/integration/tcp/test.py

anna328p/IncludeOS

b9858b8e6842d6c0cc227a34a015e7e8de9438ab

[ "Apache-2.0" ]

null

test/net/integration/tcp/test.py

anna328p/IncludeOS

b9858b8e6842d6c0cc227a34a015e7e8de9438ab

[ "Apache-2.0" ]

null

test/net/integration/tcp/test.py

anna328p/IncludeOS

b9858b8e6842d6c0cc227a34a015e7e8de9438ab

[ "Apache-2.0" ]

null

#!/usr/bin/python import socket import sys import os includeos_src = os.environ.get('INCLUDEOS_SRC', os.path.realpath(os.path.join(os.getcwd(), os.path.dirname(__file__))).split('/test')[0]) sys.path.insert(0,includeos_src + "/test") import vmrunner # Usage: python test.py $GUEST_IP $HOST_IP GUEST = '10.0.0.44' if (len(sys.argv) < 2) else sys.argv[1] HOST = '10.0.0.1' if (len(sys.argv) < 3) else sys.argv[2] def TCP_test(): def connect(port): sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM) server_address = (GUEST, port) print >>sys.stderr, 'connecting to %s port %s' % server_address sock.connect(server_address) try: while True: data = sock.recv(1024) #print >>sys.stderr, '%s' % data if data: sock.sendall(data); else: break finally: print >>sys.stderr, 'closing socket' sock.close() return connect(8081) connect(8082) connect(8083) connect(8084) def listen(port): sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM) sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1) server_address = (HOST, port) print >>sys.stderr, 'starting up on %s port %s' % server_address sock.bind(server_address) sock.listen(1) while True: connection, client_address = sock.accept() try: print >>sys.stderr, 'connection from', client_address while True: data = connection.recv(1024) if data: print >>sys.stderr, 'received data, sending data back to the client' connection.sendall(data) print >>sys.stderr, 'close connection to client' connection.close() else: print >>sys.stderr, 'no more data from', client_address break finally: connection.close() break sock.close() return listen(8085) # Get an auto-created VM from the vmrunner vm = vmrunner.vms[0] # Add custom event-handler vm.on_output("IncludeOS TCP test", TCP_test) # Boot the VM, taking a timeout as parameter vm.make().boot(80)

30.160494

120

0.546459

2924f8c5c2bfffa9cf86f04239c65439bb167693

1,080

py

Python

vapour/links/connections/affine_transform.py

speedcell4/vapour

c00b9b8fffddf0b134bec3ebb26d961e0468194a

[ "MIT" ]

null

vapour/links/connections/affine_transform.py

speedcell4/vapour

c00b9b8fffddf0b134bec3ebb26d961e0468194a

[ "MIT" ]

null

vapour/links/connections/affine_transform.py

speedcell4/vapour

c00b9b8fffddf0b134bec3ebb26d961e0468194a

[ "MIT" ]

null

import chainer.functions as F import chainer.links as L from chainer import Variable, Chain import numpy as np __all__ = [ 'AffineTransform', ] class AffineTransform(Chain): def __init__(self, *in_sizes: int, out_size: int, nonlinear=F.tanh, nobias: bool = False, initialW=None, initial_bias=None) -> None: super(AffineTransform, self).__init__() self.in_size = sum(in_sizes) self.out_size = out_size self.nonlinear = nonlinear self.nobias = nobias self.fc = L.Linear(self.in_size, self.out_size, nobias, initialW, initial_bias) def __call__(self, *xs: Variable, axis: int = 1) -> Variable: return self.fc(F.concat(xs, axis=axis)) if __name__ == '__main__': affine_transform = AffineTransform(3, 4, 5, out_size=6) x1 = Variable(np.random.random((1, 3)).astype(np.float32)) x2 = Variable(np.random.random((1, 4)).astype(np.float32)) x3 = Variable(np.random.random((1, 5)).astype(np.float32)) y = affine_transform(x1, x2, x3) print(f'y :: {y.shape} => {y.data}')

30

99

0.652778

3e349b44a16def7bcb16178ba5ee1e96ba60152b

920

py

Python

pid_contoller/pid_controller/main.py

matthaeusheer/playground

407086c8070cf71280b426db61fbe03034283760

[ "MIT" ]

null

pid_contoller/pid_controller/main.py

matthaeusheer/playground

407086c8070cf71280b426db61fbe03034283760

[ "MIT" ]

1

2020-11-14T09:42:28.000Z

pid_contoller/pid_controller/main.py

matthaeusheer/playground

407086c8070cf71280b426db61fbe03034283760

[ "MIT" ]

null

import matplotlib.pyplot as plt from pid_controller.controller import Sensor, MassSystem, PidController, Gain from pid_controller.loop import closed_loop from pid_controller.visualization import plot_control_loop_output def run_pid_control(init_state, init_velocity, desired_position, system_noise_std, sensor_noise_std, delta_time, p_gain, d_gain, i_gain, mass, eps, max_steps, max_time, gravity): system = MassSystem(init_state, init_velocity, system_noise_std, mass=mass, delta_time=delta_time, gravity=gravity) sensor = Sensor(noise_std=sensor_noise_std) gains = Gain(p_gain, d_gain, i_gain) pid_controller = PidController(gains) output_generator = closed_loop(system, pid_controller, sensor, desired_position, eps, delta_time, max_time, max_steps) plot_control_loop_output(output_generator) plt.show()

41.818182

112

0.740217

c26fef29ffec3f3054e3da8b6ac97df82591154b

7,359

py

Python

scripts/automation/trex_control_plane/client_utils/trex_yaml_gen.py

timgates42/trex-core

efe94752fcb2d0734c83d4877afe92a3dbf8eccd

[ "Apache-2.0" ]

956

2015-06-24T15:04:55.000Z

2022-03-30T06:25:04.000Z

scripts/automation/trex_control_plane/client_utils/trex_yaml_gen.py

angelyouyou/trex-core

fddf78584cae285d9298ef23f9f5c8725e16911e

[ "Apache-2.0" ]

782

2015-09-20T15:19:00.000Z

2022-03-31T23:52:05.000Z

scripts/automation/trex_control_plane/client_utils/trex_yaml_gen.py

angelyouyou/trex-core

fddf78584cae285d9298ef23f9f5c8725e16911e

[ "Apache-2.0" ]

429

2015-06-27T19:34:21.000Z

2022-03-23T11:02:51.000Z

#!/router/bin/python import pprint import yaml import os # import bisect class CTRexYaml(object): """ This class functions as a YAML generator according to TRex YAML format. CTRexYaml is compatible with both Python 2 and Python 3. """ YAML_TEMPLATE = [{'cap_info': [], 'duration': 10.0, 'generator': {'clients_end': '16.0.1.255', 'clients_per_gb': 201, 'clients_start': '16.0.0.1', 'distribution': 'seq', 'dual_port_mask': '1.0.0.0', 'min_clients': 101, 'servers_end': '48.0.0.255', 'servers_start': '48.0.0.1', 'tcp_aging': 1, 'udp_aging': 1}, 'mac' : [0x00,0x00,0x00,0x01,0x00,0x00]}] PCAP_TEMPLATE = {'cps': 1.0, 'ipg': 10000, 'name': '', 'rtt': 10000, 'w': 1} def __init__ (self, trex_files_path): """ The initialization of this class creates a CTRexYaml object with **empty** 'cap-info', and with default client-server configuration. Use class methods to add and assign pcap files and export the data to a YAML file. :parameters: trex_files_path : str a path (on TRex server side) for the pcap files using which TRex can access it. """ self.yaml_obj = list(CTRexYaml.YAML_TEMPLATE) self.empty_cap = True self.file_list = [] self.yaml_dumped = False self.trex_files_path = trex_files_path def add_pcap_file (self, local_pcap_path): """ Adds a .pcap file with recorded traffic to the yaml object by linking the file with 'cap-info' template key fields. :parameters: local_pcap_path : str a path (on client side) for the pcap file to be added. :return: + The index of the inserted item (as int) if item added successfully + -1 if pcap file already exists in 'cap_info'. """ new_pcap = dict(CTRexYaml.PCAP_TEMPLATE) new_pcap['name'] = self.trex_files_path + os.path.basename(local_pcap_path) if self.get_pcap_idx(new_pcap['name']) != -1: # pcap already exists in 'cap_info' return -1 else: self.yaml_obj[0]['cap_info'].append(new_pcap) if self.empty_cap: self.empty_cap = False self.file_list.append(local_pcap_path) return ( len(self.yaml_obj[0]['cap_info']) - 1) def get_pcap_idx (self, pcap_name): """ Checks if a certain .pcap file has been added into the yaml object. :parameters: pcap_name : str the name of the pcap file to be searched :return: + The index of the pcap file (as int) if exists + -1 if not exists. """ comp_pcap = pcap_name if pcap_name.startswith(self.trex_files_path) else (self.trex_files_path + pcap_name) for idx, pcap in enumerate(self.yaml_obj[0]['cap_info']): print (pcap['name'] == comp_pcap) if pcap['name'] == comp_pcap: return idx # pcap file wasn't found return -1 def dump_as_python_obj (self): """ dumps with nice indentation the pythonic format (dictionaries and lists) of the currently built yaml object. :parameters: None :return: None """ pprint.pprint(self.yaml_obj) def dump(self): """ dumps with nice indentation the YAML format of the currently built yaml object. :parameters: None :return: None """ print (yaml.safe_dump(self.yaml_obj, default_flow_style = False)) def to_yaml(self, filename): """ Exports to YAML file the built configuration into an actual YAML file. :parameters: filename : str a path (on client side, including filename) to store the generated yaml file. :return: None :raises: + :exc:`ValueError`, in case no pcap files has been added to the object. + :exc:`EnvironmentError`, in case of any IO error of writing to the files or OSError when trying to open it for writing. """ if self.empty_cap: raise ValueError("No .pcap file has been assigned to yaml object. Must add at least one") else: try: with open(filename, 'w') as yaml_file: yaml_file.write( yaml.safe_dump(self.yaml_obj, default_flow_style = False) ) self.yaml_dumped = True self.file_list.append(filename) except EnvironmentError as inst: raise def set_cap_info_param (self, param, value, seq): """ Set cap-info parameters' value of a specific pcap file. :parameters: param : str the name of the parameters to be set. value : int/float the desired value to be set to `param` key. seq : int an index to the relevant caps array to be changed (index supplied when adding new pcap file, see :func:`add_pcap_file`). :return: **True** on success :raises: :exc:`IndexError`, in case an out-of range index was given. """ try: self.yaml_obj[0]['cap_info'][seq][param] = value return True except IndexError: return False def set_generator_param (self, param, value): """ Set generator parameters' value of the yaml object. :parameters: param : str the name of the parameters to be set. value : int/float/str the desired value to be set to `param` key. :return: None """ self.yaml_obj[0]['generator'][param] = value def get_file_list(self): """ Returns a list of all files related to the YAML object, including the YAML filename itself. .. tip:: This method is especially useful for listing all the files that should be pushed to TRex server as part of the same yaml selection. :parameters: None :return: a list of filepaths, each is a local client-machine file path. """ if not self.yaml_dumped: print ("WARNING: .yaml file wasn't dumped yet. Files list contains only .pcap files") return self.file_list if __name__ == "__main__": pass

34.549296

149

0.511754

9e81f01caca9aa03d47bf9ebc5ae8a483191a874

11,951

py

Python

ansible/venv/lib/python2.7/site-packages/ansible/modules/network/fortios/fortios_system_vxlan.py

gvashchenkolineate/gvashchenkolineate_infra_trytravis

0fb18850afe0d8609693ba4b23f29c7cda17d97f

[ "MIT" ]

17

2017-06-07T23:15:01.000Z

2021-08-30T14:32:36.000Z

ansible/venv/lib/python2.7/site-packages/ansible/modules/network/fortios/fortios_system_vxlan.py

gvashchenkolineate/gvashchenkolineate_infra_trytravis

0fb18850afe0d8609693ba4b23f29c7cda17d97f

[ "MIT" ]

32

2018-10-09T04:13:42.000Z

2020-05-11T07:20:28.000Z

ansible/venv/lib/python2.7/site-packages/ansible/modules/network/fortios/fortios_system_vxlan.py

gvashchenkolineate/gvashchenkolineate_infra_trytravis

0fb18850afe0d8609693ba4b23f29c7cda17d97f

[ "MIT" ]

11

2018-10-09T00:14:53.000Z

2021-11-03T10:54:09.000Z

#!/usr/bin/python from __future__ import (absolute_import, division, print_function) # Copyright 2019 Fortinet, Inc. # # 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 3 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, see <https://www.gnu.org/licenses/>. __metaclass__ = type ANSIBLE_METADATA = {'status': ['preview'], 'supported_by': 'community', 'metadata_version': '1.1'} DOCUMENTATION = ''' --- module: fortios_system_vxlan short_description: Configure VXLAN devices in Fortinet's FortiOS and FortiGate. description: - This module is able to configure a FortiGate or FortiOS (FOS) device by allowing the user to set and modify system feature and vxlan category. Examples include all parameters and values need to be adjusted to datasources before usage. Tested with FOS v6.0.5 version_added: "2.9" author: - Miguel Angel Munoz (@mamunozgonzalez) - Nicolas Thomas (@thomnico) notes: - Requires fortiosapi library developed by Fortinet - Run as a local_action in your playbook requirements: - fortiosapi>=0.9.8 options: host: description: - FortiOS or FortiGate IP address. type: str required: false username: description: - FortiOS or FortiGate username. type: str required: false password: description: - FortiOS or FortiGate password. type: str default: "" vdom: description: - Virtual domain, among those defined previously. A vdom is a virtual instance of the FortiGate that can be configured and used as a different unit. type: str default: root https: description: - Indicates if the requests towards FortiGate must use HTTPS protocol. type: bool default: true ssl_verify: description: - Ensures FortiGate certificate must be verified by a proper CA. type: bool default: true state: description: - Indicates whether to create or remove the object. type: str required: true choices: - present - absent system_vxlan: description: - Configure VXLAN devices. default: null type: dict suboptions: dstport: description: - VXLAN destination port (1 - 65535). type: int interface: description: - Outgoing interface for VXLAN encapsulated traffic. Source system.interface.name. type: str ip_version: description: - IP version to use for the VXLAN interface and so for communication over the VXLAN. IPv4 or IPv6 unicast or multicast. type: str choices: - ipv4-unicast - ipv6-unicast - ipv4-multicast - ipv6-multicast multicast_ttl: description: - VXLAN multicast TTL (1-255). type: int name: description: - VXLAN device or interface name. Must be a unique interface name. required: true type: str remote_ip: description: - IPv4 address of the VXLAN interface on the device at the remote end of the VXLAN. type: list suboptions: ip: description: - IPv4 address. required: true type: str remote_ip6: description: - IPv6 IP address of the VXLAN interface on the device at the remote end of the VXLAN. type: list suboptions: ip6: description: - IPv6 address. required: true type: str vni: description: - VXLAN network ID. type: int ''' EXAMPLES = ''' - hosts: localhost vars: host: "192.168.122.40" username: "admin" password: "" vdom: "root" ssl_verify: "False" tasks: - name: Configure VXLAN devices. fortios_system_vxlan: host: "{{ host }}" username: "{{ username }}" password: "{{ password }}" vdom: "{{ vdom }}" https: "False" state: "present" system_vxlan: dstport: "3" interface: "<your_own_value> (source system.interface.name)" ip_version: "ipv4-unicast" multicast_ttl: "6" name: "default_name_7" remote_ip: - ip: "<your_own_value>" remote_ip6: - ip6: "<your_own_value>" vni: "12" ''' RETURN = ''' build: description: Build number of the fortigate image returned: always type: str sample: '1547' http_method: description: Last method used to provision the content into FortiGate returned: always type: str sample: 'PUT' http_status: description: Last result given by FortiGate on last operation applied returned: always type: str sample: "200" mkey: description: Master key (id) used in the last call to FortiGate returned: success type: str sample: "id" name: description: Name of the table used to fulfill the request returned: always type: str sample: "urlfilter" path: description: Path of the table used to fulfill the request returned: always type: str sample: "webfilter" revision: description: Internal revision number returned: always type: str sample: "17.0.2.10658" serial: description: Serial number of the unit returned: always type: str sample: "FGVMEVYYQT3AB5352" status: description: Indication of the operation's result returned: always type: str sample: "success" vdom: description: Virtual domain used returned: always type: str sample: "root" version: description: Version of the FortiGate returned: always type: str sample: "v5.6.3" ''' from ansible.module_utils.basic import AnsibleModule from ansible.module_utils.connection import Connection from ansible.module_utils.network.fortios.fortios import FortiOSHandler from ansible.module_utils.network.fortimanager.common import FAIL_SOCKET_MSG def login(data, fos): host = data['host'] username = data['username'] password = data['password'] ssl_verify = data['ssl_verify'] fos.debug('on') if 'https' in data and not data['https']: fos.https('off') else: fos.https('on') fos.login(host, username, password, verify=ssl_verify) def filter_system_vxlan_data(json): option_list = ['dstport', 'interface', 'ip_version', 'multicast_ttl', 'name', 'remote_ip', 'remote_ip6', 'vni'] dictionary = {} for attribute in option_list: if attribute in json and json[attribute] is not None: dictionary[attribute] = json[attribute] return dictionary def underscore_to_hyphen(data): if isinstance(data, list): for elem in data: elem = underscore_to_hyphen(elem) elif isinstance(data, dict): new_data = {} for k, v in data.items(): new_data[k.replace('_', '-')] = underscore_to_hyphen(v) data = new_data return data def system_vxlan(data, fos): vdom = data['vdom'] state = data['state'] system_vxlan_data = data['system_vxlan'] filtered_data = underscore_to_hyphen(filter_system_vxlan_data(system_vxlan_data)) if state == "present": return fos.set('system', 'vxlan', data=filtered_data, vdom=vdom) elif state == "absent": return fos.delete('system', 'vxlan', mkey=filtered_data['name'], vdom=vdom) def is_successful_status(status): return status['status'] == "success" or \ status['http_method'] == "DELETE" and status['http_status'] == 404 def fortios_system(data, fos): if data['system_vxlan']: resp = system_vxlan(data, fos) return not is_successful_status(resp), \ resp['status'] == "success", \ resp def main(): fields = { "host": {"required": False, "type": "str"}, "username": {"required": False, "type": "str"}, "password": {"required": False, "type": "str", "default": "", "no_log": True}, "vdom": {"required": False, "type": "str", "default": "root"}, "https": {"required": False, "type": "bool", "default": True}, "ssl_verify": {"required": False, "type": "bool", "default": True}, "state": {"required": True, "type": "str", "choices": ["present", "absent"]}, "system_vxlan": { "required": False, "type": "dict", "default": None, "options": { "dstport": {"required": False, "type": "int"}, "interface": {"required": False, "type": "str"}, "ip_version": {"required": False, "type": "str", "choices": ["ipv4-unicast", "ipv6-unicast", "ipv4-multicast", "ipv6-multicast"]}, "multicast_ttl": {"required": False, "type": "int"}, "name": {"required": True, "type": "str"}, "remote_ip": {"required": False, "type": "list", "options": { "ip": {"required": True, "type": "str"} }}, "remote_ip6": {"required": False, "type": "list", "options": { "ip6": {"required": True, "type": "str"} }}, "vni": {"required": False, "type": "int"} } } } module = AnsibleModule(argument_spec=fields, supports_check_mode=False) # legacy_mode refers to using fortiosapi instead of HTTPAPI legacy_mode = 'host' in module.params and module.params['host'] is not None and \ 'username' in module.params and module.params['username'] is not None and \ 'password' in module.params and module.params['password'] is not None if not legacy_mode: if module._socket_path: connection = Connection(module._socket_path) fos = FortiOSHandler(connection) is_error, has_changed, result = fortios_system(module.params, fos) else: module.fail_json(**FAIL_SOCKET_MSG) else: try: from fortiosapi import FortiOSAPI except ImportError: module.fail_json(msg="fortiosapi module is required") fos = FortiOSAPI() login(module.params, fos) is_error, has_changed, result = fortios_system(module.params, fos) fos.logout() if not is_error: module.exit_json(changed=has_changed, meta=result) else: module.fail_json(msg="Error in repo", meta=result) if __name__ == '__main__': main()

31.203655

139

0.566396

8f0c87ee7393b990da82c9073fa68798eec89d3e

1,042

py

Python

test/integration/test_customers.py

wolever/netsuite-sdk-py

1b1c21e2a8a532fdbf54915e7e9d30b8b5fc2d08

[ "MIT" ]

47

2019-08-15T21:36:36.000Z

2022-03-18T23:44:59.000Z

test/integration/test_customers.py

wolever/netsuite-sdk-py

1b1c21e2a8a532fdbf54915e7e9d30b8b5fc2d08

[ "MIT" ]

52

2019-06-17T09:43:04.000Z

2022-03-22T05:00:53.000Z

test/integration/test_customers.py

wolever/netsuite-sdk-py

1b1c21e2a8a532fdbf54915e7e9d30b8b5fc2d08

[ "MIT" ]

55

2019-06-02T22:18:01.000Z

2022-03-29T07:20:31.000Z

import logging import json logger = logging.getLogger(__name__) def test_get(nc): data = nc.customers.get_all() logger.debug('data = %s', data) assert data, 'get all didnt work' internal_id = data[0]['internalId'] data = nc.customers.get(internalId=internal_id) logger.debug('data = %s', data) assert data, f'No object with internalId {internal_id}' def test_post(nc): with open('./test/integration/data/customers/customer.json') as oj: s = oj.read() expr1 = json.loads(s) logger.debug('expr1 = %s', expr1) res = nc.customers.post(expr1) logger.debug('res = %s', res) assert res['externalId'] == expr1['externalId'], 'External ID does not match' assert res['type'] == 'customer', 'Type does not match' expr2 = nc.customers.get(externalId=res['externalId']) logger.debug('expr2 = %s', expr2) assert expr2['externalId'] == expr1['externalId'], 'External ID does not match' assert expr2['companyName'] == expr1['companyName'], 'companyName does not match'

34.733333

85

0.661228

350321ddde694f65058007c51cbc866eb1154634

2,051

py

Python

get_meaning.py

NamitS27/WordsAPI

e075882202a8b865ed30c47d5524ec785f7aebf0

[ "MIT" ]

null

get_meaning.py

NamitS27/WordsAPI

e075882202a8b865ed30c47d5524ec785f7aebf0

[ "MIT" ]

null

get_meaning.py

NamitS27/WordsAPI

e075882202a8b865ed30c47d5524ec785f7aebf0

[ "MIT" ]

null

from selenium import webdriver import time import json def get_meaning(searchterm): """ Fetches the meaning of the word specified :param searchterm: the word for which you want to fetch the meaning :return: json object of the meaning """ # finds the input field by id in the webpage sbox = driver.find_element_by_id('word') sbox.clear() # clears the input field sbox.send_keys(searchterm) # enters the word specified in the input field # find the 'CALL THE API' button submit = driver.find_element_by_id("getWord") submit.click() # invoking the click event on the button # waiting for the results to come time.sleep(1) # find the code tag in the webpage where the meaning of the word (result) is present code = driver.find_element_by_tag_name("code") # condition if the meaning is not found if code.text == "No results for that word.": return { 'word':searchterm } # converting the meaning of the word from string to json meaning = json.loads(code.text) # returning the meaning of word in json formart return meaning if __name__=="__main__": # URL for the Words API webpage containing a free (try it) option where one can search meaning of the provided word webpage = r"https://www.wordsapi.com/" ''' Specifying the particular version of chrome driver for the particular version of the chrome installed in your system You can download chrome from here > https://www.google.com/chrome/ You can download the specific chrome driver wrt to the versin of the chrome from here > https://chromedriver.chromium.org/downloads ''' options = webdriver.ChromeOptions() chrome_driver_binary = "chromedriver.exe" # change the path of the chrome driver (ignore if in the same folder) driver = webdriver.Chrome(chrome_driver_binary) # opening the url in chrome driver.get(webpage) # Example word = "example" # changes this as per the requirements print(get_meaning(word))

35.362069

135

0.703072

a9fb7a75f3873918d6bc58d6f2bd98e24555c079

3,836

py

Python

USB/python/test-usb-dio96H.py

org-arl/MCC_Linux_Drivers

337ec41121bbff836eb32741dadd90e044edcfc7

[ "JasPer-2.0" ]

null

USB/python/test-usb-dio96H.py

org-arl/MCC_Linux_Drivers

337ec41121bbff836eb32741dadd90e044edcfc7

[ "JasPer-2.0" ]

null

USB/python/test-usb-dio96H.py

org-arl/MCC_Linux_Drivers

337ec41121bbff836eb32741dadd90e044edcfc7

[ "JasPer-2.0" ]

null

#! /usr/bin/python3 # # Copyright (c) 2018 Warren J. Jasper <wjasper@ncsu.edu> # # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Lesser General Public # License as published by the Free Software Foundation; either # version 2.1 of the License, or (at your option) any later version. # # This library 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 # Lesser General Public License for more details. # You should have received a copy of the GNU Lesser General Public # License along with this library; if not, write to the Free Software # Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA from usb_dioSS import * # Solid State Relay class import sys import time def toContinue(): answer = input('Continue [yY]? ') if (answer == 'y' or answer == 'Y'): return True else: return False def main(): # dev = usb_dio96H() # USB-DIO96H # dev = usb_dio96H_50() # USB-DIO96H-50 dev = usb_1096HFS() # USB-1096HFS while True : print("\nUSB-DIO96H/USB_DIO96H-50/USB-1096HFS Testing") print("----------------") print("Hit 'b' to blink LED") print("Hit 'c' to test counter") print("Hit 'A' to read all inputs") print("Hit 's' to get status") print("Hit 'j' for information") print("Hit 'n' to get serial number") print("Hit 'd' to test digital I/O ") print("Hit 'r' to reset the device.") print("Hit 't' to test digital bit I/O") print("Hit 'e' to exit") ch = input('\n') if ch == 'b': dev.Blink() elif ch == 'e': dev.h.close() exit(0) elif ch == 'A': value = dev.GetAll() for i in range(4): print('Port',i,'A =',hex(value[4*i+0])) print('Port',i,'B =',hex(value[4*i+1])) print('Port',i,'C Low =',hex(value[4*i+2])) print('Port',i,'C High =',hex(value[4*i+3])) elif ch == 'c': print('Test the Counter. Connect pin P2C0 <--> CTR') dev.CInit() for i in range(20): dev.DOut(dev.DIO_PORT1C_LOW, 1) time.sleep(0.05) dev.DOut(dev.DIO_PORT1C_LOW, 0) time.sleep(0.05) print('Counter =',dev.CIn()) elif ch == 'd': print('Testing Digital I/O') print('Connect pins P1A0 through P1A7 <==> P1B0 through P1B7') print('Connect pins P1C0 through P1C3 <==> P1C4 through P1C7') while True: value = int(input('Enter a byte number [0-0xff]: '),16) dev.DOut(dev.DIO_PORT0A, value) value2 = dev.DIn(dev.DIO_PORT0B) print('The number you entered =',hex(value2)) value = int(input('Enter a nibble [0-0xf]: '),16) dev.DOut(dev.DIO_PORT0C_LOW, value) value2 = dev.DIn(dev.DIO_PORT0C_HI) print('The number you entered =',hex(value2)) if (toContinue() != True): break elif ch == 't': print('Testing Digital Bit I/O') print('Connect pins P1A0 through P1A7 <==> P1B0 through P1B7') while True: pin = int(input('Enter a pin [0-7]: ')) bit_value = int(input('Enter a bit value [0|1]: ')) dev.DBitOut(dev.DIO_PORT0A, pin, bit_value) value = dev.DBitIn(dev.DIO_PORT0B, pin) print('The value you read is',value) if toContinue() != True: break elif ch == 'n': print("Serial No: %s" % dev.h.get_serial_number_string()) elif ch == 'j': print("Manufacturer: %s" % dev.h.get_manufacturer_string()) print("Product: %s" % dev.h.get_product_string()) print("Serial No: %s" % dev.h.get_serial_number_string()) elif ch == 's': status = dev.Status() print('Status =',hex(status)) if __name__ == "__main__": main()

34.872727

78

0.606361

7e8fcf5f3d1a2302a3b9bb9cfeaab77f22e8480e

19,921

py

Python

scipy/sparse/linalg/eigen/arpack/tests/test_arpack.py

opoplawski/scipy

582d59caabb4a2a6fcdd06b512dcd14daf7ca6b2

[ "BSD-3-Clause" ]

null

scipy/sparse/linalg/eigen/arpack/tests/test_arpack.py

opoplawski/scipy

582d59caabb4a2a6fcdd06b512dcd14daf7ca6b2

[ "BSD-3-Clause" ]

null

scipy/sparse/linalg/eigen/arpack/tests/test_arpack.py

opoplawski/scipy

582d59caabb4a2a6fcdd06b512dcd14daf7ca6b2

[ "BSD-3-Clause" ]

null

from __future__ import division, print_function, absolute_import __usage__ = """ To run tests locally: python tests/test_arpack.py [-l<int>] [-v<int>] """ import warnings import numpy as np from numpy.testing import assert_allclose, \ assert_array_almost_equal_nulp, TestCase, run_module_suite, dec, \ assert_raises, verbose, assert_equal from numpy import array, finfo, argsort, dot, round, conj, random from scipy.linalg import eig, eigh from scipy.sparse import csc_matrix, csr_matrix, lil_matrix, isspmatrix from scipy.sparse.linalg import LinearOperator, aslinearoperator from scipy.sparse.linalg.eigen.arpack import eigs, eigsh, svds, \ ArpackNoConvergence from scipy.linalg import svd, hilbert # eigs() and eigsh() are called many times, so apply a filter for the warnings # they generate here. _eigs_warn_msg = "Single-precision types in `eigs` and `eighs`" def setup_module(): warnings.filterwarnings("ignore", message=_eigs_warn_msg) def teardown_module(): warnings.filterwarnings("default", message=_eigs_warn_msg) # precision for tests _ndigits = {'f': 3, 'd': 11, 'F': 3, 'D': 11} def _get_test_tolerance(type_char, mattype=None): """ Return tolerance values suitable for a given test: Parameters ---------- type_char : {'f', 'd', 'F', 'D'} Data type in ARPACK eigenvalue problem mattype : {csr_matrix, aslinearoperator, asarray}, optional Linear operator type Returns ------- tol Tolerance to pass to the ARPACK routine rtol Relative tolerance for outputs atol Absolute tolerance for outputs """ rtol = {'f': 3000 * np.finfo(np.float32).eps, 'F': 3000 * np.finfo(np.float32).eps, 'd': 2000 * np.finfo(np.float64).eps, 'D': 2000 * np.finfo(np.float64).eps}[type_char] atol = rtol tol = 0 if mattype is aslinearoperator and type_char in ('f', 'F'): # iterative methods in single precision: worse errors # also: bump ARPACK tolerance so that the iterative method converges tol = 30 * np.finfo(np.float32).eps rtol *= 5 if mattype is csr_matrix and type_char in ('f', 'F'): # sparse in single precision: worse errors rtol *= 5 return tol, rtol, atol def generate_matrix(N, complex=False, hermitian=False, pos_definite=False, sparse=False): M = np.random.random((N,N)) if complex: M = M + 1j * np.random.random((N,N)) if hermitian: if pos_definite: if sparse: i = np.arange(N) j = np.random.randint(N, size=N-2) i, j = np.meshgrid(i, j) M[i,j] = 0 M = np.dot(M.conj(), M.T) else: M = np.dot(M.conj(), M.T) if sparse: i = np.random.randint(N, size=N * N // 4) j = np.random.randint(N, size=N * N // 4) ind = np.where(i == j) j[ind] = (j[ind] + 1) % N M[i,j] = 0 M[j,i] = 0 else: if sparse: i = np.random.randint(N, size=N * N // 2) j = np.random.randint(N, size=N * N // 2) M[i,j] = 0 return M def _aslinearoperator_with_dtype(m): m = aslinearoperator(m) if not hasattr(m, 'dtype'): x = np.zeros(m.shape[1]) m.dtype = (m * x).dtype return m def assert_allclose_cc(actual, desired, **kw): """Almost equal or complex conjugates almost equal""" try: assert_allclose(actual, desired, **kw) except: assert_allclose(actual, conj(desired), **kw) def argsort_which(eval, typ, k, which, sigma=None, OPpart=None, mode=None): """Return sorted indices of eigenvalues using the "which" keyword from eigs and eigsh""" if sigma is None: reval = np.round(eval, decimals=_ndigits[typ]) else: if mode is None or mode == 'normal': if OPpart is None: reval = 1. / (eval - sigma) elif OPpart == 'r': reval = 0.5 * (1. / (eval - sigma) + 1. / (eval - np.conj(sigma))) elif OPpart == 'i': reval = -0.5j * (1. / (eval - sigma) - 1. / (eval - np.conj(sigma))) elif mode == 'cayley': reval = (eval + sigma) / (eval - sigma) elif mode == 'buckling': reval = eval / (eval - sigma) else: raise ValueError("mode='%s' not recognized" % mode) reval = np.round(reval, decimals=_ndigits[typ]) if which in ['LM', 'SM']: ind = np.argsort(abs(reval)) elif which in ['LR', 'SR', 'LA', 'SA', 'BE']: ind = np.argsort(np.real(reval)) elif which in ['LI', 'SI']: # for LI,SI ARPACK returns largest,smallest abs(imaginary) why? if typ.islower(): ind = np.argsort(abs(np.imag(reval))) else: ind = np.argsort(np.imag(reval)) else: raise ValueError("which='%s' is unrecognized" % which) if which in ['LM', 'LA', 'LR', 'LI']: return ind[-k:] elif which in ['SM', 'SA', 'SR', 'SI']: return ind[:k] elif which == 'BE': return np.concatenate((ind[:k//2], ind[k//2-k:])) def eval_evec(symmetric, d, typ, k, which, v0=None, sigma=None, mattype=np.asarray, OPpart=None, mode='normal'): general = ('bmat' in d) if symmetric: eigs_func = eigsh else: eigs_func = eigs if general: err = ("error for %s:general, typ=%s, which=%s, sigma=%s, " "mattype=%s, OPpart=%s, mode=%s" % (eigs_func.__name__, typ, which, sigma, mattype.__name__, OPpart, mode)) else: err = ("error for %s:standard, typ=%s, which=%s, sigma=%s, " "mattype=%s, OPpart=%s, mode=%s" % (eigs_func.__name__, typ, which, sigma, mattype.__name__, OPpart, mode)) a = d['mat'].astype(typ) ac = mattype(a) if general: b = d['bmat'].astype(typ.lower()) bc = mattype(b) # get exact eigenvalues exact_eval = d['eval'].astype(typ.upper()) ind = argsort_which(exact_eval, typ, k, which, sigma, OPpart, mode) exact_eval_a = exact_eval exact_eval = exact_eval[ind] # compute arpack eigenvalues kwargs = dict(which=which, v0=v0, sigma=sigma) if eigs_func is eigsh: kwargs['mode'] = mode else: kwargs['OPpart'] = OPpart # compute suitable tolerances kwargs['tol'], rtol, atol = _get_test_tolerance(typ, mattype) # solve if general: try: eval, evec = eigs_func(ac, k, bc, **kwargs) except ArpackNoConvergence: kwargs['maxiter'] = 20*a.shape[0] eval, evec = eigs_func(ac, k, bc, **kwargs) else: try: eval, evec = eigs_func(ac, k, **kwargs) except ArpackNoConvergence: kwargs['maxiter'] = 20*a.shape[0] eval, evec = eigs_func(ac, k, **kwargs) ind = argsort_which(eval, typ, k, which, sigma, OPpart, mode) eval_a = eval eval = eval[ind] evec = evec[:,ind] # check eigenvalues assert_allclose_cc(eval, exact_eval, rtol=rtol, atol=atol, err_msg=err) # check eigenvectors LHS = np.dot(a, evec) if general: RHS = eval * np.dot(b, evec) else: RHS = eval * evec assert_allclose(LHS, RHS, rtol=rtol, atol=atol, err_msg=err) class DictWithRepr(dict): def __init__(self, name): self.name = name def __repr__(self): return "<%s>" % self.name class SymmetricParams: def __init__(self): self.eigs = eigsh self.which = ['LM', 'SM', 'LA', 'SA', 'BE'] self.mattypes = [csr_matrix, aslinearoperator, np.asarray] self.sigmas_modes = {None: ['normal'], 0.5: ['normal', 'buckling', 'cayley']} # generate matrices # these should all be float32 so that the eigenvalues # are the same in float32 and float64 N = 6 np.random.seed(2300) Ar = generate_matrix(N, hermitian=True, pos_definite=True).astype('f').astype('d') M = generate_matrix(N, hermitian=True, pos_definite=True).astype('f').astype('d') Ac = generate_matrix(N, hermitian=True, pos_definite=True, complex=True).astype('F').astype('D') v0 = np.random.random(N) # standard symmetric problem SS = DictWithRepr("std-symmetric") SS['mat'] = Ar SS['v0'] = v0 SS['eval'] = eigh(SS['mat'], eigvals_only=True) # general symmetric problem GS = DictWithRepr("gen-symmetric") GS['mat'] = Ar GS['bmat'] = M GS['v0'] = v0 GS['eval'] = eigh(GS['mat'], GS['bmat'], eigvals_only=True) # standard hermitian problem SH = DictWithRepr("std-hermitian") SH['mat'] = Ac SH['v0'] = v0 SH['eval'] = eigh(SH['mat'], eigvals_only=True) # general hermitian problem GH = DictWithRepr("gen-hermitian") GH['mat'] = Ac GH['bmat'] = M GH['v0'] = v0 GH['eval'] = eigh(GH['mat'], GH['bmat'], eigvals_only=True) self.real_test_cases = [SS, GS] self.complex_test_cases = [SH, GH] class NonSymmetricParams: def __init__(self): self.eigs = eigs self.which = ['LM', 'LR', 'LI'] # , 'SM', 'LR', 'SR', 'LI', 'SI'] self.mattypes = [csr_matrix, aslinearoperator, np.asarray] self.sigmas_OPparts = {None: [None], 0.1: ['r'], 0.1 + 0.1j: ['r', 'i']} # generate matrices # these should all be float32 so that the eigenvalues # are the same in float32 and float64 N = 6 np.random.seed(2300) Ar = generate_matrix(N).astype('f').astype('d') M = generate_matrix(N, hermitian=True, pos_definite=True).astype('f').astype('d') Ac = generate_matrix(N, complex=True).astype('F').astype('D') v0 = np.random.random(N) # standard real nonsymmetric problem SNR = DictWithRepr("std-real-nonsym") SNR['mat'] = Ar SNR['v0'] = v0 SNR['eval'] = eig(SNR['mat'], left=False, right=False) # general real nonsymmetric problem GNR = DictWithRepr("gen-real-nonsym") GNR['mat'] = Ar GNR['bmat'] = M GNR['v0'] = v0 GNR['eval'] = eig(GNR['mat'], GNR['bmat'], left=False, right=False) # standard complex nonsymmetric problem SNC = DictWithRepr("std-cmplx-nonsym") SNC['mat'] = Ac SNC['v0'] = v0 SNC['eval'] = eig(SNC['mat'], left=False, right=False) # general complex nonsymmetric problem GNC = DictWithRepr("gen-cmplx-nonsym") GNC['mat'] = Ac GNC['bmat'] = M GNC['v0'] = v0 GNC['eval'] = eig(GNC['mat'], GNC['bmat'], left=False, right=False) self.real_test_cases = [SNR, GNR] self.complex_test_cases = [SNC, GNC] def test_symmetric_modes(): params = SymmetricParams() k = 2 symmetric = True for D in params.real_test_cases: for typ in 'fd': for which in params.which: for mattype in params.mattypes: for (sigma, modes) in params.sigmas_modes.items(): for mode in modes: yield (eval_evec, symmetric, D, typ, k, which, None, sigma, mattype, None, mode) def test_hermitian_modes(): params = SymmetricParams() k = 2 symmetric = True for D in params.complex_test_cases: for typ in 'FD': for which in params.which: if which == 'BE': continue # BE invalid for complex for mattype in params.mattypes: for sigma in params.sigmas_modes: yield (eval_evec, symmetric, D, typ, k, which, None, sigma, mattype) def test_symmetric_starting_vector(): params = SymmetricParams() symmetric = True for k in [1, 2, 3, 4, 5]: for D in params.real_test_cases: for typ in 'fd': v0 = random.rand(len(D['v0'])).astype(typ) yield (eval_evec, symmetric, D, typ, k, 'LM', v0) def test_symmetric_no_convergence(): np.random.seed(1234) m = generate_matrix(30, hermitian=True, pos_definite=True) tol, rtol, atol = _get_test_tolerance('d') try: w, v = eigsh(m, 4, which='LM', v0=m[:, 0], maxiter=5, tol=tol) raise AssertionError("Spurious no-error exit") except ArpackNoConvergence as err: k = len(err.eigenvalues) if k <= 0: raise AssertionError("Spurious no-eigenvalues-found case") w, v = err.eigenvalues, err.eigenvectors assert_allclose(dot(m, v), w * v, rtol=rtol, atol=atol) def test_real_nonsymmetric_modes(): params = NonSymmetricParams() k = 2 symmetric = False for D in params.real_test_cases: for typ in 'fd': for which in params.which: for mattype in params.mattypes: for sigma, OPparts in params.sigmas_OPparts.items(): for OPpart in OPparts: yield (eval_evec, symmetric, D, typ, k, which, None, sigma, mattype, OPpart) def test_complex_nonsymmetric_modes(): params = NonSymmetricParams() k = 2 symmetric = False for D in params.complex_test_cases: for typ in 'DF': for which in params.which: for mattype in params.mattypes: for sigma in params.sigmas_OPparts: yield (eval_evec, symmetric, D, typ, k, which, None, sigma, mattype) def test_standard_nonsymmetric_starting_vector(): params = NonSymmetricParams() sigma = None symmetric = False for k in [1, 2, 3, 4]: for d in params.complex_test_cases: for typ in 'FD': A = d['mat'] n = A.shape[0] v0 = random.rand(n).astype(typ) yield (eval_evec, symmetric, d, typ, k, "LM", v0, sigma) def test_general_nonsymmetric_starting_vector(): params = NonSymmetricParams() sigma = None symmetric = False for k in [1, 2, 3, 4]: for d in params.complex_test_cases: for typ in 'FD': A = d['mat'] n = A.shape[0] v0 = random.rand(n).astype(typ) yield (eval_evec, symmetric, d, typ, k, "LM", v0, sigma) def test_standard_nonsymmetric_no_convergence(): np.random.seed(1234) m = generate_matrix(30, complex=True) tol, rtol, atol = _get_test_tolerance('d') try: w, v = eigs(m, 4, which='LM', v0=m[:, 0], maxiter=5, tol=tol) raise AssertionError("Spurious no-error exit") except ArpackNoConvergence as err: k = len(err.eigenvalues) if k <= 0: raise AssertionError("Spurious no-eigenvalues-found case") w, v = err.eigenvalues, err.eigenvectors for ww, vv in zip(w, v.T): assert_allclose(dot(m, vv), ww * vv, rtol=rtol, atol=atol) def test_eigen_bad_shapes(): # A is not square. A = csc_matrix(np.zeros((2, 3))) assert_raises(ValueError, eigs, A) def test_eigen_bad_kwargs(): # Test eigen on wrong keyword argument A = csc_matrix(np.zeros((2, 2))) assert_raises(ValueError, eigs, A, which='XX') def test_ticket_1459_arpack_crash(): for dtype in [np.float32, np.float64]: # XXX: this test does not seem to catch the issue for float32, # but we made the same fix there, just to be sure N = 6 k = 2 np.random.seed(2301) A = np.random.random((N, N)).astype(dtype) v0 = np.array([-0.71063568258907849895, -0.83185111795729227424, -0.34365925382227402451, 0.46122533684552280420, -0.58001341115969040629, -0.78844877570084292984e-01], dtype=dtype) # Should not crash: evals, evecs = eigs(A, k, v0=v0) #---------------------------------------------------------------------- # sparse SVD tests def sorted_svd(m, k, which='LM'): # Compute svd of a dense matrix m, and return singular vectors/values # sorted. if isspmatrix(m): m = m.todense() u, s, vh = svd(m) if which == 'LM': ii = np.argsort(s)[-k:] elif which == 'SM': ii = np.argsort(s)[:k] else: raise ValueError("unknown which=%r" % (which,)) return u[:, ii], s[ii], vh[ii] def svd_estimate(u, s, vh): return np.dot(u, np.dot(np.diag(s), vh)) def test_svd_simple_real(): x = np.array([[1, 2, 3], [3, 4, 3], [1, 0, 2], [0, 0, 1]], np.float) y = np.array([[1, 2, 3, 8], [3, 4, 3, 5], [1, 0, 2, 3], [0, 0, 1, 0]], np.float) z = csc_matrix(x) for m in [x.T, x, y, z, z.T]: for k in range(1, min(m.shape)): u, s, vh = sorted_svd(m, k) su, ss, svh = svds(m, k) m_hat = svd_estimate(u, s, vh) sm_hat = svd_estimate(su, ss, svh) assert_array_almost_equal_nulp(m_hat, sm_hat, nulp=1000) def test_svd_simple_complex(): x = np.array([[1, 2, 3], [3, 4, 3], [1 + 1j, 0, 2], [0, 0, 1]], np.complex) y = np.array([[1, 2, 3, 8 + 5j], [3 - 2j, 4, 3, 5], [1, 0, 2, 3], [0, 0, 1, 0]], np.complex) z = csc_matrix(x) for m in [x, x.T.conjugate(), x.T, y, y.conjugate(), z, z.T]: for k in range(1, min(m.shape) - 1): u, s, vh = sorted_svd(m, k) su, ss, svh = svds(m, k) m_hat = svd_estimate(u, s, vh) sm_hat = svd_estimate(su, ss, svh) assert_array_almost_equal_nulp(m_hat, sm_hat, nulp=1000) def test_svd_maxiter(): # check that maxiter works as expected x = hilbert(6) # ARPACK shouldn't converge on such an ill-conditioned matrix with just # one iteration assert_raises(ArpackNoConvergence, svds, x, 1, maxiter=1) # but 100 iterations should be more than enough u, s, vt = svds(x, 1, maxiter=100) assert_allclose(s, [1.7], atol=0.5) def test_svd_return(): # check that the return_singular_vectors parameter works as expected x = hilbert(6) _, s, _ = sorted_svd(x, 2) ss = svds(x, 2, return_singular_vectors=False) assert_allclose(s, ss) def test_svd_which(): # check that the which parameter works as expected x = hilbert(6) for which in ['LM', 'SM']: _, s, _ = sorted_svd(x, 2, which=which) ss = svds(x, 2, which=which, return_singular_vectors=False) ss.sort() assert_allclose(s, ss, atol=np.sqrt(1e-15)) def test_svd_v0(): # check that the v0 parameter works as expected x = np.array([[1, 2, 3, 4], [5, 6, 7, 8]], float) u, s, vh = svds(x, 1) u2, s2, vh2 = svds(x, 1, v0=u[:,0]) assert_allclose(s, s2, atol=np.sqrt(1e-15)) if __name__ == "__main__": run_module_suite()

31.670906

78

0.538778

a930f5b0e874f959fb9ddcd784a17f560fcb192c

1,140

py

Python

aliyun-python-sdk-afs/aliyunsdkafs/request/v20180112/DescribeConfigNameRequest.py

yndu13/aliyun-openapi-python-sdk

12ace4fb39fe2fb0e3927a4b1b43ee4872da43f5

[ "Apache-2.0" ]

1,001

2015-07-24T01:32:41.000Z

2022-03-25T01:28:18.000Z

aliyun-python-sdk-afs/aliyunsdkafs/request/v20180112/DescribeConfigNameRequest.py

yndu13/aliyun-openapi-python-sdk

12ace4fb39fe2fb0e3927a4b1b43ee4872da43f5

[ "Apache-2.0" ]

363

2015-10-20T03:15:00.000Z

2022-03-08T12:26:19.000Z

aliyun-python-sdk-afs/aliyunsdkafs/request/v20180112/DescribeConfigNameRequest.py

yndu13/aliyun-openapi-python-sdk

12ace4fb39fe2fb0e3927a4b1b43ee4872da43f5

[ "Apache-2.0" ]

682

2015-09-22T07:19:02.000Z

2022-03-22T09:51:46.000Z

# Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you under the Apache License, Version 2.0 (the # "License"); you may not use this file except in compliance # with the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND, either express or implied. See the License for the # specific language governing permissions and limitations # under the License. from aliyunsdkcore.request import RpcRequest class DescribeConfigNameRequest(RpcRequest): def __init__(self): RpcRequest.__init__(self, 'afs', '2018-01-12', 'DescribeConfigName','afs') def get_SourceIp(self): return self.get_query_params().get('SourceIp') def set_SourceIp(self,SourceIp): self.add_query_param('SourceIp',SourceIp)

38

77

0.766667

313fdc83513b30f45dc5efdeae2e9d25d8fc40d3

30,822

py

Python

tests/regressiontests/backends/tests.py

kezabelle/django

138de533ff677b470a1e7b4b6ff084a5b7a7444b

[ "BSD-3-Clause" ]

null

tests/regressiontests/backends/tests.py

kezabelle/django

138de533ff677b470a1e7b4b6ff084a5b7a7444b

[ "BSD-3-Clause" ]

null

tests/regressiontests/backends/tests.py

kezabelle/django

138de533ff677b470a1e7b4b6ff084a5b7a7444b

[ "BSD-3-Clause" ]

1

2021-09-14T06:09:11.000Z

# -*- coding: utf-8 -*- # Unit and doctests for specific database backends. from __future__ import absolute_import, unicode_literals import datetime import threading from django.conf import settings from django.core.management.color import no_style from django.core.exceptions import ImproperlyConfigured from django.db import (backend, connection, connections, DEFAULT_DB_ALIAS, IntegrityError, transaction) from django.db.backends.signals import connection_created from django.db.backends.postgresql_psycopg2 import version as pg_version from django.db.models import fields, Sum, Avg, Variance, StdDev from django.db.utils import ConnectionHandler, DatabaseError, load_backend from django.test import (TestCase, skipUnlessDBFeature, skipIfDBFeature, TransactionTestCase) from django.test.utils import override_settings, str_prefix from django.utils import six from django.utils.six.moves import xrange from django.utils import unittest from . import models class DummyBackendTest(TestCase): def test_no_databases(self): """ Test that empty DATABASES setting default to the dummy backend. """ DATABASES = {} conns = ConnectionHandler(DATABASES) self.assertEqual(conns[DEFAULT_DB_ALIAS].settings_dict['ENGINE'], 'django.db.backends.dummy') class OracleChecks(unittest.TestCase): @unittest.skipUnless(connection.vendor == 'oracle', "No need to check Oracle quote_name semantics") def test_quote_name(self): # Check that '%' chars are escaped for query execution. name = '"SOME%NAME"' quoted_name = connection.ops.quote_name(name) self.assertEqual(quoted_name % (), name) @unittest.skipUnless(connection.vendor == 'oracle', "No need to check Oracle cursor semantics") def test_dbms_session(self): # If the backend is Oracle, test that we can call a standard # stored procedure through our cursor wrapper. convert_unicode = backend.convert_unicode cursor = connection.cursor() cursor.callproc(convert_unicode('DBMS_SESSION.SET_IDENTIFIER'), [convert_unicode('_django_testing!'),]) @unittest.skipUnless(connection.vendor == 'oracle', "No need to check Oracle cursor semantics") def test_cursor_var(self): # If the backend is Oracle, test that we can pass cursor variables # as query parameters. cursor = connection.cursor() var = cursor.var(backend.Database.STRING) cursor.execute("BEGIN %s := 'X'; END; ", [var]) self.assertEqual(var.getvalue(), 'X') @unittest.skipUnless(connection.vendor == 'oracle', "No need to check Oracle cursor semantics") def test_long_string(self): # If the backend is Oracle, test that we can save a text longer # than 4000 chars and read it properly c = connection.cursor() c.execute('CREATE TABLE ltext ("TEXT" NCLOB)') long_str = ''.join([six.text_type(x) for x in xrange(4000)]) c.execute('INSERT INTO ltext VALUES (%s)',[long_str]) c.execute('SELECT text FROM ltext') row = c.fetchone() self.assertEqual(long_str, row[0].read()) c.execute('DROP TABLE ltext') @unittest.skipUnless(connection.vendor == 'oracle', "No need to check Oracle connection semantics") def test_client_encoding(self): # If the backend is Oracle, test that the client encoding is set # correctly. This was broken under Cygwin prior to r14781. connection.cursor() # Ensure the connection is initialized. self.assertEqual(connection.connection.encoding, "UTF-8") self.assertEqual(connection.connection.nencoding, "UTF-8") @unittest.skipUnless(connection.vendor == 'oracle', "No need to check Oracle connection semantics") def test_order_of_nls_parameters(self): # an 'almost right' datetime should work with configured # NLS parameters as per #18465. c = connection.cursor() query = "select 1 from dual where '1936-12-29 00:00' < sysdate" # Test that the query succeeds without errors - pre #18465 this # wasn't the case. c.execute(query) self.assertEqual(c.fetchone()[0], 1) class MySQLTests(TestCase): @unittest.skipUnless(connection.vendor == 'mysql', "Test valid only for MySQL") def test_autoincrement(self): """ Check that auto_increment fields are reset correctly by sql_flush(). Before MySQL version 5.0.13 TRUNCATE did not do auto_increment reset. Refs #16961. """ statements = connection.ops.sql_flush(no_style(), tables=['test'], sequences=[{ 'table': 'test', 'col': 'somecol', }]) found_reset = False for sql in statements: found_reset = found_reset or 'ALTER TABLE' in sql if connection.mysql_version < (5,0,13): self.assertTrue(found_reset) else: self.assertFalse(found_reset) @unittest.skipUnless(connection.vendor == 'mysql', "Test valid only for MySQL") def test_server_version_connections(self): connection.close() connection.mysql_version self.assertTrue(connection.connection is None) class DateQuotingTest(TestCase): def test_django_date_trunc(self): """ Test the custom ``django_date_trunc method``, in particular against fields which clash with strings passed to it (e.g. 'year') - see #12818__. __: http://code.djangoproject.com/ticket/12818 """ updated = datetime.datetime(2010, 2, 20) models.SchoolClass.objects.create(year=2009, last_updated=updated) years = models.SchoolClass.objects.dates('last_updated', 'year') self.assertEqual(list(years), [datetime.datetime(2010, 1, 1, 0, 0)]) def test_django_extract(self): """ Test the custom ``django_extract method``, in particular against fields which clash with strings passed to it (e.g. 'day') - see #12818__. __: http://code.djangoproject.com/ticket/12818 """ updated = datetime.datetime(2010, 2, 20) models.SchoolClass.objects.create(year=2009, last_updated=updated) classes = models.SchoolClass.objects.filter(last_updated__day=20) self.assertEqual(len(classes), 1) @override_settings(DEBUG=True) class LastExecutedQueryTest(TestCase): def test_debug_sql(self): list(models.Tag.objects.filter(name="test")) sql = connection.queries[-1]['sql'].lower() self.assertIn("select", sql) self.assertIn(models.Tag._meta.db_table, sql) def test_query_encoding(self): """ Test that last_executed_query() returns an Unicode string """ tags = models.Tag.objects.extra(select={'föö': 1}) sql, params = tags.query.sql_with_params() cursor = tags.query.get_compiler('default').execute_sql(None) last_sql = cursor.db.ops.last_executed_query(cursor, sql, params) self.assertTrue(isinstance(last_sql, six.text_type)) @unittest.skipUnless(connection.vendor == 'sqlite', "This test is specific to SQLite.") def test_no_interpolation_on_sqlite(self): # Regression for #17158 # This shouldn't raise an exception query = "SELECT strftime('%Y', 'now');" connection.cursor().execute(query) self.assertEqual(connection.queries[-1]['sql'], str_prefix("QUERY = %(_)s\"SELECT strftime('%%Y', 'now');\" - PARAMS = ()")) class ParameterHandlingTest(TestCase): def test_bad_parameter_count(self): "An executemany call with too many/not enough parameters will raise an exception (Refs #12612)" cursor = connection.cursor() query = ('INSERT INTO %s (%s, %s) VALUES (%%s, %%s)' % ( connection.introspection.table_name_converter('backends_square'), connection.ops.quote_name('root'), connection.ops.quote_name('square') )) self.assertRaises(Exception, cursor.executemany, query, [(1,2,3),]) self.assertRaises(Exception, cursor.executemany, query, [(1,),]) # Unfortunately, the following tests would be a good test to run on all # backends, but it breaks MySQL hard. Until #13711 is fixed, it can't be run # everywhere (although it would be an effective test of #13711). class LongNameTest(TestCase): """Long primary keys and model names can result in a sequence name that exceeds the database limits, which will result in truncation on certain databases (e.g., Postgres). The backend needs to use the correct sequence name in last_insert_id and other places, so check it is. Refs #8901. """ @skipUnlessDBFeature('supports_long_model_names') def test_sequence_name_length_limits_create(self): """Test creation of model with long name and long pk name doesn't error. Ref #8901""" models.VeryLongModelNameZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZ.objects.create() @skipUnlessDBFeature('supports_long_model_names') def test_sequence_name_length_limits_m2m(self): """Test an m2m save of a model with a long name and a long m2m field name doesn't error as on Django >=1.2 this now uses object saves. Ref #8901""" obj = models.VeryLongModelNameZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZ.objects.create() rel_obj = models.Person.objects.create(first_name='Django', last_name='Reinhardt') obj.m2m_also_quite_long_zzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzz.add(rel_obj) @skipUnlessDBFeature('supports_long_model_names') def test_sequence_name_length_limits_flush(self): """Test that sequence resetting as part of a flush with model with long name and long pk name doesn't error. Ref #8901""" # A full flush is expensive to the full test, so we dig into the # internals to generate the likely offending SQL and run it manually # Some convenience aliases VLM = models.VeryLongModelNameZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZ VLM_m2m = VLM.m2m_also_quite_long_zzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzz.through tables = [ VLM._meta.db_table, VLM_m2m._meta.db_table, ] sequences = [ { 'column': VLM._meta.pk.column, 'table': VLM._meta.db_table }, ] cursor = connection.cursor() for statement in connection.ops.sql_flush(no_style(), tables, sequences): cursor.execute(statement) class SequenceResetTest(TestCase): def test_generic_relation(self): "Sequence names are correct when resetting generic relations (Ref #13941)" # Create an object with a manually specified PK models.Post.objects.create(id=10, name='1st post', text='hello world') # Reset the sequences for the database cursor = connection.cursor() commands = connections[DEFAULT_DB_ALIAS].ops.sequence_reset_sql(no_style(), [models.Post]) for sql in commands: cursor.execute(sql) # If we create a new object now, it should have a PK greater # than the PK we specified manually. obj = models.Post.objects.create(name='New post', text='goodbye world') self.assertTrue(obj.pk > 10) class PostgresVersionTest(TestCase): def assert_parses(self, version_string, version): self.assertEqual(pg_version._parse_version(version_string), version) def test_parsing(self): """Test PostgreSQL version parsing from `SELECT version()` output""" self.assert_parses("PostgreSQL 8.3 beta4", 80300) self.assert_parses("PostgreSQL 8.3", 80300) self.assert_parses("EnterpriseDB 8.3", 80300) self.assert_parses("PostgreSQL 8.3.6", 80306) self.assert_parses("PostgreSQL 8.4beta1", 80400) self.assert_parses("PostgreSQL 8.3.1 on i386-apple-darwin9.2.2, compiled by GCC i686-apple-darwin9-gcc-4.0.1 (GCC) 4.0.1 (Apple Inc. build 5478)", 80301) def test_version_detection(self): """Test PostgreSQL version detection""" # Helper mocks class CursorMock(object): "Very simple mock of DB-API cursor" def execute(self, arg): pass def fetchone(self): return ["PostgreSQL 8.3"] class OlderConnectionMock(object): "Mock of psycopg2 (< 2.0.12) connection" def cursor(self): return CursorMock() # psycopg2 < 2.0.12 code path conn = OlderConnectionMock() self.assertEqual(pg_version.get_version(conn), 80300) class PostgresNewConnectionTest(TestCase): """ #17062: PostgreSQL shouldn't roll back SET TIME ZONE, even if the first transaction is rolled back. """ @unittest.skipUnless( connection.vendor == 'postgresql' and connection.isolation_level > 0, "This test applies only to PostgreSQL without autocommit") def test_connect_and_rollback(self): new_connections = ConnectionHandler(settings.DATABASES) new_connection = new_connections[DEFAULT_DB_ALIAS] try: # Ensure the database default time zone is different than # the time zone in new_connection.settings_dict. We can # get the default time zone by reset & show. cursor = new_connection.cursor() cursor.execute("RESET TIMEZONE") cursor.execute("SHOW TIMEZONE") db_default_tz = cursor.fetchone()[0] new_tz = 'Europe/Paris' if db_default_tz == 'UTC' else 'UTC' new_connection.close() # Fetch a new connection with the new_tz as default # time zone, run a query and rollback. new_connection.settings_dict['TIME_ZONE'] = new_tz new_connection.enter_transaction_management() cursor = new_connection.cursor() new_connection.rollback() # Now let's see if the rollback rolled back the SET TIME ZONE. cursor.execute("SHOW TIMEZONE") tz = cursor.fetchone()[0] self.assertEqual(new_tz, tz) finally: try: new_connection.close() except DatabaseError: pass # Unfortunately with sqlite3 the in-memory test database cannot be # closed, and so it cannot be re-opened during testing, and so we # sadly disable this test for now. class ConnectionCreatedSignalTest(TestCase): @skipUnlessDBFeature('test_db_allows_multiple_connections') def test_signal(self): data = {} def receiver(sender, connection, **kwargs): data["connection"] = connection connection_created.connect(receiver) connection.close() cursor = connection.cursor() self.assertTrue(data["connection"].connection is connection.connection) connection_created.disconnect(receiver) data.clear() cursor = connection.cursor() self.assertTrue(data == {}) class EscapingChecks(TestCase): @unittest.skipUnless(connection.vendor == 'sqlite', "This is a sqlite-specific issue") def test_parameter_escaping(self): #13648: '%s' escaping support for sqlite3 cursor = connection.cursor() response = cursor.execute( "select strftime('%%s', date('now'))").fetchall()[0][0] self.assertNotEqual(response, None) # response should be an non-zero integer self.assertTrue(int(response)) class SqlliteAggregationTests(TestCase): """ #19360: Raise NotImplementedError when aggregating on date/time fields. """ @unittest.skipUnless(connection.vendor == 'sqlite', "No need to check SQLite aggregation semantics") def test_aggregation(self): for aggregate in (Sum, Avg, Variance, StdDev): self.assertRaises(NotImplementedError, models.Item.objects.all().aggregate, aggregate('time')) self.assertRaises(NotImplementedError, models.Item.objects.all().aggregate, aggregate('date')) self.assertRaises(NotImplementedError, models.Item.objects.all().aggregate, aggregate('last_modified')) class BackendTestCase(TestCase): def create_squares_with_executemany(self, args): cursor = connection.cursor() opts = models.Square._meta tbl = connection.introspection.table_name_converter(opts.db_table) f1 = connection.ops.quote_name(opts.get_field('root').column) f2 = connection.ops.quote_name(opts.get_field('square').column) query = 'INSERT INTO %s (%s, %s) VALUES (%%s, %%s)' % (tbl, f1, f2) cursor.executemany(query, args) def test_cursor_executemany(self): #4896: Test cursor.executemany args = [(i, i**2) for i in range(-5, 6)] self.create_squares_with_executemany(args) self.assertEqual(models.Square.objects.count(), 11) for i in range(-5, 6): square = models.Square.objects.get(root=i) self.assertEqual(square.square, i**2) def test_cursor_executemany_with_empty_params_list(self): #4765: executemany with params=[] does nothing args = [] self.create_squares_with_executemany(args) self.assertEqual(models.Square.objects.count(), 0) def test_cursor_executemany_with_iterator(self): #10320: executemany accepts iterators args = iter((i, i**2) for i in range(-3, 2)) self.create_squares_with_executemany(args) self.assertEqual(models.Square.objects.count(), 5) args = iter((i, i**2) for i in range(3, 7)) with override_settings(DEBUG=True): # same test for DebugCursorWrapper self.create_squares_with_executemany(args) self.assertEqual(models.Square.objects.count(), 9) def test_unicode_fetches(self): #6254: fetchone, fetchmany, fetchall return strings as unicode objects qn = connection.ops.quote_name models.Person(first_name="John", last_name="Doe").save() models.Person(first_name="Jane", last_name="Doe").save() models.Person(first_name="Mary", last_name="Agnelline").save() models.Person(first_name="Peter", last_name="Parker").save() models.Person(first_name="Clark", last_name="Kent").save() opts2 = models.Person._meta f3, f4 = opts2.get_field('first_name'), opts2.get_field('last_name') query2 = ('SELECT %s, %s FROM %s ORDER BY %s' % (qn(f3.column), qn(f4.column), connection.introspection.table_name_converter(opts2.db_table), qn(f3.column))) cursor = connection.cursor() cursor.execute(query2) self.assertEqual(cursor.fetchone(), ('Clark', 'Kent')) self.assertEqual(list(cursor.fetchmany(2)), [('Jane', 'Doe'), ('John', 'Doe')]) self.assertEqual(list(cursor.fetchall()), [('Mary', 'Agnelline'), ('Peter', 'Parker')]) def test_unicode_password(self): old_password = connection.settings_dict['PASSWORD'] connection.settings_dict['PASSWORD'] = "françois" try: cursor = connection.cursor() except backend.Database.DatabaseError: # As password is probably wrong, a database exception is expected pass except Exception as e: self.fail("Unexpected error raised with unicode password: %s" % e) finally: connection.settings_dict['PASSWORD'] = old_password def test_database_operations_helper_class(self): # Ticket #13630 self.assertTrue(hasattr(connection, 'ops')) self.assertTrue(hasattr(connection.ops, 'connection')) self.assertEqual(connection, connection.ops.connection) def test_cached_db_features(self): self.assertIn(connection.features.supports_transactions, (True, False)) self.assertIn(connection.features.supports_stddev, (True, False)) self.assertIn(connection.features.can_introspect_foreign_keys, (True, False)) def test_duplicate_table_error(self): """ Test that creating an existing table returns a DatabaseError """ cursor = connection.cursor() query = 'CREATE TABLE %s (id INTEGER);' % models.Article._meta.db_table with self.assertRaises(DatabaseError): cursor.execute(query) # We don't make these tests conditional because that means we would need to # check and differentiate between: # * MySQL+InnoDB, MySQL+MYISAM (something we currently can't do). # * if sqlite3 (if/once we get #14204 fixed) has referential integrity turned # on or not, something that would be controlled by runtime support and user # preference. # verify if its type is django.database.db.IntegrityError. class FkConstraintsTests(TransactionTestCase): def setUp(self): # Create a Reporter. self.r = models.Reporter.objects.create(first_name='John', last_name='Smith') def test_integrity_checks_on_creation(self): """ Try to create a model instance that violates a FK constraint. If it fails it should fail with IntegrityError. """ a = models.Article(headline="This is a test", pub_date=datetime.datetime(2005, 7, 27), reporter_id=30) try: a.save() except IntegrityError: return self.skipTest("This backend does not support integrity checks.") def test_integrity_checks_on_update(self): """ Try to update a model instance introducing a FK constraint violation. If it fails it should fail with IntegrityError. """ # Create an Article. models.Article.objects.create(headline="Test article", pub_date=datetime.datetime(2010, 9, 4), reporter=self.r) # Retrive it from the DB a = models.Article.objects.get(headline="Test article") a.reporter_id = 30 try: a.save() except IntegrityError: return self.skipTest("This backend does not support integrity checks.") def test_disable_constraint_checks_manually(self): """ When constraint checks are disabled, should be able to write bad data without IntegrityErrors. """ with transaction.commit_manually(): # Create an Article. models.Article.objects.create(headline="Test article", pub_date=datetime.datetime(2010, 9, 4), reporter=self.r) # Retrive it from the DB a = models.Article.objects.get(headline="Test article") a.reporter_id = 30 try: connection.disable_constraint_checking() a.save() connection.enable_constraint_checking() except IntegrityError: self.fail("IntegrityError should not have occurred.") finally: transaction.rollback() def test_disable_constraint_checks_context_manager(self): """ When constraint checks are disabled (using context manager), should be able to write bad data without IntegrityErrors. """ with transaction.commit_manually(): # Create an Article. models.Article.objects.create(headline="Test article", pub_date=datetime.datetime(2010, 9, 4), reporter=self.r) # Retrive it from the DB a = models.Article.objects.get(headline="Test article") a.reporter_id = 30 try: with connection.constraint_checks_disabled(): a.save() except IntegrityError: self.fail("IntegrityError should not have occurred.") finally: transaction.rollback() def test_check_constraints(self): """ Constraint checks should raise an IntegrityError when bad data is in the DB. """ with transaction.commit_manually(): # Create an Article. models.Article.objects.create(headline="Test article", pub_date=datetime.datetime(2010, 9, 4), reporter=self.r) # Retrive it from the DB a = models.Article.objects.get(headline="Test article") a.reporter_id = 30 try: with connection.constraint_checks_disabled(): a.save() with self.assertRaises(IntegrityError): connection.check_constraints() finally: transaction.rollback() class ThreadTests(TestCase): def test_default_connection_thread_local(self): """ Ensure that the default connection (i.e. django.db.connection) is different for each thread. Refs #17258. """ connections_set = set() connection.cursor() connections_set.add(connection) def runner(): # Passing django.db.connection between threads doesn't work while # connections[DEFAULT_DB_ALIAS] does. from django.db import connections connection = connections[DEFAULT_DB_ALIAS] # Allow thread sharing so the connection can be closed by the # main thread. connection.allow_thread_sharing = True connection.cursor() connections_set.add(connection) for x in range(2): t = threading.Thread(target=runner) t.start() t.join() # Check that each created connection got different inner connection. self.assertEqual( len(set([conn.connection for conn in connections_set])), 3) # Finish by closing the connections opened by the other threads (the # connection opened in the main thread will automatically be closed on # teardown). for conn in connections_set: if conn is not connection: conn.close() def test_connections_thread_local(self): """ Ensure that the connections are different for each thread. Refs #17258. """ connections_set = set() for conn in connections.all(): connections_set.add(conn) def runner(): from django.db import connections for conn in connections.all(): # Allow thread sharing so the connection can be closed by the # main thread. conn.allow_thread_sharing = True connections_set.add(conn) for x in range(2): t = threading.Thread(target=runner) t.start() t.join() self.assertEqual(len(connections_set), 6) # Finish by closing the connections opened by the other threads (the # connection opened in the main thread will automatically be closed on # teardown). for conn in connections_set: if conn is not connection: conn.close() def test_pass_connection_between_threads(self): """ Ensure that a connection can be passed from one thread to the other. Refs #17258. """ models.Person.objects.create(first_name="John", last_name="Doe") def do_thread(): def runner(main_thread_connection): from django.db import connections connections['default'] = main_thread_connection try: models.Person.objects.get(first_name="John", last_name="Doe") except Exception as e: exceptions.append(e) t = threading.Thread(target=runner, args=[connections['default']]) t.start() t.join() # Without touching allow_thread_sharing, which should be False by default. exceptions = [] do_thread() # Forbidden! self.assertIsInstance(exceptions[0], DatabaseError) # If explicitly setting allow_thread_sharing to False connections['default'].allow_thread_sharing = False exceptions = [] do_thread() # Forbidden! self.assertIsInstance(exceptions[0], DatabaseError) # If explicitly setting allow_thread_sharing to True connections['default'].allow_thread_sharing = True exceptions = [] do_thread() # All good self.assertEqual(exceptions, []) def test_closing_non_shared_connections(self): """ Ensure that a connection that is not explicitly shareable cannot be closed by another thread. Refs #17258. """ # First, without explicitly enabling the connection for sharing. exceptions = set() def runner1(): def runner2(other_thread_connection): try: other_thread_connection.close() except DatabaseError as e: exceptions.add(e) t2 = threading.Thread(target=runner2, args=[connections['default']]) t2.start() t2.join() t1 = threading.Thread(target=runner1) t1.start() t1.join() # The exception was raised self.assertEqual(len(exceptions), 1) # Then, with explicitly enabling the connection for sharing. exceptions = set() def runner1(): def runner2(other_thread_connection): try: other_thread_connection.close() except DatabaseError as e: exceptions.add(e) # Enable thread sharing connections['default'].allow_thread_sharing = True t2 = threading.Thread(target=runner2, args=[connections['default']]) t2.start() t2.join() t1 = threading.Thread(target=runner1) t1.start() t1.join() # No exception was raised self.assertEqual(len(exceptions), 0) class MySQLPKZeroTests(TestCase): """ Zero as id for AutoField should raise exception in MySQL, because MySQL does not allow zero for automatic primary key. """ @skipIfDBFeature('allows_primary_key_0') def test_zero_as_autoval(self): with self.assertRaises(ValueError): models.Square.objects.create(id=0, root=0, square=1)

42.049113

161

0.636364

30a591f29ebc30dfb2ed05f9bb735a7332d4c6fd

8,617

py

Python

ocrd_wrap/skimage_denoise.py

kba/ocrd_wrap

6d92c02e968a8622b1918fc71e4469c6413a8f9b

[ "MIT" ]

null

ocrd_wrap/skimage_denoise.py

kba/ocrd_wrap

6d92c02e968a8622b1918fc71e4469c6413a8f9b

[ "MIT" ]

null

ocrd_wrap/skimage_denoise.py

kba/ocrd_wrap

6d92c02e968a8622b1918fc71e4469c6413a8f9b

[ "MIT" ]

null

from __future__ import absolute_import import os.path from PIL import Image import numpy as np from skimage.morphology import ( remove_small_objects, remove_small_holes ) from ocrd import Processor from ocrd_utils import ( getLogger, make_file_id, assert_file_grp_cardinality, MIMETYPE_PAGE ) from ocrd_modelfactory import page_from_file from ocrd_models.ocrd_page import ( LabelType, LabelsType, MetadataItemType, AlternativeImageType, to_xml ) from .config import OCRD_TOOL TOOL = 'ocrd-skimage-denoise' LOG = getLogger('processor.SkimageDenoise') class SkimageDenoise(Processor): def __init__(self, *args, **kwargs): kwargs['ocrd_tool'] = OCRD_TOOL['tools'][TOOL] kwargs['version'] = OCRD_TOOL['version'] super(SkimageDenoise, self).__init__(*args, **kwargs) def process(self): """Performs binary denoising of segment or page images with scikit-image on the workspace. Open and deserialize PAGE input files and their respective images, then iterate over the element hierarchy down to the requested ``level-of-operation`` in the element hierarchy. For each segment element, retrieve a segment image according to the layout annotation (from an existing AlternativeImage, or by cropping via coordinates into the higher-level image, and - when applicable - deskewing), in binarized form. Next, denoise the image by removing too small connected components with skimage. Then write the new image to the workspace along with the output fileGrp, and using a file ID with suffix ``.IMG-DEN`` with further identification of the input element. Produce a new PAGE output file by serialising the resulting hierarchy. """ oplevel = self.parameter['level-of-operation'] assert_file_grp_cardinality(self.input_file_grp, 1) assert_file_grp_cardinality(self.output_file_grp, 1) for (n, input_file) in enumerate(self.input_files): file_id = make_file_id(input_file, self.output_file_grp) page_id = input_file.pageId or input_file.ID LOG.info("INPUT FILE %i / %s", n, page_id) pcgts = page_from_file(self.workspace.download_file(input_file)) page = pcgts.get_Page() metadata = pcgts.get_Metadata() # ensured by from_file() metadata.add_MetadataItem( MetadataItemType(type_="processingStep", name=self.ocrd_tool['steps'][0], value=TOOL, Labels=[LabelsType( externalModel="ocrd-tool", externalId="parameters", Label=[LabelType(type_=name, value=self.parameter[name]) for name in self.parameter.keys()])])) for page in [page]: page_image, page_coords, page_image_info = self.workspace.image_from_page( page, page_id, feature_selector='binarized') if self.parameter['dpi'] > 0: dpi = self.parameter['dpi'] LOG.info("Page '%s' images will use %d DPI from parameter override", page_id, dpi) elif page_image_info.resolution != 1: dpi = page_image_info.resolution if page_image_info.resolutionUnit == 'cm': dpi = round(dpi * 2.54) LOG.info("Page '%s' images will use %d DPI from image meta-data", page_id, dpi) else: dpi = 300 LOG.info("Page '%s' images will use 300 DPI from fall-back", page_id) maxsize = self.parameter['maxsize'] # in pt maxsize *= dpi/72 # in px #maxsize **= 2 # area if oplevel == 'page': self._process_segment(page, page_image, page_coords, maxsize, "page '%s'" % page_id, input_file.pageId, file_id + '.IMG-DEN') continue regions = page.get_AllRegions(classes=['Text']) if not regions: LOG.warning("Page '%s' contains no text regions", page_id) for region in regions: region_image, region_coords = self.workspace.image_from_segment( region, page_image, page_coords, feature_selector='binarized') if oplevel == 'region': self._process_segment(region, region_image, region_coords, maxsize, "region '%s'" % region.id, None, file_id + '.IMG-DEN_' + region.id) continue lines = region.get_TextLine() if not lines: LOG.warning("Region '%s' contains no text lines", region.id) for line in lines: line_image, line_coords = self.workspace.image_from_segment( line, region_image, region_coords, feature_selector='binarized') if oplevel == 'line': self._process_segment(line, line_image, line_coords, maxsize, "line '%s'" % line.id, None, file_id + '.IMG-DEN_' + line.id) continue words = line.get_Word() if not words: LOG.warning("Line '%s' contains no words", line.id) for word in words: word_image, word_coords = self.workspace.image_from_segment( word, line_image, line_coords, feature_selector='binarized') if oplevel == 'word': self._process_segment(word, word_image, word_coords, maxsize, "word '%s'" % word.id, None, file_id + '.IMG-DEN_' + word.id) continue glyphs = word.get_Glyph() if not glyphs: LOG.warning("Word '%s' contains no glyphs", word.id) for glyph in glyphs: glyph_image, glyph_coords = self.workspace.image_from_segment( glyph, word_image, word_coords, feature_selector='binarized') self._process_segment(glyph, glyph_image, glyph_coords, maxsize, "glyph '%s'" % glyph.id, None, file_id + '.IMG-DEN_' + glyph.id) pcgts.set_pcGtsId(file_id) self.workspace.add_file( ID=file_id, file_grp=self.output_file_grp, pageId=input_file.pageId, mimetype=MIMETYPE_PAGE, local_filename=os.path.join(self.output_file_grp, file_id + '.xml'), content=to_xml(pcgts)) def _process_segment(self, segment, image, coords, maxsize, where, page_id, file_id): features = coords['features'] # features already applied to image features += ',despeckled' array = np.array(image) # suppress bg specks in fg (holes in binary-inverted) remove_small_objects(array, min_size=maxsize, in_place=True) # suppress fg specks in bg (blobs in binary-inverted) remove_small_holes(array, area_threshold=maxsize, in_place=True) image = Image.fromarray(array) # annotate results file_path = self.workspace.save_image_file( image, file_id, file_grp=self.output_file_grp, page_id=page_id) segment.add_AlternativeImage(AlternativeImageType( filename=file_path, comments=features)) LOG.debug("Despeckled image for %s saved as '%s'", where, file_path)

49.24

102

0.525357

e58385fb835ed9c0152c4ca0775a294f61634b58

4,706

py

Python

util/data_processing.py

tonyqtian/tagpredictor

595734bfa80ac213268d36be84f082034ff4f014

[ "MIT" ]

1

2017-11-17T00:18:14.000Z

util/data_processing.py

tonyqtian/tagpredictor

595734bfa80ac213268d36be84f082034ff4f014

[ "MIT" ]

null

util/data_processing.py

tonyqtian/tagpredictor

595734bfa80ac213268d36be84f082034ff4f014

[ "MIT" ]

null

''' Created on Mar 17, 2017 @author: tonyq ''' import pandas as pd import numpy as np import re, sys from bs4 import BeautifulSoup import logging from keras.preprocessing.sequence import pad_sequences from tqdm._tqdm import tqdm from nltk.tokenize import word_tokenize from numpy import array, zeros import operator logger = logging.getLogger(__name__) uri_re = r'(?i)\b((?:https?://|www\d{0,3}[.]|[a-z0-9.\-]+[.][a-z]{2,4}/)(?:[^\s()<>]+|$([^\s()<>]+|(\([^\s()<>]+$))*\))+(?:$([^\s()<>]+|(\([^\s()<>]+$))*\)|[^\s`!()\[\]{};:\'".,<>?«»“”‘’]))' def stripTagsAndUris(x): if x: # BeautifulSoup on content soup = BeautifulSoup(x, "html.parser") # Stripping all <code> tags with their content if any if soup.code: soup.code.decompose() # Get all the text out of the html text = soup.get_text() # Returning text stripping out all uris return re.sub(uri_re, "", text) else: return "" def get_words(text): # word_split = re.compile('[^a-zA-Z0-9_\\+\\-]') # return [word.strip().lower() for word in word_split.split(text)] text = text.replace("\\", " ") return word_tokenize(text) def get_pdTable(path, notag=False): logger.info(' Processing pandas csv ') pdtable = pd.read_csv(path) if notag: return pdtable.id, pdtable.title, pdtable.content else: return pdtable.id, pdtable.title, pdtable.content, pdtable.tags def tableMerge(tableList): return [' '.join(str1) for str1 in zip(*tableList)] def tokenizeIt(table, clean=False): tokenizedTable = [] maxLen = 0 for content in tqdm(table, file=sys.stdout): if clean: text = stripTagsAndUris(content) text = get_words(text) tokenizedTable.append(text) if len(text) > maxLen: maxLen = len(text) else: text = content.split(' ') tokenizedTable.append(text) if len(text) > maxLen: maxLen = len(text) return tokenizedTable, maxLen def createVocab(tableList, min_count=1, reservedList=['<pad>', '<EOF>', '<unk>']): logger.info(' Creating vocabulary ') contentList = [] for list1 in tableList: contentList.extend(list1) wdFrq = {} total_words = 0 for line in contentList: for wd in line: try: wdFrq[wd] += 1 except KeyError: wdFrq[wd] = 1 total_words += 1 logger.info(' %i total words, %i unique words ' % (total_words, len(wdFrq))) sorted_word_freqs = sorted(wdFrq.items(), key=operator.itemgetter(1), reverse=True) vocab_size = 0 for _, freq in sorted_word_freqs: if freq >= min_count: vocab_size += 1 vocabDict = {} vocabReverseDict = [] idx = 0 for item1 in reservedList: vocabDict[item1] = idx vocabReverseDict.append(item1) idx += 1 for word, _ in sorted_word_freqs[:vocab_size]: vocabDict[word] = idx vocabReverseDict.append(word) idx += 1 logger.info(' vocab size %i ' % len(vocabReverseDict)) return vocabDict, vocabReverseDict def word2num(contentTable, vocab, unk, maxLen, padding=None, eof=None): unk_hit = 0 totalword = 0 data = [] for line in contentTable: w2num = [] for word in line: if word in vocab: w2num.append(vocab[word]) else: if not type(unk) is type(None): w2num.append(vocab[unk]) unk_hit += 1 totalword += 1 if not type(eof) is type(None): w2num.append(vocab[eof]) data.append(w2num) logger.info(' total %i tokens processed, %i unk hit ' % (totalword, unk_hit)) # pad to np array if not type(padding) is type(None): logger.info(' padding data to width %d by %s padding' % (maxLen, padding)) np_ary = pad_sequences(data, maxlen=maxLen, padding=padding) else: np_ary = array(data) return np_ary def to_categorical2D(y, nb_classes=None): if not nb_classes: nb_classes = y.max() return (np.arange(nb_classes) == y[:,:,None]).astype(int) def to_categoricalAll(y, nb_classes): categorical = zeros((len(y),nb_classes)) line_idx = 0 for line in y: for elem in line: categorical[line_idx][elem] = 1 line_idx += 1 return categorical def categorical_toary(y, round01=False): (length, nb_classes) = y.shape if round01: y = np.around(y) y_ary = [] for i in range(length): y_ary.append(np.argwhere(y[i,:] == 1).ravel().tolist()) return y_ary def prob_top_n(y, top=5): (length, nb_classes) = y.shape y_ary = [] for i in range(length): idx_prob = list(zip(list(range(nb_classes)), y[i,:])) sorted_idx_prob = sorted(idx_prob, key=operator.itemgetter(1), reverse=True)[:top] idx_round = np.around(sorted_idx_prob).astype(int) idx_pos = [] for (idx, prob) in idx_round: if prob == 1: idx_pos.append(idx) y_ary.append(idx_pos) return y_ary

28.179641

195

0.645771

8e48b92f7a0805a175511fefe3b07c3e9e6087a2

4,776

py

Python

release/src-rt-6.x.4708/router/samba3/source4/torture/drs/python/drs_base.py

zaion520/ATtomato

4d48bb79f8d147f89a568cf18da9e0edc41f93fb

[ "FSFAP" ]

2

2019-01-13T09:16:31.000Z

2019-02-15T03:30:28.000Z

release/src-rt-6.x.4708/router/samba3/source4/torture/drs/python/drs_base.py

zaion520/ATtomato

4d48bb79f8d147f89a568cf18da9e0edc41f93fb

[ "FSFAP" ]

null

release/src-rt-6.x.4708/router/samba3/source4/torture/drs/python/drs_base.py

zaion520/ATtomato

4d48bb79f8d147f89a568cf18da9e0edc41f93fb

[ "FSFAP" ]

2

2020-03-08T01:58:25.000Z

2020-12-20T10:34:54.000Z

#!/usr/bin/env python # -*- coding: utf-8 -*- # # Unix SMB/CIFS implementation. # Copyright (C) Kamen Mazdrashki <kamenim@samba.org> 2011 # # 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 3 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, see <http://www.gnu.org/licenses/>. # import sys import time import os sys.path.insert(0, "bin/python") import samba samba.ensure_external_module("testtools", "testtools") samba.ensure_external_module("subunit", "subunit/python") from ldb import ( SCOPE_BASE, Message, FLAG_MOD_REPLACE, ) import samba.tests class DrsBaseTestCase(samba.tests.BlackboxTestCase): """Base class implementation for all DRS python tests. It is intended to provide common initialization and and functionality used by all DRS tests in drs/python test package. For instance, DC1 and DC2 are always used to pass URLs for DCs to test against""" def setUp(self): super(DrsBaseTestCase, self).setUp() # connect to DCs url_dc = samba.tests.env_get_var_value("DC1") (self.ldb_dc1, self.info_dc1) = samba.tests.connect_samdb_ex(url_dc, ldap_only=True) url_dc = samba.tests.env_get_var_value("DC2") (self.ldb_dc2, self.info_dc2) = samba.tests.connect_samdb_ex(url_dc, ldap_only=True) # cache some of RootDSE props self.schema_dn = self.info_dc1["schemaNamingContext"][0] self.domain_dn = self.info_dc1["defaultNamingContext"][0] self.config_dn = self.info_dc1["configurationNamingContext"][0] self.forest_level = int(self.info_dc1["forestFunctionality"][0]) # we will need DCs DNS names for 'samba-tool drs' command self.dnsname_dc1 = self.info_dc1["dnsHostName"][0] self.dnsname_dc2 = self.info_dc2["dnsHostName"][0] def tearDown(self): super(DrsBaseTestCase, self).tearDown() def _GUID_string(self, guid): return self.ldb_dc1.schema_format_value("objectGUID", guid) def _ldap_schemaUpdateNow(self, sam_db): rec = {"dn": "", "schemaUpdateNow": "1"} m = Message.from_dict(sam_db, rec, FLAG_MOD_REPLACE) sam_db.modify(m) def _deleted_objects_dn(self, sam_ldb): wkdn = "<WKGUID=18E2EA80684F11D2B9AA00C04F79F805,%s>" % self.domain_dn res = sam_ldb.search(base=wkdn, scope=SCOPE_BASE, controls=["show_deleted:1"]) self.assertEquals(len(res), 1) return str(res[0]["dn"]) def _make_obj_name(self, prefix): return prefix + time.strftime("%s", time.gmtime()) def _samba_tool_cmdline(self, drs_command): # find out where is net command samba_tool_cmd = os.path.abspath("./bin/samba-tool") # make command line credentials string creds = self.get_credentials() cmdline_auth = "-U%s/%s%%%s" % (creds.get_domain(), creds.get_username(), creds.get_password()) # bin/samba-tool drs <drs_command> <cmdline_auth> return "%s drs %s %s" % (samba_tool_cmd, drs_command, cmdline_auth) def _net_drs_replicate(self, DC, fromDC, nc_dn=None, forced=True): if nc_dn is None: nc_dn = self.domain_dn # make base command line samba_tool_cmdline = self._samba_tool_cmdline("replicate") if forced: samba_tool_cmdline += " --sync-forced" # bin/samba-tool drs replicate <Dest_DC_NAME> <Src_DC_NAME> <Naming Context> cmd_line = "%s %s %s %s" % (samba_tool_cmdline, DC, fromDC, nc_dn) return self.check_output(cmd_line) def _enable_inbound_repl(self, DC): # make base command line samba_tool_cmd = self._samba_tool_cmdline("options") # disable replication self.check_run("%s %s --dsa-option=-DISABLE_INBOUND_REPL" %(samba_tool_cmd, DC)) def _disable_inbound_repl(self, DC): # make base command line samba_tool_cmd = self._samba_tool_cmdline("options") # disable replication self.check_run("%s %s --dsa-option=+DISABLE_INBOUND_REPL" %(samba_tool_cmd, DC))

38.829268

88

0.647613

c0c7ee9a0323b6ec3f4252fdfe897de4d50ecc85

1,726

py

Python

examples/figures.py

pbmanis/cnmodel

eee593c673752c19137658d5b9a381ea9ad4580f

[ "BSD-3-Clause" ]

5

2017-07-26T21:46:14.000Z

2020-11-27T07:53:14.000Z

examples/figures.py

pbmanis/cnmodel

eee593c673752c19137658d5b9a381ea9ad4580f

[ "BSD-3-Clause" ]

12

2017-07-26T07:16:16.000Z

2021-07-14T13:41:37.000Z

examples/figures.py

pbmanis/cnmodel

eee593c673752c19137658d5b9a381ea9ad4580f

[ "BSD-3-Clause" ]

10

2017-07-26T07:03:29.000Z

2021-06-23T15:52:37.000Z

from __future__ import print_function """ """ import sys import subprocess if len(sys.argv) < 2: # if no argument, print helpful message print("Plot selected figures from paper, Manis and Campagnola, Hearing Research. 2018") print("Usage: figures.py [2a | 2b | 2c | 3 | 4 | 5 | 6a | 6d | 7]") exit(1) arg = sys.argv[1] # get argument, check that it is valid if arg not in ['2a', '2b', '2c', '3', '4', '5', '6a', '6d', '7']: print("Usage: figures.py [2a | 2b | 2c | 3 | 4 | 5 | 6a | 6d | 7]") exit(1) if arg == '2a': proc = subprocess.Popen(['python', 'examples/test_mechanisms.py', 'klt'], stdin=subprocess.PIPE, stdout=subprocess.PIPE, stderr=subprocess.PIPE) proc.wait() print (proc.stdout.read()) # ;; # 2b) # python examples/test_mechanisms.py kht # ;; # 2c) # python examples/test_mechanisms.py ka # ;; # 3) # python examples/toy_model.py # ;; # 4) # python examples/test_synapses.py sgc bushy # ;; # 5) # python examples/test_decorator.py # ;; # 6a) # python examples/test_bushy_variation.py a # ;; # 6d) # python examples/test_bushy_variation.py d # ;; # # 7) # while true; do # echo "This figure may take hours to generate!" # read -p "Are you sure you want to run the script?" yn # case $yn in # [Yy]* ) python examples/test_physiology.py; break;; # [Nn]* ) exit;; # * ) echo "Please answer yes or no.";; # esac # done # ;; #

27.83871

91

0.505214

4ebb9c9334d36b725e072f9ec0586c516d0c5abc

68

py

Python

pyxb/bundles/reqif/driver.py

eLBati/pyxb

14737c23a125fd12c954823ad64fc4497816fae3

[ "Apache-2.0" ]

123

2015-01-12T06:43:22.000Z

2022-03-20T18:06:46.000Z

pyxb/bundles/reqif/driver.py

eLBati/pyxb

14737c23a125fd12c954823ad64fc4497816fae3

[ "Apache-2.0" ]

103

2015-01-08T18:35:57.000Z

2022-01-18T01:44:14.000Z

pyxb/bundles/reqif/driver.py

eLBati/pyxb

14737c23a125fd12c954823ad64fc4497816fae3

[ "Apache-2.0" ]

54

2015-02-15T17:12:00.000Z

2022-03-07T23:02:32.000Z

# -*- coding: utf-8 -*- from pyxb.bundles.reqif.raw.driver import *

22.666667

43

0.661765

a8476f13aa7ec8aead3ddfc3532185a0782ed373

1,254

py

Python

updater/reports/ReportOrgsAbandoned.py

rashamalek/hubble

508cc0ce4ef60fb968df5e7252ee878bf83c929f

[ "MIT" ]

null

updater/reports/ReportOrgsAbandoned.py

rashamalek/hubble

508cc0ce4ef60fb968df5e7252ee878bf83c929f

[ "MIT" ]

1

2022-03-02T10:13:49.000Z

updater/reports/ReportOrgsAbandoned.py

rashamalek/hubble

508cc0ce4ef60fb968df5e7252ee878bf83c929f

[ "MIT" ]

null

from .ReportDaily import * # Find the organizations that have not received a push for the longest time. # Only look at organizations that have not received a push for at least one # year. Only look at repositories that are still maintained (not archived!). class ReportOrgsAbandoned(ReportDaily): def name(self): return "organizations-abandoned" def updateDailyData(self): self.detailedHeader, self.detailedData = self.parseData(self.executeQuery(self.query())) if len(self.data) == 0: self.header = ["date", "abandoned organizations"] self.data.append([str(self.yesterday()), len(self.detailedData)]) self.truncateData(self.timeRangeTotal()) self.sortDataByDate() def query(self): query = ''' SELECT users.login AS "organization", DATE(MAX(pushes.created_at)) AS "last push" FROM repositories JOIN users ON repositories.owner_id = users.id JOIN pushes ON pushes.repository_id = repositories.id WHERE users.type = "organization" AND repositories.maintained = 1 ''' + \ self.andExcludedEntities("users.login") + ''' GROUP BY users.id HAVING CAST(MAX(pushes.created_at) AS DATE) < "''' + str(self.daysAgo(365)) + '''" ORDER BY MAX(pushes.created_at) ''' return query

32.153846

90

0.707337

c6a2852d37e984a32f50323523da5363f7038d6d

225,669

py

Python

spyder/plugins/editor/widgets/codeeditor.py

skjerns/spyder

c130a2e2f8782d8a27886da100e70e1e47d903f5

[ "MIT" ]

null

spyder/plugins/editor/widgets/codeeditor.py

skjerns/spyder

c130a2e2f8782d8a27886da100e70e1e47d903f5

[ "MIT" ]

null

spyder/plugins/editor/widgets/codeeditor.py

skjerns/spyder

c130a2e2f8782d8a27886da100e70e1e47d903f5

[ "MIT" ]

null

# -*- coding: utf-8 -*- # # Copyright © Spyder Project Contributors # Licensed under the terms of the MIT License # (see spyder/__init__.py for details) """ Editor widget based on QtGui.QPlainTextEdit """ # TODO: Try to separate this module from spyder to create a self # consistent editor module (Qt source code and shell widgets library) # pylint: disable=C0103 # pylint: disable=R0903 # pylint: disable=R0911 # pylint: disable=R0201 # Standard library imports from unicodedata import category import logging import functools import os.path as osp import re import sre_constants import sys import textwrap # Third party imports from diff_match_patch import diff_match_patch from IPython.core.inputtransformer2 import TransformerManager from qtpy.compat import to_qvariant from qtpy.QtCore import (QEvent, QRegExp, Qt, QTimer, QThread, QUrl, Signal, Slot) from qtpy.QtGui import (QColor, QCursor, QFont, QKeySequence, QPaintEvent, QPainter, QMouseEvent, QTextCursor, QDesktopServices, QKeyEvent, QTextDocument, QTextFormat, QTextOption, QTextCharFormat, QTextLayout) from qtpy.QtWidgets import (QApplication, QMenu, QMessageBox, QSplitter, QScrollBar) from spyder_kernels.utils.dochelpers import getobj from three_merge import merge # Local imports from spyder.api.panel import Panel from spyder.config.base import _, get_debug_level, running_under_pytest from spyder.config.manager import CONF from spyder.plugins.editor.api.decoration import TextDecoration from spyder.plugins.editor.extensions import (CloseBracketsExtension, CloseQuotesExtension, DocstringWriterExtension, QMenuOnlyForEnter, EditorExtensionsManager, SnippetsExtension) from spyder.plugins.completion.providers.kite.widgets import KiteCallToAction from spyder.plugins.completion.api import (CompletionRequestTypes, TextDocumentSyncKind, DiagnosticSeverity) from spyder.plugins.editor.panels import (ClassFunctionDropdown, DebuggerPanel, EdgeLine, FoldingPanel, IndentationGuide, LineNumberArea, PanelsManager, ScrollFlagArea) from spyder.plugins.editor.utils.editor import (TextHelper, BlockUserData, get_file_language) from spyder.plugins.editor.utils.debugger import DebuggerManager from spyder.plugins.editor.utils.kill_ring import QtKillRing from spyder.plugins.editor.utils.languages import ALL_LANGUAGES, CELL_LANGUAGES from spyder.plugins.editor.panels.utils import ( merge_folding, collect_folding_regions) from spyder.plugins.completion.decorators import ( request, handles, class_register) from spyder.plugins.editor.widgets.codeeditor_widgets import GoToLineDialog from spyder.plugins.editor.widgets.base import TextEditBaseWidget from spyder.plugins.outlineexplorer.api import (OutlineExplorerData as OED, is_cell_header) from spyder.py3compat import PY2, to_text_string, is_string, is_text_string from spyder.utils import encoding, sourcecode from spyder.utils.clipboard_helper import CLIPBOARD_HELPER from spyder.utils.icon_manager import ima from spyder.utils import syntaxhighlighters as sh from spyder.utils.palette import SpyderPalette, QStylePalette from spyder.utils.qthelpers import (add_actions, create_action, file_uri, mimedata2url, start_file) from spyder.utils.vcs import get_git_remotes, remote_to_url from spyder.utils.qstringhelpers import qstring_length try: import nbformat as nbformat from nbconvert import PythonExporter as nbexporter except Exception: nbformat = None # analysis:ignore logger = logging.getLogger(__name__) # Regexp to detect noqa inline comments. NOQA_INLINE_REGEXP = re.compile(r"#?noqa", re.IGNORECASE) @class_register class CodeEditor(TextEditBaseWidget): """Source Code Editor Widget based exclusively on Qt""" LANGUAGES = { 'Python': (sh.PythonSH, '#'), 'IPython': (sh.IPythonSH, '#'), 'Cython': (sh.CythonSH, '#'), 'Fortran77': (sh.Fortran77SH, 'c'), 'Fortran': (sh.FortranSH, '!'), 'Idl': (sh.IdlSH, ';'), 'Diff': (sh.DiffSH, ''), 'GetText': (sh.GetTextSH, '#'), 'Nsis': (sh.NsisSH, '#'), 'Html': (sh.HtmlSH, ''), 'Yaml': (sh.YamlSH, '#'), 'Cpp': (sh.CppSH, '//'), 'OpenCL': (sh.OpenCLSH, '//'), 'Enaml': (sh.EnamlSH, '#'), 'Markdown': (sh.MarkdownSH, '#'), # Every other language 'None': (sh.TextSH, ''), } TAB_ALWAYS_INDENTS = ( 'py', 'pyw', 'python', 'ipy', 'c', 'cpp', 'cl', 'h', 'pyt', 'pyi' ) # Timeout to update decorations (through a QTimer) when a position # changed is detected in the vertical scrollbar or when releasing # the up/down arrow keys. UPDATE_DECORATIONS_TIMEOUT = 500 # milliseconds # Timeouts (in milliseconds) to sychronize symbols and folding after # linting results arrive, according to the number of lines in the file. SYNC_SYMBOLS_AND_FOLDING_TIMEOUTS = { # Lines: Timeout 500: 500, 1500: 1200, 2500: 3200, 6500: 4500 } # Custom signal to be emitted upon completion of the editor's paintEvent painted = Signal(QPaintEvent) # To have these attrs when early viewportEvent's are triggered edge_line = None indent_guides = None sig_breakpoints_changed = Signal() sig_debug_stop = Signal((int,), ()) sig_debug_start = Signal() sig_breakpoints_saved = Signal() sig_filename_changed = Signal(str) sig_bookmarks_changed = Signal() go_to_definition = Signal(str, int, int) sig_show_object_info = Signal(int) sig_run_selection = Signal() sig_run_cell_and_advance = Signal() sig_run_cell = Signal() sig_re_run_last_cell = Signal() sig_debug_cell = Signal() sig_cursor_position_changed = Signal(int, int) sig_new_file = Signal(str) sig_refresh_formatting = Signal(bool) #: Signal emitted when the editor loses focus sig_focus_changed = Signal() #: Signal emitted when a key is pressed sig_key_pressed = Signal(QKeyEvent) #: Signal emitted when a key is released sig_key_released = Signal(QKeyEvent) #: Signal emitted when the alt key is pressed and the left button of the # mouse is clicked sig_alt_left_mouse_pressed = Signal(QMouseEvent) #: Signal emitted when the alt key is pressed and the cursor moves over # the editor sig_alt_mouse_moved = Signal(QMouseEvent) #: Signal emitted when the cursor leaves the editor sig_leave_out = Signal() #: Signal emitted when the flags need to be updated in the scrollflagarea sig_flags_changed = Signal() #: Signal emitted when the syntax color theme of the editor. sig_theme_colors_changed = Signal(dict) #: Signal emitted when a new text is set on the widget new_text_set = Signal() # -- LSP signals #: Signal emitted when an LSP request is sent to the LSP manager sig_perform_completion_request = Signal(str, str, dict) #: Signal emitted when a response is received from the completion plugin # For now it's only used on tests, but it could be used to track # and profile completion diagnostics. completions_response_signal = Signal(str, object) #: Signal to display object information on the Help plugin sig_display_object_info = Signal(str, bool) #: Signal only used for tests # TODO: Remove it! sig_signature_invoked = Signal(dict) #: Signal emitted when processing code analysis warnings is finished sig_process_code_analysis = Signal() # Used for testing. When the mouse moves with Ctrl/Cmd pressed and # a URI is found, this signal is emitted sig_uri_found = Signal(str) sig_file_uri_preprocessed = Signal(str) """ This signal is emitted when the go to uri for a file has been preprocessed. Parameters ---------- fpath: str The preprocessed file path. """ # Signal with the info about the current completion item documentation # str: object name # str: object signature/documentation # bool: force showing the info sig_show_completion_object_info = Signal(str, str, bool) # Used to indicate if text was inserted into the editor sig_text_was_inserted = Signal() # Used to indicate that text will be inserted into the editor sig_will_insert_text = Signal(str) # Used to indicate that a text selection will be removed sig_will_remove_selection = Signal(tuple, tuple) # Used to indicate that text will be pasted sig_will_paste_text = Signal(str) # Used to indicate that an undo operation will take place sig_undo = Signal() # Used to indicate that an undo operation will take place sig_redo = Signal() # Used to start the status spinner in the editor sig_start_operation_in_progress = Signal() # Used to start the status spinner in the editor sig_stop_operation_in_progress = Signal() def __init__(self, parent=None): TextEditBaseWidget.__init__(self, parent) self.setFocusPolicy(Qt.StrongFocus) # Projects self.current_project_path = None # Caret (text cursor) self.setCursorWidth(CONF.get('main', 'cursor/width')) self.text_helper = TextHelper(self) self._panels = PanelsManager(self) # Mouse moving timer / Hover hints handling # See: mouseMoveEvent self.tooltip_widget.sig_help_requested.connect( self.show_object_info) self.tooltip_widget.sig_completion_help_requested.connect( self.show_completion_object_info) self._last_point = None self._last_hover_word = None self._last_hover_cursor = None self._timer_mouse_moving = QTimer(self) self._timer_mouse_moving.setInterval(350) self._timer_mouse_moving.setSingleShot(True) self._timer_mouse_moving.timeout.connect(self._handle_hover) # Typing keys / handling on the fly completions # See: keyPressEvent self._last_key_pressed_text = '' self._last_pressed_key = None self._timer_autocomplete = QTimer(self) self._timer_autocomplete.setSingleShot(True) self._timer_autocomplete.timeout.connect(self._handle_completions) # Handle completions hints self._completions_hint_idle = False self._timer_completions_hint = QTimer(self) self._timer_completions_hint.setSingleShot(True) self._timer_completions_hint.timeout.connect( self._set_completions_hint_idle) self.completion_widget.sig_completion_hint.connect( self.show_hint_for_completion) # Request symbols and folding after a timeout. # See: process_diagnostics self._timer_sync_symbols_and_folding = QTimer(self) self._timer_sync_symbols_and_folding.setSingleShot(True) self._timer_sync_symbols_and_folding.timeout.connect( self.sync_symbols_and_folding) self.blockCountChanged.connect( self.set_sync_symbols_and_folding_timeout) # Goto uri self._last_hover_pattern_key = None self._last_hover_pattern_text = None # 79-col edge line self.edge_line = self.panels.register(EdgeLine(), Panel.Position.FLOATING) # indent guides self.indent_guides = self.panels.register(IndentationGuide(), Panel.Position.FLOATING) # Blanks enabled self.blanks_enabled = False # Underline errors and warnings self.underline_errors_enabled = False # Scrolling past the end of the document self.scrollpastend_enabled = False self.background = QColor('white') # Folding self.panels.register(FoldingPanel()) # Debugger panel (Breakpoints) self.debugger = DebuggerManager(self) self.panels.register(DebuggerPanel()) # Update breakpoints if the number of lines in the file changes self.blockCountChanged.connect(self.debugger.update_breakpoints) # Line number area management self.linenumberarea = self.panels.register(LineNumberArea()) # Class and Method/Function Dropdowns self.classfuncdropdown = self.panels.register( ClassFunctionDropdown(), Panel.Position.TOP, ) # Colors to be defined in _apply_highlighter_color_scheme() # Currentcell color and current line color are defined in base.py self.occurrence_color = None self.ctrl_click_color = None self.sideareas_color = None self.matched_p_color = None self.unmatched_p_color = None self.normal_color = None self.comment_color = None # --- Syntax highlight entrypoint --- # # - if set, self.highlighter is responsible for # - coloring raw text data inside editor on load # - coloring text data when editor is cloned # - updating document highlight on line edits # - providing color palette (scheme) for the editor # - providing data for Outliner # - self.highlighter is not responsible for # - background highlight for current line # - background highlight for search / current line occurrences self.highlighter_class = sh.TextSH self.highlighter = None ccs = 'Spyder' if ccs not in sh.COLOR_SCHEME_NAMES: ccs = sh.COLOR_SCHEME_NAMES[0] self.color_scheme = ccs self.highlight_current_line_enabled = False # Vertical scrollbar # This is required to avoid a "RuntimeError: no access to protected # functions or signals for objects not created from Python" in # Linux Ubuntu. See spyder-ide/spyder#5215. self.setVerticalScrollBar(QScrollBar()) # Highlights and flag colors self.warning_color = SpyderPalette.COLOR_WARN_2 self.error_color = SpyderPalette.COLOR_ERROR_1 self.todo_color = SpyderPalette.GROUP_9 self.breakpoint_color = SpyderPalette.ICON_3 self.occurrence_color = QColor(SpyderPalette.GROUP_2).lighter(160) self.found_results_color = QColor(SpyderPalette.COLOR_OCCURRENCE_4) # Scrollbar flag area self.scrollflagarea = self.panels.register(ScrollFlagArea(), Panel.Position.RIGHT) self.panels.refresh() self.document_id = id(self) # Indicate occurrences of the selected word self.cursorPositionChanged.connect(self.__cursor_position_changed) self.__find_first_pos = None self.language = None self.supported_language = False self.supported_cell_language = False self.comment_string = None self._kill_ring = QtKillRing(self) # Block user data self.blockCountChanged.connect(self.update_bookmarks) # Highlight using Pygments highlighter timer # --------------------------------------------------------------------- # For files that use the PygmentsSH we parse the full file inside # the highlighter in order to generate the correct coloring. self.timer_syntax_highlight = QTimer(self) self.timer_syntax_highlight.setSingleShot(True) self.timer_syntax_highlight.timeout.connect( self.run_pygments_highlighter) # Mark occurrences timer self.occurrence_highlighting = None self.occurrence_timer = QTimer(self) self.occurrence_timer.setSingleShot(True) self.occurrence_timer.setInterval(1500) self.occurrence_timer.timeout.connect(self.__mark_occurrences) self.occurrences = [] # Update decorations self.update_decorations_timer = QTimer(self) self.update_decorations_timer.setSingleShot(True) self.update_decorations_timer.setInterval( self.UPDATE_DECORATIONS_TIMEOUT) self.update_decorations_timer.timeout.connect( self.update_decorations) self.verticalScrollBar().valueChanged.connect( lambda value: self.update_decorations_timer.start()) # Mark found results self.textChanged.connect(self.__text_has_changed) self.found_results = [] # Docstring self.writer_docstring = DocstringWriterExtension(self) # Context menu self.gotodef_action = None self.setup_context_menu() # Tab key behavior self.tab_indents = None self.tab_mode = True # see CodeEditor.set_tab_mode # Intelligent backspace mode self.intelligent_backspace = True # Automatic (on the fly) completions self.automatic_completions = True self.automatic_completions_after_chars = 3 self.automatic_completions_after_ms = 300 # Code Folding self.code_folding = True self.update_folding_thread = QThread() # Completions hint self.completions_hint = True self.completions_hint_after_ms = 500 self.close_parentheses_enabled = True self.close_quotes_enabled = False self.add_colons_enabled = True self.auto_unindent_enabled = True # Autoformat on save self.format_on_save = False # Mouse tracking self.setMouseTracking(True) self.__cursor_changed = False self._mouse_left_button_pressed = False self.ctrl_click_color = QColor(Qt.blue) self.bookmarks = self.get_bookmarks() # Keyboard shortcuts self.shortcuts = self.create_shortcuts() # Paint event self.__visible_blocks = [] # Visible blocks, update with repaint self.painted.connect(self._draw_editor_cell_divider) # Outline explorer self.oe_proxy = None # Line stripping self.last_change_position = None self.last_position = None self.last_auto_indent = None self.skip_rstrip = False self.strip_trailing_spaces_on_modify = True # Hover hints self.hover_hints_enabled = None # Language Server self.filename = None self.completions_available = False self.text_version = 0 self.save_include_text = True self.open_close_notifications = True self.sync_mode = TextDocumentSyncKind.FULL self.will_save_notify = False self.will_save_until_notify = False self.enable_hover = True self.auto_completion_characters = [] self.resolve_completions_enabled = False self.signature_completion_characters = [] self.go_to_definition_enabled = False self.find_references_enabled = False self.highlight_enabled = False self.formatting_enabled = False self.range_formatting_enabled = False self.document_symbols_enabled = False self.formatting_characters = [] self.completion_args = None self.folding_supported = False self.is_cloned = False self.operation_in_progress = False # Diagnostics self.update_diagnostics_thread = QThread() self._diagnostics = [] # Editor Extensions self.editor_extensions = EditorExtensionsManager(self) self.editor_extensions.add(CloseQuotesExtension()) self.editor_extensions.add(SnippetsExtension()) self.editor_extensions.add(CloseBracketsExtension()) # Text diffs across versions self.differ = diff_match_patch() self.previous_text = '' self.patch = [] self.leading_whitespaces = {} # re-use parent of completion_widget (usually the main window) completion_parent = self.completion_widget.parent() self.kite_call_to_action = KiteCallToAction(self, completion_parent) # Some events should not be triggered during undo/redo # such as line stripping self.is_undoing = False self.is_redoing = False # --- Helper private methods # ------------------------------------------------------------------------ # --- Hover/Hints def _should_display_hover(self, point): """Check if a hover hint should be displayed:""" if not self._mouse_left_button_pressed: return (self.hover_hints_enabled and point and self.get_word_at(point)) def _handle_hover(self): """Handle hover hint trigger after delay.""" self._timer_mouse_moving.stop() pos = self._last_point # These are textual characters but should not trigger a completion # FIXME: update per language ignore_chars = ['(', ')', '.'] if self._should_display_hover(pos): key, pattern_text, cursor = self.get_pattern_at(pos) text = self.get_word_at(pos) if pattern_text: ctrl_text = 'Cmd' if sys.platform == "darwin" else 'Ctrl' if key in ['file']: hint_text = ctrl_text + ' + ' + _('click to open file') elif key in ['mail']: hint_text = ctrl_text + ' + ' + _('click to send email') elif key in ['url']: hint_text = ctrl_text + ' + ' + _('click to open url') else: hint_text = ctrl_text + ' + ' + _('click to open') hint_text = '<span> {} </span>'.format(hint_text) self.show_tooltip(text=hint_text, at_point=pos) return cursor = self.cursorForPosition(pos) cursor_offset = cursor.position() line, col = cursor.blockNumber(), cursor.columnNumber() self._last_point = pos if text and self._last_hover_word != text: if all(char not in text for char in ignore_chars): self._last_hover_word = text self.request_hover(line, col, cursor_offset) else: self.hide_tooltip() elif not self.is_completion_widget_visible(): self.hide_tooltip() def blockuserdata_list(self): """Get the list of all user data in document.""" block = self.document().firstBlock() while block.isValid(): data = block.userData() if data: yield data block = block.next() def outlineexplorer_data_list(self): """Get the list of all user data in document.""" for data in self.blockuserdata_list(): if data.oedata: yield data.oedata # ---- Keyboard Shortcuts def create_cursor_callback(self, attr): """Make a callback for cursor move event type, (e.g. "Start")""" def cursor_move_event(): cursor = self.textCursor() move_type = getattr(QTextCursor, attr) cursor.movePosition(move_type) self.setTextCursor(cursor) return cursor_move_event def create_shortcuts(self): """Create the local shortcuts for the CodeEditor.""" shortcut_context_name_callbacks = ( ('editor', 'code completion', self.do_completion), ('editor', 'duplicate line down', self.duplicate_line_down), ('editor', 'duplicate line up', self.duplicate_line_up), ('editor', 'delete line', self.delete_line), ('editor', 'move line up', self.move_line_up), ('editor', 'move line down', self.move_line_down), ('editor', 'go to new line', self.go_to_new_line), ('editor', 'go to definition', self.go_to_definition_from_cursor), ('editor', 'toggle comment', self.toggle_comment), ('editor', 'blockcomment', self.blockcomment), ('editor', 'unblockcomment', self.unblockcomment), ('editor', 'transform to uppercase', self.transform_to_uppercase), ('editor', 'transform to lowercase', self.transform_to_lowercase), ('editor', 'indent', lambda: self.indent(force=True)), ('editor', 'unindent', lambda: self.unindent(force=True)), ('editor', 'start of line', self.create_cursor_callback('StartOfLine')), ('editor', 'end of line', self.create_cursor_callback('EndOfLine')), ('editor', 'previous line', self.create_cursor_callback('Up')), ('editor', 'next line', self.create_cursor_callback('Down')), ('editor', 'previous char', self.create_cursor_callback('Left')), ('editor', 'next char', self.create_cursor_callback('Right')), ('editor', 'previous word', self.create_cursor_callback('PreviousWord')), ('editor', 'next word', self.create_cursor_callback('NextWord')), ('editor', 'kill to line end', self.kill_line_end), ('editor', 'kill to line start', self.kill_line_start), ('editor', 'yank', self._kill_ring.yank), ('editor', 'rotate kill ring', self._kill_ring.rotate), ('editor', 'kill previous word', self.kill_prev_word), ('editor', 'kill next word', self.kill_next_word), ('editor', 'start of document', self.create_cursor_callback('Start')), ('editor', 'end of document', self.create_cursor_callback('End')), ('editor', 'undo', self.undo), ('editor', 'redo', self.redo), ('editor', 'cut', self.cut), ('editor', 'copy', self.copy), ('editor', 'paste', self.paste), ('editor', 'delete', self.delete), ('editor', 'select all', self.selectAll), ('editor', 'docstring', self.writer_docstring.write_docstring_for_shortcut), ('editor', 'autoformatting', self.format_document_or_range), ('array_builder', 'enter array inline', self.enter_array_inline), ('array_builder', 'enter array table', self.enter_array_table) ) shortcuts = [] for context, name, callback in shortcut_context_name_callbacks: shortcuts.append( CONF.config_shortcut( callback, context=context, name=name, parent=self)) return shortcuts def get_shortcut_data(self): """ Returns shortcut data, a list of tuples (shortcut, text, default) shortcut (QShortcut or QAction instance) text (string): action/shortcut description default (string): default key sequence """ return [sc.data for sc in self.shortcuts] def closeEvent(self, event): TextEditBaseWidget.closeEvent(self, event) def get_document_id(self): return self.document_id def set_as_clone(self, editor): """Set as clone editor""" self.setDocument(editor.document()) self.document_id = editor.get_document_id() self.highlighter = editor.highlighter self.eol_chars = editor.eol_chars self._apply_highlighter_color_scheme() # ---- Widget setup and options def toggle_wrap_mode(self, enable): """Enable/disable wrap mode""" self.set_wrap_mode('word' if enable else None) def toggle_line_numbers(self, linenumbers=True, markers=False): """Enable/disable line numbers.""" self.linenumberarea.setup_margins(linenumbers, markers) @property def panels(self): """ Returns a reference to the :class:`spyder.widgets.panels.managers.PanelsManager` used to manage the collection of installed panels """ return self._panels def setup_editor(self, linenumbers=True, language=None, markers=False, font=None, color_scheme=None, wrap=False, tab_mode=True, strip_mode=False, intelligent_backspace=True, automatic_completions=True, automatic_completions_after_chars=3, automatic_completions_after_ms=300, completions_hint=True, completions_hint_after_ms=500, hover_hints=True, code_snippets=True, highlight_current_line=True, highlight_current_cell=True, occurrence_highlighting=True, scrollflagarea=True, edge_line=True, edge_line_columns=(79,), show_blanks=False, underline_errors=False, close_parentheses=True, close_quotes=False, add_colons=True, auto_unindent=True, indent_chars=" "*4, tab_stop_width_spaces=4, cloned_from=None, filename=None, occurrence_timeout=1500, show_class_func_dropdown=False, indent_guides=False, scroll_past_end=False, show_debug_panel=True, folding=True, remove_trailing_spaces=False, remove_trailing_newlines=False, add_newline=False, format_on_save=False): """ Set-up configuration for the CodeEditor instance. Usually the parameters here are related with a configurable preference in the Preference Dialog and Editor configurations: linenumbers: Enable/Disable line number panel. Default True. language: Set editor language for example python. Default None. markers: Enable/Disable markers panel. Used to show elements like Code Analysis. Default False. font: Base font for the Editor to use. Default None. color_scheme: Initial color scheme for the Editor to use. Default None. wrap: Enable/Disable line wrap. Default False. tab_mode: Enable/Disable using Tab as delimiter between word, Default True. strip_mode: strip_mode: Enable/Disable striping trailing spaces when modifying the file. Default False. intelligent_backspace: Enable/Disable automatically unindenting inserted text (unindenting happens if the leading text length of the line isn't module of the length of indentation chars being use) Default True. automatic_completions: Enable/Disable automatic completions. The behavior of the trigger of this the completions can be established with the two following kwargs. Default True. automatic_completions_after_chars: Number of charts to type to trigger an automatic completion. Default 3. automatic_completions_after_ms: Number of milliseconds to pass before an autocompletion is triggered. Default 300. completions_hint: Enable/Disable documentation hints for completions. Default True. completions_hint_after_ms: Number of milliseconds over a completion item to show the documentation hint. Default 500. hover_hints: Enable/Disable documentation hover hints. Default True. code_snippets: Enable/Disable code snippets completions. Default True. highlight_current_line: Enable/Disable current line highlighting. Default True. highlight_current_cell: Enable/Disable current cell highlighting. Default True. occurrence_highlighting: Enable/Disable highlighting of current word occurrence in the file. Default True. scrollflagarea : Enable/Disable flag area that shows at the left of the scroll bar. Default True. edge_line: Enable/Disable vertical line to show max number of characters per line. Customizable number of columns in the following kwarg. Default True. edge_line_columns: Number of columns/characters where the editor horizontal edge line will show. Default (79,). show_blanks: Enable/Disable blanks highlighting. Default False. underline_errors: Enable/Disable showing and underline to highlight errors. Default False. close_parentheses: Enable/Disable automatic parentheses closing insertion. Default True. close_quotes: Enable/Disable automatic closing of quotes. Default False. add_colons: Enable/Disable automatic addition of colons. Default True. auto_unindent: Enable/Disable automatically unindentation before else, elif, finally or except statements. Default True. indent_chars: Characters to use for indentation. Default " "*4. tab_stop_width_spaces: Enable/Disable using tabs for indentation. Default 4. cloned_from: Editor instance used as template to instantiate this CodeEditor instance. Default None. filename: Initial filename to show. Default None. occurrence_timeout : Timeout in milliseconds to start highlighting matches/occurrences for the current word under the cursor. Default 1500 ms. show_class_func_dropdown: Enable/Disable a Matlab like widget to show classes and functions available in the current file. Default False. indent_guides: Enable/Disable highlighting of code indentation. Default False. scroll_past_end: Enable/Disable possibility to scroll file passed its end. Default False. show_debug_panel: Enable/Disable debug panel. Default True. folding: Enable/Disable code folding. Default True. remove_trailing_spaces: Remove trailing whitespaces on lines. Default False. remove_trailing_newlines: Remove extra lines at the end of the file. Default False. add_newline: Add a newline at the end of the file if there is not one. Default False. format_on_save: Autoformat file automatically when saving. Default False. """ self.set_close_parentheses_enabled(close_parentheses) self.set_close_quotes_enabled(close_quotes) self.set_add_colons_enabled(add_colons) self.set_auto_unindent_enabled(auto_unindent) self.set_indent_chars(indent_chars) # Show/hide the debug panel depending on the language and parameter self.set_debug_panel(show_debug_panel, language) # Show/hide folding panel depending on parameter self.toggle_code_folding(folding) # Scrollbar flag area self.scrollflagarea.set_enabled(scrollflagarea) # Debugging self.debugger.set_filename(filename) # Edge line self.edge_line.set_enabled(edge_line) self.edge_line.set_columns(edge_line_columns) # Indent guides self.toggle_identation_guides(indent_guides) if self.indent_chars == '\t': self.indent_guides.set_indentation_width( tab_stop_width_spaces) else: self.indent_guides.set_indentation_width(len(self.indent_chars)) # Blanks self.set_blanks_enabled(show_blanks) # Remove trailing whitespaces self.set_remove_trailing_spaces(remove_trailing_spaces) # Remove trailing newlines self.set_remove_trailing_newlines(remove_trailing_newlines) # Add newline at the end self.set_add_newline(add_newline) # Scrolling past the end self.set_scrollpastend_enabled(scroll_past_end) # Line number area and indent guides if cloned_from: self.setFont(font) # this is required for line numbers area # Needed to show indent guides for splited editor panels # See spyder-ide/spyder#10900 self.patch = cloned_from.patch self.is_cloned = True self.toggle_line_numbers(linenumbers, markers) # Lexer self.filename = filename self.set_language(language, filename) # Underline errors and warnings self.set_underline_errors_enabled(underline_errors) # Highlight current cell self.set_highlight_current_cell(highlight_current_cell) # Highlight current line self.set_highlight_current_line(highlight_current_line) # Occurrence highlighting self.set_occurrence_highlighting(occurrence_highlighting) self.set_occurrence_timeout(occurrence_timeout) # Tab always indents (even when cursor is not at the begin of line) self.set_tab_mode(tab_mode) # Intelligent backspace self.toggle_intelligent_backspace(intelligent_backspace) # Automatic completions self.toggle_automatic_completions(automatic_completions) self.set_automatic_completions_after_chars( automatic_completions_after_chars) self.set_automatic_completions_after_ms(automatic_completions_after_ms) # Completions hint self.toggle_completions_hint(completions_hint) self.set_completions_hint_after_ms(completions_hint_after_ms) # Hover hints self.toggle_hover_hints(hover_hints) # Code snippets self.toggle_code_snippets(code_snippets) # Autoformat on save self.toggle_format_on_save(format_on_save) if cloned_from is not None: self.set_as_clone(cloned_from) self.panels.refresh() elif font is not None: self.set_font(font, color_scheme) elif color_scheme is not None: self.set_color_scheme(color_scheme) # Set tab spacing after font is set self.set_tab_stop_width_spaces(tab_stop_width_spaces) self.toggle_wrap_mode(wrap) # Class/Function dropdown will be disabled if we're not in a Python # file. self.classfuncdropdown.setVisible(show_class_func_dropdown and self.is_python_like()) self.set_strip_mode(strip_mode) # --- Language Server Protocol methods ----------------------------------- # ------------------------------------------------------------------------ @Slot(str, dict) def handle_response(self, method, params): if method in self.handler_registry: handler_name = self.handler_registry[method] handler = getattr(self, handler_name) handler(params) # This signal is only used on tests. # It could be used to track and profile LSP diagnostics. self.completions_response_signal.emit(method, params) def emit_request(self, method, params, requires_response): """Send request to LSP manager.""" params['requires_response'] = requires_response params['response_instance'] = self self.sig_perform_completion_request.emit( self.language.lower(), method, params) def log_lsp_handle_errors(self, message): """ Log errors when handling LSP responses. This works when debugging is on or off. """ if get_debug_level() > 0: # We log the error normally when running on debug mode. logger.error(message, exc_info=True) else: # We need this because logger.error activates our error # report dialog but it doesn't show the entire traceback # there. So we intentionally leave an error in this call # to get the entire stack info generated by it, which # gives the info we need from users. if PY2: logger.error(message, exc_info=True) print(message, file=sys.stderr) else: logger.error('%', 1, stack_info=True) # ------------- LSP: Configuration and protocol start/end ---------------- def start_completion_services(self): """Start completion services for this instance.""" self.completions_available = True if self.is_cloned: additional_msg = " cloned editor" else: additional_msg = "" self.document_did_open() logger.debug(u"Completion services available for {0}: {1}".format( additional_msg, self.filename)) def register_completion_capabilities(self, capabilities): """ Register completion server capabilities. Parameters ---------- capabilities: dict Capabilities supported by a language server. """ sync_options = capabilities['textDocumentSync'] completion_options = capabilities['completionProvider'] signature_options = capabilities['signatureHelpProvider'] range_formatting_options = ( capabilities['documentOnTypeFormattingProvider']) self.open_close_notifications = sync_options.get('openClose', False) self.sync_mode = sync_options.get('change', TextDocumentSyncKind.NONE) self.will_save_notify = sync_options.get('willSave', False) self.will_save_until_notify = sync_options.get('willSaveWaitUntil', False) self.save_include_text = sync_options['save']['includeText'] self.enable_hover = capabilities['hoverProvider'] self.folding_supported = capabilities.get( 'foldingRangeProvider', False) self.auto_completion_characters = ( completion_options['triggerCharacters']) self.resolve_completions_enabled = ( completion_options.get('resolveProvider', False)) self.signature_completion_characters = ( signature_options['triggerCharacters'] + ['=']) # FIXME: self.go_to_definition_enabled = capabilities['definitionProvider'] self.find_references_enabled = capabilities['referencesProvider'] self.highlight_enabled = capabilities['documentHighlightProvider'] self.formatting_enabled = capabilities['documentFormattingProvider'] self.range_formatting_enabled = ( capabilities['documentRangeFormattingProvider']) self.document_symbols_enabled = ( capabilities['documentSymbolProvider'] ) self.formatting_characters.append( range_formatting_options['firstTriggerCharacter']) self.formatting_characters += ( range_formatting_options.get('moreTriggerCharacter', [])) if self.formatting_enabled: self.format_action.setEnabled(True) self.sig_refresh_formatting.emit(True) self.completions_available = True def stop_completion_services(self): logger.debug('Stopping completion services for %s' % self.filename) self.completions_available = False @request(method=CompletionRequestTypes.DOCUMENT_DID_OPEN, requires_response=False) def document_did_open(self): """Send textDocument/didOpen request to the server.""" cursor = self.textCursor() text = self.get_text_with_eol() if self.is_ipython(): # Send valid python text to LSP as it doesn't support IPython text = self.ipython_to_python(text) params = { 'file': self.filename, 'language': self.language, 'version': self.text_version, 'text': text, 'codeeditor': self, 'offset': cursor.position(), 'selection_start': cursor.selectionStart(), 'selection_end': cursor.selectionEnd(), } return params # ------------- LSP: Symbols --------------------------------------- @request(method=CompletionRequestTypes.DOCUMENT_SYMBOL) def request_symbols(self): """Request document symbols.""" if not self.document_symbols_enabled: return if self.oe_proxy is not None: self.oe_proxy.emit_request_in_progress() params = {'file': self.filename} return params @handles(CompletionRequestTypes.DOCUMENT_SYMBOL) def process_symbols(self, params): """Handle symbols response.""" try: symbols = params['params'] symbols = [] if symbols is None else symbols self.classfuncdropdown.update_data(symbols) if self.oe_proxy is not None: self.oe_proxy.update_outline_info(symbols) except RuntimeError: # This is triggered when a codeeditor instance was removed # before the response can be processed. return except Exception: self.log_lsp_handle_errors("Error when processing symbols") # ------------- LSP: Linting --------------------------------------- @request( method=CompletionRequestTypes.DOCUMENT_DID_CHANGE, requires_response=False) def document_did_change(self, text=None): """Send textDocument/didChange request to the server.""" self.text_version += 1 text = self.get_text_with_eol() if self.is_ipython(): # Send valid python text to LSP text = self.ipython_to_python(text) self.patch = self.differ.patch_make(self.previous_text, text) self.previous_text = text cursor = self.textCursor() params = { 'file': self.filename, 'version': self.text_version, 'text': text, 'diff': self.patch, 'offset': cursor.position(), 'selection_start': cursor.selectionStart(), 'selection_end': cursor.selectionEnd(), } return params @handles(CompletionRequestTypes.DOCUMENT_PUBLISH_DIAGNOSTICS) def process_diagnostics(self, params): """Handle linting response.""" # The LSP spec doesn't require that folding and symbols # are treated in the same way as linting, i.e. to be # recomputed on didChange, didOpen and didSave. However, # we think that's necessary to maintain accurate folding # and symbols all the time. Therefore, we decided to call # those requests here, but after a certain timeout to # avoid performance issues. self._timer_sync_symbols_and_folding.start() # Process results (runs in a thread) self.process_code_analysis(params['params']) def set_sync_symbols_and_folding_timeout(self): """ Set timeout to sync symbols and folding according to the file size. """ current_lines = self.get_line_count() timeout = None for lines in self.SYNC_SYMBOLS_AND_FOLDING_TIMEOUTS.keys(): if (current_lines // lines) == 0: timeout = self.SYNC_SYMBOLS_AND_FOLDING_TIMEOUTS[lines] break if not timeout: timeouts = self.SYNC_SYMBOLS_AND_FOLDING_TIMEOUTS.values() timeout = list(timeouts)[-1] self._timer_sync_symbols_and_folding.setInterval(timeout) def sync_symbols_and_folding(self): """ Synchronize symbols and folding after linting results arrive. """ self.request_folding() self.request_symbols() def process_code_analysis(self, diagnostics): """Process code analysis results in a thread.""" self.cleanup_code_analysis() self._diagnostics = diagnostics # Process diagnostics in a thread to improve performance. self.update_diagnostics_thread.run = self.set_errors self.update_diagnostics_thread.finished.connect( self.finish_code_analysis) self.update_diagnostics_thread.start() def cleanup_code_analysis(self): """Remove all code analysis markers""" self.setUpdatesEnabled(False) self.clear_extra_selections('code_analysis_highlight') self.clear_extra_selections('code_analysis_underline') for data in self.blockuserdata_list(): data.code_analysis = [] self.setUpdatesEnabled(True) # When the new code analysis results are empty, it is necessary # to update manually the scrollflag and linenumber areas (otherwise, # the old flags will still be displayed): self.sig_flags_changed.emit() self.linenumberarea.update() def set_errors(self): """Set errors and warnings in the line number area.""" try: self._process_code_analysis(underline=False) except RuntimeError: # This is triggered when a codeeditor instance was removed # before the response can be processed. return except Exception: self.log_lsp_handle_errors("Error when processing linting") def underline_errors(self): """Underline errors and warnings.""" try: self._process_code_analysis(underline=True) except RuntimeError: # This is triggered when a codeeditor instance was removed # before the response can be processed. return except Exception: self.log_lsp_handle_errors("Error when processing linting") def finish_code_analysis(self): """Finish processing code analysis results.""" self.linenumberarea.update() self.underline_errors() self.update_extra_selections() self.sig_process_code_analysis.emit() self.sig_flags_changed.emit() def errors_present(self): """ Return True if there are errors or warnings present in the file. """ return bool(len(self._diagnostics)) def _process_code_analysis(self, underline): """ Process all code analysis results. Parameters ---------- underline: bool Determines if errors and warnings are going to be set in the line number area or underlined. It's better to separate these two processes for perfomance reasons. That's because setting errors can be done in a thread whereas underlining them can't. """ document = self.document() for diagnostic in self._diagnostics: if self.is_ipython() and ( diagnostic["message"] == "undefined name 'get_ipython'"): # get_ipython is defined in IPython files continue source = diagnostic.get('source', '') msg_range = diagnostic['range'] start = msg_range['start'] end = msg_range['end'] code = diagnostic.get('code', 'E') message = diagnostic['message'] severity = diagnostic.get( 'severity', DiagnosticSeverity.ERROR) block = document.findBlockByNumber(start['line']) data = block.userData() # Skip messages according to certain criteria. # This one works for any programming language if 'analysis:ignore' in block.text(): continue # This only works for Python. if self.language == 'Python': if NOQA_INLINE_REGEXP.search(block.text()) is not None: continue if not data: data = BlockUserData(self) if underline: block_nb = block.blockNumber() first, last = self.get_buffer_block_numbers() if (self.underline_errors_enabled and first <= block_nb <= last): error = severity == DiagnosticSeverity.ERROR color = self.error_color if error else self.warning_color color = QColor(color) color.setAlpha(255) block.color = color data.selection_start = start data.selection_end = end # Don't call highlight_selection with `update=True` so that # all underline selections are updated in bulk in # finish_code_analysis or update_decorations. self.highlight_selection('code_analysis_underline', data._selection(), underline_color=block.color) else: # Don't append messages to data for cloned editors to avoid # showing them twice or more times on hover. # Fixes spyder-ide/spyder#15618 if not self.is_cloned: data.code_analysis.append( (source, code, severity, message) ) block.setUserData(data) # ------------- LSP: Completion --------------------------------------- @request(method=CompletionRequestTypes.DOCUMENT_COMPLETION) def do_completion(self, automatic=False): """Trigger completion.""" cursor = self.textCursor() current_word = self.get_current_word( completion=True, valid_python_variable=False ) params = { 'file': self.filename, 'line': cursor.blockNumber(), 'column': cursor.columnNumber(), 'offset': cursor.position(), 'selection_start': cursor.selectionStart(), 'selection_end': cursor.selectionEnd(), 'current_word': current_word } self.completion_args = (self.textCursor().position(), automatic) return params @handles(CompletionRequestTypes.DOCUMENT_COMPLETION) def process_completion(self, params): """Handle completion response.""" args = self.completion_args if args is None: # This should not happen return self.completion_args = None position, automatic = args start_cursor = self.textCursor() start_cursor.movePosition(QTextCursor.StartOfBlock) line_text = self.get_text(start_cursor.position(), 'eol') leading_whitespace = self.compute_whitespace(line_text) indentation_whitespace = ' ' * leading_whitespace eol_char = self.get_line_separator() try: completions = params['params'] completions = ([] if completions is None else [completion for completion in completions if completion.get('insertText') or completion.get('textEdit', {}).get('newText')]) prefix = self.get_current_word(completion=True, valid_python_variable=False) if (len(completions) == 1 and completions[0].get('insertText') == prefix and not completions[0].get('textEdit', {}).get('newText')): completions.pop() replace_end = self.textCursor().position() under_cursor = self.get_current_word_and_position(completion=True) if under_cursor: word, replace_start = under_cursor else: word = '' replace_start = replace_end first_letter = '' if len(word) > 0: first_letter = word[0] def sort_key(completion): if 'textEdit' in completion: text_insertion = completion['textEdit']['newText'] else: text_insertion = completion['insertText'] first_insert_letter = text_insertion[0] case_mismatch = ( (first_letter.isupper() and first_insert_letter.islower()) or (first_letter.islower() and first_insert_letter.isupper()) ) # False < True, so case matches go first return (case_mismatch, completion['sortText']) completion_list = sorted(completions, key=sort_key) # Allow for textEdit completions to be filtered by Spyder # if on-the-fly completions are disabled, only if the # textEdit range matches the word under the cursor. for completion in completion_list: if 'textEdit' in completion: c_replace_start = completion['textEdit']['range']['start'] c_replace_end = completion['textEdit']['range']['end'] if (c_replace_start == replace_start and c_replace_end == replace_end): insert_text = completion['textEdit']['newText'] completion['filterText'] = insert_text completion['insertText'] = insert_text del completion['textEdit'] if 'insertText' in completion: insert_text = completion['insertText'] insert_text_lines = insert_text.splitlines() reindented_text = [insert_text_lines[0]] for insert_line in insert_text_lines[1:]: insert_line = indentation_whitespace + insert_line reindented_text.append(insert_line) reindented_text = eol_char.join(reindented_text) completion['insertText'] = reindented_text self.completion_widget.show_list( completion_list, position, automatic) self.kite_call_to_action.handle_processed_completions(completions) except RuntimeError: # This is triggered when a codeeditor instance was removed # before the response can be processed. self.kite_call_to_action.hide_coverage_cta() return except Exception: self.log_lsp_handle_errors('Error when processing completions') @request(method=CompletionRequestTypes.COMPLETION_RESOLVE) def resolve_completion_item(self, item): return { 'file': self.filename, 'completion_item': item } @handles(CompletionRequestTypes.COMPLETION_RESOLVE) def handle_completion_item_resolution(self, response): try: response = response['params'] if not response: return self.completion_widget.augment_completion_info(response) except RuntimeError: # This is triggered when a codeeditor instance was removed # before the response can be processed. return except Exception: self.log_lsp_handle_errors( "Error when handling completion item resolution") # ------------- LSP: Signature Hints ------------------------------------ @request(method=CompletionRequestTypes.DOCUMENT_SIGNATURE) def request_signature(self): """Ask for signature.""" self.document_did_change('') line, column = self.get_cursor_line_column() offset = self.get_position('cursor') params = { 'file': self.filename, 'line': line, 'column': column, 'offset': offset } return params @handles(CompletionRequestTypes.DOCUMENT_SIGNATURE) def process_signatures(self, params): """Handle signature response.""" try: signature_params = params['params'] if (signature_params is not None and 'activeParameter' in signature_params): self.sig_signature_invoked.emit(signature_params) signature_data = signature_params['signatures'] documentation = signature_data['documentation'] if isinstance(documentation, dict): documentation = documentation['value'] # The language server returns encoded text with # spaces defined as `\xa0` documentation = documentation.replace(u'\xa0', ' ') parameter_idx = signature_params['activeParameter'] parameters = signature_data['parameters'] parameter = None if len(parameters) > 0 and parameter_idx < len(parameters): parameter_data = parameters[parameter_idx] parameter = parameter_data['label'] signature = signature_data['label'] # This method is part of spyder/widgets/mixins self.show_calltip( signature=signature, parameter=parameter, language=self.language, documentation=documentation, ) except RuntimeError: # This is triggered when a codeeditor instance was removed # before the response can be processed. return except Exception: self.log_lsp_handle_errors("Error when processing signature") # ------------- LSP: Hover/Mouse --------------------------------------- @request(method=CompletionRequestTypes.DOCUMENT_CURSOR_EVENT) def request_cursor_event(self): text = self.get_text_with_eol() cursor = self.textCursor() params = { 'file': self.filename, 'version': self.text_version, 'text': text, 'offset': cursor.position(), 'selection_start': cursor.selectionStart(), 'selection_end': cursor.selectionEnd(), } return params @request(method=CompletionRequestTypes.DOCUMENT_HOVER) def request_hover(self, line, col, offset, show_hint=True, clicked=True): """Request hover information.""" params = { 'file': self.filename, 'line': line, 'column': col, 'offset': offset } self._show_hint = show_hint self._request_hover_clicked = clicked return params @handles(CompletionRequestTypes.DOCUMENT_HOVER) def handle_hover_response(self, contents): """Handle hover response.""" if running_under_pytest(): from unittest.mock import Mock # On some tests this is returning a Mock if isinstance(contents, Mock): return try: content = contents['params'] # - Don't display hover if there's no content to display. # - Prevent spurious errors when a client returns a list. if not content or isinstance(content, list): return self.sig_display_object_info.emit( content, self._request_hover_clicked ) if content is not None and self._show_hint and self._last_point: # This is located in spyder/widgets/mixins.py word = self._last_hover_word content = content.replace(u'\xa0', ' ') self.show_hint(content, inspect_word=word, at_point=self._last_point) self._last_point = None except RuntimeError: # This is triggered when a codeeditor instance was removed # before the response can be processed. return except Exception: self.log_lsp_handle_errors("Error when processing hover") # ------------- LSP: Go To Definition ---------------------------- @Slot() @request(method=CompletionRequestTypes.DOCUMENT_DEFINITION) def go_to_definition_from_cursor(self, cursor=None): """Go to definition from cursor instance (QTextCursor).""" if (not self.go_to_definition_enabled or self.in_comment_or_string()): return if cursor is None: cursor = self.textCursor() text = to_text_string(cursor.selectedText()) if len(text) == 0: cursor.select(QTextCursor.WordUnderCursor) text = to_text_string(cursor.selectedText()) if text is not None: line, column = self.get_cursor_line_column() params = { 'file': self.filename, 'line': line, 'column': column } return params @handles(CompletionRequestTypes.DOCUMENT_DEFINITION) def handle_go_to_definition(self, position): """Handle go to definition response.""" try: position = position['params'] if position is not None: def_range = position['range'] start = def_range['start'] if self.filename == position['file']: self.go_to_line(start['line'] + 1, start['character'], None, word=None) else: self.go_to_definition.emit(position['file'], start['line'] + 1, start['character']) except RuntimeError: # This is triggered when a codeeditor instance was removed # before the response can be processed. return except Exception: self.log_lsp_handle_errors( "Error when processing go to definition") # ------------- LSP: Document/Selection formatting -------------------- def format_document_or_range(self): if self.has_selected_text() and self.range_formatting_enabled: self.format_document_range() else: self.format_document() @request(method=CompletionRequestTypes.DOCUMENT_FORMATTING) def format_document(self): if not self.formatting_enabled: return using_spaces = self.indent_chars != '\t' tab_size = (len(self.indent_chars) if using_spaces else self.tab_stop_width_spaces) params = { 'file': self.filename, 'options': { 'tab_size': tab_size, 'insert_spaces': using_spaces, 'trim_trailing_whitespace': self.remove_trailing_spaces, 'insert_final_new_line': self.add_newline, 'trim_final_new_lines': self.remove_trailing_newlines } } # Sets the document into read-only and updates its corresponding # tab name to display the filename into parenthesis self.setReadOnly(True) self.document().setModified(True) self.sig_start_operation_in_progress.emit() self.operation_in_progress = True return params @request(method=CompletionRequestTypes.DOCUMENT_RANGE_FORMATTING) def format_document_range(self): if not self.range_formatting_enabled or not self.has_selected_text(): return start, end = self.get_selection_start_end() start_line, start_col = start end_line, end_col = end using_spaces = self.indent_chars != '\t' tab_size = (len(self.indent_chars) if using_spaces else self.tab_stop_width_spaces) fmt_range = { 'start': { 'line': start_line, 'character': start_col }, 'end': { 'line': end_line, 'character': end_col } } params = { 'file': self.filename, 'range': fmt_range, 'options': { 'tab_size': tab_size, 'insert_spaces': using_spaces, 'trim_trailing_whitespace': self.remove_trailing_spaces, 'insert_final_new_line': self.add_newline, 'trim_final_new_lines': self.remove_trailing_newlines } } # Sets the document into read-only and updates its corresponding # tab name to display the filename into parenthesis self.setReadOnly(True) self.document().setModified(True) self.sig_start_operation_in_progress.emit() self.operation_in_progress = True return params @handles(CompletionRequestTypes.DOCUMENT_FORMATTING) def handle_document_formatting(self, edits): try: self._apply_document_edits(edits) except RuntimeError: # This is triggered when a codeeditor instance was removed # before the response can be processed. return except Exception: self.log_lsp_handle_errors("Error when processing document " "formatting") finally: # Remove read-only parenthesis and highlight document modification self.setReadOnly(False) self.document().setModified(False) self.document().setModified(True) self.sig_stop_operation_in_progress.emit() self.operation_in_progress = False @handles(CompletionRequestTypes.DOCUMENT_RANGE_FORMATTING) def handle_document_range_formatting(self, edits): try: self._apply_document_edits(edits) except RuntimeError: # This is triggered when a codeeditor instance was removed # before the response can be processed. return except Exception: self.log_lsp_handle_errors("Error when processing document " "selection formatting") finally: # Remove read-only parenthesis and highlight document modification self.setReadOnly(False) self.document().setModified(False) self.document().setModified(True) self.sig_stop_operation_in_progress.emit() self.operation_in_progress = False def _apply_document_edits(self, edits): """Apply a set of atomic document edits to the current editor text.""" edits = edits['params'] if edits is None: return # We need to use here toPlainText and not get_text_with_eol to # to not mess up the code when applying formatting. # See spyder-ide/spyder#16180 text = self.toPlainText() text_tokens = list(text) merged_text = None for edit in edits: edit_range = edit['range'] repl_text = edit['newText'] start, end = edit_range['start'], edit_range['end'] start_line, start_col = start['line'], start['character'] end_line, end_col = end['line'], end['character'] start_pos = self.get_position_line_number(start_line, start_col) end_pos = self.get_position_line_number(end_line, end_col) text_tokens = list(text_tokens) this_edit = list(repl_text) if end_line == self.document().blockCount(): end_pos = self.get_position('eof') end_pos += 1 if (end_pos == len(text_tokens) and text_tokens[end_pos - 1] == '\n'): end_pos += 1 this_edition = (text_tokens[:max(start_pos - 1, 0)] + this_edit + text_tokens[end_pos - 1:]) text_edit = ''.join(this_edition) if merged_text is None: merged_text = text_edit else: merged_text = merge(text_edit, merged_text, text) if merged_text is not None: cursor = self.textCursor() cursor.beginEditBlock() cursor.movePosition(QTextCursor.Start) cursor.movePosition(QTextCursor.End, QTextCursor.KeepAnchor) cursor.insertText(merged_text) cursor.endEditBlock() self.document_did_change() # ------------- LSP: Code folding ranges ------------------------------- def compute_whitespace(self, line): tab_size = self.tab_stop_width_spaces whitespace_regex = re.compile(r'(\s+).*') whitespace_match = whitespace_regex.match(line) total_whitespace = 0 if whitespace_match is not None: whitespace_chars = whitespace_match.group(1) whitespace_chars = whitespace_chars.replace( '\t', tab_size * ' ') total_whitespace = len(whitespace_chars) return total_whitespace def update_whitespace_count(self, line, column): self.leading_whitespaces = {} lines = to_text_string(self.toPlainText()).splitlines() for i, text in enumerate(lines): total_whitespace = self.compute_whitespace(text) self.leading_whitespaces[i] = total_whitespace def cleanup_folding(self): """Cleanup folding pane.""" folding_panel = self.panels.get(FoldingPanel) folding_panel.folding_regions = {} @request(method=CompletionRequestTypes.DOCUMENT_FOLDING_RANGE) def request_folding(self): """Request folding.""" if not self.folding_supported or not self.code_folding: return params = {'file': self.filename} return params @handles(CompletionRequestTypes.DOCUMENT_FOLDING_RANGE) def handle_folding_range(self, response): """Handle folding response.""" ranges = response['params'] if ranges is None: return # Compute extended_ranges here because get_text_region ends up # calling paintEvent and that method can't be called in a # thread due to Qt restrictions. try: extended_ranges = [] for start, end in ranges: text_region = self.get_text_region(start, end) extended_ranges.append((start, end, text_region)) except RuntimeError: # This is triggered when a codeeditor instance was removed # before the response can be processed. return except Exception: self.log_lsp_handle_errors("Error when processing folding") # Update folding in a thread self.update_folding_thread.run = functools.partial( self.update_and_merge_folding, extended_ranges) self.update_folding_thread.finished.connect( self.finish_code_folding) self.update_folding_thread.start() def update_and_merge_folding(self, extended_ranges): """Update and merge new folding information.""" try: folding_panel = self.panels.get(FoldingPanel) current_tree, root = merge_folding( extended_ranges, folding_panel.current_tree, folding_panel.root) folding_info = collect_folding_regions(root) self._folding_info = (current_tree, root, *folding_info) except RuntimeError: # This is triggered when a codeeditor instance was removed # before the response can be processed. return except Exception: self.log_lsp_handle_errors("Error when processing folding") def finish_code_folding(self): """Finish processing code folding.""" folding_panel = self.panels.get(FoldingPanel) folding_panel.update_folding(self._folding_info) # Update indent guides, which depend on folding if self.indent_guides._enabled and len(self.patch) > 0: line, column = self.get_cursor_line_column() self.update_whitespace_count(line, column) # ------------- LSP: Save/close file ----------------------------------- @request(method=CompletionRequestTypes.DOCUMENT_DID_SAVE, requires_response=False) def notify_save(self): """Send save request.""" params = {'file': self.filename} if self.save_include_text: params['text'] = self.get_text_with_eol() return params @request(method=CompletionRequestTypes.DOCUMENT_DID_CLOSE, requires_response=False) def notify_close(self): """Send close request.""" if self.completions_available: params = { 'file': self.filename, 'codeeditor': self } return params # ------------------------------------------------------------------------- def set_debug_panel(self, show_debug_panel, language): """Enable/disable debug panel.""" debugger_panel = self.panels.get(DebuggerPanel) if (is_text_string(language) and language.lower() in ALL_LANGUAGES['Python'] and show_debug_panel): debugger_panel.setVisible(True) else: debugger_panel.setVisible(False) def update_debugger_panel_state(self, state, last_step, force=False): """Update debugger panel state.""" debugger_panel = self.panels.get(DebuggerPanel) if force: debugger_panel.start_clean() return elif state and 'fname' in last_step: fname = last_step['fname'] if (fname and self.filename and osp.normcase(fname) == osp.normcase(self.filename)): debugger_panel.start_clean() return debugger_panel.stop_clean() def set_folding_panel(self, folding): """Enable/disable folding panel.""" folding_panel = self.panels.get(FoldingPanel) folding_panel.setVisible(folding) def set_tab_mode(self, enable): """ enabled = tab always indent (otherwise tab indents only when cursor is at the beginning of a line) """ self.tab_mode = enable def set_strip_mode(self, enable): """ Strip all trailing spaces if enabled. """ self.strip_trailing_spaces_on_modify = enable def toggle_intelligent_backspace(self, state): self.intelligent_backspace = state def toggle_automatic_completions(self, state): self.automatic_completions = state def toggle_hover_hints(self, state): self.hover_hints_enabled = state def toggle_code_snippets(self, state): self.code_snippets = state def toggle_format_on_save(self, state): self.format_on_save = state def toggle_code_folding(self, state): self.code_folding = state self.set_folding_panel(state) if not state and self.indent_guides._enabled: self.code_folding = True def toggle_identation_guides(self, state): if state and not self.code_folding: self.code_folding = True self.indent_guides.set_enabled(state) def toggle_completions_hint(self, state): """Enable/disable completion hint.""" self.completions_hint = state def set_automatic_completions_after_chars(self, number): """ Set the number of characters after which auto completion is fired. """ self.automatic_completions_after_chars = number def set_automatic_completions_after_ms(self, ms): """ Set the amount of time in ms after which auto completion is fired. """ self.automatic_completions_after_ms = ms def set_completions_hint_after_ms(self, ms): """ Set the amount of time in ms after which the completions hint is shown. """ self.completions_hint_after_ms = ms def set_close_parentheses_enabled(self, enable): """Enable/disable automatic parentheses insertion feature""" self.close_parentheses_enabled = enable bracket_extension = self.editor_extensions.get(CloseBracketsExtension) if bracket_extension is not None: bracket_extension.enabled = enable def set_close_quotes_enabled(self, enable): """Enable/disable automatic quote insertion feature""" self.close_quotes_enabled = enable quote_extension = self.editor_extensions.get(CloseQuotesExtension) if quote_extension is not None: quote_extension.enabled = enable def set_add_colons_enabled(self, enable): """Enable/disable automatic colons insertion feature""" self.add_colons_enabled = enable def set_auto_unindent_enabled(self, enable): """Enable/disable automatic unindent after else/elif/finally/except""" self.auto_unindent_enabled = enable def set_occurrence_highlighting(self, enable): """Enable/disable occurrence highlighting""" self.occurrence_highlighting = enable if not enable: self.__clear_occurrences() def set_occurrence_timeout(self, timeout): """Set occurrence highlighting timeout (ms)""" self.occurrence_timer.setInterval(timeout) def set_underline_errors_enabled(self, state): """Toggle the underlining of errors and warnings.""" self.underline_errors_enabled = state if state: self.document_did_change() else: self.clear_extra_selections('code_analysis_underline') def set_highlight_current_line(self, enable): """Enable/disable current line highlighting""" self.highlight_current_line_enabled = enable if self.highlight_current_line_enabled: self.highlight_current_line() else: self.unhighlight_current_line() def set_highlight_current_cell(self, enable): """Enable/disable current line highlighting""" hl_cell_enable = enable and self.supported_cell_language self.highlight_current_cell_enabled = hl_cell_enable if self.highlight_current_cell_enabled: self.highlight_current_cell() else: self.unhighlight_current_cell() def set_language(self, language, filename=None): extra_supported_languages = {'stil': 'STIL'} self.tab_indents = language in self.TAB_ALWAYS_INDENTS self.comment_string = '' self.language = 'Text' self.supported_language = False sh_class = sh.TextSH language = 'None' if language is None else language if language is not None: for (key, value) in ALL_LANGUAGES.items(): if language.lower() in value: self.supported_language = True sh_class, comment_string = self.LANGUAGES[key] if key == 'IPython': self.language = 'Python' else: self.language = key self.comment_string = comment_string if key in CELL_LANGUAGES: self.supported_cell_language = True self.has_cell_separators = True break if filename is not None and not self.supported_language: sh_class = sh.guess_pygments_highlighter(filename) self.support_language = sh_class is not sh.TextSH if self.support_language: # Pygments report S for the lexer name of R files if sh_class._lexer.name == 'S': self.language = 'R' else: self.language = sh_class._lexer.name else: _, ext = osp.splitext(filename) ext = ext.lower() if ext in extra_supported_languages: self.language = extra_supported_languages[ext] self._set_highlighter(sh_class) self.completion_widget.set_language(self.language) def _set_highlighter(self, sh_class): self.highlighter_class = sh_class if self.highlighter is not None: # Removing old highlighter # TODO: test if leaving parent/document as is eats memory self.highlighter.setParent(None) self.highlighter.setDocument(None) self.highlighter = self.highlighter_class(self.document(), self.font(), self.color_scheme) self.highlighter._cell_list = [] self.highlighter.sig_new_cell.connect(self.add_to_cell_list) self._apply_highlighter_color_scheme() self.highlighter.editor = self def add_to_cell_list(self, oedata): """Add new cell to cell list.""" if self.highlighter is None: return self.highlighter._cell_list.append(oedata) def get_cell_list(self): """Get all cells.""" if self.highlighter is None: return [] # Filter out old cells def good(oedata): return oedata.is_valid() and oedata.def_type == oedata.CELL self.highlighter._cell_list = [ oedata for oedata in self.highlighter._cell_list if good(oedata)] return sorted( {oedata.get_block_number(): oedata for oedata in self.highlighter._cell_list}.items()) def is_json(self): return (isinstance(self.highlighter, sh.PygmentsSH) and self.highlighter._lexer.name == 'JSON') def is_python(self): return self.highlighter_class is sh.PythonSH def is_ipython(self): return self.highlighter_class is sh.IPythonSH def is_python_or_ipython(self): return self.is_python() or self.is_ipython() def is_cython(self): return self.highlighter_class is sh.CythonSH def is_enaml(self): return self.highlighter_class is sh.EnamlSH def is_python_like(self): return (self.is_python() or self.is_ipython() or self.is_cython() or self.is_enaml()) def intelligent_tab(self): """Provide intelligent behavior for Tab key press.""" leading_text = self.get_text('sol', 'cursor') if not leading_text.strip() or leading_text.endswith('#'): # blank line or start of comment self.indent_or_replace() elif self.in_comment_or_string() and not leading_text.endswith(' '): # in a word in a comment self.do_completion() elif leading_text.endswith('import ') or leading_text[-1] == '.': # blank import or dot completion self.do_completion() elif (leading_text.split()[0] in ['from', 'import'] and ';' not in leading_text): # import line with a single statement # (prevents lines like: `import pdb; pdb.set_trace()`) self.do_completion() elif leading_text[-1] in '(,' or leading_text.endswith(', '): self.indent_or_replace() elif leading_text.endswith(' '): # if the line ends with a space, indent self.indent_or_replace() elif re.search(r"[^\d\W]\w*\Z", leading_text, re.UNICODE): # if the line ends with a non-whitespace character self.do_completion() else: self.indent_or_replace() def intelligent_backtab(self): """Provide intelligent behavior for Shift+Tab key press""" leading_text = self.get_text('sol', 'cursor') if not leading_text.strip(): # blank line self.unindent() elif self.in_comment_or_string(): self.unindent() elif leading_text[-1] in '(,' or leading_text.endswith(', '): position = self.get_position('cursor') self.show_object_info(position) else: # if the line ends with any other character but comma self.unindent() def rehighlight(self): """Rehighlight the whole document.""" if self.highlighter is not None: self.highlighter.rehighlight() if self.highlight_current_cell_enabled: self.highlight_current_cell() else: self.unhighlight_current_cell() if self.highlight_current_line_enabled: self.highlight_current_line() else: self.unhighlight_current_line() def trim_trailing_spaces(self): """Remove trailing spaces""" cursor = self.textCursor() cursor.beginEditBlock() cursor.movePosition(QTextCursor.Start) while True: cursor.movePosition(QTextCursor.EndOfBlock) text = to_text_string(cursor.block().text()) length = len(text)-len(text.rstrip()) if length > 0: cursor.movePosition(QTextCursor.Left, QTextCursor.KeepAnchor, length) cursor.removeSelectedText() if cursor.atEnd(): break cursor.movePosition(QTextCursor.NextBlock) cursor.endEditBlock() self.document_did_change() def trim_trailing_newlines(self): """Remove extra newlines at the end of the document.""" cursor = self.textCursor() cursor.beginEditBlock() cursor.movePosition(QTextCursor.End) line = cursor.blockNumber() this_line = self.get_text_line(line) previous_line = self.get_text_line(line - 1) while this_line == '': cursor.movePosition(QTextCursor.PreviousBlock, QTextCursor.KeepAnchor) if self.add_newline: if this_line == '' and previous_line != '': cursor.movePosition(QTextCursor.NextBlock, QTextCursor.KeepAnchor) line -= 1 if line == 0: break this_line = self.get_text_line(line) previous_line = self.get_text_line(line - 1) if not self.add_newline: cursor.movePosition(QTextCursor.EndOfBlock, QTextCursor.KeepAnchor) cursor.removeSelectedText() cursor.endEditBlock() self.document_did_change() def add_newline_to_file(self): """Add a newline to the end of the file if it does not exist.""" cursor = self.textCursor() cursor.movePosition(QTextCursor.End) line = cursor.blockNumber() this_line = self.get_text_line(line) if this_line != '': cursor.beginEditBlock() cursor.movePosition(QTextCursor.EndOfBlock) cursor.insertText(self.get_line_separator()) cursor.endEditBlock() self.document_did_change() def fix_indentation(self): """Replace tabs by spaces.""" text_before = to_text_string(self.toPlainText()) text_after = sourcecode.fix_indentation(text_before, self.indent_chars) if text_before != text_after: # We do the following rather than using self.setPlainText # to benefit from QTextEdit's undo/redo feature. self.selectAll() self.skip_rstrip = True self.insertPlainText(text_after) self.document_did_change() self.skip_rstrip = False def get_current_object(self): """Return current object (string) """ source_code = to_text_string(self.toPlainText()) offset = self.get_position('cursor') return sourcecode.get_primary_at(source_code, offset) def next_cursor_position(self, position=None, mode=QTextLayout.SkipCharacters): """ Get next valid cursor position. Adapted from: https://github.com/qt/qtbase/blob/5.15.2/src/gui/text/qtextdocument_p.cpp#L1361 """ cursor = self.textCursor() if cursor.atEnd(): return position if position is None: position = cursor.position() else: cursor.setPosition(position) it = cursor.block() start = it.position() end = start + it.length() - 1 if (position == end): return end + 1 return it.layout().nextCursorPosition(position - start, mode) + start @Slot() def delete(self): """Remove selected text or next character.""" if not self.has_selected_text(): cursor = self.textCursor() if not cursor.atEnd(): cursor.setPosition( self.next_cursor_position(), QTextCursor.KeepAnchor) self.setTextCursor(cursor) self.remove_selected_text() #------Find occurrences def __find_first(self, text): """Find first occurrence: scan whole document""" flags = QTextDocument.FindCaseSensitively|QTextDocument.FindWholeWords cursor = self.textCursor() # Scanning whole document cursor.movePosition(QTextCursor.Start) regexp = QRegExp(r"\b%s\b" % QRegExp.escape(text), Qt.CaseSensitive) cursor = self.document().find(regexp, cursor, flags) self.__find_first_pos = cursor.position() return cursor def __find_next(self, text, cursor): """Find next occurrence""" flags = QTextDocument.FindCaseSensitively|QTextDocument.FindWholeWords regexp = QRegExp(r"\b%s\b" % QRegExp.escape(text), Qt.CaseSensitive) cursor = self.document().find(regexp, cursor, flags) if cursor.position() != self.__find_first_pos: return cursor def __cursor_position_changed(self): """Cursor position has changed""" line, column = self.get_cursor_line_column() self.sig_cursor_position_changed.emit(line, column) if self.highlight_current_cell_enabled: self.highlight_current_cell() else: self.unhighlight_current_cell() if self.highlight_current_line_enabled: self.highlight_current_line() else: self.unhighlight_current_line() if self.occurrence_highlighting: self.occurrence_timer.stop() self.occurrence_timer.start() # Strip if needed self.strip_trailing_spaces() def __clear_occurrences(self): """Clear occurrence markers""" self.occurrences = [] self.clear_extra_selections('occurrences') self.sig_flags_changed.emit() def get_selection(self, cursor, foreground_color=None, background_color=None, underline_color=None, outline_color=None, underline_style=QTextCharFormat.SingleUnderline): """Get selection.""" if cursor is None: return selection = TextDecoration(cursor) if foreground_color is not None: selection.format.setForeground(foreground_color) if background_color is not None: selection.format.setBackground(background_color) if underline_color is not None: selection.format.setProperty(QTextFormat.TextUnderlineStyle, to_qvariant(underline_style)) selection.format.setProperty(QTextFormat.TextUnderlineColor, to_qvariant(underline_color)) if outline_color is not None: selection.set_outline(outline_color) return selection def highlight_selection(self, key, cursor, foreground_color=None, background_color=None, underline_color=None, outline_color=None, underline_style=QTextCharFormat.SingleUnderline, update=False): selection = self.get_selection( cursor, foreground_color, background_color, underline_color, outline_color, underline_style) if selection is None: return extra_selections = self.get_extra_selections(key) extra_selections.append(selection) self.set_extra_selections(key, extra_selections) if update: self.update_extra_selections() def __mark_occurrences(self): """Marking occurrences of the currently selected word""" self.__clear_occurrences() if not self.supported_language: return text = self.get_selected_text().strip() if not text: text = self.get_current_word() if text is None: return if (self.has_selected_text() and self.get_selected_text().strip() != text): return if (self.is_python_like() and (sourcecode.is_keyword(to_text_string(text)) or to_text_string(text) == 'self')): return # Highlighting all occurrences of word *text* cursor = self.__find_first(text) self.occurrences = [] extra_selections = self.get_extra_selections('occurrences') first_occurrence = None while cursor: self.occurrences.append(cursor.blockNumber()) selection = self.get_selection(cursor) if len(selection.cursor.selectedText()) > 0: extra_selections.append(selection) if len(extra_selections) == 1: first_occurrence = selection else: selection.format.setBackground(self.occurrence_color) first_occurrence.format.setBackground( self.occurrence_color) cursor = self.__find_next(text, cursor) self.set_extra_selections('occurrences', extra_selections) self.update_extra_selections() if len(self.occurrences) > 1 and self.occurrences[-1] == 0: # XXX: this is never happening with PySide but it's necessary # for PyQt4... this must be related to a different behavior for # the QTextDocument.find function between those two libraries self.occurrences.pop(-1) self.sig_flags_changed.emit() #-----highlight found results (find/replace widget) def highlight_found_results(self, pattern, word=False, regexp=False, case=False): """Highlight all found patterns""" pattern = to_text_string(pattern) if not pattern: return if not regexp: pattern = re.escape(to_text_string(pattern)) pattern = r"\b%s\b" % pattern if word else pattern text = to_text_string(self.toPlainText()) re_flags = re.MULTILINE if case else re.IGNORECASE | re.MULTILINE try: regobj = re.compile(pattern, flags=re_flags) except sre_constants.error: return extra_selections = [] self.found_results = [] for match in regobj.finditer(text): pos1, pos2 = sh.get_span(match) selection = TextDecoration(self.textCursor()) selection.format.setBackground(self.found_results_color) selection.cursor.setPosition(pos1) self.found_results.append(selection.cursor.blockNumber()) selection.cursor.setPosition(pos2, QTextCursor.KeepAnchor) extra_selections.append(selection) self.set_extra_selections('find', extra_selections) self.update_extra_selections() def clear_found_results(self): """Clear found results highlighting""" self.found_results = [] self.clear_extra_selections('find') self.sig_flags_changed.emit() def __text_has_changed(self): """Text has changed, eventually clear found results highlighting""" self.last_change_position = self.textCursor().position() if self.found_results: self.clear_found_results() def get_linenumberarea_width(self): """ Return current line number area width. This method is left for backward compatibility (BaseEditMixin define it), any changes should be in LineNumberArea class. """ return self.linenumberarea.get_width() def calculate_real_position(self, point): """Add offset to a point, to take into account the panels.""" point.setX(point.x() + self.panels.margin_size(Panel.Position.LEFT)) point.setY(point.y() + self.panels.margin_size(Panel.Position.TOP)) return point def calculate_real_position_from_global(self, point): """Add offset to a point, to take into account the panels.""" point.setX(point.x() - self.panels.margin_size(Panel.Position.LEFT)) point.setY(point.y() + self.panels.margin_size(Panel.Position.TOP)) return point def get_linenumber_from_mouse_event(self, event): """Return line number from mouse event""" block = self.firstVisibleBlock() line_number = block.blockNumber() top = self.blockBoundingGeometry(block).translated( self.contentOffset()).top() bottom = top + self.blockBoundingRect(block).height() while block.isValid() and top < event.pos().y(): block = block.next() if block.isVisible(): # skip collapsed blocks top = bottom bottom = top + self.blockBoundingRect(block).height() line_number += 1 return line_number def select_lines(self, linenumber_pressed, linenumber_released): """Select line(s) after a mouse press/mouse press drag event""" find_block_by_number = self.document().findBlockByNumber move_n_blocks = (linenumber_released - linenumber_pressed) start_line = linenumber_pressed start_block = find_block_by_number(start_line - 1) cursor = self.textCursor() cursor.setPosition(start_block.position()) # Select/drag downwards if move_n_blocks > 0: for n in range(abs(move_n_blocks) + 1): cursor.movePosition(cursor.NextBlock, cursor.KeepAnchor) # Select/drag upwards or select single line else: cursor.movePosition(cursor.NextBlock) for n in range(abs(move_n_blocks) + 1): cursor.movePosition(cursor.PreviousBlock, cursor.KeepAnchor) # Account for last line case if linenumber_released == self.blockCount(): cursor.movePosition(cursor.EndOfBlock, cursor.KeepAnchor) else: cursor.movePosition(cursor.StartOfBlock, cursor.KeepAnchor) self.setTextCursor(cursor) # ----- Code bookmarks def add_bookmark(self, slot_num, line=None, column=None): """Add bookmark to current block's userData.""" if line is None: # Triggered by shortcut, else by spyder start line, column = self.get_cursor_line_column() block = self.document().findBlockByNumber(line) data = block.userData() if not data: data = BlockUserData(self) if slot_num not in data.bookmarks: data.bookmarks.append((slot_num, column)) block.setUserData(data) self.sig_bookmarks_changed.emit() def get_bookmarks(self): """Get bookmarks by going over all blocks.""" bookmarks = {} block = self.document().firstBlock() for line_number in range(0, self.document().blockCount()): data = block.userData() if data and data.bookmarks: for slot_num, column in data.bookmarks: bookmarks[slot_num] = [line_number, column] block = block.next() return bookmarks def clear_bookmarks(self): """Clear bookmarks for all blocks.""" self.bookmarks = {} for data in self.blockuserdata_list(): data.bookmarks = [] def set_bookmarks(self, bookmarks): """Set bookmarks when opening file.""" self.clear_bookmarks() for slot_num, bookmark in bookmarks.items(): self.add_bookmark(slot_num, bookmark[1], bookmark[2]) def update_bookmarks(self): """Emit signal to update bookmarks.""" self.sig_bookmarks_changed.emit() # -----Code introspection def show_completion_object_info(self, name, signature): """Trigger show completion info in Help Pane.""" force = True self.sig_show_completion_object_info.emit(name, signature, force) def show_object_info(self, position): """Trigger a calltip""" self.sig_show_object_info.emit(position) # -----blank spaces def set_blanks_enabled(self, state): """Toggle blanks visibility""" self.blanks_enabled = state option = self.document().defaultTextOption() option.setFlags(option.flags() | \ QTextOption.AddSpaceForLineAndParagraphSeparators) if self.blanks_enabled: option.setFlags(option.flags() | QTextOption.ShowTabsAndSpaces) else: option.setFlags(option.flags() & ~QTextOption.ShowTabsAndSpaces) self.document().setDefaultTextOption(option) # Rehighlight to make the spaces less apparent. self.rehighlight() def set_scrollpastend_enabled(self, state): """ Allow user to scroll past the end of the document to have the last line on top of the screen """ self.scrollpastend_enabled = state self.setCenterOnScroll(state) self.setDocument(self.document()) def resizeEvent(self, event): """Reimplemented Qt method to handle p resizing""" TextEditBaseWidget.resizeEvent(self, event) self.panels.resize() def showEvent(self, event): """Overrides showEvent to update the viewport margins.""" super(CodeEditor, self).showEvent(event) self.panels.refresh() #-----Misc. def _apply_highlighter_color_scheme(self): """Apply color scheme from syntax highlighter to the editor""" hl = self.highlighter if hl is not None: self.set_palette(background=hl.get_background_color(), foreground=hl.get_foreground_color()) self.currentline_color = hl.get_currentline_color() self.currentcell_color = hl.get_currentcell_color() self.occurrence_color = hl.get_occurrence_color() self.ctrl_click_color = hl.get_ctrlclick_color() self.sideareas_color = hl.get_sideareas_color() self.comment_color = hl.get_comment_color() self.normal_color = hl.get_foreground_color() self.matched_p_color = hl.get_matched_p_color() self.unmatched_p_color = hl.get_unmatched_p_color() self.edge_line.update_color() self.indent_guides.update_color() self.sig_theme_colors_changed.emit( {'occurrence': self.occurrence_color}) def apply_highlighter_settings(self, color_scheme=None): """Apply syntax highlighter settings""" if self.highlighter is not None: # Updating highlighter settings (font and color scheme) self.highlighter.setup_formats(self.font()) if color_scheme is not None: self.set_color_scheme(color_scheme) else: self.highlighter.rehighlight() def set_font(self, font, color_scheme=None): """Set font""" # Note: why using this method to set color scheme instead of # 'set_color_scheme'? To avoid rehighlighting the document twice # at startup. if color_scheme is not None: self.color_scheme = color_scheme self.setFont(font) self.panels.refresh() self.apply_highlighter_settings(color_scheme) def set_color_scheme(self, color_scheme): """Set color scheme for syntax highlighting""" self.color_scheme = color_scheme if self.highlighter is not None: # this calls self.highlighter.rehighlight() self.highlighter.set_color_scheme(color_scheme) self._apply_highlighter_color_scheme() if self.highlight_current_cell_enabled: self.highlight_current_cell() else: self.unhighlight_current_cell() if self.highlight_current_line_enabled: self.highlight_current_line() else: self.unhighlight_current_line() def set_text(self, text): """Set the text of the editor""" self.setPlainText(text) self.set_eol_chars(text) self.document_did_change(text) if (isinstance(self.highlighter, sh.PygmentsSH) and not running_under_pytest()): self.highlighter.make_charlist() def set_text_from_file(self, filename, language=None): """Set the text of the editor from file *fname*""" self.filename = filename text, _enc = encoding.read(filename) if language is None: language = get_file_language(filename, text) self.set_language(language, filename) self.set_text(text) def append(self, text): """Append text to the end of the text widget""" cursor = self.textCursor() cursor.movePosition(QTextCursor.End) cursor.insertText(text) self.document_did_change() def adjust_indentation(self, line, indent_adjustment): """Adjust indentation.""" if indent_adjustment == 0 or line == "": return line using_spaces = self.indent_chars != '\t' if indent_adjustment > 0: if using_spaces: return ' ' * indent_adjustment + line else: return ( self.indent_chars * (indent_adjustment // self.tab_stop_width_spaces) + line) max_indent = self.get_line_indentation(line) indent_adjustment = min(max_indent, -indent_adjustment) indent_adjustment = (indent_adjustment if using_spaces else indent_adjustment // self.tab_stop_width_spaces) return line[indent_adjustment:] @Slot() def paste(self): """ Insert text or file/folder path copied from clipboard. Reimplement QPlainTextEdit's method to fix the following issue: on Windows, pasted text has only 'LF' EOL chars even if the original text has 'CRLF' EOL chars. The function also changes the clipboard data if they are copied as files/folders but does not change normal text data except if they are multiple lines. Since we are changing clipboard data we cannot use paste, which directly pastes from clipboard instead we use insertPlainText and pass the formatted/changed text without modifying clipboard content. """ clipboard = QApplication.clipboard() text = to_text_string(clipboard.text()) if clipboard.mimeData().hasUrls(): # Have copied file and folder urls pasted as text paths. # See spyder-ide/spyder#8644 for details. urls = clipboard.mimeData().urls() if all([url.isLocalFile() for url in urls]): if len(urls) > 1: sep_chars = ',' + self.get_line_separator() text = sep_chars.join('"' + url.toLocalFile(). replace(osp.os.sep, '/') + '"' for url in urls) else: text = urls[0].toLocalFile().replace(osp.os.sep, '/') eol_chars = self.get_line_separator() if len(text.splitlines()) > 1: text = eol_chars.join((text + eol_chars).splitlines()) # Align multiline text based on first line cursor = self.textCursor() cursor.beginEditBlock() cursor.removeSelectedText() cursor.setPosition(cursor.selectionStart()) cursor.setPosition(cursor.block().position(), QTextCursor.KeepAnchor) preceding_text = cursor.selectedText() first_line_selected, *remaining_lines = (text + eol_chars).splitlines() first_line = preceding_text + first_line_selected first_line_adjustment = 0 # Dedent if automatic indentation makes code invalid # Minimum indentation = max of current and paster indentation if (self.is_python_like() and len(preceding_text.strip()) == 0 and len(first_line.strip()) > 0): # Correct indentation desired_indent = self.find_indentation() if desired_indent: # minimum indentation is either the current indentation # or the indentation of the paster text desired_indent = max( desired_indent, self.get_line_indentation(first_line_selected), self.get_line_indentation(preceding_text)) first_line_adjustment = ( desired_indent - self.get_line_indentation(first_line)) # Only dedent, don't indent first_line_adjustment = min(first_line_adjustment, 0) # Only dedent, don't indent first_line = self.adjust_indentation( first_line, first_line_adjustment) # Fix indentation of multiline text based on first line if len(remaining_lines) > 0 and len(first_line.strip()) > 0: lines_adjustment = first_line_adjustment lines_adjustment += CLIPBOARD_HELPER.remaining_lines_adjustment( preceding_text) # Make sure the code is not flattened indentations = [ self.get_line_indentation(line) for line in remaining_lines if line.strip() != ""] if indentations: max_dedent = min(indentations) lines_adjustment = max(lines_adjustment, -max_dedent) # Get new text remaining_lines = [ self.adjust_indentation(line, lines_adjustment) for line in remaining_lines] text = eol_chars.join([first_line, *remaining_lines]) self.skip_rstrip = True self.sig_will_paste_text.emit(text) cursor.removeSelectedText() cursor.insertText(text) cursor.endEditBlock() self.sig_text_was_inserted.emit() self.document_did_change(text) self.skip_rstrip = False def _save_clipboard_indentation(self): """ Save the indentation corresponding to the clipboard data. Must be called right after copying. """ cursor = self.textCursor() cursor.setPosition(cursor.selectionStart()) cursor.setPosition(cursor.block().position(), QTextCursor.KeepAnchor) preceding_text = cursor.selectedText() CLIPBOARD_HELPER.save_indentation( preceding_text, self.tab_stop_width_spaces) @Slot() def cut(self): """Reimplement cut to signal listeners about changes on the text.""" has_selected_text = self.has_selected_text() if not has_selected_text: return start, end = self.get_selection_start_end() self.sig_will_remove_selection.emit(start, end) TextEditBaseWidget.cut(self) self._save_clipboard_indentation() self.sig_text_was_inserted.emit() self.document_did_change('') @Slot() def copy(self): """Reimplement copy to save indentation.""" TextEditBaseWidget.copy(self) self._save_clipboard_indentation() @Slot() def undo(self): """Reimplement undo to decrease text version number.""" if self.document().isUndoAvailable(): self.text_version -= 1 self.skip_rstrip = True self.is_undoing = True TextEditBaseWidget.undo(self) self.document_did_change('') self.sig_undo.emit() self.sig_text_was_inserted.emit() self.is_undoing = False self.skip_rstrip = False @Slot() def redo(self): """Reimplement redo to increase text version number.""" if self.document().isRedoAvailable(): self.text_version += 1 self.skip_rstrip = True self.is_redoing = True TextEditBaseWidget.redo(self) self.document_did_change('text') self.sig_redo.emit() self.sig_text_was_inserted.emit() self.is_redoing = False self.skip_rstrip = False # ========================================================================= # High-level editor features # ========================================================================= @Slot() def center_cursor_on_next_focus(self): """QPlainTextEdit's "centerCursor" requires the widget to be visible""" self.centerCursor() self.focus_in.disconnect(self.center_cursor_on_next_focus) def go_to_line(self, line, start_column=0, end_column=0, word=''): """Go to line number *line* and eventually highlight it""" self.text_helper.goto_line(line, column=start_column, end_column=end_column, move=True, word=word) def exec_gotolinedialog(self): """Execute the GoToLineDialog dialog box""" dlg = GoToLineDialog(self) if dlg.exec_(): self.go_to_line(dlg.get_line_number()) def hide_tooltip(self): """ Hide the tooltip widget. The tooltip widget is a special QLabel that looks like a tooltip, this method is here so it can be hidden as necessary. For example, when the user leaves the Linenumber area when hovering over lint warnings and errors. """ self._timer_mouse_moving.stop() self._last_hover_word = None self.clear_extra_selections('code_analysis_highlight') if self.tooltip_widget.isVisible(): self.tooltip_widget.hide() def _set_completions_hint_idle(self): self._completions_hint_idle = True self.completion_widget.trigger_completion_hint() # --- Hint for completions def show_hint_for_completion(self, word, documentation, at_point): """Show hint for completion element.""" if self.completions_hint and self._completions_hint_idle: documentation = documentation.replace(u'\xa0', ' ') completion_doc = {'name': word, 'signature': documentation} if documentation and len(documentation) > 0: self.show_hint( documentation, inspect_word=word, at_point=at_point, completion_doc=completion_doc, max_lines=self._DEFAULT_MAX_LINES, max_width=self._DEFAULT_COMPLETION_HINT_MAX_WIDTH) self.tooltip_widget.move(at_point) return self.hide_tooltip() def update_decorations(self): """Update decorations on the visible portion of the screen.""" if self.underline_errors_enabled: # Clear current selections before painting the new ones. # This prevents accumulating them when moving around in the file, # which generated a memory leak and sluggishness in the editor # after some time. self.clear_extra_selections('code_analysis_underline') self.underline_errors() self.update_extra_selections() # This is required to update decorations whether there are or not # underline errors in the visible portion of the screen. # See spyder-ide/spyder#14268. self.decorations.update() def show_code_analysis_results(self, line_number, block_data): """Show warning/error messages.""" # Diagnostic severity icons = { DiagnosticSeverity.ERROR: 'error', DiagnosticSeverity.WARNING: 'warning', DiagnosticSeverity.INFORMATION: 'information', DiagnosticSeverity.HINT: 'hint', } code_analysis = block_data.code_analysis # Size must be adapted from font fm = self.fontMetrics() size = fm.height() template = ( '<img src="data:image/png;base64, {}"' ' height="{size}" width="{size}" /> ' '{} <i>({} {})</i>' ) msglist = [] max_lines_msglist = 25 sorted_code_analysis = sorted(code_analysis, key=lambda i: i[2]) for src, code, sev, msg in sorted_code_analysis: if src == 'pylint' and '[' in msg and ']' in msg: # Remove extra redundant info from pylint messages msg = msg.split(']')[-1] msg = msg.strip() # Avoid messing TODO, FIXME # Prevent error if msg only has one element if len(msg) > 1: msg = msg[0].upper() + msg[1:] # Get individual lines following paragraph format and handle # symbols like '<' and '>' to not mess with br tags msg = msg.replace('<', '<').replace('>', '>') paragraphs = msg.splitlines() new_paragraphs = [] long_paragraphs = 0 lines_per_message = 6 for paragraph in paragraphs: new_paragraph = textwrap.wrap( paragraph, width=self._DEFAULT_MAX_HINT_WIDTH) if lines_per_message > 2: if len(new_paragraph) > 1: new_paragraph = '<br>'.join(new_paragraph[:2]) + '...' long_paragraphs += 1 lines_per_message -= 2 else: new_paragraph = '<br>'.join(new_paragraph) lines_per_message -= 1 new_paragraphs.append(new_paragraph) if len(new_paragraphs) > 1: # Define max lines taking into account that in the same # tooltip you can find multiple warnings and messages # and each one can have multiple lines if long_paragraphs != 0: max_lines = 3 max_lines_msglist -= max_lines * 2 else: max_lines = 5 max_lines_msglist -= max_lines msg = '<br>'.join(new_paragraphs[:max_lines]) + '<br>' else: msg = '<br>'.join(new_paragraphs) base_64 = ima.base64_from_icon(icons[sev], size, size) if max_lines_msglist >= 0: msglist.append(template.format(base_64, msg, src, code, size=size)) if msglist: self.show_tooltip( title=_("Code analysis"), text='\n'.join(msglist), title_color=QStylePalette.COLOR_ACCENT_4, at_line=line_number, with_html_format=True ) self.highlight_line_warning(block_data) def highlight_line_warning(self, block_data): """Highlight errors and warnings in this editor.""" self.clear_extra_selections('code_analysis_highlight') self.highlight_selection('code_analysis_highlight', block_data._selection(), background_color=block_data.color, update=True) self.linenumberarea.update() def get_current_warnings(self): """ Get all warnings for the current editor and return a list with the message and line number. """ block = self.document().firstBlock() line_count = self.document().blockCount() warnings = [] while True: data = block.userData() if data and data.code_analysis: for warning in data.code_analysis: warnings.append([warning[-1], block.blockNumber() + 1]) # See spyder-ide/spyder#9924 if block.blockNumber() + 1 == line_count: break block = block.next() return warnings def go_to_next_warning(self): """ Go to next code warning message and return new cursor position. """ block = self.textCursor().block() line_count = self.document().blockCount() for __ in range(line_count): line_number = block.blockNumber() + 1 if line_number < line_count: block = block.next() else: block = self.document().firstBlock() data = block.userData() if data and data.code_analysis: line_number = block.blockNumber() + 1 self.go_to_line(line_number) self.show_code_analysis_results(line_number, data) return self.get_position('cursor') def go_to_previous_warning(self): """ Go to previous code warning message and return new cursor position. """ block = self.textCursor().block() line_count = self.document().blockCount() for __ in range(line_count): line_number = block.blockNumber() + 1 if line_number > 1: block = block.previous() else: block = self.document().lastBlock() data = block.userData() if data and data.code_analysis: line_number = block.blockNumber() + 1 self.go_to_line(line_number) self.show_code_analysis_results(line_number, data) return self.get_position('cursor') def cell_list(self): """Get the outline explorer data for all cells.""" for oedata in self.outlineexplorer_data_list(): if oedata.def_type == OED.CELL: yield oedata def get_cell_code(self, cell): """ Get cell code for a given cell. If the cell doesn't exist, raises an exception """ selected_block = None if is_string(cell): for oedata in self.cell_list(): if oedata.def_name == cell: selected_block = oedata.block break else: if cell == 0: selected_block = self.document().firstBlock() else: cell_list = list(self.cell_list()) if cell <= len(cell_list): selected_block = cell_list[cell - 1].block if not selected_block: raise RuntimeError("Cell {} not found.".format(repr(cell))) cursor = QTextCursor(selected_block) cell_code, _ = self.get_cell_as_executable_code(cursor) return cell_code def get_cell_count(self): """Get number of cells in document.""" return 1 + len(list(self.cell_list())) #------Tasks management def go_to_next_todo(self): """Go to next todo and return new cursor position""" block = self.textCursor().block() line_count = self.document().blockCount() while True: if block.blockNumber()+1 < line_count: block = block.next() else: block = self.document().firstBlock() data = block.userData() if data and data.todo: break line_number = block.blockNumber()+1 self.go_to_line(line_number) self.show_tooltip( title=_("To do"), text=data.todo, title_color=QStylePalette.COLOR_ACCENT_4, at_line=line_number, ) return self.get_position('cursor') def process_todo(self, todo_results): """Process todo finder results""" for data in self.blockuserdata_list(): data.todo = '' for message, line_number in todo_results: block = self.document().findBlockByNumber(line_number - 1) data = block.userData() if not data: data = BlockUserData(self) data.todo = message block.setUserData(data) self.sig_flags_changed.emit() #------Comments/Indentation def add_prefix(self, prefix): """Add prefix to current line or selected line(s)""" cursor = self.textCursor() if self.has_selected_text(): # Add prefix to selected line(s) start_pos, end_pos = cursor.selectionStart(), cursor.selectionEnd() # Let's see if selection begins at a block start first_pos = min([start_pos, end_pos]) first_cursor = self.textCursor() first_cursor.setPosition(first_pos) cursor.beginEditBlock() cursor.setPosition(end_pos) # Check if end_pos is at the start of a block: if so, starting # changes from the previous block if cursor.atBlockStart(): cursor.movePosition(QTextCursor.PreviousBlock) if cursor.position() < start_pos: cursor.setPosition(start_pos) move_number = self.__spaces_for_prefix() while cursor.position() >= start_pos: cursor.movePosition(QTextCursor.StartOfBlock) line_text = to_text_string(cursor.block().text()) if (self.get_character(cursor.position()) == ' ' and '#' in prefix and not line_text.isspace() or (not line_text.startswith(' ') and line_text != '')): cursor.movePosition(QTextCursor.Right, QTextCursor.MoveAnchor, move_number) cursor.insertText(prefix) elif '#' not in prefix: cursor.insertText(prefix) if cursor.blockNumber() == 0: # Avoid infinite loop when indenting the very first line break cursor.movePosition(QTextCursor.PreviousBlock) cursor.movePosition(QTextCursor.EndOfBlock) cursor.endEditBlock() else: # Add prefix to current line cursor.beginEditBlock() cursor.movePosition(QTextCursor.StartOfBlock) if self.get_character(cursor.position()) == ' ' and '#' in prefix: cursor.movePosition(QTextCursor.NextWord) cursor.insertText(prefix) cursor.endEditBlock() self.document_did_change() def __spaces_for_prefix(self): """Find the less indented level of text.""" cursor = self.textCursor() if self.has_selected_text(): # Add prefix to selected line(s) start_pos, end_pos = cursor.selectionStart(), cursor.selectionEnd() # Let's see if selection begins at a block start first_pos = min([start_pos, end_pos]) first_cursor = self.textCursor() first_cursor.setPosition(first_pos) cursor.beginEditBlock() cursor.setPosition(end_pos) # Check if end_pos is at the start of a block: if so, starting # changes from the previous block if cursor.atBlockStart(): cursor.movePosition(QTextCursor.PreviousBlock) if cursor.position() < start_pos: cursor.setPosition(start_pos) number_spaces = -1 while cursor.position() >= start_pos: cursor.movePosition(QTextCursor.StartOfBlock) line_text = to_text_string(cursor.block().text()) start_with_space = line_text.startswith(' ') left_number_spaces = self.__number_of_spaces(line_text) if not start_with_space: left_number_spaces = 0 if ((number_spaces == -1 or number_spaces > left_number_spaces) and not line_text.isspace() and line_text != ''): number_spaces = left_number_spaces if cursor.blockNumber() == 0: # Avoid infinite loop when indenting the very first line break cursor.movePosition(QTextCursor.PreviousBlock) cursor.movePosition(QTextCursor.EndOfBlock) cursor.endEditBlock() return number_spaces def remove_suffix(self, suffix): """ Remove suffix from current line (there should not be any selection) """ cursor = self.textCursor() cursor.setPosition(cursor.position() - qstring_length(suffix), QTextCursor.KeepAnchor) if to_text_string(cursor.selectedText()) == suffix: cursor.removeSelectedText() self.document_did_change() def remove_prefix(self, prefix): """Remove prefix from current line or selected line(s)""" cursor = self.textCursor() if self.has_selected_text(): # Remove prefix from selected line(s) start_pos, end_pos = sorted([cursor.selectionStart(), cursor.selectionEnd()]) cursor.setPosition(start_pos) if not cursor.atBlockStart(): cursor.movePosition(QTextCursor.StartOfBlock) start_pos = cursor.position() cursor.beginEditBlock() cursor.setPosition(end_pos) # Check if end_pos is at the start of a block: if so, starting # changes from the previous block if cursor.atBlockStart(): cursor.movePosition(QTextCursor.PreviousBlock) if cursor.position() < start_pos: cursor.setPosition(start_pos) cursor.movePosition(QTextCursor.StartOfBlock) old_pos = None while cursor.position() >= start_pos: new_pos = cursor.position() if old_pos == new_pos: break else: old_pos = new_pos line_text = to_text_string(cursor.block().text()) self.__remove_prefix(prefix, cursor, line_text) cursor.movePosition(QTextCursor.PreviousBlock) cursor.endEditBlock() else: # Remove prefix from current line cursor.movePosition(QTextCursor.StartOfBlock) line_text = to_text_string(cursor.block().text()) self.__remove_prefix(prefix, cursor, line_text) def __remove_prefix(self, prefix, cursor, line_text): """Handle the removal of the prefix for a single line.""" start_with_space = line_text.startswith(' ') if start_with_space: left_spaces = self.__even_number_of_spaces(line_text) else: left_spaces = False if start_with_space: right_number_spaces = self.__number_of_spaces(line_text, group=1) else: right_number_spaces = self.__number_of_spaces(line_text) # Handle prefix remove for comments with spaces if (prefix.strip() and line_text.lstrip().startswith(prefix + ' ') or line_text.startswith(prefix + ' ') and '#' in prefix): cursor.movePosition(QTextCursor.Right, QTextCursor.MoveAnchor, line_text.find(prefix)) if (right_number_spaces == 1 and (left_spaces or not start_with_space) or (not start_with_space and right_number_spaces % 2 != 0) or (left_spaces and right_number_spaces % 2 != 0)): # Handle inserted '# ' with the count of the number of spaces # at the right and left of the prefix. cursor.movePosition(QTextCursor.Right, QTextCursor.KeepAnchor, len(prefix + ' ')) else: # Handle manual insertion of '#' cursor.movePosition(QTextCursor.Right, QTextCursor.KeepAnchor, len(prefix)) cursor.removeSelectedText() # Check for prefix without space elif (prefix.strip() and line_text.lstrip().startswith(prefix) or line_text.startswith(prefix)): cursor.movePosition(QTextCursor.Right, QTextCursor.MoveAnchor, line_text.find(prefix)) cursor.movePosition(QTextCursor.Right, QTextCursor.KeepAnchor, len(prefix)) cursor.removeSelectedText() self.document_did_change() def __even_number_of_spaces(self, line_text, group=0): """ Get if there is a correct indentation from a group of spaces of a line. """ spaces = re.findall(r'\s+', line_text) if len(spaces) - 1 >= group: return len(spaces[group]) % len(self.indent_chars) == 0 def __number_of_spaces(self, line_text, group=0): """Get the number of spaces from a group of spaces in a line.""" spaces = re.findall(r'\s+', line_text) if len(spaces) - 1 >= group: return len(spaces[group]) def __get_brackets(self, line_text, closing_brackets=[]): """ Return unmatched opening brackets and left-over closing brackets. (str, []) -> ([(pos, bracket)], [bracket], comment_pos) Iterate through line_text to find unmatched brackets. Returns three objects as a tuple: 1) bracket_stack: a list of tuples of pos and char of each unmatched opening bracket 2) closing brackets: this line's unmatched closing brackets + arg closing_brackets. If this line ad no closing brackets, arg closing_brackets might be matched with previously unmatched opening brackets in this line. 3) Pos at which a # comment begins. -1 if it doesn't.' """ # Remove inline comment and check brackets bracket_stack = [] # list containing this lines unmatched opening # same deal, for closing though. Ignore if bracket stack not empty, # since they are mismatched in that case. bracket_unmatched_closing = [] comment_pos = -1 deactivate = None escaped = False pos, c = None, None for pos, c in enumerate(line_text): # Handle '\' inside strings if escaped: escaped = False # Handle strings elif deactivate: if c == deactivate: deactivate = None elif c == "\\": escaped = True elif c in ["'", '"']: deactivate = c # Handle comments elif c == "#": comment_pos = pos break # Handle brackets elif c in ('(', '[', '{'): bracket_stack.append((pos, c)) elif c in (')', ']', '}'): if bracket_stack and bracket_stack[-1][1] == \ {')': '(', ']': '[', '}': '{'}[c]: bracket_stack.pop() else: bracket_unmatched_closing.append(c) del pos, deactivate, escaped # If no closing brackets are left over from this line, # check the ones from previous iterations' prevlines if not bracket_unmatched_closing: for c in list(closing_brackets): if bracket_stack and bracket_stack[-1][1] == \ {')': '(', ']': '[', '}': '{'}[c]: bracket_stack.pop() closing_brackets.remove(c) else: break del c closing_brackets = bracket_unmatched_closing + closing_brackets return (bracket_stack, closing_brackets, comment_pos) def fix_indent(self, *args, **kwargs): """Indent line according to the preferences""" if self.is_python_like(): ret = self.fix_indent_smart(*args, **kwargs) else: ret = self.simple_indentation(*args, **kwargs) self.document_did_change() return ret def simple_indentation(self, forward=True, **kwargs): """ Simply preserve the indentation-level of the previous line. """ cursor = self.textCursor() block_nb = cursor.blockNumber() prev_block = self.document().findBlockByNumber(block_nb - 1) prevline = to_text_string(prev_block.text()) indentation = re.match(r"\s*", prevline).group() # Unident if not forward: indentation = indentation[len(self.indent_chars):] cursor.insertText(indentation) return False # simple indentation don't fix indentation def find_indentation(self, forward=True, comment_or_string=False, cur_indent=None): """ Find indentation (Python only, no text selection) forward=True: fix indent only if text is not enough indented (otherwise force indent) forward=False: fix indent only if text is too much indented (otherwise force unindent) comment_or_string: Do not adjust indent level for unmatched opening brackets and keywords max_blank_lines: maximum number of blank lines to search before giving up cur_indent: current indent. This is the indent before we started processing. E.g. when returning, indent before rstrip. Returns the indentation for the current line Assumes self.is_python_like() to return True """ cursor = self.textCursor() block_nb = cursor.blockNumber() # find the line that contains our scope line_in_block = False visual_indent = False add_indent = 0 # How many levels of indent to add prevline = None prevtext = "" empty_lines = True closing_brackets = [] for prevline in range(block_nb - 1, -1, -1): cursor.movePosition(QTextCursor.PreviousBlock) prevtext = to_text_string(cursor.block().text()).rstrip() bracket_stack, closing_brackets, comment_pos = self.__get_brackets( prevtext, closing_brackets) if not prevtext: continue if prevtext.endswith((':', '\\')): # Presume a block was started line_in_block = True # add one level of indent to correct_indent # Does this variable actually do *anything* of relevance? # comment_or_string = True if bracket_stack or not closing_brackets: break if prevtext.strip() != '': empty_lines = False if empty_lines and prevline is not None and prevline < block_nb - 2: # The previous line is too far, ignore prevtext = '' prevline = block_nb - 2 line_in_block = False # splits of prevtext happen a few times. Let's just do it once words = re.split(r'[\s$\[\{\}\]$]', prevtext.lstrip()) if line_in_block: add_indent += 1 if prevtext and not comment_or_string: if bracket_stack: # Hanging indent if prevtext.endswith(('(', '[', '{')): add_indent += 1 if words[0] in ('class', 'def', 'elif', 'except', 'for', 'if', 'while', 'with'): add_indent += 1 elif not ( # I'm not sure this block should exist here ( self.tab_stop_width_spaces if self.indent_chars == '\t' else len(self.indent_chars) ) * 2 < len(prevtext)): visual_indent = True else: # There's stuff after unmatched opening brackets visual_indent = True elif (words[-1] in ('continue', 'break', 'pass',) or words[0] == "return" and not line_in_block ): add_indent -= 1 if prevline: prevline_indent = self.get_block_indentation(prevline) else: prevline_indent = 0 if visual_indent: # can only be true if bracket_stack correct_indent = bracket_stack[-1][0] + 1 elif add_indent: # Indent if self.indent_chars == '\t': correct_indent = prevline_indent + self.tab_stop_width_spaces * add_indent else: correct_indent = prevline_indent + len(self.indent_chars) * add_indent else: correct_indent = prevline_indent # TODO untangle this block if prevline and not bracket_stack and not prevtext.endswith(':'): if forward: # Keep indentation of previous line ref_line = block_nb - 1 else: # Find indentation context ref_line = prevline if cur_indent is None: cur_indent = self.get_block_indentation(ref_line) is_blank = not self.get_text_line(ref_line).strip() trailing_text = self.get_text_line(block_nb).strip() # If brackets are matched and no block gets opened # Match the above line's indent and nudge to the next multiple of 4 if cur_indent < prevline_indent and (trailing_text or is_blank): # if line directly above is blank or there is text after cursor # Ceiling division correct_indent = -(-cur_indent // len(self.indent_chars)) * \ len(self.indent_chars) return correct_indent def fix_indent_smart(self, forward=True, comment_or_string=False, cur_indent=None): """ Fix indentation (Python only, no text selection) forward=True: fix indent only if text is not enough indented (otherwise force indent) forward=False: fix indent only if text is too much indented (otherwise force unindent) comment_or_string: Do not adjust indent level for unmatched opening brackets and keywords max_blank_lines: maximum number of blank lines to search before giving up cur_indent: current indent. This is the indent before we started processing. E.g. when returning, indent before rstrip. Returns True if indent needed to be fixed Assumes self.is_python_like() to return True """ cursor = self.textCursor() block_nb = cursor.blockNumber() indent = self.get_block_indentation(block_nb) correct_indent = self.find_indentation( forward, comment_or_string, cur_indent) if correct_indent >= 0 and not ( indent == correct_indent or forward and indent > correct_indent or not forward and indent < correct_indent ): # Insert the determined indent cursor = self.textCursor() cursor.movePosition(QTextCursor.StartOfBlock) if self.indent_chars == '\t': indent = indent // self.tab_stop_width_spaces cursor.setPosition(cursor.position()+indent, QTextCursor.KeepAnchor) cursor.removeSelectedText() if self.indent_chars == '\t': indent_text = ( '\t' * (correct_indent // self.tab_stop_width_spaces) + ' ' * (correct_indent % self.tab_stop_width_spaces) ) else: indent_text = ' '*correct_indent cursor.insertText(indent_text) return True return False @Slot() def clear_all_output(self): """Removes all output in the ipynb format (Json only)""" try: nb = nbformat.reads(self.toPlainText(), as_version=4) if nb.cells: for cell in nb.cells: if 'outputs' in cell: cell['outputs'] = [] if 'prompt_number' in cell: cell['prompt_number'] = None # We do the following rather than using self.setPlainText # to benefit from QTextEdit's undo/redo feature. self.selectAll() self.skip_rstrip = True self.insertPlainText(nbformat.writes(nb)) self.skip_rstrip = False except Exception as e: QMessageBox.critical(self, _('Removal error'), _("It was not possible to remove outputs from " "this notebook. The error is:\n\n") + \ to_text_string(e)) return @Slot() def convert_notebook(self): """Convert an IPython notebook to a Python script in editor""" try: nb = nbformat.reads(self.toPlainText(), as_version=4) script = nbexporter().from_notebook_node(nb)[0] except Exception as e: QMessageBox.critical(self, _('Conversion error'), _("It was not possible to convert this " "notebook. The error is:\n\n") + \ to_text_string(e)) return self.sig_new_file.emit(script) def indent(self, force=False): """ Indent current line or selection force=True: indent even if cursor is not a the beginning of the line """ leading_text = self.get_text('sol', 'cursor') if self.has_selected_text(): self.add_prefix(self.indent_chars) elif (force or not leading_text.strip() or (self.tab_indents and self.tab_mode)): if self.is_python_like(): if not self.fix_indent(forward=True): self.add_prefix(self.indent_chars) else: self.add_prefix(self.indent_chars) else: if len(self.indent_chars) > 1: length = len(self.indent_chars) self.insert_text(" "*(length-(len(leading_text) % length))) else: self.insert_text(self.indent_chars) def indent_or_replace(self): """Indent or replace by 4 spaces depending on selection and tab mode""" if (self.tab_indents and self.tab_mode) or not self.has_selected_text(): self.indent() else: cursor = self.textCursor() if (self.get_selected_text() == to_text_string(cursor.block().text())): self.indent() else: cursor1 = self.textCursor() cursor1.setPosition(cursor.selectionStart()) cursor2 = self.textCursor() cursor2.setPosition(cursor.selectionEnd()) if cursor1.blockNumber() != cursor2.blockNumber(): self.indent() else: self.replace(self.indent_chars) def unindent(self, force=False): """ Unindent current line or selection force=True: unindent even if cursor is not a the beginning of the line """ if self.has_selected_text(): if self.indent_chars == "\t": # Tabs, remove one tab self.remove_prefix(self.indent_chars) else: # Spaces space_count = len(self.indent_chars) leading_spaces = self.__spaces_for_prefix() remainder = leading_spaces % space_count if remainder: # Get block on "space multiple grid". # See spyder-ide/spyder#5734. self.remove_prefix(" "*remainder) else: # Unindent one space multiple self.remove_prefix(self.indent_chars) else: leading_text = self.get_text('sol', 'cursor') if (force or not leading_text.strip() or (self.tab_indents and self.tab_mode)): if self.is_python_like(): if not self.fix_indent(forward=False): self.remove_prefix(self.indent_chars) elif leading_text.endswith('\t'): self.remove_prefix('\t') else: self.remove_prefix(self.indent_chars) @Slot() def toggle_comment(self): """Toggle comment on current line or selection""" cursor = self.textCursor() start_pos, end_pos = sorted([cursor.selectionStart(), cursor.selectionEnd()]) cursor.setPosition(end_pos) last_line = cursor.block().blockNumber() if cursor.atBlockStart() and start_pos != end_pos: last_line -= 1 cursor.setPosition(start_pos) first_line = cursor.block().blockNumber() # If the selection contains only commented lines and surrounding # whitespace, uncomment. Otherwise, comment. is_comment_or_whitespace = True at_least_one_comment = False for _line_nb in range(first_line, last_line+1): text = to_text_string(cursor.block().text()).lstrip() is_comment = text.startswith(self.comment_string) is_whitespace = (text == '') is_comment_or_whitespace *= (is_comment or is_whitespace) if is_comment: at_least_one_comment = True cursor.movePosition(QTextCursor.NextBlock) if is_comment_or_whitespace and at_least_one_comment: self.uncomment() else: self.comment() def is_comment(self, block): """Detect inline comments. Return True if the block is an inline comment. """ if block is None: return False text = to_text_string(block.text()).lstrip() return text.startswith(self.comment_string) def comment(self): """Comment current line or selection.""" self.add_prefix(self.comment_string + ' ') def uncomment(self): """Uncomment current line or selection.""" blockcomment = self.unblockcomment() if not blockcomment: self.remove_prefix(self.comment_string) def __blockcomment_bar(self, compatibility=False): """Handle versions of blockcomment bar for backwards compatibility.""" # Blockcomment bar in Spyder version >= 4 blockcomment_bar = self.comment_string + ' ' + '=' * ( 79 - len(self.comment_string + ' ')) if compatibility: # Blockcomment bar in Spyder version < 4 blockcomment_bar = self.comment_string + '=' * ( 79 - len(self.comment_string)) return blockcomment_bar def transform_to_uppercase(self): """Change to uppercase current line or selection.""" cursor = self.textCursor() prev_pos = cursor.position() selected_text = to_text_string(cursor.selectedText()) if len(selected_text) == 0: prev_pos = cursor.position() cursor.select(QTextCursor.WordUnderCursor) selected_text = to_text_string(cursor.selectedText()) s = selected_text.upper() cursor.insertText(s) self.set_cursor_position(prev_pos) self.document_did_change() def transform_to_lowercase(self): """Change to lowercase current line or selection.""" cursor = self.textCursor() prev_pos = cursor.position() selected_text = to_text_string(cursor.selectedText()) if len(selected_text) == 0: prev_pos = cursor.position() cursor.select(QTextCursor.WordUnderCursor) selected_text = to_text_string(cursor.selectedText()) s = selected_text.lower() cursor.insertText(s) self.set_cursor_position(prev_pos) self.document_did_change() def blockcomment(self): """Block comment current line or selection.""" comline = self.__blockcomment_bar() + self.get_line_separator() cursor = self.textCursor() if self.has_selected_text(): self.extend_selection_to_complete_lines() start_pos, end_pos = cursor.selectionStart(), cursor.selectionEnd() else: start_pos = end_pos = cursor.position() cursor.beginEditBlock() cursor.setPosition(start_pos) cursor.movePosition(QTextCursor.StartOfBlock) while cursor.position() <= end_pos: cursor.insertText(self.comment_string + " ") cursor.movePosition(QTextCursor.EndOfBlock) if cursor.atEnd(): break cursor.movePosition(QTextCursor.NextBlock) end_pos += len(self.comment_string + " ") cursor.setPosition(end_pos) cursor.movePosition(QTextCursor.EndOfBlock) if cursor.atEnd(): cursor.insertText(self.get_line_separator()) else: cursor.movePosition(QTextCursor.NextBlock) cursor.insertText(comline) cursor.setPosition(start_pos) cursor.movePosition(QTextCursor.StartOfBlock) cursor.insertText(comline) cursor.endEditBlock() self.document_did_change() def unblockcomment(self): """Un-block comment current line or selection.""" # Needed for backward compatibility with Spyder previous blockcomments. # See spyder-ide/spyder#2845. unblockcomment = self.__unblockcomment() if not unblockcomment: unblockcomment = self.__unblockcomment(compatibility=True) else: return unblockcomment self.document_did_change() def __unblockcomment(self, compatibility=False): """Un-block comment current line or selection helper.""" def __is_comment_bar(cursor): return to_text_string(cursor.block().text() ).startswith( self.__blockcomment_bar(compatibility=compatibility)) # Finding first comment bar cursor1 = self.textCursor() if __is_comment_bar(cursor1): return while not __is_comment_bar(cursor1): cursor1.movePosition(QTextCursor.PreviousBlock) if cursor1.blockNumber() == 0: break if not __is_comment_bar(cursor1): return False def __in_block_comment(cursor): cs = self.comment_string return to_text_string(cursor.block().text()).startswith(cs) # Finding second comment bar cursor2 = QTextCursor(cursor1) cursor2.movePosition(QTextCursor.NextBlock) while not __is_comment_bar(cursor2) and __in_block_comment(cursor2): cursor2.movePosition(QTextCursor.NextBlock) if cursor2.block() == self.document().lastBlock(): break if not __is_comment_bar(cursor2): return False # Removing block comment cursor3 = self.textCursor() cursor3.beginEditBlock() cursor3.setPosition(cursor1.position()) cursor3.movePosition(QTextCursor.NextBlock) while cursor3.position() < cursor2.position(): cursor3.movePosition(QTextCursor.NextCharacter, QTextCursor.KeepAnchor) if not cursor3.atBlockEnd(): # standard commenting inserts '# ' but a trailing space on an # empty line might be stripped. if not compatibility: cursor3.movePosition(QTextCursor.NextCharacter, QTextCursor.KeepAnchor) cursor3.removeSelectedText() cursor3.movePosition(QTextCursor.NextBlock) for cursor in (cursor2, cursor1): cursor3.setPosition(cursor.position()) cursor3.select(QTextCursor.BlockUnderCursor) cursor3.removeSelectedText() cursor3.endEditBlock() return True #------Kill ring handlers # Taken from Jupyter's QtConsole # Copyright (c) 2001-2015, IPython Development Team # Copyright (c) 2015-, Jupyter Development Team def kill_line_end(self): """Kill the text on the current line from the cursor forward""" cursor = self.textCursor() cursor.clearSelection() cursor.movePosition(QTextCursor.EndOfLine, QTextCursor.KeepAnchor) if not cursor.hasSelection(): # Line deletion cursor.movePosition(QTextCursor.NextBlock, QTextCursor.KeepAnchor) self._kill_ring.kill_cursor(cursor) self.setTextCursor(cursor) self.document_did_change() def kill_line_start(self): """Kill the text on the current line from the cursor backward""" cursor = self.textCursor() cursor.clearSelection() cursor.movePosition(QTextCursor.StartOfBlock, QTextCursor.KeepAnchor) self._kill_ring.kill_cursor(cursor) self.setTextCursor(cursor) self.document_did_change() def _get_word_start_cursor(self, position): """Find the start of the word to the left of the given position. If a sequence of non-word characters precedes the first word, skip over them. (This emulates the behavior of bash, emacs, etc.) """ document = self.document() position -= 1 while (position and not self.is_letter_or_number(document.characterAt(position))): position -= 1 while position and self.is_letter_or_number( document.characterAt(position)): position -= 1 cursor = self.textCursor() cursor.setPosition(self.next_cursor_position()) return cursor def _get_word_end_cursor(self, position): """Find the end of the word to the right of the given position. If a sequence of non-word characters precedes the first word, skip over them. (This emulates the behavior of bash, emacs, etc.) """ document = self.document() cursor = self.textCursor() position = cursor.position() cursor.movePosition(QTextCursor.End) end = cursor.position() while (position < end and not self.is_letter_or_number(document.characterAt(position))): position = self.next_cursor_position(position) while (position < end and self.is_letter_or_number(document.characterAt(position))): position = self.next_cursor_position(position) cursor.setPosition(position) return cursor def kill_prev_word(self): """Kill the previous word""" position = self.textCursor().position() cursor = self._get_word_start_cursor(position) cursor.setPosition(position, QTextCursor.KeepAnchor) self._kill_ring.kill_cursor(cursor) self.setTextCursor(cursor) self.document_did_change() def kill_next_word(self): """Kill the next word""" position = self.textCursor().position() cursor = self._get_word_end_cursor(position) cursor.setPosition(position, QTextCursor.KeepAnchor) self._kill_ring.kill_cursor(cursor) self.setTextCursor(cursor) self.document_did_change() #------Autoinsertion of quotes/colons def __get_current_color(self, cursor=None): """Get the syntax highlighting color for the current cursor position""" if cursor is None: cursor = self.textCursor() block = cursor.block() pos = cursor.position() - block.position() # relative pos within block layout = block.layout() block_formats = layout.additionalFormats() if block_formats: # To easily grab current format for autoinsert_colons if cursor.atBlockEnd(): current_format = block_formats[-1].format else: current_format = None for fmt in block_formats: if (pos >= fmt.start) and (pos < fmt.start + fmt.length): current_format = fmt.format if current_format is None: return None color = current_format.foreground().color().name() return color else: return None def in_comment_or_string(self, cursor=None, position=None): """Is the cursor or position inside or next to a comment or string? If *cursor* is None, *position* is used instead. If *position* is also None, then the current cursor position is used. """ if self.highlighter: if cursor is None: cursor = self.textCursor() if position: cursor.setPosition(position) current_color = self.__get_current_color(cursor=cursor) comment_color = self.highlighter.get_color_name('comment') string_color = self.highlighter.get_color_name('string') if (current_color == comment_color) or (current_color == string_color): return True else: return False else: return False def __colon_keyword(self, text): stmt_kws = ['def', 'for', 'if', 'while', 'with', 'class', 'elif', 'except'] whole_kws = ['else', 'try', 'except', 'finally'] text = text.lstrip() words = text.split() if any([text == wk for wk in whole_kws]): return True elif len(words) < 2: return False elif any([words[0] == sk for sk in stmt_kws]): return True else: return False def __forbidden_colon_end_char(self, text): end_chars = [':', '\\', '[', '{', '(', ','] text = text.rstrip() if any([text.endswith(c) for c in end_chars]): return True else: return False def __has_colon_not_in_brackets(self, text): """ Return whether a string has a colon which is not between brackets. This function returns True if the given string has a colon which is not between a pair of (round, square or curly) brackets. It assumes that the brackets in the string are balanced. """ bracket_ext = self.editor_extensions.get(CloseBracketsExtension) for pos, char in enumerate(text): if (char == ':' and not bracket_ext.unmatched_brackets_in_line(text[:pos])): return True return False def __has_unmatched_opening_bracket(self): """ Checks if there are any unmatched opening brackets before the current cursor position. """ position = self.textCursor().position() for brace in [']', ')', '}']: match = self.find_brace_match(position, brace, forward=False) if match is not None: return True return False def autoinsert_colons(self): """Decide if we want to autoinsert colons""" bracket_ext = self.editor_extensions.get(CloseBracketsExtension) self.completion_widget.hide() line_text = self.get_text('sol', 'cursor') if not self.textCursor().atBlockEnd(): return False elif self.in_comment_or_string(): return False elif not self.__colon_keyword(line_text): return False elif self.__forbidden_colon_end_char(line_text): return False elif bracket_ext.unmatched_brackets_in_line(line_text): return False elif self.__has_colon_not_in_brackets(line_text): return False elif self.__has_unmatched_opening_bracket(): return False else: return True def next_char(self): cursor = self.textCursor() cursor.movePosition(QTextCursor.NextCharacter, QTextCursor.KeepAnchor) next_char = to_text_string(cursor.selectedText()) return next_char def in_comment(self, cursor=None, position=None): """Returns True if the given position is inside a comment. Parameters ---------- cursor : QTextCursor, optional The position to check. position : int, optional The position to check if *cursor* is None. This parameter is ignored when *cursor* is not None. If both *cursor* and *position* are none, then the position returned by self.textCursor() is used instead. """ if self.highlighter: if cursor is None: cursor = self.textCursor() if position is not None: cursor.setPosition(position) current_color = self.__get_current_color(cursor) comment_color = self.highlighter.get_color_name('comment') return (current_color == comment_color) else: return False def in_string(self, cursor=None, position=None): """Returns True if the given position is inside a string. Parameters ---------- cursor : QTextCursor, optional The position to check. position : int, optional The position to check if *cursor* is None. This parameter is ignored when *cursor* is not None. If both *cursor* and *position* are none, then the position returned by self.textCursor() is used instead. """ if self.highlighter: if cursor is None: cursor = self.textCursor() if position is not None: cursor.setPosition(position) current_color = self.__get_current_color(cursor) string_color = self.highlighter.get_color_name('string') return (current_color == string_color) else: return False # ------ Qt Event handlers def setup_context_menu(self): """Setup context menu""" self.undo_action = create_action( self, _("Undo"), icon=ima.icon('undo'), shortcut=CONF.get_shortcut('editor', 'undo'), triggered=self.undo) self.redo_action = create_action( self, _("Redo"), icon=ima.icon('redo'), shortcut=CONF.get_shortcut('editor', 'redo'), triggered=self.redo) self.cut_action = create_action( self, _("Cut"), icon=ima.icon('editcut'), shortcut=CONF.get_shortcut('editor', 'cut'), triggered=self.cut) self.copy_action = create_action( self, _("Copy"), icon=ima.icon('editcopy'), shortcut=CONF.get_shortcut('editor', 'copy'), triggered=self.copy) self.paste_action = create_action( self, _("Paste"), icon=ima.icon('editpaste'), shortcut=CONF.get_shortcut('editor', 'paste'), triggered=self.paste) selectall_action = create_action( self, _("Select All"), icon=ima.icon('selectall'), shortcut=CONF.get_shortcut('editor', 'select all'), triggered=self.selectAll) toggle_comment_action = create_action( self, _("Comment")+"/"+_("Uncomment"), icon=ima.icon('comment'), shortcut=CONF.get_shortcut('editor', 'toggle comment'), triggered=self.toggle_comment) self.clear_all_output_action = create_action( self, _("Clear all ouput"), icon=ima.icon('ipython_console'), triggered=self.clear_all_output) self.ipynb_convert_action = create_action( self, _("Convert to Python script"), icon=ima.icon('python'), triggered=self.convert_notebook) self.gotodef_action = create_action( self, _("Go to definition"), shortcut=CONF.get_shortcut('editor', 'go to definition'), triggered=self.go_to_definition_from_cursor) # Run actions self.run_cell_action = create_action( self, _("Run cell"), icon=ima.icon('run_cell'), shortcut=CONF.get_shortcut('editor', 'run cell'), triggered=self.sig_run_cell.emit) self.run_cell_and_advance_action = create_action( self, _("Run cell and advance"), icon=ima.icon('run_cell'), shortcut=CONF.get_shortcut('editor', 'run cell and advance'), triggered=self.sig_run_cell_and_advance.emit) self.re_run_last_cell_action = create_action( self, _("Re-run last cell"), icon=ima.icon('run_cell'), shortcut=CONF.get_shortcut('editor', 're-run last cell'), triggered=self.sig_re_run_last_cell.emit) self.run_selection_action = create_action( self, _("Run &selection or current line"), icon=ima.icon('run_selection'), shortcut=CONF.get_shortcut('editor', 'run selection'), triggered=self.sig_run_selection.emit) self.debug_cell_action = create_action( self, _("Debug cell"), icon=ima.icon('debug_cell'), shortcut=CONF.get_shortcut('editor', 'debug cell'), triggered=self.sig_debug_cell.emit) # Zoom actions zoom_in_action = create_action( self, _("Zoom in"), icon=ima.icon('zoom_in'), shortcut=QKeySequence(QKeySequence.ZoomIn), triggered=self.zoom_in.emit) zoom_out_action = create_action( self, _("Zoom out"), icon=ima.icon('zoom_out'), shortcut=QKeySequence(QKeySequence.ZoomOut), triggered=self.zoom_out.emit) zoom_reset_action = create_action( self, _("Zoom reset"), shortcut=QKeySequence("Ctrl+0"), triggered=self.zoom_reset.emit) # Docstring writer = self.writer_docstring self.docstring_action = create_action( self, _("Generate docstring"), shortcut=CONF.get_shortcut('editor', 'docstring'), triggered=writer.write_docstring_at_first_line_of_function) # Document formatting formatter = CONF.get( 'completions', ('provider_configuration', 'lsp', 'values', 'formatting'), '' ) self.format_action = create_action( self, _('Format file or selection with {0}').format( formatter.capitalize()), shortcut=CONF.get_shortcut('editor', 'autoformatting'), triggered=self.format_document_or_range) self.format_action.setEnabled(False) # Build menu self.menu = QMenu(self) actions_1 = [self.run_cell_action, self.run_cell_and_advance_action, self.re_run_last_cell_action, self.run_selection_action, self.gotodef_action, None, self.undo_action, self.redo_action, None, self.cut_action, self.copy_action, self.paste_action, selectall_action] actions_2 = [None, zoom_in_action, zoom_out_action, zoom_reset_action, None, toggle_comment_action, self.docstring_action, self.format_action] if nbformat is not None: nb_actions = [self.clear_all_output_action, self.ipynb_convert_action, None] actions = actions_1 + nb_actions + actions_2 add_actions(self.menu, actions) else: actions = actions_1 + actions_2 add_actions(self.menu, actions) # Read-only context-menu self.readonly_menu = QMenu(self) add_actions(self.readonly_menu, (self.copy_action, None, selectall_action, self.gotodef_action)) def keyReleaseEvent(self, event): """Override Qt method.""" self.sig_key_released.emit(event) key = event.key() direction_keys = {Qt.Key_Up, Qt.Key_Left, Qt.Key_Right, Qt.Key_Down} if key in direction_keys: self.request_cursor_event() # Update decorations after releasing these keys because they don't # trigger the emission of the valueChanged signal in # verticalScrollBar. # See https://bugreports.qt.io/browse/QTBUG-25365 if key in {Qt.Key_Up, Qt.Key_Down}: self.update_decorations_timer.start() # This necessary to run our Pygments highlighter again after the # user generated text changes if event.text(): # Stop the active timer and start it again to not run it on # every event if self.timer_syntax_highlight.isActive(): self.timer_syntax_highlight.stop() # Adjust interval to rehighlight according to the lines # present in the file total_lines = self.get_line_count() if total_lines < 1000: self.timer_syntax_highlight.setInterval(600) elif total_lines < 2000: self.timer_syntax_highlight.setInterval(800) else: self.timer_syntax_highlight.setInterval(1000) self.timer_syntax_highlight.start() self._restore_editor_cursor_and_selections() super(CodeEditor, self).keyReleaseEvent(event) event.ignore() def event(self, event): """Qt method override.""" if event.type() == QEvent.ShortcutOverride: event.ignore() return False else: return super(CodeEditor, self).event(event) def _start_completion_timer(self): """Helper to start timer or complete.""" if self.automatic_completions_after_ms > 0: self._timer_autocomplete.start( self.automatic_completions_after_ms) else: self._handle_completions() def _handle_keypress_event(self, event): """Handle keypress events.""" TextEditBaseWidget.keyPressEvent(self, event) # Trigger the following actions only if the event generates # a text change. text = to_text_string(event.text()) if text: # The next three lines are a workaround for a quirk of # QTextEdit. See spyder-ide/spyder#12663 and # https://bugreports.qt.io/browse/QTBUG-35861 cursor = self.textCursor() cursor.setPosition(cursor.position()) self.setTextCursor(cursor) self.document_did_change() self.sig_text_was_inserted.emit() def keyPressEvent(self, event): """Reimplement Qt method.""" tab_pressed = False if self.completions_hint_after_ms > 0: self._completions_hint_idle = False self._timer_completions_hint.start(self.completions_hint_after_ms) else: self._set_completions_hint_idle() # Send the signal to the editor's extension. event.ignore() self.sig_key_pressed.emit(event) self.kite_call_to_action.handle_key_press(event) key = event.key() text = to_text_string(event.text()) has_selection = self.has_selected_text() ctrl = event.modifiers() & Qt.ControlModifier shift = event.modifiers() & Qt.ShiftModifier if text: self.__clear_occurrences() # Only ask for completions if there's some text generated # as part of the event. Events such as pressing Crtl, # Shift or Alt don't generate any text. # Fixes spyder-ide/spyder#11021 self._start_completion_timer() if key in {Qt.Key_Up, Qt.Key_Left, Qt.Key_Right, Qt.Key_Down}: self.hide_tooltip() if event.isAccepted(): # The event was handled by one of the editor extension. return if key in [Qt.Key_Control, Qt.Key_Shift, Qt.Key_Alt, Qt.Key_Meta, Qt.KeypadModifier]: # The user pressed only a modifier key. if ctrl: pos = self.mapFromGlobal(QCursor.pos()) pos = self.calculate_real_position_from_global(pos) if self._handle_goto_uri_event(pos): event.accept() return if self._handle_goto_definition_event(pos): event.accept() return return # ---- Handle hard coded and builtin actions operators = {'+', '-', '*', '**', '/', '//', '%', '@', '<<', '>>', '&', '|', '^', '~', '<', '>', '<=', '>=', '==', '!='} delimiters = {',', ':', ';', '@', '=', '->', '+=', '-=', '*=', '/=', '//=', '%=', '@=', '&=', '|=', '^=', '>>=', '<<=', '**='} if text not in self.auto_completion_characters: if text in operators or text in delimiters: self.completion_widget.hide() if key in (Qt.Key_Enter, Qt.Key_Return): if not shift and not ctrl: if (self.add_colons_enabled and self.is_python_like() and self.autoinsert_colons()): self.textCursor().beginEditBlock() self.insert_text(':' + self.get_line_separator()) if self.strip_trailing_spaces_on_modify: self.fix_and_strip_indent() else: self.fix_indent() self.textCursor().endEditBlock() elif self.is_completion_widget_visible(): self.select_completion_list() else: self.textCursor().beginEditBlock() cur_indent = self.get_block_indentation( self.textCursor().blockNumber()) self._handle_keypress_event(event) # Check if we're in a comment or a string at the # current position cmt_or_str_cursor = self.in_comment_or_string() # Check if the line start with a comment or string cursor = self.textCursor() cursor.setPosition(cursor.block().position(), QTextCursor.KeepAnchor) cmt_or_str_line_begin = self.in_comment_or_string( cursor=cursor) # Check if we are in a comment or a string cmt_or_str = cmt_or_str_cursor and cmt_or_str_line_begin if self.strip_trailing_spaces_on_modify: self.fix_and_strip_indent( comment_or_string=cmt_or_str, cur_indent=cur_indent) else: self.fix_indent(comment_or_string=cmt_or_str, cur_indent=cur_indent) self.textCursor().endEditBlock() elif key == Qt.Key_Insert and not shift and not ctrl: self.setOverwriteMode(not self.overwriteMode()) elif key == Qt.Key_Backspace and not shift and not ctrl: if has_selection or not self.intelligent_backspace: self._handle_keypress_event(event) else: leading_text = self.get_text('sol', 'cursor') leading_length = len(leading_text) trailing_spaces = leading_length - len(leading_text.rstrip()) trailing_text = self.get_text('cursor', 'eol') matches = ('()', '[]', '{}', '\'\'', '""') if (not leading_text.strip() and (leading_length > len(self.indent_chars))): if leading_length % len(self.indent_chars) == 0: self.unindent() else: self._handle_keypress_event(event) elif trailing_spaces and not trailing_text.strip(): self.remove_suffix(leading_text[-trailing_spaces:]) elif (leading_text and trailing_text and (leading_text[-1] + trailing_text[0] in matches)): cursor = self.textCursor() cursor.movePosition(QTextCursor.PreviousCharacter) cursor.movePosition(QTextCursor.NextCharacter, QTextCursor.KeepAnchor, 2) cursor.removeSelectedText() self.document_did_change() else: self._handle_keypress_event(event) elif key == Qt.Key_Home: self.stdkey_home(shift, ctrl) elif key == Qt.Key_End: # See spyder-ide/spyder#495: on MacOS X, it is necessary to # redefine this basic action which should have been implemented # natively self.stdkey_end(shift, ctrl) elif text in self.auto_completion_characters: self.insert_text(text) if text == ".": if not self.in_comment_or_string(): text = self.get_text('sol', 'cursor') last_obj = getobj(text) prev_char = text[-2] if len(text) > 1 else '' if (prev_char in {')', ']', '}'} or (last_obj and not last_obj.isdigit())): # Completions should be triggered immediately when # an autocompletion character is introduced. self.do_completion(automatic=True) else: self.do_completion(automatic=True) elif (text in self.signature_completion_characters and not self.has_selected_text()): self.insert_text(text) self.request_signature() elif (key == Qt.Key_Colon and not has_selection and self.auto_unindent_enabled): leading_text = self.get_text('sol', 'cursor') if leading_text.lstrip() in ('else', 'finally'): ind = lambda txt: len(txt) - len(txt.lstrip()) prevtxt = (to_text_string(self.textCursor().block(). previous().text())) if self.language == 'Python': prevtxt = prevtxt.rstrip() if ind(leading_text) == ind(prevtxt): self.unindent(force=True) self._handle_keypress_event(event) elif (key == Qt.Key_Space and not shift and not ctrl and not has_selection and self.auto_unindent_enabled): self.completion_widget.hide() leading_text = self.get_text('sol', 'cursor') if leading_text.lstrip() in ('elif', 'except'): ind = lambda txt: len(txt)-len(txt.lstrip()) prevtxt = (to_text_string(self.textCursor().block(). previous().text())) if self.language == 'Python': prevtxt = prevtxt.rstrip() if ind(leading_text) == ind(prevtxt): self.unindent(force=True) self._handle_keypress_event(event) elif key == Qt.Key_Tab and not ctrl: # Important note: <TAB> can't be called with a QShortcut because # of its singular role with respect to widget focus management tab_pressed = True if not has_selection and not self.tab_mode: self.intelligent_tab() else: # indent the selected text self.indent_or_replace() elif key == Qt.Key_Backtab and not ctrl: # Backtab, i.e. Shift+<TAB>, could be treated as a QShortcut but # there is no point since <TAB> can't (see above) tab_pressed = True if not has_selection and not self.tab_mode: self.intelligent_backtab() else: # indent the selected text self.unindent() event.accept() elif not event.isAccepted(): self._handle_keypress_event(event) self._last_key_pressed_text = text self._last_pressed_key = key if self.automatic_completions_after_ms == 0 and not tab_pressed: self._handle_completions() if not event.modifiers(): # Accept event to avoid it being handled by the parent. # Modifiers should be passed to the parent because they # could be shortcuts event.accept() def _handle_completions(self): """Handle on the fly completions after delay.""" cursor = self.textCursor() pos = cursor.position() cursor.select(QTextCursor.WordUnderCursor) text = to_text_string(cursor.selectedText()) key = self._last_pressed_key if key is not None: if key in [Qt.Key_Return, Qt.Key_Escape, Qt.Key_Tab, Qt.Key_Backtab, Qt.Key_Space]: self._last_pressed_key = None return # Correctly handle completions when Backspace key is pressed. # We should not show the widget if deleting a space before a word. if key == Qt.Key_Backspace: cursor.setPosition(pos - 1, QTextCursor.MoveAnchor) cursor.select(QTextCursor.WordUnderCursor) prev_text = to_text_string(cursor.selectedText()) cursor.setPosition(pos - 1, QTextCursor.MoveAnchor) cursor.setPosition(pos, QTextCursor.KeepAnchor) prev_char = cursor.selectedText() if prev_text == '' or prev_char in (u'\u2029', ' ', '\t'): return # Text might be after a dot '.' if text == '': cursor.setPosition(pos - 1, QTextCursor.MoveAnchor) cursor.select(QTextCursor.WordUnderCursor) text = to_text_string(cursor.selectedText()) if text != '.': text = '' # WordUnderCursor fails if the cursor is next to a right brace. # If the returned text starts with it, we move to the left. if text.startswith((')', ']', '}')): cursor.setPosition(pos - 1, QTextCursor.MoveAnchor) cursor.select(QTextCursor.WordUnderCursor) text = to_text_string(cursor.selectedText()) self.document_did_change(text) is_backspace = ( self.is_completion_widget_visible() and key == Qt.Key_Backspace) if (len(text) >= self.automatic_completions_after_chars and self._last_key_pressed_text or is_backspace): # Perform completion on the fly if self.automatic_completions and not self.in_comment_or_string(): # Variables can include numbers and underscores if (text.isalpha() or text.isalnum() or '_' in text or '.' in text): self.do_completion(automatic=True) self._last_key_pressed_text = '' self._last_pressed_key = None def fix_and_strip_indent(self, *args, **kwargs): """ Automatically fix indent and strip previous automatic indent. args and kwargs are forwarded to self.fix_indent """ # Fix indent cursor_before = self.textCursor().position() # A change just occurred on the last line (return was pressed) if cursor_before > 0: self.last_change_position = cursor_before - 1 self.fix_indent(*args, **kwargs) cursor_after = self.textCursor().position() # Remove previous spaces and update last_auto_indent nspaces_removed = self.strip_trailing_spaces() self.last_auto_indent = (cursor_before - nspaces_removed, cursor_after - nspaces_removed) self.document_did_change() def run_pygments_highlighter(self): """Run pygments highlighter.""" if isinstance(self.highlighter, sh.PygmentsSH): self.highlighter.make_charlist() def get_pattern_at(self, coordinates): """ Return key, text and cursor for pattern (if found at coordinates). """ return self.get_pattern_cursor_at(self.highlighter.patterns, coordinates) def get_pattern_cursor_at(self, pattern, coordinates): """ Find pattern located at the line where the coordinate is located. This returns the actual match and the cursor that selects the text. """ cursor, key, text = None, None, None break_loop = False # Check if the pattern is in line line = self.get_line_at(coordinates) match = pattern.search(line) while match: for key, value in list(match.groupdict().items()): if value: start, end = sh.get_span(match) # Get cursor selection if pattern found cursor = self.cursorForPosition(coordinates) cursor.movePosition(QTextCursor.StartOfBlock) line_start_position = cursor.position() cursor.setPosition(line_start_position + start, cursor.MoveAnchor) start_rect = self.cursorRect(cursor) cursor.setPosition(line_start_position + end, cursor.MoveAnchor) end_rect = self.cursorRect(cursor) bounding_rect = start_rect.united(end_rect) # Check coordinates are located within the selection rect if bounding_rect.contains(coordinates): text = line[start:end] cursor.setPosition(line_start_position + start, cursor.KeepAnchor) break_loop = True break if break_loop: break match = pattern.search(line, end) return key, text, cursor def _preprocess_file_uri(self, uri): """Format uri to conform to absolute or relative file paths.""" fname = uri.replace('file://', '') if fname[-1] == '/': fname = fname[:-1] # ^/ is used to denote the current project root if fname.startswith("^/"): if self.current_project_path is not None: fname = osp.join(self.current_project_path, fname[2:]) else: fname = fname.replace("^/", "~/") if fname.startswith("~/"): fname = osp.expanduser(fname) dirname = osp.dirname(osp.abspath(self.filename)) if osp.isdir(dirname): if not osp.isfile(fname): # Maybe relative fname = osp.join(dirname, fname) self.sig_file_uri_preprocessed.emit(fname) return fname def _handle_goto_definition_event(self, pos): """Check if goto definition can be applied and apply highlight.""" text = self.get_word_at(pos) if text and not sourcecode.is_keyword(to_text_string(text)): if not self.__cursor_changed: QApplication.setOverrideCursor(QCursor(Qt.PointingHandCursor)) self.__cursor_changed = True cursor = self.cursorForPosition(pos) cursor.select(QTextCursor.WordUnderCursor) self.clear_extra_selections('ctrl_click') self.highlight_selection( 'ctrl_click', cursor, update=True, foreground_color=self.ctrl_click_color, underline_color=self.ctrl_click_color, underline_style=QTextCharFormat.SingleUnderline) return True else: return False def _handle_goto_uri_event(self, pos): """Check if go to uri can be applied and apply highlight.""" key, pattern_text, cursor = self.get_pattern_at(pos) if key and pattern_text and cursor: self._last_hover_pattern_key = key self._last_hover_pattern_text = pattern_text color = self.ctrl_click_color if key in ['file']: fname = self._preprocess_file_uri(pattern_text) if not osp.isfile(fname): color = QColor(SpyderPalette.COLOR_ERROR_2) self.clear_extra_selections('ctrl_click') self.highlight_selection( 'ctrl_click', cursor, update=True, foreground_color=color, underline_color=color, underline_style=QTextCharFormat.SingleUnderline) if not self.__cursor_changed: QApplication.setOverrideCursor( QCursor(Qt.PointingHandCursor)) self.__cursor_changed = True self.sig_uri_found.emit(pattern_text) return True else: self._last_hover_pattern_key = key self._last_hover_pattern_text = pattern_text return False def go_to_uri_from_cursor(self, uri): """Go to url from cursor and defined hover patterns.""" key = self._last_hover_pattern_key full_uri = uri if key in ['file']: fname = self._preprocess_file_uri(uri) if osp.isfile(fname) and encoding.is_text_file(fname): # Open in editor self.go_to_definition.emit(fname, 0, 0) else: # Use external program fname = file_uri(fname) start_file(fname) elif key in ['mail', 'url']: if '@' in uri and not uri.startswith('mailto:'): full_uri = 'mailto:' + uri quri = QUrl(full_uri) QDesktopServices.openUrl(quri) elif key in ['issue']: # Issue URI repo_url = uri.replace('#', '/issues/') if uri.startswith(('gh-', 'bb-', 'gl-')): number = uri[3:] remotes = get_git_remotes(self.filename) remote = remotes.get('upstream', remotes.get('origin')) if remote: full_uri = remote_to_url(remote) + '/issues/' + number else: full_uri = None elif uri.startswith('gh:') or ':' not in uri: # Github repo_and_issue = repo_url if uri.startswith('gh:'): repo_and_issue = repo_url[3:] full_uri = 'https://github.com/' + repo_and_issue elif uri.startswith('gl:'): # Gitlab full_uri = 'https://gitlab.com/' + repo_url[3:] elif uri.startswith('bb:'): # Bitbucket full_uri = 'https://bitbucket.org/' + repo_url[3:] if full_uri: quri = QUrl(full_uri) QDesktopServices.openUrl(quri) else: QMessageBox.information( self, _('Information'), _('This file is not part of a local repository or ' 'upstream/origin remotes are not defined!'), QMessageBox.Ok, ) self.hide_tooltip() return full_uri def line_range(self, position): """ Get line range from position. """ if position is None: return None if position >= self.document().characterCount(): return None # Check if still on the line cursor = self.textCursor() cursor.setPosition(position) line_range = (cursor.block().position(), cursor.block().position() + cursor.block().length() - 1) return line_range def strip_trailing_spaces(self): """ Strip trailing spaces if needed. Remove trailing whitespace on leaving a non-string line containing it. Return the number of removed spaces. """ if not running_under_pytest(): if not self.hasFocus(): # Avoid problem when using split editor return 0 # Update current position current_position = self.textCursor().position() last_position = self.last_position self.last_position = current_position if self.skip_rstrip: return 0 line_range = self.line_range(last_position) if line_range is None: # Doesn't apply return 0 def pos_in_line(pos): """Check if pos is in last line.""" if pos is None: return False return line_range[0] <= pos <= line_range[1] if pos_in_line(current_position): # Check if still on the line return 0 # Check if end of line in string cursor = self.textCursor() cursor.setPosition(line_range[1]) if (not self.strip_trailing_spaces_on_modify or self.in_string(cursor=cursor)): if self.last_auto_indent is None: return 0 elif (self.last_auto_indent != self.line_range(self.last_auto_indent[0])): # line not empty self.last_auto_indent = None return 0 line_range = self.last_auto_indent self.last_auto_indent = None elif not pos_in_line(self.last_change_position): # Should process if pressed return or made a change on the line: return 0 cursor.setPosition(line_range[0]) cursor.setPosition(line_range[1], QTextCursor.KeepAnchor) # remove spaces on the right text = cursor.selectedText() strip = text.rstrip() # I think all the characters we can strip are in a single QChar. # Therefore there shouldn't be any length problems. N_strip = qstring_length(text[len(strip):]) if N_strip > 0: # Select text to remove cursor.setPosition(line_range[1] - N_strip) cursor.setPosition(line_range[1], QTextCursor.KeepAnchor) cursor.removeSelectedText() self.document_did_change() # Correct last change position self.last_change_position = line_range[1] self.last_position = self.textCursor().position() return N_strip return 0 def move_line_up(self): """Move up current line or selected text""" self.__move_line_or_selection(after_current_line=False) def move_line_down(self): """Move down current line or selected text""" self.__move_line_or_selection(after_current_line=True) def __move_line_or_selection(self, after_current_line=True): cursor = self.textCursor() # Unfold any folded code block before moving lines up/down folding_panel = self.panels.get('FoldingPanel') fold_start_line = cursor.blockNumber() + 1 block = cursor.block().next() if fold_start_line in folding_panel.folding_status: fold_status = folding_panel.folding_status[fold_start_line] if fold_status: folding_panel.toggle_fold_trigger(block) if after_current_line: # Unfold any folded region when moving lines down fold_start_line = cursor.blockNumber() + 2 block = cursor.block().next().next() if fold_start_line in folding_panel.folding_status: fold_status = folding_panel.folding_status[fold_start_line] if fold_status: folding_panel.toggle_fold_trigger(block) else: # Unfold any folded region when moving lines up block = cursor.block() offset = 0 if self.has_selected_text(): ((selection_start, _), (selection_end)) = self.get_selection_start_end() if selection_end != selection_start: offset = 1 fold_start_line = block.blockNumber() - 1 - offset # Find the innermost code folding region for the current position enclosing_regions = sorted(list( folding_panel.current_tree[fold_start_line])) folding_status = folding_panel.folding_status if len(enclosing_regions) > 0: for region in enclosing_regions: fold_start_line = region.begin block = self.document().findBlockByNumber(fold_start_line) if fold_start_line in folding_status: fold_status = folding_status[fold_start_line] if fold_status: folding_panel.toggle_fold_trigger(block) self._TextEditBaseWidget__move_line_or_selection( after_current_line=after_current_line) def mouseMoveEvent(self, event): """Underline words when pressing <CONTROL>""" # Restart timer every time the mouse is moved # This is needed to correctly handle hover hints with a delay self._timer_mouse_moving.start() pos = event.pos() self._last_point = pos alt = event.modifiers() & Qt.AltModifier ctrl = event.modifiers() & Qt.ControlModifier if alt: self.sig_alt_mouse_moved.emit(event) event.accept() return if ctrl: if self._handle_goto_uri_event(pos): event.accept() return if self.has_selected_text(): TextEditBaseWidget.mouseMoveEvent(self, event) return if self.go_to_definition_enabled and ctrl: if self._handle_goto_definition_event(pos): event.accept() return if self.__cursor_changed: self._restore_editor_cursor_and_selections() else: if (not self._should_display_hover(pos) and not self.is_completion_widget_visible()): self.hide_tooltip() TextEditBaseWidget.mouseMoveEvent(self, event) def setPlainText(self, txt): """ Extends setPlainText to emit the new_text_set signal. :param txt: The new text to set. :param mime_type: Associated mimetype. Setting the mime will update the pygments lexer. :param encoding: text encoding """ super(CodeEditor, self).setPlainText(txt) self.new_text_set.emit() def focusOutEvent(self, event): """Extend Qt method""" self.sig_focus_changed.emit() self._restore_editor_cursor_and_selections() super(CodeEditor, self).focusOutEvent(event) def focusInEvent(self, event): formatting_enabled = getattr(self, 'formatting_enabled', False) self.sig_refresh_formatting.emit(formatting_enabled) super(CodeEditor, self).focusInEvent(event) def leaveEvent(self, event): """Extend Qt method""" self.sig_leave_out.emit() self._restore_editor_cursor_and_selections() TextEditBaseWidget.leaveEvent(self, event) def mousePressEvent(self, event): """Override Qt method.""" self.hide_tooltip() self.kite_call_to_action.handle_mouse_press(event) ctrl = event.modifiers() & Qt.ControlModifier alt = event.modifiers() & Qt.AltModifier pos = event.pos() self._mouse_left_button_pressed = event.button() == Qt.LeftButton if event.button() == Qt.LeftButton and ctrl: TextEditBaseWidget.mousePressEvent(self, event) cursor = self.cursorForPosition(pos) uri = self._last_hover_pattern_text if uri: self.go_to_uri_from_cursor(uri) else: self.go_to_definition_from_cursor(cursor) elif event.button() == Qt.LeftButton and alt: self.sig_alt_left_mouse_pressed.emit(event) else: TextEditBaseWidget.mousePressEvent(self, event) def mouseReleaseEvent(self, event): """Override Qt method.""" if event.button() == Qt.LeftButton: self._mouse_left_button_pressed = False self.request_cursor_event() TextEditBaseWidget.mouseReleaseEvent(self, event) def contextMenuEvent(self, event): """Reimplement Qt method""" nonempty_selection = self.has_selected_text() self.copy_action.setEnabled(nonempty_selection) self.cut_action.setEnabled(nonempty_selection) self.clear_all_output_action.setVisible(self.is_json() and nbformat is not None) self.ipynb_convert_action.setVisible(self.is_json() and nbformat is not None) self.run_cell_action.setVisible(self.is_python_or_ipython()) self.run_cell_and_advance_action.setVisible(self.is_python_or_ipython()) self.run_selection_action.setVisible(self.is_python_or_ipython()) self.re_run_last_cell_action.setVisible(self.is_python_or_ipython()) self.gotodef_action.setVisible(self.go_to_definition_enabled) formatter = CONF.get( 'completions', ('provider_configuration', 'lsp', 'values', 'formatting'), '' ) self.format_action.setText(_( 'Format file or selection with {0}').format( formatter.capitalize())) # Check if a docstring is writable writer = self.writer_docstring writer.line_number_cursor = self.get_line_number_at(event.pos()) result = writer.get_function_definition_from_first_line() if result: self.docstring_action.setEnabled(True) else: self.docstring_action.setEnabled(False) # Code duplication go_to_definition_from_cursor and mouse_move_event cursor = self.textCursor() text = to_text_string(cursor.selectedText()) if len(text) == 0: cursor.select(QTextCursor.WordUnderCursor) text = to_text_string(cursor.selectedText()) self.undo_action.setEnabled(self.document().isUndoAvailable()) self.redo_action.setEnabled(self.document().isRedoAvailable()) menu = self.menu if self.isReadOnly(): menu = self.readonly_menu menu.popup(event.globalPos()) event.accept() def _restore_editor_cursor_and_selections(self): """Restore the cursor and extra selections of this code editor.""" if self.__cursor_changed: self.__cursor_changed = False QApplication.restoreOverrideCursor() self.clear_extra_selections('ctrl_click') self._last_hover_pattern_key = None self._last_hover_pattern_text = None #------ Drag and drop def dragEnterEvent(self, event): """ Reimplemented Qt method. Inform Qt about the types of data that the widget accepts. """ logger.debug("dragEnterEvent was received") all_urls = mimedata2url(event.mimeData()) if all_urls: # Let the parent widget handle this logger.debug("Let the parent widget handle this dragEnterEvent") event.ignore() else: logger.debug("Call TextEditBaseWidget dragEnterEvent method") TextEditBaseWidget.dragEnterEvent(self, event) def dropEvent(self, event): """ Reimplemented Qt method. Unpack dropped data and handle it. """ logger.debug("dropEvent was received") if mimedata2url(event.mimeData()): logger.debug("Let the parent widget handle this") event.ignore() else: logger.debug("Call TextEditBaseWidget dropEvent method") TextEditBaseWidget.dropEvent(self, event) self.document_did_change() #------ Paint event def paintEvent(self, event): """Overrides paint event to update the list of visible blocks""" self.update_visible_blocks(event) TextEditBaseWidget.paintEvent(self, event) self.painted.emit(event) def update_visible_blocks(self, event): """Update the list of visible blocks/lines position""" self.__visible_blocks[:] = [] block = self.firstVisibleBlock() blockNumber = block.blockNumber() top = int(self.blockBoundingGeometry(block).translated( self.contentOffset()).top()) bottom = top + int(self.blockBoundingRect(block).height()) ebottom_bottom = self.height() while block.isValid(): visible = bottom <= ebottom_bottom if not visible: break if block.isVisible(): self.__visible_blocks.append((top, blockNumber+1, block)) block = block.next() top = bottom bottom = top + int(self.blockBoundingRect(block).height()) blockNumber = block.blockNumber() def _draw_editor_cell_divider(self): """Draw a line on top of a define cell""" if self.supported_cell_language: cell_line_color = self.comment_color painter = QPainter(self.viewport()) pen = painter.pen() pen.setStyle(Qt.SolidLine) pen.setBrush(cell_line_color) painter.setPen(pen) for top, line_number, block in self.visible_blocks: if is_cell_header(block): painter.drawLine(4, top, self.width(), top) @property def visible_blocks(self): """ Returns the list of visible blocks. Each element in the list is a tuple made up of the line top position, the line number (already 1 based), and the QTextBlock itself. :return: A list of tuple(top position, line number, block) :rtype: List of tuple(int, int, QtGui.QTextBlock) """ return self.__visible_blocks def is_editor(self): return True def popup_docstring(self, prev_text, prev_pos): """Show the menu for generating docstring.""" line_text = self.textCursor().block().text() if line_text != prev_text: return if prev_pos != self.textCursor().position(): return writer = self.writer_docstring if writer.get_function_definition_from_below_last_line(): point = self.cursorRect().bottomRight() point = self.calculate_real_position(point) point = self.mapToGlobal(point) self.menu_docstring = QMenuOnlyForEnter(self) self.docstring_action = create_action( self, _("Generate docstring"), icon=ima.icon('TextFileIcon'), triggered=writer.write_docstring) self.menu_docstring.addAction(self.docstring_action) self.menu_docstring.setActiveAction(self.docstring_action) self.menu_docstring.popup(point) def delayed_popup_docstring(self): """Show context menu for docstring. This method is called after typing '''. After typing ''', this function waits 300ms. If there was no input for 300ms, show the context menu. """ line_text = self.textCursor().block().text() pos = self.textCursor().position() timer = QTimer() timer.singleShot(300, lambda: self.popup_docstring(line_text, pos)) def set_current_project_path(self, root_path=None): """ Set the current active project root path. Parameters ---------- root_path: str or None, optional Path to current project root path. Default is None. """ self.current_project_path = root_path def count_leading_empty_lines(self, cell): """Count the number of leading empty cells.""" lines = cell.splitlines(keepends=True) if not lines: return 0 for i, line in enumerate(lines): if line and not line.isspace(): return i return len(lines) def ipython_to_python(self, code): """Transform IPython code to python code.""" tm = TransformerManager() number_empty_lines = self.count_leading_empty_lines(code) try: code = tm.transform_cell(code) except SyntaxError: return code return '\n' * number_empty_lines + code def is_letter_or_number(self, char): """ Returns whether the specified unicode character is a letter or a number. """ cat = category(char) return cat.startswith('L') or cat.startswith('N') # ============================================================================= # Editor + Class browser test # ============================================================================= class TestWidget(QSplitter): def __init__(self, parent): QSplitter.__init__(self, parent) self.editor = CodeEditor(self) self.editor.setup_editor(linenumbers=True, markers=True, tab_mode=False, font=QFont("Courier New", 10), show_blanks=True, color_scheme='Zenburn') self.addWidget(self.editor) self.setWindowIcon(ima.icon('spyder')) def load(self, filename): self.editor.set_text_from_file(filename) self.setWindowTitle("%s - %s (%s)" % (_("Editor"), osp.basename(filename), osp.dirname(filename))) self.editor.hide_tooltip() def test(fname): from spyder.utils.qthelpers import qapplication app = qapplication(test_time=5) win = TestWidget(None) win.show() win.load(fname) win.resize(900, 700) sys.exit(app.exec_()) if __name__ == '__main__': if len(sys.argv) > 1: fname = sys.argv[1] else: fname = __file__ test(fname)

41.233145

91

0.580793

17bcbf8ffe4bb48c0b31afd03cda79acf0cde04e

1,223

py

Python

petstagram/accounts/managers.py

ivanoff-ivan/petstagram

6ad2fa038843c6e2011fd9242e3edec857f48707

[ "MIT" ]

null

petstagram/accounts/managers.py

ivanoff-ivan/petstagram

6ad2fa038843c6e2011fd9242e3edec857f48707

[ "MIT" ]

null

petstagram/accounts/managers.py

ivanoff-ivan/petstagram

6ad2fa038843c6e2011fd9242e3edec857f48707

[ "MIT" ]

null

from django.contrib.auth import base_user as auth_base from django.contrib.auth.hashers import make_password class PetstagramUserManager(auth_base.BaseUserManager): def _create_user(self, username, password, **extra_fields): if not username: raise ValueError('The given username must be set') user = self.model(username=username, **extra_fields) user.password = make_password(password) user.save(using=self._db) return user def create_user(self, username, password=None, **extra_fields): extra_fields.setdefault('is_staff', False) extra_fields.setdefault('is_superuser', False) return self._create_user(username, password, **extra_fields) def create_superuser(self, username, password=None, **extra_fields): extra_fields.setdefault('is_staff', True) extra_fields.setdefault('is_superuser', True) if extra_fields.get('is_staff') is not True: raise ValueError('Superuser must have is_staff=True.') if extra_fields.get('is_superuser') is not True: raise ValueError('Superuser must have is_superuser=True.') return self._create_user(username, password, **extra_fields)

42.172414

72

0.705642

3ae3bdc6fdc89bc015a61bcf9d7874c9b7628332

33,498

py

Python

viper/modules/misp.py

Mario-Kart-Felix/mal-scrap

bc396a15ea5b144eb1c0f05759d1f9419d6671df

[ "BSD-3-Clause" ]

2

2015-12-17T20:25:09.000Z

2017-10-08T19:14:57.000Z

viper/modules/misp.py

Mario-Kart-Felix/mal-scrap

bc396a15ea5b144eb1c0f05759d1f9419d6671df

[ "BSD-3-Clause" ]

1

2015-01-05T18:07:13.000Z

2015-01-07T21:43:57.000Z

viper/modules/misp.py

Mario-Kart-Felix/mal-scrap

bc396a15ea5b144eb1c0f05759d1f9419d6671df

[ "BSD-3-Clause" ]

3

2017-10-18T00:56:53.000Z

2020-05-24T09:38:54.000Z

# -*- coding: utf-8 -*- # This file is part of Viper - https://github.com/viper-framework/viper # See the file 'LICENSE' for copying permission. import argparse import textwrap import os import json import logging try: from pymisp import PyMISP, PyMISPError, MISPEvent try: from pymisp import MISPEncode except ImportError: from pymisp import EncodeFull as MISPEncode HAVE_PYMISP = True except: HAVE_PYMISP = False try: import requests HAVE_REQUESTS = True except: HAVE_REQUESTS = False from viper.common.abstracts import Module from viper.core.database import Database from viper.core.session import __sessions__ from viper.core.project import __project__ from viper.core.storage import get_sample_path from viper.common.objects import MispEvent from viper.common.constants import VIPER_ROOT from viper.core.config import __config__ log = logging.getLogger('viper') cfg = __config__ cfg.parse_http_client(cfg.misp) class MISP(Module): cmd = 'misp' description = 'Upload and query IOCs to/from a MISP instance' authors = ['Raphaël Vinot'] from .misp_methods import admin # noqa from .misp_methods import create_event # noqa from .misp_methods import download # noqa from .misp_methods import check_hashes, _prepare_attributes, _populate # noqa from .misp_methods import store, _get_local_events # noqa from .misp_methods import tag # noqa from .misp_methods import galaxies # noqa from .misp_methods import version # noqa from .misp_methods import open_samples, _load_tmp_samples, _display_tmp_files, _clean_tmp_samples # noqa from .misp_methods import add, add_hashes, _check_add, _change_event # noqa def __init__(self): super(MISP, self).__init__() self.cur_path = __project__.get_path() self.parser.add_argument("--url", help='URL of the MISP instance') self.parser.add_argument("--off", action='store_true', help='Use offline (can only work on pre-downloaded events)') self.parser.add_argument("--on", action='store_true', help='Switch to online mode') self.parser.add_argument("-k", "--key", help='Your key on the MISP instance') self.parser.add_argument("-v", "--verify", action='store_false', help='Disable certificate verification (for self-signed)') subparsers = self.parser.add_subparsers(dest='subname') # ##### Upload sample to MISP ##### parser_up = subparsers.add_parser('upload', help='Send malware sample to MISP.', formatter_class=argparse.RawDescriptionHelpFormatter, description=textwrap.dedent(''' Distribution levels: * 0: Your organisation only * 1: This community only * 2: Connected communities * 3: All communities * 5: Inherit Sample categories: * 0: Payload delivery * 1: Artifacts dropped * 2: Payload installation * 3: External analysis Analysis levels: * 0: Initial * 1: Ongoing * 2: Completed Threat levels: * 0: High * 1: Medium * 2: Low * 3: Undefined ''')) parser_up.add_argument("-e", "--event", type=int, help="Event ID to update. If None, and you're not connected to a MISP event a new one is created.") parser_up.add_argument("-d", "--distrib", type=int, choices=[0, 1, 2, 3, 5], help="Distribution of the attributes for the new event.") parser_up.add_argument("-s", "--sharing", type=int, help="Sharing group ID when distribution is set to 4.") parser_up.add_argument("-ids", action='store_true', help="Is eligible for automatically creating IDS signatures.") parser_up.add_argument("-c", "--categ", type=int, choices=[0, 1, 2, 3], default=1, help="Category of the samples.") parser_up.add_argument("-i", "--info", nargs='+', help="Event info field of a new event.") parser_up.add_argument("-o", "--comment", nargs='+', help="Comment associated to the sample.") parser_up.add_argument("-a", "--analysis", type=int, choices=[0, 1, 2], help="Analysis level a new event.") parser_up.add_argument("-t", "--threat", type=int, choices=[0, 1, 2, 3], help="Threat level of a new event.") # ##### Download samples from event ##### parser_down = subparsers.add_parser('download', help='Download malware samples from MISP.') group = parser_down.add_mutually_exclusive_group() group.add_argument("-e", "--event", type=int, help="Download all the samples related to this event ID.") group.add_argument("-l", "--list", nargs='*', help="Download all the samples related to a list of events. Empty list to download all the samples of all the events stored in the current project.") # noqa group.add_argument("--hash", help="Download the sample related to this hash (only MD5).") # ##### Search in MISP ##### parser_search = subparsers.add_parser('search', help='Search in all the attributes.') parser_search.add_argument("query", nargs='*', help="String to search (if empty, search the hashes of the current file).") # ##### Check hashes on VT ##### parser_checkhashes = subparsers.add_parser('check_hashes', help='Crosscheck hashes on VT.') parser_checkhashes.add_argument("event", nargs='?', default=None, type=int, help="Lookup all the hashes of an event on VT.") parser_checkhashes.add_argument("-p", "--populate", action='store_true', help="Automatically populate event with hashes found on VT.") # ##### Download Yara rules ##### parser_checkhashes = subparsers.add_parser('yara', help='Get YARA rules of an event.') parser_checkhashes.add_argument("event", nargs='?', default=None, type=int, help="Download the yara rules of that event.") # ##### Get Events ##### parser_pull = subparsers.add_parser('pull', help='Initialize the session with an existing MISP event.') parser_pull.add_argument("event", nargs='+', type=int, help="(List of) Event(s) ID.") # ##### Create an Event ##### parser_create_event = subparsers.add_parser('create_event', help='Create a new event on MISP and initialize the session with it.', formatter_class=argparse.RawDescriptionHelpFormatter, description=textwrap.dedent(''' Distribution levels: * 0: Your organisation only * 1: This community only * 2: Connected communities * 3: All communities * 4: Sharing group Sharing Group: * #: ID of sharing group Analysis levels: * 0: Initial * 1: Ongoing * 2: Completed Threat levels: * 0: High * 1: Medium * 2: Low * 3: Undefined ''')) parser_create_event.add_argument("-d", "--distrib", type=int, choices=[0, 1, 2, 3, 4], help="Distribution of the attributes for the new event.") parser_create_event.add_argument("-s", "--sharing", type=int, help="Sharing group ID when distribution is set to 4.") parser_create_event.add_argument("-t", "--threat", type=int, choices=[0, 1, 2, 3], help="Threat level of a new event.") parser_create_event.add_argument("-a", "--analysis", type=int, choices=[0, 1, 2], help="Analysis level a new event.") parser_create_event.add_argument("-i", "--info", required=True, nargs='+', help="Event info field of a new event.") parser_create_event.add_argument("--date", help="Date of the event. (Default: today).") # ##### Add Hashes ##### h = subparsers.add_parser("add_hashes", help="If no parameters, add all the hashes of the current session.") h.add_argument("-f", "--filename", help="Filename") h.add_argument("-m", "--md5", help="MD5") h.add_argument("-s", "--sha1", help="SHA1") h.add_argument("-a", "--sha256", help="SHA256") # ##### Add attributes ##### parser_add = subparsers.add_parser('add', help='Add attributes to an existing MISP event.') subparsers_add = parser_add.add_subparsers(dest='add') # Hashes # Generic add temp_me = MISPEvent() if hasattr(temp_me, "types"): known_types = temp_me.types else: # New API known_types = temp_me.get_known_types() for t in known_types: sp = subparsers_add.add_parser(t, help="Add {} to the event.".format(t)) sp.add_argument(t, nargs='+') # ##### Show attributes ##### subparsers.add_parser('show', help='Show attributes to an existing MISP event.') # ##### Open file ##### o = subparsers.add_parser('open', help='Open a sample from the temp directory.') ox = o.add_mutually_exclusive_group(required=True) ox.add_argument("-l", "--list", action='store_true', help="List available files") ox.add_argument("-d", "--delete", help="Delete temporary files (use 'all' to remove all the local samples or an Event ID to only remove the associated samples)") ox.add_argument("sid", nargs='?', type=int, help='Sample ID to open (from the list option).') # ##### Publish an event ##### subparsers.add_parser('publish', help='Publish an existing MISP event.') # ##### Show version ##### subparsers.add_parser('version', help='Returns the version of the MISP instance.') # Store s = subparsers.add_parser('store', help='Store the current MISP event in the current project.') s.add_argument("-l", "--list", action='store_true', help="List stored MISP events") s.add_argument("-u", "--update", action='store_true', help="Update all stored MISP events") s.add_argument("-s", "--sync", action='store_true', help="Sync all MISP Events with the remote MISP instance") s.add_argument("-d", "--delete", type=int, help="Delete a stored MISP event") s.add_argument("-o", "--open", help="Open a stored MISP event") # Tags s = subparsers.add_parser('tag', help='Tag managment using MISP taxonomies.') s.add_argument("-l", "--list", action='store_true', help="List Existing taxonomies.") s.add_argument("-d", "--details", help="Display all values of a taxonomy.") s.add_argument("-s", "--search", help="Search all tags matching a value.") s.add_argument("-e", "--event", help="Add tag to the current event.") s.add_argument("-a", "--attribute", nargs='+', help="Add tag to an attribute of the current event. Syntax: <identifier for the attribute> <machinetag>") # Galaxies s = subparsers.add_parser('galaxies', help='Use misp-galaxy with PyMISPGalaxies.') s.add_argument("-l", "--list", action='store_true', help="List existing galaxies.") s.add_argument("-d", "--details", help="Display all values of a galaxy.") s.add_argument("-v", "--cluster-value", nargs='+', help="Display all details of a cluster value.") s.add_argument("-s", "--search", nargs='+', help="Search all galaxies matching a value.") # Admin s = subparsers.add_parser('admin', help='Administration options.') admin_parser = s.add_subparsers(dest='admin') # Organisation org = admin_parser.add_parser('org', help="Organisation managment.") subparsers_org = org.add_subparsers(dest='org') # Get display = subparsers_org.add_parser('display', help="Display an organisation.") display.add_argument('id', help='ID of the organisation to display. Use "local" to display all local organisations, "external" for all remote organisations, and "all", for both.') # Search search = subparsers_org.add_parser('search', help="Search an organisation by name.") search.add_argument('name', help='(Partial) name of the organisation.') search.add_argument('-t', '--type', default='local', choices=['local', 'external', 'all'], help='Use "local" to search in all local organisations, "external" for remote organisations, and "all", for both.') # Add add_org = subparsers_org.add_parser('add', help="Add an organisation.") add_org.add_argument('name', help='Organisation name.') add_org.add_argument('-u', '--uuid', default=None, help='UUID of the organisation.') add_org.add_argument('-d', '--description', default=[], nargs='+', help='Description of the organisation.') add_org.add_argument('-t', '--type', default=[], nargs='+', help='Type of the organisation.') add_org.add_argument('-n', '--nationality', default=None, help='Nationality of the organisation.') add_org.add_argument('-s', '--sector', default=[], nargs='+', help='Sector of the organisation.') add_org.add_argument('-c', '--contacts', default=[], nargs='+', help='Contact point(s) in the organisation.') add_org.add_argument('--not-local', default=True, action='store_false', help='**Not** a local organisation.') # Delete delete = subparsers_org.add_parser('delete', help="Delete an organisation.") delete.add_argument('id', help='ID of the organisation to delete.') # Edit edit = subparsers_org.add_parser('edit', help="Edit an organisation.") edit.add_argument('id', help='ID of the organisation to edit.') edit.add_argument('-n', '--name', help='Organisation name.') edit.add_argument('-u', '--uuid', help='UUID of the organisation.') edit.add_argument('-d', '--description', default=[], nargs='+', help='Description of the organisation.') edit.add_argument('-t', '--type', default=[], nargs='+', help='Type of the organisation.') edit.add_argument('--nationality', help='Nationality of the organisation.') edit.add_argument('-s', '--sector', default=[], nargs='+', help='Sector of the organisation.') edit.add_argument('-c', '--contacts', default=[], nargs='+', help='Contact point(s) in the organisation.') edit.add_argument('--not-local', default=True, action='store_false', help='**Not** a local organisation.') # User user = admin_parser.add_parser('user', help="User managment.") subparsers_user = user.add_subparsers(dest='user') # Get display = subparsers_user.add_parser('display', help="Display a user.") display.add_argument('id', help='ID of the user to display. Use "all" to display all users.') # Search search = subparsers_user.add_parser('search', help="Search a user by email.") search.add_argument('name', help='(Partial) email of the user.') # Add add_usr = subparsers_user.add_parser('add', help="Add a user.") add_usr.add_argument('email', help='User email address.') add_usr.add_argument('-o', '--org-id', default=None, help='Organisation ID of the user.') add_usr.add_argument('-r', '--role-id', default=None, help='Role of the user') add_usr.add_argument('-g', '--gpgkey', default=None, help='Path to the GPG public key export') add_usr.add_argument('-c', '--change-pw', default=None, action='store_true', help='Force thanging the password after next login') add_usr.add_argument('-t', '--termsaccepted', default=None, action='store_true', help='Set the TOC to accepted') add_usr.add_argument('-p', '--password', default=None, help='Set a new password') add_usr.add_argument('-d', '--disabled', default=None, action='store_true', help='Disable the account') # Delete delete = subparsers_user.add_parser('delete', help="Delete a user.") delete.add_argument('id', help='ID of the user to delete.') # Edit edit = subparsers_user.add_parser('edit', help="Edit a user.") edit.add_argument('id', help='ID of the user to edit.') edit.add_argument('-e', '--email', help='User email address.') edit.add_argument('-o', '--org-id', default=None, help='Organisation ID of the user.') edit.add_argument('-r', '--role-id', default=None, help='Role of the user') edit.add_argument('-g', '--gpgkey', default=None, help='Path to the GPG public key export') edit.add_argument('-c', '--change-pw', default=None, action='store_true', help='Force thanging the password after next login') edit.add_argument('-t', '--termsaccepted', default=None, action='store_true', help='Set the TOC to accepted') edit.add_argument('-p', '--password', default=None, help='Set a new password') edit.add_argument('-d', '--disabled', default=None, action='store_true', help='Disable the account') # Role role = admin_parser.add_parser('role', help="Role managment.") subparsers_role = role.add_subparsers(dest='role') # Get display = subparsers_role.add_parser('display', help="Display all the roles.") # Search search = subparsers_role.add_parser('search', help="Search a role by name.") search.add_argument('name', help='(Partial) name of the role.') # Tags t = admin_parser.add_parser('tag', help="Tag managment.") subparsers_tag = t.add_subparsers(dest='tag') # Get display = subparsers_tag.add_parser('display', help="Display all the tags.") # Search search = subparsers_tag.add_parser('search', help="Search a tag by name.") search.add_argument('name', help='(Partial) name of the tag.') self.categories = {0: 'Payload delivery', 1: 'Artifacts dropped', 2: 'Payload installation', 3: 'External analysis'} # ####### Generic Helpers ######## def _get_eventid(self, quiet=False): if vars(self.args).get('event'): return self.args.event else: # Get current event ID if possible if not __sessions__.is_attached_misp(quiet): return None return __sessions__.current.misp_event.event.id def _has_error_message(self, result): if result.get('errors'): for message in result['errors']: self.log('error', message) return True elif result.get('error'): self.log('error', result.get('error')) return True return False def _search_local_hashes(self, event, open_session=True): local = [] samples_count = 0 if isinstance(event, MISPEvent): misp_event = event elif event.get('Event') is None: self.log('error', event) return else: misp_event = MISPEvent() misp_event.load(event) for a in misp_event.attributes: row = None if a.type == 'malware-sample': samples_count += 1 if a.type in ('md5', 'sha1', 'sha256'): row = Database().find(key=a.type, value=a.value) elif a.type in ('filename|md5', 'filename|sha1', 'filename|sha256'): row = Database().find(key=a.type.split('|')[1], value=a.value.split('|')[1]) elif a.type == 'malware-sample': row = Database().find(key='md5', value=a.value.split('|')[1]) if row: local.append(row[0]) self.log('info', 'Event {} contains {} samples.'.format(misp_event.id, samples_count)) if not open_session: return shas = set([l.sha256 for l in local]) if len(shas) == 1: __sessions__.new(get_sample_path(shas.pop()), MispEvent(misp_event, self.offline_mode)) elif len(shas) > 1: self.log('success', 'The following samples are in this viper instance:') __sessions__.new(misp_event=MispEvent(misp_event, self.offline_mode)) for s in shas: self.log('item', s) else: __sessions__.new(misp_event=MispEvent(misp_event, self.offline_mode)) self.log('info', 'No known (in Viper) samples in that event.') def _find_related_id(self, event): if not event.RelatedEvent: return [] related = [(_event.id, _event.info) for _event in event.RelatedEvent] to_return = list(set(related)) to_return.sort(key=lambda tup: tup[0]) return to_return def _dump(self, event=None): event_path = os.path.join(self.cur_path, 'misp_events') if not os.path.exists(event_path): os.makedirs(event_path) if not event: to_dump = __sessions__.current.misp_event.event elif isinstance(event, MISPEvent): to_dump = event else: to_dump = MISPEvent() to_dump.load(event) if to_dump.id: filename = str(to_dump.id) elif (__sessions__.is_attached_misp(True) and __sessions__.current.misp_event.current_dump_file): filename = __sessions__.current.misp_event.current_dump_file else: i = 1 while True: filename = 'new_event_{}.json'.format(i) if not os.path.exists(os.path.join(event_path, filename)): break i += 1 path = os.path.join(event_path, filename) with open(path, 'w') as f: json.dump(to_dump, f, cls=MISPEncode) self.log('success', '{} stored successfully.'.format(filename.rstrip('.json'))) return filename # ########################################## def yara(self): if self.offline_mode: self.log('error', 'Offline mode, unable to get yara rules') return ok = False data = None event_id = self._get_eventid() if event_id is None: return ok, data = self.misp.get_yara(event_id) if not ok: self.log('error', data) return rule_path = os.path.join(VIPER_ROOT, 'data/yara', self.args.event + '.yara') if os.path.exists(rule_path): self.log('error', 'File {} already exists.'.format(rule_path)) return with open(rule_path, 'wb') as f: f.write(data.encode('utf-8')) self.log('success', 'The yara rules of event {} have been downloaded: {}'.format(self.args.event, rule_path)) def upload(self): if self.offline_mode: self.log('error', 'Offline mode, unable to upload a sample') return categ = self.categories.get(self.args.categ) if self.args.info is not None: info = ' '.join(self.args.info) else: info = None if self.args.comment is not None: comment = ' '.join(self.args.comment) else: comment = None # No need to check the output: is the event_id is none, we create a new one. event_id = self._get_eventid(True) try: result = self.misp.upload_sample(__sessions__.current.file.name, __sessions__.current.file.path, event_id, self.args.distrib, self.args.ids, categ, info, comment, self.args.analysis, self.args.threat) except Exception as e: self.log('error', e) return if not self._has_error_message(result): self.log('success', "File uploaded successfully") if event_id is None: event_id = result['id'] full_event = self.misp.get(event_id) if not self._has_error_message(full_event): return __sessions__.new(misp_event=MispEvent(full_event, self.offline_mode)) def searchall(self): if self.args.query: self._search(' '.join(self.args.query)) else: if not __sessions__.is_attached_file(True): self.log('error', "Not attached to a file, nothing to search for.") return False to_search = [__sessions__.current.file.md5, __sessions__.current.file.sha1, __sessions__.current.file.sha256] for q in to_search: self._search(q) def _search(self, query): if self.offline_mode: self.log('error', 'Offline mode, unable to search') return result = self.misp.search_all(query) if self._has_error_message(result): return self.log('success', '{} matches on the following events:'.format(query)) for e in result['response']: nb_samples = 0 nb_hashes = 0 me = MISPEvent() me.load(e) for a in me.attributes: if a.type == 'malware-sample': nb_samples += 1 if a.type in ('md5', 'sha1', 'sha256', 'filename|md5', 'filename|sha1', 'filename|sha256'): nb_hashes += 1 self.log('item', '{} ({} samples, {} hashes) - {}{}{}'.format(me.info, nb_samples, nb_hashes, self.url, '/events/view/', me.id)) def pull(self): if self.offline_mode: self.log('error', 'Offline mode, unable to pull a remote event') return open_session = len(self.args.event) == 1 for e in self.args.event: event = self.misp.get(e) if not self._has_error_message(event): self._search_local_hashes(event, open_session) self._dump(event) def publish(self): __sessions__.current.misp_event.event.publish() if self.offline_mode: self._dump() else: event = self.misp.update(__sessions__.current.misp_event.event._json()) if not self._has_error_message(event): self.log('success', 'Event {} published.'.format(event['Event']['id'])) __sessions__.new(misp_event=MispEvent(event, self.offline_mode)) def show(self): current_event = __sessions__.current.misp_event.event related = self._find_related_id(current_event) if len(related) > 0: self.log('info', 'Related events:') for r, title in related: self.log('item', '{}/events/view/{} - {}'.format(self.url.rstrip('/'), r, title)) header = ['type', 'value', 'comment', 'related'] rows = [] for a in current_event.attributes: # FIXME: this has been removed upstream: https://github.com/MISP/MISP/issues/1793 # Keeping it like that for now, until we decide how to re-enable it idlist = [] if a.RelatedAttribute: for r in a.RelatedAttribute: # idlist.append(r.id) pass rows.append([a.type, a.value, '\n'.join(textwrap.wrap(a.comment, 30)), '\n'.join(textwrap.wrap(' '.join(idlist), 15))]) self.log('table', dict(header=header, rows=rows)) if current_event.published: self.log('info', 'This event has been published') else: self.log('info', 'This event has not been published') if __sessions__.current.misp_event.event.id: self.log('info', 'Link to Event: {}/events/view/{}'.format(self.url.rstrip('/'), __sessions__.current.misp_event.event.id)) def run(self): super(MISP, self).run() if self.args is None: return if not HAVE_PYMISP: self.log('error', "Missing dependency, install pymisp (`pip install pymisp`)") return self.offline_mode = False if self.args.on: self.offline_mode = False if __sessions__.is_attached_misp(True): __sessions__.current.misp_event.off = False elif self.args.off or (__sessions__.is_attached_misp(True) and __sessions__.current.misp_event.off): self.offline_mode = True if __sessions__.is_attached_misp(True): __sessions__.current.misp_event.off = True self.url = self.args.url if self.url is None: self.url = cfg.misp.misp_url if self.url is None: self.log('error', "This command requires the URL of the MISP instance you want to query.") return self.key = self.args.key if self.key is None: self.key = cfg.misp.misp_key if self.key is None: self.log('error', "This command requires a MISP private API key.") return if not self.args.verify: verify = False else: verify = cfg.misp.tls_verify # Capture default distribution and sharing group settings. Backwards compatability and empty string check self.distribution = cfg.misp.get("misp_distribution", None) self.distribution = None if self.distribution == "" else self.distribution if type(self.distribution) not in (type(None), int): self.distribution = None self.log('info', "The distribution stored in viper config is not an integer, setting to None") self.sharinggroup = cfg.misp.get("misp_sharinggroup", None) self.sharinggroup = None if self.sharinggroup == "" else self.sharinggroup if type(self.sharinggroup) not in (type(None), int): self.sharinggroup = None self.log('info', "The sharing group stored in viper config is not an integer, setting to None") if not self.offline_mode: try: self.misp = PyMISP(self.url, self.key, ssl=verify, proxies=cfg.misp.proxies, cert=cfg.misp.cert) except PyMISPError as e: self.log('error', e.message) return # Require an open MISP session if self.args.subname in ['add_hashes', 'add', 'show', 'publish'] and not __sessions__.is_attached_misp(): return # Require an open file session if self.args.subname in ['upload'] and not __sessions__.is_attached_file(): return try: if self.args.subname == 'upload': self.upload() elif self.args.subname == 'search': self.searchall() elif self.args.subname == 'download': self.download() elif self.args.subname == 'check_hashes': self.check_hashes() elif self.args.subname == 'yara': self.yara() elif self.args.subname == 'pull': self.pull() elif self.args.subname == 'create_event': self.create_event() elif self.args.subname == 'add': self.add() elif self.args.subname == 'add_hashes': self.add_hashes() elif self.args.subname == 'show': self.show() elif self.args.subname == 'open': self.open_samples() elif self.args.subname == 'publish': self.publish() elif self.args.subname == 'version': self.version() elif self.args.subname == 'store': self.store() elif self.args.subname == 'tag': self.tag() elif self.args.subname == 'galaxies': self.galaxies() elif self.args.subname == 'admin': self.admin() else: self.log('error', "No calls defined for this command.") except requests.exceptions.HTTPError as e: self.log('error', e)

51.064024

211

0.570989

1051ff4c639fa7e4cf7b18f3439326df08db862c

31,350

py

Python

d3rlpy/base.py

Mohan-Zhang-u/d3rlpy

3ab3c0bbd6f86e73c171a6084f3130d60be85b5f

[ "MIT" ]

565

2020-08-01T02:44:28.000Z

2022-03-30T15:00:54.000Z

d3rlpy/base.py

Mohan-Zhang-u/d3rlpy

3ab3c0bbd6f86e73c171a6084f3130d60be85b5f

[ "MIT" ]

144

2020-08-01T03:45:10.000Z

2022-03-30T14:51:16.000Z

d3rlpy/base.py

Mohan-Zhang-u/d3rlpy

3ab3c0bbd6f86e73c171a6084f3130d60be85b5f

[ "MIT" ]

103

2020-08-26T13:27:34.000Z

2022-03-31T12:24:27.000Z

import copy import json from abc import ABCMeta, abstractmethod from collections import defaultdict from typing import ( Any, Callable, DefaultDict, Dict, Generator, List, Optional, Sequence, Tuple, Union, ) import gym import numpy as np from tqdm.auto import tqdm from .argument_utility import ( ActionScalerArg, RewardScalerArg, ScalerArg, UseGPUArg, check_action_scaler, check_reward_scaler, check_scaler, ) from .constants import ( CONTINUOUS_ACTION_SPACE_MISMATCH_ERROR, DISCRETE_ACTION_SPACE_MISMATCH_ERROR, IMPL_NOT_INITIALIZED_ERROR, ActionSpace, ) from .context import disable_parallel from .dataset import Episode, MDPDataset, Transition, TransitionMiniBatch from .decorators import pretty_repr from .gpu import Device from .iterators import RandomIterator, RoundIterator, TransitionIterator from .logger import LOG, D3RLPyLogger from .models.encoders import EncoderFactory, create_encoder_factory from .models.optimizers import OptimizerFactory from .models.q_functions import QFunctionFactory, create_q_func_factory from .online.utility import get_action_size_from_env from .preprocessing import ( ActionScaler, RewardScaler, Scaler, create_action_scaler, create_reward_scaler, create_scaler, ) class ImplBase(metaclass=ABCMeta): @abstractmethod def save_model(self, fname: str) -> None: pass @abstractmethod def load_model(self, fname: str) -> None: pass @property @abstractmethod def observation_shape(self) -> Sequence[int]: pass @property @abstractmethod def action_size(self) -> int: pass def _serialize_params(params: Dict[str, Any]) -> Dict[str, Any]: for key, value in params.items(): if isinstance(value, Device): params[key] = value.get_id() elif isinstance( value, ( Scaler, ActionScaler, RewardScaler, EncoderFactory, QFunctionFactory, ), ): params[key] = { "type": value.get_type(), "params": value.get_params(), } elif isinstance(value, OptimizerFactory): params[key] = value.get_params() return params def _deseriealize_params(params: Dict[str, Any]) -> Dict[str, Any]: for key, value in params.items(): if key == "scaler" and params["scaler"]: scaler_type = params["scaler"]["type"] scaler_params = params["scaler"]["params"] scaler = create_scaler(scaler_type, **scaler_params) params[key] = scaler elif key == "action_scaler" and params["action_scaler"]: scaler_type = params["action_scaler"]["type"] scaler_params = params["action_scaler"]["params"] action_scaler = create_action_scaler(scaler_type, **scaler_params) params[key] = action_scaler elif key == "reward_scaler" and params["reward_scaler"]: scaler_type = params["reward_scaler"]["type"] scaler_params = params["reward_scaler"]["params"] reward_scaler = create_reward_scaler(scaler_type, **scaler_params) params[key] = reward_scaler elif "optim_factory" in key: params[key] = OptimizerFactory(**value) elif "encoder_factory" in key: params[key] = create_encoder_factory( value["type"], **value["params"] ) elif key == "q_func_factory": params[key] = create_q_func_factory( value["type"], **value["params"] ) return params @pretty_repr class LearnableBase: _batch_size: int _n_frames: int _n_steps: int _gamma: float _scaler: Optional[Scaler] _action_scaler: Optional[ActionScaler] _reward_scaler: Optional[RewardScaler] _real_ratio: float _generated_maxlen: int _impl: Optional[ImplBase] _eval_results: DefaultDict[str, List[float]] _loss_history: DefaultDict[str, List[float]] _active_logger: Optional[D3RLPyLogger] _grad_step: int def __init__( self, batch_size: int, n_frames: int, n_steps: int, gamma: float, scaler: ScalerArg = None, action_scaler: ActionScalerArg = None, reward_scaler: RewardScalerArg = None, real_ratio: float = 1.0, generated_maxlen: int = 100000, kwargs: Optional[Dict[str, Any]] = None, ): self._batch_size = batch_size self._n_frames = n_frames self._n_steps = n_steps self._gamma = gamma self._scaler = check_scaler(scaler) self._action_scaler = check_action_scaler(action_scaler) self._reward_scaler = check_reward_scaler(reward_scaler) self._real_ratio = real_ratio self._generated_maxlen = generated_maxlen self._impl = None self._eval_results = defaultdict(list) self._loss_history = defaultdict(list) self._active_logger = None self._grad_step = 0 if kwargs and len(kwargs.keys()) > 0: LOG.warning("Unused arguments are passed.", **kwargs) def __setattr__(self, name: str, value: Any) -> None: super().__setattr__(name, value) # propagate property updates to implementation object if hasattr(self, "_impl") and self._impl and hasattr(self._impl, name): setattr(self._impl, name, value) @classmethod def from_json( cls, fname: str, use_gpu: UseGPUArg = False ) -> "LearnableBase": """Returns algorithm configured with json file. The Json file should be the one saved during fitting. .. code-block:: python from d3rlpy.algos import Algo # create algorithm with saved configuration algo = Algo.from_json('d3rlpy_logs/<path-to-json>/params.json') # ready to load algo.load_model('d3rlpy_logs/<path-to-model>/model_100.pt') # ready to predict algo.predict(...) Args: fname: file path to `params.json`. use_gpu: flag to use GPU, device ID or device. Returns: algorithm. """ with open(fname, "r") as f: params = json.load(f) observation_shape = tuple(params["observation_shape"]) action_size = params["action_size"] del params["observation_shape"] del params["action_size"] # reconstruct objects from json params = _deseriealize_params(params) # overwrite use_gpu flag params["use_gpu"] = use_gpu algo = cls(**params) algo.create_impl(observation_shape, action_size) return algo def set_params(self, **params: Any) -> "LearnableBase": """Sets the given arguments to the attributes if they exist. This method sets the given values to the attributes including ones in subclasses. If the values that don't exist as attributes are passed, they are ignored. Some of scikit-learn utilities will use this method. .. code-block:: python algo.set_params(batch_size=100) Args: params: arbitrary inputs to set as attributes. Returns: itself. """ for key, val in params.items(): if hasattr(self, key): try: setattr(self, key, val) except AttributeError: # try passing to protected keys assert hasattr(self, "_" + key), f"{key} does not exist." setattr(self, "_" + key, val) else: assert hasattr(self, "_" + key), f"{key} does not exist." setattr(self, "_" + key, val) return self def get_params(self, deep: bool = True) -> Dict[str, Any]: """Returns the all attributes. This method returns the all attributes including ones in subclasses. Some of scikit-learn utilities will use this method. .. code-block:: python params = algo.get_params(deep=True) # the returned values can be used to instantiate the new object. algo2 = AlgoBase(**params) Args: deep: flag to deeply copy objects such as `impl`. Returns: attribute values in dictionary. """ rets = {} for key in dir(self): # remove magic properties if key[:2] == "__": continue # remove specific keys if key in [ "_eval_results", "_loss_history", "_active_logger", "_grad_step", "active_logger", "grad_step", "observation_shape", "action_size", ]: continue value = getattr(self, key) # remove underscore if key[0] == "_": key = key[1:] # pick scalar parameters if np.isscalar(value): rets[key] = value elif isinstance(value, object) and not callable(value): if deep: rets[key] = copy.deepcopy(value) else: rets[key] = value return rets def save_model(self, fname: str) -> None: """Saves neural network parameters. .. code-block:: python algo.save_model('model.pt') Args: fname: destination file path. """ assert self._impl is not None, IMPL_NOT_INITIALIZED_ERROR self._impl.save_model(fname) def load_model(self, fname: str) -> None: """Load neural network parameters. .. code-block:: python algo.load_model('model.pt') Args: fname: source file path. """ assert self._impl is not None, IMPL_NOT_INITIALIZED_ERROR self._impl.load_model(fname) def fit( self, dataset: Union[List[Episode], MDPDataset], n_epochs: Optional[int] = None, n_steps: Optional[int] = None, n_steps_per_epoch: int = 10000, save_metrics: bool = True, experiment_name: Optional[str] = None, with_timestamp: bool = True, logdir: str = "d3rlpy_logs", verbose: bool = True, show_progress: bool = True, tensorboard_dir: Optional[str] = None, eval_episodes: Optional[List[Episode]] = None, save_interval: int = 1, scorers: Optional[ Dict[str, Callable[[Any, List[Episode]], float]] ] = None, shuffle: bool = True, callback: Optional[Callable[["LearnableBase", int, int], None]] = None, ) -> List[Tuple[int, Dict[str, float]]]: """Trains with the given dataset. .. code-block:: python algo.fit(episodes, n_steps=1000000) Args: dataset: list of episodes to train. n_epochs: the number of epochs to train. n_steps: the number of steps to train. n_steps_per_epoch: the number of steps per epoch. This value will be ignored when ``n_steps`` is ``None``. save_metrics: flag to record metrics in files. If False, the log directory is not created and the model parameters are not saved during training. experiment_name: experiment name for logging. If not passed, the directory name will be `{class name}_{timestamp}`. with_timestamp: flag to add timestamp string to the last of directory name. logdir: root directory name to save logs. verbose: flag to show logged information on stdout. show_progress: flag to show progress bar for iterations. tensorboard_dir: directory to save logged information in tensorboard (additional to the csv data). if ``None``, the directory will not be created. eval_episodes: list of episodes to test. save_interval: interval to save parameters. scorers: list of scorer functions used with `eval_episodes`. shuffle: flag to shuffle transitions on each epoch. callback: callable function that takes ``(algo, epoch, total_step)`` , which is called every step. Returns: list of result tuples (epoch, metrics) per epoch. """ results = list( self.fitter( dataset, n_epochs, n_steps, n_steps_per_epoch, save_metrics, experiment_name, with_timestamp, logdir, verbose, show_progress, tensorboard_dir, eval_episodes, save_interval, scorers, shuffle, callback, ) ) return results def fitter( self, dataset: Union[List[Episode], MDPDataset], n_epochs: Optional[int] = None, n_steps: Optional[int] = None, n_steps_per_epoch: int = 10000, save_metrics: bool = True, experiment_name: Optional[str] = None, with_timestamp: bool = True, logdir: str = "d3rlpy_logs", verbose: bool = True, show_progress: bool = True, tensorboard_dir: Optional[str] = None, eval_episodes: Optional[List[Episode]] = None, save_interval: int = 1, scorers: Optional[ Dict[str, Callable[[Any, List[Episode]], float]] ] = None, shuffle: bool = True, callback: Optional[Callable[["LearnableBase", int, int], None]] = None, ) -> Generator[Tuple[int, Dict[str, float]], None, None]: """Iterate over epochs steps to train with the given dataset. At each iteration algo methods and properties can be changed or queried. .. code-block:: python for epoch, metrics in algo.fitter(episodes): my_plot(metrics) algo.save_model(my_path) Args: dataset: list of episodes to train. n_epochs: the number of epochs to train. n_steps: the number of steps to train. n_steps_per_epoch: the number of steps per epoch. This value will be ignored when ``n_steps`` is ``None``. save_metrics: flag to record metrics in files. If False, the log directory is not created and the model parameters are not saved during training. experiment_name: experiment name for logging. If not passed, the directory name will be `{class name}_{timestamp}`. with_timestamp: flag to add timestamp string to the last of directory name. logdir: root directory name to save logs. verbose: flag to show logged information on stdout. show_progress: flag to show progress bar for iterations. tensorboard_dir: directory to save logged information in tensorboard (additional to the csv data). if ``None``, the directory will not be created. eval_episodes: list of episodes to test. save_interval: interval to save parameters. scorers: list of scorer functions used with `eval_episodes`. shuffle: flag to shuffle transitions on each epoch. callback: callable function that takes ``(algo, epoch, total_step)`` , which is called every step. Returns: iterator yielding current epoch and metrics dict. """ if isinstance(dataset, MDPDataset): episodes = dataset.episodes else: episodes = dataset # check action space if self.get_action_type() == ActionSpace.BOTH: pass elif len(episodes[0].actions.shape) > 1: assert ( self.get_action_type() == ActionSpace.CONTINUOUS ), CONTINUOUS_ACTION_SPACE_MISMATCH_ERROR else: assert ( self.get_action_type() == ActionSpace.DISCRETE ), DISCRETE_ACTION_SPACE_MISMATCH_ERROR iterator: TransitionIterator if n_epochs is None and n_steps is not None: assert n_steps >= n_steps_per_epoch n_epochs = n_steps // n_steps_per_epoch iterator = RandomIterator( episodes, n_steps_per_epoch, batch_size=self._batch_size, n_steps=self._n_steps, gamma=self._gamma, n_frames=self._n_frames, real_ratio=self._real_ratio, generated_maxlen=self._generated_maxlen, ) LOG.debug("RandomIterator is selected.") elif n_epochs is not None and n_steps is None: iterator = RoundIterator( episodes, batch_size=self._batch_size, n_steps=self._n_steps, gamma=self._gamma, n_frames=self._n_frames, real_ratio=self._real_ratio, generated_maxlen=self._generated_maxlen, shuffle=shuffle, ) LOG.debug("RoundIterator is selected.") else: raise ValueError("Either of n_epochs or n_steps must be given.") # setup logger logger = self._prepare_logger( save_metrics, experiment_name, with_timestamp, logdir, verbose, tensorboard_dir, ) # add reference to active logger to algo class during fit self._active_logger = logger # initialize scaler if self._scaler: LOG.debug("Fitting scaler...", scaler=self._scaler.get_type()) self._scaler.fit(episodes) # initialize action scaler if self._action_scaler: LOG.debug( "Fitting action scaler...", action_scaler=self._action_scaler.get_type(), ) self._action_scaler.fit(episodes) # initialize reward scaler if self._reward_scaler: LOG.debug( "Fitting reward scaler...", reward_scaler=self._reward_scaler.get_type(), ) self._reward_scaler.fit(episodes) # instantiate implementation if self._impl is None: LOG.debug("Building models...") transition = iterator.transitions[0] action_size = transition.get_action_size() observation_shape = tuple(transition.get_observation_shape()) self.create_impl( self._process_observation_shape(observation_shape), action_size ) LOG.debug("Models have been built.") else: LOG.warning("Skip building models since they're already built.") # save hyperparameters self.save_params(logger) # refresh evaluation metrics self._eval_results = defaultdict(list) # refresh loss history self._loss_history = defaultdict(list) # training loop total_step = 0 for epoch in range(1, n_epochs + 1): # dict to add incremental mean losses to epoch epoch_loss = defaultdict(list) range_gen = tqdm( range(len(iterator)), disable=not show_progress, desc=f"Epoch {int(epoch)}/{n_epochs}", ) iterator.reset() for itr in range_gen: # generate new transitions with dynamics models new_transitions = self.generate_new_data( transitions=iterator.transitions, ) if new_transitions: iterator.add_generated_transitions(new_transitions) LOG.debug( f"{len(new_transitions)} transitions are generated.", real_transitions=len(iterator.transitions), fake_transitions=len(iterator.generated_transitions), ) with logger.measure_time("step"): # pick transitions with logger.measure_time("sample_batch"): batch = next(iterator) # update parameters with logger.measure_time("algorithm_update"): loss = self.update(batch) # record metrics for name, val in loss.items(): logger.add_metric(name, val) epoch_loss[name].append(val) # update progress postfix with losses if itr % 10 == 0: mean_loss = { k: np.mean(v) for k, v in epoch_loss.items() } range_gen.set_postfix(mean_loss) total_step += 1 # call callback if given if callback: callback(self, epoch, total_step) # save loss to loss history dict self._loss_history["epoch"].append(epoch) self._loss_history["step"].append(total_step) for name, vals in epoch_loss.items(): if vals: self._loss_history[name].append(np.mean(vals)) if scorers and eval_episodes: self._evaluate(eval_episodes, scorers, logger) # save metrics metrics = logger.commit(epoch, total_step) # save model parameters if epoch % save_interval == 0: logger.save_model(total_step, self) yield epoch, metrics # drop reference to active logger since out of fit there is no active # logger self._active_logger = None def create_impl( self, observation_shape: Sequence[int], action_size: int ) -> None: """Instantiate implementation objects with the dataset shapes. This method will be used internally when `fit` method is called. Args: observation_shape: observation shape. action_size: dimension of action-space. """ if self._impl: LOG.warn("Parameters will be reinitialized.") self._create_impl(observation_shape, action_size) def _create_impl( self, observation_shape: Sequence[int], action_size: int ) -> None: raise NotImplementedError def build_with_dataset(self, dataset: MDPDataset) -> None: """Instantiate implementation object with MDPDataset object. Args: dataset: dataset. """ observation_shape = dataset.get_observation_shape() self.create_impl( self._process_observation_shape(observation_shape), dataset.get_action_size(), ) def build_with_env(self, env: gym.Env) -> None: """Instantiate implementation object with OpenAI Gym object. Args: env: gym-like environment. """ observation_shape = env.observation_space.shape self.create_impl( self._process_observation_shape(observation_shape), get_action_size_from_env(env), ) def _process_observation_shape( self, observation_shape: Sequence[int] ) -> Sequence[int]: if len(observation_shape) == 3: n_channels = observation_shape[0] image_size = observation_shape[1:] # frame stacking for image observation observation_shape = (self._n_frames * n_channels, *image_size) return observation_shape def update(self, batch: TransitionMiniBatch) -> Dict[str, float]: """Update parameters with mini-batch of data. Args: batch: mini-batch data. Returns: dictionary of metrics. """ loss = self._update(batch) self._grad_step += 1 return loss def _update(self, batch: TransitionMiniBatch) -> Dict[str, float]: raise NotImplementedError def generate_new_data( self, transitions: List[Transition] ) -> Optional[List[Transition]]: """Returns generated transitions for data augmentation. This method is for model-based RL algorithms. Args: transitions: list of transitions. Returns: list of new transitions. """ return None def _prepare_logger( self, save_metrics: bool, experiment_name: Optional[str], with_timestamp: bool, logdir: str, verbose: bool, tensorboard_dir: Optional[str], ) -> D3RLPyLogger: if experiment_name is None: experiment_name = self.__class__.__name__ logger = D3RLPyLogger( experiment_name, save_metrics=save_metrics, root_dir=logdir, verbose=verbose, tensorboard_dir=tensorboard_dir, with_timestamp=with_timestamp, ) return logger def _evaluate( self, episodes: List[Episode], scorers: Dict[str, Callable[[Any, List[Episode]], float]], logger: D3RLPyLogger, ) -> None: for name, scorer in scorers.items(): # evaluation with test data test_score = scorer(self, episodes) # logging metrics logger.add_metric(name, test_score) # store metric locally if test_score is not None: self._eval_results[name].append(test_score) def save_params(self, logger: D3RLPyLogger) -> None: """Saves configurations as params.json. Args: logger: logger object. """ assert self._impl is not None, IMPL_NOT_INITIALIZED_ERROR # get hyperparameters without impl params = {} with disable_parallel(): for k, v in self.get_params(deep=False).items(): if isinstance(v, (ImplBase, LearnableBase)): continue params[k] = v # save algorithm name params["algorithm"] = self.__class__.__name__ # save shapes params["observation_shape"] = self._impl.observation_shape params["action_size"] = self._impl.action_size # serialize objects params = _serialize_params(params) logger.add_params(params) def get_action_type(self) -> ActionSpace: """Returns action type (continuous or discrete). Returns: action type. """ raise NotImplementedError @property def batch_size(self) -> int: """Batch size to train. Returns: int: batch size. """ return self._batch_size @batch_size.setter def batch_size(self, batch_size: int) -> None: self._batch_size = batch_size @property def n_frames(self) -> int: """Number of frames to stack. This is only for image observation. Returns: int: number of frames to stack. """ return self._n_frames @n_frames.setter def n_frames(self, n_frames: int) -> None: self._n_frames = n_frames @property def n_steps(self) -> int: """N-step TD backup. Returns: int: N-step TD backup. """ return self._n_steps @n_steps.setter def n_steps(self, n_steps: int) -> None: self._n_steps = n_steps @property def gamma(self) -> float: """Discount factor. Returns: float: discount factor. """ return self._gamma @gamma.setter def gamma(self, gamma: float) -> None: self._gamma = gamma @property def scaler(self) -> Optional[Scaler]: """Preprocessing scaler. Returns: Optional[Scaler]: preprocessing scaler. """ return self._scaler @scaler.setter def scaler(self, scaler: Scaler) -> None: self._scaler = scaler @property def action_scaler(self) -> Optional[ActionScaler]: """Preprocessing action scaler. Returns: Optional[ActionScaler]: preprocessing action scaler. """ return self._action_scaler @action_scaler.setter def action_scaler(self, action_scaler: ActionScaler) -> None: self._action_scaler = action_scaler @property def reward_scaler(self) -> Optional[RewardScaler]: """Preprocessing reward scaler. Returns: Optional[RewardScaler]: preprocessing reward scaler. """ return self._reward_scaler @reward_scaler.setter def reward_scaler(self, reward_scaler: RewardScaler) -> None: self._reward_scaler = reward_scaler @property def impl(self) -> Optional[ImplBase]: """Implementation object. Returns: Optional[ImplBase]: implementation object. """ return self._impl @impl.setter def impl(self, impl: ImplBase) -> None: self._impl = impl @property def observation_shape(self) -> Optional[Sequence[int]]: """Observation shape. Returns: Optional[Sequence[int]]: observation shape. """ if self._impl: return self._impl.observation_shape return None @property def action_size(self) -> Optional[int]: """Action size. Returns: Optional[int]: action size. """ if self._impl: return self._impl.action_size return None @property def active_logger(self) -> Optional[D3RLPyLogger]: """Active D3RLPyLogger object. This will be only available during training. Returns: logger object. """ return self._active_logger def set_active_logger(self, logger: D3RLPyLogger) -> None: """Set active D3RLPyLogger object Args: logger: logger object. """ self._active_logger = logger @property def grad_step(self) -> int: """Total gradient step counter. This value will keep counting after ``fit`` and ``fit_online`` methods finish. Returns: total gradient step counter. """ return self._grad_step def set_grad_step(self, grad_step: int) -> None: """Set total gradient step counter. This method can be used to restart the middle of training with an arbitrary gradient step counter, which has effects on periodic functions such as the target update. Args: grad_step: total gradient step counter. """ self._grad_step = grad_step

30.675147

80

0.576651

a68807a204cd4378180400afc4a8d7373c7a168f

139

py

Python

src/main/python/601/design_lab_2.1.py

Fiegellan/Data-Engineer-Preperation

9178ea2381a1225d65c0b1761f9c38d2d8272f8a

[ "MIT" ]

null

src/main/python/601/design_lab_2.1.py

Fiegellan/Data-Engineer-Preperation

9178ea2381a1225d65c0b1761f9c38d2d8272f8a

[ "MIT" ]

null

src/main/python/601/design_lab_2.1.py

Fiegellan/Data-Engineer-Preperation

9178ea2381a1225d65c0b1761f9c38d2d8272f8a

[ "MIT" ]

1

2019-03-17T02:02:24.000Z

def fib(x): if x == 0: return 0 elif x == 1: return 1 else: return fib(x-1) + fib(x-2) print fib(10)

12.636364

34

0.438849

38eac4d497cbf5b539d211a1231955f44b02ee97

2,596

py

Python

osf/migrations/0126_update_social_data_format.py

birdbrained/osf.io

ca70cf9fdacc2f3771038c8e5bc1c19e7126fd50

[ "Apache-2.0" ]

1

2019-12-23T04:30:20.000Z

osf/migrations/0126_update_social_data_format.py

birdbrained/osf.io

ca70cf9fdacc2f3771038c8e5bc1c19e7126fd50

[ "Apache-2.0" ]

16

2020-03-24T16:30:32.000Z

2022-03-03T22:39:45.000Z

osf/migrations/0126_update_social_data_format.py

birdbrained/osf.io

ca70cf9fdacc2f3771038c8e5bc1c19e7126fd50

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- # Generated by Django 1.11.13 on 2018-07-25 20:39 from __future__ import unicode_literals import logging import progressbar from django.db import migrations, connection logger = logging.getLogger(__name__) FIELDS_TO_MIGRATE = [ 'github', 'linkedIn', 'twitter' ] class Migration(migrations.Migration): def update_social_fields(state, schema): for field in FIELDS_TO_MIGRATE: sql = """ UPDATE osf_osfuser SET social = social || json_build_object( '{0}', CASE WHEN (osf_osfuser.social ->> '{0}') = '' THEN '[]' WHEN (osf_osfuser.social ->> '{0}') IS NOT NULL AND json_typeof(osf_osfuser.social::json -> '{0}') != 'array' THEN json_build_array(osf_osfuser.social ->> '{0}') ELSE (osf_osfuser.social -> '{0}')::json END )::jsonb WHERE osf_osfuser.social ? '{0}'; """.format(field) with connection.cursor() as cursor: logger.info('Setting social fields for {}...'.format(field)) cursor.execute(sql) def reset_social_fields(state, schema): OSFUser = state.get_model('osf', 'osfuser') users_with_social = OSFUser.objects.filter(social__has_any_keys=FIELDS_TO_MIGRATE) users_to_update = users_with_social.count() logger.info('Updating social fields for {} users'.format(users_to_update)) progress_bar = progressbar.ProgressBar(maxval=users_to_update or 100).start() users_updated = 0 for user in users_with_social: old_social = {} for key, value in user.social.items(): if key in FIELDS_TO_MIGRATE: if len(value) > 1: raise ValueError('Current social list field has more than one value, cannot reset to just one value.') old_social[key] = value[0] else: old_social[key] = value user.social = old_social user.save() users_updated += 1 progress_bar.update(users_updated) progress_bar.finish() logger.info('Updated social field for {} users'.format(users_updated)) dependencies = [ ('osf', '0125_merge_20180824_1856'), ] operations = [ migrations.RunPython(update_social_fields, reset_social_fields) ]

35.561644

126

0.557396

0d79f9915228fde49bce9066b70dc46b1b3ffad3

17,499

py

Python

scripts/dual_IDG.py

ssykira/optic-nerve

0457df5c3cd58416361b107be3282745b6d4acbd

[ "MIT" ]

2

2019-07-29T09:36:10.000Z

2019-08-26T08:30:37.000Z

scripts/dual_IDG.py

ssykira/optic-nerve

0457df5c3cd58416361b107be3282745b6d4acbd

[ "MIT" ]

null

scripts/dual_IDG.py

ssykira/optic-nerve

0457df5c3cd58416361b107be3282745b6d4acbd

[ "MIT" ]

1

2019-12-18T17:30:14.000Z

"""Fairly basic set of tools for real-time data augmentation on image data. Can easily be extended to include new transformations, new preprocessing methods, etc... Based on Keras code Modified by He Xie 08/2016, Artem Sevastopolsky 10/2016 For image segmentation problem data augmentation. Transform train img data and mask img data simultaneously and in the same fashion. Omit flow from directory function. """ from __future__ import absolute_import from __future__ import print_function import numpy as np import re from scipy import linalg import scipy.ndimage as ndi # from six.moves import range import os import threading from keras import backend as K def random_channel_shift(x, intensity, channel_index=0): x = np.rollaxis(x, channel_index, 0) min_x, max_x = np.min(x), np.max(x) channel_images = [np.clip(x_channel + np.random.uniform(-intensity, intensity), min_x, max_x) for x_channel in x] x = np.stack(channel_images, axis=0) x = np.rollaxis(x, 0, channel_index+1) return x def transform_matrix_offset_center(matrix, x, y): o_x = float(x) / 2 + 0.5 o_y = float(y) / 2 + 0.5 offset_matrix = np.array([[1, 0, o_x], [0, 1, o_y], [0, 0, 1]]) reset_matrix = np.array([[1, 0, -o_x], [0, 1, -o_y], [0, 0, 1]]) transform_matrix = np.dot(np.dot(offset_matrix, matrix), reset_matrix) return transform_matrix def apply_transform(x, transform_matrix, channel_index=0, fill_mode='nearest', cval=0.): x = np.rollaxis(x, channel_index, 0) final_affine_matrix = transform_matrix[:2, :2] final_offset = transform_matrix[:2, 2] channel_images = [ndi.interpolation.affine_transform(x_channel, final_affine_matrix, final_offset, order=0, mode=fill_mode, cval=cval) for x_channel in x] x = np.stack(channel_images, axis=0) x = np.rollaxis(x, 0, channel_index+1) return x def flip_axis(x, axis): x = np.asarray(x).swapaxes(axis, 0) x = x[::-1, ...] x = x.swapaxes(0, axis) return x def array_to_img(x, dim_ordering='default', scale=True): from PIL import Image if dim_ordering == 'default': dim_ordering = K.image_dim_ordering() if dim_ordering == 'th': x = x.transpose(1, 2, 0) if scale: x += max(-np.min(x), 0) x /= np.max(x) x *= 255 if x.shape[2] == 3: # RGB return Image.fromarray(x.astype('uint8'), 'RGB') elif x.shape[2] == 1: # grayscale return Image.fromarray(x[:, :, 0].astype('uint8'), 'L') else: raise Exception('Unsupported channel number: ', x.shape[2]) def img_to_array(img, dim_ordering='default'): if dim_ordering == 'default': dim_ordering = K.image_dim_ordering() if dim_ordering not in ['th', 'tf']: raise Exception('Unknown dim_ordering: ', dim_ordering) # image has dim_ordering (height, width, channel) x = np.asarray(img, dtype='float32') if len(x.shape) == 3: if dim_ordering == 'th': x = x.transpose(2, 0, 1) elif len(x.shape) == 2: if dim_ordering == 'th': x = x.reshape((1, x.shape[0], x.shape[1])) else: x = x.reshape((x.shape[0], x.shape[1], 1)) else: raise Exception('Unsupported image shape: ', x.shape) return x class DualImageDataGenerator(object): '''Generate minibatches with real-time data augmentation. Assume X is train img, Y is train label (same size as X with only 0 and 255 for values) # Arguments featurewise_center: set input mean to 0 over the dataset. Only to X samplewise_center: set each sample mean to 0. Only to X featurewise_std_normalization: divide inputs by std of the dataset. Only to X samplewise_std_normalization: divide each input by its std. Only to X zca_whitening: apply ZCA whitening. Only to X rotation_range: degrees (0 to 180). To X and Y width_shift_range: fraction of total width. To X and Y height_shift_range: fraction of total height. To X and Y shear_range: shear intensity (shear angle in radians). To X and Y zoom_range: amount of zoom. if scalar z, zoom will be randomly picked in the range [1-z, 1+z]. A sequence of two can be passed instead to select this range. To X and Y channel_shift_range: shift range for each channels. Only to X fill_mode: points outside the boundaries are filled according to the given mode ('constant', 'nearest', 'reflect' or 'wrap'). Default is 'nearest'. For Y, always fill with constant 0 cval: value used for points outside the boundaries when fill_mode is 'constant'. Default is 0. horizontal_flip: whether to randomly flip images horizontally. To X and Y vertical_flip: whether to randomly flip images vertically. To X and Y rescale: rescaling factor. If None or 0, no rescaling is applied, otherwise we multiply the data by the value provided (before applying any other transformation). Only to X dim_ordering: 'th' or 'tf'. In 'th' mode, the channels dimension (the depth) is at index 1, in 'tf' mode it is at index 3. It defaults to the `image_dim_ordering` value found in your Keras config file at `~/.keras/keras.json`. If you never set it, then it will be "th". ''' def __init__(self, featurewise_center=False, samplewise_center=False, featurewise_std_normalization=False, samplewise_std_normalization=False, zca_whitening=False, rotation_range=0., width_shift_range=0., height_shift_range=0., shear_range=0., zoom_range=0., channel_shift_range=0., fill_mode='nearest', cval=0., horizontal_flip=False, vertical_flip=False, rescale=None, dim_ordering='default'): if dim_ordering == 'default': dim_ordering = K.image_dim_ordering() self.__dict__.update(locals()) self.mean = None self.std = None self.principal_components = None self.rescale = rescale if dim_ordering not in {'tf', 'th'}: raise Exception('dim_ordering should be "tf" (channel after row and ' 'column) or "th" (channel before row and column). ' 'Received arg: ', dim_ordering) self.dim_ordering = dim_ordering if dim_ordering == 'th': self.channel_index = 1 self.row_index = 2 self.col_index = 3 if dim_ordering == 'tf': self.channel_index = 3 self.row_index = 1 self.col_index = 2 if np.isscalar(zoom_range): self.zoom_range = [1 - zoom_range, 1 + zoom_range] elif len(zoom_range) == 2: self.zoom_range = [zoom_range[0], zoom_range[1]] else: raise Exception('zoom_range should be a float or ' 'a tuple or list of two floats. ' 'Received arg: ', zoom_range) def flow(self, X, y=None, batch_size=32, shuffle=True, seed=None, save_to_dir=None, save_prefix='', save_format='jpeg'): return NumpyArrayIterator( X, y, self, batch_size=batch_size, shuffle=shuffle, seed=seed, dim_ordering=self.dim_ordering, save_to_dir=save_to_dir, save_prefix=save_prefix, save_format=save_format) def standardize(self, x): # Only applied to X if self.rescale: x *= self.rescale # x is a single image, so it doesn't have image number at index 0 img_channel_index = self.channel_index - 1 if self.samplewise_center: x -= np.mean(x, axis=img_channel_index, keepdims=True) if self.samplewise_std_normalization: x /= (np.std(x, axis=img_channel_index, keepdims=True) + 1e-7) if self.featurewise_center: x -= self.mean if self.featurewise_std_normalization: x /= (self.std + 1e-7) if self.zca_whitening: flatx = np.reshape(x, (x.size)) whitex = np.dot(flatx, self.principal_components) x = np.reshape(whitex, (x.shape[0], x.shape[1], x.shape[2])) return x def random_transform(self, x, y): # Need to modify to transform both X and Y ---- to do # x is a single image, so it doesn't have image number at index 0 img_row_index = self.row_index - 1 img_col_index = self.col_index - 1 img_channel_index = self.channel_index - 1 # use composition of homographies to generate final transform that needs to be applied if self.rotation_range: theta = np.pi / 180 * np.random.uniform(-self.rotation_range, self.rotation_range) else: theta = 0 rotation_matrix = np.array([[np.cos(theta), -np.sin(theta), 0], [np.sin(theta), np.cos(theta), 0], [0, 0, 1]]) if self.height_shift_range: tx = np.random.uniform(-self.height_shift_range, self.height_shift_range) * x.shape[img_row_index] else: tx = 0 if self.width_shift_range: ty = np.random.uniform(-self.width_shift_range, self.width_shift_range) * x.shape[img_col_index] else: ty = 0 translation_matrix = np.array([[1, 0, tx], [0, 1, ty], [0, 0, 1]]) if self.shear_range: shear = np.random.uniform(-self.shear_range, self.shear_range) else: shear = 0 shear_matrix = np.array([[1, -np.sin(shear), 0], [0, np.cos(shear), 0], [0, 0, 1]]) if self.zoom_range[0] == 1 and self.zoom_range[1] == 1: zx, zy = 1, 1 else: zx, zy = np.random.uniform(self.zoom_range[0], self.zoom_range[1], 2) zoom_matrix = np.array([[zx, 0, 0], [0, zy, 0], [0, 0, 1]]) transform_matrix = np.dot(np.dot(np.dot(rotation_matrix, translation_matrix), shear_matrix), zoom_matrix) h, w = x.shape[img_row_index], x.shape[img_col_index] transform_matrix = transform_matrix_offset_center(transform_matrix, h, w) x = apply_transform(x, transform_matrix, img_channel_index, fill_mode=self.fill_mode, cval=self.cval) # For y, mask data, fill mode constant, cval = 0 y = apply_transform(y, transform_matrix, img_channel_index, fill_mode="constant", cval=0) if self.channel_shift_range != 0: x = random_channel_shift(x, self.channel_shift_range, img_channel_index) if self.horizontal_flip: if np.random.random() < 0.5: x = flip_axis(x, img_col_index) y = flip_axis(y, img_col_index) if self.vertical_flip: if np.random.random() < 0.5: x = flip_axis(x, img_row_index) y = flip_axis(y, img_row_index) # TODO: # channel-wise normalization # barrel/fisheye return x, y def fit(self, X, augment=False, rounds=1, seed=None): '''Required for featurewise_center, featurewise_std_normalization and zca_whitening. # Arguments X: Numpy array, the data to fit on. augment: whether to fit on randomly augmented samples rounds: if `augment`, how many augmentation passes to do over the data seed: random seed. # Only applied to X ''' X = np.copy(X) if augment: aX = np.zeros(tuple([rounds * X.shape[0]] + list(X.shape)[1:])) for r in range(rounds): for i in range(X.shape[0]): aX[i + r * X.shape[0]] = self.random_transform(X[i]) X = aX if self.featurewise_center: self.mean = np.mean(X, axis=0) X -= self.mean if self.featurewise_std_normalization: self.std = np.std(X, axis=0) X /= (self.std + 1e-7) if self.zca_whitening: flatX = np.reshape(X, (X.shape[0], X.shape[1] * X.shape[2] * X.shape[3])) sigma = np.dot(flatX.T, flatX) / flatX.shape[1] U, S, V = linalg.svd(sigma) self.principal_components = np.dot(np.dot(U, np.diag(1. / np.sqrt(S + 10e-7))), U.T) class Iterator(object): def __init__(self, N, batch_size, shuffle, seed): self.N = N self.batch_size = batch_size self.shuffle = shuffle self.batch_index = 0 self.total_batches_seen = 0 self.lock = threading.Lock() self.index_generator = self._flow_index(N, batch_size, shuffle, seed) def reset(self): self.batch_index = 0 def _flow_index(self, N, batch_size=32, shuffle=False, seed=None): # ensure self.batch_index is 0 self.reset() while 1: if self.batch_index == 0: index_array = np.arange(N) if shuffle: if seed is not None: np.random.seed(seed + self.total_batches_seen) index_array = np.random.permutation(N) current_index = (self.batch_index * batch_size) % N if N >= current_index + batch_size: current_batch_size = batch_size self.batch_index += 1 else: current_batch_size = N - current_index self.batch_index = 0 self.total_batches_seen += 1 yield (index_array[current_index: current_index + current_batch_size], current_index, current_batch_size) def __iter__(self): # needed if we want to do something like: # for x, y in data_gen.flow(...): return self def __next__(self, *args, **kwargs): # ? return self.next(*args, **kwargs) class NumpyArrayIterator(Iterator): def __init__(self, X, y, image_data_generator, batch_size=32, shuffle=False, seed=None, dim_ordering='default', save_to_dir=None, save_prefix='', save_format='jpeg'): if len(X) != len(y): raise Exception('X (images tensor) and y (labels) ' 'should have the same length. ' 'Found: X.shape = %s, y.shape = %s' % (np.asarray(X).shape, np.asarray(y).shape)) if dim_ordering == 'default': dim_ordering = K.image_dim_ordering() self.X = X self.y = y self.image_data_generator = image_data_generator self.dim_ordering = dim_ordering self.save_to_dir = save_to_dir self.save_prefix = save_prefix self.save_format = save_format super(NumpyArrayIterator, self).__init__(X.shape[0], batch_size, shuffle, seed) def next(self): # for python 2.x. # Keeps under lock only the mechanism which advances # the indexing of each batch # see http://anandology.com/blog/using-iterators-and-generators/ with self.lock: index_array, current_index, current_batch_size = next(self.index_generator) # The transformation of images is not under thread lock so it can be done in parallel batch_x = np.zeros(tuple([current_batch_size] + list(self.X.shape)[1:])) batch_y = np.zeros(tuple([current_batch_size] + list(self.y.shape)[1:])) for i, j in enumerate(index_array): x = self.X[j] label = self.y[j] x, label = self.image_data_generator.random_transform(x.astype('float32'), label.astype("float32")) x = self.image_data_generator.standardize(x) batch_x[i] = x batch_y[i] = label if self.save_to_dir: for i in range(current_batch_size): img = array_to_img(batch_x[i], self.dim_ordering, scale=True) fname = '{prefix}_{index}_{hash}.{format}'.format(prefix=self.save_prefix, index=current_index + i, hash=np.random.randint(1e4), format=self.save_format) img.save(os.path.join(self.save_to_dir, fname)) mask = array_to_img(batch_y[i], self.dim_ordering, scale=True) fname = '{prefix}_{index}_{hash}_mask.{format}'.format(prefix=self.save_prefix, index=current_index + i, hash=np.random.randint(1e4), format=self.save_format) mask.save(os.path.join(self.save_to_dir, fname)) return batch_x, batch_y

40.885514

113

0.573233

f58e4c879a6a95a925d5063390cfe2ee4bf5423e

780

py

Python

12.0-flask/src/simple_dev_sample.py

zehemz/clases-python-101

633cb5f0cbc85e64e242514f0394754a5bed0513

[ "Apache-2.0" ]

null

12.0-flask/src/simple_dev_sample.py

zehemz/clases-python-101

633cb5f0cbc85e64e242514f0394754a5bed0513

[ "Apache-2.0" ]

null

12.0-flask/src/simple_dev_sample.py

zehemz/clases-python-101

633cb5f0cbc85e64e242514f0394754a5bed0513

[ "Apache-2.0" ]

null

''' This module represents the (otherwise anonymous) scope in which the interpreter’s main program executes — commands read either from standard input, from a script file, or from an interactive prompt. It is this environment in which the idiomatic “conditional script” stanza causes a script to run: Instalación de flask: $ pip install Flask Ejecución de flask: $ python simple_dev_sample.py Esto nos proveerá de una instancia de programa corriendo y escuchando en el puerto 5000 de manera local * Running on http://localhost:5000/ ''' from flask import Flask app = Flask(__name__) if __name__ == "__main__": #Main script print("Running flask example ...") #Variable global, ejecuta run. app.run() @app.route("/") def hello(): return "Hello World!"

26

296

0.739744

e9ab4cd22c3d93750a4dcea194de1d9bd906138d

7,299

py

Python

src/converter.py

xryuseix/proofreader

b4c523d05324cf771acee688d51cfea8d6d6d114

[ "MIT" ]

null

src/converter.py

xryuseix/proofreader

b4c523d05324cf771acee688d51cfea8d6d6d114

[ "MIT" ]

5

2020-04-28T18:13:26.000Z

2020-05-17T19:09:42.000Z

src/converter.py

xryuseix/Proofreader

b4c523d05324cf771acee688d51cfea8d6d6d114

[ "MIT" ]

null

# -*- coding: utf-8 -*- import os, sys, re import read_file as File # ．，を、。に変換 def dot_to_comma(text): replaced_text = re.sub("，", r"、", text) return re.sub("．", r"。", replaced_text) # word_listを参照して警告 def word_to_word(text, file, search, root): if not os.path.isfile("%sword_list.csv" % (root)): return text word_list = File.readFile("%sword_list.csv" % (root), True) # find_listを開く if search: find_list_path = "%sfind_list.txt" % (root) if not os.path.isfile(find_list_path): find_list = [] else: find_list = File.readFile(find_list_path).split("\n") text_arr = text.splitlines() warning_list = [] find_out_list = [] for i, text in enumerate(text_arr): for word in word_list: # 文字列警告 re_obj = re.search(word[0], text) if re_obj: warning_list.append([i + 1, re_obj.start(), re_obj.group(), word[1]]) if search: # 文字列探索 for word in find_list: re_obj = re.search(word, text) if re_obj: find_out_list.append([i + 1, re_obj.start(), word]) if not search: for c in warning_list: print( "\033[33mWARNING\033[0m: %s:%s:%s: (%s) => (%s)" % (file, c[0], c[1], c[2], c[3]) ) if search: for c in find_out_list: print("\033[36mFOUND!!\033[0m: %s:%s:%s: (%s)" % (file, c[0], c[1], c[2])) return "\n".join(text_arr) # 数字を三桁ごとに区切ってカンマ class DigitComma: def __init__(self, text: str): self.text = text # 数字を三桁ごとに区切ってカンマ def __digit_comma(self, num: str): num = num.group() integer_decimal = num.split(".") commad_num = re.sub( "(\d)(?=(\d\d\d)+(?!\d))", r"\1,", integer_decimal[0] ) # 整数部 if len(integer_decimal) > 1: commad_num += "." + integer_decimal[1] # 小数部 return commad_num # textから数値の場所のみを切り出す def cut_out(self): # 数値を切り出してカンマを挿入 return re.sub(r"\d+[.,\d]*\d+", self.__digit_comma, self.text) # 数値の前後と行頭にスペースを入れる class SpaceConvert: def __init__(self, text: str, special_noun_list: list): self.text = text self.special_noun_list = special_noun_list # 数値の前後と行頭にスペースを入れる def __add_space(self, text: str): # 数値の前に空白 text = re.sub(r"([^\n\d, \.])((?:\d+\.?\d*|\.\d+))", r"\1 \2", text) # 数値の後ろに空白 text = re.sub(r"([\+\-]?(?:\d+\.?\d*|\.\d+))([^\n\d, \.])", r"\1 \2", text) # 記号の前後に空白 op = r"\+\-\*" text = re.sub(r"([^%s\n ])([%s]+)" % (op, op), r"\1 \2", text) text = re.sub(r"([%s]+)([^%s ])" % (op, op), r"\1 \2", text) # 英字の後ろに空白 symbol = r'_\.\^,:\/%<>"\'=\[\]' word = r"a-zA-Z\d" + symbol text = re.sub(r"([a-zA-Z][%s]*)([^\n%s ])" % (word, word), r"\1 \2", text) # 先頭以外の英字の前に空白 text = re.sub(r"([^\n%s ])([a-zA-Z][%s]*)" % (word, word), r"\1 \2", text) # "[日本語][スペース]?[演算子][スペース][数値]"を"[日本語][スペース][演算子][数値]"にする (あ+ 1 → あ +1) ja = r"亜-熙ぁ-んァ-ヶ" text = re.sub(r"([%s]) ?([%s]+) (\d)" % (ja, op), r"\1 \2\3", text) # "[改行][スペース]?[演算子][スペース][数値]"を"[改行][演算子][数値]"にする (+ 1 → +1) text = re.sub(r"\n ?([%s]+) (\d)" % (op), r"\n\1\2", text) return text # 前後に空白が入ってはいけない場合，削除する def __erase_invalid_spaces(self, text: str): # 累乗記号 : 前後のスペースを消す(xor記号の場合は^を使わない) text = text.replace(r" ^ ", r"^") # アンダーバー : 前後またはその片方のスペースを消す text = text.replace("_ ", "_").replace(" _", "_") # Python 3 を Python3 にする text = re.sub(r"([A-Za-z]) (\d)", r"\1\2", text) # special_noun_listに入っている，間に不要な空白が入っている単語を修正する for noun in self.special_noun_list: # 入力をエスケープ need_escape = r"\\\*\+\.\?\{\}\[\]\^\$\|" noun_invalid_reg: str = re.sub("([%s])" % (need_escape), r"\\\1", noun) noun_invalid_reg = re.sub(r"(?<!\\)(.)", r" ?\1", noun_invalid_reg)[2:] # 置換 text = re.sub(noun_invalid_reg, noun, text) return text # タグ前後の不要なスペースを削除 def __erase_invalid_before_patterns_spaces(self, text: str): text = re.sub(r" +<", r" <", text) # タグの前 text = re.sub(r"> +", r"> ", text) # タグの後 return text # 文字列を除外パターンで分離 def split_text(self): converted_text = "" # 変換対象外にするパターン一覧 rm_patterns_range = [r"</?pre>", r"</?code>", "```"] # 除外パターンでテキストを分割 text_arr = re.split("|".join(rm_patterns_range), self.text) # text_arrの各文字列がどのパターンの中にあるか ptns_in_text = [ m.group() for m in re.finditer("|".join(rm_patterns_range), self.text) ] # 現在囲まれているタグ一覧 ptn_state = [] for doc, ptn in zip(text_arr, ptns_in_text): # 終了タグと開始タグを一緒にする ptn_prot = ptn.replace("/", "") # 除外パターンに囲われていない時 if not ptn_state: # 数値にスペースを入れる doc = self.__add_space(doc) # 数値にカンマを入れる dc = DigitComma(doc) doc = dc.cut_out() doc = self.__erase_invalid_spaces(doc) converted_text += doc # 除外パターンの開始 if not ptn_prot in ptn_state: ptn_state.append(ptn_prot) # <code>の前にスペースを入れる if ptn_prot == "<code>" and converted_text[-1] != "\n": converted_text += " " # タグを整形結果に追加 converted_text += ptn # 除外パターンの終了 else: ptn_state.remove(ptn_prot) # タグを整形結果に追加 converted_text += ptn # <code>の後にスペースを入れる if ptn_prot == "<code>": converted_text += " " else: # テキストのブロック数と除外パターンの数が不一致の場合 if len(text_arr) > len(ptns_in_text) and not ptn_state: # 数値にスペースを入れる doc = self.__add_space(text_arr[-1]) # 数値にカンマを入れる dc = DigitComma(doc) doc = dc.cut_out() converted_text += self.__erase_invalid_spaces(doc) # タグ前後の不要なスペースを削除 converted_text = self.__erase_invalid_before_patterns_spaces(converted_text) return converted_text def converter(file, search): text = File.readFile(file) root = sys.argv[0][:-14] if not os.path.isfile("%sspecial_noun_list.txt" % (root)): special_noun_list = [] else: special_noun_list = File.readFile("%sspecial_noun_list.txt" % (root)).split( "\n" ) if not search: # 数値の前後，行頭の英単語の後にスペースを入れる sc = SpaceConvert(text, special_noun_list) text = sc.split_text() # ，を、に変更する text = dot_to_comma(text) # 指定した単語のWARNINGを出す text = word_to_word(text, file, search, root) with open(file, mode="w") as f: f.write(text) if __name__ == "__main__": # s = "A12 ^ 12AA<pre>Z_ 1Z 1 _ 23 - 456 Z</pre>CC- 1234C+ 12```ZZZ```AAA" s = "abc1d_aaa貼り付けco11111deでき入1123.456888 力 <code>る </code> よ\n<code></code>あっっ" print(s) sc = SpaceConvert(s) print(sc.split_text())

32.730942

86

0.511029

c09fe77938eb67c406590e65bee0751046c56ae5

17,762

py

Python

elementpath/xpath2/xpath2_constructors.py

felixonmars/elementpath

62584c7335a8188ebc7eecdcbf0cee52daebe301

[ "MIT" ]

null

elementpath/xpath2/xpath2_constructors.py

felixonmars/elementpath

62584c7335a8188ebc7eecdcbf0cee52daebe301

[ "MIT" ]

null

elementpath/xpath2/xpath2_constructors.py

felixonmars/elementpath

62584c7335a8188ebc7eecdcbf0cee52daebe301

[ "MIT" ]

null

# # Copyright (c), 2018-2021, SISSA (International School for Advanced Studies). # All rights reserved. # This file is distributed under the terms of the MIT License. # See the file 'LICENSE' in the root directory of the present # distribution, or http://opensource.org/licenses/MIT. # # @author Davide Brunato <brunato@sissa.it> # """ XPath 2.0 implementation - part 3 (XSD constructors and multi-role tokens) """ from ..exceptions import ElementPathError, ElementPathSyntaxError from ..namespaces import XSD_NAMESPACE from ..datatypes import xsd10_atomic_types, xsd11_atomic_types, GregorianDay, \ GregorianMonth, GregorianMonthDay, GregorianYear10, GregorianYear, \ GregorianYearMonth10, GregorianYearMonth, Duration, DayTimeDuration, \ YearMonthDuration, Date10, Date, DateTime10, DateTime, DateTimeStamp, \ Time, UntypedAtomic, QName, HexBinary, Base64Binary, BooleanProxy from ..xpath_token import XPathToken from .xpath2_functions import XPath2Parser register = XPath2Parser.register unregister = XPath2Parser.unregister method = XPath2Parser.method constructor = XPath2Parser.constructor ### # Constructors for string-based XSD types @constructor('normalizedString') @constructor('token') @constructor('language') @constructor('NMTOKEN') @constructor('Name') @constructor('NCName') @constructor('ID') @constructor('IDREF') @constructor('ENTITY') @constructor('anyURI') def cast(self, value): try: return xsd10_atomic_types[self.symbol](value) except ValueError as err: raise self.error('FORG0001', err) ### # Constructors for numeric XSD types @constructor('decimal') @constructor('double') @constructor('float') def cast(self, value): try: if self.parser.xsd_version == '1.0': return xsd10_atomic_types[self.symbol](value) return xsd11_atomic_types[self.symbol](value) except ValueError as err: if isinstance(value, (str, UntypedAtomic)): raise self.error('FORG0001', err) raise self.error('FOCA0002', err) @constructor('integer') @constructor('nonNegativeInteger') @constructor('positiveInteger') @constructor('nonPositiveInteger') @constructor('negativeInteger') @constructor('long') @constructor('int') @constructor('short') @constructor('byte') @constructor('unsignedLong') @constructor('unsignedInt') @constructor('unsignedShort') @constructor('unsignedByte') def cast(self, value): try: return xsd10_atomic_types[self.symbol](value) except ValueError: msg = 'could not convert {!r} to xs:{}'.format(value, self.symbol) if isinstance(value, (str, bytes, UntypedAtomic, bool)): raise self.error('FORG0001', msg) from None raise self.error('FOCA0002', msg) from None except OverflowError as err: raise self.error('FOCA0002', err) from None ### # Constructors for datetime XSD types @constructor('date') def cast(self, value): cls = Date if self.parser.xsd_version == '1.1' else Date10 if isinstance(value, cls): return value try: if isinstance(value, UntypedAtomic): return cls.fromstring(value.value) elif isinstance(value, DateTime10): return cls(value.year, value.month, value.day, value.tzinfo) return cls.fromstring(value) except OverflowError as err: raise self.error('FODT0001', err) from None except ValueError as err: raise self.error('FORG0001', err) @constructor('gDay') def cast(self, value): if isinstance(value, GregorianDay): return value try: if isinstance(value, UntypedAtomic): return GregorianDay.fromstring(value.value) elif isinstance(value, (Date10, DateTime10)): return GregorianDay(value.day, value.tzinfo) return GregorianDay.fromstring(value) except ValueError as err: raise self.error('FORG0001', err) @constructor('gMonth') def cast(self, value): if isinstance(value, GregorianMonth): return value try: if isinstance(value, UntypedAtomic): return GregorianMonth.fromstring(value.value) elif isinstance(value, (Date10, DateTime10)): return GregorianMonth(value.month, value.tzinfo) return GregorianMonth.fromstring(value) except ValueError as err: raise self.error('FORG0001', err) @constructor('gMonthDay') def cast(self, value): if isinstance(value, GregorianMonthDay): return value try: if isinstance(value, UntypedAtomic): return GregorianMonthDay.fromstring(value.value) elif isinstance(value, (Date10, DateTime10)): return GregorianMonthDay(value.month, value.day, value.tzinfo) return GregorianMonthDay.fromstring(value) except ValueError as err: raise self.error('FORG0001', err) @constructor('gYear') def cast(self, value): cls = GregorianYear if self.parser.xsd_version == '1.1' else GregorianYear10 if isinstance(value, cls): return value try: if isinstance(value, UntypedAtomic): return cls.fromstring(value.value) elif isinstance(value, (Date10, DateTime10)): return cls(value.year, value.tzinfo) return cls.fromstring(value) except OverflowError as err: raise self.error('FODT0001', err) from None except ValueError as err: raise self.error('FORG0001', err) @constructor('gYearMonth') def cast(self, value): cls = GregorianYearMonth \ if self.parser.xsd_version == '1.1' else GregorianYearMonth10 if isinstance(value, cls): return value try: if isinstance(value, UntypedAtomic): return cls.fromstring(value.value) elif isinstance(value, (Date10, DateTime10)): return cls(value.year, value.month, value.tzinfo) return cls.fromstring(value) except OverflowError as err: raise self.error('FODT0001', err) from None except ValueError as err: raise self.error('FORG0001', err) @constructor('time') def cast(self, value): if isinstance(value, Time): return value try: if isinstance(value, UntypedAtomic): return Time.fromstring(value.value) elif isinstance(value, DateTime10): return Time(value.hour, value.minute, value.second, value.microsecond, value.tzinfo) return Time.fromstring(value) except ValueError as err: raise self.error('FORG0001', err) @method('date') @method('gDay') @method('gMonth') @method('gMonthDay') @method('gYear') @method('gYearMonth') @method('time') def evaluate(self, context=None): arg = self.data_value(self.get_argument(context)) if arg is None: return [] try: return self.cast(arg) except TypeError as err: raise self.error('FORG0006', err) from None except OverflowError as err: raise self.error('FODT0001', err) from None ### # Constructors for time durations XSD types @constructor('duration') def cast(self, value): if isinstance(value, Duration): return value try: if isinstance(value, UntypedAtomic): return Duration.fromstring(value.value) return Duration.fromstring(value) except OverflowError as err: raise self.error('FODT0002', err) from None except ValueError as err: raise self.error('FORG0001', err) @constructor('yearMonthDuration') def cast(self, value): if isinstance(value, YearMonthDuration): return value elif isinstance(value, Duration): return YearMonthDuration(months=value.months) try: if isinstance(value, UntypedAtomic): return YearMonthDuration.fromstring(value.value) return YearMonthDuration.fromstring(value) except OverflowError as err: raise self.error('FODT0002', err) from None except ValueError as err: raise self.error('FORG0001', err) @constructor('dayTimeDuration') def cast(self, value): if isinstance(value, DayTimeDuration): return value elif isinstance(value, Duration): return DayTimeDuration(seconds=value.seconds) try: if isinstance(value, UntypedAtomic): return DayTimeDuration.fromstring(value.value) return DayTimeDuration.fromstring(value) except OverflowError as err: raise self.error('FODT0002', err) from None except ValueError as err: raise self.error('FORG0001', err) from None @constructor('dateTimeStamp') def cast(self, value): if isinstance(value, DateTimeStamp): return value elif isinstance(value, DateTime10): value = str(value) try: if isinstance(value, UntypedAtomic): return DateTimeStamp.fromstring(value.value) return DateTimeStamp.fromstring(value) except ValueError as err: raise self.error('FORG0001', err) from None @method('dateTimeStamp') def evaluate(self, context=None): arg = self.data_value(self.get_argument(context)) if arg is None: return [] if isinstance(arg, UntypedAtomic): return self.cast(arg.value) return self.cast(str(arg)) @method('dateTimeStamp') def nud(self): if self.parser.xsd_version == '1.0': raise self.wrong_syntax("xs:dateTimeStamp is not recognized unless XSD 1.1 is enabled") try: self.parser.advance('(') self[0:] = self.parser.expression(5), if self.parser.next_token.symbol == ',': raise self.wrong_nargs('Too many arguments: expected at most 1 argument') self.parser.advance(')') self.value = None except SyntaxError: raise self.error('XPST0017') from None return self ### # Constructors for binary XSD types @constructor('base64Binary') def cast(self, value): try: return Base64Binary(value) except ValueError as err: raise self.error('FORG0001', err) from None except TypeError as err: raise self.error('XPTY0004', err) from None @constructor('hexBinary') def cast(self, value): try: return HexBinary(value) except ValueError as err: raise self.error('FORG0001', err) from None except TypeError as err: raise self.error('XPTY0004', err) from None @method('base64Binary') @method('hexBinary') def evaluate(self, context=None): arg = self.data_value(self.get_argument(context)) if arg is None: return [] try: return self.cast(arg) except ElementPathError as err: err.token = self raise @constructor('NOTATION') def cast(self, value): raise NotImplementedError("No value is castable to xs:NOTATION") @method('NOTATION') def nud(self): self.parser.advance('(') if self.parser.next_token.symbol == ')': raise self.error('XPST0017', 'expected exactly one argument') self[0:] = self.parser.expression(5), if self.parser.next_token.symbol != ')': raise self.error('XPST0017', 'expected exactly one argument') self.parser.advance() self.value = None raise self.error('XPST0017', "no constructor function exists for xs:NOTATION") ### # Multi role-tokens constructors (function or constructor) # # Case 1: In XPath 2.0 the 'boolean' keyword is used both for boolean() function and # for boolean() constructor. unregister('boolean') @constructor('boolean', bp=90, label=('function', 'constructor function')) def cast(self, value): try: return BooleanProxy(value) except ValueError as err: raise self.error('FORG0001', err) from None except TypeError as err: raise self.error('XPTY0004', err) from None @method('boolean') def nud(self): self.parser.advance('(') if self.parser.next_token.symbol == ')': raise self.wrong_nargs('Too few arguments: expected at least 1 argument') self[0:] = self.parser.expression(5), if self.parser.next_token.symbol == ',': raise self.wrong_nargs('Too many arguments: expected at most 1 argument') self.parser.advance(')') self.value = None return self @method('boolean') def evaluate(self, context=None): if self.label == 'function': return self.boolean_value([x for x in self[0].select(context)]) # xs:boolean constructor arg = self.data_value(self.get_argument(context)) if arg is None: return [] try: return self.cast(arg) except ElementPathError as err: err.token = self raise ### # Case 2: In XPath 2.0 the 'string' keyword is used both for fn:string() and xs:string(). unregister('string') register('string', lbp=90, rbp=90, label=('function', 'constructor function'), # pragma: no cover pattern=r'\bstring(?=\s*$|\s*\(\:.*\:$\()', cast=XPathToken.string_value) @method('string') def nud(self): try: self.parser.advance('(') if self.label != 'function' or self.parser.next_token.symbol != ')': self[0:] = self.parser.expression(5), self.parser.advance(')') except ElementPathSyntaxError as err: err.code = self.error_code('XPST0017') raise self.value = None return self @method('string') def evaluate(self, context=None): if self.label == 'function': if not self: if context is None: raise self.missing_context() return self.string_value(context.item) return self.string_value(self.get_argument(context)) else: item = self.get_argument(context) return [] if item is None else self.string_value(item) # Case 3 and 4: In XPath 2.0 the XSD 'QName' and 'dateTime' types have special # constructor functions so the 'QName' keyword is used both for fn:QName() and # xs:QName(), the same for 'dateTime' keyword. # # In those cases the label at parse time is set by the nud method, in dependence # of the number of args. # @constructor('QName', bp=90, label=('function', 'constructor function')) def cast(self, value): if isinstance(value, QName): return value elif isinstance(value, UntypedAtomic): return self.cast_to_qname(value.value) elif isinstance(value, str): return self.cast_to_qname(value) else: raise self.error('XPTY0004', 'the argument has an invalid type %r' % type(value)) @constructor('dateTime', bp=90, label=('function', 'constructor function')) def cast(self, value): cls = DateTime if self.parser.xsd_version == '1.1' else DateTime10 if isinstance(value, cls): return value try: if isinstance(value, UntypedAtomic): return cls.fromstring(value.value) elif isinstance(value, Date10): return cls(value.year, value.month, value.day, tzinfo=value.tzinfo) return cls.fromstring(value) except OverflowError as err: raise self.error('FODT0001', err) from None except ValueError as err: raise self.error('FORG0001', err) from None @method('QName') @method('dateTime') def nud(self): try: self.parser.advance('(') self[0:] = self.parser.expression(5), if self.parser.next_token.symbol == ',': if self.label != 'function': raise self.error('XPST0017', 'unexpected 2nd argument') self.label = 'function' self.parser.advance(',') self[1:] = self.parser.expression(5), elif self.label != 'constructor function' or self.namespace != XSD_NAMESPACE: raise self.error('XPST0017', '2nd argument missing') else: self.label = 'constructor function' self.parser.advance(')') except SyntaxError: raise self.error('XPST0017') from None self.value = None return self @method('QName') def evaluate(self, context=None): if self.label == 'constructor function': arg = self.data_value(self.get_argument(context)) return [] if arg is None else self.cast(arg) else: uri = self.get_argument(context) qname = self.get_argument(context, index=1) try: return QName(uri, qname) except TypeError as err: raise self.error('XPTY0004', err) except ValueError as err: raise self.error('FOCA0002', err) @method('dateTime') def evaluate(self, context=None): if self.label == 'constructor function': arg = self.data_value(self.get_argument(context)) if arg is None: return [] try: return self.cast(arg) except ValueError as err: raise self.error('FORG0001', err) from None except TypeError as err: raise self.error('FORG0006', err) from None else: dt = self.get_argument(context, cls=Date10) tm = self.get_argument(context, 1, cls=Time) if dt is None or tm is None: return [] elif dt.tzinfo == tm.tzinfo or tm.tzinfo is None: tzinfo = dt.tzinfo elif dt.tzinfo is None: tzinfo = tm.tzinfo else: raise self.error('FORG0008') if self.parser.xsd_version == '1.1': return DateTime(dt.year, dt.month, dt.day, tm.hour, tm.minute, tm.second, tm.microsecond, tzinfo) return DateTime10(dt.year, dt.month, dt.day, tm.hour, tm.minute, tm.second, tm.microsecond, tzinfo) @constructor('untypedAtomic') def cast(self, value): return UntypedAtomic(value) @method('untypedAtomic') def evaluate(self, context=None): arg = self.data_value(self.get_argument(context)) if arg is None: return [] elif isinstance(arg, UntypedAtomic): return arg else: return self.cast(arg)

30.5189

98

0.656627

1a4b1fa2a88c6d3f4f11f66beb64bdb562d95994

18,819

py

Python

c2rust-refactor/doc/literate/diff.py

marcograss/c2rust

b432c3836d8cf3cb5866c0d98d93c06c900abafb

[ "BSD-3-Clause" ]

2,337

2018-04-20T00:34:36.000Z

2022-03-30T21:08:36.000Z

c2rust-refactor/doc/literate/diff.py

marcograss/c2rust

b432c3836d8cf3cb5866c0d98d93c06c900abafb

[ "BSD-3-Clause" ]

324

2018-06-20T04:14:12.000Z

2022-03-31T16:45:17.000Z

c2rust-refactor/doc/literate/diff.py

marcograss/c2rust

b432c3836d8cf3cb5866c0d98d93c06c900abafb

[ "BSD-3-Clause" ]

146

2018-06-22T20:16:11.000Z

2022-03-16T18:04:30.000Z

from collections import namedtuple, deque import difflib import pygments.formatters import pygments.lexers import pygments.token import re from typing import List, Tuple, Optional, Iterator, Iterable from literate.annot import Span, Annot, SpanMerger, \ cut_annot, merge_annot, sub_annot, fill_annot from literate.file import File, Line, Diff, DiffBlock, Hunk, OutputLine from literate.points import Point, cut_annot_at_points # Regex for finding runs of identical non-space characters RUN_RE = re.compile(r'([^ \n])\1*') def parse_intra_annot(s: str) -> Annot[str]: '''Parse an `ndiff` detail (`?`) line and convert it to an annotation indicating intraline edits in the text of the preceding line. The annotation labels inserted, deleted, and changed characters with `'ins'`, `'del'`, and `'chg'` respectively.''' spans = [] for m in RUN_RE.finditer(s): c = m.group(1) # Map the symbols used by `ndiff` to something more meaningful. label = { '+': 'ins', '-': 'del', '^': 'chg', }[c] spans.append(Span(m.start(), m.end(), label)) return spans DiffLine = Tuple[bool, bool, Optional[Annot[str]], Optional[Annot[str]]] def diff_lines(old_lines: List[str], new_lines: List[str]) -> Iterator[DiffLine]: '''Compute a diff of `old` and `new`, and yield a sequence of (old_line, new_line, old_detail, new_detail). Each `line` is a boolean indicating whether there is a line present in the old/new file, and each `detail` is an intraline edit annotation (see `parse_intra_annot`). Possible outputs: - (True, True, None, None): Unmodified/context line - (True, False, None, None): Deletion of a line from the old text. - (False, True, None, None): Insertion of a line in the new text. - (True, True, [...], [...]): Changed line, modified via the indicated intraline insertions and deletions. ''' # We buffer up to two previous result tuples. This lets us handle # intraline change markers, and in particular, the nasty '-+?' case, where # we don't find out that we're in an intraline change ('?') until we've # seen both the '-' and '+' lines. buf = deque() for dl in difflib.ndiff(old_lines, new_lines): prefix = dl[0:2] if prefix == ' ': # Context line. Flush the whole buffer. while buf: yield buf.popleft() yield (True, True, None, None) elif prefix == '- ': while buf: yield buf.popleft() buf.append((True, False, None, None)) elif prefix == '+ ': # Try to fold into a previous intraline edit quad, if one exists. if len(buf) > 0: old_line, new_line, old_detail, new_detail = buf[-1] if not new_line and old_detail is not None: # Previously saw a '-' and a '?'. Fold in this '+'. assert not new_line buf[-1] = (old_line, True, old_detail, None) continue # If there's no old_detail ('?'), then we aren't in an # intraline edit. If there's a new_line, then the intraline # edit is already finished. In either case, we want to do the # default action of just adding the '+' on its own. while len(buf) > 2: yield buf.popleft() buf.append((False, True, None, None)) elif prefix == '? ': detail = parse_intra_annot(dl[2:]) # Add this detail to the previous buffered line. We may also need # to merge a pair of previous '-' and '+' lines, if we didn't # previously know that they were part of an intraline change quad. assert len(buf) > 0 old_line, new_line, old_detail, new_detail = buf.pop() if new_line: if old_line: # The previous line is a rollup of a '-' and a '+'. # (Context lines are not included in the buffer.) assert old_detail is not None buf.append((True, True, old_detail, detail)) else: # The previous line is just a '+'. There must be a '-' # before it, so roll up both of those together with the new # detail. old_line2, new_line2, old_detail2, new_detail2 = buf.pop() assert old_line2 assert not new_line2 assert old_detail2 is None assert new_detail2 is None buf.append((True, True, None, detail)) else: # The previous line is just a '-'. Roll this detail into it. # Next we should see a '+', which will get rolled in, so this # bogus (True, False, [...], None) entry will never be yielded. buf.append((True, False, detail, None)) # Flush any remaining buffered entries. while buf: yield buf.popleft() def adjust_closing_brace(old_lines: List[str], new_lines: List[str], diff: Iterable[DiffLine]) -> Iterator[DiffLine]: '''Adjust the output of `diff_lines` to turn this: fn f() { ... +} +fn g() { + ... } into this: fn f() { ... } +fn g() { + ... +} ''' # Specifically: at the end of every run of insertions or deletions, if the # first context line after the run consists of solely a '}' character (with # whitespace), then we scan from the top of the run for an identical # inserted line. If found, we change the earlier line from an insertion to # context, and change the context line to an insertion. mode = None buf = [] buf_start = None old_i = -1 new_i = -1 for dl in diff: old_line, new_line, old_detail, new_detail = dl if old_line and not new_line: new_mode = 'del' old_i += 1 elif not old_line and new_line: new_mode = 'ins' new_i += 1 else: new_mode = None old_i += 1 new_i += 1 if new_mode != mode: if new_mode is None: # Switching from ins or del mode to context mode. If the # current line is a '}', we try to do the block adjustment. check_lines = new_lines if mode == 'ins' else old_lines i = new_i if mode == 'ins' else old_i if check_lines[i].strip() == '}': # Yield everything from buf, while scanning for an earlier # matching line. found_dl = None for j, buf_dl in enumerate(buf): if check_lines[buf_start + j] == check_lines[i]: found_dl = buf_dl yield (True, True, None, None) # We're stopping early, so yield the remaining # elements. yield from buf[j + 1:] break else: yield buf_dl if found_dl: yield found_dl else: yield (True, True, None, None) else: yield from buf yield dl mode = None buf = [] buf_start = None # We already yielded the correct info, so don't fall through to # the default logic. continue else: if mode is not None: yield from buf mode = new_mode buf = [] buf_start = new_i if mode == 'ins' else old_i if mode is None: yield dl else: buf.append(dl) # There are no more lines, so there can't be a `}` line following `buf` to # trigger our heuristic. That means we can blindly dump everything in # `buf`. yield from buf WORD_BREAK_RE = re.compile(r'\b') def token_annot(line: Line) -> Annot[None]: '''Annotate the tokens of `l`. Each token (and some sub-token strings) gets a separate span. This is a helper function for `calc_tokenized_intra`.''' annot = fill_annot(line.highlight, len(line.text)) # Special cases: treat word boundaries inside strings and comments as token # breaks. This essentially gives us the behavior of `git`'s `--word-diff` # feature. extra_cuts = [] for span in annot: # We don't handle String subtypes (only String itself) because we don't # want to break up `\x00` and similar escapes. if span.label == pygments.token.String or \ span.label in pygments.token.Comment: text = line.text[span.start : span.end] for m in WORD_BREAK_RE.finditer(text): extra_cuts.append(Point(span.start + m.start())) return cut_annot_at_points(annot, extra_cuts) def calc_tokenized_intra(l1: Line, l2: Line) -> Tuple[Annot[str], Annot[str]]: '''Calculate token-based intraline edit annotations for `l1` and `l2`. `difflib.ndiff` does a pretty good job of matching up similar lines, but it computes intraline changes character-by-character, which often produces bad results. For example, it might turn `unsafe` into `malloc` by replacing `uns` -> `m` and `fe` -> `lloc`, instead of doing `unsafe` -> `malloc` in one go. Here we calculate some intraline edits that are easier to read, using the tokenization provided by `pygments` to align edit boundaries to the boundaries of source tokens.''' annot1 = token_annot(l1) annot2 = token_annot(l2) tokens1 = [l1.text[s.start : s.end] for s in annot1] tokens2 = [l2.text[s.start : s.end] for s in annot2] intra1 = [] intra2 = [] sm = difflib.SequenceMatcher(a=tokens1, b=tokens2) for tag, i1, i2, j1, j2 in sm.get_opcodes(): if tag == 'equal': continue while i1 < i2 and tokens1[i1].isspace(): i1 += 1 while i2 > i1 and tokens1[i2 - 1].isspace(): i2 -= 1 while j1 < j2 and tokens2[j1].isspace(): j1 += 1 while j2 > j1 and tokens2[j2 - 1].isspace(): j2 -= 1 if i1 != i2: intra1.append(Span(annot1[i1].start, annot1[i2 - 1].end, 'chg' if tag == 'replace' else 'del')) if j1 != j2: intra2.append(Span(annot2[j1].start, annot2[j2 - 1].end, 'chg' if tag == 'replace' else 'ins')) return (intra1, intra2) def diff_files(f1: File, f2: File) -> Diff: '''Diff two files, returning a `Diff` between them and also setting the `intra` annotation on the lines of both files.''' dls = diff_lines(f1.line_text, f2.line_text) dls = adjust_closing_brace(f1.line_text, f2.line_text, dls) # Accumulator for diff blocks. diff_blocks = [] # Start and current position of the current block. old_start = 0 old_cur = 0 new_start = 0 new_cur = 0 # Is the current block a change? (If not, it's context.) changed = True def flush(): nonlocal old_start, new_start # This check means we can blindly call `flush()` without worrying about # cluttering the output with zero-length blocks. if old_cur - old_start > 0 or new_cur - new_start > 0: diff_blocks.append(DiffBlock(changed, Span(old_start, old_cur), Span(new_start, new_cur))) old_start = old_cur new_start = new_cur for old_line, new_line, old_detail, new_detail in dls: next_changed = not (old_line and new_line and old_detail is None and new_detail is None) has_intra = old_detail is not None or new_detail is not None if next_changed != changed: flush() if has_intra: # Emit each `intra` line as its own block, to ensure they're # aligned in the output. flush() intra1, intra2 = calc_tokenized_intra( f1.lines[old_cur], f2.lines[new_cur]) if len(intra1) > 0: f1.lines[old_cur].set_intra(intra1) if len(intra2) > 0: f2.lines[new_cur].set_intra(intra2) flush() if old_line: old_cur += 1 if new_line: new_cur += 1 changed = next_changed flush() return Diff(f1, f2, diff_blocks) def context_annot(blocks: List[DiffBlock], new: bool, context_lines: int) -> Annot[None]: '''Generate an annotation of the old or new file's lines, indicating which lines are changes or context for changes (within `context_lines` distance).''' result = SpanMerger() for (changed, old_span, new_span) in blocks: if not changed: continue span = new_span if new else old_span result.add(Span( span.start - context_lines, span.end + context_lines)) return result.finish() def split_hunks(blocks: List[DiffBlock]) -> List[Hunk]: '''Split the output of `filter_unchanged` into hunks, anywhere there's a gap in the old or new line numbers.''' last_old = 0 last_new = 0 cur = [] hunks = [] def flush(): nonlocal cur if len(cur) > 0: hunks.append(Hunk(cur)) cur = [] for b in blocks: changed, old_span, new_span = b if old_span.start != last_old or new_span.start != last_new: flush() cur.append(b) last_old = old_span.end last_new = new_span.end flush() return hunks def annotate_blocks(blocks: List[DiffBlock]) \ -> Tuple[Annot[Span[None]], Annot[Span[None]]]: '''Return annotations on the old and new files, labeling each line with the block that contains it.''' old = [] new = [] for b in blocks: old.append(Span(b.old_span.start, b.old_span.end, b)) new.append(Span(b.new_span.start, b.new_span.end, b)) return old, new def build_diff_hunks(d: Diff, context_diff: bool=True): '''Build a list of output hunks, and assign it to `d.hunks`. If `d.old_file` or `d.new_file` has a `keep_mark_lines` annotation, all annotated lines will be kept as additional context.''' # Find the set of lines each file wants to keep. def calc_file_keep(f, is_new): if context_diff: keep = context_annot(d.blocks, is_new, 5) if f.keep_mark_lines is not None: keep = merge_annot(keep, f.keep_mark_lines) else: if len(f.line_annot) > 0: keep = [Span(0, f.line_annot[-1].end)] else: keep = [] if f.drop_irrelevant_lines is not None: keep = sub_annot(keep, f.drop_irrelevant_lines) return keep keep_old = calc_file_keep(d.old_file, False) keep_new = calc_file_keep(d.new_file, True) # In unchanged blocks, add each file's keep lines to the other file's set. # This works because unchanged blocks have the same number of lines on each # side. old_blocks, new_blocks = annotate_blocks(d.blocks) extra_keep_old = [] extra_keep_new = [] for block_span, keep_spans in cut_annot(keep_old, old_blocks): if block_span.label.changed: continue base = block_span.label.new_span.start extra_keep_new.extend(s + base for s in keep_spans) for block_span, keep_spans in cut_annot(keep_new, new_blocks): if block_span.label.changed: continue base = block_span.label.old_span.start extra_keep_old.extend(s + base for s in keep_spans) keep_old = merge_annot(keep_old, extra_keep_old) keep_new = merge_annot(keep_new, extra_keep_new) # For changed blocks, we can't match up lines from different files, so we # just hope for the best. (Normally all changed lines are kept, so there's # no need to match - the only exception is when the `irrelevant_*_regex` # options are set.) # Build the filtered list of blocks. There can be different numbers of # blocks on the old and new sides. We use a fairly naive strategy to match # them up, but it generally seems to work okay. blocks = [] for (old_block, old_keeps), (new_block, new_keeps) in zip( cut_annot(keep_old, old_blocks), cut_annot(keep_new, new_blocks)): # `old_blocks` and `new_blocks` have corresponding entries (based on # the same block) at corresponding positions. assert old_block.label is new_block.label block = old_block.label # Match up `old_keeps` and `new_keeps` entries by position. In most # cases, the two lists will have the same length. for old_keep, new_keep in zip(old_keeps, new_keeps): blocks.append(DiffBlock(block.changed, old_keep + block.old_span.start, new_keep + block.new_span.start)) for old_keep in old_keeps[len(new_keeps):]: blocks.append(DiffBlock(block.changed, old_keep + block.old_span.start, Span(block.new_span.end, block.new_span.end))) for new_keep in new_keeps[len(old_keeps):]: blocks.append(DiffBlock(block.changed, Span(block.old_span.end, block.old_span.end), new_keep + block.new_span.start)) # Split the new blocks into hunks, and save them in the `Diff`. hunks = split_hunks(blocks) d.set_hunks(hunks) def hunk_output_lines(h: Hunk) -> List[OutputLine]: result = [] for changed, old_span, new_span in h.blocks: common_lines = min(len(old_span), len(new_span)) for i in range(0, common_lines): result.append(OutputLine(changed, old_span.start + i, new_span.start + i)) for i in range(common_lines, len(old_span)): result.append(OutputLine(changed, old_span.start + i, None)) for i in range(common_lines, len(new_span)): result.append(OutputLine(changed, None, new_span.start + i)) return result def build_output_lines(d: Diff): '''Build a list of two-column output lines for each hunk of `d`, and set the `Hunk.output_lines` fields.''' for h in d.hunks: output_lines = hunk_output_lines(h) h.set_output_lines(output_lines)

37.638

89

0.580955

2e81de17de3944a25d618607675c46a2ec850257

971

py

Python

sunless_web/migrations/0022_auto_20180526_1619.py

bluedisk/SunlessSeaKo

1e6d498ff7e735b8d272dd0bca6c17741a2faedb

[ "MIT" ]

2

2019-02-19T11:53:29.000Z

2021-02-18T23:57:20.000Z

sunless_web/migrations/0022_auto_20180526_1619.py

bluedisk/SunlessSeaKo

1e6d498ff7e735b8d272dd0bca6c17741a2faedb

[ "MIT" ]

4

2018-05-26T13:18:27.000Z

2018-05-26T13:19:50.000Z

sunless_web/migrations/0022_auto_20180526_1619.py

bluedisk/SunlessSeaKo

1e6d498ff7e735b8d272dd0bca6c17741a2faedb

[ "MIT" ]

null

# Generated by Django 2.0.5 on 2018-05-26 07:19 import django.db.models.deletion from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('sunless_web', '0021_auto_20180526_1607'), ] operations = [ migrations.RenameModel( old_name='EntityFile', new_name='EntityCate', ), migrations.RenameField( model_name='entitycate', old_name='filename', new_name='name', ), migrations.RemoveField( model_name='entity', name='file', ), migrations.AddField( model_name='entity', name='cate', field=models.ForeignKey(default='events', on_delete=django.db.models.deletion.CASCADE, related_name='entities', to='sunless_web.EntityCate', verbose_name='소속 파일'), preserve_default=False, ), ]

28.558824

112

0.566426

010a03d5dde055ea5698c4114064a6fa6aa0f3a0

24,708

py

Python

openbb_terminal/cryptocurrency/crypto_controller.py

tehcoderer/GamestonkTerminal

54a1b6f545a0016c576e9e00eef5c003d229dacf

[ "MIT" ]

null

openbb_terminal/cryptocurrency/crypto_controller.py

tehcoderer/GamestonkTerminal

54a1b6f545a0016c576e9e00eef5c003d229dacf

[ "MIT" ]

null

openbb_terminal/cryptocurrency/crypto_controller.py

tehcoderer/GamestonkTerminal

54a1b6f545a0016c576e9e00eef5c003d229dacf

[ "MIT" ]

null

"""Cryptocurrency Context Controller""" __docformat__ = "numpy" # pylint: disable=R0904, C0302, R1710, W0622, C0201, C0301 import argparse import logging import os from typing import List from binance.client import Client from prompt_toolkit.completion import NestedCompleter import openbb_terminal.config_terminal as cfg from openbb_terminal import feature_flags as obbff from openbb_terminal.cryptocurrency.cryptocurrency_helpers import ( FIND_KEYS, display_all_coins, find, plot_chart, ) from openbb_terminal.cryptocurrency.due_diligence import ( binance_model, binance_view, coinbase_model, coinpaprika_view, finbrain_crypto_view, pycoingecko_model, pycoingecko_view, ) from openbb_terminal.decorators import log_start_end from openbb_terminal.helper_funcs import ( EXPORT_BOTH_RAW_DATA_AND_FIGURES, EXPORT_ONLY_RAW_DATA_ALLOWED, check_positive, parse_known_args_and_warn, ) from openbb_terminal.menu import session from openbb_terminal.parent_classes import CryptoBaseController from openbb_terminal.rich_config import console # pylint: disable=import-outside-toplevel logger = logging.getLogger(__name__) CRYPTO_SOURCES = { "bin": "Binance", "cg": "CoinGecko", "cp": "CoinPaprika", "cb": "Coinbase", "yf": "YahooFinance", } class CryptoController(CryptoBaseController): """Crypto Controller""" CHOICES_COMMANDS = [ "headlines", "chart", "load", "find", "prt", "resources", ] CHOICES_MENUS = [ "ta", "dd", "ov", "disc", "onchain", "defi", "tools", "nft", "pred", "qa", ] DD_VIEWS_MAPPING = { "cg": pycoingecko_view, "cp": coinpaprika_view, "bin": binance_view, } PATH = "/crypto/" FILE_PATH = os.path.join(os.path.dirname(__file__), "README.md") def __init__(self, queue: List[str] = None): """Constructor""" super().__init__(queue) if session and obbff.USE_PROMPT_TOOLKIT: choices: dict = {c: {} for c in self.controller_choices} choices["load"]["--source"] = {c: {} for c in CRYPTO_SOURCES.keys()} choices["find"]["--source"] = {c: {} for c in CRYPTO_SOURCES.keys()} choices["find"]["-k"] = {c: {} for c in FIND_KEYS} choices["headlines"] = {c: {} for c in finbrain_crypto_view.COINS} # choices["prt"]["--vs"] = {c: {} for c in coingecko_coin_ids} # list is huge. makes typing buggy choices["support"] = self.SUPPORT_CHOICES self.completer = NestedCompleter.from_nested_dict(choices) def print_help(self): """Print help""" source_txt = CRYPTO_SOURCES.get(self.source, "?") if self.source != "" else "" has_ticker_start = "" if self.coin else "[unvl]" has_ticker_end = "" if self.coin else "[/unvl]" help_text = f"""[cmds] load load a specific cryptocurrency for analysis find find coins[/cmds] [param]Coin: [/param]{self.coin} [param]Source: [/param]{source_txt} [cmds] headlines crypto sentiment from 15+ major news headlines [src][Finbrain][/src]{has_ticker_start} chart view a candle chart for a specific cryptocurrency prt potential returns tool - check how much upside if ETH reaches BTC market cap{has_ticker_end} [/cmds][menu] > disc discover trending cryptocurrencies, e.g.: top gainers, losers, top sentiment > ov overview of the cryptocurrencies, e.g.: market cap, DeFi, latest news, top exchanges, stables > onchain information on different blockchains, e.g.: eth gas fees, whale alerts, DEXes info > defi decentralized finance information, e.g.: dpi, llama, tvl, lending, borrow, funding > tools explore different tools e.g.: apytoapr, il > nft non-fungible tokens, e.g.: today drops{has_ticker_start} > dd due-diligence for loaded coin, e.g.: coin information, social media, market stats > ta technical analysis for loaded coin, e.g.: ema, macd, rsi, adx, bbands, obv > pred prediction techniques, e.g.: regression, arima, rnn, lstm, conv1d, monte carlo > qa quantitative analysis e.g.: decompose, cusum, residuals analysis[/menu] {has_ticker_end} """ console.print(text=help_text, menu="Cryptocurrency") @log_start_end(log=logger) def call_prt(self, other_args): """Process prt command""" if self.coin: parser = argparse.ArgumentParser( add_help=False, formatter_class=argparse.ArgumentDefaultsHelpFormatter, prog="prt", description="Potential Returns Tool" "Tool to check returns if loaded coin reaches provided price or other crypto market cap" "Uses CoinGecko to grab coin data (price and market cap).", ) parser.add_argument( "--vs", help="Coin to compare with", dest="vs", type=str, required="-h" not in other_args, ) parser.add_argument( "-p", "--price", help="Desired price", dest="price", type=int, default=None, ) parser.add_argument( "-t", "--top", help="Compare with top N coins", dest="top", type=int, default=None, ) if other_args and "-" not in other_args[0][0]: other_args.insert(0, "--vs") ns_parser = parse_known_args_and_warn( parser, other_args, EXPORT_ONLY_RAW_DATA_ALLOWED ) if ns_parser: if ns_parser.vs: coin_found = pycoingecko_model.check_coin(ns_parser.vs) if not coin_found: console.print( f"VS Coin '{ns_parser.vs}' not found in CoinGecko\n" ) return pycoingecko_view.display_coin_potential_returns( self.coin_map_df["CoinGecko"], coin_found, ns_parser.top, ns_parser.price, ) else: console.print( "No coin selected. Use 'load' to load the coin you want to look at.\n" ) @log_start_end(log=logger) def call_chart(self, other_args): """Process chart command""" if self.coin: parser = argparse.ArgumentParser( add_help=False, formatter_class=argparse.ArgumentDefaultsHelpFormatter, prog="chart", description="""Display chart for loaded coin. You can specify currency vs which you want to show chart and also number of days to get data for.""", ) if self.source == "cp": parser.add_argument( "--vs", default="usd", dest="vs", help="Currency to display vs coin", choices=["usd", "btc", "BTC", "USD"], type=str, ) parser.add_argument( "-d", "--days", default=365, dest="days", help="Number of days to get data for", type=check_positive, ) if self.source == "cg": parser.add_argument( "--vs", default="usd", dest="vs", help="Currency to display vs coin" ) parser.add_argument( "-d", "--days", default=30, dest="days", help="Number of days to get data for", ) if self.source == "bin": client = Client(cfg.API_BINANCE_KEY, cfg.API_BINANCE_SECRET) interval_map = { "1day": client.KLINE_INTERVAL_1DAY, "3day": client.KLINE_INTERVAL_3DAY, "1hour": client.KLINE_INTERVAL_1HOUR, "2hour": client.KLINE_INTERVAL_2HOUR, "4hour": client.KLINE_INTERVAL_4HOUR, "6hour": client.KLINE_INTERVAL_6HOUR, "8hour": client.KLINE_INTERVAL_8HOUR, "12hour": client.KLINE_INTERVAL_12HOUR, "1week": client.KLINE_INTERVAL_1WEEK, "1min": client.KLINE_INTERVAL_1MINUTE, "3min": client.KLINE_INTERVAL_3MINUTE, "5min": client.KLINE_INTERVAL_5MINUTE, "15min": client.KLINE_INTERVAL_15MINUTE, "30min": client.KLINE_INTERVAL_30MINUTE, "1month": client.KLINE_INTERVAL_1MONTH, } _, quotes = binance_model.show_available_pairs_for_given_symbol( self.coin ) parser.add_argument( "--vs", help="Quote currency (what to view coin vs)", dest="vs", type=str, default="USDT", choices=quotes, ) parser.add_argument( "-i", "--interval", help="Interval to get data", choices=list(interval_map.keys()), dest="interval", default="1day", type=str, ) parser.add_argument( "-l", "--limit", dest="limit", default=100, help="Number to get", type=check_positive, ) if self.source == "cb": interval_map = { "1min": 60, "5min": 300, "15min": 900, "1hour": 3600, "6hour": 21600, "24hour": 86400, "1day": 86400, } _, quotes = coinbase_model.show_available_pairs_for_given_symbol( self.coin ) if len(quotes) < 0: console.print( f"Couldn't find any quoted coins for provided symbol {self.coin}" ) return parser.add_argument( "--vs", help="Quote currency (what to view coin vs)", dest="vs", type=str, default="USDT" if "USDT" in quotes else quotes[0], choices=quotes, ) parser.add_argument( "-i", "--interval", help="Interval to get data", choices=list(interval_map.keys()), dest="interval", default="1day", type=str, ) parser.add_argument( "-l", "--limit", dest="limit", default=100, help="Number to get", type=check_positive, ) if self.source == "yf": interval_map = { "1min": "1m", "2min": "2m", "5min": "5m", "15min": "15m", "30min": "30m", "60min": "60m", "90min": "90m", "1hour": "1h", "1day": "1d", "5day": "5d", "1week": "1wk", "1month": "1mo", "3month": "3mo", } parser.add_argument( "--vs", default="USD", dest="vs", help="Currency to display vs coin", choices=[ "CAD", "CNY", "ETH", "EUR", "GBP", "INR", "JPY", "KRW", "RUB", "USD", "AUD", "BTC", ], type=str, ) parser.add_argument( "-i", "--interval", help="Interval to get data", choices=list(interval_map.keys()), dest="interval", default="1day", type=str, ) parser.add_argument( "-l", "--limit", dest="limit", default=100, help="Number to get", type=check_positive, ) parser.add_argument( "-d", "--days", default=30, dest="days", help="Number of days to get data for", ) ns_parser = parse_known_args_and_warn( parser, other_args, EXPORT_BOTH_RAW_DATA_AND_FIGURES ) if ns_parser: if self.source in ["bin", "cb"]: limit = ns_parser.limit interval = ns_parser.interval days = 0 elif self.source == "yf": limit = ns_parser.limit interval = interval_map[ns_parser.interval] days = ns_parser.days else: limit = 0 interval = "1day" days = ns_parser.days plot_chart( coin_map_df=self.coin_map_df, limit=limit, interval=interval, days=days, currency=ns_parser.vs, source=self.source, ) @log_start_end(log=logger) def call_ta(self, _): """Process ta command""" from openbb_terminal.cryptocurrency.technical_analysis.ta_controller import ( TechnicalAnalysisController, ) # TODO: Play with this to get correct usage if self.coin: if self.current_currency != "" and not self.current_df.empty: self.queue = self.load_class( TechnicalAnalysisController, stock=self.current_df, coin=self.symbol, start=self.current_df.index[0], interval="", queue=self.queue, ) else: console.print("No coin selected. Use 'load' to load a coin.\n") @log_start_end(log=logger) def call_tools(self, _): """Process tools command""" from openbb_terminal.cryptocurrency.tools.tools_controller import ( ToolsController, ) self.queue = self.load_class(ToolsController, self.queue) @log_start_end(log=logger) def call_disc(self, _): """Process disc command""" from openbb_terminal.cryptocurrency.discovery.discovery_controller import ( DiscoveryController, ) self.queue = self.load_class(DiscoveryController, self.queue) @log_start_end(log=logger) def call_ov(self, _): """Process ov command""" from openbb_terminal.cryptocurrency.overview.overview_controller import ( OverviewController, ) self.queue = self.load_class(OverviewController, self.queue) @log_start_end(log=logger) def call_defi(self, _): """Process defi command""" from openbb_terminal.cryptocurrency.defi.defi_controller import DefiController self.queue = self.load_class(DefiController, self.queue) @log_start_end(log=logger) def call_headlines(self, other_args): """Process sentiment command""" parser = argparse.ArgumentParser( add_help=False, formatter_class=argparse.ArgumentDefaultsHelpFormatter, prog="headlines", description="""Display sentiment analysis from FinBrain for chosen Cryptocurrencies""", ) parser.add_argument( "-c", "--coin", default="BTC", type=str, dest="coin", help="Symbol of coin to load data for, ~100 symbols are available", choices=finbrain_crypto_view.COINS, ) if other_args and "-" not in other_args[0][0]: other_args.insert(0, "-c") ns_parser = parse_known_args_and_warn( parser, other_args, EXPORT_BOTH_RAW_DATA_AND_FIGURES ) if ns_parser: finbrain_crypto_view.display_crypto_sentiment_analysis( coin=ns_parser.coin, export=ns_parser.export ) @log_start_end(log=logger) def call_dd(self, _): """Process dd command""" if self.coin: from openbb_terminal.cryptocurrency.due_diligence.dd_controller import ( DueDiligenceController, ) self.queue = self.load_class( DueDiligenceController, self.coin, self.source, self.symbol, self.coin_map_df, queue=self.queue, ) else: console.print("No coin selected. Use 'load' to load a coin.\n") @log_start_end(log=logger) def call_qa(self, _): """Process pred command""" if self.coin: from openbb_terminal.cryptocurrency.quantitative_analysis import ( qa_controller, ) if self.current_interval != "1day": console.print("Only interval `1day` is possible for now.\n") else: self.queue = self.load_class( qa_controller.QaController, self.coin, self.current_df, self.queue, ) @log_start_end(log=logger) def call_pred(self, _): """Process pred command""" if obbff.ENABLE_PREDICT: if self.coin: try: from openbb_terminal.cryptocurrency.prediction_techniques import ( pred_controller, ) if self.current_interval != "1day": console.print("Only interval `1day` is possible for now.\n") else: self.queue = self.load_class( pred_controller.PredictionTechniquesController, self.coin, self.current_df, self.queue, ) except ImportError: logger.exception("Tensorflow not available") console.print("[red]Run pip install tensorflow to continue[/red]\n") else: console.print( "No coin selected. Use 'load' to load the coin you want to look at.\n" ) else: console.print( "Predict is disabled. Check ENABLE_PREDICT flag on feature_flags.py", "\n", ) @log_start_end(log=logger) def call_onchain(self, _): """Process onchain command""" from openbb_terminal.cryptocurrency.onchain.onchain_controller import ( OnchainController, ) self.queue = self.load_class(OnchainController, self.queue) @log_start_end(log=logger) def call_nft(self, _): """Process nft command""" from openbb_terminal.cryptocurrency.nft.nft_controller import NFTController self.queue = self.load_class(NFTController, self.queue) # TODO: merge the two views that this command calls. (find + previously called coins) @log_start_end(log=logger) def call_find(self, other_args): """Process find command""" parser = argparse.ArgumentParser( prog="find", add_help=False, formatter_class=argparse.ArgumentDefaultsHelpFormatter, description=""" Find similar coin by name, symbol, or id. If you don't remember exact name or id of the Coin at CoinGecko, Binance, Coinbase or CoinPaprika you can use this command to display coins with similar name, symbol or id to your search query. Example of usage: coin name is something like "polka". So I can try: find -c polka -k name -t 25 It will search for coin that has similar name to polka and display top 25 matches. -c, --coin stands for coin - you provide here your search query -k, --key it's a searching key. You can search by symbol, id or name of coin -l, --limit it displays top N number of records. coins: Shows list of coins available on CoinGecko, CoinPaprika and Binance.If you provide name of coin then in result you will see ids of coins with best match for all mentioned services. If you provide ALL keyword in your search query, then all coins will be displayed. To move over coins you can use pagination mechanism with skip, top params. E.g. coins ALL --skip 100 --limit 30 then all coins from 100 to 130 will be displayed. By default skip = 0, limit = 10. If you won't provide source of the data everything will be displayed (CoinGecko, CoinPaprika, Binance). If you want to search only in given source then use --source flag. E.g. if you want to find coin with name uniswap on CoinPaprika then use: coins uniswap --source cp --limit 10 """, ) parser.add_argument( "-c", "--coin", help="Symbol Name or Id of Coin", dest="coin", required="-h" not in other_args, type=str, ) parser.add_argument( "-k", "--key", dest="key", help="Specify by which column you would like to search: symbol, name, id", type=str, choices=FIND_KEYS, default="symbol", ) parser.add_argument( "-l", "--limit", default=10, dest="limit", help="Number of records to display", type=check_positive, ) parser.add_argument( "--source", dest="source", choices=CRYPTO_SOURCES.keys(), default="cg", help="Source of data.", type=str, ) parser.add_argument( "-s", "--skip", default=0, dest="skip", help="Skip n of records", type=check_positive, ) if other_args and not other_args[0][0] == "-": other_args.insert(0, "-c") ns_parser = parse_known_args_and_warn( parser, other_args, EXPORT_ONLY_RAW_DATA_ALLOWED ) # TODO: merge find + display_all_coins if ns_parser: find( coin=ns_parser.coin, source=ns_parser.source, key=ns_parser.key, top=ns_parser.limit, export=ns_parser.export, ) display_all_coins( coin=ns_parser.coin, source=ns_parser.source, top=ns_parser.limit, skip=ns_parser.skip, show_all=bool("ALL" in other_args), export=ns_parser.export, )

35.449067

118

0.496236

732afe7bb5014bc6299202562ff7bd0adc01d8c7

2,414

py

Python

test/azure/version-tolerant/Expected/AcceptanceTests/CustomUrlPagingVersionTolerant/custombaseurlpagingversiontolerant/_configuration.py

changlong-liu/autorest.python

1f03e4c6a11934d385fab050dc44041f1e91e9ff

[ "MIT" ]

null

test/azure/version-tolerant/Expected/AcceptanceTests/CustomUrlPagingVersionTolerant/custombaseurlpagingversiontolerant/_configuration.py

changlong-liu/autorest.python

1f03e4c6a11934d385fab050dc44041f1e91e9ff

[ "MIT" ]

null

test/azure/version-tolerant/Expected/AcceptanceTests/CustomUrlPagingVersionTolerant/custombaseurlpagingversiontolerant/_configuration.py

changlong-liu/autorest.python

1f03e4c6a11934d385fab050dc44041f1e91e9ff

[ "MIT" ]

null

# coding=utf-8 # -------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for license information. # Code generated by Microsoft (R) AutoRest Code Generator. # Changes may cause incorrect behavior and will be lost if the code is regenerated. # -------------------------------------------------------------------------- from typing import Any from azure.core.configuration import Configuration from azure.core.pipeline import policies from ._version import VERSION class AutoRestParameterizedHostTestPagingClientConfiguration( Configuration ): # pylint: disable=too-many-instance-attributes """Configuration for AutoRestParameterizedHostTestPagingClient. Note that all parameters used to create this instance are saved as instance attributes. :param host: A string value that is used as a global part of the parameterized host. :type host: str """ def __init__(self, host: str = "host", **kwargs: Any) -> None: super(AutoRestParameterizedHostTestPagingClientConfiguration, self).__init__(**kwargs) if host is None: raise ValueError("Parameter 'host' must not be None.") self.host = host kwargs.setdefault("sdk_moniker", "autorestparameterizedhosttestpagingclient/{}".format(VERSION)) self._configure(**kwargs) def _configure( self, **kwargs # type: Any ): # type: (...) -> None self.user_agent_policy = kwargs.get("user_agent_policy") or policies.UserAgentPolicy(**kwargs) self.headers_policy = kwargs.get("headers_policy") or policies.HeadersPolicy(**kwargs) self.proxy_policy = kwargs.get("proxy_policy") or policies.ProxyPolicy(**kwargs) self.logging_policy = kwargs.get("logging_policy") or policies.NetworkTraceLoggingPolicy(**kwargs) self.http_logging_policy = kwargs.get("http_logging_policy") or policies.HttpLoggingPolicy(**kwargs) self.retry_policy = kwargs.get("retry_policy") or policies.RetryPolicy(**kwargs) self.custom_hook_policy = kwargs.get("custom_hook_policy") or policies.CustomHookPolicy(**kwargs) self.redirect_policy = kwargs.get("redirect_policy") or policies.RedirectPolicy(**kwargs) self.authentication_policy = kwargs.get("authentication_policy")

47.333333

108

0.686827

cc7b1f0eac367bcd0aba12f3acc8c89875634cf6

10,655

py

Python

madrona/kmlapp/views.py

movermeyer/madrona

fcdced0a03408754b88a3d88f416e04d500c32d4

[ "BSD-3-Clause" ]

9

2015-03-09T11:04:21.000Z

2022-01-16T09:45:36.000Z

madrona/kmlapp/views.py

movermeyer/madrona

fcdced0a03408754b88a3d88f416e04d500c32d4

[ "BSD-3-Clause" ]

1

2020-04-24T14:38:43.000Z

madrona/kmlapp/views.py

movermeyer/madrona

fcdced0a03408754b88a3d88f416e04d500c32d4

[ "BSD-3-Clause" ]

2

2016-12-06T15:31:35.000Z

2018-03-04T20:04:44.000Z

from django.shortcuts import render_to_response from django.contrib.auth.models import * from django.template.loader import get_template from django.template import Context from django.http import HttpResponse, Http404 from madrona.common import default_mimetypes as mimetypes from madrona.common import utils from django.http import Http404 from madrona.common.utils import load_session, get_logger from django.contrib.gis.db import models from django.core.exceptions import FieldError from django.conf import settings from django.contrib.contenttypes.models import ContentType from madrona.features import get_feature_models, get_collection_models, get_feature_by_uid from madrona.features.models import FeatureCollection, Feature try: set except NameError: from sets import Set as set log = get_logger() def get_styles(features, collections, links=True): """ Based on which features and collection are provided, the styles for all features are determined here """ models = [] models.extend([f.kml_style for f in features]) models.extend([c.kml_style for c in collections]) if not links: # Collections will be represented by Folders, not NetworkLinks # So every feature n the entire tree will be in this KML Doc # We need to recurse down to determine what's in there for c in collections: children = c.feature_set(recurse=True) models.extend([child.kml_style for child in children]) unique_set = set(models) return list(unique_set) def get_user_data(user): """ Organizes user's Features and FeatureCollections. Only returns objects owned by user, not shared Returns only the features/collections at the top level, nested child features will be handled later through recursive calls to feature_set. """ toplevel_features = [] toplevel_collections = [] for fmodel in get_feature_models(): unattached = list(fmodel.objects.filter(user=user, content_type=None, object_id=None)) toplevel_features.extend(unattached) for cmodel in get_collection_models(): collections_top = list(cmodel.objects.filter(user=user, content_type=None, object_id=None)) toplevel_collections.extend(collections_top) return toplevel_features, toplevel_collections def get_data_for_feature(user, uid): try: f = get_feature_by_uid(uid) except: return False, HttpResponse("Feature %s does not exist" % uid, status=404) viewable, response = f.is_viewable(user) if not viewable: return viewable, response features = [] collections = [] if isinstance(f, FeatureCollection): obj_id = f.pk ct = ContentType.objects.get_for_model(f.__class__) for fmodel in get_feature_models(): unattached = list(fmodel.objects.filter(content_type=ct,object_id=obj_id)) features.extend(unattached) for cmodel in get_collection_models(): collections_top = list(cmodel.objects.filter(content_type=ct,object_id=obj_id)) collections.extend(collections_top) elif isinstance(f, Feature): features.append(f) return features, collections def get_public_data(): """ No login necessary, everyone sees these Public groups defined in settings.SHARING_TO_PUBLIC_GROUPS """ from django.conf import settings public_groups = Group.objects.filter(name__in=settings.SHARING_TO_PUBLIC_GROUPS) features = [] collections = [] for fmodel in get_feature_models(): unattached = list(fmodel.objects.filter(sharing_groups__in=public_groups)) features.extend(unattached) for cmodel in get_collection_models(): collections_top = list(cmodel.objects.filter(sharing_groups__in=public_groups)) collections.extend(collections_top) return features, collections def get_shared_data(shareuser, sharegroup, user): sg = Group.objects.get(pk=sharegroup) su = User.objects.get(pk=shareuser) features = [] collections = [] for fmodel in get_feature_models(): # Find top level features shared with user # top-level == not belonging to any collections # have to use content_type and object_id fields to determine unattached = list( fmodel.objects.shared_with_user(user,filter_groups=[sg]) .filter(user=su, content_type=None,object_id=None) ) features.extend(unattached) for cmodel in get_collection_models(): collections_top = list( cmodel.objects.shared_with_user(user,filter_groups=[sg]) .filter(user=su, content_type=None,object_id=None) ) collections.extend(collections_top) return features, collections def create_kmz(kml, zippath): """ Given a KML string and a "/" seperated path like "FOLDERNAME/doc.kml", creates a zipped KMZ archive buffer that can be written directly to a django response object """ import tempfile from cStringIO import StringIO import zipfile # write out the kml to tempfile #The Problem: for Windows, we need to close the file before we can access it again below (via zipout.write) # this caused a Permissions Error when running from the local dev server (on Windows) # as Windows considered the unclosed file to already be in use (and therefore unaccessible) #The Solution: adding 'delete=False' to tempfile.NamedTemporaryFiles for developing environments using Python 2.6(sf 2-16-10) # this will only happen if the user is using Python 2.6, previous versions of Python will treat the code as it was # (this delete parameter isn't available until python 2.6) #if the development environment is using 2.5 or earlier, then the temp file will still be closed via kmlfile.close() #if the development environment is using 2.6 then the temporary file is deleted manually via os.unlink(kmlfile.name) (see below) #This was reported (and perhaps more fully explained) in Issue 263 python26 = True try: kmlfile = tempfile.NamedTemporaryFile(delete=False) except: kmlfile = tempfile.NamedTemporaryFile() python26 = False kmlfile.write(kml.encode('utf-8')) kmlfile.flush() if python26: kmlfile.close() # zip it up into a kmz kmzbuffer = StringIO() zipout = zipfile.ZipFile(kmzbuffer,'w',zipfile.ZIP_DEFLATED) zipout.write(kmlfile.name, zippath.encode('ascii')) zipout.close() # close out the tempfile if python26: import os os.unlink(kmlfile.name) else: kmlfile.close() # grab the content of the stringIO buffer kmz = kmzbuffer.getvalue() # close out the stringIO buffer kmzbuffer.close() return kmz from django.views.decorators.cache import cache_control @cache_control(no_cache=True) def create_kml(request, input_username=None, input_uid=None, input_shareuser=None, input_sharegroup=None, links=False, kmz=False, session_key='0'): """ Returns a KML/KMZ containing Feautures/FeatureCollections owned by user """ load_session(request, session_key) user = request.user if input_username and user.username != input_username: log.warn("Input username from URL is %r but request.user.username is %r" % (input_username, user.username)) return HttpResponse('Access denied', status=401) if input_username: features, collections = get_user_data(user) elif input_uid: features, collections = get_data_for_feature(user, input_uid) elif input_shareuser and input_sharegroup: features, collections = get_shared_data(input_shareuser, input_sharegroup, user) else: raise Http404 if not features and isinstance(collections, HttpResponse): return collections # We got an http error going on styles = get_styles(features,collections,links) t = get_template('kmlapp/myshapes.kml') context = Context({ 'user': user, 'features': features, 'collections': collections, 'use_network_links': links, 'request_path': request.path, 'styles': styles, 'session_key': session_key, 'shareuser': input_shareuser, 'sharegroup': input_sharegroup, 'feature_id': input_uid, }) kml = t.render(context) mime = mimetypes.KML if kmz: mime = mimetypes.KMZ kml = create_kmz(kml, 'mm/doc.kml') response = HttpResponse(kml, mimetype=mime) response['Content-Disposition'] = 'attachment' return response @cache_control(no_cache=True) def create_shared_kml(request, input_username, kmz=False, session_key='0'): """ Returns a KML/KMZ containing shared MPAs (organized into folders by groups and users who have shared them) """ load_session(request, session_key) user = request.user if input_username and user.username != input_username: return HttpResponse('Access denied', status=401) from madrona.features import groups_users_sharing_with sharing_with = groups_users_sharing_with(user) t = get_template('kmlapp/shared.kml') kml = t.render(Context({'user': request.user, 'groups_users': sharing_with, 'request_path': request.path, 'session_key': session_key})) mime = mimetypes.KML if kmz: mime = mimetypes.KMZ kml = create_kmz(kml, 'mm/doc.kml') response = HttpResponse(kml, mimetype=mime) response['Content-Disposition'] = 'attachment' return response def shared_public(request, kmz=False, session_key='0'): """ Shows all publically shared arrays Must be shared with a special set of public groups defined in settings.SHARING_TO_PUBLIC_GROUPS """ load_session(request, session_key) user = request.user features, collections = get_public_data() styles = get_styles(features,collections) # determine content types for sharing t = get_template('kmlapp/public.kml') kml = t.render(Context({'loggedin_user': request.user, 'user': request.user, 'features': features, 'collections': collections, 'styles': styles, 'use_network_links': True, 'request_path': request.path, 'session_key': session_key})) mime = mimetypes.KML if kmz: mime = mimetypes.KMZ kml = create_kmz(kml, 'mm/doc.kml') response = HttpResponse(kml, mimetype=mime) response['Content-Disposition'] = 'attachment' return response

37.125436

139

0.694322

c3da59bec46bde629a6b2bc0722b66276cc27a3f

22,405

py

Python

UMLRT2Kiltera_MM/transformation_reduced/Himesis/HState2HProcDef.py

levilucio/SyVOLT

7526ec794d21565e3efcc925a7b08ae8db27d46a

[ "MIT" ]

3

2017-06-02T19:26:27.000Z

2021-06-14T04:25:45.000Z

UMLRT2Kiltera_MM/transformation_reduced/Himesis/HState2HProcDef.py

levilucio/SyVOLT

7526ec794d21565e3efcc925a7b08ae8db27d46a

[ "MIT" ]

8

2016-08-24T07:04:07.000Z

2017-05-26T16:22:47.000Z

UMLRT2Kiltera_MM/transformation_reduced/Himesis/HState2HProcDef.py

levilucio/SyVOLT

7526ec794d21565e3efcc925a7b08ae8db27d46a

[ "MIT" ]

1

2019-10-31T06:00:23.000Z

from core.himesis import Himesis import cPickle as pickle from uuid import UUID class HState2HProcDef(Himesis): def __init__(self): """ Creates the himesis graph representing the AToM3 model HState2HProcDef. """ # Flag this instance as compiled now self.is_compiled = True super(HState2HProcDef, self).__init__(name='HState2HProcDef', num_nodes=120, edges=[]) # Add the edges self.add_edges([(1, 92), (92, 8), (8, 93), (93, 18), (8, 94), (94, 19), (8, 95), (95, 20), (8, 96), (96, 11), (11, 97), (97, 15), (15, 98), (98, 7), (11, 99), (99, 16), (16, 100), (100, 9), (9, 101), (101, 13), (9, 102), (102, 12), (12, 103), (103, 17), (17, 104), (104, 14), (44, 32), (32, 80), (45, 33), (33, 81), (46, 34), (34, 82), (47, 35), (35, 83), (48, 36), (36, 84), (49, 37), (37, 85), (50, 38), (38, 86), (51, 39), (39, 87), (52, 40), (40, 88), (53, 41), (41, 89), (54, 42), (42, 90), (55, 43), (43, 91), (8, 21), (21, 57), (18, 22), (22, 58), (19, 23), (23, 59), (20, 24), (24, 60), (15, 25), (25, 61), (16, 26), (26, 62), (13, 27), (27, 63), (17, 28), (28, 64), (14, 29), (29, 65), (1, 30), (30, 66), (11, 31), (31, 67), (5, 0), (0, 105), (0, 106), (0, 107), (0, 108), (0, 109), (0, 110), (0, 111), (0, 112), (0, 113), (0, 114), (0, 115), (0, 116), (0, 117), (0, 118), (0, 119), (105, 1), (110, 1), (1, 10), (6, 2), (2, 4), (4, 3), (3, 56), (10, 4), (6, 5), (113, 11), (117, 12), (116, 13), (119, 14), (44, 68), (45, 69), (46, 70), (47, 71), (48, 72), (49, 73), (50, 74), (51, 75), (52, 76), (53, 77), (54, 78), (55, 79), (111, 7), (106, 8), (107, 18), (108, 19), (109, 20), (112, 15), (114, 16), (115, 9), (118, 17), (68, 56), (69, 57), (70, 58), (71, 59), (72, 60), (73, 61), (74, 62), (75, 63), (76, 64), (77, 65), (78, 66), (79, 67)]) # Set the graph attributes self["mm__"] = pickle.loads("""(lp1 S'UMLRT2Kiltera_MM' p2 a.""") self["name"] = """State2HProcDef""" self["GUID__"] = UUID('c9b98a9f-3ced-495d-abed-18098af469ce') # Set the node attributes self.vs[0]["mm__"] = """ApplyModel""" self.vs[0]["GUID__"] = UUID('c4ebe780-4dda-4193-abfe-1cf60931cf95') self.vs[1]["name"] = """localdef1""" self.vs[1]["classtype"] = """LocalDef""" self.vs[1]["mm__"] = """LocalDef""" self.vs[1]["cardinality"] = """1""" self.vs[1]["GUID__"] = UUID('957094bb-7101-47b7-8704-df11182d4402') self.vs[2]["mm__"] = """match_contains""" self.vs[2]["GUID__"] = UUID('f614a131-19c7-4f60-9764-94c946e58207') self.vs[3]["mm__"] = """hasAttribute_S""" self.vs[3]["GUID__"] = UUID('b8863e77-6af6-4271-83cf-f837da316564') self.vs[4]["name"] = """state1""" self.vs[4]["classtype"] = """State""" self.vs[4]["mm__"] = """State""" self.vs[4]["cardinality"] = """+""" self.vs[4]["GUID__"] = UUID('264c4013-8001-4c3b-87e6-e9e679dd7843') self.vs[5]["mm__"] = """paired_with""" self.vs[5]["GUID__"] = UUID('527cf59b-09cc-4ab5-8c6e-c91f1ec6c212') self.vs[6]["mm__"] = """MatchModel""" self.vs[6]["GUID__"] = UUID('c565c79c-1e9a-4b86-b3c1-d25278fbd0c4') self.vs[7]["name"] = """null1""" self.vs[7]["classtype"] = """Null""" self.vs[7]["mm__"] = """Null""" self.vs[7]["cardinality"] = """1""" self.vs[7]["GUID__"] = UUID('88e3915a-a880-4a3d-ad55-c9b5e288cd07') self.vs[8]["name"] = """procdef1""" self.vs[8]["classtype"] = """ProcDef""" self.vs[8]["mm__"] = """ProcDef""" self.vs[8]["cardinality"] = """1""" self.vs[8]["GUID__"] = UUID('7e6e52ca-30de-4478-81e9-7a13d925b974') self.vs[9]["name"] = """seq1""" self.vs[9]["classtype"] = """Seq""" self.vs[9]["mm__"] = """Seq""" self.vs[9]["cardinality"] = """1""" self.vs[9]["GUID__"] = UUID('45ca5c92-26e5-4b34-a335-80845327541d') self.vs[10]["type"] = """ruleDef""" self.vs[10]["mm__"] = """backward_link""" self.vs[10]["GUID__"] = UUID('1aca5e8b-b626-4a91-beed-410f6fee460a') self.vs[11]["name"] = """listen1""" self.vs[11]["classtype"] = """Listen""" self.vs[11]["mm__"] = """Listen""" self.vs[11]["cardinality"] = """1""" self.vs[11]["GUID__"] = UUID('cee60ea3-e4d6-4411-b496-1a33aa9835a5') self.vs[12]["name"] = """listen2""" self.vs[12]["classtype"] = """Listen""" self.vs[12]["mm__"] = """Listen""" self.vs[12]["cardinality"] = """1""" self.vs[12]["GUID__"] = UUID('acda3dff-f44d-4a8d-b6dc-b1ff936a1089') self.vs[13]["name"] = """triggerT1""" self.vs[13]["classtype"] = """Trigger_T""" self.vs[13]["mm__"] = """Trigger_T""" self.vs[13]["cardinality"] = """1""" self.vs[13]["GUID__"] = UUID('eb493f3e-981f-4fb5-9ad3-b1a1fb5782b3') self.vs[14]["name"] = """triggerT2""" self.vs[14]["classtype"] = """Trigger_T""" self.vs[14]["mm__"] = """Trigger_T""" self.vs[14]["cardinality"] = """1""" self.vs[14]["GUID__"] = UUID('f8618612-2d1d-48f4-baaf-d0aa71e0b095') self.vs[15]["name"] = """listenbranch1""" self.vs[15]["classtype"] = """ListenBranch""" self.vs[15]["mm__"] = """ListenBranch""" self.vs[15]["cardinality"] = """1""" self.vs[15]["GUID__"] = UUID('c1c067f2-5eb4-4ee5-96be-49a363368dc5') self.vs[16]["name"] = """listenbranch2""" self.vs[16]["classtype"] = """ListenBranch""" self.vs[16]["mm__"] = """ListenBranch""" self.vs[16]["cardinality"] = """1""" self.vs[16]["GUID__"] = UUID('4754c4f4-fdeb-49d0-b521-6ef5f2ae3f4c') self.vs[17]["name"] = """listenbranch3""" self.vs[17]["classtype"] = """ListenBranch""" self.vs[17]["mm__"] = """ListenBranch""" self.vs[17]["cardinality"] = """1""" self.vs[17]["GUID__"] = UUID('07ec92c0-7650-4a8e-98f8-1dfa5f3ce055') self.vs[18]["name"] = """name1""" self.vs[18]["classtype"] = """Name""" self.vs[18]["mm__"] = """Name""" self.vs[18]["cardinality"] = """1""" self.vs[18]["GUID__"] = UUID('1d7083d4-f529-4d56-9f67-072c911be12d') self.vs[19]["name"] = """name2""" self.vs[19]["classtype"] = """Name""" self.vs[19]["mm__"] = """Name""" self.vs[19]["cardinality"] = """1""" self.vs[19]["GUID__"] = UUID('9165afaa-5baa-4ed4-8cf8-c719572bdbfe') self.vs[20]["name"] = """name3""" self.vs[20]["classtype"] = """Name""" self.vs[20]["mm__"] = """Name""" self.vs[20]["cardinality"] = """1""" self.vs[20]["GUID__"] = UUID('213bf0fe-e6fc-49ef-bc93-ca2d477eb185') self.vs[21]["mm__"] = """hasAttribute_T""" self.vs[21]["GUID__"] = UUID('648df2ee-ee8b-4a4e-a23e-d5f99c018d51') self.vs[22]["mm__"] = """hasAttribute_T""" self.vs[22]["GUID__"] = UUID('f9a93cd2-8c30-4446-971f-651081f12dab') self.vs[23]["mm__"] = """hasAttribute_T""" self.vs[23]["GUID__"] = UUID('bcfe2e43-743d-43be-a9ce-6958972c8bb2') self.vs[24]["mm__"] = """hasAttribute_T""" self.vs[24]["GUID__"] = UUID('d40d572c-fd11-4838-991f-1fb2e35206c3') self.vs[25]["mm__"] = """hasAttribute_T""" self.vs[25]["GUID__"] = UUID('77897619-f415-4fe7-a98c-ff1e3360555e') self.vs[26]["mm__"] = """hasAttribute_T""" self.vs[26]["GUID__"] = UUID('dc74e4de-f6ff-4571-970b-9756e71f0a06') self.vs[27]["mm__"] = """hasAttribute_T""" self.vs[27]["GUID__"] = UUID('991b204c-ef20-49b1-bdad-c7339a3ff8b4') self.vs[28]["mm__"] = """hasAttribute_T""" self.vs[28]["GUID__"] = UUID('0cbb3783-fa62-4301-9e9b-aa21e95ac550') self.vs[29]["mm__"] = """hasAttribute_T""" self.vs[29]["GUID__"] = UUID('97a1e512-cc60-4871-8049-3fee09bda745') self.vs[30]["mm__"] = """hasAttribute_T""" self.vs[30]["GUID__"] = UUID('567e7d49-da49-412e-9f47-5d41023f858f') self.vs[31]["mm__"] = """hasAttribute_T""" self.vs[31]["GUID__"] = UUID('a9f55267-1d6b-4e31-a58b-22f0ffb4c15f') self.vs[32]["mm__"] = """rightExpr""" self.vs[32]["GUID__"] = UUID('7d2944db-08b2-4f20-9a99-71456f7aff90') self.vs[33]["mm__"] = """rightExpr""" self.vs[33]["GUID__"] = UUID('9183c4ab-dc4f-4aec-8df1-ddf1f15bbec2') self.vs[34]["mm__"] = """rightExpr""" self.vs[34]["GUID__"] = UUID('75d020a5-7185-4d4f-bfa1-71b33680fcc3') self.vs[35]["mm__"] = """rightExpr""" self.vs[35]["GUID__"] = UUID('98e4ca91-0063-4cc8-8755-1ab8d1da8469') self.vs[36]["mm__"] = """rightExpr""" self.vs[36]["GUID__"] = UUID('17956f1f-e2a6-4765-bd63-e6202c8315aa') self.vs[37]["mm__"] = """rightExpr""" self.vs[37]["GUID__"] = UUID('146e89a9-bf39-4133-bac1-5a4b4f53e452') self.vs[38]["mm__"] = """rightExpr""" self.vs[38]["GUID__"] = UUID('a670605a-a946-4662-8d10-97034bc6e312') self.vs[39]["mm__"] = """rightExpr""" self.vs[39]["GUID__"] = UUID('ec62d436-db5a-43f1-bd06-1c077b66fb3b') self.vs[40]["mm__"] = """rightExpr""" self.vs[40]["GUID__"] = UUID('3cd35e10-48b2-4bf7-9b8c-414d07a790e0') self.vs[41]["mm__"] = """rightExpr""" self.vs[41]["GUID__"] = UUID('13d6bce3-e399-4fb9-a40a-be6bdf18e0dd') self.vs[42]["mm__"] = """rightExpr""" self.vs[42]["GUID__"] = UUID('46c0f6d2-0745-44fb-aee5-c45a15d2b332') self.vs[43]["mm__"] = """rightExpr""" self.vs[43]["GUID__"] = UUID('621229c7-3603-42d8-9469-feefecd00066') self.vs[44]["name"] = """eq1""" self.vs[44]["mm__"] = """Equation""" self.vs[44]["GUID__"] = UUID('ef6f863f-d1f8-4c63-b68e-77dcdb16afe0') self.vs[45]["name"] = """eq2""" self.vs[45]["mm__"] = """Equation""" self.vs[45]["GUID__"] = UUID('5bdc5b24-b1f4-4698-8032-00de143e9b8e') self.vs[46]["name"] = """eq3""" self.vs[46]["mm__"] = """Equation""" self.vs[46]["GUID__"] = UUID('343fdda6-5b2b-4ac7-89ac-48ecbf57a213') self.vs[47]["name"] = """eq4""" self.vs[47]["mm__"] = """Equation""" self.vs[47]["GUID__"] = UUID('87c48d09-99c9-4c82-8f08-2c013ba1c411') self.vs[48]["name"] = """eq5""" self.vs[48]["mm__"] = """Equation""" self.vs[48]["GUID__"] = UUID('713657b3-9801-42f5-ab0e-f80eda9bac1f') self.vs[49]["name"] = """eq6""" self.vs[49]["mm__"] = """Equation""" self.vs[49]["GUID__"] = UUID('c51e8c57-4222-4e36-abcd-ce1730f50c97') self.vs[50]["name"] = """eq7""" self.vs[50]["mm__"] = """Equation""" self.vs[50]["GUID__"] = UUID('282b9799-7ff0-44f1-ab31-26a681b4a18f') self.vs[51]["name"] = """eq8""" self.vs[51]["mm__"] = """Equation""" self.vs[51]["GUID__"] = UUID('d9c9e3a3-7d1b-4344-8213-462a9642f55c') self.vs[52]["name"] = """eq9""" self.vs[52]["mm__"] = """Equation""" self.vs[52]["GUID__"] = UUID('af60f366-fc92-430a-a3f8-783660f63559') self.vs[53]["name"] = """eq10""" self.vs[53]["mm__"] = """Equation""" self.vs[53]["GUID__"] = UUID('c2d09128-0554-4a30-85ef-dfba8835a797') self.vs[54]["name"] = """eq11""" self.vs[54]["mm__"] = """Equation""" self.vs[54]["GUID__"] = UUID('f6e28d1b-728d-4d9b-b0eb-6f3f8461765a') self.vs[55]["name"] = """eq12""" self.vs[55]["mm__"] = """Equation""" self.vs[55]["GUID__"] = UUID('a99fd112-d7e2-412d-9756-9d2a0a540a4c') self.vs[56]["name"] = """isComposite""" self.vs[56]["mm__"] = """Attribute""" self.vs[56]["Type"] = """'Bool'""" self.vs[56]["GUID__"] = UUID('d4ce77e3-1757-4ab4-8e69-1d61cbbd2e1c') self.vs[57]["name"] = """name""" self.vs[57]["mm__"] = """Attribute""" self.vs[57]["Type"] = """'String'""" self.vs[57]["GUID__"] = UUID('875bd734-fa6d-4393-a55d-75672f36e41a') self.vs[58]["name"] = """literal""" self.vs[58]["mm__"] = """Attribute""" self.vs[58]["Type"] = """'String'""" self.vs[58]["GUID__"] = UUID('3212c20d-117f-4f0a-a881-90948fa722c5') self.vs[59]["name"] = """literal""" self.vs[59]["mm__"] = """Attribute""" self.vs[59]["Type"] = """'String'""" self.vs[59]["GUID__"] = UUID('3c0037c7-34a2-419d-98fd-57d2af95dbf0') self.vs[60]["name"] = """literal""" self.vs[60]["mm__"] = """Attribute""" self.vs[60]["Type"] = """'String'""" self.vs[60]["GUID__"] = UUID('6cf1ad01-09b8-4800-abc0-93675732f2ea') self.vs[61]["name"] = """channel""" self.vs[61]["mm__"] = """Attribute""" self.vs[61]["Type"] = """'String'""" self.vs[61]["GUID__"] = UUID('cdf2170c-3fb1-4eec-9850-e0b4d02c7f49') self.vs[62]["name"] = """channel""" self.vs[62]["mm__"] = """Attribute""" self.vs[62]["Type"] = """'String'""" self.vs[62]["GUID__"] = UUID('7c6d1eef-888e-4a0b-bd7f-be84a81a9f5b') self.vs[63]["name"] = """channel""" self.vs[63]["mm__"] = """Attribute""" self.vs[63]["Type"] = """'String'""" self.vs[63]["GUID__"] = UUID('daf9618a-f9d4-4b03-a116-eee67554619d') self.vs[64]["name"] = """channel""" self.vs[64]["mm__"] = """Attribute""" self.vs[64]["Type"] = """'String'""" self.vs[64]["GUID__"] = UUID('0f4f6497-dab1-4daa-b2aa-2b516f11782d') self.vs[65]["name"] = """channel""" self.vs[65]["mm__"] = """Attribute""" self.vs[65]["Type"] = """'String'""" self.vs[65]["GUID__"] = UUID('59d0854e-046b-4eed-82f0-3e01c41d7364') self.vs[66]["name"] = """pivot""" self.vs[66]["mm__"] = """Attribute""" self.vs[66]["Type"] = """'String'""" self.vs[66]["GUID__"] = UUID('d35e7fa8-4dca-474b-a604-3d0a6bf475c4') self.vs[67]["name"] = """pivot""" self.vs[67]["mm__"] = """Attribute""" self.vs[67]["Type"] = """'String'""" self.vs[67]["GUID__"] = UUID('e1cecb58-0ba5-4e92-847d-35bd83e506fb') self.vs[68]["mm__"] = """leftExpr""" self.vs[68]["GUID__"] = UUID('490c6edb-3532-4611-8e13-ffdd400a1171') self.vs[69]["mm__"] = """leftExpr""" self.vs[69]["GUID__"] = UUID('99ea6c88-3e7c-4e1a-a145-1d803265c563') self.vs[70]["mm__"] = """leftExpr""" self.vs[70]["GUID__"] = UUID('6ad9a534-e0d0-4918-83eb-fbb1fb42aab7') self.vs[71]["mm__"] = """leftExpr""" self.vs[71]["GUID__"] = UUID('65fabf71-cf72-43a5-967a-6c65ba98bf11') self.vs[72]["mm__"] = """leftExpr""" self.vs[72]["GUID__"] = UUID('acb4af4d-f61e-4ef6-b07e-7c73eb612d13') self.vs[73]["mm__"] = """leftExpr""" self.vs[73]["GUID__"] = UUID('77356899-8723-4d36-b4c6-204c0fd53ec2') self.vs[74]["mm__"] = """leftExpr""" self.vs[74]["GUID__"] = UUID('12a6616c-08fc-4302-97fe-f0bab6232ef3') self.vs[75]["mm__"] = """leftExpr""" self.vs[75]["GUID__"] = UUID('84144e3e-d49d-4610-ac70-fc3ba7a78017') self.vs[76]["mm__"] = """leftExpr""" self.vs[76]["GUID__"] = UUID('65a2f57d-f505-4aed-b901-daa4cb8c858e') self.vs[77]["mm__"] = """leftExpr""" self.vs[77]["GUID__"] = UUID('f2fdf0c4-158f-48e9-8217-400a0443fc3f') self.vs[78]["mm__"] = """leftExpr""" self.vs[78]["GUID__"] = UUID('73acaa3b-8499-4e53-9136-0a9f9e84ca79') self.vs[79]["mm__"] = """leftExpr""" self.vs[79]["GUID__"] = UUID('b6639c41-1c47-495c-997e-f0ec5c99544d') self.vs[80]["name"] = """true""" self.vs[80]["mm__"] = """Constant""" self.vs[80]["Type"] = """'Bool'""" self.vs[80]["GUID__"] = UUID('4bf7f87d-6220-4da2-aa93-10e1fa0b17eb') self.vs[81]["name"] = """H""" self.vs[81]["mm__"] = """Constant""" self.vs[81]["Type"] = """'String'""" self.vs[81]["GUID__"] = UUID('37964822-e8ba-4636-939e-16adab6e5dc5') self.vs[82]["name"] = """exit_in""" self.vs[82]["mm__"] = """Constant""" self.vs[82]["Type"] = """'String'""" self.vs[82]["GUID__"] = UUID('8a925018-5dc0-4bae-a7b5-c4e62a582197') self.vs[83]["name"] = """exack_in""" self.vs[83]["mm__"] = """Constant""" self.vs[83]["Type"] = """'String'""" self.vs[83]["GUID__"] = UUID('0224dbc3-96e1-4fd2-88d6-c451fd4d4d04') self.vs[84]["name"] = """sh_in""" self.vs[84]["mm__"] = """Constant""" self.vs[84]["Type"] = """'String'""" self.vs[84]["GUID__"] = UUID('2e131930-44c0-4b7a-80c7-490f08b98d24') self.vs[85]["name"] = """sh_in""" self.vs[85]["mm__"] = """Constant""" self.vs[85]["Type"] = """'String'""" self.vs[85]["GUID__"] = UUID('a020e333-8f08-4182-8e98-ff846a411f4d') self.vs[86]["name"] = """exit""" self.vs[86]["mm__"] = """Constant""" self.vs[86]["Type"] = """'String'""" self.vs[86]["GUID__"] = UUID('6bce0315-1b57-4513-8af3-8ecb89bdf5ef') self.vs[87]["name"] = """exit_in""" self.vs[87]["mm__"] = """Constant""" self.vs[87]["Type"] = """'String'""" self.vs[87]["GUID__"] = UUID('c12271ea-5c52-42af-944c-adcedf70278a') self.vs[88]["name"] = """exack_in""" self.vs[88]["mm__"] = """Constant""" self.vs[88]["Type"] = """'String'""" self.vs[88]["GUID__"] = UUID('4b7e1e82-2013-412f-927f-6b95f987c4b2') self.vs[89]["name"] = """exack""" self.vs[89]["mm__"] = """Constant""" self.vs[89]["Type"] = """'String'""" self.vs[89]["GUID__"] = UUID('42db34d0-e993-4400-aa6e-e3524fcdc93f') self.vs[90]["name"] = """localdefcompstate""" self.vs[90]["mm__"] = """Constant""" self.vs[90]["Type"] = """'String'""" self.vs[90]["GUID__"] = UUID('c0c414d3-71fc-4a6a-a96a-8ea77ff5c227') self.vs[91]["name"] = """listenhproc""" self.vs[91]["mm__"] = """Constant""" self.vs[91]["Type"] = """'String'""" self.vs[91]["GUID__"] = UUID('9c323a46-1015-4470-868d-ad6998f961e8') self.vs[92]["associationType"] = """def""" self.vs[92]["mm__"] = """directLink_T""" self.vs[92]["GUID__"] = UUID('b42713fe-166a-4615-bf49-c2d847cbcc66') self.vs[93]["associationType"] = """channelNames""" self.vs[93]["mm__"] = """directLink_T""" self.vs[93]["GUID__"] = UUID('1d36c993-397f-42dc-a20f-90823be162b4') self.vs[94]["associationType"] = """channelNames""" self.vs[94]["mm__"] = """directLink_T""" self.vs[94]["GUID__"] = UUID('8959bbfa-4c92-4cd3-a6f3-d2358a5c878c') self.vs[95]["associationType"] = """channelNames""" self.vs[95]["mm__"] = """directLink_T""" self.vs[95]["GUID__"] = UUID('1e0cb1f5-1a2a-45c4-ae55-bf431298d2f0') self.vs[96]["associationType"] = """p""" self.vs[96]["mm__"] = """directLink_T""" self.vs[96]["GUID__"] = UUID('92c264f5-453e-437e-8b88-c84115951f9e') self.vs[97]["associationType"] = """branches""" self.vs[97]["mm__"] = """directLink_T""" self.vs[97]["GUID__"] = UUID('46247179-13f3-4f56-938e-efad4ef8d0ce') self.vs[98]["associationType"] = """p""" self.vs[98]["mm__"] = """directLink_T""" self.vs[98]["GUID__"] = UUID('cf0e65c2-05ff-4e90-b6be-64e4a2a6bca4') self.vs[99]["associationType"] = """branches""" self.vs[99]["mm__"] = """directLink_T""" self.vs[99]["GUID__"] = UUID('2c67bb2b-2dc1-4941-9c6b-c8734694d0f8') self.vs[100]["associationType"] = """p""" self.vs[100]["mm__"] = """directLink_T""" self.vs[100]["GUID__"] = UUID('2df1aebd-c4fa-4934-95c6-81ef80096c37') self.vs[101]["associationType"] = """p""" self.vs[101]["mm__"] = """directLink_T""" self.vs[101]["GUID__"] = UUID('35a0a975-0ed3-4d31-8b24-6f5270e567f8') self.vs[102]["associationType"] = """p""" self.vs[102]["mm__"] = """directLink_T""" self.vs[102]["GUID__"] = UUID('b8a78e5b-1aaf-416c-be5d-67e07f4a3187') self.vs[103]["associationType"] = """branches""" self.vs[103]["mm__"] = """directLink_T""" self.vs[103]["GUID__"] = UUID('ca8630fd-f565-4e8c-a804-f9175932584f') self.vs[104]["associationType"] = """p""" self.vs[104]["mm__"] = """directLink_T""" self.vs[104]["GUID__"] = UUID('75b6dc51-42e4-4f0e-884f-fefe2d32a5b4') self.vs[105]["mm__"] = """apply_contains""" self.vs[105]["GUID__"] = UUID('fbee6762-5ede-4156-9562-1ee0e1ce4b7a') self.vs[106]["mm__"] = """apply_contains""" self.vs[106]["GUID__"] = UUID('4816109f-f72c-49dc-8596-5c423175ceb9') self.vs[107]["mm__"] = """apply_contains""" self.vs[107]["GUID__"] = UUID('a6277216-5bf1-47a1-9622-8c410bcac5d2') self.vs[108]["mm__"] = """apply_contains""" self.vs[108]["GUID__"] = UUID('7a7e4288-0836-4f12-9f13-a95cb48e9d04') self.vs[109]["mm__"] = """apply_contains""" self.vs[109]["GUID__"] = UUID('c64c3258-54aa-4718-9bf3-760c9fd7344a') self.vs[110]["mm__"] = """apply_contains""" self.vs[110]["GUID__"] = UUID('093b3597-0204-43f6-b05a-afaf7369748a') self.vs[111]["mm__"] = """apply_contains""" self.vs[111]["GUID__"] = UUID('433eaa35-d916-4230-ae95-4682955c1ed8') self.vs[112]["mm__"] = """apply_contains""" self.vs[112]["GUID__"] = UUID('3562a3ae-36a1-4aaa-8125-63b61fa43697') self.vs[113]["mm__"] = """apply_contains""" self.vs[113]["GUID__"] = UUID('aaefe157-c161-4519-a618-7d3e56d4aff8') self.vs[114]["mm__"] = """apply_contains""" self.vs[114]["GUID__"] = UUID('ef075067-1e3f-491c-91e7-308075a1a390') self.vs[115]["mm__"] = """apply_contains""" self.vs[115]["GUID__"] = UUID('4334ab4a-0df1-4295-a5b7-ee804e906f5e') self.vs[116]["mm__"] = """apply_contains""" self.vs[116]["GUID__"] = UUID('27ece724-de6d-4186-9d6b-2c8fb9d71089') self.vs[117]["mm__"] = """apply_contains""" self.vs[117]["GUID__"] = UUID('67c0e949-6687-4181-b5b6-2525a3d2f20c') self.vs[118]["mm__"] = """apply_contains""" self.vs[118]["GUID__"] = UUID('209d41f5-5e71-4992-9b2b-9459bade7646') self.vs[119]["mm__"] = """apply_contains""" self.vs[119]["GUID__"] = UUID('93a7ddd9-b11d-4bd2-8785-eec038f2ef86')

57.744845

1,359

0.537826

93238639fd2963f368d9ec70b1b0dcaf69edac4a

1,736

py

Python

const.py

youssefhoummad/tetris

beda6947bbf734672da453c2404e6946f44759b9

[ "MIT" ]

null

const.py

youssefhoummad/tetris

beda6947bbf734672da453c2404e6946f44759b9

[ "MIT" ]

null

const.py

youssefhoummad/tetris

beda6947bbf734672da453c2404e6946f44759b9

[ "MIT" ]

null

B = BLOCK_SIZE = 30 SURFACE_WIDTH = 300 SURFACE_HEIGHT = 600 S = [['.....', '.....', '..00.', '.00..', '.....'], ['.....', '..0..', '..00.', '...0.', '.....' ]] Z = [['.....', '.....', '.00..', '..00.', '.....'], ['.....', '..,0.', '..00.', '..0..', '.....' ]] I = [['.....', '..0..', '..0..', '..0..', '.....'], ['.....', '.....', '.000.', '.....', '.....' ]] O = [['.....', '.....', '.00..', '.00..', '.....']] J = [['.....', '.....', '.0...', '.000.', '.....'], ['.....', '..00.', '..0..', '..0..', '.....' ], ['.....', '.000.', '...0.', '.....', '.....' ], ['.....', '..0..', '..0..', '.00..', '.....' ],] L = [['.....', '...0.', '.000.', '.....', '.....'], ['.....', '..0..', '..0..', '..00.', '.....'], ['.....', '.....', '.000.', '.0...', '.....'], ['.....', '.00..', '..0..', '..0..', '.....']] T = [['.....', '..0..', '.000.', '.....', '.....'], ['.....', '..0..', '..00.', '..0..', '.....'], ['.....', '.....', '.000.', '..0..', '.....'], ['.....', '..0..', '.00..', '..0..', '.....']] SHAPES = [S, Z, I, O, L, T] # red green blue pink gray orange SHAPES_FILL = ['#f44336', '#4CAF50', '#2196F3', '#F50057', '#607D8B', '#FF9800']

16.533333

80

0.12212

41aa3c7403731d9854e1166bf116715b2fe4c459

10,268

py

Python

generate.py

gaoxiao/handwriting-generation

5e684c1adee7fb83be309f9d9c001b559b567666

[ "MIT" ]

null

generate.py

gaoxiao/handwriting-generation

5e684c1adee7fb83be309f9d9c001b559b567666

[ "MIT" ]

null

generate.py

gaoxiao/handwriting-generation

5e684c1adee7fb83be309f9d9c001b559b567666

[ "MIT" ]

null

import os import pickle import argparse import numpy as np import tensorflow as tf import matplotlib.pyplot as plt import matplotlib.cm as cm import matplotlib.mlab as mlab from matplotlib import animation import seaborn from collections import namedtuple parser = argparse.ArgumentParser() parser.add_argument('--model', dest='model_path', type=str, default=os.path.join('pretrained', 'model-29')) parser.add_argument('--text', dest='text', type=str, default=None) parser.add_argument('--style', dest='style', type=int, default=None) parser.add_argument('--bias', dest='bias', type=float, default=1.) parser.add_argument('--force', dest='force', action='store_true', default=False) parser.add_argument('--animation', dest='animation', action='store_true', default=False) parser.add_argument('--noinfo', dest='info', action='store_false', default=False) parser.add_argument('--save', dest='save', type=str, default=None) args = parser.parse_args() def sample(e, mu1, mu2, std1, std2, rho): cov = np.array([[std1 * std1, std1 * std2 * rho], [std1 * std2 * rho, std2 * std2]]) mean = np.array([mu1, mu2]) x, y = np.random.multivariate_normal(mean, cov) end = np.random.binomial(1, e) return np.array([x, y, end]) def split_strokes(points): points = np.array(points) strokes = [] b = 0 for e in range(len(points)): if points[e, 2] == 1.: strokes += [points[b: e + 1, :2].copy()] b = e + 1 return strokes def cumsum(points): sums = np.cumsum(points[:, :2], axis=0) return np.concatenate([sums, points[:, 2:]], axis=1) def sample_text(sess, args_text, translation, style=None): fields = ['coordinates', 'sequence', 'bias', 'e', 'pi', 'mu1', 'mu2', 'std1', 'std2', 'rho', 'window', 'kappa', 'phi', 'finish', 'zero_states'] vs = namedtuple('Params', fields)( *[tf.get_collection(name)[0] for name in fields] ) text = np.array([translation.get(c, 0) for c in args_text]) coord = np.array([0., 0., 1.]) coords = [coord] # Prime the model with the author style if requested prime_len, style_len = 0, 0 if style is not None: # Priming consist of joining to a real pen-position and character sequences the synthetic sequence to generate # and set the synthetic pen-position to a null vector (the positions are sampled from the MDN) style_coords, style_text = style prime_len = len(style_coords) style_len = len(style_text) prime_coords = list(style_coords) coord = prime_coords[0] # Set the first pen stroke as the first element to process text = np.r_[style_text, text] # concatenate on 1 axis the prime text + synthesis character sequence sequence_prime = np.eye(len(translation), dtype=np.float32)[style_text] sequence_prime = np.expand_dims(np.concatenate([sequence_prime, np.zeros((1, len(translation)))]), axis=0) sequence = np.eye(len(translation), dtype=np.float32)[text] sequence = np.expand_dims(np.concatenate([sequence, np.zeros((1, len(translation)))]), axis=0) phi_data, window_data, kappa_data, stroke_data = [], [], [], [] sess.run(vs.zero_states) sequence_len = len(args_text) + style_len for s in range(1, 60 * sequence_len + 1): is_priming = s < prime_len print('\r[{:5d}] sampling... {}'.format(s, 'priming' if is_priming else 'synthesis'), end='') e, pi, mu1, mu2, std1, std2, rho, \ finish, phi, window, kappa = sess.run([vs.e, vs.pi, vs.mu1, vs.mu2, vs.std1, vs.std2, vs.rho, vs.finish, vs.phi, vs.window, vs.kappa], feed_dict={ vs.coordinates: coord[None, None, ...], vs.sequence: sequence_prime if is_priming else sequence, vs.bias: args.bias }) if is_priming: # Use the real coordinate if priming coord = prime_coords[s] else: # Synthesis mode phi_data += [phi[0, :]] window_data += [window[0, :]] kappa_data += [kappa[0, :]] # --- g = np.random.choice(np.arange(pi.shape[1]), p=pi[0]) coord = sample(e[0, 0], mu1[0, g], mu2[0, g], std1[0, g], std2[0, g], rho[0, g]) coords += [coord] stroke_data += [[mu1[0, g], mu2[0, g], std1[0, g], std2[0, g], rho[0, g], coord[2]]] if not args.force and finish[0, 0] > 0.8: print('\nFinished sampling!\n') break coords = np.array(coords) coords[-1, 2] = 1. return phi_data, window_data, kappa_data, stroke_data, coords def main(): with open(os.path.join('data', 'translation.pkl'), 'rb') as file: translation = pickle.load(file) rev_translation = {v: k for k, v in translation.items()} charset = [rev_translation[i] for i in range(len(rev_translation))] charset[0] = '' config = tf.ConfigProto( device_count={'GPU': 0} ) with tf.Session(config=config) as sess: saver = tf.train.import_meta_graph(args.model_path + '.meta') saver.restore(sess, args.model_path) while True: if args.text is not None: args_text = args.text else: args_text = input('What to generate: ') style = None if args.style is not None: style = None with open(os.path.join('data', 'styles.pkl'), 'rb') as file: styles = pickle.load(file) if args.style > len(styles[0]): raise ValueError('Requested style is not in style list') style = [styles[0][args.style], styles[1][args.style]] phi_data, window_data, kappa_data, stroke_data, coords = sample_text(sess, args_text, translation, style) strokes = np.array(stroke_data) epsilon = 1e-8 strokes[:, :2] = np.cumsum(strokes[:, :2], axis=0) minx, maxx = np.min(strokes[:, 0]), np.max(strokes[:, 0]) miny, maxy = np.min(strokes[:, 1]), np.max(strokes[:, 1]) if args.info: delta = abs(maxx - minx) / 400. x = np.arange(minx, maxx, delta) y = np.arange(miny, maxy, delta) x_grid, y_grid = np.meshgrid(x, y) z_grid = np.zeros_like(x_grid) for i in range(strokes.shape[0]): gauss = mlab.bivariate_normal(x_grid, y_grid, mux=strokes[i, 0], muy=strokes[i, 1], sigmax=strokes[i, 2], sigmay=strokes[i, 3], sigmaxy=0.) # strokes[i, 4] z_grid += gauss * np.power(strokes[i, 2] + strokes[i, 3], 0.4) / (np.max(gauss) + epsilon) fig, ax = plt.subplots(2, 2) ax[0, 0].imshow(z_grid, interpolation='bilinear', aspect='auto', cmap=cm.jet) ax[0, 0].grid(False) ax[0, 0].set_title('Densities') ax[0, 0].set_aspect('equal') for stroke in split_strokes(cumsum(np.array(coords))): ax[0, 1].plot(stroke[:, 0], -stroke[:, 1]) ax[0, 1].set_title('Handwriting') ax[0, 1].set_aspect('equal') phi_img = np.vstack(phi_data).T[::-1, :] ax[1, 0].imshow(phi_img, interpolation='nearest', aspect='auto', cmap=cm.jet) ax[1, 0].set_yticks(np.arange(0, len(args_text) + 1)) ax[1, 0].set_yticklabels(list(' ' + args_text[::-1]), rotation='vertical', fontsize=8) ax[1, 0].grid(False) ax[1, 0].set_title('Phi') window_img = np.vstack(window_data).T ax[1, 1].imshow(window_img, interpolation='nearest', aspect='auto', cmap=cm.jet) ax[1, 1].set_yticks(np.arange(0, len(charset))) ax[1, 1].set_yticklabels(list(charset), rotation='vertical', fontsize=8) ax[1, 1].grid(False) ax[1, 1].set_title('Window') plt.show() else: fig, ax = plt.subplots(1, 1) for stroke in split_strokes(cumsum(np.array(coords))): plt.plot(stroke[:, 0], -stroke[:, 1], 'r') ax.set_title('Handwriting') ax.set_aspect('equal') plt.show() if args.animation: fig, ax = plt.subplots(1, 1, frameon=False, figsize=(2 * (maxx - minx + 2) / (maxy - miny + 1), 2)) ax.set_xlim(minx - 1., maxx + 1.) ax.set_ylim(-maxy - 0.5, -miny + 0.5) ax.set_aspect('equal') ax.axis('off') # ax.hold(True) plt.draw() plt.show(False) background = fig.canvas.copy_from_bbox(ax.bbox) sumed = cumsum(coords) def _update(i): c1, c2 = sumed[i: i+2] fig.canvas.restore_region(background) if c1[2] == 1. and c2[2] == 1.: line, = ax.plot([c2[0], c2[0]], [-c2[1], -c2[1]]) elif c1[2] != 1.: line, = ax.plot([c1[0], c2[0]], [-c1[1], -c2[1]]) else: line, = ax.plot([c1[0], c1[0]], [-c1[1], -c1[1]]) fig.canvas.blit(ax.bbox) return line, anim = animation.FuncAnimation(fig, _update, frames=len(sumed) - 2, interval=16, blit=True, repeat=False) if args.save is not None: anim.save(args.save, fps=60, extra_args=['-vcodec', 'libx264']) plt.show() if args.text is not None: break if __name__ == '__main__': main()

41.57085

118

0.523763

426bfe69a3bf724a1170331e637f3380fcaf80e5

35

py

Python

stam_pytorch/__init__.py

lab176344/STAM-pytorch

1ea4fd218d1567e94d27581e48e3b8ce50f985ed

[ "MIT" ]

100

2021-03-28T21:50:30.000Z

2022-03-21T06:34:59.000Z

stam_pytorch/__init__.py

lab176344/STAM-pytorch

1ea4fd218d1567e94d27581e48e3b8ce50f985ed

[ "MIT" ]

5

2021-03-29T13:24:04.000Z

2022-02-01T20:03:00.000Z

stam_pytorch/__init__.py

lab176344/STAM-pytorch

1ea4fd218d1567e94d27581e48e3b8ce50f985ed

[ "MIT" ]

13

2021-03-29T01:36:14.000Z

2022-03-30T05:59:24.000Z

from stam_pytorch.stam import STAM

17.5

34

0.857143

efe83e4fba52286da6a14089f9972879eb399e62

3,786

py

Python

tests/garage/torch/algos/test_ddpg.py

igor-krawczuk/garage

aa86ce710c6d01380477d6feddc0e38427b1e3b4

[ "MIT" ]

null

tests/garage/torch/algos/test_ddpg.py

igor-krawczuk/garage

aa86ce710c6d01380477d6feddc0e38427b1e3b4

[ "MIT" ]

null

tests/garage/torch/algos/test_ddpg.py

igor-krawczuk/garage

aa86ce710c6d01380477d6feddc0e38427b1e3b4

[ "MIT" ]

null

"""This script creates a test that fails when DDPG performance is too low.""" import gym import pytest import torch from torch.nn import functional as F # NOQA from garage.envs import normalize from garage.envs.base import GarageEnv from garage.experiment import deterministic, LocalRunner from garage.np.exploration_strategies import OUStrategy from garage.replay_buffer import SimpleReplayBuffer from garage.torch.algos import DDPG from garage.torch.policies import DeterministicMLPPolicy from garage.torch.q_functions import ContinuousMLPQFunction from tests.fixtures import snapshot_config class TestDDPG: """Test class for DDPG.""" @pytest.mark.mujoco @pytest.mark.large def test_ddpg_double_pendulum(self): """Test DDPG with Pendulum environment.""" deterministic.set_seed(0) runner = LocalRunner(snapshot_config) env = GarageEnv(gym.make('InvertedDoublePendulum-v2')) action_noise = OUStrategy(env.spec, sigma=0.2) policy = DeterministicMLPPolicy(env_spec=env.spec, hidden_sizes=[64, 64], hidden_nonlinearity=F.relu, output_nonlinearity=torch.tanh) qf = ContinuousMLPQFunction(env_spec=env.spec, hidden_sizes=[64, 64], hidden_nonlinearity=F.relu) replay_buffer = SimpleReplayBuffer(env_spec=env.spec, size_in_transitions=int(1e6), time_horizon=100) algo = DDPG(env_spec=env.spec, policy=policy, qf=qf, replay_buffer=replay_buffer, steps_per_epoch=20, n_train_steps=50, min_buffer_size=int(1e4), exploration_strategy=action_noise, target_update_tau=1e-2, discount=0.9) runner.setup(algo, env) last_avg_ret = runner.train(n_epochs=10, batch_size=100) assert last_avg_ret > 45 env.close() @pytest.mark.mujoco @pytest.mark.large def test_ddpg_pendulum(self): """Test DDPG with Pendulum environment. This environment has a [-3, 3] action_space bound. """ deterministic.set_seed(0) runner = LocalRunner(snapshot_config) env = GarageEnv(normalize(gym.make('InvertedPendulum-v2'))) action_noise = OUStrategy(env.spec, sigma=0.2) policy = DeterministicMLPPolicy(env_spec=env.spec, hidden_sizes=[64, 64], hidden_nonlinearity=F.relu, output_nonlinearity=torch.tanh) qf = ContinuousMLPQFunction(env_spec=env.spec, hidden_sizes=[64, 64], hidden_nonlinearity=F.relu) replay_buffer = SimpleReplayBuffer(env_spec=env.spec, size_in_transitions=int(1e6), time_horizon=100) algo = DDPG(env_spec=env.spec, policy=policy, qf=qf, replay_buffer=replay_buffer, steps_per_epoch=20, n_train_steps=50, min_buffer_size=int(1e4), exploration_strategy=action_noise, target_update_tau=1e-2, discount=0.9) runner.setup(algo, env) last_avg_ret = runner.train(n_epochs=10, batch_size=100) assert last_avg_ret > 10 env.close()

37.485149

77

0.559694

60b19e6bcf5095763b0c383ac66e3fc7f68041dc

23,584

py

Python

training.py

davidwfong/ViolinMelodyCNNs

79fbfc4f03f081ef4b3145e341d1aec49e934a9e

[ "Apache-2.0" ]

6

2018-06-20T10:29:22.000Z

2018-06-29T22:56:00.000Z

training.py

davidwfong/ViolinMelodyCNNs

79fbfc4f03f081ef4b3145e341d1aec49e934a9e

[ "Apache-2.0" ]

1

2018-09-09T22:08:45.000Z

2019-04-17T11:12:16.000Z

training.py

davidwfong/ViolinMelodyCNNs

79fbfc4f03f081ef4b3145e341d1aec49e934a9e

[ "Apache-2.0" ]

null

#IMPORT RELEVANT MODULES import numpy as np import matplotlib.pyplot as plt from sklearn.metrics import confusion_matrix import itertools import preprocessing from sklearn import model_selection from keras.models import Model from keras.utils import np_utils, plot_model from keras.layers import Flatten, Dropout, Activation, BatchNormalization, Concatenate from keras.layers import Input, Dense, Conv2D, Conv2DTranspose, AveragePooling2D, MaxPooling2D from keras.losses import categorical_crossentropy from keras.models import load_model from keras import optimizers from keras import callbacks from keras.callbacks import ModelCheckpoint import keras.backend as K #------------------------------------------------------------------------------------------------------------------- #FUNCTIONS def initialiseWithSeed(num): seed = num initialisation = np.random.seed(seed) return initialisation def getInputShape(rows=192,columns=11,channels=2): inputShape = (rows,columns,channels) return inputShape def splitData(XFull,yFull,valSetSize,outputToStratify): X_Train, X_Val, Y_Train, Y_Val = model_selection.train_test_split(XFull, yFull, test_size=valSetSize, stratify=None) print("Train-test split complete with "+str(valSetSize)+" validation set") return X_Train, X_Val, Y_Train, Y_Val def encodeLabels(yTrain, yTest, numClasses): yTrainEncoded = np_utils.to_categorical(yTrain, numClasses) yTestEncoded = np_utils.to_categorical(yTest, numClasses) return yTrainEncoded, yTestEncoded def encodeLabelsSingle(y, numClasses): yEncoded = np_utils.to_categorical(y, numClasses) return yEncoded def buildSingleMECNN(rows,columns,channels): inputShape = getInputShape(rows,columns,channels) fH_l1 = 4 fW_l1 = 3 nC_l1 = 64 sH_l1 = 1 sW_l1 = 1 fH_l2 = 4 fW_l2 = 3 nC_l2 = 64 sH_l2 = 1 sW_l2 = 1 fH_l2C = 1 fW_l2C = 2 sH_l2C = 1 sW_l2C = 2 fH_l2D = 2 fW_l2D = 1 sH_l2D = 2 sW_l2D = 1 fH_l3 = 4 fW_l3 = 3 nC_l3 = 128 sH_l3 = 1 sW_l3 = 1 fH_l3C = 1 fW_l3C = 2 sH_l3C = 1 sW_l3C = 2 fH_l3D = 2 fW_l3D = 1 sH_l3D = 2 sW_l3D = 1 pDropout_l4 = 0.25 fH_l4 = 12 fW_l4 = 3 nC_l4 = 256 sH_l4 = 1 sW_l4 = 1 fH_l4C = 1 fW_l4C = 2 sH_l4C = 1 sW_l4C = 2 pDropout_l5 = 0.50 hiddenUnits_l5 = 392 pDropout_l6 = 0.50 C = 49 input_l0 = Input(shape=inputShape) Conv_l1 = Conv2D(nC_l1, kernel_size=(fH_l1,fW_l1), strides=(sH_l1,sW_l1), padding='same', activation='relu', kernel_initializer='glorot_uniform', bias_initializer='zeros')(input_l0) BatchNorm_l1B = BatchNormalization(axis=-1)(Conv_l1) Conv_l2 = Conv2D(nC_l2, kernel_size=(fH_l2,fW_l2), strides=(sH_l2,sW_l2), padding='same', activation='relu', kernel_initializer='glorot_uniform', bias_initializer='zeros')(BatchNorm_l1B) BatchNorm_l2B = BatchNormalization(axis=-1)(Conv_l2) AP_l2C = AveragePooling2D(pool_size=(fH_l2C,fW_l2C), strides=(sH_l2C,sW_l2C), padding='valid')(BatchNorm_l2B) MP_l2D = MaxPooling2D(pool_size=(fH_l2D,fW_l2D), strides=(sH_l2D,sW_l2D), padding='valid')(AP_l2C) Conv_l3 = Conv2D(nC_l3, kernel_size=(fH_l3,fW_l3), strides=(sH_l3,sW_l3), padding='same', activation='relu', kernel_initializer='glorot_uniform', bias_initializer='zeros')(MP_l2D) BatchNorm_l3B = BatchNormalization(axis=-1)(Conv_l3) AP_l3C = AveragePooling2D(pool_size=(fH_l3C,fW_l3C), strides=(sH_l3C,sW_l3C), padding='valid')(BatchNorm_l3B) MP_l3D = MaxPooling2D(pool_size=(fH_l3D,fW_l3D), strides=(sH_l3D,sW_l3D), padding='valid')(AP_l3C) Dropout_l4A = Dropout(pDropout_l4)(MP_l3D) Conv_l4 = Conv2D(nC_l4, kernel_size=(fH_l4,fW_l4), strides=(sH_l4,sW_l4), padding='same', activation='relu', kernel_initializer='glorot_uniform', bias_initializer='zeros')(Dropout_l4A) BatchNorm_l4B = BatchNormalization(axis=-1)(Conv_l4) AP_l4C = AveragePooling2D(pool_size=(fH_l4C,fW_l4C), strides=(sH_l4C,sW_l4C), padding='valid')(BatchNorm_l4B) Flatten_l5A = Flatten()(AP_l4C) Dropout_l5B = Dropout(pDropout_l5)(Flatten_l5A) FC_l5 = Dense(hiddenUnits_l5, activation=None, kernel_initializer='glorot_uniform', bias_initializer='zeros')(Dropout_l5B) Dropout_l6A = Dropout(pDropout_l6)(FC_l5) FC_l6 = Dense(C, activation='softmax', kernel_initializer='glorot_uniform', bias_initializer='zeros')(Dropout_l6A) modelBuilt = Model(inputs=[input_l0], outputs=[FC_l6]) print(modelBuilt.summary()) return modelBuilt def setHyperparamsPolyMECNN(): E = 25 batchSize = 64 learningRate = 0.001 learningDecay = 1e-06 beta = 0.90 optimiser = optimizers.SGD(lr=learningRate, decay=learningDecay, momentum=beta, nesterov=True) """ beta1 = 0.90 beta2 = 0.999 optimiser = optimizers.Adam(lr=learningRate, decay=learningDecay, beta_1=beta1, beta_2=beta2) """ lossFunction = 'categorical_crossentropy' return E, batchSize, optimiser, lossFunction def setHyperparamsMonoMECNN(): E = 50 batchSize = 64 learningRate = 0.001 learningDecay = 1e-06 momentum = 0.90 optimiser = optimizers.SGD(lr=learningRate, decay=learningDecay, momentum=momentum, nesterov=True) """ beta1 = 0.90 beta2 = 0.999 optimiser = optimizers.Adam(lr=learningRate, decay=learningDecay, beta_1=beta1, beta_2=beta2) """ lossFunction = 'categorical_crossentropy' return E, batchSize, optimiser, lossFunction def buildMTMECNN(rows,columns,channels): inputShape = getInputShape(rows,columns,channels) fH_l1 = 4 fW_l1 = 3 nC_l1 = 64 sH_l1 = 1 sW_l1 = 1 fH_l2 = 4 fW_l2 = 3 nC_l2 = 64 sH_l2 = 1 sW_l2 = 1 fH_l2C = 1 fW_l2C = 2 sH_l2C = 1 sW_l2C = 2 fH_l2D = 2 fW_l2D = 1 sH_l2D = 2 sW_l2D = 1 fH_l3 = 4 fW_l3 = 3 nC_l3 = 128 sH_l3 = 1 sW_l3 = 1 fH_l3C = 1 fW_l3C = 2 sH_l3C = 1 sW_l3C = 2 fH_l3D = 2 fW_l3D = 1 sH_l3D = 2 sW_l3D = 1 pDropout_l4 = 0.25 fH_l4 = 12 fW_l4 = 3 nC_l4 = 256 sH_l4 = 1 sW_l4 = 1 fH_l4C = 1 fW_l4C = 2 sH_l4C = 1 sW_l4C = 2 pDropout_l5 = 0.50 hiddenUnits_l5 = 392 pDropout_l6 = 0.50 C = 49 input_l0_Mono = Input(shape=inputShape) input_l0_Poly = Input(shape=inputShape) Conv_l1_Mono = Conv2D(nC_l1, kernel_size=(fH_l1,fW_l1), strides=(sH_l1,sW_l1), padding='same', activation='relu')(input_l0_Mono) Conv_l1_Poly = Conv2D(nC_l1, kernel_size=(fH_l1,fW_l1), strides=(sH_l1,sW_l1), padding='same', activation='relu')(input_l0_Poly) BatchNorm_l1B_Mono = BatchNormalization(axis=-1)(Conv_l1_Mono) BatchNorm_l1B_Poly = BatchNormalization(axis=-1)(Conv_l1_Poly) Conv_l2_Mono = Conv2D(nC_l2, kernel_size=(fH_l2,fW_l2), strides=(sH_l2,sW_l2), padding='same', activation='relu')(BatchNorm_l1B_Mono) Conv_l2_Poly = Conv2D(nC_l2, kernel_size=(fH_l2,fW_l2), strides=(sH_l2,sW_l2), padding='same', activation='relu')(BatchNorm_l1B_Poly) BatchNorm_l2B_Mono = BatchNormalization(axis=-1)(Conv_l2_Mono) BatchNorm_l2B_Poly = BatchNormalization(axis=-1)(Conv_l2_Poly) AP_l2C_Mono = AveragePooling2D(pool_size=(fH_l2C,fW_l2C), strides=(sH_l2C,sW_l2C), padding='valid')(BatchNorm_l2B_Mono) AP_l2C_Poly = AveragePooling2D(pool_size=(fH_l2C,fW_l2C), strides=(sH_l2C,sW_l2C), padding='valid')(BatchNorm_l2B_Poly) MP_l2D_Mono = MaxPooling2D(pool_size=(fH_l2D,fW_l2D), strides=(sH_l2D,sW_l2D), padding='valid')(AP_l2C_Mono) MP_l2D_Poly = MaxPooling2D(pool_size=(fH_l2D,fW_l2D), strides=(sH_l2D,sW_l2D), padding='valid')(AP_l2C_Poly) Conv_l3_Mono = Conv2D(nC_l3, kernel_size=(fH_l3,fW_l3), strides=(sH_l3,sW_l3), padding='same', activation='relu')(MP_l2D_Mono) Conv_l3_Poly = Conv2D(nC_l3, kernel_size=(fH_l3,fW_l3), strides=(sH_l3,sW_l3), padding='same', activation='relu')(MP_l2D_Poly) BatchNorm_l3B_Mono = BatchNormalization(axis=-1)(Conv_l3_Mono) BatchNorm_l3B_Poly = BatchNormalization(axis=-1)(Conv_l3_Poly) AP_l3C_Mono = AveragePooling2D(pool_size=(fH_l3C,fW_l3C), strides=(sH_l3C,sW_l3C), padding='valid')(BatchNorm_l3B_Mono) AP_l3C_Poly = AveragePooling2D(pool_size=(fH_l3C,fW_l3C), strides=(sH_l3C,sW_l3C), padding='valid')(BatchNorm_l3B_Poly) MP_l3D_Mono = MaxPooling2D(pool_size=(fH_l3D,fW_l3D), strides=(sH_l3D,sW_l3D), padding='valid')(AP_l3C_Mono) MP_l3D_Poly = MaxPooling2D(pool_size=(fH_l3D,fW_l3D), strides=(sH_l3D,sW_l3D), padding='valid')(AP_l3C_Poly) Dropout_l4A_Mono = Dropout(pDropout_l4)(MP_l3D_Mono) Dropout_l4A_Poly = Dropout(pDropout_l4)(MP_l3D_Poly) Conv_l4_Mono = Conv2D(nC_l4, kernel_size=(fH_l4,fW_l4), strides=(sH_l4,sW_l4), padding='same', activation='relu')(Dropout_l4A_Mono) Conv_l4_Poly = Conv2D(nC_l4, kernel_size=(fH_l4,fW_l4), strides=(sH_l4,sW_l4), padding='same', activation='relu')(Dropout_l4A_Poly) BatchNorm_l4B_Mono = BatchNormalization(axis=-1)(Conv_l4_Mono) BatchNorm_l4B_Poly = BatchNormalization(axis=-1)(Conv_l4_Poly) AP_l4C_Mono = AveragePooling2D(pool_size=(fH_l4C,fW_l4C), strides=(sH_l4C,sW_l4C), padding='valid')(BatchNorm_l4B_Mono) AP_l4C_Poly = AveragePooling2D(pool_size=(fH_l4C,fW_l4C), strides=(sH_l4C,sW_l4C), padding='valid')(BatchNorm_l4B_Poly) Flatten_l5A_Mono = Flatten()(AP_l4C_Mono) Flatten_l5A_Poly = Flatten()(AP_l4C_Poly) Dropout_l5B_Mono = Dropout(pDropout_l5)(Flatten_l5A_Mono) Dropout_l5B_Poly = Dropout(pDropout_l5)(Flatten_l5A_Poly) FC_l5_Mono = Dense(hiddenUnits_l5, activation=None)(Dropout_l5B_Mono) FC_l5_Poly = Dense(hiddenUnits_l5, activation=None)(Dropout_l5B_Poly) Dropout_l6A_Mono = Dropout(pDropout_l6)(FC_l5_Mono) Dropout_l6A_Poly = Dropout(pDropout_l6)(FC_l5_Poly) output_l6_Mono = Dense(C, activation='softmax', name='MONOME')(Dropout_l6A_Mono) output_l6_Poly = Dense(C, activation='softmax', name='POLYME')(Dropout_l6A_Poly) modelBuilt = Model(inputs=[input_l0_Poly, input_l0_Mono], outputs=[output_l6_Poly, output_l6_Mono]) print(modelBuilt.summary()) return modelBuilt def getLossWeights(numModel): if numModel==1: gammaPoly = 0.5 elif numModel==2: gammaPoly = 0.6 elif numModel==3: gammaPoly = 0.7 elif numModel==4: gammaPoly = 0.8 elif numModel==5: gammaPoly = 0.9 else: gammaPoly = 0.5 gammaMono = 1.0 - gammaPoly lossWeights = [gammaPoly, gammaMono] return lossWeights def setHyperparamsMTMECNN(): E = 75 batchSize = 64 learningRate = 0.001 learningDecay = 1e-06 momentum = 0.90 optimiser = optimizers.SGD(lr=learningRate, decay=learningDecay, momentum=momentum, nesterov=True) """ beta1 = 0.90 beta2 = 0.999 optimiser = optimizers.Adam(lr=learningRate, decay=learningDecay, beta_1=beta1, beta_2=beta2) """ lossFunction = ['categorical_crossentropy', 'categorical_crossentropy'] return E, batchSize, optimiser, lossFunction def compileMultiTaskModel(modelBuilt, lossFunction, lossWeights, optimiser): modelBuilt.compile(loss=lossFunction, loss_weights=lossWeights, optimizer=optimiser, metrics=['accuracy']) return modelBuilt def trainMultiTaskModel(modelCompiled, trainingX1, trainingY1, trainingX2, trainingY2, E, batchSize, nameModel, numModel, pathSave, vsplitfactor=0.1): filenameModel = nameModel+'_'+str(numModel)+'.h5' checkpointer = ModelCheckpoint(filepath=pathSave+'/'+filenameModel, monitor='val_loss', verbose=2, save_best_only=True) modelHistory = modelCompiled.fit([trainingX1, trainingX2], [trainingY1, trainingY2], batch_size=batchSize, epochs=E, verbose=2, validation_split=vsplitfactor, callbacks = [checkpointer]) print('Model has been trained and will now be evaluated on test set') return modelCompiled, modelHistory def plotMultiTaskModelHistory(modelHistory, nameModel, numModel, E): print(modelHistory.history.keys()) plt.figure(1, figsize=(22, 11)) plt.rc('axes', axisbelow=True) plt.plot(modelHistory.history['POLYME_acc']) plt.plot(modelHistory.history['val_POLYME_acc']) plt.plot(modelHistory.history['MONOME_acc']) plt.plot(modelHistory.history['val_MONOME_acc']) plt.xlabel('Epochs', fontsize = 16) plt.ylabel('Accuracy', fontsize = 16) plt.grid(axis='both', zorder=0) plt.xticks(np.arange(0, E+1, 1), fontsize = 12) plt.yticks(fontsize=15) plt.legend(['Training Polyphonic','Validation Polyphonic', 'Training Monophonic', 'Validation Monophonic'], loc='upper left', fontsize=16) #plt.title('Accuracy of '+nameModel+' over '+str(E)+' training epochs') plt.savefig(nameModel+'_'+str(numModel)+'_SingleTaskAccuracy'+'.png') plt.figure(2, figsize=(22, 11)) plt.rc('axes', axisbelow=True) plt.plot(modelHistory.history['POLYME_loss']) plt.plot(modelHistory.history['val_POLYME_loss']) plt.plot(modelHistory.history['MONOME_loss']) plt.plot(modelHistory.history['val_MONOME_loss']) plt.xlabel('Epochs', fontsize = 16) plt.ylabel('Loss', fontsize = 16) plt.grid(axis='both', zorder=0) plt.xticks(np.arange(0, E+1, 1), fontsize = 12) plt.yticks(fontsize=15) plt.legend(['Training Polyphonic','Validation Polyphonic', 'Training Monophonic', 'Validation Monophonic'], loc='upper left', fontsize=16) #plt.title('Loss of '+nameModel+' over '+str(E)+' training epochs') plt.savefig(nameModel+'_'+str(numModel)+'_SingleTaskLoss'+'.png') plt.figure(3, figsize=(22, 11)) plt.rc('axes', axisbelow=True) plt.plot(modelHistory.history['loss']) plt.plot(modelHistory.history['val_loss']) plt.xlabel('Epochs', fontsize = 16) plt.ylabel('Loss', fontsize = 16) plt.grid(axis='both', zorder=0) plt.xticks(np.arange(0, E+1, 1), fontsize = 12) plt.yticks(fontsize=15) plt.legend(['Training Multi-Task','Validation Multi-Task'], loc='upper left', fontsize=16) #plt.title('Accuracy of '+nameModel+' over '+str(E)+' training epochs') plt.savefig(nameModel+'_'+str(numModel)+'_MultiTaskLoss'+'.png') def evaluateMultiTaskModel(modelTrained, batchSize, XTest, yTest): allMetrics = modelTrained.metrics_names scores = modelTrained.evaluate([XTest, XTest], [yTest, yTest], batch_size=batchSize, verbose=2) OverallLoss = scores[0] TargetLoss = scores[1] TargetAcc = scores[3] AuxLoss = scores[2] AuxAcc = scores[4] print("Test Loss for Target Task is " + str(TargetLoss)) print("Test Accuracy for Target Task is " + str(TargetAcc)) print("Test Loss for Auxiliary Task is " + str(AuxLoss)) print("Test Accuracy for Auxiliary Task is " + str(AuxAcc)) print("Overall Test Loss is " + str(OverallLoss)) return allMetrics, scores, TargetLoss, TargetAcc def saveModelDiagram(model, nameModel): plot_model(model, to_file=nameModel+'.png') print("Saved Model Graph") def compileModel(modelBuilt, lossFunction, optimiser): modelBuilt.compile(loss=lossFunction, optimizer=optimiser, metrics=['accuracy']) return modelBuilt def trainModel(modelCompiled, trainingX, trainingY, E, batchSize, nameModel, numModel, pathSave, vsplit=True, vsplitfactor=0.1, XVal=None, yVal=None): filenameModel = nameModel+'_'+str(numModel)+'.h5' checkpointer = ModelCheckpoint(filepath=pathSave+'/'+filenameModel, monitor='val_loss', verbose=2, save_best_only=True) if vsplit==True: modelHistory = modelCompiled.fit(trainingX, trainingY, batch_size=batchSize, epochs=E, verbose=2, validation_split=vsplitfactor, callbacks = [checkpointer]) elif vsplit==False: modelHistory = modelCompiled.fit(trainingX, trainingY, batch_size=batchSize, epochs=E, verbose=2, validation_data=(XVal,yVal), callbacks = [checkpointer]) print('Model has been trained and will now be evaluated on test set') return modelCompiled, modelHistory def saveModel(modelTrained, nameModel, numModel, savepath): filenameModel = nameModel+'_'+str(numModel)+'.h5' print('Saving model under filename '+filenameModel) modelTrained.save(savepath+ '/' + filenameModel) print('Saved model under filename '+filenameModel) def loadpretrainedmodel(modelfilename): loadedModel = load_model(modelfilename) print("Model loaded") return loadedModel def plotModelHistory(modelHistory, nameModel, numModel, E): print(modelHistory.history.keys()) plt.figure(1, figsize=(16, 8)) plt.rc('axes', axisbelow=True) plt.plot(modelHistory.history['acc']) plt.plot(modelHistory.history['val_acc']) plt.xlabel('Epochs', fontsize = 14) plt.ylabel('Accuracy', fontsize = 14) plt.grid(axis='both',zorder=0) plt.xticks(np.arange(0, E+1, 1), fontsize = 12) plt.legend(['Training','Validation'], loc='upper left', fontsize=14) #plt.title('Accuracy of '+nameModel+' over '+str(E)+' training epochs') plt.savefig(nameModel+'_'+str(numModel)+'_Accuracy'+'.png') plt.figure(2, figsize=(16, 8)) plt.rc('axes', axisbelow=True) plt.plot(modelHistory.history['loss']) plt.plot(modelHistory.history['val_loss']) plt.xlabel('Epochs', fontsize = 14) plt.ylabel('Loss', fontsize = 14) plt.grid(axis='both',zorder=0) plt.xticks(np.arange(0, E+1, 1), fontsize = 12) plt.legend(['Training','Validation'], loc='upper left', fontsize=14) #plt.title('Loss of '+nameModel+' over '+str(E)+' training epochs') plt.savefig(nameModel+'_'+str(numModel)+'_Loss'+'.png') print("Model has been evaluated on test set") def evaluateModel(modelTrained, batchSize, XTest, yTest): score = modelTrained.evaluate(XTest, yTest, batch_size=batchSize, verbose=2) testLoss = score[0] testAccuracy = score[1] print("Test Loss is " + str(testLoss)) print("Test Accuracy is " + str(testAccuracy)) return testLoss, testAccuracy def getClassNames(y): f0Labels = np.unique(y) lowestMIDI = preprocessing.getMIDIfromNote('G3') classNamesTest = preprocessing.getNotesfromF0Labels(f0Labels, offset=lowestMIDI-1) return lowestMIDI, f0Labels, classNamesTest def getAllClassNames(): classnamesAll = ['None', 'G3', 'G#3', 'A3', 'A#3', 'B3', 'C4', 'C#4', 'D4', 'D#4', 'E4', 'F4', 'F#4', 'G4', 'G#4', 'A4', 'A#4', 'B4', 'C5', 'C#5', 'D5', 'D#5', 'E5', 'F5', 'F#5', 'G5', 'G#5', 'A5', 'A#5', 'B5', 'C6', 'C#6', 'D6', 'D#6', 'E6', 'F6', 'F#6', 'G6', 'G#6', 'A6', 'A#6', 'B6', 'C7', 'C#7', 'D7', 'D#7', 'E7', 'F7', 'F#7'] return classnamesAll def plotConfusionMatrixSingle(model, nameModel, numModel, XTest, YTest, classNames): YHat = model.predict(np.array(XTest)) YHat_nonCategory = [np.argmax(t) for t in YHat] YTest_nonCategory = [np.argmax(t) for t in YTest] cnf_matrix = confusion_matrix(YTest_nonCategory, YHat_nonCategory) cm = cnf_matrix.astype('float') / (cnf_matrix.astype('float').sum(axis=1))[:, np.newaxis] plt.figure(figsize=(20, 20)) plt.imshow(cnf_matrix, interpolation='nearest', cmap=plt.cm.Blues) #plt.title('Confusion Matrix') #plt.colorbar() tick_marks = np.arange(len(classNames)) plt.xticks(tick_marks, classNames, rotation = 45, fontsize = 17) plt.yticks(tick_marks, classNames, fontsize = 17) fmt = '.2f' thresh = cm.max() * 0.85 for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])): plt.text(j, i, format(cm[i, j], fmt), horizontalalignment="center", color="white" if cm[i, j] > thresh else "black", fontsize = 17) plt.ylabel('Ground Truth f0 Label', fontsize = 17) plt.xlabel('Predicted f0 Label', fontsize = 17) plt.tight_layout() plt.savefig(nameModel+'_'+str(numModel)+'_ConfusionMatrix'+'.png') return cm def plotConfusionMatrixMT(model, nameModel, numModel, XTest, YTest, classNames): YHat = model.predict(XTest)[0] YTestPoly = YTest[0] YHat_nonCategory = [np.argmax(t) for t in YHat] YTest_nonCategory = [np.argmax(t) for t in YTestPoly] cnf_matrix = confusion_matrix(YTest_nonCategory, YHat_nonCategory) cm = cnf_matrix.astype('float') / (cnf_matrix.astype('float').sum(axis=1))[:, np.newaxis] plt.figure(figsize=(20, 20)) plt.imshow(cnf_matrix, interpolation='nearest', cmap=plt.cm.Blues) #plt.title('Confusion Matrix') #plt.colorbar() tick_marks = np.arange(len(classNames)) plt.xticks(tick_marks, classNames, rotation = 45, fontsize = 17) plt.yticks(tick_marks, classNames, fontsize = 17) fmt = '.2f' thresh = cm.max() * 0.85 for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])): plt.text(j, i, format(cm[i, j], fmt), horizontalalignment="center", color="white" if cm[i, j] > thresh else "black", fontsize = 17) plt.ylabel('Ground Truth f0 Label', fontsize = 17) plt.xlabel('Predicted f0 Label', fontsize = 17) plt.tight_layout() plt.savefig(nameModel+'_'+str(numModel)+'_ConfusionMatrix'+'.png') return cm

37.55414

142

0.620378

74c187eaaf50834930934613ffd8f6fd08df9468

2,768

py

Python

python3/koans/about_string_manipulation.py

mahletbogale/python_koans

61380577467de08c744c47af0efccb9459497f98

[ "MIT" ]

null

python3/koans/about_string_manipulation.py

mahletbogale/python_koans

61380577467de08c744c47af0efccb9459497f98

[ "MIT" ]

null

python3/koans/about_string_manipulation.py

mahletbogale/python_koans

61380577467de08c744c47af0efccb9459497f98

[ "MIT" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- from runner.koan import * class AboutStringManipulation(Koan): def test_use_format_to_interpolate_variables(self): value1 = 'one' value2 = 2 string = "The values are {0} and {1}".format(value1, value2) self.assertEqual('The values are one and 2', string) def test_formatted_values_can_be_shown_in_any_order_or_be_repeated(self): value1 = 'doh' value2 = 'DOH' string = "The values are {1}, {0}, {0} and {1}!".format(value1, value2) self.assertEqual('The values are DOH, doh, doh and DOH!', string) def test_any_python_expression_may_be_interpolated(self): import math # import a standard python module with math functions decimal_places = 4 string = "The square root of 5 is {0:.{1}f}".format(math.sqrt(5), decimal_places) self.assertEqual('The square root of 5 is 2.2361', string) def test_you_can_get_a_substring_from_a_string(self): string = "Bacon, lettuce and tomato" self.assertEqual('let', string[7:10]) def test_you_can_get_a_single_character_from_a_string(self): string = "Bacon, lettuce and tomato" self.assertEqual("a", string[1]) def test_single_characters_can_be_represented_by_integers(self): self.assertEqual(97, ord('a')) self.assertEqual(True, ord('b') == (ord('a') + 1)) def test_strings_can_be_split(self): string = "Sausage Egg Cheese" words = string.split() self.assertListEqual(['Sausage', 'Egg', 'Cheese'], words) def test_strings_can_be_split_with_different_patterns(self): import re #import python regular expression library string = "the,rain;in,spain" pattern = re.compile(',|;') words = pattern.split(string) self.assertListEqual(["the","rain","in","spain"], words) # Pattern is a Python regular expression pattern which matches ',' or ';' def test_raw_strings_do_not_interpret_escape_characters(self): string = r'\n' self.assertNotEqual('\n', string) self.assertEqual(r'\n', string) self.assertEqual(2, len(string)) # Useful in regular expressions, file paths, URLs, etc. def test_strings_can_be_joined(self): words = ["Now", "is", "the", "time"] self.assertEqual('Now is the time', ' '.join(words)) def test_strings_can_change_case(self): self.assertEqual('Guido', 'guido'.capitalize()) self.assertEqual('GUIDO', 'guido'.upper()) self.assertEqual('timbot', 'TimBot'.lower()) self.assertEqual('Guido Van Rossum', 'guido van rossum'.title()) self.assertEqual('tOtAlLy AwEsOmE', 'ToTaLlY aWeSoMe'.swapcase())

36.906667

81

0.649566

92ff7140afb365b2d6bb9a1214ce332a6b8a6e32

387

py

Python

blender/arm/logicnode/variable/LN_color.py

onelsonic/armory

55cfead0844923d419d75bf4bd677ebed714b4b5

[ "Zlib" ]

2,583

2016-07-27T08:25:47.000Z

2022-03-31T10:42:17.000Z

blender/arm/logicnode/variable/LN_color.py

N8n5h/armory

5b4d24f067a2354bafd3ab417bb8e30ee0c5aff8

[ "Zlib" ]

2,122

2016-07-31T14:20:04.000Z

2022-03-31T20:44:14.000Z

blender/arm/logicnode/variable/LN_color.py

N8n5h/armory

5b4d24f067a2354bafd3ab417bb8e30ee0c5aff8

[ "Zlib" ]

451

2016-08-12T05:52:58.000Z

2022-03-31T01:33:07.000Z

from arm.logicnode.arm_nodes import * class ColorNode(ArmLogicTreeNode): """Stores the given color as a variable.""" bl_idname = 'LNColorNode' bl_label = 'Color' arm_version = 1 def arm_init(self, context): self.add_input('ArmColorSocket', 'Color In', default_value=[1.0, 1.0, 1.0, 1.0]) self.add_output('ArmColorSocket', 'Color Out', is_var=True)

29.769231

88

0.669251

ced541c354c6043ad0d0cecb11d6ce920e80a9ea

242,200

py

Python

tensorflow/python/framework/ops.py

mckib2/tensorflow-icg

fede3707c8700a198c8a972978749e4a69bf9a81

[ "Apache-2.0" ]

1

2020-07-29T14:42:30.000Z

tensorflow/python/framework/ops.py

mckib2/tensorflow-icg

fede3707c8700a198c8a972978749e4a69bf9a81

[ "Apache-2.0" ]

null

tensorflow/python/framework/ops.py

mckib2/tensorflow-icg

fede3707c8700a198c8a972978749e4a69bf9a81

[ "Apache-2.0" ]

1

2020-11-10T15:45:53.000Z

# Copyright 2015 The TensorFlow 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. # ============================================================================== """Classes and functions used to construct graphs.""" # pylint: disable=g-bad-name from __future__ import absolute_import from __future__ import division from __future__ import print_function import collections import copy import re import sys import threading import numpy as np import six from six.moves import xrange # pylint: disable=redefined-builtin from tensorflow.core.framework import attr_value_pb2 from tensorflow.core.framework import function_pb2 from tensorflow.core.framework import graph_pb2 from tensorflow.core.framework import node_def_pb2 from tensorflow.core.framework import op_def_pb2 from tensorflow.core.framework import versions_pb2 from tensorflow.core.protobuf import config_pb2 from tensorflow.python import pywrap_tensorflow as c_api from tensorflow.python import tf2 from tensorflow.python.eager import context from tensorflow.python.eager import core from tensorflow.python.eager import monitoring from tensorflow.python.eager import tape from tensorflow.python.framework import c_api_util from tensorflow.python.framework import composite_tensor from tensorflow.python.framework import device as pydev from tensorflow.python.framework import dtypes from tensorflow.python.framework import errors from tensorflow.python.framework import indexed_slices from tensorflow.python.framework import op_def_registry from tensorflow.python.framework import registry from tensorflow.python.framework import tensor_conversion_registry from tensorflow.python.framework import tensor_like from tensorflow.python.framework import tensor_shape from tensorflow.python.framework import traceable_stack from tensorflow.python.framework import versions from tensorflow.python.ops import control_flow_util from tensorflow.python.platform import app from tensorflow.python.platform import tf_logging as logging from tensorflow.python.util import compat from tensorflow.python.util import decorator_utils from tensorflow.python.util import deprecation from tensorflow.python.util import function_utils from tensorflow.python.util import lock_util from tensorflow.python.util import memory from tensorflow.python.util import object_identity from tensorflow.python.util import tf_contextlib from tensorflow.python.util import tf_stack from tensorflow.python.util.compat import collections_abc from tensorflow.python.util.deprecation import deprecated_args from tensorflow.python.util.lazy_loader import LazyLoader from tensorflow.python.util.tf_export import tf_export ag_ctx = LazyLoader( "ag_ctx", globals(), "tensorflow.python.autograph.core.ag_ctx") # Temporary global switches determining if we should enable the work-in-progress # calls to the C API. These will be removed once all functionality is supported. _USE_C_API = True _USE_C_SHAPES = True _api_usage_gauge = monitoring.BoolGauge( "/tensorflow/api/ops_eager_execution", "Whether ops.enable_eager_execution() is called.") # pylint: disable=protected-access _TensorLike = tensor_like._TensorLike _DTYPES_INTERN_TABLE = dtypes._INTERN_TABLE # pylint: enable=protected-access def tensor_id(tensor): """Returns a unique identifier for this Tensor.""" return tensor._id # pylint: disable=protected-access class _UserDeviceSpec(object): """Store user-specified device and provide computation of merged device.""" def __init__(self, device_name_or_function): self._device_name_or_function = device_name_or_function self.display_name = str(self._device_name_or_function) self.function = device_name_or_function self.raw_string = None if isinstance(device_name_or_function, pydev.MergeDevice): self.is_null_merge = device_name_or_function.is_null_merge elif callable(device_name_or_function): self.is_null_merge = False dev_func = self._device_name_or_function func_name = function_utils.get_func_name(dev_func) func_code = function_utils.get_func_code(dev_func) if func_code: fname = func_code.co_filename lineno = func_code.co_firstlineno else: fname = "unknown" lineno = -1 self.display_name = "%s<%s, %d>" % (func_name, fname, lineno) elif device_name_or_function is None: # NOTE(taylorrobie): This MUST be False. None signals a break in the # device stack, so `is_null_merge` must be False for such a case to # allow callers to safely skip over null merges without missing a None. self.is_null_merge = False else: self.raw_string = device_name_or_function self.function = pydev.merge_device(device_name_or_function) self.is_null_merge = self.function.is_null_merge # We perform this check in __init__ because it is of non-trivial cost, # and self.string_merge is typically called many times. self.fast_string_merge = isinstance(self.function, pydev.MergeDevice) def string_merge(self, node_def): if self.fast_string_merge: return self.function.shortcut_string_merge(node_def) return compat.as_str(_device_string(self.function(node_def))) class NullContextmanager(object): def __init__(self, *args, **kwargs): pass def __enter__(self): pass def __exit__(self, type_arg, value_arg, traceback_arg): return False # False values do not suppress exceptions def _override_helper(clazz_object, operator, func): """Overrides (string) operator on Tensors to call func. Args: clazz_object: the class to override for; either Tensor or SparseTensor. operator: the string name of the operator to override. func: the function that replaces the overridden operator. Raises: ValueError: If operator has already been overwritten, or if operator is not allowed to be overwritten. """ existing = getattr(clazz_object, operator, None) if existing is not None: # Check to see if this is a default method-wrapper or slot wrapper which # will be true for the comparison operators. if not isinstance(existing, type(object.__lt__)): raise ValueError("operator %s cannot be overwritten again on class %s." % (operator, clazz_object)) if operator not in Tensor.OVERLOADABLE_OPERATORS: raise ValueError("Overriding %s is disallowed" % operator) setattr(clazz_object, operator, func) def _as_graph_element(obj): """Convert `obj` to a graph element if possible, otherwise return `None`. Args: obj: Object to convert. Returns: The result of `obj._as_graph_element()` if that method is available; otherwise `None`. """ conv_fn = getattr(obj, "_as_graph_element", None) if conv_fn and callable(conv_fn): return conv_fn() return None _TENSOR_LIKE_TYPES = tuple() def is_dense_tensor_like(t): """EXPERIMENTAL: Returns true if `t` implements the tensor interface. See `register_dense_tensor_like_type()` for the current definition of a "tensor-like type". Args: t: An object. Returns: True iff `t` is an instance of one of the registered "tensor-like" types. """ return isinstance(t, _TENSOR_LIKE_TYPES) def register_dense_tensor_like_type(tensor_type): """EXPERIMENTAL: Registers `tensor_type` as implementing the tensor interface. A "tensor-like type" can represent a single dense tensor, and implements the `name` and `dtype` properties. Args: tensor_type: A type implementing the tensor interface. Raises: TypeError: If `tensor_type` does not implement the tensor interface. """ try: if not isinstance(tensor_type.name, property): raise TypeError("Type %s does not define a `name` property" % tensor_type.__name__) except AttributeError: raise TypeError("Type %s does not define a `name` property" % tensor_type.__name__) try: if not isinstance(tensor_type.dtype, property): raise TypeError("Type %s does not define a `dtype` property" % tensor_type.__name__) except AttributeError: raise TypeError("Type %s does not define a `dtype` property" % tensor_type.__name__) # We expect this list to be small, so choose quadratic complexity # for registration, so that we have a tuple that can be used for # more efficient `isinstance` checks later. global _TENSOR_LIKE_TYPES _TENSOR_LIKE_TYPES = tuple(list(_TENSOR_LIKE_TYPES) + [tensor_type]) def uid(): """A unique (within this program execution) integer.""" return c_api.TFE_Py_UID() def numpy_text(tensor, is_repr=False): """Human readable representation of a tensor's numpy value.""" if tensor.dtype.is_numpy_compatible: # pylint: disable=protected-access text = repr(tensor._numpy()) if is_repr else str(tensor._numpy()) # pylint: enable=protected-access else: text = "<unprintable>" if "\n" in text: text = "\n" + text return text @tf_export(v1=["enable_tensor_equality"]) def enable_tensor_equality(): """Compare Tensors with element-wise comparison and thus be unhashable. Comparing tensors with element-wise allows comparisons such as tf.Variable(1.0) == 1.0. Element-wise equality implies that tensors are unhashable. Thus tensors can no longer be directly used in sets or as a key in a dictionary. """ Tensor._USE_EQUALITY = True # pylint: disable=protected-access @tf_export(v1=["disable_tensor_equality"]) def disable_tensor_equality(): """Compare Tensors by their id and be hashable. This is a legacy behaviour of TensorFlow and is highly discouraged. """ Tensor._USE_EQUALITY = False # pylint: disable=protected-access @tf_export("Tensor") class Tensor(_TensorLike): """Represents one of the outputs of an `Operation`. A `Tensor` is a symbolic handle to one of the outputs of an `Operation`. It does not hold the values of that operation's output, but instead provides a means of computing those values in a TensorFlow `tf.compat.v1.Session`. This class has two primary purposes: 1. A `Tensor` can be passed as an input to another `Operation`. This builds a dataflow connection between operations, which enables TensorFlow to execute an entire `Graph` that represents a large, multi-step computation. 2. After the graph has been launched in a session, the value of the `Tensor` can be computed by passing it to `tf.Session.run`. `t.eval()` is a shortcut for calling `tf.compat.v1.get_default_session().run(t)`. In the following example, `c`, `d`, and `e` are symbolic `Tensor` objects, whereas `result` is a numpy array that stores a concrete value: ```python # Build a dataflow graph. c = tf.constant([[1.0, 2.0], [3.0, 4.0]]) d = tf.constant([[1.0, 1.0], [0.0, 1.0]]) e = tf.matmul(c, d) # Construct a `Session` to execute the graph. sess = tf.compat.v1.Session() # Execute the graph and store the value that `e` represents in `result`. result = sess.run(e) ``` """ # List of Python operators that we allow to override. OVERLOADABLE_OPERATORS = { # Binary. "__add__", "__radd__", "__sub__", "__rsub__", "__mul__", "__rmul__", "__div__", "__rdiv__", "__truediv__", "__rtruediv__", "__floordiv__", "__rfloordiv__", "__mod__", "__rmod__", "__lt__", "__le__", "__gt__", "__ge__", "__ne__", "__eq__", "__and__", "__rand__", "__or__", "__ror__", "__xor__", "__rxor__", "__getitem__", "__pow__", "__rpow__", # Unary. "__invert__", "__neg__", "__abs__", "__matmul__", "__rmatmul__" } # Whether to allow hashing or numpy-style equality _USE_EQUALITY = tf2.enabled() def __init__(self, op, value_index, dtype): """Creates a new `Tensor`. Args: op: An `Operation`. `Operation` that computes this tensor. value_index: An `int`. Index of the operation's endpoint that produces this tensor. dtype: A `DType`. Type of elements stored in this tensor. Raises: TypeError: If the op is not an `Operation`. """ if not isinstance(op, Operation): raise TypeError("op needs to be an Operation: %s" % op) self._op = op self._value_index = value_index self._dtype = dtypes.as_dtype(dtype) # This will be set by self._as_tf_output(). self._tf_output = None # This will be set by self.shape(). self._shape_val = None # List of operations that use this Tensor as input. We maintain this list # to easily navigate a computation graph. self._consumers = [] self._id = uid() self._name = None @staticmethod def _create_with_tf_output(op, value_index, dtype, tf_output): ret = Tensor(op, value_index, dtype) ret._tf_output = tf_output return ret @property def op(self): """The `Operation` that produces this tensor as an output.""" return self._op @property def dtype(self): """The `DType` of elements in this tensor.""" return self._dtype @property def graph(self): """The `Graph` that contains this tensor.""" return self._op.graph @property def name(self): """The string name of this tensor.""" if self._name is None: if not self._op.name: raise ValueError("Operation was not named: %s" % self._op) self._name = "%s:%d" % (self._op.name, self._value_index) return self._name @property def device(self): """The name of the device on which this tensor will be produced, or None.""" return self._op.device @property def shape(self): """Returns the `TensorShape` that represents the shape of this tensor. The shape is computed using shape inference functions that are registered in the Op for each `Operation`. See `tf.TensorShape` for more details of what a shape represents. The inferred shape of a tensor is used to provide shape information without having to launch the graph in a session. This can be used for debugging, and providing early error messages. For example: ```python c = tf.constant([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]) print(c.shape) ==> TensorShape([Dimension(2), Dimension(3)]) d = tf.constant([[1.0, 0.0], [0.0, 1.0], [1.0, 0.0], [0.0, 1.0]]) print(d.shape) ==> TensorShape([Dimension(4), Dimension(2)]) # Raises a ValueError, because `c` and `d` do not have compatible # inner dimensions. e = tf.matmul(c, d) f = tf.matmul(c, d, transpose_a=True, transpose_b=True) print(f.shape) ==> TensorShape([Dimension(3), Dimension(4)]) ``` In some cases, the inferred shape may have unknown dimensions. If the caller has additional information about the values of these dimensions, `Tensor.set_shape()` can be used to augment the inferred shape. Returns: A `TensorShape` representing the shape of this tensor. """ if self._shape_val is None: self._shape_val = self._c_api_shape() return self._shape_val def _get_input_ops_without_shapes(self, target_op): """Returns ops needing shape inference to compute target_op's shape.""" result = [] stack = [self._op] visited = set() while stack: op = stack.pop() if op in visited: continue result.append(op) stack.extend(t.op for t in op.inputs if t._shape_val is None) visited.add(op) return result def _c_api_shape(self): """Returns the TensorShape of this tensor according to the C API.""" c_graph = self._op._graph._c_graph # pylint: disable=protected-access shape_vector, unknown_shape = c_api.TF_GraphGetTensorShapeHelper( c_graph, self._as_tf_output()) if unknown_shape: return tensor_shape.unknown_shape() else: shape_vector = [None if d == -1 else d for d in shape_vector] return tensor_shape.TensorShape(shape_vector) @property def _shape(self): logging.warning("Tensor._shape is private, use Tensor.shape " "instead. Tensor._shape will eventually be removed.") return self.shape @_shape.setter def _shape(self, value): raise ValueError( "Tensor._shape cannot be assigned, use Tensor.set_shape instead.") def _disallow_when_autograph_disabled(self, task): raise errors.OperatorNotAllowedInGraphError( "{} is not allowed: AutoGraph is disabled in this function." " Try decorating it directly with @tf.function.".format(task)) def _disallow_when_autograph_enabled(self, task): raise errors.OperatorNotAllowedInGraphError( "{} is not allowed: AutoGraph did not convert this function. Try" " decorating it directly with @tf.function.".format(task)) def _disallow_in_graph_mode(self, task): raise errors.OperatorNotAllowedInGraphError( "{} is not allowed in Graph execution. Use Eager execution or decorate" " this function with @tf.function.".format(task)) def _disallow_bool_casting(self): if ag_ctx.control_status_ctx().status == ag_ctx.Status.DISABLED: self._disallow_when_autograph_disabled( "using a `tf.Tensor` as a Python `bool`") elif ag_ctx.control_status_ctx().status == ag_ctx.Status.ENABLED: self._disallow_when_autograph_enabled( "using a `tf.Tensor` as a Python `bool`") else: # Default: V1-style Graph execution. self._disallow_in_graph_mode("using a `tf.Tensor` as a Python `bool`") def _disallow_iteration(self): if ag_ctx.control_status_ctx().status == ag_ctx.Status.DISABLED: self._disallow_when_autograph_disabled("iterating over `tf.Tensor`") elif ag_ctx.control_status_ctx().status == ag_ctx.Status.ENABLED: self._disallow_when_autograph_enabled("iterating over `tf.Tensor`") else: # Default: V1-style Graph execution. self._disallow_in_graph_mode("iterating over `tf.Tensor`") def __iter__(self): if not context.executing_eagerly(): self._disallow_iteration() shape = self._shape_tuple() if shape is None: raise TypeError("Cannot iterate over a tensor with unknown shape.") if not shape: raise TypeError("Cannot iterate over a scalar tensor.") if shape[0] is None: raise TypeError( "Cannot iterate over a tensor with unknown first dimension.") for i in xrange(shape[0]): yield self[i] def _shape_as_list(self): if self.shape.ndims is not None: return [dim.value for dim in self.shape.dims] else: return None def _shape_tuple(self): shape = self._shape_as_list() if shape is None: return None return tuple(shape) def _rank(self): """Integer rank of this Tensor, if known, else None. Returns: Integer rank or None """ return self.shape.ndims def get_shape(self): """Alias of Tensor.shape.""" return self.shape def set_shape(self, shape): """Updates the shape of this tensor. This method can be called multiple times, and will merge the given `shape` with the current shape of this tensor. It can be used to provide additional information about the shape of this tensor that cannot be inferred from the graph alone. For example, this can be used to provide additional information about the shapes of images: ```python _, image_data = tf.compat.v1.TFRecordReader(...).read(...) image = tf.image.decode_png(image_data, channels=3) # The height and width dimensions of `image` are data dependent, and # cannot be computed without executing the op. print(image.shape) ==> TensorShape([Dimension(None), Dimension(None), Dimension(3)]) # We know that each image in this dataset is 28 x 28 pixels. image.set_shape([28, 28, 3]) print(image.shape) ==> TensorShape([Dimension(28), Dimension(28), Dimension(3)]) ``` NOTE: This shape is not enforced at runtime. Setting incorrect shapes can result in inconsistencies between the statically-known graph and the runtime value of tensors. For runtime validation of the shape, use `tf.ensure_shape` instead. Args: shape: A `TensorShape` representing the shape of this tensor, a `TensorShapeProto`, a list, a tuple, or None. Raises: ValueError: If `shape` is not compatible with the current shape of this tensor. """ # Reset cached shape. self._shape_val = None # We want set_shape to be reflected in the C API graph for when we run it. if not isinstance(shape, tensor_shape.TensorShape): shape = tensor_shape.TensorShape(shape) dim_list = [] if shape.dims is None: unknown_shape = True else: unknown_shape = False for dim in shape.dims: if dim.value is None: dim_list.append(-1) else: dim_list.append(dim.value) try: c_api.TF_GraphSetTensorShape_wrapper( self._op._graph._c_graph, # pylint: disable=protected-access self._as_tf_output(), dim_list, unknown_shape) except errors.InvalidArgumentError as e: # Convert to ValueError for backwards compatibility. raise ValueError(str(e)) @property def value_index(self): """The index of this tensor in the outputs of its `Operation`.""" return self._value_index def consumers(self): """Returns a list of `Operation`s that consume this tensor. Returns: A list of `Operation`s. """ consumer_names = c_api.TF_OperationOutputConsumers_wrapper( self._as_tf_output()) # pylint: disable=protected-access return [ self.graph._get_operation_by_name_unsafe(name) for name in consumer_names ] # pylint: enable=protected-access def _as_node_def_input(self): """Return a value to use for the NodeDef "input" attribute. The returned string can be used in a NodeDef "input" attribute to indicate that the NodeDef uses this Tensor as input. Raises: ValueError: if this Tensor's Operation does not have a name. Returns: a string. """ if not self._op.name: raise ValueError("Operation was not named: %s" % self._op) if self._value_index == 0: return self._op.name else: return "%s:%d" % (self._op.name, self._value_index) def _as_tf_output(self): # pylint: disable=protected-access # NOTE: Beyond preventing unnecessary (re-)allocation, the cached object # also guarantees that a dictionary of tf_output objects will retain a # deterministic (yet unsorted) order which prevents memory blowup in the # cache of executor(s) stored for every session. if self._tf_output is None: self._tf_output = c_api_util.tf_output(self.op._c_op, self.value_index) return self._tf_output # pylint: enable=protected-access def __str__(self): return "Tensor(\"%s\"%s%s%s)" % ( self.name, (", shape=%s" % self.get_shape()) if self.get_shape().ndims is not None else "", (", dtype=%s" % self._dtype.name) if self._dtype else "", (", device=%s" % self.device) if self.device else "") def __repr__(self): return "<tf.Tensor '%s' shape=%s dtype=%s>" % (self.name, self.get_shape(), self._dtype.name) def __hash__(self): g = getattr(self, "graph", None) if (Tensor._USE_EQUALITY and executing_eagerly_outside_functions() and (g is None or g._building_function)): # pylint: disable=protected-access raise TypeError("Tensor is unhashable if Tensor equality is enabled. " "Instead, use tensor.experimental_ref() as the key.") else: return id(self) def __copy__(self): # TODO(b/77597810): get rid of Tensor copies. cls = self.__class__ result = cls.__new__(cls) result.__dict__.update(self.__dict__) return result # NOTE(mrry): This enables the Tensor's overloaded "right" binary # operators to run when the left operand is an ndarray, because it # accords the Tensor class higher priority than an ndarray, or a # numpy matrix. # TODO(mrry): Convert this to using numpy's __numpy_ufunc__ # mechanism, which allows more control over how Tensors interact # with ndarrays. __array_priority__ = 100 def __array__(self): raise NotImplementedError("Cannot convert a symbolic Tensor ({}) to a numpy" " array.".format(self.name)) def __len__(self): raise TypeError("len is not well defined for symbolic Tensors. ({}) " "Please call `x.shape` rather than `len(x)` for " "shape information.".format(self.name)) @staticmethod def _override_operator(operator, func): _override_helper(Tensor, operator, func) def __bool__(self): """Dummy method to prevent a tensor from being used as a Python `bool`. This overload raises a `TypeError` when the user inadvertently treats a `Tensor` as a boolean (most commonly in an `if` or `while` statement), in code that was not converted by AutoGraph. For example: ```python if tf.constant(True): # Will raise. # ... if tf.constant(5) < tf.constant(7): # Will raise. # ... ``` Raises: `TypeError`. """ self._disallow_bool_casting() def __nonzero__(self): """Dummy method to prevent a tensor from being used as a Python `bool`. This is the Python 2.x counterpart to `__bool__()` above. Raises: `TypeError`. """ self._disallow_bool_casting() def eval(self, feed_dict=None, session=None): """Evaluates this tensor in a `Session`. Calling this method will execute all preceding operations that produce the inputs needed for the operation that produces this tensor. *N.B.* Before invoking `Tensor.eval()`, its graph must have been launched in a session, and either a default session must be available, or `session` must be specified explicitly. Args: feed_dict: A dictionary that maps `Tensor` objects to feed values. See `tf.Session.run` for a description of the valid feed values. session: (Optional.) The `Session` to be used to evaluate this tensor. If none, the default session will be used. Returns: A numpy array corresponding to the value of this tensor. """ return _eval_using_default_session(self, feed_dict, self.graph, session) def experimental_ref(self): # tf.Variable also has the same experimental_ref() API. If you update the # documenation here, please update tf.Variable.experimental_ref() as well. """Returns a hashable reference object to this Tensor. Warning: Experimental API that could be changed or removed. The primary usecase for this API is to put tensors in a set/dictionary. We can't put tensors in a set/dictionary as `tensor.__hash__()` is no longer available starting Tensorflow 2.0. ```python import tensorflow as tf x = tf.constant(5) y = tf.constant(10) z = tf.constant(10) # The followings will raise an exception starting 2.0 # TypeError: Tensor is unhashable if Tensor equality is enabled. tensor_set = {x, y, z} tensor_dict = {x: 'five', y: 'ten', z: 'ten'} ``` Instead, we can use `tensor.experimental_ref()`. ```python tensor_set = {x.experimental_ref(), y.experimental_ref(), z.experimental_ref()} print(x.experimental_ref() in tensor_set) ==> True tensor_dict = {x.experimental_ref(): 'five', y.experimental_ref(): 'ten', z.experimental_ref(): 'ten'} print(tensor_dict[y.experimental_ref()]) ==> ten ``` Also, the reference object provides `.deref()` function that returns the original Tensor. ```python x = tf.constant(5) print(x.experimental_ref().deref()) ==> tf.Tensor(5, shape=(), dtype=int32) ``` """ return object_identity.Reference(self) # TODO(agarwal): consider getting rid of this. class _EagerTensorBase(Tensor): """Base class for EagerTensor.""" # __int__, __float__ and __index__ may copy the tensor to CPU and # only work for scalars; values are cast as per numpy. def __int__(self): return int(self._numpy()) def __long__(self): return long(self._numpy()) def __float__(self): return float(self._numpy()) def __index__(self): maybe_arr = self._numpy() if isinstance(maybe_arr, np.ndarray): return maybe_arr.__index__() return int(maybe_arr) # Must be a NumPy scalar. def __bool__(self): return bool(self._numpy()) __nonzero__ = __bool__ def __format__(self, format_spec): return self._numpy().__format__(format_spec) def __reduce__(self): return convert_to_tensor, (self._numpy(),) def __copy__(self): # Eager Tensors are immutable so it's safe to return themselves as a copy. return self def __deepcopy__(self, memo): # Eager Tensors are immutable so it's safe to return themselves as a copy. del memo return self def __str__(self): return "tf.Tensor(%s, shape=%s, dtype=%s)" % (numpy_text(self), self.shape, self.dtype.name) def __repr__(self): return "<tf.Tensor: id=%s, shape=%s, dtype=%s, numpy=%s>" % ( self._id, self.shape, self.dtype.name, numpy_text(self, is_repr=True)) def __len__(self): """Returns the length of the first dimension in the Tensor.""" if not self.shape.ndims: raise TypeError("Scalar tensor has no `len()`") return self._shape_tuple()[0] def _numpy(self): raise NotImplementedError() @property def dtype(self): # Note: using the intern table directly here as this is # performance-sensitive in some models. return dtypes._INTERN_TABLE[self._datatype_enum()] # pylint: disable=protected-access def numpy(self): """Returns a numpy array or a scalar with the same contents as the Tensor. TODO(ashankar,agarwal): Perhaps this should NOT reference the underlying buffer but instead always explicitly copy? Note that currently it may or may not copy based on whether the numpy data is properly aligned or not. Returns: A numpy array or a scalar. Numpy array may share memory with the Tensor object. Any changes to one may be reflected in the other. A scalar value is returned when self has rank 0. Raises: ValueError: if the type of this Tensor is not representable in numpy. """ maybe_arr = self._numpy() # pylint: disable=protected-access return maybe_arr.copy() if isinstance(maybe_arr, np.ndarray) else maybe_arr @property def backing_device(self): """Returns the name of the device holding this tensor's memory. `.backing_device` is usually the same as `.device`, which returns the device on which the kernel of the operation that produced this tensor ran. However, some operations can produce tensors on a different device (e.g., an operation that executes on the GPU but produces output tensors in host memory). """ raise NotImplementedError() def _datatype_enum(self): raise NotImplementedError() def _shape_tuple(self): """The shape of this Tensor, as a tuple. This is more performant than tuple(shape().as_list()) as it avoids two list and one object creation. Marked private for now as from an API perspective, it would be better to have a single performant way of getting a shape rather than exposing shape() and shape_tuple() (and heaven forbid, shape_list() etc. as well!). Punting on that for now, but ideally one would work things out and remove the need for this method. Returns: tuple with the shape. """ raise NotImplementedError() def _rank(self): """Integer rank of this Tensor. Unlike regular Tensors, the rank is always known for EagerTensors. This is more performant than len(self._shape_tuple()) Returns: Integer rank """ raise NotImplementedError() def _num_elements(self): """Number of elements of this Tensor. Unlike regular Tensors, the number of elements is always known for EagerTensors. This is more performant than tensor.shape.num_elements Returns: Long - num elements in the tensor """ raise NotImplementedError() def _copy_to_device(self, device_name): # pylint: disable=redefined-outer-name raise NotImplementedError() @staticmethod def _override_operator(name, func): setattr(_EagerTensorBase, name, func) def _copy_nograd(self, ctx=None, device_name=None): """Copies tensor to dest device, but doesn't record the operation.""" # Creates a new tensor on the dest device. if ctx is None: ctx = context.context() if device_name is None: device_name = ctx.device_name # pylint: disable=protected-access try: ctx.ensure_initialized() new_tensor = self._copy_to_device(device_name) except core._NotOkStatusException as e: six.raise_from(core._status_to_exception(e.code, e.message), None) return new_tensor def _copy(self, ctx=None, device_name=None): """Copies tensor to dest device.""" new_tensor = self._copy_nograd(ctx, device_name) # Record the copy on tape and define backprop copy as well. if context.executing_eagerly(): self_device = self.device def grad_fun(dresult): return [ dresult._copy(device_name=self_device) if hasattr(dresult, "_copy") else dresult ] tape.record_operation("_copy", [new_tensor], [self], grad_fun) return new_tensor # pylint: enable=protected-access @property def shape(self): if self._tensor_shape is None: # pylint: disable=access-member-before-definition # `_tensor_shape` is declared and defined in the definition of # `EagerTensor`, in C. self._tensor_shape = tensor_shape.TensorShape(self._shape_tuple()) return self._tensor_shape def get_shape(self): """Alias of Tensor.shape.""" return self.shape def _shape_as_list(self): """The shape of the tensor as a list.""" return list(self._shape_tuple()) @property def ndim(self): """Returns the number of Tensor dimensions.""" return self.shape.ndims @deprecation.deprecated(None, "Use tf.identity instead.") def cpu(self): """A copy of this Tensor with contents backed by host memory.""" return self._copy(context.context(), "CPU:0") @deprecation.deprecated(None, "Use tf.identity instead.") def gpu(self, gpu_index=0): """A copy of this Tensor with contents backed by memory on the GPU. Arguments: gpu_index: Identifies which GPU to place the contents on the returned Tensor in. Returns: A GPU-memory backed Tensor object initialized with the same contents as this Tensor. """ return self._copy(context.context(), "GPU:" + str(gpu_index)) def set_shape(self, shape): if not self.shape.is_compatible_with(shape): raise ValueError( "Tensor's shape %s is not compatible with supplied shape %s" % (self.shape, shape)) # Methods not supported / implemented for Eager Tensors. @property def op(self): raise AttributeError( "Tensor.op is meaningless when eager execution is enabled.") @property def graph(self): raise AttributeError( "Tensor.graph is meaningless when eager execution is enabled.") @property def name(self): raise AttributeError( "Tensor.name is meaningless when eager execution is enabled.") @property def value_index(self): raise AttributeError( "Tensor.value_index is meaningless when eager execution is enabled.") def consumers(self): raise NotImplementedError( "Tensor.consumers is meaningless when eager execution is enabled.") def _add_consumer(self, consumer): raise NotImplementedError( "_add_consumer not supported when eager execution is enabled.") def _as_node_def_input(self): raise NotImplementedError( "_as_node_def_input not supported when eager execution is enabled.") def _as_tf_output(self): raise NotImplementedError( "_as_tf_output not supported when eager execution is enabled.") def eval(self, feed_dict=None, session=None): raise NotImplementedError( "eval is not supported when eager execution is enabled, " "is .numpy() what you're looking for?") # This call creates an EagerTensor class, as a subclass of _EagerTensorBase, and # registers it with the current module. EagerTensor = c_api.TFE_Py_InitEagerTensor(_EagerTensorBase) register_dense_tensor_like_type(Tensor) @tf_export(v1=["convert_to_tensor"]) def convert_to_tensor(value, dtype=None, name=None, preferred_dtype=None, dtype_hint=None): """Converts the given `value` to a `Tensor`. This function converts Python objects of various types to `Tensor` objects. It accepts `Tensor` objects, numpy arrays, Python lists, and Python scalars. For example: ```python import numpy as np def my_func(arg): arg = tf.convert_to_tensor(arg, dtype=tf.float32) return tf.matmul(arg, arg) + arg # The following calls are equivalent. value_1 = my_func(tf.constant([[1.0, 2.0], [3.0, 4.0]])) value_2 = my_func([[1.0, 2.0], [3.0, 4.0]]) value_3 = my_func(np.array([[1.0, 2.0], [3.0, 4.0]], dtype=np.float32)) ``` This function can be useful when composing a new operation in Python (such as `my_func` in the example above). All standard Python op constructors apply this function to each of their Tensor-valued inputs, which allows those ops to accept numpy arrays, Python lists, and scalars in addition to `Tensor` objects. Note: This function diverges from default Numpy behavior for `float` and `string` types when `None` is present in a Python list or scalar. Rather than silently converting `None` values, an error will be thrown. Args: value: An object whose type has a registered `Tensor` conversion function. dtype: Optional element type for the returned tensor. If missing, the type is inferred from the type of `value`. name: Optional name to use if a new `Tensor` is created. preferred_dtype: Optional element type for the returned tensor, used when dtype is None. In some cases, a caller may not have a dtype in mind when converting to a tensor, so preferred_dtype can be used as a soft preference. If the conversion to `preferred_dtype` is not possible, this argument has no effect. dtype_hint: same meaning as preferred_dtype, and overrides it. Returns: A `Tensor` based on `value`. Raises: TypeError: If no conversion function is registered for `value` to `dtype`. RuntimeError: If a registered conversion function returns an invalid value. ValueError: If the `value` is a tensor not of given `dtype` in graph mode. """ preferred_dtype = deprecation.deprecated_argument_lookup( "dtype_hint", dtype_hint, "preferred_dtype", preferred_dtype) return convert_to_tensor_v2(value, dtype, preferred_dtype, name) @tf_export("convert_to_tensor", v1=[]) def convert_to_tensor_v2(value, dtype=None, dtype_hint=None, name=None): """Converts the given `value` to a `Tensor`. This function converts Python objects of various types to `Tensor` objects. It accepts `Tensor` objects, numpy arrays, Python lists, and Python scalars. For example: ```python import numpy as np def my_func(arg): arg = tf.convert_to_tensor(arg, dtype=tf.float32) return tf.matmul(arg, arg) + arg # The following calls are equivalent. value_1 = my_func(tf.constant([[1.0, 2.0], [3.0, 4.0]])) value_2 = my_func([[1.0, 2.0], [3.0, 4.0]]) value_3 = my_func(np.array([[1.0, 2.0], [3.0, 4.0]], dtype=np.float32)) ``` This function can be useful when composing a new operation in Python (such as `my_func` in the example above). All standard Python op constructors apply this function to each of their Tensor-valued inputs, which allows those ops to accept numpy arrays, Python lists, and scalars in addition to `Tensor` objects. Note: This function diverges from default Numpy behavior for `float` and `string` types when `None` is present in a Python list or scalar. Rather than silently converting `None` values, an error will be thrown. Args: value: An object whose type has a registered `Tensor` conversion function. dtype: Optional element type for the returned tensor. If missing, the type is inferred from the type of `value`. dtype_hint: Optional element type for the returned tensor, used when dtype is None. In some cases, a caller may not have a dtype in mind when converting to a tensor, so dtype_hint can be used as a soft preference. If the conversion to `dtype_hint` is not possible, this argument has no effect. name: Optional name to use if a new `Tensor` is created. Returns: A `Tensor` based on `value`. Raises: TypeError: If no conversion function is registered for `value` to `dtype`. RuntimeError: If a registered conversion function returns an invalid value. ValueError: If the `value` is a tensor not of given `dtype` in graph mode. """ return internal_convert_to_tensor( value=value, dtype=dtype, name=name, preferred_dtype=dtype_hint, as_ref=False) def _error_prefix(name): return "" if name is None else "%s: " % name def internal_convert_to_tensor(value, dtype=None, name=None, as_ref=False, preferred_dtype=None, ctx=None, accepted_result_types=(Tensor,)): """Implementation of the public convert_to_tensor.""" if isinstance(value, EagerTensor): if ctx is None: ctx = context.context() if not ctx.executing_eagerly(): graph = get_default_graph() if not graph.building_function: raise RuntimeError("Attempting to capture an EagerTensor without " "building a function.") return graph.capture(value, name=name) if dtype is not None: dtype = dtypes.as_dtype(dtype) if isinstance(value, Tensor): if dtype is not None and not dtype.is_compatible_with(value.dtype): raise ValueError( "Tensor conversion requested dtype %s for Tensor with dtype %s: %r" % (dtype.name, value.dtype.name, value)) return value if preferred_dtype is not None: preferred_dtype = dtypes.as_dtype(preferred_dtype) for base_type, conversion_func in tensor_conversion_registry.get(type(value)): # If dtype is None but preferred_dtype is not None, we try to # cast to preferred_dtype first. ret = None if dtype is None and preferred_dtype is not None: try: ret = conversion_func( value, dtype=preferred_dtype, name=name, as_ref=as_ref) except (TypeError, ValueError): # Could not coerce the conversion to use the preferred dtype. pass else: if (ret is not NotImplemented and ret.dtype.base_dtype != preferred_dtype.base_dtype): raise TypeError("convert_to_tensor did not convert to " "the preferred dtype: %s vs %s " % (ret.dtype.base_dtype, preferred_dtype.base_dtype)) if ret is None: ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref) if ret is NotImplemented: continue if not isinstance(ret, accepted_result_types): raise RuntimeError( "%sConversion function %r for type %s returned non-Tensor: %r" % (_error_prefix(name), conversion_func, base_type, ret)) if dtype and not dtype.is_compatible_with(ret.dtype): raise RuntimeError( "%sConversion function %r for type %s returned incompatible " "dtype: requested = %s, actual = %s" % (_error_prefix(name), conversion_func, base_type, dtype.name, ret.dtype.name)) return ret raise TypeError("%sCannot convert %r with type %s to Tensor: " "no conversion function registered." % (_error_prefix(name), value, type(value))) def internal_convert_n_to_tensor(values, dtype=None, name=None, as_ref=False, preferred_dtype=None, ctx=None): """Converts `values` to a list of `Tensor` objects. Args: values: A list of objects that can be consumed by `tf.convert_to_tensor()`. dtype: (Optional.) The required `DType` of the returned `Tensor` objects. name: (Optional.) A name prefix to used when a new `Tensor` is created, in which case element `i` will be given the name `name + '_' + i`. as_ref: True if the caller wants the results as ref tensors. preferred_dtype: Optional element type for the returned tensors, used when dtype is None. In some cases, a caller may not have a dtype in mind when converting to a tensor, so preferred_dtype can be used as a soft preference. If the conversion to `preferred_dtype` is not possible, this argument has no effect. ctx: The value of context.context(). Returns: A list of `Tensor` and/or `IndexedSlices` objects. Raises: TypeError: If no conversion function is registered for an element in `values`. RuntimeError: If a registered conversion function returns an invalid value. """ if not isinstance(values, collections_abc.Sequence): raise TypeError("values must be a sequence.") ret = [] if ctx is None: ctx = context.context() for i, value in enumerate(values): n = None if name is None else "%s_%d" % (name, i) ret.append( internal_convert_to_tensor( value, dtype=dtype, name=n, as_ref=as_ref, preferred_dtype=preferred_dtype, ctx=ctx)) return ret def convert_n_to_tensor(values, dtype=None, name=None, preferred_dtype=None): """Converts `values` to a list of `Tensor` objects. Args: values: A list of objects that can be consumed by `tf.convert_to_tensor()`. dtype: (Optional.) The required `DType` of the returned `Tensor` objects. name: (Optional.) A name prefix to used when a new `Tensor` is created, in which case element `i` will be given the name `name + '_' + i`. preferred_dtype: Optional element type for the returned tensors, used when dtype is None. In some cases, a caller may not have a dtype in mind when converting to a tensor, so preferred_dtype can be used as a soft preference. If the conversion to `preferred_dtype` is not possible, this argument has no effect. Returns: A list of `Tensor` and/or `IndexedSlices` objects. Raises: TypeError: If no conversion function is registered for an element in `values`. RuntimeError: If a registered conversion function returns an invalid value. """ return internal_convert_n_to_tensor( values=values, dtype=dtype, name=name, preferred_dtype=preferred_dtype, as_ref=False) def convert_to_tensor_or_composite(value, dtype=None, name=None): """Converts the given object to a `Tensor` or `CompositeTensor`. If `value` is a `CompositeTensor` it is returned unmodified. Otherwise, it is converted to a `Tensor` using `convert_to_tensor()`. Args: value: A `CompositeTensor` or an object that can be consumed by `convert_to_tensor()`. dtype: (Optional.) The required `DType` of the returned `Tensor` or `CompositeTensor`. name: (Optional.) A name to use if a new `Tensor` is created. Returns: A `Tensor` or `CompositeTensor`, based on `value`. Raises: ValueError: If `dtype` does not match the element type of `value`. """ return internal_convert_to_tensor_or_composite( value=value, dtype=dtype, name=name, as_ref=False) def internal_convert_to_tensor_or_composite(value, dtype=None, name=None, as_ref=False): """Converts the given object to a `Tensor` or `CompositeTensor`. If `value` is a `CompositeTensor` it is returned unmodified. Otherwise, it is converted to a `Tensor` using `convert_to_tensor()`. Args: value: A `CompositeTensor`, or an object that can be consumed by `convert_to_tensor()`. dtype: (Optional.) The required `DType` of the returned `Tensor` or `CompositeTensor`. name: (Optional.) A name to use if a new `Tensor` is created. as_ref: True if the caller wants the results as ref tensors. Returns: A `Tensor` or `CompositeTensor`, based on `value`. Raises: ValueError: If `dtype` does not match the element type of `value`. """ if isinstance(value, composite_tensor.CompositeTensor): value_dtype = getattr(value, "dtype", None) if dtype and not dtypes.as_dtype(dtype).is_compatible_with(value_dtype): raise ValueError( "Tensor conversion requested dtype %s for Tensor with dtype %s: %r" % (dtypes.as_dtype(dtype).name, value.dtype.name, str(value))) return value else: return internal_convert_to_tensor( value, dtype=dtype, name=name, as_ref=as_ref, accepted_result_types=(Tensor, composite_tensor.CompositeTensor)) def internal_convert_n_to_tensor_or_composite(values, dtype=None, name=None, as_ref=False): """Converts `values` to a list of `Tensor` or `CompositeTensor` objects. Any `CompositeTensor` objects in `values` are returned unmodified. Args: values: A list of `None`, `CompositeTensor`, or objects that can be consumed by `convert_to_tensor()`. dtype: (Optional.) The required `DType` of the returned `Tensor`s or `CompositeTensor`s. name: (Optional.) A name prefix to used when a new `Tensor` is created, in which case element `i` will be given the name `name + '_' + i`. as_ref: True if the caller wants the results as ref tensors. Returns: A list of `Tensor`, `CompositeTensor`, and/or `None` objects. Raises: TypeError: If no conversion function is registered for an element in `values`. RuntimeError: If a registered conversion function returns an invalid value. """ if not isinstance(values, collections_abc.Sequence): raise TypeError("values must be a sequence.") ret = [] for i, value in enumerate(values): if value is None: ret.append(value) else: n = None if name is None else "%s_%d" % (name, i) ret.append( internal_convert_to_tensor_or_composite( value, dtype=dtype, name=n, as_ref=as_ref)) return ret def convert_n_to_tensor_or_composite(values, dtype=None, name=None): """Converts `values` to a list of `Output` or `CompositeTensor` objects. Any `CompositeTensor` objects in `values` are returned unmodified. Args: values: A list of `None`, `CompositeTensor``, or objects that can be consumed by `convert_to_tensor()`. dtype: (Optional.) The required `DType` of the returned `Tensor`s or `CompositeTensor`s. name: (Optional.) A name prefix to used when a new `Tensor` is created, in which case element `i` will be given the name `name + '_' + i`. Returns: A list of `Tensor` and/or `CompositeTensor` objects. Raises: TypeError: If no conversion function is registered for an element in `values`. RuntimeError: If a registered conversion function returns an invalid value. """ return internal_convert_n_to_tensor_or_composite( values=values, dtype=dtype, name=name, as_ref=False) def _device_string(dev_spec): if pydev.is_device_spec(dev_spec): return dev_spec.to_string() else: return dev_spec def _NodeDef(op_type, name, device=None, attrs=None): # pylint: disable=redefined-outer-name """Create a NodeDef proto. Args: op_type: Value for the "op" attribute of the NodeDef proto. name: Value for the "name" attribute of the NodeDef proto. device: string, device, or function from NodeDef to string. Value for the "device" attribute of the NodeDef proto. attrs: Optional dictionary where the key is the attribute name (a string) and the value is the respective "attr" attribute of the NodeDef proto (an AttrValue). Returns: A node_def_pb2.NodeDef protocol buffer. """ node_def = node_def_pb2.NodeDef() node_def.op = compat.as_bytes(op_type) node_def.name = compat.as_bytes(name) if attrs is not None: for k, v in six.iteritems(attrs): node_def.attr[k].CopyFrom(v) if device is not None: if callable(device): node_def.device = device(node_def) else: node_def.device = _device_string(device) return node_def # Copied from core/framework/node_def_util.cc # TODO(mrry,josh11b): Consolidate this validation in C++ code. _VALID_OP_NAME_REGEX = re.compile("^[A-Za-z0-9.][A-Za-z0-9_.\\-/]*$") _VALID_SCOPE_NAME_REGEX = re.compile("^[A-Za-z0-9_.\\-/]*$") def _create_c_op(graph, node_def, inputs, control_inputs): """Creates a TF_Operation. Args: graph: a `Graph`. node_def: `node_def_pb2.NodeDef` for the operation to create. inputs: A list of `Tensor`s (corresponding to scalar inputs) and lists of `Tensor`s (corresponding to sequence inputs, e.g. "int64 * N", "list(int64)"). The length of the list should be equal to the number of inputs specified by this operation's op def. control_inputs: A list of `Operation`s to set as control dependencies. Returns: A wrapped TF_Operation*. """ # pylint: disable=protected-access op_desc = c_api.TF_NewOperation(graph._c_graph, compat.as_str(node_def.op), compat.as_str(node_def.name)) if node_def.device: c_api.TF_SetDevice(op_desc, compat.as_str(node_def.device)) # Add inputs for op_input in inputs: if isinstance(op_input, (list, tuple)): c_api.TF_AddInputList(op_desc, [t._as_tf_output() for t in op_input]) else: c_api.TF_AddInput(op_desc, op_input._as_tf_output()) # Add control inputs for control_input in control_inputs: c_api.TF_AddControlInput(op_desc, control_input._c_op) # pylint: enable=protected-access # Add attrs for name, attr_value in node_def.attr.items(): serialized = attr_value.SerializeToString() # TODO(skyewm): this creates and deletes a new TF_Status for every attr. # It might be worth creating a convenient way to re-use the same status. c_api.TF_SetAttrValueProto(op_desc, compat.as_str(name), serialized) try: c_op = c_api.TF_FinishOperation(op_desc) except errors.InvalidArgumentError as e: # Convert to ValueError for backwards compatibility. raise ValueError(str(e)) return c_op @tf_export("Operation") class Operation(object): """Represents a graph node that performs computation on tensors. An `Operation` is a node in a TensorFlow `Graph` that takes zero or more `Tensor` objects as input, and produces zero or more `Tensor` objects as output. Objects of type `Operation` are created by calling a Python op constructor (such as `tf.matmul`) or `tf.Graph.create_op`. For example `c = tf.matmul(a, b)` creates an `Operation` of type "MatMul" that takes tensors `a` and `b` as input, and produces `c` as output. After the graph has been launched in a session, an `Operation` can be executed by passing it to `tf.Session.run`. `op.run()` is a shortcut for calling `tf.compat.v1.get_default_session().run(op)`. """ def __init__(self, node_def, g, inputs=None, output_types=None, control_inputs=None, input_types=None, original_op=None, op_def=None): r"""Creates an `Operation`. NOTE: This constructor validates the name of the `Operation` (passed as `node_def.name`). Valid `Operation` names match the following regular expression: [A-Za-z0-9.][A-Za-z0-9_.\\-/]* Args: node_def: `node_def_pb2.NodeDef`. `NodeDef` for the `Operation`. Used for attributes of `node_def_pb2.NodeDef`, typically `name`, `op`, and `device`. The `input` attribute is irrelevant here as it will be computed when generating the model. g: `Graph`. The parent graph. inputs: list of `Tensor` objects. The inputs to this `Operation`. output_types: list of `DType` objects. List of the types of the `Tensors` computed by this operation. The length of this list indicates the number of output endpoints of the `Operation`. control_inputs: list of operations or tensors from which to have a control dependency. input_types: List of `DType` objects representing the types of the tensors accepted by the `Operation`. By default uses `[x.dtype.base_dtype for x in inputs]`. Operations that expect reference-typed inputs must specify these explicitly. original_op: Optional. Used to associate the new `Operation` with an existing `Operation` (for example, a replica with the op that was replicated). op_def: Optional. The `op_def_pb2.OpDef` proto that describes the op type that this `Operation` represents. Raises: TypeError: if control inputs are not Operations or Tensors, or if `node_def` is not a `NodeDef`, or if `g` is not a `Graph`, or if `inputs` are not tensors, or if `inputs` and `input_types` are incompatible. ValueError: if the `node_def` name is not valid. """ # For internal use only: `node_def` can be set to a TF_Operation to create # an Operation for that op. This is useful for creating Operations for ops # indirectly created by C API methods, e.g. the ops created by # TF_ImportGraphDef. When `node_def` is a TF_Operation, all optional fields # should be None. if isinstance(node_def, node_def_pb2.NodeDef): if node_def.ByteSize() >= (1 << 31) or node_def.ByteSize() < 0: raise ValueError( "Cannot create a tensor proto whose content is larger than 2GB.") if not _VALID_OP_NAME_REGEX.match(node_def.name): raise ValueError("'%s' is not a valid node name" % node_def.name) c_op = None elif type(node_def).__name__ == "SwigPyObject": assert inputs is None assert output_types is None assert control_inputs is None assert input_types is None assert original_op is None assert op_def is None c_op = node_def else: raise TypeError("node_def needs to be a NodeDef: %s" % node_def) if not isinstance(g, Graph): raise TypeError("g needs to be a Graph: %s" % g) self._graph = g if inputs is None: inputs = [] elif not isinstance(inputs, list): raise TypeError("inputs needs to be a list of Tensors: %s" % inputs) for a in inputs: if not isinstance(a, Tensor): raise TypeError("input needs to be a Tensor: %s" % a) if input_types is None: input_types = [i.dtype.base_dtype for i in inputs] else: if not all( x.is_compatible_with(i.dtype) for i, x in zip(inputs, input_types)): raise TypeError("In op '%s', input types (%s) are not compatible " "with expected types (%s)" % (node_def.name, [i.dtype for i in inputs], input_types)) # Build the list of control inputs. control_input_ops = [] if control_inputs: for c in control_inputs: control_op = None if isinstance(c, Operation): control_op = c elif isinstance(c, (Tensor, IndexedSlices)): control_op = c.op else: raise TypeError("Control input must be an Operation, " "a Tensor, or IndexedSlices: %s" % c) control_input_ops.append(control_op) # This will be set by self.inputs. self._inputs_val = None # pylint: disable=protected-access self._id_value = self._graph._next_id() self._original_op = original_op self._traceback = tf_stack.extract_stack() # List of _UserDevSpecs holding code location of device context manager # invocations and the users original argument to them. self._device_code_locations = None # Dict mapping op name to file and line information for op colocation # context managers. self._colocation_code_locations = None self._control_flow_context = self.graph._get_control_flow_context() # Initialize self._c_op. if c_op: self._c_op = c_op op_def = g._get_op_def(c_api.TF_OperationOpType(c_op)) else: if op_def is None: op_def = self._graph._get_op_def(node_def.op) # TODO(skyewm): op_def_library.apply_op() flattens the incoming inputs. # Refactor so we don't have to do this here. grouped_inputs = self._reconstruct_sequence_inputs( op_def, inputs, node_def.attr) self._c_op = _create_c_op(self._graph, node_def, grouped_inputs, control_input_ops) # pylint: enable=protected-access self._is_stateful = op_def.is_stateful # Initialize self._outputs. num_outputs = c_api.TF_OperationNumOutputs(self._c_op) self._outputs = [] for i in range(num_outputs): tf_output = c_api_util.tf_output(self._c_op, i) output_type = c_api.TF_OperationOutputType(tf_output) tensor = Tensor._create_with_tf_output(self, i, output_type, tf_output) # pylint: disable=protected-access self._outputs.append(tensor) self._graph._add_op(self) # pylint: disable=protected-access if not c_op: self._control_flow_post_processing() def _control_flow_post_processing(self): """Add this op to its control flow context. This may add new ops and change this op's inputs. self.inputs must be available before calling this method. """ for input_tensor in self.inputs: control_flow_util.CheckInputFromValidContext(self, input_tensor.op) if self._control_flow_context is not None: self._control_flow_context.AddOp(self) def _reconstruct_sequence_inputs(self, op_def, inputs, attrs): """Regroups a flat list of input tensors into scalar and sequence inputs. Args: op_def: The `op_def_pb2.OpDef` (for knowing the input types) inputs: a list of input `Tensor`s to the op. attrs: mapping from attr name to `attr_value_pb2.AttrValue` (these define how long each sequence is) Returns: A list of `Tensor`s (corresponding to scalar inputs) and lists of `Tensor`s (corresponding to sequence inputs). """ grouped_inputs = [] i = 0 for input_arg in op_def.input_arg: if input_arg.number_attr: input_len = attrs[input_arg.number_attr].i is_sequence = True elif input_arg.type_list_attr: input_len = len(attrs[input_arg.type_list_attr].list.type) is_sequence = True else: input_len = 1 is_sequence = False if is_sequence: grouped_inputs.append(inputs[i:i + input_len]) else: grouped_inputs.append(inputs[i]) i += input_len assert i == len(inputs) return grouped_inputs def colocation_groups(self): """Returns the list of colocation groups of the op.""" default_colocation_group = [compat.as_bytes("loc:@%s" % self.name)] try: class_attr = self.get_attr("_class") except ValueError: # This op has no explicit colocation group, so it is itself its # own root of a colocation group. return default_colocation_group attr_groups = [ class_name for class_name in class_attr if class_name.startswith(b"loc:@") ] # If there are no colocation groups in the explicit _class field, # return the default colocation group. return attr_groups if attr_groups else default_colocation_group def values(self): """DEPRECATED: Use outputs.""" return tuple(self.outputs) def _get_control_flow_context(self): """Returns the control flow context of this op. Returns: A context object. """ return self._control_flow_context def _set_control_flow_context(self, ctx): """Sets the current control flow context of this op. Args: ctx: a context object. """ self._control_flow_context = ctx @property def name(self): """The full name of this operation.""" return c_api.TF_OperationName(self._c_op) @property def _id(self): """The unique integer id of this operation.""" return self._id_value @property def device(self): """The name of the device to which this op has been assigned, if any. Returns: The string name of the device to which this op has been assigned, or an empty string if it has not been assigned to a device. """ return c_api.TF_OperationDevice(self._c_op) @property def _device_assignments(self): """Code locations for device context managers active at op creation. This property will return a list of traceable_stack.TraceableObject instances where .obj is a string representing the assigned device (or information about the function that would be applied to this op to compute the desired device) and the filename and lineno members record the location of the relevant device context manager. For example, suppose file_a contained these lines: file_a.py: 15: with tf.device('/gpu:0'): 16: node_b = tf.constant(4, name='NODE_B') Then a TraceableObject t_obj representing the device context manager would have these member values: t_obj.obj -> '/gpu:0' t_obj.filename = 'file_a.py' t_obj.lineno = 15 and node_b.op._device_assignments would return the list [t_obj]. Returns: [str: traceable_stack.TraceableObject, ...] as per this method's description, above. """ return self._device_code_locations or [] @property def _colocation_dict(self): """Code locations for colocation context managers active at op creation. This property will return a dictionary for which the keys are nodes with which this Operation is colocated, and for which the values are traceable_stack.TraceableObject instances. The TraceableObject instances record the location of the relevant colocation context manager but have the "obj" field set to None to prevent leaking private data. For example, suppose file_a contained these lines: file_a.py: 14: node_a = tf.constant(3, name='NODE_A') 15: with tf.compat.v1.colocate_with(node_a): 16: node_b = tf.constant(4, name='NODE_B') Then a TraceableObject t_obj representing the colocation context manager would have these member values: t_obj.obj -> None t_obj.filename = 'file_a.py' t_obj.lineno = 15 and node_b.op._colocation_dict would return the dictionary { 'NODE_A': t_obj } Returns: {str: traceable_stack.TraceableObject} as per this method's description, above. """ locations_dict = self._colocation_code_locations or {} return locations_dict.copy() @property def _output_types(self): """List this operation's output types. Returns: List of the types of the Tensors computed by this operation. Each element in the list is an integer whose value is one of the TF_DataType enums defined in c_api.h The length of this list indicates the number of output endpoints of the operation. """ num_outputs = c_api.TF_OperationNumOutputs(self._c_op) output_types = [ c_api.TF_OperationOutputType(self._tf_output(i)) for i in xrange(num_outputs) ] # In all the tests we have output_types that are passed into # Operation.__init__ are a list of ints (which is illegal according # to the docstring), but input_types are instances of DType. # This extra assert is to catch if we ever use DType for output_types. if output_types: assert isinstance(output_types[0], int) return output_types def _tf_output(self, output_idx): """Create and return a new TF_Output for output_idx'th output of this op.""" tf_output = c_api.TF_Output() tf_output.oper = self._c_op tf_output.index = output_idx return tf_output def _tf_input(self, input_idx): """Create and return a new TF_Input for input_idx'th input of this op.""" tf_input = c_api.TF_Input() tf_input.oper = self._c_op tf_input.index = input_idx return tf_input def _set_device(self, device): # pylint: disable=redefined-outer-name """Set the device of this operation. Args: device: string or device.. The device to set. """ self._set_device_from_string(compat.as_str(_device_string(device))) def _set_device_from_string(self, device_str): """Fast path to set device if the type is known to be a string. This function is called frequently enough during graph construction that there are non-trivial performance gains if the caller can guarantee that the specified device is already a string. Args: device_str: A string specifying where to place this op. """ c_api.SetRequestedDevice( self._graph._c_graph, # pylint: disable=protected-access self._c_op, # pylint: disable=protected-access device_str) def _update_input(self, index, tensor): """Update the input to this operation at the given index. NOTE: This is for TF internal use only. Please don't use it. Args: index: the index of the input to update. tensor: the Tensor to be used as the input at the given index. Raises: TypeError: if tensor is not a Tensor, or if input tensor type is not convertible to dtype. ValueError: if the Tensor is from a different graph. """ if not isinstance(tensor, Tensor): raise TypeError("tensor must be a Tensor: %s" % tensor) _assert_same_graph(self, tensor) # Reset cached inputs. self._inputs_val = None c_api.UpdateEdge( self._graph._c_graph, # pylint: disable=protected-access tensor._as_tf_output(), # pylint: disable=protected-access self._tf_input(index)) def _add_while_inputs(self, tensors): """See AddWhileInputHack in python_api.h. NOTE: This is for TF internal use only. Please don't use it. Args: tensors: list of Tensors Raises: TypeError: if tensor is not a Tensor, or if input tensor type is not convertible to dtype. ValueError: if the Tensor is from a different graph. """ for tensor in tensors: if not isinstance(tensor, Tensor): raise TypeError("tensor must be a Tensor: %s" % tensor) _assert_same_graph(self, tensor) # Reset cached inputs. self._inputs_val = None c_api.AddWhileInputHack( self._graph._c_graph, # pylint: disable=protected-access tensor._as_tf_output(), # pylint: disable=protected-access self._c_op) def _add_control_inputs(self, ops): """Add a list of new control inputs to this operation. Args: ops: the list of Operations to add as control input. Raises: TypeError: if ops is not a list of Operations. ValueError: if any op in ops is from a different graph. """ for op in ops: if not isinstance(op, Operation): raise TypeError("op must be an Operation: %s" % op) c_api.AddControlInput(self._graph._c_graph, self._c_op, op._c_op) # pylint: disable=protected-access def _add_control_input(self, op): """Add a new control input to this operation. Args: op: the Operation to add as control input. Raises: TypeError: if op is not an Operation. ValueError: if op is from a different graph. """ if not isinstance(op, Operation): raise TypeError("op must be an Operation: %s" % op) c_api.AddControlInput(self._graph._c_graph, self._c_op, op._c_op) # pylint: disable=protected-access def _remove_all_control_inputs(self): """Removes any control inputs to this operation.""" c_api.RemoveAllControlInputs(self._graph._c_graph, self._c_op) # pylint: disable=protected-access def _add_outputs(self, types, shapes): """Adds new Tensors to self.outputs. Note: this is generally unsafe to use. This is used in certain situations in conjunction with _set_type_list_attr. Arguments: types: list of DTypes shapes: list of TensorShapes """ assert len(types) == len(shapes) orig_num_outputs = len(self.outputs) for i in range(len(types)): t = Tensor(self, orig_num_outputs + i, types[i]) self._outputs.append(t) t.set_shape(shapes[i]) def __str__(self): return str(self.node_def) def __repr__(self): return "<tf.Operation '%s' type=%s>" % (self.name, self.type) @property def outputs(self): """The list of `Tensor` objects representing the outputs of this op.""" return self._outputs class _InputList(object): """Immutable input list wrapper.""" def __init__(self, inputs): self._inputs = inputs def __iter__(self): return iter(self._inputs) def __len__(self): return len(self._inputs) def __bool__(self): return bool(self._inputs) # Python 3 wants __bool__, Python 2.7 wants __nonzero__ __nonzero__ = __bool__ def __getitem__(self, i): return self._inputs[i] @property def inputs(self): """The list of `Tensor` objects representing the data inputs of this op.""" if self._inputs_val is None: tf_outputs = c_api.GetOperationInputs(self._c_op) # pylint: disable=protected-access retval = [ self.graph._get_tensor_by_tf_output(tf_output) for tf_output in tf_outputs ] # pylint: enable=protected-access self._inputs_val = Operation._InputList(retval) return self._inputs_val @property def _inputs(self): logging.warning("Operation._inputs is private, use Operation.inputs " "instead. Operation._inputs will eventually be removed.") return self.inputs @_inputs.setter def _inputs(self, value): raise ValueError("Cannot assign _inputs") @property def _input_types(self): num_inputs = c_api.TF_OperationNumInputs(self._c_op) input_types = [ dtypes.as_dtype(c_api.TF_OperationInputType(self._tf_input(i))) for i in xrange(num_inputs) ] return input_types @_input_types.setter def _input_types(self, value): raise ValueError("Cannot assign _input_types") @property def control_inputs(self): """The `Operation` objects on which this op has a control dependency. Before this op is executed, TensorFlow will ensure that the operations in `self.control_inputs` have finished executing. This mechanism can be used to run ops sequentially for performance reasons, or to ensure that the side effects of an op are observed in the correct order. Returns: A list of `Operation` objects. """ control_c_ops = c_api.TF_OperationGetControlInputs_wrapper(self._c_op) # pylint: disable=protected-access return [ self.graph._get_operation_by_name_unsafe(c_api.TF_OperationName(c_op)) for c_op in control_c_ops ] # pylint: enable=protected-access @property def _control_outputs(self): """The `Operation` objects which have a control dependency on this op. Before any of the ops in self._control_outputs can execute tensorflow will ensure self has finished executing. Returns: A list of `Operation` objects. """ control_c_ops = c_api.TF_OperationGetControlOutputs_wrapper(self._c_op) # pylint: disable=protected-access return [ self.graph._get_operation_by_name_unsafe(c_api.TF_OperationName(c_op)) for c_op in control_c_ops ] # pylint: enable=protected-access @property def _control_inputs(self): logging.warning("Operation._control_inputs is private, use " "Operation.control_inputs instead. " "Operation._control_inputs will eventually be removed.") return self.control_inputs @_control_inputs.setter def _control_inputs(self, value): logging.warning("Operation._control_inputs is private, use " "Operation.control_inputs instead. " "Operation._control_inputs will eventually be removed.") # Copy value because it may be self._control_inputs_val (in particular if # this is called from self._control_inputs += ...), and we don't want to # clear value below. value = copy.copy(value) self._remove_all_control_inputs() self._add_control_inputs(value) @property def type(self): """The type of the op (e.g. `"MatMul"`).""" return c_api.TF_OperationOpType(self._c_op) @property def graph(self): """The `Graph` that contains this operation.""" return self._graph @property def node_def(self): # pylint: disable=line-too-long """Returns the `NodeDef` representation of this operation. Returns: A [`NodeDef`](https://www.tensorflow.org/code/tensorflow/core/framework/node_def.proto) protocol buffer. """ # pylint: enable=line-too-long with c_api_util.tf_buffer() as buf: c_api.TF_OperationToNodeDef(self._c_op, buf) data = c_api.TF_GetBuffer(buf) node_def = node_def_pb2.NodeDef() node_def.ParseFromString(compat.as_bytes(data)) return node_def @property def _node_def(self): logging.warning("Operation._node_def is private, use Operation.node_def " "instead. Operation._node_def will eventually be removed.") return self.node_def @property def op_def(self): # pylint: disable=line-too-long """Returns the `OpDef` proto that represents the type of this op. Returns: An [`OpDef`](https://www.tensorflow.org/code/tensorflow/core/framework/op_def.proto) protocol buffer. """ # pylint: enable=line-too-long return self._graph._get_op_def(self.type) @property def _op_def(self): logging.warning("Operation._op_def is private, use Operation.op_def " "instead. Operation._op_def will eventually be removed.") return self.op_def @property def traceback(self): """Returns the call stack from when this operation was constructed.""" return tf_stack.convert_stack(self._traceback) @property def traceback_with_start_lines(self): """Same as traceback but includes start line of function definition. Returns: A list of 5-tuples (filename, lineno, name, code, func_start_lineno). """ return tf_stack.convert_stack( self._traceback, include_func_start_lineno=True) def _set_attr(self, attr_name, attr_value): """Private method used to set an attribute in the node_def.""" buf = c_api.TF_NewBufferFromString( compat.as_bytes(attr_value.SerializeToString())) try: # pylint: disable=protected-access c_api.SetAttr(self._graph._c_graph, self._c_op, attr_name, buf) # pylint: enable=protected-access finally: c_api.TF_DeleteBuffer(buf) def _set_func_attr(self, attr_name, func_name): """Private method used to set a function attribute in the node_def.""" func = attr_value_pb2.NameAttrList(name=func_name) self._set_attr(attr_name, attr_value_pb2.AttrValue(func=func)) def _set_func_list_attr(self, attr_name, func_names): """Private method used to set a list(function) attribute in the node_def.""" funcs = [attr_value_pb2.NameAttrList(name=func_name) for func_name in func_names] funcs_list = attr_value_pb2.AttrValue.ListValue(func=funcs) self._set_attr(attr_name, attr_value_pb2.AttrValue(list=funcs_list)) def _set_type_list_attr(self, attr_name, types): """Private method used to set a list(type) attribute in the node_def.""" if not types: return if isinstance(types[0], dtypes.DType): types = [dt.as_datatype_enum for dt in types] types_list = attr_value_pb2.AttrValue.ListValue(type=types) self._set_attr(attr_name, attr_value_pb2.AttrValue(list=types_list)) def _set_shape_list_attr(self, attr_name, shapes): """Private method used to set a list(shape) attribute in the node_def.""" shapes = [s.as_proto() for s in shapes] shapes_list = attr_value_pb2.AttrValue.ListValue(shape=shapes) self._set_attr(attr_name, attr_value_pb2.AttrValue(list=shapes_list)) def _clear_attr(self, attr_name): """Private method used to clear an attribute in the node_def.""" # pylint: disable=protected-access c_api.ClearAttr(self._graph._c_graph, self._c_op, attr_name) # pylint: enable=protected-access def get_attr(self, name): """Returns the value of the attr of this op with the given `name`. Args: name: The name of the attr to fetch. Returns: The value of the attr, as a Python object. Raises: ValueError: If this op does not have an attr with the given `name`. """ fields = ("s", "i", "f", "b", "type", "shape", "tensor", "func") try: with c_api_util.tf_buffer() as buf: c_api.TF_OperationGetAttrValueProto(self._c_op, name, buf) data = c_api.TF_GetBuffer(buf) except errors.InvalidArgumentError as e: # Convert to ValueError for backwards compatibility. raise ValueError(str(e)) x = attr_value_pb2.AttrValue() x.ParseFromString(data) oneof_value = x.WhichOneof("value") if oneof_value is None: return [] if oneof_value == "list": for f in fields: if getattr(x.list, f): if f == "type": return [dtypes.as_dtype(t) for t in x.list.type] else: return list(getattr(x.list, f)) return [] if oneof_value == "type": return dtypes.as_dtype(x.type) assert oneof_value in fields, "Unsupported field type in " + str(x) return getattr(x, oneof_value) def _get_attr_type(self, name): """Returns the value of the attr of this op with the given `name`. Args: name: The name of the attr to fetch. Returns: The value of the attr, as a Python object. Raises: ValueError: If this op does not have an attr with the given `name`. """ try: dtype_enum = c_api.TF_OperationGetAttrType(self._c_op, name) return _DTYPES_INTERN_TABLE[dtype_enum] except errors.InvalidArgumentError as e: # Convert to ValueError for backwards compatibility. raise ValueError(str(e)) def run(self, feed_dict=None, session=None): """Runs this operation in a `Session`. Calling this method will execute all preceding operations that produce the inputs needed for this operation. *N.B.* Before invoking `Operation.run()`, its graph must have been launched in a session, and either a default session must be available, or `session` must be specified explicitly. Args: feed_dict: A dictionary that maps `Tensor` objects to feed values. See `tf.Session.run` for a description of the valid feed values. session: (Optional.) The `Session` to be used to run to this operation. If none, the default session will be used. """ _run_using_default_session(self, feed_dict, self.graph, session) _gradient_registry = registry.Registry("gradient") @tf_export("RegisterGradient") class RegisterGradient(object): """A decorator for registering the gradient function for an op type. This decorator is only used when defining a new op type. For an op with `m` inputs and `n` outputs, the gradient function is a function that takes the original `Operation` and `n` `Tensor` objects (representing the gradients with respect to each output of the op), and returns `m` `Tensor` objects (representing the partial gradients with respect to each input of the op). For example, assuming that operations of type `"Sub"` take two inputs `x` and `y`, and return a single output `x - y`, the following gradient function would be registered: ```python @tf.RegisterGradient("Sub") def _sub_grad(unused_op, grad): return grad, tf.negative(grad) ``` The decorator argument `op_type` is the string type of an operation. This corresponds to the `OpDef.name` field for the proto that defines the operation. """ def __init__(self, op_type): """Creates a new decorator with `op_type` as the Operation type. Args: op_type: The string type of an operation. This corresponds to the `OpDef.name` field for the proto that defines the operation. Raises: TypeError: If `op_type` is not string. """ if not isinstance(op_type, six.string_types): raise TypeError("op_type must be a string") self._op_type = op_type def __call__(self, f): """Registers the function `f` as gradient function for `op_type`.""" _gradient_registry.register(f, self._op_type) return f @deprecation.deprecated_endpoints("NotDifferentiable", "NoGradient") @tf_export("no_gradient", v1=["no_gradient", "NotDifferentiable", "NoGradient"]) def no_gradient(op_type): """Specifies that ops of type `op_type` is not differentiable. This function should *not* be used for operations that have a well-defined gradient that is not yet implemented. This function is only used when defining a new op type. It may be used for ops such as `tf.size()` that are not differentiable. For example: ```python tf.no_gradient("Size") ``` The gradient computed for 'op_type' will then propagate zeros. For ops that have a well-defined gradient but are not yet implemented, no declaration should be made, and an error *must* be thrown if an attempt to request its gradient is made. Args: op_type: The string type of an operation. This corresponds to the `OpDef.name` field for the proto that defines the operation. Raises: TypeError: If `op_type` is not a string. """ if not isinstance(op_type, six.string_types): raise TypeError("op_type must be a string") _gradient_registry.register(None, op_type) # Aliases for the old names, will be eventually removed. NoGradient = no_gradient NotDifferentiable = no_gradient def get_gradient_function(op): """Returns the function that computes gradients for "op".""" if not op.inputs: return None try: op_type = op.get_attr("_gradient_op_type") except ValueError: op_type = op.type return _gradient_registry.lookup(op_type) _shape_registry = registry.Registry("shape functions") _default_shape_function_registry = registry.Registry("default shape functions") # These are set to common_shapes.call_cpp_shape_fn by op generated code # (generated by python_op_gen.cc). # It is set outside ops.py to avoid a circular dependency. _call_cpp_shape_fn = None _call_cpp_shape_fn_and_require_op = None def _set_call_cpp_shape_fn(call_cpp_shape_fn): """Sets default shape fns from passed common_shapes.call_cpp_shape_fn.""" global _call_cpp_shape_fn, _call_cpp_shape_fn_and_require_op if _call_cpp_shape_fn: return # already registered def call_without_requiring(op): return call_cpp_shape_fn(op, require_shape_fn=False) _call_cpp_shape_fn = call_without_requiring def call_with_requiring(op): return call_cpp_shape_fn(op, require_shape_fn=True) _call_cpp_shape_fn_and_require_op = call_with_requiring class RegisterShape(object): """No longer used. Was: A decorator for registering a shape function. Shape functions must now be registered via the SetShapeFn on the original Op specification in C++. """ def __init__(self, op_type): """Saves the `op_type` as the `Operation` type.""" if not isinstance(op_type, six.string_types): raise TypeError("op_type must be a string") self._op_type = op_type def __call__(self, f): """Registers "f" as the shape function for "op_type".""" if f is None: assert _call_cpp_shape_fn # None is a special "weak" value that provides a default shape function, # and can be overridden by a non-None registration. try: _default_shape_function_registry.register(_call_cpp_shape_fn, self._op_type) except KeyError: # Ignore duplicate registrations of the weak value. This can # occur if the op library input to wrapper generation # inadvertently links in one or more of the standard op # libraries. pass else: _shape_registry.register(f, self._op_type) return f def set_shape_and_handle_data_for_outputs(_): """No op. TODO(b/74620627): Remove this.""" pass class OpStats(object): """A holder for statistics about an operator. This class holds information about the resource requirements for an op, including the size of its weight parameters on-disk and how many FLOPS it requires to execute forward inference. If you define a new operation, you can create a function that will return a set of information about its usage of the CPU and disk space when serialized. The function itself takes a Graph object that's been set up so you can call methods like get_tensor_by_name to help calculate the results, and a NodeDef argument. """ def __init__(self, statistic_type, value=None): """Sets up the initial placeholders for the statistics.""" self.statistic_type = statistic_type self.value = value @property def statistic_type(self): return self._statistic_type @statistic_type.setter def statistic_type(self, statistic_type): self._statistic_type = statistic_type @property def value(self): return self._value @value.setter def value(self, value): self._value = value def __iadd__(self, other): if other.statistic_type != self.statistic_type: raise ValueError("Can't add an OpStat of type %s to one of %s." % (self.statistic_type, other.statistic_type)) if self.value is None: self.value = other.value elif other.value is not None: self._value += other.value return self _stats_registry = registry.Registry("statistical functions") class RegisterStatistics(object): """A decorator for registering the statistics function for an op type. This decorator can be defined for an op type so that it gives a report on the resources used by an instance of an operator, in the form of an OpStats object. Well-known types of statistics include these so far: - flops: When running a graph, the bulk of the computation happens doing numerical calculations like matrix multiplications. This type allows a node to return how many floating-point operations it takes to complete. The total number of FLOPs for a graph is a good guide to its expected latency. You can add your own statistics just by picking a new type string, registering functions for the ops you care about, and then calling get_stats_for_node_def. If a statistic for an op is registered multiple times, a KeyError will be raised. Since the statistics is counted on a per-op basis. It is not suitable for model parameters (capacity), which is expected to be counted only once, even if it is shared by multiple ops. (e.g. RNN) For example, you can define a new metric called doohickey for a Foo operation by placing this in your code: ```python @ops.RegisterStatistics("Foo", "doohickey") def _calc_foo_bojangles(unused_graph, unused_node_def): return ops.OpStats("doohickey", 20) ``` Then in client code you can retrieve the value by making this call: ```python doohickey = ops.get_stats_for_node_def(graph, node_def, "doohickey") ``` If the NodeDef is for an op with a registered doohickey function, you'll get back the calculated amount in doohickey.value, or None if it's not defined. """ def __init__(self, op_type, statistic_type): """Saves the `op_type` as the `Operation` type.""" if not isinstance(op_type, six.string_types): raise TypeError("op_type must be a string.") if "," in op_type: raise TypeError("op_type must not contain a comma.") self._op_type = op_type if not isinstance(statistic_type, six.string_types): raise TypeError("statistic_type must be a string.") if "," in statistic_type: raise TypeError("statistic_type must not contain a comma.") self._statistic_type = statistic_type def __call__(self, f): """Registers "f" as the statistics function for "op_type".""" _stats_registry.register(f, self._op_type + "," + self._statistic_type) return f def get_stats_for_node_def(graph, node, statistic_type): """Looks up the node's statistics function in the registry and calls it. This function takes a Graph object and a NodeDef from a GraphDef, and if there's an associated statistics method, calls it and returns a result. If no function has been registered for the particular node type, it returns an empty statistics object. Args: graph: A Graph object that's been set up with the node's graph. node: A NodeDef describing the operator. statistic_type: A string identifying the statistic we're interested in. Returns: An OpStats object containing information about resource usage. """ try: stats_func = _stats_registry.lookup(node.op + "," + statistic_type) result = stats_func(graph, node) except LookupError: result = OpStats(statistic_type) return result def name_from_scope_name(name): """Returns the name of an op given the name of its scope. Args: name: the name of the scope. Returns: the name of the op (equal to scope name minus any trailing slash). """ return name[:-1] if (name and name[-1] == "/") else name _MUTATION_LOCK_GROUP = 0 _SESSION_RUN_LOCK_GROUP = 1 @tf_export("Graph") class Graph(object): """A TensorFlow computation, represented as a dataflow graph. A `Graph` contains a set of `tf.Operation` objects, which represent units of computation; and `tf.Tensor` objects, which represent the units of data that flow between operations. A default `Graph` is always registered, and accessible by calling `tf.compat.v1.get_default_graph`. To add an operation to the default graph, simply call one of the functions that defines a new `Operation`: ```python c = tf.constant(4.0) assert c.graph is tf.compat.v1.get_default_graph() ``` Another typical usage involves the `tf.Graph.as_default` context manager, which overrides the current default graph for the lifetime of the context: ```python g = tf.Graph() with g.as_default(): # Define operations and tensors in `g`. c = tf.constant(30.0) assert c.graph is g ``` Important note: This class *is not* thread-safe for graph construction. All operations should be created from a single thread, or external synchronization must be provided. Unless otherwise specified, all methods are not thread-safe. A `Graph` instance supports an arbitrary number of "collections" that are identified by name. For convenience when building a large graph, collections can store groups of related objects: for example, the `tf.Variable` uses a collection (named `tf.GraphKeys.GLOBAL_VARIABLES`) for all variables that are created during the construction of a graph. The caller may define additional collections by specifying a new name. """ def __init__(self): """Creates a new, empty Graph.""" # Protects core state that can be returned via public accessors. # Thread-safety is provided on a best-effort basis to support buggy # programs, and is not guaranteed by the public `tf.Graph` API. # # NOTE(mrry): This does not protect the various stacks. A warning will # be reported if these are used from multiple threads self._lock = threading.RLock() # The group lock synchronizes Session.run calls with methods that create # and mutate ops (e.g. Graph.create_op()). This synchronization is # necessary because it's illegal to modify an operation after it's been run. # The group lock allows any number of threads to mutate ops at the same time # but if any modification is going on, all Session.run calls have to wait. # Similarly, if one or more Session.run calls are going on, all mutate ops # have to wait until all Session.run calls have finished. self._group_lock = lock_util.GroupLock(num_groups=2) self._nodes_by_id = {} # GUARDED_BY(self._lock) self._next_id_counter = 0 # GUARDED_BY(self._lock) self._nodes_by_name = {} # GUARDED_BY(self._lock) self._version = 0 # GUARDED_BY(self._lock) # Maps a name used in the graph to the next id to use for that name. self._names_in_use = {} self._stack_state_is_thread_local = False self._thread_local = threading.local() # Functions that will be applied to choose a device if none is specified. # In TF2.x or after switch_to_thread_local(), # self._thread_local._device_function_stack is used instead. self._graph_device_function_stack = traceable_stack.TraceableStack() # Default original_op applied to new ops. self._default_original_op = None # Current control flow context. It could be either CondContext or # WhileContext defined in ops/control_flow_ops.py self._control_flow_context = None # A new node will depend of the union of all of the nodes in the stack. # In TF2.x or after switch_to_thread_local(), # self._thread_local._control_dependencies_stack is used instead. self._graph_control_dependencies_stack = [] # Arbitrary collections of objects. self._collections = {} # The graph-level random seed self._seed = None # A dictionary of attributes that should be applied to all ops. self._attr_scope_map = {} # A map from op type to the kernel label that should be used. self._op_to_kernel_label_map = {} # A map from op type to an alternative op type that should be used when # computing gradients. self._gradient_override_map = {} # True if the graph is considered "finalized". In that case no # new operations can be added. self._finalized = False # Functions defined in the graph self._functions = collections.OrderedDict() # Default GraphDef versions self._graph_def_versions = versions_pb2.VersionDef( producer=versions.GRAPH_DEF_VERSION, min_consumer=versions.GRAPH_DEF_VERSION_MIN_CONSUMER) self._building_function = False # Stack of colocate_with ops. In TF2.x or after switch_to_thread_local(), # self._thread_local._colocation_stack is used instead. self._graph_colocation_stack = traceable_stack.TraceableStack() # Set of tensors that are dangerous to feed! self._unfeedable_tensors = object_identity.ObjectIdentitySet() # Set of operations that are dangerous to fetch! self._unfetchable_ops = set() # A map of tensor handle placeholder to tensor dtype. self._handle_feeders = {} # A map from tensor handle to its read op. self._handle_readers = {} # A map from tensor handle to its move op. self._handle_movers = {} # A map from tensor handle to its delete op. self._handle_deleters = {} # Allow optimizers and other objects to pseudo-uniquely key graphs (this key # will be shared when defining function graphs, for example, so optimizers # being called inside function definitions behave as if they were seeing the # actual outside graph). self._graph_key = "grap-key-%d/" % (uid(),) # A string with the last reduction method passed to # losses.compute_weighted_loss(), or None. This is required only for # backward compatibility with Estimator and optimizer V1 use cases. self._last_loss_reduction = None # Flag that is used to indicate whether loss has been scaled by optimizer. # If this flag has been set, then estimator uses it to scale losss back # before reporting. This is required only for backward compatibility with # Estimator and optimizer V1 use cases. self._is_loss_scaled_by_optimizer = False self._container = "" self._registered_ops = op_def_registry.get_registered_ops() # Set to True if this graph is being built in an # AutomaticControlDependencies context. self._add_control_dependencies = False # Cache for OpDef protobufs retrieved via the C API. self._op_def_cache = {} # Cache for constant results of `broadcast_gradient_args()`. The keys are # tuples of fully-defined shapes: (x_shape_tuple, y_shape_tuple), and the # values are tuples of reduction indices: (rx, ry). self._bcast_grad_args_cache = {} # Cache for constant results of `reduced_shape()`. The keys are pairs of # tuples: (input_shape_tuple, reduction_indices_tuple), and the values # are pairs of tuples: (output_shape_kept_dims, tile_scaling). self._reduced_shape_cache = {} # TODO(skyewm): fold as much of the above as possible into the C # implementation self._scoped_c_graph = c_api_util.ScopedTFGraph() # The C API requires all ops to have shape functions. Disable this # requirement (many custom ops do not have shape functions, and we don't # want to break these existing cases). c_api.SetRequireShapeInferenceFns(self._c_graph, False) if tf2.enabled(): self.switch_to_thread_local() # Note: this method is private because the API of tf.Graph() is public and # frozen, and this functionality is still not ready for public visibility. @tf_contextlib.contextmanager def _variable_creator_scope(self, creator, priority=100): """Scope which defines a variable creation function. Args: creator: A callable taking `next_creator` and `kwargs`. See the `tf.variable_creator_scope` docstring. priority: Creators with a higher `priority` are called first. Within the same priority, creators are called inner-to-outer. Yields: `_variable_creator_scope` is a context manager with a side effect, but doesn't return a value. Raises: RuntimeError: If variable creator scopes are not properly nested. """ # This step keeps a reference to the existing stack, and it also initializes # self._thread_local._variable_creator_stack if it doesn't exist yet. old = self._variable_creator_stack new = list(old) new.append((priority, creator)) # Sorting is stable, so we'll put higher-priority creators later in the list # but otherwise maintain registration order. new.sort(key=lambda item: item[0]) self._thread_local._variable_creator_stack = new # pylint: disable=protected-access try: yield finally: if self._thread_local._variable_creator_stack is not new: # pylint: disable=protected-access raise RuntimeError( "Exiting variable_creator_scope without proper nesting.") self._thread_local._variable_creator_stack = old # pylint: disable=protected-access # Note: this method is private because the API of tf.Graph() is public and # frozen, and this functionality is still not ready for public visibility. @property def _variable_creator_stack(self): if not hasattr(self._thread_local, "_variable_creator_stack"): self._thread_local._variable_creator_stack = [] # pylint: disable=protected-access # This previously returned a copy of the stack instead of the stack itself, # to guard against accidental mutation. Consider, however, code that wants # to save and restore the variable creator stack: # def f(): # original_stack = graph._variable_creator_stack # graph._variable_creator_stack = new_stack # ... # Some code # graph._variable_creator_stack = original_stack # # And lets say you have some code that calls this function with some # variable_creator: # def g(): # with variable_scope.variable_creator_scope(creator): # f() # When exiting the variable creator scope, it would see a different stack # object than it expected leading to a "Exiting variable_creator_scope # without proper nesting" error. return self._thread_local._variable_creator_stack # pylint: disable=protected-access @_variable_creator_stack.setter def _variable_creator_stack(self, variable_creator_stack): self._thread_local._variable_creator_stack = variable_creator_stack # pylint: disable=protected-access def _check_not_finalized(self): """Check if the graph is finalized. Raises: RuntimeError: If the graph finalized. """ if self._finalized: raise RuntimeError("Graph is finalized and cannot be modified.") def _add_op(self, op): """Adds 'op' to the graph. Args: op: the Operator or Tensor to add. Raises: TypeError: if op is not an Operation or Tensor. ValueError: if the op.name or op._id are already used. """ self._check_not_finalized() if not isinstance(op, (Tensor, Operation)): raise TypeError("op must be a Tensor or Operation: %s" % op) with self._lock: # pylint: disable=protected-access if op._id in self._nodes_by_id: raise ValueError("cannot add an op with id %d as it already " "exists in the graph" % op._id) if op.name in self._nodes_by_name: raise ValueError("cannot add op with name %s as that name " "is already used" % op.name) self._nodes_by_id[op._id] = op self._nodes_by_name[op.name] = op self._version = max(self._version, op._id) # pylint: enable=protected-access @property def _c_graph(self): if self._scoped_c_graph: return self._scoped_c_graph.graph return None @property def version(self): """Returns a version number that increases as ops are added to the graph. Note that this is unrelated to the `tf.Graph.graph_def_versions`. Returns: An integer version that increases as ops are added to the graph. """ if self._finalized: return self._version with self._lock: return self._version @property def graph_def_versions(self): # pylint: disable=line-too-long """The GraphDef version information of this graph. For details on the meaning of each version, see [`GraphDef`](https://www.tensorflow.org/code/tensorflow/core/framework/graph.proto). Returns: A `VersionDef`. """ # pylint: enable=line-too-long with c_api_util.tf_buffer() as buf: c_api.TF_GraphVersions(self._c_graph, buf) data = c_api.TF_GetBuffer(buf) version_def = versions_pb2.VersionDef() version_def.ParseFromString(compat.as_bytes(data)) return version_def @property def seed(self): """The graph-level random seed of this graph.""" return self._seed @seed.setter def seed(self, seed): self._seed = seed @property def finalized(self): """True if this graph has been finalized.""" return self._finalized def finalize(self): """Finalizes this graph, making it read-only. After calling `g.finalize()`, no new operations can be added to `g`. This method is used to ensure that no operations are added to a graph when it is shared between multiple threads, for example when using a `tf.compat.v1.train.QueueRunner`. """ self._finalized = True def _unsafe_unfinalize(self): """Opposite of `finalize`. Internal interface. NOTE: Unfinalizing a graph could have negative impact on performance, especially in a multi-threaded environment. Unfinalizing a graph when it is in use by a Session may lead to undefined behavior. Ensure that all sessions using a graph are closed before calling this method. """ self._finalized = False def _get_control_flow_context(self): """Returns the current control flow context. Returns: A context object. """ return self._control_flow_context def _set_control_flow_context(self, ctx): """Sets the current control flow context. Args: ctx: a context object. """ self._control_flow_context = ctx def _copy_functions_to_graph_def(self, graph_def, starting_bytesize): """If this graph contains functions, copy them to `graph_def`.""" bytesize = starting_bytesize for f in self._functions.values(): bytesize += f.definition.ByteSize() if bytesize >= (1 << 31) or bytesize < 0: raise ValueError("GraphDef cannot be larger than 2GB.") graph_def.library.function.extend([f.definition]) if f.grad_func_name: grad_def = function_pb2.GradientDef() grad_def.function_name = f.name grad_def.gradient_func = f.grad_func_name graph_def.library.gradient.extend([grad_def]) def _as_graph_def(self, from_version=None, add_shapes=False): # pylint: disable=line-too-long """Returns a serialized `GraphDef` representation of this graph. The serialized `GraphDef` can be imported into another `Graph` (using `tf.import_graph_def`) or used with the [C++ Session API](../../../../api_docs/cc/index.md). This method is thread-safe. Args: from_version: Optional. If this is set, returns a `GraphDef` containing only the nodes that were added to this graph since its `version` property had the given value. add_shapes: If true, adds an "_output_shapes" list attr to each node with the inferred shapes of each of its outputs. Returns: A tuple containing a [`GraphDef`](https://www.tensorflow.org/code/tensorflow/core/framework/graph.proto) protocol buffer, and the version of the graph to which that `GraphDef` corresponds. Raises: ValueError: If the `graph_def` would be too large. """ # pylint: enable=line-too-long with self._lock: with c_api_util.tf_buffer() as buf: c_api.TF_GraphToGraphDef(self._c_graph, buf) data = c_api.TF_GetBuffer(buf) graph = graph_pb2.GraphDef() graph.ParseFromString(compat.as_bytes(data)) # Strip the experimental library field iff it's empty. if not graph.library.function: graph.ClearField("library") if add_shapes: for node in graph.node: op = self._nodes_by_name[node.name] if op.outputs: node.attr["_output_shapes"].list.shape.extend( [output.get_shape().as_proto() for output in op.outputs]) for function_def in graph.library.function: defined_function = self._functions[function_def.signature.name] try: func_graph = defined_function.graph except AttributeError: # _DefinedFunction doesn't have a graph, _EagerDefinedFunction # does. Both rely on ops.py, so we can't really isinstance check # them. continue input_shapes = function_def.attr["_input_shapes"] try: func_graph_inputs = func_graph.inputs except AttributeError: continue for input_tensor in func_graph_inputs: if input_tensor.dtype == dtypes.resource: # TODO(allenl): Save and restore handle data, then save the # resource placeholder's shape. Right now some shape functions get # confused if we set the shape of the resource placeholder (to a # scalar of course) and there isn't any handle data. input_shapes.list.shape.add().CopyFrom( tensor_shape.TensorShape(None).as_proto()) else: input_shapes.list.shape.add().CopyFrom( input_tensor.get_shape().as_proto()) for node in function_def.node_def: try: op = func_graph.get_operation_by_name(node.name) except KeyError: continue node.attr["_output_shapes"].list.shape.extend( [output.get_shape().as_proto() for output in op.outputs]) return graph, self._version def as_graph_def(self, from_version=None, add_shapes=False): # pylint: disable=line-too-long """Returns a serialized `GraphDef` representation of this graph. The serialized `GraphDef` can be imported into another `Graph` (using `tf.import_graph_def`) or used with the [C++ Session API](../../api_docs/cc/index.md). This method is thread-safe. Args: from_version: Optional. If this is set, returns a `GraphDef` containing only the nodes that were added to this graph since its `version` property had the given value. add_shapes: If true, adds an "_output_shapes" list attr to each node with the inferred shapes of each of its outputs. Returns: A [`GraphDef`](https://www.tensorflow.org/code/tensorflow/core/framework/graph.proto) protocol buffer. Raises: ValueError: If the `graph_def` would be too large. """ # pylint: enable=line-too-long result, _ = self._as_graph_def(from_version, add_shapes) return result def _is_function(self, name): """Tests whether 'name' is registered in this graph's function library. Args: name: string op name. Returns: bool indicating whether or not 'name' is registered in function library. """ return compat.as_str(name) in self._functions def _get_function(self, name): """Returns the function definition for 'name'. Args: name: string function name. Returns: The function def proto. """ return self._functions.get(compat.as_str(name), None) def _add_function(self, function): """Adds a function to the graph. After the function has been added, you can call to the function by passing the function name in place of an op name to `Graph.create_op()`. Args: function: A `_DefinedFunction` object. Raises: ValueError: if another function is defined with the same name. """ name = function.name # Sanity checks on gradient definition. if (function.grad_func_name is not None) and (function.python_grad_func is not None): raise ValueError("Gradient defined twice for function %s" % name) # Add function to graph # pylint: disable=protected-access gradient = ( function._grad_func._c_func.func if function._grad_func else None) c_api.TF_GraphCopyFunction(self._c_graph, function._c_func.func, gradient) # pylint: enable=protected-access self._functions[compat.as_str(name)] = function # Need a new-enough consumer to support the functions we add to the graph. if self._graph_def_versions.min_consumer < 12: self._graph_def_versions.min_consumer = 12 @property def building_function(self): """Returns True iff this graph represents a function.""" return self._building_function # Helper functions to create operations. @deprecated_args(None, "Shapes are always computed; don't use the compute_shapes " "as it has no effect.", "compute_shapes") def create_op( self, op_type, inputs, dtypes=None, # pylint: disable=redefined-outer-name input_types=None, name=None, attrs=None, op_def=None, compute_shapes=True, compute_device=True): """Creates an `Operation` in this graph. This is a low-level interface for creating an `Operation`. Most programs will not call this method directly, and instead use the Python op constructors, such as `tf.constant()`, which add ops to the default graph. Args: op_type: The `Operation` type to create. This corresponds to the `OpDef.name` field for the proto that defines the operation. inputs: A list of `Tensor` objects that will be inputs to the `Operation`. dtypes: (Optional) A list of `DType` objects that will be the types of the tensors that the operation produces. input_types: (Optional.) A list of `DType`s that will be the types of the tensors that the operation consumes. By default, uses the base `DType` of each input in `inputs`. Operations that expect reference-typed inputs must specify `input_types` explicitly. name: (Optional.) A string name for the operation. If not specified, a name is generated based on `op_type`. attrs: (Optional.) A dictionary where the key is the attribute name (a string) and the value is the respective `attr` attribute of the `NodeDef` proto that will represent the operation (an `AttrValue` proto). op_def: (Optional.) The `OpDef` proto that describes the `op_type` that the operation will have. compute_shapes: (Optional.) Deprecated. Has no effect (shapes are always computed). compute_device: (Optional.) If True, device functions will be executed to compute the device property of the Operation. Raises: TypeError: if any of the inputs is not a `Tensor`. ValueError: if colocation conflicts with existing device assignment. Returns: An `Operation` object. """ del compute_shapes for idx, a in enumerate(inputs): if not isinstance(a, Tensor): raise TypeError("Input #%d is not a tensor: %s" % (idx, a)) return self._create_op_internal(op_type, inputs, dtypes, input_types, name, attrs, op_def, compute_device) def _create_op_internal( self, op_type, inputs, dtypes=None, # pylint: disable=redefined-outer-name input_types=None, name=None, attrs=None, op_def=None, compute_device=True): """Creates an `Operation` in this graph. Implements `Graph.create_op()` without the overhead of the deprecation wrapper. Args: op_type: The `Operation` type to create. This corresponds to the `OpDef.name` field for the proto that defines the operation. inputs: A list of `Tensor` objects that will be inputs to the `Operation`. dtypes: (Optional) A list of `DType` objects that will be the types of the tensors that the operation produces. input_types: (Optional.) A list of `DType`s that will be the types of the tensors that the operation consumes. By default, uses the base `DType` of each input in `inputs`. Operations that expect reference-typed inputs must specify `input_types` explicitly. name: (Optional.) A string name for the operation. If not specified, a name is generated based on `op_type`. attrs: (Optional.) A dictionary where the key is the attribute name (a string) and the value is the respective `attr` attribute of the `NodeDef` proto that will represent the operation (an `AttrValue` proto). op_def: (Optional.) The `OpDef` proto that describes the `op_type` that the operation will have. compute_device: (Optional.) If True, device functions will be executed to compute the device property of the Operation. Raises: ValueError: if colocation conflicts with existing device assignment. Returns: An `Operation` object. """ self._check_not_finalized() if name is None: name = op_type # If a names ends with a '/' it is a "name scope" and we use it as-is, # after removing the trailing '/'. if name and name[-1] == "/": name = name_from_scope_name(name) else: name = self.unique_name(name) node_def = _NodeDef(op_type, name, device=None, attrs=attrs) input_ops = set([t.op for t in inputs]) control_inputs = self._control_dependencies_for_inputs(input_ops) # _create_op_helper mutates the new Operation. `_mutation_lock` ensures a # Session.run call cannot occur between creating and mutating the op. with self._mutation_lock(): ret = Operation( node_def, self, inputs=inputs, output_types=dtypes, control_inputs=control_inputs, input_types=input_types, original_op=self._default_original_op, op_def=op_def) self._create_op_helper(ret, compute_device=compute_device) return ret def _create_op_from_tf_operation(self, c_op, compute_device=True): """Creates an `Operation` in this graph from the supplied TF_Operation. This method is like create_op() except the new Operation is constructed using `c_op`. The returned Operation will have `c_op` as its _c_op field. This is used to create Operation objects around TF_Operations created indirectly by the C API (e.g. by TF_ImportGraphDef, TF_FinishWhile). This function does not call Operation._control_flow_post_processing or Graph._control_dependencies_for_inputs (since the inputs may not be available yet). The caller is responsible for calling these methods. Args: c_op: a wrapped TF_Operation compute_device: (Optional.) If True, device functions will be executed to compute the device property of the Operation. Returns: An `Operation` object. """ self._check_not_finalized() ret = Operation(c_op, self) # If a name_scope was created with ret.name but no nodes were created in it, # the name will still appear in _names_in_use even though the name hasn't # been used. This is ok, just leave _names_in_use as-is in this case. # TODO(skyewm): make the C API guarantee no name conflicts. name_key = ret.name.lower() if name_key not in self._names_in_use: self._names_in_use[name_key] = 1 self._create_op_helper(ret, compute_device=compute_device) return ret def _create_op_helper(self, op, compute_device=True): """Common logic for creating an op in this graph.""" # Apply any additional attributes requested. Do not overwrite any existing # attributes. for key, value in self._attr_scope_map.items(): try: op.get_attr(key) except ValueError: if callable(value): value = value(op.node_def) if not isinstance(value, (type(None), attr_value_pb2.AttrValue)): raise TypeError( "Callable for scope map key '%s' must return either None or " "an AttrValue protocol buffer; but it returned: %s" % (key, value)) if value: op._set_attr(key, value) # pylint: disable=protected-access # Apply a kernel label if one has been specified for this op type. try: kernel_label = self._op_to_kernel_label_map[op.type] op._set_attr("_kernel", # pylint: disable=protected-access attr_value_pb2.AttrValue(s=compat.as_bytes(kernel_label))) except KeyError: pass # Apply the overriding op type for gradients if one has been specified for # this op type. try: mapped_op_type = self._gradient_override_map[op.type] op._set_attr("_gradient_op_type", # pylint: disable=protected-access attr_value_pb2.AttrValue(s=compat.as_bytes(mapped_op_type))) except KeyError: pass self._record_op_seen_by_control_dependencies(op) if compute_device: self._apply_device_functions(op) # Snapshot the colocation stack metadata before we might generate error # messages using it. Note that this snapshot depends on the actual stack # and is independent of the op's _class attribute. # pylint: disable=protected-access op._colocation_code_locations = self._snapshot_colocation_stack_metadata() # pylint: enable=protected-access if self._colocation_stack: all_colocation_groups = [] for colocation_op in self._colocation_stack.peek_objs(): all_colocation_groups.extend(colocation_op.colocation_groups()) if colocation_op.device: # pylint: disable=protected-access op._set_device(colocation_op.device) # pylint: enable=protected-access all_colocation_groups = sorted(set(all_colocation_groups)) # pylint: disable=protected-access op._set_attr( "_class", attr_value_pb2.AttrValue( list=attr_value_pb2.AttrValue.ListValue(s=all_colocation_groups))) # pylint: enable=protected-access # Sets "container" attribute if # (1) self._container is not None # (2) "is_stateful" is set in OpDef # (3) "container" attribute is in OpDef # (4) "container" attribute is None if self._container and op._is_stateful: # pylint: disable=protected-access try: container_attr = op.get_attr("container") except ValueError: # "container" attribute is not in OpDef pass else: if not container_attr: op._set_attr("container", attr_value_pb2.AttrValue( # pylint: disable=protected-access s=compat.as_bytes(self._container))) def _add_new_tf_operations(self, compute_devices=True): """Creates `Operations` in this graph for any new TF_Operations. This is useful for when TF_Operations are indirectly created by the C API outside of the Operation constructor (e.g. by TF_ImportGraphDef, TF_FinishWhile). This ensures there are corresponding Operations for all TF_Operations in the underlying TF_Graph. Args: compute_devices: (Optional.) If True, device functions will be executed to compute the device properties of each new Operation. Returns: A list of the new `Operation` objects. """ # Create all Operation objects before accessing their inputs since an op may # be created before its inputs. new_ops = [ self._create_op_from_tf_operation(c_op, compute_device=compute_devices) for c_op in c_api_util.new_tf_operations(self) ] # pylint: disable=protected-access for op in new_ops: new_control_inputs = self._control_dependencies_for_inputs(op.inputs) op._add_control_inputs(new_control_inputs) op._control_flow_post_processing() # pylint: enable=protected-access return new_ops def as_graph_element(self, obj, allow_tensor=True, allow_operation=True): """Returns the object referred to by `obj`, as an `Operation` or `Tensor`. This function validates that `obj` represents an element of this graph, and gives an informative error message if it is not. This function is the canonical way to get/validate an object of one of the allowed types from an external argument reference in the Session API. This method may be called concurrently from multiple threads. Args: obj: A `Tensor`, an `Operation`, or the name of a tensor or operation. Can also be any object with an `_as_graph_element()` method that returns a value of one of these types. Note: `_as_graph_element` will be called inside the graph's lock and so may not modify the graph. allow_tensor: If true, `obj` may refer to a `Tensor`. allow_operation: If true, `obj` may refer to an `Operation`. Returns: The `Tensor` or `Operation` in the Graph corresponding to `obj`. Raises: TypeError: If `obj` is not a type we support attempting to convert to types. ValueError: If `obj` is of an appropriate type but invalid. For example, an invalid string. KeyError: If `obj` is not an object in the graph. """ if self._finalized: return self._as_graph_element_locked(obj, allow_tensor, allow_operation) with self._lock: return self._as_graph_element_locked(obj, allow_tensor, allow_operation) def _as_graph_element_locked(self, obj, allow_tensor, allow_operation): """See `Graph.as_graph_element()` for details.""" # The vast majority of this function is figuring # out what an API user might be doing wrong, so # that we can give helpful error messages. # # Ideally, it would be nice to split it up, but we # need context to generate nice error messages. if allow_tensor and allow_operation: types_str = "Tensor or Operation" elif allow_tensor: types_str = "Tensor" elif allow_operation: types_str = "Operation" else: raise ValueError("allow_tensor and allow_operation can't both be False.") temp_obj = _as_graph_element(obj) if temp_obj is not None: obj = temp_obj # If obj appears to be a name... if isinstance(obj, compat.bytes_or_text_types): name = compat.as_str(obj) if ":" in name and allow_tensor: # Looks like a Tensor name and can be a Tensor. try: op_name, out_n = name.split(":") out_n = int(out_n) except: raise ValueError("The name %s looks a like a Tensor name, but is " "not a valid one. Tensor names must be of the " "form \"<op_name>:<output_index>\"." % repr(name)) if op_name in self._nodes_by_name: op = self._nodes_by_name[op_name] else: raise KeyError("The name %s refers to a Tensor which does not " "exist. The operation, %s, does not exist in the " "graph." % (repr(name), repr(op_name))) try: return op.outputs[out_n] except: raise KeyError("The name %s refers to a Tensor which does not " "exist. The operation, %s, exists but only has " "%s outputs." % (repr(name), repr(op_name), len(op.outputs))) elif ":" in name and not allow_tensor: # Looks like a Tensor name but can't be a Tensor. raise ValueError("Name %s appears to refer to a Tensor, not a %s." % (repr(name), types_str)) elif ":" not in name and allow_operation: # Looks like an Operation name and can be an Operation. if name not in self._nodes_by_name: raise KeyError("The name %s refers to an Operation not in the " "graph." % repr(name)) return self._nodes_by_name[name] elif ":" not in name and not allow_operation: # Looks like an Operation name but can't be an Operation. if name in self._nodes_by_name: # Yep, it's an Operation name err_msg = ("The name %s refers to an Operation, not a %s." % (repr(name), types_str)) else: err_msg = ("The name %s looks like an (invalid) Operation name, " "not a %s." % (repr(name), types_str)) err_msg += (" Tensor names must be of the form " "\"<op_name>:<output_index>\".") raise ValueError(err_msg) elif isinstance(obj, Tensor) and allow_tensor: # Actually obj is just the object it's referring to. if obj.graph is not self: raise ValueError("Tensor %s is not an element of this graph." % obj) return obj elif isinstance(obj, Operation) and allow_operation: # Actually obj is just the object it's referring to. if obj.graph is not self: raise ValueError("Operation %s is not an element of this graph." % obj) return obj else: # We give up! raise TypeError("Can not convert a %s into a %s." % (type(obj).__name__, types_str)) def get_operations(self): """Return the list of operations in the graph. You can modify the operations in place, but modifications to the list such as inserts/delete have no effect on the list of operations known to the graph. This method may be called concurrently from multiple threads. Returns: A list of Operations. """ if self._finalized: return list(self._nodes_by_id.values()) with self._lock: return list(self._nodes_by_id.values()) def get_operation_by_name(self, name): """Returns the `Operation` with the given `name`. This method may be called concurrently from multiple threads. Args: name: The name of the `Operation` to return. Returns: The `Operation` with the given `name`. Raises: TypeError: If `name` is not a string. KeyError: If `name` does not correspond to an operation in this graph. """ if not isinstance(name, six.string_types): raise TypeError("Operation names are strings (or similar), not %s." % type(name).__name__) return self.as_graph_element(name, allow_tensor=False, allow_operation=True) def _get_operation_by_name_unsafe(self, name): """Returns the `Operation` with the given `name`. This is a internal unsafe version of get_operation_by_name. It skips many checks and does not have user friedly error messages but runs considerably faster. This method may be called concurrently from multiple threads. Args: name: The name of the `Operation` to return. Returns: The `Operation` with the given `name`. Raises: KeyError: If `name` does not correspond to an operation in this graph. """ if self._finalized: return self._nodes_by_name[name] with self._lock: return self._nodes_by_name[name] def _get_operation_by_tf_operation(self, tf_oper): op_name = c_api.TF_OperationName(tf_oper) return self._get_operation_by_name_unsafe(op_name) def get_tensor_by_name(self, name): """Returns the `Tensor` with the given `name`. This method may be called concurrently from multiple threads. Args: name: The name of the `Tensor` to return. Returns: The `Tensor` with the given `name`. Raises: TypeError: If `name` is not a string. KeyError: If `name` does not correspond to a tensor in this graph. """ # Names should be strings. if not isinstance(name, six.string_types): raise TypeError("Tensor names are strings (or similar), not %s." % type(name).__name__) return self.as_graph_element(name, allow_tensor=True, allow_operation=False) def _get_tensor_by_tf_output(self, tf_output): """Returns the `Tensor` representing `tf_output`. Note that there is only one such `Tensor`, i.e. multiple calls to this function with the same TF_Output value will always return the same `Tensor` object. Args: tf_output: A wrapped `TF_Output` (the C API equivalent of `Tensor`). Returns: The `Tensor` that represents `tf_output`. """ op = self._get_operation_by_tf_operation(tf_output.oper) return op.outputs[tf_output.index] def _next_id(self): """Id for next Operation instance. Also increments the internal id.""" self._check_not_finalized() with self._lock: self._next_id_counter += 1 return self._next_id_counter @property def _last_id(self): return self._next_id_counter def _get_op_def(self, type): # pylint: disable=redefined-builtin """Returns the `OpDef` proto for `type`. `type` is a string.""" # NOTE: No locking is required because the lookup and insertion operations # on Python dictionaries are atomic. try: return self._op_def_cache[type] except KeyError: with c_api_util.tf_buffer() as buf: # pylint: disable=protected-access c_api.TF_GraphGetOpDef(self._c_graph, compat.as_bytes(type), buf) # pylint: enable=protected-access data = c_api.TF_GetBuffer(buf) op_def = op_def_pb2.OpDef() op_def.ParseFromString(compat.as_bytes(data)) self._op_def_cache[type] = op_def return op_def def as_default(self): """Returns a context manager that makes this `Graph` the default graph. This method should be used if you want to create multiple graphs in the same process. For convenience, a global default graph is provided, and all ops will be added to this graph if you do not create a new graph explicitly. Use this method with the `with` keyword to specify that ops created within the scope of a block should be added to this graph. In this case, once the scope of the `with` is exited, the previous default graph is set again as default. There is a stack, so it's ok to have multiple nested levels of `as_default` calls. The default graph is a property of the current thread. If you create a new thread, and wish to use the default graph in that thread, you must explicitly add a `with g.as_default():` in that thread's function. The following code examples are equivalent: ```python # 1. Using Graph.as_default(): g = tf.Graph() with g.as_default(): c = tf.constant(5.0) assert c.graph is g # 2. Constructing and making default: with tf.Graph().as_default() as g: c = tf.constant(5.0) assert c.graph is g ``` If eager execution is enabled ops created under this context manager will be added to the graph instead of executed eagerly. Returns: A context manager for using this graph as the default graph. """ return _default_graph_stack.get_controller(self) @property def collections(self): """Returns the names of the collections known to this graph.""" return list(self._collections) def add_to_collection(self, name, value): """Stores `value` in the collection with the given `name`. Note that collections are not sets, so it is possible to add a value to a collection several times. Args: name: The key for the collection. The `GraphKeys` class contains many standard names for collections. value: The value to add to the collection. """ # pylint: disable=g-doc-exception self._check_not_finalized() with self._lock: if name not in self._collections: self._collections[name] = [value] else: self._collections[name].append(value) def add_to_collections(self, names, value): """Stores `value` in the collections given by `names`. Note that collections are not sets, so it is possible to add a value to a collection several times. This function makes sure that duplicates in `names` are ignored, but it will not check for pre-existing membership of `value` in any of the collections in `names`. `names` can be any iterable, but if `names` is a string, it is treated as a single collection name. Args: names: The keys for the collections to add to. The `GraphKeys` class contains many standard names for collections. value: The value to add to the collections. """ # Make sure names are unique, but treat strings as a single collection name names = (names,) if isinstance(names, six.string_types) else set(names) for name in names: self.add_to_collection(name, value) def get_collection_ref(self, name): """Returns a list of values in the collection with the given `name`. If the collection exists, this returns the list itself, which can be modified in place to change the collection. If the collection does not exist, it is created as an empty list and the list is returned. This is different from `get_collection()` which always returns a copy of the collection list if it exists and never creates an empty collection. Args: name: The key for the collection. For example, the `GraphKeys` class contains many standard names for collections. Returns: The list of values in the collection with the given `name`, or an empty list if no value has been added to that collection. """ # pylint: disable=g-doc-exception with self._lock: coll_list = self._collections.get(name, None) if coll_list is None: coll_list = [] self._collections[name] = coll_list return coll_list def get_collection(self, name, scope=None): """Returns a list of values in the collection with the given `name`. This is different from `get_collection_ref()` which always returns the actual collection list if it exists in that it returns a new list each time it is called. Args: name: The key for the collection. For example, the `GraphKeys` class contains many standard names for collections. scope: (Optional.) A string. If supplied, the resulting list is filtered to include only items whose `name` attribute matches `scope` using `re.match`. Items without a `name` attribute are never returned if a scope is supplied. The choice of `re.match` means that a `scope` without special tokens filters by prefix. Returns: The list of values in the collection with the given `name`, or an empty list if no value has been added to that collection. The list contains the values in the order under which they were collected. """ # pylint: disable=g-doc-exception with self._lock: collection = self._collections.get(name, None) if collection is None: return [] if scope is None: return list(collection) else: c = [] regex = re.compile(scope) for item in collection: try: if regex.match(item.name): c.append(item) except AttributeError: # Collection items with no name are ignored. pass return c def get_all_collection_keys(self): """Returns a list of collections used in this graph.""" with self._lock: return [x for x in self._collections if isinstance(x, six.string_types)] def clear_collection(self, name): """Clears all values in a collection. Args: name: The key for the collection. The `GraphKeys` class contains many standard names for collections. """ self._check_not_finalized() with self._lock: if name in self._collections: del self._collections[name] @tf_contextlib.contextmanager def _original_op(self, op): """Python 'with' handler to help annotate ops with their originator. An op may have an 'original_op' property that indicates the op on which it was based. For example a replica op is based on the op that was replicated and a gradient op is based on the op that was differentiated. All ops created in the scope of this 'with' handler will have the given 'op' as their original op. Args: op: The Operation that all ops created in this scope will have as their original op. Yields: Nothing. """ old_original_op = self._default_original_op self._default_original_op = op try: yield finally: self._default_original_op = old_original_op @property def _name_stack(self): # This may be called from a thread where name_stack doesn't yet exist. if not hasattr(self._thread_local, "_name_stack"): self._thread_local._name_stack = "" return self._thread_local._name_stack @_name_stack.setter def _name_stack(self, name_stack): self._thread_local._name_stack = name_stack # pylint: disable=g-doc-return-or-yield,line-too-long @tf_contextlib.contextmanager def name_scope(self, name): """Returns a context manager that creates hierarchical names for operations. A graph maintains a stack of name scopes. A `with name_scope(...):` statement pushes a new name onto the stack for the lifetime of the context. The `name` argument will be interpreted as follows: * A string (not ending with '/') will create a new name scope, in which `name` is appended to the prefix of all operations created in the context. If `name` has been used before, it will be made unique by calling `self.unique_name(name)`. * A scope previously captured from a `with g.name_scope(...) as scope:` statement will be treated as an "absolute" name scope, which makes it possible to re-enter existing scopes. * A value of `None` or the empty string will reset the current name scope to the top-level (empty) name scope. For example: ```python with tf.Graph().as_default() as g: c = tf.constant(5.0, name="c") assert c.op.name == "c" c_1 = tf.constant(6.0, name="c") assert c_1.op.name == "c_1" # Creates a scope called "nested" with g.name_scope("nested") as scope: nested_c = tf.constant(10.0, name="c") assert nested_c.op.name == "nested/c" # Creates a nested scope called "inner". with g.name_scope("inner"): nested_inner_c = tf.constant(20.0, name="c") assert nested_inner_c.op.name == "nested/inner/c" # Create a nested scope called "inner_1". with g.name_scope("inner"): nested_inner_1_c = tf.constant(30.0, name="c") assert nested_inner_1_c.op.name == "nested/inner_1/c" # Treats `scope` as an absolute name scope, and # switches to the "nested/" scope. with g.name_scope(scope): nested_d = tf.constant(40.0, name="d") assert nested_d.op.name == "nested/d" with g.name_scope(""): e = tf.constant(50.0, name="e") assert e.op.name == "e" ``` The name of the scope itself can be captured by `with g.name_scope(...) as scope:`, which stores the name of the scope in the variable `scope`. This value can be used to name an operation that represents the overall result of executing the ops in a scope. For example: ```python inputs = tf.constant(...) with g.name_scope('my_layer') as scope: weights = tf.Variable(..., name="weights") biases = tf.Variable(..., name="biases") affine = tf.matmul(inputs, weights) + biases output = tf.nn.relu(affine, name=scope) ``` NOTE: This constructor validates the given `name`. Valid scope names match one of the following regular expressions: [A-Za-z0-9.][A-Za-z0-9_.\\-/]* (for scopes at the root) [A-Za-z0-9_.\\-/]* (for other scopes) Args: name: A name for the scope. Returns: A context manager that installs `name` as a new name scope. Raises: ValueError: If `name` is not a valid scope name, according to the rules above. """ if name: if isinstance(name, compat.bytes_or_text_types): name = compat.as_str(name) if self._name_stack: # Scopes created in a nested scope may have initial characters # that are illegal as the initial character of an op name # (viz. '-', '\', '/', and '_'). if not _VALID_SCOPE_NAME_REGEX.match(name): raise ValueError("'%s' is not a valid scope name" % name) else: # Scopes created in the root must match the more restrictive # op name regex, which constrains the initial character. if not _VALID_OP_NAME_REGEX.match(name): raise ValueError("'%s' is not a valid scope name" % name) old_stack = self._name_stack if not name: # Both for name=None and name="" we re-set to empty scope. new_stack = None elif name[-1] == "/": new_stack = name_from_scope_name(name) else: new_stack = self.unique_name(name) self._name_stack = new_stack try: yield "" if new_stack is None else new_stack + "/" finally: self._name_stack = old_stack # pylint: enable=g-doc-return-or-yield,line-too-long def unique_name(self, name, mark_as_used=True): """Return a unique operation name for `name`. Note: You rarely need to call `unique_name()` directly. Most of the time you just need to create `with g.name_scope()` blocks to generate structured names. `unique_name` is used to generate structured names, separated by `"/"`, to help identify operations when debugging a graph. Operation names are displayed in error messages reported by the TensorFlow runtime, and in various visualization tools such as TensorBoard. If `mark_as_used` is set to `True`, which is the default, a new unique name is created and marked as in use. If it's set to `False`, the unique name is returned without actually being marked as used. This is useful when the caller simply wants to know what the name to be created will be. Args: name: The name for an operation. mark_as_used: Whether to mark this name as being used. Returns: A string to be passed to `create_op()` that will be used to name the operation being created. """ if self._name_stack: name = self._name_stack + "/" + name # For the sake of checking for names in use, we treat names as case # insensitive (e.g. foo = Foo). name_key = name.lower() i = self._names_in_use.get(name_key, 0) # Increment the number for "name_key". if mark_as_used: self._names_in_use[name_key] = i + 1 if i > 0: base_name_key = name_key # Make sure the composed name key is not already used. while name_key in self._names_in_use: name_key = "%s_%d" % (base_name_key, i) i += 1 # Mark the composed name_key as used in case someone wants # to call unique_name("name_1"). if mark_as_used: self._names_in_use[name_key] = 1 # Return the new name with the original capitalization of the given name. name = "%s_%d" % (name, i - 1) return name def get_name_scope(self): """Returns the current name scope. For example: ```python with tf.name_scope('scope1'): with tf.name_scope('scope2'): print(tf.compat.v1.get_default_graph().get_name_scope()) ``` would print the string `scope1/scope2`. Returns: A string representing the current name scope. """ return self._name_stack @tf_contextlib.contextmanager def _colocate_with_for_gradient(self, op, gradient_uid, ignore_existing=False): with self.colocate_with(op, ignore_existing): if gradient_uid is not None and self._control_flow_context is not None: self._control_flow_context.EnterGradientColocation(op, gradient_uid) try: yield finally: self._control_flow_context.ExitGradientColocation(op, gradient_uid) else: yield @tf_contextlib.contextmanager def colocate_with(self, op, ignore_existing=False): """Returns a context manager that specifies an op to colocate with. Note: this function is not for public use, only for internal libraries. For example: ```python a = tf.Variable([1.0]) with g.colocate_with(a): b = tf.constant(1.0) c = tf.add(a, b) ``` `b` and `c` will always be colocated with `a`, no matter where `a` is eventually placed. **NOTE** Using a colocation scope resets any existing device constraints. If `op` is `None` then `ignore_existing` must be `True` and the new scope resets all colocation and device constraints. Args: op: The op to colocate all created ops with, or `None`. ignore_existing: If true, only applies colocation of this op within the context, rather than applying all colocation properties on the stack. If `op` is `None`, this value must be `True`. Raises: ValueError: if op is None but ignore_existing is False. Yields: A context manager that specifies the op with which to colocate newly created ops. """ if op is None and not ignore_existing: raise ValueError("Trying to reset colocation (op is None) but " "ignore_existing is not True") op = _op_to_colocate_with(op, self) # By default, colocate_with resets the device function stack, # since colocate_with is typically used in specific internal # library functions where colocation is intended to be "stronger" # than device functions. # # In the future, a caller may specify that device_functions win # over colocation, in which case we can add support. device_fn_tmp = self._device_function_stack self._device_function_stack = traceable_stack.TraceableStack() if ignore_existing: current_stack = self._colocation_stack self._colocation_stack = traceable_stack.TraceableStack() if op is not None: # offset refers to the stack frame used for storing code location. # We use 4, the sum of 1 to use our caller's stack frame and 3 # to jump over layers of context managers above us. self._colocation_stack.push_obj(op, offset=4) try: yield finally: # Restore device function stack self._device_function_stack = device_fn_tmp if op is not None: self._colocation_stack.pop_obj() # Reset the colocation stack if requested. if ignore_existing: self._colocation_stack = current_stack def _add_device_to_stack(self, device_name_or_function, offset=0): """Add device to stack manually, separate from a context manager.""" total_offset = 1 + offset spec = _UserDeviceSpec(device_name_or_function) self._device_function_stack.push_obj(spec, offset=total_offset) return spec @tf_contextlib.contextmanager def device(self, device_name_or_function): # pylint: disable=line-too-long """Returns a context manager that specifies the default device to use. The `device_name_or_function` argument may either be a device name string, a device function, or None: * If it is a device name string, all operations constructed in this context will be assigned to the device with that name, unless overridden by a nested `device()` context. * If it is a function, it will be treated as a function from Operation objects to device name strings, and invoked each time a new Operation is created. The Operation will be assigned to the device with the returned name. * If it is None, all `device()` invocations from the enclosing context will be ignored. For information about the valid syntax of device name strings, see the documentation in [`DeviceNameUtils`](https://www.tensorflow.org/code/tensorflow/core/util/device_name_utils.h). For example: ```python with g.device('/device:GPU:0'): # All operations constructed in this context will be placed # on GPU 0. with g.device(None): # All operations constructed in this context will have no # assigned device. # Defines a function from `Operation` to device string. def matmul_on_gpu(n): if n.type == "MatMul": return "/device:GPU:0" else: return "/cpu:0" with g.device(matmul_on_gpu): # All operations of type "MatMul" constructed in this context # will be placed on GPU 0; all other operations will be placed # on CPU 0. ``` **N.B.** The device scope may be overridden by op wrappers or other library code. For example, a variable assignment op `v.assign()` must be colocated with the `tf.Variable` `v`, and incompatible device scopes will be ignored. Args: device_name_or_function: The device name or function to use in the context. Yields: A context manager that specifies the default device to use for newly created ops. Raises: RuntimeError: If device scopes are not properly nested. """ self._add_device_to_stack(device_name_or_function, offset=2) old_top_of_stack = self._device_function_stack.peek_top_obj() try: yield finally: new_top_of_stack = self._device_function_stack.peek_top_obj() if old_top_of_stack is not new_top_of_stack: raise RuntimeError("Exiting device scope without proper scope nesting.") self._device_function_stack.pop_obj() def _apply_device_functions(self, op): """Applies the current device function stack to the given operation.""" # Apply any device functions in LIFO order, so that the most recently # pushed function has the first chance to apply a device to the op. # We apply here because the result can depend on the Operation's # signature, which is computed in the Operation constructor. # pylint: disable=protected-access prior_device_string = None for device_spec in self._device_function_stack.peek_objs(): if device_spec.is_null_merge: continue if device_spec.function is None: break device_string = device_spec.string_merge(op) # Take advantage of the fact that None is a singleton and Python interns # strings, since identity checks are faster than equality checks. if device_string is not prior_device_string: op._set_device_from_string(device_string) prior_device_string = device_string op._device_code_locations = self._snapshot_device_function_stack_metadata() # pylint: enable=protected-access # pylint: disable=g-doc-return-or-yield @tf_contextlib.contextmanager def container(self, container_name): """Returns a context manager that specifies the resource container to use. Stateful operations, such as variables and queues, can maintain their states on devices so that they can be shared by multiple processes. A resource container is a string name under which these stateful operations are tracked. These resources can be released or cleared with `tf.Session.reset()`. For example: ```python with g.container('experiment0'): # All stateful Operations constructed in this context will be placed # in resource container "experiment0". v1 = tf.Variable([1.0]) v2 = tf.Variable([2.0]) with g.container("experiment1"): # All stateful Operations constructed in this context will be # placed in resource container "experiment1". v3 = tf.Variable([3.0]) q1 = tf.queue.FIFOQueue(10, tf.float32) # All stateful Operations constructed in this context will be # be created in the "experiment0". v4 = tf.Variable([4.0]) q1 = tf.queue.FIFOQueue(20, tf.float32) with g.container(""): # All stateful Operations constructed in this context will be # be placed in the default resource container. v5 = tf.Variable([5.0]) q3 = tf.queue.FIFOQueue(30, tf.float32) # Resets container "experiment0", after which the state of v1, v2, v4, q1 # will become undefined (such as uninitialized). tf.Session.reset(target, ["experiment0"]) ``` Args: container_name: container name string. Returns: A context manager for defining resource containers for stateful ops, yields the container name. """ original_container = self._container self._container = container_name try: yield self._container finally: self._container = original_container # pylint: enable=g-doc-return-or-yield class _ControlDependenciesController(object): """Context manager for `control_dependencies()`.""" def __init__(self, graph, control_inputs): """Create a new `_ControlDependenciesController`. A `_ControlDependenciesController` is the context manager for `with tf.control_dependencies()` blocks. These normally nest, as described in the documentation for `control_dependencies()`. The `control_inputs` argument list control dependencies that must be added to the current set of control dependencies. Because of uniquification the set can be empty even if the caller passed a list of ops. The special value `None` indicates that we want to start a new empty set of control dependencies instead of extending the current set. In that case we also clear the current control flow context, which is an additional mechanism to add control dependencies. Args: graph: The graph that this controller is managing. control_inputs: List of ops to use as control inputs in addition to the current control dependencies. None to indicate that the dependencies should be cleared. """ self._graph = graph if control_inputs is None: self._control_inputs_val = [] self._new_stack = True else: self._control_inputs_val = control_inputs self._new_stack = False self._seen_nodes = set() self._old_stack = None self._old_control_flow_context = None # pylint: disable=protected-access def __enter__(self): if self._new_stack: # Clear the control_dependencies graph. self._old_stack = self._graph._control_dependencies_stack self._graph._control_dependencies_stack = [] # Clear the control_flow_context too. self._old_control_flow_context = self._graph._get_control_flow_context() self._graph._set_control_flow_context(None) self._graph._push_control_dependencies_controller(self) def __exit__(self, unused_type, unused_value, unused_traceback): self._graph._pop_control_dependencies_controller(self) if self._new_stack: self._graph._control_dependencies_stack = self._old_stack self._graph._set_control_flow_context(self._old_control_flow_context) # pylint: enable=protected-access @property def control_inputs(self): return self._control_inputs_val def add_op(self, op): if isinstance(op, Tensor): op = op.experimental_ref() self._seen_nodes.add(op) def op_in_group(self, op): if isinstance(op, Tensor): op = op.experimental_ref() return op in self._seen_nodes def _push_control_dependencies_controller(self, controller): self._control_dependencies_stack.append(controller) def _pop_control_dependencies_controller(self, controller): assert self._control_dependencies_stack[-1] is controller self._control_dependencies_stack.pop() def _current_control_dependencies(self): ret = set() for controller in self._control_dependencies_stack: for op in controller.control_inputs: ret.add(op) return ret def _control_dependencies_for_inputs(self, input_ops): """For an op that takes `input_ops` as inputs, compute control inputs. The returned control dependencies should yield an execution that is equivalent to adding all control inputs in self._control_dependencies_stack to a newly created op. However, this function attempts to prune the returned control dependencies by observing that nodes created within the same `with control_dependencies(...):` block may have data dependencies that make the explicit approach redundant. Args: input_ops: The data input ops for an op to be created. Returns: A list of control inputs for the op to be created. """ ret = [] for controller in self._control_dependencies_stack: # If any of the input_ops already depends on the inputs from controller, # we say that the new op is dominated (by that input), and we therefore # do not need to add control dependencies for this controller's inputs. dominated = False for op in input_ops: if controller.op_in_group(op): dominated = True break if not dominated: # Don't add a control input if we already have a data dependency on i. # NOTE(mrry): We do not currently track transitive data dependencies, # so we may add redundant control inputs. ret.extend([c for c in controller.control_inputs if c not in input_ops]) return ret def _record_op_seen_by_control_dependencies(self, op): """Record that the given op depends on all registered control dependencies. Args: op: An Operation. """ for controller in self._control_dependencies_stack: controller.add_op(op) def control_dependencies(self, control_inputs): """Returns a context manager that specifies control dependencies. Use with the `with` keyword to specify that all operations constructed within the context should have control dependencies on `control_inputs`. For example: ```python with g.control_dependencies([a, b, c]): # `d` and `e` will only run after `a`, `b`, and `c` have executed. d = ... e = ... ``` Multiple calls to `control_dependencies()` can be nested, and in that case a new `Operation` will have control dependencies on the union of `control_inputs` from all active contexts. ```python with g.control_dependencies([a, b]): # Ops constructed here run after `a` and `b`. with g.control_dependencies([c, d]): # Ops constructed here run after `a`, `b`, `c`, and `d`. ``` You can pass None to clear the control dependencies: ```python with g.control_dependencies([a, b]): # Ops constructed here run after `a` and `b`. with g.control_dependencies(None): # Ops constructed here run normally, not waiting for either `a` or `b`. with g.control_dependencies([c, d]): # Ops constructed here run after `c` and `d`, also not waiting # for either `a` or `b`. ``` *N.B.* The control dependencies context applies *only* to ops that are constructed within the context. Merely using an op or tensor in the context does not add a control dependency. The following example illustrates this point: ```python # WRONG def my_func(pred, tensor): t = tf.matmul(tensor, tensor) with tf.control_dependencies([pred]): # The matmul op is created outside the context, so no control # dependency will be added. return t # RIGHT def my_func(pred, tensor): with tf.control_dependencies([pred]): # The matmul op is created in the context, so a control dependency # will be added. return tf.matmul(tensor, tensor) ``` Also note that though execution of ops created under this scope will trigger execution of the dependencies, the ops created under this scope might still be pruned from a normal tensorflow graph. For example, in the following snippet of code the dependencies are never executed: ```python loss = model.loss() with tf.control_dependencies(dependencies): loss = loss + tf.constant(1) # note: dependencies ignored in the # backward pass return tf.gradients(loss, model.variables) ``` This is because evaluating the gradient graph does not require evaluating the constant(1) op created in the forward pass. Args: control_inputs: A list of `Operation` or `Tensor` objects which must be executed or computed before running the operations defined in the context. Can also be `None` to clear the control dependencies. Returns: A context manager that specifies control dependencies for all operations constructed within the context. Raises: TypeError: If `control_inputs` is not a list of `Operation` or `Tensor` objects. """ if control_inputs is None: return self._ControlDependenciesController(self, None) # First convert the inputs to ops, and deduplicate them. # NOTE(mrry): Other than deduplication, we do not currently track direct # or indirect dependencies between control_inputs, which may result in # redundant control inputs. control_ops = [] current = self._current_control_dependencies() for c in control_inputs: # The hasattr(handle) is designed to match ResourceVariables. This is so # control dependencies on a variable or on an unread variable don't # trigger reads. if (isinstance(c, IndexedSlices) or (hasattr(c, "_handle") and hasattr(c, "op"))): c = c.op c = self.as_graph_element(c) if isinstance(c, Tensor): c = c.op elif not isinstance(c, Operation): raise TypeError("Control input must be Operation or Tensor: %s" % c) if c not in current: control_ops.append(c) current.add(c) return self._ControlDependenciesController(self, control_ops) # pylint: disable=g-doc-return-or-yield @tf_contextlib.contextmanager def _attr_scope(self, attr_map): """EXPERIMENTAL: A context manager for setting attributes on operators. This context manager can be used to add additional attributes to operators within the scope of the context. For example: with ops.Graph().as_default() as g: f_1 = Foo() # No extra attributes with g._attr_scope({"_a": tf.attr_value_pb2.AttrValue(b=False)}): f_2 = Foo() # Additional attribute _a=False with g._attr_scope({"_a": tf.attr_value_pb2.AttrValue(b=True)}): f_3 = Foo() # Additional attribute _a=False with g._attr_scope({"_a": None}): f_4 = Foo() # No additional attributes. Args: attr_map: A dictionary mapping attr name strings to AttrValue protocol buffers or None. Returns: A context manager that sets the kernel label to be used for one or more ops created in that context. Raises: TypeError: If attr_map is not a dictionary mapping strings to AttrValue protobufs. """ if not isinstance(attr_map, dict): raise TypeError("attr_map must be a dictionary mapping " "strings to AttrValue protocol buffers") # The saved_attrs dictionary stores any currently-set labels that # will be overridden by this context manager. saved_attrs = {} # Install the given attribute for name, attr in attr_map.items(): if not (isinstance(name, six.string_types) and (isinstance(attr, (type(None), attr_value_pb2.AttrValue)) or callable(attr))): raise TypeError("attr_map must be a dictionary mapping " "strings to AttrValue protocol buffers or " "callables that emit AttrValue protocol buffers") try: saved_attrs[name] = self._attr_scope_map[name] except KeyError: pass if attr is None: del self._attr_scope_map[name] else: self._attr_scope_map[name] = attr try: yield # The code within the context runs here. finally: # Remove the attributes set for this context, and restore any saved # attributes. for name, attr in attr_map.items(): try: self._attr_scope_map[name] = saved_attrs[name] except KeyError: del self._attr_scope_map[name] # pylint: enable=g-doc-return-or-yield # pylint: disable=g-doc-return-or-yield @tf_contextlib.contextmanager def _kernel_label_map(self, op_to_kernel_label_map): """EXPERIMENTAL: A context manager for setting kernel labels. This context manager can be used to select particular implementations of kernels within the scope of the context. For example: with ops.Graph().as_default() as g: f_1 = Foo() # Uses the default registered kernel for the Foo op. with g.kernel_label_map({"Foo": "v_2"}): f_2 = Foo() # Uses the registered kernel with label "v_2" # for the Foo op. with g.kernel_label_map({"Foo": "v_3"}): f_3 = Foo() # Uses the registered kernel with label "v_3" # for the Foo op. with g.kernel_label_map({"Foo": ""}): f_4 = Foo() # Uses the default registered kernel # for the Foo op. Args: op_to_kernel_label_map: A dictionary mapping op type strings to kernel label strings. Returns: A context manager that sets the kernel label to be used for one or more ops created in that context. Raises: TypeError: If op_to_kernel_label_map is not a dictionary mapping strings to strings. """ if not isinstance(op_to_kernel_label_map, dict): raise TypeError("op_to_kernel_label_map must be a dictionary mapping " "strings to strings") # The saved_labels dictionary stores any currently-set labels that # will be overridden by this context manager. saved_labels = {} # Install the given label for op_type, label in op_to_kernel_label_map.items(): if not (isinstance(op_type, six.string_types) and isinstance(label, six.string_types)): raise TypeError("op_to_kernel_label_map must be a dictionary mapping " "strings to strings") try: saved_labels[op_type] = self._op_to_kernel_label_map[op_type] except KeyError: pass self._op_to_kernel_label_map[op_type] = label try: yield # The code within the context runs here. finally: # Remove the labels set for this context, and restore any saved labels. for op_type, label in op_to_kernel_label_map.items(): try: self._op_to_kernel_label_map[op_type] = saved_labels[op_type] except KeyError: del self._op_to_kernel_label_map[op_type] # pylint: enable=g-doc-return-or-yield # pylint: disable=g-doc-return-or-yield @tf_contextlib.contextmanager def gradient_override_map(self, op_type_map): """EXPERIMENTAL: A context manager for overriding gradient functions. This context manager can be used to override the gradient function that will be used for ops within the scope of the context. For example: ```python @tf.RegisterGradient("CustomSquare") def _custom_square_grad(op, grad): # ... with tf.Graph().as_default() as g: c = tf.constant(5.0) s_1 = tf.square(c) # Uses the default gradient for tf.square. with g.gradient_override_map({"Square": "CustomSquare"}): s_2 = tf.square(s_2) # Uses _custom_square_grad to compute the # gradient of s_2. ``` Args: op_type_map: A dictionary mapping op type strings to alternative op type strings. Returns: A context manager that sets the alternative op type to be used for one or more ops created in that context. Raises: TypeError: If `op_type_map` is not a dictionary mapping strings to strings. """ if not isinstance(op_type_map, dict): raise TypeError("op_type_map must be a dictionary mapping " "strings to strings") # The saved_mappings dictionary stores any currently-set mappings that # will be overridden by this context manager. saved_mappings = {} # Install the given label for op_type, mapped_op_type in op_type_map.items(): if not (isinstance(op_type, six.string_types) and isinstance(mapped_op_type, six.string_types)): raise TypeError("op_type_map must be a dictionary mapping " "strings to strings") try: saved_mappings[op_type] = self._gradient_override_map[op_type] except KeyError: pass self._gradient_override_map[op_type] = mapped_op_type try: yield # The code within the context runs here. finally: # Remove the labels set for this context, and restore any saved labels. for op_type, mapped_op_type in op_type_map.items(): try: self._gradient_override_map[op_type] = saved_mappings[op_type] except KeyError: del self._gradient_override_map[op_type] # pylint: enable=g-doc-return-or-yield def prevent_feeding(self, tensor): """Marks the given `tensor` as unfeedable in this graph.""" self._unfeedable_tensors.add(tensor) def is_feedable(self, tensor): """Returns `True` if and only if `tensor` is feedable.""" return tensor not in self._unfeedable_tensors def prevent_fetching(self, op): """Marks the given `op` as unfetchable in this graph.""" self._unfetchable_ops.add(op) def is_fetchable(self, tensor_or_op): """Returns `True` if and only if `tensor_or_op` is fetchable.""" if isinstance(tensor_or_op, Tensor): return tensor_or_op.op not in self._unfetchable_ops else: return tensor_or_op not in self._unfetchable_ops def switch_to_thread_local(self): """Make device, colocation and dependencies stacks thread-local. Device, colocation and dependencies stacks are not thread-local be default. If multiple threads access them, then the state is shared. This means that one thread may affect the behavior of another thread. After this method is called, the stacks become thread-local. If multiple threads access them, then the state is not shared. Each thread uses its own value; a thread doesn't affect other threads by mutating such a stack. The initial value for every thread's stack is set to the current value of the stack when `switch_to_thread_local()` was first called. """ if not self._stack_state_is_thread_local: self._stack_state_is_thread_local = True @property def _device_function_stack(self): if self._stack_state_is_thread_local: # This may be called from a thread where device_function_stack doesn't yet # exist. # pylint: disable=protected-access if not hasattr(self._thread_local, "_device_function_stack"): stack_copy_for_this_thread = self._graph_device_function_stack.copy() self._thread_local._device_function_stack = stack_copy_for_this_thread return self._thread_local._device_function_stack # pylint: enable=protected-access else: return self._graph_device_function_stack @property def _device_functions_outer_to_inner(self): user_device_specs = self._device_function_stack.peek_objs() device_functions = [spec.function for spec in user_device_specs] device_functions_outer_to_inner = list(reversed(device_functions)) return device_functions_outer_to_inner def _snapshot_device_function_stack_metadata(self): """Return device function stack as a list of TraceableObjects. Returns: [traceable_stack.TraceableObject, ...] where each TraceableObject's .obj member is a displayable name for the user's argument to Graph.device, and the filename and lineno members point to the code location where Graph.device was called directly or indirectly by the user. """ snapshot = [] for obj in self._device_function_stack.peek_traceable_objs(): obj_copy = obj.copy_metadata() obj_copy.obj = obj.obj.display_name snapshot.append(obj_copy) return snapshot @_device_function_stack.setter def _device_function_stack(self, device_function_stack): if self._stack_state_is_thread_local: # pylint: disable=protected-access self._thread_local._device_function_stack = device_function_stack # pylint: enable=protected-access else: self._graph_device_function_stack = device_function_stack @property def _colocation_stack(self): """Return thread-local copy of colocation stack.""" if self._stack_state_is_thread_local: # This may be called from a thread where colocation_stack doesn't yet # exist. # pylint: disable=protected-access if not hasattr(self._thread_local, "_colocation_stack"): stack_copy_for_this_thread = self._graph_colocation_stack.copy() self._thread_local._colocation_stack = stack_copy_for_this_thread return self._thread_local._colocation_stack # pylint: enable=protected-access else: return self._graph_colocation_stack def _snapshot_colocation_stack_metadata(self): """Return colocation stack metadata as a dictionary.""" return { traceable_obj.obj.name: traceable_obj.copy_metadata() for traceable_obj in self._colocation_stack.peek_traceable_objs() } @_colocation_stack.setter def _colocation_stack(self, colocation_stack): if self._stack_state_is_thread_local: # pylint: disable=protected-access self._thread_local._colocation_stack = colocation_stack # pylint: enable=protected-access else: self._graph_colocation_stack = colocation_stack @property def _control_dependencies_stack(self): if self._stack_state_is_thread_local: # This may be called from a thread where control_dependencies_stack # doesn't yet exist. if not hasattr(self._thread_local, "_control_dependencies_stack"): self._thread_local._control_dependencies_stack = ( self._graph_control_dependencies_stack[:]) return self._thread_local._control_dependencies_stack else: return self._graph_control_dependencies_stack @_control_dependencies_stack.setter def _control_dependencies_stack(self, control_dependencies): if self._stack_state_is_thread_local: self._thread_local._control_dependencies_stack = control_dependencies else: self._graph_control_dependencies_stack = control_dependencies @property def _distribution_strategy_stack(self): """A stack to maintain distribution strategy context for each thread.""" if not hasattr(self._thread_local, "_distribution_strategy_stack"): self._thread_local._distribution_strategy_stack = [] # pylint: disable=protected-access return self._thread_local._distribution_strategy_stack # pylint: disable=protected-access @_distribution_strategy_stack.setter def _distribution_strategy_stack(self, _distribution_strategy_stack): self._thread_local._distribution_strategy_stack = ( # pylint: disable=protected-access _distribution_strategy_stack) @property def _global_distribute_strategy_scope(self): """For implementing `tf.distribute.set_strategy()`.""" if not hasattr(self._thread_local, "distribute_strategy_scope"): self._thread_local.distribute_strategy_scope = None return self._thread_local.distribute_strategy_scope @_global_distribute_strategy_scope.setter def _global_distribute_strategy_scope(self, distribute_strategy_scope): self._thread_local.distribute_strategy_scope = (distribute_strategy_scope) @property def _auto_cast_variable_read_dtype(self): """The dtype that instances of `AutoCastVariable` will be casted to. This is None if `AutoCastVariables` should not be casted. See `AutoCastVariable` for more information. Returns: The dtype that instances of `AutoCastVariable` will be casted to. """ if not hasattr(self._thread_local, "_auto_cast_variable_read_dtype"): self._thread_local._auto_cast_variable_read_dtype = None # pylint: disable=protected-access return self._thread_local._auto_cast_variable_read_dtype # pylint: disable=protected-access @_auto_cast_variable_read_dtype.setter def _auto_cast_variable_read_dtype(self, dtype): if dtype: dtype = dtypes.as_dtype(dtype) self._thread_local._auto_cast_variable_read_dtype = dtype # pylint: disable=protected-access @tf_contextlib.contextmanager def _enable_auto_casting_variables(self, dtype): """Context manager to automatically cast AutoCastVariables. If an AutoCastVariable `var` is used under this context manager, it will be casted to `dtype` before being used. See `AutoCastVariable` for more information. Args: dtype: The dtype that AutoCastVariables should be casted to. Yields: Nothing. """ prev_read_dtype = self._auto_cast_variable_read_dtype try: self._auto_cast_variable_read_dtype = dtype yield finally: self._auto_cast_variable_read_dtype = prev_read_dtype def _mutation_lock(self): """Returns a lock to guard code that creates & mutates ops. See the comment for self._group_lock for more info. """ return self._group_lock.group(_MUTATION_LOCK_GROUP) def _session_run_lock(self): """Returns a lock to guard code for Session.run. See the comment for self._group_lock for more info. """ return self._group_lock.group(_SESSION_RUN_LOCK_GROUP) # TODO(agarwal): currently device directives in an outer eager scope will not # apply to inner graph mode code. Fix that. @tf_export(v1=["device"]) def device(device_name_or_function): """Wrapper for `Graph.device()` using the default graph. See `tf.Graph.device` for more details. Args: device_name_or_function: The device name or function to use in the context. Returns: A context manager that specifies the default device to use for newly created ops. Raises: RuntimeError: If eager execution is enabled and a function is passed in. """ if context.executing_eagerly(): if callable(device_name_or_function): raise RuntimeError( "tf.device does not support functions when eager execution " "is enabled.") return context.device(device_name_or_function) elif executing_eagerly_outside_functions(): @tf_contextlib.contextmanager def combined(device_name_or_function): with get_default_graph().device(device_name_or_function): if not callable(device_name_or_function): with context.device(device_name_or_function): yield else: yield return combined(device_name_or_function) else: return get_default_graph().device(device_name_or_function) @tf_export("device", v1=[]) def device_v2(device_name): """Specifies the device for ops created/executed in this context. `device_name` can be fully specified, as in "/job:worker/task:1/device:cpu:0", or partially specified, containing only a subset of the "/"-separated fields. Any fields which are specified override device annotations from outer scopes. For example: ```python with tf.device('/job:foo'): # ops created here have devices with /job:foo with tf.device('/job:bar/task:0/device:gpu:2'): # ops created here have the fully specified device above with tf.device('/device:gpu:1'): # ops created here have the device '/job:foo/device:gpu:1' ``` Args: device_name: The device name to use in the context. Returns: A context manager that specifies the default device to use for newly created ops. Raises: RuntimeError: If a function is passed in. """ if callable(device_name): raise RuntimeError("tf.device does not support functions.") return device(device_name) @tf_export(v1=["container"]) def container(container_name): """Wrapper for `Graph.container()` using the default graph. Args: container_name: The container string to use in the context. Returns: A context manager that specifies the default container to use for newly created stateful ops. """ return get_default_graph().container(container_name) def _colocate_with_for_gradient(op, gradient_uid, ignore_existing=False): if context.executing_eagerly(): if op is not None: if not hasattr(op, "device"): op = internal_convert_to_tensor_or_indexed_slices(op) return device(op.device) else: return NullContextmanager() else: default_graph = get_default_graph() if isinstance(op, EagerTensor): if default_graph.building_function: return default_graph.device(op.device) else: raise ValueError("Encountered an Eager-defined Tensor during graph " "construction, but a function was not being built.") return default_graph._colocate_with_for_gradient( op, gradient_uid=gradient_uid, ignore_existing=ignore_existing) # Internal interface to colocate_with. colocate_with has been deprecated from # public API. There are still a few internal uses of colocate_with. Add internal # only API for those uses to avoid deprecation warning. def colocate_with(op, ignore_existing=False): return _colocate_with_for_gradient(op, None, ignore_existing=ignore_existing) @deprecation.deprecated( date=None, instructions="Colocations handled automatically by placer.") @tf_export(v1=["colocate_with"]) def _colocate_with(op, ignore_existing=False): return colocate_with(op, ignore_existing) @tf_export("control_dependencies") def control_dependencies(control_inputs): """Wrapper for `Graph.control_dependencies()` using the default graph. See `tf.Graph.control_dependencies` for more details. When eager execution is enabled, any callable object in the `control_inputs` list will be called. Args: control_inputs: A list of `Operation` or `Tensor` objects which must be executed or computed before running the operations defined in the context. Can also be `None` to clear the control dependencies. If eager execution is enabled, any callable object in the `control_inputs` list will be called. Returns: A context manager that specifies control dependencies for all operations constructed within the context. """ if context.executing_eagerly(): if control_inputs: # Excute any pending callables. for control in control_inputs: if callable(control): control() return NullContextmanager() else: return get_default_graph().control_dependencies(control_inputs) class _DefaultStack(threading.local): """A thread-local stack of objects for providing implicit defaults.""" def __init__(self): super(_DefaultStack, self).__init__() self._enforce_nesting = True self.stack = [] def get_default(self): return self.stack[-1] if len(self.stack) >= 1 else None def reset(self): self.stack = [] def is_cleared(self): return not self.stack @property def enforce_nesting(self): return self._enforce_nesting @enforce_nesting.setter def enforce_nesting(self, value): self._enforce_nesting = value @tf_contextlib.contextmanager def get_controller(self, default): """A context manager for manipulating a default stack.""" self.stack.append(default) try: yield default finally: # stack may be empty if reset() was called if self.stack: if self._enforce_nesting: if self.stack[-1] is not default: raise AssertionError( "Nesting violated for default stack of %s objects" % type(default)) self.stack.pop() else: self.stack.remove(default) _default_session_stack = _DefaultStack() # pylint: disable=protected-access def default_session(session): """Python "with" handler for defining a default session. This function provides a means of registering a session for handling Tensor.eval() and Operation.run() calls. It is primarily intended for use by session.Session, but can be used with any object that implements the Session.run() interface. Use with the "with" keyword to specify that Tensor.eval() and Operation.run() invocations within the scope of a block should be executed by a particular session. The default session applies to the current thread only, so it is always possible to inspect the call stack and determine the scope of a default session. If you create a new thread, and wish to use the default session in that thread, you must explicitly add a "with ops.default_session(sess):" block in that thread's function. Example: The following code examples are equivalent: # 1. Using the Session object directly: sess = ... c = tf.constant(5.0) sess.run(c) # 2. Using default_session(): sess = ... with ops.default_session(sess): c = tf.constant(5.0) result = c.eval() # 3. Overriding default_session(): sess = ... with ops.default_session(sess): c = tf.constant(5.0) with ops.default_session(...): c.eval(session=sess) Args: session: The session to be installed as the default session. Returns: A context manager for the default session. """ return _default_session_stack.get_controller(session) @tf_export(v1=["get_default_session"]) def get_default_session(): """Returns the default session for the current thread. The returned `Session` will be the innermost session on which a `Session` or `Session.as_default()` context has been entered. NOTE: The default session is a property of the current thread. If you create a new thread, and wish to use the default session in that thread, you must explicitly add a `with sess.as_default():` in that thread's function. Returns: The default `Session` being used in the current thread. """ return _default_session_stack.get_default() def _eval_using_default_session(tensors, feed_dict, graph, session=None): """Uses the default session to evaluate one or more tensors. Args: tensors: A single Tensor, or a list of Tensor objects. feed_dict: A dictionary that maps Tensor objects (or tensor names) to lists, numpy ndarrays, TensorProtos, or strings. graph: The graph in which the tensors are defined. session: (Optional) A different session to use to evaluate "tensors". Returns: Either a single numpy ndarray if "tensors" is a single tensor; or a list of numpy ndarrays that each correspond to the respective element in "tensors". Raises: ValueError: If no default session is available; the default session does not have "graph" as its graph; or if "session" is specified, and it does not have "graph" as its graph. """ if session is None: session = get_default_session() if session is None: raise ValueError("Cannot evaluate tensor using `eval()`: No default " "session is registered. Use `with " "sess.as_default()` or pass an explicit session to " "`eval(session=sess)`") if session.graph is not graph: raise ValueError("Cannot use the default session to evaluate tensor: " "the tensor's graph is different from the session's " "graph. Pass an explicit session to " "`eval(session=sess)`.") else: if session.graph is not graph: raise ValueError("Cannot use the given session to evaluate tensor: " "the tensor's graph is different from the session's " "graph.") return session.run(tensors, feed_dict) def _run_using_default_session(operation, feed_dict, graph, session=None): """Uses the default session to run "operation". Args: operation: The Operation to be run. feed_dict: A dictionary that maps Tensor objects (or tensor names) to lists, numpy ndarrays, TensorProtos, or strings. graph: The graph in which "operation" is defined. session: (Optional) A different session to use to run "operation". Raises: ValueError: If no default session is available; the default session does not have "graph" as its graph; or if "session" is specified, and it does not have "graph" as its graph. """ if session is None: session = get_default_session() if session is None: raise ValueError("Cannot execute operation using `run()`: No default " "session is registered. Use `with " "sess.as_default():` or pass an explicit session to " "`run(session=sess)`") if session.graph is not graph: raise ValueError("Cannot use the default session to execute operation: " "the operation's graph is different from the " "session's graph. Pass an explicit session to " "run(session=sess).") else: if session.graph is not graph: raise ValueError("Cannot use the given session to execute operation: " "the operation's graph is different from the session's " "graph.") session.run(operation, feed_dict) class _DefaultGraphStack(_DefaultStack): # pylint: disable=protected-access """A thread-local stack of objects for providing an implicit default graph.""" def __init__(self): super(_DefaultGraphStack, self).__init__() self._global_default_graph = None def get_default(self): """Override that returns a global default if the stack is empty.""" ret = super(_DefaultGraphStack, self).get_default() if ret is None: ret = self._GetGlobalDefaultGraph() return ret def _GetGlobalDefaultGraph(self): if self._global_default_graph is None: # TODO(mrry): Perhaps log that the default graph is being used, or set # provide some other feedback to prevent confusion when a mixture of # the global default graph and an explicit graph are combined in the # same process. self._global_default_graph = Graph() return self._global_default_graph def reset(self): super(_DefaultGraphStack, self).reset() self._global_default_graph = None @tf_contextlib.contextmanager def get_controller(self, default): context.context().context_switches.push(default.building_function, default.as_default, default._device_function_stack) try: with super(_DefaultGraphStack, self).get_controller(default) as g, context.graph_mode(): yield g finally: # If an exception is raised here it may be hiding a related exception in # the try-block (just above). context.context().context_switches.pop() _default_graph_stack = _DefaultGraphStack() # Shared helper used in init_scope and executing_eagerly_outside_functions # to obtain the outermost context that is not building a function, and the # innermost non empty device stack. def _get_outer_context_and_inner_device_stack(): """Get the outermost context not building a function.""" default_graph = get_default_graph() outer_context = None innermost_nonempty_device_stack = default_graph._device_function_stack # pylint: disable=protected-access if not _default_graph_stack.stack: # If the default graph stack is empty, then we cannot be building a # function. Install the global graph (which, in this case, is also the # default graph) as the outer context. if default_graph.building_function: raise RuntimeError("The global graph is building a function.") outer_context = default_graph.as_default else: # Find a context that is not building a function. for stack_entry in reversed(context.context().context_switches.stack): if not innermost_nonempty_device_stack: innermost_nonempty_device_stack = stack_entry.device_stack if not stack_entry.is_building_function: outer_context = stack_entry.enter_context_fn break if outer_context is None: # As a last resort, obtain the global default graph; this graph doesn't # necessarily live on the graph stack (and hence it doesn't necessarily # live on the context stack), but it is stored in the graph stack's # encapsulating object. outer_context = _default_graph_stack._GetGlobalDefaultGraph().as_default # pylint: disable=protected-access if outer_context is None: # Sanity check; this shouldn't be triggered. raise RuntimeError("All graphs are building functions, and no " "eager context was previously active.") return outer_context, innermost_nonempty_device_stack # pylint: disable=g-doc-return-or-yield,line-too-long @tf_export("init_scope") @tf_contextlib.contextmanager def init_scope(): """A context manager that lifts ops out of control-flow scopes and function-building graphs. There is often a need to lift variable initialization ops out of control-flow scopes, function-building graphs, and gradient tapes. Entering an `init_scope` is a mechanism for satisfying these desiderata. In particular, entering an `init_scope` has three effects: (1) All control dependencies are cleared the moment the scope is entered; this is equivalent to entering the context manager returned from `control_dependencies(None)`, which has the side-effect of exiting control-flow scopes like `tf.cond` and `tf.while_loop`. (2) All operations that are created while the scope is active are lifted into the lowest context on the `context_stack` that is not building a graph function. Here, a context is defined as either a graph or an eager context. Every context switch, i.e., every installation of a graph as the default graph and every switch into eager mode, is logged in a thread-local stack called `context_switches`; the log entry for a context switch is popped from the stack when the context is exited. Entering an `init_scope` is equivalent to crawling up `context_switches`, finding the first context that is not building a graph function, and entering it. A caveat is that if graph mode is enabled but the default graph stack is empty, then entering an `init_scope` will simply install a fresh graph as the default one. (3) The gradient tape is paused while the scope is active. When eager execution is enabled, code inside an init_scope block runs with eager execution enabled even when defining graph functions via tf.contrib.eager.defun. For example: ```python tf.compat.v1.enable_eager_execution() @tf.contrib.eager.defun def func(): # A defun-decorated function constructs TensorFlow graphs, # it does not execute eagerly. assert not tf.executing_eagerly() with tf.init_scope(): # Initialization runs with eager execution enabled assert tf.executing_eagerly() ``` Raises: RuntimeError: if graph state is incompatible with this initialization. """ # pylint: enable=g-doc-return-or-yield,line-too-long if context.executing_eagerly(): # Fastpath. with tape.stop_recording(): yield else: # Retrieve the active name scope: entering an `init_scope` preserves # the name scope of the current context. scope = get_default_graph().get_name_scope() if scope and scope[-1] != "/": # Names that end with trailing slashes are treated by `name_scope` as # absolute. scope = scope + "/" outer_context, innermost_nonempty_device_stack = ( _get_outer_context_and_inner_device_stack()) outer_graph = None outer_device_stack = None try: with outer_context(), name_scope(scope), control_dependencies( None), tape.stop_recording(): context_manager = NullContextmanager context_manager_input = None if not context.executing_eagerly(): # The device stack is preserved when lifting into a graph. Eager # execution doesn't implement device stacks and in particular it # doesn't support device functions, so in general it's not possible # to do the same when lifting into the eager context. outer_graph = get_default_graph() outer_device_stack = outer_graph._device_function_stack # pylint: disable=protected-access outer_graph._device_function_stack = innermost_nonempty_device_stack # pylint: disable=protected-access elif innermost_nonempty_device_stack is not None: for device_spec in innermost_nonempty_device_stack.peek_objs(): if device_spec.function is None: break if device_spec.raw_string: context_manager = context.device context_manager_input = device_spec.raw_string break # It is currently not possible to have a device function in V2, # but in V1 we are unable to apply device functions in eager mode. # This means that we will silently skip some of the entries on the # device stack in V1 + eager mode. with context_manager(context_manager_input): yield finally: # If an exception is raised here it may be hiding a related exception in # try-block (just above). if outer_graph is not None: outer_graph._device_function_stack = outer_device_stack # pylint: disable=protected-access def executing_eagerly_outside_functions(): """Returns True if executing eagerly, even if inside a graph function.""" if context.executing_eagerly(): return True else: outer_context, _ = _get_outer_context_and_inner_device_stack() with outer_context(): return context.executing_eagerly() def inside_function(): return get_default_graph().building_function @tf_export(v1=["enable_eager_execution"]) def enable_eager_execution(config=None, device_policy=None, execution_mode=None): """Enables eager execution for the lifetime of this program. Eager execution provides an imperative interface to TensorFlow. With eager execution enabled, TensorFlow functions execute operations immediately (as opposed to adding to a graph to be executed later in a `tf.compat.v1.Session`) and return concrete values (as opposed to symbolic references to a node in a computational graph). For example: ```python tf.compat.v1.enable_eager_execution() # After eager execution is enabled, operations are executed as they are # defined and Tensor objects hold concrete values, which can be accessed as # numpy.ndarray`s through the numpy() method. assert tf.multiply(6, 7).numpy() == 42 ``` Eager execution cannot be enabled after TensorFlow APIs have been used to create or execute graphs. It is typically recommended to invoke this function at program startup and not in a library (as most libraries should be usable both with and without eager execution). Args: config: (Optional.) A `tf.compat.v1.ConfigProto` to use to configure the environment in which operations are executed. Note that `tf.compat.v1.ConfigProto` is also used to configure graph execution (via `tf.compat.v1.Session`) and many options within `tf.compat.v1.ConfigProto` are not implemented (or are irrelevant) when eager execution is enabled. device_policy: (Optional.) Policy controlling how operations requiring inputs on a specific device (e.g., a GPU 0) handle inputs on a different device (e.g. GPU 1 or CPU). When set to None, an appropriate value will be picked automatically. The value picked may change between TensorFlow releases. Valid values: - tf.contrib.eager.DEVICE_PLACEMENT_EXPLICIT: raises an error if the placement is not correct. - tf.contrib.eager.DEVICE_PLACEMENT_WARN: copies the tensors which are not on the right device but logs a warning. - tf.contrib.eager.DEVICE_PLACEMENT_SILENT: silently copies the tensors. Note that this may hide performance problems as there is no notification provided when operations are blocked on the tensor being copied between devices. - tf.contrib.eager.DEVICE_PLACEMENT_SILENT_FOR_INT32: silently copies int32 tensors, raising errors on the other ones. execution_mode: (Optional.) Policy controlling how operations dispatched are actually executed. When set to None, an appropriate value will be picked automatically. The value picked may change between TensorFlow releases. Valid values: - tf.contrib.eager.SYNC: executes each operation synchronously. - tf.contrib.eager.ASYNC: executes each operation asynchronously. These operations may return "non-ready" handles. Raises: ValueError: If eager execution is enabled after creating/executing a TensorFlow graph, or if options provided conflict with a previous call to this function. """ _api_usage_gauge.get_cell().set(True) if context.default_execution_mode != context.EAGER_MODE: return enable_eager_execution_internal( config=config, device_policy=device_policy, execution_mode=execution_mode, server_def=None) @tf_export(v1=["disable_eager_execution"]) def disable_eager_execution(): """Disables eager execution. This function can only be called before any Graphs, Ops, or Tensors have been created. It can be used at the beginning of the program for complex migration projects from TensorFlow 1.x to 2.x. """ _api_usage_gauge.get_cell().set(False) context.default_execution_mode = context.GRAPH_MODE c = context.context_safe() if c is not None: c._thread_local_data.is_eager = False # pylint: disable=protected-access def enable_eager_execution_internal(config=None, device_policy=None, execution_mode=None, server_def=None): """Enables eager execution for the lifetime of this program. Most of the doc string for enable_eager_execution is relevant here as well. Args: config: See enable_eager_execution doc string device_policy: See enable_eager_execution doc string execution_mode: See enable_eager_execution doc string server_def: (Optional.) A tensorflow::ServerDef proto. Enables execution on remote devices. GrpcServers need to be started by creating an identical server_def to this, and setting the appropriate task_indexes, so that the servers can communicate. It will then be possible to execute operations on remote devices. Raises: ValueError """ if config is not None and not isinstance(config, config_pb2.ConfigProto): raise TypeError("config must be a tf.ConfigProto, but got %s" % type(config)) if device_policy not in (None, context.DEVICE_PLACEMENT_EXPLICIT, context.DEVICE_PLACEMENT_WARN, context.DEVICE_PLACEMENT_SILENT, context.DEVICE_PLACEMENT_SILENT_FOR_INT32): raise ValueError( "device_policy must be one of None, tf.contrib.eager.DEVICE_PLACEMENT_*" ) if execution_mode not in (None, context.SYNC, context.ASYNC): raise ValueError( "execution_mode must be one of None, tf.contrib.eager.SYNC, " "tf.contrib.eager.ASYNC") if context.default_execution_mode == context.GRAPH_MODE: graph_mode_has_been_used = ( _default_graph_stack._global_default_graph is not None) # pylint: disable=protected-access if graph_mode_has_been_used: raise ValueError( "tf.enable_eager_execution must be called at program startup.") context.default_execution_mode = context.EAGER_MODE # pylint: disable=protected-access with context._context_lock: if context._context is None: context._set_context_locked(context.Context( config=config, device_policy=device_policy, execution_mode=execution_mode, server_def=server_def)) elif ((config is not None and config is not context._context._config) or (device_policy is not None and device_policy is not context._context._device_policy) or (execution_mode is not None and execution_mode is not context._context._execution_mode)): raise ValueError( "Trying to change the options of an active eager" " execution. Context config: %s, specified config:" " %s. Context device policy: %s, specified device" " policy: %s. Context execution mode: %s, " " specified execution mode %s." % (context._context._config, config, context._context._device_policy, device_policy, context._context._execution_mode, execution_mode)) else: # We already created everything, so update the thread local data. context._context._thread_local_data.is_eager = True # Monkey patch to get rid of an unnecessary conditional since the context is # now initialized. context.context = context.context_safe def eager_run(main=None, argv=None): """Runs the program with an optional main function and argv list. The program will run with eager execution enabled. Example: ```python import tensorflow as tf # Import subject to future changes: from tensorflow.contrib.eager.python import tfe def main(_): u = tf.constant(6.0) v = tf.constant(7.0) print(u * v) if __name__ == "__main__": tfe.run() ``` Args: main: the main function to run. argv: the arguments to pass to it. """ enable_eager_execution() app.run(main, argv) @tf_export(v1=["reset_default_graph"]) def reset_default_graph(): """Clears the default graph stack and resets the global default graph. NOTE: The default graph is a property of the current thread. This function applies only to the current thread. Calling this function while a `tf.compat.v1.Session` or `tf.compat.v1.InteractiveSession` is active will result in undefined behavior. Using any previously created `tf.Operation` or `tf.Tensor` objects after calling this function will result in undefined behavior. Raises: AssertionError: If this function is called within a nested graph. """ if not _default_graph_stack.is_cleared(): raise AssertionError("Do not use tf.reset_default_graph() to clear " "nested graphs. If you need a cleared graph, " "exit the nesting and create a new graph.") _default_graph_stack.reset() @tf_export(v1=["get_default_graph"]) def get_default_graph(): """Returns the default graph for the current thread. The returned graph will be the innermost graph on which a `Graph.as_default()` context has been entered, or a global default graph if none has been explicitly created. NOTE: The default graph is a property of the current thread. If you create a new thread, and wish to use the default graph in that thread, you must explicitly add a `with g.as_default():` in that thread's function. Returns: The default `Graph` being used in the current thread. """ return _default_graph_stack.get_default() def has_default_graph(): """Returns True if there is a default graph.""" return len(_default_graph_stack.stack) >= 1 def get_name_scope(): """Returns the current name scope in the default_graph. For example: ```python with tf.name_scope('scope1'): with tf.name_scope('scope2'): print(tf.get_name_scope()) ``` would print the string `scope1/scope2`. Returns: A string representing the current name scope. """ if context.executing_eagerly(): return context.context().scope_name.rstrip("/") return get_default_graph().get_name_scope() def _assert_same_graph(original_item, item): """Fail if the 2 items are from different graphs. Args: original_item: Original item to check against. item: Item to check. Raises: ValueError: if graphs do not match. """ if original_item.graph is not item.graph: raise ValueError("%s must be from the same graph as %s." % (item, original_item)) def _get_graph_from_inputs(op_input_list, graph=None): """Returns the appropriate graph to use for the given inputs. This library method provides a consistent algorithm for choosing the graph in which an Operation should be constructed: 1. If the default graph is being used to construct a function, we use the default graph. 2. If the "graph" is specified explicitly, we validate that all of the inputs in "op_input_list" are compatible with that graph. 3. Otherwise, we attempt to select a graph from the first Operation- or Tensor-valued input in "op_input_list", and validate that all other such inputs are in the same graph. 4. If the graph was not specified and it could not be inferred from "op_input_list", we attempt to use the default graph. Args: op_input_list: A list of inputs to an operation, which may include `Tensor`, `Operation`, and other objects that may be converted to a graph element. graph: (Optional) The explicit graph to use. Raises: TypeError: If op_input_list is not a list or tuple, or if graph is not a Graph. ValueError: If a graph is explicitly passed and not all inputs are from it, or if the inputs are from multiple graphs, or we could not find a graph and there was no default graph. Returns: The appropriate graph to use for the given inputs. """ current_default_graph = get_default_graph() if current_default_graph.building_function: return current_default_graph op_input_list = tuple(op_input_list) # Handle generators correctly if graph and not isinstance(graph, Graph): raise TypeError("Input graph needs to be a Graph: %s" % graph) # 1. We validate that all of the inputs are from the same graph. This is # either the supplied graph parameter, or the first one selected from one # the graph-element-valued inputs. In the latter case, we hold onto # that input in original_graph_element so we can provide a more # informative error if a mismatch is found. original_graph_element = None for op_input in op_input_list: # Determine if this is a valid graph_element. # TODO(josh11b): Note that we exclude subclasses of Tensor. Need to clean this # up. graph_element = None if (isinstance(op_input, (Operation, _TensorLike)) and ((not isinstance(op_input, Tensor)) or type(op_input) == Tensor)): # pylint: disable=unidiomatic-typecheck graph_element = op_input else: graph_element = _as_graph_element(op_input) if graph_element is not None: if not graph: original_graph_element = graph_element graph = graph_element.graph elif original_graph_element is not None: _assert_same_graph(original_graph_element, graph_element) elif graph_element.graph is not graph: raise ValueError("%s is not from the passed-in graph." % graph_element) # 2. If all else fails, we use the default graph, which is always there. return graph or current_default_graph @tf_export(v1=["GraphKeys"]) class GraphKeys(object): """Standard names to use for graph collections. The standard library uses various well-known names to collect and retrieve values associated with a graph. For example, the `tf.Optimizer` subclasses default to optimizing the variables collected under `tf.GraphKeys.TRAINABLE_VARIABLES` if none is specified, but it is also possible to pass an explicit list of variables. The following standard keys are defined: * `GLOBAL_VARIABLES`: the default collection of `Variable` objects, shared across distributed environment (model variables are subset of these). See `tf.compat.v1.global_variables` for more details. Commonly, all `TRAINABLE_VARIABLES` variables will be in `MODEL_VARIABLES`, and all `MODEL_VARIABLES` variables will be in `GLOBAL_VARIABLES`. * `LOCAL_VARIABLES`: the subset of `Variable` objects that are local to each machine. Usually used for temporarily variables, like counters. Note: use `tf.contrib.framework.local_variable` to add to this collection. * `MODEL_VARIABLES`: the subset of `Variable` objects that are used in the model for inference (feed forward). Note: use `tf.contrib.framework.model_variable` to add to this collection. * `TRAINABLE_VARIABLES`: the subset of `Variable` objects that will be trained by an optimizer. See `tf.compat.v1.trainable_variables` for more details. * `SUMMARIES`: the summary `Tensor` objects that have been created in the graph. See `tf.compat.v1.summary.merge_all` for more details. * `QUEUE_RUNNERS`: the `QueueRunner` objects that are used to produce input for a computation. See `tf.compat.v1.train.start_queue_runners` for more details. * `MOVING_AVERAGE_VARIABLES`: the subset of `Variable` objects that will also keep moving averages. See `tf.compat.v1.moving_average_variables` for more details. * `REGULARIZATION_LOSSES`: regularization losses collected during graph construction. The following standard keys are _defined_, but their collections are **not** automatically populated as many of the others are: * `WEIGHTS` * `BIASES` * `ACTIVATIONS` """ # Key to collect Variable objects that are global (shared across machines). # Default collection for all variables, except local ones. GLOBAL_VARIABLES = "variables" # Key to collect local variables that are local to the machine and are not # saved/restored. LOCAL_VARIABLES = "local_variables" # Key to collect local variables which are used to accumulate interal state # to be used in tf.metrics.*. METRIC_VARIABLES = "metric_variables" # Key to collect model variables defined by layers. MODEL_VARIABLES = "model_variables" # Key to collect Variable objects that will be trained by the # optimizers. TRAINABLE_VARIABLES = "trainable_variables" # Key to collect summaries. SUMMARIES = "summaries" # Key to collect QueueRunners. QUEUE_RUNNERS = "queue_runners" # Key to collect table initializers. TABLE_INITIALIZERS = "table_initializer" # Key to collect asset filepaths. An asset represents an external resource # like a vocabulary file. ASSET_FILEPATHS = "asset_filepaths" # Key to collect Variable objects that keep moving averages. MOVING_AVERAGE_VARIABLES = "moving_average_variables" # Key to collect regularization losses at graph construction. REGULARIZATION_LOSSES = "regularization_losses" # Key to collect concatenated sharded variables. CONCATENATED_VARIABLES = "concatenated_variables" # Key to collect savers. SAVERS = "savers" # Key to collect weights WEIGHTS = "weights" # Key to collect biases BIASES = "biases" # Key to collect activations ACTIVATIONS = "activations" # Key to collect update_ops UPDATE_OPS = "update_ops" # Key to collect losses LOSSES = "losses" # Key to collect BaseSaverBuilder.SaveableObject instances for checkpointing. SAVEABLE_OBJECTS = "saveable_objects" # Key to collect all shared resources used by the graph which need to be # initialized once per cluster. RESOURCES = "resources" # Key to collect all shared resources used in this graph which need to be # initialized once per session. LOCAL_RESOURCES = "local_resources" # Trainable resource-style variables. TRAINABLE_RESOURCE_VARIABLES = "trainable_resource_variables" # Key to indicate various ops. INIT_OP = "init_op" LOCAL_INIT_OP = "local_init_op" READY_OP = "ready_op" READY_FOR_LOCAL_INIT_OP = "ready_for_local_init_op" SUMMARY_OP = "summary_op" GLOBAL_STEP = "global_step" # Used to count the number of evaluations performed during a single evaluation # run. EVAL_STEP = "eval_step" TRAIN_OP = "train_op" # Key for control flow context. COND_CONTEXT = "cond_context" WHILE_CONTEXT = "while_context" # Used to store v2 summary names. _SUMMARY_COLLECTION = "_SUMMARY_V2" # List of all collections that keep track of variables. _VARIABLE_COLLECTIONS = [ GLOBAL_VARIABLES, LOCAL_VARIABLES, METRIC_VARIABLES, MODEL_VARIABLES, TRAINABLE_VARIABLES, MOVING_AVERAGE_VARIABLES, CONCATENATED_VARIABLES, TRAINABLE_RESOURCE_VARIABLES, ] # Key for streaming model ports. # NOTE(yuanbyu): internal and experimental. _STREAMING_MODEL_PORTS = "streaming_model_ports" @decorator_utils.classproperty @deprecation.deprecated(None, "Use `tf.GraphKeys.GLOBAL_VARIABLES` instead.") def VARIABLES(cls): # pylint: disable=no-self-argument return cls.GLOBAL_VARIABLES def dismantle_graph(graph): """Cleans up reference cycles from a `Graph`. Helpful for making sure the garbage collector doesn't need to run after a temporary `Graph` is no longer needed. Args: graph: A `Graph` object to destroy. Neither it nor any of its ops are usable after this function runs. """ memory.dismantle_ordered_dict(graph._functions) # pylint: disable=protected-access # Now clean up Operation<->Graph reference cycles by clearing all of the # attributes for the Graph and its ops. graph_operations = graph.get_operations() for op in graph_operations: op.__dict__ = {} graph.__dict__ = {} @tf_export(v1=["add_to_collection"]) def add_to_collection(name, value): """Wrapper for `Graph.add_to_collection()` using the default graph. See `tf.Graph.add_to_collection` for more details. Args: name: The key for the collection. For example, the `GraphKeys` class contains many standard names for collections. value: The value to add to the collection. @compatibility(eager) Collections are only supported in eager when variables are created inside an EagerVariableStore (e.g. as part of a layer or template). @end_compatibility """ get_default_graph().add_to_collection(name, value) @tf_export(v1=["add_to_collections"]) def add_to_collections(names, value): """Wrapper for `Graph.add_to_collections()` using the default graph. See `tf.Graph.add_to_collections` for more details. Args: names: The key for the collections. The `GraphKeys` class contains many standard names for collections. value: The value to add to the collections. @compatibility(eager) Collections are only supported in eager when variables are created inside an EagerVariableStore (e.g. as part of a layer or template). @end_compatibility """ get_default_graph().add_to_collections(names, value) @tf_export(v1=["get_collection_ref"]) def get_collection_ref(key): """Wrapper for `Graph.get_collection_ref()` using the default graph. See `tf.Graph.get_collection_ref` for more details. Args: key: The key for the collection. For example, the `GraphKeys` class contains many standard names for collections. Returns: The list of values in the collection with the given `name`, or an empty list if no value has been added to that collection. Note that this returns the collection list itself, which can be modified in place to change the collection. @compatibility(eager) Collections are not supported when eager execution is enabled. @end_compatibility """ return get_default_graph().get_collection_ref(key) @tf_export(v1=["get_collection"]) def get_collection(key, scope=None): """Wrapper for `Graph.get_collection()` using the default graph. See `tf.Graph.get_collection` for more details. Args: key: The key for the collection. For example, the `GraphKeys` class contains many standard names for collections. scope: (Optional.) If supplied, the resulting list is filtered to include only items whose `name` attribute matches using `re.match`. Items without a `name` attribute are never returned if a scope is supplied and the choice or `re.match` means that a `scope` without special tokens filters by prefix. Returns: The list of values in the collection with the given `name`, or an empty list if no value has been added to that collection. The list contains the values in the order under which they were collected. @compatibility(eager) Collections are not supported when eager execution is enabled. @end_compatibility """ return get_default_graph().get_collection(key, scope) def get_all_collection_keys(): """Returns a list of collections used in the default graph.""" return get_default_graph().get_all_collection_keys() # Named like a function for backwards compatibility with the # @tf_contextlib.contextmanager version, which was switched to a class to avoid # some object creation overhead. @tf_export(v1=["name_scope"]) class name_scope(object): # pylint: disable=invalid-name """A context manager for use when defining a Python op. This context manager validates that the given `values` are from the same graph, makes that graph the default graph, and pushes a name scope in that graph (see `tf.Graph.name_scope` for more details on that). For example, to define a new Python op called `my_op`: ```python def my_op(a, b, c, name=None): with tf.name_scope(name, "MyOp", [a, b, c]) as scope: a = tf.convert_to_tensor(a, name="a") b = tf.convert_to_tensor(b, name="b") c = tf.convert_to_tensor(c, name="c") # Define some computation that uses `a`, `b`, and `c`. return foo_op(..., name=scope) ``` """ @property def name(self): return self._name def __init__(self, name, default_name=None, values=None): """Initialize the context manager. Args: name: The name argument that is passed to the op function. default_name: The default name to use if the `name` argument is `None`. values: The list of `Tensor` arguments that are passed to the op function. Raises: TypeError: if `default_name` is passed in but not a string. """ if not (default_name is None or isinstance(default_name, six.string_types)): raise TypeError( "`default_name` type (%s) is not a string type. You likely meant to " "pass this into the `values` kwarg." % type(default_name)) self._name = default_name if name is None else name self._default_name = default_name self._values = values self._ctx = context.context() self._in_eager_mode = self._ctx.executing_eagerly() self._has_symbolic_input_in_eager = False if self._values and self._in_eager_mode: # The presence of a graph tensor in `self._values` overrides the context. for value in self._values: if hasattr(value, "graph"): self._has_symbolic_input_in_eager = True self._name_scope = value.graph.name_scope(self._name) def __enter__(self): """Start the scope block. Returns: The scope name. Raises: ValueError: if neither `name` nor `default_name` is provided but `values` are. """ if self._has_symbolic_input_in_eager: return self._name_scope.__enter__() if self._in_eager_mode: scope_name, self._old_name = enter_eager_name_scope(self._ctx, self._name) return scope_name else: if self._name is None and self._values is not None: # We only raise an error if values is not None (provided) because # currently tf.name_scope(None) (values=None then) is sometimes used as # an idiom to reset to top scope. raise ValueError( "At least one of name (%s) and default_name (%s) must be provided." % (self._name, self._default_name)) g = get_default_graph() if self._values and not g.building_function: # Specialize based on the knowledge that `_get_graph_from_inputs()` # ignores `inputs` when building a function. g_from_inputs = _get_graph_from_inputs(self._values) if g_from_inputs is not g: g = g_from_inputs self._g_manager = g.as_default() self._g_manager.__enter__() else: self._g_manager = None else: self._g_manager = None try: self._name_scope = g.name_scope(self._name) return self._name_scope.__enter__() except: if self._g_manager is not None: self._g_manager.__exit__(*sys.exc_info()) raise def __exit__(self, type_arg, value_arg, traceback_arg): if self._has_symbolic_input_in_eager: self._name_scope.__exit__(type_arg, value_arg, traceback_arg) elif self._in_eager_mode: self._ctx.scope_name = self._old_name else: self._name_scope.__exit__(type_arg, value_arg, traceback_arg) if self._g_manager is not None: self._g_manager.__exit__(type_arg, value_arg, traceback_arg) return False # False values do not suppress exceptions def enter_eager_name_scope(ctx, name): """Updates the eager context to enter the given name scope.""" old_name = ctx.scope_name if not name: scope_name = "" else: if name.endswith("/"): # A trailing slash breaks out of nested name scopes, indicating a # fully specified scope name, for compatibility with Graph.name_scope. scope_name = name else: scope_name = name + "/" if old_name: scope_name = old_name + scope_name ctx.scope_name = scope_name return scope_name, old_name @tf_export("name_scope", v1=[]) class name_scope_v2(name_scope): """A context manager for use when defining a Python op. This context manager pushes a name scope, which will make the name of all operations added within it have a prefix. For example, to define a new Python op called `my_op`: ```python def my_op(a, b, c, name=None): with tf.name_scope("MyOp") as scope: a = tf.convert_to_tensor(a, name="a") b = tf.convert_to_tensor(b, name="b") c = tf.convert_to_tensor(c, name="c") # Define some computation that uses `a`, `b`, and `c`. return foo_op(..., name=scope) ``` When executed, the Tensors `a`, `b`, `c`, will have names `MyOp/a`, `MyOp/b`, and `MyOp/c`. If the scope name already exists, the name will be made unique by appending `_n`. For example, calling `my_op` the second time will generate `MyOp_1/a`, etc. """ def __init__(self, name): """Initialize the context manager. Args: name: The prefix to use on all names created within the name scope. Raises: ValueError: If name is None, or not a string. """ if name is None or not isinstance(name, six.string_types): raise ValueError("name for name_scope must be a string.") self._name = name self._exit_fns = [] @property def name(self): return self._name def __enter__(self): """Start the scope block. Returns: The scope name. Raises: ValueError: if neither `name` nor `default_name` is provided but `values` are. """ ctx = context.context() if ctx.executing_eagerly(): scope_name, old_scope_name = enter_eager_name_scope(ctx, self._name) self._exit_fns.append( lambda *a: setattr(ctx, "scope_name", old_scope_name)) else: scope = get_default_graph().name_scope(self._name) scope_name = scope.__enter__() self._exit_fns.append(scope.__exit__) return scope_name def __exit__(self, type_arg, value_arg, traceback_arg): exit_fn = self._exit_fns.pop() exit_fn(type_arg, value_arg, traceback_arg) return False # False values do not suppress exceptions def strip_name_scope(name, export_scope): """Removes name scope from a name. Args: name: A `string` name. export_scope: Optional `string`. Name scope to remove. Returns: Name with name scope removed, or the original name if export_scope is None. """ if export_scope: if export_scope[-1] == "/": export_scope = export_scope[:-1] try: # Strips export_scope/, export_scope///, # ^export_scope/, loc:@export_scope/. str_to_replace = r"([\^]|loc:@|^)" + export_scope + r"[\/]+(.*)" return re.sub(str_to_replace, r"\1\2", compat.as_str(name), count=1) except TypeError as e: # If the name is not of a type we can process, simply return it. logging.warning(e) return name else: return name def prepend_name_scope(name, import_scope): """Prepends name scope to a name. Args: name: A `string` name. import_scope: Optional `string`. Name scope to add. Returns: Name with name scope added, or the original name if import_scope is None. """ if import_scope: if import_scope[-1] == "/": import_scope = import_scope[:-1] try: str_to_replace = r"([\^]|loc:@|^)(.*)" return re.sub(str_to_replace, r"\1" + import_scope + r"/\2", compat.as_str(name)) except TypeError as e: # If the name is not of a type we can process, simply return it. logging.warning(e) return name else: return name # pylint: disable=g-doc-return-or-yield # pylint: disable=not-context-manager @tf_export(v1=["op_scope"]) @tf_contextlib.contextmanager def op_scope(values, name, default_name=None): """DEPRECATED. Same as name_scope above, just different argument order.""" logging.warn("tf.op_scope(values, name, default_name) is deprecated," " use tf.name_scope(name, default_name, values)") with name_scope(name, default_name=default_name, values=values) as scope: yield scope _proto_function_registry = registry.Registry("proto functions") def register_proto_function(collection_name, proto_type=None, to_proto=None, from_proto=None): """Registers `to_proto` and `from_proto` functions for collection_name. `to_proto` function converts a Python object to the corresponding protocol buffer, and returns the protocol buffer. `from_proto` function converts protocol buffer into a Python object, and returns the object.. Args: collection_name: Name of the collection. proto_type: Protobuf type, such as `saver_pb2.SaverDef`, `variable_pb2.VariableDef`, `queue_runner_pb2.QueueRunnerDef`.. to_proto: Function that implements Python object to protobuf conversion. from_proto: Function that implements protobuf to Python object conversion. """ if to_proto and not callable(to_proto): raise TypeError("to_proto must be callable.") if from_proto and not callable(from_proto): raise TypeError("from_proto must be callable.") _proto_function_registry.register((proto_type, to_proto, from_proto), collection_name) def get_collection_proto_type(collection_name): """Returns the proto_type for collection_name.""" try: return _proto_function_registry.lookup(collection_name)[0] except LookupError: return None def get_to_proto_function(collection_name): """Returns the to_proto function for collection_name.""" try: return _proto_function_registry.lookup(collection_name)[1] except LookupError: return None def get_from_proto_function(collection_name): """Returns the from_proto function for collection_name.""" try: return _proto_function_registry.lookup(collection_name)[2] except LookupError: return None def _operation_conversion_error(op, dtype=None, name=None, as_ref=False): """Produce a nice error if someone converts an Operation to a Tensor.""" raise TypeError(("Can't convert Operation '%s' to Tensor " "(target dtype=%r, name=%r, as_ref=%r)") % (op.name, dtype, name, as_ref)) def _op_to_colocate_with(v, graph): """Operation object corresponding to v to use for colocation constraints.""" if v is None: return None if isinstance(v, Operation): return v # We always want to colocate with the reference op. # When 'v' is a ResourceVariable, the reference op is the handle creating op. # # What this should be is: # if isinstance(v, ResourceVariable): # return v.handle.op # However, that would require a circular import dependency. # As of October 2018, there were attempts underway to remove # colocation constraints altogether. Assuming that will # happen soon, perhaps this hack to work around the circular # import dependency is acceptable. if hasattr(v, "handle") and hasattr(v.handle, "op") and isinstance( v.handle.op, Operation): if graph.building_function: return graph.capture(v.handle).op else: return v.handle.op return internal_convert_to_tensor_or_indexed_slices(v, as_ref=True).op def _is_keras_symbolic_tensor(x): return hasattr(x, "graph") and getattr(x.graph, "name", None) == "keras_graph" tensor_conversion_registry.register_tensor_conversion_function( Operation, _operation_conversion_error) # These symbols were originally defined in this module; import them for # backwards compatibility until all references have been updated to access # them from the indexed_slices.py module. IndexedSlices = indexed_slices.IndexedSlices IndexedSlicesValue = indexed_slices.IndexedSlicesValue convert_to_tensor_or_indexed_slices = \ indexed_slices.convert_to_tensor_or_indexed_slices convert_n_to_tensor_or_indexed_slices = \ indexed_slices.convert_n_to_tensor_or_indexed_slices internal_convert_to_tensor_or_indexed_slices = \ indexed_slices.internal_convert_to_tensor_or_indexed_slices internal_convert_n_to_tensor_or_indexed_slices = \ indexed_slices.internal_convert_n_to_tensor_or_indexed_slices register_tensor_conversion_function = \ tensor_conversion_registry.register_tensor_conversion_function

36.475904

115

0.69192

7a96b61c583030bfa7d6ec7318df4537be72b4d1

1,549

py

Python

tests/records/dumpers/test_pids_dumper.py

inveniosoftware/invenio-datacite

d25e3670b74f132390fc42e5647765ae5c605ef3

[ "MIT" ]

10

2020-01-17T10:13:09.000Z

2022-03-17T10:14:41.000Z

tests/records/dumpers/test_pids_dumper.py

inveniosoftware/invenio-datacite

d25e3670b74f132390fc42e5647765ae5c605ef3

[ "MIT" ]

570

2019-08-15T16:35:25.000Z

2022-03-31T13:46:17.000Z

tests/records/dumpers/test_pids_dumper.py

inveniosoftware/invenio-datacite

d25e3670b74f132390fc42e5647765ae5c605ef3

[ "MIT" ]

57

2019-09-04T09:25:29.000Z

2022-03-30T19:32:55.000Z

# -*- coding: utf-8 -*- # # Copyright (C) 2021 CERN. # # Invenio-RDM-Records is free software; you can redistribute it and/or modify # it under the terms of the MIT License; see LICENSE file for more details. """Module tests.""" from invenio_records.dumpers import ElasticsearchDumper from invenio_rdm_records.records import RDMRecord from invenio_rdm_records.records.api import RDMParent from invenio_rdm_records.records.dumpers import PIDsDumperExt def test_esdumper_with_externalpidsext(app, db, minimal_record, location): # Create a simple extension that adds a computed field. dumper = ElasticsearchDumper( extensions=[PIDsDumperExt()] ) minimal_record["pids"] = { "doi": { "identifier": "10.5281/zenodo.1234", "provider": "datacite", "client": "zenodo" }, "handle": { "identifier": "9.12314", "provider": "cern-handle", "client": "zenodo" } } # Create the record record = RDMRecord.create(minimal_record, parent=RDMParent.create({})) db.session.commit() # Dump it dump = record.dumps(dumper=dumper) dumped_pids = dump["pids"] for dumped_pid in dumped_pids: pid_attrs = dumped_pid.keys() assert "scheme" in pid_attrs assert "identifier" in pid_attrs assert "provider" in pid_attrs assert "client" in pid_attrs # Load it new_record = RDMRecord.loads(dump, loader=dumper) assert minimal_record["pids"] == new_record["pids"]

28.685185

77

0.652679

413b5d7172b204633ad4a243279055d7e40d1bc1

292

py

Python

docs/report/cloudmesh-openapi/tests/add-json/add.py

rickotten/cybertraining-dsc.github.io

c8ea59be4f09fd543040ba0908af118df5820a70

[ "Apache-2.0" ]

7

2020-02-29T14:53:19.000Z

2021-01-17T17:08:44.000Z

docs/report/cloudmesh-openapi/tests/add-json/add.py

rickotten/cybertraining-dsc.github.io

c8ea59be4f09fd543040ba0908af118df5820a70

[ "Apache-2.0" ]

27

2020-02-29T13:38:11.000Z

2020-09-02T20:24:59.000Z

docs/report/cloudmesh-openapi/tests/add-json/add.py

rickotten/cybertraining-dsc.github.io

c8ea59be4f09fd543040ba0908af118df5820a70

[ "Apache-2.0" ]

6

2020-03-02T17:09:14.000Z

2020-10-30T22:48:01.000Z

from flask import jsonify def add(x: float, y: float) -> str: """ adding float and float. :param x: x value :type x: float :param y: y value :type y: float :return: result :return type: float """ result = {"result": x + y} return jsonify(result)

18.25

35

0.568493

cfb9c9893f84ab9eeab000956d10b0d8efbfbb9d

8,298

py

Python

tefla/da/iterator.py

SiddhantKapil/tefla

cff18fbaf5cde4ef55643978593cbcfcb35b3b37

[ "MIT" ]

3

2017-02-26T14:35:03.000Z

2020-04-26T23:06:36.000Z

tefla/da/iterator.py

SiddhantKapil/tefla

cff18fbaf5cde4ef55643978593cbcfcb35b3b37

[ "MIT" ]

null

tefla/da/iterator.py

SiddhantKapil/tefla

cff18fbaf5cde4ef55643978593cbcfcb35b3b37

[ "MIT" ]

1

2020-05-19T19:18:08.000Z

from __future__ import division, print_function, absolute_import import Queue import SharedArray import multiprocessing import os import threading from uuid import uuid4 import numpy as np from tefla.da import data class BatchIterator(object): def __init__(self, batch_size, shuffle): self.batch_size = batch_size self.shuffle = shuffle def __call__(self, X, y=None): if self.shuffle: index_array = np.random.permutation(len(X)) self.X = X[index_array] self.y = y[index_array] if y is not None else y else: self.X, self.y = X, y return self def __iter__(self): n_samples = self.X.shape[0] bs = self.batch_size for i in range((n_samples + bs - 1) // bs): sl = slice(i * bs, (i + 1) * bs) Xb = self.X[sl] if self.y is not None: yb = self.y[sl] else: yb = None yield self.transform(Xb, yb) def transform(self, Xb, yb): return Xb, yb def __getstate__(self): state = dict(self.__dict__) for attr in ('X', 'y',): if attr in state: del state[attr] return state class QueuedMixin(object): def __iter__(self): queue = Queue.Queue(maxsize=20) end_marker = object() def producer(): for Xb, yb in super(QueuedMixin, self).__iter__(): queue.put((np.array(Xb), np.array(yb))) queue.put(end_marker) thread = threading.Thread(target=producer) thread.daemon = True thread.start() item = queue.get() while item is not end_marker: yield item queue.task_done() item = queue.get() class QueuedIterator(QueuedMixin, BatchIterator): pass class DAIterator(BatchIterator): def __call__(self, X, y=None, crop_bbox=None, xform=None): self.crop_bbox = crop_bbox self.xform = xform return super(DAIterator, self).__call__(X, y) def __init__(self, batch_size, shuffle, preprocessor, crop_size, is_training, aug_params=data.no_augmentation_params, fill_mode='constant', fill_mode_cval=0, standardizer=None, save_to_dir=None): self.preprocessor = preprocessor if preprocessor else data.image_no_preprocessing self.w = crop_size[0] self.h = crop_size[1] self.is_training = is_training self.aug_params = aug_params self.fill_mode = fill_mode self.fill_mode_cval = fill_mode_cval self.standardizer = standardizer self.save_to_dir = save_to_dir if save_to_dir and not os.path.exists(save_to_dir): os.makedirs(save_to_dir) super(DAIterator, self).__init__(batch_size, shuffle) def da_args(self): kwargs = {'preprocessor': self.preprocessor, 'w': self.w, 'h': self.h, 'is_training': self.is_training, 'fill_mode': self.fill_mode, 'fill_mode_cval': self.fill_mode_cval, 'standardizer': self.standardizer, 'save_to_dir': self.save_to_dir} if self.crop_bbox is not None: assert not self.is_training, "crop bbox only in validation/prediction mode" kwargs['bbox'] = self.crop_bbox elif self.xform is not None: assert not self.is_training, "transform only in validation/prediction mode" kwargs['transform'] = self.xform else: kwargs['aug_params'] = self.aug_params return kwargs def transform(self, Xb, yb): fnames, labels = Xb, yb Xb = data.load_augmented_images(fnames, **self.da_args()) return Xb, labels class QueuedDAIterator(QueuedMixin, DAIterator): pass pool_process_seed = None def load_shared(args): import os i, array_name, fname, kwargs = args array = SharedArray.attach(array_name) global pool_process_seed if not pool_process_seed: pool_process_seed = os.getpid() # print("random seed: %d in pid %d" % (pool_process_seed, os.getpid())) np.random.seed(pool_process_seed) array[i] = data.load_augment(fname, **kwargs) class ParallelDAIterator(QueuedDAIterator): def __init__(self, batch_size, shuffle, preprocessor, crop_size, is_training, aug_params=data.no_augmentation_params, fill_mode='constant', fill_mode_cval=0, standardizer=None, save_to_dir=None): self.pool = multiprocessing.Pool() self.num_image_channels = None super(ParallelDAIterator, self).__init__(batch_size, shuffle, preprocessor, crop_size, is_training, aug_params, fill_mode, fill_mode_cval, standardizer, save_to_dir) def transform(self, Xb, yb): shared_array_name = str(uuid4()) fnames, labels = Xb, yb args = [] da_args = self.da_args() for i, fname in enumerate(fnames): args.append((i, shared_array_name, fname, da_args)) if self.num_image_channels is None: test_img = data.load_augment(fnames[0], **da_args) self.num_image_channels = test_img.shape[-1] try: shared_array = SharedArray.create( shared_array_name, [len(Xb), self.w, self.h, self.num_image_channels], dtype=np.float32) self.pool.map(load_shared, args) Xb = np.array(shared_array, dtype=np.float32) finally: SharedArray.delete(shared_array_name) # if labels is not None: # labels = labels[:, np.newaxis] return Xb, labels class BalancingDAIterator(ParallelDAIterator): def __init__( self, batch_size, shuffle, preprocessor, crop_size, is_training, balance_weights, final_balance_weights, balance_ratio, balance_epoch_count=0, aug_params=data.no_augmentation_params, fill_mode='constant', fill_mode_cval=0, standardizer=None, save_to_dir=None): self.count = balance_epoch_count self.balance_weights = balance_weights self.final_balance_weights = final_balance_weights self.balance_ratio = balance_ratio super(BalancingDAIterator, self).__init__(batch_size, shuffle, preprocessor, crop_size, is_training, aug_params, fill_mode, fill_mode_cval, standardizer, save_to_dir) def __call__(self, X, y=None): if y is not None: alpha = self.balance_ratio ** self.count class_weights = self.balance_weights * alpha + self.final_balance_weights * (1 - alpha) self.count += 1 indices = data.balance_per_class_indices(y, weights=class_weights) X = X[indices] y = y[indices] return super(BalancingDAIterator, self).__call__(X, y) # Todo remove code duplication with BalancingDAIterator (call method) class BalancingQueuedDAIterator(QueuedDAIterator): def __init__( self, batch_size, shuffle, preprocessor, crop_size, is_training, balance_weights, final_balance_weights, balance_ratio, balance_epoch_count=0, aug_params=data.no_augmentation_params, fill_mode='constant', fill_mode_cval=0, standardizer=None, save_to_dir=None): self.count = balance_epoch_count self.balance_weights = balance_weights self.final_balance_weights = final_balance_weights self.balance_ratio = balance_ratio super(BalancingQueuedDAIterator, self).__init__(batch_size, shuffle, preprocessor, crop_size, is_training, aug_params, fill_mode, fill_mode_cval, standardizer, save_to_dir) def __call__(self, X, y=None): if y is not None: alpha = self.balance_ratio ** self.count class_weights = self.balance_weights * alpha + self.final_balance_weights * (1 - alpha) self.count += 1 indices = data.balance_per_class_indices(y, weights=class_weights) X = X[indices] y = y[indices] return super(BalancingQueuedDAIterator, self).__call__(X, y)

37.210762

120

0.627501

61f55a73a88c5360e22740ba24577788bcd668b8

2,238

py

Python

labml_nn/resnet/experiment.py

Aarsh2001/annotated_deep_learning_paper_implementations

ff0d5c065da1a46769f5f66fddc252c178f8fa37

[ "MIT" ]

1

2022-03-12T11:04:19.000Z

robust StyleGAN 2/labml_nn/resnet/experiment.py

sbnietert/robust-OT

d1fc1bc0e09975687419149dbe25cce786f81fcc

[ "MIT" ]

null

robust StyleGAN 2/labml_nn/resnet/experiment.py

sbnietert/robust-OT

d1fc1bc0e09975687419149dbe25cce786f81fcc

[ "MIT" ]

1

2022-02-09T04:11:36.000Z

""" --- title: Train a ResNet on CIFAR 10 summary: > Train a ResNet on CIFAR 10 --- # Train a [ResNet](index.html) on CIFAR 10 [![View Run](https://img.shields.io/badge/labml-experiment-brightgreen)](https://app.labml.ai/run/fc5ad600e4af11ebbafd23b8665193c1) """ from typing import List, Optional from torch import nn from labml import experiment from labml.configs import option from labml_nn.experiments.cifar10 import CIFAR10Configs from labml_nn.resnet import ResNetBase class Configs(CIFAR10Configs): """ ## Configurations We use [`CIFAR10Configs`](../experiments/cifar10.html) which defines all the dataset related configurations, optimizer, and a training loop. """ # Number fo blocks for each feature map size n_blocks: List[int] = [3, 3, 3] # Number of channels for each feature map size n_channels: List[int] = [16, 32, 64] # Bottleneck sizes bottlenecks: Optional[List[int]] = None # Kernel size of the initial convolution layer first_kernel_size: int = 7 @option(Configs.model) def _resnet(c: Configs): """ ### Create model """ # [ResNet](index.html) base = ResNetBase(c.n_blocks, c.n_channels, c.bottlenecks, img_channels=3, first_kernel_size=c.first_kernel_size) # Linear layer for classification classification = nn.Linear(c.n_channels[-1], 10) # Stack them model = nn.Sequential(base, classification) # Move the model to the device return model.to(c.device) def main(): # Create experiment experiment.create(name='resnet', comment='cifar10') # Create configurations conf = Configs() # Load configurations experiment.configs(conf, { 'bottlenecks': [8, 16, 16], 'n_blocks': [6, 6, 6], 'optimizer.optimizer': 'Adam', 'optimizer.learning_rate': 2.5e-4, 'epochs': 500, 'train_batch_size': 256, 'train_dataset': 'cifar10_train_augmented', 'valid_dataset': 'cifar10_valid_no_augment', }) # Set model for saving/loading experiment.add_pytorch_models({'model': conf.model}) # Start the experiment and run the training loop with experiment.start(): conf.run() # if __name__ == '__main__': main()

26.329412

131

0.674263

c196a11a122f0464f8219ae11d9726069b0a3976

9,789

py

Python

sanic/errorpages.py

AggressivelyMeows/sanic

04427bb96f2ca756d5620c58cac7e240e313b7af

[ "MIT" ]

null

sanic/errorpages.py

AggressivelyMeows/sanic

04427bb96f2ca756d5620c58cac7e240e313b7af

[ "MIT" ]

null

sanic/errorpages.py

AggressivelyMeows/sanic

04427bb96f2ca756d5620c58cac7e240e313b7af

[ "MIT" ]

null

import sys import typing as t from functools import partial from traceback import extract_tb from sanic.exceptions import InvalidUsage, SanicException from sanic.helpers import STATUS_CODES from sanic.request import Request from sanic.response import HTTPResponse, html, json, text try: from ujson import dumps dumps = partial(dumps, escape_forward_slashes=False) except ImportError: # noqa from json import dumps # type: ignore FALLBACK_TEXT = ( "The server encountered an internal error and " "cannot complete your request." ) FALLBACK_STATUS = 500 class BaseRenderer: def __init__(self, request, exception, debug): self.request = request self.exception = exception self.debug = debug @property def headers(self): if isinstance(self.exception, SanicException): return getattr(self.exception, "headers", {}) return {} @property def status(self): if isinstance(self.exception, SanicException): return getattr(self.exception, "status_code", FALLBACK_STATUS) return FALLBACK_STATUS @property def text(self): if self.debug or isinstance(self.exception, SanicException): return str(self.exception) return FALLBACK_TEXT @property def title(self): status_text = STATUS_CODES.get(self.status, b"Error Occurred").decode() return f"{self.status} — {status_text}" def render(self): output = ( self.full if self.debug and not getattr(self.exception, "quiet", False) else self.minimal ) return output() def minimal(self): # noqa raise NotImplementedError def full(self): # noqa raise NotImplementedError class HTMLRenderer(BaseRenderer): TRACEBACK_STYLE = """ html { font-family: sans-serif } h2 { color: #888; } .tb-wrapper p { margin: 0 } .frame-border { margin: 1rem } .frame-line > * { padding: 0.3rem 0.6rem } .frame-line { margin-bottom: 0.3rem } .frame-code { font-size: 16px; padding-left: 4ch } .tb-wrapper { border: 1px solid #eee } .tb-header { background: #eee; padding: 0.3rem; font-weight: bold } .frame-descriptor { background: #e2eafb; font-size: 14px } """ TRACEBACK_WRAPPER_HTML = ( "<div class=tb-header>{exc_name}: {exc_value}</div>" "<div class=tb-wrapper>{frame_html}</div>" ) TRACEBACK_BORDER = ( "<div class=frame-border>" "The above exception was the direct cause of the following exception:" "</div>" ) TRACEBACK_LINE_HTML = ( "<div class=frame-line>" "<p class=frame-descriptor>" "File {0.filename}, line <i>{0.lineno}</i>, " "in <code><b>{0.name}</b></code>" "<p class=frame-code><code>{0.line}</code>" "</div>" ) OUTPUT_HTML = ( "<!DOCTYPE html><html lang=en>" "<meta charset=UTF-8><title>{title}</title>\n" "<style>{style}</style>\n" "<h1>{title}</h1><p>{text}\n" "{body}" ) def full(self): return html( self.OUTPUT_HTML.format( title=self.title, text=self.text, style=self.TRACEBACK_STYLE, body=self._generate_body(), ), status=self.status, ) def minimal(self): return html( self.OUTPUT_HTML.format( title=self.title, text=self.text, style=self.TRACEBACK_STYLE, body="", ), status=self.status, headers=self.headers, ) @property def text(self): return escape(super().text) @property def title(self): return escape(f"⚠️ {super().title}") def _generate_body(self): _, exc_value, __ = sys.exc_info() exceptions = [] while exc_value: exceptions.append(self._format_exc(exc_value)) exc_value = exc_value.__cause__ traceback_html = self.TRACEBACK_BORDER.join(reversed(exceptions)) appname = escape(self.request.app.name) name = escape(self.exception.__class__.__name__) value = escape(self.exception) path = escape(self.request.path) lines = [ f"<h2>Traceback of {appname} (most recent call last):</h2>", f"{traceback_html}", "<div class=summary><p>", f"<b>{name}: {value}</b> while handling path <code>{path}</code>", "</div>", ] return "\n".join(lines) def _format_exc(self, exc): frames = extract_tb(exc.__traceback__) frame_html = "".join( self.TRACEBACK_LINE_HTML.format(frame) for frame in frames ) return self.TRACEBACK_WRAPPER_HTML.format( exc_name=escape(exc.__class__.__name__), exc_value=escape(exc), frame_html=frame_html, ) class TextRenderer(BaseRenderer): OUTPUT_TEXT = "{title}\n{bar}\n{text}\n\n{body}" SPACER = " " def full(self): return text( self.OUTPUT_TEXT.format( title=self.title, text=self.text, bar=("=" * len(self.title)), body=self._generate_body(), ), status=self.status, ) def minimal(self): return text( self.OUTPUT_TEXT.format( title=self.title, text=self.text, bar=("=" * len(self.title)), body="", ), status=self.status, headers=self.headers, ) @property def title(self): return f"⚠️ {super().title}" def _generate_body(self): _, exc_value, __ = sys.exc_info() exceptions = [] lines = [ f"{self.exception.__class__.__name__}: {self.exception} while " f"handling path {self.request.path}", f"Traceback of {self.request.app.name} (most recent call last):\n", ] while exc_value: exceptions.append(self._format_exc(exc_value)) exc_value = exc_value.__cause__ return "\n".join(lines + exceptions[::-1]) def _format_exc(self, exc): frames = "\n\n".join( [ f"{self.SPACER * 2}File {frame.filename}, " f"line {frame.lineno}, in " f"{frame.name}\n{self.SPACER * 2}{frame.line}" for frame in extract_tb(exc.__traceback__) ] ) return f"{self.SPACER}{exc.__class__.__name__}: {exc}\n{frames}" class JSONRenderer(BaseRenderer): def full(self): output = self._generate_output(full=True) return json(output, status=self.status, dumps=dumps) def minimal(self): output = self._generate_output(full=False) return json(output, status=self.status, dumps=dumps) def _generate_output(self, *, full): output = { "description": self.title, "status": self.status, "message": self.text, } if full: _, exc_value, __ = sys.exc_info() exceptions = [] while exc_value: exceptions.append( { "type": exc_value.__class__.__name__, "exception": str(exc_value), "frames": [ { "file": frame.filename, "line": frame.lineno, "name": frame.name, "src": frame.line, } for frame in extract_tb(exc_value.__traceback__) ], } ) exc_value = exc_value.__cause__ output["path"] = self.request.path output["args"] = self.request.args output["exceptions"] = exceptions[::-1] return output @property def title(self): return STATUS_CODES.get(self.status, b"Error Occurred").decode() def escape(text): """Minimal HTML escaping, not for attribute values (unlike html.escape).""" return f"{text}".replace("&", "&").replace("<", "<") RENDERERS_BY_CONFIG = { "html": HTMLRenderer, "json": JSONRenderer, "text": TextRenderer, } RENDERERS_BY_CONTENT_TYPE = { "multipart/form-data": HTMLRenderer, "application/json": JSONRenderer, "text/plain": TextRenderer, } def exception_response( request: Request, exception: Exception, debug: bool, renderer: t.Type[t.Optional[BaseRenderer]] = None, ) -> HTTPResponse: """Render a response for the default FALLBACK exception handler""" if not renderer: renderer = HTMLRenderer if request: if request.app.config.FALLBACK_ERROR_FORMAT == "auto": try: renderer = JSONRenderer if request.json else HTMLRenderer except InvalidUsage: renderer = HTMLRenderer content_type, *_ = request.headers.get( "content-type", "" ).split(";") renderer = RENDERERS_BY_CONTENT_TYPE.get( content_type, renderer ) else: render_format = request.app.config.FALLBACK_ERROR_FORMAT renderer = RENDERERS_BY_CONFIG.get(render_format, renderer) renderer = t.cast(t.Type[BaseRenderer], renderer) return renderer(request, exception, debug).render()

29.663636

79

0.548473

b7415d786ff106ac51975dddea19b249d86e65ad

763

py

Python

tests/test_alpha_helix.py

xingjiepan/alpha_helix_generator

2b35691b790e6363d5c4897a72c3efa8556d0143

[ "BSD-3-Clause" ]

null

tests/test_alpha_helix.py

xingjiepan/alpha_helix_generator

2b35691b790e6363d5c4897a72c3efa8556d0143

[ "BSD-3-Clause" ]

null

tests/test_alpha_helix.py

xingjiepan/alpha_helix_generator

2b35691b790e6363d5c4897a72c3efa8556d0143

[ "BSD-3-Clause" ]

null

#!/usr/bin/env python3 import pytest import numpy as np np.seterr(all='raise') import ss_generator as ssg def test_build_beta_sheet(): print("test build beta sheet.") res_list = ssg.alpha_helix.build_ideal_straight_alpha_helix(20) ssg.IO.save_residue_list(res_list, "ideal_straight_helix.pdb") theta = np.radians(90) phi = np.radians(-4) directions = [np.array([0, np.sin(theta), np.cos(theta)])] for i in range(1, 50): directions.append(np.dot(ssg.geometry.rotation_matrix_from_axis_and_angle( np.array([0, 0, 1]), phi), directions[-1])) res_list = ssg.alpha_helix.build_alpha_helix_from_directions(directions) ssg.IO.save_residue_list(res_list, "helix_perturbed.pdb")

28.259259

82

0.685452

9f00735e14b595c0579f296dd5d2151c6d0235a8

100

py

Python

Python/DivisibilityProblem.py

Zardosh/code-forces-solutions

ea1446b8e4f391f3e9ef63094816c7bdaded1557

[ "MIT" ]

null

Python/DivisibilityProblem.py

Zardosh/code-forces-solutions

ea1446b8e4f391f3e9ef63094816c7bdaded1557

[ "MIT" ]

null

Python/DivisibilityProblem.py

Zardosh/code-forces-solutions

ea1446b8e4f391f3e9ef63094816c7bdaded1557

[ "MIT" ]

null

t = int(input()) for _ in range(t): a, b = map(int, input().split()) print((b - a % b) % b)

20

36

0.48

6695e94528d8f1a836ec7968f8855909c72a0fed

7,565

py

Python

src/oci/identity_data_plane/models/bad_user_state_authenticate_user_result.py

xjuarez/oci-python-sdk

3c1604e4e212008fb6718e2f68cdb5ef71fd5793

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

3

2020-09-10T22:09:45.000Z

2021-12-24T17:00:07.000Z

src/oci/identity_data_plane/models/bad_user_state_authenticate_user_result.py

xjuarez/oci-python-sdk

3c1604e4e212008fb6718e2f68cdb5ef71fd5793

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

null

src/oci/identity_data_plane/models/bad_user_state_authenticate_user_result.py

xjuarez/oci-python-sdk

3c1604e4e212008fb6718e2f68cdb5ef71fd5793

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

null

# coding: utf-8 # Copyright (c) 2016, 2021, Oracle and/or its affiliates. All rights reserved. # This software is dual-licensed to you under the Universal Permissive License (UPL) 1.0 as shown at https://oss.oracle.com/licenses/upl or Apache License 2.0 as shown at http://www.apache.org/licenses/LICENSE-2.0. You may choose either license. from oci.util import formatted_flat_dict, NONE_SENTINEL, value_allowed_none_or_none_sentinel # noqa: F401 from oci.decorators import init_model_state_from_kwargs @init_model_state_from_kwargs class BadUserStateAuthenticateUserResult(object): """ BadUserStateAuthenticateUserResult model. """ #: A constant which can be used with the user_state property of a BadUserStateAuthenticateUserResult. #: This constant has a value of "USER_BLOCKED" USER_STATE_USER_BLOCKED = "USER_BLOCKED" #: A constant which can be used with the user_state property of a BadUserStateAuthenticateUserResult. #: This constant has a value of "USER_DISABLED" USER_STATE_USER_DISABLED = "USER_DISABLED" #: A constant which can be used with the user_state property of a BadUserStateAuthenticateUserResult. #: This constant has a value of "ONE_TIME_PASSWORD_EXPIRED" USER_STATE_ONE_TIME_PASSWORD_EXPIRED = "ONE_TIME_PASSWORD_EXPIRED" #: A constant which can be used with the user_state property of a BadUserStateAuthenticateUserResult. #: This constant has a value of "PASSWORD_INVALID" USER_STATE_PASSWORD_INVALID = "PASSWORD_INVALID" def __init__(self, **kwargs): """ Initializes a new BadUserStateAuthenticateUserResult object with values from keyword arguments. The following keyword arguments are supported (corresponding to the getters/setters of this class): :param tenant_input: The value to assign to the tenant_input property of this BadUserStateAuthenticateUserResult. :type tenant_input: str :param user_input: The value to assign to the user_input property of this BadUserStateAuthenticateUserResult. :type user_input: str :param resolved_tenant_id: The value to assign to the resolved_tenant_id property of this BadUserStateAuthenticateUserResult. :type resolved_tenant_id: str :param resolved_user_id: The value to assign to the resolved_user_id property of this BadUserStateAuthenticateUserResult. :type resolved_user_id: str :param user_state: The value to assign to the user_state property of this BadUserStateAuthenticateUserResult. Allowed values for this property are: "USER_BLOCKED", "USER_DISABLED", "ONE_TIME_PASSWORD_EXPIRED", "PASSWORD_INVALID" :type user_state: str """ self.swagger_types = { 'tenant_input': 'str', 'user_input': 'str', 'resolved_tenant_id': 'str', 'resolved_user_id': 'str', 'user_state': 'str' } self.attribute_map = { 'tenant_input': 'tenantInput', 'user_input': 'userInput', 'resolved_tenant_id': 'resolvedTenantId', 'resolved_user_id': 'resolvedUserId', 'user_state': 'userState' } self._tenant_input = None self._user_input = None self._resolved_tenant_id = None self._resolved_user_id = None self._user_state = None @property def tenant_input(self): """ **[Required]** Gets the tenant_input of this BadUserStateAuthenticateUserResult. The tenant name. :return: The tenant_input of this BadUserStateAuthenticateUserResult. :rtype: str """ return self._tenant_input @tenant_input.setter def tenant_input(self, tenant_input): """ Sets the tenant_input of this BadUserStateAuthenticateUserResult. The tenant name. :param tenant_input: The tenant_input of this BadUserStateAuthenticateUserResult. :type: str """ self._tenant_input = tenant_input @property def user_input(self): """ **[Required]** Gets the user_input of this BadUserStateAuthenticateUserResult. The user name. :return: The user_input of this BadUserStateAuthenticateUserResult. :rtype: str """ return self._user_input @user_input.setter def user_input(self, user_input): """ Sets the user_input of this BadUserStateAuthenticateUserResult. The user name. :param user_input: The user_input of this BadUserStateAuthenticateUserResult. :type: str """ self._user_input = user_input @property def resolved_tenant_id(self): """ **[Required]** Gets the resolved_tenant_id of this BadUserStateAuthenticateUserResult. The resolved tenant id. :return: The resolved_tenant_id of this BadUserStateAuthenticateUserResult. :rtype: str """ return self._resolved_tenant_id @resolved_tenant_id.setter def resolved_tenant_id(self, resolved_tenant_id): """ Sets the resolved_tenant_id of this BadUserStateAuthenticateUserResult. The resolved tenant id. :param resolved_tenant_id: The resolved_tenant_id of this BadUserStateAuthenticateUserResult. :type: str """ self._resolved_tenant_id = resolved_tenant_id @property def resolved_user_id(self): """ **[Required]** Gets the resolved_user_id of this BadUserStateAuthenticateUserResult. The resolved user id. :return: The resolved_user_id of this BadUserStateAuthenticateUserResult. :rtype: str """ return self._resolved_user_id @resolved_user_id.setter def resolved_user_id(self, resolved_user_id): """ Sets the resolved_user_id of this BadUserStateAuthenticateUserResult. The resolved user id. :param resolved_user_id: The resolved_user_id of this BadUserStateAuthenticateUserResult. :type: str """ self._resolved_user_id = resolved_user_id @property def user_state(self): """ **[Required]** Gets the user_state of this BadUserStateAuthenticateUserResult. The bad user state. Allowed values for this property are: "USER_BLOCKED", "USER_DISABLED", "ONE_TIME_PASSWORD_EXPIRED", "PASSWORD_INVALID" :return: The user_state of this BadUserStateAuthenticateUserResult. :rtype: str """ return self._user_state @user_state.setter def user_state(self, user_state): """ Sets the user_state of this BadUserStateAuthenticateUserResult. The bad user state. :param user_state: The user_state of this BadUserStateAuthenticateUserResult. :type: str """ allowed_values = ["USER_BLOCKED", "USER_DISABLED", "ONE_TIME_PASSWORD_EXPIRED", "PASSWORD_INVALID"] if not value_allowed_none_or_none_sentinel(user_state, allowed_values): raise ValueError( "Invalid value for `user_state`, must be None or one of {0}" .format(allowed_values) ) self._user_state = user_state def __repr__(self): return formatted_flat_dict(self) def __eq__(self, other): if other is None: return False return self.__dict__ == other.__dict__ def __ne__(self, other): return not self == other

34.386364

245

0.678519

989b6c18ecf49debb2a55a4e8ef501e76e102db2

1,260

py

Python

bot.py

wazaan159/Auto-Filter-Bot-V2

e5cfd5c1d91fd049b040ff07388c0e5de7c82b1f

[ "MIT" ]

null

bot.py

wazaan159/Auto-Filter-Bot-V2

e5cfd5c1d91fd049b040ff07388c0e5de7c82b1f

[ "MIT" ]

null

bot.py

wazaan159/Auto-Filter-Bot-V2

e5cfd5c1d91fd049b040ff07388c0e5de7c82b1f

[ "MIT" ]

null

#!/usr/bin/env python3 # -*- coding: utf-8 -*- # @Cinema_FestivalTG from pyrogram import ( Client, __version__ ) from config import ( API_HASH, APP_ID, LOGGER, AUTH_USERS, TG_BOT_SESSION, TG_BOT_TOKEN, TG_BOT_WORKERS ) from user import User class Bot(Client): USER: User = None USER_ID: int = None def __init__(self): super().__init__( TG_BOT_SESSION, api_hash=API_HASH, api_id=APP_ID, plugins={ "root": "plugins" }, workers=TG_BOT_WORKERS, bot_token=TG_BOT_TOKEN ) self.LOGGER = LOGGER async def start(self): await super().start() usr_bot_me = await self.get_me() self.set_parse_mode("html") self.LOGGER(__name__).info( f"@{usr_bot_me.username} started!\n\n" f"Add @{usr_bot_me.username} as admin with all rights in your required channels\n\n" ) AUTH_USERS.add(680815375) self.USER, self.USER_ID = await User().start() async def stop(self, *args): await super().stop() self.LOGGER(__name__).info("Bot stopped. Bye.")

22.5

97

0.545238

a45a879067e597dccbbd02df6d2f9020e5cee639

1,826

py

Python

gadget/route/api/project/model.py

wvankuipers/gadget-backend

ac23c9b991c44ed343784a197ed0805b23b88f02

[ "MIT" ]

null

gadget/route/api/project/model.py

wvankuipers/gadget-backend

ac23c9b991c44ed343784a197ed0805b23b88f02

[ "MIT" ]

9

2020-09-26T11:25:11.000Z

2020-09-30T18:18:26.000Z

gadget/route/api/project/model.py

wvankuipers/gadget-backend

ac23c9b991c44ed343784a197ed0805b23b88f02

[ "MIT" ]

null

from datetime import datetime from flask import current_app from slugify import slugify from sqlalchemy.ext.hybrid import hybrid_property from gadget import db from gadget import utils class Project(db.Model): id = db.Column(db.Integer, primary_key=True) name = db.Column(db.String(250), nullable=False) _slug = db.Column('slug', db.String(250), nullable=False) created = db.Column(db.DateTime, nullable=False, default=datetime.utcnow) updated = db.Column(db.DateTime, nullable=False, default=datetime.utcnow) suites = db.relationship('Suite', backref='project', cascade='all, delete-orphan') @hybrid_property def slug(self): return self._slug @slug.setter def slug(self, slug): self._slug = slugify(slug) def create(self): db.session.add(self) db.session.commit() def update(self, name, slug): self.updated = datetime.utcnow() self.name = name self. slug = slug db.session.commit() def delete(self): db.session.delete(self) db.session.commit() def fixtures(): if current_app.config['DEBUG'] is False: raise Exception('Inserting fixtures not allowed in production') return [ Project(name='project1', slug='project1'), Project(name='project2', slug='project2'), Project(name='project3', slug='project3'), ] def serialize(self, recursive=True): result = { 'id': self.id, 'name': self.name, 'slug': self.slug, 'created': utils.to_local_js_timestamp(self.created), 'updated': utils.to_local_js_timestamp(self.updated) } if recursive: result['suites'] = utils.serialize_list(self.suites) return result

28.984127

86

0.625958

d461f2651a0bba4cc4cdea00bb54b62335f87bde

2,974

py

Python

extra_apps/xadmin/plugins/details.py

ansonsry/Education

3c96cc3e951dfdb5ab6b44997a72f61501fbe0f7

[ "MIT" ]

null

extra_apps/xadmin/plugins/details.py

ansonsry/Education

3c96cc3e951dfdb5ab6b44997a72f61501fbe0f7

[ "MIT" ]

1

2021-02-08T20:31:28.000Z

extra_apps/xadmin/plugins/details.py

ansonsry/Freshshop

79ab8beb1aa993f6365182c8d3bb478ee4e028f8

[ "MIT" ]

null

from django.utils.translation import ugettext as _ # from django.core.urlresolvers import reverse, NoReverseMatch from django.urls import NoReverseMatch, reverse from django.db import models from xadmin.sites import site from xadmin.views import BaseAdminPlugin, ListAdminView class DetailsPlugin(BaseAdminPlugin): show_detail_fields = [] show_all_rel_details = True def result_item(self, item, obj, field_name, row): if (self.show_all_rel_details or (field_name in self.show_detail_fields)): rel_obj = None if hasattr(item.field, 'rel') and isinstance(item.field.rel, models.ManyToOneRel): rel_obj = getattr(obj, field_name) elif field_name in self.show_detail_fields: rel_obj = obj if rel_obj: if rel_obj.__class__ in site._registry: try: model_admin = site._registry[rel_obj.__class__] has_view_perm = model_admin(self.admin_view.request).has_view_permission(rel_obj) has_change_perm = model_admin(self.admin_view.request).has_change_permission(rel_obj) except: has_view_perm = self.admin_view.has_model_perm(rel_obj.__class__, 'view') has_change_perm = self.has_model_perm(rel_obj.__class__, 'change') else: has_view_perm = self.admin_view.has_model_perm(rel_obj.__class__, 'view') has_change_perm = self.has_model_perm(rel_obj.__class__, 'change') if rel_obj and has_view_perm: opts = rel_obj._meta try: item_res_uri = reverse( '%s:%s_%s_detail' % (self.admin_site.app_name, opts.app_label, opts.model_name), args=(getattr(rel_obj, opts.pk.attname),)) if item_res_uri: if has_change_perm: edit_url = reverse( '%s:%s_%s_change' % (self.admin_site.app_name, opts.app_label, opts.model_name), args=(getattr(rel_obj, opts.pk.attname),)) else: edit_url = '' item.btns.append('<a data-res-uri="%s" data-edit-uri="%s" class="details-handler" rel="tooltip" title="%s"><i class="fa fa-info-circle"></i></a>' % (item_res_uri, edit_url, _(u'Details of %s') % str(rel_obj))) except NoReverseMatch: pass return item # Media def get_media(self, media): if self.show_all_rel_details or self.show_detail_fields: media = media + self.vendor('xadmin.plugin.details.js', 'xadmin.form.css') return media site.register_plugin(DetailsPlugin, ListAdminView)

45.060606

169

0.569267

557100b8218a3096e706d4775effa9bcb4cc1993

299

py

Python

source/vectorpaths/__init__.py

chrisarridge/fitCurves

44e30f480dcacfb8b05494549e6a902aaa384a79

[ "MIT" ]

2

2021-09-10T18:54:19.000Z

2021-11-26T06:17:19.000Z

source/vectorpaths/__init__.py

chrisarridge/fitCurves

44e30f480dcacfb8b05494549e6a902aaa384a79

[ "MIT" ]

null

source/vectorpaths/__init__.py

chrisarridge/fitCurves

44e30f480dcacfb8b05494549e6a902aaa384a79

[ "MIT" ]

1

2021-09-10T18:54:54.000Z

"""Path package; mainly for fitting cubic beziers to points. """ import numpy as np import matplotlib.pyplot as plt import logging __author__ = 'Chris Arridge' __version__ = '0.2' _path_logger = logging.getLogger(__name__) from .bezier import CubicBezier from .fitcurves import fit_cubic_bezier

19.933333

60

0.785953

5a0a0499ddba5b6288e040a308b7f90def81dff1

1,132

py

Python

mysite/ct/tests/test_models.py

raccoongang/socraticqs2

06201005136ee139846f857dbb2f518736e441de

[ "Apache-2.0" ]

3

2015-11-20T07:33:28.000Z

2017-01-15T23:33:50.000Z

mysite/ct/tests/test_models.py

raccoongang/socraticqs2

06201005136ee139846f857dbb2f518736e441de

[ "Apache-2.0" ]

28

2015-07-14T11:33:24.000Z

2017-11-17T15:21:22.000Z

mysite/ct/tests/test_models.py

raccoongang/socraticqs2

06201005136ee139846f857dbb2f518736e441de

[ "Apache-2.0" ]

4

2015-04-29T09:04:59.000Z

2017-07-19T14:11:16.000Z

import pytest from .models import Lesson from core.common.utils import get_onboarding_status_with_settings from core.common.onboarding import CREATE_THREAD from mysite.helpers import base64_to_file @pytest.mark.django_db def test_get_canvas_html(lesson_question_canvas, base64_gif_image): assert lesson_question_canvas.attachment is not None attachment = base64_to_file('data:image/gif;base64,{}'.format(base64_gif_image)) lesson_question_canvas.attachment.save('image.gif', attachment, save=True) assert lesson_question_canvas.attachment.url is not None assert lesson_question_canvas.get_html().find(lesson_question_canvas.attachment.url) > -1 @pytest.mark.django_db @pytest.mark.parametrize("kind, updated", [ (Lesson.BASE_EXPLANATION, True), (Lesson.EXPLANATION, True), (Lesson.ORCT_QUESTION, False), (Lesson.ANSWER, True), ]) def test_onboarding_step_5_update(unique_instructor, kind, updated): Lesson(title='Lesson test', kind=kind, addedBy=unique_instructor).save() assert get_onboarding_status_with_settings(unique_instructor.id).get(CREATE_THREAD).get('done') == updated

35.375

110

0.793286

f82ea255c74a5a9205624c13f9094974ccdffdf3

1,073

py

Python

roles/finalise/files/finish.py

cbutakoff/slurm-ansible-playbook

c957a8b96dae3a9859ab7c3095dc4d3c1619d1c0

[ "MIT" ]

null

roles/finalise/files/finish.py

cbutakoff/slurm-ansible-playbook

c957a8b96dae3a9859ab7c3095dc4d3c1619d1c0

[ "MIT" ]

null

roles/finalise/files/finish.py

cbutakoff/slurm-ansible-playbook

c957a8b96dae3a9859ab7c3095dc4d3c1619d1c0

[ "MIT" ]

null

#! /usr/bin/env python2 from __future__ import (absolute_import, division, print_function) import glob import os import subprocess finished_nodes = set(os.path.basename(file) for file in glob.glob('/mnt/shared/finalised/*')) all_nodes = {'mgmt'} unfinished_nodes = all_nodes - finished_nodes if unfinished_nodes: print('Error: The following nodes have not reported finishing their setup:') for node in sorted(unfinished_nodes): print(' ', node) print('Please allow them to finish before continuing.') print('For information about why they have not finished, SSH to that machine and check the file /root/ansible-pull.log') exit(1) if not os.path.exists('/home/opc/limits.yaml'): print('Error: Could not find limits.yaml') print('Please create the file and rerun this script.') print('See https://cluster-in-the-cloud.readthedocs.io/en/latest/finalise.html#setting-service-limits for details.') exit(1) subprocess.call(['sudo', '/usr/local/bin/update_config']) subprocess.call(['sudo', 'systemctl', 'restart', 'slurmctld'])

35.766667

124

0.72973

ff3175b68f512ce5315b2cbf21f687c63d18b7d3

11,242

py

Python

flow_collector/netflow_v5.py

xyTel/flowanalyzer-kubernetes

4a26e973a297cf08976934d5c1aac365014ac59b

[ "MIT" ]

1

2020-09-27T17:52:08.000Z

flow_collector/netflow_v5.py

xyTel/flowanalyzer-kubernetes

4a26e973a297cf08976934d5c1aac365014ac59b

[ "MIT" ]

null

flow_collector/netflow_v5.py

xyTel/flowanalyzer-kubernetes

4a26e973a297cf08976934d5c1aac365014ac59b

[ "MIT" ]

null

# Copyright (c) 2017, Manito Networks, LLC # All rights reserved. # Import what we need import time, datetime, socket, struct, sys, json, socket, logging, logging.handlers, getopt, parser_modules from struct import * from socket import inet_ntoa from elasticsearch import Elasticsearch, helpers from IPy import IP # Protocol numbers and types of traffic for comparison from protocol_numbers import protocol_type from defined_ports import registered_ports, other_ports from netflow_options import * ### Get the command line arguments ### try: arguments = getopt.getopt(sys.argv[1:], "hl:", ["--help", "log="]) for option_set in arguments: for opt, arg in option_set: if opt in ('-l', '--log'): # Log level arg = arg.upper() # Uppercase for matching and logging.basicConfig() format if arg in ["CRITICAL", "ERROR", "WARNING", "INFO", "DEBUG"]: log_level = arg # Use what was passed in arguments elif opt in ('-h', '--help'): with open("./help.txt") as help_file: print help_file.read() sys.exit() else: pass except Exception: sys.exit("Unsupported or badly formed options, see -h for available arguments.") # Set the logging level per https://docs.python.org/2/howto/logging.html try: log_level # Check if log level was passed in from command arguments except NameError: log_level = "WARNING" # Use default logging level logging.basicConfig(level = str(log_level)) # Set the logging level logging.critical('Log level set to ' + str(log_level) + " - OK") # Show the logging level for debug ### DNS Lookups ### # # Reverse lookups try: if dns is False: logging.warning("DNS reverse lookups disabled - DISABLED") elif dns is True: logging.warning("DNS reverse lookups enabled - OK") else: logging.warning("DNS enable option incorrectly set - DISABLING") dns = False except: logging.warning("DNS enable option not set - DISABLING") dns = False # RFC-1918 reverse lookups try: if lookup_internal is False: logging.warning("DNS local IP reverse lookups disabled - DISABLED") elif lookup_internal is True: logging.warning("DNS local IP reverse lookups enabled - OK") else: logging.warning("DNS local IP reverse lookups incorrectly set - DISABLING") lookup_internal = False except: logging.warning("DNS local IP reverse lookups not set - DISABLING") lookup_internal = False # Set packet information variables # # Netflow v5 packet structure is STATIC - DO NOT MODIFY THESE VALUES packet_header_size = 24 flow_record_size = 48 # Check if the Netflow v5 port is specified try: netflow_v5_port except NameError: # Not specified, use default netflow_v5_port = 2055 logging.warning("Netflow v5 port not set in netflow_options.py, defaulting to " + str(netflow_v5_port) + " - OK") # Set up the socket listener try: netflow_sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM) netflow_sock.bind(('0.0.0.0', netflow_v5_port)) logging.critical("Bound to port " + str(netflow_v5_port) + " - OK") except Exception as socket_error: logging.critical("Could not open or bind a socket on port " + str(netflow_v9_port)) logging.critical(str(socket_error)) sys.exit() # ElasticSearch class es = Elasticsearch([elasticsearch_host]) # DNS lookup class name_lookups = parser_modules.name_lookups() # TCP / UDP identification class tcp_udp = parser_modules.ports_and_protocols() ### Netflow v5 Collector ### if __name__ == "__main__": # Stage the flows for the bulk API index operation flow_dic = [] # Number of cached records record_num = 0 # Continually listen for inbound packets while True: flow_packet_contents, sensor_address = netflow_sock.recvfrom(65565) # Unpack the header try: logging.info("Unpacking header from " + str(sensor_address[0])) # Netflow v5 packet fields packet_keys = [ "netflow_version", "flow_count", "sys_uptime", "unix_secs", "unix_nsecs", "flow_seq", "engine_type", "engine_id"] packet_values = struct.unpack('!HHIIIIBB', flow_packet_contents[0:22]) packet_contents = dict(zip(packet_keys, packet_values)) # v5 packet fields and values logging.info(str(packet_contents)) logging.info("Finished unpacking header from " + str(sensor_address[0])) # Failed to unpack the header except Exception as flow_header_error: logging.warning("Failed unpacking header from " + str(sensor_address[0]) + " - " + str(flow_header_error)) continue # Timestamp for flow received now = datetime.datetime.utcnow() # Check the Netflow version if packet_contents["netflow_version"] != 5: logging.warning("Received a non-v5 Netflow packet - SKIPPING") continue # Iterate over flows in packet for flow_num in range(0, packet_contents["flow_count"]): logging.info("Parsing flow " + str(flow_num+1)) # Calculate flow starting point base = packet_header_size + (flow_num * flow_record_size) # Index for upload flow_index = { "_index": str("flow-" + now.strftime("%Y-%m-%d")), "_type": "Flow", "_source": { "Flow Type": "Netflow v5", "IP Protocol Version": 4, "Sensor": sensor_address[0], "Time": now.strftime("%Y-%m-%dT%H:%M:%S") + ".%03d" % (now.microsecond / 1000) + "Z", "Engine Type": packet_contents["engine_type"], "Engine ID": packet_contents["engine_id"] } } # Unpack flow data, populate flow_index["_source"] ( ip_source, ip_destination, next_hop, flow_index["_source"]["Input Interface"], flow_index["_source"]["Output Interface"], flow_index["_source"]["Packets In"], flow_index["_source"]["Bytes In"], flow_index["_source"]["System Uptime Start"], flow_index["_source"]["System Uptime Stop"], flow_index["_source"]["Source Port"], flow_index["_source"]["Destination Port"], pad, flow_index["_source"]["TCP Flags"], flow_index["_source"]["Protocol Number"], flow_index["_source"]["Type of Service"], flow_index["_source"]["Source AS"], flow_index["_source"]["Destination AS"], flow_index["_source"]["Source Mask"], flow_index["_source"]["Destination Mask"] ) = struct.unpack('!4s4s4shhIIIIHHcBBBhhBB', flow_packet_contents[base+0:base+46]) # Final unpack, IP addresses via inet_ntoa() flow_index["_source"]["IPv4 Source"] = inet_ntoa(ip_source) flow_index["_source"]["IPv4 Destination"] = inet_ntoa(ip_destination) flow_index["_source"]["IPv4 Next Hop"] = inet_ntoa(next_hop) # Protocols try: # Protocol name flow_index["_source"]["Protocol"] = protocol_type[flow_index["_source"]["Protocol Number"]]["Name"] except Exception as protocol_error: flow_protocol = "Other" # Should never see this unless undefined protocol in use logging.warning("Unknown protocol number - " + str(flow_index["_source"]["Protocol Number"]) + ". Please report to the author for inclusion.") logging.warning(str(protocol_error)) # If the protocol is TCP or UDP try to apply traffic labels if flow_index["_source"]["Protocol Number"] in ([6, 17, 33, 132]): traffic_and_category = tcp_udp.port_traffic_classifier(flow_index["_source"]["Source Port"], flow_index["_source"]["Destination Port"]) flow_index["_source"]["Traffic"] = traffic_and_category["Traffic"] flow_index["_source"]["Traffic Category"] = traffic_and_category["Traffic Category"] else: # Protocol category if "Category" in protocol_type[flow_index["_source"]["Protocol Number"]]: flow_index["_source"]['Traffic Category'] = protocol_type[flow_index["_source"]["Protocol Number"]]["Category"] else: flow_index["_source"]['Traffic Category'] = "Uncategorized" # Perform DNS lookups if enabled if dns is True: # Source DNS source_lookups = name_lookups.ip_names(4, flow_index["_source"]["IPv4 Source"]) flow_index["_source"]["Source FQDN"] = source_lookups["FQDN"] flow_index["_source"]["Source Domain"] = source_lookups["Domain"] # Destination DNS destination_lookups = name_lookups.ip_names(4, flow_index["_source"]["IPv4 Destination"]) flow_index["_source"]["Destination FQDN"] = destination_lookups["FQDN"] flow_index["_source"]["Destination Domain"] = destination_lookups["Domain"] # Content src_dest_categories = [source_lookups["Content"], destination_lookups["Content"]] try: # Pick unique domain Content != "Uncategorized" unique_content = [category for category in src_dest_categories if category != "Uncategorized"] flow_index["_source"]["Content"] = unique_content[0] except: # No unique domain Content flow_index["_source"]["Content"] = "Uncategorized" logging.debug("Current flow data: " + str(flow_index)) logging.info("Finished flow " + str(flow_num+1) + " of " + str(packet_contents["flow_count"])) # Add the parsed flow to flow_dic for bulk insert flow_dic.append(flow_index) # Increment the record counter record_num += 1 # Elasticsearch bulk insert if record_num >= bulk_insert_count: try: helpers.bulk(es, flow_dic) logging.info(str(record_num)+" flow(s) uploaded to Elasticsearch - OK") except ValueError as bulk_index_error: logging.critical(str(record_num)+" flow(s) DROPPED, unable to index flows - FAIL") logging.critical(bulk_index_error.message) # Reset flow_dic flow_dic = [] # Reset the record counter record_num = 0

41.179487

158

0.58913

f7913cf4d4e61ab4cb4674ea62165f9d2320b83f

4,193

py

Python

underground_box/tests.py

fga-eps-mds/2017.2-SiGI-Op_API

4532019c15414fd17e06bb3aa78501886e00da1d

[ "BSD-3-Clause" ]

6

2017-08-24T13:18:21.000Z

2017-10-03T18:06:13.000Z

underground_box/tests.py

fga-gpp-mds/2017.2-Grupo9

4532019c15414fd17e06bb3aa78501886e00da1d

[ "BSD-3-Clause" ]

173

2017-08-31T15:29:01.000Z

2017-12-14T13:40:13.000Z

underground_box/tests.py

fga-gpp-mds/2017.2-SiGI-Op_API

4532019c15414fd17e06bb3aa78501886e00da1d

[ "BSD-3-Clause" ]

2

2018-11-19T10:33:00.000Z

2019-06-19T22:35:43.000Z

from django.test import TestCase from rest_framework.test import APIRequestFactory from emendation_box.models import EmendationBox from emendation_box.models import EmendationBoxType from emendation_box.models import EmendationBoxStructure from technical_reserve.models import TechnicalReserve from .models import UndergroundBox from .models import UndergroundBoxType from .views import UndergroundBoxTypeViewSet from .views import UndergroundBoxViewSet class UndergroundBoxTest(TestCase): def test_type_view_set(self): request = APIRequestFactory().get("") undergroundbox_type_detail = UndergroundBoxTypeViewSet.as_view(actions={'get': 'retrieve'}) undergroundbox_type = UndergroundBoxType.objects.create(name="Random undergroundBox") response = undergroundbox_type_detail(request, pk=undergroundbox_type.pk) self.assertEqual(response.status_code, 200) def test_undergroundbox_view_set(self): request = APIRequestFactory().get("") undergroundbox_detail = UndergroundBoxViewSet.as_view(actions={'get':'retrieve'}) undergroundbox_type = UndergroundBoxType.objects.create(name="Random undergroundBox") emendationtype = EmendationBoxType.objects.create(description="IHATEIT") structure = EmendationBoxStructure.objects.create(description="SERIOUSLY") emendationbox = EmendationBox.objects.create( lattitude=42.42, longitude=42.42, designNumber=42, access_box=True, creation_date="2017-01-01", extinction_date="2018-01-01", emendation_type=emendationtype, emendation_structure=structure, ) reserve = TechnicalReserve.objects.create( code=666, length=8008.5, lattitude=8001, longitude=9001 ) undergroundbox = UndergroundBox.objects.create( box_type=undergroundbox_type, code=123, lattitude=123, cover_type="Cover type", longitude=321, emendation_box=emendationbox, technical_reserve=reserve ) response = undergroundbox_detail(request, pk=undergroundbox.pk) self.assertEqual(response.status_code, 200) def test_wrong_type_view_set(self): request = APIRequestFactory().get("") undergroundbox_type_detail = UndergroundBoxTypeViewSet.as_view(actions={'get': 'retrieve'}) undergroundbox_type = UndergroundBoxType.objects.create(name="Fundergroundbox") primary_key = undergroundbox_type.pk undergroundbox_type.delete() response = undergroundbox_type_detail(request, pk=primary_key) self.assertEqual(response.status_code, 404) def test_wrong_view_set(self): request = APIRequestFactory().get("") undergroundbox_detail = UndergroundBoxViewSet.as_view(actions={'get':'retrieve'}) undergroundbox_type = UndergroundBoxType.objects.create(name="Random undergroundBox") emendationtype = EmendationBoxType.objects.create(description="IHATEIT") structure = EmendationBoxStructure.objects.create(description="TOOMUCH") emendationbox = EmendationBox.objects.create( lattitude=42.42, longitude=42.42, designNumber=42, emendation_type=emendationtype, access_box=True, emendation_structure=structure, creation_date="2017-01-01", extinction_date="2018-01-01", ) reserve = TechnicalReserve.objects.create( code=666, length=8008.5, lattitude=8001, longitude=9001 ) undergroundbox = UndergroundBox.objects.create( box_type=undergroundbox_type, code=123, lattitude=123, cover_type="Cover type", longitude=321, emendation_box=emendationbox, technical_reserve=reserve ) primary_key = undergroundbox.pk undergroundbox.delete() response = undergroundbox_detail(request, pk=primary_key) self.assertEqual(response.status_code, 404)

42.353535

99

0.679227

cd3bd96d6a315026d80b4e09d946ba0179403fe0

4,096

py

Python

app/camera.py

1297rohit/PigCount

449a75e24b2f34c856e713cea5e804e858383d71

[ "MIT" ]

null

app/camera.py

1297rohit/PigCount

449a75e24b2f34c856e713cea5e804e858383d71

[ "MIT" ]

null

app/camera.py

1297rohit/PigCount

449a75e24b2f34c856e713cea5e804e858383d71

[ "MIT" ]

null

from picamera.array import PiRGBArray # Generates a 3D RGB array from picamera import PiCamera # Provides a Python interface for the RPi Camera Module import time # Provides time-related functions import cv2 import tensorflow as tf import numpy as np from azure.iot.device import IoTHubDeviceClient, Message #azure connection string CONNECTION_STRING = "HostName=HUBDEVKIT.azure-devices.net;DeviceId=piazuretest;SharedAccessKey=L000kRCO4zda4rzBp6WMJGAn4Wbpanscdj2jChib0WQ=" def iothub_client_init(): # Create an IoT Hub client client = IoTHubDeviceClient.create_from_connection_string(CONNECTION_STRING) return client MSG_TXT = '{{"pigcount": {count}}}' #model files model_filename = 'model.pb' LABELS_FILENAME = 'labels.txt' labels = None INPUT_TENSOR_NAME = 'image_tensor:0' OUTPUT_TENSOR_NAMES = ['detected_boxes:0', 'detected_scores:0', 'detected_classes:0'] graph_def = tf.compat.v1.GraphDef() with open(model_filename, 'rb') as f: graph_def.ParseFromString(f.read()) graph = tf.Graph() with graph.as_default(): tf.import_graph_def(graph_def, name='') # Get input shape with tf.compat.v1.Session(graph=graph) as sess: input_shape = sess.graph.get_tensor_by_name(INPUT_TENSOR_NAME).shape.as_list()[1:3] with open(LABELS_FILENAME) as f: labels = [l.strip() for l in f.readlines()] def predict_image(image): inputs = np.array(image, dtype=np.float32)[np.newaxis, :, :, :] with tf.compat.v1.Session(graph=graph) as sess: output_tensors = [sess.graph.get_tensor_by_name(n) for n in OUTPUT_TENSOR_NAMES] outputs = sess.run(output_tensors, {INPUT_TENSOR_NAME: inputs}) return outputs class VideoCamera(object): def __init__(self): self.camera = PiCamera() self.camera.resolution = (640, 480) self.camera.framerate = 32 self.raw_capture = PiRGBArray(self.camera, size=(640, 480)) self.client = iothub_client_init() time.sleep(0.1) def __del__(self): self.raw_capture.truncate(0) def get_frame(self,min_conf_threshold): for image in self.camera.capture_continuous(self.raw_capture, format="bgr", use_video_port=True): frame = image.array height,width,_ = frame.shape screen = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) resized = cv2.resize(screen, (tuple(input_shape)), interpolation = cv2.INTER_AREA) pred_out = predict_image(resized) predictions = [{'probability': round(float(p[1]), 8), 'tagId': int(p[2]), 'tagName': labels[p[2]], 'boundingBox': { 'left': round(float(p[0][0]), 8), 'top': round(float(p[0][1]), 8), 'width': round(float(p[0][2] - p[0][0]), 8), 'height': round(float(p[0][3] - p[0][1]), 8) } } for p in zip(*pred_out)] count = 0 for i in predictions: if i["probability"] > min_conf_threshold and i["tagName"] == "pig": count +=1 x = int(i["boundingBox"]["left"]*width) y = int(i["boundingBox"]["top"]*height) w = int(i["boundingBox"]["width"]*width) h = int(i["boundingBox"]["height"]*height) # print(x,y,w,h) cv2.rectangle(frame,(x,y),(x+w,y+h),(0,255,128), 5) cv2.putText(frame,str(count),(30,50),cv2.FONT_HERSHEY_SIMPLEX,1,(255,255,0),2,cv2.LINE_AA) msg_txt_formatted = MSG_TXT.format(count=count) message = Message(msg_txt_formatted) print( "Sending message: {}".format(message) ) self.client.send_message(message) print ( "Message successfully sent" ) time.sleep(5) ret, jpeg = cv2.imencode('.jpg', frame) self.raw_capture.truncate(0) return jpeg.tobytes()

40.554455

140

0.598877

0e8817582daacc30a7e5c3b1c68b11a27b807188

11,890

py

Python

rasa_core/training/dsl.py

ymihay/dialogue_flow

ccb7ba89d1832c23c2bd1c8b7ff2e30f5019c874

[ "Apache-2.0" ]

null

rasa_core/training/dsl.py

ymihay/dialogue_flow

ccb7ba89d1832c23c2bd1c8b7ff2e30f5019c874

[ "Apache-2.0" ]

null

rasa_core/training/dsl.py

ymihay/dialogue_flow

ccb7ba89d1832c23c2bd1c8b7ff2e30f5019c874

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- from __future__ import absolute_import from __future__ import division from __future__ import print_function from __future__ import unicode_literals import io import json import logging import os import re import warnings from typing import Optional, List, Text, Any, Dict from rasa_core import utils from rasa_core.events import ( ActionExecuted, UserUttered, Event) from rasa_core.interpreter import RegexInterpreter from rasa_core.training.structures import ( Checkpoint, STORY_END, STORY_START, StoryStep) logger = logging.getLogger(__name__) class StoryParseError(Exception): """Raised if there is an error while parsing the story file.""" def __init__(self, message): self.message = message class StoryStepBuilder(object): def __init__(self, name): self.name = name self.story_steps = [] self.current_steps = [] self.start_checkpoints = [] def add_checkpoint(self, name, conditions): # Depending on the state of the story part this # is either a start or an end check point if not self.current_steps: self.start_checkpoints.append(Checkpoint(name, conditions)) else: if conditions: logger.warn("End or intermediate checkpoints " "do not support conditions! " "(checkpoint: {})".format(name)) additional_steps = [] for t in self.current_steps: if t.end_checkpoints: tcp = t.create_copy(use_new_id=True) tcp.end_checkpoints = [Checkpoint(name)] additional_steps.append(tcp) else: t.end_checkpoints = [Checkpoint(name)] self.current_steps.extend(additional_steps) def _prev_end_checkpoints(self): if not self.current_steps: return self.start_checkpoints else: # makes sure we got each end name only once end_names = {e.name for s in self.current_steps for e in s.end_checkpoints} return [Checkpoint(name) for name in end_names] def add_user_messages(self, messages): self.ensure_current_steps() if len(messages) == 1: # If there is only one possible intent, we'll keep things simple for t in self.current_steps: t.add_user_message(messages[0]) else: # If there are multiple different intents the # user can use the express the same thing # we need to copy the blocks and create one # copy for each possible message generated_checkpoint = utils.generate_id("GENERATED_M_") updated_steps = [] for t in self.current_steps: for m in messages: copied = t.create_copy(use_new_id=True) copied.add_user_message(m) copied.end_checkpoints = [Checkpoint(generated_checkpoint)] updated_steps.append(copied) self.current_steps = updated_steps def add_event(self, event): self.ensure_current_steps() for t in self.current_steps: t.add_event(event) def ensure_current_steps(self): completed = [step for step in self.current_steps if step.end_checkpoints] unfinished = [step for step in self.current_steps if not step.end_checkpoints] self.story_steps.extend(completed) if unfinished: self.current_steps = unfinished else: self.current_steps = self._next_story_steps() def flush(self): if self.current_steps: self.story_steps.extend(self.current_steps) self.current_steps = [] def _next_story_steps(self): start_checkpoints = self._prev_end_checkpoints() if not start_checkpoints: start_checkpoints = [Checkpoint(STORY_START)] current_turns = [StoryStep(block_name=self.name, start_checkpoints=start_checkpoints)] return current_turns class StoryFileReader(object): """Helper class to read a story file.""" def __init__(self, domain, interpreter, template_vars=None): self.story_steps = [] self.current_step_builder = None # type: Optional[StoryStepBuilder] self.domain = domain self.interpreter = interpreter self.template_variables = template_vars if template_vars else {} @staticmethod def read_from_file(filename, domain, interpreter=RegexInterpreter(), template_variables=None): """Given a json file reads the contained stories.""" try: with io.open(filename, "r") as f: lines = f.readlines() reader = StoryFileReader(domain, interpreter, template_variables) return reader.process_lines(lines) except Exception: logger.exception("Failed to parse '{}'".format( os.path.abspath(filename))) raise ValueError("Invalid story file format.") @staticmethod def _parameters_from_json_string(s, line): # type: (Text, Text) -> Dict[Text, Any] """Parse the passed string as json and create a parameter dict.""" if s is None or not s.strip(): # if there is no strings there are not going to be any parameters return {} try: parsed_slots = json.loads(s) if isinstance(parsed_slots, dict): return parsed_slots else: raise Exception("Parsed value isn't a json object " "(instead parser found '{}')" ".".format(type(parsed_slots))) except Exception as e: raise ValueError("Invalid to parse arguments in line " "'{}'. Failed to decode parameters" "as a json object. Make sure the event" "name is followed by a proper json " "object. Error: {}".format(line, e)) @staticmethod def _parse_event_line(line): """Tries to parse a single line as an event with arguments.""" # the regex matches "slot{"a": 1}" m = re.search('^([^{]+)([{].+)?', line) if m is not None: event_name = m.group(1).strip() slots_str = m.group(2) parameters = StoryFileReader._parameters_from_json_string(slots_str, line) return event_name, parameters else: warnings.warn("Failed to parse action line '{}'. " "Ignoring this line.".format(line)) return "", {} def process_lines(self, lines): # type: (List[Text]) -> List[StoryStep] for idx, line in enumerate(lines): line_num = idx + 1 try: line = self._replace_template_variables( self._clean_up_line(line)) if line.strip() == "": continue elif line.startswith("#"): # reached a new story block name = line[1:].strip("# ") self.new_story_part(name) elif line.startswith(">"): # reached a checkpoint name, conditions = self._parse_event_line(line[1:].strip()) self.add_checkpoint(name, conditions) elif line.startswith( "-"): # reached a slot, event, or executed action event_name, parameters = self._parse_event_line(line[1:]) self.add_event(event_name, parameters) elif line.startswith("*"): # reached a user message user_messages = [el.strip() for el in line[1:].split(" OR ")] self.add_user_messages(user_messages, line_num) else: # reached an unknown type of line logger.warn("Skipping line {}. No valid command found. " "Line Content: '{}'".format(line_num, line)) except Exception as e: msg = "Error in line {}: {}".format(line_num, e.message) logger.error(msg, exc_info=1) raise Exception(msg) self._add_current_stories_to_result() return self.story_steps def _replace_template_variables(self, line): def process_match(matchobject): varname = matchobject.group(1) if varname in self.template_variables: return self.template_variables[varname] else: raise ValueError("Unknown variable `{var}` " "in template line '{line}'".format(var=varname, line=line)) template_rx = re.compile(r"`([^`]+)`") return template_rx.sub(process_match, line) @staticmethod def _clean_up_line(line): # type: (Text) -> Text """Removes comments and trailing spaces""" return re.sub(r'', '', line).strip() def _add_current_stories_to_result(self): if self.current_step_builder: self.current_step_builder.flush() self.story_steps.extend(self.current_step_builder.story_steps) def new_story_part(self, name): self._add_current_stories_to_result() self.current_step_builder = StoryStepBuilder(name) def add_checkpoint(self, name, conditions): # type: (Text) -> None # Ensure story part already has a name if not self.current_step_builder: raise StoryParseError("Checkpoint '{}' is at an invalid location. " "Expected a story start.".format(name)) self.current_step_builder.add_checkpoint(name, conditions) def add_user_messages(self, messages, line_num): if not self.current_step_builder: raise StoryParseError("User message '{}' at invalid location. " "Expected story start.".format(messages)) parsed_messages = [] for m in messages: parse_data = self.interpreter.parse(m) utterance = UserUttered.from_parse_data(m, parse_data) if m.startswith("_"): c = utterance.as_story_string() logger.warn("Stating user intents with a leading '_' is " "deprecated. The new format is " "'* {}'. Please update " "your example '{}' to the new format.".format(c, m)) intent_name = utterance.intent.get("name") if intent_name not in self.domain.intents: logger.warn("Found unknown intent '{}' on line {}. Please, " "make sure that all intents are listed in your " "domain yaml.".format(intent_name, line_num)) parsed_messages.append(utterance) self.current_step_builder.add_user_messages(parsed_messages) def add_event(self, event_name, parameters): parsed = Event.from_story_string(event_name, parameters, self.domain, default=ActionExecuted) if parsed is None: raise StoryParseError("Unknown event '{}'. It is Neither an event " "nor an action).".format(event_name)) self.current_step_builder.add_event(parsed)

40.719178

80

0.566442

140a876ca65171f20b607169e07f0f7d440e20e8

135

py

Python

harnessed_jobs/ptc_BOT/v0/validator_ptc_BOT.py

duncanwood/EO-analysis-jobs

26d22e49c0d2e32fbf2759f504048754f66ecc45

[ "BSD-3-Clause-LBNL" ]

2

2018-07-26T09:32:46.000Z

2019-05-28T20:57:43.000Z

harnessed_jobs/ptc_BOT/v0/validator_ptc_BOT.py

duncanwood/EO-analysis-jobs

26d22e49c0d2e32fbf2759f504048754f66ecc45

[ "BSD-3-Clause-LBNL" ]

3

2018-03-18T21:55:07.000Z

2019-04-18T18:26:06.000Z

harnessed_jobs/ptc_BOT/v0/validator_ptc_BOT.py

duncanwood/EO-analysis-jobs

26d22e49c0d2e32fbf2759f504048754f66ecc45

[ "BSD-3-Clause-LBNL" ]

2

2020-11-12T19:47:42.000Z

2022-02-25T21:43:03.000Z

#!/usr/bin/env ipython """ Validator script for BOT PTC analysis. """ from bot_eo_validators import run_validator run_validator('ptc')

19.285714

43

0.77037

133e9743309f25a4795ef62e9e686c3567ebd693

111

py

Python

timSoft/imanager/processing.py

apetcho/timSoft

90f0084969bb9d4e2d8691583d2fdab4cfd1bb2c

[ "BSD-3-Clause" ]

null

timSoft/imanager/processing.py

apetcho/timSoft

90f0084969bb9d4e2d8691583d2fdab4cfd1bb2c

[ "BSD-3-Clause" ]

null

timSoft/imanager/processing.py

apetcho/timSoft

90f0084969bb9d4e2d8691583d2fdab4cfd1bb2c

[ "BSD-3-Clause" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- """ Created on Sun Feb 16, 2020 12:00:23 PM @author: eapetcho """

18.5

39

0.612613

36736c564815bea8bff4b54b43aa9b5b3c45403b

6,681

py

Python

reachy_sdk/arm.py

Pandinosaurus/reachy-sdk

c155bc8f56488de305e6fe5bacdb77aad0383295

[ "Apache-2.0" ]

21

2021-04-20T15:37:32.000Z

2022-01-06T15:23:11.000Z

reachy_sdk/arm.py

Pandinosaurus/reachy-sdk

c155bc8f56488de305e6fe5bacdb77aad0383295

[ "Apache-2.0" ]

13

2021-03-24T13:29:08.000Z

2022-02-04T11:54:40.000Z

reachy_sdk/arm.py

Pandinosaurus/reachy-sdk

c155bc8f56488de305e6fe5bacdb77aad0383295

[ "Apache-2.0" ]

4

2021-06-23T07:44:55.000Z

2021-12-07T16:15:05.000Z

"""Reachy Arm module. Handles all specific method to an Arm (left and/or right) especially: - the forward kinematics - the inverse kinematics """ from abc import ABC from typing import List, Optional, Set import numpy as np from reachy_sdk_api.arm_kinematics_pb2_grpc import ArmKinematicsStub from reachy_sdk_api.arm_kinematics_pb2 import ArmEndEffector, ArmFKRequest, ArmIKRequest, ArmJointPosition, ArmSide from reachy_sdk_api.kinematics_pb2 import joint__pb2 from reachy_sdk_api.arm_kinematics_pb2 import kinematics__pb2 from .device_holder import DeviceHolder from .joint import Joint # Circumvent https://github.com/grpc/grpc/issues/18139 JointId = joint__pb2.JointId JointPosition = kinematics__pb2.JointPosition Matrix4x4 = kinematics__pb2.Matrix4x4 class Arm(ABC): """Arm abstract class used for both left/right arms. It exposes the kinematics of the arm: - you can access the joints actually used in the kinematic chain, - you can compute the forward and inverse kinematics """ def __init__(self, joints: List[Joint], grpc_channel) -> None: """Set up the arm with its kinematics.""" self._kin_stub = ArmKinematicsStub(grpc_channel) found_joints = [j for j in joints if j.name in self._required_joints] if len(found_joints) != len(self._required_joints): raise ValueError(f'Required joints not found {self._required_joints}') self.joints = DeviceHolder(found_joints) self._setup_joints(found_joints) self.kinematics_chain = DeviceHolder([j for j in found_joints if j.name in self._kinematics_chain]) def __repr__(self) -> str: """Clean representation of an arm state.""" return f'<Arm side="{self._side}" joints={self.joints}>' @property def _side(self) -> str: ... def _setup_joints(self, joints: List[Joint]) -> None: for j in joints: if j.name in self._required_joints: setattr(self, j.name, j) def forward_kinematics(self, joints_position: Optional[List[float]] = None) -> np.ndarray: """Compute the forward kinematics of the arm. It will return the pose 4x4 matrix (as a numpy array) expressed in Reachy coordinate systems. You can either specify a given joints position, otherwise it will use the current robot position. """ if joints_position is None: joints_position = [j.present_position for j in self.kinematics_chain.values()] if isinstance(joints_position, np.ndarray) and len(joints_position.shape) > 1: raise ValueError('Vectorized kinematics not supported!') pos = np.deg2rad(list(joints_position)) if len(pos) != len(self._kinematics_chain): raise ValueError( f'joints_position should be length {len(self._kinematics_chain)} (got {len(pos)} instead)!' ) req = ArmFKRequest( arm_position=ArmJointPosition( side=self._arm_side, positions=self._joint_position_from_pos(pos), ), ) resp = self._kin_stub.ComputeArmFK(req) if not resp.success: raise ValueError(f'No solution found for the given joints ({joints_position})!') return np.array(resp.end_effector.pose.data).reshape((4, 4)) def inverse_kinematics(self, target: np.ndarray, q0: Optional[List[float]] = None) -> List[float]: """Compute the inverse kinematics of the arm. Given a pose 4x4 target matrix (as a numpy array) expressed in Reachy coordinate systems, it will try to compute a joint solution to reach this target (or get close). It will raise a ValueError if no solution is found. You can also specify a basic joint configuration as a prior for the solution. """ if target.shape != (4, 4): raise ValueError('target shape should be (4, 4) (got {target.shape} instead)!') if q0 is not None and (len(q0) != len(self._kinematics_chain)): raise ValueError(f'q0 should be length {len(self._kinematics_chain)} (got {len(q0)} instead)!') if isinstance(q0, np.ndarray) and len(q0.shape) > 1: raise ValueError('Vectorized kinematics not supported!') req_params = { 'target': ArmEndEffector( side=self._arm_side, pose=Matrix4x4(data=target.flatten().tolist()), ) } if q0 is not None: req_params['q0'] = self._joint_position_from_pos(np.deg2rad(q0)) req = ArmIKRequest(**req_params) resp = self._kin_stub.ComputeArmIK(req) if not resp.success: raise ValueError(f'No solution found for the given target ({target})!') return np.rad2deg(resp.arm_position.positions.positions).tolist() @property def _kinematics_chain(self) -> List[str]: ... @property def _required_joints(self) -> Set[str]: ... @property def _arm_side(self): return ArmSide.LEFT if self._side == 'left' else ArmSide.RIGHT def _joint_position_from_pos(self, joints_position: List[float]) -> ArmJointPosition: return JointPosition( ids=[JointId(uid=j.uid) for j in self.kinematics_chain.values()], positions=joints_position, ) class LeftArm(Arm): """LeftArm class, all the work is actually done by the ABC Arm class. It exposes the kinematics of the arm: - you can access the joints actually used in the kinematic chain, - you can compute the forward and inverse kinematics """ _side = 'left' _kinematics_chain = ( 'l_shoulder_pitch', 'l_shoulder_roll', 'l_arm_yaw', 'l_elbow_pitch', 'l_forearm_yaw', 'l_wrist_pitch', 'l_wrist_roll', ) _required_joints = { 'l_shoulder_pitch', 'l_shoulder_roll', 'l_arm_yaw', 'l_elbow_pitch', 'l_forearm_yaw', 'l_wrist_pitch', 'l_wrist_roll', 'l_gripper', } class RightArm(Arm): """RightArm class, all the work is actually done by the ABC Arm class. It exposes the kinematics of the arm: - you can access the joints actually used in the kinematic chain, - you can compute the forward and inverse kinematics """ _side = 'right' _kinematics_chain = ( 'r_shoulder_pitch', 'r_shoulder_roll', 'r_arm_yaw', 'r_elbow_pitch', 'r_forearm_yaw', 'r_wrist_pitch', 'r_wrist_roll', ) _required_joints = { 'r_shoulder_pitch', 'r_shoulder_roll', 'r_arm_yaw', 'r_elbow_pitch', 'r_forearm_yaw', 'r_wrist_pitch', 'r_wrist_roll', 'r_gripper', }

35.163158

115

0.659332

7e325eeb655e505534f1c68a10cfae153b37b697

2,968

py

Python

MyappV2/credential.py

Khairiazim/Raspinstabot

4973f9dbea68a669b6c3a86271bd9f017dd9b6e7

[ "Apache-2.0" ]

null

MyappV2/credential.py

Khairiazim/Raspinstabot

4973f9dbea68a669b6c3a86271bd9f017dd9b6e7

[ "Apache-2.0" ]

null

MyappV2/credential.py

Khairiazim/Raspinstabot

4973f9dbea68a669b6c3a86271bd9f017dd9b6e7

[ "Apache-2.0" ]

null

# user enter credential # check credential # if true open mainwindow and store pc id # close license import sys import webbrowser from PyQt5.QtCore import QSharedMemory from PyQt5 import QtCore, QtGui, QtWidgets, uic # UI.py 1) import ui # from ui import # UI FORMAT class License_class(QtWidgets.QWidget): def __init__(self): QtWidgets.QWidget.__init__(self) uic.loadUi("ui/License.ui", self) # UI.py FORMAT 2) inherit ui class at first paramiter # class Software_class(UI_software.Ui_Dialog,QtWidgets.QDialog): # def __init__(self): # #UI.py 3) make a super # super(UI_software.Ui_Dialog,self).__init__() # self.setupUi(self) # QtCore.QCoreApplication.processEvents() self.pb_apply.clicked.connect(self.check_credential) def check_credential(self): email = str(self.le_username.text()).strip() password = str(self.le_password.text()).strip() # if email and password == true: todo # open mainwindow # else: # reply = QtWidgets.QMessageBox.information(self, 'Dear User', "You are not register,\n" # "would you like to register", # QtWidgets.QMessageBox.Yes | QtWidgets.QMessageBox.No, # QtWidgets.QMessageBox.Yes) # if reply == QtWidgets.QMessageBox.Yes: # # IF "YES" DOWNLOAD AND EXECUTE FILE WITH PROGRESSBAR/ IMPORT PROGRESS.PY # webbrowser.open('https://vetogram.com/register', new=2) # else: # # IF "NO" CLOSE MESSAGEBOX # pass print("email:", email, "password:", password) def message(self): QtWidgets.QMessageBox.information(self, "info", "Vetogram application are currently running") class MemoryCondition: def __init__(self, key='memory_condition_key'): self._shm = QSharedMemory(key) if not self._shm.attach(): if not self._shm.create(1): raise RuntimeError('error creating shared memory: %s' % self._shm.errorString()) self.condition = False def __enter__(self): self._shm.lock() if self._shm.data()[0] == b'\x00': self.condition = True self._shm.data()[0] = b'\x01' self._shm.unlock() return self.condition def __exit__(self, exc_type, exc_value, traceback): if self.condition: self._shm.lock() self._shm.data()[0] = b'\x00' self._shm.unlock() if __name__ == "__main__": with MemoryCondition() as condition: app = QtWidgets.QApplication(sys.argv) License = License_class() if condition: License.show() sys.exit(app.exec()) else: License.message()

32.26087

109

0.574124

41441423b1340898c062e3dd2e63ea64a5b76a5a

7,837

py

Python

examples/imdb_dropout.py

albietz/stochs

3c3c4860c91ab59b2d8d5762583c722f73ab8608

[ "MIT" ]

27

2017-03-22T15:21:59.000Z

2020-10-26T22:11:00.000Z

examples/imdb_dropout.py

albietz/stochs

3c3c4860c91ab59b2d8d5762583c722f73ab8608

[ "MIT" ]

null

examples/imdb_dropout.py

albietz/stochs

3c3c4860c91ab59b2d8d5762583c722f73ab8608

[ "MIT" ]

6

2017-03-26T23:56:19.000Z

2018-09-04T03:12:49.000Z

import argparse import logging import os import sys import time import numpy as np import scipy.sparse as sp import stochs from concurrent.futures import ThreadPoolExecutor logging.basicConfig(level=logging.INFO) params = { 'lmbda': [1e-2], 'delta': [0, 0.01, 0.3], 'algos': [ {'name': 'miso_nonu', 'lr': 1.0}, # {'name': 'miso', 'lr': 10.0}, # {'name': 'sgd_nonu', 'lr': 1.0}, # {'name': 'sgd', 'lr': 10.0}, # {'name': 'saga', 'lr': 10.0}, ], } start_decay = 2 num_epochs = 301 loss = b'squared_hinge' eval_delta = 10 eval_mc_samples = 0 seed = None def load_imdb(folder): def process_imdb(X, y): return X.astype(stochs.dtype), (y > 5).astype(stochs.dtype) from sklearn.datasets import load_svmlight_files Xtrain, ytrain, Xtest, ytest = load_svmlight_files( (os.path.join(folder, 'train/labeledBow.feat'), os.path.join(folder, 'test/labeledBow.feat'))) Xtrain, ytrain = process_imdb(Xtrain, ytrain) Xtest, ytest = process_imdb(Xtest, ytest) return Xtrain, ytrain, Xtest, ytest def training(lmbda, dropout_rate, solver, q=None): ep = [] loss_train = [] loss_test = [] acc_train = [] acc_test = [] random = np.random.RandomState(seed=seed or 43) # prepare evaluation set if dropout_rate > 0 and eval_mc_samples > 0: Xtrain_eval = sp.vstack((Xtrain for _ in range(eval_mc_samples))) Xtrain_eval.sort_indices() Xtrain_eval.data *= random.binomial(1, 1 - dropout_rate, size=Xtrain_eval.nnz) / (1 - dropout_rate) ytrain_eval = np.hstack((ytrain for _ in range(eval_mc_samples))) else: Xtrain_eval = Xtrain ytrain_eval = ytrain t_start = time.time() for epoch in range(num_epochs): if epoch % eval_delta == 0: ep.append(epoch) loss_train.append(solver.compute_loss(Xtrain_eval, ytrain_eval) + 0.5 * lmbda * solver.compute_squared_norm()) loss_test.append(solver.compute_loss(Xtest, ytest)) acc_train.append((((2*ytrain - 1) * Xtrain.dot(solver.w)) >= 0).mean()) acc_test.append((((2*ytest - 1) * Xtest.dot(solver.w)) >= 0).mean()) if epoch == start_decay and dropout_rate > 0: solver.start_decay() if dropout_rate > 0: idxs = random.choice(n, n, p=q) Xtt = Xtrain[idxs] Xtt.sort_indices() Xtt.data *= random.binomial(1, 1 - dropout_rate, size=Xtt.nnz) / (1 - dropout_rate) solver.iterate(Xtt, ytrain[idxs], idxs) else: idxs = random.choice(n, n, p=q) solver.iterate_indexed(Xtrain, ytrain, idxs) logging.info('elapsed: %f', time.time() - t_start) # print('lmbda', lmbda, 'delta', dropout_rate, # '=> train loss:', loss_train[-1], 'test loss:', loss_test[-1], # 'train acc:', acc_train[-1], 'test acc:', acc_test[-1]) return { 'epochs': ep, 'loss_train': loss_train, 'loss_test': loss_test, 'acc_train': acc_train, 'acc_test': acc_test, } def train_sgd(lmbda, dropout_rate, lr): solver = stochs.SparseSGD(d, lr=lr * (1 - dropout_rate)**2 / Lmax, lmbda=lmbda, loss=loss) return training(lmbda, dropout_rate, solver) def train_sgd_nonu(lmbda, dropout_rate, lr): solver = stochs.SparseSGD(d, lr=lr * (1 - dropout_rate)**2 / Lavg, lmbda=lmbda, loss=loss) q = np.asarray(Xtrain.power(2).sum(1)).flatten() q += q.mean() q /= q.sum() solver.set_q(q) return training(lmbda, dropout_rate, solver, q=q) def train_miso(lmbda, dropout_rate, lr): solver = stochs.SparseMISO(d, n, lmbda=lmbda, loss=loss) solver.init(Xtrain) solver.decay(lr * min(1, n * lmbda * (1 - dropout_rate)**2 / Lmax)) return training(lmbda, dropout_rate, solver) def train_miso_nonu(lmbda, dropout_rate, lr): alpha = lr * min(1, n * lmbda * (1 - dropout_rate)**2 / Lavg) solver = stochs.SparseMISO(d, n, alpha=alpha, lmbda=lmbda, loss=loss) solver.init(Xtrain) q = np.asarray(Xtrain.power(2).sum(1)).flatten() q += q.mean() q /= q.sum() solver.set_q(q) return training(lmbda, dropout_rate, solver, q=q) def train_saga(lmbda, dropout_rate, lr): solver = stochs.SparseSAGA(d, n, lr=lr * (1 - dropout_rate)**2 / Lmax, lmbda=lmbda, loss=loss) solver.init(Xtrain) return training(lmbda, dropout_rate, solver) train_fn = {'sgd': train_sgd, 'sgd_nonu': train_sgd_nonu, 'miso': train_miso, 'miso_nonu': train_miso_nonu, 'saga': train_saga} if __name__ == '__main__': parser = argparse.ArgumentParser(description='dropout training on imdb') parser.add_argument('--num-workers', default=1, type=int, help='number of threads for grid search') parser.add_argument('--pdf-file', default=None, help='pdf file to save to') parser.add_argument('--pkl-file', default=None, help='pickle file to save to') parser.add_argument('--seed', default=None, type=int, help='random seed') parser.add_argument('--data-folder', default='data/aclImdb', help='imdb dataset folder') args = parser.parse_args() seed = args.seed print('seed:', seed) logging.info('loading imdb data') if not os.path.exists(args.data_folder): logging.error('IMDB dataset folder {} is missing. '.format(args.data_folder) + 'Download at http://ai.stanford.edu/~amaas/data/sentiment/ or specify a different folder.') sys.exit(0) Xtrain, ytrain, Xtest, ytest = load_imdb(args.data_folder) n = Xtrain.shape[0] d = Xtrain.shape[1] Lmax = Xtrain.power(2).sum(1).max() Lavg = Xtrain.power(2).sum(1).mean() pp = None if args.pdf_file: import matplotlib matplotlib.use('Agg') from matplotlib.backends.backend_pdf import PdfPages import curves pp = PdfPages(args.pdf_file) pkl = None if args.pkl_file is not None: pkl = [] logging.info('training') futures = [] with ThreadPoolExecutor(max_workers=args.num_workers) as executor: for lmbda in params['lmbda']: for delta in params['delta']: futures.append(((lmbda, delta), [(alg, executor.submit(train_fn[alg['name']], lmbda, delta, alg['lr'])) for alg in params['algos']])) for (lmbda, delta), futs in futures: res = [(alg, f.result()) for alg, f in futs] print('lmbda', lmbda, 'delta', delta) for alg, r in res: print(alg['name'], alg['lr'], 'train loss', r['loss_train'][-1], 'test acc', r['acc_test'][-1]) if pp or pkl is not None: plot_res = {} plot_res['params'] = [dict(name=alg['name'], lr=alg['lr'], lmbda=lmbda, loss=loss) for alg, r in res] plot_res['epochs'] = res[0][1]['epochs'] def transpose(key): return list(zip(*(r[key] for alg, r in res))) plot_res['test_accs'] = transpose('acc_test') plot_res['train_accs'] = transpose('acc_train') plot_res['train_losses'] = transpose('loss_train') plot_res['test_losses'] = transpose('loss_test') if pkl is not None: pkl.append(((lmbda, delta), plot_res)) if pp: curves.plot_loss(plot_res, ty='train', log=True, step=1, last=None, small=False, legend=True, title='imdb, $\delta$ = {:.2f}'.format(delta)) pp.savefig() if pp: pp.close() if pkl: import pickle pickle.dump(pkl, open(args.pkl_file, 'wb'))

36.451163

122

0.590277

74095c2ae0f63ee6887469e4af190482f7e204a7

4,586

py

Python

sqlpython/metadata.py

fortime/sqlpython

40e6b7d0b24ffeafabac0e3719049c081e26e8b2

[ "MIT" ]

null

sqlpython/metadata.py

fortime/sqlpython

40e6b7d0b24ffeafabac0e3719049c081e26e8b2

[ "MIT" ]

null

sqlpython/metadata.py

fortime/sqlpython

40e6b7d0b24ffeafabac0e3719049c081e26e8b2

[ "MIT" ]

null

from defaultdict import defaultdict metaqueries = defaultdict(defaultdict) metaqueries['desc']['oracle'] = defaultdict(defaultdict) metaqueries['desc']['oracle']['TABLE']['long'] = ( """SELECT atc.column_id "#", atc.column_name, CASE atc.nullable WHEN 'Y' THEN 'NULL' ELSE 'NOT NULL' END "Null?", atc.data_type || CASE atc.data_type WHEN 'DATE' THEN '' ELSE '(' || CASE atc.data_type WHEN 'NUMBER' THEN TO_CHAR(atc.data_precision) || CASE atc.data_scale WHEN 0 THEN '' ELSE ',' || TO_CHAR(atc.data_scale) END ELSE TO_CHAR(atc.data_length) END END || CASE atc.data_type WHEN 'DATE' THEN '' ELSE ')' END data_type, acc.comments FROM all_tab_columns atc JOIN all_col_comments acc ON (acc.owner = atc.owner AND acc.table_name = atc.table_name AND acc.column_name = atc.column_name) WHERE atc.table_name = :object_name AND atc.owner = :owner ORDER BY atc.column_id;""",) metaqueries['desc']['oracle']['TABLE']['short'] = ( """SELECT atc.column_name, CASE atc.nullable WHEN 'Y' THEN 'NULL' ELSE 'NOT NULL' END "Null?", atc.data_type || CASE atc.data_type WHEN 'DATE' THEN '' ELSE '(' || CASE atc.data_type WHEN 'NUMBER' THEN TO_CHAR(atc.data_precision) || CASE atc.data_scale WHEN 0 THEN '' ELSE ',' || TO_CHAR(atc.data_scale) END ELSE TO_CHAR(atc.data_length) END END || CASE atc.data_type WHEN 'DATE' THEN '' ELSE ')' END data_type FROM all_tab_columns atc WHERE atc.table_name = :object_name AND atc.owner = :owner ORDER BY atc.column_id;""",) metaqueries['desc']['oracle']['PROCEDURE'] = ( """SELECT NVL(argument_name, 'Return Value') argument_name, data_type, in_out, default_value FROM all_arguments WHERE object_name = :object_name AND owner = :owner AND package_name IS NULL ORDER BY sequence;""",) metaqueries['desc']['oracle']['PackageObjects'] = ( """SELECT DISTINCT object_name FROM all_arguments WHERE package_name = :package_name AND owner = :owner""",) metaqueries['desc']['oracle']['PackageObjArgs'] = ( """SELECT object_name, argument_name, data_type, in_out, default_value FROM all_arguments WHERE package_name = :package_name AND object_name = :object_name AND owner = :owner AND argument_name IS NOT NULL ORDER BY sequence;""",) metaqueries['desc']['oracle']['TRIGGER'] = ( """SELECT description FROM all_triggers WHERE owner = :owner AND trigger_name = :object_name; """, """SELECT table_owner, base_object_type, table_name, column_name, when_clause, status, action_type, crossedition FROM all_triggers WHERE owner = :owner AND trigger_name = :object_name \\t""",) metaqueries['desc']['oracle']['INDEX'] = ( """SELECT index_type, table_owner, table_name, table_type, uniqueness, compression, partitioned, temporary, generated, secondary, dropped, visibility FROM all_indexes WHERE owner = :owner AND index_name = :object_name\\t""",) metaqueries['desc']['oracle']['VIEW'] = metaqueries['desc']['oracle']['TABLE']['short'] metaqueries['desc']['oracle']['FUNCTION'] = metaqueries['desc']['oracle']['PROCEDURE'] metaqueries['ls']['oracle'] = """ SELECT owner, object_name, object_type, status, last_ddl_time, user as my_own FROM all_objects""" metaqueries['ls']['information_schema'] = """ SELECT table_schema as owner, table_name as object_name, table_type as object_type, null as status, null as last_ddl_time, %(my_own)s as my_own FROM information_schema.tables UNION ALL SELECT trigger_schema as owner, trigger_name as object_name, 'TRIGGER' as object_type, null as status, created as last_ddl_time, %(my_own)s as my_own FROM information_schema.triggers UNION ALL SELECT routine_schema as owner, routine_name as object_name, routine_type as object_type, null as status, last_altered as last_ddl_time, %(my_own)s as my_own FROM information_schema.routines """ metaqueries['ls']['postgres'] = (metaqueries['ls']['information_schema'] + """UNION ALL SELECT sequence_schema as owner, sequence_name as object_name, 'SEQUENCE' as object_type, null as status, null as last_ddl_time, %(my_own)s as my_own FROM information_schema.sequences""") % {'my_own': "text('public')"} metaqueries['ls']['mysql'] = metaqueries['ls']['information_schema'] % {'my_own':"database()"} metaqueries['ls']['sqlite'] = """ SELECT '' as owner, tbl_name as object_name, type as object_type, null as status, null as last_ddl_time, '' as current_username FROM sqlite_master"""

27.297619

126

0.694287

dcedf9711a3e04223dfa7aa542c6feea98598e33

1,775

py

Python

src/encoded/tests/test_upgrade_analysis_step.py

Parul-Kudtarkar/t2dream-portal

ad0514e71da95cd911e874cb04112037e10c5823

[ "MIT" ]

null

src/encoded/tests/test_upgrade_analysis_step.py

Parul-Kudtarkar/t2dream-portal

ad0514e71da95cd911e874cb04112037e10c5823

[ "MIT" ]

null

src/encoded/tests/test_upgrade_analysis_step.py

Parul-Kudtarkar/t2dream-portal

ad0514e71da95cd911e874cb04112037e10c5823

[ "MIT" ]

null

import pytest @pytest.fixture def base_analysis_step(testapp, software_version): item = { 'name': 'base_analysis_step_v_1', 'title': 'base_analysis_step_v_1 title', 'analysis_step_types': ['alignments'], 'input_file_types': ['reads'], 'software_versions': [ software_version['@id'], ], } return item @pytest.fixture def analysis_step_1(base_analysis_step): item = base_analysis_step.copy() item.update({ 'schema_version': '2', 'output_file_types': ['signal of multi-mapped reads'] }) return item @pytest.fixture def analysis_step_3(base_analysis_step): item = base_analysis_step.copy() item.update({ 'schema_version': '3', 'analysis_step_types': ['alignment', 'alignment'], 'input_file_types': ['reads', 'reads'], 'output_file_types': ['transcriptome alignments', 'transcriptome alignments'] }) return item def test_analysis_step_run_2_3(registry, upgrader, analysis_step_1, threadlocals): value = upgrader.upgrade('analysis_step', analysis_step_1, current_version='2', target_version='3', registry=registry) assert 'signal of all reads' in value['output_file_types'] assert 'signal of multi-mapped reads' not in value['output_file_types'] def test_analysis_step_unique_array(upgrader, analysis_step_3): value = upgrader.upgrade('analysis_step', analysis_step_3, current_version='3', target_version='4') assert value['schema_version'] == '4' assert len(value['analysis_step_types']) == len(set(value['analysis_step_types'])) assert len(value['input_file_types']) == len(set(value['input_file_types'])) assert len(value['output_file_types']) == len(set(value['output_file_types']))

34.134615

122

0.689577

05f1f8b61e4058236338b3f9954a40173b8f9fff

3,734

py

Python

Lib/site-packages/validictory/tests/test_disallow_unknown_properties.py

Kronos3/pyexec

c9e76a0302dee047ed137bc38aa669cec04c24cd

[ "bzip2-1.0.6" ]

73

2015-07-05T12:52:55.000Z

2019-12-09T16:34:09.000Z

Lib/site-packages/validictory/tests/test_disallow_unknown_properties.py

Kronos3/pyexec

c9e76a0302dee047ed137bc38aa669cec04c24cd

[ "bzip2-1.0.6" ]

30

2015-10-13T21:47:46.000Z

2017-10-26T17:00:47.000Z

Lib/site-packages/validictory/tests/test_disallow_unknown_properties.py

Kronos3/pyexec

c9e76a0302dee047ed137bc38aa669cec04c24cd

[ "bzip2-1.0.6" ]

23

2015-07-11T05:11:36.000Z

2022-02-14T05:27:22.000Z

from unittest import TestCase import validictory class TestDisallowUnknownProperties(TestCase): def setUp(self): self.data_simple = {"name": "john doe", "age": 42} self.schema_simple = { "type": "object", "properties": { "name": {"type": "string"}, "age": {"type": "integer"} }, } self.data_complex = { "inv_number": "123", "rows": [ { "sku": "ab-456", "desc": "a description", "price": 100.45 }, { "sku": "xy-123", "desc": "another description", "price": 999.00 } ], "data": { "name": "john doe", "age": 42 } } self.schema_complex = { "type": "object", "properties": { "inv_number": {"type": "string"}, "rows": { "type": "array", "items": { "type": "object", "properties": { "sku": {"type": "string"}, "desc": {"type": "string"}, "price": {"type": "number"} } }, }, "data": { "type": ( { "type": "object", "properties": { "name": { "type": "string" }, "hair": { "type": "string" } } }, { "type": "object", "properties": { "name": { "type": "string" }, "age": { "type": "number" } } } ) } } } def test_disallow_unknown_properties_pass(self): try: validictory.validate(self.data_simple, self.schema_simple, disallow_unknown_properties=True) except ValueError as e: self.fail("Unexpected failure: %s" % e) def test_disallow_unknown_properties_fail(self): self.data_simple["sex"] = "male" self.assertRaises(validictory.SchemaError, validictory.validate, self.data_simple, self.schema_simple, disallow_unknown_properties=True) def test_disallow_unknown_properties_complex_pass(self): try: validictory.validate(self.data_complex, self.schema_complex, disallow_unknown_properties=True) except ValueError as e: self.fail("Unexpected failure: %s" % e) def test_disallow_unknown_properties_complex_fail(self): newrow = {"sku": "789", "desc": "catch me if you can", "price": 1, "rice": 666} self.data_complex["rows"].append(newrow) self.assertRaises(validictory.SchemaError, validictory.validate, self.data_complex, self.schema_complex, disallow_unknown_properties=True)

34.574074

74

0.363417