lparkourer10/starcoder-python5b · Datasets at Hugging Face

79511842dbabcea06a7c53ac39ceb0d361da63c1

2,868

py

Python

pyqtgraph/exporters/PrintExporter.py

marsipu/pyqtgraph

eb8965c2f4b8ef9393eeeb857bc23d5faf2c1baf

[ "MIT" ]

1

2021-07-09T23:27:37.000Z

pyqtgraph/exporters/PrintExporter.py

tom00ti/pyqtgraph

6b4385ce0d0f9078aa22e2e27aa5307271e95ae1

[ "MIT" ]

null

pyqtgraph/exporters/PrintExporter.py

tom00ti/pyqtgraph

6b4385ce0d0f9078aa22e2e27aa5307271e95ae1

[ "MIT" ]

null

from .Exporter import Exporter from ..parametertree import Parameter from ..Qt import QtGui, QtCore, QtSvg import re translate = QtCore.QCoreApplication.translate __all__ = ['PrintExporter'] #__all__ = [] ## Printer is disabled for now--does not work very well. class PrintExporter(Exporter): Name = "Printer" def __init__(self, item): Exporter.__init__(self, item) tr = self.getTargetRect() self.params = Parameter(name='params', type='group', children=[ {'name': 'width', 'title': translate("Exporter", 'width'), 'type': 'float', 'value': 0.1, 'limits': (0, None), 'suffix': 'm', 'siPrefix': True}, {'name': 'height', 'title': translate("Exporter", 'height'), 'type': 'float', 'value': (0.1 * tr.height()) / tr.width(), 'limits': (0, None), 'suffix': 'm', 'siPrefix': True}, ]) self.params.param('width').sigValueChanged.connect(self.widthChanged) self.params.param('height').sigValueChanged.connect(self.heightChanged) def widthChanged(self): sr = self.getSourceRect() ar = sr.height() / sr.width() self.params.param('height').setValue(self.params['width'] * ar, blockSignal=self.heightChanged) def heightChanged(self): sr = self.getSourceRect() ar = sr.width() / sr.height() self.params.param('width').setValue(self.params['height'] * ar, blockSignal=self.widthChanged) def parameters(self): return self.params def export(self, fileName=None): printer = QtGui.QPrinter(QtGui.QPrinter.HighResolution) dialog = QtGui.QPrintDialog(printer) dialog.setWindowTitle(translate('Exporter', "Print Document")) if dialog.exec_() != QtGui.QDialog.DialogCode.Accepted: return #dpi = QtGui.QDesktopWidget().physicalDpiX() #self.svg.setSize(QtCore.QSize(100,100)) #self.svg.setResolution(600) #res = printer.resolution() sr = self.getSourceRect() #res = sr.width() * .4 / (self.params['width'] * 100 / 2.54) res = QtGui.QGuiApplication.primaryScreen().physicalDotsPerInchX() printer.setResolution(res) rect = printer.pageRect() center = rect.center() h = self.params['height'] * res * 100. / 2.54 w = self.params['width'] * res * 100. / 2.54 x = center.x() - w/2. y = center.y() - h/2. targetRect = QtCore.QRect(x, y, w, h) sourceRect = self.getSourceRect() painter = QtGui.QPainter(printer) try: self.setExportMode(True, {'painter': painter}) self.getScene().render(painter, QtCore.QRectF(targetRect), QtCore.QRectF(sourceRect)) finally: self.setExportMode(False) painter.end() #PrintExporter.register()

39.287671

110

0.602859

795119d6a65d74e29656bde466342abc699b627b

650

py

Python

bin/cli.py

rmit-ir/joint-cascade-ranking

fa53c4447ce0ebe380d0ebbb5017eb7b1ac015e3

[ "MIT" ]

9

2018-12-03T12:29:54.000Z

2021-08-18T02:21:34.000Z

bin/cli.py

rmit-ir/joint-cascade-ranking

fa53c4447ce0ebe380d0ebbb5017eb7b1ac015e3

[ "MIT" ]

1

2019-11-05T14:54:50.000Z

2019-11-20T01:12:45.000Z

bin/cli.py

rmit-ir/joint-cascade-ranking

fa53c4447ce0ebe380d0ebbb5017eb7b1ac015e3

[ "MIT" ]

4

2019-10-16T02:55:11.000Z

2021-12-05T00:23:29.000Z

class AttrDict(dict): __setattr__ = dict.__setitem__ __getattr__ = dict.__getitem__ @classmethod def from_parseargs(cls, args): config = cls() config.merge_parseargs(args) return config @classmethod def from_attr_dict(cls, other): config = cls() for key, val in other.items(): setattr(config, key, val) return config def merge_parseargs(self, args): for key, val in vars(args).items(): # this class is a `dict` so use `in` instead of `hasattr` if key in self.keys() and val is not None: setattr(self, key, val)

28.26087

69

0.589231

79511bd93dd29d8fab4047de060459b0fe8f22c1

7,841

py

Python

bipartite/matching.py

severmore/pygraph

1ad4c0f6c287ef5fd4c0269495ff890b8be8a6e0

[ "MIT" ]

2

2018-06-30T10:19:41.000Z

2018-07-02T09:57:25.000Z

bipartite/matching.py

severmore/pygraph

1ad4c0f6c287ef5fd4c0269495ff890b8be8a6e0

[ "MIT" ]

10

2018-06-28T16:19:21.000Z

2018-07-06T11:48:03.000Z

bipartite/matching.py

severmore/pygraphs

1ad4c0f6c287ef5fd4c0269495ff890b8be8a6e0

[ "MIT" ]

null

""" Created by Ivanov Roman and Maxim Dudin. https://github.com/severmore/pygraphs """ from collections import deque def euler_partition(graph, sustain_graph=False): """ Finds an Euler partition of a graph. An Euler partition is a partition of edges of a graph into open and close path, each vertex of odd degree is the end of exactly one open path, and each vertex of even degree is the end of no open path [1]. In this implementation an Euler partition is implemented as a list of paths, each path is given as a list of vertices that this path passes. For that cases when the edges should be including a modificaiton of the method should be considered. After finishing this method all edges of an initial graph will be removed as the algorithm supposes. To avoid this set `sustain_graph` to True. Args: graph(:obj:`Graph`) - a graph for which a partition is to find. sustain_graph(bool) - if it is set to True graph edges will be copied. Return: :obj:`list` of :obj:`list` of int: an Euler partition representation that is a list of paths. Each path is given by the list of verteces, skipping passing enges. References: [1] Harold N. Gabow. Using Euler Partition to Edge Color Bipartite Multigraphs // The International Journal of Computer and Information Sciences, Vol. 5, No. 4, 1976. """ if sustain_graph: pass partition = list() # Populate Q in reverse order comparing to [1] queue = deque() for vertex in graph.get_vertices(): if graph.degree(vertex) % 2: queue.append(vertex) else: queue.appendleft(vertex) while queue: start = queue.pop() if graph.degree(start): path = [start] pivot = start while graph.degree(pivot): pivot_next = graph.edges[pivot][0] graph.remove_edge(pivot, pivot_next) path.append(pivot_next) pivot = pivot_next partition.append(path) if graph.degree(start): queue.appendleft(start) return partition def euler_split(graph): """ Returns an Euler split of a graph. An Euler split is two subgraphs of the initial graph such that the set of both subgraphs vertices remains unchanged, the edges of the graph is a disjoint union of subgraphs edges and these obtains by alteranatively placing the edges of the paths in the Euler partition of the graph to the subgraphs. Args: graph(:obj:`Graph`) - a graph for which a matching covering maximum degree vertices is need to find. Returns: :obj:`list` of :obj:`turple` of int - a matching to find as a list of edges. References: [1] Harold N. Gabow. Using Euler Partition to Edge Color Bipartite Multigraphs // The International Journal of Computer and Information Sciences, Vol. 5, No. 4, 1976. """ G1 = graph.__class__(vertices_num=graph.vertices_num) G2 = graph.__class__(vertices_num=graph.vertices_num) partition = euler_partition(graph) for path in partition: v_prev, path = path[0], path[1:] for index, vertex in enumerate(path): if index % 2: G1.add_edge(v_prev, vertex) else: G2.add_edge(v_prev, vertex) v_prev = vertex G1.update_max_degree() G2.update_max_degree() return G1, G2 def _covering_partition(graph): """ Finds Cole-Hopcroft graph partition - a partition into two subgraphs such that the following properties holds: - both subgraphs have the same to graph vertices, - a disjoint union of thier edges forms the edges of the initial graph, - the set of vertices having maximum degree in the graph also have maximum degree in each subgraphs. If the maximum degree of the initial graph is even then Cole-Hopcroft partition is just an Euler split. Args: graph(:obj:`Graph`) - a graph to split Returns: :obj:`Graph`, :obj:`Graph` - two subraphs of `graph`. References: [2] Richard Cole, and John Hopcroft. On Edge Coloring Bipartite Graphs // SIAM Journal on Computing, Vol. 11, No. 3, pp. 540-546, 1982. """ if graph.max_degree % 2 == 0: return euler_split(graph) D = graph.max_degree k, d = D // 4, 1 if D % 4 == 3: k, d = k + 1, -1 # Find M-containing set - a set of max degree vertices in a graph. M = { v for v in graph.get_vertices() if graph.degree(v) == D } H1, H2 = euler_split(graph) # M1 is a subset of M which vertices have degree 2 * k + d in H1. If M1 have # less vertices than half of M then swap H1 and H2, M1 and M2; so M1 should # have at least half of maximum degree vertices. M1 = { v for v in M if H1.degree(v) == 2 * k + d } M2 = M - M1 if len(M1) < len(M) / 2: H2, H1 = H1, H2 M2, M1 = M1, M2 # Iterates till in both the subraphs the set of max degree vertices equals M while M2: H21, H22 = euler_split(H2) M21 = { v for v in M2 if H21.degree(v) == k + d } M22 = M2 - M21 if len(M21) < len(M2) / 2: H22, H21 = H21, H22 M22, M21 = M21, M22 H1.union(H21) H2 = H22 if k % 2: k = k / 2 else: H1, H2 = H2, H1 k = (D - k) / 2 d = -d M1 = M1.union(M21) M2 = M22 H1.update_max_degree() H2.update_max_degree() return H1, H2 def covering_matching(graph, sustain_graph=True): """ Finds matching covering maximum degree vertices of a graph. The matching is a subset of a graph edges such that no two edges have common vertex. It is said that matching covers a set of vertices, if for all vertex from this set there is an edge from matching that contains this vertex. After finishing this method all edges of an initial graph will be removed as the algorithm supposes. If it is necessary to keep graph edges set `sustain_graph` to True, and the method return both matching edges and the rest part of the edges. If one need to recover the initial graph just union this edges into a single graph. Args: graph(:obj:`Graph`) - a graph to split sustain_graph(bool) - if it is set to True graph edges excluding the edges of matching found will be returned. Returns: :obj:`list` of :obj:`list` of int, :obj:`Graph` - a matching and the rest of `graph` if `sustain_graph` is set to True. :obj:`list` of :obj:`list` of int - a matching found if `sustain_graph` is set to False. References: [2] Richard Cole, and John Hopcroft. On Edge Coloring Bipartite Graphs // SIAM Journal on Computing, Vol. 11, No. 3, pp. 540-546, 1982. """ if sustain_graph: matching, rest = graph, graph.__class__() # The first iteration of cycle is individually coded to avoid superfluous # coping of graph edges to `rest`. if matching.max_degree > 1: matching, rest = _covering_partition(matching) if matching.max_degree > rest.max_degree: matching, rest = rest, matching while matching.max_degree > 1: G1, G2 = _covering_partition(matching) if G1.max_degree > G2.max_degree: G1, G2 = G2, G1 matching = G1 rest.union(G2) return matching, rest else: matching = graph while matching.max_degree > 1: G1, G2 = _covering_partition(matching) matching = G1 if G1.max_degree < G2.max_degree else G2 return matching if __name__ == '__main__': import bipartite.graph graph = bipartite.graph.UDGraph(edges= [ (0, 3), (0, 4), (1, 3), (1, 4), (1, 5), (2, 3)]) print('graph: ', graph) matching, rest = covering_matching(graph) print('matching: ', matching) print('rest: ', repr(rest)) rest.union(matching) graph = bipartite.graph.UDGraph(edges= [ (0, 3), (0, 4), (1, 3), (1, 4), (1, 5), (2, 3)]) print('restored: ', rest) print('equality of rest and graph: ', rest == graph)

27.416084

80

0.661268

79511e61ff4229cda4d0f13805e1c20e75406481

11,885

py

Python

jx_telebot.py

jxwhat/telegram_bot

f984eeac70efeca99c89c7e3436e88de65ed99d1

[ "MIT" ]

null

jx_telebot.py

jxwhat/telegram_bot

f984eeac70efeca99c89c7e3436e88de65ed99d1

[ "MIT" ]

null

jx_telebot.py

jxwhat/telegram_bot

f984eeac70efeca99c89c7e3436e88de65ed99d1

[ "MIT" ]

null

# this file provides supporting stuff for the telegram bot import numpy as np import pandas as pd import yfinance as yf import re import random from datetime import datetime, timedelta from datetime import date as datetype ## clumsy code but works for now import requests vocab_dict = { 'no_change': ['remained flat at {}'], 'small_up': ['inched up {}', 'edged up {}'], 'reg_up': ['gained {}', 'climbed {}', 'advanced {}', 'rose {}', 'added {}', 'was {} higher'], 'big_up': ['jumped {}', 'surged {}', 'rallied {}', 'shot up {}', 'spiked up {}'], 'small_down': ['inched down {}', 'edged down {}'], 'reg_down': ['slid {}', 'dropped {}', 'declined {}', 'fell by {}', 'eased {}', 'retreated by {}', 'slipped by {}', 'was {} lower', 'pulled back {}', 'shedded {}'], 'big_down': ['sunk {}', 'tumbled {}', 'collapsed {}', 'slumped {}'] } def write_sentence(pct_chg, close_price, vocab_dict=vocab_dict, precision=2): ''' Crafts a descriptive sentence given the parameters passed ''' if pct_chg < -0.025: a = 'big_down' elif -0.025 <= pct_chg < -0.002: a = 'reg_down' elif -0.002 <= pct_chg < 0: a = 'small_down' elif pct_chg == 0: a = 'no_change' elif 0 < pct_chg < 0.002: a = 'small_up' elif 0.002 <= pct_chg < 0.025: a = 'reg_up' elif 0.025 <= pct_chg: a = 'big_up' else: if pd.isnull(pct_chg) and pd.isnull(close_price): return 'has not been traded yet' else: return "was unchanged today" if a == 'no_change': out_str = 'remained flat at ' + str(round(close_price, 2)) else: out_str = random.choice(vocab_dict[a]).format(str(round(abs(pct_chg) * 100, 2)) + '%') if out_str[-1] == '%': out_str += ' to ' else: out_str += ' at ' out_str += str(round(close_price, precision)) return out_str def format_content(df): ''' writes sentences for every row in the df, provided that rows are indexed by the tickers returns a dictionary of sentences, with tickers as key ''' out = {} for i in range(len(df)): # check if the series is a currency, if so use 4dp precision if len(str(df.iloc[i]['short_name'])) == 7 and str(df.iloc[i]['short_name'][3]) == '/': out[df.iloc[i].name] = write_sentence(pct_chg=df.iloc[i][2], close_price=df.iloc[i][1], precision=4) else: out[df.iloc[i].name] = write_sentence(pct_chg=df.iloc[i][2], close_price=df.iloc[i][1]) return out def tellme(a): if all(x > 0 for x in a): return "up" elif all(x < 0 for x in a): return "down" else: return "mixed" def message_draft_pm(df, author, date=datetime.today(), out_path=None): ''' Drafts a message given a reference df, template, and date Saves message as a txt file if provided an out_path ''' weekday = date.strftime(format='%A') now = date.strftime(format='%I:%M %p') date = date.strftime(format='%d %b %Y') if date[0] == '0': date = date[1:] template = './msg_templates/msg_template_pm.txt' sentences = format_content(df) with open(template, 'r') as f: text = f.read() bank_rets = df.loc[['D05.SI', 'U11.SI', 'O39.SI']].pct_chg.values bank_mvmt = tellme(bank_rets) textformats = { 'weekday': weekday, 'now': now, 'msg_date': date, 'author_name': author, 'sti': sentences['^STI'], 'hsi': sentences['^HSI'], 'csi': sentences['000300.SS'], 'sse': sentences['000001.SS'], 'chnxt': sentences['399006.SZ'], 'n225': sentences['^N225'], 'asx': sentences['^AXJO'], 'aord': sentences['^AORD'], 'ftse': sentences['^FTSE'], 'cac': sentences['^FCHI'], 'dax': sentences['^GDAXI'], 'sxxp': sentences['^STOXX'], 'sx5e': sentences['^STOXX50E'], 'spf': df.loc['ES=F', 'short_name'], 'spfs': sentences['ES=F'], 'nqf': df.loc['NQ=F', 'short_name'], 'nqfs': sentences['NQ=F'], 'sgdx': sentences['SGD=X'], 'tnx': sentences['^TNX'], 'gcf': df.loc['GC=F', 'short_name'], 'gcfs': sentences['GC=F'], 'clf': df.loc['CL=F', 'short_name'], 'clfs': sentences['CL=F'], 'dbs': sentences['D05.SI'], 'uob': sentences['U11.SI'], 'ocbc': sentences['O39.SI'], 'bank_mvmt': bank_mvmt } text = text.format(**textformats) if out_path != None: with open(out_path, 'w') as f: f.write(text) return text def message_draft_am(df, author, date=datetime.today(), out_path=None): ''' Drafts a message given a reference df, template, and date Saves message as a txt file if provided an out_path ''' weekday_today = date.strftime(format='%A') weekday = (date.date() - timedelta(1)).strftime(format='%A') now = date.strftime(format='%I:%M %p') date = date.strftime(format='%d %b %Y') if date[0] == '0': date = date[1:] template = './msg_templates/msg_template_am.txt' sentences = format_content(df) with open(template, 'r') as f: text = f.read() textformats = { 'weekday': weekday, 'weekday_today': weekday_today, 'msg_date': date, 'now': now, 'author_name': author, 'spx': sentences['^GSPC'], 'ndq': sentences['^IXIC'], 'dji': sentences['^DJI'], 'fb': sentences['FB'], 'amzn': sentences['AMZN'], 'aapl': sentences['AAPL'], 'goog': sentences['GOOG'], 'ftse': sentences['^FTSE'], 'cac': sentences['^FCHI'], 'dax': sentences['^GDAXI'], 'sxxp': sentences['^STOXX'], 'sx5e': sentences['^STOXX50E'], 'sgdx': sentences['SGD=X'], 'tnx': sentences['^TNX'], 'gcf': df.loc['GC=F', 'short_name'], 'gcfs': sentences['GC=F'], 'clf': df.loc['CL=F', 'short_name'], 'clfs': sentences['CL=F'] } text = text.format(**textformats) if out_path != None: with open(out_path, 'w') as f: f.write(text) return text def get_prev_summary(tick_list, date, series_type=None): ''' Get descriptive statistics of tickers given: tick list - list of tickers date - reference date Returns data in a DataFrame ''' if not isinstance(date, datetype): date = datetime.strftime(format='%Y-%m-%d') names = [] close_price = [] pct_chg = [] for t in tick_list: print('Retrieving data for', str(t)) # initialise the ticker, get quotes and info ticker = yf.Ticker(t) quotes = ticker.history(period='5d') #5d quote used as 1d has some issues # append short name try: info = ticker.info names.append(info['shortName']) except: names.append(np.nan) # append close prices try: close_price.append(quotes.loc[date, 'Close']) except: close_price.append(np.nan) # this part handles percent changes try: a = quotes.pct_change().loc[date, 'Close'] except: a = np.nan if pd.isnull(a): try: spare_df = return_csv(series_type) mydate = spare_df.iloc[[spare_df.shape[0]-1]].index.values[0] print("{} doesn't have historical data. Attempting to retrieve values from local data dated {}.".format(t, str(mydate)[:10])) prev_close = spare_df.iloc[[spare_df.shape[0]-1]].loc[:, t][0] today_close = quotes.loc[date, 'Close'] pct_chg.append(today_close/prev_close - 1) except: print('Data for {} is unavailable, filling with NaN value instead.'.format(t)) pct_chg.append(np.nan) else: pct_chg.append(a) output = pd.DataFrame({ 'short_name': names, 'close_price': close_price, 'pct_chg': pct_chg }, index=tick_list) return output def create_csv(df, date, path): ''' creates a csv containing closing price timeseries. doesn't really need to be called day-to-day ''' if not isinstance(date, datetype): date = datetime.strptime(date, '%Y-%m-%d') data_df = pd.DataFrame(data = df['close_price'].values.reshape(df.shape[0], 1).T, columns = df.index, index = pd.DatetimeIndex([date])) data_df.index.name = 'date' data_df.to_csv(path, encoding='utf-8-sig') def update_csv(df, date, path): ''' updates the csv file containing the closing price timeseries ''' if not isinstance(date, datetype): date = datetime.strptime(date, '%Y-%m-%d') data_df = pd.DataFrame(data = df['close_price'].values.reshape(df.shape[0], 1).T, columns = df.index, index = pd.DatetimeIndex([date])) data_df.index.name = 'date' source_df = pd.read_csv(path, index_col='date') source_df = source_df.append(data_df) source_df.to_csv(path, encoding='utf-8-sig') def undo_csv(path): ''' removes the last row of the csv file ''' df = pd.read_csv(path, index_col='date') df.iloc[:len(df)-1].to_csv(path) def retrieve_csv(path): df = pd.read_csv(path, parse_dates=True, index_col='date') return df def return_csv(series_type): path_dict = { 'apac': './data_files/apac_timeseries.csv', 'useu': './data_files/useu_timeseries.csv', 'othr': './data_files/othr_timeseries.csv' } if series_type not in path_dict.keys(): raise ValueError("Bad series_type passed. Series type must be one of the following: 'apac', 'useu', 'othr'.") return retrieve_csv(path_dict[series_type]) class telegram_bot(): ''' contains info on our telegram bot ''' def __init__(self): self.token = '<YOUR_BOT_TOKEN_HERE>' self.channel_id = '<YOUR_CHANNEL_ID_HERE>' self.api_url = 'https://api.telegram.org/bot{}/sendMessage'.format(self.token) def get_details(self): return self.token, self.channel_id, self.api_url def __msgformat__(self, text): replacements = { '.': '\\.', '>': '\\>', '-': '\\-', '(': '\$', ')': '\$', '+': '\\+' } replacements = dict((re.escape(k), v) for k, v in replacements.items()) pattern = re.compile("|".join(replacements.keys())) return pattern.sub(lambda m: replacements[re.escape(m.group(0))], text) def send_msg_str(self, str_in): ''' Sends a message using the telegram bot, given: str_in: a string to be used as the message content ''' content = self.__msgformat__(str_in) payload = { 'chat_id': '-100'+self.channel_id, 'text': content, 'parse_mode': 'MarkdownV2' } r = requests.post(self.api_url, data=payload) return r.json() def send_msg_file(self, path): ''' Sends a message using the telegram bot, given: path: a path to a .txt file containing the message content ''' with open(path, 'r') as f: content = f.read() content = self.__msgformat__(content) payload = { 'chat_id': '-100'+self.channel_id, 'text': content, 'parse_mode': 'MarkdownV2' } r = requests.post(url=self.api_url, data=payload) return r.json()

33.198324

168

0.549011

79511e883f0726820a155db9a2df66110bdaecac

5,182

py

Python

src/stk/molecular/functional_groups/factories/boronic_acid_factory.py

supramolecular-toolkit/stk

0d3e1b0207aa6fa4d4d5ee8dfe3a29561abb08a2

[ "MIT" ]

21

2018-04-12T16:25:24.000Z

2022-02-14T23:05:43.000Z

src/stk/molecular/functional_groups/factories/boronic_acid_factory.py

JelfsMaterialsGroup/stk

0d3e1b0207aa6fa4d4d5ee8dfe3a29561abb08a2

[ "MIT" ]

8

2019-03-19T12:36:36.000Z

2020-11-11T12:46:00.000Z

src/stk/molecular/functional_groups/factories/boronic_acid_factory.py

supramolecular-toolkit/stk

0d3e1b0207aa6fa4d4d5ee8dfe3a29561abb08a2

[ "MIT" ]

5

2018-08-07T13:00:16.000Z

2021-11-01T00:55:10.000Z

""" Boronic Acid Factory ==================== """ from __future__ import annotations import typing from collections import abc from .functional_group_factory import FunctionalGroupFactory from .utilities import get_atom_ids from ..functional_groups import BoronicAcid from ...molecule import Molecule from ...atoms import O, H, B __all__ = ( 'BoronicAcidFactory', ) _ValidIndex = typing.Literal[0, 1, 2, 3, 4, 5] class BoronicAcidFactory(FunctionalGroupFactory): """ Creates :class:`.BoronicAcid` instances. Creates functional groups from substructures, which match the ``[*][B]([O][H])[O][H]`` functional group string. Examples: *Creating Functional Groups with the Factory* You want to create a building block which has :class:`.BoronicAcid` functional groups. You want the boron atom in those functional groups to be the *bonder* atom and the OH groups to be *deleter* atoms. .. testcode:: creating-functional-groups-with-the-factory import stk building_block = stk.BuildingBlock( smiles='OB(O)CCCB(O)O', functional_groups=(stk.BoronicAcidFactory(), ), ) .. testcode:: creating-functional-groups-with-the-factory :hide: assert all( isinstance(functional_group, stk.BoronicAcid) for functional_group in building_block.get_functional_groups() ) assert building_block.get_num_functional_groups() == 2 *Changing the Bonder and Deleter Atoms* You want to create a building block which has :class:`.BoronicAcid` functional groups. You want the oxygen atoms to be treated as *bonder* atoms, and the hydrogen atoms to be treated as *deleter* atoms. .. testcode:: changing-the-bonder-and-deleter-atoms import stk boronic_acid_factory = stk.BoronicAcidFactory( # The indices of the oxygen atoms in the functional # group string (see docstring) are 2 and 4. bonders=(2, 4), # The indices of the hydrogen atoms in the # functional group string (see docstring) are 3 and 5. deleters=(3, 5), ) building_block = stk.BuildingBlock( smiles='OB(O)CCCB(O)O', functional_groups=(boronic_acid_factory, ), ) .. testcode:: changing-the-bonder-and-deleter-atoms :hide: fg1, fg2 = building_block.get_functional_groups() assert fg1.get_num_bonders() == 2 assert sum(1 for _ in fg1.get_deleters()) == 2 assert fg2.get_num_bonders() == 2 assert sum(1 for _ in fg2.get_deleters()) == 2 assert all( isinstance(atom, stk.O) for functional_group in building_block.get_functional_groups() for atom in functional_group.get_bonders() ) assert all( isinstance(atom, stk.H) for functional_group in building_block.get_functional_groups() for atom in functional_group.get_deleters() ) See Also: :class:`.GenericFunctionalGroup` Defines *bonders* and *deleters*. """ def __init__( self, bonders: tuple[_ValidIndex, ...] = (1, ), deleters: tuple[_ValidIndex, ...] = (2, 3, 4, 5), placers: typing.Optional[tuple[_ValidIndex, ...]] = None, ) -> None: """ Initialize a :class:`.BoronicAcidFactory` instance. Parameters: bonders: The indices of atoms in the functional group string, which are *bonder* atoms. deleters: The indices of atoms in the functional group string, which are *deleter* atoms. placers: The indices of atoms in the functional group string, which are *placer* atoms. If ``None``, `bonders` will be used. """ self._bonders = bonders self._deleters = deleters self._placers = bonders if placers is None else placers def get_functional_groups( self, molecule: Molecule, ) -> abc.Iterable[BoronicAcid]: for atom_ids in get_atom_ids( query='[*][B]([O][H])[O][H]', molecule=molecule, ): atoms = tuple(molecule.get_atoms(atom_ids)) yield BoronicAcid( boron=typing.cast(B, atoms[1]), oxygen1=typing.cast(O, atoms[2]), hydrogen1=typing.cast(H, atoms[3]), oxygen2=typing.cast(O, atoms[4]), hydrogen2=typing.cast(H, atoms[5]), atom=atoms[0], bonders=tuple(atoms[i] for i in self._bonders), deleters=tuple(atoms[i] for i in self._deleters), placers=tuple(atoms[i] for i in self._placers), )

30.662722

71

0.564068

79511f8809fe03a5ecbce619085d46fad3459e42

3,327

py

Python

python/kfserving/test/test_v1beta1_alibi_explainer_spec.py

titoeb/kfserving

b072a76842b57e904dbdf46a136474a22051500d

[ "Apache-2.0" ]

6

2022-02-15T21:54:19.000Z

2022-02-16T21:18:54.000Z

python/kfserving/test/test_v1beta1_alibi_explainer_spec.py

titoeb/kfserving

b072a76842b57e904dbdf46a136474a22051500d

[ "Apache-2.0" ]

635

2021-01-29T07:06:06.000Z

2022-03-31T09:09:20.000Z

python/kfserving/test/test_v1beta1_alibi_explainer_spec.py

titoeb/kfserving

b072a76842b57e904dbdf46a136474a22051500d

[ "Apache-2.0" ]

4

2021-02-15T23:02:53.000Z

2022-01-27T22:54:16.000Z

# Copyright 2020 kubeflow.org. # # 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. # coding: utf-8 """ KFServing Python SDK for KFServing # noqa: E501 The version of the OpenAPI document: v0.1 Generated by: https://openapi-generator.tech """ from __future__ import absolute_import import unittest import datetime import kfserving from kfserving.models.v1beta1_alibi_explainer_spec import V1beta1AlibiExplainerSpec # noqa: E501 from kfserving.rest import ApiException class TestV1beta1AlibiExplainerSpec(unittest.TestCase): """V1beta1AlibiExplainerSpec unit test stubs""" def setUp(self): pass def tearDown(self): pass def make_instance(self, include_optional): """Test V1beta1AlibiExplainerSpec include_option is a boolean, when False only required params are included, when True both required and optional params are included """ # model = kfserving.models.v1beta1_alibi_explainer_spec.V1beta1AlibiExplainerSpec() # noqa: E501 if include_optional : return V1beta1AlibiExplainerSpec( args = [ '0' ], command = [ '0' ], config = { 'key' : '0' }, env = [ None ], env_from = [ None ], image = '0', image_pull_policy = '0', lifecycle = None, liveness_probe = None, name = '0', ports = [ None ], readiness_probe = None, resources = None, runtime_version = '0', security_context = None, startup_probe = None, stdin = True, stdin_once = True, storage_uri = '0', termination_message_path = '0', termination_message_policy = '0', tty = True, type = '0', volume_devices = [ None ], volume_mounts = [ None ], working_dir = '0' ) else : return V1beta1AlibiExplainerSpec( name = '0', type = '0', ) def testV1beta1AlibiExplainerSpec(self): """Test V1beta1AlibiExplainerSpec""" inst_req_only = self.make_instance(include_optional=False) inst_req_and_optional = self.make_instance(include_optional=True) if __name__ == '__main__': unittest.main()

30.245455

105

0.533514

79511fa50dbbf3c85a67a7e66a81564151308499

1,011

py

Python

airflow/www/gunicorn_config.py

IGIT-CN/airflow

a6e5bcd59198afe5716813e84ebc4c59eade532c

[ "Apache-2.0" ]

8

2017-04-20T16:15:44.000Z

2020-10-11T13:44:10.000Z

airflow/www/gunicorn_config.py

IGIT-CN/airflow

a6e5bcd59198afe5716813e84ebc4c59eade532c

[ "Apache-2.0" ]

219

2017-03-15T18:40:16.000Z

2022-02-28T22:52:43.000Z

airflow/www/gunicorn_config.py

IGIT-CN/airflow

a6e5bcd59198afe5716813e84ebc4c59eade532c

[ "Apache-2.0" ]

4

2020-07-17T14:02:28.000Z

2022-02-23T04:29:58.000Z

#!/usr/bin/env python # # 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 setproctitle from airflow import settings def post_worker_init(dummy_worker): setproctitle.setproctitle( settings.GUNICORN_WORKER_READY_PREFIX + setproctitle.getproctitle() )

34.862069

75

0.771513

79512037860f60e2420827be48ddfa72ece0fcaa

14,680

py

Python

_code_generation/mof.py

osgirl/dractor

aa0ba9f5e4802ae365df3cb7d51c31b8d1b2349b

[ "Apache-2.0" ]

null

_code_generation/mof.py

osgirl/dractor

aa0ba9f5e4802ae365df3cb7d51c31b8d1b2349b

[ "Apache-2.0" ]

2

2019-03-17T15:40:39.000Z

2019-03-17T15:40:41.000Z

_code_generation/mof.py

osgirl/dractor

aa0ba9f5e4802ae365df3cb7d51c31b8d1b2349b

[ "Apache-2.0" ]

null

# Copyright (C) 2017 Verizon. All Rights Reserved. # # File: mof.py # Author: John Hickey, Phil Chandler # Date: 2017-02-17 # # 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. """ Plumbing for converting Dell MOF files into Python objects. """ # # Imports # # Python standard library import collections import glob import logging import os # Third party import textx.metamodel # this project import _code_generation.exceptions as my_exceptions # # Module variables # # logger _LOGGER = logging.getLogger(__name__) # # Custom classes for text meta model # class Qualified(object): """ We have qualifiers at class, function, and argument levels in MOF files. This is an attempt to unify the parsing of qualifiers and provide the name property. """ def __init__(self, name, qualifiers): self._name = name self.qualifiers = {} for qualifier in qualifiers: if hasattr(qualifier, 'value'): value = qualifier.value elif hasattr(qualifier, 'values'): value = qualifier.values elif qualifier.__class__.__name__ == 'NegativeKeyword': value = False else: value = True # All qualifiers have a 'name' # Since the capitalization of qualifiers is inconsistent, # we are just going to squash case for everything self.qualifiers[qualifier.name.lower()] = value # Make sure we don't have multiple cases of the same key assert len(self.qualifiers) == len(qualifiers) @property def name(self): """ Return the Pythonic Name """ return self._name.replace("[]", "") @property def valuemap(self): """ Return the ValueMap from the qualifiers as a python dictionary """ values = self.qualifiers.get('values') valuemap = self.qualifiers.get('valuemap') final_mapping = {} if values and valuemap: raw_mapping = dict(zip(valuemap, values)) final_mapping = {} for raw_key, raw_value in raw_mapping.items(): final_mapping[raw_key.strip()] = raw_value.strip() return final_mapping @property def docstring(self): """ Return a docstring generated from the qualifiers """ raw_description = self.qualifiers.get("description") # short circuit if there isn't a description to process if not raw_description: _LOGGER.debug("No raw description found in MOF, substituting placeholder") return "No documentation in MOF" # process the raw description, normalizing whitespace and special characters _LOGGER.debug("Normalizing raw description from MOF:\n%s", raw_description) normalized_lines = [] for raw_line in raw_description: # split to normalize \n in the entry normalized_line_elements = [] for text in raw_line.split(): # strip leading/trailing whitespace stripped_text = text.strip() # escape any special rst characters escaped_text = stripped_text.replace('*', '\*') # add to normalized line elements normalized_line_elements.append(escaped_text) # create normalized line and save it normalized_line = " ".join(normalized_line_elements) normalized_lines.append(normalized_line) # create and return the normalized line block normalized_description = "\n".join(normalized_lines) _LOGGER.debug("Normalized description is:\n%s", normalized_description) return normalized_description class MOFClass(Qualified): """ MOF Class """ def __init__(self, name, qualifiers, parent_class, members): """ Our MOF classes consist of members, which are functions, and qualifiers """ self.parent_class = parent_class self.members = members super(MOFClass, self).__init__(name, qualifiers) @property def attributes(self): """ Return all methods that don't take arguments, these are populated by get/enumerate factory classes """ attributes = [] for member in self.members: if member.attribute: attributes.append(member) return attributes @property def attributes_metadata(self): """ Return the value mapping and qualifiers for every attribute as a dictionary. We can't do fancy things if we embed these in the functions like we do for methods. """ attribute_meta = collections.defaultdict(dict) for attribute in self.attributes: attribute_meta[attribute.name]['valuemap'] = attribute.valuemap attribute_meta[attribute.name]['qualifiers'] = attribute.qualifiers return dict(attribute_meta) @property def methods(self): """ Return all methods that require invoke """ methods = [] for member in self.members: if not member.attribute: methods.append(member) return methods @property def dcim_parents(self): """ Return parent classes in dractor/DCIM.py for code autogeneration """ parents = [] if self.attributes: parents.append('DCIMAttributeObject') if self.methods: parents.append('DCIMMethodObject') return parents @property def key(self): """ Return the name of our key """ for member in self.members: if member.key: return member.name @property def mof_metadata(self): """ Return a dictionary representation of the MOF file """ mof_dict = collections.defaultdict(dict) mof_dict['class'] = self.name mof_dict['parent_class'] = self.parent_class mof_dict['qualifiers'] = self.qualifiers for func in self.members: mof_dict['functions'].update(func.mof_metadata) return dict(mof_dict) class Function(Qualified): """ Member function """ def __init__(self, name, parent, qualifiers, return_type, arguments, default): # pylint: disable=too-many-arguments super(Function, self).__init__(name, qualifiers) self.parent = parent self.arguments = arguments self.return_type = return_type self.default = default @property def key(self): """ Is this a key property? """ return self.qualifiers.get("key", False) @property def required_inputs(self): """ Return arguments that have a Required qualifier """ inputs = [] for arg in self.arguments: if arg.IN and arg.required: inputs.append(arg) return inputs @property def optional_inputs(self): """ Return all arguments without the Required qualifier """ inputs = [] for arg in self.arguments: if arg.IN and not arg.required: inputs.append(arg) return inputs @property def inputs(self): """ Return all arguments, required and optional """ inputs = [] for arg in self.arguments: if arg.IN: inputs.append(arg) return inputs @property def outputs(self): """ Return all return values """ outputs = [] for arg in self.arguments: if arg.OUT: outputs.append(arg) return outputs @property def arg_str(self): """ Return a pythonic string of args """ args = ['self'] args.extend([x.name for x in self.required_inputs]) args.extend(["{}=None".format(x.name) for x in self.optional_inputs]) return ", ".join(args) @property def attribute(self): """ Is this function an attribute or method """ return not bool(self.arguments) @property def mof_metadata(self): """ Return all metadata """ func_dict = collections.defaultdict(dict) func_dict[self.name]['qualifiers'] = self.qualifiers func_dict[self.name]['valuemap'] = self.valuemap func_dict[self.name]['return_type'] = self.return_type func_dict[self.name]['optional_inputs'] = {} func_dict[self.name]['required_inputs'] = {} func_dict[self.name]['outputs'] = {} for arg in self.required_inputs: func_dict[self.name]['required_inputs'].update(arg.mof_metadata) for arg in self.optional_inputs: func_dict[self.name]['optional_inputs'].update(arg.mof_metadata) for arg in self.outputs: func_dict[self.name]['outputs'].update(arg.mof_metadata) # For the return value return dict(func_dict) class FunctionArg(Qualified): """ Arguments have metadata too """ def __init__(self, name, parent, qualifiers, ctype): super(FunctionArg, self).__init__(name, qualifiers) self.parent = parent self.ctype = ctype if '[]' in name or '[]' in ctype: # pylint: disable=simplifiable-if-statement self.is_list = True else: self.is_list = False @property def IN(self): # pylint: disable=invalid-name """ Is this a return value or input value """ # This is complicated a little bit by args like # DCIM_PhysicalComputerSystemView.SetOneTimeBootSource # which has a required arg that specifies neither in # or out. if 'out' in self.qualifiers: return False return self.qualifiers.get("in", True) @property def OUT(self): # pylint: disable=invalid-name """ Is this a return value or input value """ # Most mof files are OUT, but some say Out... return self.qualifiers.get("out", False) @property def required(self): """ Is this a required arg """ return bool(self.qualifiers.get("required", False)) @property def arg_type(self): """ Return a pythonic type for this argument """ arg_type = self.ctype if 'int' in arg_type: arg_type = 'int' if self.is_list: arg_type = 'list of {}'.format(arg_type) if 'required' in self.qualifiers: arg_type = "{}, optional".format(arg_type) return arg_type @property def mapping_description(self): """ Return a docstring friendly explanation of how this argument is mapped """ mapping_description_lines = [] if self.valuemap: for value in sorted(self.valuemap.keys()): mapping = self.valuemap[value] mapping_description_lines.append("'{}' <-> '{}'\n".format(value, mapping)) return mapping_description_lines @property def mof_metadata(self): """ Return all the information we know about this arg as a dictionary """ arg_dict = collections.defaultdict(dict) arg_dict[self.name]['type'] = self.arg_type arg_dict[self.name]['qualifiers'] = self.qualifiers arg_dict[self.name]['valuemap'] = self.valuemap return dict(arg_dict) # # MOF parser class # class MOFParser(object): """Parser for MOF data files""" # location of textx metamodel config META_MODEL_PATH = os.path.join(os.path.dirname(__file__), 'data', 'textex', 'dcim_mof_parse.tx') # custom meta model classes META_MODEL_CLASSES = [MOFClass, Function, FunctionArg] # location of mof files MOF_DIR = os.path.join(os.path.dirname(__file__), 'data', 'mof') # info on a candidate MOF file MOFFileEntry = collections.namedtuple('MOFFileEntry', field_names=['base_name', 'path']) @staticmethod def find_avail_mof_files(dell_version): """Collect list of available MOF files given the dell version""" assert dell_version is not None mof_path = os.path.join(MOFParser.MOF_DIR, dell_version) entries = [] for mof_file_name in glob.glob('{}/*.[Mm][Oo][Ff]'.format(mof_path)): mof_file_path = os.path.join(mof_path, mof_file_name) mof_file_base_name = os.path.basename(mof_file_name) entry = MOFParser.MOFFileEntry(base_name=mof_file_base_name, path=mof_file_path) entries.append(entry) _LOGGER.debug("Collected this list of available mof files for dell version %s : %s", dell_version, entries) return entries def __init__(self): _LOGGER.debug("Load textx metamodel for MOF parsing") try: metamodel = textx.metamodel.metamodel_from_file( MOFParser.META_MODEL_PATH, classes=MOFParser.META_MODEL_CLASSES) except Exception as error: raise my_exceptions.CodeGenError("Fatal error loading textx metamodel for MOF parsing", error) else: _LOGGER.debug("Successfully loaded text metamodel from %s : %s", MOFParser.META_MODEL_PATH, metamodel) self._meta_model = metamodel def parser_mof_file(self, mof_file_entry): assert isinstance(mof_file_entry, MOFParser.MOFFileEntry) _LOGGER.info("Begin parsing MOF file: %s", mof_file_entry) try: mof_class = self._meta_model.model_from_file(mof_file_entry.path) _LOGGER.debug("successfully parsed mof file") except Exception as error: raise my_exceptions.CodeGenError( "Fatal error while parsing MOF file {}".format(mof_file_entry.path), error) # now check if it has members if not mof_class.members: raise my_exceptions.SkipMOFFile( "MOF class in MOF file {} has no members, so skipping it".format( mof_file_entry.path)) _LOGGER.info("Finished parsing MOF file: %s", mof_file_entry) return mof_class

30.143737

100

0.618188

795120d9661a627b7941be18f854f41be190ccda

6,891

py

Python

src/view/helpDialog.py

Curtin-Timescales-of-Mineral-Systems/UPb-Unmixer

4391a77bbb7c198955af8637ed10854245cd15c3

[ "MIT" ]

1

2021-02-17T08:10:21.000Z

src/view/helpDialog.py

Curtin-Timescales-of-Mineral-Systems/UPb-Unmixer

4391a77bbb7c198955af8637ed10854245cd15c3

[ "MIT" ]

null

src/view/helpDialog.py

Curtin-Timescales-of-Mineral-Systems/UPb-Unmixer

4391a77bbb7c198955af8637ed10854245cd15c3

[ "MIT" ]

null

from PyQt5.QtCore import Qt from PyQt5.QtWidgets import QLabel, QTabWidget, QVBoxLayout, QWidget, QDialog, QLayout from utils import config, string, calculations class HelpDialog(QDialog): def __init__(self): super().__init__() self.setWindowTitle(config.TITLE + " help") about_label: QLabel = QLabel(self.getAboutHelpText()) about_label.title = "About" inputs_label: QLabel = QLabel(self.getInputsHelpText()) inputs_label.title = "Inputs" processing_label: QLabel = QLabel(self.getProcessingHelpText()) processing_label.title = "Processing" outputs_label: QLabel = QLabel(self.getOutputsHelpText()) outputs_label.title = "Outputs" tab_widget = QTabWidget() for label in [about_label, inputs_label, processing_label, outputs_label]: label.setTextFormat(Qt.RichText) label.setWordWrap(True) label.setTextInteractionFlags(Qt.TextSelectableByMouse|label.textInteractionFlags()) layout = QVBoxLayout() layout.addWidget(label, 0, Qt.AlignTop) widget = QWidget() widget.setLayout(layout) tab_widget.addTab(widget, label.title) layout: QLayout = QVBoxLayout() layout.addWidget(tab_widget) self.setLayout(layout) def _getStandardInputHelp(self) -> str: return \ "Data can be parsed from a range of csv file layouts by specifying which columns the required values are " \ "in. Columns can be referred to either by using:" \ "<ul>" \ " <li> numbers (1, 2, 3, ..., 26, 27, ...)" \ " <li> letters (A, B, C, ..., Z, AA, ...)" \ "</ul>" \ "Different uncertainty formats are also supported:" \ "<ul>" \ " <li> percentage vs absolute" \ " <li> 1σ vs 2σ" \ "</ul>" \ "If a row in the imported data is invalid then it will be highlighted in RED." \ " " \ "Symbols are not supported in column headings (e.g., ±, σ). Use only English alphabetic characters or " \ "numerals." def _getStandardProcessingHelp(self) -> str: return \ "Constants used:" \ "<ul>" \ "<li> ²³⁸U/²³⁵U ratio " + " " * 9 + " = " + string.getConstantStr(calculations.U238U235_RATIO) + \ "<li> ²³⁸U decay constant " + " " * 1 + " = " + string.getConstantStr(calculations.U238_DECAY_CONSTANT) + \ "<li> ²³⁵U decay constant " + " " * 1 + " = " + string.getConstantStr(calculations.U235_DECAY_CONSTANT) + \ "<li> ²³²Th decay constant = " + string.getConstantStr(calculations.TH232_DECAY_CONSTANT) + \ "<li> Avogadro constant " + " " * 3 + " = " + string.getConstantStr(calculations.AVOGADRO_NUMBER) + \ "<ul>" def _getStandardOutputsHelp(self) -> str: return \ "The plot may be fully customised (markers, colours, scale etc.) using the " \ "button in the toolbar at the bottom. The plot can also be saved to various image formats." \ " " \ "When the calculated values are exported back to a CSV file, the values are appended to the end of the " \ "columns of the original CSV file." def getInputsHelpText(self) -> str: return \ "Input data required: <ul>" \ "<li> known age (in Ma) of rim (younger) component in mixture" \ "<li> measured ²³⁸U/²⁰⁶Pb and ²⁰⁷Pb/²⁰⁶Pb ratios" \ "<li> uncertainties for all of the above" \ "<li> U and Th concentrations (ppm)" \ "</ul>" + \ self._getStandardInputHelp() def getProcessingHelpText(self) -> str: return \ "Processing the data will attempt to calculate a reconstructed core age with associated uncertainty values." \ " " \ "Uncertainties are calculated using second order error propagation. " \ "Due to the non-linearity of the concordia curve, the resulting uncertainties are not symmetric. " \ "In particular the upper uncertainty will be larger than the lower uncertainty." \ " " \ "Rows with unsuccessful calculations will be highlighted in ORANGE." \ " " \ "Rows with successful calculations that have a total score of < 0.5 will be highlighted in YELLOW." \ " " \ "Rows with successful calculations that have a total score of >= 0.5 will be highlighted in GREEN." \ " " + \ self._getStandardProcessingHelp() def getOutputsHelpText(self) -> str: return \ "The following values are output:" \ "<ul>" \ "<li> a reconstructed core age (in Ma)" \ "<li> reconstructed core ²³⁸U/²⁰⁶Pb and ²⁰⁷Pb/²⁰⁶Pb ratios " \ "<li> uncertainties for all of the above" \ "<li> metamict score for the reconstructed age" \ "<li> precision score for the reconstructed age" \ "<li> core:rim score for the reconstructed age" \ "<li> total score for the reconstructed age" \ "</ul>" \ "Ages with a total score of < 0.5 should be considered unreliable. " \ "See the paper for more details on how individual scores are calculated." \ " " \ "Clicking on an individual row permits visual inspection of the full solution " \ "on the inverse Concordia plot. " \ "Selecting multiple rows permits visual inspection of the final calculated ages of " \ "all the rows in the selection." \ " " + \ self._getStandardOutputsHelp() def getAboutHelpText(self) -> str: link = "https://github.com/Curtin-Timescales-of-Mineral-Systems/UPb-Unmixer/issues" return \ 'This program is accompanied by the following paper which should be cited if it this program is used in your results' \ 'Hugo K.H. Olierook, Christopher L. Kirkland, Milo Barham,' \ ' Matthew L. Daggitt, Julie Hollis, Michael Hartnady, ' \ ' Extracting meaningful U-Pb ages from core–rim mixtures, Gondwana Research, 2020<\\p>' \ ' ' \ 'Please report any feedback or issues that you may have with this program on the ' \ 'Github page at: <a href="' + link + '">' + link + '</a><\\p>'

51.044444

181

0.595269

79512226bda6594208f2df1b5e7f4c6bf8546918

7,380

py

Python

build/lib/WORC/classification/AdvancedSampler.py

Sikerdebaard/PREDICTFastr

e1f172c3606e6f33edf58008f958dcd1c0ac5b7b

[ "ECL-2.0", "Apache-2.0" ]

null

build/lib/WORC/classification/AdvancedSampler.py

Sikerdebaard/PREDICTFastr

e1f172c3606e6f33edf58008f958dcd1c0ac5b7b

[ "ECL-2.0", "Apache-2.0" ]

null

build/lib/WORC/classification/AdvancedSampler.py

Sikerdebaard/PREDICTFastr

e1f172c3606e6f33edf58008f958dcd1c0ac5b7b

[ "ECL-2.0", "Apache-2.0" ]

null

#!/usr/bin/env python # Copyright 2016-2019 Biomedical Imaging Group Rotterdam, Departments of # Medical Informatics and Radiology, Erasmus MC, Rotterdam, The Netherlands # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from sklearn.utils import check_random_state import numpy as np from sklearn.externals import six from ghalton import Halton # from sobol_seq import i4_sobol_generate as Sobol import scipy from scipy.stats import uniform import math class log_uniform(): def __init__(self, loc=-1, scale=0, base=10): self.loc = loc self.scale = scale self.base = base self.uniform_dist = uniform(loc=self.loc, scale=self.scale) def rvs(self, size=None, random_state=None): if size is None: return np.power(self.base, self.uniform_dist.rvs(random_state=random_state)) else: return np.power(self.base, self.uniform_dist.rvs(size=size, random_state=random_state)) class discrete_uniform(): def __init__(self, loc=-1, scale=0): self.loc = loc self.scale = scale self.uniform_dist = uniform(loc=self.loc, scale=self.scale) def rvs(self, size=None, random_state=None): if size is None: return int(self.uniform_dist.rvs(random_state=random_state)) else: return int(self.uniform_dist.rvs(size=size, random_state=random_state)) class exp_uniform(): def __init__(self, loc=-1, scale=0, base=math.e): self.loc = loc self.scale = scale self.base = base def rvs(self, size=None, random_state=None): uniform_dist = uniform(loc=self.loc, scale=self.scale) if size is None: return np.power(self.base, uniform_dist .rvs(random_state=random_state)) else: return np.power(self.base, uniform_dist .rvs(size=size, random_state=random_state)) class AdvancedSampler(object): """Generator on parameters sampled from given distributions using numerical sequences. Based on the sklearn ParameterSampler. Non-deterministic iterable over random candidate combinations for hyper- parameter search. If all parameters are presented as a list, sampling without replacement is performed. If at least one parameter is given as a distribution, sampling with replacement is used. It is highly recommended to use continuous distributions for continuous parameters. Note that before SciPy 0.16, the ``scipy.stats.distributions`` do not accept a custom RNG instance and always use the singleton RNG from ``numpy.random``. Hence setting ``random_state`` will not guarantee a deterministic iteration whenever ``scipy.stats`` distributions are used to define the parameter search space. Deterministic behavior is however guaranteed from SciPy 0.16 onwards. Read more in the :ref:`User Guide <search>`. Parameters ---------- param_distributions : dict Dictionary where the keys are parameters and values are distributions from which a parameter is to be sampled. Distributions either have to provide a ``rvs`` function to sample from them, or can be given as a list of values, where a uniform distribution is assumed. n_iter : integer Number of parameter settings that are produced. random_state : int or RandomState Pseudo random number generator state used for random uniform sampling from lists of possible values instead of scipy.stats distributions. Returns ------- params : dict of string to any **Yields** dictionaries mapping each estimator parameter to as sampled value. Examples -------- >>> from WORC.classification.AdvancedSampler import HaltonSampler >>> from scipy.stats.distributions import expon >>> import numpy as np >>> np.random.seed(0) >>> param_grid = {'a':[1, 2], 'b': expon()} >>> param_list = list(HaltonSampler(param_grid, n_iter=4)) >>> rounded_list = [dict((k, round(v, 6)) for (k, v) in d.items()) ... for d in param_list] >>> rounded_list == [{'b': 0.89856, 'a': 1}, ... {'b': 0.923223, 'a': 1}, ... {'b': 1.878964, 'a': 2}, ... {'b': 1.038159, 'a': 2}] True """ def __init__(self, param_distributions, n_iter, random_state=None, method='Halton'): self.param_distributions = param_distributions self.n_iter = n_iter self.random_state = random_state self.method = method if method == 'Halton': self.Halton = Halton(len(self.param_distributions.keys())) def __iter__(self): # Create a random state to be used rnd = check_random_state(self.random_state) # Generate the sequence generator if self.method == 'Halton': sequence = self.Halton.get(self.n_iter) elif self.method == 'Sobol': sequence = Sobol(len(self.param_distributions.keys()), self.n_iter) # Always sort the keys of a dictionary, for reproducibility items = sorted(self.param_distributions.items()) for i in six.moves.range(self.n_iter): sample = sequence[i] params = dict() for ind, (k, v) in enumerate(items): point = sample[ind] # Check if the parameter space is a distribution or a list if hasattr(v, "rvs"): print(point) # Parameter space is a distribution, hence sample params[k] = v.ppf(point) else: # Parameter space is a list, so select an index point = int(round(point*float(len(v) - 1))) print(point) params[k] = v[point] yield params # For reproducibility, reset sampler if needed if self.method == 'Halton': self.Halton.reset() def __len__(self): """Number of points that will be sampled.""" return self.n_iter if __name__ == '__main__': random_seed = np.random.randint(1, 5000) random_state = check_random_state(random_seed) param_distributions = {'kernel': ['poly', 'RGB'], 'C': scipy.stats.uniform(loc=0, scale=1E6), 'degree': scipy.stats.uniform(loc=1, scale=6), 'coef0': scipy.stats.uniform(loc=0, scale=1), 'gamma': scipy.stats.uniform(loc=1E-5, scale=1), 'histogram_features': ['True', 'False']} n_iter = 6 method = 'Sobol' sampled_params = AdvancedSampler(param_distributions, n_iter, random_state) for s in sampled_params: print(s)

37.653061

99

0.628862

795124f5956467b4e725d767c1b0c0765f150e03

257

py

Python

0x04-python-more_data_structures/10-best_score.py

Trice254/alx-higher_level_programming

b49b7adaf2c3faa290b3652ad703914f8013c67c

[ "MIT" ]

null

0x04-python-more_data_structures/10-best_score.py

Trice254/alx-higher_level_programming

b49b7adaf2c3faa290b3652ad703914f8013c67c

[ "MIT" ]

null

0x04-python-more_data_structures/10-best_score.py

Trice254/alx-higher_level_programming

b49b7adaf2c3faa290b3652ad703914f8013c67c

[ "MIT" ]

null

#!/usr/bin/python3 def best_score(a_dictionary): m = 0 res = "" if a_dictionary: for k, v in a_dictionary.items(): if v > m: res = k m = v return res else: return None

18.357143

41

0.44358

79512638a56b332004747d466d3ec9317b9bbb41

3,661

py

Python

profiles_project/profiles_api/views.py

aseem-hegshetye/Django_Rest_Api

663693b4003d012029fc3365f01b171824229344

[ "MIT" ]

null

profiles_project/profiles_api/views.py

aseem-hegshetye/Django_Rest_Api

663693b4003d012029fc3365f01b171824229344

[ "MIT" ]

15

2020-06-06T00:44:36.000Z

2022-03-12T00:18:42.000Z

profiles_project/profiles_api/views.py

aseem-hegshetye/Django_Rest_Api

663693b4003d012029fc3365f01b171824229344

[ "MIT" ]

null

from django.shortcuts import render from rest_framework.views import APIView, Response, status from rest_framework import viewsets from rest_framework.authentication import TokenAuthentication from rest_framework import filters from rest_framework.authtoken.views import ObtainAuthToken from rest_framework.settings import api_settings from rest_framework.permissions import IsAuthenticated from . import models from . import permissions from .serializers import * class HelloAPIView(APIView): """ Test API View""" def get(self, request, format=None): api_list = [ 'item1', 'item2' ] return Response({'message': 'test view', 'data': api_list}, status=status.HTTP_200_OK) def post(self, request, format=None): """create hello message with our name """ serializer = HelloSerializer(data=request.data) if serializer.is_valid(): name = serializer.validated_data.get('name') message = f'Hello {name}' return Response({'message': message}) else: return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST) def put(self, response, pk=None): return Response({'method': 'PUT'}) def patch(self, response, pk=None): return Response({'method': 'PATCH'}) def delete(self, response, pk=None): return Response({'method': 'DELETE'}) class HelloViewSet(viewsets.ViewSet): """ test api view set""" serializer_class = HelloSerializer # keep serializer class here at top so that django will show this in browsers API view def list(self, request): api_viewset = [ 'asidas', 'aosidjais' ] return Response({'message': api_viewset}, status=status.HTTP_200_OK) def create(self, request): """ create a new hello message""" serializer = self.serializer_class(data=request.data) if serializer.is_valid(): name = serializer.validated_data.get('name') message = f'hello {name}' return Response({'message': message}) else: return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST) def retrieve(self, request, pk=None): return Response({'method': 'get'}) def update(self, request, pk=None): return Response({'method': 'put'}) def partial_update(self, request, pk=None): return Response({'method': 'patch'}) def destroy(self, request, pk=None): return Response({'method': 'delete'}) class UserProfileViewSet(viewsets.ModelViewSet): """ handle creating and updating profile authentication - """ serializer_class = UserProfileSerializer queryset = models.CustomUser.objects.all() authentication_classes = (TokenAuthentication,) permission_classes = (permissions.UpdateOwnProfile,) filter_backends = (filters.SearchFilter,) search_fields = ('first_name', 'email') class UserLoginApiView(ObtainAuthToken): renderer_classes = api_settings.DEFAULT_RENDERER_CLASSES # token: 3b582ecfa00f0363ad7d306f006d0af2bf84ddc4 class UserProfileFeedViewSet(viewsets.ModelViewSet): """ handle profile feed updates and creation""" queryset = models.ProfileFeedItem.objects.all() serializer_class = ProfileFeedSerializer authentication_classes = (TokenAuthentication,) permission_classes = ( permissions.UpdateOwnStatus, IsAuthenticated ) def perform_create(self, serializer): """ overwritting POST request made to create new object""" serializer.save(user_profile=self.request.user)

32.114035

126

0.679869

795126b292ac556dd3a62d41d01831097b39fc84

3,524

py

Python

PyAxe/AFTP.py

sunjinopensource/PyAxe

4b01d0aec83006f9ab366acbefbe7a2bcd5bee10

[ "MIT" ]

null

PyAxe/AFTP.py

sunjinopensource/PyAxe

4b01d0aec83006f9ab366acbefbe7a2bcd5bee10

[ "MIT" ]

null

PyAxe/AFTP.py

sunjinopensource/PyAxe

4b01d0aec83006f9ab366acbefbe7a2bcd5bee10

[ "MIT" ]

null

import os import ftplib import re from . import ALog ANONYMOUS_USERPASS = ('', '') class FileStat: def __init__(self): self.isDir = False self.permissionBits = '' #　'rwxrwxrwx' self.name = '' self.size = 0 def dir(host, targetDir='/', userpass=ANONYMOUS_USERPASS): ret = [] def parseLine(line): """ drwxr-xr-x 1 ftp ftp 0 Nov 02 15:47 TestDir -rw-r--r-- 1 ftp ftp 0 Nov 02 14:51 test.txt -rwxr-xr-x 1 ftp ftp 2943704 Aug 02 2016 cpuz_x32.exe -rw-r--r-- 1 ftp ftp 463451034 Jul 25 2016 exe_7v7_20160725_130231_master_a1b66ed_svn1206.rar """ pattern = re.compile(r'([dl\-])([r\-][w\-][xsStT\-][r\-][w\-][xsStT\-][r\-][w\-][xsStT\-])\s+(\d+)\s+(\S+?)\s+(\S+?)\s+(\d+)\s+([a-zA-Z]{3})\s+(\d{2})\s+(\S+?)\s+(.*)') result = pattern.match(line) dir, permissionBits, fileCount, user, group, size, month, day, yearOrTime, name = result.groups() stat = FileStat() stat.isDir = dir == 'd' stat.permissionBits = permissionBits stat.name = name stat.size = int(size) ret.append(stat) with ftplib.FTP(host) as ftp: ftp.login(userpass[0], userpass[1]) ftp.cwd(targetDir) ftp.dir(parseLine) return ret def upload(host, localFilePath, targetDir='/', userpass=ANONYMOUS_USERPASS): """ 本地文件上传到目标目录目标目录必须已经存在 eg. upload('192.168.3.250', 'C:\\test\\a.txt', 'A/B') ==> A/B/a.txt """ ALog.info('-=> ftp upload(%s, %s, %s)', host, localFilePath, targetDir) with ftplib.FTP(host) as ftp: ftp.login(userpass[0], userpass[1]) ftp.cwd(targetDir) ftp.storbinary('STOR %s' % os.path.basename(localFilePath), open(localFilePath, 'rb')) def download(host, targetFilePath, localDir='.', userpass=ANONYMOUS_USERPASS): """ 目标文件下载到本地目录本地目录必须已经存在 """ ALog.info('-=> ftp download(%s, %s, %s)', host, targetFilePath, localDir) targetDir = os.path.dirname(targetFilePath) targetFileName = os.path.basename(targetFilePath) with ftplib.FTP(host) as ftp: ftp.login(userpass[0], userpass[1]) ftp.cwd(targetDir) ftp.retrbinary('RETR %s' % targetFileName, open(os.path.join(localDir, targetFileName), 'wb').write) def moveFile(host, srcPath, dstPath, userpass=ANONYMOUS_USERPASS): """ 把文件从源路径移到目标路径 """ ALog.info('-=> ftp move(%s, %s, %s)', host, srcPath, dstPath) with ftplib.FTP(host) as ftp: ftp.login(userpass[0], userpass[1]) ftp.rename(srcPath, dstPath) def isDir(host, path, userpass=ANONYMOUS_USERPASS): """ 目标目录是否存在于FTP """ try: with ftplib.FTP(host) as ftp: ftp.login(userpass[0], userpass[1]) ftp.cwd(path) return True except: return False def isFile(host, path, userpass=ANONYMOUS_USERPASS): """ 目标文件是否存在于FTP """ targetDir = os.path.dirname(path) targetName = os.path.basename(path) try: files = dir(host, targetDir, userpass) except: return False for file in files: if not file.isDir and file.name == targetName: return True return False def exists(host, path, userpass=ANONYMOUS_USERPASS): """ 目标是否存在于FTP """ return isDir(host, path, userpass) or isFile(host, path, userpass)

30.119658

177

0.571793

7951277a710e2424e66291bfe8c36119cc185bbd

929

py

Python

RecoHGCal/TICL/python/SimTracksters_cff.py

soumyadipbarman/cmssw

1e8e5a42bd6a4f7eb5a4dc2523ca21b04b687658

[ "Apache-2.0" ]

13

2015-11-30T15:49:45.000Z

2022-02-08T16:11:30.000Z

RecoHGCal/TICL/python/SimTracksters_cff.py

soumyadipbarman/cmssw

1e8e5a42bd6a4f7eb5a4dc2523ca21b04b687658

[ "Apache-2.0" ]

640

2015-02-11T18:55:47.000Z

2022-03-31T14:12:23.000Z

RecoHGCal/TICL/python/SimTracksters_cff.py

soumyadipbarman/cmssw

1e8e5a42bd6a4f7eb5a4dc2523ca21b04b687658

[ "Apache-2.0" ]

51

2015-08-11T21:01:40.000Z

2022-03-30T07:31:34.000Z

import FWCore.ParameterSet.Config as cms from RecoHGCal.TICL.trackstersFromSimClustersProducer_cfi import trackstersFromSimClustersProducer as _trackstersFromSimClustersProducer from RecoHGCal.TICL.filteredLayerClustersProducer_cfi import filteredLayerClustersProducer as _filteredLayerClustersProducer # CA - PATTERN RECOGNITION filteredLayerClustersSimTracksters = _filteredLayerClustersProducer.clone( clusterFilter = "ClusterFilterByAlgoAndSize", algo_number = 8, min_cluster_size = 0, # inclusive iteration_label = "ticlSimTracksters" ) ticlSimTracksters = _trackstersFromSimClustersProducer.clone( ) from Configuration.ProcessModifiers.premix_stage2_cff import premix_stage2 premix_stage2.toModify(ticlSimTracksters, simclusters = "mixData:MergedCaloTruth", caloparticles = "mixData:MergedCaloTruth", ) ticlSimTrackstersTask = cms.Task(filteredLayerClustersSimTracksters, ticlSimTracksters)

34.407407

136

0.8493

7951277be2e0540b73a805c24f3393759fef015e

189,965

py

Python

qiskit/circuit/quantumcircuit.py

itoko/qiskit-terra

5c61d8f2df5a2255e1f31a53be79a45a1abe0cd1

[ "Apache-2.0" ]

1,456

2017-08-05T16:33:05.000Z

2018-06-05T04:15:35.000Z

qiskit/circuit/quantumcircuit.py

itoko/qiskit-terra

5c61d8f2df5a2255e1f31a53be79a45a1abe0cd1

[ "Apache-2.0" ]

365

2017-08-04T06:09:16.000Z

2018-06-05T08:33:37.000Z

qiskit/circuit/quantumcircuit.py

itoko/qiskit-terra

5c61d8f2df5a2255e1f31a53be79a45a1abe0cd1

[ "Apache-2.0" ]

463

2017-08-05T04:10:01.000Z

2018-06-05T06:43:21.000Z

# This code is part of Qiskit. # # (C) Copyright IBM 2017. # # This code is licensed under the Apache License, Version 2.0. You may # obtain a copy of this license in the LICENSE.txt file in the root directory # of this source tree or at http://www.apache.org/licenses/LICENSE-2.0. # # Any modifications or derivative works of this code must retain this # copyright notice, and modified files need to carry a notice indicating # that they have been altered from the originals. # pylint: disable=bad-docstring-quotes,invalid-name """Quantum circuit object.""" import copy import itertools import functools import multiprocessing as mp import string import re from collections import OrderedDict, defaultdict, namedtuple from typing import ( Union, Optional, List, Dict, Tuple, Type, TypeVar, Sequence, Callable, Mapping, Set, Iterable, ) import typing import numpy as np from qiskit.exceptions import QiskitError, MissingOptionalLibraryError from qiskit.utils.multiprocessing import is_main_process from qiskit.circuit.instruction import Instruction from qiskit.circuit.gate import Gate from qiskit.circuit.parameter import Parameter from qiskit.qasm.qasm import Qasm from qiskit.qasm.exceptions import QasmError from qiskit.circuit.exceptions import CircuitError from qiskit.utils.deprecation import deprecate_function from .parameterexpression import ParameterExpression, ParameterValueType from .quantumregister import QuantumRegister, Qubit, AncillaRegister, AncillaQubit from .classicalregister import ClassicalRegister, Clbit from .parametertable import ParameterTable, ParameterView from .parametervector import ParameterVector, ParameterVectorElement from .instructionset import InstructionSet from .register import Register from .bit import Bit from .quantumcircuitdata import QuantumCircuitData from .delay import Delay from .measure import Measure from .reset import Reset try: import pygments from pygments.formatters import Terminal256Formatter # pylint: disable=no-name-in-module from qiskit.qasm.pygments import OpenQASMLexer # pylint: disable=ungrouped-imports from qiskit.qasm.pygments import QasmTerminalStyle # pylint: disable=ungrouped-imports HAS_PYGMENTS = True except Exception: # pylint: disable=broad-except HAS_PYGMENTS = False if typing.TYPE_CHECKING: import qiskit # pylint: disable=cyclic-import BitLocations = namedtuple("BitLocations", ("index", "registers")) # The following types are not marked private to avoid leaking this "private/public" abstraction out # into the documentation. They are not imported by circuit.__init__, nor are they meant to be. # Arbitrary type variables for marking up generics. S = TypeVar("S") T = TypeVar("T") # Type of the elements of QuantumCircuit._data. DataElement = Tuple[Instruction, List[Qubit], List[Clbit]] # Types that can be coerced to a valid Qubit specifier in a circuit. QubitSpecifier = Union[ Qubit, QuantumRegister, int, slice, Sequence[Union[Qubit, int]], ] # Types that can be coerced to a valid Clbit specifier in a circuit. ClbitSpecifier = Union[ Clbit, ClassicalRegister, int, slice, Sequence[Union[Clbit, int]], ] # Generic type which is either :obj:`~Qubit` or :obj:`~Clbit`, used to specify types of functions # which operate on either type of bit, but not both at the same time. BitType = TypeVar("BitType", Qubit, Clbit) # Regex pattern to match valid OpenQASM identifiers VALID_QASM2_IDENTIFIER = re.compile("[a-z][a-zA-Z_0-9]*") class QuantumCircuit: """Create a new circuit. A circuit is a list of instructions bound to some registers. Args: regs (list(:class:`Register`) or list(``int``) or list(list(:class:`Bit`))): The registers to be included in the circuit. * If a list of :class:`Register` objects, represents the :class:`QuantumRegister` and/or :class:`ClassicalRegister` objects to include in the circuit. For example: * ``QuantumCircuit(QuantumRegister(4))`` * ``QuantumCircuit(QuantumRegister(4), ClassicalRegister(3))`` * ``QuantumCircuit(QuantumRegister(4, 'qr0'), QuantumRegister(2, 'qr1'))`` * If a list of ``int``, the amount of qubits and/or classical bits to include in the circuit. It can either be a single int for just the number of quantum bits, or 2 ints for the number of quantum bits and classical bits, respectively. For example: * ``QuantumCircuit(4) # A QuantumCircuit with 4 qubits`` * ``QuantumCircuit(4, 3) # A QuantumCircuit with 4 qubits and 3 classical bits`` * If a list of python lists containing :class:`Bit` objects, a collection of :class:`Bit` s to be added to the circuit. name (str): the name of the quantum circuit. If not set, an automatically generated string will be assigned. global_phase (float or ParameterExpression): The global phase of the circuit in radians. metadata (dict): Arbitrary key value metadata to associate with the circuit. This gets stored as free-form data in a dict in the :attr:`~qiskit.circuit.QuantumCircuit.metadata` attribute. It will not be directly used in the circuit. Raises: CircuitError: if the circuit name, if given, is not valid. Examples: Construct a simple Bell state circuit. .. jupyter-execute:: from qiskit import QuantumCircuit qc = QuantumCircuit(2, 2) qc.h(0) qc.cx(0, 1) qc.measure([0, 1], [0, 1]) qc.draw() Construct a 5-qubit GHZ circuit. .. jupyter-execute:: from qiskit import QuantumCircuit qc = QuantumCircuit(5) qc.h(0) qc.cx(0, range(1, 5)) qc.measure_all() Construct a 4-qubit Bernstein-Vazirani circuit using registers. .. jupyter-execute:: from qiskit import QuantumRegister, ClassicalRegister, QuantumCircuit qr = QuantumRegister(3, 'q') anc = QuantumRegister(1, 'ancilla') cr = ClassicalRegister(3, 'c') qc = QuantumCircuit(qr, anc, cr) qc.x(anc[0]) qc.h(anc[0]) qc.h(qr[0:3]) qc.cx(qr[0:3], anc[0]) qc.h(qr[0:3]) qc.barrier(qr) qc.measure(qr, cr) qc.draw() """ instances = 0 prefix = "circuit" # Class variable OPENQASM header header = "OPENQASM 2.0;" extension_lib = 'include "qelib1.inc";' def __init__( self, *regs: Union[Register, int, Sequence[Bit]], name: Optional[str] = None, global_phase: ParameterValueType = 0, metadata: Optional[Dict] = None, ): if any(not isinstance(reg, (list, QuantumRegister, ClassicalRegister)) for reg in regs): # check if inputs are integers, but also allow e.g. 2.0 try: valid_reg_size = all(reg == int(reg) for reg in regs) except (ValueError, TypeError): valid_reg_size = False if not valid_reg_size: raise CircuitError( "Circuit args must be Registers or integers. (%s '%s' was " "provided)" % ([type(reg).__name__ for reg in regs], regs) ) regs = tuple(int(reg) for reg in regs) # cast to int self._base_name = None if name is None: self._base_name = self.cls_prefix() self._name_update() elif not isinstance(name, str): raise CircuitError( "The circuit name should be a string (or None to auto-generate a name)." ) else: self._base_name = name self.name = name self._increment_instances() # Data contains a list of instructions and their contexts, # in the order they were applied. self._data = [] # A stack to hold the instruction sets that are being built up during for-, if- and # while-block construction. These are stored as a stripped down sequence of instructions, # and sets of qubits and clbits, rather than a full QuantumCircuit instance because the # builder interfaces need to wait until they are completed before they can fill in things # like `break` and `continue`. This is because these instructions need to "operate" on the # full width of bits, but the builder interface won't know what bits are used until the end. self._control_flow_scopes = [] self.qregs = [] self.cregs = [] self._qubits = [] self._clbits = [] # Dict mapping Qubit or Clbit instances to tuple comprised of 0) the # corresponding index in circuit.{qubits,clbits} and 1) a list of # Register-int pairs for each Register containing the Bit and its index # within that register. self._qubit_indices = {} self._clbit_indices = {} self._ancillas = [] self._calibrations = defaultdict(dict) self.add_register(*regs) # Parameter table tracks instructions with variable parameters. self._parameter_table = ParameterTable() # Cache to avoid re-sorting parameters self._parameters = None self._layout = None self._global_phase: ParameterValueType = 0 self.global_phase = global_phase self.duration = None self.unit = "dt" if not isinstance(metadata, dict) and metadata is not None: raise TypeError("Only a dictionary or None is accepted for circuit metadata") self._metadata = metadata @property def data(self) -> QuantumCircuitData: """Return the circuit data (instructions and context). Returns: QuantumCircuitData: a list-like object containing the tuples for the circuit's data. Each tuple is in the format ``(instruction, qargs, cargs)``, where instruction is an Instruction (or subclass) object, qargs is a list of Qubit objects, and cargs is a list of Clbit objects. """ return QuantumCircuitData(self) @data.setter def data( self, data_input: List[Tuple[Instruction, List[QubitSpecifier], List[ClbitSpecifier]]] ): """Sets the circuit data from a list of instructions and context. Args: data_input (list): A list of instructions with context in the format (instruction, qargs, cargs), where Instruction is an Instruction (or subclass) object, qargs is a list of Qubit objects, and cargs is a list of Clbit objects. """ # If data_input is QuantumCircuitData(self), clearing self._data # below will also empty data_input, so make a shallow copy first. data_input = data_input.copy() self._data = [] self._parameter_table = ParameterTable() for inst, qargs, cargs in data_input: self.append(inst, qargs, cargs) @property def calibrations(self) -> dict: """Return calibration dictionary. The custom pulse definition of a given gate is of the form {'gate_name': {(qubits, params): schedule}} """ return dict(self._calibrations) @calibrations.setter def calibrations(self, calibrations: dict): """Set the circuit calibration data from a dictionary of calibration definition. Args: calibrations (dict): A dictionary of input in the format {'gate_name': {(qubits, gate_params): schedule}} """ self._calibrations = defaultdict(dict, calibrations) @property def metadata(self) -> dict: """The user provided metadata associated with the circuit The metadata for the circuit is a user provided ``dict`` of metadata for the circuit. It will not be used to influence the execution or operation of the circuit, but it is expected to be passed between all transforms of the circuit (ie transpilation) and that providers will associate any circuit metadata with the results it returns from execution of that circuit. """ return self._metadata @metadata.setter def metadata(self, metadata: Optional[dict]): """Update the circuit metadata""" if not isinstance(metadata, dict) and metadata is not None: raise TypeError("Only a dictionary or None is accepted for circuit metadata") self._metadata = metadata def __str__(self) -> str: return str(self.draw(output="text")) def __eq__(self, other) -> bool: if not isinstance(other, QuantumCircuit): return False # TODO: remove the DAG from this function from qiskit.converters import circuit_to_dag return circuit_to_dag(self) == circuit_to_dag(other) @classmethod def _increment_instances(cls): cls.instances += 1 @classmethod def cls_instances(cls) -> int: """Return the current number of instances of this class, useful for auto naming.""" return cls.instances @classmethod def cls_prefix(cls) -> str: """Return the prefix to use for auto naming.""" return cls.prefix def _name_update(self) -> None: """update name of instance using instance number""" if not is_main_process(): pid_name = f"-{mp.current_process().pid}" else: pid_name = "" self.name = f"{self._base_name}-{self.cls_instances()}{pid_name}" def has_register(self, register: Register) -> bool: """ Test if this circuit has the register r. Args: register (Register): a quantum or classical register. Returns: bool: True if the register is contained in this circuit. """ has_reg = False if isinstance(register, QuantumRegister) and register in self.qregs: has_reg = True elif isinstance(register, ClassicalRegister) and register in self.cregs: has_reg = True return has_reg def reverse_ops(self) -> "QuantumCircuit": """Reverse the circuit by reversing the order of instructions. This is done by recursively reversing all instructions. It does not invert (adjoint) any gate. Returns: QuantumCircuit: the reversed circuit. Examples: input: .. parsed-literal:: ┌───┐ q_0: ┤ H ├─────■────── └───┘┌────┴─────┐ q_1: ─────┤ RX(1.57) ├ └──────────┘ output: .. parsed-literal:: ┌───┐ q_0: ─────■──────┤ H ├ ┌────┴─────┐└───┘ q_1: ┤ RX(1.57) ├───── └──────────┘ """ reverse_circ = QuantumCircuit( self.qubits, self.clbits, *self.qregs, *self.cregs, name=self.name + "_reverse" ) for inst, qargs, cargs in reversed(self.data): reverse_circ._append(inst.reverse_ops(), qargs, cargs) reverse_circ.duration = self.duration reverse_circ.unit = self.unit return reverse_circ def reverse_bits(self) -> "QuantumCircuit": """Return a circuit with the opposite order of wires. The circuit is "vertically" flipped. If a circuit is defined over multiple registers, the resulting circuit will have the same registers but with their order flipped. This method is useful for converting a circuit written in little-endian convention to the big-endian equivalent, and vice versa. Returns: QuantumCircuit: the circuit with reversed bit order. Examples: input: .. parsed-literal:: ┌───┐ q_0: ┤ H ├─────■────── └───┘┌────┴─────┐ q_1: ─────┤ RX(1.57) ├ └──────────┘ output: .. parsed-literal:: ┌──────────┐ q_0: ─────┤ RX(1.57) ├ ┌───┐└────┬─────┘ q_1: ┤ H ├─────■────── └───┘ """ circ = QuantumCircuit( *reversed(self.qregs), *reversed(self.cregs), name=self.name, global_phase=self.global_phase, ) num_qubits = self.num_qubits num_clbits = self.num_clbits old_qubits = self.qubits old_clbits = self.clbits new_qubits = circ.qubits new_clbits = circ.clbits for inst, qargs, cargs in self.data: new_qargs = [new_qubits[num_qubits - old_qubits.index(q) - 1] for q in qargs] new_cargs = [new_clbits[num_clbits - old_clbits.index(c) - 1] for c in cargs] circ._append(inst, new_qargs, new_cargs) return circ def inverse(self) -> "QuantumCircuit": """Invert (take adjoint of) this circuit. This is done by recursively inverting all gates. Returns: QuantumCircuit: the inverted circuit Raises: CircuitError: if the circuit cannot be inverted. Examples: input: .. parsed-literal:: ┌───┐ q_0: ┤ H ├─────■────── └───┘┌────┴─────┐ q_1: ─────┤ RX(1.57) ├ └──────────┘ output: .. parsed-literal:: ┌───┐ q_0: ──────■──────┤ H ├ ┌─────┴─────┐└───┘ q_1: ┤ RX(-1.57) ├───── └───────────┘ """ inverse_circ = QuantumCircuit( self.qubits, self.clbits, *self.qregs, *self.cregs, name=self.name + "_dg", global_phase=-self.global_phase, ) for inst, qargs, cargs in reversed(self._data): inverse_circ._append(inst.inverse(), qargs, cargs) return inverse_circ def repeat(self, reps: int) -> "QuantumCircuit": """Repeat this circuit ``reps`` times. Args: reps (int): How often this circuit should be repeated. Returns: QuantumCircuit: A circuit containing ``reps`` repetitions of this circuit. """ repeated_circ = QuantumCircuit( self.qubits, self.clbits, *self.qregs, *self.cregs, name=self.name + f"**{reps}" ) # benefit of appending instructions: decomposing shows the subparts, i.e. the power # is actually `reps` times this circuit, and it is currently much faster than `compose`. if reps > 0: try: # try to append as gate if possible to not disallow to_gate inst: Instruction = self.to_gate() except QiskitError: inst = self.to_instruction() for _ in range(reps): repeated_circ._append(inst, self.qubits, self.clbits) return repeated_circ def power(self, power: float, matrix_power: bool = False) -> "QuantumCircuit": """Raise this circuit to the power of ``power``. If ``power`` is a positive integer and ``matrix_power`` is ``False``, this implementation defaults to calling ``repeat``. Otherwise, if the circuit is unitary, the matrix is computed to calculate the matrix power. Args: power (float): The power to raise this circuit to. matrix_power (bool): If True, the circuit is converted to a matrix and then the matrix power is computed. If False, and ``power`` is a positive integer, the implementation defaults to ``repeat``. Raises: CircuitError: If the circuit needs to be converted to a gate but it is not unitary. Returns: QuantumCircuit: A circuit implementing this circuit raised to the power of ``power``. """ if power >= 0 and isinstance(power, (int, np.integer)) and not matrix_power: return self.repeat(power) # attempt conversion to gate if self.num_parameters > 0: raise CircuitError( "Cannot raise a parameterized circuit to a non-positive power " "or matrix-power, please bind the free parameters: " "{}".format(self.parameters) ) try: gate = self.to_gate() except QiskitError as ex: raise CircuitError( "The circuit contains non-unitary operations and cannot be " "controlled. Note that no qiskit.circuit.Instruction objects may " "be in the circuit for this operation." ) from ex power_circuit = QuantumCircuit(self.qubits, self.clbits, *self.qregs, *self.cregs) power_circuit.append(gate.power(power), list(range(gate.num_qubits))) return power_circuit def control( self, num_ctrl_qubits: int = 1, label: Optional[str] = None, ctrl_state: Optional[Union[str, int]] = None, ) -> "QuantumCircuit": """Control this circuit on ``num_ctrl_qubits`` qubits. Args: num_ctrl_qubits (int): The number of control qubits. label (str): An optional label to give the controlled operation for visualization. ctrl_state (str or int): The control state in decimal or as a bitstring (e.g. '111'). If None, use ``2**num_ctrl_qubits - 1``. Returns: QuantumCircuit: The controlled version of this circuit. Raises: CircuitError: If the circuit contains a non-unitary operation and cannot be controlled. """ try: gate = self.to_gate() except QiskitError as ex: raise CircuitError( "The circuit contains non-unitary operations and cannot be " "controlled. Note that no qiskit.circuit.Instruction objects may " "be in the circuit for this operation." ) from ex controlled_gate = gate.control(num_ctrl_qubits, label, ctrl_state) control_qreg = QuantumRegister(num_ctrl_qubits) controlled_circ = QuantumCircuit( control_qreg, self.qubits, *self.qregs, name=f"c_{self.name}" ) controlled_circ.append(controlled_gate, controlled_circ.qubits) return controlled_circ @deprecate_function( "The QuantumCircuit.combine() method is being deprecated. " "Use the compose() method which is more flexible w.r.t " "circuit register compatibility." ) def combine(self, rhs: "QuantumCircuit") -> "QuantumCircuit": """DEPRECATED - Returns rhs appended to self if self contains compatible registers. Two circuits are compatible if they contain the same registers or if they contain different registers with unique names. The returned circuit will contain all unique registers between both circuits. Return self + rhs as a new object. Args: rhs (QuantumCircuit): The quantum circuit to append to the right hand side. Returns: QuantumCircuit: Returns a new QuantumCircuit object Raises: QiskitError: if the rhs circuit is not compatible """ # Check registers in LHS are compatible with RHS self._check_compatible_regs(rhs) # Make new circuit with combined registers combined_qregs = copy.deepcopy(self.qregs) combined_cregs = copy.deepcopy(self.cregs) for element in rhs.qregs: if element not in self.qregs: combined_qregs.append(element) for element in rhs.cregs: if element not in self.cregs: combined_cregs.append(element) circuit = QuantumCircuit(*combined_qregs, *combined_cregs) for instruction_context in itertools.chain(self.data, rhs.data): circuit._append(*instruction_context) circuit.global_phase = self.global_phase + rhs.global_phase for gate, cals in rhs.calibrations.items(): for key, sched in cals.items(): circuit.add_calibration(gate, qubits=key[0], schedule=sched, params=key[1]) for gate, cals in self.calibrations.items(): for key, sched in cals.items(): circuit.add_calibration(gate, qubits=key[0], schedule=sched, params=key[1]) return circuit @deprecate_function( "The QuantumCircuit.extend() method is being deprecated. Use the " "compose() (potentially with the inplace=True argument) and tensor() " "methods which are more flexible w.r.t circuit register compatibility." ) def extend(self, rhs: "QuantumCircuit") -> "QuantumCircuit": """DEPRECATED - Append QuantumCircuit to the RHS if it contains compatible registers. Two circuits are compatible if they contain the same registers or if they contain different registers with unique names. The returned circuit will contain all unique registers between both circuits. Modify and return self. Args: rhs (QuantumCircuit): The quantum circuit to append to the right hand side. Returns: QuantumCircuit: Returns this QuantumCircuit object (which has been modified) Raises: QiskitError: if the rhs circuit is not compatible """ # Check registers in LHS are compatible with RHS self._check_compatible_regs(rhs) # Add new registers for element in rhs.qregs: if element not in self.qregs: self.add_register(element) for element in rhs.cregs: if element not in self.cregs: self.add_register(element) # Copy the circuit data if rhs and self are the same, otherwise the data of rhs is # appended to both self and rhs resulting in an infinite loop data = rhs.data.copy() if rhs is self else rhs.data # Add new gates for instruction_context in data: self._append(*instruction_context) self.global_phase += rhs.global_phase for gate, cals in rhs.calibrations.items(): for key, sched in cals.items(): self.add_calibration(gate, qubits=key[0], schedule=sched, params=key[1]) return self def compose( self, other: Union["QuantumCircuit", Instruction], qubits: Optional[Sequence[Union[Qubit, int]]] = None, clbits: Optional[Sequence[Union[Clbit, int]]] = None, front: bool = False, inplace: bool = False, wrap: bool = False, ) -> Optional["QuantumCircuit"]: """Compose circuit with ``other`` circuit or instruction, optionally permuting wires. ``other`` can be narrower or of equal width to ``self``. Args: other (qiskit.circuit.Instruction or QuantumCircuit): (sub)circuit or instruction to compose onto self. If not a :obj:`.QuantumCircuit`, this can be anything that :obj:`.append` will accept. qubits (list[Qubit|int]): qubits of self to compose onto. clbits (list[Clbit|int]): clbits of self to compose onto. front (bool): If True, front composition will be performed (not implemented yet). inplace (bool): If True, modify the object. Otherwise return composed circuit. wrap (bool): If True, wraps the other circuit into a gate (or instruction, depending on whether it contains only unitary instructions) before composing it onto self. Returns: QuantumCircuit: the composed circuit (returns None if inplace==True). Raises: CircuitError: if composing on the front. QiskitError: if ``other`` is wider or there are duplicate edge mappings. Examples:: lhs.compose(rhs, qubits=[3, 2], inplace=True) .. parsed-literal:: ┌───┐ ┌─────┐ ┌───┐ lqr_1_0: ───┤ H ├─── rqr_0: ──■──┤ Tdg ├ lqr_1_0: ───┤ H ├─────────────── ├───┤ ┌─┴─┐└─────┘ ├───┤ lqr_1_1: ───┤ X ├─── rqr_1: ┤ X ├─────── lqr_1_1: ───┤ X ├─────────────── ┌──┴───┴──┐ └───┘ ┌──┴───┴──┐┌───┐ lqr_1_2: ┤ U1(0.1) ├ + = lqr_1_2: ┤ U1(0.1) ├┤ X ├─────── └─────────┘ └─────────┘└─┬─┘┌─────┐ lqr_2_0: ─────■───── lqr_2_0: ─────■───────■──┤ Tdg ├ ┌─┴─┐ ┌─┴─┐ └─────┘ lqr_2_1: ───┤ X ├─── lqr_2_1: ───┤ X ├─────────────── └───┘ └───┘ lcr_0: 0 ═══════════ lcr_0: 0 ═══════════════════════ lcr_1: 0 ═══════════ lcr_1: 0 ═══════════════════════ """ if inplace: dest = self else: dest = self.copy() if wrap: try: other = other.to_gate() except QiskitError: other = other.to_instruction() if not isinstance(other, QuantumCircuit): if qubits is None: qubits = list(range(other.num_qubits)) if clbits is None: clbits = list(range(other.num_clbits)) if front: dest.data.insert(0, (other, qubits, clbits)) else: dest.append(other, qargs=qubits, cargs=clbits) if inplace: return None return dest instrs = other.data if other.num_qubits > self.num_qubits or other.num_clbits > self.num_clbits: raise CircuitError( "Trying to compose with another QuantumCircuit which has more 'in' edges." ) # number of qubits and clbits must match number in circuit or None identity_qubit_map = dict(zip(other.qubits, self.qubits)) identity_clbit_map = dict(zip(other.clbits, self.clbits)) if qubits is None: qubit_map = identity_qubit_map elif len(qubits) != len(other.qubits): raise CircuitError( f"Number of items in qubits parameter ({len(qubits)}) does not" f" match number of qubits in the circuit ({len(other.qubits)})." ) else: qubit_map = { other.qubits[i]: (self.qubits[q] if isinstance(q, int) else q) for i, q in enumerate(qubits) } if clbits is None: clbit_map = identity_clbit_map elif len(clbits) != len(other.clbits): raise CircuitError( f"Number of items in clbits parameter ({len(clbits)}) does not" f" match number of clbits in the circuit ({len(other.clbits)})." ) else: clbit_map = { other.clbits[i]: (self.clbits[c] if isinstance(c, int) else c) for i, c in enumerate(clbits) } edge_map = {**qubit_map, **clbit_map} or {**identity_qubit_map, **identity_clbit_map} mapped_instrs = [] for instr, qargs, cargs in instrs: n_qargs = [edge_map[qarg] for qarg in qargs] n_cargs = [edge_map[carg] for carg in cargs] n_instr = instr.copy() if instr.condition is not None: from qiskit.dagcircuit import DAGCircuit # pylint: disable=cyclic-import n_instr.condition = DAGCircuit._map_condition(edge_map, instr.condition, self.cregs) mapped_instrs.append((n_instr, n_qargs, n_cargs)) if front: # adjust new instrs before original ones and update all parameters dest._data = mapped_instrs + dest._data dest._parameter_table.clear() for instr, _, _ in dest._data: dest._update_parameter_table(instr) else: # just append new instrs and parameters dest._data += mapped_instrs for instr, _, _ in mapped_instrs: dest._update_parameter_table(instr) for gate, cals in other.calibrations.items(): dest._calibrations[gate].update(cals) dest.global_phase += other.global_phase if inplace: return None return dest def tensor(self, other: "QuantumCircuit", inplace: bool = False) -> Optional["QuantumCircuit"]: """Tensor ``self`` with ``other``. Remember that in the little-endian convention the leftmost operation will be at the bottom of the circuit. See also [the docs](qiskit.org/documentation/tutorials/circuits/3_summary_of_quantum_operations.html) for more information. .. parsed-literal:: ┌────────┐ ┌─────┐ ┌─────┐ q_0: ┤ bottom ├ ⊗ q_0: ┤ top ├ = q_0: ─┤ top ├── └────────┘ └─────┘ ┌┴─────┴─┐ q_1: ┤ bottom ├ └────────┘ Args: other (QuantumCircuit): The other circuit to tensor this circuit with. inplace (bool): If True, modify the object. Otherwise return composed circuit. Examples: .. jupyter-execute:: from qiskit import QuantumCircuit top = QuantumCircuit(1) top.x(0); bottom = QuantumCircuit(2) bottom.cry(0.2, 0, 1); tensored = bottom.tensor(top) print(tensored.draw()) Returns: QuantumCircuit: The tensored circuit (returns None if inplace==True). """ num_qubits = self.num_qubits + other.num_qubits num_clbits = self.num_clbits + other.num_clbits # If a user defined both circuits with via register sizes and not with named registers # (e.g. QuantumCircuit(2, 2)) then we have a naming collision, as the registers are by # default called "q" resp. "c". To still allow tensoring we define new registers of the # correct sizes. if ( len(self.qregs) == len(other.qregs) == 1 and self.qregs[0].name == other.qregs[0].name == "q" ): # check if classical registers are in the circuit if num_clbits > 0: dest = QuantumCircuit(num_qubits, num_clbits) else: dest = QuantumCircuit(num_qubits) # handle case if ``measure_all`` was called on both circuits, in which case the # registers are both named "meas" elif ( len(self.cregs) == len(other.cregs) == 1 and self.cregs[0].name == other.cregs[0].name == "meas" ): cr = ClassicalRegister(self.num_clbits + other.num_clbits, "meas") dest = QuantumCircuit(*other.qregs, *self.qregs, cr) # Now we don't have to handle any more cases arising from special implicit naming else: dest = QuantumCircuit( other.qubits, self.qubits, other.clbits, self.clbits, *other.qregs, *self.qregs, *other.cregs, *self.cregs, ) # compose self onto the output, and then other dest.compose(other, range(other.num_qubits), range(other.num_clbits), inplace=True) dest.compose( self, range(other.num_qubits, num_qubits), range(other.num_clbits, num_clbits), inplace=True, ) # Replace information from tensored circuit into self when inplace = True if inplace: self.__dict__.update(dest.__dict__) return None return dest @property def qubits(self) -> List[Qubit]: """ Returns a list of quantum bits in the order that the registers were added. """ return self._qubits @property def clbits(self) -> List[Clbit]: """ Returns a list of classical bits in the order that the registers were added. """ return self._clbits @property def ancillas(self) -> List[AncillaQubit]: """ Returns a list of ancilla bits in the order that the registers were added. """ return self._ancillas @deprecate_function( "The QuantumCircuit.__add__() method is being deprecated." "Use the compose() method which is more flexible w.r.t " "circuit register compatibility." ) def __add__(self, rhs: "QuantumCircuit") -> "QuantumCircuit": """Overload + to implement self.combine.""" return self.combine(rhs) @deprecate_function( "The QuantumCircuit.__iadd__() method is being deprecated. Use the " "compose() (potentially with the inplace=True argument) and tensor() " "methods which are more flexible w.r.t circuit register compatibility." ) def __iadd__(self, rhs: "QuantumCircuit") -> "QuantumCircuit": """Overload += to implement self.extend.""" return self.extend(rhs) def __and__(self, rhs: "QuantumCircuit") -> "QuantumCircuit": """Overload & to implement self.compose.""" return self.compose(rhs) def __iand__(self, rhs: "QuantumCircuit") -> "QuantumCircuit": """Overload &= to implement self.compose in place.""" self.compose(rhs, inplace=True) return self def __xor__(self, top: "QuantumCircuit") -> "QuantumCircuit": """Overload ^ to implement self.tensor.""" return self.tensor(top) def __ixor__(self, top: "QuantumCircuit") -> "QuantumCircuit": """Overload ^= to implement self.tensor in place.""" self.tensor(top, inplace=True) return self def __len__(self) -> int: """Return number of operations in circuit.""" return len(self._data) @typing.overload def __getitem__(self, item: int) -> DataElement: ... @typing.overload def __getitem__(self, item: slice) -> List[DataElement]: ... def __getitem__(self, item): """Return indexed operation.""" return self._data[item] @staticmethod def cast(value: S, type_: Callable[..., T]) -> Union[S, T]: """Best effort to cast value to type. Otherwise, returns the value.""" try: return type_(value) except (ValueError, TypeError): return value def qbit_argument_conversion(self, qubit_representation: QubitSpecifier) -> List[Qubit]: """ Converts several qubit representations (such as indexes, range, etc.) into a list of qubits. Args: qubit_representation (Object): representation to expand Returns: List(Qubit): the resolved instances of the qubits. """ return _bit_argument_conversion( qubit_representation, self.qubits, self._qubit_indices, Qubit ) def cbit_argument_conversion(self, clbit_representation: ClbitSpecifier) -> List[Clbit]: """ Converts several classical bit representations (such as indexes, range, etc.) into a list of classical bits. Args: clbit_representation (Object): representation to expand Returns: List(tuple): Where each tuple is a classical bit. """ return _bit_argument_conversion( clbit_representation, self.clbits, self._clbit_indices, Clbit ) def _resolve_classical_resource(self, specifier): """Resolve a single classical resource specifier into a concrete resource, raising an error if the specifier is invalid. This is slightly different to :meth:`.cbit_argument_conversion`, because it should not unwrap :obj:`.ClassicalRegister` instances into lists, and in general it should not allow iterables or broadcasting. It is expected to be used as a callback for things like :meth:`.InstructionSet.c_if` to check the validity of their arguments. Args: specifier (Union[Clbit, ClassicalRegister, int]): a specifier of a classical resource present in this circuit. An ``int`` will be resolved into a :obj:`.Clbit` using the same conventions as measurement operations on this circuit use. Returns: Union[Clbit, ClassicalRegister]: the resolved resource. Raises: CircuitError: if the resource is not present in this circuit, or if the integer index passed is out-of-bounds. """ if isinstance(specifier, Clbit): if specifier not in self._clbit_indices: raise CircuitError(f"Clbit {specifier} is not present in this circuit.") return specifier if isinstance(specifier, ClassicalRegister): # This is linear complexity for something that should be constant, but QuantumCircuit # does not currently keep a hashmap of registers, and requires non-trivial changes to # how it exposes its registers publically before such a map can be safely stored so it # doesn't miss updates. (Jake, 2021-11-10). if specifier not in self.cregs: raise CircuitError(f"Register {specifier} is not present in this circuit.") return specifier if isinstance(specifier, int): try: return self._clbits[specifier] except IndexError: raise CircuitError(f"Classical bit index {specifier} is out-of-range.") from None raise CircuitError(f"Unknown classical resource specifier: '{specifier}'.") def append( self, instruction: Instruction, qargs: Optional[Sequence[QubitSpecifier]] = None, cargs: Optional[Sequence[ClbitSpecifier]] = None, ) -> InstructionSet: """Append one or more instructions to the end of the circuit, modifying the circuit in place. Expands qargs and cargs. Args: instruction (qiskit.circuit.Instruction): Instruction instance to append qargs (list(argument)): qubits to attach instruction to cargs (list(argument)): clbits to attach instruction to Returns: qiskit.circuit.Instruction: a handle to the instruction that was just added Raises: CircuitError: if object passed is a subclass of Instruction CircuitError: if object passed is neither subclass nor an instance of Instruction """ # Convert input to instruction if not isinstance(instruction, Instruction) and not hasattr(instruction, "to_instruction"): if issubclass(instruction, Instruction): raise CircuitError( "Object is a subclass of Instruction, please add () to " "pass an instance of this object." ) raise CircuitError( "Object to append must be an Instruction or have a to_instruction() method." ) if not isinstance(instruction, Instruction) and hasattr(instruction, "to_instruction"): instruction = instruction.to_instruction() if not isinstance(instruction, Instruction): raise CircuitError("object is not an Instruction.") # Make copy of parameterized gate instances if hasattr(instruction, "params"): is_parameter = any(isinstance(param, Parameter) for param in instruction.params) if is_parameter: instruction = copy.deepcopy(instruction) expanded_qargs = [self.qbit_argument_conversion(qarg) for qarg in qargs or []] expanded_cargs = [self.cbit_argument_conversion(carg) for carg in cargs or []] if self._control_flow_scopes: appender = self._control_flow_scopes[-1].append requester = self._control_flow_scopes[-1].request_classical_resource else: appender = self._append requester = self._resolve_classical_resource instructions = InstructionSet(resource_requester=requester) for qarg, carg in instruction.broadcast_arguments(expanded_qargs, expanded_cargs): self._check_dups(qarg) instructions.add(appender(instruction, qarg, carg), qarg, carg) return instructions def _append( self, instruction: Instruction, qargs: Sequence[Qubit], cargs: Sequence[Clbit], ) -> Instruction: """Append an instruction to the end of the circuit, modifying the circuit in place. .. warning:: This is an internal fast-path function, and it is the responsibility of the caller to ensure that all the arguments are valid; there is no error checking here. In particular, all the qubits and clbits must already exist in the circuit and there can be no duplicates in the list. .. note:: This function may be used by callers other than :obj:`.QuantumCircuit` when the caller is sure that all error-checking, broadcasting and scoping has already been performed, and the only reference to the circuit the instructions are being appended to is within that same function. In particular, it is not safe to call :meth:`QuantumCircuit._append` on a circuit that is received by a function argument. This is because :meth:`.QuantumCircuit._append` will not recognise the scoping constructs of the control-flow builder interface. Args: instruction: Instruction instance to append qargs: Qubits to attach the instruction to. cargs: Clbits to attach the instruction to. Returns: Instruction: a handle to the instruction that was just added :meta public: """ self._data.append((instruction, qargs, cargs)) self._update_parameter_table(instruction) # mark as normal circuit if a new instruction is added self.duration = None self.unit = "dt" return instruction def _update_parameter_table(self, instruction: Instruction) -> Instruction: for param_index, param in enumerate(instruction.params): if isinstance(param, (ParameterExpression, QuantumCircuit)): # Scoped constructs like the control-flow ops use QuantumCircuit as a parameter. atomic_parameters = set(param.parameters) else: atomic_parameters = set() for parameter in atomic_parameters: if parameter in self._parameter_table: if not self._check_dup_param_spec( self._parameter_table[parameter], instruction, param_index ): self._parameter_table[parameter].append((instruction, param_index)) else: if parameter.name in self._parameter_table.get_names(): raise CircuitError(f"Name conflict on adding parameter: {parameter.name}") self._parameter_table[parameter] = [(instruction, param_index)] # clear cache if new parameter is added self._parameters = None return instruction def _check_dup_param_spec( self, parameter_spec_list: Sequence[Tuple[Instruction, int]], instruction: Instruction, param_index: int, ) -> bool: for spec in parameter_spec_list: if spec[0] is instruction and spec[1] == param_index: return True return False def add_register(self, *regs: Union[Register, int, Sequence[Bit]]) -> None: """Add registers.""" if not regs: return if any(isinstance(reg, int) for reg in regs): # QuantumCircuit defined without registers if len(regs) == 1 and isinstance(regs[0], int): # QuantumCircuit with anonymous quantum wires e.g. QuantumCircuit(2) if regs[0] == 0: regs = tuple() else: regs = (QuantumRegister(regs[0], "q"),) elif len(regs) == 2 and all(isinstance(reg, int) for reg in regs): # QuantumCircuit with anonymous wires e.g. QuantumCircuit(2, 3) if regs[0] == 0: qregs = tuple() else: qregs = (QuantumRegister(regs[0], "q"),) if regs[1] == 0: cregs = tuple() else: cregs = (ClassicalRegister(regs[1], "c"),) regs = qregs + cregs else: raise CircuitError( "QuantumCircuit parameters can be Registers or Integers." " If Integers, up to 2 arguments. QuantumCircuit was called" " with %s." % (regs,) ) for register in regs: if isinstance(register, Register) and any( register.name == reg.name for reg in self.qregs + self.cregs ): raise CircuitError('register name "%s" already exists' % register.name) if isinstance(register, AncillaRegister): for bit in register: if bit not in self._qubit_indices: self._ancillas.append(bit) if isinstance(register, QuantumRegister): self.qregs.append(register) for idx, bit in enumerate(register): if bit in self._qubit_indices: self._qubit_indices[bit].registers.append((register, idx)) else: self._qubits.append(bit) self._qubit_indices[bit] = BitLocations( len(self._qubits) - 1, [(register, idx)] ) elif isinstance(register, ClassicalRegister): self.cregs.append(register) for idx, bit in enumerate(register): if bit in self._clbit_indices: self._clbit_indices[bit].registers.append((register, idx)) else: self._clbits.append(bit) self._clbit_indices[bit] = BitLocations( len(self._clbits) - 1, [(register, idx)] ) elif isinstance(register, list): self.add_bits(register) else: raise CircuitError("expected a register") def add_bits(self, bits: Iterable[Bit]) -> None: """Add Bits to the circuit.""" duplicate_bits = set(self._qubit_indices).union(self._clbit_indices).intersection(bits) if duplicate_bits: raise CircuitError(f"Attempted to add bits found already in circuit: {duplicate_bits}") for bit in bits: if isinstance(bit, AncillaQubit): self._ancillas.append(bit) if isinstance(bit, Qubit): self._qubits.append(bit) self._qubit_indices[bit] = BitLocations(len(self._qubits) - 1, []) elif isinstance(bit, Clbit): self._clbits.append(bit) self._clbit_indices[bit] = BitLocations(len(self._clbits) - 1, []) else: raise CircuitError( "Expected an instance of Qubit, Clbit, or " "AncillaQubit, but was passed {}".format(bit) ) def find_bit(self, bit: Bit) -> BitLocations: """Find locations in the circuit which can be used to reference a given :obj:`~Bit`. Args: bit (Bit): The bit to locate. Returns: namedtuple(int, List[Tuple(Register, int)]): A 2-tuple. The first element (``index``) contains the index at which the ``Bit`` can be found (in either :obj:`~QuantumCircuit.qubits`, :obj:`~QuantumCircuit.clbits`, depending on its type). The second element (``registers``) is a list of ``(register, index)`` pairs with an entry for each :obj:`~Register` in the circuit which contains the :obj:`~Bit` (and the index in the :obj:`~Register` at which it can be found). Notes: The circuit index of an :obj:`~AncillaQubit` will be its index in :obj:`~QuantumCircuit.qubits`, not :obj:`~QuantumCircuit.ancillas`. Raises: CircuitError: If the supplied :obj:`~Bit` was of an unknown type. CircuitError: If the supplied :obj:`~Bit` could not be found on the circuit. """ try: if isinstance(bit, Qubit): return self._qubit_indices[bit] elif isinstance(bit, Clbit): return self._clbit_indices[bit] else: raise CircuitError(f"Could not locate bit of unknown type: {type(bit)}") except KeyError as err: raise CircuitError( f"Could not locate provided bit: {bit}. Has it been added to the QuantumCircuit?" ) from err def _check_dups(self, qubits: Sequence[Qubit]) -> None: """Raise exception if list of qubits contains duplicates.""" squbits = set(qubits) if len(squbits) != len(qubits): raise CircuitError("duplicate qubit arguments") def to_instruction( self, parameter_map: Optional[Dict[Parameter, ParameterValueType]] = None, label: Optional[str] = None, ) -> Instruction: """Create an Instruction out of this circuit. Args: parameter_map(dict): For parameterized circuits, a mapping from parameters in the circuit to parameters to be used in the instruction. If None, existing circuit parameters will also parameterize the instruction. label (str): Optional gate label. Returns: qiskit.circuit.Instruction: a composite instruction encapsulating this circuit (can be decomposed back) """ from qiskit.converters.circuit_to_instruction import circuit_to_instruction return circuit_to_instruction(self, parameter_map, label=label) def to_gate( self, parameter_map: Optional[Dict[Parameter, ParameterValueType]] = None, label: Optional[str] = None, ) -> Gate: """Create a Gate out of this circuit. Args: parameter_map(dict): For parameterized circuits, a mapping from parameters in the circuit to parameters to be used in the gate. If None, existing circuit parameters will also parameterize the gate. label (str): Optional gate label. Returns: Gate: a composite gate encapsulating this circuit (can be decomposed back) """ from qiskit.converters.circuit_to_gate import circuit_to_gate return circuit_to_gate(self, parameter_map, label=label) def decompose( self, gates_to_decompose: Optional[ Union[Type[Gate], Sequence[Type[Gate]], Sequence[str], str] ] = None, ) -> "QuantumCircuit": """Call a decomposition pass on this circuit, to decompose one level (shallow decompose). Args: gates_to_decompose (str or list(str)): optional subset of gates to decompose. Defaults to all gates in circuit. Returns: QuantumCircuit: a circuit one level decomposed """ # pylint: disable=cyclic-import from qiskit.transpiler.passes.basis.decompose import Decompose from qiskit.converters.circuit_to_dag import circuit_to_dag from qiskit.converters.dag_to_circuit import dag_to_circuit pass_ = Decompose(gates_to_decompose=gates_to_decompose) decomposed_dag = pass_.run(circuit_to_dag(self)) return dag_to_circuit(decomposed_dag) def _check_compatible_regs(self, rhs: "QuantumCircuit") -> None: """Raise exception if the circuits are defined on incompatible registers""" list1 = self.qregs + self.cregs list2 = rhs.qregs + rhs.cregs for element1 in list1: for element2 in list2: if element2.name == element1.name: if element1 != element2: raise CircuitError( "circuits are not compatible:" f" registers {element1} and {element2} not compatible" ) def _unique_register_name(self, prefix: str = "") -> str: """Generate a register name with the given prefix, which is unique within this circuit.""" used = { reg.name[len(prefix) :] for reg in itertools.chain(self.qregs, self.cregs) if reg.name.startswith(prefix) } characters = (string.digits + string.ascii_letters) if prefix else string.ascii_letters for parts in itertools.chain.from_iterable( itertools.product(characters, repeat=n) for n in itertools.count(1) ): name = "".join(parts) if name not in used: return prefix + name # This isn't actually reachable because the above loop is infinite. return prefix def qasm( self, formatted: bool = False, filename: Optional[str] = None, encoding: Optional[str] = None, ) -> Optional[str]: """Return OpenQASM string. Args: formatted (bool): Return formatted Qasm string. filename (str): Save Qasm to file with name 'filename'. encoding (str): Optionally specify the encoding to use for the output file if ``filename`` is specified. By default this is set to the system's default encoding (ie whatever ``locale.getpreferredencoding()`` returns) and can be set to any valid codec or alias from stdlib's `codec module <https://docs.python.org/3/library/codecs.html#standard-encodings>`__ Returns: str: If formatted=False. Raises: MissingOptionalLibraryError: If pygments is not installed and ``formatted`` is ``True``. QasmError: If circuit has free parameters. """ if self.num_parameters > 0: raise QasmError("Cannot represent circuits with unbound parameters in OpenQASM 2.") existing_gate_names = [ "barrier", "measure", "reset", "u3", "u2", "u1", "cx", "id", "u0", "u", "p", "x", "y", "z", "h", "s", "sdg", "t", "tdg", "rx", "ry", "rz", "sx", "sxdg", "cz", "cy", "swap", "ch", "ccx", "cswap", "crx", "cry", "crz", "cu1", "cp", "cu3", "csx", "cu", "rxx", "rzz", "rccx", "rc3x", "c3x", "c3sx", "c4x", ] existing_composite_circuits = [] string_temp = self.header + "\n" string_temp += self.extension_lib + "\n" for register in self.qregs: string_temp += register.qasm() + "\n" for register in self.cregs: string_temp += register.qasm() + "\n" bit_labels = { bit: "%s[%d]" % (reg.name, idx) for reg in self.qregs + self.cregs for (idx, bit) in enumerate(reg) } regless_qubits = set(self.qubits) - {bit for reg in self.qregs for bit in reg} regless_clbits = set(self.clbits) - {bit for reg in self.cregs for bit in reg} if regless_qubits: register_name = self._unique_register_name("qregless_") string_temp += f"qreg {register_name}[{len(regless_qubits)}];\n" bit_labels.update( {bit: f"{register_name}[{idx}]" for idx, bit in enumerate(regless_qubits)} ) if regless_clbits: register_name = self._unique_register_name("cregless_") string_temp += f"creg {register_name}[{len(regless_clbits)}];\n" bit_labels.update( {bit: f"{register_name}[{idx}]" for idx, bit in enumerate(regless_clbits)} ) for instruction, qargs, cargs in self._data: if instruction.name == "measure": qubit = qargs[0] clbit = cargs[0] string_temp += "{} {} -> {};\n".format( instruction.qasm(), bit_labels[qubit], bit_labels[clbit], ) else: # Check instructions names or label are valid if not VALID_QASM2_IDENTIFIER.fullmatch(instruction.name): instruction = instruction.copy(name=_qasm_escape_gate_name(instruction.name)) # decompose gate using definitions if they are not defined in OpenQASM2 if ( instruction.name not in existing_gate_names and instruction not in existing_composite_circuits ): if instruction.name in [ instruction.name for instruction in existing_composite_circuits ]: # append instruction id to name of instruction copy to make it unique instruction = instruction.copy(name=f"{instruction.name}_{id(instruction)}") existing_composite_circuits.append(instruction) _add_sub_instruction_to_existing_composite_circuits( instruction, existing_gate_names, existing_composite_circuits ) # Insert qasm representation of the original instruction string_temp += "{} {};\n".format( instruction.qasm(), ",".join([bit_labels[j] for j in qargs + cargs]), ) # insert gate definitions string_temp = _insert_composite_gate_definition_qasm( string_temp, existing_composite_circuits, self.extension_lib ) if filename: with open(filename, "w+", encoding=encoding) as file: file.write(string_temp) file.close() if formatted: if not HAS_PYGMENTS: raise MissingOptionalLibraryError( libname="pygments>2.4", name="formatted QASM output", pip_install="pip install pygments", ) code = pygments.highlight( string_temp, OpenQASMLexer(), Terminal256Formatter(style=QasmTerminalStyle) ) print(code) return None else: return string_temp def draw( self, output: Optional[str] = None, scale: Optional[float] = None, filename: Optional[str] = None, style: Optional[Union[dict, str]] = None, interactive: bool = False, plot_barriers: bool = True, reverse_bits: bool = False, justify: Optional[str] = None, vertical_compression: Optional[str] = "medium", idle_wires: bool = True, with_layout: bool = True, fold: Optional[int] = None, # The type of ax is matplotlib.axes.Axes, but this is not a fixed dependency, so cannot be # safely forward-referenced. ax: Optional[typing.Any] = None, initial_state: bool = False, cregbundle: bool = True, ): """Draw the quantum circuit. Use the output parameter to choose the drawing format: **text**: ASCII art TextDrawing that can be printed in the console. **matplotlib**: images with color rendered purely in Python. **latex**: high-quality images compiled via latex. **latex_source**: raw uncompiled latex output. Args: output (str): select the output method to use for drawing the circuit. Valid choices are ``text``, ``mpl``, ``latex``, ``latex_source``. By default the `text` drawer is used unless the user config file (usually ``~/.qiskit/settings.conf``) has an alternative backend set as the default. For example, ``circuit_drawer = latex``. If the output kwarg is set, that backend will always be used over the default in the user config file. scale (float): scale of image to draw (shrink if < 1.0). Only used by the `mpl`, `latex` and `latex_source` outputs. Defaults to 1.0. filename (str): file path to save image to. Defaults to None. style (dict or str): dictionary of style or file name of style json file. This option is only used by the `mpl` or `latex` output type. If `style` is a str, it is used as the path to a json file which contains a style dict. The file will be opened, parsed, and then any style elements in the dict will replace the default values in the input dict. A file to be loaded must end in ``.json``, but the name entered here can omit ``.json``. For example, ``style='iqx.json'`` or ``style='iqx'``. If `style` is a dict and the ``'name'`` key is set, that name will be used to load a json file, followed by loading the other items in the style dict. For example, ``style={'name': 'iqx'}``. If `style` is not a str and `name` is not a key in the style dict, then the default value from the user config file (usually ``~/.qiskit/settings.conf``) will be used, for example, ``circuit_mpl_style = iqx``. If none of these are set, the `default` style will be used. The search path for style json files can be specified in the user config, for example, ``circuit_mpl_style_path = /home/user/styles:/home/user``. See: :class:`~qiskit.visualization.qcstyle.DefaultStyle` for more information on the contents. interactive (bool): when set to true, show the circuit in a new window (for `mpl` this depends on the matplotlib backend being used supporting this). Note when used with either the `text` or the `latex_source` output type this has no effect and will be silently ignored. Defaults to False. reverse_bits (bool): when set to True, reverse the bit order inside registers for the output visualization. Defaults to False. plot_barriers (bool): enable/disable drawing barriers in the output circuit. Defaults to True. justify (string): options are ``left``, ``right`` or ``none``. If anything else is supplied, it defaults to left justified. It refers to where gates should be placed in the output circuit if there is an option. ``none`` results in each gate being placed in its own column. vertical_compression (string): ``high``, ``medium`` or ``low``. It merges the lines generated by the `text` output so the drawing will take less vertical room. Default is ``medium``. Only used by the `text` output, will be silently ignored otherwise. idle_wires (bool): include idle wires (wires with no circuit elements) in output visualization. Default is True. with_layout (bool): include layout information, with labels on the physical layout. Default is True. fold (int): sets pagination. It can be disabled using -1. In `text`, sets the length of the lines. This is useful when the drawing does not fit in the console. If None (default), it will try to guess the console width using ``shutil.get_terminal_size()``. However, if running in jupyter, the default line length is set to 80 characters. In `mpl`, it is the number of (visual) layers before folding. Default is 25. ax (matplotlib.axes.Axes): Only used by the `mpl` backend. An optional Axes object to be used for the visualization output. If none is specified, a new matplotlib Figure will be created and used. Additionally, if specified there will be no returned Figure since it is redundant. initial_state (bool): optional. Adds ``|0>`` in the beginning of the wire. Default is False. cregbundle (bool): optional. If set True, bundle classical registers. Default is True. Returns: :class:`TextDrawing` or :class:`matplotlib.figure` or :class:`PIL.Image` or :class:`str`: * `TextDrawing` (output='text') A drawing that can be printed as ascii art. * `matplotlib.figure.Figure` (output='mpl') A matplotlib figure object for the circuit diagram. * `PIL.Image` (output='latex') An in-memory representation of the image of the circuit diagram. * `str` (output='latex_source') The LaTeX source code for visualizing the circuit diagram. Raises: VisualizationError: when an invalid output method is selected ImportError: when the output methods requires non-installed libraries. Example: .. jupyter-execute:: from qiskit import QuantumRegister, ClassicalRegister, QuantumCircuit from qiskit.tools.visualization import circuit_drawer q = QuantumRegister(1) c = ClassicalRegister(1) qc = QuantumCircuit(q, c) qc.h(q) qc.measure(q, c) qc.draw(output='mpl', style={'backgroundcolor': '#EEEEEE'}) """ # pylint: disable=cyclic-import from qiskit.visualization import circuit_drawer return circuit_drawer( self, scale=scale, filename=filename, style=style, output=output, interactive=interactive, plot_barriers=plot_barriers, reverse_bits=reverse_bits, justify=justify, vertical_compression=vertical_compression, idle_wires=idle_wires, with_layout=with_layout, fold=fold, ax=ax, initial_state=initial_state, cregbundle=cregbundle, ) def size(self, filter_function: Optional[callable] = lambda x: not x[0]._directive) -> int: """Returns total number of instructions in circuit. Args: filter_function (callable): a function to filter out some instructions. Should take as input a tuple of (Instruction, list(Qubit), list(Clbit)). By default filters out "directives", such as barrier or snapshot. Returns: int: Total number of gate operations. """ return sum(map(filter_function, self._data)) def depth(self, filter_function: Optional[callable] = lambda x: not x[0]._directive) -> int: """Return circuit depth (i.e., length of critical path). Args: filter_function (callable): a function to filter out some instructions. Should take as input a tuple of (Instruction, list(Qubit), list(Clbit)). By default filters out "directives", such as barrier or snapshot. Returns: int: Depth of circuit. Notes: The circuit depth and the DAG depth need not be the same. """ # Assign each bit in the circuit a unique integer # to index into op_stack. bit_indices = {bit: idx for idx, bit in enumerate(self.qubits + self.clbits)} # If no bits, return 0 if not bit_indices: return 0 # A list that holds the height of each qubit # and classical bit. op_stack = [0] * len(bit_indices) # Here we are playing a modified version of # Tetris where we stack gates, but multi-qubit # gates, or measurements have a block for each # qubit or cbit that are connected by a virtual # line so that they all stacked at the same depth. # Conditional gates act on all cbits in the register # they are conditioned on. # The max stack height is the circuit depth. for instr, qargs, cargs in self._data: levels = [] reg_ints = [] for ind, reg in enumerate(qargs + cargs): # Add to the stacks of the qubits and # cbits used in the gate. reg_ints.append(bit_indices[reg]) if filter_function((instr, qargs, cargs)): levels.append(op_stack[reg_ints[ind]] + 1) else: levels.append(op_stack[reg_ints[ind]]) # Assuming here that there is no conditional # snapshots or barriers ever. if instr.condition: # Controls operate over all bits of a classical register # or over a single bit if isinstance(instr.condition[0], Clbit): condition_bits = [instr.condition[0]] else: condition_bits = instr.condition[0] for cbit in condition_bits: idx = bit_indices[cbit] if idx not in reg_ints: reg_ints.append(idx) levels.append(op_stack[idx] + 1) max_level = max(levels) for ind in reg_ints: op_stack[ind] = max_level return max(op_stack) def width(self) -> int: """Return number of qubits plus clbits in circuit. Returns: int: Width of circuit. """ return len(self.qubits) + len(self.clbits) @property def num_qubits(self) -> int: """Return number of qubits.""" return len(self.qubits) @property def num_ancillas(self) -> int: """Return the number of ancilla qubits.""" return len(self.ancillas) @property def num_clbits(self) -> int: """Return number of classical bits.""" return len(self.clbits) # The stringified return type is because OrderedDict can't be subscripted before Python 3.9, and # typing.OrderedDict wasn't added until 3.7.2. It can be turned into a proper type once 3.6 # support is dropped. def count_ops(self) -> "OrderedDict[Instruction, int]": """Count each operation kind in the circuit. Returns: OrderedDict: a breakdown of how many operations of each kind, sorted by amount. """ count_ops: Dict[Instruction, int] = {} for instr, _, _ in self._data: count_ops[instr.name] = count_ops.get(instr.name, 0) + 1 return OrderedDict(sorted(count_ops.items(), key=lambda kv: kv[1], reverse=True)) def num_nonlocal_gates(self) -> int: """Return number of non-local gates (i.e. involving 2+ qubits). Conditional nonlocal gates are also included. """ multi_qubit_gates = 0 for instr, _, _ in self._data: if instr.num_qubits > 1 and not instr._directive: multi_qubit_gates += 1 return multi_qubit_gates def get_instructions(self, name: str) -> List[DataElement]: """Get instructions matching name. Args: name (str): The name of instruction to. Returns: list(tuple): list of (instruction, qargs, cargs). """ return [match for match in self._data if match[0].name == name] def num_connected_components(self, unitary_only: bool = False) -> int: """How many non-entangled subcircuits can the circuit be factored to. Args: unitary_only (bool): Compute only unitary part of graph. Returns: int: Number of connected components in circuit. """ # Convert registers to ints (as done in depth). bits = self.qubits if unitary_only else (self.qubits + self.clbits) bit_indices = {bit: idx for idx, bit in enumerate(bits)} # Start with each qubit or cbit being its own subgraph. sub_graphs = [[bit] for bit in range(len(bit_indices))] num_sub_graphs = len(sub_graphs) # Here we are traversing the gates and looking to see # which of the sub_graphs the gate joins together. for instr, qargs, cargs in self._data: if unitary_only: args = qargs num_qargs = len(args) else: args = qargs + cargs num_qargs = len(args) + (1 if instr.condition else 0) if num_qargs >= 2 and not instr._directive: graphs_touched = [] num_touched = 0 # Controls necessarily join all the cbits in the # register that they use. if not unitary_only: for bit in instr.condition_bits: idx = bit_indices[bit] for k in range(num_sub_graphs): if idx in sub_graphs[k]: graphs_touched.append(k) break for item in args: reg_int = bit_indices[item] for k in range(num_sub_graphs): if reg_int in sub_graphs[k]: if k not in graphs_touched: graphs_touched.append(k) break graphs_touched = list(set(graphs_touched)) num_touched = len(graphs_touched) # If the gate touches more than one subgraph # join those graphs together and return # reduced number of subgraphs if num_touched > 1: connections = [] for idx in graphs_touched: connections.extend(sub_graphs[idx]) _sub_graphs = [] for idx in range(num_sub_graphs): if idx not in graphs_touched: _sub_graphs.append(sub_graphs[idx]) _sub_graphs.append(connections) sub_graphs = _sub_graphs num_sub_graphs -= num_touched - 1 # Cannot go lower than one so break if num_sub_graphs == 1: break return num_sub_graphs def num_unitary_factors(self) -> int: """Computes the number of tensor factors in the unitary (quantum) part of the circuit only. """ return self.num_connected_components(unitary_only=True) def num_tensor_factors(self) -> int: """Computes the number of tensor factors in the unitary (quantum) part of the circuit only. Notes: This is here for backwards compatibility, and will be removed in a future release of Qiskit. You should call `num_unitary_factors` instead. """ return self.num_unitary_factors() def copy(self, name: Optional[str] = None) -> "QuantumCircuit": """Copy the circuit. Args: name (str): name to be given to the copied circuit. If None, then the name stays the same Returns: QuantumCircuit: a deepcopy of the current circuit, with the specified name """ cpy = copy.copy(self) # copy registers correctly, in copy.copy they are only copied via reference cpy.qregs = self.qregs.copy() cpy.cregs = self.cregs.copy() cpy._qubits = self._qubits.copy() cpy._ancillas = self._ancillas.copy() cpy._clbits = self._clbits.copy() cpy._qubit_indices = self._qubit_indices.copy() cpy._clbit_indices = self._clbit_indices.copy() instr_instances = {id(instr): instr for instr, _, __ in self._data} instr_copies = {id_: instr.copy() for id_, instr in instr_instances.items()} cpy._parameter_table = ParameterTable( { param: [ (instr_copies[id(instr)], param_index) for instr, param_index in self._parameter_table[param] ] for param in self._parameter_table } ) cpy._data = [ (instr_copies[id(inst)], qargs.copy(), cargs.copy()) for inst, qargs, cargs in self._data ] cpy._calibrations = copy.deepcopy(self._calibrations) cpy._metadata = copy.deepcopy(self._metadata) if name: cpy.name = name return cpy def _create_creg(self, length: int, name: str) -> ClassicalRegister: """Creates a creg, checking if ClassicalRegister with same name exists""" if name in [creg.name for creg in self.cregs]: save_prefix = ClassicalRegister.prefix ClassicalRegister.prefix = name new_creg = ClassicalRegister(length) ClassicalRegister.prefix = save_prefix else: new_creg = ClassicalRegister(length, name) return new_creg def _create_qreg(self, length: int, name: str) -> QuantumRegister: """Creates a qreg, checking if QuantumRegister with same name exists""" if name in [qreg.name for qreg in self.qregs]: save_prefix = QuantumRegister.prefix QuantumRegister.prefix = name new_qreg = QuantumRegister(length) QuantumRegister.prefix = save_prefix else: new_qreg = QuantumRegister(length, name) return new_qreg def reset(self, qubit: QubitSpecifier) -> InstructionSet: """Reset the quantum bit(s) to their default state. Args: qubit: qubit(s) to reset. Returns: qiskit.circuit.InstructionSet: handle to the added instruction. """ return self.append(Reset(), [qubit], []) def measure(self, qubit: QubitSpecifier, cbit: ClbitSpecifier) -> InstructionSet: """Measure quantum bit into classical bit (tuples). Args: qubit: qubit to measure. cbit: classical bit to place the measurement in. Returns: qiskit.circuit.InstructionSet: handle to the added instructions. Raises: CircuitError: if arguments have bad format. """ return self.append(Measure(), [qubit], [cbit]) def measure_active(self, inplace: bool = True) -> Optional["QuantumCircuit"]: """Adds measurement to all non-idle qubits. Creates a new ClassicalRegister with a size equal to the number of non-idle qubits being measured. Returns a new circuit with measurements if `inplace=False`. Args: inplace (bool): All measurements inplace or return new circuit. Returns: QuantumCircuit: Returns circuit with measurements when `inplace = False`. """ from qiskit.converters.circuit_to_dag import circuit_to_dag if inplace: circ = self else: circ = self.copy() dag = circuit_to_dag(circ) qubits_to_measure = [qubit for qubit in circ.qubits if qubit not in dag.idle_wires()] new_creg = circ._create_creg(len(qubits_to_measure), "measure") circ.add_register(new_creg) circ.barrier() circ.measure(qubits_to_measure, new_creg) if not inplace: return circ else: return None def measure_all( self, inplace: bool = True, add_bits: bool = True ) -> Optional["QuantumCircuit"]: """Adds measurement to all qubits. By default, adds new classical bits in a :obj:`.ClassicalRegister` to store these measurements. If ``add_bits=False``, the results of the measurements will instead be stored in the already existing classical bits, with qubit ``n`` being measured into classical bit ``n``. Returns a new circuit with measurements if ``inplace=False``. Args: inplace (bool): All measurements inplace or return new circuit. add_bits (bool): Whether to add new bits to store the results. Returns: QuantumCircuit: Returns circuit with measurements when ``inplace=False``. Raises: CircuitError: if ``add_bits=False`` but there are not enough classical bits. """ if inplace: circ = self else: circ = self.copy() if add_bits: new_creg = circ._create_creg(len(circ.qubits), "meas") circ.add_register(new_creg) circ.barrier() circ.measure(circ.qubits, new_creg) else: if len(circ.clbits) < len(circ.qubits): raise CircuitError( "The number of classical bits must be equal or greater than " "the number of qubits." ) circ.barrier() circ.measure(circ.qubits, circ.clbits[0 : len(circ.qubits)]) if not inplace: return circ else: return None def remove_final_measurements(self, inplace: bool = True) -> Optional["QuantumCircuit"]: """Removes final measurements and barriers on all qubits if they are present. Deletes the classical registers that were used to store the values from these measurements that become idle as a result of this operation, and deletes classical bits that are referenced only by removed registers, or that aren't referenced at all but have become idle as a result of this operation. Measurements and barriers are considered final if they are followed by no other operations (aside from other measurements or barriers.) Args: inplace (bool): All measurements removed inplace or return new circuit. Returns: QuantumCircuit: Returns the resulting circuit when ``inplace=False``, else None. """ # pylint: disable=cyclic-import from qiskit.transpiler.passes import RemoveFinalMeasurements from qiskit.converters import circuit_to_dag if inplace: circ = self else: circ = self.copy() dag = circuit_to_dag(circ) remove_final_meas = RemoveFinalMeasurements() new_dag = remove_final_meas.run(dag) kept_cregs = set(new_dag.cregs.values()) kept_clbits = set(new_dag.clbits) # Filter only cregs/clbits still in new DAG, preserving original circuit order cregs_to_add = [creg for creg in circ.cregs if creg in kept_cregs] clbits_to_add = [clbit for clbit in circ._clbits if clbit in kept_clbits] # Clear cregs and clbits circ.cregs = [] circ._clbits = [] circ._clbit_indices = {} # We must add the clbits first to preserve the original circuit # order. This way, add_register never adds clbits and just # creates registers that point to them. circ.add_bits(clbits_to_add) for creg in cregs_to_add: circ.add_register(creg) # Clear instruction info circ.data.clear() circ._parameter_table.clear() # Set circ instructions to match the new DAG for node in new_dag.topological_op_nodes(): # Get arguments for classical condition (if any) inst = node.op.copy() circ.append(inst, node.qargs, node.cargs) if not inplace: return circ else: return None @staticmethod def from_qasm_file(path: str) -> "QuantumCircuit": """Take in a QASM file and generate a QuantumCircuit object. Args: path (str): Path to the file for a QASM program Return: QuantumCircuit: The QuantumCircuit object for the input QASM """ qasm = Qasm(filename=path) return _circuit_from_qasm(qasm) @staticmethod def from_qasm_str(qasm_str: str) -> "QuantumCircuit": """Take in a QASM string and generate a QuantumCircuit object. Args: qasm_str (str): A QASM program string Return: QuantumCircuit: The QuantumCircuit object for the input QASM """ qasm = Qasm(data=qasm_str) return _circuit_from_qasm(qasm) @property def global_phase(self) -> ParameterValueType: """Return the global phase of the circuit in radians.""" return self._global_phase @global_phase.setter def global_phase(self, angle: ParameterValueType): """Set the phase of the circuit. Args: angle (float, ParameterExpression): radians """ if isinstance(angle, ParameterExpression) and angle.parameters: self._global_phase = angle else: # Set the phase to the [0, 2π) interval angle = float(angle) if not angle: self._global_phase = 0 else: self._global_phase = angle % (2 * np.pi) @property def parameters(self) -> ParameterView: """Convenience function to get the parameters defined in the parameter table.""" # parameters from gates if self._parameters is None: unsorted = self._unsorted_parameters() self._parameters = sorted(unsorted, key=functools.cmp_to_key(_compare_parameters)) # return as parameter view, which implements the set and list interface return ParameterView(self._parameters) @property def num_parameters(self) -> int: """Convenience function to get the number of parameter objects in the circuit.""" return len(self._unsorted_parameters()) def _unsorted_parameters(self) -> Set[Parameter]: """Efficiently get all parameters in the circuit, without any sorting overhead.""" parameters = set(self._parameter_table) if isinstance(self.global_phase, ParameterExpression): parameters.update(self.global_phase.parameters) return parameters def assign_parameters( self, parameters: Union[Mapping[Parameter, ParameterValueType], Sequence[ParameterValueType]], inplace: bool = False, ) -> Optional["QuantumCircuit"]: """Assign parameters to new parameters or values. The keys of the parameter dictionary must be Parameter instances in the current circuit. The values of the dictionary can either be numeric values or new parameter objects. The values can be assigned to the current circuit object or to a copy of it. Args: parameters (dict or iterable): Either a dictionary or iterable specifying the new parameter values. If a dict, it specifies the mapping from ``current_parameter`` to ``new_parameter``, where ``new_parameter`` can be a new parameter object or a numeric value. If an iterable, the elements are assigned to the existing parameters in the order of ``QuantumCircuit.parameters``. inplace (bool): If False, a copy of the circuit with the bound parameters is returned. If True the circuit instance itself is modified. Raises: CircuitError: If parameters is a dict and contains parameters not present in the circuit. ValueError: If parameters is a list/array and the length mismatches the number of free parameters in the circuit. Returns: Optional(QuantumCircuit): A copy of the circuit with bound parameters, if ``inplace`` is False, otherwise None. Examples: Create a parameterized circuit and assign the parameters in-place. .. jupyter-execute:: from qiskit.circuit import QuantumCircuit, Parameter circuit = QuantumCircuit(2) params = [Parameter('A'), Parameter('B'), Parameter('C')] circuit.ry(params[0], 0) circuit.crx(params[1], 0, 1) print('Original circuit:') print(circuit.draw()) circuit.assign_parameters({params[0]: params[2]}, inplace=True) print('Assigned in-place:') print(circuit.draw()) Bind the values out-of-place and get a copy of the original circuit. .. jupyter-execute:: from qiskit.circuit import QuantumCircuit, ParameterVector circuit = QuantumCircuit(2) params = ParameterVector('P', 2) circuit.ry(params[0], 0) circuit.crx(params[1], 0, 1) bound_circuit = circuit.assign_parameters({params[0]: 1, params[1]: 2}) print('Bound circuit:') print(bound_circuit.draw()) print('The original circuit is unchanged:') print(circuit.draw()) """ # replace in self or in a copy depending on the value of in_place if inplace: bound_circuit = self else: bound_circuit = self.copy() self._increment_instances() bound_circuit._name_update() if isinstance(parameters, dict): # unroll the parameter dictionary (needed if e.g. it contains a ParameterVector) unrolled_param_dict = self._unroll_param_dict(parameters) unsorted_parameters = self._unsorted_parameters() # check that all param_dict items are in the _parameter_table for this circuit params_not_in_circuit = [ param_key for param_key in unrolled_param_dict if param_key not in unsorted_parameters ] if len(params_not_in_circuit) > 0: raise CircuitError( "Cannot bind parameters ({}) not present in the circuit.".format( ", ".join(map(str, params_not_in_circuit)) ) ) # replace the parameters with a new Parameter ("substitute") or numeric value ("bind") for parameter, value in unrolled_param_dict.items(): bound_circuit._assign_parameter(parameter, value) else: if len(parameters) != self.num_parameters: raise ValueError( "Mismatching number of values and parameters. For partial binding " "please pass a dictionary of {parameter: value} pairs." ) # use a copy of the parameters, to ensure we don't change the contents of # self.parameters while iterating over them fixed_parameters_copy = self.parameters.copy() for i, value in enumerate(parameters): bound_circuit._assign_parameter(fixed_parameters_copy[i], value) return None if inplace else bound_circuit def bind_parameters( self, values: Union[Mapping[Parameter, float], Sequence[float]] ) -> "QuantumCircuit": """Assign numeric parameters to values yielding a new circuit. To assign new Parameter objects or bind the values in-place, without yielding a new circuit, use the :meth:`assign_parameters` method. Args: values (dict or iterable): {parameter: value, ...} or [value1, value2, ...] Raises: CircuitError: If values is a dict and contains parameters not present in the circuit. TypeError: If values contains a ParameterExpression. Returns: QuantumCircuit: copy of self with assignment substitution. """ if isinstance(values, dict): if any(isinstance(value, ParameterExpression) for value in values.values()): raise TypeError( "Found ParameterExpression in values; use assign_parameters() instead." ) return self.assign_parameters(values) else: if any(isinstance(value, ParameterExpression) for value in values): raise TypeError( "Found ParameterExpression in values; use assign_parameters() instead." ) return self.assign_parameters(values) def _unroll_param_dict( self, value_dict: Mapping[Parameter, ParameterValueType] ) -> Dict[Parameter, ParameterValueType]: unrolled_value_dict: Dict[Parameter, ParameterValueType] = {} for (param, value) in value_dict.items(): if isinstance(param, ParameterVector): if not len(param) == len(value): raise CircuitError( "ParameterVector {} has length {}, which " "differs from value list {} of " "len {}".format(param, len(param), value, len(value)) ) unrolled_value_dict.update(zip(param, value)) # pass anything else except number through. error checking is done in assign_parameter elif isinstance(param, (ParameterExpression, str)) or param is None: unrolled_value_dict[param] = value return unrolled_value_dict def _assign_parameter(self, parameter: Parameter, value: ParameterValueType) -> None: """Update this circuit where instances of ``parameter`` are replaced by ``value``, which can be either a numeric value or a new parameter expression. Args: parameter (ParameterExpression): Parameter to be bound value (Union(ParameterExpression, float, int)): A numeric or parametric expression to replace instances of ``parameter``. Raises: RuntimeError: if some internal logic error has caused the circuit instruction sequence and the parameter table to become out of sync, and the table now contains a reference to a value that cannot be assigned. """ # parameter might be in global phase only if parameter in self._parameter_table.keys(): for instr, param_index in self._parameter_table[parameter]: assignee = instr.params[param_index] # Normal ParameterExpression. if isinstance(assignee, ParameterExpression): new_param = assignee.assign(parameter, value) # if fully bound, validate if len(new_param.parameters) == 0: instr.params[param_index] = instr.validate_parameter(new_param) else: instr.params[param_index] = new_param self._rebind_definition(instr, parameter, value) # Scoped block of a larger instruction. elif isinstance(assignee, QuantumCircuit): # It's possible that someone may re-use a loop body, so we need to mutate the # parameter vector with a new circuit, rather than mutating the body. instr.params[param_index] = assignee.assign_parameters({parameter: value}) else: raise RuntimeError( # pragma: no cover "The ParameterTable or data of this QuantumCircuit have become out-of-sync." f"\nParameterTable: {self._parameter_table}" f"\nData: {self.data}" ) if isinstance(value, ParameterExpression): entry = self._parameter_table.pop(parameter) for new_parameter in value.parameters: if new_parameter in self._parameter_table: self._parameter_table[new_parameter].extend(entry) else: self._parameter_table[new_parameter] = entry else: del self._parameter_table[parameter] # clear evaluated expressions if ( isinstance(self.global_phase, ParameterExpression) and parameter in self.global_phase.parameters ): self.global_phase = self.global_phase.assign(parameter, value) # clear parameter cache self._parameters = None self._assign_calibration_parameters(parameter, value) def _assign_calibration_parameters( self, parameter: Parameter, value: ParameterValueType ) -> None: """Update parameterized pulse gate calibrations, if there are any which contain ``parameter``. This updates the calibration mapping as well as the gate definition ``Schedule``s, which also may contain ``parameter``. """ new_param: ParameterValueType for cals in self.calibrations.values(): for (qubit, cal_params), schedule in copy.copy(cals).items(): if any( isinstance(p, ParameterExpression) and parameter in p.parameters for p in cal_params ): del cals[(qubit, cal_params)] new_cal_params = [] for p in cal_params: if isinstance(p, ParameterExpression) and parameter in p.parameters: new_param = p.assign(parameter, value) if not new_param.parameters: new_param = float(new_param) new_cal_params.append(new_param) else: new_cal_params.append(p) schedule.assign_parameters({parameter: value}) cals[(qubit, tuple(new_cal_params))] = schedule def _rebind_definition( self, instruction: Instruction, parameter: Parameter, value: ParameterValueType ) -> None: if instruction._definition: for op, _, _ in instruction._definition: for idx, param in enumerate(op.params): if isinstance(param, ParameterExpression) and parameter in param.parameters: if isinstance(value, ParameterExpression): op.params[idx] = param.subs({parameter: value}) else: op.params[idx] = param.bind({parameter: value}) self._rebind_definition(op, parameter, value) def barrier(self, *qargs: QubitSpecifier) -> InstructionSet: """Apply :class:`~qiskit.circuit.Barrier`. If qargs is empty, applies to all qubits in the circuit. Returns: qiskit.circuit.InstructionSet: handle to the added instructions. """ from .barrier import Barrier qubits: List[QubitSpecifier] = [] if not qargs: # None qubits.extend(self.qubits) for qarg in qargs: if isinstance(qarg, QuantumRegister): qubits.extend([qarg[j] for j in range(qarg.size)]) elif isinstance(qarg, list): qubits.extend(qarg) elif isinstance(qarg, range): qubits.extend(list(qarg)) elif isinstance(qarg, slice): qubits.extend(self.qubits[qarg]) else: qubits.append(qarg) return self.append(Barrier(len(qubits)), qubits, []) def delay( self, duration: ParameterValueType, qarg: Optional[QubitSpecifier] = None, unit: str = "dt", ) -> InstructionSet: """Apply :class:`~qiskit.circuit.Delay`. If qarg is None, applies to all qubits. When applying to multiple qubits, delays with the same duration will be created. Args: duration (int or float or ParameterExpression): duration of the delay. qarg (Object): qubit argument to apply this delay. unit (str): unit of the duration. Supported units: 's', 'ms', 'us', 'ns', 'ps', 'dt'. Default is ``dt``, i.e. integer time unit depending on the target backend. Returns: qiskit.circuit.InstructionSet: handle to the added instructions. Raises: CircuitError: if arguments have bad format. """ qubits: List[QubitSpecifier] = [] if qarg is None: # -> apply delays to all qubits for q in self.qubits: qubits.append(q) else: if isinstance(qarg, QuantumRegister): qubits.extend([qarg[j] for j in range(qarg.size)]) elif isinstance(qarg, list): qubits.extend(qarg) elif isinstance(qarg, (range, tuple)): qubits.extend(list(qarg)) elif isinstance(qarg, slice): qubits.extend(self.qubits[qarg]) else: qubits.append(qarg) instructions = InstructionSet(resource_requester=self._resolve_classical_resource) for q in qubits: inst = (Delay(duration, unit), [q], []) self.append(*inst) instructions.add(*inst) return instructions def h(self, qubit: QubitSpecifier) -> InstructionSet: """Apply :class:`~qiskit.circuit.library.HGate`. For the full matrix form of this gate, see the underlying gate documentation. Args: qubit: The qubit(s) to apply the gate to. Returns: A handle to the instructions created. """ from .library.standard_gates.h import HGate return self.append(HGate(), [qubit], []) def ch( self, control_qubit: QubitSpecifier, target_qubit: QubitSpecifier, label: Optional[str] = None, ctrl_state: Optional[Union[str, int]] = None, ) -> InstructionSet: """Apply :class:`~qiskit.circuit.library.CHGate`. For the full matrix form of this gate, see the underlying gate documentation. Args: control_qubit: The qubit(s) used as the control. target_qubit: The qubit(s) targeted by the gate. label: The string label of the gate in the circuit. ctrl_state: The control state in decimal, or as a bitstring (e.g. '1'). Defaults to controlling on the '1' state. Returns: A handle to the instructions created. """ from .library.standard_gates.h import CHGate return self.append( CHGate(label=label, ctrl_state=ctrl_state), [control_qubit, target_qubit], [] ) def i(self, qubit: QubitSpecifier) -> InstructionSet: """Apply :class:`~qiskit.circuit.library.IGate`. For the full matrix form of this gate, see the underlying gate documentation. Args: qubit: The qubit(s) to apply the gate to. Returns: A handle to the instructions created. """ from .library.standard_gates.i import IGate return self.append(IGate(), [qubit], []) def id(self, qubit: QubitSpecifier) -> InstructionSet: # pylint: disable=invalid-name """Apply :class:`~qiskit.circuit.library.IGate`. For the full matrix form of this gate, see the underlying gate documentation. Args: qubit: The qubit(s) to apply the gate to. Returns: A handle to the instructions created. See also: QuantumCircuit.i: the same function. """ return self.i(qubit) def ms(self, theta: ParameterValueType, qubits: Sequence[QubitSpecifier]) -> InstructionSet: """Apply :class:`~qiskit.circuit.library.generalized_gates.gms.MSGate`. For the full matrix form of this gate, see the underlying gate documentation. Args: theta: The angle of the rotation. qubits: The qubits to apply the gate to. Returns: A handle to the instructions created. """ # pylint: disable=cyclic-import from .library.generalized_gates.gms import MSGate return self.append(MSGate(len(qubits), theta), qubits) def p(self, theta: ParameterValueType, qubit: QubitSpecifier) -> InstructionSet: """Apply :class:`~qiskit.circuit.library.PhaseGate`. For the full matrix form of this gate, see the underlying gate documentation. Args: theta: THe angle of the rotation. qubit: The qubit(s) to apply the gate to. Returns: A handle to the instructions created. """ from .library.standard_gates.p import PhaseGate return self.append(PhaseGate(theta), [qubit], []) def cp( self, theta: ParameterValueType, control_qubit: QubitSpecifier, target_qubit: QubitSpecifier, label: Optional[str] = None, ctrl_state: Optional[Union[str, int]] = None, ) -> InstructionSet: """Apply :class:`~qiskit.circuit.library.CPhaseGate`. For the full matrix form of this gate, see the underlying gate documentation. Args: theta: The angle of the rotation. control_qubit: The qubit(s) used as the control. target_qubit: The qubit(s) targeted by the gate. label: The string label of the gate in the circuit. ctrl_state: The control state in decimal, or as a bitstring (e.g. '1'). Defaults to controlling on the '1' state. Returns: A handle to the instructions created. """ from .library.standard_gates.p import CPhaseGate return self.append( CPhaseGate(theta, label=label, ctrl_state=ctrl_state), [control_qubit, target_qubit], [] ) def mcp( self, lam: ParameterValueType, control_qubits: Sequence[QubitSpecifier], target_qubit: QubitSpecifier, ) -> InstructionSet: """Apply :class:`~qiskit.circuit.library.MCPhaseGate`. For the full matrix form of this gate, see the underlying gate documentation. Args: lam: The angle of the rotation. control_qubits: The qubits used as the controls. target_qubit: The qubit(s) targeted by the gate. Returns: A handle to the instructions created. """ from .library.standard_gates.p import MCPhaseGate num_ctrl_qubits = len(control_qubits) return self.append( MCPhaseGate(lam, num_ctrl_qubits), control_qubits[:] + [target_qubit], [] ) def r( self, theta: ParameterValueType, phi: ParameterValueType, qubit: QubitSpecifier ) -> InstructionSet: """Apply :class:`~qiskit.circuit.library.RGate`. For the full matrix form of this gate, see the underlying gate documentation. Args: theta: The angle of the rotation. phi: The angle of the axis of rotation in the x-y plane. qubit: The qubit(s) to apply the gate to. Returns: A handle to the instructions created. """ from .library.standard_gates.r import RGate return self.append(RGate(theta, phi), [qubit], []) def rv( self, vx: ParameterValueType, vy: ParameterValueType, vz: ParameterValueType, qubit: QubitSpecifier, ) -> InstructionSet: """Apply :class:`~qiskit.circuit.library.RVGate`. For the full matrix form of this gate, see the underlying gate documentation. Rotation around an arbitrary rotation axis :math:`v`, where :math:`|v|` is the angle of rotation in radians. Args: vx: x-compenent of the rotation axis. vy: y-compenent of the rotation axis. vz: z-compenent of the rotation axis. qubit: The qubit(s) to apply the gate to. Returns: A handle to the instructions created. """ from .library.generalized_gates.rv import RVGate return self.append(RVGate(vx, vy, vz), [qubit], []) def rccx( self, control_qubit1: QubitSpecifier, control_qubit2: QubitSpecifier, target_qubit: QubitSpecifier, ) -> InstructionSet: """Apply :class:`~qiskit.circuit.library.RCCXGate`. For the full matrix form of this gate, see the underlying gate documentation. Args: control_qubit1: The qubit(s) used as the first control. control_qubit2: The qubit(s) used as the second control. target_qubit: The qubit(s) targeted by the gate. Returns: A handle to the instructions created. """ from .library.standard_gates.x import RCCXGate return self.append(RCCXGate(), [control_qubit1, control_qubit2, target_qubit], []) def rcccx( self, control_qubit1: QubitSpecifier, control_qubit2: QubitSpecifier, control_qubit3: QubitSpecifier, target_qubit: QubitSpecifier, ) -> InstructionSet: """Apply :class:`~qiskit.circuit.library.RC3XGate`. For the full matrix form of this gate, see the underlying gate documentation. Args: control_qubit1: The qubit(s) used as the first control. control_qubit2: The qubit(s) used as the second control. control_qubit3: The qubit(s) used as the third control. target_qubit: The qubit(s) targeted by the gate. Returns: A handle to the instructions created. """ from .library.standard_gates.x import RC3XGate return self.append( RC3XGate(), [control_qubit1, control_qubit2, control_qubit3, target_qubit], [] ) def rx( self, theta: ParameterValueType, qubit: QubitSpecifier, label: Optional[str] = None ) -> InstructionSet: """Apply :class:`~qiskit.circuit.library.RXGate`. For the full matrix form of this gate, see the underlying gate documentation. Args: theta: The rotation angle of the gate. qubit: The qubit(s) to apply the gate to. label: The string label of the gate in the circuit. Returns: A handle to the instructions created. """ from .library.standard_gates.rx import RXGate return self.append(RXGate(theta, label=label), [qubit], []) def crx( self, theta: ParameterValueType, control_qubit: QubitSpecifier, target_qubit: QubitSpecifier, label: Optional[str] = None, ctrl_state: Optional[Union[str, int]] = None, ) -> InstructionSet: """Apply :class:`~qiskit.circuit.library.CRXGate`. For the full matrix form of this gate, see the underlying gate documentation. Args: theta: The angle of the rotation. control_qubit: The qubit(s) used as the control. target_qubit: The qubit(s) targeted by the gate. label: The string label of the gate in the circuit. ctrl_state: The control state in decimal, or as a bitstring (e.g. '1'). Defaults to controlling on the '1' state. Returns: A handle to the instructions created. """ from .library.standard_gates.rx import CRXGate return self.append( CRXGate(theta, label=label, ctrl_state=ctrl_state), [control_qubit, target_qubit], [] ) def rxx( self, theta: ParameterValueType, qubit1: QubitSpecifier, qubit2: QubitSpecifier ) -> InstructionSet: """Apply :class:`~qiskit.circuit.library.RXXGate`. For the full matrix form of this gate, see the underlying gate documentation. Args: theta: The angle of the rotation. qubit1: The qubit(s) to apply the gate to. qubit2: The qubit(s) to apply the gate to. Returns: A handle to the instructions created. """ from .library.standard_gates.rxx import RXXGate return self.append(RXXGate(theta), [qubit1, qubit2], []) def ry( self, theta: ParameterValueType, qubit: QubitSpecifier, label: Optional[str] = None ) -> InstructionSet: """Apply :class:`~qiskit.circuit.library.RYGate`. For the full matrix form of this gate, see the underlying gate documentation. Args: theta: The rotation angle of the gate. qubit: The qubit(s) to apply the gate to. label: The string label of the gate in the circuit. Returns: A handle to the instructions created. """ from .library.standard_gates.ry import RYGate return self.append(RYGate(theta, label=label), [qubit], []) def cry( self, theta: ParameterValueType, control_qubit: QubitSpecifier, target_qubit: QubitSpecifier, label: Optional[str] = None, ctrl_state: Optional[Union[str, int]] = None, ) -> InstructionSet: """Apply :class:`~qiskit.circuit.library.CRYGate`. For the full matrix form of this gate, see the underlying gate documentation. Args: theta: The angle of the rotation. control_qubit: The qubit(s) used as the control. target_qubit: The qubit(s) targeted by the gate. label: The string label of the gate in the circuit. ctrl_state: The control state in decimal, or as a bitstring (e.g. '1'). Defaults to controlling on the '1' state. Returns: A handle to the instructions created. """ from .library.standard_gates.ry import CRYGate return self.append( CRYGate(theta, label=label, ctrl_state=ctrl_state), [control_qubit, target_qubit], [] ) def ryy( self, theta: ParameterValueType, qubit1: QubitSpecifier, qubit2: QubitSpecifier ) -> InstructionSet: """Apply :class:`~qiskit.circuit.library.RYYGate`. For the full matrix form of this gate, see the underlying gate documentation. Args: theta: The rotation angle of the gate. qubit1: The qubit(s) to apply the gate to. qubit2: The qubit(s) to apply the gate to. Returns: A handle to the instructions created. """ from .library.standard_gates.ryy import RYYGate return self.append(RYYGate(theta), [qubit1, qubit2], []) def rz(self, phi: ParameterValueType, qubit: QubitSpecifier) -> InstructionSet: """Apply :class:`~qiskit.circuit.library.RZGate`. For the full matrix form of this gate, see the underlying gate documentation. Args: phi: The rotation angle of the gate. qubit: The qubit(s) to apply the gate to. Returns: A handle to the instructions created. """ from .library.standard_gates.rz import RZGate return self.append(RZGate(phi), [qubit], []) def crz( self, theta: ParameterValueType, control_qubit: QubitSpecifier, target_qubit: QubitSpecifier, label: Optional[str] = None, ctrl_state: Optional[Union[str, int]] = None, ) -> InstructionSet: """Apply :class:`~qiskit.circuit.library.CRZGate`. For the full matrix form of this gate, see the underlying gate documentation. Args: theta: The angle of the rotation. control_qubit: The qubit(s) used as the control. target_qubit: The qubit(s) targeted by the gate. label: The string label of the gate in the circuit. ctrl_state: The control state in decimal, or as a bitstring (e.g. '1'). Defaults to controlling on the '1' state. Returns: A handle to the instructions created. """ from .library.standard_gates.rz import CRZGate return self.append( CRZGate(theta, label=label, ctrl_state=ctrl_state), [control_qubit, target_qubit], [] ) def rzx( self, theta: ParameterValueType, qubit1: QubitSpecifier, qubit2: QubitSpecifier ) -> InstructionSet: """Apply :class:`~qiskit.circuit.library.RZXGate`. For the full matrix form of this gate, see the underlying gate documentation. Args: theta: The rotation angle of the gate. qubit1: The qubit(s) to apply the gate to. qubit2: The qubit(s) to apply the gate to. Returns: A handle to the instructions created. """ from .library.standard_gates.rzx import RZXGate return self.append(RZXGate(theta), [qubit1, qubit2], []) def rzz( self, theta: ParameterValueType, qubit1: QubitSpecifier, qubit2: QubitSpecifier ) -> InstructionSet: """Apply :class:`~qiskit.circuit.library.RZZGate`. For the full matrix form of this gate, see the underlying gate documentation. Args: theta: The rotation angle of the gate. qubit1: The qubit(s) to apply the gate to. qubit2: The qubit(s) to apply the gate to. Returns: A handle to the instructions created. """ from .library.standard_gates.rzz import RZZGate return self.append(RZZGate(theta), [qubit1, qubit2], []) def ecr(self, qubit1: QubitSpecifier, qubit2: QubitSpecifier) -> InstructionSet: """Apply :class:`~qiskit.circuit.library.ECRGate`. For the full matrix form of this gate, see the underlying gate documentation. Args: qubit1, qubit2: The qubits to apply the gate to. Returns: A handle to the instructions created. """ from .library.standard_gates.ecr import ECRGate return self.append(ECRGate(), [qubit1, qubit2], []) def s(self, qubit: QubitSpecifier) -> InstructionSet: """Apply :class:`~qiskit.circuit.library.SGate`. For the full matrix form of this gate, see the underlying gate documentation. Args: qubit: The qubit(s) to apply the gate to. Returns: A handle to the instructions created. """ from .library.standard_gates.s import SGate return self.append(SGate(), [qubit], []) def sdg(self, qubit: QubitSpecifier) -> InstructionSet: """Apply :class:`~qiskit.circuit.library.SdgGate`. For the full matrix form of this gate, see the underlying gate documentation. Args: qubit: The qubit(s) to apply the gate to. Returns: A handle to the instructions created. """ from .library.standard_gates.s import SdgGate return self.append(SdgGate(), [qubit], []) def swap(self, qubit1: QubitSpecifier, qubit2: QubitSpecifier) -> InstructionSet: """Apply :class:`~qiskit.circuit.library.SwapGate`. For the full matrix form of this gate, see the underlying gate documentation. Args: qubit1, qubit2: The qubits to apply the gate to. Returns: A handle to the instructions created. """ from .library.standard_gates.swap import SwapGate return self.append(SwapGate(), [qubit1, qubit2], []) def iswap(self, qubit1: QubitSpecifier, qubit2: QubitSpecifier) -> InstructionSet: """Apply :class:`~qiskit.circuit.library.iSwapGate`. For the full matrix form of this gate, see the underlying gate documentation. Args: qubit1, qubit2: The qubits to apply the gate to. Returns: A handle to the instructions created. """ from .library.standard_gates.iswap import iSwapGate return self.append(iSwapGate(), [qubit1, qubit2], []) def cswap( self, control_qubit: QubitSpecifier, target_qubit1: QubitSpecifier, target_qubit2: QubitSpecifier, label: Optional[str] = None, ctrl_state: Optional[Union[str, int]] = None, ) -> InstructionSet: """Apply :class:`~qiskit.circuit.library.CSwapGate`. For the full matrix form of this gate, see the underlying gate documentation. Args: control_qubit: The qubit(s) used as the control. target_qubit1: The qubit(s) targeted by the gate. target_qubit2: The qubit(s) targeted by the gate. label: The string label of the gate in the circuit. ctrl_state: The control state in decimal, or as a bitstring (e.g. '1'). Defaults to controlling on the '1' state. Returns: A handle to the instructions created. """ from .library.standard_gates.swap import CSwapGate return self.append( CSwapGate(label=label, ctrl_state=ctrl_state), [control_qubit, target_qubit1, target_qubit2], [], ) def fredkin( self, control_qubit: QubitSpecifier, target_qubit1: QubitSpecifier, target_qubit2: QubitSpecifier, ) -> InstructionSet: """Apply :class:`~qiskit.circuit.library.CSwapGate`. For the full matrix form of this gate, see the underlying gate documentation. Args: control_qubit: The qubit(s) used as the control. target_qubit1: The qubit(s) targeted by the gate. target_qubit2: The qubit(s) targeted by the gate. Returns: A handle to the instructions created. See Also: QuantumCircuit.cswap: the same function with a different name. """ return self.cswap(control_qubit, target_qubit1, target_qubit2) def sx(self, qubit: QubitSpecifier) -> InstructionSet: """Apply :class:`~qiskit.circuit.library.SXGate`. For the full matrix form of this gate, see the underlying gate documentation. Args: qubit: The qubit(s) to apply the gate to. Returns: A handle to the instructions created. """ from .library.standard_gates.sx import SXGate return self.append(SXGate(), [qubit], []) def sxdg(self, qubit: QubitSpecifier) -> InstructionSet: """Apply :class:`~qiskit.circuit.library.SXdgGate`. For the full matrix form of this gate, see the underlying gate documentation. Args: qubit: The qubit(s) to apply the gate to. Returns: A handle to the instructions created. """ from .library.standard_gates.sx import SXdgGate return self.append(SXdgGate(), [qubit], []) def csx( self, control_qubit: QubitSpecifier, target_qubit: QubitSpecifier, label: Optional[str] = None, ctrl_state: Optional[Union[str, int]] = None, ) -> InstructionSet: """Apply :class:`~qiskit.circuit.library.CSXGate`. For the full matrix form of this gate, see the underlying gate documentation. Args: control_qubit: The qubit(s) used as the control. target_qubit: The qubit(s) targeted by the gate. label: The string label of the gate in the circuit. ctrl_state: The control state in decimal, or as a bitstring (e.g. '1'). Defaults to controlling on the '1' state. Returns: A handle to the instructions created. """ from .library.standard_gates.sx import CSXGate return self.append( CSXGate(label=label, ctrl_state=ctrl_state), [control_qubit, target_qubit], [], ) def t(self, qubit: QubitSpecifier) -> InstructionSet: """Apply :class:`~qiskit.circuit.library.TGate`. For the full matrix form of this gate, see the underlying gate documentation. Args: qubit: The qubit(s) to apply the gate to. Returns: A handle to the instructions created. """ from .library.standard_gates.t import TGate return self.append(TGate(), [qubit], []) def tdg(self, qubit: QubitSpecifier) -> InstructionSet: """Apply :class:`~qiskit.circuit.library.TdgGate`. For the full matrix form of this gate, see the underlying gate documentation. Args: qubit: The qubit(s) to apply the gate to. Returns: A handle to the instructions created. """ from .library.standard_gates.t import TdgGate return self.append(TdgGate(), [qubit], []) def u( self, theta: ParameterValueType, phi: ParameterValueType, lam: ParameterValueType, qubit: QubitSpecifier, ) -> InstructionSet: r"""Apply :class:`~qiskit.circuit.library.UGate`. For the full matrix form of this gate, see the underlying gate documentation. Args: theta: The :math:`\theta` rotation angle of the gate. phi: The :math:`\phi` rotation angle of the gate. lam: The :math:`\lambda` rotation angle of the gate. qubit: The qubit(s) to apply the gate to. Returns: A handle to the instructions created. """ from .library.standard_gates.u import UGate return self.append(UGate(theta, phi, lam), [qubit], []) def cu( self, theta: ParameterValueType, phi: ParameterValueType, lam: ParameterValueType, gamma: ParameterValueType, control_qubit: QubitSpecifier, target_qubit: QubitSpecifier, label: Optional[str] = None, ctrl_state: Optional[Union[str, int]] = None, ) -> InstructionSet: r"""Apply :class:`~qiskit.circuit.library.CUGate`. For the full matrix form of this gate, see the underlying gate documentation. Args: theta: The :math:`\theta` rotation angle of the gate. phi: The :math:`\phi` rotation angle of the gate. lam: The :math:`\lambda` rotation angle of the gate. gamma: The global phase applied of the U gate, if applied. control_qubit: The qubit(s) used as the control. target_qubit: The qubit(s) targeted by the gate. label: The string label of the gate in the circuit. ctrl_state: The control state in decimal, or as a bitstring (e.g. '1'). Defaults to controlling on the '1' state. Returns: A handle to the instructions created. """ from .library.standard_gates.u import CUGate return self.append( CUGate(theta, phi, lam, gamma, label=label, ctrl_state=ctrl_state), [control_qubit, target_qubit], [], ) @deprecate_function( "The QuantumCircuit.u1 method is deprecated as of " "0.16.0. It will be removed no earlier than 3 months " "after the release date. You should use the " "QuantumCircuit.p method instead, which acts " "identically." ) def u1(self, theta: ParameterValueType, qubit: QubitSpecifier) -> InstructionSet: r"""Apply :class:`~qiskit.circuit.library.U1Gate`. For the full matrix form of this gate, see the underlying gate documentation. Args: theta: The :math:`\theta` rotation angle of the gate. qubit: The qubit(s) to apply the gate to. Returns: A handle to the instructions created. """ from .library.standard_gates.u1 import U1Gate return self.append(U1Gate(theta), [qubit], []) @deprecate_function( "The QuantumCircuit.cu1 method is deprecated as of " "0.16.0. It will be removed no earlier than 3 months " "after the release date. You should use the " "QuantumCircuit.cp method instead, which acts " "identically." ) def cu1( self, theta: ParameterValueType, control_qubit: QubitSpecifier, target_qubit: QubitSpecifier, label: Optional[str] = None, ctrl_state: Optional[Union[str, int]] = None, ) -> InstructionSet: r"""Apply :class:`~qiskit.circuit.library.CU1Gate`. For the full matrix form of this gate, see the underlying gate documentation. Args: theta: The :math:`\theta` rotation angle of the gate. control_qubit: The qubit(s) used as the control. target_qubit: The qubit(s) targeted by the gate. label: The string label of the gate in the circuit. ctrl_state: The control state in decimal, or as a bitstring (e.g. '1'). Defaults to controlling on the '1' state. Returns: A handle to the instructions created. """ from .library.standard_gates.u1 import CU1Gate return self.append( CU1Gate(theta, label=label, ctrl_state=ctrl_state), [control_qubit, target_qubit], [] ) @deprecate_function( "The QuantumCircuit.mcu1 method is deprecated as of " "0.16.0. It will be removed no earlier than 3 months " "after the release date. You should use the " "QuantumCircuit.mcp method instead, which acts " "identically." ) def mcu1( self, lam: ParameterValueType, control_qubits: Sequence[QubitSpecifier], target_qubit: QubitSpecifier, ) -> InstructionSet: r"""Apply :class:`~qiskit.circuit.library.MCU1Gate`. For the full matrix form of this gate, see the underlying gate documentation. Args: lam: The :math:`\lambda` rotation angle of the gate. control_qubits: The qubits used as the controls. target_qubit: The qubit(s) targeted by the gate. Returns: A handle to the instructions created. """ from .library.standard_gates.u1 import MCU1Gate num_ctrl_qubits = len(control_qubits) return self.append(MCU1Gate(lam, num_ctrl_qubits), control_qubits[:] + [target_qubit], []) @deprecate_function( "The QuantumCircuit.u2 method is deprecated as of " "0.16.0. It will be removed no earlier than 3 months " "after the release date. You can use the general 1-" "qubit gate QuantumCircuit.u instead: u2(φ,λ) = " "u(π/2, φ, λ). Alternatively, you can decompose it in" "terms of QuantumCircuit.p and QuantumCircuit.sx: " "u2(φ,λ) = p(π/2+φ) sx p(λ-π/2) (1 pulse on hardware)." ) def u2( self, phi: ParameterValueType, lam: ParameterValueType, qubit: QubitSpecifier ) -> InstructionSet: r"""Apply :class:`~qiskit.circuit.library.U2Gate`. For the full matrix form of this gate, see the underlying gate documentation. Args: phi: The :math:`\phi` rotation angle of the gate. lam: The :math:`\lambda` rotation angle of the gate. qubit: The qubit(s) to apply the gate to. Returns: A handle to the instructions created. """ from .library.standard_gates.u2 import U2Gate return self.append(U2Gate(phi, lam), [qubit], []) @deprecate_function( "The QuantumCircuit.u3 method is deprecated as of 0.16.0. It will be " "removed no earlier than 3 months after the release date. You should use " "QuantumCircuit.u instead, which acts identically. Alternatively, you can " "decompose u3 in terms of QuantumCircuit.p and QuantumCircuit.sx: " "u3(ϴ,φ,λ) = p(φ+π) sx p(ϴ+π) sx p(λ) (2 pulses on hardware)." ) def u3( self, theta: ParameterValueType, phi: ParameterValueType, lam: ParameterValueType, qubit: QubitSpecifier, ) -> InstructionSet: r"""Apply :class:`~qiskit.circuit.library.U3Gate`. For the full matrix form of this gate, see the underlying gate documentation. Args: theta: The :math:`\theta` rotation angle of the gate. phi: The :math:`\phi` rotation angle of the gate. lam: The :math:`\lambda` rotation angle of the gate. qubit: The qubit(s) to apply the gate to. Returns: A handle to the instructions created. """ from .library.standard_gates.u3 import U3Gate return self.append(U3Gate(theta, phi, lam), [qubit], []) @deprecate_function( "The QuantumCircuit.cu3 method is deprecated as of 0.16.0. It will be " "removed no earlier than 3 months after the release date. You should " "use the QuantumCircuit.cu method instead, where " "cu3(ϴ,φ,λ) = cu(ϴ,φ,λ,0)." ) def cu3( self, theta: ParameterValueType, phi: ParameterValueType, lam: ParameterValueType, control_qubit: QubitSpecifier, target_qubit: QubitSpecifier, label: Optional[str] = None, ctrl_state: Optional[Union[str, int]] = None, ) -> InstructionSet: r"""Apply :class:`~qiskit.circuit.library.CU3Gate`. For the full matrix form of this gate, see the underlying gate documentation. Args: theta: The :math:`\theta` rotation angle of the gate. phi: The :math:`\phi` rotation angle of the gate. lam: The :math:`\lambda` rotation angle of the gate. control_qubit: The qubit(s) used as the control. target_qubit: The qubit(s) targeted by the gate. label: The string label of the gate in the circuit. ctrl_state: The control state in decimal, or as a bitstring (e.g. '1'). Defaults to controlling on the '1' state. Returns: A handle to the instructions created. """ from .library.standard_gates.u3 import CU3Gate return self.append( CU3Gate(theta, phi, lam, label=label, ctrl_state=ctrl_state), [control_qubit, target_qubit], [], ) def x(self, qubit: QubitSpecifier, label: Optional[str] = None) -> InstructionSet: r"""Apply :class:`~qiskit.circuit.library.XGate`. For the full matrix form of this gate, see the underlying gate documentation. Args: qubit: The qubit(s) to apply the gate to. label: The string label of the gate in the circuit. Returns: A handle to the instructions created. """ from .library.standard_gates.x import XGate return self.append(XGate(label=label), [qubit], []) def cx( self, control_qubit: QubitSpecifier, target_qubit: QubitSpecifier, label: Optional[str] = None, ctrl_state: Optional[Union[str, int]] = None, ) -> InstructionSet: r"""Apply :class:`~qiskit.circuit.library.CXGate`. For the full matrix form of this gate, see the underlying gate documentation. Args: control_qubit: The qubit(s) used as the control. target_qubit: The qubit(s) targeted by the gate. label: The string label of the gate in the circuit. ctrl_state: The control state in decimal, or as a bitstring (e.g. '1'). Defaults to controlling on the '1' state. Returns: A handle to the instructions created. """ from .library.standard_gates.x import CXGate return self.append( CXGate(label=label, ctrl_state=ctrl_state), [control_qubit, target_qubit], [] ) def cnot( self, control_qubit: QubitSpecifier, target_qubit: QubitSpecifier, label: Optional[str] = None, ctrl_state: Optional[Union[str, int]] = None, ) -> InstructionSet: r"""Apply :class:`~qiskit.circuit.library.CXGate`. For the full matrix form of this gate, see the underlying gate documentation. Args: control_qubit: The qubit(s) used as the control. target_qubit: The qubit(s) targeted by the gate. label: The string label of the gate in the circuit. ctrl_state: The control state in decimal, or as a bitstring (e.g. '1'). Defaults to controlling on the '1' state. Returns: A handle to the instructions created. See Also: QuantumCircuit.cx: the same function with a different name. """ return self.cx(control_qubit, target_qubit, label, ctrl_state) def dcx(self, qubit1: QubitSpecifier, qubit2: QubitSpecifier) -> InstructionSet: r"""Apply :class:`~qiskit.circuit.library.DCXGate`. For the full matrix form of this gate, see the underlying gate documentation. Args: qubit1: The qubit(s) to apply the gate to. qubit2: The qubit(s) to apply the gate to. Returns: A handle to the instructions created. """ from .library.standard_gates.dcx import DCXGate return self.append(DCXGate(), [qubit1, qubit2], []) def ccx( self, control_qubit1: QubitSpecifier, control_qubit2: QubitSpecifier, target_qubit: QubitSpecifier, ctrl_state: Optional[Union[str, int]] = None, ) -> InstructionSet: r"""Apply :class:`~qiskit.circuit.library.CCXGate`. For the full matrix form of this gate, see the underlying gate documentation. Args: control_qubit1: The qubit(s) used as the first control. control_qubit2: The qubit(s) used as the second control. target_qubit: The qubit(s) targeted by the gate. ctrl_state: The control state in decimal, or as a bitstring (e.g. '1'). Defaults to controlling on the '1' state. Returns: A handle to the instructions created. """ from .library.standard_gates.x import CCXGate return self.append( CCXGate(ctrl_state=ctrl_state), [control_qubit1, control_qubit2, target_qubit], [], ) def toffoli( self, control_qubit1: QubitSpecifier, control_qubit2: QubitSpecifier, target_qubit: QubitSpecifier, ) -> InstructionSet: r"""Apply :class:`~qiskit.circuit.library.CCXGate`. For the full matrix form of this gate, see the underlying gate documentation. Args: control_qubit1: The qubit(s) used as the first control. control_qubit2: The qubit(s) used as the second control. target_qubit: The qubit(s) targeted by the gate. Returns: A handle to the instructions created. See Also: QuantumCircuit.ccx: the same gate with a different name. """ return self.ccx(control_qubit1, control_qubit2, target_qubit) def mcx( self, control_qubits: Sequence[QubitSpecifier], target_qubit: QubitSpecifier, ancilla_qubits: Optional[Union[QubitSpecifier, Sequence[QubitSpecifier]]] = None, mode: str = "noancilla", ) -> InstructionSet: """Apply :class:`~qiskit.circuit.library.MCXGate`. The multi-cX gate can be implemented using different techniques, which use different numbers of ancilla qubits and have varying circuit depth. These modes are: - 'noancilla': Requires 0 ancilla qubits. - 'recursion': Requires 1 ancilla qubit if more than 4 controls are used, otherwise 0. - 'v-chain': Requires 2 less ancillas than the number of control qubits. - 'v-chain-dirty': Same as for the clean ancillas (but the circuit will be longer). For the full matrix form of this gate, see the underlying gate documentation. Args: control_qubits: The qubits used as the controls. target_qubit: The qubit(s) targeted by the gate. ancilla_qubits: The qubits used as the ancillae, if the mode requires them. mode: The choice of mode, explained further above. Returns: A handle to the instructions created. Raises: ValueError: if the given mode is not known, or if too few ancilla qubits are passed. AttributeError: if no ancilla qubits are passed, but some are needed. """ from .library.standard_gates.x import MCXGrayCode, MCXRecursive, MCXVChain num_ctrl_qubits = len(control_qubits) available_implementations = { "noancilla": MCXGrayCode(num_ctrl_qubits), "recursion": MCXRecursive(num_ctrl_qubits), "v-chain": MCXVChain(num_ctrl_qubits, False), "v-chain-dirty": MCXVChain(num_ctrl_qubits, dirty_ancillas=True), # outdated, previous names "advanced": MCXRecursive(num_ctrl_qubits), "basic": MCXVChain(num_ctrl_qubits, dirty_ancillas=False), "basic-dirty-ancilla": MCXVChain(num_ctrl_qubits, dirty_ancillas=True), } # check ancilla input if ancilla_qubits: _ = self.qbit_argument_conversion(ancilla_qubits) try: gate = available_implementations[mode] except KeyError as ex: all_modes = list(available_implementations.keys()) raise ValueError( f"Unsupported mode ({mode}) selected, choose one of {all_modes}" ) from ex if hasattr(gate, "num_ancilla_qubits") and gate.num_ancilla_qubits > 0: required = gate.num_ancilla_qubits if ancilla_qubits is None: raise AttributeError(f"No ancillas provided, but {required} are needed!") # convert ancilla qubits to a list if they were passed as int or qubit if not hasattr(ancilla_qubits, "__len__"): ancilla_qubits = [ancilla_qubits] if len(ancilla_qubits) < required: actually = len(ancilla_qubits) raise ValueError(f"At least {required} ancillas required, but {actually} given.") # size down if too many ancillas were provided ancilla_qubits = ancilla_qubits[:required] else: ancilla_qubits = [] return self.append(gate, control_qubits[:] + [target_qubit] + ancilla_qubits[:], []) def mct( self, control_qubits: Sequence[QubitSpecifier], target_qubit: QubitSpecifier, ancilla_qubits: Optional[Union[QubitSpecifier, Sequence[QubitSpecifier]]] = None, mode: str = "noancilla", ) -> InstructionSet: """Apply :class:`~qiskit.circuit.library.MCXGate`. The multi-cX gate can be implemented using different techniques, which use different numbers of ancilla qubits and have varying circuit depth. These modes are: - 'noancilla': Requires 0 ancilla qubits. - 'recursion': Requires 1 ancilla qubit if more than 4 controls are used, otherwise 0. - 'v-chain': Requires 2 less ancillas than the number of control qubits. - 'v-chain-dirty': Same as for the clean ancillas (but the circuit will be longer). For the full matrix form of this gate, see the underlying gate documentation. Args: control_qubits: The qubits used as the controls. target_qubit: The qubit(s) targeted by the gate. ancilla_qubits: The qubits used as the ancillae, if the mode requires them. mode: The choice of mode, explained further above. Returns: A handle to the instructions created. Raises: ValueError: if the given mode is not known, or if too few ancilla qubits are passed. AttributeError: if no ancilla qubits are passed, but some are needed. See Also: QuantumCircuit.mcx: the same gate with a different name. """ return self.mcx(control_qubits, target_qubit, ancilla_qubits, mode) def y(self, qubit: QubitSpecifier) -> InstructionSet: r"""Apply :class:`~qiskit.circuit.library.YGate`. For the full matrix form of this gate, see the underlying gate documentation. Args: qubit: The qubit(s) to apply the gate to. Returns: A handle to the instructions created. """ from .library.standard_gates.y import YGate return self.append(YGate(), [qubit], []) def cy( self, control_qubit: QubitSpecifier, target_qubit: QubitSpecifier, label: Optional[str] = None, ctrl_state: Optional[Union[str, int]] = None, ) -> InstructionSet: r"""Apply :class:`~qiskit.circuit.library.CYGate`. For the full matrix form of this gate, see the underlying gate documentation. Args: control_qubit: The qubit(s) used as the controls. target_qubit: The qubit(s) targeted by the gate. label: The string label of the gate in the circuit. ctrl_state: The control state in decimal, or as a bitstring (e.g. '1'). Defaults to controlling on the '1' state. Returns: A handle to the instructions created. """ from .library.standard_gates.y import CYGate return self.append( CYGate(label=label, ctrl_state=ctrl_state), [control_qubit, target_qubit], [] ) def z(self, qubit: QubitSpecifier) -> InstructionSet: r"""Apply :class:`~qiskit.circuit.library.ZGate`. For the full matrix form of this gate, see the underlying gate documentation. Args: qubit: The qubit(s) to apply the gate to. Returns: A handle to the instructions created. """ from .library.standard_gates.z import ZGate return self.append(ZGate(), [qubit], []) def cz( self, control_qubit: QubitSpecifier, target_qubit: QubitSpecifier, label: Optional[str] = None, ctrl_state: Optional[Union[str, int]] = None, ) -> InstructionSet: r"""Apply :class:`~qiskit.circuit.library.CZGate`. For the full matrix form of this gate, see the underlying gate documentation. Args: control_qubit: The qubit(s) used as the controls. target_qubit: The qubit(s) targeted by the gate. label: The string label of the gate in the circuit. ctrl_state: The control state in decimal, or as a bitstring (e.g. '1'). Defaults to controlling on the '1' state. Returns: A handle to the instructions created. """ from .library.standard_gates.z import CZGate return self.append( CZGate(label=label, ctrl_state=ctrl_state), [control_qubit, target_qubit], [] ) def pauli( self, pauli_string: str, qubits: Sequence[QubitSpecifier], ) -> InstructionSet: """Apply :class:`~qiskit.circuit.library.PauliGate`. Args: pauli_string: A string representing the Pauli operator to apply, e.g. 'XX'. qubits: The qubits to apply this gate to. Returns: A handle to the instructions created. """ from qiskit.circuit.library.generalized_gates.pauli import PauliGate return self.append(PauliGate(pauli_string), qubits, []) def _push_scope( self, qubits: Iterable[Qubit] = (), clbits: Iterable[Clbit] = (), registers: Iterable[Register] = (), allow_jumps: bool = True, ): """Add a scope for collecting instructions into this circuit. This should only be done by the control-flow context managers, which will handle cleaning up after themselves at the end as well. Args: qubits: Any qubits that this scope should automatically use. clbits: Any clbits that this scope should automatically use. allow_jumps: Whether this scope allows jumps to be used within it. """ # pylint: disable=cyclic-import from qiskit.circuit.controlflow.builder import ControlFlowBuilderBlock # Chain resource requests so things like registers added to inner scopes via conditions are # requested in the outer scope as well. if self._control_flow_scopes: resource_requester = self._control_flow_scopes[-1].request_classical_resource else: resource_requester = self._resolve_classical_resource self._control_flow_scopes.append( ControlFlowBuilderBlock( qubits, clbits, resource_requester=resource_requester, registers=registers, allow_jumps=allow_jumps, ) ) def _pop_scope(self) -> "qiskit.circuit.controlflow.builder.ControlFlowBuilderBlock": """Finish a scope used in the control-flow builder interface, and return it to the caller. This should only be done by the control-flow context managers, since they naturally synchronise the creation and deletion of stack elements.""" return self._control_flow_scopes.pop() def _peek_previous_instruction_in_scope( self, ) -> Tuple[Instruction, Sequence[Qubit], Sequence[Clbit]]: """Return the instruction 3-tuple of the most recent instruction in the current scope, even if that scope is currently under construction. This function is only intended for use by the control-flow ``if``-statement builders, which may need to modify a previous instruction.""" if self._control_flow_scopes: return self._control_flow_scopes[-1].peek() if not self._data: raise CircuitError("This circuit contains no instructions.") return self._data[-1] def _pop_previous_instruction_in_scope( self, ) -> Tuple[Instruction, Sequence[Qubit], Sequence[Clbit]]: """Return the instruction 3-tuple of the most recent instruction in the current scope, even if that scope is currently under construction, and remove it from that scope. This function is only intended for use by the control-flow ``if``-statement builders, which may need to replace a previous instruction with another. """ if self._control_flow_scopes: return self._control_flow_scopes[-1].pop() if not self._data: raise CircuitError("This circuit contains no instructions.") instruction, qubits, clbits = self._data.pop() self._update_parameter_table_on_instruction_removal(instruction) return instruction, qubits, clbits def _update_parameter_table_on_instruction_removal(self, instruction: Instruction) -> None: """Update the :obj:`.ParameterTable` of this circuit given that an instance of the given ``instruction`` has just been removed from the circuit. .. note:: This does not account for the possibility for the same instruction instance being added more than once to the circuit. At the time of writing (2021-11-17, main commit 271a82f) there is a defensive ``deepcopy`` of parameterised instructions inside :meth:`.QuantumCircuit.append`, so this should be safe. Trying to account for it would involve adding a potentially quadratic-scaling loop to check each entry in ``data``. """ atomic_parameters = set() for parameter in instruction.params: if isinstance(parameter, (ParameterExpression, QuantumCircuit)): atomic_parameters.update(parameter.parameters) for atomic_parameter in atomic_parameters: entries = self._parameter_table[atomic_parameter] new_entries = [ (entry_instruction, entry_index) for entry_instruction, entry_index in entries if entry_instruction is not instruction ] if not new_entries: del self._parameter_table[atomic_parameter] # Invalidate cache. self._parameters = None else: self._parameter_table[atomic_parameter] = new_entries @typing.overload def while_loop( self, condition: Tuple[Union[ClassicalRegister, Clbit], int], body: None, qubits: None, clbits: None, *, label: Optional[str], ) -> "qiskit.circuit.controlflow.while_loop.WhileLoopContext": ... @typing.overload def while_loop( self, condition: Tuple[Union[ClassicalRegister, Clbit], int], body: "QuantumCircuit", qubits: Sequence[QubitSpecifier], clbits: Sequence[ClbitSpecifier], *, label: Optional[str], ) -> InstructionSet: ... def while_loop(self, condition, body=None, qubits=None, clbits=None, *, label=None): """Create a ``while`` loop on this circuit. There are two forms for calling this function. If called with all its arguments (with the possible exception of ``label``), it will create a :obj:`~qiskit.circuit.controlflow.WhileLoopOp` with the given ``body``. If ``body`` (and ``qubits`` and ``clbits``) are *not* passed, then this acts as a context manager, which will automatically build a :obj:`~qiskit.circuit.controlflow.WhileLoopOp` when the scope finishes. In this form, you do not need to keep track of the qubits or clbits you are using, because the scope will handle it for you. Example usage:: from qiskit.circuit import QuantumCircuit, Clbit, Qubit bits = [Qubit(), Qubit(), Clbit()] qc = QuantumCircuit(bits) with qc.while_loop((bits[2], 0)): qc.h(0) qc.cx(0, 1) qc.measure(0, 0) Args: condition (Tuple[Union[ClassicalRegister, Clbit], int]): An equality condition to be checked prior to executing ``body``. The left-hand side of the condition must be a :obj:`~ClassicalRegister` or a :obj:`~Clbit`, and the right-hand side must be an integer or boolean. body (Optional[QuantumCircuit]): The loop body to be repeatedly executed. Omit this to use the context-manager mode. qubits (Optional[Sequence[Qubit]]): The circuit qubits over which the loop body should be run. Omit this to use the context-manager mode. clbits (Optional[Sequence[Clbit]]): The circuit clbits over which the loop body should be run. Omit this to use the context-manager mode. label (Optional[str]): The string label of the instruction in the circuit. Returns: InstructionSet or WhileLoopContext: If used in context-manager mode, then this should be used as a ``with`` resource, which will infer the block content and operands on exit. If the full form is used, then this returns a handle to the instructions created. Raises: CircuitError: if an incorrect calling convention is used. """ # pylint: disable=cyclic-import from qiskit.circuit.controlflow.while_loop import WhileLoopOp, WhileLoopContext if body is None: if qubits is not None or clbits is not None: raise CircuitError( "When using 'while_loop' as a context manager," " you cannot pass qubits or clbits." ) return WhileLoopContext(self, condition, label=label) elif qubits is None or clbits is None: raise CircuitError( "When using 'while_loop' with a body, you must pass qubits and clbits." ) return self.append(WhileLoopOp(condition, body, label), qubits, clbits) @typing.overload def for_loop( self, indexset: Iterable[int], loop_parameter: Optional[Parameter], body: None, qubits: None, clbits: None, *, label: Optional[str], ) -> "qiskit.circuit.controlflow.for_loop.ForLoopContext": ... @typing.overload def for_loop( self, indexset: Iterable[int], loop_parameter: Union[Parameter, None], body: "QuantumCircuit", qubits: Sequence[QubitSpecifier], clbits: Sequence[ClbitSpecifier], *, label: Optional[str], ) -> InstructionSet: ... def for_loop( self, indexset, loop_parameter=None, body=None, qubits=None, clbits=None, *, label=None ): """Create a ``for`` loop on this circuit. There are two forms for calling this function. If called with all its arguments (with the possible exception of ``label``), it will create a :obj:`~qiskit.circuit.controlflow.ForLoopOp` with the given ``body``. If ``body`` (and ``qubits`` and ``clbits``) are *not* passed, then this acts as a context manager, which, when entered, provides a loop variable (unless one is given, in which case it will be reused) and will automatically build a :obj:`~qiskit.circuit.controlflow.ForLoopOp` when the scope finishes. In this form, you do not need to keep track of the qubits or clbits you are using, because the scope will handle it for you. For example:: from qiskit import QuantumCircuit qc = QuantumCircuit(2, 1) with qc.for_loop(range(5)) as i: qc.h(0) qc.cx(0, 1) qc.measure(0, 0) qc.break_loop().c_if(0, True) Args: indexset (Iterable[int]): A collection of integers to loop over. Always necessary. loop_parameter (Optional[Parameter]): The parameter used within ``body`` to which the values from ``indexset`` will be assigned. In the context-manager form, if this argument is not supplied, then a loop parameter will be allocated for you and returned as the value of the ``with`` statement. This will only be bound into the circuit if it is used within the body. If this argument is ``None`` in the manual form of this method, ``body`` will be repeated once for each of the items in ``indexset`` but their values will be ignored. body (Optional[QuantumCircuit]): The loop body to be repeatedly executed. Omit this to use the context-manager mode. qubits (Optional[Sequence[QubitSpecifier]]): The circuit qubits over which the loop body should be run. Omit this to use the context-manager mode. clbits (Optional[Sequence[ClbitSpecifier]]): The circuit clbits over which the loop body should be run. Omit this to use the context-manager mode. label (Optional[str]): The string label of the instruction in the circuit. Returns: InstructionSet or ForLoopContext: depending on the call signature, either a context manager for creating the for loop (it will automatically be added to the circuit at the end of the block), or an :obj:`~InstructionSet` handle to the appended loop operation. Raises: CircuitError: if an incorrect calling convention is used. """ # pylint: disable=cyclic-import from qiskit.circuit.controlflow.for_loop import ForLoopOp, ForLoopContext if body is None: if qubits is not None or clbits is not None: raise CircuitError( "When using 'for_loop' as a context manager, you cannot pass qubits or clbits." ) return ForLoopContext(self, indexset, loop_parameter, label=label) elif qubits is None or clbits is None: raise CircuitError( "When using 'for_loop' with a body, you must pass qubits and clbits." ) return self.append(ForLoopOp(indexset, loop_parameter, body, label), qubits, clbits) @typing.overload def if_test( self, condition: Tuple[Union[ClassicalRegister, Clbit], int], true_body: None, qubits: None, clbits: None, *, label: Optional[str], ) -> "qiskit.circuit.controlflow.if_else.IfContext": ... @typing.overload def if_test( self, condition: Tuple[Union[ClassicalRegister, Clbit], int], true_body: "QuantumCircuit", qubits: Sequence[QubitSpecifier], clbits: Sequence[ClbitSpecifier], *, label: Optional[str] = None, ) -> InstructionSet: ... def if_test( self, condition, true_body=None, qubits=None, clbits=None, *, label=None, ): """Create an ``if`` statement on this circuit. There are two forms for calling this function. If called with all its arguments (with the possible exception of ``label``), it will create a :obj:`~qiskit.circuit.controlflow.IfElseOp` with the given ``true_body``, and there will be no branch for the ``false`` condition (see also the :meth:`.if_else` method). However, if ``true_body`` (and ``qubits`` and ``clbits``) are *not* passed, then this acts as a context manager, which can be used to build ``if`` statements. The return value of the ``with`` statement is a chainable context manager, which can be used to create subsequent ``else`` blocks. In this form, you do not need to keep track of the qubits or clbits you are using, because the scope will handle it for you. For example:: from qiskit.circuit import QuantumCircuit, Qubit, Clbit bits = [Qubit(), Qubit(), Qubit(), Clbit(), Clbit()] qc = QuantumCircuit(bits) qc.h(0) qc.cx(0, 1) qc.measure(0, 0) qc.h(0) qc.cx(0, 1) qc.measure(0, 1) with qc.if_test((bits[3], 0)) as else_: qc.x(2) with else_: qc.h(2) qc.z(2) Args: condition (Tuple[Union[ClassicalRegister, Clbit], int]): A condition to be evaluated at circuit runtime which, if true, will trigger the evaluation of ``true_body``. Can be specified as either a tuple of a ``ClassicalRegister`` to be tested for equality with a given ``int``, or as a tuple of a ``Clbit`` to be compared to either a ``bool`` or an ``int``. true_body (Optional[QuantumCircuit]): The circuit body to be run if ``condition`` is true. qubits (Optional[Sequence[QubitSpecifier]]): The circuit qubits over which the if/else should be run. clbits (Optional[Sequence[ClbitSpecifier]]): The circuit clbits over which the if/else should be run. label (Optional[str]): The string label of the instruction in the circuit. Returns: InstructionSet or IfContext: depending on the call signature, either a context manager for creating the ``if`` block (it will automatically be added to the circuit at the end of the block), or an :obj:`~InstructionSet` handle to the appended conditional operation. Raises: CircuitError: If the provided condition references Clbits outside the enclosing circuit. CircuitError: if an incorrect calling convention is used. Returns: A handle to the instruction created. """ # pylint: disable=cyclic-import from qiskit.circuit.controlflow.if_else import IfElseOp, IfContext condition = (self._resolve_classical_resource(condition[0]), condition[1]) if true_body is None: if qubits is not None or clbits is not None: raise CircuitError( "When using 'if_test' as a context manager, you cannot pass qubits or clbits." ) # We can only allow jumps if we're in a loop block, but the default path (no scopes) # also allows adding jumps to support the more verbose internal mode. in_loop = bool(self._control_flow_scopes and self._control_flow_scopes[-1].allow_jumps) return IfContext(self, condition, in_loop=in_loop, label=label) elif qubits is None or clbits is None: raise CircuitError("When using 'if_test' with a body, you must pass qubits and clbits.") return self.append(IfElseOp(condition, true_body, None, label), qubits, clbits) def if_else( self, condition: Union[ Tuple[ClassicalRegister, int], Tuple[Clbit, int], Tuple[Clbit, bool], ], true_body: "QuantumCircuit", false_body: "QuantumCircuit", qubits: Sequence[QubitSpecifier], clbits: Sequence[ClbitSpecifier], label: Optional[str] = None, ) -> InstructionSet: """Apply :class:`~qiskit.circuit.controlflow.IfElseOp`. .. note:: This method does not have an associated context-manager form, because it is already handled by the :meth:`.if_test` method. You can use the ``else`` part of that with something such as:: from qiskit.circuit import QuantumCircuit, Qubit, Clbit bits = [Qubit(), Qubit(), Clbit()] qc = QuantumCircuit(bits) qc.h(0) qc.cx(0, 1) qc.measure(0, 0) with qc.if_test((bits[2], 0)) as else_: qc.h(0) with else_: qc.x(0) Args: condition: A condition to be evaluated at circuit runtime which, if true, will trigger the evaluation of ``true_body``. Can be specified as either a tuple of a ``ClassicalRegister`` to be tested for equality with a given ``int``, or as a tuple of a ``Clbit`` to be compared to either a ``bool`` or an ``int``. true_body: The circuit body to be run if ``condition`` is true. false_body: The circuit to be run if ``condition`` is false. qubits: The circuit qubits over which the if/else should be run. clbits: The circuit clbits over which the if/else should be run. label: The string label of the instruction in the circuit. Raises: CircuitError: If the provided condition references Clbits outside the enclosing circuit. Returns: A handle to the instruction created. """ # pylint: disable=cyclic-import from qiskit.circuit.controlflow.if_else import IfElseOp condition = (self._resolve_classical_resource(condition[0]), condition[1]) return self.append(IfElseOp(condition, true_body, false_body, label), qubits, clbits) def break_loop(self) -> InstructionSet: """Apply :class:`~qiskit.circuit.controlflow.BreakLoopOp`. .. warning:: If you are using the context-manager "builder" forms of :meth:`.if_test`, :meth:`.for_loop` or :meth:`.while_loop`, you can only call this method if you are within a loop context, because otherwise the "resource width" of the operation cannot be determined. This would quickly lead to invalid circuits, and so if you are trying to construct a reusable loop body (without the context managers), you must also use the non-context-manager form of :meth:`.if_test` and :meth:`.if_else`. Take care that the :obj:`.BreakLoopOp` instruction must span all the resources of its containing loop, not just the immediate scope. Returns: A handle to the instruction created. Raises: CircuitError: if this method was called within a builder context, but not contained within a loop. """ # pylint: disable=cyclic-import from qiskit.circuit.controlflow.break_loop import BreakLoopOp, BreakLoopPlaceholder if self._control_flow_scopes: operation = BreakLoopPlaceholder() resources = operation.placeholder_resources() return self.append(operation, resources.qubits, resources.clbits) return self.append(BreakLoopOp(self.num_qubits, self.num_clbits), self.qubits, self.clbits) def continue_loop(self) -> InstructionSet: """Apply :class:`~qiskit.circuit.controlflow.ContinueLoopOp`. .. warning:: If you are using the context-manager "builder" forms of :meth:`.if_test`, :meth:`.for_loop` or :meth:`.while_loop`, you can only call this method if you are within a loop context, because otherwise the "resource width" of the operation cannot be determined. This would quickly lead to invalid circuits, and so if you are trying to construct a reusable loop body (without the context managers), you must also use the non-context-manager form of :meth:`.if_test` and :meth:`.if_else`. Take care that the :obj:`.ContinueLoopOp` instruction must span all the resources of its containing loop, not just the immediate scope. Returns: A handle to the instruction created. Raises: CircuitError: if this method was called within a builder context, but not contained within a loop. """ # pylint: disable=cyclic-import from qiskit.circuit.controlflow.continue_loop import ContinueLoopOp, ContinueLoopPlaceholder if self._control_flow_scopes: operation = ContinueLoopPlaceholder() resources = operation.placeholder_resources() return self.append(operation, resources.qubits, resources.clbits) return self.append( ContinueLoopOp(self.num_qubits, self.num_clbits), self.qubits, self.clbits ) def add_calibration( self, gate: Union[Gate, str], qubits: Sequence[int], # Schedule has the type `qiskit.pulse.Schedule`, but `qiskit.pulse` cannot be imported # while this module is, and so Sphinx will not accept a forward reference to it. Sphinx # needs the types available at runtime, whereas mypy will accept it, because it handles the # type checking by static analysis. schedule, params: Optional[Sequence[ParameterValueType]] = None, ) -> None: """Register a low-level, custom pulse definition for the given gate. Args: gate (Union[Gate, str]): Gate information. qubits (Union[int, Tuple[int]]): List of qubits to be measured. schedule (Schedule): Schedule information. params (Optional[List[Union[float, Parameter]]]): A list of parameters. Raises: Exception: if the gate is of type string and params is None. """ if isinstance(gate, Gate): self._calibrations[gate.name][(tuple(qubits), tuple(gate.params))] = schedule else: self._calibrations[gate][(tuple(qubits), tuple(params or []))] = schedule # Functions only for scheduled circuits def qubit_duration(self, *qubits: Union[Qubit, int]) -> float: """Return the duration between the start and stop time of the first and last instructions, excluding delays, over the supplied qubits. Its time unit is ``self.unit``. Args: *qubits: Qubits within ``self`` to include. Returns: Return the duration between the first start and last stop time of non-delay instructions """ return self.qubit_stop_time(*qubits) - self.qubit_start_time(*qubits) def qubit_start_time(self, *qubits: Union[Qubit, int]) -> float: """Return the start time of the first instruction, excluding delays, over the supplied qubits. Its time unit is ``self.unit``. Return 0 if there are no instructions over qubits Args: *qubits: Qubits within ``self`` to include. Integers are allowed for qubits, indicating indices of ``self.qubits``. Returns: Return the start time of the first instruction, excluding delays, over the qubits Raises: CircuitError: if ``self`` is a not-yet scheduled circuit. """ if self.duration is None: # circuit has only delays, this is kind of scheduled for inst, _, _ in self.data: if not isinstance(inst, Delay): raise CircuitError( "qubit_start_time undefined. Circuit must be scheduled first." ) return 0 qubits = [self.qubits[q] if isinstance(q, int) else q for q in qubits] starts = {q: 0 for q in qubits} dones = {q: False for q in qubits} for inst, qargs, _ in self.data: for q in qubits: if q in qargs: if isinstance(inst, Delay): if not dones[q]: starts[q] += inst.duration else: dones[q] = True if len(qubits) == len([done for done in dones.values() if done]): # all done return min(start for start in starts.values()) return 0 # If there are no instructions over bits def qubit_stop_time(self, *qubits: Union[Qubit, int]) -> float: """Return the stop time of the last instruction, excluding delays, over the supplied qubits. Its time unit is ``self.unit``. Return 0 if there are no instructions over qubits Args: *qubits: Qubits within ``self`` to include. Integers are allowed for qubits, indicating indices of ``self.qubits``. Returns: Return the stop time of the last instruction, excluding delays, over the qubits Raises: CircuitError: if ``self`` is a not-yet scheduled circuit. """ if self.duration is None: # circuit has only delays, this is kind of scheduled for inst, _, _ in self.data: if not isinstance(inst, Delay): raise CircuitError( "qubit_stop_time undefined. Circuit must be scheduled first." ) return 0 qubits = [self.qubits[q] if isinstance(q, int) else q for q in qubits] stops = {q: self.duration for q in qubits} dones = {q: False for q in qubits} for inst, qargs, _ in reversed(self.data): for q in qubits: if q in qargs: if isinstance(inst, Delay): if not dones[q]: stops[q] -= inst.duration else: dones[q] = True if len(qubits) == len([done for done in dones.values() if done]): # all done return max(stop for stop in stops.values()) return 0 # If there are no instructions over bits def _circuit_from_qasm(qasm: Qasm) -> "QuantumCircuit": # pylint: disable=cyclic-import from qiskit.converters import ast_to_dag from qiskit.converters import dag_to_circuit ast = qasm.parse() dag = ast_to_dag(ast) return dag_to_circuit(dag) def _standard_compare(value1, value2): if value1 < value2: return -1 if value1 > value2: return 1 return 0 def _compare_parameters(param1: Parameter, param2: Parameter) -> int: if isinstance(param1, ParameterVectorElement) and isinstance(param2, ParameterVectorElement): # if they belong to a vector with the same name, sort by index if param1.vector.name == param2.vector.name: return _standard_compare(param1.index, param2.index) # else sort by name return _standard_compare(param1.name, param2.name) def _add_sub_instruction_to_existing_composite_circuits( instruction: Instruction, existing_gate_names: List[str], existing_composite_circuits: List[Instruction], ) -> None: """Recursively add undefined sub-instructions in the definition of the given instruction to existing_composite_circuit list. """ for sub_instruction, _, _ in instruction.definition: # Check instructions names are valid if not VALID_QASM2_IDENTIFIER.fullmatch(sub_instruction.name): sub_instruction = sub_instruction.copy( name=_qasm_escape_gate_name(sub_instruction.name) ) if ( sub_instruction.name not in existing_gate_names and sub_instruction not in existing_composite_circuits ): existing_composite_circuits.insert(0, sub_instruction) _add_sub_instruction_to_existing_composite_circuits( sub_instruction, existing_gate_names, existing_composite_circuits ) def _qasm_escape_gate_name(name: str) -> str: """Returns a valid OpenQASM gate identifier""" # Replace all non-ASCII-word characters with the underscore. escaped_name = re.sub(r"\W", "_", name, flags=re.ASCII) if not escaped_name or escaped_name[0] not in string.ascii_lowercase: # Add an arbitrary, guaranteed-to-be-valid prefix. escaped_name = "gate_" + escaped_name return escaped_name def _get_composite_circuit_qasm_from_instruction(instruction: Instruction) -> str: """Returns OpenQASM string composite circuit given an instruction. The given instruction should be the result of composite_circuit.to_instruction().""" if instruction.definition is None: raise ValueError(f'Instruction "{instruction.name}" is not defined.') gate_parameters = ",".join(["param%i" % num for num in range(len(instruction.params))]) qubit_parameters = ",".join(["q%i" % num for num in range(instruction.num_qubits)]) composite_circuit_gates = "" definition = instruction.definition definition_bit_labels = { bit: idx for bits in (definition.qubits, definition.clbits) for idx, bit in enumerate(bits) } for sub_instruction, qargs, _ in definition: if not VALID_QASM2_IDENTIFIER.fullmatch(sub_instruction.name): sub_instruction = sub_instruction.copy( name=_qasm_escape_gate_name(sub_instruction.name) ) gate_qargs = ",".join( ["q%i" % index for index in [definition_bit_labels[qubit] for qubit in qargs]] ) composite_circuit_gates += f"{sub_instruction.qasm()} {gate_qargs}; " if composite_circuit_gates: composite_circuit_gates = composite_circuit_gates.rstrip(" ") if gate_parameters: qasm_string = "gate {}({}) {} {{ {} }}".format( instruction.name, gate_parameters, qubit_parameters, composite_circuit_gates, ) else: qasm_string = "gate {} {} {{ {} }}".format( instruction.name, qubit_parameters, composite_circuit_gates, ) return qasm_string def _insert_composite_gate_definition_qasm( string_temp: str, existing_composite_circuits: List[Instruction], extension_lib: str ) -> str: """Insert composite gate definition QASM code right after extension library in the header""" gate_definition_string = "" # Generate gate definition string for instruction in existing_composite_circuits: if hasattr(instruction, "_qasm_definition"): qasm_string = instruction._qasm_definition else: qasm_string = _get_composite_circuit_qasm_from_instruction(instruction) gate_definition_string += "\n" + qasm_string string_temp = string_temp.replace(extension_lib, f"{extension_lib}{gate_definition_string}") return string_temp def _bit_argument_conversion(specifier, bit_sequence, bit_set, type_): """Get the list of bits referred to by the specifier ``specifier``. Valid types for ``specifier`` are integers, bits of the correct type (as given in ``type_``), or iterables of one of those two scalar types. Integers are interpreted as indices into the sequence ``bit_sequence``. All allowed bits must be in ``bit_set`` (which should implement fast lookup), which is assumed to contain the same bits as ``bit_sequence``. Returns: List[Bit]: a list of the specified bits from ``bits``. Raises: CircuitError: if an incorrect type or index is encountered, if the same bit is specified more than once, or if the specifier is to a bit not in the ``bit_set``. """ # The duplication between this function and `_bit_argument_conversion_scalar` is so that fast # paths return as quickly as possible, and all valid specifiers will resolve without needing to # try/catch exceptions (which is too slow for inner-loop code). if isinstance(specifier, type_): if specifier in bit_set: return [specifier] raise CircuitError(f"Bit '{specifier}' is not in the circuit.") if isinstance(specifier, (int, np.integer)): try: return [bit_sequence[specifier]] except IndexError as ex: raise CircuitError( f"Index {specifier} out of range for size {len(bit_sequence)}." ) from ex # Slices can't raise IndexError - they just return an empty list. if isinstance(specifier, slice): return bit_sequence[specifier] try: return [ _bit_argument_conversion_scalar(index, bit_sequence, bit_set, type_) for index in specifier ] except TypeError as ex: message = ( f"Incorrect bit type: expected '{type_.__name__}' but got '{type(specifier).__name__}'" if isinstance(specifier, Bit) else f"Invalid bit index: '{specifier}' of type '{type(specifier)}'" ) raise CircuitError(message) from ex def _bit_argument_conversion_scalar(specifier, bit_sequence, bit_set, type_): if isinstance(specifier, type_): if specifier in bit_set: return specifier raise CircuitError(f"Bit '{specifier}' is not in the circuit.") if isinstance(specifier, (int, np.integer)): try: return bit_sequence[specifier] except IndexError as ex: raise CircuitError( f"Index {specifier} out of range for size {len(bit_sequence)}." ) from ex message = ( f"Incorrect bit type: expected '{type_.__name__}' but got '{type(specifier).__name__}'" if isinstance(specifier, Bit) else f"Invalid bit index: '{specifier}' of type '{type(specifier)}'" ) raise CircuitError(message)

39.135764

100

0.597489

795128014c9de92d174d1f4345ed699b13341265

414

py

Python

app/services/cli/settings.py

criticallycode/zima

cd38cac1c0c33b362d110ae28deba3828daa3f4a

[ "Apache-2.0" ]

null

app/services/cli/settings.py

criticallycode/zima

cd38cac1c0c33b362d110ae28deba3828daa3f4a

[ "Apache-2.0" ]

null

app/services/cli/settings.py

criticallycode/zima

cd38cac1c0c33b362d110ae28deba3828daa3f4a

[ "Apache-2.0" ]

null

""" For the full list of settings and their values, see https://docs.djangoproject.com/en/3.0/ref/settings/ """ #------------------------------------------------------------------------------- # Global settings #------------------------------------------------------------------------------- # Core Django settings #------------------------------------------------------------------------------- # Django Addons

31.846154

80

0.301932

7951280dde0bd6c660a84ca4935acca684c0ed84

851

py

Python

website/pagination_classes.py

edsegura30/BackEnd

82669ce4f07fa54936f33ee08f0123f64d27a83a

[ "MIT" ]

3

2019-04-05T17:39:28.000Z

2019-05-22T15:38:08.000Z

website/pagination_classes.py

edsegura30/BackEnd

82669ce4f07fa54936f33ee08f0123f64d27a83a

[ "MIT" ]

1

2019-04-05T17:39:44.000Z

2019-04-08T08:56:55.000Z

website/pagination_classes.py

edsegura30/BackEnd

82669ce4f07fa54936f33ee08f0123f64d27a83a

[ "MIT" ]

null

""" Custom paginators for API responses """ from rest_framework.pagination import PageNumberPagination from rest_framework.response import Response _BASE_URL = 'http://localhost:8000/' _EVENT_URL = 'events/' class DefaultEdgarPagination(PageNumberPagination): """ This is the default REST API paginator """ page_size = 20 page_size_query_param = 'page_size' max_page_size = 200 def get_paginated_response(self, data): return Response( { 'next': self.get_next_link(), 'previous': self.get_previous_link(), 'total_pages': self.page.paginator.num_pages, 'page': self.page.number, 'all_events': _BASE_URL + _EVENT_URL, 'count': self.page.paginator.count, 'results': data, })

28.366667

61

0.613396

795128174c98f5be3d2b79e43ab1d172dafe03fc

169,255

py

Python

nova/virt/libvirt/driver.py

bopopescu/novatest-1

16ed4510200308b8b710cac90aa032ec5eab7e9d

[ "Apache-2.0" ]

null

nova/virt/libvirt/driver.py

bopopescu/novatest-1

16ed4510200308b8b710cac90aa032ec5eab7e9d

[ "Apache-2.0" ]

null

nova/virt/libvirt/driver.py

bopopescu/novatest-1

16ed4510200308b8b710cac90aa032ec5eab7e9d

[ "Apache-2.0" ]

1

2020-07-24T08:49:47.000Z

# vim: tabstop=4 shiftwidth=4 softtabstop=4 # Copyright 2010 United States Government as represented by the # Administrator of the National Aeronautics and Space Administration. # All Rights Reserved. # Copyright (c) 2010 Citrix Systems, Inc. # Copyright (c) 2011 Piston Cloud Computing, Inc # Copyright (c) 2012 University Of Minho # (c) Copyright 2013 Hewlett-Packard Development Company, L.P. # # 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. """ A connection to a hypervisor through libvirt. Supports KVM, LXC, QEMU, UML, and XEN. **Related Flags** :libvirt_type: Libvirt domain type. Can be kvm, qemu, uml, xen (default: kvm). :libvirt_uri: Override for the default libvirt URI (depends on libvirt_type). :libvirt_disk_prefix: Override the default disk prefix for the devices attached to a server. :rescue_image_id: Rescue ami image (None = original image). :rescue_kernel_id: Rescue aki image (None = original image). :rescue_ramdisk_id: Rescue ari image (None = original image). :injected_network_template: Template file for injected network :allow_same_net_traffic: Whether to allow in project network traffic """ import errno import eventlet import functools import glob import os import shutil import socket import sys import tempfile import threading import time import uuid from eventlet import greenio from eventlet import greenthread from eventlet import patcher from eventlet import tpool from eventlet import util as eventlet_util from lxml import etree from oslo.config import cfg from xml.dom import minidom from nova.api.metadata import base as instance_metadata from nova import block_device from nova.compute import flavors from nova.compute import power_state from nova.compute import task_states from nova.compute import vm_mode from nova import context as nova_context from nova import exception from nova.image import glance from nova.openstack.common import excutils from nova.openstack.common import fileutils from nova.openstack.common import importutils from nova.openstack.common import jsonutils from nova.openstack.common import log as logging from nova.openstack.common import loopingcall from nova.openstack.common.notifier import api as notifier from nova.openstack.common import processutils from nova import utils from nova import version from nova.virt import configdrive from nova.virt.disk import api as disk from nova.virt import driver from nova.virt import event as virtevent from nova.virt import firewall from nova.virt.libvirt import blockinfo from nova.virt.libvirt import config as vconfig from nova.virt.libvirt import firewall as libvirt_firewall from nova.virt.libvirt import imagebackend from nova.virt.libvirt import imagecache from nova.virt.libvirt import utils as libvirt_utils from nova.virt import netutils native_threading = patcher.original("threading") native_Queue = patcher.original("Queue") libvirt = None LOG = logging.getLogger(__name__) libvirt_opts = [ cfg.StrOpt('rescue_image_id', default=None, help='Rescue ami image'), cfg.StrOpt('rescue_kernel_id', default=None, help='Rescue aki image'), cfg.StrOpt('rescue_ramdisk_id', default=None, help='Rescue ari image'), cfg.StrOpt('libvirt_type', default='kvm', help='Libvirt domain type (valid options are: ' 'kvm, lxc, qemu, uml, xen)'), cfg.StrOpt('libvirt_uri', default='', help='Override the default libvirt URI ' '(which is dependent on libvirt_type)'), cfg.BoolOpt('libvirt_inject_password', default=False, help='Inject the admin password at boot time, ' 'without an agent.'), cfg.BoolOpt('libvirt_inject_key', default=True, help='Inject the ssh public key at boot time'), cfg.IntOpt('libvirt_inject_partition', default=1, help='The partition to inject to : ' '-2 => disable, -1 => inspect (libguestfs only), ' '0 => not partitioned, >0 => partition number'), cfg.BoolOpt('use_usb_tablet', default=True, help='Sync virtual and real mouse cursors in Windows VMs'), cfg.StrOpt('live_migration_uri', default="qemu+tcp://%s/system", help='Migration target URI ' '(any included "%s" is replaced with ' 'the migration target hostname)'), cfg.StrOpt('live_migration_flag', default='VIR_MIGRATE_UNDEFINE_SOURCE, VIR_MIGRATE_PEER2PEER', help='Migration flags to be set for live migration'), cfg.StrOpt('block_migration_flag', default='VIR_MIGRATE_UNDEFINE_SOURCE, VIR_MIGRATE_PEER2PEER, ' 'VIR_MIGRATE_NON_SHARED_INC', help='Migration flags to be set for block migration'), cfg.IntOpt('live_migration_bandwidth', default=0, help='Maximum bandwidth to be used during migration, in Mbps'), cfg.StrOpt('snapshot_image_format', default=None, help='Snapshot image format (valid options are : ' 'raw, qcow2, vmdk, vdi). ' 'Defaults to same as source image'), cfg.StrOpt('libvirt_vif_driver', default='nova.virt.libvirt.vif.LibvirtGenericVIFDriver', help='The libvirt VIF driver to configure the VIFs.'), cfg.ListOpt('libvirt_volume_drivers', default=[ 'iscsi=nova.virt.libvirt.volume.LibvirtISCSIVolumeDriver', 'local=nova.virt.libvirt.volume.LibvirtVolumeDriver', 'fake=nova.virt.libvirt.volume.LibvirtFakeVolumeDriver', 'rbd=nova.virt.libvirt.volume.LibvirtNetVolumeDriver', 'sheepdog=nova.virt.libvirt.volume.LibvirtNetVolumeDriver', 'nfs=nova.virt.libvirt.volume.LibvirtNFSVolumeDriver', 'aoe=nova.virt.libvirt.volume.LibvirtAOEVolumeDriver', 'glusterfs=' 'nova.virt.libvirt.volume.LibvirtGlusterfsVolumeDriver', 'fibre_channel=nova.virt.libvirt.volume.' 'LibvirtFibreChannelVolumeDriver', 'scality=' 'nova.virt.libvirt.volume.LibvirtScalityVolumeDriver', ], help='Libvirt handlers for remote volumes.'), cfg.StrOpt('libvirt_disk_prefix', default=None, help='Override the default disk prefix for the devices attached' ' to a server, which is dependent on libvirt_type. ' '(valid options are: sd, xvd, uvd, vd)'), cfg.IntOpt('libvirt_wait_soft_reboot_seconds', default=120, help='Number of seconds to wait for instance to shut down after' ' soft reboot request is made. We fall back to hard reboot' ' if instance does not shutdown within this window.'), cfg.BoolOpt('libvirt_nonblocking', default=True, help='Use a separated OS thread pool to realize non-blocking' ' libvirt calls'), cfg.StrOpt('libvirt_cpu_mode', default=None, help='Set to "host-model" to clone the host CPU feature flags; ' 'to "host-passthrough" to use the host CPU model exactly; ' 'to "custom" to use a named CPU model; ' 'to "none" to not set any CPU model. ' 'If libvirt_type="kvm|qemu", it will default to ' '"host-model", otherwise it will default to "none"'), cfg.StrOpt('libvirt_cpu_model', default=None, help='Set to a named libvirt CPU model (see names listed ' 'in /usr/share/libvirt/cpu_map.xml). Only has effect if ' 'libvirt_cpu_mode="custom" and libvirt_type="kvm|qemu"'), cfg.StrOpt('libvirt_snapshots_directory', default='$instances_path/snapshots', help='Location where libvirt driver will store snapshots ' 'before uploading them to image service'), cfg.StrOpt('xen_hvmloader_path', default='/usr/lib/xen/boot/hvmloader', help='Location where the Xen hvmloader is kept'), cfg.ListOpt('disk_cachemodes', default=[], help='Specific cachemodes to use for different disk types ' 'e.g: ["file=directsync","block=none"]'), cfg.StrOpt('vcpu_pin_set', default=None, help='Which pcpus can be used by vcpus of instance ' 'e.g: "4-12,^8,15"'), ] CONF = cfg.CONF CONF.register_opts(libvirt_opts) CONF.import_opt('host', 'nova.netconf') CONF.import_opt('my_ip', 'nova.netconf') CONF.import_opt('default_ephemeral_format', 'nova.virt.driver') CONF.import_opt('use_cow_images', 'nova.virt.driver') CONF.import_opt('live_migration_retry_count', 'nova.compute.manager') CONF.import_opt('vncserver_proxyclient_address', 'nova.vnc') CONF.import_opt('server_proxyclient_address', 'nova.spice', group='spice') DEFAULT_FIREWALL_DRIVER = "%s.%s" % ( libvirt_firewall.__name__, libvirt_firewall.IptablesFirewallDriver.__name__) MAX_CONSOLE_BYTES = 102400 def patch_tpool_proxy(): """eventlet.tpool.Proxy doesn't work with old-style class in __str__() or __repr__() calls. See bug #962840 for details. We perform a monkey patch to replace those two instance methods. """ def str_method(self): return str(self._obj) def repr_method(self): return repr(self._obj) tpool.Proxy.__str__ = str_method tpool.Proxy.__repr__ = repr_method patch_tpool_proxy() VIR_DOMAIN_NOSTATE = 0 VIR_DOMAIN_RUNNING = 1 VIR_DOMAIN_BLOCKED = 2 VIR_DOMAIN_PAUSED = 3 VIR_DOMAIN_SHUTDOWN = 4 VIR_DOMAIN_SHUTOFF = 5 VIR_DOMAIN_CRASHED = 6 VIR_DOMAIN_PMSUSPENDED = 7 LIBVIRT_POWER_STATE = { VIR_DOMAIN_NOSTATE: power_state.NOSTATE, VIR_DOMAIN_RUNNING: power_state.RUNNING, # NOTE(maoy): The DOMAIN_BLOCKED state is only valid in Xen. # It means that the VM is running and the vCPU is idle. So, # we map it to RUNNING VIR_DOMAIN_BLOCKED: power_state.RUNNING, VIR_DOMAIN_PAUSED: power_state.PAUSED, # NOTE(maoy): The libvirt API doc says that DOMAIN_SHUTDOWN # means the domain is being shut down. So technically the domain # is still running. SHUTOFF is the real powered off state. # But we will map both to SHUTDOWN anyway. # http://libvirt.org/html/libvirt-libvirt.html VIR_DOMAIN_SHUTDOWN: power_state.SHUTDOWN, VIR_DOMAIN_SHUTOFF: power_state.SHUTDOWN, VIR_DOMAIN_CRASHED: power_state.CRASHED, VIR_DOMAIN_PMSUSPENDED: power_state.SUSPENDED, } MIN_LIBVIRT_VERSION = (0, 9, 6) # When the above version matches/exceeds this version # delete it & corresponding code using it MIN_LIBVIRT_HOST_CPU_VERSION = (0, 9, 10) MIN_LIBVIRT_CLOSE_CALLBACK_VERSION = (1, 0, 1) # Live snapshot requirements REQ_HYPERVISOR_LIVESNAPSHOT = "QEMU" MIN_LIBVIRT_LIVESNAPSHOT_VERSION = (1, 0, 0) MIN_QEMU_LIVESNAPSHOT_VERSION = (1, 3, 0) def libvirt_error_handler(context, err): # Just ignore instead of default outputting to stderr. pass class LibvirtDriver(driver.ComputeDriver): capabilities = { "has_imagecache": True, "supports_recreate": True, } def __init__(self, virtapi, read_only=False): super(LibvirtDriver, self).__init__(virtapi) global libvirt if libvirt is None: libvirt = __import__('libvirt') self._host_state = None self._initiator = None self._fc_wwnns = None self._fc_wwpns = None self._wrapped_conn = None self._wrapped_conn_lock = threading.Lock() self._caps = None self._vcpu_total = 0 self.read_only = read_only self.firewall_driver = firewall.load_driver( DEFAULT_FIREWALL_DRIVER, self.virtapi, get_connection=self._get_connection) vif_class = importutils.import_class(CONF.libvirt_vif_driver) self.vif_driver = vif_class(self._get_connection) self.volume_drivers = driver.driver_dict_from_config( CONF.libvirt_volume_drivers, self) self._host_state = None self._event_queue = None self._disk_cachemode = None self.image_cache_manager = imagecache.ImageCacheManager() self.image_backend = imagebackend.Backend(CONF.use_cow_images) self.disk_cachemodes = {} self.valid_cachemodes = ["default", "none", "writethrough", "writeback", "directsync", "writethrough", "unsafe", ] for mode_str in CONF.disk_cachemodes: disk_type, sep, cache_mode = mode_str.partition('=') if cache_mode not in self.valid_cachemodes: LOG.warn(_('Invalid cachemode %(cache_mode)s specified ' 'for disk type %(disk_type)s.'), {'cache_mode': cache_mode, 'disk_type': disk_type}) continue self.disk_cachemodes[disk_type] = cache_mode @property def disk_cachemode(self): if self._disk_cachemode is None: # We prefer 'none' for consistent performance, host crash # safety & migration correctness by avoiding host page cache. # Some filesystems (eg GlusterFS via FUSE) don't support # O_DIRECT though. For those we fallback to 'writethrough' # which gives host crash safety, and is safe for migration # provided the filesystem is cache coherant (cluster filesystems # typically are, but things like NFS are not). self._disk_cachemode = "none" if not self._supports_direct_io(CONF.instances_path): self._disk_cachemode = "writethrough" return self._disk_cachemode @property def host_state(self): if not self._host_state: self._host_state = HostState(self) return self._host_state def set_cache_mode(self, conf): """Set cache mode on LibvirtConfigGuestDisk object.""" try: source_type = conf.source_type driver_cache = conf.driver_cache except AttributeError: return cache_mode = self.disk_cachemodes.get(source_type, driver_cache) conf.driver_cache = cache_mode def has_min_version(self, lv_ver=None, hv_ver=None, hv_type=None): def _munge_version(ver): return ver[0] * 1000000 + ver[1] * 1000 + ver[2] try: if lv_ver is not None: libvirt_version = self._conn.getLibVersion() if libvirt_version < _munge_version(lv_ver): return False if hv_ver is not None: hypervisor_version = self._conn.getVersion() if hypervisor_version < _munge_version(hv_ver): return False if hv_type is not None: hypervisor_type = self._conn.getType() if hypervisor_type != hv_type: return False return True except Exception: return False def _native_thread(self): """Receives async events coming in from libvirtd. This is a native thread which runs the default libvirt event loop implementation. This processes any incoming async events from libvirtd and queues them for later dispatch. This thread is only permitted to use libvirt python APIs, and the driver.queue_event method. In particular any use of logging is forbidden, since it will confuse eventlet's greenthread integration """ while True: libvirt.virEventRunDefaultImpl() def _dispatch_thread(self): """Dispatches async events coming in from libvirtd. This is a green thread which waits for events to arrive from the libvirt event loop thread. This then dispatches the events to the compute manager. """ while True: self._dispatch_events() @staticmethod def _event_lifecycle_callback(conn, dom, event, detail, opaque): """Receives lifecycle events from libvirt. NB: this method is executing in a native thread, not an eventlet coroutine. It can only invoke other libvirt APIs, or use self.queue_event(). Any use of logging APIs in particular is forbidden. """ self = opaque uuid = dom.UUIDString() transition = None if event == libvirt.VIR_DOMAIN_EVENT_STOPPED: transition = virtevent.EVENT_LIFECYCLE_STOPPED elif event == libvirt.VIR_DOMAIN_EVENT_STARTED: transition = virtevent.EVENT_LIFECYCLE_STARTED elif event == libvirt.VIR_DOMAIN_EVENT_SUSPENDED: transition = virtevent.EVENT_LIFECYCLE_PAUSED elif event == libvirt.VIR_DOMAIN_EVENT_RESUMED: transition = virtevent.EVENT_LIFECYCLE_RESUMED if transition is not None: self._queue_event(virtevent.LifecycleEvent(uuid, transition)) def _queue_event(self, event): """Puts an event on the queue for dispatch. This method is called by the native event thread to put events on the queue for later dispatch by the green thread. """ if self._event_queue is None: LOG.debug("Event loop thread is not active, " "discarding event %s" % event) return # Queue the event... self._event_queue.put(event) # ...then wakeup the green thread to dispatch it c = ' '.encode() self._event_notify_send.write(c) self._event_notify_send.flush() def _dispatch_events(self): """Wait for & dispatch events from native thread Blocks until native thread indicates some events are ready. Then dispatches all queued events. """ # Wait to be notified that there are some # events pending try: _c = self._event_notify_recv.read(1) assert _c except ValueError: return # will be raised when pipe is closed # Process as many events as possible without # blocking while not self._event_queue.empty(): try: event = self._event_queue.get(block=False) self.emit_event(event) except native_Queue.Empty: pass def _init_events_pipe(self): """Create a self-pipe for the native thread to synchronize on. This code is taken from the eventlet tpool module, under terms of the Apache License v2.0. """ self._event_queue = native_Queue.Queue() try: rpipe, wpipe = os.pipe() self._event_notify_send = greenio.GreenPipe(wpipe, 'wb', 0) self._event_notify_recv = greenio.GreenPipe(rpipe, 'rb', 0) except (ImportError, NotImplementedError): # This is Windows compatibility -- use a socket instead # of a pipe because pipes don't really exist on Windows. sock = eventlet_util.__original_socket__(socket.AF_INET, socket.SOCK_STREAM) sock.bind(('localhost', 0)) sock.listen(50) csock = eventlet_util.__original_socket__(socket.AF_INET, socket.SOCK_STREAM) csock.connect(('localhost', sock.getsockname()[1])) nsock, addr = sock.accept() self._event_notify_send = nsock.makefile('wb', 0) gsock = greenio.GreenSocket(csock) self._event_notify_recv = gsock.makefile('rb', 0) def _init_events(self): """Initializes the libvirt events subsystem. This requires running a native thread to provide the libvirt event loop integration. This forwards events to a green thread which does the actual dispatching. """ self._init_events_pipe() LOG.debug("Starting native event thread") event_thread = native_threading.Thread(target=self._native_thread) event_thread.setDaemon(True) event_thread.start() LOG.debug("Starting green dispatch thread") eventlet.spawn(self._dispatch_thread) def init_host(self, host): if not self.has_min_version(MIN_LIBVIRT_VERSION): major = MIN_LIBVIRT_VERSION[0] minor = MIN_LIBVIRT_VERSION[1] micro = MIN_LIBVIRT_VERSION[2] LOG.error(_('Nova requires libvirt version ' '%(major)i.%(minor)i.%(micro)i or greater.'), {'major': major, 'minor': minor, 'micro': micro}) libvirt.registerErrorHandler(libvirt_error_handler, None) libvirt.virEventRegisterDefaultImpl() self._init_events() def _get_connection(self): with self._wrapped_conn_lock: wrapped_conn = self._wrapped_conn if not wrapped_conn or not self._test_connection(wrapped_conn): LOG.debug(_('Connecting to libvirt: %s'), self.uri()) if not CONF.libvirt_nonblocking: wrapped_conn = self._connect(self.uri(), self.read_only) else: wrapped_conn = tpool.proxy_call( (libvirt.virDomain, libvirt.virConnect), self._connect, self.uri(), self.read_only) with self._wrapped_conn_lock: self._wrapped_conn = wrapped_conn try: LOG.debug("Registering for lifecycle events %s" % str(self)) wrapped_conn.domainEventRegisterAny( None, libvirt.VIR_DOMAIN_EVENT_ID_LIFECYCLE, self._event_lifecycle_callback, self) except Exception: LOG.warn(_("URI %s does not support events"), self.uri()) if self.has_min_version(MIN_LIBVIRT_CLOSE_CALLBACK_VERSION): try: LOG.debug("Registering for connection events: %s" % str(self)) wrapped_conn.registerCloseCallback( self._close_callback, None) except libvirt.libvirtError: LOG.debug(_("URI %s does not support connection events"), self.uri()) return wrapped_conn _conn = property(_get_connection) def _close_callback(self, conn, reason, opaque): with self._wrapped_conn_lock: if conn == self._wrapped_conn: LOG.info(_("Connection to libvirt lost: %s") % reason) self._wrapped_conn = None @staticmethod def _test_connection(conn): try: conn.getLibVersion() return True except libvirt.libvirtError as e: if (e.get_error_code() in (libvirt.VIR_ERR_SYSTEM_ERROR, libvirt.VIR_ERR_INTERNAL_ERROR) and e.get_error_domain() in (libvirt.VIR_FROM_REMOTE, libvirt.VIR_FROM_RPC)): LOG.debug(_('Connection to libvirt broke')) return False raise @staticmethod def uri(): if CONF.libvirt_type == 'uml': uri = CONF.libvirt_uri or 'uml:///system' elif CONF.libvirt_type == 'xen': uri = CONF.libvirt_uri or 'xen:///' elif CONF.libvirt_type == 'lxc': uri = CONF.libvirt_uri or 'lxc:///' else: uri = CONF.libvirt_uri or 'qemu:///system' return uri @staticmethod def _connect(uri, read_only): def _connect_auth_cb(creds, opaque): if len(creds) == 0: return 0 LOG.warning( _("Can not handle authentication request for %d credentials") % len(creds)) raise exception.NovaException( _("Can not handle authentication request for %d credentials") % len(creds)) auth = [[libvirt.VIR_CRED_AUTHNAME, libvirt.VIR_CRED_ECHOPROMPT, libvirt.VIR_CRED_REALM, libvirt.VIR_CRED_PASSPHRASE, libvirt.VIR_CRED_NOECHOPROMPT, libvirt.VIR_CRED_EXTERNAL], _connect_auth_cb, None] try: if read_only: return libvirt.openReadOnly(uri) else: return libvirt.openAuth(uri, auth, 0) except libvirt.libvirtError as ex: LOG.exception(_("Connection to libvirt failed: %s"), ex) payload = dict(ip=LibvirtDriver.get_host_ip_addr(), method='_connect', reason=ex) notifier.notify(nova_context.get_admin_context(), notifier.publisher_id('compute'), 'compute.libvirt.error', notifier.ERROR, payload) pass def get_num_instances(self): """Efficient override of base instance_exists method.""" return self._conn.numOfDomains() def instance_exists(self, instance_name): """Efficient override of base instance_exists method.""" try: self._lookup_by_name(instance_name) return True except exception.NovaException: return False def legacy_nwinfo(self): return True # TODO(Shrews): Remove when libvirt Bugzilla bug # 836647 is fixed. def list_instance_ids(self): if self._conn.numOfDomains() == 0: return [] return self._conn.listDomainsID() def list_instances(self): names = [] for domain_id in self.list_instance_ids(): try: # We skip domains with ID 0 (hypervisors). if domain_id != 0: domain = self._lookup_by_id(domain_id) names.append(domain.name()) except exception.InstanceNotFound: # Ignore deleted instance while listing continue # extend instance list to contain also defined domains names.extend([vm for vm in self._conn.listDefinedDomains() if vm not in names]) return names def list_instance_uuids(self): uuids = set() for domain_id in self.list_instance_ids(): try: # We skip domains with ID 0 (hypervisors). if domain_id != 0: domain = self._lookup_by_id(domain_id) uuids.add(domain.UUIDString()) except exception.InstanceNotFound: # Ignore deleted instance while listing continue # extend instance list to contain also defined domains for domain_name in self._conn.listDefinedDomains(): try: uuids.add(self._lookup_by_name(domain_name).UUIDString()) except exception.InstanceNotFound: # Ignore deleted instance while listing continue return list(uuids) def plug_vifs(self, instance, network_info): """Plug VIFs into networks.""" for (network, mapping) in network_info: self.vif_driver.plug(instance, (network, mapping)) def unplug_vifs(self, instance, network_info): """Unplug VIFs from networks.""" for (network, mapping) in network_info: self.vif_driver.unplug(instance, (network, mapping)) def _destroy(self, instance): try: virt_dom = self._lookup_by_name(instance['name']) except exception.NotFound: virt_dom = None # If the instance is already terminated, we're still happy # Otherwise, destroy it old_domid = -1 if virt_dom is not None: try: old_domid = virt_dom.ID() virt_dom.destroy() except libvirt.libvirtError as e: is_okay = False errcode = e.get_error_code() if errcode == libvirt.VIR_ERR_OPERATION_INVALID: # If the instance is already shut off, we get this: # Code=55 Error=Requested operation is not valid: # domain is not running (state, _max_mem, _mem, _cpus, _t) = virt_dom.info() state = LIBVIRT_POWER_STATE[state] if state == power_state.SHUTDOWN: is_okay = True elif errcode == libvirt.VIR_ERR_OPERATION_TIMEOUT: LOG.warn(_("Cannot destroy instance, operation time out"), instance=instance) reason = _("operation time out") raise exception.InstancePowerOffFailure(reason=reason) if not is_okay: with excutils.save_and_reraise_exception(): LOG.error(_('Error from libvirt during destroy. ' 'Code=%(errcode)s Error=%(e)s'), {'errcode': errcode, 'e': e}, instance=instance) def _wait_for_destroy(expected_domid): """Called at an interval until the VM is gone.""" # NOTE(vish): If the instance disappears during the destroy # we ignore it so the cleanup can still be # attempted because we would prefer destroy to # never fail. try: dom_info = self.get_info(instance) state = dom_info['state'] new_domid = dom_info['id'] except exception.NotFound: LOG.error(_("During wait destroy, instance disappeared."), instance=instance) raise loopingcall.LoopingCallDone() if state == power_state.SHUTDOWN: LOG.info(_("Instance destroyed successfully."), instance=instance) raise loopingcall.LoopingCallDone() # NOTE(wangpan): If the instance was booted again after destroy, # this may be a endless loop, so check the id of # domain here, if it changed and the instance is # still running, we should destroy it again. # see https://bugs.launchpad.net/nova/+bug/1111213 for more details if new_domid != expected_domid: LOG.info(_("Instance may be started again."), instance=instance) kwargs['is_running'] = True raise loopingcall.LoopingCallDone() kwargs = {'is_running': False} timer = loopingcall.FixedIntervalLoopingCall(_wait_for_destroy, old_domid) timer.start(interval=0.5).wait() if kwargs['is_running']: LOG.info(_("Going to destroy instance again."), instance=instance) self._destroy(instance) def destroy(self, instance, network_info, block_device_info=None, destroy_disks=True): self._destroy(instance) self._cleanup(instance, network_info, block_device_info, destroy_disks) def _undefine_domain(self, instance): try: virt_dom = self._lookup_by_name(instance['name']) except exception.NotFound: virt_dom = None if virt_dom: try: try: virt_dom.undefineFlags( libvirt.VIR_DOMAIN_UNDEFINE_MANAGED_SAVE) except libvirt.libvirtError: LOG.debug(_("Error from libvirt during undefineFlags." " Retrying with undefine"), instance=instance) virt_dom.undefine() except AttributeError: # NOTE(vish): Older versions of libvirt don't support # undefine flags, so attempt to do the # right thing. try: if virt_dom.hasManagedSaveImage(0): virt_dom.managedSaveRemove(0) except AttributeError: pass virt_dom.undefine() except libvirt.libvirtError as e: with excutils.save_and_reraise_exception(): errcode = e.get_error_code() LOG.error(_('Error from libvirt during undefine. ' 'Code=%(errcode)s Error=%(e)s') % {'errcode': errcode, 'e': e}, instance=instance) def _cleanup(self, instance, network_info, block_device_info, destroy_disks): self._undefine_domain(instance) self.unplug_vifs(instance, network_info) retry = True while retry: try: self.firewall_driver.unfilter_instance(instance, network_info=network_info) except libvirt.libvirtError as e: try: state = self.get_info(instance)['state'] except exception.NotFound: state = power_state.SHUTDOWN if state != power_state.SHUTDOWN: LOG.warn(_("Instance may be still running, destroy " "it again."), instance=instance) self._destroy(instance) else: retry = False errcode = e.get_error_code() LOG.error(_('Error from libvirt during unfilter. ' 'Code=%(errcode)s Error=%(e)s') % {'errcode': errcode, 'e': e}, instance=instance) reason = "Error unfiltering instance." raise exception.InstanceTerminationFailure(reason=reason) except Exception: retry = False raise else: retry = False # FIXME(wangpan): if the instance is booted again here, such as the # the soft reboot operation boot it here, it will # become "running deleted", should we check and destroy # it at the end of this method? # NOTE(vish): we disconnect from volumes regardless block_device_mapping = driver.block_device_info_get_mapping( block_device_info) for vol in block_device_mapping: connection_info = vol['connection_info'] disk_dev = vol['mount_device'].rpartition("/")[2] self.volume_driver_method('disconnect_volume', connection_info, disk_dev) if destroy_disks: target = libvirt_utils.get_instance_path(instance) LOG.info(_('Deleting instance files %s'), target, instance=instance) if os.path.exists(target): # If we fail to get rid of the directory # tree, this shouldn't block deletion of # the instance as whole. try: shutil.rmtree(target) except OSError as e: LOG.error(_('Failed to cleanup directory %(target)s: ' '%(e)s'), {'target': target, 'e': e}) #NOTE(bfilippov): destroy all LVM disks for this instance self._cleanup_lvm(instance) def _cleanup_lvm(self, instance): """Delete all LVM disks for given instance object.""" disks = self._lvm_disks(instance) if disks: libvirt_utils.remove_logical_volumes(*disks) def _lvm_disks(self, instance): """Returns all LVM disks for given instance object.""" if CONF.libvirt_images_volume_group: vg = os.path.join('/dev', CONF.libvirt_images_volume_group) if not os.path.exists(vg): return [] pattern = '%s_' % instance['name'] def belongs_to_instance(disk): return disk.startswith(pattern) def fullpath(name): return os.path.join(vg, name) logical_volumes = libvirt_utils.list_logical_volumes(vg) disk_names = filter(belongs_to_instance, logical_volumes) disks = map(fullpath, disk_names) return disks return [] def get_volume_connector(self, instance): if not self._initiator: self._initiator = libvirt_utils.get_iscsi_initiator() if not self._initiator: LOG.debug(_('Could not determine iscsi initiator name'), instance=instance) if not self._fc_wwnns: self._fc_wwnns = libvirt_utils.get_fc_wwnns() if not self._fc_wwnns or len(self._fc_wwnns) == 0: LOG.debug(_('Could not determine fibre channel ' 'world wide node names'), instance=instance) if not self._fc_wwpns: self._fc_wwpns = libvirt_utils.get_fc_wwpns() if not self._fc_wwpns or len(self._fc_wwpns) == 0: LOG.debug(_('Could not determine fibre channel ' 'world wide port names'), instance=instance) connector = {'ip': CONF.my_ip, 'host': CONF.host} if self._initiator: connector['initiator'] = self._initiator if self._fc_wwnns and self._fc_wwpns: connector["wwnns"] = self._fc_wwnns connector["wwpns"] = self._fc_wwpns return connector def _cleanup_resize(self, instance, network_info): target = libvirt_utils.get_instance_path(instance) + "_resize" if os.path.exists(target): shutil.rmtree(target) if instance['host'] != CONF.host: self._undefine_domain(instance) self.unplug_vifs(instance, network_info) self.firewall_driver.unfilter_instance(instance, network_info) def volume_driver_method(self, method_name, connection_info, *args, **kwargs): driver_type = connection_info.get('driver_volume_type') if driver_type not in self.volume_drivers: raise exception.VolumeDriverNotFound(driver_type=driver_type) driver = self.volume_drivers[driver_type] method = getattr(driver, method_name) return method(connection_info, *args, **kwargs) def attach_volume(self, connection_info, instance, mountpoint): instance_name = instance['name'] virt_dom = self._lookup_by_name(instance_name) disk_dev = mountpoint.rpartition("/")[2] disk_info = { 'dev': disk_dev, 'bus': blockinfo.get_disk_bus_for_disk_dev(CONF.libvirt_type, disk_dev), 'type': 'disk', } conf = self.volume_driver_method('connect_volume', connection_info, disk_info) self.set_cache_mode(conf) try: # NOTE(vish): We can always affect config because our # domains are persistent, but we should only # affect live if the domain is running. flags = libvirt.VIR_DOMAIN_AFFECT_CONFIG state = LIBVIRT_POWER_STATE[virt_dom.info()[0]] if state == power_state.RUNNING: flags |= libvirt.VIR_DOMAIN_AFFECT_LIVE virt_dom.attachDeviceFlags(conf.to_xml(), flags) except Exception as ex: if isinstance(ex, libvirt.libvirtError): errcode = ex.get_error_code() if errcode == libvirt.VIR_ERR_OPERATION_FAILED: self.volume_driver_method('disconnect_volume', connection_info, disk_dev) raise exception.DeviceIsBusy(device=disk_dev) with excutils.save_and_reraise_exception(): self.volume_driver_method('disconnect_volume', connection_info, disk_dev) @staticmethod def _get_disk_xml(xml, device): """Returns the xml for the disk mounted at device.""" try: doc = etree.fromstring(xml) except Exception: return None ret = doc.findall('./devices/disk') for node in ret: for child in node.getchildren(): if child.tag == 'target': if child.get('dev') == device: return etree.tostring(node) def _get_existing_domain_xml(self, instance, network_info, block_device_info=None): try: virt_dom = self._lookup_by_name(instance['name']) xml = virt_dom.XMLDesc(0) except exception.InstanceNotFound: disk_info = blockinfo.get_disk_info(CONF.libvirt_type, instance, block_device_info) xml = self.to_xml(instance, network_info, disk_info, block_device_info=block_device_info) return xml def detach_volume(self, connection_info, instance, mountpoint): instance_name = instance['name'] disk_dev = mountpoint.rpartition("/")[2] try: virt_dom = self._lookup_by_name(instance_name) xml = self._get_disk_xml(virt_dom.XMLDesc(0), disk_dev) if not xml: raise exception.DiskNotFound(location=disk_dev) else: # NOTE(vish): We can always affect config because our # domains are persistent, but we should only # affect live if the domain is running. flags = libvirt.VIR_DOMAIN_AFFECT_CONFIG state = LIBVIRT_POWER_STATE[virt_dom.info()[0]] if state == power_state.RUNNING: flags |= libvirt.VIR_DOMAIN_AFFECT_LIVE virt_dom.detachDeviceFlags(xml, flags) except libvirt.libvirtError as ex: # NOTE(vish): This is called to cleanup volumes after live # migration, so we should still disconnect even if # the instance doesn't exist here anymore. error_code = ex.get_error_code() if error_code == libvirt.VIR_ERR_NO_DOMAIN: # NOTE(vish): LOG.warn(_("During detach_volume, instance disappeared.")) else: raise self.volume_driver_method('disconnect_volume', connection_info, disk_dev) @exception.wrap_exception() def attach_interface(self, instance, image_meta, network_info): virt_dom = self._lookup_by_name(instance['name']) for (network, mapping) in network_info: self.vif_driver.plug(instance, (network, mapping)) self.firewall_driver.setup_basic_filtering(instance, [(network, mapping)]) cfg = self.vif_driver.get_config(instance, network, mapping, image_meta) try: flags = libvirt.VIR_DOMAIN_AFFECT_CONFIG state = LIBVIRT_POWER_STATE[virt_dom.info()[0]] if state == power_state.RUNNING: flags |= libvirt.VIR_DOMAIN_AFFECT_LIVE virt_dom.attachDeviceFlags(cfg.to_xml(), flags) except libvirt.libvirtError: LOG.error(_('attaching network adapter failed.'), instance=instance) self.vif_driver.unplug(instance, (network, mapping)) raise exception.InterfaceAttachFailed(instance) @exception.wrap_exception() def detach_interface(self, instance, network_info): virt_dom = self._lookup_by_name(instance['name']) for (network, mapping) in network_info: cfg = self.vif_driver.get_config(instance, network, mapping, None) try: self.vif_driver.unplug(instance, (network, mapping)) flags = libvirt.VIR_DOMAIN_AFFECT_CONFIG state = LIBVIRT_POWER_STATE[virt_dom.info()[0]] if state == power_state.RUNNING: flags |= libvirt.VIR_DOMAIN_AFFECT_LIVE virt_dom.detachDeviceFlags(cfg.to_xml(), flags) except libvirt.libvirtError as ex: error_code = ex.get_error_code() if error_code == libvirt.VIR_ERR_NO_DOMAIN: LOG.warn(_("During detach_interface, " "instance disappeared."), instance=instance) else: LOG.error(_('detaching network adapter failed.'), instance=instance) raise exception.InterfaceDetachFailed(instance) def snapshot(self, context, instance, image_href, update_task_state): """Create snapshot from a running VM instance. This command only works with qemu 0.14+ """ try: virt_dom = self._lookup_by_name(instance['name']) except exception.InstanceNotFound: raise exception.InstanceNotRunning(instance_id=instance['uuid']) (image_service, image_id) = glance.get_remote_image_service( context, instance['image_ref']) try: base = image_service.show(context, image_id) except exception.ImageNotFound: base = {} _image_service = glance.get_remote_image_service(context, image_href) snapshot_image_service, snapshot_image_id = _image_service snapshot = snapshot_image_service.show(context, snapshot_image_id) metadata = {'is_public': False, 'status': 'active', 'name': snapshot['name'], 'properties': { 'kernel_id': instance['kernel_id'], 'image_location': 'snapshot', 'image_state': 'available', 'owner_id': instance['project_id'], 'ramdisk_id': instance['ramdisk_id'], } } disk_path = libvirt_utils.find_disk(virt_dom) source_format = libvirt_utils.get_disk_type(disk_path) image_format = CONF.snapshot_image_format or source_format # NOTE(bfilippov): save lvm as raw if image_format == 'lvm': image_format = 'raw' # NOTE(vish): glance forces ami disk format to be ami if base.get('disk_format') == 'ami': metadata['disk_format'] = 'ami' else: metadata['disk_format'] = image_format metadata['container_format'] = base.get('container_format', 'bare') snapshot_name = uuid.uuid4().hex (state, _max_mem, _mem, _cpus, _t) = virt_dom.info() state = LIBVIRT_POWER_STATE[state] # NOTE(rmk): Live snapshots require QEMU 1.3 and Libvirt 1.0.0. # These restrictions can be relaxed as other configurations # can be validated. if self.has_min_version(MIN_LIBVIRT_LIVESNAPSHOT_VERSION, MIN_QEMU_LIVESNAPSHOT_VERSION, REQ_HYPERVISOR_LIVESNAPSHOT) \ and not source_format == "lvm": live_snapshot = True # Abort is an idempotent operation, so make sure any block # jobs which may have failed are ended. This operation also # confims the running instance, as opposed to the system as a # whole, has a new enough version of the hypervisor (bug 1193146). try: virt_dom.blockJobAbort(disk_path, 0) except libvirt.libvirtError as ex: error_code = ex.get_error_code() if error_code == libvirt.VIR_ERR_CONFIG_UNSUPPORTED: live_snapshot = False else: pass else: live_snapshot = False # NOTE(rmk): We cannot perform live snapshots when a managedSave # file is present, so we will use the cold/legacy method # for instances which are shutdown. if state == power_state.SHUTDOWN: live_snapshot = False # NOTE(dkang): managedSave does not work for LXC if CONF.libvirt_type != 'lxc' and not live_snapshot: if state == power_state.RUNNING or state == power_state.PAUSED: virt_dom.managedSave(0) snapshot_backend = self.image_backend.snapshot(disk_path, snapshot_name, image_type=source_format) if live_snapshot: LOG.info(_("Beginning live snapshot process"), instance=instance) else: LOG.info(_("Beginning cold snapshot process"), instance=instance) snapshot_backend.snapshot_create() update_task_state(task_state=task_states.IMAGE_PENDING_UPLOAD) snapshot_directory = CONF.libvirt_snapshots_directory fileutils.ensure_tree(snapshot_directory) with utils.tempdir(dir=snapshot_directory) as tmpdir: try: out_path = os.path.join(tmpdir, snapshot_name) if live_snapshot: # NOTE (rmk): libvirt needs to be able to write to the # temp directory, which is owned nova. utils.execute('chmod', '777', tmpdir, run_as_root=True) self._live_snapshot(virt_dom, disk_path, out_path, image_format) else: snapshot_backend.snapshot_extract(out_path, image_format) finally: if not live_snapshot: snapshot_backend.snapshot_delete() # NOTE(dkang): because previous managedSave is not called # for LXC, _create_domain must not be called. if CONF.libvirt_type != 'lxc' and not live_snapshot: if state == power_state.RUNNING: self._create_domain(domain=virt_dom) elif state == power_state.PAUSED: self._create_domain(domain=virt_dom, launch_flags=libvirt.VIR_DOMAIN_START_PAUSED) LOG.info(_("Snapshot extracted, beginning image upload"), instance=instance) # Upload that image to the image service update_task_state(task_state=task_states.IMAGE_UPLOADING, expected_state=task_states.IMAGE_PENDING_UPLOAD) with libvirt_utils.file_open(out_path) as image_file: image_service.update(context, image_href, metadata, image_file) LOG.info(_("Snapshot image upload complete"), instance=instance) def _live_snapshot(self, domain, disk_path, out_path, image_format): """Snapshot an instance without downtime.""" # Save a copy of the domain's running XML file xml = domain.XMLDesc(0) def _wait_for_block_job(domain, disk_path): status = domain.blockJobInfo(disk_path, 0) try: cur = status.get('cur', 0) end = status.get('end', 0) except Exception: return False if cur == end and cur != 0 and end != 0: return False else: return True # NOTE (rmk): We are using shallow rebases as a workaround to a bug # in QEMU 1.3. In order to do this, we need to create # a destination image with the original backing file # and matching size of the instance root disk. src_disk_size = libvirt_utils.get_disk_size(disk_path) src_back_path = libvirt_utils.get_disk_backing_file(disk_path, basename=False) disk_delta = out_path + '.delta' libvirt_utils.create_cow_image(src_back_path, disk_delta, src_disk_size) try: # NOTE (rmk): blockRebase cannot be executed on persistent # domains, so we need to temporarily undefine it. # If any part of this block fails, the domain is # re-defined regardless. if domain.isPersistent(): domain.undefine() # NOTE (rmk): Establish a temporary mirror of our root disk and # issue an abort once we have a complete copy. domain.blockRebase(disk_path, disk_delta, 0, libvirt.VIR_DOMAIN_BLOCK_REBASE_COPY | libvirt.VIR_DOMAIN_BLOCK_REBASE_REUSE_EXT | libvirt.VIR_DOMAIN_BLOCK_REBASE_SHALLOW) while _wait_for_block_job(domain, disk_path): time.sleep(0.5) domain.blockJobAbort(disk_path, 0) libvirt_utils.chown(disk_delta, os.getuid()) finally: self._conn.defineXML(xml) # Convert the delta (CoW) image with a backing file to a flat # image with no backing file. libvirt_utils.extract_snapshot(disk_delta, 'qcow2', None, out_path, image_format) def reboot(self, context, instance, network_info, reboot_type='SOFT', block_device_info=None, bad_volumes_callback=None): """Reboot a virtual machine, given an instance reference.""" if reboot_type == 'SOFT': # NOTE(vish): This will attempt to do a graceful shutdown/restart. if self._soft_reboot(instance): LOG.info(_("Instance soft rebooted successfully."), instance=instance) return else: LOG.warn(_("Failed to soft reboot instance."), instance=instance) return self._hard_reboot(context, instance, network_info, block_device_info) def _soft_reboot(self, instance): """Attempt to shutdown and restart the instance gracefully. We use shutdown and create here so we can return if the guest responded and actually rebooted. Note that this method only succeeds if the guest responds to acpi. Therefore we return success or failure so we can fall back to a hard reboot if necessary. :returns: True if the reboot succeeded """ dom = self._lookup_by_name(instance["name"]) (state, _max_mem, _mem, _cpus, _t) = dom.info() state = LIBVIRT_POWER_STATE[state] old_domid = dom.ID() # NOTE(vish): This check allows us to reboot an instance that # is already shutdown. if state == power_state.RUNNING: dom.shutdown() # NOTE(vish): This actually could take slighty longer than the # FLAG defines depending on how long the get_info # call takes to return. for x in xrange(CONF.libvirt_wait_soft_reboot_seconds): dom = self._lookup_by_name(instance["name"]) (state, _max_mem, _mem, _cpus, _t) = dom.info() state = LIBVIRT_POWER_STATE[state] new_domid = dom.ID() # NOTE(ivoks): By checking domain IDs, we make sure we are # not recreating domain that's already running. if old_domid != new_domid: if state in [power_state.SHUTDOWN, power_state.CRASHED]: LOG.info(_("Instance shutdown successfully."), instance=instance) self._create_domain(domain=dom) timer = loopingcall.FixedIntervalLoopingCall( self._wait_for_running, instance) timer.start(interval=0.5).wait() return True else: LOG.info(_("Instance may have been rebooted during soft " "reboot, so return now."), instance=instance) return True greenthread.sleep(1) return False def _hard_reboot(self, context, instance, network_info, block_device_info=None): """Reboot a virtual machine, given an instance reference. Performs a Libvirt reset (if supported) on the domain. If Libvirt reset is unavailable this method actually destroys and re-creates the domain to ensure the reboot happens, as the guest OS cannot ignore this action. If xml is set, it uses the passed in xml in place of the xml from the existing domain. """ self._destroy(instance) disk_info = blockinfo.get_disk_info(CONF.libvirt_type, instance, block_device_info) # NOTE(vish): This could generate the wrong device_format if we are # using the raw backend and the images don't exist yet. # The create_images_and_backing below doesn't properly # regenerate raw backend images, however, so when it # does we need to (re)generate the xml after the images # are in place. xml = self.to_xml(instance, network_info, disk_info, block_device_info=block_device_info, write_to_disk=True) # NOTE (rmk): Re-populate any missing backing files. disk_info_json = self.get_instance_disk_info(instance['name'], xml, block_device_info) self._create_images_and_backing(context, instance, disk_info_json) # Initialize all the necessary networking, block devices and # start the instance. self._create_domain_and_network(xml, instance, network_info, block_device_info) def _wait_for_reboot(): """Called at an interval until the VM is running again.""" state = self.get_info(instance)['state'] if state == power_state.RUNNING: LOG.info(_("Instance rebooted successfully."), instance=instance) raise loopingcall.LoopingCallDone() timer = loopingcall.FixedIntervalLoopingCall(_wait_for_reboot) timer.start(interval=0.5).wait() def pause(self, instance): """Pause VM instance.""" dom = self._lookup_by_name(instance['name']) dom.suspend() def unpause(self, instance): """Unpause paused VM instance.""" dom = self._lookup_by_name(instance['name']) dom.resume() def power_off(self, instance): """Power off the specified instance.""" self._destroy(instance) def power_on(self, context, instance, network_info, block_device_info=None): """Power on the specified instance.""" # We use _hard_reboot here to ensure that all backing files, # network, and block device connections, etc. are established # and available before we attempt to start the instance. self._hard_reboot(context, instance, network_info, block_device_info) def suspend(self, instance): """Suspend the specified instance.""" dom = self._lookup_by_name(instance['name']) dom.managedSave(0) def resume(self, instance, network_info, block_device_info=None): """resume the specified instance.""" xml = self._get_existing_domain_xml(instance, network_info, block_device_info) self._create_domain_and_network(xml, instance, network_info, block_device_info) def resume_state_on_host_boot(self, context, instance, network_info, block_device_info=None): """resume guest state when a host is booted.""" # Check if the instance is running already and avoid doing # anything if it is. if self.instance_exists(instance['name']): domain = self._lookup_by_name(instance['name']) state = LIBVIRT_POWER_STATE[domain.info()[0]] ignored_states = (power_state.RUNNING, power_state.SUSPENDED, power_state.NOSTATE, power_state.PAUSED) if state in ignored_states: return # Instance is not up and could be in an unknown state. # Be as absolute as possible about getting it back into # a known and running state. self._hard_reboot(context, instance, network_info, block_device_info) def rescue(self, context, instance, network_info, image_meta, rescue_password): """Loads a VM using rescue images. A rescue is normally performed when something goes wrong with the primary images and data needs to be corrected/recovered. Rescuing should not edit or over-ride the original image, only allow for data recovery. """ instance_dir = libvirt_utils.get_instance_path(instance) unrescue_xml = self._get_existing_domain_xml(instance, network_info) unrescue_xml_path = os.path.join(instance_dir, 'unrescue.xml') libvirt_utils.write_to_file(unrescue_xml_path, unrescue_xml) rescue_images = { 'image_id': CONF.rescue_image_id or instance['image_ref'], 'kernel_id': CONF.rescue_kernel_id or instance['kernel_id'], 'ramdisk_id': CONF.rescue_ramdisk_id or instance['ramdisk_id'], } disk_info = blockinfo.get_disk_info(CONF.libvirt_type, instance, None, image_meta, rescue=True) self._create_image(context, instance, disk_info['mapping'], '.rescue', rescue_images, network_info=network_info, admin_pass=rescue_password) xml = self.to_xml(instance, network_info, disk_info, image_meta, rescue=rescue_images, write_to_disk=True) self._destroy(instance) self._create_domain(xml) def unrescue(self, instance, network_info): """Reboot the VM which is being rescued back into primary images. """ instance_dir = libvirt_utils.get_instance_path(instance) unrescue_xml_path = os.path.join(instance_dir, 'unrescue.xml') xml = libvirt_utils.load_file(unrescue_xml_path) virt_dom = self._lookup_by_name(instance['name']) self._destroy(instance) self._create_domain(xml, virt_dom) libvirt_utils.file_delete(unrescue_xml_path) rescue_files = os.path.join(instance_dir, "*.rescue") for rescue_file in glob.iglob(rescue_files): libvirt_utils.file_delete(rescue_file) def poll_rebooting_instances(self, timeout, instances): pass def _enable_hairpin(self, xml): interfaces = self.get_interfaces(xml) for interface in interfaces: utils.execute('tee', '/sys/class/net/%s/brport/hairpin_mode' % interface, process_input='1', run_as_root=True, check_exit_code=[0, 1]) # NOTE(ilyaalekseyev): Implementation like in multinics # for xenapi(tr3buchet) def spawn(self, context, instance, image_meta, injected_files, admin_password, network_info=None, block_device_info=None): disk_info = blockinfo.get_disk_info(CONF.libvirt_type, instance, block_device_info, image_meta) self._create_image(context, instance, disk_info['mapping'], network_info=network_info, block_device_info=block_device_info, files=injected_files, admin_pass=admin_password) xml = self.to_xml(instance, network_info, disk_info, image_meta, block_device_info=block_device_info, write_to_disk=True) self._create_domain_and_network(xml, instance, network_info, block_device_info) LOG.debug(_("Instance is running"), instance=instance) def _wait_for_boot(): """Called at an interval until the VM is running.""" state = self.get_info(instance)['state'] if state == power_state.RUNNING: LOG.info(_("Instance spawned successfully."), instance=instance) raise loopingcall.LoopingCallDone() timer = loopingcall.FixedIntervalLoopingCall(_wait_for_boot) timer.start(interval=0.5).wait() def _flush_libvirt_console(self, pty): out, err = utils.execute('dd', 'if=%s' % pty, 'iflag=nonblock', run_as_root=True, check_exit_code=False) return out def _append_to_file(self, data, fpath): LOG.info(_('data: %(data)r, fpath: %(fpath)r'), {'data': data, 'fpath': fpath}) fp = open(fpath, 'a+') fp.write(data) return fpath def get_console_output(self, instance): virt_dom = self._lookup_by_name(instance['name']) xml = virt_dom.XMLDesc(0) tree = etree.fromstring(xml) console_types = {} # NOTE(comstud): We want to try 'file' types first, then try 'pty' # types. We can't use Python 2.7 syntax of: # tree.find("./devices/console[@type='file']/source") # because we need to support 2.6. console_nodes = tree.findall('./devices/console') for console_node in console_nodes: console_type = console_node.get('type') console_types.setdefault(console_type, []) console_types[console_type].append(console_node) # If the guest has a console logging to a file prefer to use that if console_types.get('file'): for file_console in console_types.get('file'): source_node = file_console.find('./source') if source_node is None: continue path = source_node.get("path") if not path: continue libvirt_utils.chown(path, os.getuid()) with libvirt_utils.file_open(path, 'rb') as fp: log_data, remaining = utils.last_bytes(fp, MAX_CONSOLE_BYTES) if remaining > 0: LOG.info(_('Truncated console log returned, %d bytes ' 'ignored'), remaining, instance=instance) return log_data # Try 'pty' types if console_types.get('pty'): for pty_console in console_types.get('pty'): source_node = pty_console.find('./source') if source_node is None: continue pty = source_node.get("path") if not pty: continue break else: msg = _("Guest does not have a console available") raise exception.NovaException(msg) self._chown_console_log_for_instance(instance) data = self._flush_libvirt_console(pty) console_log = self._get_console_log_path(instance) fpath = self._append_to_file(data, console_log) with libvirt_utils.file_open(fpath, 'rb') as fp: log_data, remaining = utils.last_bytes(fp, MAX_CONSOLE_BYTES) if remaining > 0: LOG.info(_('Truncated console log returned, %d bytes ignored'), remaining, instance=instance) return log_data @staticmethod def get_host_ip_addr(): return CONF.my_ip def get_vnc_console(self, instance): def get_vnc_port_for_instance(instance_name): virt_dom = self._lookup_by_name(instance_name) xml = virt_dom.XMLDesc(0) # TODO(sleepsonthefloor): use etree instead of minidom dom = minidom.parseString(xml) for graphic in dom.getElementsByTagName('graphics'): if graphic.getAttribute('type') == 'vnc': return graphic.getAttribute('port') port = get_vnc_port_for_instance(instance['name']) host = CONF.vncserver_proxyclient_address return {'host': host, 'port': port, 'internal_access_path': None} @exception.wrap_exception() def get_spice_console(self, instance): def get_spice_ports_for_instance(instance_name): virt_dom = self._lookup_by_name(instance_name) xml = virt_dom.XMLDesc(0) # TODO(sleepsonthefloor): use etree instead of minidom dom = minidom.parseString(xml) for graphic in dom.getElementsByTagName('graphics'): if graphic.getAttribute('type') == 'spice': return (graphic.getAttribute('port'), graphic.getAttribute('tlsPort')) return (None, None) ports = get_spice_ports_for_instance(instance['name']) host = CONF.spice.server_proxyclient_address return {'host': host, 'port': ports[0], 'tlsPort': ports[1], 'internal_access_path': None} @staticmethod def _supports_direct_io(dirpath): if not hasattr(os, 'O_DIRECT'): LOG.debug("This python runtime does not support direct I/O") return False testfile = os.path.join(dirpath, ".directio.test") hasDirectIO = True try: f = os.open(testfile, os.O_CREAT | os.O_WRONLY | os.O_DIRECT) os.close(f) LOG.debug(_("Path '%(path)s' supports direct I/O") % {'path': dirpath}) except OSError as e: if e.errno == errno.EINVAL: LOG.debug(_("Path '%(path)s' does not support direct I/O: " "'%(ex)s'") % {'path': dirpath, 'ex': str(e)}) hasDirectIO = False else: with excutils.save_and_reraise_exception(): LOG.error(_("Error on '%(path)s' while checking " "direct I/O: '%(ex)s'") % {'path': dirpath, 'ex': str(e)}) except Exception as e: with excutils.save_and_reraise_exception(): LOG.error(_("Error on '%(path)s' while checking direct I/O: " "'%(ex)s'") % {'path': dirpath, 'ex': str(e)}) finally: try: os.unlink(testfile) except Exception: pass return hasDirectIO @staticmethod def _create_local(target, local_size, unit='G', fs_format=None, label=None): """Create a blank image of specified size.""" if not fs_format: fs_format = CONF.default_ephemeral_format if not CONF.libvirt_images_type == "lvm": libvirt_utils.create_image('raw', target, '%d%c' % (local_size, unit)) if fs_format: utils.mkfs(fs_format, target, label) def _create_ephemeral(self, target, ephemeral_size, fs_label, os_type): self._create_local(target, ephemeral_size) disk.mkfs(os_type, fs_label, target) @staticmethod def _create_swap(target, swap_mb): """Create a swap file of specified size.""" libvirt_utils.create_image('raw', target, '%dM' % swap_mb) utils.mkfs('swap', target) @staticmethod def _get_console_log_path(instance): return os.path.join(libvirt_utils.get_instance_path(instance), 'console.log') def _chown_console_log_for_instance(self, instance): console_log = self._get_console_log_path(instance) if os.path.exists(console_log): libvirt_utils.chown(console_log, os.getuid()) def _create_image(self, context, instance, disk_mapping, suffix='', disk_images=None, network_info=None, block_device_info=None, files=None, admin_pass=None): if not suffix: suffix = '' booted_from_volume = ( (not bool(instance.get('image_ref'))) or 'disk' not in disk_mapping ) # syntactic nicety def basepath(fname='', suffix=suffix): return os.path.join(libvirt_utils.get_instance_path(instance), fname + suffix) def image(fname, image_type=CONF.libvirt_images_type): return self.image_backend.image(instance, fname + suffix, image_type) def raw(fname): return image(fname, image_type='raw') # ensure directories exist and are writable fileutils.ensure_tree(basepath(suffix='')) LOG.info(_('Creating image'), instance=instance) # NOTE(dprince): for rescue console.log may already exist... chown it. self._chown_console_log_for_instance(instance) # NOTE(vish): No need add the suffix to console.log libvirt_utils.write_to_file( self._get_console_log_path(instance), '', 7) if not disk_images: disk_images = {'image_id': instance['image_ref'], 'kernel_id': instance['kernel_id'], 'ramdisk_id': instance['ramdisk_id']} if disk_images['kernel_id']: fname = imagecache.get_cache_fname(disk_images, 'kernel_id') raw('kernel').cache(fetch_func=libvirt_utils.fetch_image, context=context, filename=fname, image_id=disk_images['kernel_id'], user_id=instance['user_id'], project_id=instance['project_id']) if disk_images['ramdisk_id']: fname = imagecache.get_cache_fname(disk_images, 'ramdisk_id') raw('ramdisk').cache(fetch_func=libvirt_utils.fetch_image, context=context, filename=fname, image_id=disk_images['ramdisk_id'], user_id=instance['user_id'], project_id=instance['project_id']) inst_type = flavors.extract_flavor(instance) # NOTE(ndipanov): Even if disk_mapping was passed in, which # currently happens only on rescue - we still don't want to # create a base image. if not booted_from_volume: root_fname = imagecache.get_cache_fname(disk_images, 'image_id') size = instance['root_gb'] * 1024 * 1024 * 1024 if size == 0 or suffix == '.rescue': size = None image('disk').cache(fetch_func=libvirt_utils.fetch_image, context=context, filename=root_fname, size=size, image_id=disk_images['image_id'], user_id=instance['user_id'], project_id=instance['project_id']) # Lookup the filesystem type if required os_type_with_default = instance['os_type'] if not os_type_with_default: os_type_with_default = 'default' ephemeral_gb = instance['ephemeral_gb'] if 'disk.local' in disk_mapping: fn = functools.partial(self._create_ephemeral, fs_label='ephemeral0', os_type=instance["os_type"]) fname = "ephemeral_%s_%s" % (ephemeral_gb, os_type_with_default) size = ephemeral_gb * 1024 * 1024 * 1024 image('disk.local').cache(fetch_func=fn, filename=fname, size=size, ephemeral_size=ephemeral_gb) for eph in driver.block_device_info_get_ephemerals(block_device_info): fn = functools.partial(self._create_ephemeral, fs_label='ephemeral%d' % eph['num'], os_type=instance["os_type"]) size = eph['size'] * 1024 * 1024 * 1024 fname = "ephemeral_%s_%s" % (eph['size'], os_type_with_default) image(blockinfo.get_eph_disk(eph)).cache( fetch_func=fn, filename=fname, size=size, ephemeral_size=eph['size']) if 'disk.swap' in disk_mapping: mapping = disk_mapping['disk.swap'] swap_mb = 0 swap = driver.block_device_info_get_swap(block_device_info) if driver.swap_is_usable(swap): swap_mb = swap['swap_size'] elif (inst_type['swap'] > 0 and not block_device.volume_in_mapping( mapping['dev'], block_device_info)): swap_mb = inst_type['swap'] if swap_mb > 0: size = swap_mb * 1024 * 1024 image('disk.swap').cache(fetch_func=self._create_swap, filename="swap_%s" % swap_mb, size=size, swap_mb=swap_mb) # Config drive if configdrive.required_by(instance): LOG.info(_('Using config drive'), instance=instance) extra_md = {} if admin_pass: extra_md['admin_pass'] = admin_pass inst_md = instance_metadata.InstanceMetadata(instance, content=files, extra_md=extra_md, network_info=network_info) with configdrive.ConfigDriveBuilder(instance_md=inst_md) as cdb: configdrive_path = basepath(fname='disk.config') LOG.info(_('Creating config drive at %(path)s'), {'path': configdrive_path}, instance=instance) try: cdb.make_drive(configdrive_path) except processutils.ProcessExecutionError as e: with excutils.save_and_reraise_exception(): LOG.error(_('Creating config drive failed ' 'with error: %s'), e, instance=instance) # File injection elif CONF.libvirt_inject_partition != -2: target_partition = None if not instance['kernel_id']: target_partition = CONF.libvirt_inject_partition if target_partition == 0: target_partition = None if CONF.libvirt_type == 'lxc': target_partition = None if CONF.libvirt_inject_key and instance['key_data']: key = str(instance['key_data']) else: key = None net = netutils.get_injected_network_template(network_info) metadata = instance.get('metadata') if not CONF.libvirt_inject_password: admin_pass = None if any((key, net, metadata, admin_pass, files)): # If we're not using config_drive, inject into root fs injection_path = image('disk').path img_id = instance['image_ref'] for inj, val in [('key', key), ('net', net), ('metadata', metadata), ('admin_pass', admin_pass), ('files', files)]: if val: LOG.info(_('Injecting %(inj)s into image ' '%(img_id)s'), {'inj': inj, 'img_id': img_id}, instance=instance) try: disk.inject_data(injection_path, key, net, metadata, admin_pass, files, partition=target_partition, use_cow=CONF.use_cow_images, mandatory=('files',)) except Exception as e: with excutils.save_and_reraise_exception(): LOG.error(_('Error injecting data into image ' '%(img_id)s (%(e)s)'), {'img_id': img_id, 'e': e}, instance=instance) if CONF.libvirt_type == 'uml': libvirt_utils.chown(image('disk').path, 'root') def get_host_capabilities(self): """Returns an instance of config.LibvirtConfigCaps representing the capabilities of the host. """ if not self._caps: xmlstr = self._conn.getCapabilities() self._caps = vconfig.LibvirtConfigCaps() self._caps.parse_str(xmlstr) return self._caps def get_host_uuid(self): """Returns a UUID representing the host.""" caps = self.get_host_capabilities() return caps.host.uuid def get_host_cpu_for_guest(self): """Returns an instance of config.LibvirtConfigGuestCPU representing the host's CPU model & topology with policy for configuring a guest to match """ caps = self.get_host_capabilities() hostcpu = caps.host.cpu guestcpu = vconfig.LibvirtConfigGuestCPU() guestcpu.model = hostcpu.model guestcpu.vendor = hostcpu.vendor guestcpu.arch = hostcpu.arch guestcpu.match = "exact" for hostfeat in hostcpu.features: guestfeat = vconfig.LibvirtConfigGuestCPUFeature(hostfeat.name) guestfeat.policy = "require" guestcpu.features.append(guestfeat) return guestcpu def get_guest_cpu_config(self): mode = CONF.libvirt_cpu_mode model = CONF.libvirt_cpu_model if mode is None: if CONF.libvirt_type == "kvm" or CONF.libvirt_type == "qemu": mode = "host-model" else: mode = "none" if mode == "none": return None if CONF.libvirt_type != "kvm" and CONF.libvirt_type != "qemu": msg = _("Config requested an explicit CPU model, but " "the current libvirt hypervisor '%s' does not " "support selecting CPU models") % CONF.libvirt_type raise exception.Invalid(msg) if mode == "custom" and model is None: msg = _("Config requested a custom CPU model, but no " "model name was provided") raise exception.Invalid(msg) elif mode != "custom" and model is not None: msg = _("A CPU model name should not be set when a " "host CPU model is requested") raise exception.Invalid(msg) LOG.debug(_("CPU mode '%(mode)s' model '%(model)s' was chosen") % {'mode': mode, 'model': (model or "")}) # TODO(berrange): in the future, when MIN_LIBVIRT_VERSION is # updated to be at least this new, we can kill off the elif # blocks here if self.has_min_version(MIN_LIBVIRT_HOST_CPU_VERSION): cpu = vconfig.LibvirtConfigGuestCPU() cpu.mode = mode cpu.model = model elif mode == "custom": cpu = vconfig.LibvirtConfigGuestCPU() cpu.model = model elif mode == "host-model": cpu = self.get_host_cpu_for_guest() elif mode == "host-passthrough": msg = _("Passthrough of the host CPU was requested but " "this libvirt version does not support this feature") raise exception.NovaException(msg) return cpu def get_guest_disk_config(self, instance, name, disk_mapping, inst_type, image_type=None): image = self.image_backend.image(instance, name, image_type) disk_info = disk_mapping[name] return image.libvirt_info(disk_info['bus'], disk_info['dev'], disk_info['type'], self.disk_cachemode, inst_type['extra_specs'], self.get_hypervisor_version()) def get_guest_storage_config(self, instance, image_meta, disk_info, rescue, block_device_info, inst_type): devices = [] disk_mapping = disk_info['mapping'] block_device_mapping = driver.block_device_info_get_mapping( block_device_info) if CONF.libvirt_type == "lxc": fs = vconfig.LibvirtConfigGuestFilesys() fs.source_type = "mount" fs.source_dir = os.path.join( libvirt_utils.get_instance_path(instance), 'rootfs') devices.append(fs) else: if rescue: diskrescue = self.get_guest_disk_config(instance, 'disk.rescue', disk_mapping, inst_type) devices.append(diskrescue) diskos = self.get_guest_disk_config(instance, 'disk', disk_mapping, inst_type) devices.append(diskos) else: if 'disk' in disk_mapping: diskos = self.get_guest_disk_config(instance, 'disk', disk_mapping, inst_type) devices.append(diskos) if 'disk.local' in disk_mapping: disklocal = self.get_guest_disk_config(instance, 'disk.local', disk_mapping, inst_type) devices.append(disklocal) self.virtapi.instance_update( nova_context.get_admin_context(), instance['uuid'], {'default_ephemeral_device': '/dev/' + disklocal.target_dev}) for eph in driver.block_device_info_get_ephemerals( block_device_info): diskeph = self.get_guest_disk_config( instance, blockinfo.get_eph_disk(eph), disk_mapping, inst_type) devices.append(diskeph) if 'disk.swap' in disk_mapping: diskswap = self.get_guest_disk_config(instance, 'disk.swap', disk_mapping, inst_type) devices.append(diskswap) self.virtapi.instance_update( nova_context.get_admin_context(), instance['uuid'], {'default_swap_device': '/dev/' + diskswap.target_dev}) for vol in block_device_mapping: connection_info = vol['connection_info'] info = disk_mapping[vol['mount_device']] cfg = self.volume_driver_method('connect_volume', connection_info, info) devices.append(cfg) if 'disk.config' in disk_mapping: diskconfig = self.get_guest_disk_config(instance, 'disk.config', disk_mapping, inst_type, 'raw') devices.append(diskconfig) for d in devices: self.set_cache_mode(d) return devices def get_guest_config_sysinfo(self, instance): sysinfo = vconfig.LibvirtConfigGuestSysinfo() sysinfo.system_manufacturer = version.vendor_string() sysinfo.system_product = version.product_string() sysinfo.system_version = version.version_string_with_package() sysinfo.system_serial = self.get_host_uuid() sysinfo.system_uuid = instance['uuid'] return sysinfo def get_guest_config(self, instance, network_info, image_meta, disk_info, rescue=None, block_device_info=None): """Get config data for parameters. :param rescue: optional dictionary that should contain the key 'ramdisk_id' if a ramdisk is needed for the rescue image and 'kernel_id' if a kernel is needed for the rescue image. """ inst_type = self.virtapi.instance_type_get( nova_context.get_admin_context(read_deleted='yes'), instance['instance_type_id']) inst_path = libvirt_utils.get_instance_path(instance) disk_mapping = disk_info['mapping'] CONSOLE = "console=tty0 console=ttyS0" guest = vconfig.LibvirtConfigGuest() guest.virt_type = CONF.libvirt_type guest.name = instance['name'] guest.uuid = instance['uuid'] guest.memory = inst_type['memory_mb'] * 1024 guest.vcpus = inst_type['vcpus'] guest.cpuset = CONF.vcpu_pin_set quota_items = ['cpu_shares', 'cpu_period', 'cpu_quota'] for key, value in inst_type['extra_specs'].iteritems(): scope = key.split(':') if len(scope) > 1 and scope[0] == 'quota': if scope[1] in quota_items: setattr(guest, scope[1], value) guest.cpu = self.get_guest_cpu_config() if 'root' in disk_mapping and disk_mapping['root']['dev'] is not None: root_device_name = "/dev/" + disk_mapping['root']['dev'] else: root_device_name = None if root_device_name: # NOTE(yamahata): # for nova.api.ec2.cloud.CloudController.get_metadata() self.virtapi.instance_update( nova_context.get_admin_context(), instance['uuid'], {'root_device_name': root_device_name}) guest.os_type = vm_mode.get_from_instance(instance) if guest.os_type is None: if CONF.libvirt_type == "lxc": guest.os_type = vm_mode.EXE elif CONF.libvirt_type == "uml": guest.os_type = vm_mode.UML elif CONF.libvirt_type == "xen": guest.os_type = vm_mode.XEN else: guest.os_type = vm_mode.HVM if CONF.libvirt_type == "xen" and guest.os_type == vm_mode.HVM: guest.os_loader = CONF.xen_hvmloader_path if CONF.libvirt_type in ("kvm", "qemu"): caps = self.get_host_capabilities() if caps.host.cpu.arch in ("i686", "x86_64"): guest.sysinfo = self.get_guest_config_sysinfo(instance) guest.os_smbios = vconfig.LibvirtConfigGuestSMBIOS() if CONF.libvirt_type == "lxc": guest.os_type = vm_mode.EXE guest.os_init_path = "/sbin/init" guest.os_cmdline = CONSOLE elif CONF.libvirt_type == "uml": guest.os_type = vm_mode.UML guest.os_kernel = "/usr/bin/linux" guest.os_root = root_device_name else: if CONF.libvirt_type == "xen" and guest.os_type == vm_mode.XEN: guest.os_root = root_device_name else: guest.os_type = vm_mode.HVM if rescue: if rescue.get('kernel_id'): guest.os_kernel = os.path.join(inst_path, "kernel.rescue") if CONF.libvirt_type == "xen": guest.os_cmdline = "ro" else: guest.os_cmdline = ("root=%s %s" % (root_device_name, CONSOLE)) if rescue.get('ramdisk_id'): guest.os_initrd = os.path.join(inst_path, "ramdisk.rescue") elif instance['kernel_id']: guest.os_kernel = os.path.join(inst_path, "kernel") if CONF.libvirt_type == "xen": guest.os_cmdline = "ro" else: guest.os_cmdline = ("root=%s %s" % (root_device_name, CONSOLE)) if instance['ramdisk_id']: guest.os_initrd = os.path.join(inst_path, "ramdisk") else: guest.os_boot_dev = "hd" if CONF.libvirt_type != "lxc" and CONF.libvirt_type != "uml": guest.acpi = True guest.apic = True clk = vconfig.LibvirtConfigGuestClock() clk.offset = "utc" guest.set_clock(clk) if CONF.libvirt_type == "kvm": # TODO(berrange) One day this should be per-guest # OS type configurable tmpit = vconfig.LibvirtConfigGuestTimer() tmpit.name = "pit" tmpit.tickpolicy = "delay" tmrtc = vconfig.LibvirtConfigGuestTimer() tmrtc.name = "rtc" tmrtc.tickpolicy = "catchup" clk.add_timer(tmpit) clk.add_timer(tmrtc) for cfg in self.get_guest_storage_config(instance, image_meta, disk_info, rescue, block_device_info, inst_type): guest.add_device(cfg) for (network, mapping) in network_info: cfg = self.vif_driver.get_config(instance, network, mapping, image_meta, inst_type) guest.add_device(cfg) if CONF.libvirt_type == "qemu" or CONF.libvirt_type == "kvm": # The QEMU 'pty' driver throws away any data if no # client app is connected. Thus we can't get away # with a single type=pty console. Instead we have # to configure two separate consoles. consolelog = vconfig.LibvirtConfigGuestSerial() consolelog.type = "file" consolelog.source_path = self._get_console_log_path(instance) guest.add_device(consolelog) consolepty = vconfig.LibvirtConfigGuestSerial() consolepty.type = "pty" guest.add_device(consolepty) else: consolepty = vconfig.LibvirtConfigGuestConsole() consolepty.type = "pty" guest.add_device(consolepty) # We want a tablet if VNC is enabled, # or SPICE is enabled and the SPICE agent is disabled # NB: this implies that if both SPICE + VNC are enabled # at the same time, we'll get the tablet whether the # SPICE agent is used or not. need_usb_tablet = False if CONF.vnc_enabled: need_usb_tablet = CONF.use_usb_tablet elif CONF.spice.enabled and not CONF.spice.agent_enabled: need_usb_tablet = CONF.use_usb_tablet if need_usb_tablet and guest.os_type == vm_mode.HVM: tablet = vconfig.LibvirtConfigGuestInput() tablet.type = "tablet" tablet.bus = "usb" guest.add_device(tablet) if CONF.spice.enabled and CONF.spice.agent_enabled and \ CONF.libvirt_type not in ('lxc', 'uml', 'xen'): channel = vconfig.LibvirtConfigGuestChannel() channel.target_name = "com.redhat.spice.0" guest.add_device(channel) # NB some versions of libvirt support both SPICE and VNC # at the same time. We're not trying to second guess which # those versions are. We'll just let libvirt report the # errors appropriately if the user enables both. if CONF.vnc_enabled and CONF.libvirt_type not in ('lxc', 'uml'): graphics = vconfig.LibvirtConfigGuestGraphics() graphics.type = "vnc" graphics.keymap = CONF.vnc_keymap graphics.listen = CONF.vncserver_listen guest.add_device(graphics) if CONF.spice.enabled and \ CONF.libvirt_type not in ('lxc', 'uml', 'xen'): graphics = vconfig.LibvirtConfigGuestGraphics() graphics.type = "spice" graphics.keymap = CONF.spice.keymap graphics.listen = CONF.spice.server_listen guest.add_device(graphics) return guest def to_xml(self, instance, network_info, disk_info, image_meta=None, rescue=None, block_device_info=None, write_to_disk=False): LOG.debug(_('Start to_xml instance=%(instance)s ' 'network_info=%(network_info)s ' 'disk_info=%(disk_info)s ' 'image_meta=%(image_meta)s rescue=%(rescue)s' 'block_device_info=%(block_device_info)s'), {'instance': instance, 'network_info': network_info, 'disk_info': disk_info, 'image_meta': image_meta, 'rescue': rescue, 'block_device_info': block_device_info}) conf = self.get_guest_config(instance, network_info, image_meta, disk_info, rescue, block_device_info) xml = conf.to_xml() if write_to_disk: instance_dir = libvirt_utils.get_instance_path(instance) xml_path = os.path.join(instance_dir, 'libvirt.xml') libvirt_utils.write_to_file(xml_path, xml) LOG.debug(_('End to_xml instance=%(instance)s xml=%(xml)s'), {'instance': instance, 'xml': xml}) return xml def _lookup_by_id(self, instance_id): """Retrieve libvirt domain object given an instance id. All libvirt error handling should be handled in this method and relevant nova exceptions should be raised in response. """ try: return self._conn.lookupByID(instance_id) except libvirt.libvirtError as ex: error_code = ex.get_error_code() if error_code == libvirt.VIR_ERR_NO_DOMAIN: raise exception.InstanceNotFound(instance_id=instance_id) msg = (_("Error from libvirt while looking up %(instance_id)s: " "[Error Code %(error_code)s] %(ex)s") % {'instance_id': instance_id, 'error_code': error_code, 'ex': ex}) raise exception.NovaException(msg) def _lookup_by_name(self, instance_name): """Retrieve libvirt domain object given an instance name. All libvirt error handling should be handled in this method and relevant nova exceptions should be raised in response. """ try: return self._conn.lookupByName(instance_name) except libvirt.libvirtError as ex: error_code = ex.get_error_code() if error_code == libvirt.VIR_ERR_NO_DOMAIN: raise exception.InstanceNotFound(instance_id=instance_name) msg = (_('Error from libvirt while looking up %(instance_name)s: ' '[Error Code %(error_code)s] %(ex)s') % {'instance_name': instance_name, 'error_code': error_code, 'ex': ex}) raise exception.NovaException(msg) def get_info(self, instance): """Retrieve information from libvirt for a specific instance name. If a libvirt error is encountered during lookup, we might raise a NotFound exception or Error exception depending on how severe the libvirt error is. """ virt_dom = self._lookup_by_name(instance['name']) (state, max_mem, mem, num_cpu, cpu_time) = virt_dom.info() return {'state': LIBVIRT_POWER_STATE[state], 'max_mem': max_mem, 'mem': mem, 'num_cpu': num_cpu, 'cpu_time': cpu_time, 'id': virt_dom.ID()} def _create_domain(self, xml=None, domain=None, instance=None, launch_flags=0, power_on=True): """Create a domain. Either domain or xml must be passed in. If both are passed, then the domain definition is overwritten from the xml. """ inst_path = None if instance: inst_path = libvirt_utils.get_instance_path(instance) if CONF.libvirt_type == 'lxc': if not inst_path: inst_path = None container_dir = os.path.join(inst_path, 'rootfs') fileutils.ensure_tree(container_dir) image = self.image_backend.image(instance, 'disk') disk.setup_container(image.path, container_dir=container_dir, use_cow=CONF.use_cow_images) if xml: try: domain = self._conn.defineXML(xml) except Exception as e: LOG.error(_("An error occurred while trying to define a domain" " with xml: %s") % xml) raise e if power_on: try: domain.createWithFlags(launch_flags) except Exception as e: with excutils.save_and_reraise_exception(): LOG.error(_("An error occurred while trying to launch a " "defined domain with xml: %s") % domain.XMLDesc(0)) try: self._enable_hairpin(domain.XMLDesc(0)) except Exception: with excutils.save_and_reraise_exception(): LOG.error(_("An error occurred while enabling hairpin mode on " "domain with xml: %s") % domain.XMLDesc(0)) # NOTE(uni): Now the container is running with its own private mount # namespace and so there is no need to keep the container rootfs # mounted in the host namespace if CONF.libvirt_type == 'lxc': state = self.get_info(instance)['state'] container_dir = os.path.join(inst_path, 'rootfs') if state == power_state.RUNNING: disk.clean_lxc_namespace(container_dir=container_dir) else: disk.teardown_container(container_dir=container_dir) return domain def _create_domain_and_network(self, xml, instance, network_info, block_device_info=None, power_on=True): """Do required network setup and create domain.""" block_device_mapping = driver.block_device_info_get_mapping( block_device_info) for vol in block_device_mapping: connection_info = vol['connection_info'] disk_dev = vol['mount_device'].rpartition("/")[2] disk_info = { 'dev': disk_dev, 'bus': blockinfo.get_disk_bus_for_disk_dev(CONF.libvirt_type, disk_dev), 'type': 'disk', } self.volume_driver_method('connect_volume', connection_info, disk_info) self.plug_vifs(instance, network_info) self.firewall_driver.setup_basic_filtering(instance, network_info) self.firewall_driver.prepare_instance_filter(instance, network_info) domain = self._create_domain(xml, instance=instance, power_on=power_on) self.firewall_driver.apply_instance_filter(instance, network_info) return domain def get_all_block_devices(self): """ Return all block devices in use on this node. """ devices = [] for dom_id in self.list_instance_ids(): try: domain = self._lookup_by_id(dom_id) doc = etree.fromstring(domain.XMLDesc(0)) except exception.InstanceNotFound: LOG.info(_("libvirt can't find a domain with id: %s") % dom_id) continue except Exception: continue ret = doc.findall('./devices/disk') for node in ret: if node.get('type') != 'block': continue for child in node.getchildren(): if child.tag == 'source': devices.append(child.get('dev')) return devices def get_disks(self, instance_name): """ Note that this function takes an instance name. Returns a list of all block devices for this domain. """ domain = self._lookup_by_name(instance_name) xml = domain.XMLDesc(0) try: doc = etree.fromstring(xml) except Exception: return [] return filter(bool, [target.get("dev") for target in doc.findall('devices/disk/target')]) def get_interfaces(self, xml): """ Note that this function takes a domain xml. Returns a list of all network interfaces for this instance. """ doc = None try: doc = etree.fromstring(xml) except Exception: return [] interfaces = [] ret = doc.findall('./devices/interface') for node in ret: devdst = None for child in list(node): if child.tag == 'target': devdst = child.attrib['dev'] if devdst is None: continue interfaces.append(devdst) return interfaces def _get_cpuset_ids(self): """ Parsing vcpu_pin_set config. Returns a list of pcpu ids can be used by instances. """ cpuset_ids = set() cpuset_reject_ids = set() for rule in CONF.vcpu_pin_set.split(','): rule = rule.strip() # Handle multi ',' if len(rule) < 1: continue # Note the count limit in the .split() call range_parts = rule.split('-', 1) if len(range_parts) > 1: # So, this was a range; start by converting the parts to ints try: start, end = [int(p.strip()) for p in range_parts] except ValueError: raise exception.Invalid(_("Invalid range expression %r") % rule) # Make sure it's a valid range if start > end: raise exception.Invalid(_("Invalid range expression %r") % rule) # Add available pcpu ids to set cpuset_ids |= set(range(start, end + 1)) elif rule[0] == '^': # Not a range, the rule is an exclusion rule; convert to int try: cpuset_reject_ids.add(int(rule[1:].strip())) except ValueError: raise exception.Invalid(_("Invalid exclusion " "expression %r") % rule) else: # OK, a single PCPU to include; convert to int try: cpuset_ids.add(int(rule)) except ValueError: raise exception.Invalid(_("Invalid inclusion " "expression %r") % rule) # Use sets to handle the exclusion rules for us cpuset_ids -= cpuset_reject_ids if not cpuset_ids: raise exception.Invalid(_("No CPUs available after parsing %r") % CONF.vcpu_pin_set) # This will convert the set to a sorted list for us return sorted(cpuset_ids) def get_vcpu_total(self): """Get available vcpu number of physical computer. :returns: the number of cpu core instances can be used. """ if self._vcpu_total != 0: return self._vcpu_total try: total_pcpus = self._conn.getInfo()[2] except libvirt.libvirtError: LOG.warn(_("Cannot get the number of cpu, because this " "function is not implemented for this platform. ")) return 0 if CONF.vcpu_pin_set is None: self._vcpu_total = total_pcpus return self._vcpu_total available_ids = self._get_cpuset_ids() if available_ids[-1] >= total_pcpus: raise exception.Invalid(_("Invalid vcpu_pin_set config, " "out of hypervisor cpu range.")) self._vcpu_total = len(available_ids) return self._vcpu_total def get_memory_mb_total(self): """Get the total memory size(MB) of physical computer. :returns: the total amount of memory(MB). """ return self._conn.getInfo()[1] @staticmethod def get_local_gb_info(): """Get local storage info of the compute node in GB. :returns: A dict containing: :total: How big the overall usable filesystem is (in gigabytes) :free: How much space is free (in gigabytes) :used: How much space is used (in gigabytes) """ if CONF.libvirt_images_type == 'lvm': info = libvirt_utils.get_volume_group_info( CONF.libvirt_images_volume_group) else: info = libvirt_utils.get_fs_info(CONF.instances_path) for (k, v) in info.iteritems(): info[k] = v / (1024 ** 3) return info def get_vcpu_used(self): """Get vcpu usage number of physical computer. :returns: The total number of vcpu that currently used. """ total = 0 if CONF.libvirt_type == 'lxc': return total + 1 dom_ids = self.list_instance_ids() for dom_id in dom_ids: try: dom = self._lookup_by_id(dom_id) vcpus = dom.vcpus() if vcpus is None: LOG.debug(_("couldn't obtain the vpu count from domain id:" " %s") % dom_id) else: total += len(vcpus[1]) except exception.InstanceNotFound: LOG.info(_("libvirt can't find a domain with id: %s") % dom_id) continue # NOTE(gtt116): give change to do other task. greenthread.sleep(0) return total def get_memory_mb_used(self): """Get the free memory size(MB) of physical computer. :returns: the total usage of memory(MB). """ if sys.platform.upper() not in ['LINUX2', 'LINUX3']: return 0 m = open('/proc/meminfo').read().split() idx1 = m.index('MemFree:') idx2 = m.index('Buffers:') idx3 = m.index('Cached:') if CONF.libvirt_type == 'xen': used = 0 for domain_id in self.list_instance_ids(): try: dom_mem = int(self._lookup_by_id(domain_id).info()[2]) except exception.InstanceNotFound: LOG.info(_("libvirt can't find a domain with id: %s") % domain_id) continue # skip dom0 if domain_id != 0: used += dom_mem else: # the mem reported by dom0 is be greater of what # it is being used used += (dom_mem - (int(m[idx1 + 1]) + int(m[idx2 + 1]) + int(m[idx3 + 1]))) # Convert it to MB return used / 1024 else: avail = (int(m[idx1 + 1]) + int(m[idx2 + 1]) + int(m[idx3 + 1])) # Convert it to MB return self.get_memory_mb_total() - avail / 1024 def get_hypervisor_type(self): """Get hypervisor type. :returns: hypervisor type (ex. qemu) """ return self._conn.getType() def get_hypervisor_version(self): """Get hypervisor version. :returns: hypervisor version (ex. 12003) """ # NOTE(justinsb): getVersion moved between libvirt versions # Trying to do be compatible with older versions is a lost cause # But ... we can at least give the user a nice message method = getattr(self._conn, 'getVersion', None) if method is None: raise exception.NovaException(_("libvirt version is too old" " (does not support getVersion)")) # NOTE(justinsb): If we wanted to get the version, we could: # method = getattr(libvirt, 'getVersion', None) # NOTE(justinsb): This would then rely on a proper version check return method() def get_hypervisor_hostname(self): """Returns the hostname of the hypervisor.""" return self._conn.getHostname() def get_instance_capabilities(self): """Get hypervisor instance capabilities Returns a list of tuples that describe instances the hypervisor is capable of hosting. Each tuple consists of the triplet (arch, hypervisor_type, vm_mode). :returns: List of tuples describing instance capabilities """ caps = self.get_host_capabilities() instance_caps = list() for g in caps.guests: for dt in g.domtype: instance_cap = (g.arch, dt, g.ostype) instance_caps.append(instance_cap) return instance_caps def get_cpu_info(self): """Get cpuinfo information. Obtains cpu feature from virConnect.getCapabilities, and returns as a json string. :return: see above description """ caps = self.get_host_capabilities() cpu_info = dict() cpu_info['arch'] = caps.host.cpu.arch cpu_info['model'] = caps.host.cpu.model cpu_info['vendor'] = caps.host.cpu.vendor topology = dict() topology['sockets'] = caps.host.cpu.sockets topology['cores'] = caps.host.cpu.cores topology['threads'] = caps.host.cpu.threads cpu_info['topology'] = topology features = list() for f in caps.host.cpu.features: features.append(f.name) cpu_info['features'] = features # TODO(berrange): why do we bother converting the # libvirt capabilities XML into a special JSON format ? # The data format is different across all the drivers # so we could just return the raw capabilties XML # which 'compare_cpu' could use directly # # That said, arch_filter.py now seems to rely on # the libvirt drivers format which suggests this # data format needs to be standardized across drivers return jsonutils.dumps(cpu_info) def get_all_volume_usage(self, context, compute_host_bdms): """Return usage info for volumes attached to vms on a given host. """ vol_usage = [] for instance_bdms in compute_host_bdms: instance = instance_bdms['instance'] for bdm in instance_bdms['instance_bdms']: vol_stats = [] mountpoint = bdm['device_name'] if mountpoint.startswith('/dev/'): mountpoint = mountpoint[5:] volume_id = bdm['volume_id'] LOG.debug(_("Trying to get stats for the volume %s"), volume_id) vol_stats = self.block_stats(instance['name'], mountpoint) if vol_stats: stats = dict(volume=volume_id, instance=instance, rd_req=vol_stats[0], rd_bytes=vol_stats[1], wr_req=vol_stats[2], wr_bytes=vol_stats[3], flush_operations=vol_stats[4]) LOG.debug( _("Got volume usage stats for the volume=%(volume)s," " instance=%(instance)s, rd_req=%(rd_req)d," " rd_bytes=%(rd_bytes)d, wr_req=%(wr_req)d," " wr_bytes=%(wr_bytes)d") % stats) vol_usage.append(stats) return vol_usage def block_stats(self, instance_name, disk): """ Note that this function takes an instance name. """ try: domain = self._lookup_by_name(instance_name) return domain.blockStats(disk) except libvirt.libvirtError as e: errcode = e.get_error_code() LOG.info(_('Getting block stats failed, device might have ' 'been detached. Instance=%(instance_name)s ' 'Disk=%(disk)s Code=%(errcode)s Error=%(e)s'), {'instance_name': instance_name, 'disk': disk, 'errcode': errcode, 'e': e}) except exception.InstanceNotFound: LOG.info(_('Could not find domain in libvirt for instance %s. ' 'Cannot get block stats for device'), instance_name) def interface_stats(self, instance_name, interface): """ Note that this function takes an instance name. """ domain = self._lookup_by_name(instance_name) return domain.interfaceStats(interface) def get_console_pool_info(self, console_type): #TODO(mdragon): console proxy should be implemented for libvirt, # in case someone wants to use it with kvm or # such. For now return fake data. return {'address': '127.0.0.1', 'username': 'fakeuser', 'password': 'fakepassword'} def refresh_security_group_rules(self, security_group_id): self.firewall_driver.refresh_security_group_rules(security_group_id) def refresh_security_group_members(self, security_group_id): self.firewall_driver.refresh_security_group_members(security_group_id) def refresh_instance_security_rules(self, instance): self.firewall_driver.refresh_instance_security_rules(instance) def refresh_provider_fw_rules(self): self.firewall_driver.refresh_provider_fw_rules() def get_available_resource(self, nodename): """Retrieve resource info. This method is called as a periodic task and is used only in live migration currently. :param nodename: ignored in this driver :returns: dictionary containing resource info """ def _get_disk_available_least(): """Return total real disk available least size. The size of available disk, when block_migration command given disk_over_commit param is FALSE. The size that deducted real instance disk size from the total size of the virtual disk of all instances. """ disk_free_gb = disk_info_dict['free'] disk_over_committed = self.get_disk_over_committed_size_total() # Disk available least size available_least = disk_free_gb * (1024 ** 3) - disk_over_committed return (available_least / (1024 ** 3)) disk_info_dict = self.get_local_gb_info() dic = {'vcpus': self.get_vcpu_total(), 'memory_mb': self.get_memory_mb_total(), 'local_gb': disk_info_dict['total'], 'vcpus_used': self.get_vcpu_used(), 'memory_mb_used': self.get_memory_mb_used(), 'local_gb_used': disk_info_dict['used'], 'hypervisor_type': self.get_hypervisor_type(), 'hypervisor_version': self.get_hypervisor_version(), 'hypervisor_hostname': self.get_hypervisor_hostname(), 'cpu_info': self.get_cpu_info(), 'disk_available_least': _get_disk_available_least()} return dic def check_instance_shared_storage_local(self, context, instance): dirpath = libvirt_utils.get_instance_path(instance) if not os.path.exists(dirpath): return None fd, tmp_file = tempfile.mkstemp(dir=dirpath) LOG.debug(_("Creating tmpfile %s to verify with other " "compute node that the instance is on " "the same shared storage.") % tmp_file) os.close(fd) return {"filename": tmp_file} def check_instance_shared_storage_remote(self, context, data): return os.path.exists(data['filename']) def check_instance_shared_storage_cleanup(self, context, data): fileutils.delete_if_exists(data["filename"]) def check_can_live_migrate_destination(self, context, instance, src_compute_info, dst_compute_info, block_migration=False, disk_over_commit=False): """Check if it is possible to execute live migration. This runs checks on the destination host, and then calls back to the source host to check the results. :param context: security context :param instance: nova.db.sqlalchemy.models.Instance :param block_migration: if true, prepare for block migration :param disk_over_commit: if true, allow disk over commit :returns: a dict containing: :filename: name of the tmpfile under CONF.instances_path :block_migration: whether this is block migration :disk_over_commit: disk-over-commit factor on dest host :disk_available_mb: available disk space on dest host """ disk_available_mb = None if block_migration: disk_available_gb = dst_compute_info['disk_available_least'] disk_available_mb = \ (disk_available_gb * 1024) - CONF.reserved_host_disk_mb # Compare CPU source_cpu_info = src_compute_info['cpu_info'] self._compare_cpu(source_cpu_info) # Create file on storage, to be checked on source host filename = self._create_shared_storage_test_file() return {"filename": filename, "block_migration": block_migration, "disk_over_commit": disk_over_commit, "disk_available_mb": disk_available_mb} def check_can_live_migrate_destination_cleanup(self, context, dest_check_data): """Do required cleanup on dest host after check_can_live_migrate calls :param context: security context """ filename = dest_check_data["filename"] self._cleanup_shared_storage_test_file(filename) def check_can_live_migrate_source(self, context, instance, dest_check_data): """Check if it is possible to execute live migration. This checks if the live migration can succeed, based on the results from check_can_live_migrate_destination. :param context: security context :param instance: nova.db.sqlalchemy.models.Instance :param dest_check_data: result of check_can_live_migrate_destination :returns: a dict containing migration info """ # Checking shared storage connectivity # if block migration, instances_paths should not be on shared storage. source = CONF.host filename = dest_check_data["filename"] block_migration = dest_check_data["block_migration"] is_volume_backed = dest_check_data.get('is_volume_backed', False) shared = self._check_shared_storage_test_file(filename) if block_migration: if shared: reason = _("Block migration can not be used " "with shared storage.") raise exception.InvalidLocalStorage(reason=reason, path=source) self._assert_dest_node_has_enough_disk(context, instance, dest_check_data['disk_available_mb'], dest_check_data['disk_over_commit']) elif not shared and not is_volume_backed: reason = _("Live migration can not be used " "without shared storage.") raise exception.InvalidSharedStorage(reason=reason, path=source) dest_check_data.update({"is_shared_storage": shared}) # NOTE(mikal): include the instance directory name here because it # doesn't yet exist on the destination but we want to force that # same name to be used instance_path = libvirt_utils.get_instance_path(instance, relative=True) dest_check_data['instance_relative_path'] = instance_path return dest_check_data def _assert_dest_node_has_enough_disk(self, context, instance, available_mb, disk_over_commit): """Checks if destination has enough disk for block migration.""" # Libvirt supports qcow2 disk format,which is usually compressed # on compute nodes. # Real disk image (compressed) may enlarged to "virtual disk size", # that is specified as the maximum disk size. # (See qemu-img -f path-to-disk) # Scheduler recognizes destination host still has enough disk space # if real disk size < available disk size # if disk_over_commit is True, # otherwise virtual disk size < available disk size. available = 0 if available_mb: available = available_mb * (1024 ** 2) ret = self.get_instance_disk_info(instance['name']) disk_infos = jsonutils.loads(ret) necessary = 0 if disk_over_commit: for info in disk_infos: necessary += int(info['disk_size']) else: for info in disk_infos: necessary += int(info['virt_disk_size']) # Check that available disk > necessary disk if (available - necessary) < 0: reason = (_('Unable to migrate %(instance_uuid)s: ' 'Disk of instance is too large(available' ' on destination host:%(available)s ' '< need:%(necessary)s)') % {'instance_uuid': instance['uuid'], 'available': available, 'necessary': necessary}) raise exception.MigrationPreCheckError(reason=reason) def _compare_cpu(self, cpu_info): """Checks the host cpu is compatible to a cpu given by xml. "xml" must be a part of libvirt.openReadonly().getCapabilities(). return values follows by virCPUCompareResult. if 0 > return value, do live migration. 'http://libvirt.org/html/libvirt-libvirt.html#virCPUCompareResult' :param cpu_info: json string that shows cpu feature(see get_cpu_info()) :returns: None. if given cpu info is not compatible to this server, raise exception. """ # NOTE(berendt): virConnectCompareCPU not working for Xen if CONF.libvirt_type == 'xen': return 1 info = jsonutils.loads(cpu_info) LOG.info(_('Instance launched has CPU info:\n%s') % cpu_info) cpu = vconfig.LibvirtConfigCPU() cpu.arch = info['arch'] cpu.model = info['model'] cpu.vendor = info['vendor'] cpu.sockets = info['topology']['sockets'] cpu.cores = info['topology']['cores'] cpu.threads = info['topology']['threads'] for f in info['features']: cpu.add_feature(vconfig.LibvirtConfigCPUFeature(f)) u = "http://libvirt.org/html/libvirt-libvirt.html#virCPUCompareResult" m = _("CPU doesn't have compatibility.\n\n%(ret)s\n\nRefer to %(u)s") # unknown character exists in xml, then libvirt complains try: ret = self._conn.compareCPU(cpu.to_xml(), 0) except libvirt.libvirtError as e: with excutils.save_and_reraise_exception(): ret = e.message LOG.error(m, {'ret': ret, 'u': u}) if ret <= 0: LOG.error(m, {'ret': ret, 'u': u}) raise exception.InvalidCPUInfo(reason=m % {'ret': ret, 'u': u}) def _create_shared_storage_test_file(self): """Makes tmpfile under CONF.instances_path.""" dirpath = CONF.instances_path fd, tmp_file = tempfile.mkstemp(dir=dirpath) LOG.debug(_("Creating tmpfile %s to notify to other " "compute nodes that they should mount " "the same storage.") % tmp_file) os.close(fd) return os.path.basename(tmp_file) def _check_shared_storage_test_file(self, filename): """Confirms existence of the tmpfile under CONF.instances_path. Cannot confirm tmpfile return False. """ tmp_file = os.path.join(CONF.instances_path, filename) if not os.path.exists(tmp_file): return False else: return True def _cleanup_shared_storage_test_file(self, filename): """Removes existence of the tmpfile under CONF.instances_path.""" tmp_file = os.path.join(CONF.instances_path, filename) os.remove(tmp_file) def ensure_filtering_rules_for_instance(self, instance, network_info, time_module=None): """Ensure that an instance's filtering rules are enabled. When migrating an instance, we need the filtering rules to be configured on the destination host before starting the migration. Also, when restarting the compute service, we need to ensure that filtering rules exist for all running services. """ if not time_module: time_module = greenthread self.firewall_driver.setup_basic_filtering(instance, network_info) self.firewall_driver.prepare_instance_filter(instance, network_info) # nwfilters may be defined in a separate thread in the case # of libvirt non-blocking mode, so we wait for completion timeout_count = range(CONF.live_migration_retry_count) while timeout_count: if self.firewall_driver.instance_filter_exists(instance, network_info): break timeout_count.pop() if len(timeout_count) == 0: msg = _('The firewall filter for %s does not exist') raise exception.NovaException(msg % instance["name"]) time_module.sleep(1) def filter_defer_apply_on(self): self.firewall_driver.filter_defer_apply_on() def filter_defer_apply_off(self): self.firewall_driver.filter_defer_apply_off() def live_migration(self, context, instance, dest, post_method, recover_method, block_migration=False, migrate_data=None): """Spawning live_migration operation for distributing high-load. :params context: security context :params instance: nova.db.sqlalchemy.models.Instance object instance object that is migrated. :params dest: destination host :params block_migration: destination host :params post_method: post operation method. expected nova.compute.manager.post_live_migration. :params recover_method: recovery method when any exception occurs. expected nova.compute.manager.recover_live_migration. :params block_migration: if true, do block migration. :params migrate_data: implementation specific params """ greenthread.spawn(self._live_migration, context, instance, dest, post_method, recover_method, block_migration, migrate_data) def _live_migration(self, context, instance, dest, post_method, recover_method, block_migration=False, migrate_data=None): """Do live migration. :params context: security context :params instance: nova.db.sqlalchemy.models.Instance object instance object that is migrated. :params dest: destination host :params post_method: post operation method. expected nova.compute.manager.post_live_migration. :params recover_method: recovery method when any exception occurs. expected nova.compute.manager.recover_live_migration. :params migrate_data: implementation specific params """ # Do live migration. try: if block_migration: flaglist = CONF.block_migration_flag.split(',') else: flaglist = CONF.live_migration_flag.split(',') flagvals = [getattr(libvirt, x.strip()) for x in flaglist] logical_sum = reduce(lambda x, y: x | y, flagvals) dom = self._lookup_by_name(instance["name"]) dom.migrateToURI(CONF.live_migration_uri % dest, logical_sum, None, CONF.live_migration_bandwidth) except Exception as e: with excutils.save_and_reraise_exception(): LOG.error(_("Live Migration failure: %s"), e, instance=instance) recover_method(context, instance, dest, block_migration) # Waiting for completion of live_migration. timer = loopingcall.FixedIntervalLoopingCall(f=None) def wait_for_live_migration(): """waiting for live migration completion.""" try: self.get_info(instance)['state'] except exception.NotFound: timer.stop() post_method(context, instance, dest, block_migration, migrate_data) timer.f = wait_for_live_migration timer.start(interval=0.5).wait() def _fetch_instance_kernel_ramdisk(self, context, instance): """Download kernel and ramdisk for instance in instance directory.""" instance_dir = libvirt_utils.get_instance_path(instance) if instance['kernel_id']: libvirt_utils.fetch_image(context, os.path.join(instance_dir, 'kernel'), instance['kernel_id'], instance['user_id'], instance['project_id']) if instance['ramdisk_id']: libvirt_utils.fetch_image(context, os.path.join(instance_dir, 'ramdisk'), instance['ramdisk_id'], instance['user_id'], instance['project_id']) def pre_live_migration(self, context, instance, block_device_info, network_info, migrate_data=None): """Preparation live migration.""" # Steps for volume backed instance live migration w/o shared storage. is_shared_storage = True is_volume_backed = False is_block_migration = True instance_relative_path = None if migrate_data: is_shared_storage = migrate_data.get('is_shared_storage', True) is_volume_backed = migrate_data.get('is_volume_backed', False) is_block_migration = migrate_data.get('block_migration', True) instance_relative_path = migrate_data.get('instance_relative_path') if not is_shared_storage: # NOTE(mikal): this doesn't use libvirt_utils.get_instance_path # because we are ensuring that the same instance directory name # is used as was at the source if instance_relative_path: instance_dir = os.path.join(CONF.instances_path, instance_relative_path) else: instance_dir = libvirt_utils.get_instance_path(instance) if os.path.exists(instance_dir): raise exception.DestinationDiskExists(path=instance_dir) os.mkdir(instance_dir) if is_volume_backed and not (is_block_migration or is_shared_storage): # Touch the console.log file, required by libvirt. console_file = self._get_console_log_path(instance) libvirt_utils.file_open(console_file, 'a').close() # if image has kernel and ramdisk, just download # following normal way. self._fetch_instance_kernel_ramdisk(context, instance) # Establishing connection to volume server. block_device_mapping = driver.block_device_info_get_mapping( block_device_info) for vol in block_device_mapping: connection_info = vol['connection_info'] disk_dev = vol['mount_device'].rpartition("/")[2] disk_info = { 'dev': disk_dev, 'bus': blockinfo.get_disk_bus_for_disk_dev(CONF.libvirt_type, disk_dev), 'type': 'disk', } self.volume_driver_method('connect_volume', connection_info, disk_info) # We call plug_vifs before the compute manager calls # ensure_filtering_rules_for_instance, to ensure bridge is set up # Retry operation is necessary because continuously request comes, # concorrent request occurs to iptables, then it complains. max_retry = CONF.live_migration_retry_count for cnt in range(max_retry): try: self.plug_vifs(instance, network_info) break except processutils.ProcessExecutionError: if cnt == max_retry - 1: raise else: LOG.warn(_('plug_vifs() failed %(cnt)d. Retry up to ' '%(max_retry)d.'), {'cnt': cnt, 'max_retry': max_retry}, instance=instance) greenthread.sleep(1) def pre_block_migration(self, context, instance, disk_info_json): """Preparation for block migration.""" # NOTE (rmk): When preparing for a block migration, the instance dir # should not exist on the destination hypervisor. instance_dir = libvirt_utils.get_instance_path(instance) if os.path.exists(instance_dir): raise exception.DestinationDiskExists(path=instance_dir) os.mkdir(instance_dir) self._create_images_and_backing(context, instance, disk_info_json) def _create_images_and_backing(self, context, instance, disk_info_json): """ :params context: security context :params instance: nova.db.sqlalchemy.models.Instance object instance object that is migrated. :params disk_info_json: json strings specified in get_instance_disk_info """ disk_info = jsonutils.loads(disk_info_json) instance_dir = libvirt_utils.get_instance_path(instance) for info in disk_info: base = os.path.basename(info['path']) # Get image type and create empty disk image, and # create backing file in case of qcow2. instance_disk = os.path.join(instance_dir, base) if not info['backing_file'] and not os.path.exists(instance_disk): libvirt_utils.create_image(info['type'], instance_disk, info['disk_size']) else: # Creating backing file follows same way as spawning instances. cache_name = os.path.basename(info['backing_file']) image = self.image_backend.image(instance, instance_disk, CONF.libvirt_images_type) image.cache(fetch_func=libvirt_utils.fetch_image, context=context, filename=cache_name, image_id=instance['image_ref'], user_id=instance['user_id'], project_id=instance['project_id'], size=info['virt_disk_size']) # if image has kernel and ramdisk, just download # following normal way. self._fetch_instance_kernel_ramdisk(context, instance) def post_live_migration_at_destination(self, context, instance, network_info, block_migration, block_device_info=None): """Post operation of live migration at destination host. :param context: security context :param instance: nova.db.sqlalchemy.models.Instance object instance object that is migrated. :param network_info: instance network information :param block_migration: if true, post operation of block_migraiton. """ # Define migrated instance, otherwise, suspend/destroy does not work. dom_list = self._conn.listDefinedDomains() if instance["name"] not in dom_list: # In case of block migration, destination does not have # libvirt.xml disk_info = blockinfo.get_disk_info(CONF.libvirt_type, instance) self.to_xml(instance, network_info, disk_info, block_device_info, write_to_disk=True) # libvirt.xml should be made by to_xml(), but libvirt # does not accept to_xml() result, since uuid is not # included in to_xml() result. dom = self._lookup_by_name(instance["name"]) self._conn.defineXML(dom.XMLDesc(0)) def get_instance_disk_info(self, instance_name, xml=None, block_device_info=None): """Preparation block migration. :params instance: nova.db.sqlalchemy.models.Instance object instance object that is migrated. :return: json strings with below format:: "[{'path':'disk', 'type':'raw', 'virt_disk_size':'10737418240', 'backing_file':'backing_file', 'disk_size':'83886080'},...]" """ # NOTE (rmk): Passing the domain XML into this function is optional. # When it is not passed, we attempt to extract it from # the pre-existing definition. if xml is None: try: virt_dom = self._lookup_by_name(instance_name) xml = virt_dom.XMLDesc(0) except libvirt.libvirtError as ex: error_code = ex.get_error_code() msg = (_('Error from libvirt while getting description of ' '%(instance_name)s: [Error Code %(error_code)s] ' '%(ex)s') % {'instance_name': instance_name, 'error_code': error_code, 'ex': ex}) LOG.warn(msg) raise exception.InstanceNotFound(instance_id=instance_name) # NOTE (rmk): When block_device_info is provided, we will use it to # filter out devices which are actually volumes. block_device_mapping = driver.block_device_info_get_mapping( block_device_info) volume_devices = set() for vol in block_device_mapping: disk_dev = vol['mount_device'].rpartition("/")[2] volume_devices.add(disk_dev) disk_info = [] doc = etree.fromstring(xml) disk_nodes = doc.findall('.//devices/disk') path_nodes = doc.findall('.//devices/disk/source') driver_nodes = doc.findall('.//devices/disk/driver') target_nodes = doc.findall('.//devices/disk/target') for cnt, path_node in enumerate(path_nodes): disk_type = disk_nodes[cnt].get('type') path = path_node.get('file') target = target_nodes[cnt].attrib['dev'] if disk_type != 'file': LOG.debug(_('skipping %s since it looks like volume'), path) continue if not path: LOG.debug(_('skipping disk for %s as it does not have a path'), instance_name) continue if target in volume_devices: LOG.debug(_('skipping disk %(path)s (%(target)s) as it is a ' 'volume'), {'path': path, 'target': target}) continue # get the real disk size or # raise a localized error if image is unavailable dk_size = int(os.path.getsize(path)) disk_type = driver_nodes[cnt].get('type') if disk_type == "qcow2": backing_file = libvirt_utils.get_disk_backing_file(path) virt_size = disk.get_disk_size(path) over_commit_size = int(virt_size) - dk_size else: backing_file = "" virt_size = 0 over_commit_size = 0 disk_info.append({'type': disk_type, 'path': path, 'virt_disk_size': virt_size, 'backing_file': backing_file, 'disk_size': dk_size, 'over_committed_disk_size': over_commit_size}) return jsonutils.dumps(disk_info) def get_disk_over_committed_size_total(self): """Return total over committed disk size for all instances.""" # Disk size that all instance uses : virtual_size - disk_size instances_name = self.list_instances() disk_over_committed_size = 0 for i_name in instances_name: try: disk_infos = jsonutils.loads( self.get_instance_disk_info(i_name)) for info in disk_infos: disk_over_committed_size += int( info['over_committed_disk_size']) except OSError as e: if e.errno == errno.ENOENT: LOG.error(_('Getting disk size of %(i_name)s: %(e)s'), {'i_name': i_name, 'e': e}) else: raise except exception.InstanceNotFound: # Instance was deleted during the check so ignore it pass # NOTE(gtt116): give change to do other task. greenthread.sleep(0) return disk_over_committed_size def unfilter_instance(self, instance, network_info): """See comments of same method in firewall_driver.""" self.firewall_driver.unfilter_instance(instance, network_info=network_info) def get_host_stats(self, refresh=False): """Return the current state of the host. If 'refresh' is True, run update the stats first. """ return self.host_state.get_host_stats(refresh=refresh) def get_host_uptime(self, host): """Returns the result of calling "uptime".""" #NOTE(dprince): host seems to be ignored for this call and in # other compute drivers as well. Perhaps we should remove it? out, err = utils.execute('env', 'LANG=C', 'uptime') return out def manage_image_cache(self, context, all_instances): """Manage the local cache of images.""" self.image_cache_manager.verify_base_images(context, all_instances) def _cleanup_remote_migration(self, dest, inst_base, inst_base_resize, shared_storage=False): """Used only for cleanup in case migrate_disk_and_power_off fails.""" try: if os.path.exists(inst_base_resize): utils.execute('rm', '-rf', inst_base) utils.execute('mv', inst_base_resize, inst_base) if not shared_storage: utils.execute('ssh', dest, 'rm', '-rf', inst_base) except Exception: pass def _is_storage_shared_with(self, dest, inst_base): # NOTE (rmk): There are two methods of determining whether we are # on the same filesystem: the source and dest IP are the # same, or we create a file on the dest system via SSH # and check whether the source system can also see it. shared_storage = (dest == self.get_host_ip_addr()) if not shared_storage: tmp_file = uuid.uuid4().hex + '.tmp' tmp_path = os.path.join(inst_base, tmp_file) try: utils.execute('ssh', dest, 'touch', tmp_path) if os.path.exists(tmp_path): shared_storage = True os.unlink(tmp_path) else: utils.execute('ssh', dest, 'rm', tmp_path) except Exception: pass return shared_storage def migrate_disk_and_power_off(self, context, instance, dest, instance_type, network_info, block_device_info=None): LOG.debug(_("Starting migrate_disk_and_power_off"), instance=instance) disk_info_text = self.get_instance_disk_info(instance['name'], block_device_info=block_device_info) disk_info = jsonutils.loads(disk_info_text) # copy disks to destination # rename instance dir to +_resize at first for using # shared storage for instance dir (eg. NFS). inst_base = libvirt_utils.get_instance_path(instance) inst_base_resize = inst_base + "_resize" shared_storage = self._is_storage_shared_with(dest, inst_base) # try to create the directory on the remote compute node # if this fails we pass the exception up the stack so we can catch # failures here earlier if not shared_storage: utils.execute('ssh', dest, 'mkdir', '-p', inst_base) self.power_off(instance) block_device_mapping = driver.block_device_info_get_mapping( block_device_info) for vol in block_device_mapping: connection_info = vol['connection_info'] disk_dev = vol['mount_device'].rpartition("/")[2] self.volume_driver_method('disconnect_volume', connection_info, disk_dev) try: utils.execute('mv', inst_base, inst_base_resize) # if we are migrating the instance with shared storage then # create the directory. If it is a remote node the directory # has already been created if shared_storage: dest = None utils.execute('mkdir', '-p', inst_base) for info in disk_info: # assume inst_base == dirname(info['path']) img_path = info['path'] fname = os.path.basename(img_path) from_path = os.path.join(inst_base_resize, fname) if info['type'] == 'qcow2' and info['backing_file']: tmp_path = from_path + "_rbase" # merge backing file utils.execute('qemu-img', 'convert', '-f', 'qcow2', '-O', 'qcow2', from_path, tmp_path) if shared_storage: utils.execute('mv', tmp_path, img_path) else: libvirt_utils.copy_image(tmp_path, img_path, host=dest) utils.execute('rm', '-f', tmp_path) else: # raw or qcow2 with no backing file libvirt_utils.copy_image(from_path, img_path, host=dest) except Exception: with excutils.save_and_reraise_exception(): self._cleanup_remote_migration(dest, inst_base, inst_base_resize, shared_storage) return disk_info_text def _wait_for_running(self, instance): state = self.get_info(instance)['state'] if state == power_state.RUNNING: LOG.info(_("Instance running successfully."), instance=instance) raise loopingcall.LoopingCallDone() def finish_migration(self, context, migration, instance, disk_info, network_info, image_meta, resize_instance, block_device_info=None, power_on=True): LOG.debug(_("Starting finish_migration"), instance=instance) # resize disks. only "disk" and "disk.local" are necessary. disk_info = jsonutils.loads(disk_info) for info in disk_info: fname = os.path.basename(info['path']) if fname == 'disk': size = instance['root_gb'] elif fname == 'disk.local': size = instance['ephemeral_gb'] else: size = 0 size *= 1024 * 1024 * 1024 # If we have a non partitioned image that we can extend # then ensure we're in 'raw' format so we can extend file system. fmt = info['type'] if (size and fmt == 'qcow2' and disk.can_resize_fs(info['path'], size, use_cow=True)): path_raw = info['path'] + '_raw' utils.execute('qemu-img', 'convert', '-f', 'qcow2', '-O', 'raw', info['path'], path_raw) utils.execute('mv', path_raw, info['path']) fmt = 'raw' if size: disk.extend(info['path'], size) if fmt == 'raw' and CONF.use_cow_images: # back to qcow2 (no backing_file though) so that snapshot # will be available path_qcow = info['path'] + '_qcow' utils.execute('qemu-img', 'convert', '-f', 'raw', '-O', 'qcow2', info['path'], path_qcow) utils.execute('mv', path_qcow, info['path']) disk_info = blockinfo.get_disk_info(CONF.libvirt_type, instance, block_device_info, image_meta) # assume _create_image do nothing if a target file exists. # TODO(oda): injecting files is not necessary self._create_image(context, instance, disk_mapping=disk_info['mapping'], network_info=network_info, block_device_info=None) xml = self.to_xml(instance, network_info, disk_info, block_device_info=block_device_info, write_to_disk=True) self._create_domain_and_network(xml, instance, network_info, block_device_info, power_on) if power_on: timer = loopingcall.FixedIntervalLoopingCall( self._wait_for_running, instance) timer.start(interval=0.5).wait() def _cleanup_failed_migration(self, inst_base): """Make sure that a failed migrate doesn't prevent us from rolling back in a revert. """ shutil.rmtree(inst_base) def finish_revert_migration(self, instance, network_info, block_device_info=None, power_on=True): LOG.debug(_("Starting finish_revert_migration"), instance=instance) inst_base = libvirt_utils.get_instance_path(instance) inst_base_resize = inst_base + "_resize" # NOTE(danms): if we're recovering from a failed migration, # make sure we don't have a left-over same-host base directory # that would conflict. Also, don't fail on the rename if the # failure happened early. if os.path.exists(inst_base_resize): if os.path.exists(inst_base): self._cleanup_failed_migration(inst_base) utils.execute('mv', inst_base_resize, inst_base) disk_info = blockinfo.get_disk_info(CONF.libvirt_type, instance, block_device_info) xml = self.to_xml(instance, network_info, disk_info, block_device_info=block_device_info) self._create_domain_and_network(xml, instance, network_info, block_device_info, power_on) if power_on: timer = loopingcall.FixedIntervalLoopingCall( self._wait_for_running, instance) timer.start(interval=0.5).wait() def confirm_migration(self, migration, instance, network_info): """Confirms a resize, destroying the source VM.""" self._cleanup_resize(instance, network_info) def get_diagnostics(self, instance): def get_io_devices(xml_doc): """get the list of io devices from the xml document.""" result = {"volumes": [], "ifaces": []} try: doc = etree.fromstring(xml_doc) except Exception: return result blocks = [('./devices/disk', 'volumes'), ('./devices/interface', 'ifaces')] for block, key in blocks: section = doc.findall(block) for node in section: for child in node.getchildren(): if child.tag == 'target' and child.get('dev'): result[key].append(child.get('dev')) return result domain = self._lookup_by_name(instance['name']) output = {} # get cpu time, might launch an exception if the method # is not supported by the underlying hypervisor being # used by libvirt try: cputime = domain.vcpus()[0] for i in range(len(cputime)): output["cpu" + str(i) + "_time"] = cputime[i][2] except libvirt.libvirtError: pass # get io status xml = domain.XMLDesc(0) dom_io = get_io_devices(xml) for disk in dom_io["volumes"]: try: # blockStats might launch an exception if the method # is not supported by the underlying hypervisor being # used by libvirt stats = domain.blockStats(disk) output[disk + "_read_req"] = stats[0] output[disk + "_read"] = stats[1] output[disk + "_write_req"] = stats[2] output[disk + "_write"] = stats[3] output[disk + "_errors"] = stats[4] except libvirt.libvirtError: pass for interface in dom_io["ifaces"]: try: # interfaceStats might launch an exception if the method # is not supported by the underlying hypervisor being # used by libvirt stats = domain.interfaceStats(interface) output[interface + "_rx"] = stats[0] output[interface + "_rx_packets"] = stats[1] output[interface + "_rx_errors"] = stats[2] output[interface + "_rx_drop"] = stats[3] output[interface + "_tx"] = stats[4] output[interface + "_tx_packets"] = stats[5] output[interface + "_tx_errors"] = stats[6] output[interface + "_tx_drop"] = stats[7] except libvirt.libvirtError: pass output["memory"] = domain.maxMemory() # memoryStats might launch an exception if the method # is not supported by the underlying hypervisor being # used by libvirt try: mem = domain.memoryStats() for key in mem.keys(): output["memory-" + key] = mem[key] except (libvirt.libvirtError, AttributeError): pass return output def add_to_aggregate(self, context, aggregate, host, **kwargs): """Add a compute host to an aggregate.""" #NOTE(jogo) Currently only used for XenAPI-Pool pass def remove_from_aggregate(self, context, aggregate, host, **kwargs): """Remove a compute host from an aggregate.""" pass def undo_aggregate_operation(self, context, op, aggregate, host, set_error=True): """only used for Resource Pools.""" pass def instance_on_disk(self, instance): # ensure directories exist and are writable instance_path = libvirt_utils.get_instance_path(instance) LOG.debug(_('Checking instance files accessability %s'), instance_path) return os.access(instance_path, os.W_OK) def inject_network_info(self, instance, nw_info): self.firewall_driver.setup_basic_filtering(instance, nw_info) class HostState(object): """Manages information about the compute node through libvirt.""" def __init__(self, driver): super(HostState, self).__init__() self._stats = {} self.driver = driver self.update_status() def get_host_stats(self, refresh=False): """Return the current state of the host. If 'refresh' is True, run update the stats first. """ if refresh: self.update_status() return self._stats def update_status(self): """Retrieve status info from libvirt.""" LOG.debug(_("Updating host stats")) data = {} data["vcpus"] = self.driver.get_vcpu_total() data["vcpus_used"] = self.driver.get_vcpu_used() data["cpu_info"] = jsonutils.loads(self.driver.get_cpu_info()) disk_info_dict = self.driver.get_local_gb_info() data["disk_total"] = disk_info_dict['total'] data["disk_used"] = disk_info_dict['used'] data["disk_available"] = disk_info_dict['free'] data["host_memory_total"] = self.driver.get_memory_mb_total() data["host_memory_free"] = (data["host_memory_total"] - self.driver.get_memory_mb_used()) data["hypervisor_type"] = self.driver.get_hypervisor_type() data["hypervisor_version"] = self.driver.get_hypervisor_version() data["hypervisor_hostname"] = self.driver.get_hypervisor_hostname() data["supported_instances"] = \ self.driver.get_instance_capabilities() self._stats = data return data

41.957115

79

0.560308

79512840cdc2dd833c98c35c8ab44be0a49cc05b

4,935

py

Python

pipenv/patched/crayons.py

craynic/pipenv

e137d4334a5d225a06bf41b21e2eef746c19c3cb

[ "MIT" ]

null

pipenv/patched/crayons.py

craynic/pipenv

e137d4334a5d225a06bf41b21e2eef746c19c3cb

[ "MIT" ]

null

pipenv/patched/crayons.py

craynic/pipenv

e137d4334a5d225a06bf41b21e2eef746c19c3cb

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ clint.colored ~~~~~~~~~~~~~ This module provides a simple and elegant wrapper for colorama. """ import os import re import sys import shellingham import colorama PY3 = sys.version_info[0] >= 3 __all__ = ( "red", "green", "yellow", "blue", "black", "magenta", "cyan", "white", "normal", "clean", "disable", ) COLORS = __all__[:-2] is_ipython = "get_ipython" in dir() if ( os.environ.get("CMDER_ROOT") or os.environ.get("VSCODE_PID") or os.environ.get("TERM_PROGRAM") == "Hyper" or "VSCODE_CWD" in os.environ ): is_native_powershell = False else: is_native_powershell = True try: is_powershell = "powershell" in shellingham.detect_shell()[0] except shellingham.ShellDetectionFailure: is_powershell = False if is_ipython or (is_powershell and is_native_powershell): """when ipython is fired lot of variables like _oh, etc are used. There are so many ways to find current python interpreter is ipython. get_ipython is easiest is most appealing for readers to understand. """ DISABLE_COLOR = True else: DISABLE_COLOR = False class ColoredString(object): """Enhanced string for __len__ operations on Colored output.""" def __init__(self, color, s, always_color=False, bold=False): super(ColoredString, self).__init__() if not PY3 and isinstance(s, unicode): self.s = s.encode("utf-8") else: self.s = s self.color = color self.always_color = always_color self.bold = bold if os.environ.get("PIPENV_FORCE_COLOR"): self.always_color = True def __getattr__(self, att): def func_help(*args, **kwargs): result = getattr(self.s, att)(*args, **kwargs) try: is_result_string = isinstance(result, basestring) except NameError: is_result_string = isinstance(result, str) if is_result_string: return self._new(result) elif isinstance(result, list): return [self._new(x) for x in result] else: return result return func_help @property def color_str(self): style = "BRIGHT" if self.bold else "NORMAL" c = "%s%s%s%s%s" % ( getattr(colorama.Fore, self.color), getattr(colorama.Style, style), self.s, colorama.Fore.RESET, getattr(colorama.Style, "NORMAL"), ) if self.always_color: return c elif sys.stdout.isatty() and not DISABLE_COLOR: return c else: return self.s def __len__(self): return len(self.s) def __repr__(self): return "<%s-string: '%s'>" % (self.color, self.s) def __unicode__(self): value = self.color_str if isinstance(value, bytes): return value.decode("utf8") return value if PY3: __str__ = __unicode__ else: def __str__(self): return self.color_str def __iter__(self): return iter(self.color_str) def __add__(self, other): return str(self.color_str) + str(other) def __radd__(self, other): return str(other) + str(self.color_str) def __mul__(self, other): return self.color_str * other def _new(self, s): return ColoredString(self.color, s) def clean(s): strip = re.compile( "([^-_a-zA-Z0-9!@#%&=,/'\";:~`\$\^\*\+\[\]\.\{\}\|\?\<\>\\]+|[^\s]+)" ) txt = strip.sub("", str(s)) strip = re.compile(r"\[\d+m") txt = strip.sub("", txt) return txt def normal(string, always=False, bold=False): return ColoredString("RESET", string, always_color=always, bold=bold) def black(string, always=False, bold=False): return ColoredString("BLACK", string, always_color=always, bold=bold) def red(string, always=False, bold=False): return ColoredString("RED", string, always_color=always, bold=bold) def green(string, always=False, bold=False): return ColoredString("GREEN", string, always_color=always, bold=bold) def yellow(string, always=False, bold=False): return ColoredString("YELLOW", string, always_color=always, bold=bold) def blue(string, always=False, bold=False): return ColoredString("BLUE", string, always_color=always, bold=bold) def magenta(string, always=False, bold=False): return ColoredString("MAGENTA", string, always_color=always, bold=bold) def cyan(string, always=False, bold=False): return ColoredString("CYAN", string, always_color=always, bold=bold) def white(string, always=False, bold=False): # This upsets people... return ColoredString("WHITE", string, always_color=always, bold=bold) def disable(): """Disables colors.""" global DISABLE_COLOR DISABLE_COLOR = True

24.552239

81

0.613982

795129c42bbde563f6643398aaa67a5593d36874

20,542

py

Python

qgitc/mergewidget.py

timxx/qgitc

514333c80ce79c795ebe7c70ef74cfa91a9f529b

[ "Apache-2.0" ]

2

2020-05-06T19:56:15.000Z

2022-02-08T01:02:19.000Z

qgitc/mergewidget.py

timxx/qgitc

514333c80ce79c795ebe7c70ef74cfa91a9f529b

[ "Apache-2.0" ]

null

qgitc/mergewidget.py

timxx/qgitc

514333c80ce79c795ebe7c70ef74cfa91a9f529b

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- from PySide6.QtGui import * from PySide6.QtWidgets import * from PySide6.QtCore import * from .gitutils import Git, GitProcess from .stylehelper import dpiScaled from .conflictlog import ( ConflictLogExcel, ConflictLogXlsx, HAVE_EXCEL_API, HAVE_XLSX_WRITER, MergeInfo) from .events import CopyConflictCommit from .common import dataDirPath from datetime import datetime import shutil STATE_CONFLICT = 0 STATE_RESOLVED = 1 RESOLVE_SUCCEEDED = 0 RESOLVE_FAILED = 1 StateRole = Qt.UserRole + 1 class MergeWidget(QWidget): requestResolve = Signal(str) resolveFinished = Signal(int) def __init__(self, parent=None): super(MergeWidget, self).__init__(parent) self.setWindowFlags(Qt.WindowMinMaxButtonsHint) self.setWindowTitle(self.tr("Conflict List")) self.resolvedCount = 0 self.iconResolved = self.__makeTextIcon(chr(0x2714), Qt.green) self.iconConflict = self.__makeTextIcon('!', Qt.red) self.resolveIndex = -1 self.process = None self._firstShown = True self.log = None self.__setupUi() self.__setupSignals() self._mergeInfo = None def __setupUi(self): self.view = QListView(self) self.model = QStandardItemModel(self) self.proxyModel = QSortFilterProxyModel(self) self.proxyModel.setSourceModel(self.model) self.view.setSelectionMode(QAbstractItemView.ExtendedSelection) # for Ctrl+C self.view.installEventFilter(self) self.view.setModel(self.proxyModel) self.view.setEditTriggers(QAbstractItemView.NoEditTriggers) self.leFilter = QLineEdit(self) self.lbFilter = QLabel("0", self) filterLayout = QHBoxLayout() filterLayout.addWidget(self.leFilter) filterLayout.addWidget(self.lbFilter) vlayout = QVBoxLayout(self) vlayout.addLayout(filterLayout) vlayout.addWidget(self.view) hlayout = QHBoxLayout() self.status = QLabel(self) self.status.setToolTip(self.tr("Click to refresh the list")) self.cbAutoNext = QCheckBox(self.tr("Continuous resolve")) self.btnResolve = QPushButton(self.tr("Resolve")) hlayout.addWidget(self.status) hlayout.addSpacerItem(QSpacerItem( 20, 20, QSizePolicy.MinimumExpanding)) hlayout.addWidget(self.cbAutoNext) hlayout.addWidget(self.btnResolve) vlayout.addLayout(hlayout) self.cbAutoLog = QCheckBox(self.tr("Log conflicts to"), self) self.leLogFile = QLineEdit(self) self.btnChooseLog = QPushButton(self.tr("Choose"), self) hlayout = QHBoxLayout() hlayout.addWidget(self.cbAutoLog) hlayout.addWidget(self.leLogFile) hlayout.addWidget(self.btnChooseLog) vlayout.addLayout(hlayout) self.cbAutoNext.setChecked(True) if HAVE_EXCEL_API or HAVE_XLSX_WRITER: self.cbAutoLog.setChecked(True) self.__onAutoLogChanged(Qt.Checked) else: self.cbAutoLog.setChecked(False) self.cbAutoLog.setEnabled(False) self.cbAutoLog.setToolTip( self.tr("No pywin32/pywpsrpc or openpyxl found, feature disabled.")) self.__onAutoLogChanged(Qt.Unchecked) self.leLogFile.setText(self.__defaultLogFile()) self.__setupMenu() def __defaultLogFile(self): dir = QStandardPaths.writableLocation(QStandardPaths.DocumentsLocation) dt = datetime.now() fileName = "conflicts-{}.xlsx".format(dt.strftime("%Y%m%d%H%M%S")) return dir + QDir.separator() + fileName def __ensureLogWriter(self): if self.log is not None: return logFile = self.leLogFile.text() shutil.copy(dataDirPath() + "/templates/builtin.xlsx", logFile) if HAVE_EXCEL_API: self.log = ConflictLogExcel(logFile) elif HAVE_XLSX_WRITER: self.log = ConflictLogXlsx(logFile) if self._mergeInfo is not None: self.log.setMergeInfo(self._mergeInfo) def __setupMenu(self): self.menu = QMenu() self.acResolve = self.menu.addAction( self.tr("&Resolve"), self.__onMenuResolve) self.acUndoMerge = self.menu.addAction( self.tr("&Undo merge"), self.__onMenuUndoMerge) self.menu.addSeparator() self.acUseOurs = self.menu.addAction( self.tr("Use &ours"), self.__onMenuUseOurs) self.acUseTheirs = self.menu.addAction( self.tr("Use &theirs"), self.__onMenuUseTheirs) self.menu.addSeparator() self.menu.addAction(self.tr("&Copy Path"), self.__onMenuCopyPath, QKeySequence("Ctrl+C")) self.menu.addAction(self.tr("Copy &Windows Path"), self.__onMenuCopyWinPath) self.menu.addSeparator() self.menu.addAction(self.tr("Select &All"), self.__onMenuSelectAll, QKeySequence("Ctrl+A")) def __setupSignals(self): self.btnResolve.clicked.connect(self.__onResolveClicked) self.view.doubleClicked.connect(self.__onItemDoubleClicked) self.status.linkActivated.connect(self.__onStatusRefresh) self.leFilter.textChanged.connect(self.__onFilterChanged) self.cbAutoLog.stateChanged.connect(self.__onAutoLogChanged) self.btnChooseLog.clicked.connect(self.__onChooseLogFile) def __makeTextIcon(self, text, color): img = QPixmap(dpiScaled(QSize(32, 32))) img.fill(Qt.transparent) painter = QPainter(img) painter.setPen(color) font = QFont() font.setPixelSize(dpiScaled(32)) painter.setFont(font) painter.drawText(img.rect(), Qt.AlignCenter, text) painter = None return QIcon(img) def __updateStatus(self): # just don't wanna inherit a QLabel LoL total = self.model.rowCount() self.status.setText( "<a href='#refresh'>{}/{}</a>".format(self.resolvedCount, total)) def __updateFilterCount(self): text = self.proxyModel.filterRegularExpression().pattern() count = self.proxyModel.rowCount() if text else 0 self.lbFilter.setText("{}".format(count)) def __resolvedIndex(self, index): index = self.proxyModel.mapToSource(index) item = self.model.itemFromIndex(index) item.setData(STATE_RESOLVED, StateRole) item.setIcon(self.iconResolved) self.resolvedCount += 1 self.__updateStatus() def __checkCurrentResolve(self, index): if self.resolveIndex == index.row(): text = self.tr( "You are resolving this file, please close it first.") QMessageBox.information(self, qApp.applicationName(), text) return False return True def __onResolveClicked(self, checked=False): index = self.view.currentIndex() self.resolve(index) def __onItemDoubleClicked(self, index): self.resolve(index) def __onStatusRefresh(self, link): if self.process: QMessageBox.information(self, qApp.applicationName(), self.tr("You can't refresh before close the merge window.")) return self.updateList() def __onFilterChanged(self, text): self.proxyModel.setFilterRegularExpression(text) self.__updateFilterCount() def __onAutoLogChanged(self, state): enabled = state == Qt.Checked self.leLogFile.setEnabled(enabled) self.btnChooseLog.setEnabled(enabled) def __onChooseLogFile(self, checked): f, _ = QFileDialog.getSaveFileName( self, self.tr("Choose file"), dir=QStandardPaths.writableLocation(QStandardPaths.DocumentsLocation)) if f: self.leLogFile.setText(f) def __onMenuResolve(self): self.__onResolveClicked() def __onMenuUndoMerge(self): index = self.view.currentIndex() if index.data(StateRole) != STATE_RESOLVED: return if Git.undoMerge(index.data()): index = self.proxyModel.mapToSource(index) item = self.model.itemFromIndex(index) item.setData(STATE_CONFLICT, StateRole) item.setIcon(self.iconConflict) self.resolvedCount -= 1 self.__updateStatus() def __onMenuUseOurs(self): index = self.view.currentIndex() if not self.__checkCurrentResolve(index): return if index.data(StateRole) == STATE_CONFLICT: if Git.resolveBy(True, index.data()): self.__resolvedIndex(index) if self.logEnabled(): self.__ensureLogWriter() self.log.setResolveMethod( index.data(), self.tr("Local Branch")) def __onMenuUseTheirs(self): index = self.view.currentIndex() if not self.__checkCurrentResolve(index): return if index.data(StateRole) == STATE_CONFLICT: if Git.resolveBy(False, index.data()): self.__resolvedIndex(index) if self.logEnabled(): self.__ensureLogWriter() self.log.setResolveMethod( index.data(), self.tr("Remote Branch")) def __doCopyPath(self, asWin=False): indexList = self.view.selectionModel().selectedRows() paths = "" for index in indexList: path = index.data(Qt.DisplayRole) if asWin: path = path.replace('/', '\\') paths += path + "\n" if paths: qApp.clipboard().setText(paths) def __onMenuCopyPath(self): self.__doCopyPath() def __onMenuCopyWinPath(self): self.__doCopyPath(True) def __onMenuSelectAll(self): self.view.selectAll() def __onReadyRead(self): # FIXME: since git might not flush all output at one time # delay some time to read all data for "Deleted merge" QTimer.singleShot(50, self.__onResolveReadyRead) def __onResolveReadyRead(self): if not self.process or not self.process.bytesAvailable(): return data = self.process.readAllStandardOutput().data() # seems no options to control this buggy prompt if b'Continue merging other unresolved paths [y/n]?' in data: self.process.write(b"n\n") elif b'Deleted merge conflict for' in data: text = data.decode("utf-8") isCreated = "(c)reated" in text if isCreated: text = text.replace("(c)reated", "created") else: text = text.replace("(m)odified", "modified") text = text.replace("(d)eleted", "deleted") text = text.replace("(a)bort", "abort") msgBox = QMessageBox( QMessageBox.Question, qApp.applicationName(), text, QMessageBox.NoButton, self) msgBox.addButton(self.tr("Use &created") if isCreated else self.tr("Use &modified"), QMessageBox.AcceptRole) msgBox.addButton(self.tr("&Deleted file"), QMessageBox.RejectRole) msgBox.addButton(QMessageBox.Abort) r = msgBox.exec_() if r == QMessageBox.AcceptRole: if isCreated: self.process.write(b"c\n") else: self.process.write(b"m\n") elif r == QMessageBox.RejectRole: self.process.write(b"d\n") else: # r == QMessageBox.Abort: self.process.write(b"a\n") elif b'Symbolic link merge conflict for' in data: text = data.decode("utf-8") text = text.replace("(l)ocal", "local") text = text.replace("(r)emote", "remote") text = text.replace("(a)bort", "abort") msgBox = QMessageBox( QMessageBox.Question, qApp.applicationName(), text, QMessageBox.NoButton, self) msgBox.addButton(self.tr("Use &local"), QMessageBox.AcceptRole) msgBox.addButton(self.tr("Use &remote"), QMessageBox.RejectRole) msgBox.addButton(QMessageBox.Abort) r = msgBox.exec_() if r == QMessageBox.AcceptRole: self.process.write(b"l\n") elif r == QMessageBox.RejectRole: self.process.write(b"r\n") else: self.process.write(b"a\n") elif b'Was the merge successful [y/n]?' in data: # TODO: self.process.write(b"n\n") elif b'?' in data: # TODO: might have other prompt need yes no print("unhandled prompt", data) def __onResolveFinished(self, exitCode, exitStatus): errorData = None if exitCode == 0: index = self.proxyModel.index(self.resolveIndex, 0) self.__resolvedIndex(index) else: errorData = self.process.readAllStandardError() self.process = None curRow = self.resolveIndex self.resolveIndex = -1 self.resolveFinished.emit(RESOLVE_SUCCEEDED if exitCode == 0 else RESOLVE_FAILED) self.leFilter.setEnabled(True) self.cbAutoLog.setEnabled(True) self.__onAutoLogChanged(self.cbAutoLog.checkState()) # auto next only when success if exitCode != 0: if errorData: QMessageBox.critical( self, self.window().windowTitle(), errorData.data().decode("utf-8")) return if not self.cbAutoNext.isChecked(): return if self.resolvedCount == self.model.rowCount(): QMessageBox.information(self, qApp.applicationName(), self.tr("All resolved!")) return index = None allFilterResolved = True noEndConflicts = True # search to the end for i in range(curRow + 1, self.proxyModel.rowCount()): index = self.proxyModel.index(i, 0) if index.data(StateRole) == STATE_CONFLICT: allFilterResolved = False noEndConflicts = False break index = None # search from beginning if not index: for i in range(curRow): index = self.proxyModel.index(i, 0) if index.data(StateRole) == STATE_CONFLICT: allFilterResolved = False break index = None # to avoid show two messagebox if reach to the end if allFilterResolved: text = self.tr( "All filter conflicts are resolved, please clear the filter to resolve the rest.") QMessageBox.information(self, qApp.applicationName(), text) return elif noEndConflicts: text = self.tr( "Resolve reach to the end of list, do you want to resolve from beginning?") r = QMessageBox.question( self, qApp.applicationName(), text, QMessageBox.Yes, QMessageBox.No) if r == QMessageBox.No: return self.view.setCurrentIndex(index) self.resolve(index) def __onFirstShow(self): self.updateList() if self.model.rowCount() == 0: QMessageBox.information( self, self.window().windowTitle(), self.tr("No conflict files to resolve!"), QMessageBox.Ok) def contextMenuEvent(self, event): index = self.view.currentIndex() enabled = index.data(StateRole) == STATE_RESOLVED self.acResolve.setEnabled(not enabled) # TODO: handle multiple files if len(self.view.selectionModel().selectedRows()) > 1: self.acUndoMerge.setEnabled(False) self.acUseOurs.setEnabled(False) self.acUseTheirs.setEnabled(False) else: self.acUndoMerge.setEnabled(enabled) self.acUseOurs.setEnabled(not self.isResolving()) self.acUseTheirs.setEnabled(not self.isResolving()) self.menu.exec_(event.globalPos()) def paintEvent(self, event): super().paintEvent(event) if self._firstShown: self._firstShown = False QTimer.singleShot(0, self.__onFirstShow) def sizeHint(self): return dpiScaled(QSize(500, 700)) def updateList(self): if not Git.available(): return files = Git.conflictFiles() self.model.clear() if files: for f in files: item = QStandardItem(self.iconConflict, f) item.setData(STATE_CONFLICT, StateRole) self.model.appendRow(item) index = self.proxyModel.index(0, 0) self.view.setCurrentIndex(index) self.resolvedCount = 0 self.__updateStatus() self.__updateFilterCount() for action in self.menu.actions(): action.setEnabled(not files is None) self.btnResolve.setEnabled(not files is None) def resolve(self, index): if not index.isValid(): return if index.data(StateRole) == STATE_RESOLVED: QMessageBox.information(self, qApp.applicationName(), self.tr("This file is already resolved.")) return if self.process: QMessageBox.information(self, qApp.applicationName(), self.tr("Please resolve current conflicts before start a new one.")) return # since we saved the index, so disabled ... self.leFilter.setEnabled(False) self.cbAutoLog.setEnabled(False) self.__onAutoLogChanged(Qt.Unchecked) self.resolveIndex = index.row() file = index.data() args = ["mergetool", "--no-prompt"] toolName = None tools = qApp.settings().mergeToolList() # ignored case even on Unix platform lowercase_file = file.lower() for tool in tools: if tool.canMerge() and tool.isValid(): if lowercase_file.endswith(tool.suffix.lower()): toolName = tool.command break if not toolName: toolName = qApp.settings().mergeToolName() if toolName: args.append("--tool=%s" % toolName) args.append(file) # subprocess is not suitable here self.process = QProcess(self) self.process.readyReadStandardOutput.connect(self.__onReadyRead) self.process.finished.connect(self.__onResolveFinished) self.process.setWorkingDirectory(Git.REPO_DIR) self.process.start(GitProcess.GIT_BIN, args) self.requestResolve.emit(file) if self.logEnabled(): self.__ensureLogWriter() self.log.addFile(file) def event(self, e): if e.type() == CopyConflictCommit.Type: if self.isResolving() and self.logEnabled(): self.log.addCommit(e.commit) return True return super().event(e) def eventFilter(self, obj, event): if obj == self.view and event.type() == QEvent.KeyPress: if event.matches(QKeySequence.Copy): self.__doCopyPath() return True return super().eventFilter(obj, event) def isResolving(self): return self.process is not None def logEnabled(self): return self.cbAutoLog.isChecked() def queryClose(self): if self.log: self.log.save() self.log = None return True def setBranches(self, localBranch, remoteBranch): self._mergeInfo = MergeInfo( localBranch, remoteBranch.replace("remotes/origin/", ""), Git.getConfigValue("user.name", False) )

34.582492

104

0.587285

79512bd8dcda8ddf1e1029dfa070c2e791fce916

3,817

py

Python

app/equipment_type/routes.py

kid-kodi/BioBank

27c7cb7286dcae737fa53c245456d60857fe949f

[ "MIT" ]

null

app/equipment_type/routes.py

kid-kodi/BioBank

27c7cb7286dcae737fa53c245456d60857fe949f

[ "MIT" ]

null

app/equipment_type/routes.py

kid-kodi/BioBank

27c7cb7286dcae737fa53c245456d60857fe949f

[ "MIT" ]

null

from datetime import datetime from flask import render_template, flash, redirect, url_for, request, g, \ jsonify, current_app from flask_login import current_user, login_required from flask_babel import _, get_locale from guess_language import guess_language from app import db from app.equipment_type.forms import EquipmentTypeForm, SearchForm from app.models import Customer, EquipmentType from app.translate import translate from app.equipment_type import bp @bp.route('/equipment_type', methods=['GET', 'POST']) @login_required def index(): pagination = [] search_form = SearchForm() page = request.args.get('page', 1, type=int) if search_form.validate_on_submit(): name = search_form.name.data if name != '': pagination = EquipmentType.query.filter_by(name=name) \ .order_by(EquipmentType.created_at.desc()).paginate( page, per_page=current_app.config['FLASK_PER_PAGE'], error_out=False) else: pagination = EquipmentType.query \ .order_by(EquipmentType.created_at.desc()).paginate( page, per_page=current_app.config['FLASK_PER_PAGE'], error_out=False) else: pagination = EquipmentType.query \ .order_by(EquipmentType.created_at.desc()).paginate( page, per_page=current_app.config['FLASK_PER_PAGE'], error_out=False) list = pagination.items return render_template('equipment_type/list.html', list=list, pagination=pagination, title="equipment", search_form=search_form) @bp.route('/equipment_type/add', methods=['GET', 'POST']) @login_required def add(): add = True form = EquipmentTypeForm() if form.validate_on_submit(): equipementType = EquipmentType(name=form.name.data, description=form.description.data, created_at=datetime.utcnow(), created_by=current_user.id) db.session.add(equipementType) db.session.commit() flash(_('Data saved!')) return redirect(url_for('equipment_type.index')) return render_template('equipment_type/form.html', action="Add", add=add, form=form, title="Add equipment_type") @bp.route('/equipment_type/edit/<int:id>', methods=['GET', 'POST']) @login_required def edit(id): add = False equipementType = EquipmentType.query.get_or_404(id) form = EquipmentTypeForm(obj=equipementType) if form.validate_on_submit(): equipementType.name = form.name.data equipementType.description = form.description.data db.session.commit() flash('You have successfully edited the equipment_type.') # redirect to the bps page return redirect(url_for('equipment_type.index')) form.name.data = equipementType.name return render_template('equipment_type/form.html', action="Edit", add=add, form=form, equipementType=equipementType, title="Edit equipment_type") @bp.route('/equipment_type/<int:id>', methods=['GET', 'POST']) @login_required def detail(id): equipementType = EquipmentType.query.get_or_404(id) return render_template('equipment_type/detail.html', equipementType=equipementType) @bp.route('/equipment_type/delete/<int:id>', methods=['GET', 'POST']) @login_required def delete(id): equipementType = EquipmentType.query.get_or_404(id) db.session.delete(equipementType) db.session.commit() flash('You have successfully deleted the equipment_type.') # redirect to the bps page return redirect(url_for('equipment_type.index'))

38.17

87

0.653655

79512cb9149491e8e325c294dc06fc126ca13e56

4,077

py

Python

vint/ast/parsing.py

mosheavni/vint

9078dd626415cfe37ddaf03032e714bbaca8b336

[ "MIT" ]

538

2015-01-03T18:54:53.000Z

2020-01-11T01:34:51.000Z

vint/ast/parsing.py

mosheavni/vint

9078dd626415cfe37ddaf03032e714bbaca8b336

[ "MIT" ]

235

2015-01-01T06:20:01.000Z

2020-01-17T11:32:39.000Z

vint/ast/parsing.py

mosheavni/vint

9078dd626415cfe37ddaf03032e714bbaca8b336

[ "MIT" ]

43

2015-01-23T16:59:49.000Z

2019-12-27T10:56:12.000Z

from typing import Dict, Any # noqa: F401 import re from vint._bundles import vimlparser from vint.ast.traversing import traverse from vint.encodings.decoder import Decoder from vint.encodings.decoding_strategy import default_decoding_strategy from vint.linting.lint_target import AbstractLintTarget class Parser(object): def __init__(self, plugins=None, enable_neovim=False): """ Initialize Parser with the specified plugins. The plugins can add attributes to the AST. """ self.plugins = plugins if plugins else [] self._enable_neovim = enable_neovim def parse(self, lint_target): # type: (AbstractLintTarget) -> Dict[str, Any] """ Parse vim script file and return the AST. """ decoder = Decoder(default_decoding_strategy) decoded = decoder.decode(lint_target.read()) decoded_and_lf_normalized = decoded.replace('\r\n', '\n') return self.parse_string(decoded_and_lf_normalized) def parse_string(self, string): # type: (str) -> Dict[str, Any] """ Parse vim script string and return the AST. """ lines = string.split('\n') reader = vimlparser.StringReader(lines) parser = vimlparser.VimLParser(self._enable_neovim) ast = parser.parse(reader) # TOPLEVEL does not have a pos, but we need pos for all nodes ast['pos'] = {'col': 1, 'i': 0, 'lnum': 1} for plugin in self.plugins: plugin.process(ast) return ast def parse_redir(self, redir_cmd): """ Parse a command :redir content. """ redir_cmd_str = redir_cmd['str'] matched = re.match(r'redir?!?\s*(=>>?\s*)(\S+)', redir_cmd_str) if matched: redir_cmd_op = matched.group(1) redir_cmd_body = matched.group(2) arg_pos = redir_cmd['ea']['argpos'] # Position of the "redir_cmd_body" start_pos = { 'col': arg_pos['col'] + len(redir_cmd_op), 'i': arg_pos['i'] + len(redir_cmd_op), 'lnum': arg_pos['lnum'], } # NOTE: This is a hack to parse variable node. raw_ast = self.parse_string('echo ' + redir_cmd_body) # We need the left node of ECHO node redir_cmd_ast = raw_ast['body'][0]['list'][0] def adjust_position(node): pos = node['pos'] # Care 1-based index and the length of "echo ". pos['col'] += start_pos['col'] - 1 - 5 # Care the length of "echo ". pos['i'] += start_pos['i'] - 5 # Care 1-based index pos['lnum'] += start_pos['lnum'] - 1 traverse(redir_cmd_ast, on_enter=adjust_position) return redir_cmd_ast return None def parse_string_expr(self, string_expr_node): """ Parse a string node content. """ string_expr_node_value = string_expr_node['value'] string_expr_str = string_expr_node_value[1:-1] # Care escaped string literals if string_expr_node_value[0] == "'": string_expr_str = string_expr_str.replace("''", "'") else: string_expr_str = string_expr_str.replace('\\"', '"') # NOTE: This is a hack to parse expr1. See :help expr1 raw_ast = self.parse_string('echo ' + string_expr_str) # We need the left node of ECHO node parsed_string_expr_nodes = raw_ast['body'][0]['list'] start_pos = string_expr_node['pos'] def adjust_position(node): pos = node['pos'] # Care 1-based index and the length of "echo ". pos['col'] += start_pos['col'] - 1 - 5 # Care the length of "echo ". pos['i'] += start_pos['i'] - 5 # Care 1-based index pos['lnum'] += start_pos['lnum'] - 1 for parsed_string_expr_node in parsed_string_expr_nodes: traverse(parsed_string_expr_node, on_enter=adjust_position) return parsed_string_expr_nodes

33.418033

81

0.590385

79512e3fdd0749567f943a81625f02d4b71c9138

3,240

py

Python

main.py

n0vuh/antizon

bc9b8765add79a5a4f9f225f8e14a79aa3e10291

[ "BSD-3-Clause" ]

3

2021-08-13T21:30:47.000Z

2021-11-21T23:40:06.000Z

main.py

n0vuh/antizon

bc9b8765add79a5a4f9f225f8e14a79aa3e10291

[ "BSD-3-Clause" ]

1

2021-08-12T21:53:50.000Z

2021-08-12T21:56:24.000Z

main.py

n0vuh/antizon

bc9b8765add79a5a4f9f225f8e14a79aa3e10291

[ "BSD-3-Clause" ]

null

# developed by novuh (github.com/n0vuh) # AntiZon is intended for the purpose of getting a consumer a better price for a product. # AntiZon is not intended to be used as financial advice. # source code, p.s: ignore lazy code, it'll be fixed import json import os from colorama import Back, Fore, Style from src.utils.console import clear, title, tags from src.amzn.item import AMZN from src.gogl.find import SERP, process def query_loop(amzn: AMZN, serp: SERP): query = input(tags.default + "Amazon Product ASIN >> ") print(tags.default + f"Searching Amazon for '{query}'...") amazon_data = amzn.get_item(query) try: amzn_value = amazon_data.value amzn_title = amazon_data.name print(tags.okay + "Found a result!") except AttributeError: print(tags.error + str(amazon_data["message"])) query_loop(amzn, serp) print(tags.default + f"Searching Google for '{amzn_title}'...") try: r = process(serp.get(amzn_title)) print(tags.okay + f"Found {len(r['extracted'])} result(s)!") except Exception as e: print(tags.error + f"Error occured while searching Google: {e}") query_loop(amzn, serp) print(tags.default + "Processing retrieved data...") cheaper = [] for res in r["extracted"]: if res["price"] < amzn_value: cheaper.append( { "price": res["price"], "source": res["supplier"], "link": res["link"] } ) print(tags.okay + f"Found {len(cheaper)} cheaper listings!") print(tags.default + "Writing...") with open("output.txt", "a") as fp: for res in cheaper: fp.write(f"${res['price']} | {res['link']} | {res['source']}\n") print(tags.okay + "Done! You can view the results inside output.txt.") input(Fore.BLACK + Style.BRIGHT + "Press ENTER to quit.") exit() def main(): clear() # title stuff title("AntiZon by novuh") length = os.get_terminal_size().columns print(Style.BRIGHT + Fore.WHITE + Back.YELLOW + "AntiZon by github.com/n0vuh".center(length, " ") + Back.RESET + "\n") # load config file print(Back.RESET + tags.default + "Loading config file...") cfg = json.load(open("src/resources/config.json", "r")) # check to see if config has any data, if not, require user to give required data dump = False if cfg["serpkey"] == "": print(tags.error + "You do not have a SerpAPI key! Get one @ serpapi.com") serp_key = input(tags.default + "SerpAPI Key >> ") dump = True if cfg["amznkey"] == "": print(tags.error + "You do not have a Rainforest key! Get one @ rainforestapi.com") amzn_key = input(tags.default + "Rainforest Key >> ") dump = True if dump: json.dump({"serpkey": serp_key, "amznkey": amzn_key}, open("src/resources/config.json", "w")) main() # setup api classes amzn = AMZN(cfg["amznkey"]) serp = SERP(cfg["serpkey"]) # search loop query_loop(amzn, serp) if __name__ == "__main__": main()

34.105263

123

0.587346

79512f31d9a702aa7d3cacd949fcd92b5eaa25ef

1,277

py

Python

jsonfmt.py

r15ch13/scoops

82da457b00f583627ad83512c0de68f54c615083

[ "MIT" ]

null

jsonfmt.py

r15ch13/scoops

82da457b00f583627ad83512c0de68f54c615083

[ "MIT" ]

null

jsonfmt.py

r15ch13/scoops

82da457b00f583627ad83512c0de68f54c615083

[ "MIT" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- """ @todo add docstring """ # ### imports ### from __future__ import ( absolute_import, division, print_function # , # unicode_literals ) from jsoncomment import JsonComment # from jsonschema import validate import json import os import sys def decode(s): if sys.version_info >= (3, 0): return s for encoding in 'utf-8-sig', 'utf-16': try: return s.decode(encoding) except UnicodeDecodeError: continue return s.decode('latin-1') def touch(filename, mtime): with open(filename, 'a+'): pass os.utime(filename, (mtime, mtime)) return 0 file = sys.argv[1] print('Updating', file) mtime = os.path.getmtime(file) with open(file, 'r') as f: jstr = f.read() jstr_no_bom = decode(jstr) parser = JsonComment(json) json_data = parser.loads(jstr_no_bom) new_data = json.dumps( json_data, sort_keys=True, indent=4, separators=(',', ': ')) with open(file + '.tmp', 'wb') as f: new_data = new_data.encode('utf-8') new_data += b"\n" f.write(new_data) if os.path.isfile(file + '.bak'): os.remove(file + '.bak') os.rename(file, file + '.bak') os.rename(file + '.tmp', file) touch(file, mtime) sys.exit(0)

18.779412

64

0.618637

79512fe05d993ffaabcea1e53f67931b5550932e

17,292

py

Python

cube_algorithm/cube.py

Khja/CubeAlgAddon

84961f3dadbccfeed208169776dee35e0a7e969d

[ "MIT" ]

4

2020-07-23T19:39:12.000Z

2022-01-16T15:52:01.000Z

cube_algorithm/cube.py

Khja/CubeAlgAddon

84961f3dadbccfeed208169776dee35e0a7e969d

[ "MIT" ]

null

cube_algorithm/cube.py

Khja/CubeAlgAddon

84961f3dadbccfeed208169776dee35e0a7e969d

[ "MIT" ]

null

# MIT License # Copyright (c) 2020 Khja # Please read before using this piece of software. css = """\ .card { font-family: arial; font-size: 30px; text-align: center; color: black; background-color: white; } :root { --thickness: 4; --size: 20; --G: #39EA5C; --W: #FFFFFF; --R: #DC0010; --O: #FF9E00; --Y: #FFFF00; --B: #0027D7; } \ """ n_back = '''{{FrontSide}} <hr id=answer> {{Case}} {{Algorithm}} ''' _front = """ <canvas id="cube" width=150 height=150></canvas> <script> //Colors. You may change the RGB codes, don't touch the names var colors = { G: getComputedStyle(document.documentElement).getPropertyValue('--G'), W: getComputedStyle(document.documentElement).getPropertyValue('--W'), R: getComputedStyle(document.documentElement).getPropertyValue('--R'), O: getComputedStyle(document.documentElement).getPropertyValue('--O'), Y: getComputedStyle(document.documentElement).getPropertyValue('--Y'), B: getComputedStyle(document.documentElement).getPropertyValue('--B') }; var border = getComputedStyle(document.documentElement).getPropertyValue('--Border') //Size of the cube var size = parseInt(getComputedStyle(document.documentElement).getPropertyValue('--size')) //Thickness of the border var thickness = parseInt(getComputedStyle(document.documentElement).getPropertyValue('--thickness')) //*****DON'T TOUCH FURTHER!!!!*****// //Faces var F = Array(9).fill("G"), U = Array(9).fill("W"), R = Array(9).fill("R"), B = Array(9).fill("B") D = Array(9).fill("Y"), L = Array(9).fill("O"), cube = [U,F,R,D,B,L]; function Random(max) { return Math.round(Math.random() * (max-1)); } /*Rotation functions*/ //Rotate_s the face colors clockwise. Center stays in center function Rotate_Face_1(face) { var top = Array() top.push(face[6]) top.push(face[3]) top.push(face[0]) top.push(face[7]) top.push(face[4]) top.push(face[1]) top.push(face[8]) top.push(face[5]) top.push(face[2]) return top } //Rotate_s the face colors counterclockwise. Center stays in center function Rotate_Face_2(face) { return Rotate_Face_1(Rotate_Face_1(Rotate_Face_1(face))) } //Rotate_s all objects in the Arrays clockwise function Rotate_Layer(U,F,L,B,R) { var u = Rotate_Face_1(U) var f = Array() var l = Array() var b = Array() var r = Array() //Rotate_s the lateral objects f.push(R[0]) f.push(R[1]) f.push(R[2]) f.push(F[3]) f.push(F[4]) f.push(F[5]) f.push(F[6]) f.push(F[7]) f.push(F[8]) l.push(F[0]) l.push(F[1]) l.push(F[2]) l.push(L[3]) l.push(L[4]) l.push(L[5]) l.push(L[6]) l.push(L[7]) l.push(L[8]) b.push(L[0]) b.push(L[1]) b.push(L[2]) b.push(B[3]) b.push(B[4]) b.push(B[5]) b.push(B[6]) b.push(B[7]) b.push(B[8]) r.push(B[0]) r.push(B[1]) r.push(B[2]) r.push(R[3]) r.push(R[4]) r.push(R[5]) r.push(R[6]) r.push(R[7]) r.push(R[8]) return [u, f, l, b, r] } /*Moves*/ //Rotations function Rotate_X() { var u = cube[0], f = cube[1], r = cube[2], d = cube[3], b = cube[4], l = cube[5] cube[0] = f cube[1] = d cube[2] = Rotate_Face_1(r) cube[3] = Rotate_Face_1(Rotate_Face_1(b)) cube[4] = Rotate_Face_1(Rotate_Face_1(u)) cube[5] = Rotate_Face_2(l) } function Rotate_Xi() {Rotate_X(); Rotate_X(); Rotate_X()}; function Rotate_Y() {Rotate_Yi(); Rotate_Yi(); Rotate_Yi()}; function Rotate_Yi() { var u = cube[0], f = cube[1], r = cube[2], d = cube[3], b = cube[4], l = cube[5] cube[0] = Rotate_Face_2(u) cube[1] = l cube[2] = f cube[3] = Rotate_Face_1(d) cube[4] = r cube[5] = b } function Rotate_Zi() {Rotate_Yi(); Rotate_Xi(); Rotate_Y()}; function Rotate_Z() {Rotate_Zi(); Rotate_Zi(); Rotate_Zi()}; //Layers function Rotate_U() { var all = Rotate_Layer(cube[0],cube[1],cube[5],cube[4],cube[2]) cube[0] = all[0] cube[1] = all[1] cube[5] = all[2] cube[4] = all[3] cube[2] = all[4] } function Rotate_Ui() {Rotate_U(); Rotate_U(); Rotate_U()}; function Rotate_F() {Rotate_X(); Rotate_U(); Rotate_Xi()}; function Rotate_Fi() {Rotate_X(); Rotate_Ui(); Rotate_Xi()}; function Rotate_R() {Rotate_Zi(); Rotate_U(); Rotate_Z()}; function Rotate_Ri() {Rotate_R(); Rotate_R(); Rotate_R()}; function Rotate_D() {Rotate_X(); Rotate_X(); Rotate_U(); Rotate_X(); Rotate_X()}; function Rotate_Di() {Rotate_D(); Rotate_D(); Rotate_D()}; function Rotate_B() {Rotate_Xi(); Rotate_U(); Rotate_X()}; function Rotate_Bi() {Rotate_Xi(); Rotate_Ui(); Rotate_X()}; function Rotate_L() {Rotate_Z(); Rotate_U(); Rotate_Zi()}; function Rotate_Li() {Rotate_Z(); Rotate_Ui(); Rotate_Zi()}; function Rotate_M() {Rotate_X(); Rotate_Ri(); Rotate_L()}; function Rotate_Mi() {Rotate_Xi(); Rotate_R(); Rotate_Li()}; function Rotate_r() {Rotate_L(); Rotate_X()} function Rotate_ri() {Rotate_Li(); Rotate_Xi()} function Rotate_l() {Rotate_R(); Rotate_X()} function Rotate_li() {Rotate_Ri(); Rotate_Xi()} function Rotate_u() {Rotate_Di(); Rotate_Yi()} function Rotate_ui() {Rotate_D(); Rotate_Y()} function Rotate_b() {Rotate_Fi(); Rotate_Z()} function Rotate_bi() {Rotate_F(); Rotate_Zi()} function Rotate_d() {Rotate_Ui(); Rotate_Y()} function Rotate_di() {Rotate_U(); Rotate_Yi()} function Rotate_f() {Rotate_B(); Rotate_Z()} function Rotate_fi() {Rotate_Bi(); Rotate_Zi()} /*Drawing functions*/ function rect(ctx,l,x,y,a,b,color) { ctx.fillStyle = color; ctx.beginPath(); ctx.moveTo(x*l, y*l); ctx.lineTo(x*l, y*l+a*l); ctx.lineTo(x*l+b*l, y*l+a*l); ctx.lineTo(x*l+b*l, y*l); ctx.lineTo(x*l, y*l); ctx.fill(); ctx.stroke(); } function Display(cube) { var u = cube[0], f = cube[1], r = cube[2], b = cube[4], l = cube[5]; var canvas = document.getElementById('cube'); if (canvas.getContext) { var ctx = canvas.getContext('2d'); ctx.canvas.width = 8 * size ctx.canvas.height = 8 * size ctx.strokeStyle = border; ctx.lineWidth = thickness; //U Face rect(ctx, size, 1, 5, 2, 2, colors[u[6]]) rect(ctx, size, 3, 5, 2, 2, colors[u[7]]) rect(ctx, size, 5, 5, 2, 2, colors[u[8]]) rect(ctx, size, 1, 3, 2, 2, colors[u[3]]) rect(ctx, size, 3, 3, 2, 2, colors[u[4]]) rect(ctx, size, 5, 3, 2, 2, colors[u[5]]) rect(ctx, size, 1, 1, 2, 2, colors[u[0]]) rect(ctx, size, 3, 1, 2, 2, colors[u[1]]) rect(ctx, size, 5, 1, 2, 2, colors[u[2]]) //F Face rect(ctx, size, 1, 7, 1, 2, colors[f[0]]) rect(ctx, size, 3, 7, 1, 2, colors[f[1]]) rect(ctx, size, 5, 7, 1, 2, colors[f[2]]) /* //B Face rect(ctx, size, 1, -1, colors[b[2]]) rect(ctx, size, 3, -1, colors[b[1]]) rect(ctx, size, 5, -1, colors[b[0]]) */ //R Face rect(ctx, size, 7, 1, 2, 1, colors[r[2]]) rect(ctx, size, 7, 3, 2, 1, colors[r[1]]) rect(ctx, size, 7, 5, 2, 1, colors[r[0]]) //L Face rect(ctx, size, 0, 1, 2, 1, colors[l[0]]) rect(ctx, size, 0, 3, 2, 1, colors[l[1]]) rect(ctx, size, 0, 5, 2, 1, colors[l[2]]) } } function Reverse(alg) { var reverse = Array() var alg = alg.reverse() for (i = 0; i < alg.length; i++) { //x if (alg[i] == "x") {reverse.push("x'")} if (alg[i] == "x'") {reverse.push("x")} if (alg[i] == "x2" || alg[i] == "x2'") {reverse.push("x2")} //y if (alg[i] == "y") {reverse.push("y'")} if (alg[i] == "y'") {reverse.push("y")} if (alg[i] == "y2" || alg[i] == "y2'") {reverse.push("y2")} //z if (alg[i] == "z") {reverse.push("z'")} if (alg[i] == "z'") {reverse.push("z")} if (alg[i] == "z2" || alg[i] == "z2'") {reverse.push("z2")} //U if (alg[i] == "U") {reverse.push("U'")} if (alg[i] == "U'") {reverse.push("U")} if (alg[i] == "U2" || alg[i] == "U2'") {reverse.push("U2")} //F if (alg[i] == "F") {reverse.push("F'")} if (alg[i] == "F'") {reverse.push("F")} if (alg[i] == "F2" || alg[i] == "F2'") {reverse.push("F2")} //R if (alg[i] == "R") {reverse.push("R'")} if (alg[i] == "R'") {reverse.push("R")} if (alg[i] == "R2" || alg[i] == "R2'") {reverse.push("R2")} //D if (alg[i] == "D") {reverse.push("D'")} if (alg[i] == "D'") {reverse.push("D")} if (alg[i] == "D2" || alg[i] == "D2'") {reverse.push("D2")} //B if (alg[i] == "B") {reverse.push("B'")} if (alg[i] == "B'") {reverse.push("B")} if (alg[i] == "B2" || alg[i] == "B2'") {reverse.push("B2")} //L if (alg[i] == "L") {reverse.push("L'")} if (alg[i] == "L'") {reverse.push("L")} if (alg[i] == "L2" || alg[i] == "L2'") {reverse.push("L2")} //M if (alg[i] == "M") {reverse.push("M'")} if (alg[i] == "M'") {reverse.push("M")} if (alg[i] == "M2" || alg[i] == "M2'") {reverse.push("M2")} //u if (alg[i] == "u") {reverse.push("u'")} if (alg[i] == "u'") {reverse.push("u")} if (alg[i] == "u2" || alg[i] == "u2'") {reverse.push("u2")} //f if (alg[i] == "f") {reverse.push("f'")} if (alg[i] == "f'") {reverse.push("f")} if (alg[i] == "f2" || alg[i] == "f2'") {reverse.push("f2")} //r if (alg[i] == "r") {reverse.push("r'")} if (alg[i] == "r'") {reverse.push("r")} if (alg[i] == "r2" || alg[i] == "r2'") {reverse.push("r2")} //d if (alg[i] == "d") {reverse.push("d'")} if (alg[i] == "d'") {reverse.push("d")} if (alg[i] == "d2" || alg[i] == "d2'") {reverse.push("d2")} //b if (alg[i] == "b") {reverse.push("b'")} if (alg[i] == "b'") {reverse.push("b")} if (alg[i] == "b2" || alg[i] == "b2'") {reverse.push("b2")} //l if (alg[i] == "l") {reverse.push("l'")} if (alg[i] == "l'") {reverse.push("l")} if (alg[i] == "l2" || alg[i] == "l2'") {reverse.push("l2")} } return reverse } function Perform(alg) { for (i = 0; i < alg.length; i++) { //x if (alg[i] == "x") {Rotate_X()} if (alg[i] == "x'") {Rotate_Xi()} if (alg[i] == "x2" || alg[i] == "x2'") {Rotate_X(); Rotate_X()} //y if (alg[i] == "y") {Rotate_Y()} if (alg[i] == "y'") {Rotate_Yi()} if (alg[i] == "y2" || alg[i] == "y2'") {Rotate_Y(); Rotate_Y();} //z if (alg[i] == "z") {Rotate_Z()} if (alg[i] == "z'") {Rotate_Zi()} if (alg[i] == "z2" || alg[i] == "z2'") {Rotate_Z(); Rotate_Z();} //U if (alg[i] == "U") {Rotate_U()} if (alg[i] == "U'") {Rotate_Ui()} if (alg[i] == "U2" || alg[i] == "U2'") {Rotate_U(); Rotate_U()} //F if (alg[i] == "F") {Rotate_F()} if (alg[i] == "F'") {Rotate_Fi()} if (alg[i] == "F2" || alg[i] == "F2'") {Rotate_F(); Rotate_F()} //R if (alg[i] == "R") {Rotate_R()} if (alg[i] == "R'") {Rotate_Ri()} if (alg[i] == "R2" || alg[i] == "R2'") {Rotate_R();Rotate_R();} //D if (alg[i] == "D") {Rotate_D();} if (alg[i] == "D'") {Rotate_Di();} if (alg[i] == "D2" || alg[i] == "D2'") {Rotate_D(); Rotate_D()} //B if (alg[i] == "B") {Rotate_B()} if (alg[i] == "B'") {Rotate_Bi()} if (alg[i] == "B2" || alg[i] == "B2'") {Rotate_B(); Rotate_B()} //L if (alg[i] == "L") {Rotate_L()} if (alg[i] == "L'") {Rotate_Li()} if (alg[i] == "L2" || alg[i] == "L2'") {Rotate_L(); Rotate_L()} //M if (alg[i] == "M") {Rotate_M()} if (alg[i] == "M'") {Rotate_Mi()} if (alg[i] == "M2" || alg[i] == "M2'") {Rotate_M(); Rotate_M()} //u if (alg[i] == "u") { Rotate_u()} if (alg[i] == "u'") {Rotate_ui()} if (alg[i] == "u2" || alg[i] == "u2'") {Rotate_u(); Rotate_u()} //f if (alg[i] == "f") {Rotate_f()} if (alg[i] == "f'") {Rotate_fi()} if (alg[i] == "f2" || alg[i] == "f2'") {Rotate_f(); Rotate_f()} //r if (alg[i] == "r") {Rotate_r()} if (alg[i] == "r'") {Rotate_ri()} if (alg[i] == "r2" || alg[i] == "r2'") {Rotate_r(); Rotate_r()} //d if (alg[i] == "d") {Rotate_d()} if (alg[i] == "d'") {Rotate_di()} if (alg[i] == "d2" || alg[i] == "d2'") {Rotate_d(); Rotate_d()} //b if (alg[i] == "b") {Rotate_b()} if (alg[i] == "b'") {Rotate_bi()} if (alg[i] == "b2" || alg[i] == "b2'") {Rotate_b(); Rotate_b()} //l if (alg[i] == "l") {Rotate_R(); Rotate_Xi()} if (alg[i] == "l'") {Rotate_li()} if (alg[i] == "l2" || alg[i] == "l2'") {Rotate_l(); Rotate_l()} } } var neutrality = "{{Neutrality}}".trim().split(' // '); function getNeutral(neutrality) { var colors = Array(); for (i = 0; i < neutrality.length; i ++) { color = neutrality[i]; for (k = 0; k < 10; k ++) { if (color == "W") {colors.push("")}; if (color == "Y") {colors.push("x2")}; if (color == "G") {colors.push("x")}; if (color == "B") {colors.push("x'")}; if (color == "R") {colors.push("z'")}; if (color == "O") {colors.push("z")}; } } return colors } function Scramble(moves,number) { var scramble = Array(); var i = 0; while (i < number) { var k = Random(moves.length) var move = moves[k] if (move.length > 2) { move = move.split(' ') } else { move = [move] } Perform(move) i ++ } } function getAlg(alg,s) { alg = alg.replace(/$/g, "") alg = alg.replace(/$/g, "") return alg.trim().split(s) } var moves = getAlg("{{Algorithm}}",' '); var scramble_size = {{ScrambleSize}}; var scramble = getAlg("{{Scramble}}"," // "); var aufmoves = ["U","U'","U2"]; var reverse = Reverse(moves); //Apply algorithm and scramble and AUF var ufaces = getNeutral(neutrality) Perform([ufaces[Random(ufaces.length)]]) Scramble(['y','y2'],6) Scramble(scramble,scramble_size); Perform(reverse); Perform([aufmoves[0]]) //Draw Cube Display(cube); </script> """ u_back = '''\ {{FrontSide}} <hr id=answer> {{Case}} <div id='alg'></div> <script> if ("{{U Algorithm}}".length > 0) { document.getElementById('alg').innerHTML = "{{U Algorithm}}" } else { document.getElementById('alg').innerHTML = "(U) "+"{{Algorithm}}" } </script> \ ''' n_front = _front.replace("Perform([aufmoves[0]])","") n_back = '''\ {{FrontSide}} <hr id=answer> {{Case}} {{Algorithm}} \ ''' u_front = "{{#U}}"+_front+"{{/U}}" ui_front = "{{#U'}}"+_front.replace("[aufmoves[0]]","[aufmoves[1]]")+"{{/U'}}" ui_back = u_back.replace("U Algorithm","U' Algorithm") ui_back = ui_back.replace('"(U) "+"{{Algorithm}}"', '''"(U') "+"{{Algorithm}}"''') u2_front = "{{#U2}}"+_front.replace("[aufmoves[0]]","[aufmoves[2]]")+"{{/U2}}" u2_back = u_back.replace("U Algorithm","U2 Algorithm") u2_back = u2_back.replace('"(U) "+"{{Algorithm}}"', '''"(U2) "+"{{Algorithm}}"''')

29.558974

104

0.446623

7951300735316b86476229d4a28cbf569ac524f6

397

py

Python

rimproject/wsgi.py

mostateresnet/rimproject

ef1952ee386b62ea7b49c139a3c66c718fb310d0

[ "MIT" ]

null

rimproject/wsgi.py

mostateresnet/rimproject

ef1952ee386b62ea7b49c139a3c66c718fb310d0

[ "MIT" ]

55

2018-04-18T19:57:56.000Z

2021-09-22T18:51:16.000Z

rimproject/wsgi.py

mostateresnet/rimproject

ef1952ee386b62ea7b49c139a3c66c718fb310d0

[ "MIT" ]

null

""" WSGI config for rimproject project. It exposes the WSGI callable as a module-level variable named ``application``. For more information on this file, see https://docs.djangoproject.com/en/2.0/howto/deployment/wsgi/ """ import os from django.core.wsgi import get_wsgi_application os.environ.setdefault("DJANGO_SETTINGS_MODULE", "rimproject.settings") application = get_wsgi_application()

23.352941

78

0.788413

79513118d1365989722c33e13c769768443be583

344

py

Python

src/utils/outliers.py

Hydrapse/pytorch-template

d7ea2f19bbdc032b8663ca432c1ef9012fe6180b

[ "MIT", "Unlicense" ]

null

src/utils/outliers.py

Hydrapse/pytorch-template

d7ea2f19bbdc032b8663ca432c1ef9012fe6180b

[ "MIT", "Unlicense" ]

null

src/utils/outliers.py

Hydrapse/pytorch-template

d7ea2f19bbdc032b8663ca432c1ef9012fe6180b

[ "MIT", "Unlicense" ]

null

import math import torch def replace_nan(val, nan_to=0., inf_to=1.): val = torch.where(torch.isinf(val), torch.full_like(val, nan_to), val) return torch.where(torch.isnan(val), torch.full_like(val, inf_to), val) def clip_int(val, lower, upper): val = 0 if math.isnan(val) else round(val) return max(lower, min(val, upper))

24.571429

75

0.69186

795131ae170c11d294ae427dbeebeaf1d4b452a8

346

py

Python

hammock_example.py

voidabhi/python-scripts

a6d06bd3ccf4ec24df521a3cf305d22176f68a18

[ "MIT" ]

2

2015-06-01T18:33:38.000Z

2018-11-21T19:40:37.000Z

hammock_example.py

voidabhi/python-scripts

a6d06bd3ccf4ec24df521a3cf305d22176f68a18

[ "MIT" ]

102

2015-01-20T17:26:52.000Z

2017-12-28T17:32:51.000Z

hammock_example.py

voidabhi/python-scripts

a6d06bd3ccf4ec24df521a3cf305d22176f68a18

[ "MIT" ]

3

2020-03-02T06:54:18.000Z

2021-01-07T16:36:35.000Z

from hammock import Hammock as Github github = Github('https://api.github.com') headers = {'Accept': 'application/vnd.github.preview'} resp = github.search.repositories.GET(params={'q': 'language:python', 'sort': 'stars', 'per_page': 10, 'page': 1}, headers=headers) res = resp.json() print res['items'][0]['full_name'] print len(res['items'])

34.6

131

0.696532

7951320e962063cabd7ef45ebee81d8768cc2366

22,908

py

Python

core/tests/tests_forms.py

mbaitelman/babybuddy

ea6476f9cac83959470a2fdfdd2d40441bfde061

[ "BSD-2-Clause" ]

null

core/tests/tests_forms.py

mbaitelman/babybuddy

ea6476f9cac83959470a2fdfdd2d40441bfde061

[ "BSD-2-Clause" ]

null

core/tests/tests_forms.py

mbaitelman/babybuddy

ea6476f9cac83959470a2fdfdd2d40441bfde061

[ "BSD-2-Clause" ]

null

# -*- coding: utf-8 -*- from django.contrib.auth.models import User from django.core.management import call_command from django.test import TestCase from django.test import Client as HttpClient from django.utils import timezone from django.utils.formats import get_format from faker import Factory from core import models class FormsTestCaseBase(TestCase): c = None child = None user = None @classmethod def setUpClass(cls): super(FormsTestCaseBase, cls).setUpClass() fake = Factory.create() call_command('migrate', verbosity=0) cls.c = HttpClient() fake_user = fake.simple_profile() credentials = { 'username': fake_user['username'], 'password': fake.password() } cls.user = User.objects.create_user( is_superuser=True, **credentials) cls.c.login(**credentials) cls.child = models.Child.objects.create( first_name='Child', last_name='One', birth_date=timezone.localdate() ) @staticmethod def localdate_string(datetime=None): """ Converts an object to a local date string for form input. """ date_format = get_format('DATE_INPUT_FORMATS')[0] return timezone.localdate(datetime).strftime(date_format) @staticmethod def localtime_string(datetime=None): """ Converts an object to a local time string for form input. """ datetime_format = get_format('DATETIME_INPUT_FORMATS')[0] return timezone.localtime(datetime).strftime(datetime_format) class InitialValuesTestCase(FormsTestCaseBase): @classmethod def setUpClass(cls): super(InitialValuesTestCase, cls).setUpClass() cls.timer = models.Timer.objects.create( user=cls.user, start=timezone.localtime() - timezone.timedelta(minutes=30) ) def test_child_with_one_child(self): page = self.c.get('/sleep/add/') self.assertEqual(page.context['form'].initial['child'], self.child) def test_child_with_parameter(self): child_two = models.Child.objects.create( first_name='Child', last_name='Two', birth_date=timezone.localdate() ) page = self.c.get('/sleep/add/') self.assertTrue('child' not in page.context['form'].initial) page = self.c.get('/sleep/add/?child={}'.format(self.child.slug)) self.assertEqual(page.context['form'].initial['child'], self.child) page = self.c.get('/sleep/add/?child={}'.format(child_two.slug)) self.assertEqual(page.context['form'].initial['child'], child_two) def test_feeding_type(self): child_two = models.Child.objects.create( first_name='Child', last_name='Two', birth_date=timezone.localdate() ) f_one = models.Feeding.objects.create( child=self.child, start=timezone.localtime() - timezone.timedelta(hours=4), end=timezone.localtime() - timezone.timedelta(hours=3, minutes=30), type='breast milk', method='left breast' ) f_two = models.Feeding.objects.create( child=child_two, start=timezone.localtime() - timezone.timedelta(hours=4), end=timezone.localtime() - timezone.timedelta(hours=3, minutes=30), type='formula', method='bottle' ) page = self.c.get('/feedings/add/') self.assertTrue('type' not in page.context['form'].initial) page = self.c.get('/feedings/add/?child={}'.format(self.child.slug)) self.assertEqual(page.context['form'].initial['type'], f_one.type) page = self.c.get('/feedings/add/?child={}'.format(child_two.slug)) self.assertEqual(page.context['form'].initial['type'], f_two.type) def test_timer_set(self): self.timer.stop() page = self.c.get('/sleep/add/') self.assertTrue('start' not in page.context['form'].initial) self.assertTrue('end' not in page.context['form'].initial) page = self.c.get('/sleep/add/?timer={}'.format(self.timer.id)) self.assertEqual(page.context['form'].initial['start'], self.timer.start) self.assertEqual(page.context['form'].initial['end'], self.timer.end) def test_timer_stop_on_save(self): end = timezone.localtime() params = { 'child': self.child.id, 'start': self.localtime_string(self.timer.start), 'end': self.localtime_string(end) } page = self.c.post('/sleep/add/?timer={}'.format(self.timer.id), params, follow=True) self.assertEqual(page.status_code, 200) self.timer.refresh_from_db() self.assertFalse(self.timer.active) self.assertEqual(self.localtime_string(self.timer.end), params['end']) class ChildFormsTestCase(FormsTestCaseBase): @classmethod def setUpClass(cls): super(ChildFormsTestCase, cls).setUpClass() cls.child = models.Child.objects.first() def test_add(self): params = { 'first_name': 'Child', 'last_name': 'Two', 'birth_date': timezone.localdate() } page = self.c.post('/children/add/', params, follow=True) self.assertEqual(page.status_code, 200) self.assertContains(page, 'Child entry added') def test_edit(self): params = { 'first_name': 'Name', 'last_name': 'Changed', 'birth_date': self.child.birth_date } page = self.c.post('/children/{}/edit/'.format(self.child.slug), params, follow=True) self.assertEqual(page.status_code, 200) self.child.refresh_from_db() self.assertEqual(self.child.last_name, params['last_name']) self.assertContains(page, 'Child entry updated') def test_delete(self): params = {'confirm_name': 'Incorrect'} page = self.c.post('/children/{}/delete/'.format(self.child.slug), params, follow=True) self.assertEqual(page.status_code, 200) self.assertFormError(page, 'form', 'confirm_name', 'Name does not match child name.') params['confirm_name'] = str(self.child) page = self.c.post('/children/{}/delete/'.format(self.child.slug), params, follow=True) self.assertEqual(page.status_code, 200) self.assertContains(page, 'Child entry deleted') class DiaperChangeFormsTestCase(FormsTestCaseBase): @classmethod def setUpClass(cls): super(DiaperChangeFormsTestCase, cls).setUpClass() cls.change = models.DiaperChange.objects.create( child=cls.child, time=timezone.localtime(), wet=True, solid=True, color='black', amount=0.45 ) def test_add(self): child = models.Child.objects.first() params = { 'child': child.id, 'time': self.localtime_string(), 'color': 'black', 'amount': 0.45 } page = self.c.post('/changes/add/', params) self.assertEqual(page.status_code, 200) self.assertFormError(page, 'form', None, 'Wet and/or solid is required.') params.update({'wet': 1, 'solid': 1, 'color': 'black'}) page = self.c.post('/changes/add/', params, follow=True) self.assertEqual(page.status_code, 200) self.assertContains( page, 'Diaper Change entry for {} added'.format(str(child)) ) def test_edit(self): params = { 'child': self.change.child.id, 'time': self.localtime_string(), 'wet': self.change.wet, 'solid': self.change.solid, 'color': self.change.color, 'amount': 1.23 } page = self.c.post('/changes/{}/'.format(self.change.id), params, follow=True) self.assertEqual(page.status_code, 200) self.change.refresh_from_db() self.assertEqual(self.change.amount, params['amount']) self.assertContains( page, 'Diaper Change entry for {} updated'.format(str(self.change.child)) ) def test_delete(self): page = self.c.post('/changes/{}/delete/'.format(self.change.id), follow=True) self.assertEqual(page.status_code, 200) self.assertContains(page, 'Diaper Change entry deleted') class FeedingFormsTestCase(FormsTestCaseBase): @classmethod def setUpClass(cls): super(FeedingFormsTestCase, cls).setUpClass() cls.feeding = models.Feeding.objects.create( child=cls.child, start=timezone.localtime() - timezone.timedelta(hours=2), end=timezone.localtime() - timezone.timedelta(hours=1, minutes=30), type='breast milk', method='left breast', amount=2.5 ) def test_add(self): end = timezone.localtime() start = end - timezone.timedelta(minutes=30) params = { 'child': self.child.id, 'start': self.localtime_string(start), 'end': self.localtime_string(end), 'type': 'formula', 'method': 'left breast', 'amount': 0 } page = self.c.post('/feedings/add/', params) self.assertEqual(page.status_code, 200) self.assertFormError( page, 'form', 'method', 'Only "Bottle" method is allowed with "Formula" type.') params.update({'method': 'bottle'}) page = self.c.post('/feedings/add/', params, follow=True) self.assertEqual(page.status_code, 200) self.assertContains( page, 'Feeding entry for {} added'.format(str(self.child)) ) def test_edit(self): end = timezone.localtime() start = end - timezone.timedelta(minutes=30) params = { 'child': self.feeding.child.id, 'start': self.localtime_string(start), 'end': self.localtime_string(end), 'type': self.feeding.type, 'method': self.feeding.method, 'amount': 100 } page = self.c.post('/feedings/{}/'.format(self.feeding.id), params, follow=True) self.assertEqual(page.status_code, 200) self.feeding.refresh_from_db() self.assertEqual(self.feeding.amount, params['amount']) self.assertContains( page, 'Feeding entry for {} updated'.format(str(self.feeding.child)) ) def test_delete(self): page = self.c.post('/feedings/{}/delete/'.format(self.feeding.id), follow=True) self.assertEqual(page.status_code, 200) self.assertContains(page, 'Feeding entry deleted') class SleepFormsTestCase(FormsTestCaseBase): @classmethod def setUpClass(cls): super(SleepFormsTestCase, cls).setUpClass() cls.sleep = models.Sleep.objects.create( child=cls.child, start=timezone.localtime() - timezone.timedelta(hours=6), end=timezone.localtime() - timezone.timedelta(hours=4) ) def test_add(self): end = timezone.localtime() start = end - timezone.timedelta(minutes=2) params = { 'child': self.child.id, 'start': self.localtime_string(start), 'end': self.localtime_string(end), } page = self.c.post('/sleep/add/', params, follow=True) self.assertEqual(page.status_code, 200) self.assertContains( page, 'Sleep entry for {} added'.format(str(self.child)) ) def test_edit(self): end = timezone.localtime() start = end - timezone.timedelta(minutes=2) params = { 'child': self.sleep.child.id, 'start': self.localtime_string(start), 'end': self.localtime_string(end), } page = self.c.post('/sleep/{}/'.format(self.sleep.id), params, follow=True) self.assertEqual(page.status_code, 200) self.sleep.refresh_from_db() self.assertEqual( self.localtime_string(self.sleep.end), params['end'] ) self.assertContains( page, 'Sleep entry for {} updated'.format(str(self.sleep.child)) ) def test_delete(self): page = self.c.post('/sleep/{}/delete/'.format(self.sleep.id), follow=True) self.assertEqual(page.status_code, 200) self.assertContains(page, 'Sleep entry deleted') class TemperatureFormsTestCase(FormsTestCaseBase): @classmethod def setUpClass(cls): super(TemperatureFormsTestCase, cls).setUpClass() cls.temp = models.Temperature.objects.create( child=cls.child, temperature=98.6, time=timezone.localtime() - timezone.timedelta(days=1) ) def test_add(self): params = { 'child': self.child.id, 'temperature': '98.6', 'time': self.localtime_string() } page = self.c.post('/temperature/add/', params, follow=True) self.assertEqual(page.status_code, 200) self.assertContains( page, 'Temperature entry for {} added'.format(str(self.child)) ) def test_edit(self): params = { 'child': self.temp.child.id, 'temperature': self.temp.temperature + 2, 'time': self.localtime_string() } page = self.c.post('/temperature/{}/'.format(self.temp.id), params, follow=True) self.assertEqual(page.status_code, 200) self.temp.refresh_from_db() self.assertEqual(self.temp.temperature, params['temperature']) self.assertContains( page, 'Temperature entry for {} updated'.format(str(self.temp.child)) ) def test_delete(self): page = self.c.post('/temperature/{}/delete/'.format(self.temp.id), follow=True) self.assertEqual(page.status_code, 200) self.assertContains(page, 'Temperature entry deleted') class TummyTimeFormsTestCase(FormsTestCaseBase): @classmethod def setUpClass(cls): super(TummyTimeFormsTestCase, cls).setUpClass() cls.tt = models.TummyTime.objects.create( child=cls.child, start=timezone.localtime() - timezone.timedelta(hours=2), end=timezone.localtime() - timezone.timedelta(hours=1, minutes=50) ) def test_add(self): end = timezone.localtime() start = end - timezone.timedelta(minutes=2) params = { 'child': self.child.id, 'start': self.localtime_string(start), 'end': self.localtime_string(end), 'milestone': '' } page = self.c.post('/tummy-time/add/', params, follow=True) self.assertEqual(page.status_code, 200) self.assertContains( page, 'Tummy Time entry for {} added'.format(str(self.child)) ) def test_edit(self): end = timezone.localtime() start = end - timezone.timedelta(minutes=1, seconds=32) params = { 'child': self.tt.child.id, 'start': self.localtime_string(start), 'end': self.localtime_string(end), 'milestone': 'Moved head!' } page = self.c.post('/tummy-time/{}/'.format(self.tt.id), params, follow=True) self.assertEqual(page.status_code, 200) self.tt.refresh_from_db() self.assertEqual(self.tt.milestone, params['milestone']) self.assertContains( page, 'Tummy Time entry for {} updated'.format(str(self.tt.child)) ) def test_delete(self): page = self.c.post('/tummy-time/{}/delete/'.format(self.tt.id), follow=True) self.assertEqual(page.status_code, 200) self.assertContains(page, 'Tummy Time entry deleted') class TimerFormsTestCase(FormsTestCaseBase): @classmethod def setUpClass(cls): super(TimerFormsTestCase, cls).setUpClass() cls.timer = models.Timer.objects.create(user=cls.user) def test_add(self): params = { 'child': self.child.id, 'name': 'Test Timer', 'start': self.localtime_string() } page = self.c.post('/timers/add/', params, follow=True) self.assertEqual(page.status_code, 200) self.assertContains(page, params['name']) self.assertContains(page, params['child']) def test_edit(self): start_time = self.timer.start - timezone.timedelta(hours=1) params = { 'name': 'New Timer Name', 'start': self.localtime_string(start_time) } page = self.c.post('/timers/{}/edit/'.format(self.timer.id), params, follow=True) self.assertEqual(page.status_code, 200) self.assertContains(page, params['name']) self.timer.refresh_from_db() self.assertEqual( self.localtime_string(self.timer.start), params['start']) def test_edit_stopped(self): self.timer.stop() params = { 'name': 'Edit stopped timer', 'start': self.localtime_string(self.timer.start), 'end': self.localtime_string(self.timer.end), } page = self.c.post('/timers/{}/edit/'.format(self.timer.id), params, follow=True) self.assertEqual(page.status_code, 200) def test_delete_inactive(self): models.Timer.objects.create(user=self.user) self.assertEqual(models.Timer.objects.count(), 2) self.timer.stop() page = self.c.post('/timers/delete-inactive/', follow=True) self.assertEqual(page.status_code, 200) messages = list(page.context['messages']) self.assertEqual(len(messages), 1) self.assertEqual(str(messages[0]), 'All inactive timers deleted.') self.assertEqual(models.Timer.objects.count(), 1) class ValidationsTestCase(FormsTestCaseBase): def test_validate_date(self): future = timezone.localtime() + timezone.timedelta(days=1) params = { 'child': self.child, 'weight': '8.5', 'date': self.localdate_string(future) } entry = models.Weight.objects.create(**params) page = self.c.post('/weight/{}/'.format(entry.id), params, follow=True) self.assertEqual(page.status_code, 200) self.assertFormError(page, 'form', 'date', 'Date can not be in the future.') def test_validate_duration(self): end = timezone.localtime() - timezone.timedelta(minutes=10) start = end + timezone.timedelta(minutes=5) params = { 'child': self.child, 'start': self.localtime_string(start), 'end': self.localtime_string(end), 'milestone': '' } page = self.c.post('/tummy-time/add/', params, follow=True) self.assertEqual(page.status_code, 200) self.assertFormError(page, 'form', None, 'Start time must come before end time.') start = end - timezone.timedelta(weeks=53) params['start'] = self.localtime_string(start) page = self.c.post('/tummy-time/add/', params, follow=True) self.assertEqual(page.status_code, 200) self.assertFormError(page, 'form', None, 'Duration too long.') def test_validate_time(self): future = timezone.localtime() + timezone.timedelta(hours=1) params = { 'child': self.child, 'start': self.localtime_string(), 'end': self.localtime_string(future), 'milestone': '' } page = self.c.post('/tummy-time/add/', params, follow=True) self.assertEqual(page.status_code, 200) self.assertFormError(page, 'form', 'end', 'Date/time can not be in the future.') def test_validate_unique_period(self): entry = models.TummyTime.objects.create( child=self.child, start=timezone.localtime() - timezone.timedelta(minutes=10), end=timezone.localtime() - timezone.timedelta(minutes=5), ) start = entry.start - timezone.timedelta(minutes=2) end = entry.end + timezone.timedelta(minutes=2) params = { 'child': entry.child.id, 'start': self.localtime_string(start), 'end': self.localtime_string(end), 'milestone': '' } page = self.c.post('/tummy-time/add/', params, follow=True) self.assertEqual(page.status_code, 200) self.assertFormError( page, 'form', None, 'Another entry intersects the specified time period.') class WeightFormsTest(FormsTestCaseBase): @classmethod def setUpClass(cls): super(WeightFormsTest, cls).setUpClass() cls.weight = models.Weight.objects.create( child=cls.child, weight=8, date=timezone.localdate() - timezone.timedelta(days=2) ) def test_add(self): params = { 'child': self.child.id, 'weight': 8.5, 'date': self.localdate_string() } page = self.c.post('/weight/add/', params, follow=True) self.assertEqual(page.status_code, 200) self.assertContains( page, 'Weight entry for {} added'.format(str(self.child)) ) def test_edit(self): params = { 'child': self.weight.child.id, 'weight': self.weight.weight + 1, 'date': self.localdate_string() } page = self.c.post('/weight/{}/'.format(self.weight.id), params, follow=True) self.assertEqual(page.status_code, 200) self.weight.refresh_from_db() self.assertEqual(self.weight.weight, params['weight']) self.assertContains( page, 'Weight entry for {} updated'.format(str(self.weight.child)) ) def test_delete(self): page = self.c.post('/weight/{}/delete/'.format(self.weight.id), follow=True) self.assertEqual(page.status_code, 200) self.assertContains(page, 'Weight entry deleted')

35.849765

79

0.579492

795133ac7c51cc9421f5dbe95cf8b7c9b0e242fc

1,766

py

Python

test/test_feature_extraction.py

kyledecker/project_ksd15_wjl11

f17b62ce1affaaaec65cdb0ea16c376ae00027d0

[ "MIT" ]

2

2017-03-29T06:06:49.000Z

2017-06-11T22:04:59.000Z

test/test_feature_extraction.py

kyledecker/project_ksd15_wjl11

f17b62ce1affaaaec65cdb0ea16c376ae00027d0

[ "MIT" ]

null

test/test_feature_extraction.py

kyledecker/project_ksd15_wjl11

f17b62ce1affaaaec65cdb0ea16c376ae00027d0

[ "MIT" ]

null

import os import sys sys.path.insert(0, os.path.abspath('./src/')) def test_calc_mode(): from feature_extraction import calc_mode import numpy as np hist = np.zeros(256) hist[5] = 0.4 hist[20] = 0.5 hist[100] = 0.2 actual = calc_mode(hist) expected = 20 assert actual == expected def test_calc_median(): from feature_extraction import calc_median import numpy as np data = [ii for ii in range(0, 3)] actual = calc_median(data) expected = 1. assert actual == expected def test_calc_variance(): from feature_extraction import calc_variance import numpy as np data = [ii for ii in range(0, 4)] actual = calc_variance(data, omit=(0,)) expected = np.std([1, 2, 3]) assert actual == expected def test_extract_features(): from feature_extraction import extract_features import numpy as np r = 5*np.ones([3, 3]) r[1, 1] = 0 g = np.zeros([3, 3]) b = [[ii for ii in range(0, 3)] for _ in range(0, 3)] img = np.zeros([3, 3, 3]) img[:, :, 0] = r img[:, :, 1] = g img[:, :, 2] = b actual, labels = extract_features(img, 'gb', 'r', 'rg', pct_yellow=True) expected = [np.median(g), np.median(b), np.std(r), 5, 0, 0] assert np.array_equal(actual, expected) actual, labels = extract_features(img, 'r', 'r', 'r', omit=0) expected = [5, 0, 5] assert np.array_equal(actual, expected) def test_calc_pct_yellow(): from feature_extraction import calc_pct_yellow import numpy as np rgb = np.ones((10, 10, 3)) rgb[1, 1, :] = [255, 255, 40] rgb[0, 1, :] = [0, 0, 0] rgb[0, 0, :] = [np.nan, np.nan, np.nan] actual = calc_pct_yellow(rgb) expected = 100/98 assert actual == expected

21.277108

76

0.603624

7951341f439b8f790edb531621dce45c8df11cad

637

py

Python

server/ui/admin/pces/urls.py

elise-baumgartner/onramp

beb3c807264fcb70d8069ff2e3990b0ce3f59912

[ "BSD-3-Clause" ]

2

2016-09-09T04:19:01.000Z

2019-02-15T20:28:13.000Z

server/ui/admin/pces/urls.py

elise-baumgartner/onramp

beb3c807264fcb70d8069ff2e3990b0ce3f59912

[ "BSD-3-Clause" ]

67

2016-06-02T19:37:56.000Z

2018-02-22T05:23:45.000Z

server/ui/admin/pces/urls.py

elise-baumgartner/onramp

beb3c807264fcb70d8069ff2e3990b0ce3f59912

[ "BSD-3-Clause" ]

9

2015-06-22T22:10:22.000Z

2016-04-26T15:35:45.000Z

from django.conf.urls import url import views urlpatterns = [ url(r'GetAll/$', views.get_all_pces), url(r'Modules/$', views.get_pce_modules), url(r'Module/Add/$', views.add_pce_module), url(r'Module/Edit/$', views.edit_pce_module), url(r'Module/Delete/$', views.delete_pce_module), url(r'Module/State/$', views.get_module_state), url(r'Module/Deploy/$', views.deploy_module), url(r'Add/$', views.add_pce), url(r'Edit/$', views.edit_pce), url(r'Delete/$', views.delete_pce), url(r'Workspaces/$', views.get_pce_workspaces), url(r'Jobs/$', views.get_pce_jobs), url(r'^$', views.main), ]

33.526316

53

0.660911

7951347e2f67a50c020dfa873b5f3d4cd20285da

927

py

Python

ioflo/base/test/_testServing.py

BradyHammond/ioflo

177ac656d7c4ff801aebb0d8b401db365a5248ce

[ "ECL-2.0", "Apache-2.0", "MIT" ]

128

2015-01-14T12:26:56.000Z

2021-11-06T07:09:29.000Z

ioflo/base/test/_testServing.py

BradyHammond/ioflo

177ac656d7c4ff801aebb0d8b401db365a5248ce

[ "ECL-2.0", "Apache-2.0", "MIT" ]

17

2015-01-28T18:26:50.000Z

2020-11-19T22:08:06.000Z

ioflo/base/test/_testServing.py

BradyHammond/ioflo

177ac656d7c4ff801aebb0d8b401db365a5248ce

[ "ECL-2.0", "Apache-2.0", "MIT" ]

29

2015-01-27T23:28:31.000Z

2021-05-04T16:37:30.000Z

def TestOpenStuff(): """ """ import ioflo.base.storing as storing storing.Store.Clear() #clear registry s1 = ServerTask(store = storing.Store()) s2 = ServerTask(store = storing.Store()) print(s1.server.reopen()) print(s2.server.reopen()) def Test(verbose = False): """Module self test """ import ioflo.base.storing as storing import ioflo.base.tasking as tasking storing.Store.Clear() #clear registry tasking.Tasker.Clear() s = Server(store = storing.Store()) s.store.expose() print("ready to go") status = s.start() while (not (status == STOPPED or status == ABORTED)): try: status = s.run() except KeyboardInterrupt: #CNTL-C shutdown skedder print(" Keyboard Interrupt manual shutdown of taskers ...") s.server.close() break if __name__ == "__main__": Test()

18.918367

74

0.597627

795134d7fb28a97c4dbc051effb6f36b34559e9d

1,028

py

Python

test/typedlist_test.py

DamavandiKamali/TinCanPython

a3fb50babcb09eb87999aea67b4a266a58b02949

[ "Apache-2.0" ]

2

2018-08-01T18:50:36.000Z

2019-04-17T15:24:16.000Z

test/typedlist_test.py

DamavandiKamali/TinCanPython

a3fb50babcb09eb87999aea67b4a266a58b02949

[ "Apache-2.0" ]

5

2019-03-22T23:14:19.000Z

2020-07-02T14:43:49.000Z

test/typedlist_test.py

DamavandiKamali/TinCanPython

a3fb50babcb09eb87999aea67b4a266a58b02949

[ "Apache-2.0" ]

2

2018-07-19T09:54:24.000Z

2020-02-12T12:03:31.000Z

# Copyright 2014 Rustici Software # # 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 unittest if __name__ == '__main__': from main import setup_tincan_path setup_tincan_path() from tincan import TypedList class TypedListTest(unittest.TestCase): def test_Init(self): with self.assertRaises(ValueError): TypedList() if __name__ == "__main__": suite = unittest.TestLoader().loadTestsFromTestCase(TypedListTest) unittest.TextTestRunner(verbosity=2).run(suite)

31.151515

77

0.73249

795135b09cfbea7fc12ba338e77551aa658829b4

1,324

py

Python

tensorflow_datasets/text/pg19_test.py

jvishnuvardhan/datasets

b8e38187058f1221e67c6291b3f29385ebb35fa2

[ "Apache-2.0" ]

3,380

2018-09-11T05:03:31.000Z

2022-03-31T20:04:57.000Z

tensorflow_datasets/text/pg19_test.py

jvishnuvardhan/datasets

b8e38187058f1221e67c6291b3f29385ebb35fa2

[ "Apache-2.0" ]

3,142

2018-09-14T10:09:00.000Z

2022-03-31T18:25:44.000Z

tensorflow_datasets/text/pg19_test.py

jvishnuvardhan/datasets

b8e38187058f1221e67c6291b3f29385ebb35fa2

[ "Apache-2.0" ]

1,438

2018-09-16T13:58:22.000Z

2022-03-31T11:19:54.000Z

# coding=utf-8 # Copyright 2021 The TensorFlow Datasets 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. """Test for PG-19 dataset module.""" import os import tensorflow_datasets.public_api as tfds from tensorflow_datasets.text import pg19 class Pg19Test(tfds.testing.DatasetBuilderTestCase): @classmethod def setUpClass(cls): super(Pg19Test, cls).setUpClass() pg19._DATA_DIR = os.path.join( os.path.normpath(os.path.dirname(__file__) + "/../"), "testing", "test_data", "fake_examples", "pg19", ) DATASET_CLASS = pg19.Pg19 SPLITS = { "train": 3, # Number of fake train example "test": 1, # Number of fake test example "validation": 1 # Number of fake validation example } if __name__ == "__main__": tfds.testing.test_main()

28.170213

74

0.702417

795137205fa5e1e34c71b16dce76bf7de63de1c7

886

py

Python

unreleased/azure-mgmt-intune/azure/mgmt/intune/models/application_paged.py

azuresdkci1x/azure-sdk-for-python-1722

e08fa6606543ce0f35b93133dbb78490f8e6bcc9

[ "MIT" ]

1

2018-11-09T06:16:34.000Z

unreleased/azure-mgmt-intune/azure/mgmt/intune/models/application_paged.py

azuresdkci1x/azure-sdk-for-python-1722

e08fa6606543ce0f35b93133dbb78490f8e6bcc9

[ "MIT" ]

null

unreleased/azure-mgmt-intune/azure/mgmt/intune/models/application_paged.py

azuresdkci1x/azure-sdk-for-python-1722

e08fa6606543ce0f35b93133dbb78490f8e6bcc9

[ "MIT" ]

1

2018-11-09T06:17:41.000Z

# coding=utf-8 # -------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for # license information. # # Code generated by Microsoft (R) AutoRest Code Generator. # Changes may cause incorrect behavior and will be lost if the code is # regenerated. # -------------------------------------------------------------------------- from msrest.paging import Paged class ApplicationPaged(Paged): """ A paging container for iterating over a list of Application object """ _attribute_map = { 'next_link': {'key': 'nextlink', 'type': 'str'}, 'current_page': {'key': 'value', 'type': '[Application]'} } def __init__(self, *args, **kwargs): super(ApplicationPaged, self).__init__(*args, **kwargs)

31.642857

76

0.563205

795138ea891126576d3124ad0c9655f5fe1b33cd

1,764

py

Python

nipype/interfaces/camino/tests/test_auto_SFLUTGen.py

lucindasisk/nipype

d037ed577615feb11cab20d66a06a8ecd63e5034

[ "Apache-2.0" ]

null

nipype/interfaces/camino/tests/test_auto_SFLUTGen.py

lucindasisk/nipype

d037ed577615feb11cab20d66a06a8ecd63e5034

[ "Apache-2.0" ]

2

2018-04-17T19:18:16.000Z

2020-03-04T22:05:02.000Z

nipype/interfaces/camino/tests/test_auto_SFLUTGen.py

oesteban/nipype

c14f24eba1da08711bbb894e049ee858ed740096

[ "Apache-2.0" ]

null

# AUTO-GENERATED by tools/checkspecs.py - DO NOT EDIT from __future__ import unicode_literals from ..calib import SFLUTGen def test_SFLUTGen_inputs(): input_map = dict( args=dict(argstr='%s', ), binincsize=dict( argstr='-binincsize %d', units='NA', ), directmap=dict(argstr='-directmap', ), environ=dict( nohash=True, usedefault=True, ), in_file=dict( argstr='-inputfile %s', extensions=None, mandatory=True, ), info_file=dict( argstr='-infofile %s', extensions=None, mandatory=True, ), minvectsperbin=dict( argstr='-minvectsperbin %d', units='NA', ), order=dict( argstr='-order %d', units='NA', ), out_file=dict( argstr='> %s', genfile=True, position=-1, ), outputstem=dict( argstr='-outputstem %s', usedefault=True, ), pdf=dict( argstr='-pdf %s', usedefault=True, ), ) inputs = SFLUTGen.input_spec() for key, metadata in list(input_map.items()): for metakey, value in list(metadata.items()): assert getattr(inputs.traits()[key], metakey) == value def test_SFLUTGen_outputs(): output_map = dict( lut_one_fibre=dict(extensions=None, ), lut_two_fibres=dict(extensions=None, ), ) outputs = SFLUTGen.output_spec() for key, metadata in list(output_map.items()): for metakey, value in list(metadata.items()): assert getattr(outputs.traits()[key], metakey) == value

27.138462

67

0.520975

795139a349552a4b3b1dae5abc3ac265e6a518fc

1,833

py

Python

python/281_zigzag_iterator.py

ufjfeng/leetcode-soln

cbf2db0d81d5ef98f48c8d1df486559f89142bfd

[ "MIT" ]

null

python/281_zigzag_iterator.py

ufjfeng/leetcode-soln

cbf2db0d81d5ef98f48c8d1df486559f89142bfd

[ "MIT" ]

null

python/281_zigzag_iterator.py

ufjfeng/leetcode-soln

cbf2db0d81d5ef98f48c8d1df486559f89142bfd

[ "MIT" ]

1

2019-11-22T19:28:11.000Z

""" Given two 1d vectors, implement an iterator to return their elements alternately. For example, given two 1d vectors: v1 = [1, 2] v2 = [3, 4, 5, 6] By calling next repeatedly until hasNext returns false, the order of elements returned by next should be: [1, 3, 2, 4, 5, 6]. Follow up: What if you are given k 1d vectors? How well can your code be extended to such cases? Clarification for the follow up question - Update (2015-09-18): The "Zigzag" order is not clearly defined and is ambiguous for k > 2 cases. If "Zigzag" does not look right to you, replace "Zigzag" with "Cyclic". For example, given the following input: [1,2,3] [4,5,6,7] [8,9] It should return [1,4,8,2,5,9,3,6,7]. """ class ZigzagIterator(object): def __init__(self, v1, v2): """ Initialize your data structure here. :type v1: List[int] :type v2: List[int] """ self.v1 = [c for c in reversed(v1)] self.v2 = [c for c in reversed(v2)] self.loc = 0 def next(self): """ :rtype: int """ if self.loc: self.loc = not self.loc if len(self.v2) > 0: return self.v2.pop() else: return self.next() else: self.loc = not self.loc if len(self.v1) > 0: return self.v1.pop() else: return self.next() def hasNext(self): """ :rtype: bool """ return len(self.v1) > 0 or len(self.v2) > 0 a = ZigzagIterator([1,2],[3,4,5,6]) while a.hasNext(): print(a.next()) # Your ZigzagIterator object will be instantiated and called as such: # i, v = ZigzagIterator(v1, v2), [] # while i.hasNext(): v.append(i.next())

26.955882

72

0.546645

79513af29d2839e0633dd44a8917cb93a1a6ea0e

49,467

py

Python

tests/api/v2_1_1/test_discovery.py

nonstdout/dnacentersdk

dbbbc4baa5300aa9e5c9193f2ea71438018095f5

[ "MIT" ]

null

tests/api/v2_1_1/test_discovery.py

nonstdout/dnacentersdk

dbbbc4baa5300aa9e5c9193f2ea71438018095f5

[ "MIT" ]

null

tests/api/v2_1_1/test_discovery.py

nonstdout/dnacentersdk

dbbbc4baa5300aa9e5c9193f2ea71438018095f5

[ "MIT" ]

null

# -*- coding: utf-8 -*- """DNACenterAPI discovery API fixtures and tests. Copyright (c) 2019-2020 Cisco and/or its affiliates. Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. """ import pytest from tests.environment import DNA_CENTER_VERSION pytestmark = pytest.mark.skipif(DNA_CENTER_VERSION != '2.1.1', reason='version does not match') def is_valid_get_count_of_all_discovery_jobs(json_schema_validate, obj): json_schema_validate('jsd_069d9823451b892d_v2_1_1').validate(obj) return True def get_count_of_all_discovery_jobs(api): endpoint_result = api.discovery.get_count_of_all_discovery_jobs( ) return endpoint_result @pytest.mark.discovery def test_get_count_of_all_discovery_jobs(api, validator): assert is_valid_get_count_of_all_discovery_jobs( validator, get_count_of_all_discovery_jobs(api) ) def get_count_of_all_discovery_jobs_default(api): endpoint_result = api.discovery.get_count_of_all_discovery_jobs( ) return endpoint_result @pytest.mark.discovery def test_get_count_of_all_discovery_jobs_default(api, validator): try: assert is_valid_get_count_of_all_discovery_jobs( validator, get_count_of_all_discovery_jobs_default(api) ) except Exception as original_e: with pytest.raises(TypeError, match="but instead we received None"): raise original_e def is_valid_create_netconf_credentials(json_schema_validate, obj): json_schema_validate('jsd_17929bc7465bb564_v2_1_1').validate(obj) return True def create_netconf_credentials(api): endpoint_result = api.discovery.create_netconf_credentials( active_validation=True, payload=[{'comments': 'string', 'credentialType': 'GLOBAL', 'description': 'string', 'id': 'string', 'instanceTenantId': 'string', 'instanceUuid': 'string', 'netconfPort': 'string'}] ) return endpoint_result @pytest.mark.discovery def test_create_netconf_credentials(api, validator): assert is_valid_create_netconf_credentials( validator, create_netconf_credentials(api) ) def create_netconf_credentials_default(api): endpoint_result = api.discovery.create_netconf_credentials( active_validation=True, payload=None ) return endpoint_result @pytest.mark.discovery def test_create_netconf_credentials_default(api, validator): try: assert is_valid_create_netconf_credentials( validator, create_netconf_credentials_default(api) ) except Exception as original_e: with pytest.raises(TypeError, match="but instead we received None"): raise original_e def is_valid_update_snmp_write_community(json_schema_validate, obj): json_schema_validate('jsd_10b06a6a4f7bb3cb_v2_1_1').validate(obj) return True def update_snmp_write_community(api): endpoint_result = api.discovery.update_snmp_write_community( active_validation=True, comments='string', credentialType='GLOBAL', description='string', id='string', instanceTenantId='string', instanceUuid='string', payload=None, writeCommunity='string' ) return endpoint_result @pytest.mark.discovery def test_update_snmp_write_community(api, validator): assert is_valid_update_snmp_write_community( validator, update_snmp_write_community(api) ) def update_snmp_write_community_default(api): endpoint_result = api.discovery.update_snmp_write_community( active_validation=True, comments=None, credentialType=None, description=None, id=None, instanceTenantId=None, instanceUuid=None, payload=None, writeCommunity=None ) return endpoint_result @pytest.mark.discovery def test_update_snmp_write_community_default(api, validator): try: assert is_valid_update_snmp_write_community( validator, update_snmp_write_community_default(api) ) except Exception as original_e: with pytest.raises(TypeError, match="but instead we received None"): raise original_e def is_valid_update_snmpv3_credentials(json_schema_validate, obj): json_schema_validate('jsd_1da5ebdd434aacfe_v2_1_1').validate(obj) return True def update_snmpv3_credentials(api): endpoint_result = api.discovery.update_snmpv3_credentials( active_validation=True, authPassword='string', authType='SHA', comments='string', credentialType='GLOBAL', description='string', id='string', instanceTenantId='string', instanceUuid='string', payload=None, privacyPassword='string', privacyType='DES', snmpMode='AUTHPRIV', username='string' ) return endpoint_result @pytest.mark.discovery def test_update_snmpv3_credentials(api, validator): assert is_valid_update_snmpv3_credentials( validator, update_snmpv3_credentials(api) ) def update_snmpv3_credentials_default(api): endpoint_result = api.discovery.update_snmpv3_credentials( active_validation=True, authPassword=None, authType=None, comments=None, credentialType=None, description=None, id=None, instanceTenantId=None, instanceUuid=None, payload=None, privacyPassword=None, privacyType=None, snmpMode=None, username=None ) return endpoint_result @pytest.mark.discovery def test_update_snmpv3_credentials_default(api, validator): try: assert is_valid_update_snmpv3_credentials( validator, update_snmpv3_credentials_default(api) ) except Exception as original_e: with pytest.raises(TypeError, match="but instead we received None"): raise original_e def is_valid_get_discoveries_by_range(json_schema_validate, obj): json_schema_validate('jsd_33b799d04d0a8907_v2_1_1').validate(obj) return True def get_discoveries_by_range(api): endpoint_result = api.discovery.get_discoveries_by_range( records_to_return=0, start_index=0 ) return endpoint_result @pytest.mark.discovery def test_get_discoveries_by_range(api, validator): assert is_valid_get_discoveries_by_range( validator, get_discoveries_by_range(api) ) def get_discoveries_by_range_default(api): endpoint_result = api.discovery.get_discoveries_by_range( records_to_return=0, start_index=0 ) return endpoint_result @pytest.mark.discovery def test_get_discoveries_by_range_default(api, validator): try: assert is_valid_get_discoveries_by_range( validator, get_discoveries_by_range_default(api) ) except Exception as original_e: with pytest.raises(TypeError, match="but instead we received None"): raise original_e def is_valid_get_network_devices_from_discovery(json_schema_validate, obj): json_schema_validate('jsd_3d9b99c343398a27_v2_1_1').validate(obj) return True def get_network_devices_from_discovery(api): endpoint_result = api.discovery.get_network_devices_from_discovery( cli_status='value1,value2', http_status='value1,value2', id='string', ip_address='value1,value2', netconf_status='value1,value2', ping_status='value1,value2', snmp_status='value1,value2', sort_by='string', sort_order='string', task_id='string' ) return endpoint_result @pytest.mark.discovery def test_get_network_devices_from_discovery(api, validator): assert is_valid_get_network_devices_from_discovery( validator, get_network_devices_from_discovery(api) ) def get_network_devices_from_discovery_default(api): endpoint_result = api.discovery.get_network_devices_from_discovery( cli_status=None, http_status=None, id='string', ip_address=None, netconf_status=None, ping_status=None, snmp_status=None, sort_by=None, sort_order=None, task_id=None ) return endpoint_result @pytest.mark.discovery def test_get_network_devices_from_discovery_default(api, validator): try: assert is_valid_get_network_devices_from_discovery( validator, get_network_devices_from_discovery_default(api) ) except Exception as original_e: with pytest.raises(TypeError, match="but instead we received None"): raise original_e def is_valid_get_snmp_properties(json_schema_validate, obj): json_schema_validate('jsd_44974ba5435a801d_v2_1_1').validate(obj) return True def get_snmp_properties(api): endpoint_result = api.discovery.get_snmp_properties( ) return endpoint_result @pytest.mark.discovery def test_get_snmp_properties(api, validator): assert is_valid_get_snmp_properties( validator, get_snmp_properties(api) ) def get_snmp_properties_default(api): endpoint_result = api.discovery.get_snmp_properties( ) return endpoint_result @pytest.mark.discovery def test_get_snmp_properties_default(api, validator): try: assert is_valid_get_snmp_properties( validator, get_snmp_properties_default(api) ) except Exception as original_e: with pytest.raises(TypeError, match="but instead we received None"): raise original_e def is_valid_update_snmp_read_community(json_schema_validate, obj): json_schema_validate('jsd_47a1b84b4e1b8044_v2_1_1').validate(obj) return True def update_snmp_read_community(api): endpoint_result = api.discovery.update_snmp_read_community( active_validation=True, comments='string', credentialType='GLOBAL', description='string', id='string', instanceTenantId='string', instanceUuid='string', payload=None, readCommunity='string' ) return endpoint_result @pytest.mark.discovery def test_update_snmp_read_community(api, validator): assert is_valid_update_snmp_read_community( validator, update_snmp_read_community(api) ) def update_snmp_read_community_default(api): endpoint_result = api.discovery.update_snmp_read_community( active_validation=True, comments=None, credentialType=None, description=None, id=None, instanceTenantId=None, instanceUuid=None, payload=None, readCommunity=None ) return endpoint_result @pytest.mark.discovery def test_update_snmp_read_community_default(api, validator): try: assert is_valid_update_snmp_read_community( validator, update_snmp_read_community_default(api) ) except Exception as original_e: with pytest.raises(TypeError, match="but instead we received None"): raise original_e def is_valid_create_http_write_credentials(json_schema_validate, obj): json_schema_validate('jsd_4d9ca8e2431a8a24_v2_1_1').validate(obj) return True def create_http_write_credentials(api): endpoint_result = api.discovery.create_http_write_credentials( active_validation=True, payload=[{'comments': 'string', 'credentialType': 'GLOBAL', 'description': 'string', 'id': 'string', 'instanceTenantId': 'string', 'instanceUuid': 'string', 'password': 'string', 'port': 0, 'secure': True, 'username': 'string'}] ) return endpoint_result @pytest.mark.discovery def test_create_http_write_credentials(api, validator): assert is_valid_create_http_write_credentials( validator, create_http_write_credentials(api) ) def create_http_write_credentials_default(api): endpoint_result = api.discovery.create_http_write_credentials( active_validation=True, payload=None ) return endpoint_result @pytest.mark.discovery def test_create_http_write_credentials_default(api, validator): try: assert is_valid_create_http_write_credentials( validator, create_http_write_credentials_default(api) ) except Exception as original_e: with pytest.raises(TypeError, match="but instead we received None"): raise original_e def is_valid_delete_discovery_by_id(json_schema_validate, obj): json_schema_validate('jsd_4c8cab5f435a80f4_v2_1_1').validate(obj) return True def delete_discovery_by_id(api): endpoint_result = api.discovery.delete_discovery_by_id( id='string' ) return endpoint_result @pytest.mark.discovery def test_delete_discovery_by_id(api, validator): assert is_valid_delete_discovery_by_id( validator, delete_discovery_by_id(api) ) def delete_discovery_by_id_default(api): endpoint_result = api.discovery.delete_discovery_by_id( id='string' ) return endpoint_result @pytest.mark.discovery def test_delete_discovery_by_id_default(api, validator): try: assert is_valid_delete_discovery_by_id( validator, delete_discovery_by_id_default(api) ) except Exception as original_e: with pytest.raises(TypeError, match="but instead we received None"): raise original_e def is_valid_start_discovery(json_schema_validate, obj): json_schema_validate('jsd_55b439dc4239b140_v2_1_1').validate(obj) return True def start_discovery(api): endpoint_result = api.discovery.start_discovery( active_validation=True, cdpLevel=0, discoveryType='string', enablePasswordList=['string'], globalCredentialIdList=['string'], httpReadCredential={'comments': 'string', 'credentialType': 'GLOBAL', 'description': 'string', 'id': 'string', 'instanceTenantId': 'string', 'instanceUuid': 'string', 'password': 'string', 'port': 0, 'secure': True, 'username': 'string'}, httpWriteCredential={'comments': 'string', 'credentialType': 'GLOBAL', 'description': 'string', 'id': 'string', 'instanceTenantId': 'string', 'instanceUuid': 'string', 'password': 'string', 'port': 0, 'secure': True, 'username': 'string'}, ipAddressList='string', ipFilterList=['string'], lldpLevel=0, name='string', netconfPort='string', noAddNewDevice=True, parentDiscoveryId='string', passwordList=['string'], payload=None, preferredMgmtIPMethod='string', protocolOrder='string', reDiscovery=True, retry=0, snmpAuthPassphrase='string', snmpAuthProtocol='string', snmpMode='string', snmpPrivPassphrase='string', snmpPrivProtocol='string', snmpROCommunity='string', snmpROCommunityDesc='string', snmpRWCommunity='string', snmpRWCommunityDesc='string', snmpUserName='string', snmpVersion='string', timeout=0, updateMgmtIp=True, userNameList=['string'] ) return endpoint_result @pytest.mark.discovery def test_start_discovery(api, validator): assert is_valid_start_discovery( validator, start_discovery(api) ) def start_discovery_default(api): endpoint_result = api.discovery.start_discovery( active_validation=True, cdpLevel=None, discoveryType=None, enablePasswordList=None, globalCredentialIdList=None, httpReadCredential=None, httpWriteCredential=None, ipAddressList=None, ipFilterList=None, lldpLevel=None, name=None, netconfPort=None, noAddNewDevice=None, parentDiscoveryId=None, passwordList=None, payload=None, preferredMgmtIPMethod=None, protocolOrder=None, reDiscovery=None, retry=None, snmpAuthPassphrase=None, snmpAuthProtocol=None, snmpMode=None, snmpPrivPassphrase=None, snmpPrivProtocol=None, snmpROCommunity=None, snmpROCommunityDesc=None, snmpRWCommunity=None, snmpRWCommunityDesc=None, snmpUserName=None, snmpVersion=None, timeout=None, updateMgmtIp=None, userNameList=None ) return endpoint_result @pytest.mark.discovery def test_start_discovery_default(api, validator): try: assert is_valid_start_discovery( validator, start_discovery_default(api) ) except Exception as original_e: with pytest.raises(TypeError, match="but instead we received None"): raise original_e def is_valid_get_credential_sub_type_by_credential_id(json_schema_validate, obj): json_schema_validate('jsd_58a3699e489b9529_v2_1_1').validate(obj) return True def get_credential_sub_type_by_credential_id(api): endpoint_result = api.discovery.get_credential_sub_type_by_credential_id( id='string' ) return endpoint_result @pytest.mark.discovery def test_get_credential_sub_type_by_credential_id(api, validator): assert is_valid_get_credential_sub_type_by_credential_id( validator, get_credential_sub_type_by_credential_id(api) ) def get_credential_sub_type_by_credential_id_default(api): endpoint_result = api.discovery.get_credential_sub_type_by_credential_id( id='string' ) return endpoint_result @pytest.mark.discovery def test_get_credential_sub_type_by_credential_id_default(api, validator): try: assert is_valid_get_credential_sub_type_by_credential_id( validator, get_credential_sub_type_by_credential_id_default(api) ) except Exception as original_e: with pytest.raises(TypeError, match="but instead we received None"): raise original_e def is_valid_create_snmp_write_community(json_schema_validate, obj): json_schema_validate('jsd_6bacb8d14639bdc7_v2_1_1').validate(obj) return True def create_snmp_write_community(api): endpoint_result = api.discovery.create_snmp_write_community( active_validation=True, payload=[{'comments': 'string', 'credentialType': 'GLOBAL', 'description': 'string', 'id': 'string', 'instanceTenantId': 'string', 'instanceUuid': 'string', 'writeCommunity': 'string'}] ) return endpoint_result @pytest.mark.discovery def test_create_snmp_write_community(api, validator): assert is_valid_create_snmp_write_community( validator, create_snmp_write_community(api) ) def create_snmp_write_community_default(api): endpoint_result = api.discovery.create_snmp_write_community( active_validation=True, payload=None ) return endpoint_result @pytest.mark.discovery def test_create_snmp_write_community_default(api, validator): try: assert is_valid_create_snmp_write_community( validator, create_snmp_write_community_default(api) ) except Exception as original_e: with pytest.raises(TypeError, match="but instead we received None"): raise original_e def is_valid_get_discovery_by_id(json_schema_validate, obj): json_schema_validate('jsd_63bb88b74f59aa17_v2_1_1').validate(obj) return True def get_discovery_by_id(api): endpoint_result = api.discovery.get_discovery_by_id( id='string' ) return endpoint_result @pytest.mark.discovery def test_get_discovery_by_id(api, validator): assert is_valid_get_discovery_by_id( validator, get_discovery_by_id(api) ) def get_discovery_by_id_default(api): endpoint_result = api.discovery.get_discovery_by_id( id='string' ) return endpoint_result @pytest.mark.discovery def test_get_discovery_by_id_default(api, validator): try: assert is_valid_get_discovery_by_id( validator, get_discovery_by_id_default(api) ) except Exception as original_e: with pytest.raises(TypeError, match="but instead we received None"): raise original_e def is_valid_update_global_credentials(json_schema_validate, obj): json_schema_validate('jsd_709fda3c42b8877a_v2_1_1').validate(obj) return True def update_global_credentials(api): endpoint_result = api.discovery.update_global_credentials( active_validation=True, global_credential_id='string', payload=None, siteUuids=['string'] ) return endpoint_result @pytest.mark.discovery def test_update_global_credentials(api, validator): assert is_valid_update_global_credentials( validator, update_global_credentials(api) ) def update_global_credentials_default(api): endpoint_result = api.discovery.update_global_credentials( active_validation=True, global_credential_id='string', payload=None, siteUuids=None ) return endpoint_result @pytest.mark.discovery def test_update_global_credentials_default(api, validator): try: assert is_valid_update_global_credentials( validator, update_global_credentials_default(api) ) except Exception as original_e: with pytest.raises(TypeError, match="but instead we received None"): raise original_e def is_valid_create_snmp_read_community(json_schema_validate, obj): json_schema_validate('jsd_7aa3da9d4e098ef2_v2_1_1').validate(obj) return True def create_snmp_read_community(api): endpoint_result = api.discovery.create_snmp_read_community( active_validation=True, payload=[{'comments': 'string', 'credentialType': 'GLOBAL', 'description': 'string', 'id': 'string', 'instanceTenantId': 'string', 'instanceUuid': 'string', 'readCommunity': 'string'}] ) return endpoint_result @pytest.mark.discovery def test_create_snmp_read_community(api, validator): assert is_valid_create_snmp_read_community( validator, create_snmp_read_community(api) ) def create_snmp_read_community_default(api): endpoint_result = api.discovery.create_snmp_read_community( active_validation=True, payload=None ) return endpoint_result @pytest.mark.discovery def test_create_snmp_read_community_default(api, validator): try: assert is_valid_create_snmp_read_community( validator, create_snmp_read_community_default(api) ) except Exception as original_e: with pytest.raises(TypeError, match="but instead we received None"): raise original_e def is_valid_create_snmpv3_credentials(json_schema_validate, obj): json_schema_validate('jsd_979688084b7ba60d_v2_1_1').validate(obj) return True def create_snmpv3_credentials(api): endpoint_result = api.discovery.create_snmpv3_credentials( active_validation=True, payload=[{'authPassword': 'string', 'authType': 'SHA', 'comments': 'string', 'credentialType': 'GLOBAL', 'description': 'string', 'id': 'string', 'instanceTenantId': 'string', 'instanceUuid': 'string', 'privacyPassword': 'string', 'privacyType': 'DES', 'snmpMode': 'AUTHPRIV', 'username': 'string'}] ) return endpoint_result @pytest.mark.discovery def test_create_snmpv3_credentials(api, validator): assert is_valid_create_snmpv3_credentials( validator, create_snmpv3_credentials(api) ) def create_snmpv3_credentials_default(api): endpoint_result = api.discovery.create_snmpv3_credentials( active_validation=True, payload=None ) return endpoint_result @pytest.mark.discovery def test_create_snmpv3_credentials_default(api, validator): try: assert is_valid_create_snmpv3_credentials( validator, create_snmpv3_credentials_default(api) ) except Exception as original_e: with pytest.raises(TypeError, match="but instead we received None"): raise original_e def is_valid_update_http_read_credential(json_schema_validate, obj): json_schema_validate('jsd_89b36b4649999d81_v2_1_1').validate(obj) return True def update_http_read_credential(api): endpoint_result = api.discovery.update_http_read_credential( active_validation=True, comments='string', credentialType='GLOBAL', description='string', id='string', instanceTenantId='string', instanceUuid='string', password='string', payload=None, port=0, secure=True, username='string' ) return endpoint_result @pytest.mark.discovery def test_update_http_read_credential(api, validator): assert is_valid_update_http_read_credential( validator, update_http_read_credential(api) ) def update_http_read_credential_default(api): endpoint_result = api.discovery.update_http_read_credential( active_validation=True, comments=None, credentialType=None, description=None, id=None, instanceTenantId=None, instanceUuid=None, password=None, payload=None, port=None, secure=None, username=None ) return endpoint_result @pytest.mark.discovery def test_update_http_read_credential_default(api, validator): try: assert is_valid_update_http_read_credential( validator, update_http_read_credential_default(api) ) except Exception as original_e: with pytest.raises(TypeError, match="but instead we received None"): raise original_e def is_valid_get_list_of_discoveries_by_discovery_id(json_schema_validate, obj): json_schema_validate('jsd_99872a134d0a9fb4_v2_1_1').validate(obj) return True def get_list_of_discoveries_by_discovery_id(api): endpoint_result = api.discovery.get_list_of_discoveries_by_discovery_id( id='string', ip_address='string', limit=0, offset=0 ) return endpoint_result @pytest.mark.discovery def test_get_list_of_discoveries_by_discovery_id(api, validator): assert is_valid_get_list_of_discoveries_by_discovery_id( validator, get_list_of_discoveries_by_discovery_id(api) ) def get_list_of_discoveries_by_discovery_id_default(api): endpoint_result = api.discovery.get_list_of_discoveries_by_discovery_id( id='string', ip_address=None, limit=None, offset=None ) return endpoint_result @pytest.mark.discovery def test_get_list_of_discoveries_by_discovery_id_default(api, validator): try: assert is_valid_get_list_of_discoveries_by_discovery_id( validator, get_list_of_discoveries_by_discovery_id_default(api) ) except Exception as original_e: with pytest.raises(TypeError, match="but instead we received None"): raise original_e def is_valid_create_cli_credentials(json_schema_validate, obj): json_schema_validate('jsd_948ea8194348bc0b_v2_1_1').validate(obj) return True def create_cli_credentials(api): endpoint_result = api.discovery.create_cli_credentials( active_validation=True, payload=[{'comments': 'string', 'credentialType': 'GLOBAL', 'description': 'string', 'enablePassword': 'string', 'id': 'string', 'instanceTenantId': 'string', 'instanceUuid': 'string', 'password': 'string', 'username': 'string'}] ) return endpoint_result @pytest.mark.discovery def test_create_cli_credentials(api, validator): assert is_valid_create_cli_credentials( validator, create_cli_credentials(api) ) def create_cli_credentials_default(api): endpoint_result = api.discovery.create_cli_credentials( active_validation=True, payload=None ) return endpoint_result @pytest.mark.discovery def test_create_cli_credentials_default(api, validator): try: assert is_valid_create_cli_credentials( validator, create_cli_credentials_default(api) ) except Exception as original_e: with pytest.raises(TypeError, match="but instead we received None"): raise original_e def is_valid_create_update_snmp_properties(json_schema_validate, obj): json_schema_validate('jsd_a5ac99774c6bb541_v2_1_1').validate(obj) return True def create_update_snmp_properties(api): endpoint_result = api.discovery.create_update_snmp_properties( active_validation=True, payload=[{'id': 'string', 'instanceTenantId': 'string', 'instanceUuid': 'string', 'intValue': 0, 'systemPropertyName': 'string'}] ) return endpoint_result @pytest.mark.discovery def test_create_update_snmp_properties(api, validator): assert is_valid_create_update_snmp_properties( validator, create_update_snmp_properties(api) ) def create_update_snmp_properties_default(api): endpoint_result = api.discovery.create_update_snmp_properties( active_validation=True, payload=None ) return endpoint_result @pytest.mark.discovery def test_create_update_snmp_properties_default(api, validator): try: assert is_valid_create_update_snmp_properties( validator, create_update_snmp_properties_default(api) ) except Exception as original_e: with pytest.raises(TypeError, match="but instead we received None"): raise original_e def is_valid_get_devices_discovered_by_id(json_schema_validate, obj): json_schema_validate('jsd_a6965b454c9a8663_v2_1_1').validate(obj) return True def get_devices_discovered_by_id(api): endpoint_result = api.discovery.get_devices_discovered_by_id( id='string', task_id='string' ) return endpoint_result @pytest.mark.discovery def test_get_devices_discovered_by_id(api, validator): assert is_valid_get_devices_discovered_by_id( validator, get_devices_discovered_by_id(api) ) def get_devices_discovered_by_id_default(api): endpoint_result = api.discovery.get_devices_discovered_by_id( id='string', task_id=None ) return endpoint_result @pytest.mark.discovery def test_get_devices_discovered_by_id_default(api, validator): try: assert is_valid_get_devices_discovered_by_id( validator, get_devices_discovered_by_id_default(api) ) except Exception as original_e: with pytest.raises(TypeError, match="but instead we received None"): raise original_e def is_valid_updates_discovery_by_id(json_schema_validate, obj): json_schema_validate('jsd_9788b8fc4418831d_v2_1_1').validate(obj) return True def updates_discovery_by_id(api): endpoint_result = api.discovery.updates_discovery_by_id( active_validation=True, attributeInfo={}, cdpLevel=0, deviceIds='string', discoveryCondition='string', discoveryStatus='string', discoveryType='string', enablePasswordList='string', globalCredentialIdList=['string'], httpReadCredential={'comments': 'string', 'credentialType': 'GLOBAL', 'description': 'string', 'id': 'string', 'instanceTenantId': 'string', 'instanceUuid': 'string', 'password': 'string', 'port': 0, 'secure': True, 'username': 'string'}, httpWriteCredential={'comments': 'string', 'credentialType': 'GLOBAL', 'description': 'string', 'id': 'string', 'instanceTenantId': 'string', 'instanceUuid': 'string', 'password': 'string', 'port': 0, 'secure': True, 'username': 'string'}, id='string', ipAddressList='string', ipFilterList='string', isAutoCdp=True, lldpLevel=0, name='string', netconfPort='string', numDevices=0, parentDiscoveryId='string', passwordList='string', payload=None, preferredMgmtIPMethod='string', protocolOrder='string', retryCount=0, snmpAuthPassphrase='string', snmpAuthProtocol='string', snmpMode='string', snmpPrivPassphrase='string', snmpPrivProtocol='string', snmpRoCommunity='string', snmpRoCommunityDesc='string', snmpRwCommunity='string', snmpRwCommunityDesc='string', snmpUserName='string', timeOut=0, updateMgmtIp=True, userNameList='string' ) return endpoint_result @pytest.mark.discovery def test_updates_discovery_by_id(api, validator): assert is_valid_updates_discovery_by_id( validator, updates_discovery_by_id(api) ) def updates_discovery_by_id_default(api): endpoint_result = api.discovery.updates_discovery_by_id( active_validation=True, attributeInfo=None, cdpLevel=None, deviceIds=None, discoveryCondition=None, discoveryStatus=None, discoveryType=None, enablePasswordList=None, globalCredentialIdList=None, httpReadCredential=None, httpWriteCredential=None, id=None, ipAddressList=None, ipFilterList=None, isAutoCdp=None, lldpLevel=None, name=None, netconfPort=None, numDevices=None, parentDiscoveryId=None, passwordList=None, payload=None, preferredMgmtIPMethod=None, protocolOrder=None, retryCount=None, snmpAuthPassphrase=None, snmpAuthProtocol=None, snmpMode=None, snmpPrivPassphrase=None, snmpPrivProtocol=None, snmpRoCommunity=None, snmpRoCommunityDesc=None, snmpRwCommunity=None, snmpRwCommunityDesc=None, snmpUserName=None, timeOut=None, updateMgmtIp=None, userNameList=None ) return endpoint_result @pytest.mark.discovery def test_updates_discovery_by_id_default(api, validator): try: assert is_valid_updates_discovery_by_id( validator, updates_discovery_by_id_default(api) ) except Exception as original_e: with pytest.raises(TypeError, match="but instead we received None"): raise original_e def is_valid_get_discovery_jobs_by_ip(json_schema_validate, obj): json_schema_validate('jsd_a4967be64dfaaa1a_v2_1_1').validate(obj) return True def get_discovery_jobs_by_ip(api): endpoint_result = api.discovery.get_discovery_jobs_by_ip( ip_address='string', limit=0, name='string', offset=0 ) return endpoint_result @pytest.mark.discovery def test_get_discovery_jobs_by_ip(api, validator): assert is_valid_get_discovery_jobs_by_ip( validator, get_discovery_jobs_by_ip(api) ) def get_discovery_jobs_by_ip_default(api): endpoint_result = api.discovery.get_discovery_jobs_by_ip( ip_address=None, limit=None, name=None, offset=None ) return endpoint_result @pytest.mark.discovery def test_get_discovery_jobs_by_ip_default(api, validator): try: assert is_valid_get_discovery_jobs_by_ip( validator, get_discovery_jobs_by_ip_default(api) ) except Exception as original_e: with pytest.raises(TypeError, match="but instead we received None"): raise original_e def is_valid_get_discovered_devices_by_range(json_schema_validate, obj): json_schema_validate('jsd_a6b798ab4acaa34e_v2_1_1').validate(obj) return True def get_discovered_devices_by_range(api): endpoint_result = api.discovery.get_discovered_devices_by_range( id='string', records_to_return=0, start_index=0, task_id='string' ) return endpoint_result @pytest.mark.discovery def test_get_discovered_devices_by_range(api, validator): assert is_valid_get_discovered_devices_by_range( validator, get_discovered_devices_by_range(api) ) def get_discovered_devices_by_range_default(api): endpoint_result = api.discovery.get_discovered_devices_by_range( id='string', records_to_return=0, start_index=0, task_id=None ) return endpoint_result @pytest.mark.discovery def test_get_discovered_devices_by_range_default(api, validator): try: assert is_valid_get_discovered_devices_by_range( validator, get_discovered_devices_by_range_default(api) ) except Exception as original_e: with pytest.raises(TypeError, match="but instead we received None"): raise original_e def is_valid_create_http_read_credentials(json_schema_validate, obj): json_schema_validate('jsd_bf859ac64a0ba19c_v2_1_1').validate(obj) return True def create_http_read_credentials(api): endpoint_result = api.discovery.create_http_read_credentials( active_validation=True, payload=[{'comments': 'string', 'credentialType': 'GLOBAL', 'description': 'string', 'id': 'string', 'instanceTenantId': 'string', 'instanceUuid': 'string', 'password': 'string', 'port': 0, 'secure': True, 'username': 'string'}] ) return endpoint_result @pytest.mark.discovery def test_create_http_read_credentials(api, validator): assert is_valid_create_http_read_credentials( validator, create_http_read_credentials(api) ) def create_http_read_credentials_default(api): endpoint_result = api.discovery.create_http_read_credentials( active_validation=True, payload=None ) return endpoint_result @pytest.mark.discovery def test_create_http_read_credentials_default(api, validator): try: assert is_valid_create_http_read_credentials( validator, create_http_read_credentials_default(api) ) except Exception as original_e: with pytest.raises(TypeError, match="but instead we received None"): raise original_e def is_valid_update_http_write_credentials(json_schema_validate, obj): json_schema_validate('jsd_b68a6bd8473a9a25_v2_1_1').validate(obj) return True def update_http_write_credentials(api): endpoint_result = api.discovery.update_http_write_credentials( active_validation=True, comments='string', credentialType='GLOBAL', description='string', id='string', instanceTenantId='string', instanceUuid='string', password='string', payload=None, port=0, secure=True, username='string' ) return endpoint_result @pytest.mark.discovery def test_update_http_write_credentials(api, validator): assert is_valid_update_http_write_credentials( validator, update_http_write_credentials(api) ) def update_http_write_credentials_default(api): endpoint_result = api.discovery.update_http_write_credentials( active_validation=True, comments=None, credentialType=None, description=None, id=None, instanceTenantId=None, instanceUuid=None, password=None, payload=None, port=None, secure=None, username=None ) return endpoint_result @pytest.mark.discovery def test_update_http_write_credentials_default(api, validator): try: assert is_valid_update_http_write_credentials( validator, update_http_write_credentials_default(api) ) except Exception as original_e: with pytest.raises(TypeError, match="but instead we received None"): raise original_e def is_valid_update_netconf_credentials(json_schema_validate, obj): json_schema_validate('jsd_c5acd9fa4c1a8abc_v2_1_1').validate(obj) return True def update_netconf_credentials(api): endpoint_result = api.discovery.update_netconf_credentials( active_validation=True, comments='string', credentialType='GLOBAL', description='string', id='string', instanceTenantId='string', instanceUuid='string', netconfPort='string', payload=None ) return endpoint_result @pytest.mark.discovery def test_update_netconf_credentials(api, validator): assert is_valid_update_netconf_credentials( validator, update_netconf_credentials(api) ) def update_netconf_credentials_default(api): endpoint_result = api.discovery.update_netconf_credentials( active_validation=True, comments=None, credentialType=None, description=None, id=None, instanceTenantId=None, instanceUuid=None, netconfPort=None, payload=None ) return endpoint_result @pytest.mark.discovery def test_update_netconf_credentials_default(api, validator): try: assert is_valid_update_netconf_credentials( validator, update_netconf_credentials_default(api) ) except Exception as original_e: with pytest.raises(TypeError, match="but instead we received None"): raise original_e def is_valid_delete_all_discovery(json_schema_validate, obj): json_schema_validate('jsd_db8e09234a988bab_v2_1_1').validate(obj) return True def delete_all_discovery(api): endpoint_result = api.discovery.delete_all_discovery( ) return endpoint_result @pytest.mark.discovery def test_delete_all_discovery(api, validator): assert is_valid_delete_all_discovery( validator, delete_all_discovery(api) ) def delete_all_discovery_default(api): endpoint_result = api.discovery.delete_all_discovery( ) return endpoint_result @pytest.mark.discovery def test_delete_all_discovery_default(api, validator): try: assert is_valid_delete_all_discovery( validator, delete_all_discovery_default(api) ) except Exception as original_e: with pytest.raises(TypeError, match="but instead we received None"): raise original_e def is_valid_delete_discovery_by_specified_range(json_schema_validate, obj): json_schema_validate('jsd_c1ba9a424c08a01b_v2_1_1').validate(obj) return True def delete_discovery_by_specified_range(api): endpoint_result = api.discovery.delete_discovery_by_specified_range( records_to_delete=0, start_index=0 ) return endpoint_result @pytest.mark.discovery def test_delete_discovery_by_specified_range(api, validator): assert is_valid_delete_discovery_by_specified_range( validator, delete_discovery_by_specified_range(api) ) def delete_discovery_by_specified_range_default(api): endpoint_result = api.discovery.delete_discovery_by_specified_range( records_to_delete=0, start_index=0 ) return endpoint_result @pytest.mark.discovery def test_delete_discovery_by_specified_range_default(api, validator): try: assert is_valid_delete_discovery_by_specified_range( validator, delete_discovery_by_specified_range_default(api) ) except Exception as original_e: with pytest.raises(TypeError, match="but instead we received None"): raise original_e def is_valid_delete_global_credentials_by_id(json_schema_validate, obj): json_schema_validate('jsd_f5ac590c4ca9975a_v2_1_1').validate(obj) return True def delete_global_credentials_by_id(api): endpoint_result = api.discovery.delete_global_credentials_by_id( global_credential_id='string' ) return endpoint_result @pytest.mark.discovery def test_delete_global_credentials_by_id(api, validator): assert is_valid_delete_global_credentials_by_id( validator, delete_global_credentials_by_id(api) ) def delete_global_credentials_by_id_default(api): endpoint_result = api.discovery.delete_global_credentials_by_id( global_credential_id='string' ) return endpoint_result @pytest.mark.discovery def test_delete_global_credentials_by_id_default(api, validator): try: assert is_valid_delete_global_credentials_by_id( validator, delete_global_credentials_by_id_default(api) ) except Exception as original_e: with pytest.raises(TypeError, match="but instead we received None"): raise original_e def is_valid_update_cli_credentials(json_schema_validate, obj): json_schema_validate('jsd_fba0d80747eb82e8_v2_1_1').validate(obj) return True def update_cli_credentials(api): endpoint_result = api.discovery.update_cli_credentials( active_validation=True, comments='string', credentialType='GLOBAL', description='string', enablePassword='string', id='string', instanceTenantId='string', instanceUuid='string', password='string', payload=None, username='string' ) return endpoint_result @pytest.mark.discovery def test_update_cli_credentials(api, validator): assert is_valid_update_cli_credentials( validator, update_cli_credentials(api) ) def update_cli_credentials_default(api): endpoint_result = api.discovery.update_cli_credentials( active_validation=True, comments=None, credentialType=None, description=None, enablePassword=None, id=None, instanceTenantId=None, instanceUuid=None, password=None, payload=None, username=None ) return endpoint_result @pytest.mark.discovery def test_update_cli_credentials_default(api, validator): try: assert is_valid_update_cli_credentials( validator, update_cli_credentials_default(api) ) except Exception as original_e: with pytest.raises(TypeError, match="but instead we received None"): raise original_e def is_valid_get_discovered_network_devices_by_discovery_id(json_schema_validate, obj): json_schema_validate('jsd_f6ac994f451ba011_v2_1_1').validate(obj) return True def get_discovered_network_devices_by_discovery_id(api): endpoint_result = api.discovery.get_discovered_network_devices_by_discovery_id( id='string', task_id='string' ) return endpoint_result @pytest.mark.discovery def test_get_discovered_network_devices_by_discovery_id(api, validator): assert is_valid_get_discovered_network_devices_by_discovery_id( validator, get_discovered_network_devices_by_discovery_id(api) ) def get_discovered_network_devices_by_discovery_id_default(api): endpoint_result = api.discovery.get_discovered_network_devices_by_discovery_id( id='string', task_id=None ) return endpoint_result @pytest.mark.discovery def test_get_discovered_network_devices_by_discovery_id_default(api, validator): try: assert is_valid_get_discovered_network_devices_by_discovery_id( validator, get_discovered_network_devices_by_discovery_id_default(api) ) except Exception as original_e: with pytest.raises(TypeError, match="but instead we received None"): raise original_e def is_valid_get_global_credentials(json_schema_validate, obj): json_schema_validate('jsd_ff816b8e435897eb_v2_1_1').validate(obj) return True def get_global_credentials(api): endpoint_result = api.discovery.get_global_credentials( credential_sub_type='string', order='string', sort_by='string' ) return endpoint_result @pytest.mark.discovery def test_get_global_credentials(api, validator): assert is_valid_get_global_credentials( validator, get_global_credentials(api) ) def get_global_credentials_default(api): endpoint_result = api.discovery.get_global_credentials( credential_sub_type=None, order=None, sort_by=None ) return endpoint_result @pytest.mark.discovery def test_get_global_credentials_default(api, validator): try: assert is_valid_get_global_credentials( validator, get_global_credentials_default(api) ) except Exception as original_e: with pytest.raises(TypeError, match="but instead we received None"): raise original_e

29.064042

307

0.709887

79513d233eab518d227b47f1a7b12d4b8aafee9a

2,677

py

Python

sip/execution_control/processing_block_controller/tests/test_processing_block_controller.py

SKA-ScienceDataProcessor/integration-prototype

5875dc0489f707232534ce75daf3707f909bcd15

[ "BSD-3-Clause" ]

3

2016-11-08T02:27:05.000Z

2018-01-22T13:26:11.000Z

sip/execution_control/processing_block_controller/tests/test_processing_block_controller.py

SKA-ScienceDataProcessor/integration-prototype

5875dc0489f707232534ce75daf3707f909bcd15

[ "BSD-3-Clause" ]

87

2016-11-24T11:09:01.000Z

2021-03-25T22:23:59.000Z

sip/execution_control/processing_block_controller/tests/test_processing_block_controller.py

SKA-ScienceDataProcessor/integration-prototype

5875dc0489f707232534ce75daf3707f909bcd15

[ "BSD-3-Clause" ]

10

2016-05-18T09:41:36.000Z

2019-07-04T10:19:24.000Z

# coding=utf-8 """Unit tests of the Processing Block Controller. http://docs.celeryproject.org/en/latest/userguide/testing.html FIXME(BMo) At the moment these tests require that the PBC has started. Its possible that this requirement is not needed using some Celery testing magic. """ import json from os.path import dirname, join import redis import celery import pytest from sip_config_db import ConfigDb from sip_config_db.scheduling import SchedulingBlockInstance from sip_pbc import echo, execute_processing_block, version from sip_pbc.release import __version__ from ._test_utils import add_workflow_definitions DB = ConfigDb() def test_pbc_inspect_workers(): """Test the Celery API for inspecting the Celery workers.""" try: celery.current_app.broker_connection().connect() except redis.exceptions.ConnectionError: pytest.fail('Failed to connect to broker: {}' .format(celery.current_app.broker_connection().as_uri()), pytrace=False) inspect = celery.current_app.control.inspect() workers = inspect.ping() if workers is None: pytest.skip('Unable to find any celery workers!') for worker in workers: assert not inspect.scheduled()[worker] assert not inspect.active()[worker] registered_tasks = inspect.registered_tasks()[worker] assert 'sip_pbc.tasks.execute_processing_block' in registered_tasks def test_pbc_echo(): """Test the SIP PBC echo method.""" message = "Hello there!" result = echo.apply(args=(message,)) assert result.get(timeout=3) == message def test_pbc_version(): """Test the SIP PBC version method.""" result = version.apply() assert result.get(timeout=3) == __version__ def test_pbc_execute_workflow(): """Test the SIP PBC execute_processing_block method.""" try: DB.flush_db() except ConnectionError: pytest.fail('Failed to a connect a configuration database (Redis) ' 'instance!', pytrace=False) data_dir = join(dirname(__file__), 'data') add_workflow_definitions(join(data_dir, 'workflow_definitions')) with open(join(data_dir, 'sbi_config_3.json')) as _file: sbi_config = json.load(_file) sbi = SchedulingBlockInstance.from_config(sbi_config) pb_ids = sbi.processing_block_ids assert len(pb_ids) == 1 assert pb_ids[0] == 'PB-20181116-sip-001' assert isinstance(pb_ids[0], str) result = execute_processing_block.apply(args=(pb_ids[0],), kwargs=dict(log_level='WARNING')) assert result.get(timeout=10.0) == 'completed'

32.646341

77

0.692193

79513d3e73de790c57df097eafeae26964ff282c

1,451

py

Python

setup.py

MihaiBojin/python-cli-project-template

0caef1d48e6d428f793ccbe91a30670a8c2620d9

[ "Apache-2.0" ]

null

setup.py

MihaiBojin/python-cli-project-template

0caef1d48e6d428f793ccbe91a30670a8c2620d9

[ "Apache-2.0" ]

null

setup.py

MihaiBojin/python-cli-project-template

0caef1d48e6d428f793ccbe91a30670a8c2620d9

[ "Apache-2.0" ]

null

from distutils.core import setup import setuptools from src.pkg.version import __version__, __title__ with open("README.md", "r", encoding='utf8') as fh: long_description = fh.read() # Parse requirements.txt packages = list() with open('requirements/prod.txt') as fh: for line in fh.readlines(): dep = line.strip() # skip empty lines if len(dep) == 0: continue # skip comments if dep[0] == '#': continue # Extract any comments parts = dep.split('#', 1) packages += [parts[0]] setup( name=__title__, version=__version__, author='John Doe', author_email='', packages=setuptools.find_packages(where='src'), package_dir={ '': 'src', }, # https://setuptools.readthedocs.io/en/latest/setuptools.html package_data={ '': ['*.txt', '*.yml', '*.json'], }, scripts=[], entry_points={ 'console_scripts': [ 'tool = pkg.main:main', ] }, url='http://pypi.python.org/pypi/[your-project-name-here]/', license='LICENSE', description='Python CLI tool template', long_description=long_description, long_description_content_type="text/markdown", install_requires=packages, classifiers=[ "Programming Language :: Python :: 3.7", "License :: OSI Approved :: Apache License Version 2.0", "Operating System :: POSIX", ], )

25.910714

65

0.592006

79513d5c4a03ddefdd0c36260d0e50ecb46d4eef

10,105

py

Python

scripts/rev_version.py

skallaher/RevEng_PAJ7620

3165e9953f4845e050d0daad023529c2d410bc02

[ "MIT" ]

8

2021-02-16T16:39:09.000Z

2021-09-17T01:30:56.000Z

scripts/rev_version.py

skallaher/RevEng_PAJ7620

3165e9953f4845e050d0daad023529c2d410bc02

[ "MIT" ]

38

2020-12-21T08:13:14.000Z

2021-02-16T08:54:50.000Z

scripts/rev_version.py

skallaher/RevEng_PAJ7620

3165e9953f4845e050d0daad023529c2d410bc02

[ "MIT" ]

5

2020-12-22T05:25:32.000Z

2021-09-30T15:35:21.000Z

#!/usr/bin/env python """Rev Version Updates the version of the library in the following files - Doxyfile # Doxygen - library.properties # Arduino Also updates the version of any examples which have been updated since the last version (in <path to examples>/examples.doc) """ import os import re import subprocess from copy import deepcopy from typing import List from packaging import version import click def get_updated_version_copy(existing_version: version.Version, major: int = None, minor: int = None, micro: int = None) -> version.Version: """get_updated_version_copy Generates a copy of a version.Version with the specified major, minor, micro version. None values leaves the version components unchanged inputs: existing_version (version.Version): The existing version to base the changes on major (int): The major version to write to the version copy. Unchanged if None minor (int): The minor version to write to the version copy. Unchanged if None micro (int): The micro version to write to the version copy. Unchanged if None outputs: version.Version: The version copy with modified values """ # Unroll version data new_version = deepcopy(existing_version) new_version_data = new_version._version # pylint: disable=W0212 new_release = list(new_version_data.release) new_version_data = list(new_version_data) if major: new_release[0] = major if minor: new_release[1] = minor if micro: new_release[2] = micro new_version_data[1] = tuple(new_release) new_version_data = version._Version(*new_version_data) # pylint: disable=W0212 new_version._version = new_version_data # pylint: disable=W0212 return new_version def rev_doxygen_project_number(current_version: version.Version, next_version: version.Version): """rev_doxygen_project_number Updates any version references within Doxyfile inputs: current_version (version.Version): The current version of the library as it appears in Doxyfile next_version (version.Version): The next version of the library to update Doxyfile to """ with open("Doxyfile", 'r+') as doxyfile: content = doxyfile.read() new_content, num_replaced = re.subn(current_version.base_version, next_version.base_version, content, flags=re.M) if not num_replaced: print("Failed to find {} in Doxyfile to update version.".format(current_version)) return with open("Doxyfile", 'w') as doxyfile: doxyfile.write(new_content) def rev_library_properties_version(current_version: version.Version, next_version: version.Version): """rev_library_properties_version Updates any version references within library.properties inputs: current_version (version.Version): The current version of the library as it appears in library.properties next_version (version.Version): The next version of the library to update library.properties to """ with open("library.properties", "r+") as props: content = props.read() new_content, num_replaced = re.subn(current_version.base_version, next_version.base_version, content, flags=re.M) if not num_replaced: print("Failed to find {} in library.properties to update version.".format(current_version)) return with open("library.properties", "w") as props: props.write(new_content) def get_examples_changed(example_dir: str, previous_version: version.Version) -> List[str]: """get_examples_changed Fetch all of the example files which have changed since the last version inputs: example_dir (str): The directory which contains the examples. previous_version (version.Version): The previous version of the library, used to check against for example changes. outputs: List[str]: The filenames (without paths) of each file in the example_dir which has changed """ # Get all example files changed since current version tag changed_examples = subprocess.run(args=["git", "diff", "--name-only", "v{}".format(previous_version.base_version), "--", example_dir], capture_output=True, check=True) changed_examples_str = changed_examples.stdout.decode() return [os.path.basename(f) for f in changed_examples_str.split('\n') if os.path.basename(f)] def rev_example_versions(example_dir: str, previous_version: version.Version): """rev_example_versions Update the version of each example iff the example file has changed inputs: example_dir (str): The directory which contains the examples. previous_version (version.Version): The previous version of the library, used to check against for example changes. """ with open(os.path.join(example_dir, "examples.doc"), 'r+') as ex_file: examples = re.findall(r'/\*\*(.*?)\*/', ex_file.read(), flags=re.M | re.DOTALL) new_examples = [] changed_files = get_examples_changed(example_dir, previous_version) print("Changed: {}".format(changed_files)) for example in examples: example_filename = re.search(r'example\s+(.*\..*)\n', example)[1] if example_filename in changed_files: ex_version = version.parse(re.search(r'version\s+({})\n'.format(version.VERSION_PATTERN), example, flags=re.VERBOSE | re.IGNORECASE | re.M)[1]) ex_version = get_updated_version_copy(ex_version, minor=ex_version.minor + 1) new_example = re.sub(r'(version\s+)(.*)\n', r'\g<1>{}\n'.format(ex_version.base_version), example) print("NEW: {}".format(example_filename)) new_examples.append(new_example) else: print("OLD: {}".format(example_filename)) new_examples.append(example) output = '\n\n'.join([r'/**{}*/'.format(e) for e in new_examples]) with open(os.path.join(example_dir, "examples.doc"), 'w') as ex_file: ex_file.write(output) def get_current_version() -> version.Version: """get_current_version Gets the current version as it exists in library.properties outputs: version.Version: The version as it exists in library.properties """ with open("library.properties", 'r') as props: content = props.read() existing_version = version.parse(re.search("version=({})".format(version.VERSION_PATTERN), content, flags=re.VERBOSE | re.IGNORECASE)[1]) return existing_version def calculate_next_version(current_version: version.Version) -> version.Version: """calculate_next_version Get the next version based upon the current one by incrementing the minor version and setting the micro version to 0 inputs: current_version (version.Version): The current version of the library outputs: version.Version: The next version of the library to rev to """ return get_updated_version_copy(current_version, minor=current_version.minor + 1, micro=0) def calculate_prev_version(current_version: version.Version) -> version.Version: """calculate_prev_version Get the previous version based upon the current one by decrementing the minor version if micro version is 0, else by decrementing the micro version inputs: current_version (version.Version): The current version of the library outputs: version.Version: The previous version of the library """ if current_version.micro == 0: return get_updated_version_copy(current_version, minor=current_version.minor - 1) return get_updated_version_copy(current_version, micro=current_version.micro - 1) @click.command() @click.option("--current-version", "--current", help="Current version of the library. Fetched from library.properties by default") @click.option("--previous-version", "--previous", help="Previous version of the library. Defaults to current version with minor version - 1 " "if micro version is 0 (e.g. 1.4.0 -> 1.3.0), or micro version - 1 " "(e.g. 1.4.2 -> 1.4.1)") @click.option("--next-version", "--next", help="Version to rev the library to. Defaults to current version with minor version + 1 " "and micro version set to 0 (e.g. 1.3.2 -> 1.4.0).") @click.option("--example-dir", help="Path to the examples directory", default="examples") def cmd(current_version, previous_version, next_version, example_dir): """Run the rev version """ # Move to project root to find files relative to project_root = os.path.join(os.path.dirname(os.path.realpath(__file__)), "..") os.chdir(project_root) if not current_version: current_version = get_current_version() print("Found current version {}".format(current_version.base_version)) if not previous_version: previous_version = calculate_prev_version(current_version=current_version) print("Found previous version {}".format(previous_version.base_version)) if not next_version: next_version = calculate_next_version(current_version=current_version) print("Found next version {}".format(next_version.base_version)) rev_doxygen_project_number(current_version=current_version, next_version=next_version) rev_library_properties_version(current_version=current_version, next_version=next_version) rev_example_versions(example_dir=example_dir, previous_version=previous_version) if __name__ == "__main__": cmd() # pylint: disable=E1120

41.929461

108

0.66858

79513dee3364d4b596b7a4fe17f4f9211297bbb2

3,283

py

Python

torchFI/modules/linear.py

bfgoldstein/torchfi

6a735ecf81c39bfdfa770e6a41a66e4f88ea808b

[ "Apache-2.0" ]

6

2019-10-17T17:52:43.000Z

2020-08-12T09:08:45.000Z

torchFI/modules/linear.py

bfgoldstein/torchfi

6a735ecf81c39bfdfa770e6a41a66e4f88ea808b

[ "Apache-2.0" ]

1

2021-05-04T02:57:38.000Z

torchFI/modules/linear.py

bfgoldstein/torchfi

6a735ecf81c39bfdfa770e6a41a66e4f88ea808b

[ "Apache-2.0" ]

1

2020-08-12T09:08:47.000Z

############################################################### # This file was created using part of Distiller project developed by: # NervanaSystems https://github.com/NervanaSystems/distiller # # Changes were applied to satisfy torchFI project needs ############################################################### import math import numpy as np from enum import Enum from collections import OrderedDict import torch import torch.nn as nn from util.log import * class FILinear(nn.Linear): def __init__(self, fi, name, in_features, out_features, weight=None, bias=None): self.fi = fi self.name = name self.id = fi.addNewLayer(name, FILinear) super(FILinear, self).__init__(in_features, out_features, True if bias is not None else False) if weight is not None: self.weight = weight if bias is not None: self.bias = bias def forward(self, input): if self.fi.injectionMode and self.id == self.fi.injectionLayer: # XNOR Operation # True only if both injectionFeatures and injectionWeights are True or False # False if one of them is True if not(self.fi.injectionFeatures ^ self.fi.injectionWeights): # decide where to apply injection # weights = 0, activations = 1 # locInjection = np.random.randint(0, 2) locInjection = np.random.binomial(1, .5) else: locInjection = self.fi.injectionFeatures if locInjection: if self.fi.log: logWarning("\tInjecting Fault into feature data of Linear " + self.name + " layer.") faulty_res = self.fi.injectFeatures(input.data) for idx, (indices, faulty_val) in enumerate(faulty_res): # add idx as batch index to indices array input.data[tuple([idx] + indices)] = faulty_val return nn.functional.linear(input, self.weight, self.bias) else: # create new tensor to apply FI weightFI = self.weight.clone() if self.fi.log: logWarning("\tInjecting Fault into weight data of Linear " + self.name + " layer.") indices, faulty_val = self.fi.inject(weightFI.data) weightFI.data[tuple(indices)] = faulty_val return nn.functional.linear(input, weightFI, self.bias) else: return super(FILinear, self).forward(input) @staticmethod def from_pytorch_impl(fi, name, linear: nn.Linear): return FILinear(fi, name, linear.in_features, linear.out_features, linear.weight, linear.bias) def __repr__(self): return "%s(in_features=%d, out_features=%d, bias=%s, id=%d)" % ( self.__class__.__name__, self.in_features, self.out_features, str(True if self.bias is not None else False), self.id)

37.735632

88

0.531831

79513e55f7625ed7448cfb04b651a6129c72aa02

9,927

py

Python

mnist_4.0_batchnorm_five_layers_sigmoid.py

hughkong/asstarer

ed331aa2b4c7665f10214117510cfee099216ede

[ "Apache-2.0" ]

1

2021-06-20T11:44:05.000Z

mnist_4.0_batchnorm_five_layers_sigmoid.py

hughkong/asstarer

ed331aa2b4c7665f10214117510cfee099216ede

[ "Apache-2.0" ]

null

mnist_4.0_batchnorm_five_layers_sigmoid.py

hughkong/asstarer

ed331aa2b4c7665f10214117510cfee099216ede

[ "Apache-2.0" ]

1

2020-05-05T17:21:15.000Z

# encoding: UTF-8 # Copyright 2016 Google.com # # 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 tensorflow as tf import tensorflowvisu import math from tensorflow.contrib.learn.python.learn.datasets.mnist import read_data_sets tf.set_random_seed(0) # neural network with 5 layers # # · · · · · · · · · · (input data, flattened pixels) X [batch, 784] # 784 = 28*28 # \x/x\x/x\x/x\x/x\x/ -- fully connected layer (sigmoid+BN) W1 [784, 200] B1[200] # · · · · · · · · · Y1 [batch, 200] # \x/x\x/x\x/x\x/ -- fully connected layer (sigmoid+BN) W2 [200, 100] B2[100] # · · · · · · · Y2 [batch, 100] # \x/x\x/x\x/ -- fully connected layer (sigmoid+BN) W3 [100, 60] B3[60] # · · · · · Y3 [batch, 60] # \x/x\x/ -- fully connected layer (sigmoid+BN) W4 [60, 30] B4[30] # · · · Y4 [batch, 30] # \x/ -- fully connected layer (softmax+BN) W5 [30, 10] B5[10] # · Y5 [batch, 10] # Download images and labels into mnist.test (10K images+labels) and mnist.train (60K images+labels) mnist = read_data_sets("data", one_hot=True, reshape=False, validation_size=0) # input X: 28x28 grayscale images, the first dimension (None) will index the images in the mini-batch X = tf.placeholder(tf.float32, [None, 28, 28, 1]) # correct answers will go here Y_ = tf.placeholder(tf.float32, [None, 10]) # variable learning rate lr = tf.placeholder(tf.float32) # train/test selector for batch normalisation tst = tf.placeholder(tf.bool) # training iteration iter = tf.placeholder(tf.int32) # five layers and their number of neurons (tha last layer has 10 softmax neurons) L = 200 M = 100 N = 60 P = 30 Q = 10 # Weights initialised with small random values between -0.2 and +0.2 # When using RELUs, make sure biases are initialised with small *positive* values for example 0.1 = tf.ones([K])/10 W1 = tf.Variable(tf.truncated_normal([784, L], stddev=0.1)) # 784 = 28 * 28 S1 = tf.Variable(tf.ones([L])) O1 = tf.Variable(tf.zeros([L])) W2 = tf.Variable(tf.truncated_normal([L, M], stddev=0.1)) S2 = tf.Variable(tf.ones([M])) O2 = tf.Variable(tf.zeros([M])) W3 = tf.Variable(tf.truncated_normal([M, N], stddev=0.1)) S3 = tf.Variable(tf.ones([N])) O3 = tf.Variable(tf.zeros([N])) W4 = tf.Variable(tf.truncated_normal([N, P], stddev=0.1)) S4 = tf.Variable(tf.ones([P])) O4 = tf.Variable(tf.zeros([P])) W5 = tf.Variable(tf.truncated_normal([P, Q], stddev=0.1)) B5 = tf.Variable(tf.zeros([Q])) ## Batch normalisation conclusions with sigmoid activation function: # BN is applied between logits and the activation function # On Sigmoids it is very clear that without BN, the sigmoids saturate, with BN, they output # a clean gaussian distribution of values, especially with high initial learning rates. # sigmoid, no batch-norm, lr(0.003, 0.0001, 2000) => 97.5% # sigmoid, batch-norm lr(0.03, 0.0001, 1000) => 98% # sigmoid, batch-norm, no offsets => 97.3% # sigmoid, batch-norm, no scales => 98.1% but cannot hold fast learning rate at start # sigmoid, batch-norm, no scales, no offsets => 96% # Both scales and offsets are useful with sigmoids. # With RELUs, the scale variables can be omitted. # Biases are not useful with batch norm, offsets are to be used instead # Steady 98.5% accuracy using these parameters: # moving average decay: 0.998 (equivalent to averaging over two epochs) # learning rate decay from 0.03 to 0.0001 speed 1000 => max 98.59 at 6500 iterations, 98.54 at 10K it, 98% at 1300it, 98.5% at 3200it def batchnorm(Ylogits, Offset, Scale, is_test, iteration): exp_moving_avg = tf.train.ExponentialMovingAverage(0.998, iteration) # adding the iteration prevents from averaging across non-existing iterations bnepsilon = 1e-5 mean, variance = tf.nn.moments(Ylogits, [0]) update_moving_everages = exp_moving_avg.apply([mean, variance]) m = tf.cond(is_test, lambda: exp_moving_avg.average(mean), lambda: mean) v = tf.cond(is_test, lambda: exp_moving_avg.average(variance), lambda: variance) Ybn = tf.nn.batch_normalization(Ylogits, m, v, Offset, Scale, bnepsilon) return Ybn, update_moving_everages def no_batchnorm(Ylogits, Offset, Scale, is_test, iteration): return Ylogits, tf.no_op() # The model XX = tf.reshape(X, [-1, 784]) Y1l = tf.matmul(XX, W1) Y1bn, update_ema1 = batchnorm(Y1l, O1, S1, tst, iter) Y1 = tf.nn.sigmoid(Y1bn) Y2l = tf.matmul(Y1, W2) Y2bn, update_ema2 = batchnorm(Y2l, O2, S2, tst, iter) Y2 = tf.nn.sigmoid(Y2bn) Y3l = tf.matmul(Y2, W3) Y3bn, update_ema3 = batchnorm(Y3l, O3, S3, tst, iter) Y3 = tf.nn.sigmoid(Y3bn) Y4l = tf.matmul(Y3, W4) Y4bn, update_ema4 = batchnorm(Y4l, O4, S4, tst, iter) Y4 = tf.nn.sigmoid(Y4bn) Ylogits = tf.matmul(Y4, W5) + B5 Y = tf.nn.softmax(Ylogits) update_ema = tf.group(update_ema1, update_ema2, update_ema3, update_ema4) # cross-entropy loss function (= -sum(Y_i * log(Yi)) ), normalised for batches of 100 images # TensorFlow provides the softmax_cross_entropy_with_logits function to avoid numerical stability # problems with log(0) which is NaN cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits=Ylogits, labels=Y_) cross_entropy = tf.reduce_mean(cross_entropy)*100 # accuracy of the trained model, between 0 (worst) and 1 (best) correct_prediction = tf.equal(tf.argmax(Y, 1), tf.argmax(Y_, 1)) accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) # matplotlib visualisation allweights = tf.concat([tf.reshape(W1, [-1]), tf.reshape(W2, [-1]), tf.reshape(W3, [-1]), tf.reshape(W4, [-1]), tf.reshape(W5, [-1])], 0) allbiases = tf.concat([tf.reshape(O1, [-1]), tf.reshape(O2, [-1]), tf.reshape(O3, [-1]), tf.reshape(O4, [-1]), tf.reshape(B5, [-1])], 0) # to use for sigmoid allactivations = tf.concat([tf.reshape(Y1, [-1]), tf.reshape(Y2, [-1]), tf.reshape(Y3, [-1]), tf.reshape(Y4, [-1])], 0) # to use for RELU #allactivations = tf.concat([tf.reduce_max(Y1, [0]), tf.reduce_max(Y2, [0]), tf.reduce_max(Y3, [0]), tf.reduce_max(Y4, [0])], 0) alllogits = tf.concat([tf.reshape(Y1l, [-1]), tf.reshape(Y2l, [-1]), tf.reshape(Y3l, [-1]), tf.reshape(Y4l, [-1])], 0) I = tensorflowvisu.tf_format_mnist_images(X, Y, Y_) It = tensorflowvisu.tf_format_mnist_images(X, Y, Y_, 1000, lines=25) datavis = tensorflowvisu.MnistDataVis(title4="Logits", title5="activations", histogram4colornum=2, histogram5colornum=2) # training step, the learning rate is a placeholder train_step = tf.train.AdamOptimizer(lr).minimize(cross_entropy) # init init = tf.global_variables_initializer() sess = tf.Session() sess.run(init) # You can call this function in a loop to train the model, 100 images at a time def training_step(i, update_test_data, update_train_data): # training on batches of 100 images with 100 labels batch_X, batch_Y = mnist.train.next_batch(100) # learning rate decay (without batch norm) #max_learning_rate = 0.003 #min_learning_rate = 0.0001 #decay_speed = 2000 # learning rate decay (with batch norm) max_learning_rate = 0.03 min_learning_rate = 0.0001 decay_speed = 1000.0 learning_rate = min_learning_rate + (max_learning_rate - min_learning_rate) * math.exp(-i/decay_speed) # compute training values for visualisation if update_train_data: a, c, im, al, ac = sess.run([accuracy, cross_entropy, I, alllogits, allactivations], {X: batch_X, Y_: batch_Y, tst: False}) print(str(i) + ": accuracy:" + str(a) + " loss: " + str(c) + " (lr:" + str(learning_rate) + ")") datavis.append_training_curves_data(i, a, c) datavis.update_image1(im) datavis.append_data_histograms(i, al, ac) # compute test values for visualisation if update_test_data: a, c, im = sess.run([accuracy, cross_entropy, It], {X: mnist.test.images, Y_: mnist.test.labels, tst: True}) print(str(i) + ": ********* epoch " + str(i*100//mnist.train.images.shape[0]+1) + " ********* test accuracy:" + str(a) + " test loss: " + str(c)) datavis.append_test_curves_data(i, a, c) datavis.update_image2(im) # the backpropagation training step sess.run(train_step, {X: batch_X, Y_: batch_Y, lr: learning_rate, tst: False}) sess.run(update_ema, {X: batch_X, Y_: batch_Y, tst: False, iter: i}) datavis.animate(training_step, iterations=10000+1, train_data_update_freq=20, test_data_update_freq=100, more_tests_at_start=True) # to save the animation as a movie, add save_movie=True as an argument to datavis.animate # to disable the visualisation use the following line instead of the datavis.animate line # for i in range(10000+1): training_step(i, i % 100 == 0, i % 20 == 0) print("max test accuracy: " + str(datavis.get_max_test_accuracy())) # Some results to expect: # (In all runs, if sigmoids are used, all biases are initialised at 0, if RELUs are used, # all biases are initialised at 0.1 apart from the last one which is initialised at 0.) ## decaying learning rate from 0.003 to 0.0001 decay_speed 2000, 10K iterations # final test accuracy = 0.9813 (sigmoid - training cross-entropy not stabilised) # final test accuracy = 0.9842 (relu - training set fully learned, test accuracy stable)

46.825472

153

0.676639

79513e5a37a266822c68841af92a103880eed399

1,252

py

Python

main.py

itsnikhil/tnb-analysis

c14bff7b0b17ae0cb49a3d23720632d1329d2bdc

[ "MIT" ]

25

2020-12-04T01:40:14.000Z

2021-12-16T13:10:20.000Z

main.py

itsnikhil/tnb-analysis

c14bff7b0b17ae0cb49a3d23720632d1329d2bdc

[ "MIT" ]

3

2021-06-06T12:40:03.000Z

2022-01-10T14:20:50.000Z

main.py

itsnikhil/tnb-analysis

c14bff7b0b17ae0cb49a3d23720632d1329d2bdc

[ "MIT" ]

6

2021-03-28T16:34:09.000Z

2021-07-31T22:27:28.000Z

import os from datetime import datetime from thenewboston.constants.network import MAX_POINT_VALUE from thenewboston.utils.network import fetch from utils.files import write_json from utils.format_results import format_results PRIMARY_VALIDATOR_IP = '54.219.234.129' def fetch_account_data(): """ Fetch all account data from primary validator Return list of accounts """ results = [] next_url = f'http://{PRIMARY_VALIDATOR_IP}/accounts' while next_url: print(next_url) data = fetch(url=next_url, headers={}) accounts = data['results'] results += accounts next_url = data['next'] return results def run(): """ Run main application """ now = datetime.utcnow() date_time = now.strftime('%Y-%m-%d-%H_%M_%S') data = format_results(fetch_account_data()) verify_results(data=data) write_json( file=f'./account_backups/{date_time}.json', data=data ) def verify_results(*, data): """ Ensure total coins is equal to """ total = sum(v['balance'] for k, v in data.items()) if total == MAX_POINT_VALUE: print('\nValid') else: print('\nInvalid') if __name__ == '__main__': run()

19.5625

58

0.639776

79513e949943ac9e7656cca2c5431b1415205ecb

780

py

Python

src/models/model.py

evanaze/captcha

62d226742be7f4091e54a7ea960703812bd44fd5

[ "MIT" ]

null

src/models/model.py

evanaze/captcha

62d226742be7f4091e54a7ea960703812bd44fd5

[ "MIT" ]

3

2021-03-26T18:14:39.000Z

2021-09-20T14:09:05.000Z

src/models/model.py

evanaze/captcha

62d226742be7f4091e54a7ea960703812bd44fd5

[ "MIT" ]

null

import torch import torch.nn as nn import torch.nn.functional as F class Net(nn.Module): def __init__(self): super(Net, self).__init__() self.conv1 = nn.Conv2d(1, 32, 5, 2) self.conv2 = nn.Conv2d(32, 64, 7, 3) self.dropout1 = nn.Dropout2d(0.25) self.dropout2 = nn.Dropout2d(0.5) self.fc1 = nn.Linear(36864, 128) self.fc2 = nn.Linear(128, 10) def forward(self, x): x = self.conv1(x) x = F.relu(x) x = self.conv2(x) x = F.relu(x) x = F.max_pool2d(x, 2) x = self.dropout1(x) x = torch.flatten(x, 1) x = self.fc1(x) x = F.relu(x) x = self.dropout2(x) x = self.fc2(x) output = F.log_softmax(x, dim=1) return output

26.896552

44

0.528205

79513e961b4549f94402760f10315c38cbfc605b

17,069

py

Python

qiskit_experiments/library/characterization/fine_amplitude.py

yoshida-ryuhei/qiskit-experiments

82561acf86b407dcda0a9ec69fe18de2b0a592a2

[ "Apache-2.0" ]

null

qiskit_experiments/library/characterization/fine_amplitude.py

yoshida-ryuhei/qiskit-experiments

82561acf86b407dcda0a9ec69fe18de2b0a592a2

[ "Apache-2.0" ]

null

qiskit_experiments/library/characterization/fine_amplitude.py

yoshida-ryuhei/qiskit-experiments

82561acf86b407dcda0a9ec69fe18de2b0a592a2

[ "Apache-2.0" ]

null

# This code is part of Qiskit. # # (C) Copyright IBM 2021. # # This code is licensed under the Apache License, Version 2.0. You may # obtain a copy of this license in the LICENSE.txt file in the root directory # of this source tree or at http://www.apache.org/licenses/LICENSE-2.0. # # Any modifications or derivative works of this code must retain this # copyright notice, and modified files need to carry a notice indicating # that they have been altered from the originals. """Fine amplitude characterization experiment.""" from typing import List, Optional, Sequence import numpy as np from qiskit import QuantumCircuit from qiskit.circuit import Gate from qiskit.circuit.library import XGate, SXGate from qiskit.providers.backend import Backend from qiskit_experiments.data_processing import DataProcessor, nodes from qiskit_experiments.framework import BaseExperiment, Options from qiskit_experiments.framework.restless_mixin import RestlessMixin from qiskit_experiments.library.characterization.analysis import FineAmplitudeAnalysis class FineAmplitude(BaseExperiment, RestlessMixin): r"""Error amplifying fine amplitude calibration experiment. # section: overview The :class:`FineAmplitude` calibration experiment repeats N times a gate with a pulse to amplify the under-/over-rotations in the gate to determine the optimal amplitude. The circuits are therefore of the form: .. parsed-literal:: ┌──────┐ ┌──────┐ ░ ┌─┐ q_0: ┤ Gate ├─ ... ─┤ Gate ├─░─┤M├ └──────┘ └──────┘ ░ └╥┘ measure: 1/═════════ ... ═════════════╩═ 0 Here, Gate is the name of the gate which will be repeated. The user can optionally add a square-root of X pulse before the gates are repeated. This square-root of X pulse allows the analysis to differentiate between over rotations and under rotations in the case of pi-pulses. Importantly, the resulting data is analyzed by a fit to a cosine function in which we try to determine the over/under rotation given an intended rotation angle per gate which must also be specified by the user. Error amplifying experiments are most sensitive to angle errors when we measure points along the equator of the Bloch sphere. This is why users should insert a square-root of X pulse before running calibrations for :math:`\pm\pi` rotations. When all data points are close to the equator, it is difficult for a fitter to infer the overall scale of the error. When calibrating a :math:`pi` rotation, one can use ``add_xp_circuit = True`` to insert one circuit that puts the qubit in the excited state to set the scale for the other circuits. Furthermore, when running calibrations for :math:`\pm\pi/2` rotations users are advised to use an odd number of repetitions, e.g. [1, 2, 3, 5, 7, ...] to ensure that the ideal points are on the equator of the Bloch sphere. Note the presence of two repetitions which allows us to prepare the excited state. Therefore, ``add_xp_circuit = True`` is not needed in this case. # section: example The steps to run a fine amplitude experiment are .. code-block:: python qubit = 3 amp_cal = FineAmplitude(qubit, SXGate()) amp_cal.set_experiment_options( angle_per_gate=np.pi/2, add_xp_circuit=False, add_sx=False ) amp_cal.run(backend) Note that there are subclasses of :class:`FineAmplitude` such as :class:`FineSXAmplitude` that set the appropriate options by default. # section: analysis_ref :py:class:`FineAmplitudeAnalysis` # section: reference .. ref_arxiv:: 1 1504.06597 # section: tutorial :doc:`/tutorials/fine_calibrations` """ @classmethod def _default_experiment_options(cls) -> Options: r"""Default values for the fine amplitude experiment. Experiment Options: repetitions (List[int]): A list of the number of times that the gate is repeated. gate_type (Gate): This is a gate class such as XGate, so that one can obtain a gate by doing :code:`options.gate_class()`. normalization (bool): If set to True the DataProcessor will normalized the measured signal to the interval [0, 1]. Defaults to True. add_cal_circuits (bool): If set to True then two circuits to calibrate 0 and 1 points will be added. These circuits are often needed to properly calibrate the amplitude of the ping-pong oscillation that encodes the errors. This helps account for state preparation and measurement errors. """ options = super()._default_experiment_options() options.repetitions = list(range(1, 15)) options.gate = None options.normalization = True options.add_cal_circuits = True return options def __init__( self, qubits: Sequence[int], gate: Gate, backend: Optional[Backend] = None, measurement_qubits: Sequence[int] = None, ): """Setup a fine amplitude experiment on the given qubit. Args: qubits: The qubit(s) on which to run the fine amplitude calibration experiment. gate: The gate that will be repeated. backend: Optional, the backend to run the experiment on. measurement_qubits: The qubits in the given physical qubits that need to be measured. """ super().__init__(qubits, analysis=FineAmplitudeAnalysis(), backend=backend) self.set_experiment_options(gate=gate) if measurement_qubits is not None: self._measurement_qubits = [self.physical_qubits.index(q) for q in measurement_qubits] else: self._measurement_qubits = range(self.num_qubits) def _spam_cal_circuits(self, meas_circuit: QuantumCircuit) -> List[QuantumCircuit]: """This method returns the calibration circuits. Calibration circuits allow the experiment to overcome state preparation and measurement errors which cause ideal probabilities to be below 1. Args: meas_circuit: The measurement circuit, so that we only apply x gates to the measured qubits. Returns: Two circuits that calibrate the spam errors for the 0 and 1 state. """ cal_circuits = [] for add_x in [0, 1]: circ = QuantumCircuit(self.num_qubits, meas_circuit.num_clbits) if add_x: qubits = meas_circuit.get_instructions("measure")[0][1] circ.x(qubits) circ.compose(meas_circuit, inplace=True) circ.metadata = { "experiment_type": self._type, "qubits": self.physical_qubits, "xval": add_x, "unit": "gate number", "series": "spam-cal", } cal_circuits.append(circ) return cal_circuits def _pre_circuit(self, num_clbits: int) -> QuantumCircuit: """Return a preparation circuit. This method can be overridden by subclasses e.g. to calibrate gates on transitions other than the 0 <-> 1 transition. """ return QuantumCircuit(self.num_qubits, num_clbits) def _measure_circuit(self) -> QuantumCircuit: """Create the measurement part of the quantum circuit. Sub-classes may override this function. Returns: A quantum circuit which defines the qubits that will be measured. """ circuit = QuantumCircuit(self.num_qubits, len(self._measurement_qubits)) for idx, qubit in enumerate(self._measurement_qubits): circuit.measure(qubit, idx) return circuit def circuits(self) -> List[QuantumCircuit]: """Create the circuits for the fine amplitude calibration experiment. Returns: A list of circuits with a variable number of gates. Raises: CalibrationError: If the analysis options do not contain the angle_per_gate. """ repetitions = self.experiment_options.get("repetitions") qubits = range(self.num_qubits) meas_circ = self._measure_circuit() pre_circ = self._pre_circuit(meas_circ.num_clbits) if self.experiment_options.add_cal_circuits: circuits = self._spam_cal_circuits(meas_circ) else: circuits = [] for repetition in repetitions: circuit = QuantumCircuit(self.num_qubits, meas_circ.num_clbits) # Add pre-circuit circuit.compose(pre_circ, qubits, range(meas_circ.num_clbits), inplace=True) for _ in range(repetition): circuit.append(self.experiment_options.gate, qubits) # Add the measurement part of the circuit circuit.compose(meas_circ, qubits, range(meas_circ.num_clbits), inplace=True) circuit.metadata = { "experiment_type": self._type, "qubits": self.physical_qubits, "xval": repetition, "unit": "gate number", "series": 1, } circuits.append(circuit) return circuits def _metadata(self): metadata = super()._metadata() # Store measurement level and meas return if they have been # set for the experiment for run_opt in ["meas_level", "meas_return"]: if hasattr(self.run_options, run_opt): metadata[run_opt] = getattr(self.run_options, run_opt) return metadata class FineXAmplitude(FineAmplitude): r"""A fine amplitude experiment with all the options set for the :math:`\pi`-rotation. # section: overview :class:`FineXAmplitude` is a subclass of :class:`FineAmplitude` and is used to set the appropriate values for the default options. """ def __init__(self, qubit: int, backend: Optional[Backend] = None): """Initialize the experiment.""" super().__init__([qubit], XGate(), backend=backend) # Set default analysis options self.analysis.set_options( fixed_parameters={ "angle_per_gate": np.pi, "phase_offset": np.pi / 2, } ) @classmethod def _default_experiment_options(cls) -> Options: r"""Default values for the fine amplitude experiment. Experiment Options: gate (Gate): Gate to characterize. Defaults to an XGate. """ options = super()._default_experiment_options() options.gate = XGate() return options def _pre_circuit(self, num_clbits: int) -> QuantumCircuit: """The preparation circuit is an sx gate to move to the equator of the Bloch sphere.""" circuit = QuantumCircuit(self.num_qubits, num_clbits) circuit.sx(0) return circuit class FineSXAmplitude(FineAmplitude): r"""A fine amplitude experiment with all the options set for the :math:`\pi/2`-rotation. # section: overview :class:`FineSXAmplitude` is a subclass of :class:`FineAmplitude` and is used to set the appropriate values for the default options. """ def __init__(self, qubit: int, backend: Optional[Backend] = None): """Initialize the experiment.""" super().__init__([qubit], SXGate(), backend=backend) # Set default analysis options self.analysis.set_options( fixed_parameters={ "angle_per_gate": np.pi / 2, "phase_offset": np.pi, } ) @classmethod def _default_experiment_options(cls) -> Options: r"""Default values for the fine amplitude experiment. Experiment Options: gate (Gate): FineSXAmplitude calibrates an SXGate. add_cal_circuits (bool): If set to True then two circuits to calibrate 0 and 1 points will be added. This option is set to False by default for ``FineSXAmplitude`` since the amplitude calibration can be achieved with two SX gates and this is included in the repetitions. repetitions (List[int]): By default the repetitions take on odd numbers for :math:`\pi/2` target angles as this ideally prepares states on the equator of the Bloch sphere. Note that the repetitions include two repetitions which plays the same role as including a circuit with an X gate. """ options = super()._default_experiment_options() options.gate = SXGate() options.add_cal_circuits = False options.repetitions = [0, 1, 2, 3, 5, 7, 9, 11, 13, 15, 17, 21, 23, 25] return options class FineZXAmplitude(FineAmplitude): r"""A fine amplitude experiment for the :code:`RZXGate(np.pi / 2)`. # section: overview :class:`FineZXAmplitude` is a subclass of :class:`FineAmplitude` and is used to set the appropriate values for the default options to calibrate a :code:`RZXGate(np.pi / 2)`. # section: example To run this experiment the user will have to provide the instruction schedule map in the transpile options that contains the schedule for the experiment. ..code-block:: python qubits = (1, 2) inst_map = InstructionScheduleMap() inst_map.add("szx", qubits, my_schedule) fine_amp = FineZXAmplitude(qubits, backend) fine_amp.set_transpile_options(inst_map=inst_map) Here, :code:`my_schedule` is the pulse schedule that will implement the :code:`RZXGate(np.pi / 2)` rotation. """ def __init__(self, qubits: Sequence[int], backend: Optional[Backend] = None): """Initialize the experiment.""" # We cannot use RZXGate since it has a parameter so we redefine the gate. # Failing to do so causes issues with QuantumCircuit.calibrations. gate = Gate("szx", 2, []) super().__init__(qubits, gate, backend=backend, measurement_qubits=[qubits[1]]) # Set default analysis options self.analysis.set_options( fixed_parameters={ "angle_per_gate": np.pi / 2, "phase_offset": np.pi, }, outcome="1", ) @classmethod def _default_experiment_options(cls) -> Options: r"""Default values for the fine amplitude experiment. Experiment Options: add_cal_circuits (bool): If set to True then two circuits to calibrate 0 and 1 points will be added. This option is set to False by default for ``FineZXAmplitude`` since the amplitude calibration can be achieved with two RZX gates and this is included in the repetitions. repetitions (List[int]): A list of the number of times that the gate is repeated. """ options = super()._default_experiment_options() options.add_cal_circuits = False options.repetitions = [0, 1, 2, 3, 4, 5, 7, 9, 11, 13] return options @classmethod def _default_transpile_options(cls) -> Options: """Default transpile options for the fine amplitude experiment. Experiment Options: basis_gates: Set to :code:`["szx"]`. inst_map: The instruction schedule map that will contain the schedule for the Rzx(pi/2) gate. This schedule should be stored under the instruction name ``szx``. """ options = super()._default_transpile_options() options.basis_gates = ["szx"] options.inst_map = None return options def enable_restless( self, rep_delay: Optional[float] = None, override_processor_by_restless: bool = True ): """Enable restless measurements. We wrap the method of the :class:`RestlessMixin` to readout both qubits. This forces the control qubit to be in either the 0 or 1 state before the next circuit starts since restless measurements do not reset qubits. """ self.analysis.set_options(outcome="11") super().enable_restless(rep_delay, override_processor_by_restless) self._measurement_qubits = range(self.num_qubits) def _get_restless_processor(self, meas_level: int = 2) -> DataProcessor: """Marginalize the counts after the restless shot reordering.""" return DataProcessor( "memory", [ nodes.RestlessToCounts(self._num_qubits), nodes.MarginalizeCounts({1}), # keep only the target. nodes.Probability("1"), ], )

39.603248

100

0.635948

79513eeb499b2e5f0077364077eda705ac684812

993

py

Python

moex/migrations/0035_auto_20201129_2111.py

ghostforpy/bonds-docker

fda77225b85264cb4ba06b15ff63bc807858425a

[ "MIT" ]

2

2020-09-08T12:51:56.000Z

2021-08-18T15:27:52.000Z

moex/migrations/0035_auto_20201129_2111.py

ghostforpy/bonds-docker

fda77225b85264cb4ba06b15ff63bc807858425a

[ "MIT" ]

1

2021-12-13T20:43:35.000Z

moex/migrations/0035_auto_20201129_2111.py

ghostforpy/bonds-docker

fda77225b85264cb4ba06b15ff63bc807858425a

[ "MIT" ]

null

# Generated by Django 3.0.7 on 2020-11-29 18:11 from django.db import migrations from decimal import Decimal from ..models import SecurityPortfolios,Security def refresh_total_cost_in_rub(apps, schema_editor): s_p = SecurityPortfolios.objects.\ select_related('security').\ select_related('portfolio').\ all() for i in s_p: if i.security.main_board_faceunit != 'SUR': valute = Security.objects.filter( shortname=i.security.main_board_faceunit ).get() i.total_cost_in_rub = Decimal( i.total_cost) * Decimal(valute.today_price) else: i.total_cost_in_rub = i.total_cost i.save(update_fields=['total_cost_in_rub']) i.portfolio.refresh_portfolio() class Migration(migrations.Migration): dependencies = [ ('moex', '0034_auto_20201129_1819'), ] operations = [ migrations.RunPython(refresh_total_cost_in_rub), ]

30.090909

59

0.643505

79513f12efe5e747361181458ea2794fcc5ae115

8,797

py

Python

ucscsdk/mometa/adaptor/AdaptorMenloHostPortStatsHist.py

parag-may4/ucscsdk

2ea762fa070330e3a4e2c21b46b157469555405b

[ "Apache-2.0" ]

9

2016-12-22T08:39:25.000Z

2019-09-10T15:36:19.000Z

ucscsdk/mometa/adaptor/AdaptorMenloHostPortStatsHist.py

parag-may4/ucscsdk

2ea762fa070330e3a4e2c21b46b157469555405b

[ "Apache-2.0" ]

10

2017-01-31T06:59:56.000Z

2021-11-09T09:14:37.000Z

ucscsdk/mometa/adaptor/AdaptorMenloHostPortStatsHist.py

parag-may4/ucscsdk

2ea762fa070330e3a4e2c21b46b157469555405b

[ "Apache-2.0" ]

13

2016-11-14T07:42:58.000Z

2022-02-10T17:32:05.000Z

"""This module contains the general information for AdaptorMenloHostPortStatsHist ManagedObject.""" from ...ucscmo import ManagedObject from ...ucsccoremeta import UcscVersion, MoPropertyMeta, MoMeta from ...ucscmeta import VersionMeta class AdaptorMenloHostPortStatsHistConsts(): MOST_RECENT_FALSE = "false" MOST_RECENT_NO = "no" MOST_RECENT_TRUE = "true" MOST_RECENT_YES = "yes" SUSPECT_FALSE = "false" SUSPECT_NO = "no" SUSPECT_TRUE = "true" SUSPECT_YES = "yes" class AdaptorMenloHostPortStatsHist(ManagedObject): """This is AdaptorMenloHostPortStatsHist class.""" consts = AdaptorMenloHostPortStatsHistConsts() naming_props = set([u'id']) mo_meta = MoMeta("AdaptorMenloHostPortStatsHist", "adaptorMenloHostPortStatsHist", "[id]", VersionMeta.Version111a, "OutputOnly", 0xf, [], ["read-only"], [u'adaptorMenloHostPortStats'], [], [None]) prop_meta = { "child_action": MoPropertyMeta("child_action", "childAction", "string", VersionMeta.Version111a, MoPropertyMeta.INTERNAL, None, None, None, r"""((deleteAll|ignore|deleteNonPresent),){0,2}(deleteAll|ignore|deleteNonPresent){0,1}""", [], []), "dn": MoPropertyMeta("dn", "dn", "string", VersionMeta.Version111a, MoPropertyMeta.READ_ONLY, 0x2, 0, 256, None, [], []), "id": MoPropertyMeta("id", "id", "ulong", VersionMeta.Version111a, MoPropertyMeta.NAMING, None, None, None, None, [], []), "most_recent": MoPropertyMeta("most_recent", "mostRecent", "string", VersionMeta.Version111a, MoPropertyMeta.READ_ONLY, None, None, None, None, ["false", "no", "true", "yes"], []), "rn": MoPropertyMeta("rn", "rn", "string", VersionMeta.Version111a, MoPropertyMeta.READ_ONLY, 0x4, 0, 256, None, [], []), "rx_pause_cfc": MoPropertyMeta("rx_pause_cfc", "rxPauseCFC", "ulong", VersionMeta.Version111a, MoPropertyMeta.READ_ONLY, None, None, None, None, [], []), "rx_pause_cfc_delta": MoPropertyMeta("rx_pause_cfc_delta", "rxPauseCFCDelta", "ulong", VersionMeta.Version111a, MoPropertyMeta.READ_ONLY, None, None, None, None, [], []), "rx_pause_cfc_delta_avg": MoPropertyMeta("rx_pause_cfc_delta_avg", "rxPauseCFCDeltaAvg", "ulong", VersionMeta.Version111a, MoPropertyMeta.READ_ONLY, None, None, None, None, [], []), "rx_pause_cfc_delta_max": MoPropertyMeta("rx_pause_cfc_delta_max", "rxPauseCFCDeltaMax", "ulong", VersionMeta.Version111a, MoPropertyMeta.READ_ONLY, None, None, None, None, [], []), "rx_pause_cfc_delta_min": MoPropertyMeta("rx_pause_cfc_delta_min", "rxPauseCFCDeltaMin", "ulong", VersionMeta.Version111a, MoPropertyMeta.READ_ONLY, None, None, None, None, [], []), "rx_pause_pfc": MoPropertyMeta("rx_pause_pfc", "rxPausePFC", "ulong", VersionMeta.Version111a, MoPropertyMeta.READ_ONLY, None, None, None, None, [], []), "rx_pause_pfc_delta": MoPropertyMeta("rx_pause_pfc_delta", "rxPausePFCDelta", "ulong", VersionMeta.Version111a, MoPropertyMeta.READ_ONLY, None, None, None, None, [], []), "rx_pause_pfc_delta_avg": MoPropertyMeta("rx_pause_pfc_delta_avg", "rxPausePFCDeltaAvg", "ulong", VersionMeta.Version111a, MoPropertyMeta.READ_ONLY, None, None, None, None, [], []), "rx_pause_pfc_delta_max": MoPropertyMeta("rx_pause_pfc_delta_max", "rxPausePFCDeltaMax", "ulong", VersionMeta.Version111a, MoPropertyMeta.READ_ONLY, None, None, None, None, [], []), "rx_pause_pfc_delta_min": MoPropertyMeta("rx_pause_pfc_delta_min", "rxPausePFCDeltaMin", "ulong", VersionMeta.Version111a, MoPropertyMeta.READ_ONLY, None, None, None, None, [], []), "status": MoPropertyMeta("status", "status", "string", VersionMeta.Version111a, MoPropertyMeta.READ_WRITE, 0x8, None, None, r"""((removed|created|modified|deleted),){0,3}(removed|created|modified|deleted){0,1}""", [], []), "suspect": MoPropertyMeta("suspect", "suspect", "string", VersionMeta.Version111a, MoPropertyMeta.READ_ONLY, None, None, None, None, ["false", "no", "true", "yes"], []), "thresholded": MoPropertyMeta("thresholded", "thresholded", "string", VersionMeta.Version111a, MoPropertyMeta.READ_ONLY, None, None, None, None, [], []), "time_collected": MoPropertyMeta("time_collected", "timeCollected", "string", VersionMeta.Version111a, MoPropertyMeta.READ_ONLY, None, None, None, r"""([0-9]){4}-([0-9]){2}-([0-9]){2}T([0-9]){2}:([0-9]){2}:([0-9]){2}((\.([0-9]){3})){0,1}""", [], []), "tx_pause_cfc": MoPropertyMeta("tx_pause_cfc", "txPauseCFC", "ulong", VersionMeta.Version111a, MoPropertyMeta.READ_ONLY, None, None, None, None, [], []), "tx_pause_cfc_delta": MoPropertyMeta("tx_pause_cfc_delta", "txPauseCFCDelta", "ulong", VersionMeta.Version111a, MoPropertyMeta.READ_ONLY, None, None, None, None, [], []), "tx_pause_cfc_delta_avg": MoPropertyMeta("tx_pause_cfc_delta_avg", "txPauseCFCDeltaAvg", "ulong", VersionMeta.Version111a, MoPropertyMeta.READ_ONLY, None, None, None, None, [], []), "tx_pause_cfc_delta_max": MoPropertyMeta("tx_pause_cfc_delta_max", "txPauseCFCDeltaMax", "ulong", VersionMeta.Version111a, MoPropertyMeta.READ_ONLY, None, None, None, None, [], []), "tx_pause_cfc_delta_min": MoPropertyMeta("tx_pause_cfc_delta_min", "txPauseCFCDeltaMin", "ulong", VersionMeta.Version111a, MoPropertyMeta.READ_ONLY, None, None, None, None, [], []), "tx_pause_pfc": MoPropertyMeta("tx_pause_pfc", "txPausePFC", "ulong", VersionMeta.Version111a, MoPropertyMeta.READ_ONLY, None, None, None, None, [], []), "tx_pause_pfc_delta": MoPropertyMeta("tx_pause_pfc_delta", "txPausePFCDelta", "ulong", VersionMeta.Version111a, MoPropertyMeta.READ_ONLY, None, None, None, None, [], []), "tx_pause_pfc_delta_avg": MoPropertyMeta("tx_pause_pfc_delta_avg", "txPausePFCDeltaAvg", "ulong", VersionMeta.Version111a, MoPropertyMeta.READ_ONLY, None, None, None, None, [], []), "tx_pause_pfc_delta_max": MoPropertyMeta("tx_pause_pfc_delta_max", "txPausePFCDeltaMax", "ulong", VersionMeta.Version111a, MoPropertyMeta.READ_ONLY, None, None, None, None, [], []), "tx_pause_pfc_delta_min": MoPropertyMeta("tx_pause_pfc_delta_min", "txPausePFCDeltaMin", "ulong", VersionMeta.Version111a, MoPropertyMeta.READ_ONLY, None, None, None, None, [], []), } prop_map = { "childAction": "child_action", "dn": "dn", "id": "id", "mostRecent": "most_recent", "rn": "rn", "rxPauseCFC": "rx_pause_cfc", "rxPauseCFCDelta": "rx_pause_cfc_delta", "rxPauseCFCDeltaAvg": "rx_pause_cfc_delta_avg", "rxPauseCFCDeltaMax": "rx_pause_cfc_delta_max", "rxPauseCFCDeltaMin": "rx_pause_cfc_delta_min", "rxPausePFC": "rx_pause_pfc", "rxPausePFCDelta": "rx_pause_pfc_delta", "rxPausePFCDeltaAvg": "rx_pause_pfc_delta_avg", "rxPausePFCDeltaMax": "rx_pause_pfc_delta_max", "rxPausePFCDeltaMin": "rx_pause_pfc_delta_min", "status": "status", "suspect": "suspect", "thresholded": "thresholded", "timeCollected": "time_collected", "txPauseCFC": "tx_pause_cfc", "txPauseCFCDelta": "tx_pause_cfc_delta", "txPauseCFCDeltaAvg": "tx_pause_cfc_delta_avg", "txPauseCFCDeltaMax": "tx_pause_cfc_delta_max", "txPauseCFCDeltaMin": "tx_pause_cfc_delta_min", "txPausePFC": "tx_pause_pfc", "txPausePFCDelta": "tx_pause_pfc_delta", "txPausePFCDeltaAvg": "tx_pause_pfc_delta_avg", "txPausePFCDeltaMax": "tx_pause_pfc_delta_max", "txPausePFCDeltaMin": "tx_pause_pfc_delta_min", } def __init__(self, parent_mo_or_dn, id, **kwargs): self._dirty_mask = 0 self.id = id self.child_action = None self.most_recent = None self.rx_pause_cfc = None self.rx_pause_cfc_delta = None self.rx_pause_cfc_delta_avg = None self.rx_pause_cfc_delta_max = None self.rx_pause_cfc_delta_min = None self.rx_pause_pfc = None self.rx_pause_pfc_delta = None self.rx_pause_pfc_delta_avg = None self.rx_pause_pfc_delta_max = None self.rx_pause_pfc_delta_min = None self.status = None self.suspect = None self.thresholded = None self.time_collected = None self.tx_pause_cfc = None self.tx_pause_cfc_delta = None self.tx_pause_cfc_delta_avg = None self.tx_pause_cfc_delta_max = None self.tx_pause_cfc_delta_min = None self.tx_pause_pfc = None self.tx_pause_pfc_delta = None self.tx_pause_pfc_delta_avg = None self.tx_pause_pfc_delta_max = None self.tx_pause_pfc_delta_min = None ManagedObject.__init__(self, "AdaptorMenloHostPortStatsHist", parent_mo_or_dn, **kwargs)

71.520325

259

0.689781

79513f1eacf0b08d6f373e50d327514b98fdf7e2

2,297

py

Python

src/install_venv_app.py

muravjov/fablib

e64ba200651fa92555af6bc2a2f74606ac6495fe

[ "MIT" ]

null

src/install_venv_app.py

muravjov/fablib

e64ba200651fa92555af6bc2a2f74606ac6495fe

[ "MIT" ]

null

src/install_venv_app.py

muravjov/fablib

e64ba200651fa92555af6bc2a2f74606ac6495fe

[ "MIT" ]

null

#!/usr/bin/env python # coding: utf-8 # # install_venv_app - утилита прописывания пути к приложению посредством механизма .pth/site.py # # Причина: для конечных web-приложений на Python не требуется полноценная упаковка в пакеты для virtualenv, # так как они только потребители кода других библиотек; однако прописаться в .pth venv-а со временем становится # необходимой потребностью # # Для этого данный скрипт на лету создает в venv-е минимальный пакет, единственно важное действие которого - добавить # в easy_install.pth указанную вторым аргументом папку # Расчет папки в venv, где будут находиться .pth: # # distutils.dist.py: Distribution.get_command_obj("install") => Command.ensure_finalized(self) => # => distutils/command/install.py : install.finalize_options # # См. шаблоны в INSTALL_SCHEMES, например: # 'unix_prefix': { # 'purelib': '$base/lib/python$py_version_short/site-packages', # 'platlib': '$platbase/lib/python$py_version_short/site-packages', # 'headers': '$base/include/python$py_version_short/$dist_name', # 'scripts': '$base/bin', # 'data' : '$base', # }, # # Собственно обновление .pth проходит в setuptools/command/easy_install.py: update_pth() import os def install(pkg_name, src_path): # :TRICKY: приходится менять путь old_dir = os.getcwd() os.chdir(src_path) try: from setuptools import setup setup( name = pkg_name, version = 1, # выбор директории, которая будет прописана в .pth - см. код egg_info.finalize_options(): # self.egg_base = (self.distribution.package_dir or {}).get('',os.curdir) #package_dir = {'': 'src'}, # с этими же аргументами запускает setup.py и venv/pip install -e src_path, # см. install_editable() script_args = ["develop", "--no-deps"], script_name = '', # :KLUDGE: без setup.py (но warning остается) ) finally: os.chdir(old_dir) def main(): import argparse parser = argparse.ArgumentParser() parser.add_argument("pkg_name") parser.add_argument("src_path") args = parser.parse_args() install(args.pkg_name, args.src_path) if __name__ == "__main__": main()

34.283582

117

0.659991

79513fac9ff47c3aebaaa2425e05b686ee952104

1,225

py

Python

manualtest/kuafu_mem.py

haosulab/SAPIEN

6bc3f4e2be910199b793f185aea5791d9f193e4c

[ "MIT" ]

21

2021-10-13T11:56:45.000Z

2022-03-30T16:09:21.000Z

manualtest/kuafu_mem.py

haosulab/SAPIEN

6bc3f4e2be910199b793f185aea5791d9f193e4c

[ "MIT" ]

25

2021-10-20T20:14:37.000Z

2022-03-30T05:55:15.000Z

manualtest/kuafu_mem.py

haosulab/SAPIEN

6bc3f4e2be910199b793f185aea5791d9f193e4c

[ "MIT" ]

5

2021-10-31T17:43:52.000Z

2022-03-01T09:45:53.000Z

# A minimal example of using KuafuRenderer # # By Jet <i@jetd.me> # import sapien.core as sapien import numpy as np def main(): sim = sapien.Engine() sapien.KuafuRenderer.set_log_level("debug") config = sapien.KuafuConfig() config.use_viewer = True config.spp = 1 renderer = sapien.KuafuRenderer(config) sim.set_renderer(renderer) config = sapien.SceneConfig() scene = sim.create_scene(config) scene.add_ground(0) scene.set_timestep(1 / 60) mount = scene.create_actor_builder().build_static() cam1 = scene.add_mounted_camera( "cam", mount, sapien.Pose(), 800, 600, 0, 1.0, 0.1, 100) mount.set_pose(sapien.Pose([-12, 0, 3])) for i in range(128): builder = scene.create_actor_builder() builder.add_capsule_visual() builder.add_capsule_collision() sphere = builder.build() sphere.set_pose(sapien.Pose(p=[np.random.rand(), np.random.rand(), i * 10])) scene.set_ambient_light([0.4, 0.4, 0.4]) dirlight = scene.add_directional_light( [-1, -1, -1], color=[3.0, 3.0, 3.0] ) while renderer.is_running: scene.step() scene.update_render() cam1.take_picture() main()

23.557692

84

0.638367

79513fe0e38eb905f0efffedb478ff4f3a314d11

6,209

py

Python

vit_pytorch/vit.py

rocke2020/vit-pytorch

a1f828da0c952fa56a90a71f7c88c8e0025c1d42

[ "MIT" ]

null

vit_pytorch/vit.py

rocke2020/vit-pytorch

a1f828da0c952fa56a90a71f7c88c8e0025c1d42

[ "MIT" ]

null

vit_pytorch/vit.py

rocke2020/vit-pytorch

a1f828da0c952fa56a90a71f7c88c8e0025c1d42

[ "MIT" ]

null

import torch from torch import nn, einsum import torch.nn.functional as F from einops import rearrange, repeat from einops.layers.torch import Rearrange # helpers def pair(t): return t if isinstance(t, tuple) else (t, t) # classes class PreNorm(nn.Module): def __init__(self, dim, fn): super().__init__() self.norm = nn.LayerNorm(dim) self.fn = fn def forward(self, x, **kwargs): return self.fn(self.norm(x), **kwargs) class FeedForward(nn.Module): def __init__(self, dim, hidden_dim, dropout = 0.): super().__init__() self.net = nn.Sequential( nn.Linear(dim, hidden_dim), nn.GELU(), nn.Dropout(dropout), nn.Linear(hidden_dim, dim), nn.Dropout(dropout) ) def forward(self, x): return self.net(x) class Attention(nn.Module): def __init__(self, dim, heads = 8, dim_head = 64, dropout = 0.): super().__init__() inner_dim = dim_head * heads project_out = not (heads == 1 and dim_head == dim) self.heads = heads self.scale = dim_head ** -0.5 self.attend = nn.Softmax(dim = -1) self.to_qkv = nn.Linear(dim, inner_dim * 3, bias = False) self.to_out = nn.Sequential( nn.Linear(inner_dim, dim), nn.Dropout(dropout) ) if project_out else nn.Identity() def forward(self, x): qkv = self.to_qkv(x).chunk(3, dim = -1) q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = self.heads), qkv) dots = einsum('b h i d, b h j d -> b h i j', q, k) * self.scale attn = self.attend(dots) out = einsum('b h i j, b h j d -> b h i d', attn, v) out = rearrange(out, 'b h n d -> b n (h d)') return self.to_out(out) class Transformer(nn.Module): def __init__(self, dim, depth, heads, dim_head, mlp_dim, dropout = 0.): super().__init__() self.layers = nn.ModuleList([]) for _ in range(depth): self.layers.append(nn.ModuleList([ PreNorm(dim, Attention(dim, heads = heads, dim_head = dim_head, dropout = dropout)), PreNorm(dim, FeedForward(dim, mlp_dim, dropout = dropout)) ])) def forward(self, x): for attn, ff in self.layers: x = attn(x) + x x = ff(x) + x return x class ViT(nn.Module): def __init__(self, *, image_size, patch_size, num_classes, dim, depth, heads, mlp_dim, pool = 'cls', channels = 3, dim_head = 64, dropout = 0., emb_dropout = 0.): super().__init__() image_height, image_width = pair(image_size) patch_height, patch_width = pair(patch_size) assert image_height % patch_height == 0 and image_width % patch_width == 0, 'Image dimensions must be divisible by the patch size.' num_patches = (image_height // patch_height) * (image_width // patch_width) patch_dim = channels * patch_height * patch_width assert pool in {'cls', 'mean'}, 'pool type must be either cls (cls token) or mean (mean pooling)' self.to_patch_embedding = nn.Sequential( Rearrange('b c (h p1) (w p2) -> b (h w) (p1 p2 c)', p1 = patch_height, p2 = patch_width), nn.Linear(patch_dim, dim), ) self.pos_embedding = nn.Parameter(torch.randn(1, num_patches + 1, dim)) self.cls_token = nn.Parameter(torch.randn(1, 1, dim)) self.dropout = nn.Dropout(emb_dropout) self.transformer = Transformer(dim, depth, heads, dim_head, mlp_dim, dropout) self.pool = pool self.to_latent = nn.Identity() self.mlp_head = nn.Sequential( nn.LayerNorm(dim), nn.Linear(dim, num_classes) ) def forward(self, img): x = self.to_patch_embedding(img) b, n, _ = x.shape cls_tokens = repeat(self.cls_token, '() n d -> b n d', b = b) x = torch.cat((cls_tokens, x), dim=1) # TODO maybe no need :(n+1), just self.pos_embedding is OK. x += self.pos_embedding[:, :(n + 1)] x = self.dropout(x) # x.shape, b, n+1, d x = self.transformer(x) x = x.mean(dim = 1) if self.pool == 'mean' else x[:, 0] x = self.to_latent(x) return self.mlp_head(x) class AttentionWithMask(nn.Module): def __init__(self, dim, heads = 8, dim_head = 64, dropout = 0.): super().__init__() inner_dim = dim_head * heads # 64 x 8 self.heads = heads # 8 self.scale = dim_head ** -0.5 self.to_qkv = nn.Linear(dim, inner_dim * 3, bias = False) self.to_out = nn.Sequential( nn.Linear(inner_dim, dim), nn.Dropout(dropout) ) def forward(self, x, mask = None): # n is the patch_num + 1, patch_num = (img_size/patch_size)**2. # just assume img_size 224, patch_size 32, 224/32=7 it is 7*7+1=50 here. # yolo-v1 also use patch num 7*7. b, n, _, h = *x.shape, self.heads # n=50,h=8, # self.to_qkv(x)得到的尺寸为[b,50,64x8x3],然后chunk成3份 # 也就是说，qkv是一个三元tuple,每一份都是[b,50,64x8]的大小 qkv = self.to_qkv(x).chunk(3, dim = -1) # 把每一份从[b,50,64x8]变成[b,8,50,64]的形式 q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = h), qkv) # 这一步不太好理解，q和k都是[b,8,50,64]的形式，50理解为特征数量，64为特征变量 # dots.shape=[b,8,50,50] dots = torch.einsum('bhid,bhjd->bhij', q, k) * self.scale # 不考虑mask这一块的内容 mask_value = -torch.finfo(dots.dtype).max if mask is not None: mask = F.pad(mask.flatten(1), (1, 0), value = True) assert mask.shape[-1] == dots.shape[-1], 'mask has incorrect dimensions' mask = mask[:, None, :] * mask[:, :, None] dots.masked_fill_(~mask, mask_value) del mask # 对[b,8,50,50]的最后一个维度做softmax attn = dots.softmax(dim=-1) # 这个attn就是计算出来的自注意力值，和v做点乘，out.shape=[b,8,50,64] out = torch.einsum('bhij,bhjd->bhid', attn, v) # out.shape变成[b,50,8x64] out = rearrange(out, 'b h n d -> b n (h d)') # out.shape重新变成[b,60,128] out = self.to_out(out) return out

35.278409

139

0.568691

79514130475e9f190c083c6272011b4579940bfa

5,284

py

Python

examples/pyopencap/pyscf/cap_trajectory.py

trex47/opencap

fd641133b2abaab22c13912d97fe1fe64b132dd7

[ "MIT" ]

null

examples/pyopencap/pyscf/cap_trajectory.py

trex47/opencap

fd641133b2abaab22c13912d97fe1fe64b132dd7

[ "MIT" ]

null

examples/pyopencap/pyscf/cap_trajectory.py

trex47/opencap

fd641133b2abaab22c13912d97fe1fe64b132dd7

[ "MIT" ]

null

import os import numpy as np import functools from numpy import linalg as LA import matplotlib.pyplot as plt import argparse ######################################### # Alter these values to suit your purposes ref_energy = -1.1738730219803575 guess = 13.1 eta_list = np.linspace(0,10000,1001) ########################### au2eV= 27.2113961 eta_list = eta_list * 1E-5 # a root is a single eigenvalue of the cap hamiltonian at a particular value of eta @functools.total_ordering class root(): def __init__(self, energy, eta): self.eta = eta self.energy = energy def __lt__(self, other): return self.eta < other.eta def __eq__(self, other): return self.eta == other.eta and self.eta == other.eta # a trajectory is a group of eigenvalues over a range of eta values, grouped by proximity to an initial guess class trajectory(): def __init__(self,states,guess): min=500 cur=-1 for st in states: if np.absolute(st.energy-guess)<min: cur=st min=np.absolute(st.energy-guess) self.last=cur self.states=[cur] # picks out the state from the list of states whose energy is closest to the previous entry in the trajectory def add_state(self,states): min=500 cur=-1 for st in states: if np.absolute(st.energy-self.last.energy)<min: cur=st min=np.absolute(st.energy-self.last.energy) self.last = cur self.states.append(cur) # applies first order correciton def get_corrections(self): energies=[] etas=[] for st in self.states: energies.append(st.energy) etas.append(st.eta) derivs=list(np.gradient(energies)/np.gradient(etas)) for i in range(0,len(self.states)): self.states[i].corr_energy=self.states[i].energy-derivs[i]*self.states[i].eta # read in data from opencap output file parser = argparse.ArgumentParser() parser.add_argument('pos_arg', type=str, help='Name of OpenCAP output file') args = parser.parse_args() with open(args.pos_arg, 'r') as file : filedata = file.readlines() idx=-1 for i in range(0,len(filedata)): line = filedata[i] if "Printing out matrices required for Perturbative CAP calculation." in line: idx=i num_roots=int(filedata[idx+1].split()[-1]) start=idx+3 # zeroth order hamiltonian first H_0=[] for i in range(start,start+num_roots): l1 = filedata[i].split() l1= [float(x) for x in l1] H_0+=l1 H_0 = np.reshape(H_0,(num_roots,num_roots)) start2=start+num_roots+1 # now the cap matrix cap_mat=[] for i in range(start2,start2+num_roots): l1 = filedata[i].split() l1= [float(x) for x in l1] cap_mat+=l1 cap_mat= np.reshape(cap_mat,(num_roots,num_roots)) all_roots=[] # diagonalize over range of eta values and generate trajectories for i in range(0,len(eta_list)): eta=eta_list[i] roots=[] fullH = H_0 +1.0j * eta * cap_mat eigv,eigvc=LA.eig(fullH) for eig in eigv: E = (eig - ref_energy) * au2eV roots.append(root(E,eta)) all_roots.append(root(E,eta)) if i==0: traj=trajectory(roots,guess) else: traj.add_state(roots) # first lets plot everything re_traj = [] im_traj = [] energies=[] for root in all_roots: re_traj.append(np.real(root.energy)) im_traj.append(np.imag(root.energy)) energies.append(root.energy) plt.title("Eigenvalue trajectories") plt.xlabel("Re(E)[eV]") plt.ylabel("Im(E)[eV]") plt.plot(re_traj,im_traj,'ro') plt.show() # lets get the corrected trajectory traj.get_corrections() re_traj = [] im_traj = [] corr_re=[] corr_im=[] uc_energies=[] corr_energies=[] for root in traj.states: uc_energies.append(root.energy) re_traj.append(np.real(root.energy)) im_traj.append(np.imag(root.energy)) corr_re.append(np.real(root.corr_energy)) corr_im.append(np.imag(root.corr_energy)) corr_energies.append(root.corr_energy) # plot uncorrected and corrected trajectory plt.title("Resonance trajectory") plt.plot(re_traj,im_traj,'-ro',label="Uncorrected trajectory") plt.plot(corr_re,corr_im,'-bo',label="Corrected trajectory") plt.xlabel("Re(E)[eV]") plt.ylabel("Im(E)[eV]") plt.legend() plt.show() # plot derivative, find stationary point on uncorrected trajectory derivs=list(np.absolute(np.gradient(uc_energies)/np.gradient(eta_list))) plt.plot(eta_list,derivs) plt.title("Uncorrected derivative") plt.show() sorted_derivs = sorted(derivs) points = [] etas = [] for i in range(0,5): points.append(uc_energies[derivs.index(sorted_derivs[i])]) etas.append(eta_list[derivs.index(sorted_derivs[i])]) print("Uncorrected:") print(points) print(sorted_derivs[:5]) print(etas) # plot derivative, find stationary point on corrected trajectory derivs=list(np.absolute(np.gradient(corr_energies)/np.gradient(eta_list))) plt.plot(eta_list,derivs) plt.title("Corrected derivative") plt.show() sorted_derivs = sorted(derivs) points = [] etas = [] for i in range(0,5): points.append(corr_energies[derivs.index(sorted_derivs[i])]) etas.append(eta_list[derivs.index(sorted_derivs[i])]) print("Corrected:") print(points) print(sorted_derivs[:5]) print(etas)

28.562162

113

0.672218

79514177db332a6e0db5a798c31983cac75fa3eb

1,562

py

Python

test.py

mudathirlawal/python-playground

db405904fc83f5ad0caedccbf08c2fe7f62abf0b

[ "MIT" ]

null

test.py

mudathirlawal/python-playground

db405904fc83f5ad0caedccbf08c2fe7f62abf0b

[ "MIT" ]

null

test.py

mudathirlawal/python-playground

db405904fc83f5ad0caedccbf08c2fe7f62abf0b

[ "MIT" ]

null

def pig_latin(text): say = " " # Separate the text into words words = text.split() new_words_list = [] for word in words: # Create the pig latin word and add it to the list new_words_list.append('{}{}{}'.format(word[1:], word[0], 'ay')) # Turn the list back into a phrase new_text = say.join(new_words_list) return new_text print('\n') print(pig_latin("hello how are you")) # Should be "ellohay owhay reaay ouyay" print(pig_latin("programming in python is fun")) # Should be "rogrammingpay niay ythonpay siay unfay" def group_list(group, users): members = "" for user in users: members += (' {},'.format(user)) return '{}: {}'.format(group, members.__(len(users) - 1)) print(group_list("Marketing", ["Mike", "Karen", "Jake", "Tasha"])) # Should be "Marketing: Mike, Karen, Jake, Tasha" print(group_list("Engineering", ["Kim", "Jay", "Tom"])) # Should be "Engineering: Kim, Jay, Tom" print(group_list("Users", "")) # Should be "Users:" print('Adding test string ...') # UNDER CONSTRUCTION: # def pig_latin(text): # say = "" # # Separate the text into words # words = text.split() # print(words) # for word in words: # # Create the pig latin word and add it to the list # words.append('{}{}'.format(text[0], 'ay')) # # Turn the list back into a phrase # words = say.join(words) # return words # print(pig_latin("hello how are you")) # Should be "ellohay owhay reaay ouyay" # print(pig_latin("programming in python is fun")) # Should be "rogrammingpay niay ythonpay siay unfay"

31.877551

116

0.650448

7951428aab3ff47b78666dacdd5263c055c8b1c5

1,158

py

Python

setup.py

EliasCampos/minorm

ed0eea4b448eed2a7ffedf41301c7f226e938dfd

[ "MIT" ]

2

2020-12-20T08:07:32.000Z

2022-02-25T18:49:08.000Z

setup.py

EliasCampos/minorm

ed0eea4b448eed2a7ffedf41301c7f226e938dfd

[ "MIT" ]

null

setup.py

EliasCampos/minorm

ed0eea4b448eed2a7ffedf41301c7f226e938dfd

[ "MIT" ]

null

from pathlib import Path from setuptools import setup def get_version(root_path): version_file = root_path / 'minorm' / '__init__.py' with version_file.open() as f: for line in f: if line.startswith('__version__'): return line.split('=')[1].strip().strip('"').strip("'") ROOT_PATH = Path(__file__).parent README = ROOT_PATH / 'README.rst' setup( name='minorm', version=get_version(ROOT_PATH), description='A minimalistic ORM with basic features.', long_description=README.read_text(), long_description_content_type='text/x-rst', url='https://github.com/EliasCampos/minorm', author='Campos Ilya', author_email='camposylia@gmail.com', license="MIT", classifiers=[ 'Development Status :: 2 - Pre-Alpha', 'Intended Audience :: Developers', 'License :: OSI Approved :: MIT License', 'Operating System :: OS Independent', 'Programming Language :: Python', 'Programming Language :: Python :: 3', 'Programming Language :: Python :: 3.6', 'Programming Language :: Python :: 3.7', ], packages=['minorm'], )

30.473684

71

0.630397

7951436a3c5c9513cee4d685b40ae9c85b96b4e5

1,552

py

Python

config/wsgi.py

misael1999/taximil-api

7c69a7ee27cb0223c7ba61aa08116faf0f95da46

[ "MIT" ]

null

config/wsgi.py

misael1999/taximil-api

7c69a7ee27cb0223c7ba61aa08116faf0f95da46

[ "MIT" ]

null

config/wsgi.py

misael1999/taximil-api

7c69a7ee27cb0223c7ba61aa08116faf0f95da46

[ "MIT" ]

null

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

41.945946

79

0.79317

7951436e30a57826ae0379cd8681c5286b78abb6

4,022

py

Python

tensorflow/contrib/cluster_resolver/python/training/tpu_cluster_resolver.py

dantkz/tensorflow

5333bbeb3142af2a06f1ebd971061fc4e28da743

[ "Apache-2.0" ]

null

tensorflow/contrib/cluster_resolver/python/training/tpu_cluster_resolver.py

dantkz/tensorflow

5333bbeb3142af2a06f1ebd971061fc4e28da743

[ "Apache-2.0" ]

null

tensorflow/contrib/cluster_resolver/python/training/tpu_cluster_resolver.py

dantkz/tensorflow

5333bbeb3142af2a06f1ebd971061fc4e28da743

[ "Apache-2.0" ]

null

# Copyright 2017 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== """Implementation of Cluster Resolvers for Cloud TPUs.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function from tensorflow.contrib.cluster_resolver.python.training.cluster_resolver import ClusterResolver from tensorflow.python.training.server_lib import ClusterSpec _GOOGLE_API_CLIENT_INSTALLED = True try: from googleapiclient import discovery # pylint: disable=g-import-not-at-top from oauth2client.client import GoogleCredentials # pylint: disable=g-import-not-at-top except ImportError: _GOOGLE_API_CLIENT_INSTALLED = False class TPUClusterResolver(ClusterResolver): """Cluster Resolver for Google Cloud TPUs. This is an implementation of cluster resolvers for the Google Cloud TPU service. As Cloud TPUs are in alpha, you will need to specify a API definition file for this to consume, in addition to a list of Cloud TPUs in your Google Cloud Platform project. """ def __init__(self, project, zone, tpu_names, job_name='tpu_worker', credentials='default', service=None): """Creates a new TPUClusterResolver object. The ClusterResolver will then use the parameters to query the Cloud TPU APIs for the IP addresses and ports of each Cloud TPU listed. Args: project: Name of the GCP project containing Cloud TPUs zone: Zone where the TPUs are located tpu_names: A list of names of the target Cloud TPUs. job_name: Name of the TensorFlow job the TPUs belong to. credentials: GCE Credentials. If None, then we use default credentials from the oauth2client service: The GCE API object returned by the googleapiclient.discovery function. If you specify a custom service object, then the credentials parameter will be ignored. Raises: ImportError: If the googleapiclient is not installed. """ self._project = project self._zone = zone self._tpu_names = tpu_names self._job_name = job_name self._credentials = credentials if credentials == 'default': if _GOOGLE_API_CLIENT_INSTALLED: self._credentials = GoogleCredentials.get_application_default() if service is None: if not _GOOGLE_API_CLIENT_INSTALLED: raise ImportError('googleapiclient must be installed before using the ' 'TPU cluster resolver') self._service = discovery.build( 'tpu', 'v1alpha1', credentials=self._credentials) else: self._service = service def cluster_spec(self): """Returns a ClusterSpec object based on the latest TPU information. We retrieve the information from the GCE APIs every time this method is called. Returns: A ClusterSpec containing host information returned from Cloud TPUs. """ worker_list = [] for tpu_name in self._tpu_names: full_name = 'projects/%s/locations/%s/nodes/%s' % ( self._project, self._zone, tpu_name) request = self._service.projects().locations().nodes().get(name=full_name) response = request.execute() instance_url = '%s:%s' % (response['ipAddress'], response['port']) worker_list.append(instance_url) return ClusterSpec({self._job_name: worker_list})

36.563636

96

0.701144

7951449f13b4cd597030b6462fadf2c181c117c4

809

py

Python

setup.py

topicaxis/text-analysis-service

e94dfd3ea4e9e2259ba61f135e77c6d702c440e6

[ "MIT" ]

1

2020-01-07T00:08:40.000Z

setup.py

pmatigakis/text-analysis-service

e94dfd3ea4e9e2259ba61f135e77c6d702c440e6

[ "MIT" ]

1

2019-11-02T15:41:28.000Z

setup.py

pmatigakis/text-analysis-service

e94dfd3ea4e9e2259ba61f135e77c6d702c440e6

[ "MIT" ]

null

from setuptools import setup, find_packages def get_requirements(): with open("requirements.txt") as f: requirements = [ line.strip() for line in f if not line.startswith("-e") ] return requirements def get_test_requirements(): with open("requirements-test.txt") as f: requirements = [ line.strip() for line in f ] return requirements setup( name="Text analysis service", version="0.10.3", packages=find_packages(exclude=["tests"]), include_package_data=True, zip_safe=False, install_requires=get_requirements(), test_suite="nose.collector", tests_require=get_test_requirements(), entry_points={ 'console_scripts': ['tas-cli=tas.cli:main'], } )

21.289474

52

0.613103

7951478782d8e34effe06bdad0b23b6fc2dd0a4f

4,884

py

Python

teuthology/contextutil.py

shrek-github/teuthology

5a89fb217aaf2cd7948f7419f431230ab25053c6

[ "MIT" ]

null

teuthology/contextutil.py

shrek-github/teuthology

5a89fb217aaf2cd7948f7419f431230ab25053c6

[ "MIT" ]

1

2020-03-05T03:00:08.000Z

teuthology/contextutil.py

shrek-github/teuthology

5a89fb217aaf2cd7948f7419f431230ab25053c6

[ "MIT" ]

1

2020-03-04T03:04:06.000Z

import contextlib import sys import logging import time import itertools from teuthology.config import config from teuthology.exceptions import MaxWhileTries from six import reraise log = logging.getLogger(__name__) @contextlib.contextmanager def nested(*managers): """ Like contextlib.nested but takes callables returning context managers, to avoid the major reason why contextlib.nested was deprecated. This version also logs any exceptions early, much like run_tasks, to ease debugging. TODO combine nested and run_tasks. """ exits = [] vars = [] exc = (None, None, None) try: for mgr_fn in managers: mgr = mgr_fn() exit = mgr.__exit__ enter = mgr.__enter__ vars.append(enter()) exits.append(exit) yield vars except Exception: log.exception('Saw exception from nested tasks') exc = sys.exc_info() # FIXME this needs to be more generic if config.ctx and config.ctx.config.get('interactive-on-error'): config.ctx.config['interactive-on-error'] = False from teuthology.task import interactive log.warning('Saw failure, going into interactive mode...') interactive.task(ctx=config.ctx, config=None) finally: while exits: exit = exits.pop() try: if exit(*exc): exc = (None, None, None) except Exception: exc = sys.exc_info() if exc != (None, None, None): # Don't rely on sys.exc_info() still containing # the right information. Another exception may # have been raised and caught by an exit method reraise(*exc) class safe_while(object): """ A context manager to remove boiler plate code that deals with `while` loops that need a given number of tries and some seconds to sleep between each one of those tries. The most simple example possible will try 10 times sleeping for 6 seconds: >>> from teuthology.contexutil import safe_while >>> with safe_while() as proceed: ... while proceed(): ... # repetitive code here ... print("hello world") ... Traceback (most recent call last): ... MaxWhileTries: reached maximum tries (5) after waiting for 75 seconds Yes, this adds yet another level of indentation but it allows you to implement while loops exactly the same as before with just 1 more indentation level and one extra call. Everything else stays the same, code-wise. So adding this helper to existing code is simpler. :param sleep: The amount of time to sleep between tries. Default 6 :param increment: The amount to add to the sleep value on each try. Default 0. :param tries: The amount of tries before giving up. Default 10. :param action: The name of the action being attempted. Default none. :param _raise: Whether to raise an exception (or log a warning). Default True. :param _sleeper: The function to use to sleep. Only used for testing. Default time.sleep """ def __init__(self, sleep=6, increment=0, tries=10, action=None, _raise=True, _sleeper=None): self.sleep = sleep self.increment = increment self.tries = tries self.counter = 0 self.sleep_current = sleep self.action = action self._raise = _raise self.sleeper = _sleeper or time.sleep def _make_error_msg(self): """ Sum the total number of seconds we waited while providing the number of tries we attempted """ total_seconds_waiting = sum( itertools.islice( itertools.count(self.sleep, self.increment), self.tries ) ) msg = 'reached maximum tries ({tries})' + \ ' after waiting for {total} seconds' if self.action: msg = "'{action}' " + msg msg = msg.format( action=self.action, tries=self.tries, total=total_seconds_waiting, ) return msg def __call__(self): self.counter += 1 if self.counter == 1: return True if self.counter > self.tries: error_msg = self._make_error_msg() if self._raise: raise MaxWhileTries(error_msg) else: log.warning(error_msg) return False self.sleeper(self.sleep_current) self.sleep_current += self.increment return True def __enter__(self): return self def __exit__(self, exc_type, exc_val, exc_tb): return False

33.22449

79

0.59869

795148dd80fc92a0199d6c5937f92fc2dab57954

3,666

py

Python

second/builder/dataset_builder.py

zhb0920/second.pytorch

f980f3d18749b7a2830983222d1695b5cb321dae

[ "MIT" ]

null

second/builder/dataset_builder.py

zhb0920/second.pytorch

f980f3d18749b7a2830983222d1695b5cb321dae

[ "MIT" ]

null

second/builder/dataset_builder.py

zhb0920/second.pytorch

f980f3d18749b7a2830983222d1695b5cb321dae

[ "MIT" ]

null

from second.protos import input_reader_pb2 from second.data.dataset import KittiDataset from second.data.preprocess import prep_pointcloud import numpy as np from second.builder import dbsampler_builder from functools import partial from second.utils import config_tool def build(input_reader_config, model_config, training, voxel_generator, target_assigner=None): """Builds a tensor dictionary based on the InputReader config. Args: input_reader_config: A input_reader_pb2.InputReader object. Returns: A tensor dict based on the input_reader_config. Raises: ValueError: On invalid input reader proto. ValueError: If no input paths are specified. """ if not isinstance(input_reader_config, input_reader_pb2.InputReader): raise ValueError('input_reader_config not of type ' 'input_reader_pb2.InputReader.') generate_bev = model_config.use_bev without_reflectivity = model_config.without_reflectivity num_point_features = model_config.num_point_features downsample_factor = config_tool.get_downsample_factor(model_config) cfg = input_reader_config db_sampler_cfg = input_reader_config.database_sampler db_sampler = None if len(db_sampler_cfg.sample_groups) > 0: # enable sample db_sampler = dbsampler_builder.build(db_sampler_cfg) u_db_sampler_cfg = input_reader_config.unlabeled_database_sampler u_db_sampler = None if len(u_db_sampler_cfg.sample_groups) > 0: # enable sample u_db_sampler = dbsampler_builder.build(u_db_sampler_cfg) grid_size = voxel_generator.grid_size # [352, 400] feature_map_size = grid_size[:2] // downsample_factor feature_map_size = [*feature_map_size, 1][::-1] print("feature_map_size", feature_map_size) assert all([n != '' for n in target_assigner.classes]), "you must specify class_name in anchor_generators." prep_func = partial( prep_pointcloud, root_path=cfg.kitti_root_path, class_names=target_assigner.classes, voxel_generator=voxel_generator, target_assigner=target_assigner, training=training, max_voxels=cfg.max_number_of_voxels, remove_outside_points=False, remove_unknown=cfg.remove_unknown_examples, create_targets=training, shuffle_points=cfg.shuffle_points, gt_rotation_noise=list(cfg.groundtruth_rotation_uniform_noise), gt_loc_noise_std=list(cfg.groundtruth_localization_noise_std), global_rotation_noise=list(cfg.global_rotation_uniform_noise), global_scaling_noise=list(cfg.global_scaling_uniform_noise), global_random_rot_range=list( cfg.global_random_rotation_range_per_object), db_sampler=db_sampler, unlabeled_db_sampler=u_db_sampler, generate_bev=generate_bev, without_reflectivity=without_reflectivity, num_point_features=num_point_features, anchor_area_threshold=cfg.anchor_area_threshold, gt_points_drop=cfg.groundtruth_points_drop_percentage, gt_drop_max_keep=cfg.groundtruth_drop_max_keep_points, remove_points_after_sample=cfg.remove_points_after_sample, remove_environment=cfg.remove_environment, use_group_id=cfg.use_group_id, downsample_factor=downsample_factor) dataset = KittiDataset( info_path=cfg.kitti_info_path, root_path=cfg.kitti_root_path, num_point_features=num_point_features, target_assigner=target_assigner, feature_map_size=feature_map_size, prep_func=prep_func) return dataset

42.137931

111

0.743317

7951490c0d2695e682766ac457b586dbc2e538a2

3,080

py

Python

pg_copy.py

ollieglass/sqlalchemy-pg-copy

882747b7a4764631c6c5c4217a28ceb15f13ac4f

[ "MIT" ]

2

2019-12-18T16:41:49.000Z

2020-07-31T18:09:31.000Z

pg_copy.py

ollieglass/sqlalchemy-pg-copy

882747b7a4764631c6c5c4217a28ceb15f13ac4f

[ "MIT" ]

null

pg_copy.py

ollieglass/sqlalchemy-pg-copy

882747b7a4764631c6c5c4217a28ceb15f13ac4f

[ "MIT" ]

null

import json import io import datetime as dt # ------------------------------------------------------------------------------ # CSV pre-processing def string_to_pg_csv_string(v): v = v.replace('\\', '\\\\') # replace single \ with \\ v = v.replace('\n', '\\n') v = v.replace('\r', '\\r') v = v.replace('"', '""') # can't store unicode null in Postgres so replace with empty string # https://stackoverflow.com/a/28816294/160406 v = v.replace('\0', '') v = v.replace('\x00', '') v = v.replace(u'\u0000', '') return f'"{v}"' def value_to_pg_csv_value(v): if v is None: return r'\N' elif isinstance(v, str): return string_to_pg_csv_string(v) elif isinstance(v, dt.datetime): return f'"{v.isoformat()}"' return str(v) def list_of_dicts_to_pg_csv_lines(objs): columns = objs[0].keys() for o in objs: values = [ value_to_pg_csv_value(o[c]) for c in columns ] line = ','.join(values) + '\n' yield line # ------------------------------------------------------------------------------ # StringIO def lines_to_stringio(lines): f = io.StringIO() for l in lines: f.write(l) f.seek(0) return f # ------------------------------------------------------------------------------ # Insert def insert_with_copy(engine, objs, target_table, target_schema=None, ignore_duplicates=True): """Fast insert to a Postgres SQL table using COPY Ignores duplicates by - inserting to a temp table - INSERT INTO from temp to target with ON CONFLICT DO NOTHING - dropping temp table Args: engine: SQLAclhemy engine object objs: list of dictionaries. Keys must be identical target_table: name of table to be uploaded to target_schema: optional ignore_duplicates: optional """ if target_schema: target_table = f'{target_schema}.{target_table}' column_names = ','.join(objs[0].keys()) pg_csv_lines = list_of_dicts_to_pg_csv_lines(objs) f = lines_to_stringio(pg_csv_lines) conn = engine.raw_connection() with conn.cursor() as cursor: if ignore_duplicates: staging_table = f'staging_{target_table}' cursor.execute(f''' CREATE TEMP TABLE {staging_table} ON COMMIT DROP AS SELECT * FROM {target_table} WITH NO DATA ''') cursor.copy_expert(f""" COPY {staging_table} ({column_names}) FROM STDIN WITH CSV NULL '\\N' """, f) cursor.execute(f''' INSERT INTO {target_table} ({column_names}) SELECT {column_names} FROM {staging_table} ON CONFLICT DO NOTHING ''') else: cursor.copy_expert(f""" COPY {target_table} ({column_names}) FROM STDIN WITH CSV NULL '\\N' """, f) conn.commit() conn.close()

26.324786

93

0.520455

7951497d1bde555444080015e51aeaf7dd26effe

547

py

Python

python/9th/test_2.py

Elendeer/homework

b17db0dddc8ce23ce047cb764f759d9ec47f6a8b

[ "MIT" ]

1

2020-10-14T03:45:24.000Z

python/9th/test_2.py

Elendeer/homework

b17db0dddc8ce23ce047cb764f759d9ec47f6a8b

[ "MIT" ]

null

python/9th/test_2.py

Elendeer/homework

b17db0dddc8ce23ce047cb764f759d9ec47f6a8b

[ "MIT" ]

null

''' Author : Daniel_Elendeer Date : 2020-11-21 18:33:18 LastEditors : Daniel_Elendeer LastEditTime : 2020-11-21 18:55:46 Description : ''' if __name__ == '__main__': n, k = map(int, input().split()) lst = list(range(1, n + 1)) print(lst) idx = 0 count = k while len(lst) != 1: count -= 1 if idx >= len(lst): idx = 0 if count == 0: lst.remove(lst[idx]) print(lst) count = k else : idx += 1 print(lst.pop())

18.233333

36

0.47532

795149baf617c7d31780bf1b40d91a07ae35b243

2,482

py

Python

benchmarks/benchmarks/signal_filtering.py

smola/scipy

ff8b9d9e87a585a820846d7f459d6156ba621c4d

[ "BSD-3-Clause" ]

2

2020-06-20T14:11:14.000Z

2020-10-12T07:11:36.000Z

benchmarks/benchmarks/signal_filtering.py

smola/scipy

ff8b9d9e87a585a820846d7f459d6156ba621c4d

[ "BSD-3-Clause" ]

null

benchmarks/benchmarks/signal_filtering.py

smola/scipy

ff8b9d9e87a585a820846d7f459d6156ba621c4d

[ "BSD-3-Clause" ]

1

2021-10-20T08:10:46.000Z

from __future__ import division, absolute_import, print_function import numpy as np import timeit from concurrent.futures import ThreadPoolExecutor, wait try: from scipy.signal import lfilter, firwin, decimate, butter, sosfilt except ImportError: pass from .common import Benchmark class Decimate(Benchmark): param_names = ['q', 'ftype', 'zero_phase'] params = [ [2, 10, 30], ['iir', 'fir'], [True, False] ] def setup(self, q, ftype, zero_phase): np.random.seed(123456) sample_rate = 10000. t = np.arange(int(1e6), dtype=np.float64) / sample_rate self.sig = np.sin(2*np.pi*500*t) + 0.3 * np.sin(2*np.pi*4e3*t) def time_decimate(self, q, ftype, zero_phase): decimate(self.sig, q, ftype=ftype, zero_phase=zero_phase) class Lfilter(Benchmark): param_names = ['n_samples', 'numtaps'] params = [ [1e3, 50e3, 1e6], [9, 23, 51] ] def setup(self, n_samples, numtaps): np.random.seed(125678) sample_rate = 25000. t = np.arange(n_samples, dtype=np.float64) / sample_rate nyq_rate = sample_rate / 2. cutoff_hz = 3000.0 self.sig = np.sin(2*np.pi*500*t) + 0.3 * np.sin(2*np.pi*11e3*t) self.coeff = firwin(numtaps, cutoff_hz/nyq_rate) def time_lfilter(self, n_samples, numtaps): lfilter(self.coeff, 1.0, self.sig) class ParallelSosfilt(Benchmark): timeout = 100 timer = timeit.default_timer param_names = ['n_samples', 'threads'] params = [ [1e3, 10e3], [1, 2, 4] ] def setup(self, n_samples, threads): self.filt = butter(8, 8e-6, "lowpass", output="sos") self.data = np.arange(int(n_samples) * 3000).reshape(int(n_samples), 3000) self.chunks = np.array_split(self.data, threads) def time_sosfilt(self, n_samples, threads): pool = ThreadPoolExecutor(max_workers=threads) futures = [] for i in range(threads): futures.append(pool.submit(sosfilt, self.filt, self.chunks[i])) wait(futures) class Sosfilt(Benchmark): param_names = ['n_samples', 'order'] params = [ [1000, 1000000], [6, 20] ] def setup(self, n_samples, order): self.sos = butter(order, [0.1575, 0.1625], 'band', output='sos') self.y = np.random.RandomState(0).randn(n_samples) def time_sosfilt_basic(self, n_samples, order): sosfilt(self.sos, self.y)

28.204545

82

0.616035

79514ac8aaf153c1a4bc7e7dba08e9a507ad15fa

1,031

py

Python

python/test/test_inference_service_list.py

adriangonz/seldon-deploy-sdk

c5504838630a87053387cec57ec2e1e7251971e2

[ "Apache-2.0" ]

6

2021-02-18T14:37:54.000Z

2022-01-13T13:27:43.000Z

python/test/test_inference_service_list.py

adriangonz/seldon-deploy-sdk

c5504838630a87053387cec57ec2e1e7251971e2

[ "Apache-2.0" ]

14

2021-01-04T16:32:03.000Z

2021-12-13T17:53:59.000Z

python/test/test_inference_service_list.py

adriangonz/seldon-deploy-sdk

c5504838630a87053387cec57ec2e1e7251971e2

[ "Apache-2.0" ]

7

2021-03-17T09:05:55.000Z

2022-01-05T10:39:56.000Z

# coding: utf-8 """ Seldon Deploy API API to interact and manage the lifecycle of your machine learning models deployed through Seldon Deploy. # noqa: E501 OpenAPI spec version: v1alpha1 Contact: hello@seldon.io Generated by: https://github.com/swagger-api/swagger-codegen.git """ from __future__ import absolute_import import unittest import seldon_deploy_sdk from seldon_deploy_sdk.models.inference_service_list import InferenceServiceList # noqa: E501 from seldon_deploy_sdk.rest import ApiException class TestInferenceServiceList(unittest.TestCase): """InferenceServiceList unit test stubs""" def setUp(self): pass def tearDown(self): pass def testInferenceServiceList(self): """Test InferenceServiceList""" # FIXME: construct object with mandatory attributes with example values # model = seldon_deploy_sdk.models.inference_service_list.InferenceServiceList() # noqa: E501 pass if __name__ == '__main__': unittest.main()

25.146341

122

0.734239

79514bea4a06e27854cdd81f2a682b80f6c6fa3d

1,916

py

Python

TUI/PlaySound.py

ApachePointObservatory/TUI

8f130368254161a2748167b7c8260cc24170c28c

[ "BSD-3-Clause" ]

1

2020-01-28T06:28:00.000Z

TUI/PlaySound.py

ApachePointObservatory/TUI

8f130368254161a2748167b7c8260cc24170c28c

[ "BSD-3-Clause" ]

1

2017-06-05T22:53:58.000Z

TUI/PlaySound.py

r-owen/TUI

8f130368254161a2748167b7c8260cc24170c28c

[ "BSD-3-Clause" ]

1

2020-01-28T06:28:02.000Z

#!/usr/bin/env python """Play each of the standard sounds for TUI. Gets the sounds from TUI preferences. 2003-04-28 ROwen Minimal implementation. 2003-10-30 ROwen Added msgReceived. 2003-11-24 ROwen Moved to TUI.Sounds; changed to use sound prefs. 2003-12-03 ROwen Added exposureBegins, exposureEnds, guidingBegins, guidingEnds. 2003-12-09 ROwen Modified to import TUI.TUIModel when it's used; this allows TUI.Sounds to be imported before TUI.TUIModel. 2004-05-18 ROwen Stopped importing RO.Wdg; it wasn't used. 2005-08-02 ROwen Moved from Sounds/PlaySounds.py -> PlaySound.py 2006-04-14 ROwen Added guideModeChanges. 2006-10-24 ROwen Added logHighlightedText. 2009-11-09 ROwen Added support for Play Sounds preference. 2011-01-31 ROwen Added exposureFailed. """ import TUI.TUIModel _Prefs = None _PlaySoundsPref = None def _playSound(name): if name is None: return global _Prefs, _PlaySoundsPref if _Prefs is None: _Prefs = TUI.TUIModel.getModel().prefs _PlaySoundsPref = _Prefs.getPrefVar("Play Sounds") if _PlaySoundsPref.getValue(): _Prefs.getPrefVar(name).play() def axisHalt(): _playSound("Axis Halt") def axisSlew(): _playSound("Axis Slew") def axisTrack(): _playSound("Axis Track") def cmdDone(): _playSound("Command Done") def cmdFailed(): _playSound("Command Failed") def exposureBegins(): _playSound("Exposure Begins") def exposureEnds(): _playSound("Exposure Ends") def exposureFailed(): _playSound("Exposure Failed") def guidingBegins(): _playSound("Guiding Begins") def guideModeChanges(): _playSound("Guide Mode Changes") def guidingEnds(): _playSound("Guiding Ends") def msgReceived(): _playSound("Message Received") def noGuideStar(): _playSound("No Guide Star") def logHighlightedText(): _playSound("Log Highlighted Text")

25.891892

84

0.7119

79514c332631574ac04f6a851b3e3e7f51959d91

2,088

py

Python

apps/contact/views.py

Kpaubert/onlineweb4

9ac79f163bc3a816db57ffa8477ea88770d97807

[ "MIT" ]

32

2017-02-22T13:38:38.000Z

2022-03-31T23:29:54.000Z

apps/contact/views.py

Kpaubert/onlineweb4

9ac79f163bc3a816db57ffa8477ea88770d97807

[ "MIT" ]

694

2017-02-15T23:09:52.000Z

2022-03-31T23:16:07.000Z

apps/contact/views.py

Kpaubert/onlineweb4

9ac79f163bc3a816db57ffa8477ea88770d97807

[ "MIT" ]

35

2017-09-02T21:13:09.000Z

2022-02-21T11:30:30.000Z

import logging from django.contrib import messages from django.core.mail import EmailMessage from django.shortcuts import redirect, render from apps.contact.forms import ContactForm # Index page def index(request): context = {"form": ContactForm} return render(request, "contact/index.html", context) def contact_submit(request): log = logging.getLogger(__name__) if request.method == "POST": form = ContactForm(request.POST) if form.is_valid(): name = form.cleaned_data["contact_name"] content = form.cleaned_data["content"] from_email = form.cleaned_data["contact_email"] to_email = [form.cleaned_data["contact_receiver"]] client_ip = get_client_ip(request) if not name: name = "Noen" if request.user.is_authenticated: username = request.user.username log.info( "{username} has tried to contact {to_email}".format( username=username, to_email=to_email ) ) else: log.info( "A user at {client_ip} has tried to contact {to_email}".format( client_ip=client_ip, to_email=to_email ) ) subject = ( "[Kontakt] {name} har kontaktet dere gjennom online.ntnu.no".format( name=name ) ) EmailMessage(subject, content, from_email, to_email).send() messages.success(request, "Meldingen ble sendt") else: messages.error( request, "Meldingen ble ikke sendt. Prøv igjen eller send mail direkte til dotkom", ) return redirect("contact_index") def get_client_ip(request): x_forwarded_for = request.META.get("HTTP_X_FORWARDED_FOR") if x_forwarded_for: ip = x_forwarded_for.split(",")[0] else: ip = request.META.get("REMOTE_ADDR") return ip

29.828571

90

0.566092

79514dc63cefb88fa908e188c15efb3d20f5fd3f

26,829

py

Python

CountyMapbook/CountyMapbook_v2.py

adambreznicky/smudge_python

af7ba221890253ac6fe7f38691b351861f8b3d96

[ "MIT" ]

1

2017-05-24T02:05:20.000Z

CountyMapbook/CountyMapbook_v2.py

adambreznicky/smudge_python

af7ba221890253ac6fe7f38691b351861f8b3d96

[ "MIT" ]

null

CountyMapbook/CountyMapbook_v2.py

adambreznicky/smudge_python

af7ba221890253ac6fe7f38691b351861f8b3d96

[ "MIT" ]

null

__file__ = 'CountyMapbook_v1' __date__ = '6/18/2014' __author__ = 'ABREZNIC' import os, arcpy,datetime from arcpy import env from reportlab.lib import colors from reportlab.lib.units import inch from reportlab.platypus import BaseDocTemplate, Paragraph, frames, Table, TableStyle, Frame, flowables, Flowable, PageTemplate #date now = datetime.datetime.now() curMonth = now.strftime("%m") curDay = now.strftime("%d") curYear = now.strftime("%Y") today = curYear + "_" + curMonth + "_" + curDay #variables cofolder = "C:\\TxDOT\\CountyMapbook" workspace = cofolder + "\\" + curYear database = workspace + "\\Working.gdb" comanche = "Connection to Comanche.sde" histmarkers = "Database Connections\\" + comanche + "\\TPP_GIS.APP_TPP_GIS_ADMIN.Texas_Historical_Commission\\TPP_GIS.APP_TPP_GIS_ADMIN.Historical_Markers" campgrounds = "" restareas = "Database Connections\\" + comanche + "\\TPP_GIS.APP_TPP_GIS_ADMIN.Travel\\TPP_GIS.APP_TPP_GIS_ADMIN.REST_AREA_PNT" parks = "Database Connections\\" + comanche + "\\TPP_GIS.APP_TPP_GIS_ADMIN.Park\\TPP_GIS.APP_TPP_GIS_ADMIN.Public_Lands_2014" cemeteries = "Database Connections\\" + comanche + "\\TPP_GIS.APP_TPP_GIS_ADMIN.Cemetery\\TPP_GIS.APP_TPP_GIS_ADMIN.Cemetery" cemeteriesPT = "Database Connections\\" + comanche + "\\TPP_GIS.APP_TPP_GIS_ADMIN.Cemetery\\TPP_GIS.APP_TPP_GIS_ADMIN.Cemetery_Points" roads = "Database Connections\\" + comanche + "\\TPP_GIS.APP_TPP_GIS_ADMIN.Roadways\\TPP_GIS.APP_TPP_GIS_ADMIN.TXDOT_Roadways" counties = "Database Connections\\" + comanche + "\\TPP_GIS.APP_TPP_GIS_ADMIN.County\\TPP_GIS.APP_TPP_GIS_ADMIN.County" airports = "Database Connections\\" + comanche + "\\TPP_GIS.APP_TPP_GIS_ADMIN.Airport\\TPP_GIS.APP_TPP_GIS_ADMIN.Airport" airportsPT = "Database Connections\\" + comanche + "\\TPP_GIS.APP_TPP_GIS_ADMIN.Airport\\TPP_GIS.APP_TPP_GIS_ADMIN.Airport_Points" prisons = "Database Connections\\" + comanche + "\\TPP_GIS.APP_TPP_GIS_ADMIN.Base_Map_Layers\\TPP_GIS.APP_TPP_GIS_ADMIN.Prisons" military = "Database Connections\\" + comanche + "\\TPP_GIS.APP_TPP_GIS_ADMIN.Base_Map_Layers\\TPP_GIS.APP_TPP_GIS_ADMIN.Military" schools = "Database Connections\\" + comanche + "\\TPP_GIS.APP_TPP_GIS_ADMIN.Base_Map_Layers\\TPP_GIS.APP_TPP_GIS_ADMIN.Education" cities = "Database Connections\\" + comanche + "\\TPP_GIS.APP_TPP_GIS_ADMIN.City\\TPP_GIS.APP_TPP_GIS_ADMIN.City" citiesPT = "Database Connections\\" + comanche + "\\TPP_GIS.APP_TPP_GIS_ADMIN.City\\TPP_GIS.APP_TPP_GIS_ADMIN.City_Points" lakes = "Database Connections\\" + comanche + "\\TPP_GIS.APP_TPP_GIS_ADMIN.Water\\TPP_GIS.APP_TPP_GIS_ADMIN.Water_Bodies" railroads = "Database Connections\\" + comanche + "\\TPP_GIS.APP_TPP_GIS_ADMIN.Railroad\\TPP_GIS.APP_TPP_GIS_ADMIN.Railroads" rivers = "Database Connections\\" + comanche + "\\TPP_GIS.APP_TPP_GIS_ADMIN.Water\\TPP_GIS.APP_TPP_GIS_ADMIN.Streams" grid = "Database Connections\\" + comanche + "\\TPP_GIS.MCHAMB1.Map_Index_Grids\\TPP_GIS.MCHAMB1.State_Grid_120K" def preparation(): print "Creating database..." if not os.path.exists(workspace): os.makedirs(workspace) else: try: arcpy.Delete_management(database) except: pass for file in os.listdir(workspace): thefile = os.path.join(workspace, file) os.remove(thefile) arcpy.CreateFileGDB_management(workspace, "Working.gdb") print "Copying historical markers..." arcpy.Select_analysis(histmarkers, database + "\\histmarkers", "markernum IS NOT NULL and indexname IS NOT NULL") arcpy.AddField_management(database + "\\histmarkers", "label", "TEXT", "", "", 200) cursor = arcpy.UpdateCursor(database + "\\histmarkers") for row in cursor: row.setValue("label", str(row.markernum) + " - " + row.indexname) cursor.updateRow(row) del cursor del row print "Copying campgrounds..." print "Copying rest areas..." arcpy.Copy_management(restareas, database + "\\restareas") arcpy.AddField_management(database + "\\restareas", "label", "TEXT", "", "", 200) cursor = arcpy.UpdateCursor(database + "\\restareas") for row in cursor: if row.RA_TYPE_NM == "Picnic": row.setValue("label", "Picnic Area") elif row.RA_TYPE_NM == "Rest": row.setValue("label", "Rest Area") elif row.RA_TYPE_NM == "Rest_EX": row.setValue("label", "Rest Area (EX)") elif row.RA_TYPE_NM == "TIC": row.setValue("label", "Travel Information Center") else: cursor.deleteRow(row) cursor.updateRow(row) del cursor del row print "Copying parks..." arcpy.Select_analysis(parks, database + "\\parks", "(GOVT_JURIS = '3' OR GOVT_JURIS = '4') AND LAND_NM IS NOT NULL AND LAND_NM <> ''") print "Copying cemeteries..." arcpy.Select_analysis(cemeteries, database + "\\cemeteries", "CEMETERY_NM IS NOT NULL AND CEMETERY_NM <> ''") arcpy.Select_analysis(cemeteriesPT, database + "\\cemeteries_point", "CEMETERY_NM IS NOT NULL AND CEMETERY_NM <> ''") print "Copying highways..." arcpy.Select_analysis(roads, database + "\\highways", "(( RTE_CLASS = '1' OR RTE_CLASS = '6' ) AND RDBD_TYPE = 'KG' AND RTE_OPEN = 1 ) OR (RTE_NM = 'SL0008' AND RDBD_TYPE = 'KG' AND RTE_OPEN = 1 )") print "Copying counties..." arcpy.Copy_management(counties, database + "\\counties") print "Copying airports..." arcpy.Select_analysis(airports, database + "\\airports", "ARPRT_NM <> '' AND ARPRT_NM IS NOT NULL") arcpy.Select_analysis(airportsPT, database + "\\airports_point", "DISPLAY = 'Yes'") print "Copying county roads..." arcpy.Select_analysis(roads, database + "\\countyroads", "RTE_CLASS = '2' AND RTE_OPEN = 1 AND RDBD_TYPE = 'KG'") print "Copying prisons..." arcpy.Copy_management(prisons, database + "\\prisons") print "Copying military..." arcpy.Copy_management(military, database + "\\military") print "Copying schools..." arcpy.Copy_management(schools, database + "\\schools") print "Copying cities..." arcpy.Copy_management(cities, database + "\\cities") arcpy.Select_analysis(citiesPT, database + "\\cities_point", "INC = 'N'") print "Copying lakes..." arcpy.Select_analysis(lakes, database + "\\lakes", "BODY_NM IS NOT NULL AND BODY_NM <> '' AND BODY_TYPE = '1'") print "Copying railroads..." arcpy.Select_analysis(railroads, database + "\\railroads", "RR_TYPE = 'M' AND RR_STAT = 'A'") print "Copying rivers..." arcpy.Select_analysis(rivers, database + "\\rivers", "STRM_TYPE = '1'") print "Copying federal roads..." arcpy.Select_analysis(roads, database + "\\federalroads", "RTE_CLASS = '7' AND RTE_OPEN = 1 AND RDBD_TYPE = 'KG' AND FULL_ST_NM <> '' AND FULL_ST_NM IS NOT NULL") print "Copying grid..." arcpy.Copy_management(grid, database + "\\grid") # print "Renumbering grid..." cursor = arcpy.UpdateCursor(database + "\\grid") for row in cursor: row.setValue("ID", row.ID - 66) row.setValue("STATE_ID", row.STATE_ID - 66) if row.NORTH != 0: row.setValue("NORTH", row.NORTH - 66) if row.SOUTH != 0: row.setValue("SOUTH", row.SOUTH - 66) if row.EAST != 0: row.setValue("EAST", row.EAST - 66) if row.WEST != 0: row.setValue("WEST", row.WEST - 66) cursor.updateRow(row) del cursor del row print "Creating union..." arcpy.Union_analysis([database + "\\grid", database + "\\counties"], database + "\\union") cursor = arcpy.UpdateCursor(database + "\\union") for row in cursor: if row.CNTY_NM == "" or row.CNTY_NM is None or row.ID == 0: cursor.deleteRow(row) del cursor del row def intersects(): env.workspace = database print "Creating field dictionary..." dict = {} dict["histmarkers"] = "label" #dict["campgrounds"] = "" dict["restareas"] = "label" dict["parks"] = "LAND_NM" dict["cemeteries"] = "CEMETERY_NM" dict["cemeteries_point"] = "CEMETERY_NM" dict["highways"] = "FULL_ST_NM" dict["counties"] = "CNTY_NM" dict["airports"] = "ARPRT_NM" dict["airports_point"] = "ARPRT_NM" dict["countyroads"] = "FULL_ST_NM" dict["prisons"] = "PRISON_NM" dict["military"] = "BASE_NM" dict["schools"] = "SCHOOL_NM" dict["cities"] = "CITY_NM" dict["cities_point"] = "CITY_NM" dict["lakes"] = "BODY_NM" dict["railroads"] = "RR_NM" dict["rivers"] = "STRM_NM" dict["federalroads"] = "FULL_ST_NM" print "Performing intersects..." fcList = arcpy.ListFeatureClasses() for fc in fcList: if fc != "union" and fc != "grid": print str(fc) arcpy.Intersect_analysis(["union", fc], fc + "__INTERSECT") del fcList del fc print "Summarizing..." fcList = arcpy.ListFeatureClasses() for fc in fcList: if fc.split("__")[-1] == "INTERSECT": dictname = fc.split("__")[0] print dictname field = dict[dictname] arcpy.AddField_management(fc, "UNIQUE", "TEXT", "", "", 250) cursor = arcpy.UpdateCursor(fc) for row in cursor: value = row.getValue(field) if value is None: value = "" row.setValue("UNIQUE", str(row.ID) + row.CNTY_NM + value) cursor.updateRow(row) del cursor del row arcpy.Statistics_analysis(fc, dictname + "_SUMMARIZED", [["ID", "MIN"], ["CNTY_NM", "FIRST"], [dict[dictname], "FIRST"]], ["UNIQUE"]) print "Merging with point tables..." arcpy.Merge_management(["cemeteries_SUMMARIZED", "cemeteries_point_SUMMARIZED"], "cemeteries_all_SUMMARIZED") arcpy.Merge_management(["airports_SUMMARIZED", "airports_point_SUMMARIZED"], "airports_all_SUMMARIZED") arcpy.Merge_management(["cities_SUMMARIZED", "cities_point_SUMMARIZED"], "cities_all_SUMMARIZED") print "Renaming tables..." arcpy.Rename_management("cemeteries_SUMMARIZED", "cemeteries_SUMpreMERGE") arcpy.Rename_management("cemeteries_point_SUMMARIZED", "cemeteries_point_SUMpreMERGE") arcpy.Rename_management("airports_SUMMARIZED", "airports_SUMpreMERGE") arcpy.Rename_management("airports_point_SUMMARIZED", "airports_point_SUMpreMERGE") arcpy.Rename_management("cities_SUMMARIZED", "cities_SUMpreMERGE") arcpy.Rename_management("cities_point_SUMMARIZED", "cities_point_SUMpreMERGE") def pdf(): print "Making directory..." if not os.path.exists(workspace + "\\PDF"): os.makedirs(workspace + "\\PDF") else: for file in os.listdir(workspace + "\\PDF"): thefile = os.path.join(workspace + "\\PDF", file) os.remove(thefile) # print "Creating field dictionary..." dict = {} dict["histmarkers"] = "label" #dict["campgrounds"] = "" dict["restareas"] = "label" dict["parks"] = "LAND_NM" dict["cemeteries"] = "CEMETERY_NM" dict["cemeteries_point"] = "CEMETERY_NM" dict["highways"] = "FULL_ST_NM" dict["counties"] = "CNTY_NM" dict["airports"] = "ARPRT_NM" dict["airports_point"] = "ARPRT_NM" dict["countyroads"] = "FULL_ST_NM" dict["prisons"] = "PRISON_NM" dict["military"] = "BASE_NM" dict["schools"] = "SCHOOL_NM" dict["cities"] = "CITY_NM" dict["cities_point"] = "CITY_NM" dict["lakes"] = "BODY_NM" dict["railroads"] = "RR_NM" dict["rivers"] = "STRM_NM" dict["federalroads"] = "FULL_ST_NM" # print "Creating label dictionary..." dict2 = {} dict2["histmarkers"] = ["Marker", "Historical Markers", 1] #dict2["campgrounds"] = [""] dict2["restareas"] = ["Rest Area", "Rest Areas", 1] dict2["parks"] = ["Park", "Parks", 1] dict2["cemeteries"] = ["Cemetery", "Cemeteries", 1] dict2["cemeteries_point"] = ["Cemetery", "Cemeteries", 1] dict2["highways"] = ["Highway", "Highways", 1] dict2["counties"] = ["County", "Counties", 1] dict2["airports"] = ["Airport", "Airports", 1] dict2["airports_point"] = ["Airport", "Airports", 1.925] dict2["countyroads"] = ["Street", "County Roads", 1] dict2["prisons"] = ["Prison", "Prisons", 1] dict2["military"] = ["Military Base", "Military Bases", 1] dict2["schools"] = ["School", "Schools", 1] dict2["cities"] = ["City", "Cities", 1] dict2["cities_point"] = ["City", "Cities", 1] dict2["lakes"] = ["Lake", "Lakes", 1] dict2["railroads"] = ["Railroad", "Railroads", 1] dict2["rivers"] = ["River", "Major Rivers", 1] dict2["federalroads"] = ["Roadway", "Federal Roads", 1] # print "Creating index PDF pages..." env.workspace = database fcList = arcpy.ListTables() fcList.sort() for fc in fcList: if fc.split("_")[-1] == "SUMMARIZED": feature = fc.split("_")[0] print feature title = dict2[feature][1] subtitle = dict2[feature][0] wide = dict2[feature][2] from reportlab.lib.pagesizes import ELEVENSEVENTEEN, landscape width, height = landscape(ELEVENSEVENTEEN) f = frames.Frame(.8*inch, .8*inch, wide*inch, 9.4*inch, bottomPadding=1) f2 = frames.Frame((.8 + 2 * wide)*inch, .8*inch, wide*inch, 9.4*inch, bottomPadding=1) f3 = frames.Frame((.8 + 3 * wide), .8*inch, wide*inch, 9.4*inch, bottomPadding=1) f4 = frames.Frame((.8 + 4 * wide), .8*inch, wide*inch, 9.4*inch, bottomPadding=1) f5 = frames.Frame((.8 + 5 * wide), .8*inch, wide*inch, 9.4*inch, bottomPadding=1) f6 = frames.Frame((.8 + 6 * wide), .8*inch, wide*inch, 9.4*inch, bottomPadding=1) f7 = frames.Frame((.8 + 7 * wide), .8*inch, wide*inch, 9.4*inch, bottomPadding=1) f8 = frames.Frame((.8 + 8 * wide), .8*inch, wide*inch, 9.4*inch, bottomPadding=1) doc = BaseDocTemplate(workspace + "\\PDF\\" + fc + ".pdf", pagesize=landscape(ELEVENSEVENTEEN)) def thecanvas(c, doc): from reportlab.lib.pagesizes import ELEVENSEVENTEEN, landscape width, height = landscape(ELEVENSEVENTEEN) c.setFont("Helvetica-Bold", 18) c.setFillColorRGB(0, 0, 1) c.drawCentredString(width/2,height - .7*inch, title.upper()) c.setFillColorRGB(.5, .3, .1) #hortizontal lines c.line(.75*inch, height - .75*inch, width - .75*inch, height - .75*inch) c.line(.75*inch, .75*inch, width - .75*inch, .75*inch) c.setFont("Times-Bold", 8) c.setFillColorRGB(0, 0, 0) pageNUM = c.getPageNumber() c.drawRightString(width-.75*inch, .6*inch, "Page " + str(pageNUM)) doc.addPageTemplates([PageTemplate(frames=[f, f2, f3, f4, f5, f6, f7, f8], onPage=thecanvas)]) # elements = [] data = [] dictvalue = dict[fc.split("_")[0]] cursor = arcpy.SearchCursor(fc, "", "", "", "FIRST_CNTY_NM A; FIRST_" + dictvalue + " A; MIN_ID A") starter = 0 counto = int(arcpy.GetCount_management(fc).getOutput(0)) total = counto-1 previous = "" for row in cursor: current = row.FIRST_CNTY_NM if starter == 0: line = ["", current, ""] data.append(line) t = Table(data, colWidths=[.1*inch, 1.725*inch, .1*inch], rowHeights=[.3*inch]*len(data), ) t.setStyle(TableStyle([ ('FONTNAME', (1, 0), (1, 0), 'Times-Bold'), ('FONTSIZE', (1, 0), (1, 0), 12), ('TEXTCOLOR', (1, 0), (1, 0), colors.red), ('ALIGN', (1, 0), (1, 0), 'CENTER'), ('VALIGN', (1, 0), (1, 0), 'MIDDLE'), ('LINEBEFORE', (0, 0), (0, 0), 1, colors.saddlebrown), ('LINEAFTER', (2, 0), (2, 0), 1, colors.saddlebrown), ('LINEBELOW', (1, 0), (1, 0), 1, colors.saddlebrown) ])) elements.append(t) data = [] line = [""] data.append(line) t = Table(data, colWidths=[1.925*inch], rowHeights=[.1*inch]*len(data), ) t.setStyle(TableStyle([ ('LINEBEFORE', (0, 0), (0, 0), 1, colors.saddlebrown), ('LINEAFTER', (0, 0), (0, 0), 1, colors.saddlebrown) ])) elements.append(t) data = [] line = [subtitle, "Page"] data.append(line) t = Table(data, colWidths=[1.625*inch, .3*inch], rowHeights=[.12*inch]*len(data)) t.setStyle(TableStyle([ ('FONTNAME', (0, 0), (1, (len(data)-1)), 'Times-BoldItalic'), ('FONTSIZE', (0, 0), (1, (len(data)-1)), 6), ('TEXTCOLOR', (0, 0), (1, (len(data)-1)), colors.darkblue), ('ALIGN', (0, 0), (0, (len(data)-1)), 'LEFT'), ('ALIGN', (1, 0), (1, (len(data)-1)), 'RIGHT'), ('LINEBEFORE', (0, 0), (0, (len(data)-1)), 1, colors.saddlebrown), ('LINEAFTER', (1, 0), (1, (len(data)-1)), 1, colors.saddlebrown), ('BOTTOMPADDING', (0, 0), (1, (len(data)-1)), -1) ])) elements.append(t) data = [] cellvalue = row.getValue("FIRST_" + dictvalue) line = [cellvalue, int(row.MIN_ID)] data.append(line) previous = current elif current == previous and starter != total: cellvalue = row.getValue("FIRST_" + dictvalue) line = [cellvalue, int(row.MIN_ID)] data.append(line) previous = current elif current != previous and starter != total: t = Table(data, colWidths=[1.625*inch, .3*inch], rowHeights=[.12*inch]*len(data)) t.setStyle(TableStyle([ ('FONTSIZE', (0, 0), (1, (len(data)-1)), 6), ('ALIGN', (0, 0), (0, (len(data)-1)), 'LEFT'), ('ALIGN', (1, 0), (1, (len(data)-1)), 'RIGHT'), ('BACKGROUND', (1, 0), (1, (len(data)-1)), colors.Color(.9, .9, .9)), ('LINEBEFORE', (0, 0), (0, (len(data)-1)), 1, colors.saddlebrown), ('LINEAFTER', (1, 0), (1, (len(data)-1)), 1, colors.saddlebrown), ('BOTTOMPADDING', (0, 0), (1, (len(data)-1)), -4) ])) elements.append(t) previous = current data = [] line = ["", current, ""] data.append(line) t = Table(data, colWidths=[.1*inch, 1.725*inch, .1*inch], rowHeights=[.3*inch]*len(data), ) t.setStyle(TableStyle([ ('FONTNAME', (1, 0), (1, 0), 'Times-Bold'), ('FONTSIZE', (1, 0), (1, 0), 12), ('TEXTCOLOR', (1, 0), (1, 0), colors.red), ('ALIGN', (1, 0), (1, 0), 'CENTER'), ('VALIGN', (1, 0), (1, 0), 'MIDDLE'), ('LINEBEFORE', (0, 0), (0, 0), 1, colors.saddlebrown), ('LINEAFTER', (2, 0), (2, 0), 1, colors.saddlebrown), ('LINEBELOW', (1, 0), (1, 0), 1, colors.saddlebrown) ])) elements.append(t) data = [] line = [""] data.append(line) t = Table(data, colWidths=[1.925*inch], rowHeights=[.1*inch]*len(data), ) t.setStyle(TableStyle([ ('LINEBEFORE', (0, 0), (0, 0), 1, colors.saddlebrown), ('LINEAFTER', (0, 0), (0, 0), 1, colors.saddlebrown) ])) elements.append(t) data = [] line = [subtitle, "Page"] data.append(line) t = Table(data, colWidths=[1.625*inch, .3*inch], rowHeights=[.12*inch]*len(data)) t.setStyle(TableStyle([ ('FONTNAME', (0, 0), (1, (len(data)-1)), 'Times-BoldItalic'), ('FONTSIZE', (0, 0), (1, (len(data)-1)), 6), ('TEXTCOLOR', (0, 0), (1, (len(data)-1)), colors.darkblue), ('ALIGN', (0, 0), (0, (len(data)-1)), 'LEFT'), ('ALIGN', (1, 0), (1, (len(data)-1)), 'RIGHT'), ('LINEBEFORE', (0, 0), (0, (len(data)-1)), 1, colors.saddlebrown), ('LINEAFTER', (1, 0), (1, (len(data)-1)), 1, colors.saddlebrown), ('BOTTOMPADDING', (0, 0), (1, (len(data)-1)), -1) ])) elements.append(t) data = [] cellvalue = row.getValue("FIRST_" + dictvalue) line = [cellvalue, int(row.MIN_ID)] data.append(line) elif current == previous and starter == total: cellvalue = row.getValue("FIRST_" + dictvalue) line = [cellvalue, int(row.MIN_ID)] data.append(line) t = Table(data, colWidths=[1.625*inch, .3*inch], rowHeights=[.12*inch]*len(data)) t.setStyle(TableStyle([ ('FONTSIZE', (0, 0), (1, (len(data)-1)), 6), ('ALIGN', (0, 0), (0, (len(data)-1)), 'LEFT'), ('ALIGN', (1, 0), (1, (len(data)-1)), 'RIGHT'), ('BACKGROUND', (1, 0), (1, (len(data)-1)), colors.Color(.9, .9, .9)), ('LINEBEFORE', (0, 0), (0, (len(data)-1)), 1, colors.saddlebrown), ('LINEAFTER', (1, 0), (1, (len(data)-1)), 1, colors.saddlebrown), ('BOTTOMPADDING', (0, 0), (1, (len(data)-1)), -4) ])) elements.append(t) elif current != previous and starter == total: t = Table(data, colWidths=[1.625*inch, .3*inch], rowHeights=[.12*inch]*len(data)) t.setStyle(TableStyle([ ('FONTSIZE', (0, 0), (1, (len(data)-1)), 6), ('ALIGN', (0, 0), (0, (len(data)-1)), 'LEFT'), ('ALIGN', (1, 0), (1, (len(data)-1)), 'RIGHT'), ('BACKGROUND', (1, 0), (1, (len(data)-1)), colors.Color(.9, .9, .9)), ('LINEBEFORE', (0, 0), (0, (len(data)-1)), 1, colors.saddlebrown), ('LINEAFTER', (1, 0), (1, (len(data)-1)), 1, colors.saddlebrown), ('BOTTOMPADDING', (0, 0), (1, (len(data)-1)), -4) ])) elements.append(t) previous = current data = [] line = ["", current, ""] data.append(line) t = Table(data, colWidths=[.1*inch, 1.725*inch, .1*inch], rowHeights=[.3*inch]*len(data), ) t.setStyle(TableStyle([ ('FONTNAME', (1, 0), (1, 0), 'Times-Bold'), ('FONTSIZE', (1, 0), (1, 0), 12), ('TEXTCOLOR', (1, 0), (1, 0), colors.red), ('ALIGN', (1, 0), (1, 0), 'CENTER'), ('VALIGN', (1, 0), (1, 0), 'MIDDLE'), ('LINEBEFORE', (0, 0), (0, 0), 1, colors.saddlebrown), ('LINEAFTER', (2, 0), (2, 0), 1, colors.saddlebrown), ('LINEBELOW', (1, 0), (1, 0), 1, colors.saddlebrown) ])) elements.append(t) data = [] line = [""] data.append(line) t = Table(data, colWidths=[1.925*inch], rowHeights=[.1*inch]*len(data), ) t.setStyle(TableStyle([ ('LINEBEFORE', (0, 0), (0, 0), 1, colors.saddlebrown), ('LINEAFTER', (0, 0), (0, 0), 1, colors.saddlebrown) ])) elements.append(t) data = [] line = [subtitle, "Page"] data.append(line) t = Table(data, colWidths=[1.625*inch, .3*inch], rowHeights=[.12*inch]*len(data)) t.setStyle(TableStyle([ ('FONTNAME', (0, 0), (1, (len(data)-1)), 'Times-BoldItalic'), ('FONTSIZE', (0, 0), (1, (len(data)-1)), 6), ('TEXTCOLOR', (0, 0), (1, (len(data)-1)), colors.darkblue), ('ALIGN', (0, 0), (0, (len(data)-1)), 'LEFT'), ('ALIGN', (1, 0), (1, (len(data)-1)), 'RIGHT'), ('LINEBEFORE', (0, 0), (0, (len(data)-1)), 1, colors.saddlebrown), ('LINEAFTER', (1, 0), (1, (len(data)-1)), 1, colors.saddlebrown), ('BOTTOMPADDING', (0, 0), (1, (len(data)-1)), -1) ])) elements.append(t) data = [] cellvalue = row.getValue("FIRST_" + dictvalue) line = [cellvalue, int(row.MIN_ID)] data.append(line) t = Table(data, colWidths=[1.625*inch, .3*inch], rowHeights=[.12*inch]*len(data)) t.setStyle(TableStyle([ ('FONTSIZE', (0, 0), (1, (len(data)-1)), 6), ('ALIGN', (0, 0), (0, (len(data)-1)), 'LEFT'), ('ALIGN', (1, 0), (1, (len(data)-1)), 'RIGHT'), ('BACKGROUND', (1, 0), (1, (len(data)-1)), colors.Color(.9, .9, .9)), ('LINEBEFORE', (0, 0), (0, (len(data)-1)), 1, colors.saddlebrown), ('LINEAFTER', (1, 0), (1, (len(data)-1)), 1, colors.saddlebrown), ('BOTTOMPADDING', (0, 0), (1, (len(data)-1)), -4) ])) elements.append(t) starter += 1 doc.build(elements) del cursor #preparation() #intersects() pdf() print "That's all folks!"

53.021739

202

0.523948

79514e09e1d51f8e7065d45bcfab6dc44035c06a

11,386

py

Python

auth0/v3/management/users.py

emfloyd2/auth0-python

c99e7fccb050740051f13785775a116684cf0006

[ "MIT" ]

null

auth0/v3/management/users.py

emfloyd2/auth0-python

c99e7fccb050740051f13785775a116684cf0006

[ "MIT" ]

null

auth0/v3/management/users.py

emfloyd2/auth0-python

c99e7fccb050740051f13785775a116684cf0006

[ "MIT" ]

null

from .rest import RestClient class Users(object): """Auth0 users endpoints Args: domain (str): Your Auth0 domain, e.g: 'username.auth0.com' token (str): Management API v2 Token telemetry (bool, optional): Enable or disable Telemetry (defaults to True) """ def __init__(self, domain, token, telemetry=True): self.domain = domain self.client = RestClient(jwt=token, telemetry=telemetry) def _url(self, id=None): url = 'https://{}/api/v2/users'.format(self.domain) if id is not None: return '{}/{}'.format(url, id) return url def list(self, page=0, per_page=25, sort=None, connection=None, q=None, search_engine=None, include_totals=True, fields=None, include_fields=True): """List or search users. Args: page (int, optional): The result's page number (zero based). per_page (int, optional): The amount of entries per page. sort (str, optional): The field to use for sorting. 1 == ascending and -1 == descending. (e.g: email:1) connection (str, optional): Connection filter. q (str, optional): Query in Lucene query string syntax. Only fields in app_metadata, user_metadata or the normalized user profile are searchable. search_engine (str, optional): The version of the search_engine to use when querying for users. Will default to the latest version available. See: https://auth0.com/docs/users/search include_totals (bool, optional): True if the query summary is to be included in the result, False otherwise. fields (list of str, optional): A list of fields to include or exclude from the result (depending on include_fields). Empty to retrieve all fields. include_fields (bool, optional): True if the fields specified are to be include in the result, False otherwise. See: https://auth0.com/docs/api/management/v2#!/Users/get_users """ params = { 'per_page': per_page, 'page': page, 'include_totals': str(include_totals).lower(), 'sort': sort, 'connection': connection, 'fields': fields and ','.join(fields) or None, 'include_fields': str(include_fields).lower(), 'q': q, 'search_engine': search_engine } return self.client.get(self._url(), params=params) def create(self, body): """Creates a new user. Args: body (dict): Please see: https://auth0.com/docs/api/v2#!/Users/post_users """ return self.client.post(self._url(), data=body) def delete_all_users(self): """Deletes all users (USE WITH CAUTION). Args: """ return self.client.delete(self._url()) def get(self, id, fields=None, include_fields=True): """Get a user. Args: id (str): The user_id of the user to retrieve. fields (list of str, optional): A list of fields to include or exclude from the result (depending on include_fields). Empty to retrieve all fields. include_fields (bool, optional): True if the fields specified are to be included in the result, False otherwise. See: https://auth0.com/docs/api/management/v2#!/Users/get_users_by_id """ params = { 'fields': fields and ','.join(fields) or None, 'include_fields': str(include_fields).lower() } return self.client.get(self._url(id), params=params) def delete(self, id): """Delete a user. Args: id (str): The user_id of the user to delete. See: https://auth0.com/docs/api/management/v2#!/Users/delete_users_by_id """ return self.client.delete(self._url(id)) def update(self, id, body): """Update a user with the attributes passed in 'body' Args: id (str): The user_id of the user to update. body (dict): Please see: https://auth0.com/docs/api/v2#!/Users/patch_users_by_id """ return self.client.patch(self._url(id), data=body) def list_roles(self, id, page=0, per_page=25, include_totals=True): """List the roles associated with a user. Args: id (str): The user's id. page (int, optional): The result's page number (zero based). per_page (int, optional): The amount of entries per page. include_totals (bool, optional): True if the query summary is to be included in the result, False otherwise. See https://auth0.com/docs/api/management/v2#!/Users/get_user_roles """ params = { 'per_page': per_page, 'page': page, 'include_totals': str(include_totals).lower() } url = self._url('{}/roles'.format(id)) return self.client.get(url, params=params) def remove_roles(self, id, roles): """Removes an array of roles from a user. Args: id (str): The user's id. roles (list of str): A list of roles ids to unassociate from the user. See https://auth0.com/docs/api/management/v2#!/Users/delete_user_roles """ url = self._url('{}/roles'.format(id)) body = {'roles': roles} return self.client.delete(url, data=body) def add_roles(self, id, roles): """Associate an array of roles with a user. Args: id (str): The user's id. roles (list of str): A list of roles ids to associated with the user. See https://auth0.com/docs/api/management/v2#!/Users/post_user_roles """ url = self._url('{}/roles'.format(id)) body = {'roles': roles} return self.client.post(url, data=body) def list_permissions(self, id, page=0, per_page=25, include_totals=True): """List the permissions associated to the user. Args: id (str): The user's id. page (int, optional): The result's page number (zero based). per_page (int, optional): The amount of entries per page. include_totals (bool, optional): True if the query summary is to be included in the result, False otherwise. See https://auth0.com/docs/api/management/v2#!/Users/get_permissions """ params = { 'per_page': per_page, 'page': page, 'include_totals': str(include_totals).lower() } url = self._url('{}/permissions'.format(id)) return self.client.get(url, params=params) def remove_permissions(self, id, permissions): """Removes permissions from a user. Args: id (str): The user's id. permissions (list of str): A list of permission ids to unassociate from the user. See https://auth0.com/docs/api/management/v2#!/Users/delete_permissions """ url = self._url('{}/permissions'.format(id)) body = {'permissions': permissions} return self.client.delete(url, data=body) def add_permissions(self, id, permissions): """Assign permissions to a user. Args: id (str): The user's id. permissions (list of str): A list of permission ids to associated with the user. See https://auth0.com/docs/api/management/v2#!/Users/post_permissions """ url = self._url('{}/permissions'.format(id)) body = {'permissions': permissions} return self.client.post(url, data=body) def delete_multifactor(self, id, provider): """Delete a user's multifactor provider. Args: id (str): The user's id. provider (str): The multifactor provider. Supported values 'duo' or 'google-authenticator' See: https://auth0.com/docs/api/management/v2#!/Users/delete_multifactor_by_provider """ url = self._url('{}/multifactor/{}'.format(id, provider)) return self.client.delete(url) def unlink_user_account(self, id, provider, user_id): """Unlink a user account Args: id (str): The user_id of the user identity. provider (str): The type of identity provider (e.g: facebook). user_id (str): The unique identifier for the user for the identity. See: https://auth0.com/docs/api/management/v2#!/Users/delete_user_identity_by_user_id """ url = self._url('{}/identities/{}/{}'.format(id, provider, user_id)) return self.client.delete(url) def link_user_account(self, user_id, body): """Link user accounts. Links the account specified in the body (secondary account) to the account specified by the id param of the URL (primary account). Args: id (str): The user_id of the primary identity where you are linking the secondary account to. body (dict): Please see: https://auth0.com/docs/api/v2#!/Users/post_identities """ url = self._url('{}/identities'.format(user_id)) return self.client.post(url, data=body) def regenerate_recovery_code(self, user_id): """Removes the current recovery token, generates and returns a new one Args: user_id (str): The user_id of the user identity. See: https://auth0.com/docs/api/management/v2#!/Users/post_recovery_code_regeneration """ url = self._url('{}/recovery-code-regeneration'.format(user_id)) return self.client.post(url) def get_guardian_enrollments(self, user_id): """Retrieves all Guardian enrollments. Args: user_id (str): The user_id of the user to retrieve See: https://auth0.com/docs/api/management/v2#!/Users/get_enrollments """ url = self._url('{}/enrollments'.format(user_id)) return self.client.get(url) def get_log_events(self, user_id, page=0, per_page=50, sort=None, include_totals=False): """Retrieve every log event for a specific user id Args: user_id (str): The user_id of the logs to retrieve page (int, optional): The result's page number (zero based). per_page (int, optional): The amount of entries per page. Default: 50. Max value: 100 sort (str, optional): The field to use for sorting. Use field:order where order is 1 for ascending and -1 for descending. For example date:-1 include_totals (bool, optional): True if the query summary is to be included in the result, False otherwise. See: https://auth0.com/docs/api/management/v2#!/Users/get_logs_by_user """ params = { 'per_page': per_page, 'page': page, 'include_totals': str(include_totals).lower(), 'sort': sort } url = self._url('{}/logs'.format(user_id)) return self.client.get(url, params=params)

34.192192

93

0.589232

79514f822ed57b90a3ac05a7838024972a9877ea

76,582

py

Python

nova/tests/network/test_neutronv2.py

citrix-openstack-build/nova

e5fc28adda4b95325f74402fe63ea969e816e6c3

[ "Apache-2.0" ]

null

nova/tests/network/test_neutronv2.py

citrix-openstack-build/nova

e5fc28adda4b95325f74402fe63ea969e816e6c3

[ "Apache-2.0" ]

null

nova/tests/network/test_neutronv2.py

citrix-openstack-build/nova

e5fc28adda4b95325f74402fe63ea969e816e6c3

[ "Apache-2.0" ]

null

# Copyright 2012 OpenStack Foundation # 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. # # vim: tabstop=4 shiftwidth=4 softtabstop=4 import uuid import mox from neutronclient.common import exceptions from neutronclient.v2_0 import client from oslo.config import cfg from nova.compute import flavors from nova.conductor import api as conductor_api from nova import context from nova import exception from nova.network import model from nova.network import neutronv2 from nova.network.neutronv2 import api as neutronapi from nova.network.neutronv2 import constants from nova.openstack.common import jsonutils from nova import test from nova import utils CONF = cfg.CONF #NOTE: Neutron client raises Exception which is discouraged by HACKING. # We set this variable here and use it for assertions below to avoid # the hacking checks until we can make neutron client throw a custom # exception class instead. NEUTRON_CLIENT_EXCEPTION = Exception class MyComparator(mox.Comparator): def __init__(self, lhs): self.lhs = lhs def _com_dict(self, lhs, rhs): if len(lhs) != len(rhs): return False for key, value in lhs.iteritems(): if key not in rhs: return False rhs_value = rhs[key] if not self._com(value, rhs_value): return False return True def _com_list(self, lhs, rhs): if len(lhs) != len(rhs): return False for lhs_value in lhs: if lhs_value not in rhs: return False return True def _com(self, lhs, rhs): if lhs is None: return rhs is None if isinstance(lhs, dict): if not isinstance(rhs, dict): return False return self._com_dict(lhs, rhs) if isinstance(lhs, list): if not isinstance(rhs, list): return False return self._com_list(lhs, rhs) if isinstance(lhs, tuple): if not isinstance(rhs, tuple): return False return self._com_list(lhs, rhs) return lhs == rhs def equals(self, rhs): return self._com(self.lhs, rhs) def __repr__(self): return str(self.lhs) class TestNeutronClient(test.TestCase): def test_withtoken(self): self.flags(neutron_url='http://anyhost/') self.flags(neutron_url_timeout=30) my_context = context.RequestContext('userid', 'my_tenantid', auth_token='token') self.mox.StubOutWithMock(client.Client, "__init__") client.Client.__init__( endpoint_url=CONF.neutron_url, token=my_context.auth_token, timeout=CONF.neutron_url_timeout, insecure=False, ca_cert=None).AndReturn(None) self.mox.ReplayAll() neutronv2.get_client(my_context) def test_withouttoken_keystone_connection_error(self): self.flags(neutron_auth_strategy='keystone') self.flags(neutron_url='http://anyhost/') my_context = context.RequestContext('userid', 'my_tenantid') self.assertRaises(NEUTRON_CLIENT_EXCEPTION, neutronv2.get_client, my_context) def test_withouttoken_keystone_not_auth(self): self.flags(neutron_auth_strategy=None) self.flags(neutron_url='http://anyhost/') self.flags(neutron_url_timeout=30) my_context = context.RequestContext('userid', 'my_tenantid') self.mox.StubOutWithMock(client.Client, "__init__") client.Client.__init__( endpoint_url=CONF.neutron_url, auth_strategy=None, timeout=CONF.neutron_url_timeout, insecure=False, ca_cert=None).AndReturn(None) self.mox.ReplayAll() neutronv2.get_client(my_context) class TestNeutronv2Base(test.TestCase): def setUp(self): super(TestNeutronv2Base, self).setUp() self.context = context.RequestContext('userid', 'my_tenantid') setattr(self.context, 'auth_token', 'bff4a5a6b9eb4ea2a6efec6eefb77936') self.instance = {'project_id': '9d049e4b60b64716978ab415e6fbd5c0', 'uuid': str(uuid.uuid4()), 'display_name': 'test_instance', 'availability_zone': 'nova', 'host': 'some_host', 'security_groups': []} self.instance2 = {'project_id': '9d049e4b60b64716978ab415e6fbd5c0', 'uuid': str(uuid.uuid4()), 'display_name': 'test_instance2', 'availability_zone': 'nova', 'security_groups': []} self.nets1 = [{'id': 'my_netid1', 'name': 'my_netname1', 'tenant_id': 'my_tenantid'}] self.nets2 = [] self.nets2.append(self.nets1[0]) self.nets2.append({'id': 'my_netid2', 'name': 'my_netname2', 'tenant_id': 'my_tenantid'}) self.nets3 = self.nets2 + [{'id': 'my_netid3', 'name': 'my_netname3', 'tenant_id': 'my_tenantid'}] self.nets4 = [{'id': 'his_netid4', 'name': 'his_netname4', 'tenant_id': 'his_tenantid'}] self.nets = [self.nets1, self.nets2, self.nets3, self.nets4] self.port_address = '10.0.1.2' self.port_data1 = [{'network_id': 'my_netid1', 'device_id': self.instance2['uuid'], 'device_owner': 'compute:nova', 'id': 'my_portid1', 'fixed_ips': [{'ip_address': self.port_address, 'subnet_id': 'my_subid1'}], 'mac_address': 'my_mac1', }] self.float_data1 = [{'port_id': 'my_portid1', 'fixed_ip_address': self.port_address, 'floating_ip_address': '172.0.1.2'}] self.dhcp_port_data1 = [{'fixed_ips': [{'ip_address': '10.0.1.9', 'subnet_id': 'my_subid1'}]}] self.port_address2 = '10.0.2.2' self.port_data2 = [] self.port_data2.append(self.port_data1[0]) self.port_data2.append({'network_id': 'my_netid2', 'device_id': self.instance['uuid'], 'device_owner': 'compute:nova', 'id': 'my_portid2', 'fixed_ips': [{'ip_address': self.port_address2, 'subnet_id': 'my_subid2'}], 'mac_address': 'my_mac2', }) self.float_data2 = [] self.float_data2.append(self.float_data1[0]) self.float_data2.append({'port_id': 'my_portid2', 'fixed_ip_address': '10.0.2.2', 'floating_ip_address': '172.0.2.2'}) self.port_data3 = [{'network_id': 'my_netid1', 'device_id': 'device_id3', 'device_owner': 'compute:nova', 'id': 'my_portid3', 'fixed_ips': [], # no fixed ip 'mac_address': 'my_mac3', }] self.subnet_data1 = [{'id': 'my_subid1', 'cidr': '10.0.1.0/24', 'network_id': 'my_netid1', 'gateway_ip': '10.0.1.1', 'dns_nameservers': ['8.8.1.1', '8.8.1.2']}] self.subnet_data2 = [] self.subnet_data_n = [{'id': 'my_subid1', 'cidr': '10.0.1.0/24', 'network_id': 'my_netid1', 'gateway_ip': '10.0.1.1', 'dns_nameservers': ['8.8.1.1', '8.8.1.2']}, {'id': 'my_subid2', 'cidr': '20.0.1.0/24', 'network_id': 'my_netid2', 'gateway_ip': '20.0.1.1', 'dns_nameservers': ['8.8.1.1', '8.8.1.2']}] self.subnet_data2.append({'id': 'my_subid2', 'cidr': '10.0.2.0/24', 'network_id': 'my_netid2', 'gateway_ip': '10.0.2.1', 'dns_nameservers': ['8.8.2.1', '8.8.2.2']}) self.fip_pool = {'id': '4fdbfd74-eaf8-4884-90d9-00bd6f10c2d3', 'name': 'ext_net', 'router:external': True, 'tenant_id': 'admin_tenantid'} self.fip_pool_nova = {'id': '435e20c3-d9f1-4f1b-bee5-4611a1dd07db', 'name': 'nova', 'router:external': True, 'tenant_id': 'admin_tenantid'} self.fip_unassociated = {'tenant_id': 'my_tenantid', 'id': 'fip_id1', 'floating_ip_address': '172.24.4.227', 'floating_network_id': self.fip_pool['id'], 'port_id': None, 'fixed_ip_address': None, 'router_id': None} fixed_ip_address = self.port_data2[1]['fixed_ips'][0]['ip_address'] self.fip_associated = {'tenant_id': 'my_tenantid', 'id': 'fip_id2', 'floating_ip_address': '172.24.4.228', 'floating_network_id': self.fip_pool['id'], 'port_id': self.port_data2[1]['id'], 'fixed_ip_address': fixed_ip_address, 'router_id': 'router_id1'} self._returned_nw_info = [] self.mox.StubOutWithMock(neutronv2, 'get_client') self.moxed_client = self.mox.CreateMock(client.Client) self.addCleanup(CONF.reset) self.addCleanup(self.mox.VerifyAll) self.addCleanup(self.mox.UnsetStubs) self.addCleanup(self.stubs.UnsetAll) def _stub_allocate_for_instance(self, net_idx=1, **kwargs): api = neutronapi.API() self.mox.StubOutWithMock(api, '_get_instance_nw_info') has_portbinding = False has_extra_dhcp_opts = False # Note: (dkehn) this option check should be removed as soon as support # in neutron released, see https://bugs.launchpad.net/nova/+bug/1214162 if (cfg.CONF.dhcp_options_enabled == True and kwargs.get( 'dhcp_options', None) != None): has_extra_dhcp_opts = True dhcp_options = kwargs.get('dhcp_options') if kwargs.get('portbinding'): has_portbinding = True api.extensions[constants.PORTBINDING_EXT] = 1 self.mox.StubOutWithMock(api, '_refresh_neutron_extensions_cache') neutronv2.get_client(mox.IgnoreArg()).MultipleTimes().AndReturn( self.moxed_client) neutronv2.get_client( mox.IgnoreArg(), admin=True).MultipleTimes().AndReturn( self.moxed_client) api._refresh_neutron_extensions_cache() else: self.mox.StubOutWithMock(api, '_populate_neutron_extension_values') self.mox.StubOutWithMock(api, '_has_port_binding_extension') # Net idx is 1-based for compatibility with existing unit tests nets = self.nets[net_idx - 1] ports = {} fixed_ips = {} macs = kwargs.get('macs') if macs: macs = set(macs) req_net_ids = [] if 'requested_networks' in kwargs: for id, fixed_ip, port_id in kwargs['requested_networks']: if port_id: self.moxed_client.show_port(port_id).AndReturn( {'port': {'id': 'my_portid1', 'network_id': 'my_netid1', 'mac_address': 'my_mac1', 'device_id': kwargs.get('_device') and self.instance2['uuid'] or ''}}) ports['my_netid1'] = self.port_data1[0] id = 'my_netid1' if macs is not None: macs.discard('my_mac1') else: fixed_ips[id] = fixed_ip req_net_ids.append(id) expected_network_order = req_net_ids else: expected_network_order = [n['id'] for n in nets] if kwargs.get('_break') == 'pre_list_networks': self.mox.ReplayAll() return api search_ids = [net['id'] for net in nets if net['id'] in req_net_ids] if search_ids: mox_list_params = {'id': mox.SameElementsAs(search_ids)} self.moxed_client.list_networks( **mox_list_params).AndReturn({'networks': nets}) else: mox_list_params = {'tenant_id': self.instance['project_id'], 'shared': False} self.moxed_client.list_networks( **mox_list_params).AndReturn({'networks': nets}) mox_list_params = {'shared': True} self.moxed_client.list_networks( **mox_list_params).AndReturn({'networks': []}) for net_id in expected_network_order: port_req_body = { 'port': { 'device_id': self.instance['uuid'], 'device_owner': 'compute:nova', }, } if has_portbinding: port_req_body['port']['binding:host_id'] = ( self.instance.get('host')) port = ports.get(net_id, None) if not has_portbinding: api._populate_neutron_extension_values( self.instance, mox.IgnoreArg()).AndReturn(None) else: # since _populate_neutron_extension_values() will call # _has_port_binding_extension() api._has_port_binding_extension().AndReturn(has_portbinding) api._has_port_binding_extension().AndReturn(has_portbinding) if port: port_id = port['id'] self.moxed_client.update_port(port_id, MyComparator(port_req_body) ).AndReturn( {'port': port}) else: fixed_ip = fixed_ips.get(net_id) if fixed_ip: port_req_body['port']['fixed_ips'] = [{'ip_address': fixed_ip}] port_req_body['port']['network_id'] = net_id port_req_body['port']['admin_state_up'] = True port_req_body['port']['tenant_id'] = \ self.instance['project_id'] if macs: port_req_body['port']['mac_address'] = macs.pop() if has_portbinding: port_req_body['port']['binding:host_id'] = ( self.instance.get('host')) res_port = {'port': {'id': 'fake'}} if has_extra_dhcp_opts: port_req_body['port']['extra_dhcp_opts'] = dhcp_options if kwargs.get('_break') == 'mac' + net_id: self.mox.ReplayAll() return api self.moxed_client.create_port( MyComparator(port_req_body)).AndReturn(res_port) api._get_instance_nw_info(mox.IgnoreArg(), self.instance, networks=nets).AndReturn( self._returned_nw_info) self.mox.ReplayAll() return api def _verify_nw_info(self, nw_inf, index=0): id_suffix = index + 1 self.assertEquals('10.0.%s.2' % id_suffix, nw_inf.fixed_ips()[index]['address']) self.assertEquals('172.0.%s.2' % id_suffix, nw_inf.fixed_ips()[index].floating_ip_addresses()[0]) self.assertEquals('my_netname%s' % id_suffix, nw_inf[index]['network']['label']) self.assertEquals('my_portid%s' % id_suffix, nw_inf[index]['id']) self.assertEquals('my_mac%s' % id_suffix, nw_inf[index]['address']) self.assertEquals('10.0.%s.0/24' % id_suffix, nw_inf[index]['network']['subnets'][0]['cidr']) self.assertTrue(model.IP(address='8.8.%s.1' % id_suffix) in nw_inf[index]['network']['subnets'][0]['dns']) def _get_instance_nw_info(self, number): api = neutronapi.API() self.mox.StubOutWithMock(api.db, 'instance_info_cache_update') api.db.instance_info_cache_update(mox.IgnoreArg(), self.instance['uuid'], mox.IgnoreArg()) port_data = number == 1 and self.port_data1 or self.port_data2 self.mox.StubOutWithMock(conductor_api.API, 'instance_get_by_uuid') net_info_cache = [] for port in port_data: net_info_cache.append({"network": {"id": port['network_id']}}) info_cache = {'info_cache': {'network_info': jsonutils.dumps(net_info_cache)}} api.conductor_api.instance_get_by_uuid( mox.IgnoreArg(), mox.IgnoreArg()).AndReturn(info_cache) self.moxed_client.list_ports( tenant_id=self.instance['project_id'], device_id=self.instance['uuid']).AndReturn( {'ports': port_data}) nets = number == 1 and self.nets1 or self.nets2 self.moxed_client.list_networks( tenant_id=self.instance['project_id'], shared=False).AndReturn({'networks': nets}) self.moxed_client.list_networks( shared=True).AndReturn({'networks': []}) for i in xrange(1, number + 1): float_data = number == 1 and self.float_data1 or self.float_data2 for ip in port_data[i - 1]['fixed_ips']: float_data = [x for x in float_data if x['fixed_ip_address'] == ip['ip_address']] self.moxed_client.list_floatingips( fixed_ip_address=ip['ip_address'], port_id=port_data[i - 1]['id']).AndReturn( {'floatingips': float_data}) subnet_data = i == 1 and self.subnet_data1 or self.subnet_data2 self.moxed_client.list_subnets( id=mox.SameElementsAs(['my_subid%s' % i])).AndReturn( {'subnets': subnet_data}) self.moxed_client.list_ports( network_id=subnet_data[0]['network_id'], device_owner='network:dhcp').AndReturn( {'ports': []}) self.mox.ReplayAll() nw_inf = api.get_instance_nw_info(self.context, self.instance) for i in xrange(0, number): self._verify_nw_info(nw_inf, i) def _allocate_for_instance(self, net_idx=1, **kwargs): api = self._stub_allocate_for_instance(net_idx, **kwargs) return api.allocate_for_instance(self.context, self.instance, **kwargs) class TestNeutronv2(TestNeutronv2Base): def setUp(self): super(TestNeutronv2, self).setUp() neutronv2.get_client(mox.IgnoreArg()).MultipleTimes().AndReturn( self.moxed_client) def test_get_instance_nw_info_1(self): # Test to get one port in one network and subnet. neutronv2.get_client(mox.IgnoreArg(), admin=True).MultipleTimes().AndReturn( self.moxed_client) self._get_instance_nw_info(1) def test_get_instance_nw_info_2(self): # Test to get one port in each of two networks and subnets. neutronv2.get_client(mox.IgnoreArg(), admin=True).MultipleTimes().AndReturn( self.moxed_client) self._get_instance_nw_info(2) def test_get_instance_nw_info_with_nets(self): # Test get instance_nw_info with networks passed in. api = neutronapi.API() self.mox.StubOutWithMock(api.db, 'instance_info_cache_update') api.db.instance_info_cache_update( mox.IgnoreArg(), self.instance['uuid'], mox.IgnoreArg()) self.moxed_client.list_ports( tenant_id=self.instance['project_id'], device_id=self.instance['uuid']).AndReturn( {'ports': self.port_data1}) port_data = self.port_data1 for ip in port_data[0]['fixed_ips']: self.moxed_client.list_floatingips( fixed_ip_address=ip['ip_address'], port_id=port_data[0]['id']).AndReturn( {'floatingips': self.float_data1}) self.moxed_client.list_subnets( id=mox.SameElementsAs(['my_subid1'])).AndReturn( {'subnets': self.subnet_data1}) self.moxed_client.list_ports( network_id='my_netid1', device_owner='network:dhcp').AndReturn( {'ports': self.dhcp_port_data1}) neutronv2.get_client(mox.IgnoreArg(), admin=True).MultipleTimes().AndReturn( self.moxed_client) self.mox.ReplayAll() nw_inf = api.get_instance_nw_info(self.context, self.instance, networks=self.nets1) self._verify_nw_info(nw_inf, 0) def test_get_instance_nw_info_without_subnet(self): # Test get instance_nw_info for a port without subnet. api = neutronapi.API() self.mox.StubOutWithMock(api.db, 'instance_info_cache_update') api.db.instance_info_cache_update( mox.IgnoreArg(), self.instance['uuid'], mox.IgnoreArg()) self.moxed_client.list_ports( tenant_id=self.instance['project_id'], device_id=self.instance['uuid']).AndReturn( {'ports': self.port_data3}) self.moxed_client.list_networks( shared=False, tenant_id=self.instance['project_id']).AndReturn( {'networks': self.nets1}) self.moxed_client.list_networks( shared=True).AndReturn({'networks': []}) neutronv2.get_client(mox.IgnoreArg(), admin=True).MultipleTimes().AndReturn( self.moxed_client) self.mox.StubOutWithMock(conductor_api.API, 'instance_get_by_uuid') net_info_cache = [] for port in self.port_data3: net_info_cache.append({"network": {"id": port['network_id']}}) info_cache = {'info_cache': {'network_info': jsonutils.dumps(net_info_cache)}} api.conductor_api.instance_get_by_uuid( mox.IgnoreArg(), mox.IgnoreArg()).AndReturn(info_cache) self.mox.ReplayAll() nw_inf = api.get_instance_nw_info(self.context, self.instance) id_suffix = 3 self.assertEquals(0, len(nw_inf.fixed_ips())) self.assertEquals('my_netname1', nw_inf[0]['network']['label']) self.assertEquals('my_portid%s' % id_suffix, nw_inf[0]['id']) self.assertEquals('my_mac%s' % id_suffix, nw_inf[0]['address']) self.assertEquals(0, len(nw_inf[0]['network']['subnets'])) def test_refresh_neutron_extensions_cache(self): api = neutronapi.API() self.moxed_client.list_extensions().AndReturn( {'extensions': [{'name': 'nvp-qos'}]}) self.mox.ReplayAll() api._refresh_neutron_extensions_cache() self.assertEquals({'nvp-qos': {'name': 'nvp-qos'}}, api.extensions) def test_populate_neutron_extension_values_rxtx_factor(self): api = neutronapi.API() self.moxed_client.list_extensions().AndReturn( {'extensions': [{'name': 'nvp-qos'}]}) self.mox.ReplayAll() instance_type = flavors.get_default_flavor() instance_type['rxtx_factor'] = 1 sys_meta = utils.dict_to_metadata( flavors.save_flavor_info({}, instance_type)) instance = {'system_metadata': sys_meta} port_req_body = {'port': {}} api._populate_neutron_extension_values(instance, port_req_body) self.assertEquals(port_req_body['port']['rxtx_factor'], 1) def test_allocate_for_instance_1(self): # Allocate one port in one network env. self._allocate_for_instance(1) def test_allocate_for_instance_2(self): # Allocate one port in two networks env. self._allocate_for_instance(2) def test_allocate_for_instance_accepts_macs_kwargs_None(self): # The macs kwarg should be accepted as None. self._allocate_for_instance(1, macs=None) def test_allocate_for_instance_accepts_macs_kwargs_set(self): # The macs kwarg should be accepted, as a set, the # _allocate_for_instance helper checks that the mac is used to create a # port. self._allocate_for_instance(1, macs=set(['ab:cd:ef:01:23:45'])) def test_allocate_for_instance_accepts_only_portid(self): # Make sure allocate_for_instance works when only a portid is provided self._returned_nw_info = self.port_data1 result = self._allocate_for_instance( requested_networks=[(None, None, 'my_portid1')]) self.assertEqual(self.port_data1, result) def test_allocate_for_instance_not_enough_macs_via_ports(self): # using a hypervisor MAC via a pre-created port will stop it being # used to dynamically create a port on a network. We put the network # first in requested_networks so that if the code were to not pre-check # requested ports, it would incorrectly assign the mac and not fail. requested_networks = [ (self.nets2[1]['id'], None, None), (None, None, 'my_portid1')] api = self._stub_allocate_for_instance( net_idx=2, requested_networks=requested_networks, macs=set(['my_mac1']), _break='mac' + self.nets2[1]['id']) self.assertRaises(exception.PortNotFree, api.allocate_for_instance, self.context, self.instance, requested_networks=requested_networks, macs=set(['my_mac1'])) def test_allocate_for_instance_not_enough_macs(self): # If not enough MAC addresses are available to allocate to networks, an # error should be raised. # We could pass in macs=set(), but that wouldn't tell us that # allocate_for_instance tracks used macs properly, so we pass in one # mac, and ask for two networks. requested_networks = [ (self.nets2[1]['id'], None, None), (self.nets2[0]['id'], None, None)] api = self._stub_allocate_for_instance( net_idx=2, requested_networks=requested_networks, macs=set(['my_mac2']), _break='mac' + self.nets2[0]['id']) self.assertRaises(exception.PortNotFree, api.allocate_for_instance, self.context, self.instance, requested_networks=requested_networks, macs=set(['my_mac2'])) def test_allocate_for_instance_two_macs_two_networks(self): # If two MACs are available and two networks requested, two new ports # get made and no exceptions raised. requested_networks = [ (self.nets2[1]['id'], None, None), (self.nets2[0]['id'], None, None)] self._allocate_for_instance( net_idx=2, requested_networks=requested_networks, macs=set(['my_mac2', 'my_mac1'])) def test_allocate_for_instance_mac_conflicting_requested_port(self): # specify only first and last network requested_networks = [(None, None, 'my_portid1')] api = self._stub_allocate_for_instance( net_idx=1, requested_networks=requested_networks, macs=set(['unknown:mac']), _break='pre_list_networks') self.assertRaises(exception.PortNotUsable, api.allocate_for_instance, self.context, self.instance, requested_networks=requested_networks, macs=set(['unknown:mac'])) def test_allocate_for_instance_with_requested_networks(self): # specify only first and last network requested_networks = [ (net['id'], None, None) for net in (self.nets3[1], self.nets3[0], self.nets3[2])] self._allocate_for_instance(net_idx=3, requested_networks=requested_networks) def test_allocate_for_instance_with_requested_networks_with_fixedip(self): # specify only first and last network requested_networks = [(self.nets1[0]['id'], '10.0.1.0/24', None)] self._allocate_for_instance(net_idx=1, requested_networks=requested_networks) def test_allocate_for_instance_with_requested_networks_with_port(self): requested_networks = [(None, None, 'myportid1')] self._allocate_for_instance(net_idx=1, requested_networks=requested_networks) def test_allocate_for_instance_no_networks(self): """verify the exception thrown when there are no networks defined.""" api = neutronapi.API() self.moxed_client.list_networks( tenant_id=self.instance['project_id'], shared=False).AndReturn( {'networks': []}) self.moxed_client.list_networks(shared=True).AndReturn( {'networks': []}) self.mox.ReplayAll() nwinfo = api.allocate_for_instance(self.context, self.instance) self.assertEqual(len(nwinfo), 0) def test_allocate_for_instance_ex1(self): """verify we will delete created ports if we fail to allocate all net resources. Mox to raise exception when creating a second port. In this case, the code should delete the first created port. """ api = neutronapi.API() self.mox.StubOutWithMock(api, '_populate_neutron_extension_values') self.mox.StubOutWithMock(api, '_has_port_binding_extension') api._has_port_binding_extension().MultipleTimes().AndReturn(False) self.moxed_client.list_networks( tenant_id=self.instance['project_id'], shared=False).AndReturn( {'networks': self.nets2}) self.moxed_client.list_networks(shared=True).AndReturn( {'networks': []}) index = 0 for network in self.nets2: binding_port_req_body = { 'port': { 'device_id': self.instance['uuid'], 'device_owner': 'compute:nova', }, } port_req_body = { 'port': { 'network_id': network['id'], 'admin_state_up': True, 'tenant_id': self.instance['project_id'], }, } port_req_body['port'].update(binding_port_req_body['port']) port = {'id': 'portid_' + network['id']} api._populate_neutron_extension_values( self.instance, binding_port_req_body).AndReturn(None) if index == 0: self.moxed_client.create_port( MyComparator(port_req_body)).AndReturn({'port': port}) else: NeutronOverQuota = exceptions.NeutronClientException( message="Quota exceeded for resources: ['port']", status_code=409) self.moxed_client.create_port( MyComparator(port_req_body)).AndRaise(NeutronOverQuota) index += 1 self.moxed_client.delete_port('portid_' + self.nets2[0]['id']) self.mox.ReplayAll() self.assertRaises(exception.PortLimitExceeded, api.allocate_for_instance, self.context, self.instance) def test_allocate_for_instance_ex2(self): """verify we have no port to delete if we fail to allocate the first net resource. Mox to raise exception when creating the first port. In this case, the code should not delete any ports. """ api = neutronapi.API() self.moxed_client.list_networks( tenant_id=self.instance['project_id'], shared=False).AndReturn( {'networks': self.nets2}) self.moxed_client.list_networks(shared=True).AndReturn( {'networks': []}) port_req_body = { 'port': { 'network_id': self.nets2[0]['id'], 'admin_state_up': True, 'device_id': self.instance['uuid'], 'tenant_id': self.instance['project_id'], }, } self.moxed_client.create_port( MyComparator(port_req_body)).AndRaise( Exception("fail to create port")) self.mox.ReplayAll() self.assertRaises(NEUTRON_CLIENT_EXCEPTION, api.allocate_for_instance, self.context, self.instance) def test_allocate_for_instance_no_port_or_network(self): class BailOutEarly(Exception): pass api = neutronapi.API() self.mox.StubOutWithMock(api, '_get_available_networks') # Make sure we get an empty list and then bail out of the rest # of the function api._get_available_networks(self.context, self.instance['project_id'], []).AndRaise(BailOutEarly) self.mox.ReplayAll() self.assertRaises(BailOutEarly, api.allocate_for_instance, self.context, self.instance, requested_networks=[(None, None, None)]) def test_allocate_for_instance_second_time(self): # Make sure that allocate_for_instance only returns ports that it # allocated during _that_ run. new_port = {'id': 'fake'} self._returned_nw_info = self.port_data1 + [new_port] nw_info = self._allocate_for_instance() self.assertEqual(nw_info, [new_port]) def test_allocate_for_instance_port_in_use(self): # If a port is already in use, an exception should be raised. requested_networks = [(None, None, 'my_portid1')] api = self._stub_allocate_for_instance( requested_networks=requested_networks, _break='pre_list_networks', _device=True) self.assertRaises(exception.PortInUse, api.allocate_for_instance, self.context, self.instance, requested_networks=requested_networks) def _deallocate_for_instance(self, number): port_data = number == 1 and self.port_data1 or self.port_data2 self.moxed_client.list_ports( device_id=self.instance['uuid']).AndReturn( {'ports': port_data}) for port in port_data: self.moxed_client.delete_port(port['id']) self.mox.ReplayAll() api = neutronapi.API() api.deallocate_for_instance(self.context, self.instance) def test_deallocate_for_instance_1(self): # Test to deallocate in one port env. self._deallocate_for_instance(1) def test_deallocate_for_instance_2(self): # Test to deallocate in two ports env. self._deallocate_for_instance(2) def _test_deallocate_port_for_instance(self, number): port_data = number == 1 and self.port_data1 or self.port_data2 self.moxed_client.delete_port(port_data[0]['id']) self.mox.StubOutWithMock(conductor_api.API, 'instance_get_by_uuid') net_info_cache = [] for port in port_data: net_info_cache.append({"network": {"id": port['network_id']}}) info_cache = {'info_cache': {'network_info': jsonutils.dumps(net_info_cache)}} api = neutronapi.API() api.conductor_api.instance_get_by_uuid( mox.IgnoreArg(), mox.IgnoreArg()).AndReturn(info_cache) neutronv2.get_client(mox.IgnoreArg(), admin=True).AndReturn( self.moxed_client) self.moxed_client.list_ports( tenant_id=self.instance['project_id'], device_id=self.instance['uuid']).AndReturn( {'ports': port_data[1:]}) neutronv2.get_client(mox.IgnoreArg()).MultipleTimes().AndReturn( self.moxed_client) self.moxed_client.list_networks( tenant_id=self.instance['project_id'], shared=False).AndReturn( {'networks': [self.nets2[1]]}) self.moxed_client.list_networks(shared=True).AndReturn( {'networks': []}) float_data = number == 1 and self.float_data1 or self.float_data2 for data in port_data[1:]: for ip in data['fixed_ips']: self.moxed_client.list_floatingips( fixed_ip_address=ip['ip_address'], port_id=data['id']).AndReturn( {'floatingips': float_data[1:]}) for port in port_data[1:]: self.moxed_client.list_subnets(id=['my_subid2']).AndReturn({}) self.mox.ReplayAll() nwinfo = api.deallocate_port_for_instance(self.context, self.instance, port_data[0]['id']) self.assertEqual(len(nwinfo), len(port_data[1:])) if len(port_data) > 1: self.assertEqual(nwinfo[0]['network']['id'], 'my_netid2') def test_deallocate_port_for_instance_1(self): # Test to deallocate the first and only port self._test_deallocate_port_for_instance(1) def test_deallocate_port_for_instance_2(self): # Test to deallocate the first port of two self._test_deallocate_port_for_instance(2) def test_list_ports(self): search_opts = {'parm': 'value'} self.moxed_client.list_ports(**search_opts) self.mox.ReplayAll() neutronapi.API().list_ports(self.context, **search_opts) def test_show_port(self): self.moxed_client.show_port('foo') self.mox.ReplayAll() neutronapi.API().show_port(self.context, 'foo') def test_validate_networks(self): requested_networks = [('my_netid1', 'test', None), ('my_netid2', 'test2', None)] ids = ['my_netid1', 'my_netid2'] self.moxed_client.list_networks( id=mox.SameElementsAs(ids)).AndReturn( {'networks': self.nets2}) self.mox.ReplayAll() api = neutronapi.API() api.validate_networks(self.context, requested_networks) def test_validate_networks_ex_1(self): requested_networks = [('my_netid1', 'test', None)] self.moxed_client.list_networks( id=mox.SameElementsAs(['my_netid1'])).AndReturn( {'networks': self.nets1}) self.mox.ReplayAll() api = neutronapi.API() try: api.validate_networks(self.context, requested_networks) except exception.NetworkNotFound as ex: self.assertTrue("my_netid2" in str(ex)) def test_validate_networks_ex_2(self): requested_networks = [('my_netid1', 'test', None), ('my_netid2', 'test2', None), ('my_netid3', 'test3', None)] ids = ['my_netid1', 'my_netid2', 'my_netid3'] self.moxed_client.list_networks( id=mox.SameElementsAs(ids)).AndReturn( {'networks': self.nets1}) self.mox.ReplayAll() api = neutronapi.API() try: api.validate_networks(self.context, requested_networks) except exception.NetworkNotFound as ex: self.assertTrue("my_netid2, my_netid3" in str(ex)) def test_validate_networks_duplicate(self): """Verify that the correct exception is thrown when duplicate network ids are passed to validate_networks. """ requested_networks = [('my_netid1', None, None), ('my_netid1', None, None)] self.mox.ReplayAll() # Expected call from setUp. neutronv2.get_client(None) api = neutronapi.API() self.assertRaises(exception.NetworkDuplicated, api.validate_networks, self.context, requested_networks) def test_validate_networks_not_specified(self): requested_networks = [] self.moxed_client.list_networks( tenant_id=self.context.project_id, shared=False).AndReturn( {'networks': self.nets1}) self.moxed_client.list_networks( shared=True).AndReturn( {'networks': self.nets2}) self.mox.ReplayAll() api = neutronapi.API() self.assertRaises(exception.NetworkAmbiguous, api.validate_networks, self.context, requested_networks) def test_validate_networks_port_not_found(self): # Verify that the correct exception is thrown when a non existent # port is passed to validate_networks. requested_networks = [('my_netid1', None, '3123-ad34-bc43-32332ca33e')] NeutronNotFound = neutronv2.exceptions.NeutronClientException( status_code=404) self.moxed_client.show_port(requested_networks[0][2]).AndRaise( NeutronNotFound) self.mox.ReplayAll() # Expected call from setUp. neutronv2.get_client(None) api = neutronapi.API() self.assertRaises(exception.PortNotFound, api.validate_networks, self.context, requested_networks) def test_validate_networks_port_in_use(self): requested_networks = [(None, None, self.port_data3[0]['id'])] self.moxed_client.show_port(self.port_data3[0]['id']).\ AndReturn({'port': self.port_data3[0]}) self.mox.ReplayAll() api = neutronapi.API() self.assertRaises(exception.PortInUse, api.validate_networks, self.context, requested_networks) def test_validate_networks_ports_in_same_network(self): port_a = self.port_data3[0] port_b = self.port_data1[0] self.assertEqual(port_a['network_id'], port_b['network_id']) for port in [port_a, port_b]: port['device_id'] = None port['device_owner'] = None requested_networks = [(None, None, port_a['id']), (None, None, port_b['id'])] self.moxed_client.show_port(port_a['id']).AndReturn({'port': port_a}) self.moxed_client.show_port(port_b['id']).AndReturn({'port': port_b}) self.mox.ReplayAll() api = neutronapi.API() self.assertRaises(exception.NetworkDuplicated, api.validate_networks, self.context, requested_networks) def test_validate_networks_ports_not_in_same_network(self): port_a = self.port_data3[0] port_b = self.port_data2[1] self.assertNotEqual(port_a['network_id'], port_b['network_id']) for port in [port_a, port_b]: port['device_id'] = None port['device_owner'] = None requested_networks = [(None, None, port_a['id']), (None, None, port_b['id'])] self.moxed_client.show_port(port_a['id']).AndReturn({'port': port_a}) self.moxed_client.show_port(port_b['id']).AndReturn({'port': port_b}) search_opts = {'id': [port_a['network_id'], port_b['network_id']]} self.moxed_client.list_networks( **search_opts).AndReturn({'networks': self.nets2}) self.mox.ReplayAll() api = neutronapi.API() api.validate_networks(self.context, requested_networks) def _mock_list_ports(self, port_data=None): if port_data is None: port_data = self.port_data2 address = self.port_address self.moxed_client.list_ports( fixed_ips=MyComparator('ip_address=%s' % address)).AndReturn( {'ports': port_data}) self.mox.ReplayAll() return address def test_get_instance_uuids_by_ip_filter(self): self._mock_list_ports() filters = {'ip': '^10\\.0\\.1\\.2$'} api = neutronapi.API() result = api.get_instance_uuids_by_ip_filter(self.context, filters) self.assertEquals(self.instance2['uuid'], result[0]['instance_uuid']) self.assertEquals(self.instance['uuid'], result[1]['instance_uuid']) def test_get_fixed_ip_by_address_fails_for_no_ports(self): address = self._mock_list_ports(port_data=[]) api = neutronapi.API() self.assertRaises(exception.FixedIpNotFoundForAddress, api.get_fixed_ip_by_address, self.context, address) def test_get_fixed_ip_by_address_succeeds_for_1_port(self): address = self._mock_list_ports(port_data=self.port_data1) api = neutronapi.API() result = api.get_fixed_ip_by_address(self.context, address) self.assertEquals(self.instance2['uuid'], result['instance_uuid']) def test_get_fixed_ip_by_address_fails_for_more_than_1_port(self): address = self._mock_list_ports() api = neutronapi.API() self.assertRaises(exception.FixedIpAssociatedWithMultipleInstances, api.get_fixed_ip_by_address, self.context, address) def _get_available_networks(self, prv_nets, pub_nets, req_ids=None): api = neutronapi.API() nets = prv_nets + pub_nets if req_ids: mox_list_params = {'id': req_ids} self.moxed_client.list_networks( **mox_list_params).AndReturn({'networks': nets}) else: mox_list_params = {'tenant_id': self.instance['project_id'], 'shared': False} self.moxed_client.list_networks( **mox_list_params).AndReturn({'networks': prv_nets}) mox_list_params = {'shared': True} self.moxed_client.list_networks( **mox_list_params).AndReturn({'networks': pub_nets}) self.mox.ReplayAll() rets = api._get_available_networks(self.context, self.instance['project_id'], req_ids) self.assertEqual(rets, nets) def test_get_available_networks_all_private(self): self._get_available_networks(prv_nets=self.nets2, pub_nets=[]) def test_get_available_networks_all_public(self): self._get_available_networks(prv_nets=[], pub_nets=self.nets2) def test_get_available_networks_private_and_public(self): self._get_available_networks(prv_nets=self.nets1, pub_nets=self.nets4) def test_get_available_networks_with_network_ids(self): prv_nets = [self.nets3[0]] pub_nets = [self.nets3[-1]] # specify only first and last network req_ids = [net['id'] for net in (self.nets3[0], self.nets3[-1])] self._get_available_networks(prv_nets, pub_nets, req_ids) def test_get_floating_ip_pools(self): api = neutronapi.API() search_opts = {'router:external': True} self.moxed_client.list_networks(**search_opts).\ AndReturn({'networks': [self.fip_pool, self.fip_pool_nova]}) self.mox.ReplayAll() pools = api.get_floating_ip_pools(self.context) expected = [{'name': self.fip_pool['name']}, {'name': self.fip_pool_nova['name']}] self.assertEqual(expected, pools) def _get_expected_fip_model(self, fip_data, idx=0): expected = {'id': fip_data['id'], 'address': fip_data['floating_ip_address'], 'pool': self.fip_pool['name'], 'project_id': fip_data['tenant_id'], 'fixed_ip_id': fip_data['port_id'], 'fixed_ip': {'address': fip_data['fixed_ip_address']}, 'instance': ({'uuid': self.port_data2[idx]['device_id']} if fip_data['port_id'] else None)} return expected def _test_get_floating_ip(self, fip_data, idx=0, by_address=False): api = neutronapi.API() fip_id = fip_data['id'] net_id = fip_data['floating_network_id'] address = fip_data['floating_ip_address'] if by_address: self.moxed_client.list_floatingips(floating_ip_address=address).\ AndReturn({'floatingips': [fip_data]}) else: self.moxed_client.show_floatingip(fip_id).\ AndReturn({'floatingip': fip_data}) self.moxed_client.show_network(net_id).\ AndReturn({'network': self.fip_pool}) if fip_data['port_id']: self.moxed_client.show_port(fip_data['port_id']).\ AndReturn({'port': self.port_data2[idx]}) self.mox.ReplayAll() expected = self._get_expected_fip_model(fip_data, idx) if by_address: fip = api.get_floating_ip_by_address(self.context, address) else: fip = api.get_floating_ip(self.context, fip_id) self.assertEqual(expected, fip) def test_get_floating_ip_unassociated(self): self._test_get_floating_ip(self.fip_unassociated, idx=0) def test_get_floating_ip_associated(self): self._test_get_floating_ip(self.fip_associated, idx=1) def test_get_floating_ip_by_address(self): self._test_get_floating_ip(self.fip_unassociated, idx=0, by_address=True) def test_get_floating_ip_by_address_associated(self): self._test_get_floating_ip(self.fip_associated, idx=1, by_address=True) def test_get_floating_ip_by_address_not_found(self): api = neutronapi.API() address = self.fip_unassociated['floating_ip_address'] self.moxed_client.list_floatingips(floating_ip_address=address).\ AndReturn({'floatingips': []}) self.mox.ReplayAll() self.assertRaises(exception.FloatingIpNotFoundForAddress, api.get_floating_ip_by_address, self.context, address) def test_get_floating_ip_by_id_not_found(self): api = neutronapi.API() NeutronNotFound = neutronv2.exceptions.NeutronClientException( status_code=404) floating_ip_id = self.fip_unassociated['id'] self.moxed_client.show_floatingip(floating_ip_id).\ AndRaise(NeutronNotFound) self.mox.ReplayAll() self.assertRaises(exception.FloatingIpNotFound, api.get_floating_ip, self.context, floating_ip_id) def test_get_floating_ip_by_address_multiple_found(self): api = neutronapi.API() address = self.fip_unassociated['floating_ip_address'] self.moxed_client.list_floatingips(floating_ip_address=address).\ AndReturn({'floatingips': [self.fip_unassociated] * 2}) self.mox.ReplayAll() self.assertRaises(exception.FloatingIpMultipleFoundForAddress, api.get_floating_ip_by_address, self.context, address) def test_get_floating_ips_by_project(self): api = neutronapi.API() project_id = self.context.project_id self.moxed_client.list_floatingips(tenant_id=project_id).\ AndReturn({'floatingips': [self.fip_unassociated, self.fip_associated]}) search_opts = {'router:external': True} self.moxed_client.list_networks(**search_opts).\ AndReturn({'networks': [self.fip_pool, self.fip_pool_nova]}) self.moxed_client.list_ports(tenant_id=project_id).\ AndReturn({'ports': self.port_data2}) self.mox.ReplayAll() expected = [self._get_expected_fip_model(self.fip_unassociated), self._get_expected_fip_model(self.fip_associated, idx=1)] fips = api.get_floating_ips_by_project(self.context) self.assertEqual(expected, fips) def _test_get_instance_id_by_floating_address(self, fip_data, associated=False): api = neutronapi.API() address = fip_data['floating_ip_address'] self.moxed_client.list_floatingips(floating_ip_address=address).\ AndReturn({'floatingips': [fip_data]}) if associated: self.moxed_client.show_port(fip_data['port_id']).\ AndReturn({'port': self.port_data2[1]}) self.mox.ReplayAll() if associated: expected = self.port_data2[1]['device_id'] else: expected = None fip = api.get_instance_id_by_floating_address(self.context, address) self.assertEqual(expected, fip) def test_get_instance_id_by_floating_address(self): self._test_get_instance_id_by_floating_address(self.fip_unassociated) def test_get_instance_id_by_floating_address_associated(self): self._test_get_instance_id_by_floating_address(self.fip_associated, associated=True) def test_allocate_floating_ip(self): api = neutronapi.API() pool_name = self.fip_pool['name'] pool_id = self.fip_pool['id'] search_opts = {'router:external': True, 'fields': 'id', 'name': pool_name} self.moxed_client.list_networks(**search_opts).\ AndReturn({'networks': [self.fip_pool]}) self.moxed_client.create_floatingip( {'floatingip': {'floating_network_id': pool_id}}).\ AndReturn({'floatingip': self.fip_unassociated}) self.mox.ReplayAll() fip = api.allocate_floating_ip(self.context, 'ext_net') self.assertEqual(fip, self.fip_unassociated['floating_ip_address']) def test_allocate_floating_ip_with_pool_id(self): api = neutronapi.API() pool_id = self.fip_pool['id'] search_opts = {'router:external': True, 'fields': 'id', 'id': pool_id} self.moxed_client.list_networks(**search_opts).\ AndReturn({'networks': [self.fip_pool]}) self.moxed_client.create_floatingip( {'floatingip': {'floating_network_id': pool_id}}).\ AndReturn({'floatingip': self.fip_unassociated}) self.mox.ReplayAll() fip = api.allocate_floating_ip(self.context, pool_id) self.assertEqual(fip, self.fip_unassociated['floating_ip_address']) def test_allocate_floating_ip_with_default_pool(self): api = neutronapi.API() pool_name = self.fip_pool_nova['name'] pool_id = self.fip_pool_nova['id'] search_opts = {'router:external': True, 'fields': 'id', 'name': pool_name} self.moxed_client.list_networks(**search_opts).\ AndReturn({'networks': [self.fip_pool_nova]}) self.moxed_client.create_floatingip( {'floatingip': {'floating_network_id': pool_id}}).\ AndReturn({'floatingip': self.fip_unassociated}) self.mox.ReplayAll() fip = api.allocate_floating_ip(self.context) self.assertEqual(fip, self.fip_unassociated['floating_ip_address']) def test_release_floating_ip(self): api = neutronapi.API() address = self.fip_unassociated['floating_ip_address'] fip_id = self.fip_unassociated['id'] self.moxed_client.list_floatingips(floating_ip_address=address).\ AndReturn({'floatingips': [self.fip_unassociated]}) self.moxed_client.delete_floatingip(fip_id) self.mox.ReplayAll() api.release_floating_ip(self.context, address) def test_release_floating_ip_associated(self): api = neutronapi.API() address = self.fip_associated['floating_ip_address'] self.moxed_client.list_floatingips(floating_ip_address=address).\ AndReturn({'floatingips': [self.fip_associated]}) self.mox.ReplayAll() self.assertRaises(exception.FloatingIpAssociated, api.release_floating_ip, self.context, address) def _setup_mock_for_refresh_cache(self, api, instances): nw_info = self.mox.CreateMock(model.NetworkInfo) self.mox.StubOutWithMock(api, '_get_instance_nw_info') self.mox.StubOutWithMock(api.db, 'instance_info_cache_update') for instance in instances: nw_info.json() api._get_instance_nw_info(mox.IgnoreArg(), instance).\ AndReturn(nw_info) api.db.instance_info_cache_update(mox.IgnoreArg(), instance['uuid'], mox.IgnoreArg()) def test_associate_floating_ip(self): api = neutronapi.API() address = self.fip_unassociated['floating_ip_address'] fixed_address = self.port_address2 fip_id = self.fip_unassociated['id'] search_opts = {'device_owner': 'compute:nova', 'device_id': self.instance['uuid']} self.moxed_client.list_ports(**search_opts).\ AndReturn({'ports': [self.port_data2[1]]}) self.moxed_client.list_floatingips(floating_ip_address=address).\ AndReturn({'floatingips': [self.fip_unassociated]}) self.moxed_client.update_floatingip( fip_id, {'floatingip': {'port_id': self.fip_associated['port_id'], 'fixed_ip_address': fixed_address}}) self._setup_mock_for_refresh_cache(api, [self.instance]) self.mox.ReplayAll() api.associate_floating_ip(self.context, self.instance, address, fixed_address) def test_reassociate_floating_ip(self): api = neutronapi.API() address = self.fip_associated['floating_ip_address'] old_fixed_address = self.fip_associated['fixed_ip_address'] new_fixed_address = self.port_address fip_id = self.fip_associated['id'] search_opts = {'device_owner': 'compute:nova', 'device_id': self.instance2['uuid']} self.moxed_client.list_ports(**search_opts).\ AndReturn({'ports': [self.port_data2[0]]}) self.moxed_client.list_floatingips(floating_ip_address=address).\ AndReturn({'floatingips': [self.fip_associated]}) self.moxed_client.update_floatingip( fip_id, {'floatingip': {'port_id': 'my_portid1', 'fixed_ip_address': new_fixed_address}}) self.moxed_client.show_port(self.fip_associated['port_id']).\ AndReturn({'port': self.port_data2[1]}) self.mox.StubOutWithMock(api.db, 'instance_get_by_uuid') api.db.instance_get_by_uuid(mox.IgnoreArg(), self.instance['uuid']).\ AndReturn(self.instance) self._setup_mock_for_refresh_cache(api, [self.instance, self.instance2]) self.mox.ReplayAll() api.associate_floating_ip(self.context, self.instance2, address, new_fixed_address) def test_associate_floating_ip_not_found_fixed_ip(self): api = neutronapi.API() address = self.fip_associated['floating_ip_address'] fixed_address = self.fip_associated['fixed_ip_address'] search_opts = {'device_owner': 'compute:nova', 'device_id': self.instance['uuid']} self.moxed_client.list_ports(**search_opts).\ AndReturn({'ports': [self.port_data2[0]]}) self.mox.ReplayAll() self.assertRaises(exception.FixedIpNotFoundForAddress, api.associate_floating_ip, self.context, self.instance, address, fixed_address) def test_disassociate_floating_ip(self): api = neutronapi.API() address = self.fip_associated['floating_ip_address'] fip_id = self.fip_associated['id'] self.moxed_client.list_floatingips(floating_ip_address=address).\ AndReturn({'floatingips': [self.fip_associated]}) self.moxed_client.update_floatingip( fip_id, {'floatingip': {'port_id': None}}) self._setup_mock_for_refresh_cache(api, [self.instance]) self.mox.ReplayAll() api.disassociate_floating_ip(self.context, self.instance, address) def test_add_fixed_ip_to_instance(self): api = neutronapi.API() self._setup_mock_for_refresh_cache(api, [self.instance]) network_id = 'my_netid1' search_opts = {'network_id': network_id} self.moxed_client.list_subnets( **search_opts).AndReturn({'subnets': self.subnet_data_n}) search_opts = {'device_id': self.instance['uuid'], 'device_owner': 'compute:nova', 'network_id': network_id} self.moxed_client.list_ports( **search_opts).AndReturn({'ports': self.port_data1}) port_req_body = { 'port': { 'fixed_ips': [{'subnet_id': 'my_subid1'}, {'subnet_id': 'my_subid1'}], }, } port = self.port_data1[0] port['fixed_ips'] = [{'subnet_id': 'my_subid1'}] self.moxed_client.update_port('my_portid1', MyComparator(port_req_body)).AndReturn({'port': port}) self.mox.ReplayAll() api.add_fixed_ip_to_instance(self.context, self.instance, network_id) def test_remove_fixed_ip_from_instance(self): api = neutronapi.API() self._setup_mock_for_refresh_cache(api, [self.instance]) address = '10.0.0.3' zone = 'compute:%s' % self.instance['availability_zone'] search_opts = {'device_id': self.instance['uuid'], 'device_owner': zone, 'fixed_ips': 'ip_address=%s' % address} self.moxed_client.list_ports( **search_opts).AndReturn({'ports': self.port_data1}) port_req_body = { 'port': { 'fixed_ips': [], }, } port = self.port_data1[0] port['fixed_ips'] = [] self.moxed_client.update_port('my_portid1', MyComparator(port_req_body)).AndReturn({'port': port}) self.mox.ReplayAll() api.remove_fixed_ip_from_instance(self.context, self.instance, address) def test_list_floating_ips_without_l3_support(self): api = neutronapi.API() NeutronNotFound = exceptions.NeutronClientException( status_code=404) self.moxed_client.list_floatingips( fixed_ip_address='1.1.1.1', port_id=1).AndRaise(NeutronNotFound) self.mox.ReplayAll() neutronv2.get_client('fake') floatingips = api._get_floating_ips_by_fixed_and_port( self.moxed_client, '1.1.1.1', 1) self.assertEqual(floatingips, []) def test_nw_info_get_ips(self): fake_port = { 'fixed_ips': [ {'ip_address': '1.1.1.1'}], 'id': 'port-id', } api = neutronapi.API() self.mox.StubOutWithMock(api, '_get_floating_ips_by_fixed_and_port') api._get_floating_ips_by_fixed_and_port( self.moxed_client, '1.1.1.1', 'port-id').AndReturn( [{'floating_ip_address': '10.0.0.1'}]) self.mox.ReplayAll() neutronv2.get_client('fake') result = api._nw_info_get_ips(self.moxed_client, fake_port) self.assertEqual(len(result), 1) self.assertEqual(result[0]['address'], '1.1.1.1') self.assertEqual(result[0]['floating_ips'][0]['address'], '10.0.0.1') def test_nw_info_get_subnets(self): fake_port = { 'fixed_ips': [ {'ip_address': '1.1.1.1'}, {'ip_address': '2.2.2.2'}], 'id': 'port-id', } fake_subnet = model.Subnet(cidr='1.0.0.0/8') fake_ips = [model.IP(x['ip_address']) for x in fake_port['fixed_ips']] api = neutronapi.API() self.mox.StubOutWithMock(api, '_get_subnets_from_port') api._get_subnets_from_port(self.context, fake_port).AndReturn( [fake_subnet]) self.mox.ReplayAll() neutronv2.get_client('fake') subnets = api._nw_info_get_subnets(self.context, fake_port, fake_ips) self.assertEqual(len(subnets), 1) self.assertEqual(len(subnets[0]['ips']), 1) self.assertEqual(subnets[0]['ips'][0]['address'], '1.1.1.1') def _test_nw_info_build_network(self, vif_type): fake_port = { 'fixed_ips': [{'ip_address': '1.1.1.1'}], 'id': 'port-id', 'network_id': 'net-id', 'binding:vif_type': vif_type, } fake_subnets = [model.Subnet(cidr='1.0.0.0/8')] fake_nets = [{'id': 'net-id', 'name': 'foo', 'tenant_id': 'tenant'}] api = neutronapi.API() self.mox.ReplayAll() neutronv2.get_client('fake') net, iid = api._nw_info_build_network(fake_port, fake_nets, fake_subnets) self.assertEqual(net['subnets'], fake_subnets) self.assertEqual(net['id'], 'net-id') self.assertEqual(net['label'], 'foo') self.assertEqual(net.get_meta('tenant_id'), 'tenant') self.assertEqual(net.get_meta('injected'), CONF.flat_injected) return net, iid def test_nw_info_build_network_ovs(self): net, iid = self._test_nw_info_build_network(model.VIF_TYPE_OVS) self.assertEqual(net['bridge'], CONF.neutron_ovs_bridge) self.assertFalse('should_create_bridge' in net) self.assertEqual(iid, 'port-id') def test_nw_info_build_network_bridge(self): net, iid = self._test_nw_info_build_network(model.VIF_TYPE_BRIDGE) self.assertEqual(net['bridge'], 'brqnet-id') self.assertTrue(net['should_create_bridge']) self.assertEqual(iid, None) def test_nw_info_build_network_other(self): net, iid = self._test_nw_info_build_network(None) self.assertEqual(net['bridge'], None) self.assertFalse('should_create_bridge' in net) self.assertEqual(iid, None) def test_build_network_info_model(self): api = neutronapi.API() fake_inst = {'project_id': 'fake', 'uuid': 'uuid'} fake_ports = [ {'id': 'port0', 'network_id': 'net-id', 'fixed_ips': [{'ip_address': '1.1.1.1'}], 'mac_address': 'de:ad:be:ef:00:01', 'binding:vif_type': model.VIF_TYPE_BRIDGE, }, # This does not match the networks we provide below, # so it should be ignored (and is here to verify that) {'id': 'port1', 'network_id': 'other-net-id', }, ] fake_subnets = [model.Subnet(cidr='1.0.0.0/8')] fake_nets = [ {'id': 'net-id', 'name': 'foo', 'tenant_id': 'fake', } ] neutronv2.get_client(mox.IgnoreArg(), admin=True).MultipleTimes( ).AndReturn(self.moxed_client) self.moxed_client.list_ports( tenant_id='fake', device_id='uuid').AndReturn( {'ports': fake_ports}) self.mox.StubOutWithMock(api, '_get_floating_ips_by_fixed_and_port') api._get_floating_ips_by_fixed_and_port( self.moxed_client, '1.1.1.1', 'port0').AndReturn( [{'floating_ip_address': '10.0.0.1'}]) self.mox.StubOutWithMock(api, '_get_subnets_from_port') api._get_subnets_from_port(self.context, fake_ports[0]).AndReturn( fake_subnets) self.mox.ReplayAll() neutronv2.get_client('fake') nw_info = api._build_network_info_model(self.context, fake_inst, fake_nets) self.assertEqual(len(nw_info), 1) self.assertEqual(nw_info[0]['id'], 'port0') self.assertEqual(nw_info[0]['address'], 'de:ad:be:ef:00:01') self.assertEqual(nw_info[0]['devname'], 'tapport0') self.assertEqual(nw_info[0]['ovs_interfaceid'], None) self.assertEqual(nw_info[0]['type'], model.VIF_TYPE_BRIDGE) self.assertEqual(nw_info[0]['network']['bridge'], 'brqnet-id') def test_get_all_empty_list_networks(self): api = neutronapi.API() self.moxed_client.list_networks().AndReturn({'networks': []}) self.mox.ReplayAll() networks = api.get_all(self.context) self.assertEqual(networks, []) class TestNeutronv2ModuleMethods(test.TestCase): def test_ensure_requested_network_ordering_no_preference_ids(self): l = [1, 2, 3] neutronapi._ensure_requested_network_ordering( lambda x: x, l, None) def test_ensure_requested_network_ordering_no_preference_hashes(self): l = [{'id': 3}, {'id': 1}, {'id': 2}] neutronapi._ensure_requested_network_ordering( lambda x: x['id'], l, None) self.assertEqual(l, [{'id': 3}, {'id': 1}, {'id': 2}]) def test_ensure_requested_network_ordering_with_preference(self): l = [{'id': 3}, {'id': 1}, {'id': 2}] neutronapi._ensure_requested_network_ordering( lambda x: x['id'], l, [1, 2, 3]) self.assertEqual(l, [{'id': 1}, {'id': 2}, {'id': 3}]) class TestNeutronv2Portbinding(TestNeutronv2Base): def test_allocate_for_instance_portbinding(self): self._allocate_for_instance(1, portbinding=True) def test_populate_neutron_extension_values_binding(self): api = neutronapi.API() neutronv2.get_client(mox.IgnoreArg()).AndReturn( self.moxed_client) self.moxed_client.list_extensions().AndReturn( {'extensions': [{'name': constants.PORTBINDING_EXT}]}) self.mox.ReplayAll() host_id = 'my_host_id' instance = {'host': host_id} port_req_body = {'port': {}} api._populate_neutron_extension_values(instance, port_req_body) self.assertEquals(port_req_body['port']['binding:host_id'], host_id) def test_migrate_instance_finish_binding_false(self): api = neutronapi.API() self.mox.StubOutWithMock(api, '_has_port_binding_extension') api._has_port_binding_extension(refresh_cache=True).AndReturn(False) self.mox.ReplayAll() api.migrate_instance_finish(self.context, None, None) def test_migrate_instance_finish_binding_true(self): api = neutronapi.API() self.mox.StubOutWithMock(api, '_has_port_binding_extension') api._has_port_binding_extension(refresh_cache=True).AndReturn(True) neutronv2.get_client(mox.IgnoreArg(), admin=True).AndReturn( self.moxed_client) search_opts = {'device_id': self.instance['uuid'], 'tenant_id': self.instance['project_id']} ports = {'ports': [{'id': 'test1'}]} self.moxed_client.list_ports(**search_opts).AndReturn(ports) migration = {'source_compute': self.instance.get('host'), 'dest_compute': 'dest_host', } port_req_body = {'port': {'binding:host_id': migration['dest_compute']}} self.moxed_client.update_port('test1', port_req_body).AndReturn(None) self.mox.ReplayAll() api.migrate_instance_finish(self.context, self.instance, migration) def test_migrate_instance_finish_binding_true_exception(self): api = neutronapi.API() self.mox.StubOutWithMock(api, '_has_port_binding_extension') api._has_port_binding_extension(refresh_cache=True).AndReturn(True) neutronv2.get_client(mox.IgnoreArg(), admin=True).AndReturn( self.moxed_client) search_opts = {'device_id': self.instance['uuid'], 'tenant_id': self.instance['project_id']} ports = {'ports': [{'id': 'test1'}]} self.moxed_client.list_ports(**search_opts).AndReturn(ports) migration = {'source_compute': self.instance.get('host'), 'dest_compute': 'dest_host', } port_req_body = {'port': {'binding:host_id': migration['dest_compute']}} self.moxed_client.update_port('test1', port_req_body).AndRaise( Exception("fail to update port")) self.mox.ReplayAll() self.assertRaises(NEUTRON_CLIENT_EXCEPTION, api.migrate_instance_finish, self.context, self.instance, migration) class TestNeutronv2ExtraDhcpOpts(TestNeutronv2Base): def setUp(self): super(TestNeutronv2ExtraDhcpOpts, self).setUp() neutronv2.get_client(mox.IgnoreArg()).MultipleTimes().AndReturn( self.moxed_client) def test_allocate_for_instance_1_with_extra_dhcp_opts_turned_off(self): # Note: (dkehn) this option check should be removed as soon as support # in neutron released, see https://bugs.launchpad.net/nova/+bug/1214162 CONF.set_override('dhcp_options_enabled', True) self._allocate_for_instance(1, extra_dhcp_opts=False) CONF.set_override('dhcp_options_enabled', False) def test_allocate_for_instance_extradhcpopts(self): # Note: (dkehn) this option check should be removed as soon as support # in neutron released, see https://bugs.launchpad.net/nova/+bug/1214162 CONF.set_override('dhcp_options_enabled', True) dhcp_opts = [{'opt_name': 'bootfile-name', 'opt_value': 'pxelinux.0'}, {'opt_name': 'tftp-server', 'opt_value': '123.123.123.123'}, {'opt_name': 'server-ip-address', 'opt_value': '123.123.123.456'}] self._allocate_for_instance(1, dhcp_options=dhcp_opts) CONF.set_override('dhcp_options_enabled', False)

44.55032

79

0.584889

79515123dae0695a6504167d5adb4253989226f5

886

py

Python

pygmsh/opencascade/box.py

jorgensd/pygmsh

64fa6dbc78c6270241c1576b80370123436cd07b

[ "MIT" ]

1

2022-03-13T04:39:03.000Z

pygmsh/opencascade/box.py

kurtsansom/pygmsh

9c2cfe1c6c7f80943f61f3e695a453e343544619

[ "MIT" ]

null

pygmsh/opencascade/box.py

kurtsansom/pygmsh

9c2cfe1c6c7f80943f61f3e695a453e343544619

[ "MIT" ]

null

from .volume_base import VolumeBase class Box(VolumeBase): """ Creates a box. Parameters ---------- x0 : array-like[3] List containing the x, y, z values of the start point. extends : array-like[3] List of the 3 extents of the box edges. char_length : float Characteristic length of the mesh elements of this polygon. """ def __init__(self, x0, extents, char_length=None): super().__init__() assert len(x0) == 3 assert len(extents) == 3 self.x0 = x0 self.extents = extents self.char_length = char_length args = list(x0) + list(extents) args = ", ".join([f"{arg}" for arg in args]) self.code = "\n".join( [f"{self.id} = newv;", f"Box({self.id}) = {{{args}}};"] + self.char_length_code(char_length) ) return

24.611111

67

0.551919

79515210babf07fcb797cec5475cafbc2f0e44e6

735

py

Python

ecg/examples/cinc17/entry/evaler.py

yanagiragi/ECG-acquisition-classification

1c84211a304dacd18232c86fe38811a7bb4b7b77

[ "Apache-2.0" ]

35

2019-05-15T16:11:00.000Z

2022-01-12T03:29:10.000Z

ecg/examples/cinc17/entry/evaler.py

HabibMrad/ECG-acquisition-classification

d50985c8a774d8f6eebcc2a9750fe3485b389815

[ "Apache-2.0" ]

9

2020-01-28T22:44:44.000Z

2022-02-10T00:09:38.000Z

ecg/examples/cinc17/entry/evaler.py

HabibMrad/ECG-acquisition-classification

d50985c8a774d8f6eebcc2a9750fe3485b389815

[ "Apache-2.0" ]

26

2019-05-21T06:23:20.000Z

2022-03-10T17:35:13.000Z

import json import keras import numpy as np import scipy.io as sio import scipy.stats as sst import load import network import util def predict(record): ecg = load.load_ecg(record +".mat") preproc = util.load(".") x = preproc.process_x([ecg]) params = json.load(open("config.json")) params.update({ "compile" : False, "input_shape": [None, 1], "num_categories": len(preproc.classes) }) model = network.build_network(**params) model.load_weights('model.hdf5') probs = model.predict(x) prediction = sst.mode(np.argmax(probs, axis=2).squeeze())[0][0] return preproc.int_to_class[prediction] if __name__ == '__main__': import sys print predict(sys.argv[1])

22.272727

67

0.657143

79515322ebad3fb53310683ab29ba78b416618b4

5,960

py

Python

syntropy_sdk/models/user_data_response.py

SyntropyNet/syntropy-python-sdk

27b7756b136f83886fd2a6e342fa4d4073779ff7

[ "MIT" ]

1

2020-12-17T17:30:12.000Z

syntropy_sdk/models/user_data_response.py

SyntropyNet/syntropy-python-sdk

27b7756b136f83886fd2a6e342fa4d4073779ff7

[ "MIT" ]

null

syntropy_sdk/models/user_data_response.py

SyntropyNet/syntropy-python-sdk

27b7756b136f83886fd2a6e342fa4d4073779ff7

[ "MIT" ]

null

# coding: utf-8 """ syntropy-auth-service No description provided (generated by Swagger Codegen https://github.com/swagger-api/swagger-codegen) # noqa: E501 OpenAPI spec version: 0.1.0 Generated by: https://github.com/swagger-api/swagger-codegen.git """ import pprint import re # noqa: F401 import six class UserDataResponse(object): """NOTE: This class is auto generated by the swagger code generator program. Do not edit the class manually. """ """ Attributes: swagger_types (dict): The key is attribute name and the value is attribute type. attribute_map (dict): The key is attribute name and the value is json key in definition. """ swagger_types = { "user_id": "float", "user_email": "str", "user_settings": "UserSettingsObject", "user_scopes": "list[str]", } attribute_map = { "user_id": "user_id", "user_email": "user_email", "user_settings": "user_settings", "user_scopes": "user_scopes", } def __init__( self, user_id=None, user_email=None, user_settings=None, user_scopes=None ): # noqa: E501 """UserDataResponse - a model defined in Swagger""" # noqa: E501 self._user_id = None self._user_email = None self._user_settings = None self._user_scopes = None self.discriminator = None self.user_id = user_id self.user_email = user_email self.user_settings = user_settings self.user_scopes = user_scopes @property def user_id(self): """Gets the user_id of this UserDataResponse. # noqa: E501 :return: The user_id of this UserDataResponse. # noqa: E501 :rtype: float """ return self._user_id @user_id.setter def user_id(self, user_id): """Sets the user_id of this UserDataResponse. :param user_id: The user_id of this UserDataResponse. # noqa: E501 :type: float """ if user_id is None: raise ValueError( "Invalid value for `user_id`, must not be `None`" ) # noqa: E501 self._user_id = user_id @property def user_email(self): """Gets the user_email of this UserDataResponse. # noqa: E501 :return: The user_email of this UserDataResponse. # noqa: E501 :rtype: str """ return self._user_email @user_email.setter def user_email(self, user_email): """Sets the user_email of this UserDataResponse. :param user_email: The user_email of this UserDataResponse. # noqa: E501 :type: str """ if user_email is None: raise ValueError( "Invalid value for `user_email`, must not be `None`" ) # noqa: E501 self._user_email = user_email @property def user_settings(self): """Gets the user_settings of this UserDataResponse. # noqa: E501 :return: The user_settings of this UserDataResponse. # noqa: E501 :rtype: UserSettingsObject """ return self._user_settings @user_settings.setter def user_settings(self, user_settings): """Sets the user_settings of this UserDataResponse. :param user_settings: The user_settings of this UserDataResponse. # noqa: E501 :type: UserSettingsObject """ if user_settings is None: raise ValueError( "Invalid value for `user_settings`, must not be `None`" ) # noqa: E501 self._user_settings = user_settings @property def user_scopes(self): """Gets the user_scopes of this UserDataResponse. # noqa: E501 :return: The user_scopes of this UserDataResponse. # noqa: E501 :rtype: list[str] """ return self._user_scopes @user_scopes.setter def user_scopes(self, user_scopes): """Sets the user_scopes of this UserDataResponse. :param user_scopes: The user_scopes of this UserDataResponse. # noqa: E501 :type: list[str] """ if user_scopes is None: raise ValueError( "Invalid value for `user_scopes`, must not be `None`" ) # noqa: E501 self._user_scopes = user_scopes def to_dict(self): """Returns the model properties as a dict""" result = {} for attr, _ in six.iteritems(self.swagger_types): value = getattr(self, attr) if isinstance(value, list): result[attr] = list( map(lambda x: x.to_dict() if hasattr(x, "to_dict") else x, value) ) elif hasattr(value, "to_dict"): result[attr] = value.to_dict() elif isinstance(value, dict): result[attr] = dict( map( lambda item: (item[0], item[1].to_dict()) if hasattr(item[1], "to_dict") else item, value.items(), ) ) else: result[attr] = value if issubclass(UserDataResponse, dict): for key, value in self.items(): result[key] = value return result def to_str(self): """Returns the string representation of the model""" return pprint.pformat(self.to_dict()) def __repr__(self): """For `print` and `pprint`""" return self.to_str() def __eq__(self, other): """Returns true if both objects are equal""" if not isinstance(other, UserDataResponse): return False return self.__dict__ == other.__dict__ def __ne__(self, other): """Returns true if both objects are not equal""" return not self == other

28.792271

119

0.574161

795153994b28df8d73264f3cd68bd874c69e86e9

13,739

py

Python

extract_color_mesh.py

U-sepSick/NeRF

c5910f84321eb5f72e3332507b0384f1b23f51f7

[ "MIT" ]

993

2020-04-20T02:32:58.000Z

2022-03-31T15:51:51.000Z

extract_color_mesh.py

U-sepSick/NeRF

c5910f84321eb5f72e3332507b0384f1b23f51f7

[ "MIT" ]

142

2020-04-20T15:15:30.000Z

2022-03-31T11:54:27.000Z

extract_color_mesh.py

U-sepSick/NeRF

c5910f84321eb5f72e3332507b0384f1b23f51f7

[ "MIT" ]

210

2020-05-03T06:16:24.000Z

2022-03-30T06:30:57.000Z

import torch import os import numpy as np import cv2 from PIL import Image from collections import defaultdict from tqdm import tqdm import mcubes import open3d as o3d from plyfile import PlyData, PlyElement from argparse import ArgumentParser from models.rendering import * from models.nerf import * from utils import load_ckpt from datasets import dataset_dict torch.backends.cudnn.benchmark = True def get_opts(): parser = ArgumentParser() parser.add_argument('--root_dir', type=str, default='/home/ubuntu/data/nerf_example_data/nerf_synthetic/lego', help='root directory of dataset') parser.add_argument('--dataset_name', type=str, default='blender', choices=['blender', 'llff'], help='which dataset to validate') parser.add_argument('--scene_name', type=str, default='test', help='scene name, used as output ply filename') parser.add_argument('--img_wh', nargs="+", type=int, default=[800, 800], help='resolution (img_w, img_h) of the image') parser.add_argument('--N_samples', type=int, default=64, help='number of samples to infer the acculmulated opacity') parser.add_argument('--chunk', type=int, default=32*1024, help='chunk size to split the input to avoid OOM') parser.add_argument('--ckpt_path', type=str, required=True, help='pretrained checkpoint path to load') parser.add_argument('--N_grid', type=int, default=256, help='size of the grid on 1 side, larger=higher resolution') parser.add_argument('--x_range', nargs="+", type=float, default=[-1.0, 1.0], help='x range of the object') parser.add_argument('--y_range', nargs="+", type=float, default=[-1.0, 1.0], help='x range of the object') parser.add_argument('--z_range', nargs="+", type=float, default=[-1.0, 1.0], help='x range of the object') parser.add_argument('--sigma_threshold', type=float, default=20.0, help='threshold to consider a location is occupied') parser.add_argument('--occ_threshold', type=float, default=0.2, help='''threshold to consider a vertex is occluded. larger=fewer occluded pixels''') #### method using vertex normals #### parser.add_argument('--use_vertex_normal', action="store_true", help='use vertex normals to compute color') parser.add_argument('--N_importance', type=int, default=64, help='number of fine samples to infer the acculmulated opacity') parser.add_argument('--near_t', type=float, default=1.0, help='the near bound factor to start the ray') return parser.parse_args() @torch.no_grad() def f(models, embeddings, rays, N_samples, N_importance, chunk, white_back): """Do batched inference on rays using chunk.""" B = rays.shape[0] results = defaultdict(list) for i in range(0, B, chunk): rendered_ray_chunks = \ render_rays(models, embeddings, rays[i:i+chunk], N_samples, False, 0, 0, N_importance, chunk, white_back, test_time=True) for k, v in rendered_ray_chunks.items(): results[k] += [v] for k, v in results.items(): results[k] = torch.cat(v, 0) return results if __name__ == "__main__": args = get_opts() kwargs = {'root_dir': args.root_dir, 'img_wh': tuple(args.img_wh)} if args.dataset_name == 'llff': kwargs['spheric_poses'] = True kwargs['split'] = 'test' else: kwargs['split'] = 'train' dataset = dataset_dict[args.dataset_name](**kwargs) embedding_xyz = Embedding(3, 10) embedding_dir = Embedding(3, 4) embeddings = [embedding_xyz, embedding_dir] nerf_fine = NeRF() load_ckpt(nerf_fine, args.ckpt_path, model_name='nerf_fine') nerf_fine.cuda().eval() # define the dense grid for query N = args.N_grid xmin, xmax = args.x_range ymin, ymax = args.y_range zmin, zmax = args.z_range # assert xmax-xmin == ymax-ymin == zmax-zmin, 'the ranges must have the same length!' x = np.linspace(xmin, xmax, N) y = np.linspace(ymin, ymax, N) z = np.linspace(zmin, zmax, N) xyz_ = torch.FloatTensor(np.stack(np.meshgrid(x, y, z), -1).reshape(-1, 3)).cuda() dir_ = torch.zeros_like(xyz_).cuda() # sigma is independent of direction, so any value here will produce the same result # predict sigma (occupancy) for each grid location print('Predicting occupancy ...') with torch.no_grad(): B = xyz_.shape[0] out_chunks = [] for i in tqdm(range(0, B, args.chunk)): xyz_embedded = embedding_xyz(xyz_[i:i+args.chunk]) # (N, embed_xyz_channels) dir_embedded = embedding_dir(dir_[i:i+args.chunk]) # (N, embed_dir_channels) xyzdir_embedded = torch.cat([xyz_embedded, dir_embedded], 1) out_chunks += [nerf_fine(xyzdir_embedded)] rgbsigma = torch.cat(out_chunks, 0) sigma = rgbsigma[:, -1].cpu().numpy() sigma = np.maximum(sigma, 0).reshape(N, N, N) # perform marching cube algorithm to retrieve vertices and triangle mesh print('Extracting mesh ...') vertices, triangles = mcubes.marching_cubes(sigma, args.sigma_threshold) ##### Until mesh extraction here, it is the same as the original repo. ###### vertices_ = (vertices/N).astype(np.float32) ## invert x and y coordinates (WHY? maybe because of the marching cubes algo) x_ = (ymax-ymin) * vertices_[:, 1] + ymin y_ = (xmax-xmin) * vertices_[:, 0] + xmin vertices_[:, 0] = x_ vertices_[:, 1] = y_ vertices_[:, 2] = (zmax-zmin) * vertices_[:, 2] + zmin vertices_.dtype = [('x', 'f4'), ('y', 'f4'), ('z', 'f4')] face = np.empty(len(triangles), dtype=[('vertex_indices', 'i4', (3,))]) face['vertex_indices'] = triangles PlyData([PlyElement.describe(vertices_[:, 0], 'vertex'), PlyElement.describe(face, 'face')]).write(f'{args.scene_name}.ply') # remove noise in the mesh by keeping only the biggest cluster print('Removing noise ...') mesh = o3d.io.read_triangle_mesh(f"{args.scene_name}.ply") idxs, count, _ = mesh.cluster_connected_triangles() max_cluster_idx = np.argmax(count) triangles_to_remove = [i for i in range(len(face)) if idxs[i] != max_cluster_idx] mesh.remove_triangles_by_index(triangles_to_remove) mesh.remove_unreferenced_vertices() print(f'Mesh has {len(mesh.vertices)/1e6:.2f} M vertices and {len(mesh.triangles)/1e6:.2f} M faces.') vertices_ = np.asarray(mesh.vertices).astype(np.float32) triangles = np.asarray(mesh.triangles) # perform color prediction # Step 0. define constants (image width, height and intrinsics) W, H = args.img_wh K = np.array([[dataset.focal, 0, W/2], [0, dataset.focal, H/2], [0, 0, 1]]).astype(np.float32) # Step 1. transform vertices into world coordinate N_vertices = len(vertices_) vertices_homo = np.concatenate([vertices_, np.ones((N_vertices, 1))], 1) # (N, 4) if args.use_vertex_normal: ## use normal vector method as suggested by the author. ## see https://github.com/bmild/nerf/issues/44 mesh.compute_vertex_normals() rays_d = torch.FloatTensor(np.asarray(mesh.vertex_normals)) near = dataset.bounds.min() * torch.ones_like(rays_d[:, :1]) far = dataset.bounds.max() * torch.ones_like(rays_d[:, :1]) rays_o = torch.FloatTensor(vertices_) - rays_d * near * args.near_t nerf_coarse = NeRF() load_ckpt(nerf_coarse, args.ckpt_path, model_name='nerf_coarse') nerf_coarse.cuda().eval() results = f([nerf_coarse, nerf_fine], embeddings, torch.cat([rays_o, rays_d, near, far], 1).cuda(), args.N_samples, args.N_importance, args.chunk, dataset.white_back) else: ## use my color average method. see README_mesh.md ## buffers to store the final averaged color non_occluded_sum = np.zeros((N_vertices, 1)) v_color_sum = np.zeros((N_vertices, 3)) # Step 2. project the vertices onto each training image to infer the color print('Fusing colors ...') for idx in tqdm(range(len(dataset.image_paths))): ## read image of this pose image = Image.open(dataset.image_paths[idx]).convert('RGB') image = image.resize(tuple(args.img_wh), Image.LANCZOS) image = np.array(image) ## read the camera to world relative pose P_c2w = np.concatenate([dataset.poses[idx], np.array([0, 0, 0, 1]).reshape(1, 4)], 0) P_w2c = np.linalg.inv(P_c2w)[:3] # (3, 4) ## project vertices from world coordinate to camera coordinate vertices_cam = (P_w2c @ vertices_homo.T) # (3, N) in "right up back" vertices_cam[1:] *= -1 # (3, N) in "right down forward" ## project vertices from camera coordinate to pixel coordinate vertices_image = (K @ vertices_cam).T # (N, 3) depth = vertices_image[:, -1:]+1e-5 # the depth of the vertices, used as far plane vertices_image = vertices_image[:, :2]/depth vertices_image = vertices_image.astype(np.float32) vertices_image[:, 0] = np.clip(vertices_image[:, 0], 0, W-1) vertices_image[:, 1] = np.clip(vertices_image[:, 1], 0, H-1) ## compute the color on these projected pixel coordinates ## using bilinear interpolation. ## NOTE: opencv's implementation has a size limit of 32768 pixels per side, ## so we split the input into chunks. colors = [] remap_chunk = int(3e4) for i in range(0, N_vertices, remap_chunk): colors += [cv2.remap(image, vertices_image[i:i+remap_chunk, 0], vertices_image[i:i+remap_chunk, 1], interpolation=cv2.INTER_LINEAR)[:, 0]] colors = np.vstack(colors) # (N_vertices, 3) ## predict occlusion of each vertex ## we leverage the concept of NeRF by constructing rays coming out from the camera ## and hitting each vertex; by computing the accumulated opacity along this path, ## we can know if the vertex is occluded or not. ## for vertices that appear to be occluded from every input view, we make the ## assumption that its color is the same as its neighbors that are facing our side. ## (think of a surface with one side facing us: we assume the other side has the same color) ## ray's origin is camera origin rays_o = torch.FloatTensor(dataset.poses[idx][:, -1]).expand(N_vertices, 3) ## ray's direction is the vector pointing from camera origin to the vertices rays_d = torch.FloatTensor(vertices_) - rays_o # (N_vertices, 3) rays_d = rays_d / torch.norm(rays_d, dim=-1, keepdim=True) near = dataset.bounds.min() * torch.ones_like(rays_o[:, :1]) ## the far plane is the depth of the vertices, since what we want is the accumulated ## opacity along the path from camera origin to the vertices far = torch.FloatTensor(depth) * torch.ones_like(rays_o[:, :1]) results = f([nerf_fine], embeddings, torch.cat([rays_o, rays_d, near, far], 1).cuda(), args.N_samples, 0, args.chunk, dataset.white_back) opacity = results['opacity_coarse'].cpu().numpy()[:, np.newaxis] # (N_vertices, 1) opacity = np.nan_to_num(opacity, 1) non_occluded = np.ones_like(non_occluded_sum) * 0.1/depth # weight by inverse depth # near=more confident in color non_occluded += opacity < args.occ_threshold v_color_sum += colors * non_occluded non_occluded_sum += non_occluded # Step 3. combine the output and write to file if args.use_vertex_normal: v_colors = results['rgb_fine'].cpu().numpy() * 255.0 else: ## the combined color is the average color among all views v_colors = v_color_sum/non_occluded_sum v_colors = v_colors.astype(np.uint8) v_colors.dtype = [('red', 'u1'), ('green', 'u1'), ('blue', 'u1')] vertices_.dtype = [('x', 'f4'), ('y', 'f4'), ('z', 'f4')] vertex_all = np.empty(N_vertices, vertices_.dtype.descr+v_colors.dtype.descr) for prop in vertices_.dtype.names: vertex_all[prop] = vertices_[prop][:, 0] for prop in v_colors.dtype.names: vertex_all[prop] = v_colors[prop][:, 0] face = np.empty(len(triangles), dtype=[('vertex_indices', 'i4', (3,))]) face['vertex_indices'] = triangles PlyData([PlyElement.describe(vertex_all, 'vertex'), PlyElement.describe(face, 'face')]).write(f'{args.scene_name}.ply') print('Done!')

45.796667

105

0.594803

7951549885efb88bc1edb83caae759ffad5ff681

794

py

Python

firstapp/firstapp/urls.py

aiegoo/django

2f508a318edd26403509a61eb44e99fda8b7ed64

[ "bzip2-1.0.6", "MIT" ]

null

firstapp/firstapp/urls.py

aiegoo/django

2f508a318edd26403509a61eb44e99fda8b7ed64

[ "bzip2-1.0.6", "MIT" ]

8

2020-02-12T03:14:20.000Z

2022-03-11T23:59:53.000Z

firstapp/firstapp/urls.py

aiegoo/django

2f508a318edd26403509a61eb44e99fda8b7ed64

[ "bzip2-1.0.6", "MIT" ]

null

"""firstapp URL Configuration The `urlpatterns` list routes URLs to views. For more information please see: https://docs.djangoproject.com/en/2.2/topics/http/urls/ Examples: Function views 1. Add an import: from my_app import views 2. Add a URL to urlpatterns: path('', views.home, name='home') Class-based views 1. Add an import: from other_app.views import Home 2. Add a URL to urlpatterns: path('', Home.as_view(), name='home') Including another URLconf 1. Import the include() function: from django.urls import include, path 2. Add a URL to urlpatterns: path('blog/', include('blog.urls')) """ from django.contrib import admin from django.conf.urls import url urlpatterns = [ url(r'^admin/', admin.site.urls), url(r'^posts/$', admin.site.urls), ]

34.521739

77

0.701511

795155ed2440b3ec0207c8d5c044c269d9b27057

10,940

py

Python

linux/bin/dmenu-frecency.py

nevesnunes/env

7a5e3816334337e04a87e1a2e4dc322215901744

[ "MIT" ]

4

2020-04-07T14:45:02.000Z

2021-12-28T22:43:16.000Z

linux/bin/dmenu-frecency.py

nevesnunes/env

7a5e3816334337e04a87e1a2e4dc322215901744

[ "MIT" ]

null

linux/bin/dmenu-frecency.py

nevesnunes/env

7a5e3816334337e04a87e1a2e4dc322215901744

[ "MIT" ]

2

2020-04-08T03:12:06.000Z

2021-03-04T20:33:03.000Z

#!/usr/bin/env python """Dmenu launcher with history sorted by frecency. Usage: dmenu-frecency [--read-apps] Options: --read-apps rereads all .desktop files. """ from docopt import docopt import os import sys import xdg.BaseDirectory from xdg.DesktopEntry import DesktopEntry from subprocess import Popen, PIPE from datetime import datetime from collections import defaultdict import pickle import re import gzip import json import tempfile import shlex CONFIG_DIR = xdg.BaseDirectory.save_config_path('dmenu-frecency') # Python 2 compatibility try: FileNotFoundError except NameError: FileNotFoundError = IOError class Application: def __init__(self, name, command_line, mtime=None, path=None, is_desktop=False): self.name = name self.path = path self.command_line = command_line self.is_desktop = is_desktop self.show_command = False if mtime is None: self.mtime = datetime.now() else: self.mtime = mtime def run(self): if os.fork() == 0: if self.path: os.chdir(self.path) os.execvp(self.command_line[0], self.command_line) def __lt__(self, other): return (self.is_desktop, self.mtime) < (other.is_desktop, other.mtime) def __eq__(self, other): return self.name == other.name def __hash__(self): return hash(self.name) def __str__(self): return "<Application: {} {!r}>".format(self.name, self.command_line) STATE_VERSION = 4 def get_command(desktop_entry): tokens = [] for token in shlex.split(desktop_entry.getExec()): if token == '%i': if desktop_entry.getIcon(): tokens.append('--icon') tokens.append(desktop_entry.getIcon()) else: i = 0 newtok = "" nc = len(token) while i < nc: c = token[i] if c == '%' and i < nc - 1: i += 1 code = token[i] if code == 'c' and desktop_entry.getName(): newtok += desktop_entry.getName() elif code == '%': newtok += '%' else: newtok += c i += 1 if newtok: tokens.append(newtok) return tuple(tokens) class LauncherState: STATE_FILENAME = os.path.join(CONFIG_DIR, 'state') def __init__(self, config): self.version = STATE_VERSION self.config = config self.find_apps() self.apps_generated_at = datetime.now() self.visits = defaultdict(list) self.visit_count = defaultdict(int) self.app_last_visit = None self.frecency_cache = {} def apps_by_frecency(self): app_last_visit = self.app_last_visit if self.config['preselect-last-visit'] else None if app_last_visit is not None: yield app_last_visit for app, frec in sorted(self.frecency_cache.items(), key=lambda x: (-x[1], x[0])): if app_last_visit is None or app_last_visit != app: yield app for app in self.sorted_apps: if app not in self.frecency_cache: if app_last_visit is None or app_last_visit != app: yield app def add_visit(self, app): if not app.is_desktop and app.command_line in self.command_apps: app = self.command_apps[app.command_line] app.show_command = True try: self.sorted_apps.remove(app) except ValueError: pass # not in list vs = self.visits[app] now = datetime.now() vs.append(now) self.visit_count[app] += 1 self.visits[app] = vs[-self.config['frecency-visits']:] self.app_last_visit = app if self.config['preselect-last-visit'] else None def update_frecencies(self): for app in self.visits.keys(): self.frecency_cache[app] = self.frecency(app) def frecency(self, app): points = 0 for v in self.visits[app]: days_ago = (datetime.now() - v).days if days_ago < 4: points += 100 elif days_ago < 14: points += 70 elif days_ago < 31: points += 50 elif days_ago < 90: points += 30 else: points += 10 return int(self.visit_count[app] * points / len(self.visits[app])) @classmethod def load(cls, config): try: with gzip.open(cls.STATE_FILENAME, 'rb') as f: obj = pickle.load(f) version = getattr(obj, 'version', 0) if version < STATE_VERSION: new_obj = cls(config) if version <= 1: for app, vs in obj.visits.items(): vc = obj.visit_count[app] app.is_desktop = True new_obj.visit_count[app] = vc new_obj.visits[app] = vs new_obj.find_apps() new_obj.clean_cache() new_obj.update_frecencies() new_obj.config = config return new_obj else: obj.config = config return obj except FileNotFoundError: return cls(config) def save(self): with tempfile.NamedTemporaryFile( 'wb', dir=os.path.dirname(self.STATE_FILENAME), delete=False) as tf: with gzip.open(tf, 'wb') as gzipf: pickle.dump(self, gzipf) tempname = tf.name os.rename(tempname, self.STATE_FILENAME) def find_apps(self): self.apps = {} self.command_apps = {} if self.config['scan-desktop-files']: for applications_directory in xdg.BaseDirectory.load_data_paths("applications"): if os.path.exists(applications_directory): for dirpath, dirnames, filenames in os.walk(applications_directory): for f in filenames: if f.endswith('.desktop'): full_filename = os.path.join(dirpath, f) self.add_desktop(full_filename) if self.config['scan-path']: for pathdir in os.environ["PATH"].split(os.pathsep): pathdir = pathdir.strip('"') if not os.path.isdir(pathdir): continue for f in os.listdir(pathdir): filename = os.path.join(pathdir, f) if os.path.isfile(filename) and os.access(filename, os.X_OK): app = Application( name=f, command_line=(f,), mtime=datetime.fromtimestamp(os.path.getmtime(filename))) self.add_app(app) self.sorted_apps = sorted(self.apps.values(), reverse=True) def add_desktop(self, filename): try: d = DesktopEntry(filename) if d.getHidden() or d.getNoDisplay() or d.getTerminal() or d.getType() != 'Application': return app = Application( name=d.getName(), command_line=get_command(d), mtime=datetime.fromtimestamp(os.path.getmtime(filename)), is_desktop=True) if d.getPath(): app.path = d.getPath() self.add_app(app) except (xdg.Exceptions.ParsingError, xdg.Exceptions.DuplicateGroupError, xdg.Exceptions.DuplicateKeyError): pass def add_app(self, app): if app.command_line not in self.command_apps: self.apps[app.name] = app self.command_apps[app.command_line] = app def clean_cache(self): for app in list(self.frecency_cache.keys()): if app.is_desktop and app.name not in self.apps: del self.frecency_cache[app] if self.app_last_visit is not None and self.app_last_visit.name not in self.apps: self.app_last_visit = None class DmenuFrecency: CONFIG_FILENAME = os.path.join(CONFIG_DIR, 'config.json') DEFAULT_CONFIG = { 'dmenu': 'dmenu', 'dmenu-args': ['-i'], 'cache-days': 1, 'frecency-visits': 10, 'preselect-last-visit': False, 'scan-desktop-files': True, 'scan-path': False, } NAME_WITH_COMMAND = re.compile(r"(.+) $[^()]+$") def __init__(self, arguments): self.read_apps = arguments['--read-apps'] self.load_config() self.state = LauncherState.load(self.config) assert self.state, "Failed to load state." def load_config(self): self.config = {} self.config.update(self.DEFAULT_CONFIG) try: with open(self.CONFIG_FILENAME, 'r') as f: self.config.update(json.load(f)) except FileNotFoundError: with open(self.CONFIG_FILENAME, 'w') as f: json.dump(self.config, f, sort_keys=True, indent=4) f.write('\n') def main(self): if self.read_apps: self.state.find_apps() self.state.clean_cache() self.state.save() return dmenu = Popen([self.config['dmenu']] + self.config['dmenu-args'], stdin=PIPE, stdout=PIPE) for app in self.state.apps_by_frecency(): app_name = app.name.encode('utf-8') dmenu.stdin.write(app_name) if app.show_command and app.name != app.command_line[0]: dmenu.stdin.write(" ({})".format(' '.join(app.command_line)).encode('utf-8')) dmenu.stdin.write(b'\n') stdout, stderr = dmenu.communicate() result = stdout.decode('utf-8').strip() if not result: return if result in self.state.apps: app = self.state.apps[result] else: m = self.NAME_WITH_COMMAND.match(result) if m and m.group(1) in self.state.apps: app = self.state.apps[m.group(1)] else: app = Application( name=result, command_line=tuple(shlex.split(result))) app.run() self.state.add_visit(app) self.state.update_frecencies() if (datetime.now() - self.state.apps_generated_at).days >= self.config['cache-days']: self.state.find_apps() self.state.clean_cache() self.state.save() if __name__ == '__main__': arguments = docopt(__doc__, version="0.1") DmenuFrecency(arguments).main()

33.25228

100

0.543601

7951566bdd729b13d1042abd4eb51e249413f327

452

py

Python

clusterapp/features/TimeParameters/StartTimeParameter.py

ealmuina/thesis

d436ae0c6f775c56b2072889ceafae1507291c74

[ "MIT" ]

1

2018-02-11T07:36:31.000Z

clusterapp/features/TimeParameters/StartTimeParameter.py

ealmuina/thesis

d436ae0c6f775c56b2072889ceafae1507291c74

[ "MIT" ]

null

clusterapp/features/TimeParameters/StartTimeParameter.py

ealmuina/thesis

d436ae0c6f775c56b2072889ceafae1507291c74

[ "MIT" ]

null

import numpy as np from .TimeParameter import TimeParameter from .__init__ import * class StartTimeParameter(TimeParameter): name = 'StartTime' """docstring for StartTimeParameter""" def __init__(self): super(TimeParameter, self).__init__() def measure(self, segment): value = segment.IndexFrom / segment.samplingRate segment.measures_dict[self.name] = np.round(value, DECIMAL_PLACES) return True

22.6

74

0.70354

7951568f8bb478a934e278554c4b65789cd0c5f2

7,973

py

Python

OnlineJudge/account/views/admin.py

FrozenWhalePP/OnlineJudge

aec81292046b4a0896ff164565b490dc37bd91cb

[ "MIT" ]

2

2020-03-07T02:26:00.000Z

2020-06-01T15:03:17.000Z

OnlineJudge/account/views/admin.py

FrozenWhalePP/OnlineJudge

aec81292046b4a0896ff164565b490dc37bd91cb

[ "MIT" ]

null

OnlineJudge/account/views/admin.py

FrozenWhalePP/OnlineJudge

aec81292046b4a0896ff164565b490dc37bd91cb

[ "MIT" ]

null

import os import re import xlsxwriter from django.db import transaction, IntegrityError from django.db.models import Q from django.http import HttpResponse from django.contrib.auth.hashers import make_password from submission.models import Submission from utils.api import APIView, validate_serializer from utils.shortcuts import rand_str from ..decorators import super_admin_required from ..models import AdminType, ProblemPermission, User, UserProfile from ..serializers import EditUserSerializer, UserAdminSerializer, GenerateUserSerializer from ..serializers import ImportUserSeralizer class UserAdminAPI(APIView): @validate_serializer(ImportUserSeralizer) @super_admin_required def post(self, request): """ Import User """ data = request.data["users"] user_list = [] for user_data in data: if len(user_data) != 3 or len(user_data[0]) > 32: return self.error(f"Error occurred while processing data '{user_data}'") user_list.append(User(username=user_data[0], password=make_password(user_data[1]), email=user_data[2])) try: with transaction.atomic(): ret = User.objects.bulk_create(user_list) UserProfile.objects.bulk_create([UserProfile(user=user) for user in ret]) return self.success() except IntegrityError as e: # Extract detail from exception message # duplicate key value violates unique constraint "user_username_key" # DETAIL: Key (username)=(root11) already exists. return self.error(str(e).split("\n")[1]) @validate_serializer(EditUserSerializer) @super_admin_required def put(self, request): """ Edit user api """ data = request.data try: user = User.objects.get(id=data["id"]) except User.DoesNotExist: return self.error("User does not exist") if User.objects.filter(username=data["username"].lower()).exclude(id=user.id).exists(): return self.error("Username already exists") if User.objects.filter(email=data["email"].lower()).exclude(id=user.id).exists(): return self.error("Email already exists") pre_username = user.username user.username = data["username"].lower() user.email = data["email"].lower() user.admin_type = data["admin_type"] user.is_disabled = data["is_disabled"] if data["admin_type"] == AdminType.ADMIN: user.problem_permission = data["problem_permission"] elif data["admin_type"] == AdminType.SUPER_ADMIN: user.problem_permission = ProblemPermission.ALL else: user.problem_permission = ProblemPermission.NONE if data["password"]: user.set_password(data["password"]) if data["open_api"]: # Avoid reset user appkey after saving changes if not user.open_api: user.open_api_appkey = rand_str() else: user.open_api_appkey = None user.open_api = data["open_api"] if data["two_factor_auth"]: # Avoid reset user tfa_token after saving changes if not user.two_factor_auth: user.tfa_token = rand_str() else: user.tfa_token = None user.two_factor_auth = data["two_factor_auth"] user.save() if pre_username != user.username: Submission.objects.filter(username=pre_username).update(username=user.username) UserProfile.objects.filter(user=user).update(real_name=data["real_name"]) return self.success(UserAdminSerializer(user).data) @super_admin_required def get(self, request): """ User list api / Get user by id """ user_id = request.GET.get("id") if user_id: try: user = User.objects.get(id=user_id) except User.DoesNotExist: return self.error("User does not exist") return self.success(UserAdminSerializer(user).data) user = User.objects.all().order_by("-create_time") keyword = request.GET.get("keyword", None) if keyword: user = user.filter(Q(username__icontains=keyword) | Q(userprofile__real_name__icontains=keyword) | Q(email__icontains=keyword)) return self.success(self.paginate_data(request, user, UserAdminSerializer)) @super_admin_required def delete(self, request): id = request.GET.get("id") if not id: return self.error("Invalid Parameter, id is required") ids = id.split(",") if str(request.user.id) in ids: return self.error("Current user can not be deleted") User.objects.filter(id__in=ids).delete() return self.success() class GenerateUserAPI(APIView): @super_admin_required def get(self, request): """ download users excel """ file_id = request.GET.get("file_id") if not file_id: return self.error("Invalid Parameter, file_id is required") if not re.match(r"^[a-zA-Z0-9]+$", file_id): return self.error("Illegal file_id") file_path = f"/tmp/{file_id}.xlsx" if not os.path.isfile(file_path): return self.error("File does not exist") with open(file_path, "rb") as f: raw_data = f.read() os.remove(file_path) response = HttpResponse(raw_data) response["Content-Disposition"] = f"attachment; filename=users.xlsx" response["Content-Type"] = "application/xlsx" return response @validate_serializer(GenerateUserSerializer) @super_admin_required def post(self, request): """ Generate User """ data = request.data number_max_length = max(len(str(data["number_from"])), len(str(data["number_to"]))) if number_max_length + len(data["prefix"]) + len(data["suffix"]) > 32: return self.error("Username should not more than 32 characters") if data["number_from"] > data["number_to"]: return self.error("Start number must be lower than end number") file_id = rand_str(8) filename = f"/tmp/{file_id}.xlsx" workbook = xlsxwriter.Workbook(filename) worksheet = workbook.add_worksheet() worksheet.set_column("A:B", 20) worksheet.write("A1", "Username") worksheet.write("B1", "Password") i = 1 user_list = [] for number in range(data["number_from"], data["number_to"] + 1): raw_password = rand_str(data["password_length"]) user = User(username=f"{data['prefix']}{number}{data['suffix']}", password=make_password(raw_password)) user.raw_password = raw_password user_list.append(user) try: with transaction.atomic(): ret = User.objects.bulk_create(user_list) UserProfile.objects.bulk_create([UserProfile(user=user) for user in ret]) for item in user_list: worksheet.write_string(i, 0, item.username) worksheet.write_string(i, 1, item.raw_password) i += 1 workbook.close() return self.success({"file_id": file_id}) except IntegrityError as e: # Extract detail from exception message # duplicate key value violates unique constraint "user_username_key" # DETAIL: Key (username)=(root11) already exists. return self.error(str(e).split("\n")[1])

39.275862

116

0.598269

79515834f7dfae204a53de88d3192612ce8acb78

2,181

py

Python

run.py

eric-sentient/MIDI-reAE-chords

ac16210bbeb822cb4babb95974a4a05d527763cc

[ "MIT" ]

null

run.py

eric-sentient/MIDI-reAE-chords

ac16210bbeb822cb4babb95974a4a05d527763cc

[ "MIT" ]

null

run.py

eric-sentient/MIDI-reAE-chords

ac16210bbeb822cb4babb95974a4a05d527763cc

[ "MIT" ]

null

import numpy as np from sklearn.model_selection import train_test_split from sklearn.utils import shuffle from keras.callbacks import EarlyStopping, ModelCheckpoint from keras import models import model import load_train from random import randrange BATCH_SIZE = 400 EPOCHS = 100 VERBOSE = True LOAD = False GENERATE = True GEN_SAMPLES = 6 X, y = None, None if LOAD: model = models.load_model('MIDI-reAE.h5') model.summary() else: X, y = load_train.get_train() X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42) X_valid, X_test, y_valid, y_test = train_test_split(X_test, y_test, test_size=0.5, random_state=42) checkpoint_name = 'weights/Weights-{epoch:03d}--{val_loss:.5f}.hdf5' checkpoint = ModelCheckpoint( checkpoint_name, monitor='val_loss', verbose=1, save_best_only=True, mode='auto') model = model.make_model() history = model.fit( X_train, y_train, batch_size=BATCH_SIZE, verbose=VERBOSE, callbacks=[checkpoint], epochs=EPOCHS, validation_data=(X_valid, y_valid)) model.save('MIDI-reAE.h5') if GENERATE: if not X: X, y = load_train.get_train() for _ in range(GEN_SAMPLES): i = randrange(len(X) - 1) randX, randy = X[i], y[i] num_notes = round(0.5 * sum([np.count_nonzero(b) for b in randX])) yhat = model.predict(randX.reshape(1,2,101)) ypred = [(x.argsort()[-num_notes:][::-1]) for x in yhat.reshape(2, 101)] def decode(vector): return [i for i in range(len(vector)) if vector[i] == 1] print("input sequence:") print([decode(b) for b in randX]) print("generated output:") print() print("actual output:") print([decode(b) for b in randy]) import mido import matplotlib mid = MidiFile(type=0) track = MidiTrack() mid.tracks.append(track) pred = randX + ypred t = 0 for i in range(len(pred)): for note in pred[i]: track.append(Message('note_on', note=note, velocity=80, time=t)) t = t + 400 for note in pred[i]: track.append(Message('note_off', note=note, velocity=80,time=t)) mid.draw_roll()

24.784091

101

0.659789

7951589169affccbe27ad8c97ce896cd3d70f086

26,486

py

Python

tests/test_cli.py

alandtse/python-semantic-release

b58fbc16bf5145bc512ccce71561ed6fada68b90

[ "MIT" ]

3

2019-09-30T12:47:32.000Z

2020-06-15T14:55:16.000Z

tests/test_cli.py

alandtse/python-semantic-release

b58fbc16bf5145bc512ccce71561ed6fada68b90

[ "MIT" ]

null

tests/test_cli.py

alandtse/python-semantic-release

b58fbc16bf5145bc512ccce71561ed6fada68b90

[ "MIT" ]

1

2019-09-30T12:47:49.000Z

from click.testing import CliRunner import semantic_release from semantic_release.cli import changelog, main, publish, version from semantic_release.errors import GitError, ImproperConfigurationError from . import mock, pytest, reset_config from .mocks import mock_version_file assert reset_config @pytest.fixture def runner(): return CliRunner() def test_main_should_call_correct_function(mocker, runner): mock_version = mocker.patch('semantic_release.cli.version') result = runner.invoke(main, ['version']) mock_version.assert_called_once_with( noop=False, post=False, force_level=None, retry=False, define=(), ) assert result.exit_code == 0 def test_version_by_commit_should_call_correct_functions(mocker, runner): mocker.patch('semantic_release.cli.config.getboolean', lambda *x: False) mock_tag_new_version = mocker.patch('semantic_release.cli.tag_new_version') mock_commit_new_version = mocker.patch('semantic_release.cli.commit_new_version') mock_set_new_version = mocker.patch('semantic_release.cli.set_new_version') mock_new_version = mocker.patch('semantic_release.cli.get_new_version', return_value='2.0.0') mock_evaluate_bump = mocker.patch('semantic_release.cli.evaluate_version_bump', return_value='major') mock_current_version = mocker.patch('semantic_release.cli.get_current_version', return_value='1.2.3') result = runner.invoke(main, ['version']) mock_current_version.assert_called_once_with() mock_evaluate_bump.assert_called_once_with('1.2.3', None) mock_new_version.assert_called_once_with('1.2.3', 'major') mock_set_new_version.assert_called_once_with('2.0.0') mock_commit_new_version.assert_called_once_with('2.0.0') mock_tag_new_version.assert_called_once_with('2.0.0') assert result.exit_code == 0 def test_version_by_tag_should_call_correct_functions(mocker, runner): orig = semantic_release.cli.config.get def wrapped_config_get(*args, **kwargs): if (len(args) >= 2 and args[0] == 'semantic_release' and args[1] == 'version_source'): return 'tag' elif (len(args) >= 2 and args[0] == 'semantic_release' and args[1] == 'commit_version_number'): return True return orig(*args, **kwargs) mocker.patch('semantic_release.cli.config.get', wrapped_config_get) mocker.patch('semantic_release.cli.config.getboolean', lambda *x: False) mock_set_new_version = mocker.patch('semantic_release.cli.set_new_version') mock_tag_new_version = mocker.patch('semantic_release.cli.tag_new_version') mock_commit_new_version = mocker.patch('semantic_release.cli.commit_new_version') mock_new_version = mocker.patch('semantic_release.cli.get_new_version', return_value='2.0.0') mock_evaluate_bump = mocker.patch('semantic_release.cli.evaluate_version_bump', return_value='major') mock_current_version = mocker.patch('semantic_release.cli.get_current_version', return_value='1.2.3') result = runner.invoke(main, ['version']) mock_current_version.assert_called_once_with() mock_evaluate_bump.assert_called_once_with('1.2.3', None) mock_new_version.assert_called_once_with('1.2.3', 'major') mock_set_new_version.assert_called_once_with('2.0.0') mock_commit_new_version.assert_called_once_with('2.0.0') mock_tag_new_version.assert_called_once_with('2.0.0') assert result.exit_code == 0 def test_version_by_tag_with_commit_version_number_should_call_correct_functions(mocker, runner): orig = semantic_release.cli.config.get def wrapped_config_get(*args, **kwargs): if len(args) >= 2 and args[0] == 'semantic_release' and args[1] == 'version_source': return 'tag' return orig(*args, **kwargs) mocker.patch('semantic_release.cli.config.get', wrapped_config_get) mocker.patch('semantic_release.cli.config.getboolean', lambda *x: False) mock_set_new_version = mocker.patch('semantic_release.cli.set_new_version') mock_tag_new_version = mocker.patch('semantic_release.cli.tag_new_version') mock_new_version = mocker.patch('semantic_release.cli.get_new_version', return_value='2.0.0') mock_evaluate_bump = mocker.patch('semantic_release.cli.evaluate_version_bump', return_value='major') mock_current_version = mocker.patch('semantic_release.cli.get_current_version', return_value='1.2.3') result = runner.invoke(main, ['version']) mock_current_version.assert_called_once_with() mock_evaluate_bump.assert_called_once_with('1.2.3', None) mock_new_version.assert_called_once_with('1.2.3', 'major') mock_set_new_version.assert_called_once_with('2.0.0') mock_tag_new_version.assert_called_once_with('2.0.0') assert result.exit_code == 0 def test_force_major(mocker, runner): mock_version = mocker.patch('semantic_release.cli.version') result = runner.invoke(main, ['version', '--major']) mock_version.assert_called_once_with( noop=False, post=False, force_level='major', retry=False, define=(), ) assert mock_version.call_args_list[0][1]['force_level'] == 'major' assert result.exit_code == 0 def test_force_minor(mocker, runner): mock_version = mocker.patch('semantic_release.cli.version') result = runner.invoke(main, ['version', '--minor']) mock_version.assert_called_once_with( noop=False, post=False, force_level='minor', retry=False, define=(), ) assert mock_version.call_args_list[0][1]['force_level'] == 'minor' assert result.exit_code == 0 def test_force_patch(mocker, runner): mock_version = mocker.patch('semantic_release.cli.version') result = runner.invoke(main, ['version', '--patch']) mock_version.assert_called_once_with( noop=False, post=False, force_level='patch', retry=False, define=(), ) assert mock_version.call_args_list[0][1]['force_level'] == 'patch' assert result.exit_code == 0 def test_retry(mocker, runner): mock_version = mocker.patch('semantic_release.cli.version') result = runner.invoke(main, ['version', '--retry']) mock_version.assert_called_once_with( noop=False, post=False, force_level=None, retry=True, define=(), ) assert result.exit_code == 0 def test_noop_mode(mocker, runner): mock_tag_new_version = mocker.patch('semantic_release.cli.tag_new_version') mock_set_new = mocker.patch('semantic_release.cli.commit_new_version') mock_commit_new = mocker.patch('semantic_release.cli.set_new_version') mocker.patch('semantic_release.cli.evaluate_version_bump', lambda *x: 'major') result = runner.invoke(main, ['version', '--noop']) assert not mock_set_new.called assert not mock_commit_new.called assert not mock_tag_new_version.called assert result.exit_code == 0 def test_version_no_change(mocker, runner): mock_tag_new_version = mocker.patch('semantic_release.cli.tag_new_version') mock_commit_new_version = mocker.patch('semantic_release.cli.commit_new_version') mock_set_new_version = mocker.patch('semantic_release.cli.set_new_version') mock_new_version = mocker.patch('semantic_release.cli.get_new_version', return_value='1.2.3') mock_evaluate_bump = mocker.patch('semantic_release.cli.evaluate_version_bump', return_value=None) mock_current_version = mocker.patch('semantic_release.cli.get_current_version', return_value='1.2.3') result = runner.invoke(main, ['version']) mock_current_version.assert_called_once_with() mock_evaluate_bump.assert_called_once_with('1.2.3', None) mock_new_version.assert_called_once_with('1.2.3', None) assert not mock_set_new_version.called assert not mock_commit_new_version.called assert not mock_tag_new_version.called assert result.exit_code == 0 def test_version_check_build_status_fails(mocker, runner): mock_check_build_status = mocker.patch('semantic_release.cli.check_build_status', return_value=False) mock_tag_new_version = mocker.patch('semantic_release.cli.tag_new_version') mock_commit_new = mocker.patch('semantic_release.cli.commit_new_version') mock_set_new = mocker.patch('semantic_release.cli.set_new_version') mocker.patch('semantic_release.cli.config.getboolean', lambda *x: True) mocker.patch('semantic_release.cli.evaluate_version_bump', lambda *x: 'major') result = runner.invoke(main, ['version']) assert mock_check_build_status.called assert not mock_set_new.called assert not mock_commit_new.called assert not mock_tag_new_version.called assert result.exit_code == 0 def test_version_by_commit_check_build_status_succeeds(mocker, runner): mocker.patch('semantic_release.cli.config.getboolean', lambda *x: True) mock_check_build_status = mocker.patch('semantic_release.cli.check_build_status', return_value=True) mock_tag_new_version = mocker.patch('semantic_release.cli.tag_new_version') mocker.patch('semantic_release.cli.evaluate_version_bump', lambda *x: 'major') mock_commit_new = mocker.patch('semantic_release.cli.commit_new_version') mock_set_new = mocker.patch('semantic_release.cli.set_new_version') result = runner.invoke(main, ['version']) assert mock_check_build_status.called assert mock_set_new.called assert mock_commit_new.called assert mock_tag_new_version.called assert result.exit_code == 0 def test_version_by_tag_check_build_status_succeeds(mocker, runner): orig = semantic_release.cli.config.get def wrapped_config_get(*args, **kwargs): if len(args) >= 2 and args[0] == 'semantic_release' and args[1] == 'version_source': return 'tag' return orig(*args, **kwargs) mocker.patch('semantic_release.cli.config.get', wrapped_config_get) mocker.patch('semantic_release.cli.config.getboolean', lambda *x: True) mock_check_build_status = mocker.patch('semantic_release.cli.check_build_status', return_value=True) mock_set_new_version = mocker.patch('semantic_release.cli.set_new_version') mock_tag_new_version = mocker.patch('semantic_release.cli.tag_new_version') mocker.patch('semantic_release.cli.evaluate_version_bump', lambda *x: 'major') result = runner.invoke(main, ['version']) assert mock_check_build_status.called assert mock_set_new_version.called assert mock_tag_new_version.called assert result.exit_code == 0 def test_version_check_build_status_not_called_if_disabled(mocker, runner): mock_check_build_status = mocker.patch('semantic_release.cli.check_build_status') mocker.patch('semantic_release.cli.config.getboolean', lambda *x: False) mocker.patch('semantic_release.cli.tag_new_version', None) mocker.patch('semantic_release.cli.evaluate_version_bump', lambda *x: 'major') mocker.patch('semantic_release.cli.commit_new_version', None) mocker.patch('semantic_release.cli.set_new_version', None) runner.invoke(main, ['version']) assert not mock_check_build_status.called def test_version_retry_and_giterror(mocker): mocker.patch('semantic_release.cli.get_current_version', mock.Mock(side_effect=GitError())) result = version(retry=True) assert not result def test_version_retry(mocker): mock_get_current = mocker.patch('semantic_release.cli.get_current_version', return_value='current') mock_evaluate_bump = mocker.patch('semantic_release.cli.evaluate_version_bump', return_value='patch') mock_get_new = mocker.patch('semantic_release.cli.get_new_version', return_value='new') mocker.patch('semantic_release.cli.config.getboolean', lambda *x: False) result = version(noop=False, retry=True, force_level=False) assert result mock_get_current.assert_called_once_with() mock_evaluate_bump.assert_called_once_with('current', False) mock_get_new.assert_called_once_with('current', 'patch') def test_publish_should_not_upload_to_pypi_if_option_is_false(mocker, runner): mocker.patch('semantic_release.cli.checkout') mocker.patch('semantic_release.cli.ci_checks.check') mock_upload = mocker.patch('semantic_release.cli.upload_to_pypi') mocker.patch('semantic_release.cli.post_changelog', lambda *x: True) mocker.patch('semantic_release.cli.push_new_version', lambda *x: True) mocker.patch('semantic_release.cli.version', lambda: True) mocker.patch('semantic_release.cli.markdown_changelog', lambda *x, **y: 'CHANGES') mocker.patch('semantic_release.cli.get_new_version', lambda *x: '2.0.0') mocker.patch('semantic_release.cli.check_token', lambda: True) mocker.patch('semantic_release.cli.config.getboolean', lambda *x: False) runner.invoke(main, ['publish']) assert not mock_upload.called def test_publish_should_do_nothing_when_version_fails(mocker, runner): mocker.patch('semantic_release.cli.checkout') mocker.patch('semantic_release.cli.get_new_version', lambda *x: '2.0.0') mocker.patch('semantic_release.cli.evaluate_version_bump', lambda *x: 'feature') mocker.patch('semantic_release.cli.generate_changelog') mock_log = mocker.patch('semantic_release.cli.post_changelog') mock_upload = mocker.patch('semantic_release.cli.upload_to_pypi') mock_push = mocker.patch('semantic_release.cli.push_new_version') mock_ci_check = mocker.patch('semantic_release.ci_checks.check') mock_version = mocker.patch('semantic_release.cli.version', return_value=False) result = runner.invoke(main, ['publish']) mock_version.assert_called_once_with( noop=False, post=False, force_level=None, retry=False, define=(), ) assert not mock_push.called assert not mock_upload.called assert not mock_log.called assert mock_ci_check.called assert result.exit_code == 0 def test_publish_should_call_functions(mocker, runner): mock_push = mocker.patch('semantic_release.cli.push_new_version') mock_checkout = mocker.patch('semantic_release.cli.checkout') mock_version = mocker.patch('semantic_release.cli.version', return_value=True) mock_log = mocker.patch('semantic_release.cli.post_changelog') mock_ci_check = mocker.patch('semantic_release.ci_checks.check') mock_pypi = mocker.patch('semantic_release.cli.upload_to_pypi') mocker.patch('semantic_release.cli.get_repository_owner_and_name', return_value=('relekang', 'python-semantic-release')) mocker.patch('semantic_release.cli.evaluate_version_bump', lambda *x: 'feature') mocker.patch('semantic_release.cli.generate_changelog') mocker.patch('semantic_release.cli.markdown_changelog', lambda *x, **y: 'CHANGES') mocker.patch('semantic_release.cli.get_new_version', lambda *x: '2.0.0') mocker.patch('semantic_release.cli.check_token', lambda: True) result = runner.invoke(main, ['publish']) assert result.exit_code == 0 assert mock_ci_check.called assert mock_push.called assert mock_pypi.called mock_version.assert_called_once_with( noop=False, post=False, force_level=None, retry=False, define=(), ) mock_log.assert_called_once_with(u'relekang', 'python-semantic-release', '2.0.0', 'CHANGES') mock_checkout.assert_called_once_with('master') def test_publish_retry_version_fail(mocker): mock_get_current = mocker.patch('semantic_release.cli.get_current_version', return_value='current') mock_get_previous = mocker.patch('semantic_release.cli.get_previous_version', return_value='previous') mock_get_owner_name = mocker.patch('semantic_release.cli.get_repository_owner_and_name', return_value=('owner', 'name')) mock_ci_check = mocker.patch('semantic_release.ci_checks.check') mock_checkout = mocker.patch('semantic_release.cli.checkout') mocker.patch('semantic_release.cli.config.get', lambda *x: 'my_branch') mock_version = mocker.patch('semantic_release.cli.version', return_value=False) publish(noop=False, retry=True, force_level=False) mock_get_current.assert_called_once_with() mock_get_previous.assert_called_once_with('current') mock_get_owner_name.assert_called_once_with() mock_ci_check.assert_called() mock_checkout.assert_called_once_with('my_branch') mock_version.assert_called_once_with(noop=False, retry=True, force_level=False) def test_publish_bad_token(mocker): mock_get_current = mocker.patch('semantic_release.cli.get_current_version', return_value='current') mock_get_previous = mocker.patch('semantic_release.cli.get_previous_version', return_value='previous') mock_get_owner_name = mocker.patch('semantic_release.cli.get_repository_owner_and_name', return_value=('owner', 'name')) mock_ci_check = mocker.patch('semantic_release.ci_checks.check') mock_checkout = mocker.patch('semantic_release.cli.checkout') mocker.patch('semantic_release.cli.config.get', return_value='my_branch') mocker.patch('semantic_release.cli.config.getboolean', return_value=False) mock_version = mocker.patch('semantic_release.cli.version') mock_get_token = mocker.patch('semantic_release.cli.get_token', return_value='SUPERTOKEN') mock_get_domain = mocker.patch('semantic_release.cli.get_domain', return_value='domain') mock_push = mocker.patch('semantic_release.cli.push_new_version') mock_check_token = mocker.patch('semantic_release.cli.check_token', return_value=False) publish(noop=False, retry=True, force_level=False) mock_get_current.assert_called_once_with() mock_get_previous.assert_called_once_with('current') mock_get_owner_name.assert_called_once_with() mock_ci_check.assert_called() mock_checkout.assert_called_once_with('my_branch') mock_version.assert_called_once_with(noop=False, retry=True, force_level=False) mock_get_token.assert_called() mock_get_domain.assert_called() mock_push.assert_called_once_with(auth_token='SUPERTOKEN', owner='owner', name='name', branch='my_branch', domain='domain') mock_check_token.assert_called_once_with() def test_publish_giterror_when_posting(mocker): mock_get_current = mocker.patch('semantic_release.cli.get_current_version', return_value='current') mock_evaluate = mocker.patch('semantic_release.cli.evaluate_version_bump', return_value='patch') mock_get_new = mocker.patch('semantic_release.cli.get_new_version', return_value='new') mock_get_owner_name = mocker.patch('semantic_release.cli.get_repository_owner_and_name', return_value=('owner', 'name')) mock_ci_check = mocker.patch('semantic_release.ci_checks.check') mock_checkout = mocker.patch('semantic_release.cli.checkout') mocker.patch('semantic_release.cli.config.get', return_value='my_branch') mocker.patch('semantic_release.cli.config.getboolean', return_value=False) mock_version = mocker.patch('semantic_release.cli.version') mock_get_token = mocker.patch('semantic_release.cli.get_token', return_value='SUPERTOKEN') mock_get_domain = mocker.patch('semantic_release.cli.get_domain', return_value='domain') mock_push = mocker.patch('semantic_release.cli.push_new_version') mock_check_token = mocker.patch('semantic_release.cli.check_token', return_value=True) mock_generate = mocker.patch('semantic_release.cli.generate_changelog', return_value='super changelog') mock_markdown = mocker.patch('semantic_release.cli.markdown_changelog', return_value='super md changelog') mock_post = mocker.patch('semantic_release.cli.post_changelog', mock.Mock(side_effect=GitError())) publish(noop=False, retry=False, force_level=False) mock_get_current.assert_called_once_with() mock_evaluate.assert_called_once_with('current', False) mock_get_new.assert_called_once_with('current', 'patch') mock_get_owner_name.assert_called_once_with() mock_ci_check.assert_called() mock_checkout.assert_called_once_with('my_branch') mock_version.assert_called_once_with(noop=False, retry=False, force_level=False) mock_get_token.assert_called_once_with() mock_get_domain.assert_called_once_with() mock_push.assert_called_once_with(auth_token='SUPERTOKEN', owner='owner', name='name', branch='my_branch', domain='domain') mock_check_token.assert_called_once_with() mock_generate.assert_called_once_with('current', 'new') mock_markdown.assert_called_once_with('new', 'super changelog', header=False) mock_post.assert_called_once_with('owner', 'name', 'new', 'super md changelog') def test_changelog_should_call_functions(mocker, runner): mock_changelog = mocker.patch('semantic_release.cli.changelog', return_value=True) result = runner.invoke(main, ['changelog']) assert result.exit_code == 0 mock_changelog.assert_called_once_with( noop=False, post=False, force_level=None, retry=False, unreleased=False, define=(), ) def test_overload_by_cli(mocker, runner): mock_open = mocker.patch('semantic_release.history.open', mock_version_file) runner.invoke(main, ['version', '--noop', '--patch', '-D', 'version_variable=my_version_path:my_version_var']) mock_open.assert_called_once_with('my_version_path', 'r') mock_open.reset_mock() def test_changelog_noop(mocker): mocker.patch('semantic_release.cli.get_current_version', return_value='current') mock_previous_version = mocker.patch('semantic_release.cli.get_previous_version', return_value='previous') mock_generate_changelog = mocker.patch('semantic_release.cli.generate_changelog', return_value='super changelog') mock_markdown_changelog = mocker.patch('semantic_release.cli.markdown_changelog', return_value='super changelog') changelog(noop=True, unreleased=False) mock_previous_version.assert_called_once_with('current') mock_generate_changelog.assert_called_once_with('previous', 'current') mock_markdown_changelog.assert_called_once_with('current', 'super changelog', header=False) def test_changelog_post_unreleased_no_token(mocker): mocker.patch('semantic_release.cli.get_current_version', return_value='current') mock_previous_version = mocker.patch('semantic_release.cli.get_previous_version', return_value='previous') mock_generate_changelog = mocker.patch('semantic_release.cli.generate_changelog', return_value='super changelog') mock_markdown_changelog = mocker.patch('semantic_release.cli.markdown_changelog', return_value='super changelog') mock_check_token = mocker.patch('semantic_release.cli.check_token', return_value=False) changelog(unreleased=True, post=True) mock_previous_version.assert_called_once_with('current') mock_generate_changelog.assert_called_once_with('current', None) mock_markdown_changelog.assert_called_once_with('current', 'super changelog', header=False) mock_check_token.assert_called_once_with() def test_changelog_post_complete(mocker): mocker.patch('semantic_release.cli.get_current_version', return_value='current') mock_previous_version = mocker.patch('semantic_release.cli.get_previous_version', return_value='previous') mock_generate_changelog = mocker.patch('semantic_release.cli.generate_changelog', return_value='super changelog') mock_markdown_changelog = mocker.patch('semantic_release.cli.markdown_changelog', return_value='super md changelog') mock_check_token = mocker.patch('semantic_release.cli.check_token', return_value=True) mock_get_owner_name = mocker.patch('semantic_release.cli.get_repository_owner_and_name', return_value=('owner', 'name')) mock_post_changelog = mocker.patch('semantic_release.cli.post_changelog') changelog(unreleased=True, post=True) mock_previous_version.assert_called_once_with('current') mock_generate_changelog.assert_called_once_with('current', None) mock_markdown_changelog.assert_any_call('current', 'super changelog', header=False) mock_check_token.assert_called_once_with() mock_get_owner_name.assert_called_once_with() mock_post_changelog.assert_called_once_with('owner', 'name', 'current', 'super md changelog') def test_changelog_raises_exception_when_no_current_version(mocker): mocker.patch('semantic_release.cli.get_current_version', return_value=None) with pytest.raises(ImproperConfigurationError): changelog() @mock.patch('semantic_release.cli.debug') def test_main_debug(mock_debug, runner): runner.invoke(main, ['version', '--noop']) assert mock_debug.called

47.808664

97

0.708941

795158bc3d98d0b60da961b8d2ff0a91e982952d

73,097

py

Python

examples/pytorch_pretrained_bert/modeling_conv.py

ankit-ai/BertQA-Attention-on-Steroids

49c3de360f88f55c8442b9f8153af56c28a689a9

[ "Apache-2.0" ]

122

2019-03-21T05:47:45.000Z

2021-12-16T06:57:37.000Z

examples/pytorch_pretrained_bert/modeling_conv.py

TigerMachineLearning/BertQA-Attention-on-Steroids

49c3de360f88f55c8442b9f8153af56c28a689a9

[ "Apache-2.0" ]

2

2019-06-10T03:35:06.000Z

2020-04-16T07:23:23.000Z

examples/pytorch_pretrained_bert/modeling_conv.py

TigerMachineLearning/BertQA-Attention-on-Steroids

49c3de360f88f55c8442b9f8153af56c28a689a9

[ "Apache-2.0" ]

16

2019-03-21T12:21:10.000Z

2021-12-16T06:47:59.000Z

# coding=utf-8 # Copyright 2018 The Google AI Language Team Authors and The HugginFace Inc. team. # Copyright (c) 2018, NVIDIA CORPORATION. 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. """PyTorch BERT model.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import os import copy import json import math import logging import tarfile import tempfile import shutil import torch from torch import nn from torch.nn import CrossEntropyLoss from .file_utils import cached_path logger = logging.getLogger(__name__) PRETRAINED_MODEL_ARCHIVE_MAP = { 'bert-base-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased.tar.gz", 'bert-large-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased.tar.gz", 'bert-base-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-cased.tar.gz", 'bert-large-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased.tar.gz", 'bert-base-multilingual-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-uncased.tar.gz", 'bert-base-multilingual-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-cased.tar.gz", 'bert-base-chinese': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-chinese.tar.gz", } CONFIG_NAME = 'bert_config.json' WEIGHTS_NAME = 'pytorch_model.bin' def gelu(x): """Implementation of the gelu activation function. For information: OpenAI GPT's gelu is slightly different (and gives slightly different results): 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3)))) """ return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0))) def swish(x): return x * torch.sigmoid(x) ACT2FN = {"gelu": gelu, "relu": torch.nn.functional.relu, "swish": swish} class BertConfig(object): """Configuration class to store the configuration of a `BertModel`. """ def __init__(self, vocab_size_or_config_json_file, hidden_size=768, num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=512, type_vocab_size=2, initializer_range=0.02): """Constructs BertConfig. Args: vocab_size_or_config_json_file: Vocabulary size of `inputs_ids` in `BertModel`. hidden_size: Size of the encoder layers and the pooler layer. num_hidden_layers: Number of hidden layers in the Transformer encoder. num_attention_heads: Number of attention heads for each attention layer in the Transformer encoder. intermediate_size: The size of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. hidden_act: The non-linear activation function (function or string) in the encoder and pooler. If string, "gelu", "relu" and "swish" are supported. hidden_dropout_prob: The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler. attention_probs_dropout_prob: The dropout ratio for the attention probabilities. max_position_embeddings: The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 512 or 1024 or 2048). type_vocab_size: The vocabulary size of the `token_type_ids` passed into `BertModel`. initializer_range: The sttdev of the truncated_normal_initializer for initializing all weight matrices. """ if isinstance(vocab_size_or_config_json_file, str): with open(vocab_size_or_config_json_file, "r", encoding='utf-8') as reader: json_config = json.loads(reader.read()) for key, value in json_config.items(): self.__dict__[key] = value elif isinstance(vocab_size_or_config_json_file, int): self.vocab_size = vocab_size_or_config_json_file self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.hidden_act = hidden_act self.intermediate_size = intermediate_size self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.max_position_embeddings = max_position_embeddings self.type_vocab_size = type_vocab_size self.initializer_range = initializer_range else: raise ValueError("First argument must be either a vocabulary size (int)" "or the path to a pretrained model config file (str)") @classmethod def from_dict(cls, json_object): """Constructs a `BertConfig` from a Python dictionary of parameters.""" config = BertConfig(vocab_size_or_config_json_file=-1) for key, value in json_object.items(): config.__dict__[key] = value return config @classmethod def from_json_file(cls, json_file): """Constructs a `BertConfig` from a json file of parameters.""" with open(json_file, "r", encoding='utf-8') as reader: text = reader.read() return cls.from_dict(json.loads(text)) def __repr__(self): return str(self.to_json_string()) def to_dict(self): """Serializes this instance to a Python dictionary.""" output = copy.deepcopy(self.__dict__) return output def to_json_string(self): """Serializes this instance to a JSON string.""" return json.dumps(self.to_dict(), indent=2, sort_keys=True) + "\n" try: from apex.normalization.fused_layer_norm import FusedLayerNorm as BertLayerNorm except ImportError: print("Better speed can be achieved with apex installed from https://www.github.com/nvidia/apex.") class BertLayerNorm(nn.Module): def __init__(self, hidden_size, eps=1e-12): """Construct a layernorm module in the TF style (epsilon inside the square root). """ super(BertLayerNorm, self).__init__() self.weight = nn.Parameter(torch.ones(hidden_size)) self.bias = nn.Parameter(torch.zeros(hidden_size)) self.variance_epsilon = eps def forward(self, x): u = x.mean(-1, keepdim=True) s = (x - u).pow(2).mean(-1, keepdim=True) x = (x - u) / torch.sqrt(s + self.variance_epsilon) return self.weight * x + self.bias class BertEmbeddings(nn.Module): """Construct the embeddings from word, position and token_type embeddings. """ def __init__(self, config): super(BertEmbeddings, self).__init__() self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size) self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size) self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size) # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load # any TensorFlow checkpoint file self.LayerNorm = BertLayerNorm(config.hidden_size, eps=1e-12) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, input_ids, token_type_ids=None): seq_length = input_ids.size(1) position_ids = torch.arange(seq_length, dtype=torch.long, device=input_ids.device) position_ids = position_ids.unsqueeze(0).expand_as(input_ids) if token_type_ids is None: token_type_ids = torch.zeros_like(input_ids) words_embeddings = self.word_embeddings(input_ids) position_embeddings = self.position_embeddings(position_ids) token_type_embeddings = self.token_type_embeddings(token_type_ids) embeddings = words_embeddings + position_embeddings + token_type_embeddings embeddings = self.LayerNorm(embeddings) embeddings = self.dropout(embeddings) return embeddings class BertSelfAttention(nn.Module): def __init__(self, config): super(BertSelfAttention, self).__init__() if config.hidden_size % config.num_attention_heads != 0: raise ValueError( "The hidden size (%d) is not a multiple of the number of attention " "heads (%d)" % (config.hidden_size, config.num_attention_heads)) self.num_attention_heads = config.num_attention_heads self.attention_head_size = int(config.hidden_size / config.num_attention_heads) self.all_head_size = self.num_attention_heads * self.attention_head_size self.query = nn.Linear(config.hidden_size, self.all_head_size) self.key = nn.Linear(config.hidden_size, self.all_head_size) self.value = nn.Linear(config.hidden_size, self.all_head_size) self.dropout = nn.Dropout(config.attention_probs_dropout_prob) def transpose_for_scores(self, x): new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size) x = x.view(*new_x_shape) return x.permute(0, 2, 1, 3) def forward(self, hidden_states, attention_mask): mixed_query_layer = self.query(hidden_states) mixed_key_layer = self.key(hidden_states) mixed_value_layer = self.value(hidden_states) query_layer = self.transpose_for_scores(mixed_query_layer) key_layer = self.transpose_for_scores(mixed_key_layer) value_layer = self.transpose_for_scores(mixed_value_layer) # Take the dot product between "query" and "key" to get the raw attention scores. attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) attention_scores = attention_scores / math.sqrt(self.attention_head_size) # Apply the attention mask is (precomputed for all layers in BertModel forward() function) attention_scores = attention_scores + attention_mask # Normalize the attention scores to probabilities. attention_probs = nn.Softmax(dim=-1)(attention_scores) # This is actually dropping out entire tokens to attend to, which might # seem a bit unusual, but is taken from the original Transformer paper. attention_probs = self.dropout(attention_probs) context_layer = torch.matmul(attention_probs, value_layer) context_layer = context_layer.permute(0, 2, 1, 3).contiguous() new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) context_layer = context_layer.view(*new_context_layer_shape) return context_layer class BertDeSelfOutput(nn.Module): def __init__(self, config): super(BertDeSelfOutput, self).__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.LayerNorm = BertLayerNorm(config.hidden_size, eps=1e-12) self.dropout = nn.Dropout(config.hidden_dropout_prob) self.qdense = nn.Linear(config.hidden_size, config.hidden_size) self.qLayerNorm = BertLayerNorm(config.hidden_size, eps=1e-12) self.qdropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, chidden_states,qhidden_states, cinput_tensor,qinput_tensor): chidden_states = self.dense(chidden_states) chidden_states = self.dropout(chidden_states) chidden_states = self.LayerNorm(chidden_states + cinput_tensor) qhidden_states = self.dense(qhidden_states) qhidden_states = self.dropout(qhidden_states) qhidden_states = self.LayerNorm(qhidden_states + cinput_tensor) return chidden_states,qhidden_states class BertDeAttention(nn.Module): def __init__(self, config): super(BertDeAttention, self).__init__() self.self = BertMultiAttention(config) self.output = BertDeSelfOutput(config) #Can use De here def forward(self, cinput_tensor,qinput_tensor, attention_mask, qattention_mask): cself_output,qself_output = self.self(cinput_tensor,qinput_tensor, attention_mask,qattention_mask) cattention_output,qattention_output = self.output(cself_output,qself_output, cinput_tensor,qinput_tensor) return cattention_output,qattention_output class BertDeIntermediate(nn.Module): def __init__(self, config): super(BertDeIntermediate, self).__init__() self.dense = nn.Linear(config.hidden_size, config.intermediate_size) self.qdense = nn.Linear(config.hidden_size, config.intermediate_size) self.intermediate_act_fn = ACT2FN[config.hidden_act] \ if isinstance(config.hidden_act, str) else config.hidden_act def forward(self, chidden_states,qhidden_states): #print('In DeIntermediate -dim of chidden_states is',chidden_states.size()) chidden_states = self.dense(chidden_states) chidden_states = self.intermediate_act_fn(chidden_states) qhidden_states = self.qdense(qhidden_states) qhidden_states = self.intermediate_act_fn(qhidden_states) return chidden_states,qhidden_states class BertDeOutput(nn.Module): def __init__(self, config): super(BertDeOutput, self).__init__() self.dense = nn.Linear(config.intermediate_size, config.hidden_size) self.LayerNorm = BertLayerNorm(config.hidden_size, eps=1e-12) self.dropout = nn.Dropout(config.hidden_dropout_prob) self.qdense = nn.Linear(config.intermediate_size, config.hidden_size) self.qLayerNorm = BertLayerNorm(config.hidden_size, eps=1e-12) self.qdropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, chidden_states,qhidden_states, cinput_tensor,qinput_tensor): #print('In BertDeOutput - size of chidden_states is',chidden_states.size()) chidden_states = self.dense(chidden_states) chidden_states = self.dropout(chidden_states) chidden_states = self.LayerNorm(chidden_states + cinput_tensor) qhidden_states = self.qdense(qhidden_states) qhidden_states = self.qdropout(qhidden_states) qhidden_states = self.qLayerNorm(qhidden_states + qinput_tensor) return chidden_states,qhidden_states class BertDeLayer(nn.Module): def __init__(self, config): super(BertDeLayer, self).__init__() self.attention = BertDeAttention(config) self.intermediate = BertDeIntermediate(config) self.output = BertDeOutput(config) def forward(self, chidden_states,qhidden_states, attention_mask,qattention_mask): cattention_output,qattention_output = self.attention(chidden_states,qhidden_states, attention_mask,qattention_mask) #Call this one more time to calculaye qattention_output^ #print('In DeLayer - dim of cattention_output',cattention_output.size()) cintermediate_output,qintermediate_output = self.intermediate(cattention_output,qattention_output) clayer_output,qlayer_output = self.output(cintermediate_output,qintermediate_output,cattention_output,qattention_output) return clayer_output,qlayer_output class BertDecoder(nn.Module): def __init__(self, config): super(BertDecoder, self).__init__() layer = BertDeLayer(config) self.layer = nn.ModuleList([copy.deepcopy(layer) for _ in range(6)]) def forward(self, hidden_states, cattention_mask, qattention_mask, output_all_deencoded_layers=True): call_deencoder_layers = [] qall_deencoder_layers = [] chidden_states = hidden_states qhidden_states = hidden_states for layer_module in self.layer: chidden_states,qhidden_states = layer_module(chidden_states,qhidden_states, cattention_mask,qattention_mask) if output_all_deencoded_layers: call_deencoder_layers.append(chidden_states) qall_deencoder_layers.append(qhidden_states) if not output_all_deencoded_layers: call_deencoder_layers.append(chidden_states) qall_deencoder_layers.append(qhidden_states) return call_deencoder_layers,qall_deencoder_layers class BertSelfOutput(nn.Module): def __init__(self, config): super(BertSelfOutput, self).__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.LayerNorm = BertLayerNorm(config.hidden_size, eps=1e-12) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, hidden_states, input_tensor): hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states) hidden_states = self.LayerNorm(hidden_states + input_tensor) return hidden_states class BertMultiAttention(nn.Module): def __init__(self, config): super(BertMultiAttention, self).__init__() if config.hidden_size % config.num_attention_heads != 0: raise ValueError( "The hidden size (%d) is not a multiple of the number of attention " "heads (%d)" % (config.hidden_size, config.num_attention_heads)) self.num_attention_heads = config.num_attention_heads self.attention_head_size = int(config.hidden_size / config.num_attention_heads) self.all_head_size = self.num_attention_heads * self.attention_head_size #print('config.hidden_size is',config.hidden_size) self.query = nn.Linear(config.hidden_size, self.all_head_size) torch.nn.init.xavier_uniform_(self.query.weight) self.key = nn.Linear(config.hidden_size, self.all_head_size) torch.nn.init.xavier_uniform_(self.key.weight) self.value = nn.Linear(config.hidden_size, self.all_head_size) torch.nn.init.xavier_uniform_(self.value.weight) self.qquery = nn.Linear(config.hidden_size, self.all_head_size) torch.nn.init.xavier_uniform_(self.qquery.weight) self.qkey = nn.Linear(config.hidden_size, self.all_head_size) torch.nn.init.xavier_uniform_(self.qkey.weight) self.qvalue = nn.Linear(config.hidden_size, self.all_head_size) torch.nn.init.xavier_uniform_(self.qvalue.weight) self.cdropout = nn.Dropout(config.attention_probs_dropout_prob) self.qdropout = nn.Dropout(config.attention_probs_dropout_prob) def transpose_for_scores(self, x): new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size) x = x.view(*new_x_shape) return x.permute(0, 2, 1, 3) #NOTE - # 1. enc_hidden_states is context embeddings # 2. dec_hidden_states is question embeddings # start expt with query from dec_hidden_states # key and value from context def forward(self, enc_hidden_states,dec_hidden_states, attention_mask,qattention_mask): #print('forward of decoder') #print('shape of dec_hidden_states is',dec_hidden_states.shape) #print('size of self.all_head_size is',self.all_head_size) mixed_query_layer = self.query(dec_hidden_states) mixed_key_layer = self.key(enc_hidden_states) mixed_value_layer = self.value(enc_hidden_states) query_layer = self.transpose_for_scores(mixed_query_layer) key_layer = self.transpose_for_scores(mixed_key_layer) value_layer = self.transpose_for_scores(mixed_value_layer) # Take the dot product between "query" and "key" to get the raw attention scores. attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) attention_scores = attention_scores / math.sqrt(self.attention_head_size) # Apply the attention mask is (precomputed for all layers in BertModel forward() function) attention_scores = attention_scores + attention_mask # Normalize the attention scores to probabilities. attention_probs = nn.Softmax(dim=-1)(attention_scores) # This is actually dropping out entire tokens to attend to, which might # seem a bit unusual, but is taken from the original Transformer paper. attention_probs = self.cdropout(attention_probs) context_layer = torch.matmul(attention_probs, value_layer) context_layer = context_layer.permute(0, 2, 1, 3).contiguous() new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) context_layer = context_layer.view(*new_context_layer_shape) #--- Q2C qmixed_query_layer = self.qquery(enc_hidden_states) qmixed_key_layer = self.qkey(dec_hidden_states) qmixed_value_layer = self.qvalue(dec_hidden_states) qquery_layer = self.transpose_for_scores(qmixed_query_layer) qkey_layer = self.transpose_for_scores(qmixed_key_layer) qvalue_layer = self.transpose_for_scores(qmixed_value_layer) # Take the dot product between "query" and "key" to get the raw attention scores. qattention_scores = torch.matmul(qquery_layer, qkey_layer.transpose(-1, -2)) qattention_scores = qattention_scores / math.sqrt(self.attention_head_size) # Apply the attention mask is (precomputed for all layers in BertModel forward() function) qattention_scores = qattention_scores + qattention_mask # Normalize the attention scores to probabilities. qattention_probs = nn.Softmax(dim=-1)(qattention_scores) # This is actually dropping out entire tokens to attend to, which might # seem a bit unusual, but is taken from the original Transformer paper. qattention_probs = self.qdropout(qattention_probs) q_layer = torch.matmul(qattention_probs, qvalue_layer) q_layer = q_layer.permute(0, 2, 1, 3).contiguous() new_q_layer_shape = q_layer.size()[:-2] + (self.all_head_size,) q_layer = q_layer.view(*new_q_layer_shape) return context_layer,q_layer class BertAttention(nn.Module): def __init__(self, config): super(BertAttention, self).__init__() self.self = BertSelfAttention(config) self.output = BertSelfOutput(config) def forward(self, input_tensor, attention_mask): self_output = self.self(input_tensor, attention_mask) attention_output = self.output(self_output, input_tensor) return attention_output class BertIntermediate(nn.Module): def __init__(self, config): super(BertIntermediate, self).__init__() self.dense = nn.Linear(config.hidden_size, config.intermediate_size) self.intermediate_act_fn = ACT2FN[config.hidden_act] \ if isinstance(config.hidden_act, str) else config.hidden_act def forward(self, hidden_states): hidden_states = self.dense(hidden_states) hidden_states = self.intermediate_act_fn(hidden_states) return hidden_states class BertOutput(nn.Module): def __init__(self, config): super(BertOutput, self).__init__() self.dense = nn.Linear(config.intermediate_size, config.hidden_size) self.LayerNorm = BertLayerNorm(config.hidden_size, eps=1e-12) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, hidden_states, input_tensor): hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states) hidden_states = self.LayerNorm(hidden_states + input_tensor) return hidden_states class BertLayer(nn.Module): def __init__(self, config): super(BertLayer, self).__init__() self.attention = BertAttention(config) self.intermediate = BertIntermediate(config) self.output = BertOutput(config) def forward(self, hidden_states, attention_mask): attention_output = self.attention(hidden_states, attention_mask) #print('attention_output is',attention_output.shape) intermediate_output = self.intermediate(attention_output) #print('intermediate_output is',intermediate_output.shape) layer_output = self.output(intermediate_output, attention_output) #print('layer_output is',layer_output.shape) return layer_output class BertEncoder(nn.Module): def __init__(self, config): super(BertEncoder, self).__init__() layer = BertLayer(config) self.layer = nn.ModuleList([copy.deepcopy(layer) for _ in range(config.num_hidden_layers)]) def forward(self, hidden_states, attention_mask, output_all_encoded_layers=True): all_encoder_layers = [] for layer_module in self.layer: hidden_states = layer_module(hidden_states, attention_mask) #print('size of hidden_states in BertEncoder is',hidden_states.shape) if output_all_encoded_layers: all_encoder_layers.append(hidden_states) if not output_all_encoded_layers: all_encoder_layers.append(hidden_states) return all_encoder_layers class BertPooler(nn.Module): def __init__(self, config): super(BertPooler, self).__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.activation = nn.Tanh() def forward(self, hidden_states): # We "pool" the model by simply taking the hidden state corresponding # to the first token. first_token_tensor = hidden_states[:, 0] pooled_output = self.dense(first_token_tensor) pooled_output = self.activation(pooled_output) return pooled_output class BertPredictionHeadTransform(nn.Module): def __init__(self, config): super(BertPredictionHeadTransform, self).__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.transform_act_fn = ACT2FN[config.hidden_act] \ if isinstance(config.hidden_act, str) else config.hidden_act self.LayerNorm = BertLayerNorm(config.hidden_size, eps=1e-12) def forward(self, hidden_states): hidden_states = self.dense(hidden_states) hidden_states = self.transform_act_fn(hidden_states) hidden_states = self.LayerNorm(hidden_states) return hidden_states class BertLMPredictionHead(nn.Module): def __init__(self, config, bert_model_embedding_weights): super(BertLMPredictionHead, self).__init__() self.transform = BertPredictionHeadTransform(config) # The output weights are the same as the input embeddings, but there is # an output-only bias for each token. self.decoder = nn.Linear(bert_model_embedding_weights.size(1), bert_model_embedding_weights.size(0), bias=False) self.decoder.weight = bert_model_embedding_weights self.bias = nn.Parameter(torch.zeros(bert_model_embedding_weights.size(0))) def forward(self, hidden_states): hidden_states = self.transform(hidden_states) hidden_states = self.decoder(hidden_states) + self.bias return hidden_states class BertOnlyMLMHead(nn.Module): def __init__(self, config, bert_model_embedding_weights): super(BertOnlyMLMHead, self).__init__() self.predictions = BertLMPredictionHead(config, bert_model_embedding_weights) def forward(self, sequence_output): prediction_scores = self.predictions(sequence_output) return prediction_scores class BertOnlyNSPHead(nn.Module): def __init__(self, config): super(BertOnlyNSPHead, self).__init__() self.seq_relationship = nn.Linear(config.hidden_size, 2) def forward(self, pooled_output): seq_relationship_score = self.seq_relationship(pooled_output) return seq_relationship_score class BertPreTrainingHeads(nn.Module): def __init__(self, config, bert_model_embedding_weights): super(BertPreTrainingHeads, self).__init__() self.predictions = BertLMPredictionHead(config, bert_model_embedding_weights) self.seq_relationship = nn.Linear(config.hidden_size, 2) def forward(self, sequence_output, pooled_output): prediction_scores = self.predictions(sequence_output) seq_relationship_score = self.seq_relationship(pooled_output) return prediction_scores, seq_relationship_score class PreTrainedBertModel(nn.Module): """ An abstract class to handle weights initialization and a simple interface for dowloading and loading pretrained models. """ def __init__(self, config, *inputs, **kwargs): super(PreTrainedBertModel, self).__init__() if not isinstance(config, BertConfig): raise ValueError( "Parameter config in `{}(config)` should be an instance of class `BertConfig`. " "To create a model from a Google pretrained model use " "`model = {}.from_pretrained(PRETRAINED_MODEL_NAME)`".format( self.__class__.__name__, self.__class__.__name__ )) self.config = config def init_bert_weights(self, module): """ Initialize the weights. """ if isinstance(module, (nn.Linear, nn.Embedding)): # Slightly different from the TF version which uses truncated_normal for initialization # cf https://github.com/pytorch/pytorch/pull/5617 module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) elif isinstance(module, BertLayerNorm): module.bias.data.zero_() module.weight.data.fill_(1.0) if isinstance(module, nn.Linear) and module.bias is not None: module.bias.data.zero_() @classmethod def from_pretrained(cls, pretrained_model_name, state_dict=None, cache_dir=None, *inputs, **kwargs): """ Instantiate a PreTrainedBertModel from a pre-trained model file or a pytorch state dict. Download and cache the pre-trained model file if needed. Params: pretrained_model_name: either: - a str with the name of a pre-trained model to load selected in the list of: . `bert-base-uncased` . `bert-large-uncased` . `bert-base-cased` . `bert-large-cased` . `bert-base-multilingual-uncased` . `bert-base-multilingual-cased` . `bert-base-chinese` - a path or url to a pretrained model archive containing: . `bert_config.json` a configuration file for the model . `pytorch_model.bin` a PyTorch dump of a BertForPreTraining instance cache_dir: an optional path to a folder in which the pre-trained models will be cached. state_dict: an optional state dictionnary (collections.OrderedDict object) to use instead of Google pre-trained models *inputs, **kwargs: additional input for the specific Bert class (ex: num_labels for BertForSequenceClassification) """ if pretrained_model_name in PRETRAINED_MODEL_ARCHIVE_MAP: archive_file = PRETRAINED_MODEL_ARCHIVE_MAP[pretrained_model_name] else: archive_file = pretrained_model_name # redirect to the cache, if necessary try: resolved_archive_file = cached_path(archive_file, cache_dir=cache_dir) except FileNotFoundError: logger.error( "Model name '{}' was not found in model name list ({}). " "We assumed '{}' was a path or url but couldn't find any file " "associated to this path or url.".format( pretrained_model_name, ', '.join(PRETRAINED_MODEL_ARCHIVE_MAP.keys()), archive_file)) return None if resolved_archive_file == archive_file: logger.info("loading archive file {}".format(archive_file)) else: logger.info("loading archive file {} from cache at {}".format( archive_file, resolved_archive_file)) tempdir = None if os.path.isdir(resolved_archive_file): serialization_dir = resolved_archive_file else: # Extract archive to temp dir tempdir = tempfile.mkdtemp() logger.info("extracting archive file {} to temp dir {}".format( resolved_archive_file, tempdir)) with tarfile.open(resolved_archive_file, 'r:gz') as archive: archive.extractall(tempdir) serialization_dir = tempdir # Load config config_file = os.path.join(serialization_dir, CONFIG_NAME) config = BertConfig.from_json_file(config_file) logger.info("Model config {}".format(config)) # Instantiate model. model = cls(config, *inputs, **kwargs) if state_dict is None: weights_path = os.path.join(serialization_dir, WEIGHTS_NAME) state_dict = torch.load(weights_path) old_keys = [] new_keys = [] for key in state_dict.keys(): new_key = None if 'gamma' in key: new_key = key.replace('gamma', 'weight') if 'beta' in key: new_key = key.replace('beta', 'bias') if new_key: old_keys.append(key) new_keys.append(new_key) for old_key, new_key in zip(old_keys, new_keys): state_dict[new_key] = state_dict.pop(old_key) missing_keys = [] unexpected_keys = [] error_msgs = [] # copy state_dict so _load_from_state_dict can modify it metadata = getattr(state_dict, '_metadata', None) state_dict = state_dict.copy() if metadata is not None: state_dict._metadata = metadata def load(module, prefix=''): local_metadata = {} if metadata is None else metadata.get(prefix[:-1], {}) module._load_from_state_dict( state_dict, prefix, local_metadata, True, missing_keys, unexpected_keys, error_msgs) for name, child in module._modules.items(): if child is not None: load(child, prefix + name + '.') load(model, prefix='' if hasattr(model, 'bert') else 'bert.') if len(missing_keys) > 0: logger.info("Weights of {} not initialized from pretrained model: {}".format( model.__class__.__name__, missing_keys)) if len(unexpected_keys) > 0: logger.info("Weights from pretrained model not used in {}: {}".format( model.__class__.__name__, unexpected_keys)) if tempdir: # Clean up temp dir shutil.rmtree(tempdir) return model class BertModel(PreTrainedBertModel): """BERT model ("Bidirectional Embedding Representations from a Transformer"). Params: config: a BertConfig class instance with the configuration to build a new model Inputs: `input_ids`: a torch.LongTensor of shape [batch_size, sequence_length] with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts `extract_features.py`, `run_classifier.py` and `run_squad.py`) `token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to a `sentence B` token (see BERT paper for more details). `attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max input sequence length in the current batch. It's the mask that we typically use for attention when a batch has varying length sentences. `output_all_encoded_layers`: boolean which controls the content of the `encoded_layers` output as described below. Default: `True`. Outputs: Tuple of (encoded_layers, pooled_output) `encoded_layers`: controled by `output_all_encoded_layers` argument: - `output_all_encoded_layers=True`: outputs a list of the full sequences of encoded-hidden-states at the end of each attention block (i.e. 12 full sequences for BERT-base, 24 for BERT-large), each encoded-hidden-state is a torch.FloatTensor of size [batch_size, sequence_length, hidden_size], - `output_all_encoded_layers=False`: outputs only the full sequence of hidden-states corresponding to the last attention block of shape [batch_size, sequence_length, hidden_size], `pooled_output`: a torch.FloatTensor of size [batch_size, hidden_size] which is the output of a classifier pretrained on top of the hidden state associated to the first character of the input (`CLF`) to train on the Next-Sentence task (see BERT's paper). Example usage: ```python # Already been converted into WordPiece token ids input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]]) input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]]) token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]]) config = modeling.BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768, num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072) model = modeling.BertModel(config=config) all_encoder_layers, pooled_output = model(input_ids, token_type_ids, input_mask) ``` """ def __init__(self, config): super(BertModel, self).__init__(config) self.embeddings = BertEmbeddings(config) self.encoder = BertEncoder(config) self.pooler = BertPooler(config) self.apply(self.init_bert_weights) def forward(self, input_ids, token_type_ids=None, attention_mask=None, output_all_encoded_layers=True): if attention_mask is None: attention_mask = torch.ones_like(input_ids) if token_type_ids is None: token_type_ids = torch.zeros_like(input_ids) # We create a 3D attention mask from a 2D tensor mask. # Sizes are [batch_size, 1, 1, to_seq_length] # So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length] # this attention mask is more simple than the triangular masking of causal attention # used in OpenAI GPT, we just need to prepare the broadcast dimension here. extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2) # Since attention_mask is 1.0 for positions we want to attend and 0.0 for # masked positions, this operation will create a tensor which is 0.0 for # positions we want to attend and -10000.0 for masked positions. # Since we are adding it to the raw scores before the softmax, this is # effectively the same as removing these entirely. extended_attention_mask = extended_attention_mask.to(dtype=next(self.parameters()).dtype) # fp16 compatibility extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0 #print('extended_attention_mask',extended_attention_mask) #print('attention_mask',attention_mask) #print('token_type_ids',token_type_ids) qattention_mask = attention_mask - token_type_ids cattention_mask = attention_mask - qattention_mask #print('*************************') #print('cattention_mask',cattention_mask) #print('qattention_mask',qattention_mask) cextended_attention_mask = cattention_mask.unsqueeze(1).unsqueeze(2) cextended_attention_mask = cextended_attention_mask.to(dtype=next(self.parameters()).dtype) # fp16 compatibility cextended_attention_mask = (1.0 - cextended_attention_mask) * -10000.0 qextended_attention_mask = qattention_mask.unsqueeze(1).unsqueeze(2) qextended_attention_mask = qextended_attention_mask.to(dtype=next(self.parameters()).dtype) # fp16 compatibility qextended_attention_mask = (1.0 - qextended_attention_mask) * -10000.0 #raise SystemExit embedding_output = self.embeddings(input_ids, token_type_ids) encoded_layers = self.encoder(embedding_output, extended_attention_mask, output_all_encoded_layers=output_all_encoded_layers) #print('*** encoded_layers is',encoded_layers.shape) sequence_output = encoded_layers[-1] #print('*** sequence_output is',sequence_output.shape) pooled_output = self.pooler(sequence_output) if not output_all_encoded_layers: encoded_layers = encoded_layers[-1] return cextended_attention_mask,qextended_attention_mask,sequence_output#encoded_layers, pooled_output class BertForPreTraining(PreTrainedBertModel): """BERT model with pre-training heads. This module comprises the BERT model followed by the two pre-training heads: - the masked language modeling head, and - the next sentence classification head. Params: config: a BertConfig class instance with the configuration to build a new model. Inputs: `input_ids`: a torch.LongTensor of shape [batch_size, sequence_length] with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts `extract_features.py`, `run_classifier.py` and `run_squad.py`) `token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to a `sentence B` token (see BERT paper for more details). `attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max input sequence length in the current batch. It's the mask that we typically use for attention when a batch has varying length sentences. `masked_lm_labels`: masked language modeling labels: torch.LongTensor of shape [batch_size, sequence_length] with indices selected in [-1, 0, ..., vocab_size]. All labels set to -1 are ignored (masked), the loss is only computed for the labels set in [0, ..., vocab_size] `next_sentence_label`: next sentence classification loss: torch.LongTensor of shape [batch_size] with indices selected in [0, 1]. 0 => next sentence is the continuation, 1 => next sentence is a random sentence. Outputs: if `masked_lm_labels` and `next_sentence_label` are not `None`: Outputs the total_loss which is the sum of the masked language modeling loss and the next sentence classification loss. if `masked_lm_labels` or `next_sentence_label` is `None`: Outputs a tuple comprising - the masked language modeling logits of shape [batch_size, sequence_length, vocab_size], and - the next sentence classification logits of shape [batch_size, 2]. Example usage: ```python # Already been converted into WordPiece token ids input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]]) input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]]) token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]]) config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768, num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072) model = BertForPreTraining(config) masked_lm_logits_scores, seq_relationship_logits = model(input_ids, token_type_ids, input_mask) ``` """ def __init__(self, config): super(BertForPreTraining, self).__init__(config) self.bert = BertModel(config) self.cls = BertPreTrainingHeads(config, self.bert.embeddings.word_embeddings.weight) self.apply(self.init_bert_weights) def forward(self, input_ids, token_type_ids=None, attention_mask=None, masked_lm_labels=None, next_sentence_label=None): sequence_output, pooled_output = self.bert(input_ids, token_type_ids, attention_mask, output_all_encoded_layers=False) prediction_scores, seq_relationship_score = self.cls(sequence_output, pooled_output) if masked_lm_labels is not None and next_sentence_label is not None: loss_fct = CrossEntropyLoss(ignore_index=-1) masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), masked_lm_labels.view(-1)) next_sentence_loss = loss_fct(seq_relationship_score.view(-1, 2), next_sentence_label.view(-1)) total_loss = masked_lm_loss + next_sentence_loss return total_loss else: return prediction_scores, seq_relationship_score class BertForMaskedLM(PreTrainedBertModel): """BERT model with the masked language modeling head. This module comprises the BERT model followed by the masked language modeling head. Params: config: a BertConfig class instance with the configuration to build a new model. Inputs: `input_ids`: a torch.LongTensor of shape [batch_size, sequence_length] with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts `extract_features.py`, `run_classifier.py` and `run_squad.py`) `token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to a `sentence B` token (see BERT paper for more details). `attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max input sequence length in the current batch. It's the mask that we typically use for attention when a batch has varying length sentences. `masked_lm_labels`: masked language modeling labels: torch.LongTensor of shape [batch_size, sequence_length] with indices selected in [-1, 0, ..., vocab_size]. All labels set to -1 are ignored (masked), the loss is only computed for the labels set in [0, ..., vocab_size] Outputs: if `masked_lm_labels` is not `None`: Outputs the masked language modeling loss. if `masked_lm_labels` is `None`: Outputs the masked language modeling logits of shape [batch_size, sequence_length, vocab_size]. Example usage: ```python # Already been converted into WordPiece token ids input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]]) input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]]) token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]]) config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768, num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072) model = BertForMaskedLM(config) masked_lm_logits_scores = model(input_ids, token_type_ids, input_mask) ``` """ def __init__(self, config): super(BertForMaskedLM, self).__init__(config) self.bert = BertModel(config) self.cls = BertOnlyMLMHead(config, self.bert.embeddings.word_embeddings.weight) self.apply(self.init_bert_weights) def forward(self, input_ids, token_type_ids=None, attention_mask=None, masked_lm_labels=None): sequence_output, _ = self.bert(input_ids, token_type_ids, attention_mask, output_all_encoded_layers=False) prediction_scores = self.cls(sequence_output) if masked_lm_labels is not None: loss_fct = CrossEntropyLoss(ignore_index=-1) masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), masked_lm_labels.view(-1)) return masked_lm_loss else: return prediction_scores class BertForNextSentencePrediction(PreTrainedBertModel): """BERT model with next sentence prediction head. This module comprises the BERT model followed by the next sentence classification head. Params: config: a BertConfig class instance with the configuration to build a new model. Inputs: `input_ids`: a torch.LongTensor of shape [batch_size, sequence_length] with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts `extract_features.py`, `run_classifier.py` and `run_squad.py`) `token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to a `sentence B` token (see BERT paper for more details). `attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max input sequence length in the current batch. It's the mask that we typically use for attention when a batch has varying length sentences. `next_sentence_label`: next sentence classification loss: torch.LongTensor of shape [batch_size] with indices selected in [0, 1]. 0 => next sentence is the continuation, 1 => next sentence is a random sentence. Outputs: if `next_sentence_label` is not `None`: Outputs the total_loss which is the sum of the masked language modeling loss and the next sentence classification loss. if `next_sentence_label` is `None`: Outputs the next sentence classification logits of shape [batch_size, 2]. Example usage: ```python # Already been converted into WordPiece token ids input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]]) input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]]) token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]]) config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768, num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072) model = BertForNextSentencePrediction(config) seq_relationship_logits = model(input_ids, token_type_ids, input_mask) ``` """ def __init__(self, config): super(BertForNextSentencePrediction, self).__init__(config) self.bert = BertModel(config) self.cls = BertOnlyNSPHead(config) self.apply(self.init_bert_weights) def forward(self, input_ids, token_type_ids=None, attention_mask=None, next_sentence_label=None): _, pooled_output = self.bert(input_ids, token_type_ids, attention_mask, output_all_encoded_layers=False) seq_relationship_score = self.cls( pooled_output) if next_sentence_label is not None: loss_fct = CrossEntropyLoss(ignore_index=-1) next_sentence_loss = loss_fct(seq_relationship_score.view(-1, 2), next_sentence_label.view(-1)) return next_sentence_loss else: return seq_relationship_score class BertForSequenceClassification(PreTrainedBertModel): """BERT model for classification. This module is composed of the BERT model with a linear layer on top of the pooled output. Params: `config`: a BertConfig class instance with the configuration to build a new model. `num_labels`: the number of classes for the classifier. Default = 2. Inputs: `input_ids`: a torch.LongTensor of shape [batch_size, sequence_length] with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts `extract_features.py`, `run_classifier.py` and `run_squad.py`) `token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to a `sentence B` token (see BERT paper for more details). `attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max input sequence length in the current batch. It's the mask that we typically use for attention when a batch has varying length sentences. `labels`: labels for the classification output: torch.LongTensor of shape [batch_size] with indices selected in [0, ..., num_labels]. Outputs: if `labels` is not `None`: Outputs the CrossEntropy classification loss of the output with the labels. if `labels` is `None`: Outputs the classification logits of shape [batch_size, num_labels]. Example usage: ```python # Already been converted into WordPiece token ids input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]]) input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]]) token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]]) config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768, num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072) num_labels = 2 model = BertForSequenceClassification(config, num_labels) logits = model(input_ids, token_type_ids, input_mask) ``` """ def __init__(self, config, num_labels=2): super(BertForSequenceClassification, self).__init__(config) self.num_labels = num_labels self.bert = BertModel(config) self.dropout = nn.Dropout(config.hidden_dropout_prob) self.classifier = nn.Linear(config.hidden_size, num_labels) self.apply(self.init_bert_weights) def forward(self, input_ids, token_type_ids=None, attention_mask=None, labels=None): _, pooled_output = self.bert(input_ids, token_type_ids, attention_mask, output_all_encoded_layers=False) pooled_output = self.dropout(pooled_output) logits = self.classifier(pooled_output) if labels is not None: loss_fct = CrossEntropyLoss() loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) return loss else: return logits class BertForMultipleChoice(PreTrainedBertModel): """BERT model for multiple choice tasks. This module is composed of the BERT model with a linear layer on top of the pooled output. Params: `config`: a BertConfig class instance with the configuration to build a new model. `num_choices`: the number of classes for the classifier. Default = 2. Inputs: `input_ids`: a torch.LongTensor of shape [batch_size, num_choices, sequence_length] with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts `extract_features.py`, `run_classifier.py` and `run_squad.py`) `token_type_ids`: an optional torch.LongTensor of shape [batch_size, num_choices, sequence_length] with the token types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to a `sentence B` token (see BERT paper for more details). `attention_mask`: an optional torch.LongTensor of shape [batch_size, num_choices, sequence_length] with indices selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max input sequence length in the current batch. It's the mask that we typically use for attention when a batch has varying length sentences. `labels`: labels for the classification output: torch.LongTensor of shape [batch_size] with indices selected in [0, ..., num_choices]. Outputs: if `labels` is not `None`: Outputs the CrossEntropy classification loss of the output with the labels. if `labels` is `None`: Outputs the classification logits of shape [batch_size, num_labels]. Example usage: ```python # Already been converted into WordPiece token ids input_ids = torch.LongTensor([[[31, 51, 99], [15, 5, 0]], [[12, 16, 42], [14, 28, 57]]]) input_mask = torch.LongTensor([[[1, 1, 1], [1, 1, 0]],[[1,1,0], [1, 0, 0]]]) token_type_ids = torch.LongTensor([[[0, 0, 1], [0, 1, 0]],[[0, 1, 1], [0, 0, 1]]]) config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768, num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072) num_choices = 2 model = BertForMultipleChoice(config, num_choices) logits = model(input_ids, token_type_ids, input_mask) ``` """ def __init__(self, config, num_choices=2): super(BertForMultipleChoice, self).__init__(config) self.num_choices = num_choices self.bert = BertModel(config) self.dropout = nn.Dropout(config.hidden_dropout_prob) self.classifier = nn.Linear(config.hidden_size, 1) self.apply(self.init_bert_weights) def forward(self, input_ids, token_type_ids=None, attention_mask=None, labels=None): flat_input_ids = input_ids.view(-1, input_ids.size(-1)) flat_token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) flat_attention_mask = attention_mask.view(-1, attention_mask.size(-1)) _, pooled_output = self.bert(flat_input_ids, flat_token_type_ids, flat_attention_mask, output_all_encoded_layers=False) pooled_output = self.dropout(pooled_output) logits = self.classifier(pooled_output) reshaped_logits = logits.view(-1, self.num_choices) if labels is not None: loss_fct = CrossEntropyLoss() loss = loss_fct(reshaped_logits, labels) return loss else: return reshaped_logits class BertForTokenClassification(PreTrainedBertModel): """BERT model for token-level classification. This module is composed of the BERT model with a linear layer on top of the full hidden state of the last layer. Params: `config`: a BertConfig class instance with the configuration to build a new model. `num_labels`: the number of classes for the classifier. Default = 2. Inputs: `input_ids`: a torch.LongTensor of shape [batch_size, sequence_length] with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts `extract_features.py`, `run_classifier.py` and `run_squad.py`) `token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to a `sentence B` token (see BERT paper for more details). `attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max input sequence length in the current batch. It's the mask that we typically use for attention when a batch has varying length sentences. `labels`: labels for the classification output: torch.LongTensor of shape [batch_size, sequence_length] with indices selected in [0, ..., num_labels]. Outputs: if `labels` is not `None`: Outputs the CrossEntropy classification loss of the output with the labels. if `labels` is `None`: Outputs the classification logits of shape [batch_size, sequence_length, num_labels]. Example usage: ```python # Already been converted into WordPiece token ids input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]]) input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]]) token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]]) config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768, num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072) num_labels = 2 model = BertForTokenClassification(config, num_labels) logits = model(input_ids, token_type_ids, input_mask) ``` """ def __init__(self, config, num_labels=2): super(BertForTokenClassification, self).__init__(config) self.num_labels = num_labels self.bert = BertModel(config) self.dropout = nn.Dropout(config.hidden_dropout_prob) self.classifier = nn.Linear(config.hidden_size, num_labels) self.apply(self.init_bert_weights) def forward(self, input_ids, token_type_ids=None, attention_mask=None, labels=None): sequence_output, _ = self.bert(input_ids, token_type_ids, attention_mask, output_all_encoded_layers=False) sequence_output = self.dropout(sequence_output) logits = self.classifier(sequence_output) if labels is not None: loss_fct = CrossEntropyLoss() # Only keep active parts of the loss if attention_mask is not None: active_loss = attention_mask.view(-1) == 1 active_logits = logits.view(-1, self.num_labels)[active_loss] active_labels = labels.view(-1)[active_loss] loss = loss_fct(active_logits, active_labels) else: loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) return loss else: return logits class BertForQuestionAnswering(PreTrainedBertModel): """BERT model for Question Answering (span extraction). This module is composed of the BERT model with a linear layer on top of the sequence output that computes start_logits and end_logits Params: `config`: a BertConfig class instance with the configuration to build a new model. Inputs: `input_ids`: a torch.LongTensor of shape [batch_size, sequence_length] with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts `extract_features.py`, `run_classifier.py` and `run_squad.py`) `token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to a `sentence B` token (see BERT paper for more details). `attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max input sequence length in the current batch. It's the mask that we typically use for attention when a batch has varying length sentences. `start_positions`: position of the first token for the labeled span: torch.LongTensor of shape [batch_size]. Positions are clamped to the length of the sequence and position outside of the sequence are not taken into account for computing the loss. `end_positions`: position of the last token for the labeled span: torch.LongTensor of shape [batch_size]. Positions are clamped to the length of the sequence and position outside of the sequence are not taken into account for computing the loss. Outputs: if `start_positions` and `end_positions` are not `None`: Outputs the total_loss which is the sum of the CrossEntropy loss for the start and end token positions. if `start_positions` or `end_positions` is `None`: Outputs a tuple of start_logits, end_logits which are the logits respectively for the start and end position tokens of shape [batch_size, sequence_length]. Example usage: ```python # Already been converted into WordPiece token ids input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]]) input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]]) token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]]) config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768, num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072) model = BertForQuestionAnswering(config) start_logits, end_logits = model(input_ids, token_type_ids, input_mask) ``` """ def __init__(self, config): super(BertForQuestionAnswering, self).__init__(config) self.bert = BertModel(config) self.enc_trans = nn.Linear(2*config.max_position_embeddings,config.max_position_embeddings) # TODO check with Google if it's normal there is no dropout on the token classifier of SQuAD in the TF version # self.dropout = nn.Dropout(config.hidden_dropout_prob) self.qa_outputs = nn.Linear(config.hidden_size, 2) #print('hidden size in QA is',config.hidden_size) self.apply(self.init_bert_weights) self.conv1d = nn.Conv1d(in_channels=2,out_channels=1,kernel_size=3,padding=1) self.decoder = BertDecoder(config) self.LayerNorm = BertLayerNorm(config.hidden_size, eps=1e-12) ks=3 #convs self.conv_512_1= nn.Conv1d(in_channels=768, out_channels=512, kernel_size=ks, padding=1) self.conv_512_2= nn.Conv1d(in_channels=512, out_channels=512, kernel_size=ks, padding=1) self.conv_256_1= nn.Conv1d(in_channels=512, out_channels=256, kernel_size=ks, padding=1) self.conv_256_2= nn.Conv1d(in_channels=256, out_channels=256, kernel_size=ks, padding=1) self.conv_128_1= nn.Conv1d(in_channels=256, out_channels=128, kernel_size=ks, padding=1) self.conv_128_2= nn.Conv1d(in_channels=128, out_channels=128, kernel_size=ks, padding=1) self.conv_64_1= nn.Conv1d(in_channels=128, out_channels=64, kernel_size=ks, padding=1) self.conv_64_2= nn.Conv1d(in_channels=64, out_channels=64, kernel_size=ks, padding=1) self.conv_32_1= nn.Conv1d(in_channels=64, out_channels=32, kernel_size=ks, padding=1) self.conv_32_2= nn.Conv1d(in_channels=32, out_channels=32, kernel_size=ks, padding=1) self.conv_16_1= nn.Conv1d(in_channels=32, out_channels=16, kernel_size=ks, padding=1) self.conv_16_2= nn.Conv1d(in_channels=16, out_channels=16, kernel_size=ks, padding=1) self.conv_8_1= nn.Conv1d(in_channels=16, out_channels=8, kernel_size=ks, padding=1) self.conv_8_2= nn.Conv1d(in_channels=8, out_channels=8, kernel_size=ks, padding=1) self.conv_4_1= nn.Conv1d(in_channels=8, out_channels=4, kernel_size=ks, padding=1) self.conv_4_2= nn.Conv1d(in_channels=4, out_channels=4, kernel_size=ks, padding=1) self.conv_out=nn.Conv1d(in_channels=4, out_channels=2, kernel_size=ks, padding=1) #Freeze embedding layers of Bert #for param in self.bert.parameters(): # param.requires_grad = False # print(param) def forward(self, input_ids, token_type_ids=None, attention_mask=None, start_positions=None, end_positions=None): c_attention_mask,q_attention_mask,sequence_output = self.bert(input_ids, token_type_ids, attention_mask, output_all_encoded_layers=False) #print('shape of sequence_output',sequence_output.shape) #Ankit addition - Decoder cdeencoded_layers,qdeencoded_layers = self.decoder(sequence_output, #2d --> 1d translated c_attention_mask,q_attention_mask, output_all_deencoded_layers=False)#output_all_encoded_layers) cdeencoded_layers = cdeencoded_layers[-1] qdeencoded_layers = qdeencoded_layers[-1] cdeencoded_layers = cdeencoded_layers.unsqueeze(-1) qdeencoded_layers = qdeencoded_layers.unsqueeze(-1) enc_cat = torch.cat((cdeencoded_layers,qdeencoded_layers), dim=-1) #print('enc_cat size is',enc_cat.size()) #enc_cat = enc_cat.permute(0,2,1) encshape = enc_cat.shape #print('AFTERPERMUTE - enc_cat size is',enc_cat.size()) #sequence_output1d = self.enc_trans(enc_cat) #print('Dim of sequence_output is',sequence_output1d.size()) enc_cat = enc_cat.reshape(-1,enc_cat.shape[2],enc_cat.shape[3]).contiguous() #print('AFTER : enc_cat size is',enc_cat.size()) enc_cat = enc_cat.permute(0,2,1).contiguous() sequence_output1d = self.conv1d(enc_cat) #print('shape of sequence_output1d',sequence_output1d.shape) sequence_output1d = sequence_output1d.squeeze(1).contiguous() sequence_output1d = sequence_output1d.reshape(encshape[0],encshape[1],encshape[2]) #Skip connection with bert embeddings sequence_output1d = self.LayerNorm(sequence_output + sequence_output1d) sequence_output1d = sequence_output1d.permute(0,2,1).contiguous() #print('seq after perm: ', sequence_output.shape) out_512_1=self.conv_512_1(sequence_output1d) #print('out 512 1 shape', out_512_1.shape) out_512_2=self.conv_512_2(out_512_1) #print('out 512 2 shape', out_512_2.shape) #elem_1=self.LayerNorm(sequence_output+out_512_2) #print('512 size: ', elem_1.shape) out_256_1=self.conv_256_1(out_512_2) out_256_2=self.conv_256_2(out_256_1) #elem_2=self.LayerNorm(sequence_output+out_256_2) #print('256 size: ', elem_2.shape) out_128_1=self.conv_128_1(out_256_2) out_128_2=self.conv_128_2(out_128_1) #elem_3=self.LayerNorm(sequence_output+out_128_2) #print('128 size: ', elem_3.shape) out_64_1=self.conv_64_1(out_128_2) out_64_2=self.conv_64_2(out_64_1) #elem_4=self.LayerNorm(sequence_output+out_64_2) #print('64 size: ', elem_4.shape) out_32_1=self.conv_32_1(out_64_2) out_32_2=self.conv_32_2(out_32_1) #elem_5=self.LayerNorm(sequence_output+out_32_2) #print('32 size: ', elem_5.shape) out_16_1=self.conv_16_1(out_32_2) out_16_2=self.conv_16_2(out_16_1) #elem_6=self.LayerNorm(sequence_output+out_16_2) #print('16 size: ', elem_6.shape) out_8_1=self.conv_8_1(out_16_2) out_8_2=self.conv_8_2(out_8_1) #elem_7=self.LayerNorm(sequence_output+out_8_2) #print('8 size: ', elem_7.shape) out_4_1=self.conv_4_1(out_8_2) out_4_2=self.conv_4_2(out_4_1) #elem_8=self.LayerNorm(sequence_output+out_4_2) #print('4 size: ', elem_8.shape) out=self.conv_out(out_4_2) #print('out before perm: ', out.shape) out = out.permute(0,2,1).contiguous() #out = self.LayerNorm2(out) #print('out after perm: ', out.shape) logits=out #logits = self.qa_outputs(sequence_output1d) #print('Dim of logits is',logits.size()) start_logits, end_logits = logits.split(1, dim=-1) start_logits = start_logits.squeeze(-1) end_logits = end_logits.squeeze(-1) if start_positions is not None and end_positions is not None: # If we are on multi-GPU, split add a dimension if len(start_positions.size()) > 1: start_positions = start_positions.squeeze(-1) if len(end_positions.size()) > 1: end_positions = end_positions.squeeze(-1) # sometimes the start/end positions are outside our model inputs, we ignore these terms ignored_index = start_logits.size(1) start_positions.clamp_(0, ignored_index) end_positions.clamp_(0, ignored_index) loss_fct = CrossEntropyLoss(ignore_index=ignored_index) start_loss = loss_fct(start_logits, start_positions) end_loss = loss_fct(end_logits, end_positions) total_loss = (start_loss + end_loss) / 2 return total_loss else: return start_logits, end_logits

49.557288

145

0.686198

795159781025b9c3bf600d1d006e88ef620ca937

5,909

py

Python

src/3-postprocess/peaks.py

DirkEilander/compound_hotspots

f9d7960633be80e8e24d2f2563df367cc3f060c6

[ "BSD-3-Clause" ]

1

2022-01-17T07:02:13.000Z

src/3-postprocess/peaks.py

DirkEilander/compound_hotspots

f9d7960633be80e8e24d2f2563df367cc3f060c6

[ "BSD-3-Clause" ]

null

src/3-postprocess/peaks.py

DirkEilander/compound_hotspots

f9d7960633be80e8e24d2f2563df367cc3f060c6

[ "BSD-3-Clause" ]

1

2022-01-17T02:48:28.000Z

#!/usr/bin/env python # -*- coding: utf-8 -*- # # Author: Dirk Eilander (contact: dirk.eilander@vu.nl) # Created: Nov 2nd 2018 import xarray as xr import numpy as np import scipy __all__ = ['get_peaks', 'peaks_over_threshold', 'annual_max'] def get_peaks(ts, min_dist=1, dim='time', chunks={}): """Returns a DataArray with peak values, all other values are set to NaN. Peaks are defined as a high data point surrounded by lower data points. If multiple high data points are surrounded by lower data points (a flat peak) only the first high data point is selected. The minimum distance between peaks can be set using the min_dist argument. Parameters ---------- ts : xarray DataArray time series min_dist : int, optional minimum distance between peaks [ts step] (the default is 1) dim : str, optional name of time dimension in ts (the default is 'time') Returns ------- xarray DataArray timeseries of peaks """ # get sign of trend trend = xr.ufuncs.sign(ts.diff(dim=dim)) # set flats to negative trend to deal with flat peaks trend = xr.where(trend==0, -1, trend) # peaks where previous and next points are lower peaks = ts.where(trend.diff(dim=dim, label='lower')==-2).chunk(chunks) if min_dist > 1: # import pdb; pdb.set_trace() # max_in_wdw = peaks.rolling(center=True, min_periods=1, **{dim: min_dist*2}).max() # see git issue https://github.com/pydata/xarray/issues/3165 max_in_wdw = peaks.rolling(center=True, min_periods=1, **{dim: min_dist*2}).construct('window').max('window') peaks = peaks.where(max_in_wdw==peaks) return peaks def peaks_over_threshold(ts, threshold, min_dist=1, dim='time', chunks={}): """Returns a DataArray with Peaks Over Threshold (POT), all other values are set to NaN. Peaks are defined as a high data point surrounded by lower data points. If multiple high data points are surrounded by lower data points (a flat peak) only the first high data point is selected. The minimum distance between peaks can be set using the min_dist argument. Parameters ---------- ts : xarray DataArray time series threshold : float threshold value min_dist : int, optional minimum distance between peaks [ts step] (the default is 0) dim : str, optional name of time dimension in ts (the default is 'time') Returns ------- xarray DataArray timeseries of peaks over threshold """ peaks = get_peaks(ts, dim=dim, min_dist=min_dist, chunks=chunks) # peaks over threshold (POT) peaks = peaks.where(peaks > threshold) return peaks def annual_max(da, min_dist=1, dim='time', reduce=False): """Returns a DataArray with Annual Maxima (AM) peaks Peaks are defined as a high data point surrounded by lower data points. If multiple high data points are surrounded by lower data points (a flat peak) only the first high data point is selected. The minimum distance between peaks can be set using the min_dist argument. Parameters ---------- ts : xarray DataArray time series threshold : float threshold value min_dist : int, optional minimum distance between peaks [ts step] (the default is 0) dim : str, optional name of time dimension in ts (the default is 'time') reduce : bool, optional if True, reduce the AM series to a year timestep; if False, keep full timeseries with all values but the AM set to NaN (the default is False) Returns ------- xarray DataArray timeseries of annual maxima peaks """ peaks = get_peaks(da, min_dist=min_dist, dim=dim) grp = '{}.year'.format(dim) peaks_grp = peaks.groupby(grp) if reduce == False: peaks_am = peaks.where(peaks_grp.max(dim=dim) == peaks_grp) else: peaks_am = peaks_grp.max(dim=dim) return peaks_am def nanpercentile(da, q, dim='time', interpolation='linear'): """Returns the qth percentile of the data along the specified core dimension, while ignoring nan values. Parameters ---------- da: xarray DataArray Input data array q : float in range of [0,100] (or sequence of floats) Percentile to compute, which must be between 0 and 100 inclusive. dim : str, optional name of the core dimension (the default is 'time') interpolation : {'linear', 'lower', 'higher', 'midpoint', 'nearest'} This optional parameter specifies the interpolation method to use when the desired quantile lies between two data points Returns ------- percentile : xarray DataArray The core dimension is reduce to quantiles and returned at the last dimension. """ def _nanpercentile(*args, **kwargs): """nanpercentile, but with q moved to the last axis""" return np.moveaxis(np.nanpercentile(*args, **kwargs), 0, -1) # nanpercentile parameters q = np.atleast_1d(q) q_kwargs = dict(q=q, axis=-1, interpolation=interpolation) # apply_ufunc parameters kwargs = dict( input_core_dims=[[dim]], output_core_dims=[['percentile']], dask='parallelized', output_dtypes=[float], output_sizes={'percentile': q.size} # on output, <dim> is reduced to length q.size ) if 'percentile' in da.coords: da = da.drop('percentile') percentile = xr.apply_ufunc(_nanpercentile, da.chunk({dim: -1}), kwargs=q_kwargs, **kwargs) percentile['percentile'] = xr.Variable('percentile', q) return percentile.squeeze() # if q.size=1 remove dim

37.636943

118

0.636994

79515a5b7fa0aea185bc44cdb12a87246824ca6a

12,386

py

Python

nipype/utils/config.py

PAmcconnell/nipype

39fbd5411a844ce7c023964d3295eb7643b95af5

[ "Apache-2.0" ]

null

nipype/utils/config.py

PAmcconnell/nipype

39fbd5411a844ce7c023964d3295eb7643b95af5

[ "Apache-2.0" ]

2

2018-04-26T12:09:32.000Z

2018-04-27T06:36:49.000Z

nipype/utils/config.py

PAmcconnell/nipype

39fbd5411a844ce7c023964d3295eb7643b95af5

[ "Apache-2.0" ]

1

2019-11-14T14:16:57.000Z

# -*- coding: utf-8 -*- # emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ''' Created on 20 Apr 2010 logging options : INFO, DEBUG hash_method : content, timestamp @author: Chris Filo Gorgolewski ''' import os import sys import errno import atexit from warnings import warn from distutils.version import LooseVersion import configparser import numpy as np from simplejson import load, dump from .misc import str2bool from filelock import SoftFileLock CONFIG_DEPRECATIONS = { 'profile_runtime': ('monitoring.enabled', '1.0'), 'filemanip_level': ('logging.utils_level', '1.0'), } NUMPY_MMAP = LooseVersion(np.__version__) >= LooseVersion('1.12.0') DEFAULT_CONFIG_TPL = """\ [logging] workflow_level = INFO utils_level = INFO interface_level = INFO log_to_file = false log_directory = {log_dir} log_size = 16384000 log_rotate = 4 [execution] create_report = true crashdump_dir = {crashdump_dir} hash_method = timestamp job_finished_timeout = 5 keep_inputs = false local_hash_check = true matplotlib_backend = Agg plugin = Linear remove_node_directories = false remove_unnecessary_outputs = true try_hard_link_datasink = true single_thread_matlab = true crashfile_format = pklz stop_on_first_crash = false stop_on_first_rerun = false use_relative_paths = false stop_on_unknown_version = false write_provenance = false parameterize_dirs = true poll_sleep_duration = 2 xvfb_max_wait = 10 check_version = true [monitoring] enabled = false sample_frequency = 1 summary_append = true [check] interval = 1209600 """.format def mkdir_p(path): try: os.makedirs(path) except OSError as exc: if exc.errno == errno.EEXIST and os.path.isdir(path): pass else: raise class NipypeConfig(object): """Base nipype config class""" def __init__(self, *args, **kwargs): self._config = configparser.ConfigParser() self._cwd = None config_dir = os.path.expanduser('~/.nipype') self.data_file = os.path.join(config_dir, 'nipype.json') self.set_default_config() self._display = None self._resource_monitor = None self._config.read( [os.path.join(config_dir, 'nipype.cfg'), 'nipype.cfg']) for option in CONFIG_DEPRECATIONS: for section in ['execution', 'logging', 'monitoring']: if self.has_option(section, option): new_section, new_option = CONFIG_DEPRECATIONS[option][ 0].split('.') if not self.has_option(new_section, new_option): # Warn implicit in get self.set(new_section, new_option, self.get(section, option)) @property def cwd(self): """Cache current working directory ASAP""" # Run getcwd only once, preventing multiproc to finish # with error having changed to the wrong path if self._cwd is None: try: self._cwd = os.getcwd() except OSError: warn('Trying to run Nipype from a nonexistent directory "{}".'. format(os.getenv('PWD', 'unknown')), RuntimeWarning) raise return self._cwd def set_default_config(self): """Read default settings template and set into config object""" default_cfg = DEFAULT_CONFIG_TPL( log_dir=os.path.expanduser( '~'), # Get $HOME in a platform-agnostic way crashdump_dir=self.cwd # Read cached cwd ) try: self._config.read_string(default_cfg) # Python >= 3.2 except AttributeError: from io import StringIO self._config.readfp(StringIO(default_cfg)) def enable_debug_mode(self): """Enables debug configuration""" from .. import logging self._config.set('execution', 'stop_on_first_crash', 'true') self._config.set('execution', 'remove_unnecessary_outputs', 'false') self._config.set('execution', 'keep_inputs', 'true') self._config.set('logging', 'workflow_level', 'DEBUG') self._config.set('logging', 'interface_level', 'DEBUG') self._config.set('logging', 'utils_level', 'DEBUG') logging.update_logging(self._config) def set_log_dir(self, log_dir): """Sets logging directory This should be the first thing that is done before any nipype class with logging is imported. """ self._config.set('logging', 'log_directory', log_dir) def get(self, section, option, default=None): """Get an option""" if option in CONFIG_DEPRECATIONS: msg = ('Config option "%s" has been deprecated as of nipype %s. ' 'Please use "%s" instead.') % ( option, CONFIG_DEPRECATIONS[option][1], CONFIG_DEPRECATIONS[option][0]) warn(msg) section, option = CONFIG_DEPRECATIONS[option][0].split('.') if self._config.has_option(section, option): return self._config.get(section, option) return default def set(self, section, option, value): """Set new value on option""" if isinstance(value, bool): value = str(value) if option in CONFIG_DEPRECATIONS: msg = ('Config option "%s" has been deprecated as of nipype %s. ' 'Please use "%s" instead.') % ( option, CONFIG_DEPRECATIONS[option][1], CONFIG_DEPRECATIONS[option][0]) warn(msg) section, option = CONFIG_DEPRECATIONS[option][0].split('.') return self._config.set(section, option, value) def getboolean(self, section, option): """Get a boolean option from section""" return self._config.getboolean(section, option) def has_option(self, section, option): """Check if option exists in section""" return self._config.has_option(section, option) @property def _sections(self): return self._config._sections def get_data(self, key): """Read options file""" if not os.path.exists(self.data_file): return None with SoftFileLock('%s.lock' % self.data_file): with open(self.data_file, 'rt') as file: datadict = load(file) if key in datadict: return datadict[key] return None def save_data(self, key, value): """Store config flie""" datadict = {} if os.path.exists(self.data_file): with SoftFileLock('%s.lock' % self.data_file): with open(self.data_file, 'rt') as file: datadict = load(file) else: dirname = os.path.dirname(self.data_file) if not os.path.exists(dirname): mkdir_p(dirname) with SoftFileLock('%s.lock' % self.data_file): with open(self.data_file, 'wt') as file: datadict[key] = value dump(datadict, file) def update_config(self, config_dict): """Extend internal dictionary with config_dict""" for section in ['execution', 'logging', 'check']: if section in config_dict: for key, val in list(config_dict[section].items()): if not key.startswith('__'): self._config.set(section, key, str(val)) def update_matplotlib(self): """Set backend on matplotlib from options""" import matplotlib matplotlib.use(self.get('execution', 'matplotlib_backend')) def enable_provenance(self): """Sets provenance storing on""" self._config.set('execution', 'write_provenance', 'true') self._config.set('execution', 'hash_method', 'content') @property def resource_monitor(self): """Check if resource_monitor is available""" if self._resource_monitor is not None: return self._resource_monitor # Cache config from nipype config self.resource_monitor = str2bool( self._config.get('monitoring', 'enabled')) or False return self._resource_monitor @resource_monitor.setter def resource_monitor(self, value): # Accept string true/false values if isinstance(value, (str, bytes)): value = str2bool(value.lower()) if value is False: self._resource_monitor = False elif value is True: if not self._resource_monitor: # Before setting self._resource_monitor check psutil # availability self._resource_monitor = False try: import psutil self._resource_monitor = LooseVersion( psutil.__version__) >= LooseVersion('5.0') except ImportError: pass finally: if not self._resource_monitor: warn('Could not enable the resource monitor: ' 'psutil>=5.0 could not be imported.') self._config.set('monitoring', 'enabled', ('%s' % self._resource_monitor).lower()) def enable_resource_monitor(self): """Sets the resource monitor on""" self.resource_monitor = True def disable_resource_monitor(self): """Sets the resource monitor off""" self.resource_monitor = False def get_display(self): """Returns the first display available""" # Check if an Xorg server is listening # import subprocess as sp # if not hasattr(sp, 'DEVNULL'): # setattr(sp, 'DEVNULL', os.devnull) # x_listening = bool(sp.call('ps au | grep -v grep | grep -i xorg', # shell=True, stdout=sp.DEVNULL)) if self._display is not None: return ':%d' % self._display.new_display sysdisplay = None if self._config.has_option('execution', 'display_variable'): sysdisplay = self._config.get('execution', 'display_variable') sysdisplay = sysdisplay or os.getenv('DISPLAY') if sysdisplay: from collections import namedtuple def _mock(): pass # Store a fake Xvfb object. Format - <host>:<display>[.<screen>] ndisp = sysdisplay.split(':')[-1].split('.')[0] Xvfb = namedtuple('Xvfb', ['new_display', 'stop']) self._display = Xvfb(int(ndisp), _mock) return self.get_display() else: if 'darwin' in sys.platform: raise RuntimeError( 'Xvfb requires root permissions to run in OSX. Please ' 'make sure that an X server is listening and set the ' 'appropriate config on either $DISPLAY or nipype\'s ' '"display_variable" config. Valid X servers include ' 'VNC, XQuartz, or manually started Xvfb.') # If $DISPLAY is empty, it confuses Xvfb so unset if sysdisplay == '': del os.environ['DISPLAY'] try: from xvfbwrapper import Xvfb except ImportError: raise RuntimeError( 'A display server was required, but $DISPLAY is not ' 'defined and Xvfb could not be imported.') self._display = Xvfb(nolisten='tcp') self._display.start() # Older versions of xvfbwrapper used vdisplay_num if not hasattr(self._display, 'new_display'): setattr(self._display, 'new_display', self._display.vdisplay_num) return self.get_display() def stop_display(self): """Closes the display if started""" if self._display is not None: from .. import logging self._display.stop() logging.getLogger('nipype.interface').debug( 'Closing display (if virtual)') @atexit.register def free_display(): """Stop virtual display (if it is up)""" from .. import config config.stop_display()

34.121212

79

0.591313

79515bdb18c08bbced49fbaf4464b1d47a1db895

6,434

py

Python

furnace/engine/evaluator.py

lxxue/cil-road-segmentation-2019

c6477556dc3d6d9c8ed2f2a3f185b4d986a03bb4

[ "MIT" ]

null

furnace/engine/evaluator.py

lxxue/cil-road-segmentation-2019

c6477556dc3d6d9c8ed2f2a3f185b4d986a03bb4

[ "MIT" ]

null

furnace/engine/evaluator.py

lxxue/cil-road-segmentation-2019

c6477556dc3d6d9c8ed2f2a3f185b4d986a03bb4

[ "MIT" ]

1

2020-06-08T02:09:18.000Z

import os import os.path as osp import cv2 import numpy as np import time from tqdm import tqdm import torch import torch.nn.functional as F import torch.multiprocessing as mp from engine.logger import get_logger from utils.pyt_utils import load_model, link_file, ensure_dir from utils.img_utils import pad_image_to_shape, normalize logger = get_logger() # Evaluator class manage whole evaluation process with distributed processing # and non-distributed processing class Evaluator(object): def __init__(self, dataset, class_num, image_mean, image_std, network, multi_scales, is_flip, devices, verbose=False, save_path=None): self.dataset = dataset self.ndata = self.dataset.get_length() self.class_num = class_num self.image_mean = image_mean self.image_std = image_std self.multi_scales = multi_scales self.is_flip = is_flip self.network = network self.devices = devices self.context = mp.get_context('spawn') self.val_func = None self.results_queue = self.context.Queue(self.ndata) self.verbose = verbose self.save_path = save_path if save_path is not None: ensure_dir(save_path) # Function for running evaluation process def run(self, model_path, model_indice, log_file, log_file_link): """Evaluate models.""" if '.pth' in model_indice: models = [model_indice, ] else: models = [os.path.join(model_path, 'epoch-%s.pth' % model_indice), ] results = open(log_file, 'a') link_file(log_file, log_file_link) for model in models: logger.info("Load Model: %s" % model) self.val_func = load_model(self.network, model) result_line = self.multi_process_evaluation() results.write('Model: ' + model + '\n') results.write(result_line) results.write('\n') results.flush() results.close() # multi-device distributed processing if the dataset is too large def multi_process_evaluation(self): start_eval_time = time.perf_counter() nr_devices = len(self.devices) stride = int(np.ceil(self.ndata / nr_devices)) # start multi-process on multi-gpu procs = [] for d in range(nr_devices): e_record = min((d + 1) * stride, self.ndata) shred_list = list(range(d * stride, e_record)) device = self.devices[d] logger.info( 'GPU %s handle %d data.' % (device, len(shred_list))) p = self.context.Process(target=self.worker, args=(shred_list, device)) procs.append(p) for p in procs: p.start() all_results = [] for _ in tqdm(range(self.ndata)): t = self.results_queue.get() all_results.append(t) if self.verbose: self.compute_metric(all_results) for p in procs: p.join() result_line = self.compute_metric(all_results) logger.info( 'Evaluation Elapsed Time: %.2fs' % ( time.perf_counter() - start_eval_time)) return result_line def worker(self, shred_list, device): start_load_time = time.time() logger.info('Load Model on Device %d: %.2fs' % ( device, time.time() - start_load_time)) for idx in shred_list: dd = self.dataset[idx] results_dict = self.func_per_iteration(dd, device) self.results_queue.put(results_dict) def func_per_iteration(self, data, device): raise NotImplementedError # inherited from eval.py def compute_metric(self, results): raise NotImplementedError # function for evaluating the whole image at once and select the most # probable prediction amongst all. def whole_eval(self, img, output_size, input_size=None, device=None): if input_size is not None: img, margin = self.process_image(img, input_size) else: img = self.process_image(img, input_size) pred = self.val_func_process(img, device) if input_size is not None: pred = pred[:, margin[0]:(pred.shape[1] - margin[1]), margin[2]:(pred.shape[2] - margin[3])] pred = pred.permute(1, 2, 0) pred = pred.cpu().numpy() if output_size is not None: pred = cv2.resize(pred, (output_size[1], output_size[0]), interpolation=cv2.INTER_LINEAR) # pred = pred.argmax(2) pred = pred[:, :, 1] return pred # function for loading image into model and form prediction def val_func_process(self, input_data, device=None): input_data = np.ascontiguousarray(input_data[None, :, :, :], dtype=np.float32) input_data = torch.FloatTensor(input_data).cuda(device) with torch.cuda.device(input_data.get_device()): self.val_func.eval() self.val_func.to(input_data.get_device()) with torch.no_grad(): score = self.val_func(input_data) score = score[0] if self.is_flip: input_data = input_data.flip(-1) score_flip = self.val_func(input_data) score_flip = score_flip[0] score += score_flip.flip(-1) score = torch.exp(score) # score = score.data return score # function for input image munipulation to correct dimension. def process_image(self, img, crop_size=None): p_img = img if img.shape[2] < 3: im_b = p_img im_g = p_img im_r = p_img p_img = np.concatenate((im_b, im_g, im_r), axis=2) p_img = normalize(p_img, self.image_mean, self.image_std) if crop_size is not None: p_img, margin = pad_image_to_shape(p_img, crop_size, cv2.BORDER_CONSTANT, value=0) p_img = p_img.transpose(2, 0, 1) return p_img, margin p_img = p_img.transpose(2, 0, 1) return p_img

34.042328

77

0.580199

79515c155a33611319d9e17abf536a7e050a3441

15,510

py

Python

test/vanilla/legacy/Expected/AcceptanceTests/BodyComplex/bodycomplex/operations/_basic_operations.py

Azure/autorest.python

c36f5c1a2d614a1eeba6fec6a2c02517f2d1cce7

[ "MIT" ]

35

2018-04-03T12:15:53.000Z

2022-03-11T14:03:34.000Z

test/vanilla/legacy/Expected/AcceptanceTests/BodyComplex/bodycomplex/operations/_basic_operations.py

Azure/autorest.python

c36f5c1a2d614a1eeba6fec6a2c02517f2d1cce7

[ "MIT" ]

652

2017-08-28T22:44:41.000Z

2022-03-31T21:20:31.000Z

test/vanilla/legacy/Expected/AcceptanceTests/BodyComplex/bodycomplex/operations/_basic_operations.py

Azure/autorest.python

c36f5c1a2d614a1eeba6fec6a2c02517f2d1cce7

[ "MIT" ]

29

2017-08-28T20:57:01.000Z

2022-03-11T14:03:38.000Z

# coding=utf-8 # -------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for license information. # Code generated by Microsoft (R) AutoRest Code Generator. # Changes may cause incorrect behavior and will be lost if the code is regenerated. # -------------------------------------------------------------------------- import functools from typing import TYPE_CHECKING import warnings from azure.core.exceptions import ( ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error, ) from azure.core.pipeline import PipelineResponse from azure.core.pipeline.transport import HttpResponse from azure.core.rest import HttpRequest from azure.core.tracing.decorator import distributed_trace from msrest import Serializer from .. import models as _models from .._vendor import _convert_request if TYPE_CHECKING: # pylint: disable=unused-import,ungrouped-imports from typing import Any, Callable, Dict, Generic, Optional, TypeVar T = TypeVar("T") ClsType = Optional[Callable[[PipelineResponse[HttpRequest, HttpResponse], T, Dict[str, Any]], Any]] _SERIALIZER = Serializer() # fmt: off def build_get_valid_request( **kwargs # type: Any ): # type: (...) -> HttpRequest accept = "application/json" # Construct URL url = kwargs.pop("template_url", '/complex/basic/valid') # Construct headers header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any] header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str') return HttpRequest( method="GET", url=url, headers=header_parameters, **kwargs ) def build_put_valid_request( **kwargs # type: Any ): # type: (...) -> HttpRequest content_type = kwargs.pop('content_type', None) # type: Optional[str] api_version = "2016-02-29" accept = "application/json" # Construct URL url = kwargs.pop("template_url", '/complex/basic/valid') # Construct parameters query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any] query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str') # Construct headers header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any] if content_type is not None: header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str') header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str') return HttpRequest( method="PUT", url=url, params=query_parameters, headers=header_parameters, **kwargs ) def build_get_invalid_request( **kwargs # type: Any ): # type: (...) -> HttpRequest accept = "application/json" # Construct URL url = kwargs.pop("template_url", '/complex/basic/invalid') # Construct headers header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any] header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str') return HttpRequest( method="GET", url=url, headers=header_parameters, **kwargs ) def build_get_empty_request( **kwargs # type: Any ): # type: (...) -> HttpRequest accept = "application/json" # Construct URL url = kwargs.pop("template_url", '/complex/basic/empty') # Construct headers header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any] header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str') return HttpRequest( method="GET", url=url, headers=header_parameters, **kwargs ) def build_get_null_request( **kwargs # type: Any ): # type: (...) -> HttpRequest accept = "application/json" # Construct URL url = kwargs.pop("template_url", '/complex/basic/null') # Construct headers header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any] header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str') return HttpRequest( method="GET", url=url, headers=header_parameters, **kwargs ) def build_get_not_provided_request( **kwargs # type: Any ): # type: (...) -> HttpRequest accept = "application/json" # Construct URL url = kwargs.pop("template_url", '/complex/basic/notprovided') # Construct headers header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any] header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str') return HttpRequest( method="GET", url=url, headers=header_parameters, **kwargs ) # fmt: on class BasicOperations(object): """BasicOperations operations. You should not instantiate this class directly. Instead, you should create a Client instance that instantiates it for you and attaches it as an attribute. :ivar models: Alias to model classes used in this operation group. :type models: ~bodycomplex.models :param client: Client for service requests. :param config: Configuration of service client. :param serializer: An object model serializer. :param deserializer: An object model deserializer. """ models = _models def __init__(self, client, config, serializer, deserializer): self._client = client self._serialize = serializer self._deserialize = deserializer self._config = config @distributed_trace def get_valid( self, **kwargs # type: Any ): # type: (...) -> "_models.Basic" """Get complex type {id: 2, name: 'abc', color: 'YELLOW'}. :keyword callable cls: A custom type or function that will be passed the direct response :return: Basic, or the result of cls(response) :rtype: ~bodycomplex.models.Basic :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop("cls", None) # type: ClsType["_models.Basic"] error_map = {401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError} error_map.update(kwargs.pop("error_map", {})) request = build_get_valid_request( template_url=self.get_valid.metadata["url"], ) request = _convert_request(request) request.url = self._client.format_url(request.url) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize.failsafe_deserialize(_models.Error, pipeline_response) raise HttpResponseError(response=response, model=error) deserialized = self._deserialize("Basic", pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized get_valid.metadata = {"url": "/complex/basic/valid"} # type: ignore @distributed_trace def put_valid( self, complex_body, # type: "_models.Basic" **kwargs # type: Any ): # type: (...) -> None """Please put {id: 2, name: 'abc', color: 'Magenta'}. :param complex_body: Please put {id: 2, name: 'abc', color: 'Magenta'}. :type complex_body: ~bodycomplex.models.Basic :keyword callable cls: A custom type or function that will be passed the direct response :return: None, or the result of cls(response) :rtype: None :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop("cls", None) # type: ClsType[None] error_map = {401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError} error_map.update(kwargs.pop("error_map", {})) content_type = kwargs.pop("content_type", "application/json") # type: Optional[str] json = self._serialize.body(complex_body, "Basic") request = build_put_valid_request( content_type=content_type, json=json, template_url=self.put_valid.metadata["url"], ) request = _convert_request(request) request.url = self._client.format_url(request.url) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize.failsafe_deserialize(_models.Error, pipeline_response) raise HttpResponseError(response=response, model=error) if cls: return cls(pipeline_response, None, {}) put_valid.metadata = {"url": "/complex/basic/valid"} # type: ignore @distributed_trace def get_invalid( self, **kwargs # type: Any ): # type: (...) -> "_models.Basic" """Get a basic complex type that is invalid for the local strong type. :keyword callable cls: A custom type or function that will be passed the direct response :return: Basic, or the result of cls(response) :rtype: ~bodycomplex.models.Basic :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop("cls", None) # type: ClsType["_models.Basic"] error_map = {401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError} error_map.update(kwargs.pop("error_map", {})) request = build_get_invalid_request( template_url=self.get_invalid.metadata["url"], ) request = _convert_request(request) request.url = self._client.format_url(request.url) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize.failsafe_deserialize(_models.Error, pipeline_response) raise HttpResponseError(response=response, model=error) deserialized = self._deserialize("Basic", pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized get_invalid.metadata = {"url": "/complex/basic/invalid"} # type: ignore @distributed_trace def get_empty( self, **kwargs # type: Any ): # type: (...) -> "_models.Basic" """Get a basic complex type that is empty. :keyword callable cls: A custom type or function that will be passed the direct response :return: Basic, or the result of cls(response) :rtype: ~bodycomplex.models.Basic :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop("cls", None) # type: ClsType["_models.Basic"] error_map = {401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError} error_map.update(kwargs.pop("error_map", {})) request = build_get_empty_request( template_url=self.get_empty.metadata["url"], ) request = _convert_request(request) request.url = self._client.format_url(request.url) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize.failsafe_deserialize(_models.Error, pipeline_response) raise HttpResponseError(response=response, model=error) deserialized = self._deserialize("Basic", pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized get_empty.metadata = {"url": "/complex/basic/empty"} # type: ignore @distributed_trace def get_null( self, **kwargs # type: Any ): # type: (...) -> "_models.Basic" """Get a basic complex type whose properties are null. :keyword callable cls: A custom type or function that will be passed the direct response :return: Basic, or the result of cls(response) :rtype: ~bodycomplex.models.Basic :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop("cls", None) # type: ClsType["_models.Basic"] error_map = {401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError} error_map.update(kwargs.pop("error_map", {})) request = build_get_null_request( template_url=self.get_null.metadata["url"], ) request = _convert_request(request) request.url = self._client.format_url(request.url) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize.failsafe_deserialize(_models.Error, pipeline_response) raise HttpResponseError(response=response, model=error) deserialized = self._deserialize("Basic", pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized get_null.metadata = {"url": "/complex/basic/null"} # type: ignore @distributed_trace def get_not_provided( self, **kwargs # type: Any ): # type: (...) -> "_models.Basic" """Get a basic complex type while the server doesn't provide a response payload. :keyword callable cls: A custom type or function that will be passed the direct response :return: Basic, or the result of cls(response) :rtype: ~bodycomplex.models.Basic :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop("cls", None) # type: ClsType["_models.Basic"] error_map = {401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError} error_map.update(kwargs.pop("error_map", {})) request = build_get_not_provided_request( template_url=self.get_not_provided.metadata["url"], ) request = _convert_request(request) request.url = self._client.format_url(request.url) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize.failsafe_deserialize(_models.Error, pipeline_response) raise HttpResponseError(response=response, model=error) deserialized = self._deserialize("Basic", pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized get_not_provided.metadata = {"url": "/complex/basic/notprovided"} # type: ignore

36.153846

106

0.655255

79515c9cac1a0457daf2ae4d45c2a78f723e64f1

6,991

py

Python

multiagent/core.py

Latronists/Two-Layer-Reinforcement-Learning

91dbadf1a385e42c0eeea8a6329ed164edddb41b

[ "MIT" ]

null

multiagent/core.py

Latronists/Two-Layer-Reinforcement-Learning

91dbadf1a385e42c0eeea8a6329ed164edddb41b

[ "MIT" ]

null

multiagent/core.py

Latronists/Two-Layer-Reinforcement-Learning

91dbadf1a385e42c0eeea8a6329ed164edddb41b

[ "MIT" ]

null

import numpy as np # physical/external base state of all entites class EntityState(object): def __init__(self): # physical position self.p_pos = None # physical velocity self.p_vel = None # state of agents (including communication and internal/mental state) class AgentState(EntityState): def __init__(self): super(AgentState, self).__init__() # communication utterance self.c = None # action of the agent class Action(object): def __init__(self): # physical action self.u = None # communication action self.c = None # properties and state of physical world entity class Entity(object): def __init__(self): # name self.name = '' # properties: self.size = 0.050 # entity can move / be pushed self.movable = False # entity collides with others self.collide = True # material density (affects mass) self.density = 25.0 # color self.color = None # max speed and accel self.max_speed = None self.accel = None # state self.state = EntityState() # mass self.initial_mass = 1.0 @property def mass(self): return self.initial_mass # properties of landmark entities class Landmark(Entity): def __init__(self): super(Landmark, self).__init__() # properties of agent entities class Agent(Entity): def __init__(self): super(Agent, self).__init__() # agents are movable by default self.movable = True # cannot send communication signals self.silent = False # cannot observe the world self.blind = False # physical motor noise amount self.u_noise = None # communication noise amount self.c_noise = None # control range self.u_range = 1.0 # state self.state = AgentState() # action self.action = Action() # script behavior to execute self.action_callback = None # multi-agent world class World(object): def __init__(self): # list of agents and entities (can change at execution-time!) self.agents = [] self.landmarks = [] # communication channel dimensionality self.dim_c = 0 # position dimensionality self.dim_p = 2 # color dimensionality self.dim_color = 3 # simulation timestep self.dt = 0.1 # physical damping self.damping = 0.25 # contact response parameters self.contact_force = 1e+2 self.contact_margin = 1e-3 # return all entities in the world @property def entities(self): return self.agents + self.landmarks # return all agents controllable by external policies @property def policy_agents(self): return [agent for agent in self.agents if agent.action_callback is None] # return all agents controlled by world scripts @property def scripted_agents(self): return [agent for agent in self.agents if agent.action_callback is not None] # update state of the world def step(self): # set actions for scripted agents for agent in self.scripted_agents: agent.action = agent.action_callback(agent, self) # gather forces applied to entities p_force = [None] * len(self.entities) # apply agent physical controls p_force = self.apply_action_force(p_force) # apply environment forces p_force = self.apply_environment_force(p_force) # integrate physical state self.integrate_state(p_force) # update agent state for agent in self.agents: self.update_agent_state(agent) # gather agent action forces def apply_action_force(self, p_force): # set applied forces for i,agent in enumerate(self.agents): if agent.movable: noise = np.random.randn(*agent.action.u.shape) * agent.u_noise if agent.u_noise else 0.0 p_force[i] = agent.action.u + noise return p_force # gather physical forces acting on entities def apply_environment_force(self, p_force): # simple (but inefficient) collision response for a,entity_a in enumerate(self.entities): for b,entity_b in enumerate(self.entities): if(b <= a): continue [f_a, f_b] = self.get_collision_force(entity_a, entity_b) if(f_a is not None): if(p_force[a] is None): p_force[a] = 0.0 p_force[a] = f_a + p_force[a] if(f_b is not None): if(p_force[b] is None): p_force[b] = 0.0 p_force[b] = f_b + p_force[b] return p_force # integrate physical state def integrate_state(self, p_force): for i,entity in enumerate(self.entities): if not entity.movable: continue entity.state.p_vel = entity.state.p_vel * (1 - self.damping) if (p_force[i] is not None): entity.state.p_vel += (p_force[i] / entity.mass) * self.dt if entity.max_speed is not None: speed = np.sqrt(np.square(entity.state.p_vel[0]) + np.square(entity.state.p_vel[1])) if speed > entity.max_speed: entity.state.p_vel = entity.state.p_vel / np.sqrt(np.square(entity.state.p_vel[0]) + np.square(entity.state.p_vel[1])) * entity.max_speed entity.state.p_pos += entity.state.p_vel * self.dt def update_agent_state(self, agent): # set communication state (directly for now) if agent.silent: agent.state.c = np.zeros(self.dim_c) else: noise = np.random.randn(*agent.action.c.shape) * agent.c_noise if agent.c_noise else 0.0 agent.state.c = agent.action.c + noise # get collision forces for any contact between two entities def get_collision_force(self, entity_a, entity_b): if (not entity_a.collide) or (not entity_b.collide): return [None, None] # not a collider if (entity_a is entity_b): return [None, None] # don't collide against itself # compute actual distance between entities delta_pos = entity_a.state.p_pos - entity_b.state.p_pos dist = np.sqrt(np.sum(np.square(delta_pos))) # minimum allowable distance dist_min = entity_a.size + entity_b.size # softmax penetration k = self.contact_margin penetration = np.logaddexp(0, -(dist - dist_min)/k)*k force = self.contact_force * delta_pos / dist * penetration force_a = +force if entity_a.movable else None force_b = -force if entity_b.movable else None return [force_a, force_b]

35.48731

118

0.602918

79515d2f037a3eec6a827a2696f87ca8b9a82a69

98

py

Python

fibonacci-serisi.py

melihcemipek/python

a0e6e8e5fbffb481c158f19181c10de6e58a8a13

[ "MIT" ]

null

fibonacci-serisi.py

melihcemipek/python

a0e6e8e5fbffb481c158f19181c10de6e58a8a13

[ "MIT" ]

null

fibonacci-serisi.py

melihcemipek/python

a0e6e8e5fbffb481c158f19181c10de6e58a8a13

[ "MIT" ]

null

maks = 100 a, b = 1, 1 print(a) for i in range(maks): print(b) t = b b += a a = t

10.888889

21

0.44898

79515ddfdb84ae73131a49d896ea33ace93c571b

2,834

py

Python

0608.Labour & Income - European Union - Eurostat - Labour Market - Unemployment Rate.py

alphacastio/connectors-eurostats

297c92f667649469def9b1d140eab0aecb86fa89

[ "MIT" ]

null

0608.Labour & Income - European Union - Eurostat - Labour Market - Unemployment Rate.py

alphacastio/connectors-eurostats

297c92f667649469def9b1d140eab0aecb86fa89

[ "MIT" ]

null

0608.Labour & Income - European Union - Eurostat - Labour Market - Unemployment Rate.py

alphacastio/connectors-eurostats

297c92f667649469def9b1d140eab0aecb86fa89

[ "MIT" ]

null

#!/usr/bin/env python # coding: utf-8 # In[1]: import requests import numpy as np import pandas as pd from alphacast import Alphacast from dotenv import dotenv_values API_KEY = dotenv_values(".env").get("API_KEY") alphacast = Alphacast(API_KEY) # In[2]: df = pd.read_csv('https://ec.europa.eu/eurostat/databrowser-backend/api/extraction/1.0/LIVE/false/sdmx/csv/UNE_RT_M?compressed=true', compression='gzip') # In[3]: dict_countries = {'AT':'Austria','BE':'Belgium','BG':'Bulgaria','CH':'Switzerland','CY':'Cyprus', 'CZ':'Czechia','DE':'Germany (until 1990 former territory of the FRG)','DK':'Denmark', 'EA':'Euro area (EA11-1999, EA12-2001, EA13-2007, EA...','EA18':'Euro area - 18 countries (2014)', 'EA19':'Euro area - 19 countries (from 2015)','EE':'Estonia','EL':'Greece','ES':'Spain', 'EU25':'European Union - 25 countries (2004-2006)','EU27_2007':'European Union - 27 countries (2007-2013)', 'EU27_2020':'European Union - 27 countries (from 2020)','EU28':'European Union - 28 countries (2013-2020)', 'FI':'Finland','FR':'France','HR':'Croatia','HU':'Hungary','IE':'Ireland','IS':'Iceland', 'IT':'Italy','JP':'Japan','LT':'Lithuania','LU':'Luxembourg','LV':'Latvia','MT':'Malta', 'NL':'Netherlands','NO':'Norway','PL':'Poland','PT':'Portugal','RO':'Romania', 'SE':'Sweden','SI':'Slovenia','SK':'Slovakia','TR':'Turkey','UK':'United Kingdom','US':'United States'} dict_sex = {'F':'Female', 'M':'Male', 'T':'Total'} #Hago el reemplazo de los codigos de los paises y de la columna sexo df['geo'].replace(dict_countries, inplace=True) df['sex'].replace(dict_sex, inplace=True) # In[4]: #Mantengo solo el SA y NSA y que tenga el porcentaje, no los miles df = df[(df['s_adj'] != 'TC') & (df['unit'] == 'PC_ACT')] # In[5]: #Concateno la variable df['variable'] = df['sex'] + ' - ' + df['s_adj'] #Mantengo solo unas variables df = df[['variable','geo', 'TIME_PERIOD', 'OBS_VALUE']] # In[6]: #Pivoteo los datos df = df.pivot_table(index=['TIME_PERIOD', 'geo'], columns = 'variable', values='OBS_VALUE', aggfunc=np.sum).reset_index() # In[7]: #Reemplazo el nombre de las columnas df.rename(columns = {'TIME_PERIOD':'Date', 'geo':'country'}, inplace=True) #Cambio el formato de fecha df['Date'] = pd.to_datetime(df['Date'], format = '%Y-%m') df.set_index('Date', inplace=True) df.rename_axis(None, axis=1, inplace=True) # In[8]: #Agrego el prefijo y corrijo el de country df = df.add_prefix('Unemployment Rate - ') df.rename(columns={'Unemployment Rate - country': 'country'}, inplace=True) alphacast.datasets.dataset(608).upload_data_from_df(df, deleteMissingFromDB = True, onConflictUpdateDB = True, uploadIndex=True)

32.574713

133

0.632322

79515e47eb5124f52dec27a29f8af3c5dd927343

27,136

py

Python

python/orca/src/bigdl/orca/learn/bigdl/estimator.py

sgwhat/BigDL

25b402666fbb26b0bc18fc8100e9a00469844778

[ "Apache-2.0" ]

null

python/orca/src/bigdl/orca/learn/bigdl/estimator.py

sgwhat/BigDL

25b402666fbb26b0bc18fc8100e9a00469844778

[ "Apache-2.0" ]

null

python/orca/src/bigdl/orca/learn/bigdl/estimator.py

sgwhat/BigDL

25b402666fbb26b0bc18fc8100e9a00469844778

[ "Apache-2.0" ]

null

# # Copyright 2016 The BigDL 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. # from bigdl.orca.learn.metrics import Metric from bigdl.orca.learn.utils import bigdl_metric_results_to_dict from bigdl.dllib.nnframes import NNEstimator, NNModel from bigdl.dllib.estimator import Estimator as SparkEstimator from bigdl.orca.learn.spark_estimator import Estimator as OrcaSparkEstimator from bigdl.orca.data import SparkXShards from bigdl.dllib.optim.optimizer import MaxEpoch from bigdl.dllib.feature.common import FeatureSet from bigdl.orca.learn.metrics import Accuracy from pyspark.sql.dataframe import DataFrame from bigdl.dllib.utils.log4Error import * class Estimator(object): @staticmethod def from_bigdl(*, model, loss=None, optimizer=None, metrics=None, feature_preprocessing=None, label_preprocessing=None, model_dir=None): """ Construct an Estimator with BigDL model, loss function and Preprocessing for feature and label data. :param model: BigDL Model to be trained. :param loss: BigDL criterion. :param optimizer: BigDL optimizer. :param metrics: A evaluation metric or a list of evaluation metrics :param feature_preprocessing: Used when data in `fit` and `predict` is a Spark DataFrame. The param converts the data in feature column to a Tensor or to a Sample directly. It expects a List of Int as the size of the converted Tensor, or a Preprocessing[F, Tensor[T]] If a List of Int is set as feature_preprocessing, it can only handle the case that feature column contains the following data types: Float, Double, Int, Array[Float], Array[Double], Array[Int] and MLlib Vector. The feature data are converted to Tensors with the specified sizes before sending to the model. Internally, a SeqToTensor is generated according to the size, and used as the feature_preprocessing. Alternatively, user can set feature_preprocessing as Preprocessing[F, Tensor[T]] that transforms the feature data to a Tensor[T]. Some pre-defined Preprocessing are provided in package bigdl.dllib.feature. Multiple Preprocessing can be combined as a ChainedPreprocessing. The feature_preprocessing will also be copied to the generated NNModel and applied to feature column during transform. :param label_preprocessing: Used when data in `fit` and `predict` is a Spark DataFrame. similar to feature_preprocessing, but applies to Label data. :param model_dir: The path to save model. During the training, if checkpoint_trigger is defined and triggered, the model will be saved to model_dir. :return: """ return BigDLEstimator(model=model, loss=loss, optimizer=optimizer, metrics=metrics, feature_preprocessing=feature_preprocessing, label_preprocessing=label_preprocessing, model_dir=model_dir) class BigDLEstimator(OrcaSparkEstimator): def __init__(self, *, model, loss, optimizer=None, metrics=None, feature_preprocessing=None, label_preprocessing=None, model_dir=None): self.loss = loss self.optimizer = optimizer self.metrics = Metric.convert_metrics_list(metrics) self.feature_preprocessing = feature_preprocessing self.label_preprocessing = label_preprocessing self.model_dir = model_dir self.model = model self.nn_model = NNModel(self.model, feature_preprocessing=self.feature_preprocessing) self.nn_estimator = NNEstimator(self.model, self.loss, self.feature_preprocessing, self.label_preprocessing) if self.optimizer is None: from bigdl.dllib.optim.optimizer import SGD self.optimizer = SGD() self.nn_estimator.setOptimMethod(self.optimizer) self.estimator = SparkEstimator(self.model, self.optimizer, self.model_dir) self.log_dir = None self.app_name = None self.is_nnframe_fit = False def fit(self, data, epochs, batch_size=32, feature_cols="features", label_cols="label", caching_sample=True, validation_data=None, validation_trigger=None, checkpoint_trigger=None): """ Train this BigDL model with train data. :param data: train data. It can be XShards or Spark DataFrame. If data is XShards, each partition is a dictionary of {'x': feature, 'y': label}, where feature(label) is a numpy array or a list of numpy arrays. :param epochs: Number of epochs to train the model. :param batch_size: Batch size used for training. Default: 32. :param feature_cols: Feature column name(s) of data. Only used when data is a Spark DataFrame. Default: "features". :param label_cols: Label column name(s) of data. Only used when data is a Spark DataFrame. Default: "label". :param caching_sample: whether to cache the Samples after preprocessing. Default: True :param validation_data: Validation data. XShards and Spark DataFrame are supported. If data is XShards, each partition is a dictionary of {'x': feature, 'y': label}, where feature(label) is a numpy array or a list of numpy arrays. :param validation_trigger: Orca Trigger to trigger validation computation. :param checkpoint_trigger: Orca Trigger to set a checkpoint. :return: """ from bigdl.orca.learn.trigger import Trigger invalidInputError(batch_size > 0, "batch_size should be greater than 0") if validation_data is not None: invalidInputError(self.metrics is not None, "You should provide metrics when creating this estimator" " if you provide validation_data.") if isinstance(data, DataFrame): if isinstance(feature_cols, list): data, validation_data, feature_cols = \ BigDLEstimator._combine_cols(data, feature_cols, col_name="features", val_data=validation_data) if isinstance(label_cols, list): data, validation_data, label_cols = \ BigDLEstimator._combine_cols(data, label_cols, col_name="label", val_data=validation_data) self.nn_estimator.setBatchSize(batch_size).setMaxEpoch(epochs) \ .setCachingSample(caching_sample).setFeaturesCol(feature_cols) \ .setLabelCol(label_cols) if validation_data is not None: invalidInputError(isinstance(validation_data, DataFrame), "validation_data should be a spark DataFrame.") invalidInputError(validation_trigger is not None, "You should provide validation_trigger if you provide" " validation_data.") validation_trigger = Trigger.convert_trigger(validation_trigger) self.nn_estimator.setValidation(validation_trigger, validation_data, self.metrics, batch_size) if self.log_dir is not None and self.app_name is not None: from bigdl.dllib.optim.optimizer import TrainSummary from bigdl.dllib.optim.optimizer import ValidationSummary train_summary = TrainSummary(log_dir=self.log_dir, app_name=self.app_name) self.nn_estimator.setTrainSummary(train_summary) val_summary = ValidationSummary(log_dir=self.log_dir, app_name=self.app_name) self.nn_estimator.setValidationSummary(val_summary) if self.model_dir is not None and checkpoint_trigger is not None: checkpoint_trigger = Trigger.convert_trigger(checkpoint_trigger) self.nn_estimator.setCheckpoint(self.model_dir, checkpoint_trigger) self.nn_model = self.nn_estimator.fit(data) self.is_nnframe_fit = True elif isinstance(data, SparkXShards): from bigdl.orca.data.utils import xshard_to_sample end_trigger = MaxEpoch(epochs) checkpoint_trigger = Trigger.convert_trigger(checkpoint_trigger) if isinstance(data, SparkXShards): train_rdd = data.rdd.flatMap(xshard_to_sample) train_feature_set = FeatureSet.sample_rdd(train_rdd) if validation_data is None: val_feature_set = None else: invalidInputError(isinstance(validation_data, SparkXShards), "validation_data should be a XShards") val_feature_set = FeatureSet.sample_rdd( validation_data.rdd.flatMap(xshard_to_sample)) if self.log_dir is not None and self.app_name is not None: self.estimator.set_tensorboard(self.log_dir, self.app_name) self.estimator.train(train_feature_set, self.loss, end_trigger, checkpoint_trigger, val_feature_set, self.metrics, batch_size) self.is_nnframe_fit = False else: invalidInputError(False, "Data and validation data should be XShards, but get " + data.__class__.__name__) else: invalidInputError(False, "Data should be XShards or Spark DataFrame, but get " + data.__class__.__name__) return self def predict(self, data, batch_size=4, feature_cols="features", sample_preprocessing=None): """ Predict input data :param data: predict input data. It can be XShards or Spark DataFrame. If data is XShards, each partition is a dictionary of {'x': feature}, where feature is a numpy array or a list of numpy arrays. :param batch_size: Batch size used for inference. Default: 4. :param feature_cols: Feature column name(s) of data. Only used when data is a Spark DataFrame. Default: "features". :param sample_preprocessing: Used when data is a Spark DataFrame. If the user want change the default feature_preprocessing specified in Estimator.from_bigdl, the user can pass the new sample_preprocessing methods. :return: predicted result. If input data is Spark DataFrame, the predict result is a DataFrame which includes original columns plus 'prediction' column. The 'prediction' column can be FloatType, VectorUDT or Array of VectorUDT depending on model outputs shape. If input data is an XShards, the predict result is a XShards, each partition of the XShards is a dictionary of {'prediction': result}, where result is a numpy array or a list of numpy arrays. """ if isinstance(data, DataFrame): if isinstance(feature_cols, list): data, _, feature_cols = \ BigDLEstimator._combine_cols(data, feature_cols, col_name="features") self.nn_model.setBatchSize(batch_size).setFeaturesCol(feature_cols) if sample_preprocessing is not None: self.nn_model.setSamplePreprocessing(sample_preprocessing) return self.nn_model.transform(data) elif isinstance(data, SparkXShards): from bigdl.orca.data.utils import xshard_to_sample from bigdl.orca.learn.utils import convert_predict_rdd_to_xshard sample_rdd = data.rdd.flatMap(xshard_to_sample) result_rdd = self.model.predict(sample_rdd) return convert_predict_rdd_to_xshard(data, result_rdd) else: invalidInputError(False, "Data should be XShards or Spark DataFrame, but get " + data.__class__.__name__) def evaluate(self, data, batch_size=32, feature_cols="features", label_cols="label"): """ Evaluate model. :param data: validation data. It can be XShardsor or Spark DataFrame, each partition is a dictionary of {'x': feature, 'y': label}, where feature(label) is a numpy array or a list of numpy arrays. :param batch_size: Batch size used for validation. Default: 32. :param feature_cols: (Not supported yet) Feature column name(s) of data. Only used when data is a Spark DataFrame. Default: None. :param label_cols: (Not supported yet) Label column name(s) of data. Only used when data is a Spark DataFrame. Default: None. :return: """ invalidInputError(data is not None, "validation data shouldn't be None") invalidInputError(self.metrics is not None, "metrics shouldn't be None, please specify the metrics" " argument when creating this estimator.") if isinstance(data, DataFrame): if isinstance(feature_cols, list): data, _, feature_cols = \ BigDLEstimator._combine_cols(data, [feature_cols], col_name="features") if isinstance(label_cols, list): data, _, label_cols = \ BigDLEstimator._combine_cols(data, label_cols, col_name="label") self.nn_estimator._setNNBatchSize(batch_size)._setNNFeaturesCol(feature_cols) \ ._setNNLabelCol(label_cols) self.nn_estimator.setValidation(None, None, self.metrics, batch_size) if self.log_dir is not None and self.app_name is not None: from bigdl.dllib.optim.optimizer import TrainSummary from bigdl.dllib.optim.optimizer import ValidationSummary val_summary = ValidationSummary(log_dir=self.log_dir, app_name=self.app_name) self.nn_estimator.setValidationSummary(val_summary) result = self.nn_estimator._eval(data) elif isinstance(data, SparkXShards): from bigdl.orca.data.utils import xshard_to_sample val_feature_set = FeatureSet.sample_rdd(data.rdd.flatMap(xshard_to_sample)) result = self.estimator.evaluate(val_feature_set, self.metrics, batch_size) else: invalidInputError(False, "Data should be XShards or Spark DataFrame, but get " + data.__class__.__name__) return bigdl_metric_results_to_dict(result) def get_model(self): """ Get the trained BigDL model :return: The trained BigDL model """ return self.model def save(self, model_path): """ Save the BigDL model to model_path :param model_path: path to save the trained model. :return: """ try: model = self.get_model() model.saveModel(model_path + ".bigdl", model_path + ".bin", True) except ValueError: invalidInputError(False, "You should fit before calling save") def load(self, checkpoint, optimizer=None, loss=None, feature_preprocessing=None, label_preprocessing=None, model_dir=None, is_checkpoint=False): """ Load existing BigDL model or checkpoint :param checkpoint: Path to the existing model or checkpoint. :param optimizer: BigDL optimizer. :param loss: BigDL criterion. :param feature_preprocessing: Used when data in `fit` and `predict` is a Spark DataFrame. The param converts the data in feature column to a Tensor or to a Sample directly. It expects a List of Int as the size of the converted Tensor, or a Preprocessing[F, Tensor[T]] If a List of Int is set as feature_preprocessing, it can only handle the case that feature column contains the following data types: Float, Double, Int, Array[Float], Array[Double], Array[Int] and MLlib Vector. The feature data are converted to Tensors with the specified sizes before sending to the model. Internally, a SeqToTensor is generated according to the size, and used as the feature_preprocessing. Alternatively, user can set feature_preprocessing as Preprocessing[F, Tensor[T]] that transforms the feature data to a Tensor[T]. Some pre-defined Preprocessing are provided in package bigdl.dllib.feature. Multiple Preprocessing can be combined as a ChainedPreprocessing. The feature_preprocessing will also be copied to the generated NNModel and applied to feature column during transform. :param label_preprocessing: Used when data in `fit` and `predict` is a Spark DataFrame. similar to feature_preprocessing, but applies to Label data. :param model_dir: The path to save model. During the training, if checkpoint_trigger is defined and triggered, the model will be saved to model_dir. :param is_checkpoint: Whether the path is a checkpoint or a saved BigDL model. Default: False. :return: The loaded estimator object. """ if loss is not None: self.loss = loss if optimizer is not None: self.optimizer = optimizer if feature_preprocessing is not None: self.feature_preprocessing = feature_preprocessing if label_preprocessing is not None: self.label_preprocessing = label_preprocessing if model_dir is not None: self.model_dir = model_dir if is_checkpoint: self.load_orca_checkpoint(checkpoint) else: from bigdl.dllib.net import Net self.model = Net.load_bigdl(checkpoint + ".bigdl", checkpoint + ".bin") self.nn_estimator = NNEstimator(self.model, self.loss, self.feature_preprocessing, self.label_preprocessing) if self.optimizer is None: from bigdl.dllib.optim.optimizer import SGD self.optimizer = SGD() self.nn_estimator.setOptimMethod(self.optimizer) self.estimator = SparkEstimator(self.model, self.optimizer, self.model_dir) self.nn_model = NNModel(self.model, feature_preprocessing=self.feature_preprocessing) return self def __load_bigdl_model(self, path, bigdl_type="float"): from bigdl.dllib.utils.common import callBigDlFunc from bigdl.dllib.nn.layer import Layer jmodel = callBigDlFunc(bigdl_type, "loadBigDL", path) return Layer.of(jmodel) def load_orca_checkpoint(self, path, version=None, prefix=None): """ Load existing checkpoint. To load a specific checkpoint, please provide both `version` and `perfix`. If `version` is None, then the latest checkpoint under the specified directory will be loaded. :param path: Path to the existing checkpoint (or directory containing Orca checkpoint files). :param version: checkpoint version, which is the suffix of model.* file, i.e., for modle.4 file, the version is 4. If it is None, then load the latest checkpoint. :param prefix: optimMethod prefix, for example 'optimMethod-Sequentialf53bddcc' :return: """ from bigdl.dllib.nn.layer import Model, Container from bigdl.dllib.optim.optimizer import OptimMethod from bigdl.orca.learn.utils import find_latest_checkpoint import os if version is None: path, prefix, version = find_latest_checkpoint(path, model_type="bigdl") if path is None: invalidInputError(False, "Cannot find BigDL checkpoint, please check your checkpoint" " path.") else: invalidInputError(prefix is not None, "You should provide optimMethod prefix, " "for example 'optimMethod-TorchModelf53bddcc'") try: self.model = self.__load_bigdl_model(os.path.join(path, "model.{}".format(version))) invalidInputError(isinstance(self.model, Container), "The loaded model should be a Container, please check your" " checkpoint type.") self.optimizer = OptimMethod.load(os.path.join(path, "{}.{}".format(prefix, version))) except Exception: invalidInputError(False, "Cannot load BigDL checkpoint, please check your checkpoint path " "and checkpoint type.") self.estimator = SparkEstimator(self.model, self.optimizer, self.model_dir) self.nn_estimator = NNEstimator(self.model, self.loss, self.feature_preprocessing, self.label_preprocessing) if self.optimizer is not None: self.nn_estimator.setOptimMethod(self.optimizer) self.nn_model = NNModel(self.model, feature_preprocessing=self.feature_preprocessing) def clear_gradient_clipping(self): """ Clear gradient clipping parameters. In this case, gradient clipping will not be applied. In order to take effect, it needs to be called before fit. :return: """ self.nn_estimator.clearGradientClipping() self.estimator.clear_gradient_clipping() def set_constant_gradient_clipping(self, min, max): """ Set constant gradient clipping during the training process. In order to take effect, it needs to be called before fit. :param min: The minimum value to clip by. :param max: The maximum value to clip by. :return: """ self.nn_estimator.setConstantGradientClipping(min, max) self.estimator.set_constant_gradient_clipping(min, max) def set_l2_norm_gradient_clipping(self, clip_norm): """ Clip gradient to a maximum L2-Norm during the training process. In order to take effect, it needs to be called before fit. :param clip_norm: Gradient L2-Norm threshold. :return: """ self.nn_estimator.setGradientClippingByL2Norm(clip_norm) self.estimator.set_l2_norm_gradient_clipping(clip_norm) def get_train_summary(self, tag=None): """ Get the scalar from model train summary. This method will return a list of summary data of [iteration_number, scalar_value, timestamp]. :param tag: The string variable represents the scalar wanted """ # Exception handle if tag != "Loss" and tag != "LearningRate" and tag != "Throughput": invalidInputError(False, 'Only "Loss", "LearningRate", "Throughput"' + 'are supported in train summary') if self.is_nnframe_fit: train_summary = self.nn_estimator.getTrainSummary() return train_summary.read_scalar(tag=tag) else: return self.estimator.get_train_summary(tag=tag) def get_validation_summary(self, tag=None): """ Get the scalar from model validation summary. This method will return a list of summary data of [iteration_number, scalar_value, timestamp]. Note that the metric and tag may not be consistent. Please look up following form to pass tag parameter. Left side is your metric during compile. Right side is the tag you should pass. >>> 'Accuracy' | 'Top1Accuracy' >>> 'BinaryAccuracy' | 'Top1Accuracy' >>> 'CategoricalAccuracy' | 'Top1Accuracy' >>> 'SparseCategoricalAccuracy' | 'Top1Accuracy' >>> 'AUC' | 'AucScore' >>> 'HitRatio' | 'HitRate@k' (k is Top-k) >>> 'Loss' | 'Loss' >>> 'MAE' | 'MAE' >>> 'NDCG' | 'NDCG' >>> 'TFValidationMethod' | '${name + " " + valMethod.toString()}' >>> 'Top5Accuracy' | 'Top5Accuracy' >>> 'TreeNNAccuracy' | 'TreeNNAccuracy()' >>> 'MeanAveragePrecision' | 'MAP@k' (k is Top-k) (BigDL) >>> 'MeanAveragePrecision' | 'PascalMeanAveragePrecision' (Zoo) >>> 'StatelessMetric' | '${name}' :param tag: The string variable represents the scalar wanted """ if self.is_nnframe_fit: invalidInputError(tag is not None, "You should provide tag which should match the name of " "the ValidationMethod set into the optimizer. " "e.g.'MAE', 'Top1AccuracyLoss', 'Top1Accuracy' or " "'Top5Accuracy'.") val_summary = self.nn_estimator.getValidationSummary() return val_summary.read_scalar(tag=tag) else: return self.estimator.get_validation_summary(tag=tag) @staticmethod def _combine_cols(data, cols, col_name="features", val_data=None): if isinstance(cols, list): if len(cols) == 1: col_name = cols[0] else: from pyspark.ml.feature import VectorAssembler assembler = VectorAssembler( inputCols=cols, outputCol=col_name) data = assembler.transform(data) if val_data is not None: val_data = assembler.transform(val_data) return data, val_data, col_name

51.103578

99

0.621315

79515e4e562fb7390476bdcffeb32decbf7ee83b

1,853

py

Python

behavior_analysis/apps/utils/table_subjects.py

Tauffer-Consulting/behavior-analysis

2286354d733e1ff947ba9bbc410aa51aaa85340b

[ "MIT" ]

null

behavior_analysis/apps/utils/table_subjects.py

Tauffer-Consulting/behavior-analysis

2286354d733e1ff947ba9bbc410aa51aaa85340b

[ "MIT" ]

null

behavior_analysis/apps/utils/table_subjects.py

Tauffer-Consulting/behavior-analysis

2286354d733e1ff947ba9bbc410aa51aaa85340b

[ "MIT" ]

null

from ...models import Experiment, Group, Subject import dash import dash_html_components as html import dash_core_components as dcc import dash_bootstrap_components as dbc from dash.dependencies import Input, Output, State, ALL from dash.dash import no_update import random import string class SubjectsTable(dbc.Table): def __init__(self, parent_app, subjects, id=None): super().__init__() self.parent_app = parent_app # User chosen or randomly generated unique id if id is None: self.id = ''.join(random.SystemRandom().choice(string.ascii_uppercase + string.digits) for _ in range(6)) else: self.id = id self.bordered = False self.hover = True self.responsive = True self.striped = False table_header = html.Thead(html.Tr([ html.Th(dbc.Checkbox(id='check-subject-all')), html.Th("ID"), html.Th("Experiment"), html.Th("Group"), html.Th("Date") ])) table_body = html.Tbody([]) for sub in subjects: group = sub.group # Group.query.filter_by(name=s['group']).first() row = html.Tr([ html.Td(dbc.Checkbox(id={'type': 'check-subject', 'index': sub.id})), html.Td(sub.id), html.Td(group.experiment.name), html.Td(group.name), html.Td(str(sub.date.date())) ]) table_body.children.append(row) # @self.parent_app.callback( # Output({'type': 'check-subject', 'index': 2}, 'checked'), # [Input('check-subject-all', 'checked')], # ) # def toggle_check_all(chk): # return no_update self.children = [ table_header, table_body ]

31.40678

117

0.562331

79515e70ffeb688736deadf3743e6e19f3b7b77d

10,701

py

Python

filter/filter.py

CRAWLZSTAGE/infra

d0f4d60a63cabf424f76dfbeba33dd64b48c7c98

[ "MIT" ]

null

filter/filter.py

CRAWLZSTAGE/infra

d0f4d60a63cabf424f76dfbeba33dd64b48c7c98

[ "MIT" ]

null

filter/filter.py

CRAWLZSTAGE/infra

d0f4d60a63cabf424f76dfbeba33dd64b48c7c98

[ "MIT" ]

null

import os, sys import pika import json import time import traceback from peewee import * from datetime import datetime MQTT_HOST = os.environ.get('MQTT_HOST') MQTT_USER = os.environ.get('MQTT_USER') MQTT_PASSWORD = os.environ.get('MQTT_PASSWORD') DB_HOST = os.environ.get('DB_HOST') DB_PASSWORD = os.environ.get('DB_PASSWORD') DB_USER = os.environ.get('DB_USER') DB_NAME = os.environ.get('DB_NAME') RECORD_TIMEOUT = os.environ.get('RECORD_TIMEOUT') """In Seconds""" import sys import signal def handler(signum, frame): sys.exit(1) signal.signal(signal.SIGTERM, handler) """ PSQL ORM courtesy of PeeWee No need for schema.sql since PeeWee can take care of this for us! """ psql_db = PostgresqlDatabase(DB_NAME, user=DB_USER, password=DB_PASSWORD, host=DB_HOST) class BaseModel(Model): class Meta: database = psql_db class Record_LinkedIn(BaseModel): url = CharField(primary_key=True) last_accessed = DateTimeField(default=datetime.utcnow()) class Record_Fb(BaseModel): fb_id = CharField(primary_key=True) last_accessed = DateTimeField(default=datetime.utcnow()) class Record_Fsquare(BaseModel): fsquare_id = CharField(primary_key=True) last_accessed = DateTimeField(default=datetime.utcnow()) class Record_Google(BaseModel): google_id = CharField(primary_key=True) last_accessed = DateTimeField(default=datetime.utcnow()) while True: try: psql_db.connect() break except Exception: time.sleep(5) if not Record_LinkedIn.table_exists(): Record_LinkedIn.create_table() if not Record_Fb.table_exists(): Record_Fb.create_table() if not Record_Fsquare.table_exists(): Record_Fsquare.create_table() if not Record_Google.table_exists(): Record_Google.create_table() """ RabbitMQ support courtesy of Pika """ while True: try: _credentials = pika.PlainCredentials(MQTT_USER, MQTT_PASSWORD) mqtt_connection = pika.BlockingConnection(pika.ConnectionParameters(host=MQTT_HOST, credentials=_credentials)) break except Exception: time.sleep(5) pqdata = dict() pqdata['x-max-priority'] = 5 ingress_channel = mqtt_connection.channel() ingress_channel.exchange_declare(exchange='admin', type='fanout') ingress_channel.queue_declare(queue='filter', durable=True, arguments=pqdata) admin_queue = ingress_channel.queue_declare(arguments=pqdata) ingress_channel.queue_bind(exchange="admin", queue=admin_queue.method.queue) egress_channel = mqtt_connection.channel() egress_channel.queue_declare(queue='fetch', durable=True, arguments=pqdata) """ Selectors """ def retrieve_Fb(facebook_id): return Record_Fb.select().where(Record_Fb.fb_id == facebook_id).get() def seen_fb(facebook_id): try: retrieve_Fb(facebook_id) return True except Exception: return False def retrieve_LinkedIn(website): return Record_LinkedIn.select().where(Record_LinkedIn.url == website).get() def seen_website(website): """ TODO: test this! """ try: retrieve_LinkedIn(website) return True except Exception: return False def retrieve_Fsquare(foursquare_id): return Record_Fsquare.select().where(Record_Fsquare.fsquare_id == foursquare_id).get() def seen_fsquare(foursquare_id): try: retrieve_Fsquare(foursquare_id) return True except Exception: return False def retrieve_Google(google_id): return Record_Google.select().where(Record_Google.google_id == google_id).get() def seen_google(google_id): try: retrieve_Google(google_id) return True except Exception: return False """ Message Handling """ def seen_fb_time_ago(lead): if (datetime.utcnow() - retrieve_Fb(lead).last_accessed).seconds > RECORD_TIMEOUT: return True return False def seen_linkedin_time_ago(lead): if (datetime.utcnow() - retrieve_LinkedIn(lead).last_accessed).seconds > RECORD_TIMEOUT: return True return False def seen_fsquare_time_ago(lead): if (datetime.utcnow() - retrieve_Fsquare(lead).last_accessed).seconds > RECORD_TIMEOUT: return True return False def seen_google_time_ago(lead): if (datetime.utcnow() - retrieve_Google(lead).last_accessed).seconds > RECORD_TIMEOUT: return True return False def callback(ch, method, properties, body): try: raw_data = json.loads(body) if (not raw_data.has_key("potential_leads") or not raw_data.has_key("protocol") or not raw_data.has_key("depth")): if raw_data.has_key("delete") and raw_data.has_key("resource_locator") and raw_data.has_key("protocol"): if raw_data["protocol"] == "fb": """ sys.stderr.write("Deleted: " + str(raw_data["resource_locator"]) + "\n") sys.stderr.flush() """ if seen_fb(raw_data["resource_locator"]): retrieve_Fb(raw_data["resource_locator"]).delete_instance() return if raw_data["protocol"] == "linkedin": if seen_website(raw_data["resource_locator"]): retrieve_LinkedIn(raw_data["resource_locator"]).delete_instance() return if raw_data["protocol"] == "fsquare": if seen_fsquare(raw_data["resource_locator"]): retrieve_Fsquare(raw_data["resource_locator"]).delete_instance() return if raw_data["protocol"] == "google": if seen_google(raw_data["resource_locator"]): retrieve_Google(raw_data["resource_locator"]).delete_instance() return raise Exception("Unknown protocol requested during deletion") else: raise Exception("Body malformed") potential_leads = raw_data["potential_leads"] protocol = raw_data["protocol"] for lead in potential_leads: try: if protocol == "fb": if not seen_fb(lead): newRecord = Record_Fb(fb_id=lead, last_accessed = datetime.utcnow()) newRecord.save(force_insert=True) """ TODO: Handle elif difference """ elif seen_fb_time_ago(lead): Record_Fb.update(last_accessed = datetime.utcnow()).where(fb_id == lead).execute() sys.stderr.write("Updating: \n" + lead + "\n") sys.stderr.flush() else: #return continue if protocol == "linkedin": if not seen_website(lead): newRecord = Record_LinkedIn(url=lead, last_accessed = datetime.utcnow()) newRecord.save(force_insert=True) """ TODO: Handle elif difference """ elif seen_linkedin_time_ago(lead): Record_LinkedIn.update(last_accessed = datetime.utcnow()).where(url == lead).execute() sys.stderr.write("Updating: \n" + lead + "\n") sys.stderr.flush() else: return if protocol == "fsquare": if not seen_fsquare(lead): newRecord = Record_Fsquare(fsquare_id=lead, last_accessed= datetime.utcnow()) newRecord.save(force_insert=True) elif seen_fsquare_time_ago(lead): Record_Fsquare.update(last_accessed = datetime.utcnow()).where(fsquare_id == lead).execute() sys.stderr.write("Updating: \n" + lead + "\n") sys.stderr.flush() else: continue #return if protocol == "google": if not seen_google(lead): newRecord = Record_Google(google_id=lead, last_accessed= datetime.utcnow()) newRecord.save(force_insert=True) elif seen_google_time_ago(lead): Record_Google.update(last_accessed=datetime.utcnow()).where(google_id == lead).execute() sys.stderr.write("Updating: \n" + lead + "\n") sys.stderr.flush() else: # we go on to the next lead if we see a familiar lead # and if that familiar lead is not due for an update continue except Exception as e: try: sys.stderr.write("Attempting to rollback db: \n" + str(e) + "\n") psql_db.rollback() except Exception as e: sys.stderr.write("DB connection is messed up: \n" + str(e) + "\n") psql_db.close() psql_db.connect() fetch_data = {"protocol": raw_data["protocol"], "resource_locator": lead, "depth": raw_data["depth"]} egress_channel.basic_publish( exchange='', routing_key='fetch', body=json.dumps(fetch_data), properties=pika.BasicProperties( delivery_mode = 1, priority = int(raw_data["priority"]) if raw_data.has_key("priority") else 0 # default priority ) ) except Exception as e: sys.stderr.write(str(e) + "Unable to filter: \n" + body + "\n") traceback.print_exc() try: psql_db.rollback() except: psql_db.close() psql_db.connect() sys.stderr.flush() finally: ingress_channel.basic_ack(delivery_tag = method.delivery_tag) def admin_callback(ch, method, properties, body): try: data = json.loads(body) return except Exception as e: sys.stderr.write(str(e) + "Unable to fetch: \n" + body + "\n") traceback.print_exc() sys.stderr.flush() finally: ingress_channel.basic_ack(delivery_tag = method.delivery_tag) ingress_channel.basic_qos(prefetch_count=1) ingress_channel.basic_consume(callback, queue='filter') ingress_channel.basic_consume(admin_callback, queue=admin_queue.method.queue) ingress_channel.start_consuming()

34.298077

122

0.59387

79515f4ed96d0b75eedf24bf6631955c7498b999

1,608

py

Python

byceps/services/ticketing/dbmodels/archived_attendance.py

homeworkprod/byceps

cd0f9f37f7b5eb517106ec761acc7e0bdf75e22e

[ "BSD-3-Clause" ]

23

2015-08-03T23:28:54.000Z

2018-12-12T20:11:45.000Z

byceps/services/ticketing/dbmodels/archived_attendance.py

homeworkprod/byceps

cd0f9f37f7b5eb517106ec761acc7e0bdf75e22e

[ "BSD-3-Clause" ]

1

2018-09-30T18:18:24.000Z

byceps/services/ticketing/dbmodels/archived_attendance.py

homeworkprod/byceps

cd0f9f37f7b5eb517106ec761acc7e0bdf75e22e

[ "BSD-3-Clause" ]

9

2015-08-06T16:41:36.000Z

2018-09-25T11:17:31.000Z

""" byceps.services.ticketing.dbmodels.archived_attendance ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ :Copyright: 2014-2022 Jochen Kupperschmidt :License: Revised BSD (see `LICENSE` file for details) """ from datetime import datetime from ....database import db from ....typing import PartyID, UserID from ....util.instances import ReprBuilder class ArchivedAttendance(db.Model): """A user's attendance of a party. This is a link between a party and a user that attended it. While such a link is usually established through a ticket for a party that is assigned to a user, this entity was introduced for legacy data for which no information on tickets, orders, seating areas and so on exists anymore (or should not be migrated). The data for this entity is expected to be inserted from the outside. BYCEPS itself currently does not write any archived attendances (but incorporates them to be displayed on user profiles). """ __tablename__ = 'user_archived_party_attendances' user_id = db.Column(db.Uuid, db.ForeignKey('users.id'), primary_key=True) party_id = db.Column(db.UnicodeText, db.ForeignKey('parties.id'), primary_key=True) created_at = db.Column(db.DateTime, default=datetime.utcnow, nullable=False) def __init__(self, user_id: UserID, party_id: PartyID) -> None: self.user_id = user_id self.party_id = party_id def __repr__(self) -> str: return ReprBuilder(self) \ .add('user_id', str(self.user_id)) \ .add('party_id', self.party_id) \ .build()

34.212766

87

0.682836

795160b511a4ad501525fe77bb37ed063e487a72

3,116

py

Python

src/graphql/utilities/value_from_ast_untyped.py

KingDarBoja/graphql-core

22970e94f1016e813848fc0ab5d1e7ab9ad612e4

[ "MIT" ]

590

2015-10-06T18:22:49.000Z

2022-03-22T16:32:17.000Z

src/graphql/utilities/value_from_ast_untyped.py

KingDarBoja/graphql-core

22970e94f1016e813848fc0ab5d1e7ab9ad612e4

[ "MIT" ]

300

2015-10-06T18:58:11.000Z

2022-03-22T14:01:44.000Z

src/graphql/utilities/value_from_ast_untyped.py

KingDarBoja/graphql-core

22970e94f1016e813848fc0ab5d1e7ab9ad612e4

[ "MIT" ]

270

2015-10-08T19:47:38.000Z

2022-03-10T04:17:51.000Z

from math import nan from typing import Any, Callable, Dict, Optional, Union from ..language import ( ValueNode, BooleanValueNode, EnumValueNode, FloatValueNode, IntValueNode, ListValueNode, NullValueNode, ObjectValueNode, StringValueNode, VariableNode, ) from ..pyutils import inspect, Undefined __all__ = ["value_from_ast_untyped"] def value_from_ast_untyped( value_node: ValueNode, variables: Optional[Dict[str, Any]] = None ) -> Any: """Produce a Python value given a GraphQL Value AST. Unlike :func:`~graphql.utilities.value_from_ast`, no type is provided. The resulting Python value will reflect the provided GraphQL value AST. =================== ============== ================ GraphQL Value JSON Value Python Value =================== ============== ================ Input Object Object dict List Array list Boolean Boolean bool String / Enum String str Int / Float Number int / float Null null None =================== ============== ================ """ func = _value_from_kind_functions.get(value_node.kind) if func: return func(value_node, variables) # Not reachable. All possible value nodes have been considered. raise TypeError( # pragma: no cover f"Unexpected value node: {inspect(value_node)}." ) def value_from_null(_value_node: NullValueNode, _variables: Any) -> Any: return None def value_from_int(value_node: IntValueNode, _variables: Any) -> Any: try: return int(value_node.value) except ValueError: return nan def value_from_float(value_node: FloatValueNode, _variables: Any) -> Any: try: return float(value_node.value) except ValueError: return nan def value_from_string( value_node: Union[BooleanValueNode, EnumValueNode, StringValueNode], _variables: Any ) -> Any: return value_node.value def value_from_list( value_node: ListValueNode, variables: Optional[Dict[str, Any]] ) -> Any: return [value_from_ast_untyped(node, variables) for node in value_node.values] def value_from_object( value_node: ObjectValueNode, variables: Optional[Dict[str, Any]] ) -> Any: return { field.name.value: value_from_ast_untyped(field.value, variables) for field in value_node.fields } def value_from_variable( value_node: VariableNode, variables: Optional[Dict[str, Any]] ) -> Any: variable_name = value_node.name.value if not variables: return Undefined return variables.get(variable_name, Undefined) _value_from_kind_functions: Dict[str, Callable] = { "null_value": value_from_null, "int_value": value_from_int, "float_value": value_from_float, "string_value": value_from_string, "enum_value": value_from_string, "boolean_value": value_from_string, "list_value": value_from_list, "object_value": value_from_object, "variable": value_from_variable, }

28.072072

88

0.64249

795161216b55a848c6544b630313a9a7c9ec5eed

3,780

py

Python

clients/python/openapi_client/api/search_api.py

Soluto/tweek-openapi-clients

feee32006743ea4bb815f2608bd95950439388c3

[ "Apache-2.0" ]

null

clients/python/openapi_client/api/search_api.py

Soluto/tweek-openapi-clients

feee32006743ea4bb815f2608bd95950439388c3

[ "Apache-2.0" ]

null

clients/python/openapi_client/api/search_api.py

Soluto/tweek-openapi-clients

feee32006743ea4bb815f2608bd95950439388c3

[ "Apache-2.0" ]

null

# coding: utf-8 """ Tweek No description provided (generated by Openapi Generator https://github.com/openapitools/openapi-generator) # noqa: E501 OpenAPI spec version: 0.1.0 Generated by: https://openapi-generator.tech """ from __future__ import absolute_import import re # noqa: F401 # python 2 and python 3 compatibility library import six from openapi_client.api_client import ApiClient class SearchApi(object): """NOTE: This class is auto generated by OpenAPI Generator Ref: https://openapi-generator.tech Do not edit the class manually. """ def __init__(self, api_client=None): if api_client is None: api_client = ApiClient() self.api_client = api_client def search(self, **kwargs): # noqa: E501 """search # noqa: E501 Search # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.search(async_req=True) >>> result = thread.get() :param async_req bool :return: list[object] If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.search_with_http_info(**kwargs) # noqa: E501 else: (data) = self.search_with_http_info(**kwargs) # noqa: E501 return data def search_with_http_info(self, **kwargs): # noqa: E501 """search # noqa: E501 Search # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.search_with_http_info(async_req=True) >>> result = thread.get() :param async_req bool :return: list[object] If the method is called asynchronously, returns the request thread. """ local_var_params = locals() all_params = [] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for key, val in six.iteritems(local_var_params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method search" % key ) local_var_params[key] = val del local_var_params['kwargs'] collection_formats = {} path_params = {} query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['bearerAuth'] # noqa: E501 return self.api_client.call_api( '/search', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='list[object]', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats)

30.983607

124

0.607937

795163f7738e16cd3c27c8c3f0469c4324e77114

81

py

Python

chobo/week3/10818.py

Kwak-JunYoung/154Algoritm-5weeks

fa18ae5f68a1ee722a30a05309214247f7fbfda4

[ "MIT" ]

3

2022-01-24T03:06:32.000Z

2022-01-30T08:43:58.000Z

chobo/week3/10818.py

Kwak-JunYoung/154Algoritm-5weeks

fa18ae5f68a1ee722a30a05309214247f7fbfda4

[ "MIT" ]

null

chobo/week3/10818.py

Kwak-JunYoung/154Algoritm-5weeks

fa18ae5f68a1ee722a30a05309214247f7fbfda4

[ "MIT" ]

2

2022-01-24T02:27:40.000Z

2022-01-30T08:57:03.000Z

input() nums = list(map(int, input().split())) print(f'{min(nums)} {max(nums)}')

20.25

38

0.604938

7951641b6f30250d91b693fda49f1bfd1072509d

1,459

py

Python

allennlp/common/testing/test_case.py

craigbig/allennlp

3f15a8bdcae366f3ef732eec1e9df26d91521582

[ "Apache-2.0" ]

1

2020-02-24T10:21:37.000Z

allennlp/common/testing/test_case.py

craigbig/allennlp

3f15a8bdcae366f3ef732eec1e9df26d91521582

[ "Apache-2.0" ]

null

allennlp/common/testing/test_case.py

craigbig/allennlp

3f15a8bdcae366f3ef732eec1e9df26d91521582

[ "Apache-2.0" ]

null

import logging import os import pathlib import shutil import tempfile from unittest import TestCase from allennlp.common.checks import log_pytorch_version_info TEST_DIR = tempfile.mkdtemp(prefix="allennlp_tests") class AllenNlpTestCase(TestCase): """ A custom subclass of `unittest.TestCase` that disables some of the more verbose AllenNLP logging and that creates and destroys a temp directory as a test fixture. """ PROJECT_ROOT = (pathlib.Path(__file__).parent / ".." / ".." / "..").resolve() MODULE_ROOT = PROJECT_ROOT / "allennlp" TOOLS_ROOT = MODULE_ROOT / "tools" TESTS_ROOT = MODULE_ROOT / "tests" FIXTURES_ROOT = TESTS_ROOT / "fixtures" def setUp(self): logging.basicConfig( format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", level=logging.DEBUG ) # Disabling some of the more verbose logging statements that typically aren't very helpful # in tests. logging.getLogger("allennlp.common.params").disabled = True logging.getLogger("allennlp.nn.initializers").disabled = True logging.getLogger("allennlp.modules.token_embedders.embedding").setLevel(logging.INFO) logging.getLogger("urllib3.connectionpool").disabled = True log_pytorch_version_info() self.TEST_DIR = pathlib.Path(TEST_DIR) os.makedirs(self.TEST_DIR, exist_ok=True) def tearDown(self): shutil.rmtree(self.TEST_DIR)

33.930233

98

0.697738

795164b26388a35bad25c9f4a102944a758b997a

18,797

py

Python

consolidation.py

frc1678/server-2019

5ccf17945cbed9dd9587d55349a59a73ed06063c

[ "MIT" ]

2

2019-07-31T14:18:11.000Z

2019-11-19T14:16:36.000Z

consolidation.py

frc1678/server-2019

5ccf17945cbed9dd9587d55349a59a73ed06063c

[ "MIT" ]

null

consolidation.py

frc1678/server-2019

5ccf17945cbed9dd9587d55349a59a73ed06063c

[ "MIT" ]

3

2019-07-30T21:57:21.000Z

2020-01-01T16:03:06.000Z

"""Functions that are used for the consolidation of tempTIMDs into one TIMD. The main function in this file is consolidate_temp_timds(), which is called in calculate_timd.py.""" # External imports import collections import numpy as np # No internal imports def consolidate_times(times): """Takes in multiple time options and consolidates them into one time. times is a dictionary of each scout to their respective time value.""" times = {scout: time for scout, time in times.items() if time is not None} # Creates a list of the times in the form of floats instead of their # tempTIMD format of strings. Does this in order to use them for # calculations. float_list = [float(time) for time in times.values()] # Finds the mean and standard deviation for the array of floats, # these metrics are used for the reciprocal z-scores used later on. mean = np.mean(float_list) std = np.std(float_list) # If the standard deviation is zero, all the times are the same, so # it just returns the mean. if std == 0: return round(mean, 1) # If one of the float_list items is equivalent to the mean, it's # weight will be undefined, so we can just return the mean. if mean in float_list: return mean # Creates a list of tuples with the first item as the time and the # second item as the weight (squared reciprocal of the z-score for # each time). These values are how much each time is weighted when # calculating the final weighted average. The lower the value on # this list the time is, the farther away from the mean it is, and # the less it is weighted. reciprocal_zscores = [(number, (1 / ((mean - number) / std)) ** 2) for number in float_list] # Multiplies each time by its corresponding reciprocal z-score # value, creating a weighted time. weighted_times = [number * zscore_weight for number, zscore_weight in reciprocal_zscores] # Adds up all the weighted times and divides it by the sum of the # reciprocal_zscore_list. Does this in order to get a reasonable # time, if this step is not taken, the weighted time makes no sense. weighted_average = sum(weighted_times) / sum([zscore[1] for zscore \ in reciprocal_zscores]) # Formats each average to a float with one decimal place. return round(weighted_average, 1) def convert_float_time(time): """Converts a time from a string to a float. time is the time that needs to be converted.""" # If an asterisk is in the time, the time is in the wrong time # period. If the asterisk time is in teleop, the time period is # supposed to be in sandstorm, so it sets the time to the lowest # time in sandstorm, and vice versa for when the time is in # sandstorm. if '*' in time: if float(time[:-1]) >= 135.1: return 135.0 else: return 135.1 else: return float(time) def max_occurrences(comparison_list, sprking): """Takes in a dictionary of scouts to their value and returns the majority. If there is no clear winner, returns the value for the best spr scout. comparison_list is a dictionary of each of the scouts to their input on a specific decision (value for a data field, amount of actions, etc). sprking is the scout with the best spr out of the scouts, used if there is no clear majority.""" # If the sprking is not part of the comparison_list, another scout # is randomly selected. if sprking not in list(comparison_list.keys()): correct_scout = list(comparison_list.keys())[-1] else: correct_scout = sprking # Each item in the list to how many times it appeared in the list. # Uses the collections module to count how many appearances each # item has in the list. occurence_list = dict(collections.Counter(comparison_list.values())) # Handling for an empty occurrence list. if len(occurence_list.values()) == 0: return None # If the highest occurrence on the occurrence list is the same as # the lowest occurrence, the correct value for the data point is # the value output by the scout with the best spr. This triggers # both when all the scout values are the same (The max and min # would both be three) and when all the scout values are # different (The max and min are both 1). In the case of any # other scenario, the max is trusted because it would suggest # the max is the 2 in a 2 scout versus 1 split decision. elif max(occurence_list.values()) == min(occurence_list.values()): return comparison_list[correct_scout] else: return max(occurence_list, key=occurence_list.get) def consolidate_timeline_action(temp_timd_timelines, action_type, sprking): """Takes an action type out of timelines and consolidates it separately. Returns a consolidated timeline only made up of the action type that was passed as action_type. input_timelines is the dictionary of the scouts to their specific timelines. action_type is the action type that the function is consolidating. sprking is the scout with the best spr out of the scouts, used when max_occurrences is called.""" # The dictionary of three timelines with only the types specified # in the function. simplified_timelines = {scout: [] for scout in temp_timd_timelines.keys()} # Takes the three different timelines and cuts out any types of # data points which are not the specified types. for scout, timeline in temp_timd_timelines.items(): for action in timeline: if action.get('type') == action_type: simplified_timelines[scout].append(action) # For each action in each scouts list of actions, the time is # converted from a string to a float. for scout, simplified_timeline in simplified_timelines.items(): for action in simplified_timeline: action['time'] = convert_float_time(action['time']) # Scouts to the amount of actions of the specified type are in the # timeline. count_timelines = {scout: len(timeline) for scout, timeline in simplified_timelines.items()} # Finds the majority amount of actions in the timeline to see # which amount of actions is the correct amount. majority_length = max_occurrences(count_timelines, sprking) # Creates a dictionary of scouts to their timelines which follow the # majority length of timeline. correct_length_timelines = {} for scout, timeline_length in count_timelines.items(): if timeline_length == majority_length: correct_length_timelines[scout] = simplified_timelines[scout] # If there are scouts that don't agree with the majority timeline, # creates a time_reference to line up against. time_reference = {} if sprking in correct_length_timelines.keys(): correct_scout = sprking else: correct_scout = list(correct_length_timelines.keys())[-1] reference_timeline = correct_length_timelines[correct_scout] time_reference[correct_scout] = [action['time'] for action in reference_timeline] # If there are scouts that do not agree with the correct timeline # length, find out which of their action times agree with the time # reference the best, and line it up against the reference in the # correct_length_timelines dictionary. for scout in simplified_timelines.keys(): if scout not in correct_length_timelines.keys(): correct_length_timelines[scout] = [{} for action in range(majority_length)] # In order to find the best option for timings, it sets # up a matrix of time differences between each action in # each tempTIMD. timings = np.zeros((len(simplified_timelines[scout]), majority_length)) for false_index, false_action in \ enumerate(simplified_timelines[scout]): for comparison_index, comparison_action in \ enumerate(list(time_reference.values())[0]): timings[false_index][comparison_index] = \ abs(float(comparison_action) - float(false_action['time'])) # Once the matrix of timing differences has been # created, the lowest difference is used to line up the # incorrect length timeline with the correct length # timeline. To avoid one action being compared with multiple # other actions, all other instances of the action (The row # and column) are set to 200 to signify that it has been # used. 200 is used because it is higher than any possible # time difference. if timings.size > 0: # The loop runs until there are no more time differences # in the matrix less than 200. while timings.min() < 200: # lowest_index is in the format of ([y coordinate], # [x coordinate]), which requires lowest_index[1][0] # to get the x coordinate, and lowest_index[0][0] # for the y coordinate. lowest_index = np.where(timings == timings.min()) correct_length_timelines[scout][lowest_index[1][0]] = \ simplified_timelines[scout][lowest_index[0][0]] timings[int(lowest_index[0][0])] = \ np.full([1, len(timings[0])], 200) for row in range(len(timings)): timings[row][int(lowest_index[1][0])] = 200 final_simplified_timd = [{} for action in range(majority_length)] # Iterates through the sprking's timeline to compare all the actions. # If the majority 'type' for the action is None, the majority of # scouts did not record this action, and this action should not # appear in the consolidated TIMD. for action_index, action in enumerate(correct_length_timelines[sprking]): comparison_dict = {scout: timeline[action_index] for scout, timeline in correct_length_timelines.items()} types = {scout: action.get('type') for scout, action in comparison_dict.items()} if max_occurrences(types, sprking) is None: # Skips current iteration continue # Deletes scouts that did not record this action. for scout in list(comparison_dict): if comparison_dict[scout] == {}: comparison_dict.pop(scout) # All of the possible keys for a tempTIMD for this action. keys = set() for action in comparison_dict.values(): for key in action.keys(): keys.add(key) for key in keys: # For every key that isn't time, which can't realistically # have a majority, the majority opinion is set to the final # timd. scout_to_keys = {scout: action.get(key) for scout, action in comparison_dict.items()} if key == 'time': # If the key is time, finds the correct time using the # consolidate_times algorithm. final_simplified_timd[action_index]['time'] = \ consolidate_times(scout_to_keys) else: # For every key in the dictionary other than time, it just # takes the majority value for the key. final_simplified_timd[action_index][key] = \ max_occurrences(scout_to_keys, sprking) # Returns the final created timeline return final_simplified_timd def climb_consolidation(input_timelines, sprking): """Takes climb out of the timelines of the tempTIMDs and consolidates it. Returns a timeline only with climb inside it to add to the final timeline for the timd. input_timelines is the dictionary of the scouts to their specific timelines. sprking is the scout with the best spr out of the scouts, used when max_occurrences is called. More info in the docstring for max_occurrences. """ # Scout name to climb dictionary. simplified_timelines = {} # Fills in 'simplified_timelines' with the scout and the climb # dictionary from the three tempTIMDs. for scout, timeline in input_timelines.items(): for action in timeline: if action.get('type') == 'climb': simplified_timelines[scout] = action # Returns None if no climb was recorded. if simplified_timelines == {}: return None final_simplified_timd = {'type': 'climb', 'attempted': {}, 'actual': {}} # Consolidates time first final_simplified_timd['time'] = consolidate_times({ scout: convert_float_time(climb['time']) for scout, climb in simplified_timelines.items()}) for key in ['attempted', 'actual']: for robot in ['self', 'robot1', 'robot2']: final_simplified_timd[key][robot] = max_occurrences({ scout: climb[key][robot] for scout, climb in simplified_timelines.items()}, sprking) # Returns the final created timeline return final_simplified_timd def consolidate_temp_timds(temp_timds): """Consolidates between 1-3 temp_timds into one final timd. This is the main function of consolidation.py, and is called by calculate_timd.py. temp_timds is a dictionary with scout names as keys and their respective tempTIMD as a value. """ # 'sprking' is the scout with the best (lowest) SPR #TODO: Implement spr system sprking = list(temp_timds.keys())[0] final_timd = {} # Iterates through the keys of the best scout's tempTIMD and # consolidates each data_field one at a time. for data_field in list(temp_timds[sprking]): if data_field == 'timeline': # In order to compute the timeline properly, it is split # into a list of the timelines. timelines = {} for scout, temp_timd in temp_timds.items(): temp_timeline = temp_timd.get('timeline', []) timelines[scout] = temp_timeline # If the list of timelines only includes one timeline, that # timeline is taken as the correct one and put into the # final TIMD. if len(timelines.values()) == 1: # Converts all times to floats and removes asterisk to # put it into the format of a timd. final_timeline = [] for action in timelines[sprking]: action_time = action.get('time') # Takes the time before the asterisk, if there is no # asterisk, .split() still returns a list, a list of # only the time, meaning [0] works in both # instances. action['time'] = float(action_time.split('*')[0]) final_timd['timeline'] = timelines[sprking] # If the list has more than one tempTIMD, the process for # computation has to be split up by each of the types of # actions in the timeline. else: # Creates the final timeline which is passed as the # timeline for the final timd at the end of # consolidation. final_timeline = [] # Separates all the basic actions out and consolidates # them one at a time. All the actions are consolidated # separately so that the timings on each action are # split apart, making it more easy to line up, identify, # and consolidate the timeline. for action_type in ['pinningFoul', 'incap', 'unincap', \ 'drop', 'startDefense', 'endDefense', \ 'placement', 'intake']: final_timeline += consolidate_timeline_action( timelines, action_type, sprking) # Also consolidates climb separately in order to # separate it from intakes and placements. Climb needs a # separate function because of its relatively strange # structure. climb = climb_consolidation(timelines, sprking) if climb is not None: final_timeline.append(climb) # Deletes any blank actions. final_timeline = [action for action in final_timeline if action != {}] # Once the timeline is finally completed, it is sorted # by time, and added to the final timd. final_timd['timeline'] = sorted(final_timeline, \ key=lambda action: action['time'], reverse=True) # When consolidating non-timed keys, it is easy to consolidate # them, as you can simply find which value is the most common in # the set of three possibilities. The other data_fields that # are not included in this set, such as timerStarted, are scout # diagnostics, and not included in the final TIMD. elif data_field not in ['timeline', 'timerStarted', 'currentCycle', 'scoutID', 'scoutName', 'appVersion', 'assignmentMode', 'assignmentFileTimestamp', 'matchesNotScouted']: # Creates a dictionary of each scout to the key from their # tempTIMD to compare against each other. (Code note - This # code is using .get and not simply referencing the key out # of the dictionary because .get doesn't error out when the # key doesn't exist. Instead, .get returns NoneType). data_field_comparison_list = {} for scout, temp_timd in temp_timds.items(): temp_data_field = temp_timd.get(data_field) if temp_data_field is not None: data_field_comparison_list[scout] = temp_data_field # Uses the max_occurrences function to find the correct value # for the data field. data_occurence_max = max_occurrences( data_field_comparison_list, sprking) final_timd[data_field] = data_occurence_max return final_timd

45.293976

79

0.63026

795164e9b019d5e0233e60502428b4c2cb401ddf

4,647

py

Python

scripts/scrape_cgc.py

eklipse2009/ZX-Pokemaster

113bf2e242347b475cca9eadbae4f1b67f498466

[ "MIT" ]

8

2018-11-18T00:37:25.000Z

2020-12-06T13:17:53.000Z

scripts/scrape_cgc.py

eklipse2009/ZX-Pokemaster

113bf2e242347b475cca9eadbae4f1b67f498466

[ "MIT" ]

8

2017-08-21T10:07:58.000Z

2020-03-29T18:23:37.000Z

scripts/scrape_cgc.py

eklipse2009/ZX-Pokemaster

113bf2e242347b475cca9eadbae4f1b67f498466

[ "MIT" ]

1

2021-03-04T17:43:36.000Z

import os import glob import shutil import zipfile from functions.game_name_functions import * if (os.getcwd().endswith('scripts')): os.chdir('..') from classes.scraper import * def scrape_csscgc(): # if os.path.exists('tosec\\CSSCGC Games'): # shutil.rmtree('tosec\\CSSCGC Games') s = Scraper() template = 'https://www.yoursinclair.co.uk/csscgc/csscgc.cgi?year=' for year in range(1996, 2017): files_extracted = [] page = template + str(year) selector = s.loadUrl(page) games_tables = selector.xpath('//table[@border="1"]').extract_all() for game_table in games_tables: cells = Selector(game_table).xpath('//td//text()').extract_all() game_name = cells[0] author = cells[2] if not author.startswith('Mr'): author = putInitialsToEnd(author) filenames = list(set(cells[4].split(' ')+[cells[4]])) format = cells[10] game_represented = False for filename in filenames: if not filename: continue filename = os.path.basename(filename) ext = os.path.splitext(filename)[-1].lower() tosec_name = '{} ({})({})({})[CSSCGC]{}'.format(game_name, str(year), author, format, ext) tosec_name = tosec_name.replace('(Spectrum)', '').replace('ZX Spectrum ', '').replace('(48K)', '') tosec_name = tosec_name.replace('(128K Spectrum)', '(128K)') tosec_name = tosec_name.replace('(128K-+2)', '(+2)') tosec_name =tosec_name.replace('(unknown)', '(-)') tosec_name = getFileSystemFriendlyName(tosec_name) src = os.path.join('tosec', 'csscgc scrape', 'CSSCGC' + str(year), filename) dest = os.path.join('tosec', 'CSSCGC Games', str(year), tosec_name) # print(src, dest) if not os.path.exists(src): # print('File does not exist:', filename, 'Year:', year) continue if os.path.exists(dest): print('Conflict:', tosec_name, filename, 'Year:', year) continue os.makedirs(os.path.dirname(dest), exist_ok=True) if ext == '.zip': with zipfile.ZipFile(src, 'r') as zf: files_to_extract = [] conflict = False for zfname in zf.namelist(): zfname_ext = zfname.split('.')[-1].lower() if zfname_ext in GAME_EXTENSIONS: files_to_extract.append(zfname) for each in GAME_EXTENSIONS: if len([x for x in files_to_extract if x.endswith(each)])>1: print('Conflict:', tosec_name, src, files_to_extract, 'Year:', year) conflict = True break if not conflict and files_to_extract: for file in files_to_extract: data = zf.read(files_to_extract[0]) ext = os.path.splitext(files_to_extract[0])[-1].lower() dest = dest.replace('.zip', ext) with open(dest, 'wb+') as output: output.write(data) game_represented = True files_extracted.append(src) else: shutil.copy(src, dest) files_extracted.append(src) game_represented = True if not game_represented: print('Game not represented:', tosec_name, cells[4], 'Year:', year) for src in glob.glob(os.path.join('tosec', 'csscgc scrape', 'CSSCGC'+str(year), '*')): filename, ext = os.path.splitext(os.path.basename(src)) if ext[1:] not in GAME_EXTENSIONS+['zip']: continue if src in files_extracted: continue else: tosec_name = '{} ({})(-)[CSSCGC]{}'.format(filename.title() , str(year), ext) dest = os.path.join('tosec', 'CSSCGC Games', str(year), 'unsorted', tosec_name) os.makedirs(os.path.dirname(dest), exist_ok=True) shutil.copy(src, dest) print('Copied: ', src, 'to:', dest, 'Year:', year) if __name__=='__main__': scrape_csscgc()

49.967742

114

0.497095

795165d401ebb174438275a9ed6fbb6232480959

53,255

py

Python

python/ccxt/async_support/bittrex.py

xeddmc/ccxt

9ddd88e6bbc4b2162cf45d331995bb86235d2a59

[ "MIT" ]

1

2021-03-01T17:45:33.000Z

python/ccxt/async_support/bittrex.py

xeddmc/ccxt

9ddd88e6bbc4b2162cf45d331995bb86235d2a59

[ "MIT" ]

4

2020-09-07T00:20:58.000Z

2021-05-10T09:04:56.000Z

python/ccxt/async_support/bittrex.py

xeddmc/ccxt

9ddd88e6bbc4b2162cf45d331995bb86235d2a59

[ "MIT" ]

1

2019-11-08T12:36:13.000Z

# -*- coding: utf-8 -*- # PLEASE DO NOT EDIT THIS FILE, IT IS GENERATED AND WILL BE OVERWRITTEN: # https://github.com/ccxt/ccxt/blob/master/CONTRIBUTING.md#how-to-contribute-code from ccxt.async_support.base.exchange import Exchange # ----------------------------------------------------------------------------- try: basestring # Python 3 except NameError: basestring = str # Python 2 import hashlib import math from ccxt.base.errors import ExchangeError from ccxt.base.errors import AuthenticationError from ccxt.base.errors import PermissionDenied from ccxt.base.errors import InsufficientFunds from ccxt.base.errors import AddressPending from ccxt.base.errors import InvalidOrder from ccxt.base.errors import OrderNotFound from ccxt.base.errors import DDoSProtection from ccxt.base.errors import ExchangeNotAvailable from ccxt.base.decimal_to_precision import TRUNCATE from ccxt.base.decimal_to_precision import DECIMAL_PLACES class bittrex (Exchange): def describe(self): return self.deep_extend(super(bittrex, self).describe(), { 'id': 'bittrex', 'name': 'Bittrex', 'countries': ['US'], 'version': 'v1.1', 'rateLimit': 1500, 'certified': True, # new metainfo interface 'has': { 'CORS': True, 'createMarketOrder': False, 'fetchDepositAddress': True, 'fetchClosedOrders': True, 'fetchCurrencies': True, 'fetchMyTrades': 'emulated', 'fetchOHLCV': True, 'fetchOrder': True, 'fetchOpenOrders': True, 'fetchTickers': True, 'withdraw': True, 'fetchDeposits': True, 'fetchWithdrawals': True, 'fetchTransactions': False, }, 'timeframes': { '1m': 'oneMin', '5m': 'fiveMin', '30m': 'thirtyMin', '1h': 'hour', '1d': 'day', }, 'hostname': 'bittrex.com', 'urls': { 'logo': 'https://user-images.githubusercontent.com/1294454/27766352-cf0b3c26-5ed5-11e7-82b7-f3826b7a97d8.jpg', 'api': { 'public': 'https://{hostname}/api', 'account': 'https://{hostname}/api', 'market': 'https://{hostname}/api', 'v2': 'https://{hostname}/api/v2.0/pub', 'v3': 'https://api.bittrex.com/v3', 'v3public': 'https://api.bittrex.com/v3', }, 'www': 'https://bittrex.com', 'doc': [ 'https://bittrex.github.io/api/', 'https://bittrex.github.io/api/v3', 'https://www.npmjs.com/package/bittrex-node', ], 'fees': [ 'https://bittrex.zendesk.com/hc/en-us/articles/115003684371-BITTREX-SERVICE-FEES-AND-WITHDRAWAL-LIMITATIONS', 'https://bittrex.zendesk.com/hc/en-us/articles/115000199651-What-fees-does-Bittrex-charge-', ], }, 'api': { 'v3': { 'get': [ 'account', 'addresses', 'addresses/{currencySymbol}', 'balances', 'balances/{currencySymbol}', 'currencies', 'currencies/{symbol}', 'deposits/open', 'deposits/closed', 'deposits/ByTxId/{txId}', 'deposits/{depositId}', 'orders/closed', 'orders/open', 'orders/{orderId}', 'ping', 'subaccounts/{subaccountId}', 'subaccounts', 'withdrawals/open', 'withdrawals/closed', 'withdrawals/ByTxId/{txId}', 'withdrawals/{withdrawalId}', ], 'post': [ 'addresses', 'orders', 'subaccounts', 'withdrawals', ], 'delete': [ 'orders/{orderId}', 'withdrawals/{withdrawalId}', ], }, 'v3public': { 'get': [ 'markets', 'markets/summaries', 'markets/{marketSymbol}', 'markets/{marketSymbol}/summary', 'markets/{marketSymbol}/orderbook', 'markets/{marketSymbol}/trades', 'markets/{marketSymbol}/ticker', 'markets/{marketSymbol}/candles', ], }, 'v2': { 'get': [ 'currencies/GetBTCPrice', 'market/GetTicks', 'market/GetLatestTick', 'Markets/GetMarketSummaries', 'market/GetLatestTick', ], }, 'public': { 'get': [ 'currencies', 'markethistory', 'markets', 'marketsummaries', 'marketsummary', 'orderbook', 'ticker', ], }, 'account': { 'get': [ 'balance', 'balances', 'depositaddress', 'deposithistory', 'order', 'orders', 'orderhistory', 'withdrawalhistory', 'withdraw', ], }, 'market': { 'get': [ 'buylimit', 'buymarket', 'cancel', 'openorders', 'selllimit', 'sellmarket', ], }, }, 'fees': { 'trading': { 'tierBased': False, 'percentage': True, 'maker': 0.0025, 'taker': 0.0025, }, 'funding': { 'tierBased': False, 'percentage': False, 'withdraw': { 'BTC': 0.0005, 'LTC': 0.01, 'DOGE': 2, 'VTC': 0.02, 'PPC': 0.02, 'FTC': 0.2, 'RDD': 2, 'NXT': 2, 'DASH': 0.05, 'POT': 0.002, 'BLK': 0.02, 'EMC2': 0.2, 'XMY': 0.2, 'GLD': 0.0002, 'SLR': 0.2, 'GRS': 0.2, }, 'deposit': { 'BTC': 0, 'LTC': 0, 'DOGE': 0, 'VTC': 0, 'PPC': 0, 'FTC': 0, 'RDD': 0, 'NXT': 0, 'DASH': 0, 'POT': 0, 'BLK': 0, 'EMC2': 0, 'XMY': 0, 'GLD': 0, 'SLR': 0, 'GRS': 0, }, }, }, 'exceptions': { # 'Call to Cancel was throttled. Try again in 60 seconds.': DDoSProtection, # 'Call to GetBalances was throttled. Try again in 60 seconds.': DDoSProtection, 'APISIGN_NOT_PROVIDED': AuthenticationError, 'INVALID_SIGNATURE': AuthenticationError, 'INVALID_CURRENCY': ExchangeError, 'INVALID_PERMISSION': AuthenticationError, 'INSUFFICIENT_FUNDS': InsufficientFunds, 'QUANTITY_NOT_PROVIDED': InvalidOrder, 'MIN_TRADE_REQUIREMENT_NOT_MET': InvalidOrder, 'ORDER_NOT_OPEN': OrderNotFound, 'INVALID_ORDER': InvalidOrder, 'UUID_INVALID': OrderNotFound, 'RATE_NOT_PROVIDED': InvalidOrder, # createLimitBuyOrder('ETH/BTC', 1, 0) 'WHITELIST_VIOLATION_IP': PermissionDenied, 'DUST_TRADE_DISALLOWED_MIN_VALUE': InvalidOrder, }, 'options': { 'parseOrderStatus': False, 'hasAlreadyAuthenticatedSuccessfully': False, # a workaround for APIKEY_INVALID 'symbolSeparator': '-', # With certain currencies, like # AEON, BTS, GXS, NXT, SBD, STEEM, STR, XEM, XLM, XMR, XRP # an additional tag / memo / payment id is usually required by exchanges. # With Bittrex some currencies imply the "base address + tag" logic. # The base address for depositing is stored on self.currencies[code] # The base address identifies the exchange as the recipient # while the tag identifies the user account within the exchange # and the tag is retrieved with fetchDepositAddress. 'tag': { 'NXT': True, # NXT, BURST 'CRYPTO_NOTE_PAYMENTID': True, # AEON, XMR 'BITSHAREX': True, # BTS 'RIPPLE': True, # XRP 'NEM': True, # XEM 'STELLAR': True, # XLM 'STEEM': True, # SBD, GOLOS # https://github.com/ccxt/ccxt/issues/4794 # 'LISK': True, # LSK }, 'subaccountId': None, # see the implementation of fetchClosedOrdersV3 below 'fetchClosedOrdersMethod': 'fetch_closed_orders_v3', 'fetchClosedOrdersFilterBySince': True, }, 'commonCurrencies': { 'BITS': 'SWIFT', 'CPC': 'CapriCoin', }, }) def cost_to_precision(self, symbol, cost): return self.decimal_to_precision(cost, TRUNCATE, self.markets[symbol]['precision']['price'], DECIMAL_PLACES) def fee_to_precision(self, symbol, fee): return self.decimal_to_precision(fee, TRUNCATE, self.markets[symbol]['precision']['price'], DECIMAL_PLACES) async def fetch_markets(self, params={}): response = await self.v3publicGetMarkets(params) # # [ # { # "symbol":"LTC-BTC", # "baseCurrencySymbol":"LTC", # "quoteCurrencySymbol":"BTC", # "minTradeSize":"0.01686767", # "precision":8, # "status":"ONLINE", # "OFFLINE" # "createdAt":"2014-02-13T00:00:00Z" # }, # { # "symbol":"VDX-USDT", # "baseCurrencySymbol":"VDX", # "quoteCurrencySymbol":"USDT", # "minTradeSize":"300.00000000", # "precision":8, # "status":"ONLINE", # "OFFLINE" # "createdAt":"2019-05-23T00:41:21.843Z", # "notice":"USDT has swapped to an ERC20-based token as of August 5, 2019." # } # ] # result = [] # markets = self.safe_value(response, 'result') for i in range(0, len(response)): market = response[i] baseId = self.safe_string(market, 'baseCurrencySymbol') quoteId = self.safe_string(market, 'quoteCurrencySymbol') # bittrex v2 uses inverted pairs, v3 uses regular pairs # we use v3 for fetchMarkets and v2 throughout the rest of self implementation # therefore we swap the base ←→ quote here to be v2-compatible # https://github.com/ccxt/ccxt/issues/5634 # id = self.safe_string(market, 'symbol') id = quoteId + self.options['symbolSeparator'] + baseId base = self.safe_currency_code(baseId) quote = self.safe_currency_code(quoteId) symbol = base + '/' + quote pricePrecision = self.safe_integer(market, 'precision', 8) precision = { 'amount': 8, 'price': pricePrecision, } status = self.safe_string(market, 'status') active = (status == 'ONLINE') result.append({ 'id': id, 'symbol': symbol, 'base': base, 'quote': quote, 'baseId': baseId, 'quoteId': quoteId, 'active': active, 'info': market, 'precision': precision, 'limits': { 'amount': { 'min': self.safe_float(market, 'minTradeSize'), 'max': None, }, 'price': { 'min': math.pow(10, -precision['price']), 'max': None, }, }, }) return result async def fetch_balance(self, params={}): await self.load_markets() response = await self.accountGetBalances(params) balances = self.safe_value(response, 'result') result = {'info': balances} indexed = self.index_by(balances, 'Currency') currencyIds = list(indexed.keys()) for i in range(0, len(currencyIds)): currencyId = currencyIds[i] code = self.safe_currency_code(currencyId) account = self.account() balance = indexed[currencyId] account['free'] = self.safe_float(balance, 'Available') account['total'] = self.safe_float(balance, 'Balance') result[code] = account return self.parse_balance(result) async def fetch_order_book(self, symbol, limit=None, params={}): await self.load_markets() request = { 'market': self.market_id(symbol), 'type': 'both', } response = await self.publicGetOrderbook(self.extend(request, params)) orderbook = response['result'] if 'type' in params: if params['type'] == 'buy': orderbook = { 'buy': response['result'], 'sell': [], } elif params['type'] == 'sell': orderbook = { 'buy': [], 'sell': response['result'], } return self.parse_order_book(orderbook, None, 'buy', 'sell', 'Rate', 'Quantity') def parse_ticker(self, ticker, market=None): timestamp = self.parse8601(self.safe_string(ticker, 'TimeStamp')) symbol = None if market is not None: symbol = market['symbol'] previous = self.safe_float(ticker, 'PrevDay') last = self.safe_float(ticker, 'Last') change = None percentage = None if last is not None: if previous is not None: change = last - previous if previous > 0: percentage = (change / previous) * 100 return { 'symbol': symbol, 'timestamp': timestamp, 'datetime': self.iso8601(timestamp), 'high': self.safe_float(ticker, 'High'), 'low': self.safe_float(ticker, 'Low'), 'bid': self.safe_float(ticker, 'Bid'), 'bidVolume': None, 'ask': self.safe_float(ticker, 'Ask'), 'askVolume': None, 'vwap': None, 'open': previous, 'close': last, 'last': last, 'previousClose': None, 'change': change, 'percentage': percentage, 'average': None, 'baseVolume': self.safe_float(ticker, 'Volume'), 'quoteVolume': self.safe_float(ticker, 'BaseVolume'), 'info': ticker, } async def fetch_currencies(self, params={}): response = await self.publicGetCurrencies(params) # # { # "success": True, # "message": "", # "result": [ # { # "Currency": "BTC", # "CurrencyLong":"Bitcoin", # "MinConfirmation":2, # "TxFee":0.00050000, # "IsActive":true, # "IsRestricted":false, # "CoinType":"BITCOIN", # "BaseAddress":"1N52wHoVR79PMDishab2XmRHsbekCdGquK", # "Notice":null # }, # ..., # ] # } # currencies = self.safe_value(response, 'result', []) result = {} for i in range(0, len(currencies)): currency = currencies[i] id = self.safe_string(currency, 'Currency') # todo: will need to rethink the fees # to add support for multiple withdrawal/deposit methods and # differentiated fees for each particular method code = self.safe_currency_code(id) precision = 8 # default precision, todo: fix "magic constants" address = self.safe_value(currency, 'BaseAddress') fee = self.safe_float(currency, 'TxFee') # todo: redesign result[code] = { 'id': id, 'code': code, 'address': address, 'info': currency, 'type': currency['CoinType'], 'name': currency['CurrencyLong'], 'active': currency['IsActive'], 'fee': fee, 'precision': precision, 'limits': { 'amount': { 'min': math.pow(10, -precision), 'max': math.pow(10, precision), }, 'price': { 'min': math.pow(10, -precision), 'max': math.pow(10, precision), }, 'cost': { 'min': None, 'max': None, }, 'withdraw': { 'min': fee, 'max': math.pow(10, precision), }, }, } return result async def fetch_tickers(self, symbols=None, params={}): await self.load_markets() response = await self.publicGetMarketsummaries(params) tickers = self.safe_value(response, 'result') result = {} for t in range(0, len(tickers)): ticker = tickers[t] marketId = self.safe_string(ticker, 'MarketName') market = None symbol = marketId if marketId in self.markets_by_id: market = self.markets_by_id[marketId] symbol = market['symbol'] else: symbol = self.parse_symbol(marketId) result[symbol] = self.parse_ticker(ticker, market) return result async def fetch_ticker(self, symbol, params={}): await self.load_markets() market = self.market(symbol) request = { 'market': market['id'], } response = await self.publicGetMarketsummary(self.extend(request, params)) ticker = response['result'][0] return self.parse_ticker(ticker, market) def parse_trade(self, trade, market=None): timestamp = self.parse8601(trade['TimeStamp'] + '+00:00') side = None if trade['OrderType'] == 'BUY': side = 'buy' elif trade['OrderType'] == 'SELL': side = 'sell' id = self.safe_string_2(trade, 'Id', 'ID') symbol = None if market is not None: symbol = market['symbol'] cost = None price = self.safe_float(trade, 'Price') amount = self.safe_float(trade, 'Quantity') if amount is not None: if price is not None: cost = price * amount return { 'info': trade, 'timestamp': timestamp, 'datetime': self.iso8601(timestamp), 'symbol': symbol, 'id': id, 'order': None, 'type': 'limit', 'takerOrMaker': None, 'side': side, 'price': price, 'amount': amount, 'cost': cost, 'fee': None, } async def fetch_trades(self, symbol, since=None, limit=None, params={}): await self.load_markets() market = self.market(symbol) request = { 'market': market['id'], } response = await self.publicGetMarkethistory(self.extend(request, params)) if 'result' in response: if response['result'] is not None: return self.parse_trades(response['result'], market, since, limit) raise ExchangeError(self.id + ' fetchTrades() returned None response') def parse_ohlcv(self, ohlcv, market=None, timeframe='1d', since=None, limit=None): timestamp = self.parse8601(ohlcv['T'] + '+00:00') return [ timestamp, ohlcv['O'], ohlcv['H'], ohlcv['L'], ohlcv['C'], ohlcv['V'], ] async def fetch_ohlcv(self, symbol, timeframe='1m', since=None, limit=None, params={}): await self.load_markets() market = self.market(symbol) request = { 'tickInterval': self.timeframes[timeframe], 'marketName': market['id'], } response = await self.v2GetMarketGetTicks(self.extend(request, params)) if 'result' in response: if response['result']: return self.parse_ohlcvs(response['result'], market, timeframe, since, limit) async def fetch_open_orders(self, symbol=None, since=None, limit=None, params={}): await self.load_markets() request = {} market = None if symbol is not None: market = self.market(symbol) request['market'] = market['id'] response = await self.marketGetOpenorders(self.extend(request, params)) result = self.safe_value(response, 'result', []) orders = self.parse_orders(result, market, since, limit) return self.filter_by_symbol(orders, symbol) async def create_order(self, symbol, type, side, amount, price=None, params={}): if type != 'limit': raise ExchangeError(self.id + ' allows limit orders only') await self.load_markets() market = self.market(symbol) method = 'marketGet' + self.capitalize(side) + type request = { 'market': market['id'], 'quantity': self.amount_to_precision(symbol, amount), 'rate': self.price_to_precision(symbol, price), } # if type == 'limit': # order['rate'] = self.price_to_precision(symbol, price) response = await getattr(self, method)(self.extend(request, params)) orderIdField = self.get_order_id_field() orderId = self.safe_string(response['result'], orderIdField) return { 'info': response, 'id': orderId, 'symbol': symbol, 'type': type, 'side': side, 'status': 'open', } def get_order_id_field(self): return 'uuid' async def cancel_order(self, id, symbol=None, params={}): await self.load_markets() orderIdField = self.get_order_id_field() request = {} request[orderIdField] = id response = await self.marketGetCancel(self.extend(request, params)) # # { # "success": True, # "message": "''", # "result": { # "uuid": "614c34e4-8d71-11e3-94b5-425861b86ab6" # } # } # return self.extend(self.parse_order(response), { 'status': 'canceled', }) async def fetch_deposits(self, code=None, since=None, limit=None, params={}): await self.load_markets() # https://support.bittrex.com/hc/en-us/articles/115003723911 request = {} currency = None if code is not None: currency = self.currency(code) request['currency'] = currency['id'] response = await self.accountGetDeposithistory(self.extend(request, params)) # # {success: True, # message: "", # result: [{ Id: 22578097, # Amount: 0.3, # Currency: "ETH", # Confirmations: 15, # LastUpdated: "2018-06-10T07:12:10.57", # TxId: "0xf50b5ba2ca5438b58f93516eaa523eaf35b4420ca0f24061003df1be7…", # CryptoAddress: "0xb25f281fa51f1635abd4a60b0870a62d2a7fa404" }]} # # we cannot filter by `since` timestamp, as it isn't set by Bittrex # see https://github.com/ccxt/ccxt/issues/4067 # return self.parseTransactions(response['result'], currency, since, limit) return self.parseTransactions(response['result'], currency, None, limit) async def fetch_withdrawals(self, code=None, since=None, limit=None, params={}): await self.load_markets() # https://support.bittrex.com/hc/en-us/articles/115003723911 request = {} currency = None if code is not None: currency = self.currency(code) request['currency'] = currency['id'] response = await self.accountGetWithdrawalhistory(self.extend(request, params)) # # { # "success" : True, # "message" : "", # "result" : [{ # "PaymentUuid" : "b32c7a5c-90c6-4c6e-835c-e16df12708b1", # "Currency" : "BTC", # "Amount" : 17.00000000, # "Address" : "1DfaaFBdbB5nrHj87x3NHS4onvw1GPNyAu", # "Opened" : "2014-07-09T04:24:47.217", # "Authorized" : True, # "PendingPayment" : False, # "TxCost" : 0.00020000, # "TxId" : null, # "Canceled" : True, # "InvalidAddress" : False # }, { # "PaymentUuid" : "d193da98-788c-4188-a8f9-8ec2c33fdfcf", # "Currency" : "XC", # "Amount" : 7513.75121715, # "Address" : "TcnSMgAd7EonF2Dgc4c9K14L12RBaW5S5J", # "Opened" : "2014-07-08T23:13:31.83", # "Authorized" : True, # "PendingPayment" : False, # "TxCost" : 0.00002000, # "TxId" : "d8a575c2a71c7e56d02ab8e26bb1ef0a2f6cf2094f6ca2116476a569c1e84f6e", # "Canceled" : False, # "InvalidAddress" : False # } # ] # } # return self.parseTransactions(response['result'], currency, since, limit) def parse_transaction(self, transaction, currency=None): # # fetchDeposits # # { # Id: 72578097, # Amount: 0.3, # Currency: "ETH", # Confirmations: 15, # LastUpdated: "2018-06-17T07:12:14.57", # TxId: "0xb31b5ba2ca5438b58f93516eaa523eaf35b4420ca0f24061003df1be7…", # CryptoAddress: "0x2d5f281fa51f1635abd4a60b0870a62d2a7fa404" # } # # fetchWithdrawals # # { # "PaymentUuid" : "e293da98-788c-4188-a8f9-8ec2c33fdfcf", # "Currency" : "XC", # "Amount" : 7513.75121715, # "Address" : "EVnSMgAd7EonF2Dgc4c9K14L12RBaW5S5J", # "Opened" : "2014-07-08T23:13:31.83", # "Authorized" : True, # "PendingPayment" : False, # "TxCost" : 0.00002000, # "TxId" : "b4a575c2a71c7e56d02ab8e26bb1ef0a2f6cf2094f6ca2116476a569c1e84f6e", # "Canceled" : False, # "InvalidAddress" : False # } # id = self.safe_string_2(transaction, 'Id', 'PaymentUuid') amount = self.safe_float(transaction, 'Amount') address = self.safe_string_2(transaction, 'CryptoAddress', 'Address') txid = self.safe_string(transaction, 'TxId') updated = self.parse8601(self.safe_string(transaction, 'LastUpdated')) opened = self.parse8601(self.safe_string(transaction, 'Opened')) timestamp = opened if opened else updated type = 'deposit' if (opened is None) else 'withdrawal' currencyId = self.safe_string(transaction, 'Currency') code = self.safe_currency_code(currencyId, currency) status = 'pending' if type == 'deposit': # # deposits numConfirmations never reach the minConfirmations number # we set all of them to 'ok', otherwise they'd all be 'pending' # # numConfirmations = self.safe_integer(transaction, 'Confirmations', 0) # minConfirmations = self.safe_integer(currency['info'], 'MinConfirmation') # if numConfirmations >= minConfirmations: # status = 'ok' # } # status = 'ok' else: authorized = self.safe_value(transaction, 'Authorized', False) pendingPayment = self.safe_value(transaction, 'PendingPayment', False) canceled = self.safe_value(transaction, 'Canceled', False) invalidAddress = self.safe_value(transaction, 'InvalidAddress', False) if invalidAddress: status = 'failed' elif canceled: status = 'canceled' elif pendingPayment: status = 'pending' elif authorized and(txid is not None): status = 'ok' feeCost = self.safe_float(transaction, 'TxCost') if feeCost is None: if type == 'deposit': # according to https://support.bittrex.com/hc/en-us/articles/115000199651-What-fees-does-Bittrex-charge- feeCost = 0 # FIXME: remove hardcoded value that may change any time return { 'info': transaction, 'id': id, 'currency': code, 'amount': amount, 'address': address, 'tag': None, 'status': status, 'type': type, 'updated': updated, 'txid': txid, 'timestamp': timestamp, 'datetime': self.iso8601(timestamp), 'fee': { 'currency': code, 'cost': feeCost, }, } def parse_symbol(self, id): quoteId, baseId = id.split(self.options['symbolSeparator']) base = self.safe_currency_code(baseId) quote = self.safe_currency_code(quoteId) return base + '/' + quote def parse_order(self, order, market=None): if 'marketSymbol' in order: return self.parse_order_v3(order, market) else: return self.parse_order_v2(order, market) def parse_orders(self, orders, market=None, since=None, limit=None, params={}): if self.options['fetchClosedOrdersFilterBySince']: return super(bittrex, self).parse_orders(orders, market, since, limit, params) else: return super(bittrex, self).parse_orders(orders, market, None, limit, params) def parse_order_status(self, status): statuses = { 'CLOSED': 'closed', 'OPEN': 'open', 'CANCELLED': 'canceled', 'CANCELED': 'canceled', } return self.safe_string(statuses, status, status) def parse_order_v3(self, order, market=None): # # { # id: '1be35109-b763-44ce-b6ea-05b6b0735c0c', # marketSymbol: 'LTC-ETH', # direction: 'BUY', # type: 'LIMIT', # quantity: '0.50000000', # limit: '0.17846699', # timeInForce: 'GOOD_TIL_CANCELLED', # fillQuantity: '0.50000000', # commission: '0.00022286', # proceeds: '0.08914915', # status: 'CLOSED', # createdAt: '2018-06-23T13:14:28.613Z', # updatedAt: '2018-06-23T13:14:30.19Z', # closedAt: '2018-06-23T13:14:30.19Z' # } # marketSymbol = self.safe_string(order, 'marketSymbol') symbol = None feeCurrency = None if marketSymbol is not None: baseId, quoteId = marketSymbol.split('-') base = self.safe_currency_code(baseId) quote = self.safe_currency_code(quoteId) symbol = base + '/' + quote feeCurrency = quote direction = self.safe_string_lower(order, 'direction') createdAt = self.safe_string(order, 'createdAt') updatedAt = self.safe_string(order, 'updatedAt') closedAt = self.safe_string(order, 'closedAt') lastTradeTimestamp = None if closedAt is not None: lastTradeTimestamp = self.parse8601(closedAt) elif updatedAt: lastTradeTimestamp = self.parse8601(updatedAt) timestamp = self.parse8601(createdAt) type = self.safe_string_lower(order, 'type') quantity = self.safe_float(order, 'quantity') limit = self.safe_float(order, 'limit') fillQuantity = self.safe_float(order, 'fillQuantity') commission = self.safe_float(order, 'commission') proceeds = self.safe_float(order, 'proceeds') status = self.safe_string_lower(order, 'status') average = None remaining = None if fillQuantity is not None: if proceeds is not None: if fillQuantity > 0: average = proceeds / fillQuantity elif proceeds == 0: average = 0 if quantity is not None: remaining = quantity - fillQuantity return { 'id': self.safe_string(order, 'id'), 'timestamp': timestamp, 'datetime': self.iso8601(timestamp), 'lastTradeTimestamp': lastTradeTimestamp, 'symbol': symbol, 'type': type, 'side': direction, 'price': limit, 'cost': proceeds, 'average': average, 'amount': quantity, 'filled': fillQuantity, 'remaining': remaining, 'status': status, 'fee': { 'cost': commission, 'currency': feeCurrency, }, 'info': order, } def parse_order_v2(self, order, market=None): # # { # "Uuid": "string(uuid)", # "OrderUuid": "8925d746-bc9f-4684-b1aa-e507467aaa99", # "Exchange": "BTC-LTC", # "OrderType": "string", # "Quantity": 100000, # "QuantityRemaining": 100000, # "Limit": 1e-8, # "CommissionPaid": 0, # "Price": 0, # "PricePerUnit": null, # "Opened": "2014-07-09T03:55:48.583", # "Closed": null, # "CancelInitiated": "boolean", # "ImmediateOrCancel": "boolean", # "IsConditional": "boolean" # } # side = self.safe_string_2(order, 'OrderType', 'Type') isBuyOrder = (side == 'LIMIT_BUY') or (side == 'BUY') isSellOrder = (side == 'LIMIT_SELL') or (side == 'SELL') if isBuyOrder: side = 'buy' if isSellOrder: side = 'sell' # We parse different fields in a very specific order. # Order might well be closed and then canceled. status = None if ('Opened' in list(order.keys())) and order['Opened']: status = 'open' if ('Closed' in list(order.keys())) and order['Closed']: status = 'closed' if ('CancelInitiated' in list(order.keys())) and order['CancelInitiated']: status = 'canceled' if ('Status' in list(order.keys())) and self.options['parseOrderStatus']: status = self.parse_order_status(self.safe_string(order, 'Status')) symbol = None if 'Exchange' in order: marketId = self.safe_string(order, 'Exchange') if marketId is not None: if marketId in self.markets_by_id: market = self.markets_by_id[marketId] symbol = market['symbol'] else: symbol = self.parse_symbol(marketId) else: if market is not None: symbol = market['symbol'] timestamp = None opened = self.safe_string(order, 'Opened') if opened is not None: timestamp = self.parse8601(opened + '+00:00') created = self.safe_string(order, 'Created') if created is not None: timestamp = self.parse8601(created + '+00:00') lastTradeTimestamp = None lastTimestamp = self.safe_string(order, 'TimeStamp') if lastTimestamp is not None: lastTradeTimestamp = self.parse8601(lastTimestamp + '+00:00') closed = self.safe_string(order, 'Closed') if closed is not None: lastTradeTimestamp = self.parse8601(closed + '+00:00') if timestamp is None: timestamp = lastTradeTimestamp fee = None feeCost = self.safe_float_2(order, 'Commission', 'CommissionPaid') if feeCost is not None: fee = { 'cost': feeCost, } if market is not None: fee['currency'] = market['quote'] elif symbol is not None: currencyIds = symbol.split('/') quoteCurrencyId = currencyIds[1] fee['currency'] = self.safe_currency_code(quoteCurrencyId) price = self.safe_float(order, 'Limit') cost = self.safe_float(order, 'Price') amount = self.safe_float(order, 'Quantity') remaining = self.safe_float(order, 'QuantityRemaining') filled = None if amount is not None and remaining is not None: filled = amount - remaining if (status == 'closed') and(remaining > 0): status = 'canceled' if not cost: if price and filled: cost = price * filled if not price: if cost and filled: price = cost / filled average = self.safe_float(order, 'PricePerUnit') id = self.safe_string_2(order, 'OrderUuid', 'OrderId') return { 'info': order, 'id': id, 'timestamp': timestamp, 'datetime': self.iso8601(timestamp), 'lastTradeTimestamp': lastTradeTimestamp, 'symbol': symbol, 'type': 'limit', 'side': side, 'price': price, 'cost': cost, 'average': average, 'amount': amount, 'filled': filled, 'remaining': remaining, 'status': status, 'fee': fee, } async def fetch_order(self, id, symbol=None, params={}): await self.load_markets() response = None try: orderIdField = self.get_order_id_field() request = {} request[orderIdField] = id response = await self.accountGetOrder(self.extend(request, params)) except Exception as e: if self.last_json_response: message = self.safe_string(self.last_json_response, 'message') if message == 'UUID_INVALID': raise OrderNotFound(self.id + ' fetchOrder() error: ' + self.last_http_response) raise e if not response['result']: raise OrderNotFound(self.id + ' order ' + id + ' not found') return self.parse_order(response['result']) def order_to_trade(self, order): # self entire method should be moved to the base class timestamp = self.safe_integer_2(order, 'lastTradeTimestamp', 'timestamp') return { 'id': self.safe_string(order, 'id'), 'side': self.safe_string(order, 'side'), 'order': self.safe_string(order, 'id'), 'price': self.safe_float(order, 'average'), 'amount': self.safe_float(order, 'filled'), 'cost': self.safe_float(order, 'cost'), 'symbol': self.safe_string(order, 'symbol'), 'timestamp': timestamp, 'datetime': self.iso8601(timestamp), 'fee': self.safe_value(order, 'fee'), 'info': order, } def orders_to_trades(self, orders): # self entire method should be moved to the base class result = [] for i in range(0, len(orders)): result.append(self.order_to_trade(orders[i])) return result async def fetch_my_trades(self, symbol=None, since=None, limit=None, params={}): orders = await self.fetch_closed_orders(symbol, since, limit, params) return self.orders_to_trades(orders) async def fetch_closed_orders(self, symbol=None, since=None, limit=None, params={}): method = self.safe_string(self.options, 'fetchClosedOrdersMethod', 'fetch_closed_orders_v3') return await getattr(self, method)(symbol, since, limit, params) async def fetch_closed_orders_v2(self, symbol=None, since=None, limit=None, params={}): await self.load_markets() request = {} market = None if symbol is not None: market = self.market(symbol) request['market'] = market['id'] response = await self.accountGetOrderhistory(self.extend(request, params)) result = self.safe_value(response, 'result', []) orders = self.parse_orders(result, market, since, limit) if symbol is not None: return self.filter_by_symbol(orders, symbol) return orders async def fetch_closed_orders_v3(self, symbol=None, since=None, limit=None, params={}): await self.load_markets() request = {} if limit is not None: request['pageSize'] = limit if since is not None: request['startDate'] = self.ymdhms(since, 'T') + 'Z' market = None if symbol is not None: market = self.market(symbol) # because of self line we will have to rethink the entire v3 # in other words, markets define all the rest of the API # and v3 market ids are reversed in comparison to v2 # v3 has to be a completely separate implementation # otherwise we will have to shuffle symbols and currencies everywhere # which is prone to errors, as was shown here # https://github.com/ccxt/ccxt/pull/5219#issuecomment-499646209 request['marketSymbol'] = market['base'] + '-' + market['quote'] response = await self.v3GetOrdersClosed(self.extend(request, params)) orders = self.parse_orders(response, market, since, limit) if symbol is not None: return self.filter_by_symbol(orders, symbol) return orders async def fetch_deposit_address(self, code, params={}): await self.load_markets() currency = self.currency(code) request = { 'currency': currency['id'], } response = await self.accountGetDepositaddress(self.extend(request, params)) # # {"success": False, "message": "ADDRESS_GENERATING", "result": null} # # {success: True, # message: "", # result: {Currency: "INCNT", # Address: "3PHvQt9bK21f7eVQVdJzrNPcsMzXabEA5Ha"} }} # address = self.safe_string(response['result'], 'Address') message = self.safe_string(response, 'message') if not address or message == 'ADDRESS_GENERATING': raise AddressPending(self.id + ' the address for ' + code + ' is being generated(pending, not ready yet, retry again later)') tag = None if currency['type'] in self.options['tag']: tag = address address = currency['address'] self.check_address(address) return { 'currency': code, 'address': address, 'tag': tag, 'info': response, } async def withdraw(self, code, amount, address, tag=None, params={}): self.check_address(address) await self.load_markets() currency = self.currency(code) request = { 'currency': currency['id'], 'quantity': amount, 'address': address, } if tag is not None: request['paymentid'] = tag response = await self.accountGetWithdraw(self.extend(request, params)) result = self.safe_value(response, 'result', {}) id = self.safe_string(result, 'uuid') return { 'info': response, 'id': id, } def sign(self, path, api='public', method='GET', params={}, headers=None, body=None): url = self.implode_params(self.urls['api'][api], { 'hostname': self.hostname, }) + '/' if api != 'v2' and api != 'v3' and api != 'v3public': url += self.version + '/' if api == 'public': url += api + '/' + method.lower() + path if params: url += '?' + self.urlencode(params) elif api == 'v3public': url += path if params: url += '?' + self.urlencode(params) elif api == 'v2': url += path if params: url += '?' + self.urlencode(params) elif api == 'v3': url += path if params: url += '?' + self.rawencode(params) contentHash = self.hash(self.encode(''), 'sha512', 'hex') timestamp = str(self.milliseconds()) auth = timestamp + url + method + contentHash subaccountId = self.safe_value(self.options, 'subaccountId') if subaccountId is not None: auth += subaccountId signature = self.hmac(self.encode(auth), self.encode(self.secret), hashlib.sha512) headers = { 'Api-Key': self.apiKey, 'Api-Timestamp': timestamp, 'Api-Content-Hash': contentHash, 'Api-Signature': signature, } if subaccountId is not None: headers['Api-Subaccount-Id'] = subaccountId else: self.check_required_credentials() url += api + '/' if ((api == 'account') and(path != 'withdraw')) or (path == 'openorders'): url += method.lower() request = { 'apikey': self.apiKey, } disableNonce = self.safe_value(self.options, 'disableNonce') if (disableNonce is None) or not disableNonce: request['nonce'] = self.nonce() url += path + '?' + self.urlencode(self.extend(request, params)) signature = self.hmac(self.encode(url), self.encode(self.secret), hashlib.sha512) headers = {'apisign': signature} return {'url': url, 'method': method, 'body': body, 'headers': headers} def handle_errors(self, code, reason, url, method, headers, body, response): if response is None: return # fallback to default error handler # # {success: False, message: "message"} # if body[0] == '{': success = self.safe_value(response, 'success') if success is None: raise ExchangeError(self.id + ': malformed response: ' + self.json(response)) if isinstance(success, basestring): # bleutrade uses string instead of boolean success = True if (success == 'true') else False if not success: message = self.safe_string(response, 'message') feedback = self.id + ' ' + self.json(response) exceptions = self.exceptions if message == 'APIKEY_INVALID': if self.options['hasAlreadyAuthenticatedSuccessfully']: raise DDoSProtection(feedback) else: raise AuthenticationError(feedback) # https://github.com/ccxt/ccxt/issues/4932 # the following two lines are now redundant, see line 171 in describe() # # if message == 'DUST_TRADE_DISALLOWED_MIN_VALUE_50K_SAT': # raise InvalidOrder(self.id + ' order cost should be over 50k satoshi ' + self.json(response)) # if message == 'INVALID_ORDER': # Bittrex will return an ambiguous INVALID_ORDER message # upon canceling already-canceled and closed orders # therefore self special case for cancelOrder # url = 'https://bittrex.com/api/v1.1/market/cancel?apikey=API_KEY&uuid=ORDER_UUID' cancel = 'cancel' indexOfCancel = url.find(cancel) if indexOfCancel >= 0: urlParts = url.split('?') numParts = len(urlParts) if numParts > 1: query = urlParts[1] params = query.split('&') numParams = len(params) orderId = None for i in range(0, numParams): param = params[i] keyValue = param.split('=') if keyValue[0] == 'uuid': orderId = keyValue[1] break if orderId is not None: raise OrderNotFound(self.id + ' cancelOrder ' + orderId + ' ' + self.json(response)) else: raise OrderNotFound(self.id + ' cancelOrder ' + self.json(response)) if message in exceptions: raise exceptions[message](feedback) if message is not None: if message.find('throttled. Try again') >= 0: raise DDoSProtection(feedback) if message.find('problem') >= 0: raise ExchangeNotAvailable(feedback) # 'There was a problem processing your request. If self problem persists, please contact...') raise ExchangeError(feedback) async def request(self, path, api='public', method='GET', params={}, headers=None, body=None): response = await self.fetch2(path, api, method, params, headers, body) # a workaround for APIKEY_INVALID if (api == 'account') or (api == 'market'): self.options['hasAlreadyAuthenticatedSuccessfully'] = True return response

41.540562

156

0.489738

795165d849e9147efabacf6e1a21c6ee5e001e71

2,609

py

Python

cogs/utils/activity.py

keyb0ards/reaction-light

7297c6b9aad9c1fd06480f93f0edd1e59922f5a5

[ "MIT" ]

123

2019-12-07T02:32:50.000Z

2022-03-15T14:38:13.000Z

cogs/utils/activity.py

keyb0ards/reaction-light

7297c6b9aad9c1fd06480f93f0edd1e59922f5a5

[ "MIT" ]

72

2020-01-21T06:45:13.000Z

2022-03-27T01:41:54.000Z

cogs/utils/activity.py

keyb0ards/reaction-light

7297c6b9aad9c1fd06480f93f0edd1e59922f5a5

[ "MIT" ]

80

2019-11-11T13:55:10.000Z

2022-03-18T21:44:20.000Z

""" MIT License Copyright (c) 2019-present eibex Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. """ import csv from os import path from shutil import copy from itertools import cycle class Activities: def __init__(self, file): self.file = file self.load() def load(self): self.activity_list = [] if not path.isfile(self.file): # Create activities.csv from the sample if it does not already exist copy(f"{self.file}.sample", self.file) with open(self.file, "r") as f: # Get activities.csv contents reader = csv.reader(f, delimiter=",") for row in reader: try: activity = row[0] self.activity_list.append(activity) except IndexError: pass if not self.activity_list: self.activity_list = ["with reactions"] self.loop = cycle(self.activity_list) def get(self): return next(self.loop) def add(self, activity): with open(self.file, "a", encoding="utf-8") as f: w = csv.writer(f, delimiter=",", lineterminator="\n") w.writerow([activity]) self.load() def remove(self, activity): if activity not in self.activity_list: return False self.activity_list.remove(activity) with open(self.file, "w", encoding="utf-8") as f: w = csv.writer(f, delimiter=",", lineterminator="\n") for row in self.activity_list: w.writerow([row]) self.load() return True

32.6125

80

0.653124

795166d065cee9e4322604c4ab94b5f40733aad8

5,538

py

Python

tanit/master/client/user_service.py

yassineazzouz/kraken

30d536eae2583e6fff51becbff836301058b8e69

[ "MIT" ]

1

2020-09-01T15:16:11.000Z

tanit/master/client/user_service.py

yassineazzouz/kraken

30d536eae2583e6fff51becbff836301058b8e69

[ "MIT" ]

null

tanit/master/client/user_service.py

yassineazzouz/kraken

30d536eae2583e6fff51becbff836301058b8e69

[ "MIT" ]

null

import abc import logging as lg import json from thrift.protocol import TBinaryProtocol from thrift.protocol.TMultiplexedProtocol import TMultiplexedProtocol from thrift.transport import TSocket from thrift.transport import TTransport import six from ..core.worker.worker import WorkerStats from ...common.thrift.utils import connect from ...filesystem.model import FileSystem from ...filesystem.model import FileSystemMounts from ...common.model.worker import Worker from ...thrift.master.service import MasterUserService from ...thrift.common.model.ttypes import FileSystem as TFileSystem from ...common.config.configuration_keys import Keys from ...common.config.configuration import TanitConfigurationException, TanitConfiguration _logger = lg.getLogger(__name__) @six.add_metaclass(abc.ABCMeta) class UserServiceClientIFace(object): @abc.abstractmethod def start(self): raise NotImplementedError @abc.abstractmethod def list_workers(self): raise NotImplementedError @abc.abstractmethod def deactivate_worker(self, wid): raise NotImplementedError @abc.abstractmethod def activate_worker(self, wid): raise NotImplementedError @abc.abstractmethod def register_filesystem(self, filesystem): raise NotImplementedError @abc.abstractmethod def mount_filesystem(self, name, path): raise NotImplementedError @abc.abstractmethod def umount_filesystem(self, mount_point): raise NotImplementedError @abc.abstractmethod def stop(self): raise NotImplementedError class ThriftUserServiceClient(UserServiceClientIFace): def __init__(self): self.configuration = TanitConfiguration.getInstance() master_host = self.configuration.get(Keys.MASTER_HOSTNAME) if master_host is None: raise TanitConfigurationException("Missing required configuration '%s'", Keys.MASTER_HOSTNAME) rpc_port = self.configuration.get_int(Keys.MASTER_RPC_PORT) if rpc_port is None: raise TanitConfigurationException("Missing required configuration '%s'", Keys.MASTER_RPC_PORT) self.transport = TTransport.TFramedTransport( TSocket.TSocket(master_host, rpc_port) ) self.client = MasterUserService.Client( TMultiplexedProtocol( TBinaryProtocol.TBinaryProtocol(self.transport), "UserService" ) ) def start(self): connect( self.transport, self.configuration.get(Keys.RPC_CLIENT_MAX_RETRIES), self.configuration.get(Keys.RPC_CLIENT_RETRY_INTERVAL)/1000 ) def list_workers(self): wkr_list = [] for wkr in self.client.list_workers(): wkr_list.append(Worker(wkr.wid, wkr.address, wkr.port)) return wkr_list def deactivate_worker(self, wid): self.client.deactivate_worker(wid) def activate_worker(self, wid): self.client.activate_worker(wid) def worker_stats(self, wid): stats = self.client.worker_stats(wid) return WorkerStats( wid=stats.wid, state=stats.state, last_heartbeat=stats.last_heartbeat, running_tasks=stats.running_tasks, pending_tasks=stats.pending_tasks, available_cores=stats.available_cores, ) def register_filesystem(self, filesystem): self.client.register_filesystem( TFileSystem( filesystem.name, filesystem.type, json.dumps(filesystem.parameters) ) ) def mount_filesystem(self, name, mount_point, mount_path=""): if mount_path is None: mount_path = "" self.client.mount_filesystem(name, mount_point, mount_path) def umount_filesystem(self, mount_point): self.client.umount_filesystem(mount_point) def list_filesystems(self): filesystems = [] for filesystem_mount in self.client.list_filesystems(): filesystems.append( FileSystemMounts( FileSystem( filesystem_mount.filesystem.name, filesystem_mount.filesystem.type, {} ), filesystem_mount.mounts ) ) return filesystems def stop(self): self.transport.close() class LocalUserServiceClient(UserServiceClientIFace): def __init__(self, master, configuration=None): self.master = master def configure(self, configuration): pass def start(self): # do nothing return def list_workers(self): return self.master.list_workers() def deactivate_worker(self, wid): self.master.deactivate_worker(wid) def activate_worker(self, wid): self.master.activate_worker(wid) def worker_stats(self, wid): return self.master.get_worker_stats(wid) def register_filesystem(self, filesystem): self.master.register_filesystem(filesystem) def mount_filesystem(self, name, mount_point, mount_path=""): if mount_path is None: mount_path = "" self.master.mount_filesystem(name, mount_point, mount_path) def umount_filesystem(self, mount_point): self.master.umount_filesystem(mount_point) def list_filesystems(self): return self.master.list_filesystems() def stop(self): # do nothing return

29.935135

106

0.670639

795167846abefd942dbedb043c952ea8e794be24

4,655

py

Python

CWS/binance.py

aktasr/CWS

9829e5fcca958e5c875d8ffdeeffe5d9b1ac68a6

[ "MIT" ]

2

2021-04-21T21:31:52.000Z

2021-09-10T00:43:04.000Z

CWS/binance.py

aktasr/CWS

9829e5fcca958e5c875d8ffdeeffe5d9b1ac68a6

[ "MIT" ]

null

CWS/binance.py

aktasr/CWS

9829e5fcca958e5c875d8ffdeeffe5d9b1ac68a6

[ "MIT" ]

null

#Author : Ramazan AKTAS #Date : 20.02.2021 22:30 from base.exchange import exchange import time class binance(exchange): """description of class""" # user can be subscribe one or more market(symbol). F.e: suscriber_trade = ['BTCTRY', 'BTCUSDT'] # also user can be subscribe all markets(symbols). F.e: suscriber_trade = ['all'] def __init__(self, subscribe_ticker:list = [], subscribe_trade:list = [], subscribe_orderbook:list = [], subscribe_obDiff:list = []): super().__init__({ 'onOpenEvent' : { 'subscribe_ticker' : subscribe_ticker, 'subscribe_trade' : subscribe_trade, 'subscribe_orderbook' : subscribe_orderbook, 'subscribe_orderbookDiff' : subscribe_obDiff }}) self.connect() def describe(self): return self.deep_extend(super().describe(), { 'id':'binance', 'name':'BINANCE', 'url':'wss://stream.binance.com:9443/ws', 'wsLogEnable' : 'False', 'Subscribe' : 'SUBSCRIBE', 'Result' : 'result', 'OrderBook' : 'depth20', 'OrderBookDifference' : 'depth', 'TickerAll' : 401, 'TickerPair' : 402, 'Channel':['ticker','trades','depth20@100ms','depth'], 'Symbols': { 'BTCUSDT' : 'btcusdt', 'BTCTRY' : 'btctry', 'XRPUSDT' : 'xrpusdt', 'ETHUSDT' : 'ethusdt', 'USDTTRY' : 'usdttry', 'ADAUSDT' : 'adausdt', 'TRXUSDT' : 'trxusdt', 'EOSUSDT' : 'eosusdt', 'DOTUSDT' : 'dotusdt', 'XTZUSDT' : 'xtzusdt', 'BTCUSDT' : 'btcusdt', 'XRPTRY' : 'xrptry', 'MATICUSDT' : 'maticusdt', 'ETHTRY' : 'ethtry', 'DOGETRY' : 'dogetry', 'DOGEUSDT' : 'dogeusdt', 'AVAXUSDT': 'avaxusdt', 'AVAXTRY': 'avaxtry', 'SOLTRY': 'soltry', 'SOLUSDT': 'solusdt', 'CHZTRY': 'chztry', 'DOTTRY': 'dottry', 'MATICTRY': 'matictry', 'NEOTRY': 'neotry', 'ALL' : 'all', }, 'has': { 'tickerSubscribe': True, 'tradeSubscribe': True, 'orderbookSubscribe': True, 'orderbookDiffSubscribe': True }}) def subscribe_ticker(self, symbol, params={}): try: self.subscribe(self.Channel[0], self.Symbols[symbol]) except: print("Holaa") def subscribe_trade(self, symbol, params={}): self.subscribe(self.Channel[1],self.Symbols[symbol]) def subscribe_orderbook(self, symbolArr:list = [], params = {}): symbols = [] for x in symbolArr: symbols.append(self.Symbols[x]) self.subscribe(self.Channel[2],symbols) def subscribe_orderbookDiff(self, symbol, params={}): self.subscribe(self.Channel[3],self.Symbols[symbol]) def subscribe(self, *params): if self.isConnected is False: raise PermissionError('Not connected to websocket server.') subscribeParams = [] symbols = params[1] channel = params[0] for symbol in symbols: param = symbol + "@" + channel subscribeParams.append(param) jsonData = {"method": self.Subscribe, "params" : subscribeParams, "id" : 1} self.sendMessage(self.json(jsonData)) def orderbook_received(self, message): symbol = self.getSymbolFromStream(message['stream']) orderbook = message['data'] self.parse_orderbook(symbol, orderbook) def onMessage(self, message): channel = self.getChannelFromStream(message['stream']) self.parseAndRouteMessage(channel, message) def onOpen(self): ''' in this project, combined subscribe is used. Response data must include owner stream information. ''' data = {"method": "SET_PROPERTY", "params": ["combined",True],"id": 5} self.sendMessage(self.json(data)) super().onOpen() def getSymbolFromStream(self, stream): ''' class specific function ''' return (self.parseStream(stream)[0]) def getChannelFromStream(self, stream): ''' class specific function ''' return (self.parseStream(stream)[1]) def parseStream(self, stream): ''' class specific function ''' return stream.split("@")

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