str:\n if self.__current_method in self.symbols.values():\n index = list(self.symbols.values()).index(self.__current_method)\n return list(self.symbols.keys())[index]","title":""},{"docid":"e72b41d0ca38fbfbd61243bead6632e3","score":"0.46881253","text":"def id(self):\r\n return b2h_rev(self.hash())","title":""},{"docid":"2a85cf1e0a41ee8f5f6f0dc6d1c8d48a","score":"0.46875","text":"def get_the_buyer_id_spent_most_and_the_money_spent():\n\n # your code\n name_and_sum = get_the_buyer_name_spent_most_and_the_money_spent()\n sales_table = sales.insert_customer_id_to_table()\n id_ = []\n \n for i in range(len(sales_table)):\n if name_and_sum[0] == sales_table[i][7]:\n id_.append(sales_table[i][6])\n break\n id_sum = [id_[0], name_and_sum[1]] \n \n return tuple(id_sum)","title":""},{"docid":"589ade813f21600f88472c54af9f2124","score":"0.46868056","text":"def getSymbolData2(self) -> int:\n ...","title":""},{"docid":"74777c71ea9f17cdf33880bd32d6010f","score":"0.46757942","text":"def exp_id(self):\n return self._exp_id","title":""},{"docid":"74777c71ea9f17cdf33880bd32d6010f","score":"0.46757942","text":"def exp_id(self):\n return self._exp_id","title":""},{"docid":"8d3e74cb5d13b691f39892fbfa0a1051","score":"0.46747747","text":"def get_date_price(self,bar):\n\t\tdate = str(self.data_run.index[bar])[:10]\n\t\tprice = self.data_run['Close'].ix[bar]\n\t\treturn date,price","title":""},{"docid":"5811e6aafe4a687619d1b072fc3b10f8","score":"0.46699798","text":"def iceberg_no(self):\n return self._iceberg_no","title":""},{"docid":"4b3cd43a7dd58ab350c0285675e146e7","score":"0.46663254","text":"def buy_position(self, code, price, datetime, position):\n # check\n assert(price > 0)\n assert((0 < position) and (position <= 1))\n # compute how many can we buy\n bid = self._capital * position\n # can we buy one board lot?\n oneboardlot = 100 * price # One board lot is 100 shares in Chinese market \n if bid < oneboardlot: \n raise BidTooLow(need_cash = oneboardlot, bid = bid)\n if self._cash < oneboardlot: \n raise NotEnoughMoney(need_cash = oneboardlot, cash = self._cash)\n # can we buy more?\n quantity = int(math.floor(bid / oneboardlot))\n need_cash = quantity * oneboardlot # if run to here, we must have bid >= need_cash\n while self._cash < need_cash:\n quantity = quantity - 1\n need_cash = quantity * oneboardlot\n # now buy it\n self._cash -= need_cash\n try:\n self._shares[code] += quantity\n except KeyError:\n self._shares[code] = quantity\n # commission charge deduction\n if (self.CommissionChargeRate > 0): # require consider commission charge\n commission_charge = max(need_cash*self.CommissionChargeRate, 5) # at least 5 yuan \n else: \n commission_charge = 0\n self._cash -= commission_charge\n # update total commission charge \n self._total_commission_charge += commission_charge\n # add transaction record\n self._records.append((datetime, self._cash, self._shares.copy()))\n # print infomation\n self.o.report('- - '*20)\n self.o.report(\"[\" + str(datetime) + \"] \") \n self.o.report(\"[stock code: {:s}]\".format(code)) \n self.o.report(\"Buy {0:d} board lot shares at price {1:.2f} (per share)\".format(quantity, price))\n self.o.report(\"Commission charge: {:.2f}\".format(commission_charge))\n self.o.report(\"Account: \")\n self.o.account(self._shares, self._cash)\n # return the number of shares (unit: boardlot) you buy this time \n # and the the amount of current cash and shares (unit:boardlot) in the account\n return quantity, self._shares, self._cash","title":""},{"docid":"5ec726d6e0cbe5cc3ec7609996b05bf1","score":"0.46640787","text":"def get_ticker(self):\n self.ticker = get('https://api.binance.com/api/v1/ticker/allBookTickers')\n if self.ticker:\n for x in self.ticker:\n pairing_id = self._get_pairing_id(x['symbol'])\n if pairing_id:\n self.coins[pairing_id] = {\n 'buy':x[\"askPrice\"],\n 'sell':x[\"bidPrice\"]\n }\n\n else:\n self.ticker = []\n self.coins = {}","title":""},{"docid":"5345572db3b5d58ca56514a4568fd182","score":"0.4661168","text":"def _get_price_id_from_options(self, ds_options, option, value,\n item_id=False):\n ds_obj = ServerCreateOptions()\n\n for _, options in ds_obj.get_create_options(ds_options, option, False):\n for item_options in options:\n if item_options[0] == value:\n if not item_id:\n return item_options[1]\n return item_options[2]","title":""},{"docid":"7377d846447bde39fb2482c57b36cb87","score":"0.4660437","text":"def get_balance(self, ticker):\n \n # return self.balance_base*(1-self.commision) * ticker['close'] + self.balance_counter\n return self.balance_base* ticker['close'] + self.balance_counter","title":""},{"docid":"6e08def1fd3e942c8e71566b65019174","score":"0.46601424","text":"def get_id(self, x):\n return numpy.dot(self.POWERS_OF_TWO, numpy.array(x, dtype=int))","title":""},{"docid":"38fb38bc739e59c8e975e2e7ff739c5a","score":"0.4658523","text":"def store_price(self):\n if self.nameid:\n low = 0\n volume, med = market_api.get_bulkitem_price(self.nameid)\n else:\n volume, low, med = market_api.get_item_price(self.name)\n\n # This is to debug some BULLLLSHIT ass stuff\n log.debug(\"Store Price [%s]: %s, %s, %s\" % (self.id, volume, low, med))\n\n # (-1, 0, 0) implies that steam doesnt have data on the item currently\n if volume == -1 and low == 0 and med == 0:\n volume = 0\n last_mipp = self.get_latest_mipp()\n low = last_mipp.lowest\n med = last_mipp.median\n\n five_minutes_ago = datetime.datetime.utcnow() - relativedelta(minutes=5)\n\n # Find a MIPP in the latest five minutes\n q = MarketItemPricePoint.select().where(\n (MarketItemPricePoint.item == self) &\n (MarketItemPricePoint.time >= five_minutes_ago)\n )\n\n # If we have one, we just update that, otherwise create a fresh one\n if q.count():\n mipp = q.get()\n else:\n mipp = MarketItemPricePoint()\n\n mipp.item = self\n mipp.volume = volume if volume != -1 else 0\n mipp.lowest = low\n mipp.median = med\n mipp.save()","title":""},{"docid":"419e9a7064c8f2d6ff17e9fabe0fa301","score":"0.4654737","text":"def get_win_flag_value(self, price_buy, price_sell):\n result = (price_buy < price_sell)\n return 1 if result else 0","title":""},{"docid":"5c720ff08980cc625dc052a5cc889148","score":"0.46517062","text":"def unique_id(self):\n return f\"{self.airly.latitude}-{self.airly.longitude}\"","title":""},{"docid":"a747a40cebfb623c55eb21e7845e4ba7","score":"0.46489996","text":"def myID() -> np.int:\r\n return 315674028","title":""},{"docid":"6833fde825aa1296a2efd1134b694e07","score":"0.4644364","text":"def _buy_belief(self, bid):\n if bid <= self.model.outstanding_bid:\n return 0\n\n def num_geq(history, price):\n return len([d for d in history if d >= price])\n\n def num_leq(history, price):\n return len([d for d in history if d <= price])\n\n history = self.model.history\n bids = history.get_bids(length=self.mem_length)\n accepted_asks = history.get_accepted_asks(length=self.mem_length)\n accepted_bids = history.get_accepted_bids(length=self.mem_length)\n\n TBL = num_leq(accepted_bids, bid)\n TAL = num_leq(accepted_asks, bid)\n RBG = num_geq(bids, bid) - num_geq(accepted_bids, bid)\n\n if TBL + TAL == 0:\n return self.eta\n else:\n return (TBL + TAL) / (TBL + TAL + RBG)","title":""},{"docid":"a569e1390402298aac8aac91bf3431af","score":"0.46344066","text":"def bond_convexity(price, par, T, coup, freq, dy=0.01):\r\n ytm = bond_ytm(price, par, T, coup, freq, guess=0.01)\r\n price_neg = bond_price(ytm=(ytm - dy), par=par, T=T, coup=coup, freq=freq)\r\n price_pos = bond_price(ytm=(ytm + dy), par=par, T=T, coup=coup, freq=freq)\r\n \r\n return (price_neg + price_pos - 2*price) / (price * dy**2)","title":""},{"docid":"6974ea70a21086f509085f05584093c8","score":"0.46282148","text":"def get_trigger_price(self):\n return int(self.pxSpin.get())","title":""},{"docid":"6974ea70a21086f509085f05584093c8","score":"0.46282148","text":"def get_trigger_price(self):\n return int(self.pxSpin.get())","title":""}],"string":"[\n {\n \"docid\": \"fd513a4c15168533dc835befb5e43cb2\",\n \"score\": \"0.5583561\",\n \"text\": \"def getClosePrice(ticker):\\n try:\\n yesterday = time.strftime(\\\"%m/%d/%y\\\", time.localtime(time.time() - 86400))\\n close = list(si.get_data(ticker, start_date=yesterday).to_dict()[\\\"close\\\"].values())[0]\\n return close\\n except AssertionError:\\n print(\\\"Invalid Ticker Symbol: {0}\\\".format(ticker))\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59e4d01fc858b23bfa2812494e29b6fc\",\n \"score\": \"0.5540152\",\n \"text\": \"def best_bid(self) -> int:\\n return self.__bid_prices[-1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"283299e92ce25f7cd4d9a808a756f53f\",\n \"score\": \"0.55386126\",\n \"text\": \"def side(self) -> str:\\n return const.OFFER_ORDER_SIDES_MAP[self.price_type]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9880dc9a2fa39ac7194acdf41a257149\",\n \"score\": \"0.542594\",\n \"text\": \"def get_bid(self, lastbid):\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3fd088b343a3883dce6cb0c2e6c6fae3\",\n \"score\": \"0.54183376\",\n \"text\": \"def get_ticker_bid_ask(ticker):\\n\\n stock = yf.Ticker(ticker)\\n info = stock.info\\n\\n return f'{format(info[\\\"bid\\\"], \\\".2f\\\")} - {format(info[\\\"ask\\\"], \\\".2f\\\")} {info[\\\"currency\\\"]}'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"48e86a54f228eaebccdb07425d1866a4\",\n \"score\": \"0.5411271\",\n \"text\": \"def get_current_bid(self):\\n\\n\\t\\treturn self.Bittrex.get_ticker(self.market)['result']['Bid']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79b374e1d73b9005764f856a7788a7d8\",\n \"score\": \"0.53998446\",\n \"text\": \"def spot(symbol=\\\"btcusdt\\\"):\\n\\tresp = web.get(base_uri + f\\\"market/detail/merged?symbol={symbol}\\\")\\n\\tresp = resp[\\\"tick\\\"]\\n\\task = resp[\\\"ask\\\"][0]\\n\\tbid = resp[\\\"bid\\\"][0]\\n\\treturn (ask + bid)/2.0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9af09e5396652381a74a14fbe27cadeb\",\n \"score\": \"0.53973025\",\n \"text\": \"def get_best_bid_ask(self, ticker: str) -> Tuple[Any, Any]:\\n if ticker in self.tickers:\\n if \\\"bid\\\" in self.tickers[ticker] & \\\"ask\\\" in self.tickers[ticker]:\\n bid = self.tickers[ticker][\\\"bid\\\"]\\n ask = self.tickers[ticker][\\\"ask\\\"]\\n return bid, ask\\n print(f\\\"Bid/ask values for ticker {ticker} are not available from the PriceHandler.\\\")\\n return None, None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"184e8f1bf77e5e9530b657b8bad8a029\",\n \"score\": \"0.53740263\",\n \"text\": \"def getOpenPrice(ticker):\\n try:\\n today = time.strftime(\\\"%m/%d/%y\\\")\\n open = list(si.get_data(ticker, start_date=today).to_dict()[\\\"open\\\"].values())[0]\\n return open\\n except AssertionError:\\n print(\\\"Invalid Ticker Symbol: {0}\\\".format(ticker))\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff976975128469ded0a2651c4ffa0e4d\",\n \"score\": \"0.5307488\",\n \"text\": \"def pairing_id(self):\\n return self._pairing_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4fbcb9d9e7d462b2bb068d4c3cf5ff31\",\n \"score\": \"0.52453524\",\n \"text\": \"def unique_id(self) -> str:\\n return self.pairing_data[\\\"AccessoryPairingID\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f1ba3f122d4e835e04dbfaf32adbb81\",\n \"score\": \"0.52275264\",\n \"text\": \"def close_price(symbol, start, end):\\n\\n # get close_price from yahoo\\n px = web.get_data_yahoo(symbol, start=start, end=end)\\n close = px[['Close']].dropna()\\n close.columns = [symbol]\\n\\n\\n return close[symbol]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ce1c668c31b6846df5247e5cc281450\",\n \"score\": \"0.5219979\",\n \"text\": \"def _key(self):\\n return self.card, self.series\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e528bf203e1a6ac275ee7c80a253cbde\",\n \"score\": \"0.52181906\",\n \"text\": \"def sell_signals(self) -> pd.Series:\\n # The rule of the indicator is to close long positions when the price goes below the exit long\\n ce = self.calculate()\\n # A boolean vector\\n sell_decisions = (self._history['Close'].shift(1) > ce) & (self._history['Close'] <= ce)\\n return pd.Series(data=sell_decisions, index=self._history.index)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1841d535c07e825b562011660a95bbf8\",\n \"score\": \"0.5214548\",\n \"text\": \"def bid_price(self) -> int:\\n return pulumi.get(self, \\\"bid_price\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a7979e78b86152c327af5367eb567b25\",\n \"score\": \"0.51899916\",\n \"text\": \"def trade_id(self):\\n return self._trade_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c76886bea6cb2321dfe230ad0f63a7d1\",\n \"score\": \"0.51724946\",\n \"text\": \"def obter_id(self):\\n\\t\\treturn self._ident\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00bf41eab8583088ca295f1237d11673\",\n \"score\": \"0.51409745\",\n \"text\": \"def _buy_strategy(self):\\n # print(\\\"buyer:\\\", self.unique_id, \\\"value:\\\", self.value)\\n if round(self.value, 2) <= self.model.outstanding_bid:\\n self.do_nothing()\\n #print()\\n return\\n\\n if self.model.num_traded == 0:\\n # If no trade has occurred, be a smarter ZI.\\n a = self.model.outstanding_bid\\n b = self.value\\n else:\\n prices = self.model.history.get_prices(length=self.mem_length)\\n\\n if self.lowest_bid not in prices:\\n prices.append(self.lowest_bid)\\n if self.highest_ask not in prices:\\n prices.append(self.highest_ask)\\n\\n prices.sort()\\n #print(\\\"prices:\\\", prices)\\n max_index = self._buy_surplus_maximizer(prices)\\n max_price = prices[max_index]\\n #print(\\\"max:\\\", max_price)\\n\\n if max_index == 0:\\n smallest_gap = prices[max_index + 1] - max_price\\n elif max_index == len(prices) - 1:\\n smallest_gap = max_price - prices[max_index - 1]\\n else:\\n smallest_gap = min(max_price - prices[max_index - 1],\\n prices[max_index + 1] - max_price)\\n # max_price - smallest_gap results in the outstanding ask\\n # if eta is given for ask >= oa\\n a = max(max_price - smallest_gap, self.model.outstanding_bid + 0.01)\\n b = min(a + 2 * smallest_gap, self.value) #@@@\\n\\n #print(\\\"OB:\\\", self.model.outstanding_bid)\\n #print(\\\"(a, b):\\\", (a, b))\\n planned_bid = uniform_dollar(a, b)\\n #print(\\\"Bid:\\\", planned_bid)\\n #print()\\n self.bid(planned_bid)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4940bf271aba5e57cef90ae37d1effea\",\n \"score\": \"0.5140772\",\n \"text\": \"def unique_id(self):\\n return _MpatiBlocks_swig.chirpSignal2_f_sptr_unique_id(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca4fa3288e61a1cb8ed25f197cc6f8a3\",\n \"score\": \"0.5135209\",\n \"text\": \"def get_info(self, symbol):\\r\\n dv_id = SQL.get_data_vendor_id(self.name)\\r\\n symbol_id = SQL.get_symbol_id(symbol)\\r\\n now = dt.datetime.now()\\r\\n return dv_id, symbol_id, now\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fbd901e280c50076345d3ce6223f08c0\",\n \"score\": \"0.5085969\",\n \"text\": \"def _sell(self, amount, price):\\n response = self._place_order(amount, price, \\\"sell\\\")\\n if not response[\\\"status\\\"]:\\n raise TradeException(response[\\\"error\\\"])\\n return response[\\\"order\\\"][\\\"id\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cea1f2435e35fbe0ade0545efaa12033\",\n \"score\": \"0.5023448\",\n \"text\": \"def get_openquote_id(self, trade_id):\\r\\n row = self._find_row(trade_id)\\r\\n if row:\\r\\n return row.getOpenQuoteID()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc797f07956474f751b35e854629ba7b\",\n \"score\": \"0.50132555\",\n \"text\": \"def spot(symbol1=\\\"BTC\\\", symbol2=\\\"USD\\\"):\\n\\tresp = web.get(base_uri + f\\\"ticker/{symbol1}/{symbol2}\\\")\\n\\treturn float(resp[\\\"last\\\"])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"03ec23a001a73d05b9facd381086b1cd\",\n \"score\": \"0.49746224\",\n \"text\": \"def _sell_strategy(self):\\n #print(\\\"seller:\\\", self.unique_id, \\\"value:\\\", self.value)\\n if round(self.value, 2) >= self.model.outstanding_ask:\\n self.do_nothing()\\n #print()\\n return\\n\\n if self.model.num_traded == 0:\\n # If no trade has occurred, be a smarter ZI.\\n a = self.value\\n b = min(self.model.outstanding_ask, self.highest_ask)\\n else:\\n prices = self.model.history.get_prices(length=self.mem_length)\\n\\n if self.lowest_bid not in prices:\\n prices.append(self.lowest_bid)\\n if self.highest_ask not in prices:\\n prices.append(self.highest_ask)\\n\\n prices.sort()\\n #print(\\\"prices:\\\", prices)\\n max_index = self._sell_surplus_maximizer(prices)\\n max_price = prices[max_index]\\n #print(\\\"max:\\\", max_price)\\n\\n if max_index == 0:\\n smallest_gap = prices[max_index + 1] - max_price\\n elif max_index == len(prices) - 1:\\n smallest_gap = max_price - prices[max_index - 1]\\n else:\\n smallest_gap = min(max_price - prices[max_index - 1],\\n prices[max_index + 1] - max_price)\\n # max_price - smallest_gap results in the outstanding ask\\n # if eta is given for ask >= oa\\n b = min(max_price + smallest_gap, self.model.outstanding_ask - 0.01)\\n a = max(b - 2 * smallest_gap, self.value) # @@@\\n\\n #print(\\\"OA:\\\", self.model.outstanding_ask)\\n #print(\\\"(a, b):\\\", (a, b))\\n planned_ask = uniform_dollar(a, b)\\n #print(\\\"Ask:\\\", planned_ask)\\n #print()\\n self.ask(planned_ask)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8294b9938fe51bee91204391cd620f83\",\n \"score\": \"0.49634215\",\n \"text\": \"def advance_pid(self, symbol):\\n new_pid = [None] * (self.dataset.nb_transactions+1)\\n for tid, symbols_position in enumerate(self.dataset.symbols_positions):\\n if symbol in symbols_position:\\n new_pid[tid] = next_larger_element(symbols_position[symbol], self.pid[tid], default=self.dataset.transactions_len[tid])\\n else:\\n new_pid[tid] = self.dataset.transactions_len[tid]\\n return new_pid\",\n \"title\": \"\"\n },\n {\n \"docid\": \"67b081fbca5c02dbae8c983fb739c975\",\n \"score\": \"0.49566147\",\n \"text\": \"def getClose(self, symbol, date):\\r\\n \\r\\n self._check_date(symbol, date)\\r\\n \\r\\n return self.getPriceForDate(symbol, date).close\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7cc06c60eabf60c142a309d6e359d8f4\",\n \"score\": \"0.4947353\",\n \"text\": \"def _on_op_private_ticker(self, msg):\\n msg = msg[\\\"ticker\\\"]\\n if msg[\\\"sell\\\"][\\\"currency\\\"] != self.currency:\\n return\\n ask = int(msg[\\\"sell\\\"][\\\"value_int\\\"])\\n bid = int(msg[\\\"buy\\\"][\\\"value_int\\\"])\\n\\n#added \\n now = float(msg[\\\"now\\\"]) / 1E6\\n if now - self.LASTTICKER > 20: #only show the ticker every 20 seconds.\\n self.LASTTICKER = now\\n self.debug(\\\"\\\\nBid:\\\", int2str(bid, self.currency),\\\"\\\\nAsk:\\\", int2str(ask, self.currency),\\\"\\\\n\\\") \\n \\n self.signal_ticker(self, (bid, ask))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d63c5abfded0c49105f0b32d23dae023\",\n \"score\": \"0.4928197\",\n \"text\": \"def get_last_close(self, ticker: str) -> Optional[float]:\\n if ticker in self.tickers:\\n if \\\"close\\\" in self.tickers[ticker]:\\n close_price = self.tickers[ticker][\\\"close\\\"]\\n return close_price\\n print(f\\\"Close price for ticker {ticker} is not available from the PriceHandler.\\\")\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a2d4dc19420e9e500c81e4330b6c55d\",\n \"score\": \"0.4920476\",\n \"text\": \"def holding_value_nb(close, shares):\\n return close * shares\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d99a781ca0d7cd10cce351e6ccb021a\",\n \"score\": \"0.49082682\",\n \"text\": \"def calc_id(self):\\n return 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae103406d6bfd1454ae9f3097d59d9ba\",\n \"score\": \"0.49040267\",\n \"text\": \"def _buy_belief(self, bid):\\n if bid <= self.model.outstanding_bid:\\n return 0\\n\\n try:\\n if bid <= self.model.history.min_last_period:\\n return 0 #\\n except TypeError:\\n pass\\n\\n try:\\n if bid <= self.model.history.min_current_period:\\n return 0 #\\n except TypeError:\\n pass\\n\\n def num_geq(history, price):\\n return len([d for d in history if d >= price])\\n\\n def num_leq(history, price):\\n return len([d for d in history if d <= price])\\n\\n history = self.model.history\\n bids = history.get_bids(length=self.mem_length)\\n accepted_asks = history.get_accepted_asks(length=self.mem_length)\\n accepted_bids = history.get_accepted_bids(length=self.mem_length)\\n\\n TBL = num_leq(accepted_bids, bid)\\n TAL = num_leq(accepted_asks, bid)\\n RBG = num_geq(bids, bid) - num_geq(accepted_bids, bid)\\n\\n if TBL + TAL == 0:\\n return self.eta\\n else:\\n return (TBL + TAL) / (TBL + TAL + RBG)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e7daaf5e85d306f4b4151c2117d7a2f\",\n \"score\": \"0.48918793\",\n \"text\": \"def guid(self):\\n return self.__pointid\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e35a4033dd4471a54e570b98c173827\",\n \"score\": \"0.4891633\",\n \"text\": \"def get_id(self):\\n self.dev.write(b'?\\\\n')\\n l = self.dev.readline().decode('utf-8')\\n parts = l.split()\\n if parts[1] != 'OpenPD':\\n raise RuntimeError('Invalid response to ? command')\\n\\n version = parts[2]\\n dev_id = parts[3]\\n return (version, dev_id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45d70cce5b389af6f1bf22143131c05e\",\n \"score\": \"0.4880384\",\n \"text\": \"def getCurrentPrice(self, data):\\n\\t meta = data[\\\"Meta Data\\\"]\\n\\t lastUpdated = meta[\\\"3. Last Refreshed\\\"]\\n\\t ts = data[\\\"Time Series (Daily)\\\"]\\n\\t #get the first word (the date)\\n\\t date = lastUpdated.split()[0]\\n\\t price = ts[date][\\\"5. adjusted close\\\"]\\n\\t return price\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dca8faa5f804768e9d8b2b9cef1c547c\",\n \"score\": \"0.48762462\",\n \"text\": \"def findfar():\\n originobj = findrandom()\\n price = originobj[1]\\n currentdiff = 0\\n for i in fetchall():\\n diff = abs(i[1]-price)\\n if diff > currentdiff:\\n currentdiff = diff\\n name = i[0]\\n priceobj = i[1]\\n return name, priceobj, currentdiff\",\n \"title\": \"\"\n },\n {\n \"docid\": \"302bf8c12ded587d03f1240bc4a37f95\",\n \"score\": \"0.48736987\",\n \"text\": \"def pair_trading_strategy(context,data):\\r\\n\\r\\n try:\\r\\n # Get the historic data for the stocks pair\\r\\n prices = data.history( assets = context.universe,\\r\\n fields = \\\"price\\\",\\r\\n bar_count = context.lookback,\\r\\n frequency = \\\"1m\\\"\\r\\n )\\r\\n except:\\r\\n return\\r\\n\\r\\n # Take log of the prices\\r\\n prices = np.log(prices)\\r\\n\\r\\n # Store the price data in y and x\\r\\n \\r\\n for i in range(0, len(context.universe), 2):\\r\\n\\r\\n\\r\\n y = prices[context.universe[i+1]]\\r\\n x = prices[context.universe[i]]\\r\\n\\r\\n # Calculate the hedge ratio and z_score\\r\\n _, context.hedge_ratio, resids = hedge_ratio(y, x)\\r\\n zscore = z_score(resids, lookback=context.z_window)\\r\\n # Compute the trading signal\\r\\n context.signal = trading_signal(context, data, zscore, i/2)\\r\\n print(zscore, context.signal, \\\" {}\\\" .format(get_datetime()))\\r\\n\\r\\n # Place the order to trade the pair\\r\\n #place_order(context)\\r\\n if context.signal == 999:\\r\\n continue\\r\\n\\r\\n weight = context.signal*context.leverage/len(context.universe)\\r\\n print (weight)\\r\\n\\r\\n cancel_all_open_orders(context)\\r\\n\\r\\n order_target_percent(context.universe[i], -1*weight)\\r\\n order_target_percent(context.universe[i+1], weight)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f72e71f41615974f9910bac58f1cf14e\",\n \"score\": \"0.48691663\",\n \"text\": \"def midpoint_price(self) -> int:\\n return round((self.__bid_prices[-1] - self.__ask_prices[-1]) / 2.0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ef4f40f56cf4793bba1f050f2d106fc\",\n \"score\": \"0.48652336\",\n \"text\": \"def get_buyin(self):\\n return self.buyin\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08d6ef15f6a595b367d4fd4326f38451\",\n \"score\": \"0.4861038\",\n \"text\": \"def UniquePair(self):\\n pair_object = None\\n if self.Type() in ['MT544', 'MT545', 'MT546', 'MT547']:\\n #settlement_num = self.Identifier()\\n settlement_num = self.Identifier().split('-')[0] if len(self.Identifier().split('-')) == 1 else self.Identifier().split('-')[1]\\n pair_object = acm.FSettlement[str(settlement_num)]\\n if not pair_object:\\n notifier.INFO('Settlement ' + str(settlement_num) + ' not found' + '\\\\n' + self.SwiftData())\\n return pair_object\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07db9faac98b0b570a92528af13c811c\",\n \"score\": \"0.48568624\",\n \"text\": \"def tickPrice(self, reqId, tickType, price, attrib):\\n cTime = time.strftime(\\\"%H:%M:%S\\\", time.localtime())\\n print(f'Id: {reqId}, Time: {cTime} TickType: {tickType}, Price: {price}')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e57ab43e34236ff2cf34b64888af626\",\n \"score\": \"0.48545673\",\n \"text\": \"def getPreviousClosingPrice(self):\\r\\n return self.previousClosingPrice\",\n \"title\": \"\"\n },\n {\n \"docid\": \"13be79a777779c3242ac3673854f813e\",\n \"score\": \"0.48538187\",\n \"text\": \"def obs_id(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c9e087be08b32cfb20b5218344906a5e\",\n \"score\": \"0.48497474\",\n \"text\": \"def spot_bid_prices(self) -> Optional[Sequence['outputs.ClusterNodeGroupSpotBidPrice']]:\\n return pulumi.get(self, \\\"spot_bid_prices\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c69afa09d3c76e18204900d6469e2934\",\n \"score\": \"0.4845219\",\n \"text\": \"def bid(self, price):\\n self.model.update_ob(self, price)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"02f07949865e0375ecb156ae16da55ef\",\n \"score\": \"0.48361677\",\n \"text\": \"def get_ticker_coin(symbol) -> TickerCoin:\\n # stock_id = int(stock_id)\\n session = Session()\\n\\n query = session.query(DBTickerCoin).filter(DBTickerCoin.symbol==symbol).statement\\n dfticker = pd.read_sql(query, session.connection())\\n ticker = TickerCoin(dfticker)\\n return ticker\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e500b84f336fd3b3f6c7ed0d5b92bb0e\",\n \"score\": \"0.48200643\",\n \"text\": \"def id(self,i):\\n return self.comp.id(i)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd5fdd1d66b4e0ff9a3cbf35bbf94721\",\n \"score\": \"0.4812996\",\n \"text\": \"def identifier(self):\\n return self.hash160()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e67d4854b05e601a629b97964e9c1dc\",\n \"score\": \"0.48081696\",\n \"text\": \"def asSymbolId(self,symbol,typeName=None):\\n typeName = self._fillDefault(typeName)\\n if self.schema.hasId(typeName,symbol):\\n return self.schema.getId(typeName,symbol)\\n else:\\n return -1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ddd3bbc0907ca554bfb66584958de03\",\n \"score\": \"0.48018435\",\n \"text\": \"def get_id(self, symbol):\\n for k, v in variables.items():\\n if v == symbol:\\n return v\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c8cf38f6e34e0f98074695d6dd812760\",\n \"score\": \"0.48016253\",\n \"text\": \"def bid_price(self) -> typing.Optional[str]:\\n return self._values.get('bid_price')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c8cf38f6e34e0f98074695d6dd812760\",\n \"score\": \"0.48016253\",\n \"text\": \"def bid_price(self) -> typing.Optional[str]:\\n return self._values.get('bid_price')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a532237dbd97c02ad7ff9665664d9286\",\n \"score\": \"0.47949976\",\n \"text\": \"def _sell_belief(self, ask):\\n if ask >= self.model.outstanding_ask:\\n return 0\\n\\n try:\\n if ask >= self.model.history.max_last_period:\\n return 0 #\\n except TypeError:\\n pass\\n\\n try:\\n if ask >= self.model.history.max_current_period:\\n return 0 #\\n except TypeError:\\n pass\\n\\n def num_geq(history, price):\\n return len([d for d in history if d >= price])\\n\\n def num_leq(history, price):\\n return len([d for d in history if d <= price])\\n\\n history = self.model.history\\n asks = history.get_asks(length=self.mem_length)\\n accepted_asks = history.get_accepted_asks(length=self.mem_length)\\n accepted_bids = history.get_accepted_bids(length=self.mem_length)\\n\\n TAG = num_geq(accepted_asks, ask)\\n TBG = num_geq(accepted_bids, ask)\\n RAL = num_leq(asks, ask) - num_leq(accepted_asks, ask)\\n\\n if TAG + TBG == 0:\\n return self.eta\\n else:\\n return (TAG + TBG) / (TAG + TBG + RAL)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5fa023bd4c1107a226050044022ef366\",\n \"score\": \"0.47846314\",\n \"text\": \"def __init__(self, symbol, name, previousClosingPrice, currentPrice):\\r\\n self.symbol = symbol\\r\\n self.name = name\\r\\n self.previousClosingPrice = previousClosingPrice\\r\\n self.currentPrice = currentPrice\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f06956cb03794199fd5603238c1122a8\",\n \"score\": \"0.4779203\",\n \"text\": \"def unique_id(self):\\n return _raw_ofdm.raw_ofdm_demod_sptr_unique_id(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8295ad0fc65f3efed0050db36308715a\",\n \"score\": \"0.47780028\",\n \"text\": \"def bid_prices(self):\\n return [float(bid[0]) for bid in self.__bid]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"034856f72fc892abf24e8ebd53433124\",\n \"score\": \"0.47725224\",\n \"text\": \"def get_curr_price(self):\\n return dm.get_data_provider().curr_price(self.symbol)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a820d22575279eec9ea9d6d401247dd5\",\n \"score\": \"0.47656605\",\n \"text\": \"def fetch_position(self, symbol: str, params={}):\\n self.load_markets()\\n market = self.market(symbol)\\n method = None\\n request = {\\n 'instrument_id': market['id'],\\n # 'order_id': id, # string\\n # 'after': '1', # pagination of data to return records earlier than the requested ledger_id\\n # 'before': '1', # P=pagination of data to return records newer than the requested ledger_id\\n # 'limit': limit, # optional, number of results per request, default = maximum = 100\\n }\\n type = market['type']\\n if (type == 'futures') or (type == 'swap'):\\n method = type + 'GetInstrumentIdPosition'\\n elif type == 'option':\\n underlying = self.safe_string(params, 'underlying')\\n if underlying is None:\\n raise ArgumentsRequired(self.id + ' fetchPosition() requires an underlying parameter for ' + type + ' market ' + symbol)\\n method = type + 'GetUnderlyingPosition'\\n else:\\n raise NotSupported(self.id + ' fetchPosition() does not support ' + type + ' market ' + symbol + ', supported market types are futures, swap or option')\\n response = getattr(self, method)(self.extend(request, params))\\n #\\n # futures\\n #\\n # crossed margin mode\\n #\\n # {\\n # \\\"result\\\": True,\\n # \\\"holding\\\": [\\n # {\\n # \\\"long_qty\\\": \\\"2\\\",\\n # \\\"long_avail_qty\\\": \\\"2\\\",\\n # \\\"long_avg_cost\\\": \\\"8260\\\",\\n # \\\"long_settlement_price\\\": \\\"8260\\\",\\n # \\\"realised_pnl\\\": \\\"0.00020928\\\",\\n # \\\"short_qty\\\": \\\"2\\\",\\n # \\\"short_avail_qty\\\": \\\"2\\\",\\n # \\\"short_avg_cost\\\": \\\"8259.99\\\",\\n # \\\"short_settlement_price\\\": \\\"8259.99\\\",\\n # \\\"liquidation_price\\\": \\\"113.81\\\",\\n # \\\"instrument_id\\\": \\\"BTC-USD-191227\\\",\\n # \\\"leverage\\\": \\\"10\\\",\\n # \\\"created_at\\\": \\\"2019-09-25T07:58:42.129Z\\\",\\n # \\\"updated_at\\\": \\\"2019-10-08T14:02:51.029Z\\\",\\n # \\\"margin_mode\\\": \\\"crossed\\\",\\n # \\\"short_margin\\\": \\\"0.00242197\\\",\\n # \\\"short_pnl\\\": \\\"6.63E-6\\\",\\n # \\\"short_pnl_ratio\\\": \\\"0.002477997\\\",\\n # \\\"short_unrealised_pnl\\\": \\\"6.63E-6\\\",\\n # \\\"long_margin\\\": \\\"0.00242197\\\",\\n # \\\"long_pnl\\\": \\\"-6.65E-6\\\",\\n # \\\"long_pnl_ratio\\\": \\\"-0.002478\\\",\\n # \\\"long_unrealised_pnl\\\": \\\"-6.65E-6\\\",\\n # \\\"long_settled_pnl\\\": \\\"0\\\",\\n # \\\"short_settled_pnl\\\": \\\"0\\\",\\n # \\\"last\\\": \\\"8257.57\\\"\\n # }\\n # ],\\n # \\\"margin_mode\\\": \\\"crossed\\\"\\n # }\\n #\\n # fixed margin mode\\n #\\n # {\\n # \\\"result\\\": True,\\n # \\\"holding\\\": [\\n # {\\n # \\\"long_qty\\\": \\\"4\\\",\\n # \\\"long_avail_qty\\\": \\\"4\\\",\\n # \\\"long_margin\\\": \\\"0.00323844\\\",\\n # \\\"long_liqui_price\\\": \\\"7762.09\\\",\\n # \\\"long_pnl_ratio\\\": \\\"0.06052306\\\",\\n # \\\"long_avg_cost\\\": \\\"8234.43\\\",\\n # \\\"long_settlement_price\\\": \\\"8234.43\\\",\\n # \\\"realised_pnl\\\": \\\"-0.00000296\\\",\\n # \\\"short_qty\\\": \\\"2\\\",\\n # \\\"short_avail_qty\\\": \\\"2\\\",\\n # \\\"short_margin\\\": \\\"0.00241105\\\",\\n # \\\"short_liqui_price\\\": \\\"9166.74\\\",\\n # \\\"short_pnl_ratio\\\": \\\"0.03318052\\\",\\n # \\\"short_avg_cost\\\": \\\"8295.13\\\",\\n # \\\"short_settlement_price\\\": \\\"8295.13\\\",\\n # \\\"instrument_id\\\": \\\"BTC-USD-191227\\\",\\n # \\\"long_leverage\\\": \\\"15\\\",\\n # \\\"short_leverage\\\": \\\"10\\\",\\n # \\\"created_at\\\": \\\"2019-09-25T07:58:42.129Z\\\",\\n # \\\"updated_at\\\": \\\"2019-10-08T13:12:09.438Z\\\",\\n # \\\"margin_mode\\\": \\\"fixed\\\",\\n # \\\"short_margin_ratio\\\": \\\"0.10292507\\\",\\n # \\\"short_maint_margin_ratio\\\": \\\"0.005\\\",\\n # \\\"short_pnl\\\": \\\"7.853E-5\\\",\\n # \\\"short_unrealised_pnl\\\": \\\"7.853E-5\\\",\\n # \\\"long_margin_ratio\\\": \\\"0.07103743\\\",\\n # \\\"long_maint_margin_ratio\\\": \\\"0.005\\\",\\n # \\\"long_pnl\\\": \\\"1.9841E-4\\\",\\n # \\\"long_unrealised_pnl\\\": \\\"1.9841E-4\\\",\\n # \\\"long_settled_pnl\\\": \\\"0\\\",\\n # \\\"short_settled_pnl\\\": \\\"0\\\",\\n # \\\"last\\\": \\\"8266.99\\\"\\n # }\\n # ],\\n # \\\"margin_mode\\\": \\\"fixed\\\"\\n # }\\n #\\n # swap\\n #\\n # crossed margin mode\\n #\\n # {\\n # \\\"margin_mode\\\": \\\"crossed\\\",\\n # \\\"timestamp\\\": \\\"2019-09-27T03:49:02.018Z\\\",\\n # \\\"holding\\\": [\\n # {\\n # \\\"avail_position\\\": \\\"3\\\",\\n # \\\"avg_cost\\\": \\\"59.49\\\",\\n # \\\"instrument_id\\\": \\\"LTC-USD-SWAP\\\",\\n # \\\"last\\\": \\\"55.98\\\",\\n # \\\"leverage\\\": \\\"10.00\\\",\\n # \\\"liquidation_price\\\": \\\"4.37\\\",\\n # \\\"maint_margin_ratio\\\": \\\"0.0100\\\",\\n # \\\"margin\\\": \\\"0.0536\\\",\\n # \\\"position\\\": \\\"3\\\",\\n # \\\"realized_pnl\\\": \\\"0.0000\\\",\\n # \\\"unrealized_pnl\\\": \\\"0\\\",\\n # \\\"settled_pnl\\\": \\\"-0.0330\\\",\\n # \\\"settlement_price\\\": \\\"55.84\\\",\\n # \\\"side\\\": \\\"long\\\",\\n # \\\"timestamp\\\": \\\"2019-09-27T03:49:02.018Z\\\"\\n # },\\n # ]\\n # }\\n #\\n # fixed margin mode\\n #\\n # {\\n # \\\"margin_mode\\\": \\\"fixed\\\",\\n # \\\"timestamp\\\": \\\"2019-09-27T03:47:37.230Z\\\",\\n # \\\"holding\\\": [\\n # {\\n # \\\"avail_position\\\": \\\"20\\\",\\n # \\\"avg_cost\\\": \\\"8025.0\\\",\\n # \\\"instrument_id\\\": \\\"BTC-USD-SWAP\\\",\\n # \\\"last\\\": \\\"8113.1\\\",\\n # \\\"leverage\\\": \\\"15.00\\\",\\n # \\\"liquidation_price\\\": \\\"7002.6\\\",\\n # \\\"maint_margin_ratio\\\": \\\"0.0050\\\",\\n # \\\"margin\\\": \\\"0.0454\\\",\\n # \\\"position\\\": \\\"20\\\",\\n # \\\"realized_pnl\\\": \\\"-0.0001\\\",\\n # \\\"unrealized_pnl\\\": \\\"0\\\",\\n # \\\"settled_pnl\\\": \\\"0.0076\\\",\\n # \\\"settlement_price\\\": \\\"8279.2\\\",\\n # \\\"side\\\": \\\"long\\\",\\n # \\\"timestamp\\\": \\\"2019-09-27T03:47:37.230Z\\\"\\n # }\\n # ]\\n # }\\n #\\n # option\\n #\\n # {\\n # \\\"holding\\\":[\\n # {\\n # \\\"instrument_id\\\":\\\"BTC-USD-190927-12500-C\\\",\\n # \\\"position\\\":\\\"20\\\",\\n # \\\"avg_cost\\\":\\\"3.26\\\",\\n # \\\"avail_position\\\":\\\"20\\\",\\n # \\\"settlement_price\\\":\\\"0.017\\\",\\n # \\\"total_pnl\\\":\\\"50\\\",\\n # \\\"pnl_ratio\\\":\\\"0.3\\\",\\n # \\\"realized_pnl\\\":\\\"40\\\",\\n # \\\"unrealized_pnl\\\":\\\"10\\\",\\n # \\\"pos_margin\\\":\\\"100\\\",\\n # \\\"option_value\\\":\\\"70\\\",\\n # \\\"created_at\\\":\\\"2019-08-30T03:09:20.315Z\\\",\\n # \\\"updated_at\\\":\\\"2019-08-30T03:40:18.318Z\\\"\\n # },\\n # {\\n # \\\"instrument_id\\\":\\\"BTC-USD-190927-12500-P\\\",\\n # \\\"position\\\":\\\"20\\\",\\n # \\\"avg_cost\\\":\\\"3.26\\\",\\n # \\\"avail_position\\\":\\\"20\\\",\\n # \\\"settlement_price\\\":\\\"0.019\\\",\\n # \\\"total_pnl\\\":\\\"50\\\",\\n # \\\"pnl_ratio\\\":\\\"0.3\\\",\\n # \\\"realized_pnl\\\":\\\"40\\\",\\n # \\\"unrealized_pnl\\\":\\\"10\\\",\\n # \\\"pos_margin\\\":\\\"100\\\",\\n # \\\"option_value\\\":\\\"70\\\",\\n # \\\"created_at\\\":\\\"2019-08-30T03:09:20.315Z\\\",\\n # \\\"updated_at\\\":\\\"2019-08-30T03:40:18.318Z\\\"\\n # }\\n # ]\\n # }\\n #\\n # todo unify parsePosition/parsePositions\\n return response\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24c428818d4fe1bb7c1cdb6e6db1b7d9\",\n \"score\": \"0.4755885\",\n \"text\": \"def position_to_iid(self,x,y):\\n id = self.canvas.find_closest(x,y)[0]\\n tags = self.canvas.itemcget(id,\\\"tags\\\")\\n item_id=self.parse_tags(tags,\\\"iid\\\")\\n return item_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a31d1a247d38dbb60f493a37d1262357\",\n \"score\": \"0.47530997\",\n \"text\": \"def get_close_price_n_time(trade_data):\\n trade_close = []\\n trade_time = []\\n for trade in trade_data:\\n trade_close.append(float(trade[4]))\\n\\n trade_time.append(format_time(trade[6]))\\n return trade_close, trade_time\",\n \"title\": \"\"\n },\n {\n \"docid\": \"478ac6f1a7710f7e8575f04c16cc95ff\",\n \"score\": \"0.47501865\",\n \"text\": \"def get_handle_id_idempotent(self, handle_type, name):\\n\\n is_new = False\\n handle_id = 0\\n for handle in self._handles.values():\\n if handle.get_name() == name:\\n handle_id = handle.get_id()\\n break\\n\\n # if the handle doesn't already exist, create a new one\\n if handle_id <= 0:\\n handle_id = self.get_handle_id()\\n is_new = True\\n \\n\\tprint \\\"Connection - get_handle_id_idempotent in handle_type - %s, name - %s... out handle_id - %s, is_new - %s\\\" % (handle_type, name, handle_id, is_new)\\n\\n return handle_id, is_new\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d59e54678d2e582e7fc535d4f73145e\",\n \"score\": \"0.47461802\",\n \"text\": \"def open_trade_position(self, market_id, commodity_id, table_class):\\n result = -1\\n try:\\n session = self.Session()\\n #NOTE: id_buy or id_sell must be -1\\n # but both can't be filled in (= finished trade)\\n first_obj = session.query(table_class).filter(\\n table_class.market_id == market_id,\\n table_class.commodity_id == commodity_id,\\n table_class.active == 1).filter(\\n or_(\\n table_class.id_buy == -1,\\n table_class.id_sell == -1\\n )).filter(\\n table_class.id_buy != table_class.id_sell\\n ).first()\\n if first_obj is not None:\\n result = first_obj.trade_id\\n except Exception as ex:\\n print Error.INVADE_ALREADY_STARTED, ex\\n finally:\\n session.rollback()\\n session = None\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ec0ba7d49ce047c6fc034ef0f3da817\",\n \"score\": \"0.47399038\",\n \"text\": \"def _buy_strategy(self):\\n price = np.random.uniform(self.lowest_bid, self.value)\\n self.bid(price)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"180b4b3e7e2a3af4e651c5ba9008f197\",\n \"score\": \"0.47370905\",\n \"text\": \"def get_max_close(symbol):\\n\\n\\tdf = pd.read_csv(\\\"/Users/priyambasu/desktop/code/ML-For-Trading/{}.csv\\\".format(Goldmann-Sachs.csv)) #Reading the data\\n\\treturn df['Close'].max() #Compute and return max\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da28b5d174bcaf3aa04d20cf09b47836\",\n \"score\": \"0.47336978\",\n \"text\": \"def _get_first_close(self, df: pd.DataFrame) -> float:\\n df = df.loc[df[\\\"close\\\"].first_valid_index() :]\\n _close = df[\\\"close\\\"].iloc[0]\\n return _close\",\n \"title\": \"\"\n },\n {\n \"docid\": \"378eda120a393040806ab6146c8b7ecf\",\n \"score\": \"0.47191328\",\n \"text\": \"def _fetchClosePrice(self, symbol: str, requested_date: datetime.date) -> dict:\\n requested_str_date = str(requested_date)\\n try:\\n close = float(pdr.get_data_yahoo(symbol)[\\\"Close\\\"][requested_str_date])\\n asset_type = \\\"stock\\\"\\n\\n # symbol not found in stock market, find it in mutual fund\\n except pdr._utils.RemoteDataError:\\n try:\\n a_nav = nav.get(symbol)\\n if str(a_nav.updated)[:10] != requested_str_date:\\n raise GetNAVPriceError(\\n f\\\"NAV for symbol {symbol} on {requested_str_date} is not available!\\\"\\n )\\n else:\\n close = a_nav.value\\n asset_type = \\\"mutual fund\\\"\\n\\n # symbol is not valid\\n except KeyError:\\n raise SymbolNotFoundError(f\\\"Symbol {symbol} is not found!\\\")\\n\\n # symbol is found in stock market, but the price on the date is not available\\n except KeyError as x:\\n raise GetNAVPriceError(\\n f\\\"Price for symbol {symbol} on {requested_str_date} is not available!\\\"\\n )\\n\\n return {\\\"close\\\": close, \\\"asset_type\\\": asset_type}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ce61df4a8353d6304868075b254fe63\",\n \"score\": \"0.47184458\",\n \"text\": \"def balance_id(self):\\n return self.__balance_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d1a1203610a97d2947bcd17e449e2e6a\",\n \"score\": \"0.47175372\",\n \"text\": \"def _get_identifier_for_new_conn(self):\\n # BUG len(identifiers_seen) == 0 , identifiers_used == 0\\n # NORMAL len(identifiers_seen) == 1, identifiers_used == 0\\n # BUG len(identifiers_seen) == 2, identifiers_used == 3\\n if (self.identifiers_used_n >= len(self.identifiers_seen)):\\n logging.warning(\\\"Not enough identifiers for new connection (%d, %s)\\\" %\\n (self.identifiers_used_n, str(self.identifiers_seen)))\\n assert(False)\\n\\n identifier = self.identifiers_seen[self.identifiers_used_n]\\n self.identifiers_used_n += 1\\n\\n return identifier\",\n \"title\": \"\"\n },\n {\n \"docid\": \"edfa54c1fdd8019f86007f9cac64e081\",\n \"score\": \"0.47155914\",\n \"text\": \"def test_get_bond_id():\\n expected_bond_id = '0xf1570226030766ce222ffa240231bbfc2a8de995516e63927c672b1b46c7f2c6'\\n bond_id = Adex._get_bond_id(\\n identity_address=TEST_ADDR_USER_IDENTITY,\\n amount=10661562521452745365522,\\n pool_id=HexStr(TOM_POOL_ID),\\n nonce=1596569185,\\n )\\n assert bond_id == expected_bond_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d09780a54c6aa6f9833791c5607e415\",\n \"score\": \"0.47150353\",\n \"text\": \"def sell_qty_det(self):\\n \\n client = config.client\\n #ex_rate = client.get_symbol_ticker(symbol=self.coin_pair)[\\\"price\\\"]\\n #asset_balance = float(client.get_asset_balance(asset=self.coin_pair.replace(\\\"USDT\\\", \\\"\\\"))[\\\"free\\\"])\\n\\n return round((self.amount / float(self.ex_rate) * 0.998), 2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e6169d438ae0ef9d202269c265a3e17\",\n \"score\": \"0.47119248\",\n \"text\": \"def get_closing_day(self, year):\\n return self.openAndCloseDays[year][1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"485d3033562143a43a8f1c6b9b282643\",\n \"score\": \"0.47112247\",\n \"text\": \"def choose_couple(self):\\r\\n # g2 dovrebbe essere scelto simile a g1 per qty?\\r\\n g1 = self.choose_one()\\r\\n g2 = self.choose_one()\\r\\n while (g1 is g2):\\r\\n g2 = self.choose_one()\\r\\n return (g1, g2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b5a68ae2d5c28f5c074ddb37a5e8793\",\n \"score\": \"0.47044444\",\n \"text\": \"def get_currency_id(self):\\n return self._make_request('getcurrencyid')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"174b8f12d898172d2355c25f48a1db6c\",\n \"score\": \"0.47026637\",\n \"text\": \"def getNextAvailableId(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dac74451d060ab899a6a414ad60816bb\",\n \"score\": \"0.47018328\",\n \"text\": \"def upper_ind_id(self):\\n return self._upper_ind_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a26267e21656cd9af762601b5ac30fa\",\n \"score\": \"0.4698976\",\n \"text\": \"def unique_id(self):\\n return _raw_ofdm.raw_pnc_demod_sptr_unique_id(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e943b135621b51dcfa3cca7e8f4845c9\",\n \"score\": \"0.46920982\",\n \"text\": \"def _get_slot_id(self):\\n return self.__slot_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e943b135621b51dcfa3cca7e8f4845c9\",\n \"score\": \"0.46920982\",\n \"text\": \"def _get_slot_id(self):\\n return self.__slot_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af9f77bde6b80f9e88ce38c4f5947dad\",\n \"score\": \"0.46898872\",\n \"text\": \"def unique_id(self):\\n return _MpatiBlocks_swig.chirp_signal_f_sptr_unique_id(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93a0efc8ba653735c62e279c480c364f\",\n \"score\": \"0.46898207\",\n \"text\": \"def _buy(self, amount, price):\\n response = self._place_order(amount, price, \\\"buy\\\")\\n if not response[\\\"status\\\"]:\\n raise TradeException(response[\\\"error\\\"])\\n return response[\\\"order\\\"][\\\"id\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd84d9cd3e51b72d6c2304146bcf94d5\",\n \"score\": \"0.4689799\",\n \"text\": \"def __current_method_id(self) -> str:\\n if self.__current_method in self.symbols.values():\\n index = list(self.symbols.values()).index(self.__current_method)\\n return list(self.symbols.keys())[index]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e72b41d0ca38fbfbd61243bead6632e3\",\n \"score\": \"0.46881253\",\n \"text\": \"def id(self):\\r\\n return b2h_rev(self.hash())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a85cf1e0a41ee8f5f6f0dc6d1c8d48a\",\n \"score\": \"0.46875\",\n \"text\": \"def get_the_buyer_id_spent_most_and_the_money_spent():\\n\\n # your code\\n name_and_sum = get_the_buyer_name_spent_most_and_the_money_spent()\\n sales_table = sales.insert_customer_id_to_table()\\n id_ = []\\n \\n for i in range(len(sales_table)):\\n if name_and_sum[0] == sales_table[i][7]:\\n id_.append(sales_table[i][6])\\n break\\n id_sum = [id_[0], name_and_sum[1]] \\n \\n return tuple(id_sum)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"589ade813f21600f88472c54af9f2124\",\n \"score\": \"0.46868056\",\n \"text\": \"def getSymbolData2(self) -> int:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"74777c71ea9f17cdf33880bd32d6010f\",\n \"score\": \"0.46757942\",\n \"text\": \"def exp_id(self):\\n return self._exp_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"74777c71ea9f17cdf33880bd32d6010f\",\n \"score\": \"0.46757942\",\n \"text\": \"def exp_id(self):\\n return self._exp_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d3e74cb5d13b691f39892fbfa0a1051\",\n \"score\": \"0.46747747\",\n \"text\": \"def get_date_price(self,bar):\\n\\t\\tdate = str(self.data_run.index[bar])[:10]\\n\\t\\tprice = self.data_run['Close'].ix[bar]\\n\\t\\treturn date,price\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5811e6aafe4a687619d1b072fc3b10f8\",\n \"score\": \"0.46699798\",\n \"text\": \"def iceberg_no(self):\\n return self._iceberg_no\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b3cd43a7dd58ab350c0285675e146e7\",\n \"score\": \"0.46663254\",\n \"text\": \"def buy_position(self, code, price, datetime, position):\\n # check\\n assert(price > 0)\\n assert((0 < position) and (position <= 1))\\n # compute how many can we buy\\n bid = self._capital * position\\n # can we buy one board lot?\\n oneboardlot = 100 * price # One board lot is 100 shares in Chinese market \\n if bid < oneboardlot: \\n raise BidTooLow(need_cash = oneboardlot, bid = bid)\\n if self._cash < oneboardlot: \\n raise NotEnoughMoney(need_cash = oneboardlot, cash = self._cash)\\n # can we buy more?\\n quantity = int(math.floor(bid / oneboardlot))\\n need_cash = quantity * oneboardlot # if run to here, we must have bid >= need_cash\\n while self._cash < need_cash:\\n quantity = quantity - 1\\n need_cash = quantity * oneboardlot\\n # now buy it\\n self._cash -= need_cash\\n try:\\n self._shares[code] += quantity\\n except KeyError:\\n self._shares[code] = quantity\\n # commission charge deduction\\n if (self.CommissionChargeRate > 0): # require consider commission charge\\n commission_charge = max(need_cash*self.CommissionChargeRate, 5) # at least 5 yuan \\n else: \\n commission_charge = 0\\n self._cash -= commission_charge\\n # update total commission charge \\n self._total_commission_charge += commission_charge\\n # add transaction record\\n self._records.append((datetime, self._cash, self._shares.copy()))\\n # print infomation\\n self.o.report('- - '*20)\\n self.o.report(\\\"[\\\" + str(datetime) + \\\"] \\\") \\n self.o.report(\\\"[stock code: {:s}]\\\".format(code)) \\n self.o.report(\\\"Buy {0:d} board lot shares at price {1:.2f} (per share)\\\".format(quantity, price))\\n self.o.report(\\\"Commission charge: {:.2f}\\\".format(commission_charge))\\n self.o.report(\\\"Account: \\\")\\n self.o.account(self._shares, self._cash)\\n # return the number of shares (unit: boardlot) you buy this time \\n # and the the amount of current cash and shares (unit:boardlot) in the account\\n return quantity, self._shares, self._cash\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ec726d6e0cbe5cc3ec7609996b05bf1\",\n \"score\": \"0.46640787\",\n \"text\": \"def get_ticker(self):\\n self.ticker = get('https://api.binance.com/api/v1/ticker/allBookTickers')\\n if self.ticker:\\n for x in self.ticker:\\n pairing_id = self._get_pairing_id(x['symbol'])\\n if pairing_id:\\n self.coins[pairing_id] = {\\n 'buy':x[\\\"askPrice\\\"],\\n 'sell':x[\\\"bidPrice\\\"]\\n }\\n\\n else:\\n self.ticker = []\\n self.coins = {}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5345572db3b5d58ca56514a4568fd182\",\n \"score\": \"0.4661168\",\n \"text\": \"def _get_price_id_from_options(self, ds_options, option, value,\\n item_id=False):\\n ds_obj = ServerCreateOptions()\\n\\n for _, options in ds_obj.get_create_options(ds_options, option, False):\\n for item_options in options:\\n if item_options[0] == value:\\n if not item_id:\\n return item_options[1]\\n return item_options[2]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7377d846447bde39fb2482c57b36cb87\",\n \"score\": \"0.4660437\",\n \"text\": \"def get_balance(self, ticker):\\n \\n # return self.balance_base*(1-self.commision) * ticker['close'] + self.balance_counter\\n return self.balance_base* ticker['close'] + self.balance_counter\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e08def1fd3e942c8e71566b65019174\",\n \"score\": \"0.46601424\",\n \"text\": \"def get_id(self, x):\\n return numpy.dot(self.POWERS_OF_TWO, numpy.array(x, dtype=int))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38fb38bc739e59c8e975e2e7ff739c5a\",\n \"score\": \"0.4658523\",\n \"text\": \"def store_price(self):\\n if self.nameid:\\n low = 0\\n volume, med = market_api.get_bulkitem_price(self.nameid)\\n else:\\n volume, low, med = market_api.get_item_price(self.name)\\n\\n # This is to debug some BULLLLSHIT ass stuff\\n log.debug(\\\"Store Price [%s]: %s, %s, %s\\\" % (self.id, volume, low, med))\\n\\n # (-1, 0, 0) implies that steam doesnt have data on the item currently\\n if volume == -1 and low == 0 and med == 0:\\n volume = 0\\n last_mipp = self.get_latest_mipp()\\n low = last_mipp.lowest\\n med = last_mipp.median\\n\\n five_minutes_ago = datetime.datetime.utcnow() - relativedelta(minutes=5)\\n\\n # Find a MIPP in the latest five minutes\\n q = MarketItemPricePoint.select().where(\\n (MarketItemPricePoint.item == self) &\\n (MarketItemPricePoint.time >= five_minutes_ago)\\n )\\n\\n # If we have one, we just update that, otherwise create a fresh one\\n if q.count():\\n mipp = q.get()\\n else:\\n mipp = MarketItemPricePoint()\\n\\n mipp.item = self\\n mipp.volume = volume if volume != -1 else 0\\n mipp.lowest = low\\n mipp.median = med\\n mipp.save()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"419e9a7064c8f2d6ff17e9fabe0fa301\",\n \"score\": \"0.4654737\",\n \"text\": \"def get_win_flag_value(self, price_buy, price_sell):\\n result = (price_buy < price_sell)\\n return 1 if result else 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c720ff08980cc625dc052a5cc889148\",\n \"score\": \"0.46517062\",\n \"text\": \"def unique_id(self):\\n return f\\\"{self.airly.latitude}-{self.airly.longitude}\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a747a40cebfb623c55eb21e7845e4ba7\",\n \"score\": \"0.46489996\",\n \"text\": \"def myID() -> np.int:\\r\\n return 315674028\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6833fde825aa1296a2efd1134b694e07\",\n \"score\": \"0.4644364\",\n \"text\": \"def _buy_belief(self, bid):\\n if bid <= self.model.outstanding_bid:\\n return 0\\n\\n def num_geq(history, price):\\n return len([d for d in history if d >= price])\\n\\n def num_leq(history, price):\\n return len([d for d in history if d <= price])\\n\\n history = self.model.history\\n bids = history.get_bids(length=self.mem_length)\\n accepted_asks = history.get_accepted_asks(length=self.mem_length)\\n accepted_bids = history.get_accepted_bids(length=self.mem_length)\\n\\n TBL = num_leq(accepted_bids, bid)\\n TAL = num_leq(accepted_asks, bid)\\n RBG = num_geq(bids, bid) - num_geq(accepted_bids, bid)\\n\\n if TBL + TAL == 0:\\n return self.eta\\n else:\\n return (TBL + TAL) / (TBL + TAL + RBG)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a569e1390402298aac8aac91bf3431af\",\n \"score\": \"0.46344066\",\n \"text\": \"def bond_convexity(price, par, T, coup, freq, dy=0.01):\\r\\n ytm = bond_ytm(price, par, T, coup, freq, guess=0.01)\\r\\n price_neg = bond_price(ytm=(ytm - dy), par=par, T=T, coup=coup, freq=freq)\\r\\n price_pos = bond_price(ytm=(ytm + dy), par=par, T=T, coup=coup, freq=freq)\\r\\n \\r\\n return (price_neg + price_pos - 2*price) / (price * dy**2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6974ea70a21086f509085f05584093c8\",\n \"score\": \"0.46282148\",\n \"text\": \"def get_trigger_price(self):\\n return int(self.pxSpin.get())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6974ea70a21086f509085f05584093c8\",\n \"score\": \"0.46282148\",\n \"text\": \"def get_trigger_price(self):\\n return int(self.pxSpin.get())\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1186,"cells":{"query_id":{"kind":"string","value":"02ccca2b4b0c4d9786cbcb62426bbd92"},"query":{"kind":"string","value":"Use positive semidefiniteness to identify likely zerovalued eigenvalues due to floating point imprecision."},"positive_passages":{"kind":"list like","value":[{"docid":"bcb2f01e5855d0ebefec3ad2f381a49a","score":"0.0","text":"def from_correlations(\n cls: Type[Eigenvalues],\n data: ndarray,\n atol: float = float(1e3 * np.finfo(np.float64).eps),\n lower: bool = True,\n ) -> Eigenvalues:\n if len(data.shape) > 2:\n raise ValueError(\"`data` must be either flat, 1-dimensional, or 2-dimensional.\")\n if len(data.shape) == 2 and np.min(data.shape) > 1:\n eigs = np.linalg.eigvalsh(data, \"L\" if lower else \"U\")\n eigs = data.reshape(-1) # equivalent to ravel but less likely to copy\n return cls(eigs[np.abs(eigs) < atol])","title":""}],"string":"[\n {\n \"docid\": \"bcb2f01e5855d0ebefec3ad2f381a49a\",\n \"score\": \"0.0\",\n \"text\": \"def from_correlations(\\n cls: Type[Eigenvalues],\\n data: ndarray,\\n atol: float = float(1e3 * np.finfo(np.float64).eps),\\n lower: bool = True,\\n ) -> Eigenvalues:\\n if len(data.shape) > 2:\\n raise ValueError(\\\"`data` must be either flat, 1-dimensional, or 2-dimensional.\\\")\\n if len(data.shape) == 2 and np.min(data.shape) > 1:\\n eigs = np.linalg.eigvalsh(data, \\\"L\\\" if lower else \\\"U\\\")\\n eigs = data.reshape(-1) # equivalent to ravel but less likely to copy\\n return cls(eigs[np.abs(eigs) < atol])\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"ffa4c774fada3e0ed2a46ad5aab82bec","score":"0.74455935","text":"def test_eigenvalues_of_too_few_points_results_in_0():\n a = np.array([5])\n pc = create_point_cloud(a, a, a)\n\n compute_features(pc, [[0]], pc, [\"eigenv_1\", \"eigenv_2\", \"eigenv_3\"], InfiniteCylinder(5))\n\n eigen_val_123 = np.array([pc[keys.point]['eigenv_{}'.format(i)]['data'] for i in [1, 2, 3]])\n assert not np.any(np.isnan(eigen_val_123))\n assert not np.any(np.isinf(eigen_val_123))","title":""},{"docid":"d8a85699193ab642a6cf084a26cc92ef","score":"0.63962203","text":"def erfc(x):\n return 0.0","title":""},{"docid":"08a5825d42445c1cc5cb055261cf4cd0","score":"0.63627434","text":"def calculate_E0(self) -> float:\n noisy = self.kernel_eigenvectors_[-1].copy()\n np.random.shuffle(noisy)\n\n kernel_eigenvectors = self.kernel_eigenvectors_[:-1]\n kernel_eigenvectors.append(noisy)\n\n eigenvectors_matrix = scipy.sparse.csr_matrix(\n np.column_stack([eigenvector for eigenvector in kernel_eigenvectors])\n )\n\n if len(kernel_eigenvectors) == 2:\n ev0 = kernel_eigenvectors[0]\n ev1 = kernel_eigenvectors[1]\n _, Gamma, _ = scipy.sparse.linalg.svds(\n ev0.T @ ev1, k=self.n_jointly_smooth_functions, which=\"LM\"\n )\n else:\n _, Gamma, _ = scipy.sparse.linalg.svds(\n eigenvectors_matrix, k=self.n_jointly_smooth_functions, which=\"LM\"\n )\n\n Gamma.sort()\n gamma2 = Gamma[-2]\n E0 = (1 + gamma2) / 2\n return E0","title":""},{"docid":"8310530bc0d41d9678403ca6d8a985f6","score":"0.6325769","text":"def fun(self, X):\n w = np.linalg.eigvalsh(X)\n # check for negative eigenvalues, but be forgiving for very small\n # negative values relative to the maximum eignvalue\n if np.any(np.min(w, axis=-1) < -np.spacing(np.max(w, axis=-1))):\n return np.inf\n else:\n return 0","title":""},{"docid":"1a7a74e53c50df24de81957fff40abae","score":"0.6216401","text":"def test_eigen_basis_non_orthogonal():\n\n basis = [HarmonicOscillator(n=i, center=0.25) for i in range(2)] + [\n HarmonicOscillator(n=i, center=-0.25) for i in range(2)\n ]\n\n S = pairwise_array_from_func(basis, Overlap())\n H = pairwise_array_from_func(basis, Hamiltonian(Harmonic(center=0.0)))\n eigb = EigenBasis.from_basis(basis, H, S)\n\n # check the first 3 energy levels, we won't have converged\n # the higher ones wrt basis set size\n expected_energies = [(n + 0.5) for n in range(3)]\n\n diffs = [e1 - e2 for e1, e2 in zip(sorted(eigb.energies), expected_energies)]\n\n # a little lenient due to convergence of basis to keep test fast\n assert all(math.isclose(d, 0.0, abs_tol=1e-3) for d in diffs)","title":""},{"docid":"c1e2d551632b270ab4d564dbf36ab84c","score":"0.61725926","text":"def erf(x):\n return 0.0","title":""},{"docid":"83276e0fec0f1d569af9dce47321b14d","score":"0.6149356","text":"def test_finite_well():\n file = \"tests/finite_well/schroedinger.inp\"\n x_min, x_max, n_point, ev_first, ev_last, interp_type, potential_decl, alpha = sd.read_inp(file)\n potential_dat = sd.potential_discret(x_min, x_max, n_point, interp_type, potential_decl)\n eigenvektors, eigenvalues = sd.solve_wavefuncs(n_point, ev_first, ev_last, potential_dat, alpha)\n sd.solve_expvalues(ev_first, ev_last, potential_dat, eigenvektors)\n potential_exp = np.loadtxt(\"tests/finite_well/potential.exp\")\n eigenvalues_exp = np.loadtxt(\"tests/finite_well/eigenvalues.exp\")\n assert np.all(eigenvalues-eigenvalues_exp < 1e-10)\n assert np.all(potential_dat-potential_exp < 1e-10)","title":""},{"docid":"8f96c75c5eff24ca569d81aec89a335e","score":"0.602242","text":"def _verify_spatial_precision(self, Q):\n eig = eigsh(Q, k=1, which='SA', return_eigenvectors=False, sigma=0.001)\n if eig[0] >= 1e-4:\n raise ValueError('Spatial precision matrix Q must be singular.')","title":""},{"docid":"5ad0043d9331065ae89732206e76b743","score":"0.593529","text":"def is_positive_semi_definite(mat):\n eigvals, _ = np.linalg.eigh(mat)\n return np.all(eigvals >= -1e-14)","title":""},{"docid":"6ecc854433d7b0727068571f3999e176","score":"0.5915554","text":"def test_check_conformer_energy(self):\n v_list = [-272.2779012225, -272.2774933703, -272.2768397635, -272.2778432059, -272.278645477, -272.2789602654,\n -272.2788749196, -272.278496709, -272.2779350675, -272.2777008843, -272.2777167286, -272.2780937643,\n -272.2784838846, -272.2788050464, -272.2787865352, -272.2785091607, -272.2779977452, -272.2777957743,\n -272.2779134906, -272.2781827547, -272.278443339, -272.2788244214, -272.2787748749]\n v_list = np.array(v_list, np.float64)\n v_diff = (v_list[0] - np.min(v_list)) * constants.E_h * constants.Na / 1000\n self.assertAlmostEqual(v_diff / 2.7805169838282797, 1, 5)","title":""},{"docid":"8d9401fe732896df5a0902d73b1bbf6f","score":"0.5865272","text":"def test_finite(self):\n \n Number_of_tests = 1000\n low = -1000\n high = 1000\n for i in range(Number_of_tests):\n x = np.random.rand(100) * (high - low) + low\n y = aux_functions.softmax_base(x)\n\n # This should be True if all are finite\n all_finite = np.isfinite(y).all()\n self.assertTrue(all_finite)","title":""},{"docid":"09e17c22b276387d2800c87205ee4d0d","score":"0.5832907","text":"def is_pos_semidef(X, positive_definite=False):\n if not positive_definite:\n return np.all(np.linalg.eigvals(X) >= 0)\n else:\n return np.all(np.linalg.eigvals(X) > 0)","title":""},{"docid":"72c1090fd08e6a1cf6cc1a33dbe33ded","score":"0.5799744","text":"def calculate_eigenvalues(self):\n self.__eigenvalues = []\n dictionary = np.linalg.eig(np.array(self.__A))\n indicator = True\n sum1 = 0\n for i in range(self.__A.shape[0]):\n if all(self.__A[i, j] == 0 for j in range(self.__A.shape[1])):\n indicator = all(self.__B[i,j] for j in range(self.__B.shape[1]))\n if (indicator):\n sum1 += 1\n \n for val in dictionary[0]:\n if (val != 0):\n self.__eigenvalues.append(complex(val))\n elif (indicator) and (sum1 > 0):\n sum1 -= 1\n self.__eigenvalues.append(complex(val))","title":""},{"docid":"45d9ab80a3556d344da1d0229ab9e56a","score":"0.5743405","text":"def testLogPartitionInfinityIsAccurate(self):\n alpha = float('inf')\n log_z_true = 0.70526025442 # From mathematica.\n log_z = self.variant(self._distribution.log_base_partition_function)(alpha)\n chex.assert_tree_all_close(log_z, log_z_true, atol=1e-7, rtol=1e-7)","title":""},{"docid":"bdb9e38577f9b1abadaf22fd583b739e","score":"0.56634724","text":"def eigensolve(self, epsilon=0.85):\n raise NotImplementedError(\"eigensolve Incomplete\")","title":""},{"docid":"42b01d8c381bf5bff2a8ecf62c5dd996","score":"0.56549835","text":"def test_no_el():\n levels = np.array([959., 867.9, 779.2, 647.5, 472.5, 321.9, 251.]) * units.mbar\n temperatures = np.array([22.2, 17.4, 14.6, 1.4, -17.6, -39.4, -52.5]) * units.celsius\n dewpoints = np.array([19., 14.3, -11.2, -16.7, -21., -43.3, -56.7]) * units.celsius\n el_pressure, el_temperature = el(levels, temperatures, dewpoints)\n assert_nan(el_pressure, levels.units)\n assert_nan(el_temperature, temperatures.units)","title":""},{"docid":"52b7d13448227d396194788d49643b02","score":"0.56495357","text":"def test_e0_prod(self):\n self.assertAlmostEqual(self.tunneling.E0_prod.value_si * 0.001, self.E0_prod, 4)","title":""},{"docid":"8ec3e6b93c2f3dbd019c490d3e4bbfb0","score":"0.56463265","text":"def is_posdef(X):\n return np.min(np.linalg.eigvals(X)) > 0","title":""},{"docid":"d60ee2f59e1f697d14a900945217b96c","score":"0.56452477","text":"def test_negative_electrode_potential_profile(self):\n np.testing.assert_array_almost_equal(self.phi_s_n(self.t, x=0), 0, decimal=5)","title":""},{"docid":"6961920783941077064e6241459ed256","score":"0.56451756","text":"def test_intra_power_law_fit_no_model(self):\n\n\t\tdetails= self.watcher.analyze(model=self.model, layers=self.fc_layers, intra=True, randomize=False, vectors=False)\n\t\tactual_alpha = details.alpha[0]\n\n\t\texpected_alpha = 2.654 # not very accurate because of the sparisify transform\n\t\tself.assertAlmostEqual(actual_alpha,expected_alpha, places=1)","title":""},{"docid":"a066a303238caaa53bd27738889aaead","score":"0.5635319","text":"def matrix_eig(\n self,\n chis=None,\n eps=0,\n print_errors=\"deprecated\",\n hermitian=False,\n break_degenerate=False,\n degeneracy_eps=1e-6,\n sparse=False,\n trunc_err_func=None,\n evenTrunc = False,\n ):\n if print_errors != \"deprecated\":\n msg = (\n \"The `print_errors` keyword argument has been deprecated, \"\n \"and has no effect. Rely instead on getting the error as a \"\n \"return value, and print it yourself.\"\n )\n warnings.warn(msg)\n # If chis is not specfied, there is no even truncation scheme; else, we\n # keep track of the chi we specfied\n if chis is None:\n evenTrunc = False\n else:\n try:\n chis = list(chis)\n except TypeError:\n chis = [chis]\n chiSpec = max(chis)\n chis = self._matrix_decomp_format_chis(chis, eps)\n maxchi = max(chis)\n assert self.defval == 0\n assert self.invar\n assert self.charge == 0\n assert self.dirs[0] + self.dirs[1] == 0\n assert set(zip(self.qhape[0], self.shape[0])) == set(\n zip(self.qhape[1], self.shape[1])\n )\n\n S_dtype = np.float_ if hermitian else np.complex_\n U_dtype = self.dtype if hermitian else np.complex_\n\n # Eigenvalue decompose each sector at a time.\n # While doing so, also keep track of a list of all eigenvalues, as well\n # as a heap that gives the negative of the absolute value of the\n # largest eigenvalue in each sector. These will be needed later when\n # deciding how to truncate the eigenvalues.\n eigdecomps = {}\n dims = {}\n minusabs_next_eigs = []\n all_eigs = []\n for k, v in self.sects.items():\n if 0 in v.shape:\n # This matrix is empty and trivial.\n shp = v.shape\n m = min(shp)\n u = np.empty((shp[0], m), dtype=U_dtype)\n s = np.empty((m,), dtype=S_dtype)\n eigdecomp = (s, u)\n else:\n if sparse and maxchi < min(v.shape) - 1:\n if hermitian:\n s, u = spsla.eighs(\n v, k=maxchi, return_eigenvectors=True\n )\n else:\n s, u = spsla.eigs(\n v, k=maxchi, return_eigenvectors=True\n )\n else:\n if hermitian:\n s, u = np.linalg.eigh(v)\n else:\n s, u = np.linalg.eig(v)\n order = np.argsort(-np.abs(s))\n s = s[order]\n u = u[:, order]\n s = s.astype(S_dtype)\n u = u.astype(U_dtype)\n eigdecomp = (s, u)\n eigdecomps[k] = eigdecomp\n dims[k] = 0\n all_eigs.append(s)\n if 0 not in s.shape:\n heapq.heappush(minusabs_next_eigs, (-np.abs(s[0]), k))\n try:\n all_eigs = np.concatenate(all_eigs)\n except ValueError:\n all_eigs = np.array((0,))\n\n if sparse:\n norm_sq = self.norm_sq()\n else:\n norm_sq = None\n\n # Figure out what bond dimension to truncate to, how this bond\n # dimension is distributed over the different sectors, and what the\n # truncation error is.\n chi, dims, rel_err = type(self)._find_trunc_dim(\n all_eigs,\n eigdecomps,\n minusabs_next_eigs,\n dims,\n chis=chis,\n eps=eps,\n break_degenerate=break_degenerate,\n degeneracy_eps=degeneracy_eps,\n trunc_err_func=trunc_err_func,\n norm_sq=norm_sq,\n )\n\n # truncate in both sectors evenly\n if evenTrunc and chiSpec == chi:\n # This piece of codes is only designed\n # with Z2 symmetry tensor in mind\n errmeg = \"The matrix should have two sectors (0,0) and (1,1).\"\n assert len(dims) == 2, errmeg\n if chiSpec % 2 == 0:\n dims[(0, 0)] = int(chiSpec / 2)\n dims[(1, 1)] = int(chiSpec / 2)\n else:\n dims[(0, 0)] = int((chiSpec + 1) / 2)\n dims[(1, 1)] = int((chiSpec - 1) / 2)\n\n # Truncate each block and create the dim for the new index.\n new_dim = []\n new_qim = []\n eigdecomps = {k: v for k, v in eigdecomps.items() if dims[k] > 0}\n for k, v in eigdecomps.items():\n d = dims[k]\n if d > 0:\n new_dim.append(d)\n new_qim.append(k[0])\n eigdecomps[k] = (v[0][:d], v[1][:, :d])\n else:\n del eigdecomps[k]\n\n # Initialize S and U.\n d = self.dirs[0]\n S = type(self)(\n [new_dim],\n qhape=[new_qim],\n dirs=[d],\n qodulus=self.qodulus,\n dtype=S_dtype,\n invar=False,\n charge=0,\n )\n U = type(self)(\n [self.shape[0], new_dim],\n qhape=[self.qhape[0], new_qim],\n dirs=[d, -d],\n qodulus=self.qodulus,\n dtype=U_dtype,\n charge=0,\n )\n\n # Set the blocks of U, S and V.\n for k, v in eigdecomps.items():\n S[(k[0],)] = v[0]\n k_U = (k[0], k[0])\n U[k_U] = v[1]\n\n return S, U, rel_err","title":""},{"docid":"e69eef8b97a70ce5fff729dc3d202250","score":"0.56225336","text":"def test_rescale_eigenvalues(self):\n\t\t\n\t\tevals = self.watcher.get_ESD(layer=self.fc2_layer)\n\t\trescaled_evals, Wscale = RMT_Util.rescale_eigenvalues(evals)\n\t\tun_rescaled_evals = RMT_Util.un_rescale_eigenvalues(rescaled_evals, Wscale)\n\n\t\tactual = np.max(evals)\n\t\texpected = np.max(un_rescaled_evals)\n\t\tself.assertAlmostEqual(actual, expected)","title":""},{"docid":"fac7548ede07ec0fcc47724ca607c6a9","score":"0.5614238","text":"def test_Sobol_G_raises_error_if_values_lt_zero():\n evaluate(np.array([0, -1, -.02, 1, 1, -0.1, -0, -12]))","title":""},{"docid":"0d2fc31427e60ea3e94259b8ff063f1b","score":"0.56049544","text":"def test_j0_root():\n import time\n t1 = time.time()\n\n # Our version uses tabulated values up to 40, so a useful test of the extrapolation\n # requires this to have more than 40 items.\n vals1 = [ galsim.bessel.j0_root(s) for s in range(1,51) ]\n print 'vals1 = ',vals1\n\n try:\n import scipy.special\n vals2 = scipy.special.jn_zeros(0,50)\n print 'vals2 = ',vals2\n np.testing.assert_almost_equal(\n vals1, vals2, 8, \"bessel.j0_root disagrees with scipy.special.jn_zeros\")\n except ImportError:\n print 'Unable to import scipy. Skipping scipy tests of j0_root.'\n\n # These values are what scipy returns. Check against these, so not require scipy.\n vals2 = [ 2.40482556, 5.52007811, 8.65372791, 11.79153444,\n 14.93091771, 18.07106397, 21.21163663, 24.35247153,\n 27.49347913, 30.63460647, 33.77582021, 36.91709835,\n 40.05842576, 43.19979171, 46.34118837, 49.4826099 ,\n 52.62405184, 55.76551076, 58.90698393, 62.04846919,\n 65.1899648 , 68.33146933, 71.4729816 , 74.61450064,\n 77.75602563, 80.89755587, 84.03909078, 87.18062984,\n 90.32217264, 93.46371878, 96.60526795, 99.74681986,\n 102.88837425, 106.02993092, 109.17148965, 112.31305028,\n 115.45461265, 118.59617663, 121.73774209, 124.87930891,\n 128.02087701, 131.16244628, 134.30401664, 137.44558802,\n 140.58716035, 143.72873357, 146.87030763, 150.01188246,\n 153.15345802, 156.29503427\n ]\n np.testing.assert_almost_equal(\n vals1, vals2, 8, \"bessel.j0_root disagrees with reference values\")\n\n t2 = time.time()\n print 'time for %s = %.2f'%(funcname(),t2-t1)","title":""},{"docid":"feb0ad2226494b58ecc42d4051fb5796","score":"0.5604412","text":"def is_positive_definite(x):\n return np.all(np.linalg.eigvals(x) > 0)","title":""},{"docid":"ed1fe34e52de6416571f92aa2feb845a","score":"0.56007063","text":"def test_difficulties_without_eps_multi(self):\n\n def get_beamformer(A, B):\n return get_mvdr_vector_souden(\n A, B,\n eps=0,\n return_ref_channel=True\n )\n\n for args in [\n (\n [self.PhiXX * 0, self.PhiXX],\n [self.PhiNN, self.PhiNN],\n ),\n (\n [self.PhiXX, self.PhiXX],\n [self.PhiNN * 0, self.PhiNN],\n ),\n (\n [self.PhiXX * 0, self.PhiXX],\n [self.PhiNN * 0, self.PhiNN],\n ),\n (\n [self.PhiXX * np.inf, self.PhiXX],\n [self.PhiNN, self.PhiNN],\n ),\n (\n [self.PhiXX, self.PhiXX],\n [self.PhiNN * np.inf, self.PhiNN],\n ),\n (\n [self.PhiXX * np.inf, self.PhiXX],\n [self.PhiNN * np.inf, self.PhiNN],\n ),\n ]:\n with tc.assert_raises(AssertionError):\n get_beamformer(*args)","title":""},{"docid":"c6f96be8460dc39965bf228e4b5824b4","score":"0.5582808","text":"def test_grade_infinity(self):\r\n\r\n sample_dict = {'x': (1, 2)}\r\n\r\n # Test problem\r\n problem = self.build_problem(sample_dict=sample_dict,\r\n num_samples=10,\r\n tolerance=\"1%\",\r\n answer=\"x\")\r\n # Expect such a large answer to be marked incorrect\r\n input_formula = \"x*1e999\"\r\n self.assert_grade(problem, input_formula, \"incorrect\")\r\n # Expect such a large negative answer to be marked incorrect\r\n input_formula = \"-x*1e999\"\r\n self.assert_grade(problem, input_formula, \"incorrect\")","title":""},{"docid":"3970ea1a8f568df9981d1731c7a124ff","score":"0.55817133","text":"def ok_floats():\n # copy float list so we don't change the numpy global\n floats = np.sctypes['float'][:]\n if best_float() != np.longdouble and np.longdouble in floats:\n floats.remove(np.longdouble)\n return sorted(floats, key=lambda f: type_info(f)['nmant'])","title":""},{"docid":"45df7ff562ca7471ea1578e57649ddaa","score":"0.5577971","text":"def assert_no_error(self): \r\n Nx = self['Nx']\r\n Nt = self.m.Nt\r\n L, T = self.problem['L T'.split()]\r\n L = L/2 # only half the domain used (symmetry)\r\n x = np.linspace(0, L, Nx+1) # Mesh points in space \r\n t = np.linspace(0, T, Nt+1) # Mesh points in time\r\n \r\n for n in range(len(t)):\r\n u_e = self.problem.u_exact(x, t[n])\r\n diff = np.abs(self.f.u[n,:] - u_e).max()\r\n print 'diff:', diff\r\n tol = 1E-13\r\n assert diff < tol","title":""},{"docid":"ecccf4e39c0a5d64bf9ca9b59352f795","score":"0.55736053","text":"def find_single_eigen(Potential,\n energy_guess,\n rmin, rmax, npoints,\n tolerance,\n max_iterations=100,\n diagnostics=False):\n\n count = 0\n energies = [energy_guess, ]\n h = (rmax - rmin) / (npoints - 1)\n while count < max_iterations:\n count += 1\n if diagnostics:\n print(\"Trying energy: \", energies[-1])\n new_energy, psi = update_energy(Potential, energies[-1],\n rmin, rmax, npoints)\n if diagnostics:\n f = open(\"diagnostic_wavefunction_generated.dat\", \"w\")\n for i, apsi in enumerate(psi):\n f.write(\"%f %f\\n\" % (rmin + i * h, apsi))\n f.close()\n\n energies.append(new_energy)\n if ((count >= 3) and\n (abs(energies[-1] - energies[-2]) <= tolerance) and\n (abs(energies[-2] - energies[-3]) <= tolerance)):\n break # energy converged\n else: # \"max_iterations\" reached without success:\n message = (\"Maximum iterations exceeded: %d\" % max_iterations)\n raise MaxIterationsError(message)\n # normalize wavefunction:\n factor = 1.0 / math.sqrt(h * sum([apsi * apsi for apsi in psi]))\n normalized_psi = [apsi * factor for apsi in psi]\n return {\"energy\": energies[-2], # this is energy corresponding to psi\n # not the most recent\n \"psi\": normalized_psi,\n \"rmin\": rmin,\n \"h\": h}","title":""},{"docid":"066c0d37e4d28cf72b273e077a9bc391","score":"0.5562425","text":"def set_normal_free_energy(self):\n\t\t\n\t\tself.eps_base = self.mu_eps + self.normal_eps_tuning_prefactor* \\\n\t\t\t\t\t\tsp.exp(-(1.*sp.arange(self.Mm))**2.0/(2.0* \\\n\t\t\t\t\t\tself.normal_eps_tuning_width)**2.0)\n\t\t\t\t\t\t\n\t\tself.eps_base += random_matrix(self.Mm, params=[0, self.sigma_eps], \n\t\t\t\t\t\t\t\t\t\tseed=self.seed_eps)\n\t\t\n\t\t# If dual signal, use the average of the FULL signal nonzero components\n\t\tif self.Kk_split == 0:\n\t\t\tself.eps = self.WL_scaling*sp.log(self.mu_Ss0) + self.eps_base \n\t\telse:\n\t\t\tself.eps = self.WL_scaling*sp.log(sp.average(self.Ss\\\n\t\t\t\t\t\t\t[self.Ss != 0])) + self.eps_base\n\t\t\n\t\t# Apply max epsilon value to each component\n\t\tfor iM in range(self.Mm):\n\t\t\tif self.eps[iM] > self.max_eps:\n\t\t\t\tself.eps[iM] = self.max_eps\n\t\t\tif self.eps[iM] < self.min_eps:\n\t\t\t\tself.eps[iM] = self.min_eps","title":""},{"docid":"c7e8773431bb01f3ccdb53ff719d33f6","score":"0.55589235","text":"def check_k_matrix_stability(self):\r\n K = self.make_k_matrix()\r\n vals, vects = scipy_sparse_eigens(K)\r\n principal_val = vals.max()\r\n print(\"ht3_solver:\\t'Stiffness' matrix principal eigenvalue was \"\r\n + str(principal_val))\r\n if principal_val > 1:\r\n print(\"##########################################################\")\r\n print(\"ht3_solver:\\tWARNING\")\r\n print(\"ht3_solver:\\tPrincipal eigenvalue is more than one.\")\r\n print(\"ht3_solver:\\tThe analysis will be unstable.\")\r\n print(\"ht3_solver:\\tIf this is OK, just go and modify the code \"\r\n + \"or something.\")\r\n print(\"##########################################################\")\r\n raise(AssertionError)","title":""},{"docid":"8b589fb1b89b8cabc6ced0f1103e9917","score":"0.554395","text":"def E0_prime(self):\n delta_electrons = self._GetElectronDiff()\n assert delta_electrons != 0\n return - self.DeltaG0Prime() / (constants.F*delta_electrons)","title":""},{"docid":"3d6e6188733afde76946bf09ea21a57e","score":"0.5536128","text":"def is_close_to_zero(value: Union[float, np.ndarray]) -> Union[bool, np.ndarray]:\n return abs(value) < 1.0e-10","title":""},{"docid":"072dd08b86cee3f6734a572821c9903d","score":"0.5534667","text":"def test_inf_well():\n file = \"tests/inf_well/schroedinger.inp\"\n x_min, x_max, n_point, ev_first, ev_last, interp_type, potential_decl, alpha = sd.read_inp(file)\n potential_dat = sd.potential_discret(x_min, x_max, n_point, interp_type, potential_decl)\n eigenvektors, eigenvalues = sd.solve_wavefuncs(n_point, ev_first, ev_last, potential_dat, alpha)\n sd.solve_expvalues(ev_first, ev_last, potential_dat, eigenvektors)\n potential_exp = np.loadtxt(\"tests/inf_well/potential.exp\")\n eigenvalues_exp = np.loadtxt(\"tests/inf_well/eigenvalues.exp\")\n assert np.all(eigenvalues-eigenvalues_exp < 1e-10)\n assert np.all(potential_dat-potential_exp < 1e-10)","title":""},{"docid":"022d249f29e5e66564cc01738016e5c5","score":"0.5523558","text":"def testscfenergy(self):\r\n scf = self.data.scfenergies[-1]\r\n ref = self.b3lyp_energy\r\n tol = self.b3lyp_tolerance\r\n msg = f\"Final SCF energy: {scf:f} not {int(ref)} +- {int(tol)}eV\"\r\n assert abs(scf-ref) < 40, msg","title":""},{"docid":"d940b4cf846b1c6d4329024c5b85db94","score":"0.5522695","text":"def _compute_bare_spectrum_constant(self):\n eigendata = []\n for subsys in self._hilbertspace:\n if subsys not in self.subsys_update_list:\n evals_count = subsys.truncated_dim\n eigendata.append(subsys.eigensys(evals_count=evals_count))\n else:\n eigendata.append(None)\n return eigendata","title":""},{"docid":"8034fe1393e60001cb7a32cfeeb6dac9","score":"0.55207247","text":"def get_initial_epsilon(self):\n return self.epsilon_percentile, True, self.max_rounds == 0","title":""},{"docid":"65a6a3e877c2b16774dd5e7656a89979","score":"0.5489698","text":"def test_Sobol_G_raises_error_if_values_lt_zero():\n with raises(ValueError):\n evaluate(np.array([0, -1, -.02, 1, 1, -0.1, -0, -12]))","title":""},{"docid":"fbf432a5c0a8b6c1fc5149f3083a13b4","score":"0.54879385","text":"def est_nul(self):\n\t\tif self.__valide:\n\t\t\treturn (self.degre() == 0) and (self.valuation().est_nul())\n\t\telse:\n\t\t\treturn False","title":""},{"docid":"75f510b8e03695c87794c65df128ca6c","score":"0.5480385","text":"def read_eigenvalues(self):\n # Eigenvalue line indexes\n index_eig_begin = None\n for iline, line in enumerate(self.lines):\n fstring = 'eigenvalues '\n if line.find(fstring) >= 0:\n index_eig_begin = iline + 1\n line1 = line.replace(':', ',')\n ncol, nband, nkpt, nspin = map(int, line1.split(',')[-4:])\n break\n else:\n return None\n\n # Take into account that the last row may lack \n # columns if nkpt * nspin * nband % ncol != 0\n nrow = int(np.ceil(nkpt * nspin * nband * 1. / ncol))\n index_eig_end = index_eig_begin + nrow\n ncol_last = len(self.lines[index_eig_end - 1].split())\n self.lines[index_eig_end - 1] += ' 0.0 ' * (ncol - ncol_last)\n\n eig = np.loadtxt(self.lines[index_eig_begin:index_eig_end]).flatten()\n eig *= Hartree\n N = nkpt * nband\n eigenvalues = [eig[i * N:(i + 1) * N].reshape((nkpt, nband))\n for i in range(nspin)]\n\n return eigenvalues","title":""},{"docid":"8041aef006a353635358a390e2c13d21","score":"0.5478622","text":"def test_undesirable_deviations():\n f = np.asarray([\n [0.99, 1.0, 0.5],\n [0.69, 0.6, 0.6]])\n R = common_metrics.undesirable_deviations(f, maximise=True)\n expected = np.asarray(\n [-0.245, 0.0])\n assert np.allclose(R, expected)\n R = common_metrics.undesirable_deviations(f, maximise=False)\n expected = np.asarray(\n [-0.005, -0.045])\n assert np.allclose(R, expected)","title":""},{"docid":"ab0c2905a9dbcbd43e2c5259ba0f5d54","score":"0.54771477","text":"def calculate_eigens(self):\n covariance_matrix = np.cov(self.predictor_vars_train.T)\n eigenvalues, eigenvectors = np.linalg.eig(covariance_matrix)\n idx = eigenvalues.argsort()[::-1]\n # Create \"All\" version\n self.eigenvalues_all = eigenvalues[idx]\n self.eigenvectors_all = eigenvectors[:, idx]\n # Create selected percentage version with cutoff\n eigenvalues_pct = self.eigenvalues_all / np.sum(self.eigenvalues_all)\n self.pct_var_exp_cumulative_all = np.cumsum(eigenvalues_pct)\n self.pct_var_exp_cumulative = self.pct_var_exp_cumulative_all[\n self.pct_var_exp_cumulative_all <= self.variance_explained_cutoff\n ]\n self.eigenvectors = self.eigenvectors_all[:, : len(self.pct_var_exp_cumulative)]\n self.eigenvalues = self.eigenvalues_all[: len(self.pct_var_exp_cumulative)]","title":""},{"docid":"a7fbb9d785edc7943aff4b35aa98746e","score":"0.54747474","text":"def read_eigenvalues(self):\n # Eigenvalue line indexes\n index_eig_begin = None\n for iline, line in enumerate(self.lines):\n fstring = 'eigenvalues '\n if line.find(fstring) >= 0:\n index_eig_begin = iline + 1\n line1 = line.replace(':', ',')\n ncol, nband, nkpt, nspin = map(int, line1.split(',')[-4:])\n break\n else:\n return None\n\n # Take into account that the last row may lack\n # columns if nkpt * nspin * nband % ncol != 0\n nrow = int(np.ceil(nkpt * nspin * nband * 1. / ncol))\n index_eig_end = index_eig_begin + nrow\n ncol_last = len(self.lines[index_eig_end - 1].split())\n if ncol - ncol_last > 0:\n self.lines[index_eig_end - 1] = self.lines[index_eig_end - 1].replace('\\n', '')\n self.lines[index_eig_end - 1] += ' 0.0 ' * (ncol - ncol_last)\n self.lines[index_eig_end - 1] += '\\n'\n eig = np.loadtxt(self.lines[index_eig_begin:index_eig_end]).flatten()\n eig *= Hartree\n N = nkpt * nband\n eigenvalues = [eig[i * N:(i + 1) * N].reshape((nkpt, nband))\n for i in range(nspin)]\n\n return eigenvalues","title":""},{"docid":"2083bcc905565c4f414c5276ff54b3e3","score":"0.5474369","text":"def is_pos_def(x):\n rtol = 1e-05\n atol = 1e-08\n return np.all(np.linalg.eigvals(x) > 0) and np.allclose(\n x, x.T, rtol=rtol, atol=atol\n )","title":""},{"docid":"e870de10676816106b6804e091728777","score":"0.5466653","text":"def is_zero(self):\n return -0.0001 <= self.l2_norm() <= 0.0001","title":""},{"docid":"8cb07ccae35458fc2f4d9d96d3bd285c","score":"0.54654443","text":"def testCalcCorrNaN(self):\n self.assertEqual(INF, calc_corr(2.050000, INF, EXP_KBT))","title":""},{"docid":"e5260feb1a675afd22378df2a742ef22","score":"0.5460692","text":"def test_null_from_exponential(self):\n scale = 5\n floats = False\n actual_out = null_from_exponential(self.samples, self.otus, scale, floats)\n expected_out = \\\n array([[ 4., 6., 5., 4., 3.],\n [ 5., 3., 11., 17., 2.],\n [ 8., 4., 4., 13., 0.],\n [ 0., 0., 9., 8., 10.],\n [ 19., 8., 3., 8., 1.],\n [ 5., 1., 14., 4., 3.],\n [ 2., 7., 3., 4., 0.],\n [ 5., 5., 5., 14., 6.],\n [ 2., 3., 6., 0., 5.],\n [ 6., 1., 1., 2., 2.]])\n self.assertEqual(expected_out, actual_out)\n #test with ints off\n floats = True\n actual_out = null_from_exponential(self.samples, self.otus, scale, floats)\n expected_out = \\\n array([[ 4.2221389 , 2.88662155, 22.2724869 , 0.53817556,\n 1.17150758],\n [ 0.87956777, 5.29371362, 1.46040267, 3.13970791,\n 1.40138507],\n [ 0.86563726, 0.58477706, 5.34036095, 0.74356136,\n 1.09440301],\n [ 2.30006983, 8.60165826, 0.51072443, 9.09909462,\n 0.50517391],\n [ 18.74515725, 3.16168301, 18.80963586, 4.64239251,\n 6.72122632],\n [ 0.19988151, 1.66205123, 0.64028381, 1.75588043,\n 0.63194321],\n [ 1.91350479, 2.67442193, 0.33148698, 5.89594764,\n 4.18048773],\n [ 1.5420742 , 3.70379476, 0.49325157, 4.28947821,\n 13.24627963],\n [ 1.91780105, 5.50422961, 0.70665357, 6.29966916,\n 1.70827085],\n [ 1.01175218, 4.41564523, 0.10156227, 8.82870539,\n 0.02353267]])\n assert_allclose(expected_out, actual_out, rtol=1e-06)\n #using allclose because floats get truncated","title":""},{"docid":"8a60d5ad6e7b614d601efc8416842d00","score":"0.5459371","text":"def longdouble_precision_improved():\n return not longdouble_lte_float64() and _LD_LTE_FLOAT64","title":""},{"docid":"0002f06e866834591163dc87a7a3bbc1","score":"0.54586226","text":"def test_frequency():\n assert abs(4.59043034 - waves.frequency(waves.g, 3.0, 0.3)) < 1e-7\n assert abs(0.0 - waves.frequency(waves.g, 0.0, 0.3)) < 1e-7\n assert abs(4.59043034 - waves.frequency(waves.g, -3.0, 0.3)) < 1e-7\n\n assert np.isnan(waves.frequency(waves.g, -3.0, 0.3)) == 0","title":""},{"docid":"eb818ed71f96657a9c08851f3363c5dd","score":"0.54503137","text":"def isfinite(self):\n return not self.isAny( (lambda x: not np.isfinite(x)) )","title":""},{"docid":"480fd6a24bd04472b9f73809040910f5","score":"0.54431283","text":"def fix_special_floats(value, _inf=INFINITY, _neginf=-INFINITY):\n\n try:\n value = convert_tensor_to_numpy(value)\n\n # Check if the value is Nan, equivalent of math.isnan\n if math.isnan(value):\n return \"NaN\"\n\n elif value == _inf:\n return \"Infinity\"\n\n elif value == _neginf:\n return \"-Infinity\"\n\n except Exception:\n # Value cannot be compared\n return value\n\n return value","title":""},{"docid":"6be3feaff7acee4b8205fcd4dc6dc61f","score":"0.54395276","text":"def aerosols(self):\n return 1.","title":""},{"docid":"714207a343873794986da19260446933","score":"0.54391634","text":"def eigenvalues(self, expand=False, factor=False, simplify=False):\n raise NotImplementedError","title":""},{"docid":"0a853ebc8741f672a05509dd6a142380","score":"0.54379994","text":"def _sigma_ee_nonrel(self,gam,eps):\n s0 = 4 * r0**2 * alpha / (15 * eps)\n x = 4 * eps / (gam**2 - 1)\n sigma_nonrel = s0 * self._F(x,gam)\n sigma_nonrel[np.where(eps >= 0.25*(gam**2 - 1.))] = 0.0\n sigma_nonrel[np.where(gam*np.ones_like(eps) < 1.0)] = 0.0\n return sigma_nonrel / mec2_unit","title":""},{"docid":"f005a64a38cbbb18698c392aca929f04","score":"0.5433856","text":"def get_strict_valence_flag(self):\n d_min = self.fmodel.f_obs().d_min()\n return (d_min < self.params.d_min_strict_valence)","title":""},{"docid":"b549bc31e189444d305eb5e65f6825e9","score":"0.5432304","text":"def explore_eigenvalues(self, X_train, X_test) :\r\n\r\n # scaler = StandardScaler()\r\n # X_train = scaler.fit_transform(X_train)\r\n # X_test = scaler.transform(X_test)\r\n\r\n print (\"Train feature shape:\", X_train.shape)\r\n print(\"Train feature shape:\", X_test.shape)\r\n \r\n\r\n mean_vec = np.mean(X_test, axis=0)\r\n cov_mat = (X_test - mean_vec).T.dot((X_test - mean_vec)) / (X_test.shape[0]-1)\r\n print('Covariance matrix \\n%s' %cov_mat)\r\n print('NumPy covariance matrix: \\n%s' %np.cov(X_test.T))\r\n eig_vals, eig_vecs = np.linalg.eig(cov_mat)\r\n print('Eigenvectors \\n%s' %eig_vecs)\r\n print(\"---------------------------------\")\r\n print('\\nEigenvalues \\n%s' %eig_vals)\r\n # Make a list of (eigenvalue, eigenvector) tuples\r\n eig_pairs = [(np.abs(eig_vals[i]), eig_vecs[:,i]) for i in range(len(eig_vals))]\r\n\r\n # Sort the (eigenvalue, eigenvector) tuples from high to low\r\n eig_pairs.sort(key=lambda x: x[0], reverse=True)\r\n\r\n # Visually confirm that the list is correctly sorted by decreasing eigenvalues\r\n print(\"---------------------------------\")\r\n print('Eigenvalues in descending order:')\r\n for i in eig_pairs:\r\n print(i[0])\r\n # tot = sum(eig_vals)\r\n # var_exp = [(i / tot)*100 for i in sorted(eig_vals, reverse=True)]\r\n # print(\"Explained variance : \\n{}\".format(var_exp))\r\n \r\n\r\n return X_train, X_test","title":""},{"docid":"d0044fe2a026c628775c1df00ceec4fb","score":"0.54288155","text":"def F0(t):\n if (t < 1e-6):\n return 1.0 - t / 3.0\n else:\n return 0.5 * (np.pi / t) ** 0.5 * sp.erf(t ** 0.5)","title":""},{"docid":"b8a215f14fd96ba997da4a3dcffe6444","score":"0.54255927","text":"def get_eig(self, tolerance=None):\r\n E, V = scipy.linalg.eig(self.data)\r\n E = pd.Series(E.real, name=\"EIG\")\r\n V = pd.DataFrame(V.real)\r\n if tolerance is not None:\r\n E[E/E.max() < tolerance] = 0\r\n return E, V","title":""},{"docid":"8228a0812813301ac11ed9a9d6a8d52a","score":"0.5424792","text":"def testEllipticalGaussian(self):\n #\n # Make and the objects\n #\n ab_vals = [(1.0, 0.0), (2.0, 0.0), (2.0, 0.1), ]\n nIter = 2\n for dx in (0.0, 0.5,):\n for dy in (0.0, 0.5,):\n for alpha, b in ab_vals:\n for objFlux in (1e2, 1e3, 1e4, 1e5, 1e6, ):\n flux, fluxErr, flags, psfFlux = self.makeAndMeasure(objFlux, alpha, b, dx=dx, dy=dy)\n \n failFlux = math.isnan(flux) or flags or abs(flux/objFlux - 1) > 0.25e-2\n \n ID = \"alpha,b %4.1f, %5.2f dx,dy = %.1f,%.1f \" % (alpha, b, dx, dy)\n msg = \"%s flux_DeconvolvedPsf: %9.4g v. exact value %9.4g (error %5.2f%%) (psfFlux error %5.2f%%)\" % \\\n (ID, flux, objFlux, 100*(flux/objFlux - 1), 100*(psfFlux/objFlux - 1))\n\n if False:\n print msg\n continue\n\n self.assertFalse(failFlux, msg)","title":""},{"docid":"ef5fb9e47201425978feddfea7fe425f","score":"0.54162824","text":"def almost_eq(e1,e2) :\n\treturn round(e1-e2,4) == 0.0","title":""},{"docid":"c14002fcee78cd10f0424b6ca88e5093","score":"0.5415454","text":"def eigvals(self):\n raise NotImplementedError","title":""},{"docid":"ff05d02cb50d5cd17acec412a88ea202","score":"0.5401089","text":"def test_basic_newton_finder(self):\n forwards = np.array([1.0, 1.0, 1.0, 1.0, 1.0, 1.0])\n strikes = np.array([1.0, 2.0, 1.0, 0.5, 1.0, 1.0])\n expiries = np.array([1.0, 1.0, 1.0, 1.0, 0.5, 2.0])\n discounts = np.array([1.0, 1.0, 1.0, 1.0, 1.0, 1.0])\n init_vols = np.array([2.0, 0.5, 2.0, 0.5, 1.5, 1.5])\n option_signs = np.array([1.0, 1.0, -1.0, -1.0, 1.0, 1.0])\n volatilities = np.array([1.0, 1.0, 1.0, 1.0, 1.0, 1.0])\n prices = np.array([\n 0.38292492, 0.19061012, 0.38292492, 0.09530506, 0.27632639, 0.52049988\n ])\n results = implied_vol(\n forwards,\n strikes,\n expiries,\n discounts,\n prices,\n option_signs,\n initial_volatilities=init_vols,\n max_iterations=100)\n implied_vols, converged, failed = self.evaluate(results)\n num_volatilities = len(volatilities)\n self.assertAllEqual(np.ones(num_volatilities, dtype=np.bool), converged)\n self.assertAllEqual(np.zeros(num_volatilities, dtype=np.bool), failed)\n self.assertArrayNear(volatilities, implied_vols, 1e-7)","title":""},{"docid":"778ab4b78a9d229986c50a1854562467","score":"0.5401","text":"def test_floats(self):\r\n problem_setup = [\r\n # [given_answer, [list of correct responses], [list of incorrect responses]]\r\n [1, [\"1\"], [\"1.1\"]],\r\n [2.0, [\"2.0\"], [\"1.0\"]],\r\n [4, [\"4.0\", \"4.00004\"], [\"4.00005\"]],\r\n [0.00016, [\"1.6*10^-4\"], [\"\"]],\r\n [0.000016, [\"1.6*10^-5\"], [\"0.000165\"]],\r\n [1.9e24, [\"1.9*10^24\"], [\"1.9001*10^24\"]],\r\n [2e-15, [\"2*10^-15\"], [\"\"]],\r\n [3141592653589793238., [\"3141592653589793115.\"], [\"\"]],\r\n [0.1234567, [\"0.123456\", \"0.1234561\"], [\"0.123451\"]],\r\n [1e-5, [\"1e-5\", \"1.0e-5\"], [\"-1e-5\", \"2*1e-5\"]],\r\n ]\r\n for given_answer, correct_responses, incorrect_responses in problem_setup:\r\n problem = self.build_problem(answer=given_answer)\r\n self.assert_multiple_grade(problem, correct_responses, incorrect_responses)","title":""},{"docid":"feef2f4eb26da6761fe6f1b7fe68ca2d","score":"0.5391306","text":"def test_derivative_not_in_basis_zeros(self):\n nodes = np.random.random((10, 3))\n\n basis = PowerSeries(L=3)\n ft = basis.evaluate(nodes, derivatives=[0, 1, 0])\n fz = basis.evaluate(nodes, derivatives=[0, 0, 1])\n assert np.all(ft == 0)\n assert np.all(fz == 0)\n\n basis = FourierSeries(N=4)\n fr = basis.evaluate(nodes, derivatives=[1, 0, 0])\n ft = basis.evaluate(nodes, derivatives=[0, 1, 0])\n assert np.all(fr == 0)\n assert np.all(ft == 0)\n\n basis = DoubleFourierSeries(M=2, N=4)\n fr = basis.evaluate(nodes, derivatives=[1, 0, 0])\n assert np.all(fr == 0)\n\n basis = ZernikePolynomial(L=2, M=3)\n fz = basis.evaluate(nodes, derivatives=[0, 0, 1])\n assert np.all(fz == 0)","title":""},{"docid":"c2d9903b78b2d2f95b9713cfe89f067a","score":"0.5390967","text":"def df0dE(E, fermi, T):\n exponent = (E - fermi) / (k_B * T)\n if exponent > 40 or exponent < -40: # This is necessary so at too low numbers python doesn't return NaN\n return 1e-32\n else:\n return -1 / (k_B * T) * np.exp((E - fermi) / (k_B * T)) / (1 + np.exp((E - fermi) / (k_B * T))) ** 2","title":""},{"docid":"f6ce6ad51577b2ff2646bbaa637ad6ff","score":"0.53877646","text":"def check(mat, otp):\n prd = mat*otp\n eigval = prd[0]/otp[0]\n print 'computed eigenvalue :' , eigval\n [eigs, vecs] = np.linalg.eig(mat)\n abseigs = list(abs(eigs))\n ind = abseigs.index(max(abseigs))\n print ' largest eigenvalue :', eigs[ind]","title":""},{"docid":"68b13223b82afd948fe9784cccc8c4e1","score":"0.5386818","text":"def check_nullification(full_gam, feature_combination_full, threshold = 1e-7):\n\n nullified_features = []\n\n for i in range(len(feature_combination_full)):\n XX = full_gam.generate_X_grid(term=i)\n\n term_funct = full_gam.partial_dependence(term=i, X=XX)\n\n std = np.std(term_funct)# Use standard deviation to check for nullification\n\n if std < threshold:\n nullified_features.append(feature_combination_full[i])\n\n return nullified_features","title":""},{"docid":"a202e2124a7cfa9853dfebad0fcdb08b","score":"0.53823644","text":"def test_single_spectrum_with_positive_power(self):\n power = 0.69314718055\n nspec = 1\n assert np.isclose(cospectra_pvalue(power, nspec), 0.25)","title":""},{"docid":"7bedb59ea48a3b72b1622a45bde8aac5","score":"0.5382051","text":"def eigenalgo(self, accuracy: float = 0, cap: int = 50000, version: str = \"Givens\", not_skip: bool = True):\n j, temps, verify_accuracy = 0, 0, np.ones((self.N, self.N), dtype=bool) ^ np.eye(self.N, dtype=bool)\n if version == \"Gram-Schmidt\":\n temps = time()\n while np.any(abs(self.vap[verify_accuracy]) > accuracy) and j < cap:\n j += 1\n q, r = self.gram_schmidt_qr()\n self.vap, self.vep = r @ q, self.vep @ q\n\n elif version == \"Givens\":\n verify_accuracy = np.ones((self.N, self.N), dtype=bool) ^ np.eye(self.N, dtype=bool)\n temps = time()\n while np.any(abs(self.vap[verify_accuracy]) > accuracy) and j < cap:\n j += 1\n q, r = self.givens_qr()\n self.vap, self.vep = r @ q, self.vep @ q\n\n elif version == \"Rayleigh\":\n not_sing, diff, cond, j = True, accuracy + 1, True, 0\n temps = time()\n while cond: # Stop condition, all eigenvalues must be different\n while diff > accuracy and j < cap and not_sing:\n j += 1\n self.rvap, self.vep, diff, not_sing = self.rayleigh_iteration(self.rvap, self.vep)\n\n cond = False\n if j < cap:\n self.calc, first, not_sing = np.zeros(self.N, dtype=bool), True, True\n for i in range(self.N):\n if np.sum(np.less(np.abs(self.rvap - self.rvap[i]), 10 ** -6)) != 1:\n self.rvap[i + 1:] += self.memorize[i]\n if first:\n self.memorize[i] += 0.5\n self.vep[i + 1:, i + 1:] = np.eye(self.N - i - 1)\n first, cond, diff = False, True, accuracy + 1\n self.calc[i + 1:] = 1\n temps = time() - temps\n return self.rvap, self.vep, diff, j, temps\n\n else:\n print(\"Please select an appropriate value for the version parameter\")\n\n temps = time() - temps\n diff = np.max(abs(self.vap[verify_accuracy]))\n return np.diag(self.vap), self.vep, diff, j, temps","title":""},{"docid":"e5481a624d60eb548367738fe7d4a5f4","score":"0.53814787","text":"def test_parallel_resistors_with_zero(self):\r\n self.assertTrue(numpy.isnan(calc.evaluator({}, {}, '0||1')))\r\n self.assertTrue(numpy.isnan(calc.evaluator({}, {}, '0.0||1')))\r\n self.assertTrue(numpy.isnan(calc.evaluator({'x': 0.0}, {}, 'x||1')))","title":""},{"docid":"335a38bbc3d15d4806763baa5f001a04","score":"0.53618467","text":"def best_float():\n try:\n long_info = type_info(np.longdouble)\n except FloatingError:\n return np.float64\n if (long_info['nmant'] > type_info(np.float64)['nmant'] and\n machine() != 'sparc64'): # sparc has crazy-slow float128\n return np.longdouble\n return np.float64","title":""},{"docid":"dc9f84e5243b65b17672ceed6890db36","score":"0.5360974","text":"def _compute_bare_spectrum_constant(self) -> List[Tuple[ndarray, ndarray]]:\n eigendata = []\n for subsys in self._hilbertspace:\n if subsys not in self.subsys_update_list:\n evals_count = subsys.truncated_dim\n eigendata.append(subsys.eigensys(evals_count=evals_count))\n else:\n eigendata.append(None) # type: ignore\n return eigendata","title":""},{"docid":"44afc9ad5a864c55382161eae9ddaf88","score":"0.5353698","text":"def Find_Lowest_Energy_Structure_Electrostatics(self):\n n_Na = self.structure.composition['Na']\n n_S = self.structure.composition['S']\n n_O = self.structure.composition['O']\n n_N = self.structure.composition['N']\n n_Fe = self.structure.composition['Fe']\n\n n_Fe_reduced = self.variable_magnetization_dict['Fe']['n_reduced']\n n_Fe_oxidized = n_Fe-n_Fe_reduced \n\n N_charge = ( 2.*n_O-6.*n_S-n_Na-3.*n_Fe_oxidized-2.*n_Fe_reduced )/n_N\n\n oxidation_states = {'Na':+1, 'Fe':+3, 'O':-2,'S':+6,'N':N_charge}\n Fe_2plus = pymatgen.Specie('Fe',oxidation_state=+2)\n\n structure_with_charges = self.structure.copy()\n structure_with_charges.add_oxidation_state_by_element(oxidation_states) \n\n # identify Fe sites\n list_Fe_indices = []\n for i,site in enumerate(structure_with_charges):\n if site.specie.symbol == 'Fe':\n list_Fe_indices.append(i)\n\n # Generate all possible permutation of sites and compute \n # Ewald energy\n ewald_model = EwaldElectrostaticModel(acc_factor=6)\n list_reduced_sets = []\n list_ewald_energy = []\n for reduced_set in itertools.combinations(list_Fe_indices,n_Fe_reduced):\n list_reduced_sets.append(reduced_set) \n\n struct = structure_with_charges.copy()\n for i in reduced_set:\n struct.replace(i, Fe_2plus)\n\n list_ewald_energy.append(ewald_model.get_energy(struct))\n\n if len(list_ewald_energy) == 0:\n # all sites are oxidized. No sorting involved \n list_reduced_site_indices = []\n list_oxidized_site_indices = list_Fe_indices\n else:\n # some reduction takes place. Identify best electrostatic choice\n\n imin = np.argmin(list_ewald_energy)\n\n list_reduced_site_indices = list_reduced_sets[imin] \n list_oxidized_site_indices = []\n for i in list_Fe_indices:\n if i not in list_reduced_site_indices: \n list_oxidized_site_indices.append(i) \n\n\n return list_reduced_site_indices, list_oxidized_site_indices","title":""},{"docid":"659fb48fbf121e98f085112af7dc78d0","score":"0.5352033","text":"def test_difficulties_eps_multi(self):\n well_w = self.get_w_well_behaviour()\n\n def get_beamformer(A, B):\n return get_mvdr_vector_souden(\n A, B,\n return_ref_channel=True\n )\n\n for args in [\n (\n [self.PhiXX * 0, self.PhiXX],\n [self.PhiNN, self.PhiNN],\n ),\n (\n [self.PhiXX, self.PhiXX],\n [self.PhiNN * 0, self.PhiNN],\n ),\n (\n [self.PhiXX * 0, self.PhiXX],\n [self.PhiNN * 0, self.PhiNN],\n ),\n ]:\n w, ref_channel = get_beamformer(*args)\n assert ref_channel == 2, ref_channel\n np.testing.assert_allclose(\n w,\n np.array([[0., 0., 0.], well_w])\n )\n\n for args in [\n (\n [self.PhiXX * np.inf, self.PhiXX],\n [self.PhiNN, self.PhiNN],\n ),\n (\n [self.PhiXX, self.PhiXX],\n [self.PhiNN * np.inf, self.PhiNN],\n ),\n (\n [self.PhiXX * np.inf, self.PhiXX],\n [self.PhiNN * np.inf, self.PhiNN],\n ),\n ]:\n with tc.assert_raises(AssertionError):\n get_beamformer(*args)","title":""},{"docid":"b969f7e9aba15a3964446cc91fb14f80","score":"0.53512454","text":"def quadratic_root_goes_to_infinity():\n for dt in 1E-7, 1E-12, 1E-16:\n a = dt\n b = 1 - dt\n c = -0.1\n print((dt, quadratic_roots(a, b, c)))","title":""},{"docid":"5c8a44f4738504b2c54e89b999a320de","score":"0.53359556","text":"def test_zero_parameters(self):\n problem = TimeEvolutionProblem(self.hamiltonian, time=0.02)\n sampler = Sampler()\n fidelity_primitive = ComputeUncompute(sampler)\n\n pvqd = PVQD(\n fidelity_primitive,\n QuantumCircuit(2),\n np.array([]),\n optimizer=SPSA(maxiter=10, learning_rate=0.1, perturbation=0.01),\n )\n\n with self.assertRaises(QiskitError):\n _ = pvqd.evolve(problem)","title":""},{"docid":"8984f8a8ef4ef9b03fd89c8afd9bce71","score":"0.5334759","text":"def find_epsilon(self, inverse=False):\n e_s = self.e_s if not inverse else self.e_s_inv\n\n max_score = -10000000\n\n for z in np.arange(2.5, self.sd_lim, 0.5):\n epsilon = self.mean_e_s + (self.sd_e_s * z)\n\n pruned_e_s = e_s[e_s < epsilon]\n\n i_anom = np.argwhere(e_s >= epsilon).reshape(-1,)\n buffer = np.arange(1, self._error_buffer)\n i_anom = np.sort(np.concatenate((i_anom,\n np.array([i+buffer for i in i_anom])\n .flatten(),\n np.array([i-buffer for i in i_anom])\n .flatten())))\n i_anom = i_anom[(i_anom < len(e_s)) & (i_anom >= 0)]\n i_anom = np.sort(np.unique(i_anom))\n\n if len(i_anom) > 0:\n # group anomalous indices into continuous sequences\n groups = [list(group) for group\n in mit.consecutive_groups(i_anom)]\n E_seq = [(g[0], g[-1]) for g in groups if not g[0] == g[-1]]\n\n mean_perc_decrease = (self.mean_e_s - np.mean(pruned_e_s)) \\\n / self.mean_e_s\n sd_perc_decrease = (self.sd_e_s - np.std(pruned_e_s)) \\\n / self.sd_e_s\n score = (mean_perc_decrease + sd_perc_decrease) \\\n / (len(E_seq) ** 2 + len(i_anom))\n\n # sanity checks / guardrails\n if score >= max_score and len(E_seq) <= 5 and \\\n len(i_anom) < (len(e_s) * 0.5):\n max_score = score\n if not inverse:\n self.sd_threshold = z\n self.epsilon = self.mean_e_s + z * self.sd_e_s\n else:\n self.sd_threshold_inv = z\n self.epsilon_inv = self.mean_e_s + z * self.sd_e_s","title":""},{"docid":"348e67507831e864863e5d5c3c1c750d","score":"0.5327928","text":"def epsilon():\n return _EPSILON","title":""},{"docid":"8f0b46b1b2d14e862e3ea2ce0a3e1cb5","score":"0.53216165","text":"def check_for_float(check):","title":""},{"docid":"eaa4d37dac6a3f3905583b1138408c9e","score":"0.5314363","text":"def test_exp():\n x = np.linspace(-3,3,13)\n\n default_use_numexpr = accel_math._USE_NUMEXPR\n\n accel_math._USE_NUMEXPR = True\n r1 = accel_math._exp(x)\n\n accel_math._USE_NUMEXPR = False\n r2 = accel_math._exp(x)\n\n np.testing.assert_almost_equal(r1,r2)\n\n accel_math._USE_NUMEXPR = default_use_numexpr","title":""},{"docid":"c9e615ff81ee864a740cdc85707ec10c","score":"0.53078824","text":"def test_e0_ts(self):\n self.assertAlmostEqual(self.tunneling.E0_TS.value_si * 0.001, self.E0_TS, 4)","title":""},{"docid":"3b76c31b45f88183f86caa3196c5e4c2","score":"0.5299172","text":"def is_finite(self):\n return False","title":""},{"docid":"78d0239fb2935ad8e43d85c99af624c6","score":"0.5298558","text":"def ensure_positive_precision(K):\n K_diag = np.diag(np.diag(K))\n K = np.where(np.any(np.diag(K) < 0), np.where(K_diag < 0, 1e-2, K_diag), K)\n return K","title":""},{"docid":"ee76ef2f6d9a294c26ef7b55509f3a29","score":"0.52962893","text":"def test_despike(curve):\n assert curve.df.max()[0] - curve.despike(50, z=1).df.max()[0] - 91.83918 < 0.001","title":""},{"docid":"426541e10f1f88608c357473d1e5e8aa","score":"0.52865666","text":"def e(self):\n if self._e is None:\n # self._e = self.distributions.uniform(0.3,0.33)\n # return self._e\n # max is set by q but also limited by users choice of e_max.\n res_a = 29.9*((self.j[0]/self.k[0])**(2/3))\n q = self.distributions.truncated_normal(self.q_c, self.q_w, res_a*(1-0.8), res_a*(1-0.001))\n self._e = 1 - q/res_a\n return self._e","title":""},{"docid":"ca0767e345ac1daf9c0428736e8a73a6","score":"0.52842927","text":"def test_Sobol_G_raises_error_if_values_gt_one():\n evaluate(np.array([0, 1, .02, 0.23, 1.234, 0.02848848, 0, 0.78]))","title":""},{"docid":"0b22ba99427f15fc2ce8cb809852bc6a","score":"0.5277587","text":"def convergence_check(self):\n air = self.air_alias.val\n flue_gas = self.fuel_alias.val + '_fg'\n fuel = self.fuel_alias.val\n\n for c in self.outl:\n if not c.fluid.val_set[air]:\n if c.fluid.val[air] > 0.95:\n c.fluid.val[air] = 0.95\n if c.fluid.val[air] < 0.5:\n c.fluid.val[air] = 0.5\n\n if not c.fluid.val_set[flue_gas]:\n if c.fluid.val[flue_gas] > 0.5:\n c.fluid.val[flue_gas] = 0.5\n if c.fluid.val[flue_gas] < 0.05:\n c.fluid.val[flue_gas] = 0.05\n\n if not c.fluid.val_set[fuel]:\n if c.fluid.val[fuel] > 0:\n c.fluid.val[fuel] = 0\n\n c.target.propagate_fluid_to_target(c, c.target)\n\n for i in self.inl:\n if i.m.val_SI < 0 and not i.m.val_set:\n i.m.val_SI = 0.01\n\n for c in self.outl:\n if c.m.val_SI < 0 and not c.m.val_set:\n c.m.val_SI = 10\n c.target.propagate_fluid_to_target(c, c.target)\n\n if self.lamb.val < 1 and not self.lamb.is_set:\n self.lamb.val = 2","title":""},{"docid":"360d1c6fa02dbde7f3d2e3fc817a67c1","score":"0.5276822","text":"def log_det_precisions(self):\n raise NotImplementedError","title":""},{"docid":"d13ed02b9a47e485024fe75a6510a7b9","score":"0.52733415","text":"def _check_eigenmatrices(self):\n if self._has(\"P\") and self._has(\"Q\") and \\\n _simplify(_expand(self._.P * self._.Q)) \\\n != self.order(expand=True, simplify=True) \\\n * identity_matrix(SR, self._.d + 1):\n warn(Warning(\"the eigenmatrices do not multiply \"\n \"into a multiple of the identity matrix\"))","title":""},{"docid":"1f4a575228e08d8e753165779bcea945","score":"0.52689487","text":"def eigenvalues(self) -> ndarray:\n return self._vals","title":""},{"docid":"a2c8ff7438c88632b3f58fb97e9b97c1","score":"0.52661246","text":"def test_xml_eigenvalues_specific(xml_parser_specific):\n\n eigenvalues = xml_parser_specific.get_eigenvalues_specific()\n kpoints = xml_parser_specific.get_kpoints_specific()\n kpointsw = xml_parser_specific.get_kpointsw_specific()\n assert eigenvalues['total'].shape == (20, 10)\n assert kpoints.shape == (10, 3)\n assert kpointsw.shape == (10,)\n test = np.array([-6.076 , -6.0578, -6.0031, -5.9122, -5.7858, -5.6246, -5.4302,\n -5.2048, -4.9517, -4.6768])\n np.testing.assert_allclose(eigenvalues['total'][0], test) \n test = np.array([ 9.5694, 9.6943, 10.0793, 10.646 , 11.2894, 11.9561, 12.6122,\n 13.2292, 13.6978, 13.9563])\n np.testing.assert_allclose(eigenvalues['total'][7], test) \n test = np.array([[ 0. ,0. ,0. ],\n [ 0.03703704 ,0. ,0. ],\n [ 0.07407407 ,0. ,-0. ],\n [ 0.11111111 ,0. ,-0. ],\n [ 0.14814815 ,0. ,-0. ],\n [ 0.18518519 ,-0. ,0. ],\n [ 0.22222222 ,-0. ,0. ],\n [ 0.25925926 ,-0. ,0. ],\n [ 0.2962963 ,-0. ,-0. ],\n [ 0.33333333 ,-0. ,-0. ]])\n np.testing.assert_allclose(kpoints, test)\n test = np.array([5.0810e-05, 4.0644e-04, 4.0644e-04, 4.0644e-04, 4.0644e-04, 4.0644e-04,\n 4.0644e-04, 4.0644e-04, 4.0644e-04, 4.0644e-04])\n np.testing.assert_allclose(kpointsw, test)","title":""},{"docid":"605a0731763311f822306d8abb0b7d69","score":"0.5262909","text":"def test_extreme_values(self):\n with pytest.warns(RuntimeWarning) as warninfo:\n assert np.exp(laplace_approx(999999, 999999, self.data)) == 0\n with pytest.warns(RuntimeWarning) as warninfo:\n assert np.exp(laplace_approx(999999, 999999, self.data)) == 0","title":""},{"docid":"45429e7c4fe6bc1af8a88ae383e35347","score":"0.5252851","text":"def is_algebraically_stable(self):\n import numpy as np\n\n if np.any(self.b < 0):\n return False\n\n # Check the eigenvalues for positive definiteness.\n #\n # We could also check whether the Cholesky factorization fails (=> not pos. def.).\n # However, since we are interested in positive SEMIdefinite matrices, we have to\n # regularize M by adding a small multiple of the identity matrix. Since \"small\"\n # depends on the floating point type and this is probably not a bottleneck, using\n # the eigenvalues seems to be fine.\n rk = self.__num__()\n B = np.diag(rk.b)\n M = B.dot(rk.A) + rk.A.T.dot(B) - np.outer(rk.b, rk.b)\n isposdef = np.all(np.linalg.eigvals(M) >= -10 * np.finfo(M.dtype).eps)\n\n return isposdef","title":""},{"docid":"3950f1f164983d9b867dea18836366ad","score":"0.5246538","text":"def check_non_singular(self, Am):\r\n det = self.detf(Am)\r\n if det != 0:\r\n return det\r\n else:\r\n raise ArithmeticError(\"Singular Matrix!\")","title":""},{"docid":"a8c23d69f397617a7dd870598be50e51","score":"0.52420014","text":"def test_eigenvalues_in_cylinders(self):\n random.seed(102938482634)\n point_cloud = load(os.path.join('testdata', 'AHN3.las'))\n num_all_pc_points = len(point_cloud[keys.point][\"x\"][\"data\"])\n rand_indices = [random.randint(0, num_all_pc_points) for _ in range(20)]\n target_point_cloud = utils.copy_point_cloud(point_cloud, rand_indices)\n radius = 2.5\n neighbors = compute_neighbors.compute_cylinder_neighborhood(point_cloud, target_point_cloud, radius)\n\n compute_features(point_cloud, neighbors, target_point_cloud,\n [\"eigenv_1\", \"eigenv_2\", \"eigenv_3\"], InfiniteCylinder(5))\n\n self.assertEqual(\"laserchicken.feature_extractor.eigenvals_feature_extractor\",\n target_point_cloud[keys.provenance][-1][\"module\"])","title":""},{"docid":"266a60926c33696c39d99b4424dc0e00","score":"0.5241262","text":"def test_t0(self):\n sol = Mader(p_cj=3.0e11, d_cj=8.0e5, gamma=3.0, u_piston=0.0)\n # r must contain 2 elements, otherwise the density and pressure are nan\n r = np.array([0.7, 0.8])\n t = 0.0\n solrt = sol(r, t)\n for quant in ['velocity', 'pressure', 'sound_speed', 'density', 'xdet']:\n assert np.all(np.isnan(solrt[quant]))","title":""},{"docid":"1fc8ecb6a21bffdb74a1a8406e059a7b","score":"0.523657","text":"def test_float(self):\n self.assertFalse(validate_measure_input('0.0', self.measures))\n self.assertFalse(validate_measure_input('1.0', self.measures))\n self.assertFalse(validate_measure_input('1.1', self.measures))","title":""},{"docid":"b558219319b55b133660de9340d4e33a","score":"0.5231793","text":"def check_all_finite(value):\n if all_finite(value):\n return\n\n invalid = np.logical_not(np.isfinite(value))\n msg = show_some(value, invalid, 'finite')\n\n msg += '\\n%s %s ' % (np.min(value), np.max(value))\n raise ValueError(msg)","title":""},{"docid":"adb2a7d77cd3bdf5303b291e87b8f1eb","score":"0.52210504","text":"def guestimate_init_cond(x_data):\n x0 = np.clip(np.max(x_data, 1), 1e-4, np.inf)\n return x0","title":""},{"docid":"23c3229ae2f3a12d0666a4f15107d4ed","score":"0.5219297","text":"def iszero(a: float) -> bool:\n return np.isclose(a, 0.0, atol=1.0e-12, rtol=0.0)","title":""}],"string":"[\n {\n \"docid\": \"ffa4c774fada3e0ed2a46ad5aab82bec\",\n \"score\": \"0.74455935\",\n \"text\": \"def test_eigenvalues_of_too_few_points_results_in_0():\\n a = np.array([5])\\n pc = create_point_cloud(a, a, a)\\n\\n compute_features(pc, [[0]], pc, [\\\"eigenv_1\\\", \\\"eigenv_2\\\", \\\"eigenv_3\\\"], InfiniteCylinder(5))\\n\\n eigen_val_123 = np.array([pc[keys.point]['eigenv_{}'.format(i)]['data'] for i in [1, 2, 3]])\\n assert not np.any(np.isnan(eigen_val_123))\\n assert not np.any(np.isinf(eigen_val_123))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d8a85699193ab642a6cf084a26cc92ef\",\n \"score\": \"0.63962203\",\n \"text\": \"def erfc(x):\\n return 0.0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08a5825d42445c1cc5cb055261cf4cd0\",\n \"score\": \"0.63627434\",\n \"text\": \"def calculate_E0(self) -> float:\\n noisy = self.kernel_eigenvectors_[-1].copy()\\n np.random.shuffle(noisy)\\n\\n kernel_eigenvectors = self.kernel_eigenvectors_[:-1]\\n kernel_eigenvectors.append(noisy)\\n\\n eigenvectors_matrix = scipy.sparse.csr_matrix(\\n np.column_stack([eigenvector for eigenvector in kernel_eigenvectors])\\n )\\n\\n if len(kernel_eigenvectors) == 2:\\n ev0 = kernel_eigenvectors[0]\\n ev1 = kernel_eigenvectors[1]\\n _, Gamma, _ = scipy.sparse.linalg.svds(\\n ev0.T @ ev1, k=self.n_jointly_smooth_functions, which=\\\"LM\\\"\\n )\\n else:\\n _, Gamma, _ = scipy.sparse.linalg.svds(\\n eigenvectors_matrix, k=self.n_jointly_smooth_functions, which=\\\"LM\\\"\\n )\\n\\n Gamma.sort()\\n gamma2 = Gamma[-2]\\n E0 = (1 + gamma2) / 2\\n return E0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8310530bc0d41d9678403ca6d8a985f6\",\n \"score\": \"0.6325769\",\n \"text\": \"def fun(self, X):\\n w = np.linalg.eigvalsh(X)\\n # check for negative eigenvalues, but be forgiving for very small\\n # negative values relative to the maximum eignvalue\\n if np.any(np.min(w, axis=-1) < -np.spacing(np.max(w, axis=-1))):\\n return np.inf\\n else:\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a7a74e53c50df24de81957fff40abae\",\n \"score\": \"0.6216401\",\n \"text\": \"def test_eigen_basis_non_orthogonal():\\n\\n basis = [HarmonicOscillator(n=i, center=0.25) for i in range(2)] + [\\n HarmonicOscillator(n=i, center=-0.25) for i in range(2)\\n ]\\n\\n S = pairwise_array_from_func(basis, Overlap())\\n H = pairwise_array_from_func(basis, Hamiltonian(Harmonic(center=0.0)))\\n eigb = EigenBasis.from_basis(basis, H, S)\\n\\n # check the first 3 energy levels, we won't have converged\\n # the higher ones wrt basis set size\\n expected_energies = [(n + 0.5) for n in range(3)]\\n\\n diffs = [e1 - e2 for e1, e2 in zip(sorted(eigb.energies), expected_energies)]\\n\\n # a little lenient due to convergence of basis to keep test fast\\n assert all(math.isclose(d, 0.0, abs_tol=1e-3) for d in diffs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c1e2d551632b270ab4d564dbf36ab84c\",\n \"score\": \"0.61725926\",\n \"text\": \"def erf(x):\\n return 0.0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83276e0fec0f1d569af9dce47321b14d\",\n \"score\": \"0.6149356\",\n \"text\": \"def test_finite_well():\\n file = \\\"tests/finite_well/schroedinger.inp\\\"\\n x_min, x_max, n_point, ev_first, ev_last, interp_type, potential_decl, alpha = sd.read_inp(file)\\n potential_dat = sd.potential_discret(x_min, x_max, n_point, interp_type, potential_decl)\\n eigenvektors, eigenvalues = sd.solve_wavefuncs(n_point, ev_first, ev_last, potential_dat, alpha)\\n sd.solve_expvalues(ev_first, ev_last, potential_dat, eigenvektors)\\n potential_exp = np.loadtxt(\\\"tests/finite_well/potential.exp\\\")\\n eigenvalues_exp = np.loadtxt(\\\"tests/finite_well/eigenvalues.exp\\\")\\n assert np.all(eigenvalues-eigenvalues_exp < 1e-10)\\n assert np.all(potential_dat-potential_exp < 1e-10)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f96c75c5eff24ca569d81aec89a335e\",\n \"score\": \"0.602242\",\n \"text\": \"def _verify_spatial_precision(self, Q):\\n eig = eigsh(Q, k=1, which='SA', return_eigenvectors=False, sigma=0.001)\\n if eig[0] >= 1e-4:\\n raise ValueError('Spatial precision matrix Q must be singular.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ad0043d9331065ae89732206e76b743\",\n \"score\": \"0.593529\",\n \"text\": \"def is_positive_semi_definite(mat):\\n eigvals, _ = np.linalg.eigh(mat)\\n return np.all(eigvals >= -1e-14)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ecc854433d7b0727068571f3999e176\",\n \"score\": \"0.5915554\",\n \"text\": \"def test_check_conformer_energy(self):\\n v_list = [-272.2779012225, -272.2774933703, -272.2768397635, -272.2778432059, -272.278645477, -272.2789602654,\\n -272.2788749196, -272.278496709, -272.2779350675, -272.2777008843, -272.2777167286, -272.2780937643,\\n -272.2784838846, -272.2788050464, -272.2787865352, -272.2785091607, -272.2779977452, -272.2777957743,\\n -272.2779134906, -272.2781827547, -272.278443339, -272.2788244214, -272.2787748749]\\n v_list = np.array(v_list, np.float64)\\n v_diff = (v_list[0] - np.min(v_list)) * constants.E_h * constants.Na / 1000\\n self.assertAlmostEqual(v_diff / 2.7805169838282797, 1, 5)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d9401fe732896df5a0902d73b1bbf6f\",\n \"score\": \"0.5865272\",\n \"text\": \"def test_finite(self):\\n \\n Number_of_tests = 1000\\n low = -1000\\n high = 1000\\n for i in range(Number_of_tests):\\n x = np.random.rand(100) * (high - low) + low\\n y = aux_functions.softmax_base(x)\\n\\n # This should be True if all are finite\\n all_finite = np.isfinite(y).all()\\n self.assertTrue(all_finite)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"09e17c22b276387d2800c87205ee4d0d\",\n \"score\": \"0.5832907\",\n \"text\": \"def is_pos_semidef(X, positive_definite=False):\\n if not positive_definite:\\n return np.all(np.linalg.eigvals(X) >= 0)\\n else:\\n return np.all(np.linalg.eigvals(X) > 0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72c1090fd08e6a1cf6cc1a33dbe33ded\",\n \"score\": \"0.5799744\",\n \"text\": \"def calculate_eigenvalues(self):\\n self.__eigenvalues = []\\n dictionary = np.linalg.eig(np.array(self.__A))\\n indicator = True\\n sum1 = 0\\n for i in range(self.__A.shape[0]):\\n if all(self.__A[i, j] == 0 for j in range(self.__A.shape[1])):\\n indicator = all(self.__B[i,j] for j in range(self.__B.shape[1]))\\n if (indicator):\\n sum1 += 1\\n \\n for val in dictionary[0]:\\n if (val != 0):\\n self.__eigenvalues.append(complex(val))\\n elif (indicator) and (sum1 > 0):\\n sum1 -= 1\\n self.__eigenvalues.append(complex(val))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45d9ab80a3556d344da1d0229ab9e56a\",\n \"score\": \"0.5743405\",\n \"text\": \"def testLogPartitionInfinityIsAccurate(self):\\n alpha = float('inf')\\n log_z_true = 0.70526025442 # From mathematica.\\n log_z = self.variant(self._distribution.log_base_partition_function)(alpha)\\n chex.assert_tree_all_close(log_z, log_z_true, atol=1e-7, rtol=1e-7)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bdb9e38577f9b1abadaf22fd583b739e\",\n \"score\": \"0.56634724\",\n \"text\": \"def eigensolve(self, epsilon=0.85):\\n raise NotImplementedError(\\\"eigensolve Incomplete\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42b01d8c381bf5bff2a8ecf62c5dd996\",\n \"score\": \"0.56549835\",\n \"text\": \"def test_no_el():\\n levels = np.array([959., 867.9, 779.2, 647.5, 472.5, 321.9, 251.]) * units.mbar\\n temperatures = np.array([22.2, 17.4, 14.6, 1.4, -17.6, -39.4, -52.5]) * units.celsius\\n dewpoints = np.array([19., 14.3, -11.2, -16.7, -21., -43.3, -56.7]) * units.celsius\\n el_pressure, el_temperature = el(levels, temperatures, dewpoints)\\n assert_nan(el_pressure, levels.units)\\n assert_nan(el_temperature, temperatures.units)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"52b7d13448227d396194788d49643b02\",\n \"score\": \"0.56495357\",\n \"text\": \"def test_e0_prod(self):\\n self.assertAlmostEqual(self.tunneling.E0_prod.value_si * 0.001, self.E0_prod, 4)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ec3e6b93c2f3dbd019c490d3e4bbfb0\",\n \"score\": \"0.56463265\",\n \"text\": \"def is_posdef(X):\\n return np.min(np.linalg.eigvals(X)) > 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d60ee2f59e1f697d14a900945217b96c\",\n \"score\": \"0.56452477\",\n \"text\": \"def test_negative_electrode_potential_profile(self):\\n np.testing.assert_array_almost_equal(self.phi_s_n(self.t, x=0), 0, decimal=5)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6961920783941077064e6241459ed256\",\n \"score\": \"0.56451756\",\n \"text\": \"def test_intra_power_law_fit_no_model(self):\\n\\n\\t\\tdetails= self.watcher.analyze(model=self.model, layers=self.fc_layers, intra=True, randomize=False, vectors=False)\\n\\t\\tactual_alpha = details.alpha[0]\\n\\n\\t\\texpected_alpha = 2.654 # not very accurate because of the sparisify transform\\n\\t\\tself.assertAlmostEqual(actual_alpha,expected_alpha, places=1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a066a303238caaa53bd27738889aaead\",\n \"score\": \"0.5635319\",\n \"text\": \"def matrix_eig(\\n self,\\n chis=None,\\n eps=0,\\n print_errors=\\\"deprecated\\\",\\n hermitian=False,\\n break_degenerate=False,\\n degeneracy_eps=1e-6,\\n sparse=False,\\n trunc_err_func=None,\\n evenTrunc = False,\\n ):\\n if print_errors != \\\"deprecated\\\":\\n msg = (\\n \\\"The `print_errors` keyword argument has been deprecated, \\\"\\n \\\"and has no effect. Rely instead on getting the error as a \\\"\\n \\\"return value, and print it yourself.\\\"\\n )\\n warnings.warn(msg)\\n # If chis is not specfied, there is no even truncation scheme; else, we\\n # keep track of the chi we specfied\\n if chis is None:\\n evenTrunc = False\\n else:\\n try:\\n chis = list(chis)\\n except TypeError:\\n chis = [chis]\\n chiSpec = max(chis)\\n chis = self._matrix_decomp_format_chis(chis, eps)\\n maxchi = max(chis)\\n assert self.defval == 0\\n assert self.invar\\n assert self.charge == 0\\n assert self.dirs[0] + self.dirs[1] == 0\\n assert set(zip(self.qhape[0], self.shape[0])) == set(\\n zip(self.qhape[1], self.shape[1])\\n )\\n\\n S_dtype = np.float_ if hermitian else np.complex_\\n U_dtype = self.dtype if hermitian else np.complex_\\n\\n # Eigenvalue decompose each sector at a time.\\n # While doing so, also keep track of a list of all eigenvalues, as well\\n # as a heap that gives the negative of the absolute value of the\\n # largest eigenvalue in each sector. These will be needed later when\\n # deciding how to truncate the eigenvalues.\\n eigdecomps = {}\\n dims = {}\\n minusabs_next_eigs = []\\n all_eigs = []\\n for k, v in self.sects.items():\\n if 0 in v.shape:\\n # This matrix is empty and trivial.\\n shp = v.shape\\n m = min(shp)\\n u = np.empty((shp[0], m), dtype=U_dtype)\\n s = np.empty((m,), dtype=S_dtype)\\n eigdecomp = (s, u)\\n else:\\n if sparse and maxchi < min(v.shape) - 1:\\n if hermitian:\\n s, u = spsla.eighs(\\n v, k=maxchi, return_eigenvectors=True\\n )\\n else:\\n s, u = spsla.eigs(\\n v, k=maxchi, return_eigenvectors=True\\n )\\n else:\\n if hermitian:\\n s, u = np.linalg.eigh(v)\\n else:\\n s, u = np.linalg.eig(v)\\n order = np.argsort(-np.abs(s))\\n s = s[order]\\n u = u[:, order]\\n s = s.astype(S_dtype)\\n u = u.astype(U_dtype)\\n eigdecomp = (s, u)\\n eigdecomps[k] = eigdecomp\\n dims[k] = 0\\n all_eigs.append(s)\\n if 0 not in s.shape:\\n heapq.heappush(minusabs_next_eigs, (-np.abs(s[0]), k))\\n try:\\n all_eigs = np.concatenate(all_eigs)\\n except ValueError:\\n all_eigs = np.array((0,))\\n\\n if sparse:\\n norm_sq = self.norm_sq()\\n else:\\n norm_sq = None\\n\\n # Figure out what bond dimension to truncate to, how this bond\\n # dimension is distributed over the different sectors, and what the\\n # truncation error is.\\n chi, dims, rel_err = type(self)._find_trunc_dim(\\n all_eigs,\\n eigdecomps,\\n minusabs_next_eigs,\\n dims,\\n chis=chis,\\n eps=eps,\\n break_degenerate=break_degenerate,\\n degeneracy_eps=degeneracy_eps,\\n trunc_err_func=trunc_err_func,\\n norm_sq=norm_sq,\\n )\\n\\n # truncate in both sectors evenly\\n if evenTrunc and chiSpec == chi:\\n # This piece of codes is only designed\\n # with Z2 symmetry tensor in mind\\n errmeg = \\\"The matrix should have two sectors (0,0) and (1,1).\\\"\\n assert len(dims) == 2, errmeg\\n if chiSpec % 2 == 0:\\n dims[(0, 0)] = int(chiSpec / 2)\\n dims[(1, 1)] = int(chiSpec / 2)\\n else:\\n dims[(0, 0)] = int((chiSpec + 1) / 2)\\n dims[(1, 1)] = int((chiSpec - 1) / 2)\\n\\n # Truncate each block and create the dim for the new index.\\n new_dim = []\\n new_qim = []\\n eigdecomps = {k: v for k, v in eigdecomps.items() if dims[k] > 0}\\n for k, v in eigdecomps.items():\\n d = dims[k]\\n if d > 0:\\n new_dim.append(d)\\n new_qim.append(k[0])\\n eigdecomps[k] = (v[0][:d], v[1][:, :d])\\n else:\\n del eigdecomps[k]\\n\\n # Initialize S and U.\\n d = self.dirs[0]\\n S = type(self)(\\n [new_dim],\\n qhape=[new_qim],\\n dirs=[d],\\n qodulus=self.qodulus,\\n dtype=S_dtype,\\n invar=False,\\n charge=0,\\n )\\n U = type(self)(\\n [self.shape[0], new_dim],\\n qhape=[self.qhape[0], new_qim],\\n dirs=[d, -d],\\n qodulus=self.qodulus,\\n dtype=U_dtype,\\n charge=0,\\n )\\n\\n # Set the blocks of U, S and V.\\n for k, v in eigdecomps.items():\\n S[(k[0],)] = v[0]\\n k_U = (k[0], k[0])\\n U[k_U] = v[1]\\n\\n return S, U, rel_err\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e69eef8b97a70ce5fff729dc3d202250\",\n \"score\": \"0.56225336\",\n \"text\": \"def test_rescale_eigenvalues(self):\\n\\t\\t\\n\\t\\tevals = self.watcher.get_ESD(layer=self.fc2_layer)\\n\\t\\trescaled_evals, Wscale = RMT_Util.rescale_eigenvalues(evals)\\n\\t\\tun_rescaled_evals = RMT_Util.un_rescale_eigenvalues(rescaled_evals, Wscale)\\n\\n\\t\\tactual = np.max(evals)\\n\\t\\texpected = np.max(un_rescaled_evals)\\n\\t\\tself.assertAlmostEqual(actual, expected)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fac7548ede07ec0fcc47724ca607c6a9\",\n \"score\": \"0.5614238\",\n \"text\": \"def test_Sobol_G_raises_error_if_values_lt_zero():\\n evaluate(np.array([0, -1, -.02, 1, 1, -0.1, -0, -12]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d2fc31427e60ea3e94259b8ff063f1b\",\n \"score\": \"0.56049544\",\n \"text\": \"def test_j0_root():\\n import time\\n t1 = time.time()\\n\\n # Our version uses tabulated values up to 40, so a useful test of the extrapolation\\n # requires this to have more than 40 items.\\n vals1 = [ galsim.bessel.j0_root(s) for s in range(1,51) ]\\n print 'vals1 = ',vals1\\n\\n try:\\n import scipy.special\\n vals2 = scipy.special.jn_zeros(0,50)\\n print 'vals2 = ',vals2\\n np.testing.assert_almost_equal(\\n vals1, vals2, 8, \\\"bessel.j0_root disagrees with scipy.special.jn_zeros\\\")\\n except ImportError:\\n print 'Unable to import scipy. Skipping scipy tests of j0_root.'\\n\\n # These values are what scipy returns. Check against these, so not require scipy.\\n vals2 = [ 2.40482556, 5.52007811, 8.65372791, 11.79153444,\\n 14.93091771, 18.07106397, 21.21163663, 24.35247153,\\n 27.49347913, 30.63460647, 33.77582021, 36.91709835,\\n 40.05842576, 43.19979171, 46.34118837, 49.4826099 ,\\n 52.62405184, 55.76551076, 58.90698393, 62.04846919,\\n 65.1899648 , 68.33146933, 71.4729816 , 74.61450064,\\n 77.75602563, 80.89755587, 84.03909078, 87.18062984,\\n 90.32217264, 93.46371878, 96.60526795, 99.74681986,\\n 102.88837425, 106.02993092, 109.17148965, 112.31305028,\\n 115.45461265, 118.59617663, 121.73774209, 124.87930891,\\n 128.02087701, 131.16244628, 134.30401664, 137.44558802,\\n 140.58716035, 143.72873357, 146.87030763, 150.01188246,\\n 153.15345802, 156.29503427\\n ]\\n np.testing.assert_almost_equal(\\n vals1, vals2, 8, \\\"bessel.j0_root disagrees with reference values\\\")\\n\\n t2 = time.time()\\n print 'time for %s = %.2f'%(funcname(),t2-t1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"feb0ad2226494b58ecc42d4051fb5796\",\n \"score\": \"0.5604412\",\n \"text\": \"def is_positive_definite(x):\\n return np.all(np.linalg.eigvals(x) > 0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed1fe34e52de6416571f92aa2feb845a\",\n \"score\": \"0.56007063\",\n \"text\": \"def test_difficulties_without_eps_multi(self):\\n\\n def get_beamformer(A, B):\\n return get_mvdr_vector_souden(\\n A, B,\\n eps=0,\\n return_ref_channel=True\\n )\\n\\n for args in [\\n (\\n [self.PhiXX * 0, self.PhiXX],\\n [self.PhiNN, self.PhiNN],\\n ),\\n (\\n [self.PhiXX, self.PhiXX],\\n [self.PhiNN * 0, self.PhiNN],\\n ),\\n (\\n [self.PhiXX * 0, self.PhiXX],\\n [self.PhiNN * 0, self.PhiNN],\\n ),\\n (\\n [self.PhiXX * np.inf, self.PhiXX],\\n [self.PhiNN, self.PhiNN],\\n ),\\n (\\n [self.PhiXX, self.PhiXX],\\n [self.PhiNN * np.inf, self.PhiNN],\\n ),\\n (\\n [self.PhiXX * np.inf, self.PhiXX],\\n [self.PhiNN * np.inf, self.PhiNN],\\n ),\\n ]:\\n with tc.assert_raises(AssertionError):\\n get_beamformer(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c6f96be8460dc39965bf228e4b5824b4\",\n \"score\": \"0.5582808\",\n \"text\": \"def test_grade_infinity(self):\\r\\n\\r\\n sample_dict = {'x': (1, 2)}\\r\\n\\r\\n # Test problem\\r\\n problem = self.build_problem(sample_dict=sample_dict,\\r\\n num_samples=10,\\r\\n tolerance=\\\"1%\\\",\\r\\n answer=\\\"x\\\")\\r\\n # Expect such a large answer to be marked incorrect\\r\\n input_formula = \\\"x*1e999\\\"\\r\\n self.assert_grade(problem, input_formula, \\\"incorrect\\\")\\r\\n # Expect such a large negative answer to be marked incorrect\\r\\n input_formula = \\\"-x*1e999\\\"\\r\\n self.assert_grade(problem, input_formula, \\\"incorrect\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3970ea1a8f568df9981d1731c7a124ff\",\n \"score\": \"0.55817133\",\n \"text\": \"def ok_floats():\\n # copy float list so we don't change the numpy global\\n floats = np.sctypes['float'][:]\\n if best_float() != np.longdouble and np.longdouble in floats:\\n floats.remove(np.longdouble)\\n return sorted(floats, key=lambda f: type_info(f)['nmant'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45df7ff562ca7471ea1578e57649ddaa\",\n \"score\": \"0.5577971\",\n \"text\": \"def assert_no_error(self): \\r\\n Nx = self['Nx']\\r\\n Nt = self.m.Nt\\r\\n L, T = self.problem['L T'.split()]\\r\\n L = L/2 # only half the domain used (symmetry)\\r\\n x = np.linspace(0, L, Nx+1) # Mesh points in space \\r\\n t = np.linspace(0, T, Nt+1) # Mesh points in time\\r\\n \\r\\n for n in range(len(t)):\\r\\n u_e = self.problem.u_exact(x, t[n])\\r\\n diff = np.abs(self.f.u[n,:] - u_e).max()\\r\\n print 'diff:', diff\\r\\n tol = 1E-13\\r\\n assert diff < tol\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ecccf4e39c0a5d64bf9ca9b59352f795\",\n \"score\": \"0.55736053\",\n \"text\": \"def find_single_eigen(Potential,\\n energy_guess,\\n rmin, rmax, npoints,\\n tolerance,\\n max_iterations=100,\\n diagnostics=False):\\n\\n count = 0\\n energies = [energy_guess, ]\\n h = (rmax - rmin) / (npoints - 1)\\n while count < max_iterations:\\n count += 1\\n if diagnostics:\\n print(\\\"Trying energy: \\\", energies[-1])\\n new_energy, psi = update_energy(Potential, energies[-1],\\n rmin, rmax, npoints)\\n if diagnostics:\\n f = open(\\\"diagnostic_wavefunction_generated.dat\\\", \\\"w\\\")\\n for i, apsi in enumerate(psi):\\n f.write(\\\"%f %f\\\\n\\\" % (rmin + i * h, apsi))\\n f.close()\\n\\n energies.append(new_energy)\\n if ((count >= 3) and\\n (abs(energies[-1] - energies[-2]) <= tolerance) and\\n (abs(energies[-2] - energies[-3]) <= tolerance)):\\n break # energy converged\\n else: # \\\"max_iterations\\\" reached without success:\\n message = (\\\"Maximum iterations exceeded: %d\\\" % max_iterations)\\n raise MaxIterationsError(message)\\n # normalize wavefunction:\\n factor = 1.0 / math.sqrt(h * sum([apsi * apsi for apsi in psi]))\\n normalized_psi = [apsi * factor for apsi in psi]\\n return {\\\"energy\\\": energies[-2], # this is energy corresponding to psi\\n # not the most recent\\n \\\"psi\\\": normalized_psi,\\n \\\"rmin\\\": rmin,\\n \\\"h\\\": h}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"066c0d37e4d28cf72b273e077a9bc391\",\n \"score\": \"0.5562425\",\n \"text\": \"def set_normal_free_energy(self):\\n\\t\\t\\n\\t\\tself.eps_base = self.mu_eps + self.normal_eps_tuning_prefactor* \\\\\\n\\t\\t\\t\\t\\t\\tsp.exp(-(1.*sp.arange(self.Mm))**2.0/(2.0* \\\\\\n\\t\\t\\t\\t\\t\\tself.normal_eps_tuning_width)**2.0)\\n\\t\\t\\t\\t\\t\\t\\n\\t\\tself.eps_base += random_matrix(self.Mm, params=[0, self.sigma_eps], \\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tseed=self.seed_eps)\\n\\t\\t\\n\\t\\t# If dual signal, use the average of the FULL signal nonzero components\\n\\t\\tif self.Kk_split == 0:\\n\\t\\t\\tself.eps = self.WL_scaling*sp.log(self.mu_Ss0) + self.eps_base \\n\\t\\telse:\\n\\t\\t\\tself.eps = self.WL_scaling*sp.log(sp.average(self.Ss\\\\\\n\\t\\t\\t\\t\\t\\t\\t[self.Ss != 0])) + self.eps_base\\n\\t\\t\\n\\t\\t# Apply max epsilon value to each component\\n\\t\\tfor iM in range(self.Mm):\\n\\t\\t\\tif self.eps[iM] > self.max_eps:\\n\\t\\t\\t\\tself.eps[iM] = self.max_eps\\n\\t\\t\\tif self.eps[iM] < self.min_eps:\\n\\t\\t\\t\\tself.eps[iM] = self.min_eps\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c7e8773431bb01f3ccdb53ff719d33f6\",\n \"score\": \"0.55589235\",\n \"text\": \"def check_k_matrix_stability(self):\\r\\n K = self.make_k_matrix()\\r\\n vals, vects = scipy_sparse_eigens(K)\\r\\n principal_val = vals.max()\\r\\n print(\\\"ht3_solver:\\\\t'Stiffness' matrix principal eigenvalue was \\\"\\r\\n + str(principal_val))\\r\\n if principal_val > 1:\\r\\n print(\\\"##########################################################\\\")\\r\\n print(\\\"ht3_solver:\\\\tWARNING\\\")\\r\\n print(\\\"ht3_solver:\\\\tPrincipal eigenvalue is more than one.\\\")\\r\\n print(\\\"ht3_solver:\\\\tThe analysis will be unstable.\\\")\\r\\n print(\\\"ht3_solver:\\\\tIf this is OK, just go and modify the code \\\"\\r\\n + \\\"or something.\\\")\\r\\n print(\\\"##########################################################\\\")\\r\\n raise(AssertionError)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b589fb1b89b8cabc6ced0f1103e9917\",\n \"score\": \"0.554395\",\n \"text\": \"def E0_prime(self):\\n delta_electrons = self._GetElectronDiff()\\n assert delta_electrons != 0\\n return - self.DeltaG0Prime() / (constants.F*delta_electrons)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d6e6188733afde76946bf09ea21a57e\",\n \"score\": \"0.5536128\",\n \"text\": \"def is_close_to_zero(value: Union[float, np.ndarray]) -> Union[bool, np.ndarray]:\\n return abs(value) < 1.0e-10\",\n \"title\": \"\"\n },\n {\n \"docid\": \"072dd08b86cee3f6734a572821c9903d\",\n \"score\": \"0.5534667\",\n \"text\": \"def test_inf_well():\\n file = \\\"tests/inf_well/schroedinger.inp\\\"\\n x_min, x_max, n_point, ev_first, ev_last, interp_type, potential_decl, alpha = sd.read_inp(file)\\n potential_dat = sd.potential_discret(x_min, x_max, n_point, interp_type, potential_decl)\\n eigenvektors, eigenvalues = sd.solve_wavefuncs(n_point, ev_first, ev_last, potential_dat, alpha)\\n sd.solve_expvalues(ev_first, ev_last, potential_dat, eigenvektors)\\n potential_exp = np.loadtxt(\\\"tests/inf_well/potential.exp\\\")\\n eigenvalues_exp = np.loadtxt(\\\"tests/inf_well/eigenvalues.exp\\\")\\n assert np.all(eigenvalues-eigenvalues_exp < 1e-10)\\n assert np.all(potential_dat-potential_exp < 1e-10)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"022d249f29e5e66564cc01738016e5c5\",\n \"score\": \"0.5523558\",\n \"text\": \"def testscfenergy(self):\\r\\n scf = self.data.scfenergies[-1]\\r\\n ref = self.b3lyp_energy\\r\\n tol = self.b3lyp_tolerance\\r\\n msg = f\\\"Final SCF energy: {scf:f} not {int(ref)} +- {int(tol)}eV\\\"\\r\\n assert abs(scf-ref) < 40, msg\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d940b4cf846b1c6d4329024c5b85db94\",\n \"score\": \"0.5522695\",\n \"text\": \"def _compute_bare_spectrum_constant(self):\\n eigendata = []\\n for subsys in self._hilbertspace:\\n if subsys not in self.subsys_update_list:\\n evals_count = subsys.truncated_dim\\n eigendata.append(subsys.eigensys(evals_count=evals_count))\\n else:\\n eigendata.append(None)\\n return eigendata\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8034fe1393e60001cb7a32cfeeb6dac9\",\n \"score\": \"0.55207247\",\n \"text\": \"def get_initial_epsilon(self):\\n return self.epsilon_percentile, True, self.max_rounds == 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"65a6a3e877c2b16774dd5e7656a89979\",\n \"score\": \"0.5489698\",\n \"text\": \"def test_Sobol_G_raises_error_if_values_lt_zero():\\n with raises(ValueError):\\n evaluate(np.array([0, -1, -.02, 1, 1, -0.1, -0, -12]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fbf432a5c0a8b6c1fc5149f3083a13b4\",\n \"score\": \"0.54879385\",\n \"text\": \"def est_nul(self):\\n\\t\\tif self.__valide:\\n\\t\\t\\treturn (self.degre() == 0) and (self.valuation().est_nul())\\n\\t\\telse:\\n\\t\\t\\treturn False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"75f510b8e03695c87794c65df128ca6c\",\n \"score\": \"0.5480385\",\n \"text\": \"def read_eigenvalues(self):\\n # Eigenvalue line indexes\\n index_eig_begin = None\\n for iline, line in enumerate(self.lines):\\n fstring = 'eigenvalues '\\n if line.find(fstring) >= 0:\\n index_eig_begin = iline + 1\\n line1 = line.replace(':', ',')\\n ncol, nband, nkpt, nspin = map(int, line1.split(',')[-4:])\\n break\\n else:\\n return None\\n\\n # Take into account that the last row may lack \\n # columns if nkpt * nspin * nband % ncol != 0\\n nrow = int(np.ceil(nkpt * nspin * nband * 1. / ncol))\\n index_eig_end = index_eig_begin + nrow\\n ncol_last = len(self.lines[index_eig_end - 1].split())\\n self.lines[index_eig_end - 1] += ' 0.0 ' * (ncol - ncol_last)\\n\\n eig = np.loadtxt(self.lines[index_eig_begin:index_eig_end]).flatten()\\n eig *= Hartree\\n N = nkpt * nband\\n eigenvalues = [eig[i * N:(i + 1) * N].reshape((nkpt, nband))\\n for i in range(nspin)]\\n\\n return eigenvalues\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8041aef006a353635358a390e2c13d21\",\n \"score\": \"0.5478622\",\n \"text\": \"def test_undesirable_deviations():\\n f = np.asarray([\\n [0.99, 1.0, 0.5],\\n [0.69, 0.6, 0.6]])\\n R = common_metrics.undesirable_deviations(f, maximise=True)\\n expected = np.asarray(\\n [-0.245, 0.0])\\n assert np.allclose(R, expected)\\n R = common_metrics.undesirable_deviations(f, maximise=False)\\n expected = np.asarray(\\n [-0.005, -0.045])\\n assert np.allclose(R, expected)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab0c2905a9dbcbd43e2c5259ba0f5d54\",\n \"score\": \"0.54771477\",\n \"text\": \"def calculate_eigens(self):\\n covariance_matrix = np.cov(self.predictor_vars_train.T)\\n eigenvalues, eigenvectors = np.linalg.eig(covariance_matrix)\\n idx = eigenvalues.argsort()[::-1]\\n # Create \\\"All\\\" version\\n self.eigenvalues_all = eigenvalues[idx]\\n self.eigenvectors_all = eigenvectors[:, idx]\\n # Create selected percentage version with cutoff\\n eigenvalues_pct = self.eigenvalues_all / np.sum(self.eigenvalues_all)\\n self.pct_var_exp_cumulative_all = np.cumsum(eigenvalues_pct)\\n self.pct_var_exp_cumulative = self.pct_var_exp_cumulative_all[\\n self.pct_var_exp_cumulative_all <= self.variance_explained_cutoff\\n ]\\n self.eigenvectors = self.eigenvectors_all[:, : len(self.pct_var_exp_cumulative)]\\n self.eigenvalues = self.eigenvalues_all[: len(self.pct_var_exp_cumulative)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a7fbb9d785edc7943aff4b35aa98746e\",\n \"score\": \"0.54747474\",\n \"text\": \"def read_eigenvalues(self):\\n # Eigenvalue line indexes\\n index_eig_begin = None\\n for iline, line in enumerate(self.lines):\\n fstring = 'eigenvalues '\\n if line.find(fstring) >= 0:\\n index_eig_begin = iline + 1\\n line1 = line.replace(':', ',')\\n ncol, nband, nkpt, nspin = map(int, line1.split(',')[-4:])\\n break\\n else:\\n return None\\n\\n # Take into account that the last row may lack\\n # columns if nkpt * nspin * nband % ncol != 0\\n nrow = int(np.ceil(nkpt * nspin * nband * 1. / ncol))\\n index_eig_end = index_eig_begin + nrow\\n ncol_last = len(self.lines[index_eig_end - 1].split())\\n if ncol - ncol_last > 0:\\n self.lines[index_eig_end - 1] = self.lines[index_eig_end - 1].replace('\\\\n', '')\\n self.lines[index_eig_end - 1] += ' 0.0 ' * (ncol - ncol_last)\\n self.lines[index_eig_end - 1] += '\\\\n'\\n eig = np.loadtxt(self.lines[index_eig_begin:index_eig_end]).flatten()\\n eig *= Hartree\\n N = nkpt * nband\\n eigenvalues = [eig[i * N:(i + 1) * N].reshape((nkpt, nband))\\n for i in range(nspin)]\\n\\n return eigenvalues\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2083bcc905565c4f414c5276ff54b3e3\",\n \"score\": \"0.5474369\",\n \"text\": \"def is_pos_def(x):\\n rtol = 1e-05\\n atol = 1e-08\\n return np.all(np.linalg.eigvals(x) > 0) and np.allclose(\\n x, x.T, rtol=rtol, atol=atol\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e870de10676816106b6804e091728777\",\n \"score\": \"0.5466653\",\n \"text\": \"def is_zero(self):\\n return -0.0001 <= self.l2_norm() <= 0.0001\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8cb07ccae35458fc2f4d9d96d3bd285c\",\n \"score\": \"0.54654443\",\n \"text\": \"def testCalcCorrNaN(self):\\n self.assertEqual(INF, calc_corr(2.050000, INF, EXP_KBT))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5260feb1a675afd22378df2a742ef22\",\n \"score\": \"0.5460692\",\n \"text\": \"def test_null_from_exponential(self):\\n scale = 5\\n floats = False\\n actual_out = null_from_exponential(self.samples, self.otus, scale, floats)\\n expected_out = \\\\\\n array([[ 4., 6., 5., 4., 3.],\\n [ 5., 3., 11., 17., 2.],\\n [ 8., 4., 4., 13., 0.],\\n [ 0., 0., 9., 8., 10.],\\n [ 19., 8., 3., 8., 1.],\\n [ 5., 1., 14., 4., 3.],\\n [ 2., 7., 3., 4., 0.],\\n [ 5., 5., 5., 14., 6.],\\n [ 2., 3., 6., 0., 5.],\\n [ 6., 1., 1., 2., 2.]])\\n self.assertEqual(expected_out, actual_out)\\n #test with ints off\\n floats = True\\n actual_out = null_from_exponential(self.samples, self.otus, scale, floats)\\n expected_out = \\\\\\n array([[ 4.2221389 , 2.88662155, 22.2724869 , 0.53817556,\\n 1.17150758],\\n [ 0.87956777, 5.29371362, 1.46040267, 3.13970791,\\n 1.40138507],\\n [ 0.86563726, 0.58477706, 5.34036095, 0.74356136,\\n 1.09440301],\\n [ 2.30006983, 8.60165826, 0.51072443, 9.09909462,\\n 0.50517391],\\n [ 18.74515725, 3.16168301, 18.80963586, 4.64239251,\\n 6.72122632],\\n [ 0.19988151, 1.66205123, 0.64028381, 1.75588043,\\n 0.63194321],\\n [ 1.91350479, 2.67442193, 0.33148698, 5.89594764,\\n 4.18048773],\\n [ 1.5420742 , 3.70379476, 0.49325157, 4.28947821,\\n 13.24627963],\\n [ 1.91780105, 5.50422961, 0.70665357, 6.29966916,\\n 1.70827085],\\n [ 1.01175218, 4.41564523, 0.10156227, 8.82870539,\\n 0.02353267]])\\n assert_allclose(expected_out, actual_out, rtol=1e-06)\\n #using allclose because floats get truncated\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a60d5ad6e7b614d601efc8416842d00\",\n \"score\": \"0.5459371\",\n \"text\": \"def longdouble_precision_improved():\\n return not longdouble_lte_float64() and _LD_LTE_FLOAT64\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0002f06e866834591163dc87a7a3bbc1\",\n \"score\": \"0.54586226\",\n \"text\": \"def test_frequency():\\n assert abs(4.59043034 - waves.frequency(waves.g, 3.0, 0.3)) < 1e-7\\n assert abs(0.0 - waves.frequency(waves.g, 0.0, 0.3)) < 1e-7\\n assert abs(4.59043034 - waves.frequency(waves.g, -3.0, 0.3)) < 1e-7\\n\\n assert np.isnan(waves.frequency(waves.g, -3.0, 0.3)) == 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb818ed71f96657a9c08851f3363c5dd\",\n \"score\": \"0.54503137\",\n \"text\": \"def isfinite(self):\\n return not self.isAny( (lambda x: not np.isfinite(x)) )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"480fd6a24bd04472b9f73809040910f5\",\n \"score\": \"0.54431283\",\n \"text\": \"def fix_special_floats(value, _inf=INFINITY, _neginf=-INFINITY):\\n\\n try:\\n value = convert_tensor_to_numpy(value)\\n\\n # Check if the value is Nan, equivalent of math.isnan\\n if math.isnan(value):\\n return \\\"NaN\\\"\\n\\n elif value == _inf:\\n return \\\"Infinity\\\"\\n\\n elif value == _neginf:\\n return \\\"-Infinity\\\"\\n\\n except Exception:\\n # Value cannot be compared\\n return value\\n\\n return value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6be3feaff7acee4b8205fcd4dc6dc61f\",\n \"score\": \"0.54395276\",\n \"text\": \"def aerosols(self):\\n return 1.\",\n \"title\": \"\"\n },\n {\n \"docid\": \"714207a343873794986da19260446933\",\n \"score\": \"0.54391634\",\n \"text\": \"def eigenvalues(self, expand=False, factor=False, simplify=False):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a853ebc8741f672a05509dd6a142380\",\n \"score\": \"0.54379994\",\n \"text\": \"def _sigma_ee_nonrel(self,gam,eps):\\n s0 = 4 * r0**2 * alpha / (15 * eps)\\n x = 4 * eps / (gam**2 - 1)\\n sigma_nonrel = s0 * self._F(x,gam)\\n sigma_nonrel[np.where(eps >= 0.25*(gam**2 - 1.))] = 0.0\\n sigma_nonrel[np.where(gam*np.ones_like(eps) < 1.0)] = 0.0\\n return sigma_nonrel / mec2_unit\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f005a64a38cbbb18698c392aca929f04\",\n \"score\": \"0.5433856\",\n \"text\": \"def get_strict_valence_flag(self):\\n d_min = self.fmodel.f_obs().d_min()\\n return (d_min < self.params.d_min_strict_valence)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b549bc31e189444d305eb5e65f6825e9\",\n \"score\": \"0.5432304\",\n \"text\": \"def explore_eigenvalues(self, X_train, X_test) :\\r\\n\\r\\n # scaler = StandardScaler()\\r\\n # X_train = scaler.fit_transform(X_train)\\r\\n # X_test = scaler.transform(X_test)\\r\\n\\r\\n print (\\\"Train feature shape:\\\", X_train.shape)\\r\\n print(\\\"Train feature shape:\\\", X_test.shape)\\r\\n \\r\\n\\r\\n mean_vec = np.mean(X_test, axis=0)\\r\\n cov_mat = (X_test - mean_vec).T.dot((X_test - mean_vec)) / (X_test.shape[0]-1)\\r\\n print('Covariance matrix \\\\n%s' %cov_mat)\\r\\n print('NumPy covariance matrix: \\\\n%s' %np.cov(X_test.T))\\r\\n eig_vals, eig_vecs = np.linalg.eig(cov_mat)\\r\\n print('Eigenvectors \\\\n%s' %eig_vecs)\\r\\n print(\\\"---------------------------------\\\")\\r\\n print('\\\\nEigenvalues \\\\n%s' %eig_vals)\\r\\n # Make a list of (eigenvalue, eigenvector) tuples\\r\\n eig_pairs = [(np.abs(eig_vals[i]), eig_vecs[:,i]) for i in range(len(eig_vals))]\\r\\n\\r\\n # Sort the (eigenvalue, eigenvector) tuples from high to low\\r\\n eig_pairs.sort(key=lambda x: x[0], reverse=True)\\r\\n\\r\\n # Visually confirm that the list is correctly sorted by decreasing eigenvalues\\r\\n print(\\\"---------------------------------\\\")\\r\\n print('Eigenvalues in descending order:')\\r\\n for i in eig_pairs:\\r\\n print(i[0])\\r\\n # tot = sum(eig_vals)\\r\\n # var_exp = [(i / tot)*100 for i in sorted(eig_vals, reverse=True)]\\r\\n # print(\\\"Explained variance : \\\\n{}\\\".format(var_exp))\\r\\n \\r\\n\\r\\n return X_train, X_test\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d0044fe2a026c628775c1df00ceec4fb\",\n \"score\": \"0.54288155\",\n \"text\": \"def F0(t):\\n if (t < 1e-6):\\n return 1.0 - t / 3.0\\n else:\\n return 0.5 * (np.pi / t) ** 0.5 * sp.erf(t ** 0.5)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8a215f14fd96ba997da4a3dcffe6444\",\n \"score\": \"0.54255927\",\n \"text\": \"def get_eig(self, tolerance=None):\\r\\n E, V = scipy.linalg.eig(self.data)\\r\\n E = pd.Series(E.real, name=\\\"EIG\\\")\\r\\n V = pd.DataFrame(V.real)\\r\\n if tolerance is not None:\\r\\n E[E/E.max() < tolerance] = 0\\r\\n return E, V\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8228a0812813301ac11ed9a9d6a8d52a\",\n \"score\": \"0.5424792\",\n \"text\": \"def testEllipticalGaussian(self):\\n #\\n # Make and the objects\\n #\\n ab_vals = [(1.0, 0.0), (2.0, 0.0), (2.0, 0.1), ]\\n nIter = 2\\n for dx in (0.0, 0.5,):\\n for dy in (0.0, 0.5,):\\n for alpha, b in ab_vals:\\n for objFlux in (1e2, 1e3, 1e4, 1e5, 1e6, ):\\n flux, fluxErr, flags, psfFlux = self.makeAndMeasure(objFlux, alpha, b, dx=dx, dy=dy)\\n \\n failFlux = math.isnan(flux) or flags or abs(flux/objFlux - 1) > 0.25e-2\\n \\n ID = \\\"alpha,b %4.1f, %5.2f dx,dy = %.1f,%.1f \\\" % (alpha, b, dx, dy)\\n msg = \\\"%s flux_DeconvolvedPsf: %9.4g v. exact value %9.4g (error %5.2f%%) (psfFlux error %5.2f%%)\\\" % \\\\\\n (ID, flux, objFlux, 100*(flux/objFlux - 1), 100*(psfFlux/objFlux - 1))\\n\\n if False:\\n print msg\\n continue\\n\\n self.assertFalse(failFlux, msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef5fb9e47201425978feddfea7fe425f\",\n \"score\": \"0.54162824\",\n \"text\": \"def almost_eq(e1,e2) :\\n\\treturn round(e1-e2,4) == 0.0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c14002fcee78cd10f0424b6ca88e5093\",\n \"score\": \"0.5415454\",\n \"text\": \"def eigvals(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff05d02cb50d5cd17acec412a88ea202\",\n \"score\": \"0.5401089\",\n \"text\": \"def test_basic_newton_finder(self):\\n forwards = np.array([1.0, 1.0, 1.0, 1.0, 1.0, 1.0])\\n strikes = np.array([1.0, 2.0, 1.0, 0.5, 1.0, 1.0])\\n expiries = np.array([1.0, 1.0, 1.0, 1.0, 0.5, 2.0])\\n discounts = np.array([1.0, 1.0, 1.0, 1.0, 1.0, 1.0])\\n init_vols = np.array([2.0, 0.5, 2.0, 0.5, 1.5, 1.5])\\n option_signs = np.array([1.0, 1.0, -1.0, -1.0, 1.0, 1.0])\\n volatilities = np.array([1.0, 1.0, 1.0, 1.0, 1.0, 1.0])\\n prices = np.array([\\n 0.38292492, 0.19061012, 0.38292492, 0.09530506, 0.27632639, 0.52049988\\n ])\\n results = implied_vol(\\n forwards,\\n strikes,\\n expiries,\\n discounts,\\n prices,\\n option_signs,\\n initial_volatilities=init_vols,\\n max_iterations=100)\\n implied_vols, converged, failed = self.evaluate(results)\\n num_volatilities = len(volatilities)\\n self.assertAllEqual(np.ones(num_volatilities, dtype=np.bool), converged)\\n self.assertAllEqual(np.zeros(num_volatilities, dtype=np.bool), failed)\\n self.assertArrayNear(volatilities, implied_vols, 1e-7)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"778ab4b78a9d229986c50a1854562467\",\n \"score\": \"0.5401\",\n \"text\": \"def test_floats(self):\\r\\n problem_setup = [\\r\\n # [given_answer, [list of correct responses], [list of incorrect responses]]\\r\\n [1, [\\\"1\\\"], [\\\"1.1\\\"]],\\r\\n [2.0, [\\\"2.0\\\"], [\\\"1.0\\\"]],\\r\\n [4, [\\\"4.0\\\", \\\"4.00004\\\"], [\\\"4.00005\\\"]],\\r\\n [0.00016, [\\\"1.6*10^-4\\\"], [\\\"\\\"]],\\r\\n [0.000016, [\\\"1.6*10^-5\\\"], [\\\"0.000165\\\"]],\\r\\n [1.9e24, [\\\"1.9*10^24\\\"], [\\\"1.9001*10^24\\\"]],\\r\\n [2e-15, [\\\"2*10^-15\\\"], [\\\"\\\"]],\\r\\n [3141592653589793238., [\\\"3141592653589793115.\\\"], [\\\"\\\"]],\\r\\n [0.1234567, [\\\"0.123456\\\", \\\"0.1234561\\\"], [\\\"0.123451\\\"]],\\r\\n [1e-5, [\\\"1e-5\\\", \\\"1.0e-5\\\"], [\\\"-1e-5\\\", \\\"2*1e-5\\\"]],\\r\\n ]\\r\\n for given_answer, correct_responses, incorrect_responses in problem_setup:\\r\\n problem = self.build_problem(answer=given_answer)\\r\\n self.assert_multiple_grade(problem, correct_responses, incorrect_responses)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"feef2f4eb26da6761fe6f1b7fe68ca2d\",\n \"score\": \"0.5391306\",\n \"text\": \"def test_derivative_not_in_basis_zeros(self):\\n nodes = np.random.random((10, 3))\\n\\n basis = PowerSeries(L=3)\\n ft = basis.evaluate(nodes, derivatives=[0, 1, 0])\\n fz = basis.evaluate(nodes, derivatives=[0, 0, 1])\\n assert np.all(ft == 0)\\n assert np.all(fz == 0)\\n\\n basis = FourierSeries(N=4)\\n fr = basis.evaluate(nodes, derivatives=[1, 0, 0])\\n ft = basis.evaluate(nodes, derivatives=[0, 1, 0])\\n assert np.all(fr == 0)\\n assert np.all(ft == 0)\\n\\n basis = DoubleFourierSeries(M=2, N=4)\\n fr = basis.evaluate(nodes, derivatives=[1, 0, 0])\\n assert np.all(fr == 0)\\n\\n basis = ZernikePolynomial(L=2, M=3)\\n fz = basis.evaluate(nodes, derivatives=[0, 0, 1])\\n assert np.all(fz == 0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c2d9903b78b2d2f95b9713cfe89f067a\",\n \"score\": \"0.5390967\",\n \"text\": \"def df0dE(E, fermi, T):\\n exponent = (E - fermi) / (k_B * T)\\n if exponent > 40 or exponent < -40: # This is necessary so at too low numbers python doesn't return NaN\\n return 1e-32\\n else:\\n return -1 / (k_B * T) * np.exp((E - fermi) / (k_B * T)) / (1 + np.exp((E - fermi) / (k_B * T))) ** 2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6ce6ad51577b2ff2646bbaa637ad6ff\",\n \"score\": \"0.53877646\",\n \"text\": \"def check(mat, otp):\\n prd = mat*otp\\n eigval = prd[0]/otp[0]\\n print 'computed eigenvalue :' , eigval\\n [eigs, vecs] = np.linalg.eig(mat)\\n abseigs = list(abs(eigs))\\n ind = abseigs.index(max(abseigs))\\n print ' largest eigenvalue :', eigs[ind]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"68b13223b82afd948fe9784cccc8c4e1\",\n \"score\": \"0.5386818\",\n \"text\": \"def check_nullification(full_gam, feature_combination_full, threshold = 1e-7):\\n\\n nullified_features = []\\n\\n for i in range(len(feature_combination_full)):\\n XX = full_gam.generate_X_grid(term=i)\\n\\n term_funct = full_gam.partial_dependence(term=i, X=XX)\\n\\n std = np.std(term_funct)# Use standard deviation to check for nullification\\n\\n if std < threshold:\\n nullified_features.append(feature_combination_full[i])\\n\\n return nullified_features\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a202e2124a7cfa9853dfebad0fcdb08b\",\n \"score\": \"0.53823644\",\n \"text\": \"def test_single_spectrum_with_positive_power(self):\\n power = 0.69314718055\\n nspec = 1\\n assert np.isclose(cospectra_pvalue(power, nspec), 0.25)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7bedb59ea48a3b72b1622a45bde8aac5\",\n \"score\": \"0.5382051\",\n \"text\": \"def eigenalgo(self, accuracy: float = 0, cap: int = 50000, version: str = \\\"Givens\\\", not_skip: bool = True):\\n j, temps, verify_accuracy = 0, 0, np.ones((self.N, self.N), dtype=bool) ^ np.eye(self.N, dtype=bool)\\n if version == \\\"Gram-Schmidt\\\":\\n temps = time()\\n while np.any(abs(self.vap[verify_accuracy]) > accuracy) and j < cap:\\n j += 1\\n q, r = self.gram_schmidt_qr()\\n self.vap, self.vep = r @ q, self.vep @ q\\n\\n elif version == \\\"Givens\\\":\\n verify_accuracy = np.ones((self.N, self.N), dtype=bool) ^ np.eye(self.N, dtype=bool)\\n temps = time()\\n while np.any(abs(self.vap[verify_accuracy]) > accuracy) and j < cap:\\n j += 1\\n q, r = self.givens_qr()\\n self.vap, self.vep = r @ q, self.vep @ q\\n\\n elif version == \\\"Rayleigh\\\":\\n not_sing, diff, cond, j = True, accuracy + 1, True, 0\\n temps = time()\\n while cond: # Stop condition, all eigenvalues must be different\\n while diff > accuracy and j < cap and not_sing:\\n j += 1\\n self.rvap, self.vep, diff, not_sing = self.rayleigh_iteration(self.rvap, self.vep)\\n\\n cond = False\\n if j < cap:\\n self.calc, first, not_sing = np.zeros(self.N, dtype=bool), True, True\\n for i in range(self.N):\\n if np.sum(np.less(np.abs(self.rvap - self.rvap[i]), 10 ** -6)) != 1:\\n self.rvap[i + 1:] += self.memorize[i]\\n if first:\\n self.memorize[i] += 0.5\\n self.vep[i + 1:, i + 1:] = np.eye(self.N - i - 1)\\n first, cond, diff = False, True, accuracy + 1\\n self.calc[i + 1:] = 1\\n temps = time() - temps\\n return self.rvap, self.vep, diff, j, temps\\n\\n else:\\n print(\\\"Please select an appropriate value for the version parameter\\\")\\n\\n temps = time() - temps\\n diff = np.max(abs(self.vap[verify_accuracy]))\\n return np.diag(self.vap), self.vep, diff, j, temps\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5481a624d60eb548367738fe7d4a5f4\",\n \"score\": \"0.53814787\",\n \"text\": \"def test_parallel_resistors_with_zero(self):\\r\\n self.assertTrue(numpy.isnan(calc.evaluator({}, {}, '0||1')))\\r\\n self.assertTrue(numpy.isnan(calc.evaluator({}, {}, '0.0||1')))\\r\\n self.assertTrue(numpy.isnan(calc.evaluator({'x': 0.0}, {}, 'x||1')))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"335a38bbc3d15d4806763baa5f001a04\",\n \"score\": \"0.53618467\",\n \"text\": \"def best_float():\\n try:\\n long_info = type_info(np.longdouble)\\n except FloatingError:\\n return np.float64\\n if (long_info['nmant'] > type_info(np.float64)['nmant'] and\\n machine() != 'sparc64'): # sparc has crazy-slow float128\\n return np.longdouble\\n return np.float64\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc9f84e5243b65b17672ceed6890db36\",\n \"score\": \"0.5360974\",\n \"text\": \"def _compute_bare_spectrum_constant(self) -> List[Tuple[ndarray, ndarray]]:\\n eigendata = []\\n for subsys in self._hilbertspace:\\n if subsys not in self.subsys_update_list:\\n evals_count = subsys.truncated_dim\\n eigendata.append(subsys.eigensys(evals_count=evals_count))\\n else:\\n eigendata.append(None) # type: ignore\\n return eigendata\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44afc9ad5a864c55382161eae9ddaf88\",\n \"score\": \"0.5353698\",\n \"text\": \"def Find_Lowest_Energy_Structure_Electrostatics(self):\\n n_Na = self.structure.composition['Na']\\n n_S = self.structure.composition['S']\\n n_O = self.structure.composition['O']\\n n_N = self.structure.composition['N']\\n n_Fe = self.structure.composition['Fe']\\n\\n n_Fe_reduced = self.variable_magnetization_dict['Fe']['n_reduced']\\n n_Fe_oxidized = n_Fe-n_Fe_reduced \\n\\n N_charge = ( 2.*n_O-6.*n_S-n_Na-3.*n_Fe_oxidized-2.*n_Fe_reduced )/n_N\\n\\n oxidation_states = {'Na':+1, 'Fe':+3, 'O':-2,'S':+6,'N':N_charge}\\n Fe_2plus = pymatgen.Specie('Fe',oxidation_state=+2)\\n\\n structure_with_charges = self.structure.copy()\\n structure_with_charges.add_oxidation_state_by_element(oxidation_states) \\n\\n # identify Fe sites\\n list_Fe_indices = []\\n for i,site in enumerate(structure_with_charges):\\n if site.specie.symbol == 'Fe':\\n list_Fe_indices.append(i)\\n\\n # Generate all possible permutation of sites and compute \\n # Ewald energy\\n ewald_model = EwaldElectrostaticModel(acc_factor=6)\\n list_reduced_sets = []\\n list_ewald_energy = []\\n for reduced_set in itertools.combinations(list_Fe_indices,n_Fe_reduced):\\n list_reduced_sets.append(reduced_set) \\n\\n struct = structure_with_charges.copy()\\n for i in reduced_set:\\n struct.replace(i, Fe_2plus)\\n\\n list_ewald_energy.append(ewald_model.get_energy(struct))\\n\\n if len(list_ewald_energy) == 0:\\n # all sites are oxidized. No sorting involved \\n list_reduced_site_indices = []\\n list_oxidized_site_indices = list_Fe_indices\\n else:\\n # some reduction takes place. Identify best electrostatic choice\\n\\n imin = np.argmin(list_ewald_energy)\\n\\n list_reduced_site_indices = list_reduced_sets[imin] \\n list_oxidized_site_indices = []\\n for i in list_Fe_indices:\\n if i not in list_reduced_site_indices: \\n list_oxidized_site_indices.append(i) \\n\\n\\n return list_reduced_site_indices, list_oxidized_site_indices\",\n \"title\": \"\"\n },\n {\n \"docid\": \"659fb48fbf121e98f085112af7dc78d0\",\n \"score\": \"0.5352033\",\n \"text\": \"def test_difficulties_eps_multi(self):\\n well_w = self.get_w_well_behaviour()\\n\\n def get_beamformer(A, B):\\n return get_mvdr_vector_souden(\\n A, B,\\n return_ref_channel=True\\n )\\n\\n for args in [\\n (\\n [self.PhiXX * 0, self.PhiXX],\\n [self.PhiNN, self.PhiNN],\\n ),\\n (\\n [self.PhiXX, self.PhiXX],\\n [self.PhiNN * 0, self.PhiNN],\\n ),\\n (\\n [self.PhiXX * 0, self.PhiXX],\\n [self.PhiNN * 0, self.PhiNN],\\n ),\\n ]:\\n w, ref_channel = get_beamformer(*args)\\n assert ref_channel == 2, ref_channel\\n np.testing.assert_allclose(\\n w,\\n np.array([[0., 0., 0.], well_w])\\n )\\n\\n for args in [\\n (\\n [self.PhiXX * np.inf, self.PhiXX],\\n [self.PhiNN, self.PhiNN],\\n ),\\n (\\n [self.PhiXX, self.PhiXX],\\n [self.PhiNN * np.inf, self.PhiNN],\\n ),\\n (\\n [self.PhiXX * np.inf, self.PhiXX],\\n [self.PhiNN * np.inf, self.PhiNN],\\n ),\\n ]:\\n with tc.assert_raises(AssertionError):\\n get_beamformer(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b969f7e9aba15a3964446cc91fb14f80\",\n \"score\": \"0.53512454\",\n \"text\": \"def quadratic_root_goes_to_infinity():\\n for dt in 1E-7, 1E-12, 1E-16:\\n a = dt\\n b = 1 - dt\\n c = -0.1\\n print((dt, quadratic_roots(a, b, c)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c8a44f4738504b2c54e89b999a320de\",\n \"score\": \"0.53359556\",\n \"text\": \"def test_zero_parameters(self):\\n problem = TimeEvolutionProblem(self.hamiltonian, time=0.02)\\n sampler = Sampler()\\n fidelity_primitive = ComputeUncompute(sampler)\\n\\n pvqd = PVQD(\\n fidelity_primitive,\\n QuantumCircuit(2),\\n np.array([]),\\n optimizer=SPSA(maxiter=10, learning_rate=0.1, perturbation=0.01),\\n )\\n\\n with self.assertRaises(QiskitError):\\n _ = pvqd.evolve(problem)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8984f8a8ef4ef9b03fd89c8afd9bce71\",\n \"score\": \"0.5334759\",\n \"text\": \"def find_epsilon(self, inverse=False):\\n e_s = self.e_s if not inverse else self.e_s_inv\\n\\n max_score = -10000000\\n\\n for z in np.arange(2.5, self.sd_lim, 0.5):\\n epsilon = self.mean_e_s + (self.sd_e_s * z)\\n\\n pruned_e_s = e_s[e_s < epsilon]\\n\\n i_anom = np.argwhere(e_s >= epsilon).reshape(-1,)\\n buffer = np.arange(1, self._error_buffer)\\n i_anom = np.sort(np.concatenate((i_anom,\\n np.array([i+buffer for i in i_anom])\\n .flatten(),\\n np.array([i-buffer for i in i_anom])\\n .flatten())))\\n i_anom = i_anom[(i_anom < len(e_s)) & (i_anom >= 0)]\\n i_anom = np.sort(np.unique(i_anom))\\n\\n if len(i_anom) > 0:\\n # group anomalous indices into continuous sequences\\n groups = [list(group) for group\\n in mit.consecutive_groups(i_anom)]\\n E_seq = [(g[0], g[-1]) for g in groups if not g[0] == g[-1]]\\n\\n mean_perc_decrease = (self.mean_e_s - np.mean(pruned_e_s)) \\\\\\n / self.mean_e_s\\n sd_perc_decrease = (self.sd_e_s - np.std(pruned_e_s)) \\\\\\n / self.sd_e_s\\n score = (mean_perc_decrease + sd_perc_decrease) \\\\\\n / (len(E_seq) ** 2 + len(i_anom))\\n\\n # sanity checks / guardrails\\n if score >= max_score and len(E_seq) <= 5 and \\\\\\n len(i_anom) < (len(e_s) * 0.5):\\n max_score = score\\n if not inverse:\\n self.sd_threshold = z\\n self.epsilon = self.mean_e_s + z * self.sd_e_s\\n else:\\n self.sd_threshold_inv = z\\n self.epsilon_inv = self.mean_e_s + z * self.sd_e_s\",\n \"title\": \"\"\n },\n {\n \"docid\": \"348e67507831e864863e5d5c3c1c750d\",\n \"score\": \"0.5327928\",\n \"text\": \"def epsilon():\\n return _EPSILON\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f0b46b1b2d14e862e3ea2ce0a3e1cb5\",\n \"score\": \"0.53216165\",\n \"text\": \"def check_for_float(check):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eaa4d37dac6a3f3905583b1138408c9e\",\n \"score\": \"0.5314363\",\n \"text\": \"def test_exp():\\n x = np.linspace(-3,3,13)\\n\\n default_use_numexpr = accel_math._USE_NUMEXPR\\n\\n accel_math._USE_NUMEXPR = True\\n r1 = accel_math._exp(x)\\n\\n accel_math._USE_NUMEXPR = False\\n r2 = accel_math._exp(x)\\n\\n np.testing.assert_almost_equal(r1,r2)\\n\\n accel_math._USE_NUMEXPR = default_use_numexpr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c9e615ff81ee864a740cdc85707ec10c\",\n \"score\": \"0.53078824\",\n \"text\": \"def test_e0_ts(self):\\n self.assertAlmostEqual(self.tunneling.E0_TS.value_si * 0.001, self.E0_TS, 4)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b76c31b45f88183f86caa3196c5e4c2\",\n \"score\": \"0.5299172\",\n \"text\": \"def is_finite(self):\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78d0239fb2935ad8e43d85c99af624c6\",\n \"score\": \"0.5298558\",\n \"text\": \"def ensure_positive_precision(K):\\n K_diag = np.diag(np.diag(K))\\n K = np.where(np.any(np.diag(K) < 0), np.where(K_diag < 0, 1e-2, K_diag), K)\\n return K\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee76ef2f6d9a294c26ef7b55509f3a29\",\n \"score\": \"0.52962893\",\n \"text\": \"def test_despike(curve):\\n assert curve.df.max()[0] - curve.despike(50, z=1).df.max()[0] - 91.83918 < 0.001\",\n \"title\": \"\"\n },\n {\n \"docid\": \"426541e10f1f88608c357473d1e5e8aa\",\n \"score\": \"0.52865666\",\n \"text\": \"def e(self):\\n if self._e is None:\\n # self._e = self.distributions.uniform(0.3,0.33)\\n # return self._e\\n # max is set by q but also limited by users choice of e_max.\\n res_a = 29.9*((self.j[0]/self.k[0])**(2/3))\\n q = self.distributions.truncated_normal(self.q_c, self.q_w, res_a*(1-0.8), res_a*(1-0.001))\\n self._e = 1 - q/res_a\\n return self._e\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca0767e345ac1daf9c0428736e8a73a6\",\n \"score\": \"0.52842927\",\n \"text\": \"def test_Sobol_G_raises_error_if_values_gt_one():\\n evaluate(np.array([0, 1, .02, 0.23, 1.234, 0.02848848, 0, 0.78]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b22ba99427f15fc2ce8cb809852bc6a\",\n \"score\": \"0.5277587\",\n \"text\": \"def convergence_check(self):\\n air = self.air_alias.val\\n flue_gas = self.fuel_alias.val + '_fg'\\n fuel = self.fuel_alias.val\\n\\n for c in self.outl:\\n if not c.fluid.val_set[air]:\\n if c.fluid.val[air] > 0.95:\\n c.fluid.val[air] = 0.95\\n if c.fluid.val[air] < 0.5:\\n c.fluid.val[air] = 0.5\\n\\n if not c.fluid.val_set[flue_gas]:\\n if c.fluid.val[flue_gas] > 0.5:\\n c.fluid.val[flue_gas] = 0.5\\n if c.fluid.val[flue_gas] < 0.05:\\n c.fluid.val[flue_gas] = 0.05\\n\\n if not c.fluid.val_set[fuel]:\\n if c.fluid.val[fuel] > 0:\\n c.fluid.val[fuel] = 0\\n\\n c.target.propagate_fluid_to_target(c, c.target)\\n\\n for i in self.inl:\\n if i.m.val_SI < 0 and not i.m.val_set:\\n i.m.val_SI = 0.01\\n\\n for c in self.outl:\\n if c.m.val_SI < 0 and not c.m.val_set:\\n c.m.val_SI = 10\\n c.target.propagate_fluid_to_target(c, c.target)\\n\\n if self.lamb.val < 1 and not self.lamb.is_set:\\n self.lamb.val = 2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"360d1c6fa02dbde7f3d2e3fc817a67c1\",\n \"score\": \"0.5276822\",\n \"text\": \"def log_det_precisions(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d13ed02b9a47e485024fe75a6510a7b9\",\n \"score\": \"0.52733415\",\n \"text\": \"def _check_eigenmatrices(self):\\n if self._has(\\\"P\\\") and self._has(\\\"Q\\\") and \\\\\\n _simplify(_expand(self._.P * self._.Q)) \\\\\\n != self.order(expand=True, simplify=True) \\\\\\n * identity_matrix(SR, self._.d + 1):\\n warn(Warning(\\\"the eigenmatrices do not multiply \\\"\\n \\\"into a multiple of the identity matrix\\\"))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f4a575228e08d8e753165779bcea945\",\n \"score\": \"0.52689487\",\n \"text\": \"def eigenvalues(self) -> ndarray:\\n return self._vals\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2c8ff7438c88632b3f58fb97e9b97c1\",\n \"score\": \"0.52661246\",\n \"text\": \"def test_xml_eigenvalues_specific(xml_parser_specific):\\n\\n eigenvalues = xml_parser_specific.get_eigenvalues_specific()\\n kpoints = xml_parser_specific.get_kpoints_specific()\\n kpointsw = xml_parser_specific.get_kpointsw_specific()\\n assert eigenvalues['total'].shape == (20, 10)\\n assert kpoints.shape == (10, 3)\\n assert kpointsw.shape == (10,)\\n test = np.array([-6.076 , -6.0578, -6.0031, -5.9122, -5.7858, -5.6246, -5.4302,\\n -5.2048, -4.9517, -4.6768])\\n np.testing.assert_allclose(eigenvalues['total'][0], test) \\n test = np.array([ 9.5694, 9.6943, 10.0793, 10.646 , 11.2894, 11.9561, 12.6122,\\n 13.2292, 13.6978, 13.9563])\\n np.testing.assert_allclose(eigenvalues['total'][7], test) \\n test = np.array([[ 0. ,0. ,0. ],\\n [ 0.03703704 ,0. ,0. ],\\n [ 0.07407407 ,0. ,-0. ],\\n [ 0.11111111 ,0. ,-0. ],\\n [ 0.14814815 ,0. ,-0. ],\\n [ 0.18518519 ,-0. ,0. ],\\n [ 0.22222222 ,-0. ,0. ],\\n [ 0.25925926 ,-0. ,0. ],\\n [ 0.2962963 ,-0. ,-0. ],\\n [ 0.33333333 ,-0. ,-0. ]])\\n np.testing.assert_allclose(kpoints, test)\\n test = np.array([5.0810e-05, 4.0644e-04, 4.0644e-04, 4.0644e-04, 4.0644e-04, 4.0644e-04,\\n 4.0644e-04, 4.0644e-04, 4.0644e-04, 4.0644e-04])\\n np.testing.assert_allclose(kpointsw, test)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"605a0731763311f822306d8abb0b7d69\",\n \"score\": \"0.5262909\",\n \"text\": \"def test_extreme_values(self):\\n with pytest.warns(RuntimeWarning) as warninfo:\\n assert np.exp(laplace_approx(999999, 999999, self.data)) == 0\\n with pytest.warns(RuntimeWarning) as warninfo:\\n assert np.exp(laplace_approx(999999, 999999, self.data)) == 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45429e7c4fe6bc1af8a88ae383e35347\",\n \"score\": \"0.5252851\",\n \"text\": \"def is_algebraically_stable(self):\\n import numpy as np\\n\\n if np.any(self.b < 0):\\n return False\\n\\n # Check the eigenvalues for positive definiteness.\\n #\\n # We could also check whether the Cholesky factorization fails (=> not pos. def.).\\n # However, since we are interested in positive SEMIdefinite matrices, we have to\\n # regularize M by adding a small multiple of the identity matrix. Since \\\"small\\\"\\n # depends on the floating point type and this is probably not a bottleneck, using\\n # the eigenvalues seems to be fine.\\n rk = self.__num__()\\n B = np.diag(rk.b)\\n M = B.dot(rk.A) + rk.A.T.dot(B) - np.outer(rk.b, rk.b)\\n isposdef = np.all(np.linalg.eigvals(M) >= -10 * np.finfo(M.dtype).eps)\\n\\n return isposdef\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3950f1f164983d9b867dea18836366ad\",\n \"score\": \"0.5246538\",\n \"text\": \"def check_non_singular(self, Am):\\r\\n det = self.detf(Am)\\r\\n if det != 0:\\r\\n return det\\r\\n else:\\r\\n raise ArithmeticError(\\\"Singular Matrix!\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a8c23d69f397617a7dd870598be50e51\",\n \"score\": \"0.52420014\",\n \"text\": \"def test_eigenvalues_in_cylinders(self):\\n random.seed(102938482634)\\n point_cloud = load(os.path.join('testdata', 'AHN3.las'))\\n num_all_pc_points = len(point_cloud[keys.point][\\\"x\\\"][\\\"data\\\"])\\n rand_indices = [random.randint(0, num_all_pc_points) for _ in range(20)]\\n target_point_cloud = utils.copy_point_cloud(point_cloud, rand_indices)\\n radius = 2.5\\n neighbors = compute_neighbors.compute_cylinder_neighborhood(point_cloud, target_point_cloud, radius)\\n\\n compute_features(point_cloud, neighbors, target_point_cloud,\\n [\\\"eigenv_1\\\", \\\"eigenv_2\\\", \\\"eigenv_3\\\"], InfiniteCylinder(5))\\n\\n self.assertEqual(\\\"laserchicken.feature_extractor.eigenvals_feature_extractor\\\",\\n target_point_cloud[keys.provenance][-1][\\\"module\\\"])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"266a60926c33696c39d99b4424dc0e00\",\n \"score\": \"0.5241262\",\n \"text\": \"def test_t0(self):\\n sol = Mader(p_cj=3.0e11, d_cj=8.0e5, gamma=3.0, u_piston=0.0)\\n # r must contain 2 elements, otherwise the density and pressure are nan\\n r = np.array([0.7, 0.8])\\n t = 0.0\\n solrt = sol(r, t)\\n for quant in ['velocity', 'pressure', 'sound_speed', 'density', 'xdet']:\\n assert np.all(np.isnan(solrt[quant]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1fc8ecb6a21bffdb74a1a8406e059a7b\",\n \"score\": \"0.523657\",\n \"text\": \"def test_float(self):\\n self.assertFalse(validate_measure_input('0.0', self.measures))\\n self.assertFalse(validate_measure_input('1.0', self.measures))\\n self.assertFalse(validate_measure_input('1.1', self.measures))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b558219319b55b133660de9340d4e33a\",\n \"score\": \"0.5231793\",\n \"text\": \"def check_all_finite(value):\\n if all_finite(value):\\n return\\n\\n invalid = np.logical_not(np.isfinite(value))\\n msg = show_some(value, invalid, 'finite')\\n\\n msg += '\\\\n%s %s ' % (np.min(value), np.max(value))\\n raise ValueError(msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"adb2a7d77cd3bdf5303b291e87b8f1eb\",\n \"score\": \"0.52210504\",\n \"text\": \"def guestimate_init_cond(x_data):\\n x0 = np.clip(np.max(x_data, 1), 1e-4, np.inf)\\n return x0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23c3229ae2f3a12d0666a4f15107d4ed\",\n \"score\": \"0.5219297\",\n \"text\": \"def iszero(a: float) -> bool:\\n return np.isclose(a, 0.0, atol=1.0e-12, rtol=0.0)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1187,"cells":{"query_id":{"kind":"string","value":"014f941460669fd601096f185f218dca"},"query":{"kind":"string","value":"estimator a la scikit but we want to customize a few thing in particular the predict we add a predict_verbose method which gives the argmax of predict and all other kind of info"},"positive_passages":{"kind":"list like","value":[{"docid":"b33b48f7146ba4208a877df886f8387b","score":"0.0","text":"def __init__(self,method=\"manhattan\"):\n\t\tself.liste_of_games=[]\n\t\tself.dictofplayer={}\n\t\t#self.liste_of_players=[]\n\t\tself.liste=False #do we use a list of game or a dict of player with a list of games","title":""}],"string":"[\n {\n \"docid\": \"b33b48f7146ba4208a877df886f8387b\",\n \"score\": \"0.0\",\n \"text\": \"def __init__(self,method=\\\"manhattan\\\"):\\n\\t\\tself.liste_of_games=[]\\n\\t\\tself.dictofplayer={}\\n\\t\\t#self.liste_of_players=[]\\n\\t\\tself.liste=False #do we use a list of game or a dict of player with a list of games\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"37fc8baf70ddba5073708b5e25b2031b","score":"0.68523747","text":"def meta_predict_clf():\n pass","title":""},{"docid":"1bdc25b629b990ea75d72e0f323a6ef5","score":"0.66119105","text":"def getPrediction(self,data_arg1,data_arg2,data_lbl):","title":""},{"docid":"6bd871203cb7e2d8dd2307e1c731ce16","score":"0.6543709","text":"def predict():\n pass","title":""},{"docid":"145663ca7e52492983f8265c81eaa8c2","score":"0.65347403","text":"def predict():\n pass","title":""},{"docid":"ead94255e20e230a8ccbf08c2b925e1a","score":"0.6513237","text":"def _model_fn_predict(self, mode, features, logits):\n assert mode == tf.estimator.ModeKeys.PREDICT\n predictions = {\n # We don't actually use classes downstream right now.\n # 'classes': tf.argmax(input=logits, axis=1, output_type=tf.int32),\n 'probabilities': tf.nn.softmax(logits, name='softmax_tensor'),\n # DV2 call_variants wants these passed through.\n 'variant': features['variant'],\n 'alt_allele_indices': features['alt_allele_indices'],\n }\n if 'label' in features:\n predictions['label'] = features['label']\n if 'locus' in features:\n predictions['locus'] = features['locus']\n if self.use_tpu:\n return tpu_estimator.TPUEstimatorSpec(mode=mode, predictions=predictions)\n else:\n return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions)","title":""},{"docid":"70f1de2b3e9f3c0f0674d1789f7fd1ae","score":"0.644738","text":"def predict_log_proba(self):\n ...","title":""},{"docid":"d22bcb5135a72e4247c3ea6942c2d313","score":"0.6432428","text":"def explain_prediction(estimator, doc, **kwargs):\n return Explanation(\n estimator=repr(estimator),\n error=\"estimator %r is not supported\" % estimator,\n )","title":""},{"docid":"2f311aa91d8be71794e0b6e48e26ece1","score":"0.6431149","text":"def predict(self, *args, **kwargs):\n raise NotImplementedError","title":""},{"docid":"97b304d4c7793fc545f4f9babd4d0783","score":"0.6424372","text":"def _internal_predict(self, x_test):","title":""},{"docid":"65040de2315e5c40c0684b26d6725f0c","score":"0.64239514","text":"def main():\n\n #scaler, classifier = train()\n\n #predict(scaler, classifier, [[27.23, 0.53, 0, 3.31]])","title":""},{"docid":"a861b98b68e422028569c0b6d622dd7a","score":"0.64121616","text":"def fit_predict(self, X, y=...):\n ...","title":""},{"docid":"a861b98b68e422028569c0b6d622dd7a","score":"0.64121616","text":"def fit_predict(self, X, y=...):\n ...","title":""},{"docid":"845116bc58bd4b6cc232a77e0debd1b2","score":"0.64028764","text":"def predict_proba(self):\n ...","title":""},{"docid":"773a5266eba3d5e65ed5f57bc4bd8d9e","score":"0.63921565","text":"def predict(self):\n pass","title":""},{"docid":"773a5266eba3d5e65ed5f57bc4bd8d9e","score":"0.63921565","text":"def predict(self):\n pass","title":""},{"docid":"773a5266eba3d5e65ed5f57bc4bd8d9e","score":"0.63921565","text":"def predict(self):\n pass","title":""},{"docid":"eca43b88add93efa16cc13031a6bc73c","score":"0.6388775","text":"def predict(self, X, **kw):","title":""},{"docid":"5dbccd53cd5de75e01676d0990dad361","score":"0.632211","text":"def predict_additional_output(self, logits):\n raise NotImplementedError","title":""},{"docid":"b4878e30027bbf0fdd4a07423d3c9b26","score":"0.62751275","text":"def get_best_ova( clf, hyper, score, X_train, y_train):\r\n ovr = OneVsRestClassifier(clf).fit(X_train,y_train)\r\n best = customGridSearch( ovr, param_grid=hyper, score=score )\r\n best.fit( X_train, y_train )\r\n return best.best_estimator_","title":""},{"docid":"db46c54eef48ac7435e03dbbe95c6193","score":"0.6217643","text":"def predict(self):\n raise NotImplementedError","title":""},{"docid":"db46c54eef48ac7435e03dbbe95c6193","score":"0.6217643","text":"def predict(self):\n raise NotImplementedError","title":""},{"docid":"7ad969d40f35f6bf6862261831801758","score":"0.6210032","text":"def _predict(self):\n pass","title":""},{"docid":"f9d0dd69354c041468c899fab923dac6","score":"0.6196931","text":"def predict():\n Classification_predict()","title":""},{"docid":"84bf4d2544ab5f8485485f35b420dbfb","score":"0.61920846","text":"def predict(self, X):","title":""},{"docid":"84bf4d2544ab5f8485485f35b420dbfb","score":"0.61920846","text":"def predict(self, X):","title":""},{"docid":"282e5d06712513c7de727d8014dd318c","score":"0.6161895","text":"def predict(self, *args, **kwargs):\r\n raise TypeError","title":""},{"docid":"d97b47f7e7ca1adefee482353d531bfe","score":"0.6140748","text":"def predict(self, X):\n ...","title":""},{"docid":"d97b47f7e7ca1adefee482353d531bfe","score":"0.6140748","text":"def predict(self, X):\n ...","title":""},{"docid":"bae6b93061f402cb05020a2da6ee04b8","score":"0.61373365","text":"def predict(self):\n self.y_pred = np.argmax(np.round(self.model.predict(self.X_test)),axis=1)\n print(\"dim de y_pred : \" + str(self.y_pred.shape))\n print(\"dim de y_test : \" + str(self.y_test.shape))","title":""},{"docid":"2c6256e2b59cf8fe8573d1aaa3e55036","score":"0.6116803","text":"def _predict(self, X):","title":""},{"docid":"b8a1d79dc168d9831a4ed934dc3fac37","score":"0.61030585","text":"def ML_predict(train_data, lag_size, folds, method='RF', exog_data = None, lagsx = None):\n\n if method == 'RF':\n rfr = RandomForestRegressor(n_estimators=590,criterion='mse', oob_score=True)\n if method == 'SVR':\n rfr = SVR(kernel='linear', epsilon=2.831, C=1.661)\n if method == 'ET':\n rfr = ExtraTreesRegressor(n_estimators=680, criterion='mse', oob_score=True, bootstrap=True)\n #todo: importance chcek\n if exog_data is not None and lagsx is not None:\n print(\"Running exogenous mode\")\n error, regressor, res, preds, ident = ML_external(train_data, lag_size, folds, rfr, exog_data, lagsx)\n return error, regressor, res, preds, ident\n else:\n print(\"Running internal mode\")\n error, regressor, res, preds, ident = ML_internal(train_data, lag_size, folds, rfr)\n return error, regressor, res, preds, ident","title":""},{"docid":"1702e51288fb7c655a73597a2d323435","score":"0.6055545","text":"def predict(self, *args, **kwargs):\n return super(BaseModel, self).__call__(*args, **kwargs)","title":""},{"docid":"e099193df0c90bdfdf229618cd37f144","score":"0.6048183","text":"def predict(model_path, X_test, is_lgbm=False, is_catboost=False, is_cnn=False, maxlen=400, lgbm_threshold=0.5):\n\n print('\\n === predict === \\n')\n\n if is_lgbm:\n # lightgbm\n model = lgb.Booster(model_file=model_path)\n elif is_catboost:\n model = CatBoostClassifier()\n model = model.load_model(model_path)\n elif is_cnn:\n model = load_model(model_path)\n else:\n # sklearn\n # xgboost\n model = joblib.load(model_path)\n\n # y_pred = model.predict_prob(X_test)\n y_pred = model.predict(X_test)\n\n if is_lgbm:\n #print('==')\n #print(y_pred)\n y_output = []\n for y in y_pred:\n if y > lgbm_threshold:\n y_output.append(1)\n else:\n y_output.append(0)\n #print('==')\n #print(y_output)\n return(np.array(y_output))\n #return np.array([np.argmax(y) for y in y_pred])\n elif is_cnn:\n # X_test = sequence.pad_sequences(X_test, maxlen=maxlen)\n y_pred = model.predict(X_test)\n y_pred = [np.argmax(y) for y in y_pred]\n return np.array(y_pred)\n else:\n return y_pred","title":""},{"docid":"2f59a20d13d905ebe2d422eaf5d80203","score":"0.6029188","text":"def predict(self, desc):\n return np.argmax(self.predict_proba(desc))","title":""},{"docid":"6a9140a47719762b0340b00cb18685c8","score":"0.60189235","text":"def predict(self, X): \n return super().predict(X)","title":""},{"docid":"e43c9b4b75ad7b16481d0a5ec106380a","score":"0.6013293","text":"def fit(self, **kwargs):","title":""},{"docid":"72e0aafa0846d3766820601c3d84c1fa","score":"0.6012597","text":"def predict(self,traindata,verbose=False):\n\t\tif not self.isFitted:\n\t\t\traise UnFittedModelError(\"Attempting to predict on a unfitted model\")\n\n\t\tmodelResults=[]\n\t\tensembleResult=[]\n\t\tfor model in self.model:\n\t\t\tmodelResults.append(model.predict(traindata))\n\t\tfor i in range(len(modelResults[0])):\n\t\t\ttmpResult=[]\n\t\t\tfor j in range(len(modelResults)):\n\t\t\t\ttmpResult.append(int(modelResults[j][i]))\n\t\t\tensembleResult.append(str(resolveSign(tmpResult,self.model_confidence)))\n\t\treturn(ensembleResult)","title":""},{"docid":"11702257ac9a1ce0d270be0b257f1324","score":"0.59984756","text":"def predict_next(self, **kwargs):\n pass","title":""},{"docid":"cf375ac95d684c33a6d83e61969d5096","score":"0.5974168","text":"def cmdl(ctx, provider, filename, model_id, threshold, locally, docker, output):\n ctx.setup_logger(format='')\n if docker:\n locally = \"docker\"\n A2ML(ctx, provider).predict( model_id, filename=filename, \n threshold=threshold, score=score, score_true_data=score_true_path,\n locally=locally, output=output)","title":""},{"docid":"dc77a7942d9d4d4b24a109182e4963f5","score":"0.59619737","text":"def predict_data(self):\n pass","title":""},{"docid":"0ac7c975aaefab59c2b67c651780c379","score":"0.594237","text":"def predict_(self, X, i):\n raise NotImplementedError(\"\")","title":""},{"docid":"e272e5fee03c4bde643b35da3aea8732","score":"0.59126353","text":"def predict(self, X):\r\n Util.abstract()","title":""},{"docid":"fdde0f15ca65629fae9545114631dbef","score":"0.59091765","text":"def predict(self, X):\r\n pass","title":""},{"docid":"1d80e5a8d43987c11edf17105874b8d4","score":"0.5893054","text":"def predict():\n # load the save model\n classifier = pickle.load(open('best_model.pkl'))\n\n # compile a predictor function\n predict_model = theano.function(inputs=[classifier.input], outputs=classifier.y_pred)\n\n # we can test it on some examples from test test\n dataset = 'mnist.pkl.gz'\n datasets = load_data(dataset)\n test_set_x, test_set_y = datasets[2]\n test_set_x = test_set_x.get_value()\n\n predict_values = predict_model(test_set_x[:10])\n print \"predict values for the first example in test set:\"\n print predict_values","title":""},{"docid":"19a8fd64f12968f0dc46a82244000ad8","score":"0.587905","text":"def predict(self, x, *args, **kwargs):\n raise NotImplementedError()","title":""},{"docid":"50ce39d34d2b8540ffc295463e5572df","score":"0.586703","text":"def predict(self, X): \n if self.mode == \"regex\":\n X = self._regex_subview(X)\n elif self.mode == \"index\":\n X = self._index_subview(X)\n elif self.mode == \"include\":\n X = self._include_subview(X)\n\n return self.base_estimator_.predict(X)","title":""},{"docid":"ab565e0069fb0bf28d6b6dbef8aa8716","score":"0.58650553","text":"def prediction(inp):\n pass","title":""},{"docid":"05de433bc715d57fa0bde1a32b058213","score":"0.58586246","text":"def update_parametrized_models(self):\n\n logger.log(\"fitting baseline...\")\n self.algo.baseline.fit(self.experience_replay[-1])\n logger.log(\"fitted\")\n\n if self.algo.exploration_bonus:\n logger.log(\"fitting exploration bonus model...\")\n self.algo.exploration_bonus.fit(self.experience_replay[-1])\n logger.log(\"fitted\")","title":""},{"docid":"0e6e9d2a8365e377a56c6ee7066953fe","score":"0.5853979","text":"def tuning_model():","title":""},{"docid":"2e4e68237167bb972832ff6d9698b412","score":"0.5841047","text":"def predict_recommender(self, data):","title":""},{"docid":"382e4836b988bfb226b586f894466d0e","score":"0.58212394","text":"def model(Xtrain,Ytrain,Xtest,Ytest):\n model = linear_model.SGDClassifier(loss='hinge', penalty='l2', alpha=OPT_ALPHA,\n l1_ratio=L1_RATIO, fit_intercept=True, n_iter=N_ITER, shuffle=True, verbose=0,\n epsilon=0.1, n_jobs=1, random_state=None, learning_rate='optimal',\n eta0=0.0, power_t=0.5, class_weight=None, warm_start=False, average=False)\n #model = MultinomialNB(alpha=OPT_ALPHA)\n model.fit(Xtrain,Ytrain)\n\n pred = model.predict(Xtest)\n\n i=0\n for a,b in zip(pred,Ytest):\n if i>20:\n break\n i+=1\n print(a,b)\n\n R0_Baseline = mode(Ytest)[1]/len(Ytest)\n print(\"Accuracy: \",metrics.accuracy_score(Ytest,pred))\n print(metrics.accuracy_score(Ytest,pred)*(len(Ytest)))\n print(\"Baseline: \",R0_Baseline,\" Guessing \",mode(Ytest)[0])\n print(metrics.classification_report(Ytest,pred))\n\n \"\"\"\n #hyper-parameter optimization for MultinomialNB\n\n parameters = {'alpha':np.linspace(-10,10,50)}\n\n meta_opt = GridSearchCV(model, parameters)\n meta_opt.fit(Xtrain, Ytrain)\n\n print(meta_opt.best_params_)\n \"\"\" \n\n \"\"\"\n #hyper-parameter optimization for SGDClassifier\n\n parameters = {'alpha':np.linspace(0.0001,10,10),\n 'l1_ratio':np.linspace(0,1,10),\n 'n_iter':np.linspace(50,200,4)}\n\n meta_opt = GridSearchCV(model, parameters,verbose=1)\n meta_opt.fit(Xtrain, Ytrain)\n\n print(meta_opt.best_params_)\n \"\"\"\n return metrics.accuracy_score(Ytest,pred),Ytest,pred","title":""},{"docid":"3bf69a27c84f0c8c33301379ece05b33","score":"0.5812485","text":"def predict(self, X):\n\t\t### YOUR CODE HERE\n\n\t\t### END YOUR CODE","title":""},{"docid":"7a6d37ad418489ee2666675aa2d71714","score":"0.58112264","text":"def classify(trait_arg, params_arg, performance_dict):\n\n activation_type, learning_rate_type, solver_type = params_arg\n print(\"Using Parameters : \", params_arg)\n x = df['essay'][1:]\n x = x.str.lower()\n y = df[trait_arg][1:]\n\n print(\"Predicting \", trait_arg)\n\n # TF-IDF vectorizer\n vectorizer = TfidfVectorizer()\n\n # 10 fold\n kf = KFold(n_splits=10)\n\n # MLP classifier\n classifier = MLPClassifier(activation=activation_type, batch_size='auto',\n hidden_layer_sizes=(60), learning_rate=learning_rate_type, max_iter=20,\n random_state=None, solver=solver_type)\n\n ind = 0\n precision_dict = dict()\n recall_dict = dict()\n accuracy_dict = dict()\n for train_indices, test_indices in kf.split(x, y):\n x_train, x_test, y_train, y_test = x[train_indices][1:], x[test_indices].tolist()[1:], \\\n y[train_indices][1:], y[test_indices].tolist()[1:]\n train_x_vector = vectorizer.fit_transform(x_train)\n test_X_vector = vectorizer.transform(x_test)\n classifier.fit(train_x_vector, y_train)\n guess = classifier.predict(test_X_vector)\n rep = classification_report(y_test, guess)\n a, b, c, d = dict(parse_classification_report(rep))['avg']\n precision_dict[ind] = a\n recall_dict[ind] = b\n accuracy_dict[ind] = accuracy_score(y_test, guess)\n ind = ind + 1\n\n p, r, a = (float(sum(precision_dict.values())) / 10), (float(sum(recall_dict.values())) / 10)\\\n , (float(sum(accuracy_dict.values())) / 10)\n performance_dict[params_arg] = (p + r + a) * 33.33\n print(\"Precision : \", p * 100, \", Recall : \", r * 100, \", Accuracy : \", a * 100)\n return performance_dict","title":""},{"docid":"b8f564d1efc31a35f3becb69aa3e7ce2","score":"0.57959634","text":"def predict(self, data):\n prediction = super().predict(data)\n\n if self.predict_strategy == 'argmax':\n predicted = prediction\n elif self.predict_strategy == 'relative':\n predicted = self._get_relative_proba(prediction)\n elif self.predict_strategy == 'class_distribution':\n predicted = self._get_class_distribution_proba(prediction)\n else:\n raise ValueError(\n f'Strategy \\'{self.predict_strategy}\\' not implemented.')\n\n predicted = np.argmax(predicted, axis=1)\n\n return self._classes_to_labels(predicted)","title":""},{"docid":"c280054b53891488ef4ae1fe1bbb9589","score":"0.57914215","text":"def predict(data, model):\n print('{\"value\": ' + model['p'] + '}')","title":""},{"docid":"5f53eb539efb62c04ac71f83aa60b9be","score":"0.5765077","text":"def estimator_info(self):\n pass","title":""},{"docid":"9160cd10038a30e2f62e653e4a098765","score":"0.5764108","text":"def predict(self, features):\n pass","title":""},{"docid":"2c7036d3edcc22b5ea42d413aaa5c1cf","score":"0.57541066","text":"def viz_val_decorated(*args, **kwargs):\r\n\r\n if self.reason.lower().find('val')>=0: \r\n self.reason ='val'\r\n train_score , val_score, switch, param_range,\\\r\n pname, val_kws, train_kws =func(*args, **kwargs)\r\n \r\n elif self.reason.lower().find('learn')>=0: \r\n self.reason ='learn'\r\n param_range, train_score , val_score, switch,\\\r\n pname, val_kws, train_kws=func(*args, **kwargs)\r\n \r\n if val_kws is not None : \r\n self.val_kws =val_kws \r\n if train_kws is not None: \r\n self.train_kws = train_kws\r\n \r\n # add the name of parameters.\r\n if pname !='': \r\n self.xlabel = {'xlabel':'Evaluation of parameter %s'%pname , \r\n 'fontsize': self.font_size}\r\n\r\n if switch is not None :\r\n self.turn =switch \r\n \r\n # if param_range is not None :\r\n # k= param_range \r\n \r\n plt.figure(figsize=self.fig_size)\r\n\r\n if self.turn in ['on', 1, True]: \r\n # if not isinstance(param_range, bool): \r\n self._plot_train_val_score(train_score, val_score, k=param_range )\r\n \r\n if self.savefig is not None: \r\n if isinstance(self.savefig, dict):\r\n plt.savefig(**self.savefig)\r\n else : \r\n plt.savefig(self.savefig)\r\n \r\n # initialize the trainscore_dict \r\n train_score=dict() \r\n return func(*args, **kwargs)","title":""},{"docid":"f73a13c67a6b92449c48f0494ac377ab","score":"0.5751417","text":"def predict(self, x):\n pass","title":""},{"docid":"2f676f56d2a4b7c3e913dd16feefb20e","score":"0.57491755","text":"def predict(self, test_data, fcn_obs, par=None):\n pass","title":""},{"docid":"2f676f56d2a4b7c3e913dd16feefb20e","score":"0.57491755","text":"def predict(self, test_data, fcn_obs, par=None):\n pass","title":""},{"docid":"2f676f56d2a4b7c3e913dd16feefb20e","score":"0.57491755","text":"def predict(self, test_data, fcn_obs, par=None):\n pass","title":""},{"docid":"26ed9b101db6c4996c1dcb275ca985f7","score":"0.5746194","text":"def predict(example):\n print([predictor(example) for predictor in predictors])\n return mode(predictor(example) for predictor in predictors)","title":""},{"docid":"b652494908b3c3c6a26dc5ba19522b0a","score":"0.5743264","text":"def get_predictions(self):\n pass","title":""},{"docid":"9f242e56bd1d384e111136055f019716","score":"0.57417655","text":"def predict(image_data):\n \n \n \n \n PAYLOAD = {}\n PAYLOAD[\"timestamp\"] = str(datetime.datetime.now())\n PAYLOAD[\"inferencetype\"] = \"object-detection\"\n PAYLOAD[\"inferencedescription\"] = \"Top {} predictions with score {} or above \".format(\n config_utils.MAX_NO_OF_RESULTS, config_utils.SCORE_THRESHOLD\n )\n \n PAYLOAD[\"inferenceresults\"] = []\n \n \n try:\n image_data = np.expand_dims(image_data, axis=0).astype(np.uint8)\n interpreter.set_tensor(input_details[0][\"index\"], image_data)\n interpreter.invoke()\n boxes = interpreter.get_tensor(output_details[0][\"index\"])[0]\n classes = interpreter.get_tensor(output_details[1][\"index\"])[0]\n scores = interpreter.get_tensor(output_details[2][\"index\"])[0]\n predicted = []\n \n for i in range(len(scores)):\n c = int(classes[i])\n if len(predicted) == config_utils.MAX_NO_OF_RESULTS:\n break\n if scores[i] >= config_utils.SCORE_THRESHOLD:\n predicted.append(c)\n result = {\n \"Label\": str(labels[c]),\n \"Score\": str(scores[i]),\n \"Boundaries\": str(boxes[i]),\n }\n PAYLOAD[\"inferenceresults\"].append(result)\n config_utils.logger.info(json.dumps(PAYLOAD))\n \n PAYLOAD[\"position\"] = getPositionData()\n \n if config_utils.TOPIC.strip() != \"\":\n ipc_utils.IPCUtils().publish_results_to_cloud(PAYLOAD)\n else:\n config_utils.logger.info(\"No topic set to publish the inference results to the cloud.\") \n return PAYLOAD[\"inferenceresults\"]\n except Exception as e:\n config_utils.logger.error(\"Exception occured during prediction: {}\".format(e))","title":""},{"docid":"ee204aa532cbed03d2f369b7858014b7","score":"0.5720965","text":"def predict(self, data):\n raise NotImplementedError","title":""},{"docid":"d9b2b7e330b7711ddd24679bc24587ac","score":"0.57202613","text":"def train(self, trainingData, trainingLabels, validationData, validationLabels): \n \n # might be useful in your code later...\n # this is a list of all features in the training set.\n self.features = list(set([ f for datum in trainingData for f in datum.keys() ]))\n svr = svm.SVC()\n parameters = {'kernel':('linear', 'rbf', 'sigmoid'), 'C':[1, 10]}\n self.clf = grid_search.GridSearchCV(svr, parameters)\n tdata = self.formattingData(trainingData, self.features, None, None)\n X = tdata\n #print(tdata)\n Y = trainingLabels\n #print(trainingLabels)\n #print(trainingData)\n #for i in range(len(trainingData)):\n #data = self.formattingData(trainingData, self.features, None, None)\n #label = self.legalLabels\n self.clf.fit(X, Y)\n #clf.score(tdata, trainingLabels)\n \n #print(validationLabels)\n #print(clf.score(X,Y))\n #self.myclassifier = clf.predict(X)\n \n #print(Y)\n #print(self.myclassifier)\n \n \n #print(clf.predict(X1))\n #print(self.myclassfier.predict(X1))\n \n #self.classify(X)\n #for x in self.classifer.predict(X)\n #self.weight[\n #print(trainingLabels[x])\n #print(self.myclassfier.predict(X)[x])\n \n\n \n \n \n \n \n \n \n \n \n\n \n #method fit, grab sym always linear and call fit on it","title":""},{"docid":"97261af3ed24c77a1d912925dd9fca43","score":"0.571568","text":"def on_predict_end(self, logs=None):\n super().on_predict_end(logs)\n self._set_logging_mode(LoggingMode.PREDICT)","title":""},{"docid":"64e86fa195cde726a6c51bac6f440bf0","score":"0.5710891","text":"def get_best( clf, hyper, score, X_train, y_train ):\r\n best = customGridSearch( clf, param_grid=hyper, score=score ) #uses cross validation\r\n best.fit(X_train,y_train)\r\n return best.best_estimator_","title":""},{"docid":"e374be4895d74c285cea112611b6659e","score":"0.571062","text":"def predict(self,X):\n if self.approach == \"always\":\n value = self.value\n y_pred = [value]*len(X)\n return y_pred\n elif self.approach == \"most\":\n most_common = self.most_common\n y_pred = most_common*len(X)\n return y_pred\n elif self.approach == \"distribution\":\n distribution=self.distribution\n y_pred = [predict_from_distribution(distribution) for i in range(len(X))]\n return y_pred\n elif self.approach == \"equal\":\n distributioneq=self.distributioneq\n y_pred = [predict_from_distribution(distributioneq) for i in range(len(X))]\n return y_pred","title":""},{"docid":"fbb2858246e1f1ca6d19e787bab0b707","score":"0.57083166","text":"def predict(self, dataset):\n\n data = np.column_stack([dataset.features, dataset.labels])\n columns = dataset.feature_names + dataset.label_names\n test_df = pd.DataFrame(data=data, columns=columns)\n x_test = dataset.features\n #y_test = np.array([1 if y == [dataset.favorable_label] else -1 for y in dataset.labels])\n #x_control_test = np.array(test_df[self.sensitive_attr])\n\n all_sensitive_attributes = dataset.protected_attribute_names\n \n model = self.model_name\n predictions, y_res, scores = [], [], []\n for x in x_test:\n t = model(x)\n if t > 0 :\n predictions.append(1)\n y_res.append(1)\n else:\n predictions.append(0)\n y_res.append(-1)\n scores.append((t+1)/2)\n\n\n #print(\"Gamma: \", self.obj.getGamma(y_test, y_res, x_control_test))\n pred_dataset = dataset.copy()\n pred_dataset.labels = np.array(predictions)\n pred_dataset.scores = np.array(scores)\n\n return pred_dataset","title":""},{"docid":"a113d1de58af574b69cface15bec07f2","score":"0.5704807","text":"def predict(\n fitted_model, test_data_x: pd.DataFrame, test_data_y: pd.DataFrame\n) -> np.ndarray:\n preds = fitted_model.predict(test_data_x)\n conf_mat = pd.crosstab(\n test_data_y, preds, rownames=[\"Actual Species\"], colnames=[\"Predicted Species\"]\n )\n\n # 5. Save performance metric\n pai.log_metrics({\"accuracy\": accuracy_score(test_data_y, preds)})\n pai.log_metrics({\"f1\": f1_score(test_data_y, preds, average=\"macro\")})\n pai.log_metrics({\"precision\": precision_score(test_data_y, preds, average=\"macro\")})\n pai.log_metrics({\"recall\": recall_score(test_data_y, preds, average=\"macro\")})\n\n # 6. Save other artifacts, e.g. the confusion matrix\n pai.log_artifacts({\"confusion_matrix\": conf_mat})\n pai.log_artifacts({\"plot\": generate_plot()})\n\n return preds","title":""},{"docid":"30c89510eb77ed1f02ed9fdccdb8f847","score":"0.5704508","text":"def _fit_and_predict(estimator, X, y, train, test, verbose, fit_params,\n parameters=None, method='predict'):\n # Adjust length of sample weights\n fit_params = fit_params if fit_params is not None else {}\n fit_params = dict([(k, _index_param_value(X, v, train))\n for k, v in fit_params.items()])\n\n X_train, y_train = _safe_split(estimator, X, y, train)\n X_test, _ = _safe_split(estimator, X, y, test, train)\n\n if parameters is not None:\n estimator.set_params(**parameters)\n\n if y_train is None:\n estimator.fit(X_train, **fit_params)\n else:\n estimator.fit(X_train, y_train, **fit_params)\n func = getattr(estimator, method)\n predictions = func(X_test)\n if method in ['decision_function', 'predict_proba', 'predict_log_proba']:\n n_classes = len(set(y))\n if n_classes != len(estimator.classes_):\n recommendation = (\n 'To fix this, use a cross-validation '\n 'technique resulting in properly '\n 'stratified folds')\n warnings.warn('Number of classes in training fold ({}) does '\n 'not match total number of classes ({}). '\n 'Results may not be appropriate for your use case. '\n '{}'.format(len(estimator.classes_),\n n_classes, recommendation),\n RuntimeWarning)\n if method == 'decision_function':\n if (predictions.ndim == 2 and\n predictions.shape[1] != len(estimator.classes_)):\n # This handles the case when the shape of predictions\n # does not match the number of classes used to train\n # it with. This case is found when sklearn.svm.SVC is\n # set to `decision_function_shape='ovo'`.\n raise ValueError('Output shape {} of {} does not match '\n 'number of classes ({}) in fold. '\n 'Irregular decision_function outputs '\n 'are not currently supported by '\n 'cross_val_predict'.format(\n predictions.shape, method,\n len(estimator.classes_),\n recommendation))\n if len(estimator.classes_) <= 2:\n # In this special case, `predictions` contains a 1D array.\n raise ValueError('Only {} class/es in training fold, this '\n 'is not supported for decision_function '\n 'with imbalanced folds. {}'.format(\n len(estimator.classes_),\n recommendation))\n\n float_min = np.finfo(predictions.dtype).min\n default_values = {'decision_function': float_min,\n 'predict_log_proba': float_min,\n 'predict_proba': 0}\n predictions_for_all_classes = np.full((_num_samples(predictions),\n n_classes),\n default_values[method])\n predictions_for_all_classes[:, estimator.classes_] = predictions\n predictions = predictions_for_all_classes\n return predictions, test","title":""},{"docid":"73f3b377317cf0e721704a3551a5a07a","score":"0.57019013","text":"def analyseChangeInHyperParameter(n_est_lst, max_features_lst, min_samples_split_lst, X_train, Y_train, X_val, Y_val, X_test, Y_test, smooth_garph=True, repeat_exp=3):\n val_score_lst, train_score_lst, test_score_lst, oob_score_lst = [], [], [], []\n params = list(itertools.product(n_est_lst, max_features_lst, min_samples_split_lst))\n for n_est, max_feat, min_sam_split in tqdm(params):\n train_score, test_score, val_score, oob_score = 0,0,0,0\n for i in range(repeat_exp):\n clf = RandomForestClassifier(n_estimators=n_est, max_features=max_feat, min_samples_split=min_sam_split\\\n , oob_score=False, n_jobs=-2).fit(X_train, Y_train)\n train_score+=(clf.score(X_train, Y_train)*100)\n test_score+=(clf.score(X_test, Y_test)*100)\n val_score+=(clf.score(X_val, Y_val)*100)\n train_score_lst.append(train_score/3)\n test_score_lst.append(test_score/3)\n val_score_lst.append(val_score/3)\n if len(n_est_lst)!=1:\n xlabel = 'n_estimators'\n title = 'n_estimators v/s Accuracy'\n X=n_est_lst\n elif len(max_features_lst)!=1:\n xlabel = 'max_features'\n title = 'max_features v/s Accuracy'\n X=max_features_lst\n else:\n xlabel = 'min_samples_split'\n title = 'min_samples_split v/s Accuracy'\n X=min_samples_split_lst\n\n Y_train_smoothed = make_interp_spline(X, train_score_lst, k=3)(xnew)\n Y_test_smoothed = make_interp_spline(X, test_score_lst, k=3)(xnew)\n Y_val_smoothed = make_interp_spline(X, val_score_lst, k=3)(xnew)\n if not smooth_garph:\n xnew = X\n Y_train_smoothed=train_score_lst\n Y_test_smoothed=test_score_lst\n Y_val_smoothed=val_score_lst\n gs = gridspec.GridSpec(2, 2)\n\n fig = plt.figure(figsize=(30,20))\n ax1 = fig.add_subplot(gs[0, 0])\n ax1.plot(xnew, Y_train_smoothed, label='Train Score')\n ax1.legend()\n ax1.set_title(xlabel+' v/s Train Accuracy')\n ax1.set_xlabel(xlabel)\n ax1.set_ylabel('Train Accuracy')\n\n ax1 = fig.add_subplot(gs[0, 1])\n ax1.plot(xnew, Y_test_smoothed, label='Tes Score')\n ax1.legend()\n ax1.set_title(xlabel+' v/s Test Accuracy')\n ax1.set_xlabel(xlabel)\n ax1.set_ylabel('Test Accuracy')\n\n ax1 = fig.add_subplot(gs[1, 0])\n ax1.plot(xnew, Y_val_smoothed, label='Validation Score')\n ax1.legend()\n ax1.set_title(xlabel+' v/s Validation Accuracy')\n ax1.set_xlabel(xlabel)\n ax1.set_ylabel('Validation Accuracy')\n\n ax1 = fig.add_subplot(gs[1, 1])\n ax1.plot(xnew, Y_test_smoothed, label='Test Score')\n ax1.plot(xnew, Y_val_smoothed, label='Validation Score')\n ax1.plot(xnew, Y_train_smoothed, label='Train Score')\n ax1.legend()\n ax1.set_title(xlabel+' v/s Test, Validation and Train Accuracy')\n ax1.set_xlabel(xlabel)\n ax1.set_ylabel('Accuracy')\n\n plt.show()","title":""},{"docid":"de700cd20c85daf4b5e49be92109aad6","score":"0.5695684","text":"def ALM(eval_pts, uneval_pts,model,k ):\n \n post_means, post_vars = model.predict(uneval_pts)\n \n # Point with highest variance\n new_point = uneval_pts[np.argmax(post_vars)]\n indx = np.argmax(post_vars)\n return [new_point], [indx]","title":""},{"docid":"14845b911679b2746072b19bb443c0f9","score":"0.56943643","text":"def predict(self, uedata):\n return \"Overloaded!\"","title":""},{"docid":"0dbe749244a9371cb2af13d397a014cc","score":"0.5690516","text":"def predict(self, X):\n pass","title":""},{"docid":"0dbe749244a9371cb2af13d397a014cc","score":"0.5690516","text":"def predict(self, X):\n pass","title":""},{"docid":"0dbe749244a9371cb2af13d397a014cc","score":"0.5690516","text":"def predict(self, X):\n pass","title":""},{"docid":"c72d2e4eb76eef156fded7cb49da58ef","score":"0.56883866","text":"def FindPredictions(self):\n return None","title":""},{"docid":"03a11d8bb078babc5bf7471cbff68b44","score":"0.56841344","text":"def predict(self,X):\n\t\t# below for outputting the index with the highest probability\n\t\t#labels = self.clf.decision_function(X)\n\t\t#return list(labels).index(max(labels))\n\n\t\t# below for outputting the index of first 1\n\t\tprint self.clf.predict(X)\n\t\treturn np.argmax(self.clf.predict(X))","title":""},{"docid":"e4dfb3e73c50241f53e88a0e63a57132","score":"0.56840223","text":"def display_results(self,index):\n self.best_features = literal_eval(self.res_df.iloc[index][\"Feauteres_decor_ind\"])\n \n if self.cv is not None:\n # if task is regression\n if is_regressor(self.model):\n# =============================================================================\n# Running with general algorithm and calculating accuracies\n# ============================================================================= \n\n # calculating accuracies for generalized model \n self.model.fit(self.x,self.y)\n self.y_pred_cal_gen = np.squeeze(self.model.predict(self.x)).squeeze()\n self.r2_cal_gen = r2_score(self.y,self.y_pred_cal_gen)\n self.r2_adj_cal_gen = adj_r2_score(self.y,self.y_pred_cal_gen, self.x, self.r2_cal_gen)\n self.rmse_cal_gen = mean_squared_error(self.y, self.y_pred_cal_gen, squared = False)\n self.rmspe_cal_gen = rmspe_score(self.y, self.y_pred_cal_gen)\n\n if self.cv.__str__() == \"LeaveOneOut()\":\n score_r2 = cross_validate_LOO(self.model,self.x,self.y,self.cv,\n scoring=r2_score) \n\n score_rmse = cross_validate_LOO(self.model,self.x,self.y,self.cv,\n scoring=mean_squared_error)\n \n self.y_pred_cv_gen = cross_val_predict(self.model, self.x, self.y, n_jobs = -1, cv=self.cv).squeeze() \n self.r2_cv_gen = score_r2 \n self.r2_adj_cv_gen = adj_r2_score(self.y, x=self.x, r2=self.r2_cv_gen)\n self.rmse_cv_gen = np.sqrt(score_rmse)\n self.rmspe_cv_gen = rmspe_score(self.y, self.y_pred_cv_gen) \n \n else:\n scores = cross_validate(self.model,self.x,self.y,\n n_jobs = -1, \n cv=self.cv,\n scoring=['r2','neg_mean_squared_error']) \n \n self.y_pred_cv_gen = cross_val_predict(self.model, self.x, self.y, n_jobs = -1, cv=self.cv).squeeze() \n self.r2_cv_gen = scores['test_r2'].mean() \n self.r2_adj_cv_gen = adj_r2_score(self.y, x=self.x, r2=self.r2_cv_gen)\n self.rmse_cv_gen = np.sqrt(scores['test_neg_mean_squared_error'].mean())\n self.rmspe_cv_gen = rmspe_score(self.y, self.y_pred_cv_gen) \n\n \n # =============================================================================\n # Running optimized model and calculate accuracies\n # =============================================================================\n \n self.model_best.fit(self.x[:,self.best_features],self.y) \n self.y_pred_cal_op = np.squeeze(self.model_best.predict(self.x[:,self.best_features])).squeeze()\n self.r2_cal_op = r2_score(self.y,self.y_pred_cal_op)\n self.r2_adj_cal_op = adj_r2_score(self.y,self.y_pred_cal_op,self.x[:,self.best_features],self.r2_cal_op)\n self.rmse_cal_op = mean_squared_error(self.y, self.y_pred_cal_op, squared = False)\n self.rmspe_cal_op = rmspe_score(self.y, self.y_pred_cal_op)\n \n if self.cv.__str__() == \"LeaveOneOut()\":\n \n score_r2 = cross_validate_LOO(self.model_best,self.x[:,self.best_features],self.y,self.cv,\n scoring=r2_score) \n\n score_rmse = cross_validate_LOO(self.model_best,self.x[:,self.best_features],self.y,self.cv,\n scoring=mean_squared_error) \n \n self.y_pred_cv_op = cross_val_predict(self.model_best, self.x[:,self.best_features], self.y, n_jobs = -1, cv=self.cv).squeeze() \n self.r2_cv_op = score_r2 \n self.r2_adj_cv_op = adj_r2_score(self.y,x = self.x[:,self.best_features],r2= self.r2_cv_op)\n self.rmse_cv_op = np.sqrt(score_rmse)\n self.rmspe_cv_op = rmspe_score(self.y, self.y_pred_cv_op) \n \n\n else:\n scores = cross_validate(self.model_best,self.x[:,self.best_features],self.y,\n n_jobs = -1, \n cv=self.cv,\n scoring=['r2','neg_mean_squared_error']) \n \n self.y_pred_cv_op = cross_val_predict(self.model_best, self.x[:,self.best_features], self.y, n_jobs = -1, cv=self.cv).squeeze() \n self.r2_cv_op = scores['test_r2'].mean() \n self.r2_adj_cv_op = adj_r2_score(self.y,x = self.x[:,self.best_features],r2= self.r2_cv_op)\n self.rmse_cv_op = np.sqrt(scores['test_neg_mean_squared_error'].mean())\n self.rmspe_cv_op = rmspe_score(self.y, self.y_pred_cv_op)\n\n # =============================================================================\n # Plotting generalized model and optimized model \n # =============================================================================\n # generalized model\n fig,axes = plt.subplots(2,3,figsize=(15,10))\n sns.regplot(x=self.y, y=self.y_pred_cal_gen, color=\"k\",ax=axes[0, 0])\n axes[0,0].plot([np.min([self.y, self.y_pred_cal_gen]) - 0.1*np.std(self.y), np.max([self.y, self.y_pred_cal_gen]) + 0.1*np.std(self.y)],\n [np.min([self.y, self.y_pred_cal_gen]) - 0.1*np.std(self.y), np.max([self.y, self.y_pred_cal_gen]) + 0.1*np.std(self.y)],'r--')\n axes[0,0].text(0.05,0.80,r\"RMSE={:.3f}\".format(self.rmse_cal_gen) + \"\\n\"+ \n r\"RMSPE={:.3f}\".format(self.rmspe_cal_gen) + \"\\n\"+ \n r\"$R^2$={:.3f}\".format(self.r2_cal_gen) + \"\\n\"+\n r\"$Adj. R^2$={:.3f}\".format(self.r2_adj_cal_gen),\n transform=axes[0, 0].transAxes,\n color='k',\n bbox=dict(facecolor=(1,1,1,0.5),edgecolor=(0,0,0,1)))\n axes[0,0].set_xlabel(\"Measured\")\n axes[0,0].set_ylabel(\"Predicted\")\n axes[0,0].set_title(\"General model - Cal.\")\n axes[0,0].ticklabel_format(axis=\"both\", style = \"sci\", scilimits = (0,2))\n \n sns.regplot(x=self.y, y=self.y_pred_cv_gen, color=\"k\",ax=axes[1, 0])\n axes[1,0].plot([np.min([self.y, self.y_pred_cv_gen]) - 0.1*np.std(self.y), np.max([self.y, self.y_pred_cv_gen]) + 0.1*np.std(self.y)],\n [np.min([self.y, self.y_pred_cv_gen]) - 0.1*np.std(self.y), np.max([self.y, self.y_pred_cv_gen]) + 0.1*np.std(self.y)],'r--') \n axes[1,0].text(0.05,0.80,r\"RMSE={:.3f}\".format(self.rmse_cv_gen) + \"\\n\"+ \n r\"RMSPE={:.3f}\".format(self.rmspe_cv_gen) + \"\\n\"+ \n r\"$R^2$={:.3f}\".format(self.r2_cv_gen) + \"\\n\"+\n r\"$Adj. R^2$={:.3f}\".format(self.r2_adj_cv_gen),\n transform=axes[1, 0].transAxes,\n color='k',\n bbox=dict(facecolor=(1,1,1,0.5),edgecolor=(0,0,0,1)))\n axes[1,0].set_xlabel(\"Measured\")\n axes[1,0].set_ylabel(\"Predicted\")\n axes[1,0].set_title(\"General model - CV\")\n axes[1,0].ticklabel_format(axis=\"both\", style = \"sci\", scilimits = (0,2))\n \n # optimized model\n sns.regplot(x=self.y, y=self.y_pred_cal_op, color=\"k\",ax=axes[0, 1])\n axes[0,1].plot([np.min([self.y, self.y_pred_cal_op]) - 0.1*np.std(self.y), np.max([self.y, self.y_pred_cal_op]) + 0.1*np.std(self.y)],\n [np.min([self.y, self.y_pred_cal_op]) - 0.1*np.std(self.y), np.max([self.y, self.y_pred_cal_op]) + 0.1*np.std(self.y)],'r--') \n axes[0,1].text(0.05,0.80,r\"RMSE={:.3f}\".format(self.rmse_cal_op) + \"\\n\"+ \n r\"RMSPE={:.3f}\".format(self.rmspe_cal_op) + \"\\n\"+ \n r\"$R^2$={:.3f}\".format(self.r2_cal_op)+ \"\\n\"+\n r\"$Adj. R^2$={:.3f}\".format(self.r2_adj_cal_op),\n transform=axes[0, 1].transAxes,\n color='k',\n bbox=dict(facecolor=(1,1,1,0.5),edgecolor=(0,0,0,1)))\n axes[0,1].set_xlabel(\"Measured\")\n axes[0,1].set_ylabel(\"Predicted\")\n axes[0,1].set_title(\"Optimized model - Cal.\")\n axes[0,1].ticklabel_format(axis=\"both\", style = \"sci\", scilimits = (0,2)) \n \n sns.regplot(x=self.y, y=self.y_pred_cv_op, color=\"k\",ax=axes[1, 1])\n axes[1,1].plot([np.min([self.y, self.y_pred_cv_op]) - 0.1*np.std(self.y), np.max([self.y, self.y_pred_cv_op]) + 0.1*np.std(self.y)],\n [np.min([self.y, self.y_pred_cv_op]) - 0.1*np.std(self.y), np.max([self.y, self.y_pred_cv_op]) + 0.1*np.std(self.y)],'r--') \n axes[1,1].text(0.05,0.80,r\"RMSE={:.3f}\".format(self.rmse_cv_op) + \"\\n\"+ \n r\"RMSPE={:.3f}\".format(self.rmspe_cv_op) + \"\\n\"+ \n r\"$R^2$={:.3f}\".format(self.r2_cv_op)+ \"\\n\"+\n r\"$Adj. R^2$={:.3f}\".format(self.r2_adj_cv_op),\n transform=axes[1, 1].transAxes,\n color='k',\n bbox=dict(facecolor=(1,1,1,0.5),edgecolor=(0,0,0,1)))\n axes[1,1].set_xlabel(\"Measured\")\n axes[1,1].set_ylabel(\"Predicted\")\n axes[1,1].set_title(\"Optimized model - CV\")\n axes[1,1].ticklabel_format(axis=\"both\", style = \"sci\", scilimits = (0,2)) \n \n #residuals plot\n sns.residplot(self.y_pred_cal_op, self.y - self.y_pred_cal_op, lowess=False, color=\"k\",ax=axes[0, 2])\n axes[0,2].set_xlabel(\"Predicted\")\n axes[0,2].set_ylabel(\"Residuals\")\n axes[0,2].set_title(\"Optimized model - Cal. Residuals\")\n axes[0,2].ticklabel_format(axis=\"both\", style = \"sci\", scilimits = (0,2)) \n\n \n sns.residplot(self.y_pred_cv_op, self.y - self.y_pred_cv_op, lowess=False, color=\"k\",ax=axes[1, 2])\n axes[1,2].set_xlabel(\"Predicted\")\n axes[1,2].set_ylabel(\"Residuals\")\n axes[1,2].set_title(\"Optimized model - CV Residuals\")\n axes[1,2].ticklabel_format(axis=\"both\", style = \"sci\", scilimits = (0,2)) \n \n \n\n results = {\"r2_cal_gen\":[self.r2_cal_gen],'rmse_cal_gen':[self.rmse_cal_gen],'rmspe_cal_gen':[self.rmspe_cal_gen],'r2_adj_cal_gen':[self.r2_adj_cal_gen],\n 'r2_cv_gen':[self.r2_cv_gen],'rmse_cv_gen':[self.rmse_cv_gen],'rmspe_cv_gen':[self.rmspe_cv_gen],'r2_adj_cv_gen':[self.r2_adj_cv_gen],\n \"r2_cal_op\":[self.r2_cal_op],'rmse_cal_op':[self.rmse_cal_op],'rmspe_cal_op':[self.rmspe_cal_op],'r2_adj_cal_op':[self.r2_adj_cal_op],\n 'r2_cv_op':[self.r2_cv_op],'rmse_cv_op':[self.rmse_cv_op],'rmspe_cv_op':[self.rmspe_cv_op],'r2_adj_cv_op':[self.r2_adj_cv_op]} \n \n # if task is classification\n else:\n if len(self.class_sep) == 2:\n fig,axes = plt.subplots(2,3,figsize=(15,10))\n else:\n fig,axes = plt.subplots(2,2,figsize=(10 + len(self.class_sep),10 + len(self.class_sep)))\n # general model\n \n self.model.fit(self.x,self.y)\n y_train_pred = self.model.predict(self.x)\n self.acc_train_gen = self.model.score(self.x,self.y)\n cm = confusion_matrix(self.y,y_train_pred.squeeze())\n cm = np.stack(list(map(lambda x: x/x.sum(),cm)))\n conf_matrix=pd.DataFrame(cm,columns = self.class_sep,index= self.class_sep)\n sns.heatmap(conf_matrix,cmap=\"coolwarm\", annot=True, ax=axes[0,0],vmin=0,vmax=1)\n axes[0,0].set_title('General model - Cal. confusion matrix')\n axes[0,0].set_ylabel('True label')\n axes[0,0].set_xlabel('Predicted label') \n \n \n y_test_pred = cross_val_predict(self.model, self.x, self.y, \n n_jobs = -1,\n cv=self.cv).squeeze() \n\n if self.cv.__str__() == \"LeaveOneOut()\": \n score_acc = cross_validate_LOO(self.model,self.x,self.y,self.cv,\n scoring=accuracy_score) \n\n self.acc_test_gen = score_acc \n else:\n scores = cross_validate(self.model, self.x, self.y, \n n_jobs = -1,\n cv=self.cv,\n scoring = \"accuracy\") \n \n self.acc_test_gen = scores['test_score'].mean() \n\n cm = confusion_matrix(self.y,y_test_pred)\n cm = np.stack(list(map(lambda x: x/x.sum(),cm)))\n conf_matrix=pd.DataFrame(cm,columns = self.class_sep,index= self.class_sep)\n sns.heatmap(conf_matrix,cmap=\"coolwarm\", annot=True,ax = axes[1,0],vmin=0,vmax=1)\n axes[1,0].set_title('General model - CV confusion matrix') \n axes[1,0].set_ylabel('True label')\n axes[1,0].set_xlabel('Predicted label') \n \n \n # optimized model\n self.model_best.fit(self.x[:,self.best_features],self.y)\n y_train_pred = self.model_best.predict(self.x[:,self.best_features])\n self.acc_train_op = self.model_best.score(self.x[:,self.best_features],self.y)\n cm = confusion_matrix(self.y, y_train_pred.squeeze())\n cm = np.stack(list(map(lambda x: x/x.sum(),cm)))\n conf_matrix=pd.DataFrame(cm,columns = self.class_sep,index= self.class_sep)\n sns.heatmap(conf_matrix,cmap=\"coolwarm\", annot=True, ax=axes[0,1],vmin=0,vmax=1)\n axes[0,1].set_title('Optimized model - Cal. confusion matrix') \n axes[0,1].set_ylabel('True label')\n axes[0,1].set_xlabel('Predicted label') \n \n \n y_test_pred = cross_val_predict(self.model_best, self.x[:,self.best_features], self.y, \n n_jobs = -1,\n cv=self.cv).squeeze() \n \n if self.cv.__str__() == \"LeaveOneOut()\": \n score_acc = cross_validate_LOO(self.model_best,self.x[:,self.best_features],self.y,\n self.cv,\n scoring=accuracy_score) \n \n self.acc_test_op = score_acc \n else:\n scores = cross_validate(self.model_best, self.x[:,self.best_features], self.y, \n n_jobs = -1,\n cv=self.cv,\n scoring = \"accuracy\") \n\n self.acc_test_op = scores['test_score'].mean()\n \n cm = confusion_matrix(self.y,y_test_pred)\n cm = np.stack(list(map(lambda x: x/x.sum(),cm)))\n conf_matrix=pd.DataFrame(cm,columns = self.class_sep,index= self.class_sep)\n sns.heatmap(conf_matrix,cmap=\"coolwarm\", annot=True,ax = axes[1,1],vmin=0,vmax=1)\n axes[1,1].set_title('Optimized model - CV confusion matrix') \n axes[1,1].set_ylabel('True label')\n axes[1,1].set_xlabel('Predicted label') \n \n if len(self.class_sep) == 2:\n #optimized model Roc curves\n self.model_best.fit(self.x[:,self.best_features],self.y)\n y_train_pred_prob = self.model_best.predict_proba(self.x[:,self.best_features])\n fpr, tpr, thr = roc_curve(self.y, y_train_pred_prob[:, 1].squeeze()) \n self.auc_train_op = auc(fpr, tpr)\n axes[0,2].plot([0,1],[0,1],linestyle = '--',lw = 2,color = 'black')\n axes[0,2].plot(fpr, tpr, color='blue',\n label=r'$ROC_{train}$ (AUC = %0.2f)' % (auc(fpr, tpr)),lw=2, alpha=1)\n axes[0,2].set_xlabel('False Positive Rate')\n axes[0,2].set_ylabel('True Positive Rate')\n axes[0,2].set_title('Optimized model - Cal. ROC Curve')\n axes[0,2].legend(loc=\"lower right\")\n \n y_test_pred_prob = cross_val_predict(self.model_best, self.x[:,self.best_features], self.y, \n n_jobs = -1,\n cv=self.cv,\n method='predict_proba') \n \n \n fpr, tpr, thr = roc_curve(self.y, y_test_pred_prob[:, 1].squeeze()) \n self.auc_test_op = auc(fpr, tpr)\n axes[1,2].plot([0,1],[0,1],linestyle = '--',lw = 2,color = 'black')\n axes[1,2].plot(fpr, tpr, color='blue',\n label=r'$ROC_{test}$ (AUC = %0.2f)' % (auc(fpr, tpr)),lw=2, alpha=1)\n axes[1,2].set_xlabel('False Positive Rate')\n axes[1,2].set_ylabel('True Positive Rate')\n axes[1,2].set_title('Optimized model- CV ROC Curve')\n axes[1,2].legend(loc=\"lower right\")\n \n results = {\"acc_gen\":[self.acc_train_gen],'acc_cv_gen':[self.acc_test_gen],\n 'acc_op':[self.acc_train_op],'acc_cv_op':[self.acc_test_op],\n \"auc_op\":[self.auc_train_op],'auc_cv_op':[self.auc_test_op]}\n \n results = {\"acc_gen\":[self.acc_train_gen],'acc_cv_gen':[self.acc_test_gen],\n 'acc_op':[self.acc_train_op],'acc_cv_op':[self.acc_test_op]} \n \n # if cv is not defined\n else:\n if is_regressor(self.model):\n if hasattr(self, 'x_train') and hasattr(self, 'x_test') and hasattr(self, 'y_train') and hasattr(self, 'y_test'): \n\n # calculating accuracies for generalized model\n self.model.fit(self.x_train,self.y_train)\n self.y_pred_cal_gen = np.squeeze(self.model.predict(self.x_train)).squeeze()\n self.r2_cal_gen = r2_score(self.y_train,self.y_pred_cal_gen)\n #self.r2_adj_cal_gen = adj_r2_score(self.y_train,self.y_pred_cal_gen,x = self.x_train, r2= self.r2_cal_gen)\n self.r2_adj_cal_gen = adj_r2_score(self.y_train,self.y_pred_cal_gen,x = self.x, r2= self.r2_cal_gen)\n self.rmse_cal_gen = mean_squared_error(self.y_train, self.y_pred_cal_gen, squared = False)\n self.rmspe_cal_gen = rmspe_score(self.y_train, self.y_pred_cal_gen)\n \n self.y_pred_val_gen = np.squeeze(self.model.predict(self.x_test)).squeeze()\n self.r2_val_gen = r2_score(self.y_test,self.y_pred_val_gen)\n #self.r2_adj_val_gen = adj_r2_score(self.y_test, x=self.x_test, r2=self.r2_val_gen)\n self.r2_adj_val_gen = adj_r2_score(self.y_test, x=self.x, r2=self.r2_val_gen)\n self.rmse_val_gen = mean_squared_error(self.y_test, self.y_pred_val_gen, squared = False)\n self.rmspe_val_gen = rmspe_score(self.y_test, self.y_pred_val_gen)\n \n # calculating accuracies for optimized model\n self.model_best.fit(self.x_train[:,self.best_features],self.y_train)\n self.y_pred_cal_op = np.squeeze(self.model_best.predict(self.x_train[:,self.best_features])).squeeze()\n self.r2_cal_op = r2_score(self.y_train,self.y_pred_cal_op)\n #self.r2_adj_cal_op = adj_r2_score(self.y_train, x=self.x_train[:,self.best_features],r2=self.r2_cal_op)\n self.r2_adj_cal_op = adj_r2_score(self.y_train, x=self.x[:,self.best_features],r2=self.r2_cal_op)\n self.rmse_cal_op = mean_squared_error(self.y_train, self.y_pred_cal_op, squared = False) \n self.rmspe_cal_op = rmspe_score(self.y_train, self.y_pred_cal_op)\n \n self.y_pred_val_op = np.squeeze(self.model_best.predict(self.x_test[:,self.best_features])).squeeze()\n self.r2_val_op = r2_score(self.y_test,self.y_pred_val_op)\n #self.r2_adj_val_op = adj_r2_score(self.y_test, x= self.x_test[:,self.best_features],r2=self.r2_val_op)\n self.r2_adj_val_op = adj_r2_score(self.y_test, x= self.x[:,self.best_features],r2=self.r2_val_op)\n self.rmse_val_op = mean_squared_error(self.y_test, self.y_pred_val_op, squared = False)\n self.rmspe_val_op = rmspe_score(self.y_test, self.y_pred_val_op)\n \n # generalized model\n fig,axes = plt.subplots(2,3,figsize=(15,10))\n sns.regplot(x=self.y_train, y=self.y_pred_cal_gen, color=\"k\",ax=axes[0, 0])\n axes[0,0].plot([np.min([self.y_train, self.y_pred_cal_gen]) - 0.1*np.std(self.y), np.max([self.y_train, self.y_pred_cal_gen]) + 0.1*np.std(self.y)],\n [np.min([self.y_train, self.y_pred_cal_gen]) - 0.1*np.std(self.y), np.max([self.y_train, self.y_pred_cal_gen]) + 0.1*np.std(self.y)],'r--')\n axes[0,0].text(0.05,0.80,r\"RMSE={:.3f}\".format(self.rmse_cal_gen) + \"\\n\"+ \n r\"RMSPE={:.3f}\".format(self.rmspe_cal_gen) + \"\\n\"+ \n r\"$R^2$={:.3f}\".format(self.r2_cal_gen)+ \"\\n\"+\n r\"$Adj. R^2$={:.3f}\".format(self.r2_adj_cal_gen),\n transform=axes[0, 0].transAxes,\n color='k',\n bbox=dict(facecolor=(1,1,1,0.5),edgecolor=(0,0,0,1)))\n axes[0,0].set_xlabel(\"Measured\")\n axes[0,0].set_ylabel(\"Predicted\")\n axes[0,0].set_title(\"General model - Cal.\")\n axes[0,0].ticklabel_format(axis=\"both\", style = \"sci\", scilimits = (0,2)) \n \n sns.regplot(x=self.y_test, y=self.y_pred_val_gen, color=\"k\",ax=axes[1, 0])\n axes[1,0].plot([np.min([self.y_test, self.y_pred_val_gen]) - 0.1*np.std(self.y), np.max([self.y_test, self.y_pred_val_gen]) + 0.1*np.std(self.y)],\n [np.min([self.y_test, self.y_pred_val_gen]) - 0.1*np.std(self.y), np.max([self.y_test, self.y_pred_val_gen]) + 0.1*np.std(self.y)],'r--') \n axes[1,0].text(0.05,0.80,r\"RMSE={:.3f}\".format(self.rmse_val_gen) + \"\\n\"+ \n r\"RMSPE={:.3f}\".format(self.rmspe_val_gen) + \"\\n\"+ \n r\"$R^2$={:.3f}\".format(self.r2_val_gen)+ \"\\n\"+\n r\"$Adj. R^2$={:.3f}\".format(self.r2_adj_val_gen),\n transform=axes[1, 0].transAxes,\n color='k',\n bbox=dict(facecolor=(1,1,1,0.5),edgecolor=(0,0,0,1)))\n axes[1,0].set_xlabel(\"Measured\")\n axes[1,0].set_ylabel(\"Predicted\")\n axes[1,0].set_title(\"General model - Val.\")\n axes[1,0].ticklabel_format(axis=\"both\", style = \"sci\", scilimits = (0,2)) \n\n # optimized model\n sns.regplot(x=self.y_train, y=self.y_pred_cal_op, color=\"k\",ax=axes[0, 1])\n axes[0,1].plot([np.min([self.y_train, self.y_pred_cal_op]) - 0.1*np.std(self.y), np.max([self.y_train, self.y_pred_cal_op]) + 0.1*np.std(self.y)],\n [np.min([self.y_train, self.y_pred_cal_op]) - 0.1*np.std(self.y), np.max([self.y_train, self.y_pred_cal_op]) + 0.1*np.std(self.y)],'r--') \n axes[0,1].text(0.05,0.80,r\"RMSE={:.3f}\".format(self.rmse_cal_op) + \"\\n\"+ \n r\"RMSPE={:.3f}\".format(self.rmspe_cal_op) + \"\\n\"+ \n r\"$R^2$={:.3f}\".format(self.r2_cal_op)+ \"\\n\"+\n r\"$Adj. R^2$={:.3f}\".format(self.r2_adj_cal_op),\n transform=axes[0, 1].transAxes,\n color='k',\n bbox=dict(facecolor=(1,1,1,0.5),edgecolor=(0,0,0,1)))\n axes[0,1].set_xlabel(\"Measured\")\n axes[0,1].set_ylabel(\"Predicted\")\n axes[0,1].set_title(\"Optimized model - Cal.\")\n axes[0,1].ticklabel_format(axis=\"both\", style = \"sci\", scilimits = (0,2)) \n \n sns.regplot(x=self.y_test, y=self.y_pred_val_op, color=\"k\",ax=axes[1, 1])\n axes[1,1].plot([np.min([self.y_test, self.y_pred_val_op]) - 0.1*np.std(self.y), np.max([self.y_test, self.y_pred_val_op]) + 0.1*np.std(self.y)],\n [np.min([self.y_test, self.y_pred_val_op]) - 0.1*np.std(self.y), np.max([self.y_test, self.y_pred_val_op]) + 0.1*np.std(self.y)],'r--') \n axes[1,1].text(0.05,0.80,r\"RMSE={:.3f}\".format(self.rmse_val_op) + \"\\n\"+ \n r\"RMSPE={:.3f}\".format(self.rmspe_val_op) + \"\\n\"+ \n r\"$R^2$={:.3f}\".format(self.r2_val_op)+ \"\\n\"+\n r\"$Adj. R^2$={:.3f}\".format(self.r2_adj_val_op),\n transform=axes[1, 1].transAxes,\n color='k',\n bbox=dict(facecolor=(1,1,1,0.5),edgecolor=(0,0,0,1)))\n axes[1,1].set_xlabel(\"Measured\")\n axes[1,1].set_ylabel(\"Predicted\")\n axes[1,1].set_title(\"Optimized model - Val.\")\n axes[1,1].ticklabel_format(axis=\"both\", style = \"sci\", scilimits = (0,2)) \n \n #residuals plot\n sns.residplot(self.y_pred_cal_op, self.y_train - self.y_pred_cal_op, lowess=False, color=\"k\",ax=axes[0, 2])\n axes[0,2].set_xlabel(\"Predicted\")\n axes[0,2].set_ylabel(\"Residuals\")\n axes[0,2].set_title(\"Optimized model - Cal. Residuals\")\n axes[0,2].ticklabel_format(axis=\"both\", style = \"sci\", scilimits = (0,2)) \n \n \n sns.residplot(self.y_pred_val_op, self.y_test - self.y_pred_val_op, lowess=False, color=\"k\",ax=axes[1, 2])\n axes[1,2].set_xlabel(\"Predicted\")\n axes[1,2].set_ylabel(\"Residuals\")\n axes[1,2].set_title(\"Optimized model - Val. Residuals\") \n axes[1,2].ticklabel_format(axis=\"both\", style = \"sci\", scilimits = (0,2)) \n \n results = {\"r2_cal_gen\":[self.r2_cal_gen],'rmse_cal_gen':[self.rmse_cal_gen], 'rmspe_cal_gen':[self.rmspe_cal_gen], 'r2_adj_cal_gen':[self.r2_adj_cal_gen],\n 'r2_val_gen':[self.r2_val_gen],'rmse_val_gen':[self.rmse_val_gen],'rmspe_val_gen':[self.rmspe_val_gen], 'r2_adj_val_gen':[self.r2_adj_val_gen],\n \"r2_cal_op\":[self.r2_cal_op],'rmse_cal_op':[self.rmse_cal_op],'rmspe_cal_op':[self.rmspe_cal_op], 'r2_adj_cal_op':[self.r2_adj_cal_op],\n 'r2_val_op':[self.r2_val_op],'rmse_val_op':[self.rmse_val_op],'rmspe_val_op':[self.rmspe_val_op], 'r2_adj_val_op':[self.r2_adj_val_op]} \n \n # test partitions not defined \n else:\n # calculating accuracies for generalized model\n self.model.fit(self.x,self.y)\n self.y_pred_cal_gen = np.squeeze(self.model.predict(self.x)).squeeze()\n self.r2_cal_gen = r2_score(self.y,self.y_pred_cal_gen)\n self.r2_adj_cal_gen = adj_r2_score(self.y,x=self.x, r2=self.r2_cal_gen)\n self.rmse_cal_gen = mean_squared_error(self.y, self.y_pred_cal_gen, squared = False) \n self.rmspe_cal_gen = rmspe_score(self.y, self.y_pred_cal_gen) \n \n # calculating accuracies for optimized model\n self.model_best.fit(self.x[:,self.best_features],self.y)\n self.y_pred_cal_op = np.squeeze(self.model_best.predict(self.x[:,self.best_features])).squeeze()\n self.r2_cal_op = r2_score(self.y,self.y_pred_cal_op)\n self.r2_adj_cal_op = adj_r2_score(self.y,x=self.x[:,self.best_features], r2=self.r2_cal_op)\n self.rmse_cal_op = mean_squared_error(self.y, self.y_pred_cal_op, squared = False) \n self.rmspe_cal_op = rmspe_score(self.y, self.y_pred_cal_op) \n \n # generalized model\n fig,axes = plt.subplots(1,3,figsize=(15,5))\n sns.regplot(x=self.y, y=self.y_pred_cal_gen, color=\"k\",ax=axes[0])\n axes[0].plot([np.min([self.y, self.y_pred_cal_gen]) - 0.1*np.std(self.y), np.max([self.y, self.y_pred_cal_gen]) + 0.1*np.std(self.y)],\n [np.min([self.y, self.y_pred_cal_gen]) - 0.1*np.std(self.y), np.max([self.y, self.y_pred_cal_gen]) + 0.1*np.std(self.y)],'r--')\n axes[0].text(0.05,0.80,r\"RMSE={:.3f}\".format(self.rmse_cal_gen) + \"\\n\"+ \n r\"RMSPE={:.3f}\".format(self.rmspe_cal_gen) + \"\\n\"+ \n r\"$R^2$={:.3f}\".format(self.r2_cal_gen)+ \"\\n\"+\n r\"$Adj. R^2$={:.3f}\".format(self.r2_adj_cal_gen),\n transform=axes[0].transAxes,\n color='k',\n bbox=dict(facecolor=(1,1,1,0.5),edgecolor=(0,0,0,1)))\n axes[0].set_xlabel(\"Measured\")\n axes[0].set_ylabel(\"Predicted\")\n axes[0].set_title(\"General model - Cal.\")\n axes[0].ticklabel_format(axis=\"both\", style = \"sci\", scilimits = (0,2)) \n \n \n # optimized model\n sns.regplot(x=self.y, y=self.y_pred_cal_op, color=\"k\",ax=axes[1])\n axes[1].text(0.05,0.80,r\"RMSE={:.3f}\".format(self.rmse_cal_op) + \"\\n\"+ \n r\"RMSPE={:.3f}\".format(self.rmspe_cal_op) + \"\\n\"+ \n r\"$R^2$={:.3f}\".format(self.r2_cal_op)+ \"\\n\"+\n r\"$Adj. R^2$={:.3f}\".format(self.r2_adj_cal_op),\n transform=axes[1].transAxes,\n color='k',\n bbox=dict(facecolor=(1,1,1,0.5),edgecolor=(0,0,0,1)))\n axes[1].plot([np.min([self.y, self.y_pred_cal_op]) - 0.1*np.std(self.y), np.max([self.y, self.y_pred_cal_op]) + 0.1*np.std(self.y)],\n [np.min([self.y, self.y_pred_cal_op]) - 0.1*np.std(self.y), np.max([self.y, self.y_pred_cal_op]) + 0.1*np.std(self.y)],'r--')\n axes[1].set_xlabel(\"Measured\")\n axes[1].set_ylabel(\"Predicted\")\n axes[1].set_title(\"Optimized model - Cal.\")\n axes[1].ticklabel_format(axis=\"both\", style = \"sci\", scilimits = (0,2)) \n plt.show(block=False)\n \n sns.residplot(self.y_pred_cal_op, self.y - self.y_pred_cal_op, lowess=False, color=\"r\",ax=axes[2])\n axes[2].set_xlabel(\"Predicted\")\n axes[2].set_ylabel(\"Residuals\")\n axes[2].set_title(\"Optimized model - Cal. Residuals\") \n axes[2].ticklabel_format(axis=\"both\", style = \"sci\", scilimits = (0,2)) \n\n results = {\"r2_cal_gen\":[self.r2_cal_gen],'rmse_cal_gen':[self.rmse_cal_gen],'rmspe_cal_gen':[self.rmspe_cal_gen],\"r2_adj_cal_gen\":[self.r2_adj_cal_gen],\n \"r2_cal_op\":[self.r2_cal_op],'rmse_cal_op':[self.rmse_cal_op],'rmspe_cal_op':[self.rmspe_cal_op], \"r2_adj_cal_op\":[self.r2_adj_cal_op]} \n \n # if the task is classification\n else:\n if hasattr(self, 'x_train') and hasattr(self, 'x_test') and hasattr(self, 'y_train') and hasattr(self, 'y_test'): \n if len(self.class_sep) == 2:\n fig,axes = plt.subplots(2,3,figsize=(15,10))\n else:\n fig,axes = plt.subplots(2,2,figsize=(10 + len(self.class_sep),10 + len(self.class_sep)))\n # general model\n self.model.fit(self.x_train,self.y_train)\n y_train_pred = self.model.predict(self.x_train)\n self.acc_train_gen = self.model.score(self.x_train,self.y_train)\n cm = confusion_matrix(self.y_train,y_train_pred.squeeze())\n cm = np.stack(list(map(lambda x: x/x.sum(),cm)))\n conf_matrix=pd.DataFrame(cm,columns = self.class_sep,index= self.class_sep)\n sns.heatmap(conf_matrix,cmap=\"coolwarm\", annot=True, ax=axes[0,0],vmin=0,vmax=1)\n axes[0,0].set_title('General model - Cal. confusion matrix')\n axes[0,0].set_ylabel('True label')\n axes[0,0].set_xlabel('Predicted label')\n\n self.model.fit(self.x_train,self.y_train)\n y_test_pred = self.model.predict(self.x_test)\n self.acc_test_gen = self.model.score(self.x_test,self.y_test)\n cm = confusion_matrix(self.y_test,y_test_pred.squeeze())\n cm = np.stack(list(map(lambda x: x/x.sum(),cm)))\n conf_matrix=pd.DataFrame(cm,columns = self.class_sep,index= self.class_sep)\n sns.heatmap(conf_matrix,cmap=\"coolwarm\", annot=True,ax = axes[1,0],vmin=0,vmax=1)\n axes[1,0].set_title('General model - Val. confusion matrix') \n axes[1,0].set_ylabel('True label')\n axes[1,0].set_xlabel('Predicted label')\n \n # optimized model\n self.model_best.fit(self.x_train[:,self.best_features],self.y_train)\n y_train_pred = self.model_best.predict(self.x_train[:,self.best_features])\n self.acc_train_op = self.model_best.score(self.x_train[:,self.best_features],self.y_train)\n cm = confusion_matrix(self.y_train,y_train_pred.squeeze())\n cm = np.stack(list(map(lambda x: x/x.sum(),cm)))\n conf_matrix=pd.DataFrame(cm,columns = self.class_sep,index= self.class_sep)\n sns.heatmap(conf_matrix,cmap=\"coolwarm\", annot=True, ax=axes[0,1],vmin=0,vmax=1)\n axes[0,1].set_title('Optimized model - Cal. confusion matrix') \n axes[0,1].set_ylabel('True label')\n axes[0,1].set_xlabel('Predicted label') \n \n self.model_best.fit(self.x_train[:,self.best_features],self.y_train)\n y_test_pred = self.model_best.predict(self.x_test[:,self.best_features])\n self.acc_test_op = self.model_best.score(self.x_test[:,self.best_features],self.y_test)\n cm = confusion_matrix(self.y_test,y_test_pred.squeeze())\n cm = np.stack(list(map(lambda x: x/x.sum(),cm)))\n conf_matrix=pd.DataFrame(cm,columns = self.class_sep,index= self.class_sep)\n sns.heatmap(conf_matrix,cmap=\"coolwarm\", annot=True,ax = axes[1,1],vmin=0,vmax=1)\n axes[1,1].set_title('Optimized model - Val. confusion matrix') \n axes[1,1].set_ylabel('True label')\n axes[1,1].set_xlabel('Predicted label')\n \n if len(self.class_sep) == 2:\n #optimized model Roc curves\n self.model_best.fit(self.x_train[:,self.best_features],self.y_train)\n y_train_pred_prob = self.model_best.predict_proba(self.x_train[:,self.best_features])\n fpr, tpr, thr = roc_curve(self.y_train, y_train_pred_prob[:, 1].squeeze()) \n self.auc_train_op = auc(fpr, tpr)\n axes[0,2].plot([0,1],[0,1],linestyle = '--',lw = 2,color = 'black')\n axes[0,2].plot(fpr, tpr, color='blue',\n label=r'$ROC_{train}$ (AUC = %0.2f)' % (auc(fpr, tpr)),lw=2, alpha=1)\n axes[0,2].set_xlabel('False Positive Rate')\n axes[0,2].set_ylabel('True Positive Rate')\n axes[0,2].set_title('ROC Curve')\n axes[0,2].legend(loc=\"lower right\")\n \n self.model_best.fit(self.x_train[:,self.best_features],self.y_train)\n y_test_pred_prob = self.model_best.predict_proba(self.x_test[:,self.best_features])\n fpr, tpr, thr = roc_curve(self.y_test, y_test_pred_prob[:, 1].squeeze()) \n self.auc_test_op = auc(fpr, tpr)\n axes[1,2].plot([0,1],[0,1],linestyle = '--',lw = 2,color = 'black')\n axes[1,2].plot(fpr, tpr, color='blue',\n label=r'$ROC_{test}$ (AUC = %0.2f)' % (auc(fpr, tpr)),lw=2, alpha=1)\n axes[1,2].set_xlabel('False Positive Rate')\n axes[1,2].set_ylabel('True Positive Rate')\n axes[1,2].set_title('ROC Curve')\n axes[1,2].legend(loc=\"lower right\")\n \n results = {\"acc_train_gen\":[self.acc_train_gen],'acc_test_gen':[self.acc_test_gen],\n 'acc_train_op':[self.acc_train_op],'acc_test_op':[self.acc_test_op],\n \"auc_train_op\":[self.auc_train_op],'auc_test_op':[self.auc_test_op]}\n \n results = {\"acc_train_gen\":[self.acc_train_gen],'acc_test_gen':[self.acc_test_gen],\n 'acc_train_op':[self.acc_train_op],'acc_test_op':[self.acc_test_op]}\n \n # if train and test not defined\n else:\n warnings.warn(\"Neither cv_model nor test partition (x_test/y_test) are defined, predicting on x/y.\")\n if len(self.class_sep) == 2:\n fig,axes = plt.subplots(1,3,figsize=(15,5))\n else:\n fig,axes = plt.subplots(1,2,figsize=(10 + len(self.class_sep),5 + len(self.class_sep))) \n # general model\n self.model.fit(self.x,self.y)\n y_train_pred = self.model.predict(self.x)\n self.acc_train_gen = self.model.score(self.x,self.y)\n cm = confusion_matrix(self.y,y_train_pred.squeeze())\n cm = np.stack(list(map(lambda x: x/x.sum(),cm)))\n conf_matrix=pd.DataFrame(cm,columns = self.class_sep,index= self.class_sep)\n sns.heatmap(conf_matrix,cmap=\"coolwarm\", annot=True, ax=axes[0],vmin=0,vmax=1)\n axes[0].set_title('General model - Cal. confusion matrix')\n axes[0].set_ylabel('True label')\n axes[0].set_xlabel('Predicted label')\n \n # optimized model\n self.model_best.fit(self.x[:,self.best_features],self.y)\n y_train_pred = self.model_best.predict(self.x[:,self.best_features])\n self.acc_train_op = self.model_best.score(self.x[:,self.best_features],self.y)\n cm = confusion_matrix(self.y,y_train_pred.squeeze())\n cm = np.stack(list(map(lambda x: x/x.sum(),cm)))\n conf_matrix=pd.DataFrame(cm,columns = self.class_sep,index= self.class_sep)\n sns.heatmap(conf_matrix,cmap=\"coolwarm\", annot=True, ax=axes[1],vmin=0,vmax=1)\n axes[1].set_title('Optimized model - Cal. confusion matrix') \n axes[1].set_ylabel('True label')\n axes[1].set_xlabel('Predicted label') \n \n \n if len(self.class_sep) == 2:\n #optimized model Roc curves\n self.model_best.fit(self.x[:,self.best_features],self.y)\n y_train_pred_prob = self.model_best.predict_proba(self.x[:,self.best_features])\n fpr, tpr, thr = roc_curve(self.y, y_train_pred_prob[:, 1].squeeze()) \n self.auc_train_op = auc(fpr, tpr)\n axes[2].plot([0,1],[0,1],linestyle = '--',lw = 2,color = 'black')\n axes[2].plot(fpr, tpr, color='blue',\n label=r'$ROC_{train}$ (AUC = %0.2f)' % (auc(fpr, tpr)),lw=2, alpha=1)\n axes[2].set_xlabel('False Positive Rate')\n axes[2].set_ylabel('True Positive Rate')\n axes[2].set_title('ROC Curve')\n axes[2].legend(loc=\"lower right\")\n \n \n results = {\"acc_train_gen\":[self.acc_train_gen],\n 'acc_train_op':[self.acc_train_op],\n \"auc_train_op\":[self.auc_train_op]}\n \n results = {\"acc_train_gen\":[self.acc_train_gen],\n 'acc_train_op':[self.acc_train_op]}\n \n plt.show(block=False) \n return results, fig","title":""},{"docid":"66dc9873ef8a207c1b06acb2dbcc70b8","score":"0.5680571","text":"def predict(self, features):\n raise NotImplementedError","title":""},{"docid":"b8b532b5ed1811e46e5fe12407f4c93b","score":"0.5679583","text":"def fit_predict(self, X, y=None, **kwargs):\n # we do not route parameters here, since consumers don't route. But\n # since it's possible for a `predict` method to also consume\n # metadata, we check if that's the case, and we raise a warning telling\n # users that they should implement a custom `fit_predict` method\n # to forward metadata to `predict` as well.\n #\n # For that, we calculate routing and check if anything would be routed\n # to `predict` if we were to route them.\n if _routing_enabled():\n transform_params = self.get_metadata_routing().consumes(\n method=\"predict\", params=kwargs.keys()\n )\n if transform_params:\n warnings.warn(\n (\n f\"This object ({self.__class__.__name__}) has a `predict` \"\n \"method which consumes metadata, but `fit_predict` does not \"\n \"forward metadata to `predict`. Please implement a custom \"\n \"`fit_predict` method to forward metadata to `predict` as well.\"\n \"Alternatively, you can explicitly do `set_predict_request`\"\n \"and set all values to `False` to disable metadata routed to \"\n \"`predict`, if that's an option.\"\n ),\n UserWarning,\n )\n\n # override for transductive outlier detectors like LocalOulierFactor\n return self.fit(X, **kwargs).predict(X)","title":""},{"docid":"c0826d3305203badafb8621785541b35","score":"0.5678741","text":"def predict(\r\n self, \r\n image, \r\n top_idxes=[0],\r\n class_names=None\r\n ):\r\n image1 = tf.expand_dims(image, axis=0)\r\n \r\n # the ensemble model outputs, len(logits) = top\r\n logits = self.model(image1)\r\n logits = [logits[i] for i in top_idxes]\r\n \r\n # predicted class IDs, [top, ]\r\n class_ids = []\r\n for logit in logits:\r\n class_ids.append(np.argmax(logit, axis=1))\r\n class_ids = np.array(class_ids)\r\n unique_class_ids, counts = np.unique(class_ids, return_counts=True)\r\n idx = np.argmax(counts)\r\n ensemble_class_id = unique_class_ids[idx]\r\n \r\n def vis(image, class_names, probs):\r\n \"\"\"\r\n Displays the image with the predicted probabilities.\r\n\r\n Parameters\r\n ----------\r\n image, class_names : same as above.\r\n probs : numpy array, [top, num_classes]\r\n The predicted probabilities over ensembled class ids.\r\n\r\n Returns\r\n -------\r\n None.\r\n\r\n \"\"\"\r\n plt.imshow(tf.cast(image, tf.int32))\r\n for i in range(len(class_names)):\r\n if i == np.argmax(probs):\r\n plt.text(\r\n image.shape[1] + 10, \r\n 10 + i*20,\r\n \"%s: %.2f%%\" % (class_names[i], probs[i]*100),\r\n bbox={'facecolor':'gray', 'alpha':0.3, 'pad':5})\r\n else:\r\n plt.text(\r\n image.shape[1] + 10, \r\n 10 + i*20,\r\n \"%s: %.2f%%\" % (class_names[i], probs[i]*100))\r\n plt.axis('off')\r\n \r\n if class_names is not None:\r\n # probs, [top, num_classes]\r\n probs = tf.squeeze(tf.nn.softmax(logits))\r\n # probs corresponding to ensemble_class_id\r\n idxes = tf.where(tf.argmax(probs, axis=1) == ensemble_class_id)[:,0]\r\n probs = tf.gather(probs, idxes, axis=0)\r\n # mean probs over ensembled class ids, [num_classes, ]\r\n mean_probs = tf.reduce_mean(probs, axis=0).numpy()\r\n vis(image, class_names, mean_probs)\r\n plt.title(class_names[ensemble_class_id])\r\n plt.show()\r\n \r\n return ensemble_class_id","title":""},{"docid":"7215b58dcfc54c3b899ed45c8a141789","score":"0.5678406","text":"def predict(self):\n self.kf.predict()\n\n if self.kf.x[3] >= np.pi: self.kf.x[3] -= np.pi * 2\n if self.kf.x[3] < -np.pi: self.kf.x[3] += np.pi * 2\n\n self.age += 1\n\n self.history.append(self.kf.x)\n\n #KalmanBoxTracker predict\n # Multiply by p_s\n self.w *= self.ps\n\n return self.history[-1]","title":""},{"docid":"6a8e263d3c7637350e745862c2ae1c09","score":"0.56733644","text":"def __init__(self):\n super().__init__()\n self.printTag = 'KerasRegression'","title":""},{"docid":"483433132212a5d9e3ed0686784aba07","score":"0.56711423","text":"def __init__(self):\n super().__init__()\n self.multioutputWrapper = True\n import sklearn\n import sklearn.ensemble\n self.model = sklearn.ensemble.AdaBoostRegressor","title":""},{"docid":"44f85f3f7034268e3da0bcee6055440a","score":"0.5667656","text":"def model_fn(features, labels, mode, params):\n model = create_model(params['data_format'])\n image = features\n if isinstance(image, dict):\n image = features['image']\n\n if mode == tf.estimator.ModeKeys.PREDICT:\n logits = model(image, training=False)\n predictions = {\n 'classes': tf.argmax(logits, axis=1),\n 'probabilities': tf.nn.softmax(logits),\n }\n return tf.estimator.EstimatorSpec(\n mode=tf.estimator.ModeKeys.PREDICT,\n predictions=predictions,\n export_outputs={\n 'classify': tf.estimator.export.PredictOutput(predictions)\n })\n if mode == tf.estimator.ModeKeys.TRAIN:\n optimizer = tf.train.AdamOptimizer(learning_rate=params['learning_rate'])\n #optimizer = tf.train.SyncReplicasOptimizer(optimizer, replicas_to_aggregate=2)\n\n # If we are running multi-GPU, we need to wrap the optimizer.\n optimizer = tf.contrib.estimator.TowerOptimizer(optimizer)\n\n logits = model(image, training=True)\n loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits)\n accuracy = tf.metrics.accuracy(\n labels=labels, predictions=tf.argmax(logits, axis=1))\n\n # Name tensors to be logged with LoggingTensorHook.\n tf.identity(loss, 'cross_entropy')\n tf.identity(accuracy[1], name='train_accuracy')\n\n # Save accuracy scalar to Tensorboard output.\n tf.summary.scalar('train_accuracy', accuracy[1])\n update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)\n with tf.control_dependencies(update_ops):\n train_op = optimizer.minimize(\n loss=loss, global_step=tf.train.get_global_step())\n\n return tf.estimator.EstimatorSpec(\n mode=tf.estimator.ModeKeys.TRAIN,\n loss=loss,\n train_op=train_op)\n\n if mode == tf.estimator.ModeKeys.EVAL:\n logits = model(image, training=False)\n loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits)\n return tf.estimator.EstimatorSpec(\n mode=tf.estimator.ModeKeys.EVAL,\n loss=loss,\n eval_metric_ops={\n 'accuracy':\n tf.metrics.accuracy(\n labels=labels, predictions=tf.argmax(logits, axis=1)),\n })","title":""},{"docid":"f2079e1eeb7e4e8628bf88b3ee629f31","score":"0.56617534","text":"def test_model(model,\n model_name,\n predictors: np.ndarray,\n y_true: np.ndarray,\n threshold: float,\n plot: bool = True,\n verbose: bool = True):\n y_pred = model.predict(predictors)\n metrics = calculate_metrics(y_true, y_pred, threshold)\n\n if plot:\n plot_model_performance(y_true, y_pred, metrics, model_name)\n\n if verbose:\n print_metrics({model_name: metrics})\n\n return model, y_pred, metrics","title":""},{"docid":"a4c68d6e67534b14a430283a0d941f7e","score":"0.5650589","text":"def predict(self, instances):\n\n if self.verbose: logging.info(\"Obtaining final predictions from {} models.\".format(\n len(self.ensemble_of_learners)))\n\n if not self.ensemble_of_learners:\n if self.verbose: logging.info(\"Please, evolve the model first!\")\n return None\n\n else:\n\n instances = self.return_dataframe_from_text(instances)\n transformed_instances = self.vectorizer.transform(instances)\n prediction_space = []\n\n # transformed_instances = self.update_intermediary_feature_space(custom_space = transformed_instances)\n if self.verbose: logging.info(\"Representation obtained ..\")\n for learner_tuple in self.ensemble_of_learners:\n\n try:\n\n ## get the solution.\n learner, individual, score = learner_tuple\n learner = learner.best_estimator_\n\n ## Subset the matrix.\n subsetted_space = self.apply_weights(\n individual,\n custom_feature_space=True,\n custom_feature_matrix=transformed_instances)\n\n ## obtain the predictions.\n if not prediction_space is None:\n prediction_space.append(\n learner.predict(subsetted_space).tolist())\n\n else:\n prediction_space.append(\n learner.predict(subsetted_space).tolist())\n\n except Exception as es:\n print(\n es,\n \"Please, re-check the data you are predicting from!\")\n\n ## generate the prediction matrix by maximum voting scheme.\n pspace = np.matrix(prediction_space).T\n all_predictions = pd.DataFrame(pspace).mode(\n axis=1).values.reshape(-1)\n if self.verbose: logging.info(\"Predictions obtained\")\n all_predictions = self.label_encoder.inverse_transform(\n all_predictions)\n return all_predictions","title":""},{"docid":"0ec6f54818d4b362c2befd1f6e5dfdd7","score":"0.5650204","text":"def classifier(X_Train, Y_Train, X_Test):\n\n \"\"\"\n Create a Logistic Regression instance and fit it to the kernelized data.\n ==========================================================================\n \"\"\"\n\n regression = ElasticNet(l1_ratio=0.05, max_iter=10000)\n regression.fit(X_Train, Y_Train)\n\n \"\"\"\n ==========================================================================\n \"\"\"\n\n Y_Predict = regression.predict(X_Test)\n \n for y in Y_Predict:\n label = 0\n if y > 0.5:\n label += 1\n print label","title":""},{"docid":"3c3aa60b2c61cd62b65a4e32429589d9","score":"0.56437147","text":"def tune_hyperparam(self, estimator, random_state, verbose):\n\n model_type = _get_model_type(estimator)\n\n if model_type == \"DecisionTreeClassifier\":\n param_distributions = {\"max_features\": [\"auto\", \"sqrt\", \"log2\", None], \"max_depth\": range(1, 15)}\n if model_type == \"SVC\":\n param_distributions = [{'kernel': ['rbf'], 'gamma': [1e-3, 1e-4],\n 'C': [1, 10, 100, 1000]},\n {'kernel': ['linear'], 'C': [1, 10, 100, 1000]}]\n if model_type == \"RandomForestClassifier\":\n param_distributions = {\"n_estimators\": [1, 10, 25,50, 100, 200, 500],\n \"max_features\": [\"auto\", \"sqrt\", \"log2\", None],\n \"max_depth\": range(1, 15)}\n\n clf = RandomizedSearchCV(estimator=estimator, param_distributions=param_distributions,\n random_state=random_state, n_iter=5, n_jobs=-1, cv=5, scoring=SCORING,\n verbose=verbose, refit='f1_macro')\n\n clf.fit(self.X_val, self.y_val)\n\n if verbose > 0:\n print(f\"Best {model_type} estimator:\")\n print(f\"accuracy: {clf.cv_results_['mean_test_accuracy'].mean()}\")\n print(f\"precision: {clf.cv_results_['mean_test_precision'].mean()}\")\n print(f\"recall: {clf.cv_results_['mean_test_recall'].mean()}\")\n print(f\"F1 Score: {clf.best_score_}\\n\")\n\n return clf.best_estimator_","title":""},{"docid":"bf537d6632dc29876dad1260e7e8d7f9","score":"0.5638667","text":"def run_demo():\n clf = InOutClassifier(verbose=True)\n indoor_a = [130, 9, -1, -1]\n indoor_b = [1400, 9, 2.0, -1]\n outdoor_a = [120, 4, 2.0, 20]\n outdoor_b = [40, 4, 2.0, 1.5]\n prediction = clf.predict([indoor_a, indoor_b, outdoor_a, outdoor_b])\n print prediction","title":""},{"docid":"2fc2025814e3709c9d8994eb05766669","score":"0.56380373","text":"def predicting(model, x_train, y_train, x_test, y_test, smp_idx_train, smp_idx_test, unlabelled_data, impl_type, **fit_params):\n # set y_pred_prob to None in case the probabilities can't be predicted\n y_pred_prob = None\n\n # fitting and prediction based on the model implementation type\n if impl_type == \"scikit\":\n # fitting the model\n start_time = time.time()\n model.fit(x_train, y_train)\n fit_time = time.time() - start_time\n # predicting\n start_time = time.time()\n y_pred = model.predict(x_test)\n score_time = time.time() - start_time\n # predict proba - special case SVM: change model, fit newly and predict\n if isinstance(model, SVC):\n # change model\n model.probability = True\n model.fit(x_train, y_train)\n # predicting probability\n if hasattr(model, \"predict_proba\"): y_pred_prob = model.predict_proba(x_test)\n\n elif impl_type == \"keras\":\n # fitting the model\n start_time = time.time()\n fit_ann_model(model, x_train, y_train, smp_idx_train, **fit_params)\n fit_time = time.time() - start_time\n # predicting + predicting probability\n start_time = time.time()\n y_pred, y_pred_prob = predict_ann_model(model, x_test, y_test, smp_idx_test, predict_proba=True, **fit_params)\n score_time = time.time() - start_time\n\n elif impl_type == \"baseline\":\n # fitting the model\n start_time = time.time()\n majority_vote = fit_baseline(y_train)\n fit_time = time.time() - start_time\n # predicting + predicting probability\n start_time = time.time()\n y_pred = predict_baseline(majority_vote, x_test)\n score_time = time.time() - start_time\n\n elif impl_type == \"semi_manual\":\n # fitting the model\n start_time = time.time()\n # concat unlabelled and labelled data and fit it\n model = model.fit(pd.concat([unlabelled_data, x_train]))\n fit_time = time.time() - start_time\n\n # determine the number of clusters/components\n if hasattr(model, \"cluster_centers_\"):\n num_components = model.cluster_centers_.shape[0]\n elif hasattr(model, \"weights_\"):\n num_components = model.weights_.shape[0]\n # prediction\n start_time = time.time()\n test_clusters = model.predict(x_test)\n y_pred = assign_clusters(y_test, test_clusters, num_components)\n score_time = time.time() - start_time\n\n else:\n raise ValueError(\"\"\"This Model implementation types does not exist.\n Choose one of the following: \\\"scikit\\\" (for rf, svm, knn, easy_ensemble, self_trainer),\n \\\"semi_manual\\\" (for kmean, gmm, bmm), \\\"keras\\\" (for lstm, blsm, enc_dec),\n or \\\"baseline\\\" (for the majority vote baseline)\"\"\")\n\n return y_pred, y_pred_prob, fit_time, score_time","title":""},{"docid":"f4097451071cb691bbbce23c30e41d76","score":"0.56368095","text":"def test(self):\r\n # self.clf = AdaBoostClassifier(base_estimator=DecisionTreeClassifier(), n_estimators=100, learning_rate=1.5, algorithm='SAMME.R')\r\n self.clf = AdaBoostClassifier()\r\n self.clf.set_params(**self.best_parameter)\r\n print(\"*** Test Result for AdaBoost ***\")\r\n ModelEvaluation.evaluateModelWithCV(self.clf, self.dataset_x, self.dataset_y, cv=10)","title":""},{"docid":"bd85d38012931561e78fe6b730e361ad","score":"0.5633781","text":"def predict(self, x):\n raise NotImplementedError","title":""},{"docid":"83067c1af22ecaa91632ff21b24d0130","score":"0.5632875","text":"def predict(self, dataset):\n raise NotImplementedError","title":""},{"docid":"d9c7908c4c729833a789ef8d83a7f31e","score":"0.56293464","text":"def postprocessing(self, model, input):\n pred_num = np.argmax(input)\n pred_score = np.max(input)\n return json.dumps({\"pred_num\": int(pred_num), \"pred_score\": float(pred_score)})","title":""},{"docid":"fa01c7483eeda73f7b5e3ef415fdf65e","score":"0.5629328","text":"def on_predict_begin(self, logs=None):\n super().on_predict_begin(logs)\n self._set_logging_mode(LoggingMode.PREDICT)","title":""},{"docid":"38291060a639a2424d3b57024da1dc1d","score":"0.5629008","text":"def predict(self, x, y):\n\t\tpass","title":""}],"string":"[\n {\n \"docid\": \"37fc8baf70ddba5073708b5e25b2031b\",\n \"score\": \"0.68523747\",\n \"text\": \"def meta_predict_clf():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1bdc25b629b990ea75d72e0f323a6ef5\",\n \"score\": \"0.66119105\",\n \"text\": \"def getPrediction(self,data_arg1,data_arg2,data_lbl):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6bd871203cb7e2d8dd2307e1c731ce16\",\n \"score\": \"0.6543709\",\n \"text\": \"def predict():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"145663ca7e52492983f8265c81eaa8c2\",\n \"score\": \"0.65347403\",\n \"text\": \"def predict():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ead94255e20e230a8ccbf08c2b925e1a\",\n \"score\": \"0.6513237\",\n \"text\": \"def _model_fn_predict(self, mode, features, logits):\\n assert mode == tf.estimator.ModeKeys.PREDICT\\n predictions = {\\n # We don't actually use classes downstream right now.\\n # 'classes': tf.argmax(input=logits, axis=1, output_type=tf.int32),\\n 'probabilities': tf.nn.softmax(logits, name='softmax_tensor'),\\n # DV2 call_variants wants these passed through.\\n 'variant': features['variant'],\\n 'alt_allele_indices': features['alt_allele_indices'],\\n }\\n if 'label' in features:\\n predictions['label'] = features['label']\\n if 'locus' in features:\\n predictions['locus'] = features['locus']\\n if self.use_tpu:\\n return tpu_estimator.TPUEstimatorSpec(mode=mode, predictions=predictions)\\n else:\\n return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70f1de2b3e9f3c0f0674d1789f7fd1ae\",\n \"score\": \"0.644738\",\n \"text\": \"def predict_log_proba(self):\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d22bcb5135a72e4247c3ea6942c2d313\",\n \"score\": \"0.6432428\",\n \"text\": \"def explain_prediction(estimator, doc, **kwargs):\\n return Explanation(\\n estimator=repr(estimator),\\n error=\\\"estimator %r is not supported\\\" % estimator,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f311aa91d8be71794e0b6e48e26ece1\",\n \"score\": \"0.6431149\",\n \"text\": \"def predict(self, *args, **kwargs):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"97b304d4c7793fc545f4f9babd4d0783\",\n \"score\": \"0.6424372\",\n \"text\": \"def _internal_predict(self, x_test):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"65040de2315e5c40c0684b26d6725f0c\",\n \"score\": \"0.64239514\",\n \"text\": \"def main():\\n\\n #scaler, classifier = train()\\n\\n #predict(scaler, classifier, [[27.23, 0.53, 0, 3.31]])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a861b98b68e422028569c0b6d622dd7a\",\n \"score\": \"0.64121616\",\n \"text\": \"def fit_predict(self, X, y=...):\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a861b98b68e422028569c0b6d622dd7a\",\n \"score\": \"0.64121616\",\n \"text\": \"def fit_predict(self, X, y=...):\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"845116bc58bd4b6cc232a77e0debd1b2\",\n \"score\": \"0.64028764\",\n \"text\": \"def predict_proba(self):\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"773a5266eba3d5e65ed5f57bc4bd8d9e\",\n \"score\": \"0.63921565\",\n \"text\": \"def predict(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"773a5266eba3d5e65ed5f57bc4bd8d9e\",\n \"score\": \"0.63921565\",\n \"text\": \"def predict(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"773a5266eba3d5e65ed5f57bc4bd8d9e\",\n \"score\": \"0.63921565\",\n \"text\": \"def predict(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eca43b88add93efa16cc13031a6bc73c\",\n \"score\": \"0.6388775\",\n \"text\": \"def predict(self, X, **kw):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5dbccd53cd5de75e01676d0990dad361\",\n \"score\": \"0.632211\",\n \"text\": \"def predict_additional_output(self, logits):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b4878e30027bbf0fdd4a07423d3c9b26\",\n \"score\": \"0.62751275\",\n \"text\": \"def get_best_ova( clf, hyper, score, X_train, y_train):\\r\\n ovr = OneVsRestClassifier(clf).fit(X_train,y_train)\\r\\n best = customGridSearch( ovr, param_grid=hyper, score=score )\\r\\n best.fit( X_train, y_train )\\r\\n return best.best_estimator_\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db46c54eef48ac7435e03dbbe95c6193\",\n \"score\": \"0.6217643\",\n \"text\": \"def predict(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db46c54eef48ac7435e03dbbe95c6193\",\n \"score\": \"0.6217643\",\n \"text\": \"def predict(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ad969d40f35f6bf6862261831801758\",\n \"score\": \"0.6210032\",\n \"text\": \"def _predict(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f9d0dd69354c041468c899fab923dac6\",\n \"score\": \"0.6196931\",\n \"text\": \"def predict():\\n Classification_predict()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"84bf4d2544ab5f8485485f35b420dbfb\",\n \"score\": \"0.61920846\",\n \"text\": \"def predict(self, X):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"84bf4d2544ab5f8485485f35b420dbfb\",\n \"score\": \"0.61920846\",\n \"text\": \"def predict(self, X):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"282e5d06712513c7de727d8014dd318c\",\n \"score\": \"0.6161895\",\n \"text\": \"def predict(self, *args, **kwargs):\\r\\n raise TypeError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d97b47f7e7ca1adefee482353d531bfe\",\n \"score\": \"0.6140748\",\n \"text\": \"def predict(self, X):\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d97b47f7e7ca1adefee482353d531bfe\",\n \"score\": \"0.6140748\",\n \"text\": \"def predict(self, X):\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bae6b93061f402cb05020a2da6ee04b8\",\n \"score\": \"0.61373365\",\n \"text\": \"def predict(self):\\n self.y_pred = np.argmax(np.round(self.model.predict(self.X_test)),axis=1)\\n print(\\\"dim de y_pred : \\\" + str(self.y_pred.shape))\\n print(\\\"dim de y_test : \\\" + str(self.y_test.shape))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c6256e2b59cf8fe8573d1aaa3e55036\",\n \"score\": \"0.6116803\",\n \"text\": \"def _predict(self, X):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8a1d79dc168d9831a4ed934dc3fac37\",\n \"score\": \"0.61030585\",\n \"text\": \"def ML_predict(train_data, lag_size, folds, method='RF', exog_data = None, lagsx = None):\\n\\n if method == 'RF':\\n rfr = RandomForestRegressor(n_estimators=590,criterion='mse', oob_score=True)\\n if method == 'SVR':\\n rfr = SVR(kernel='linear', epsilon=2.831, C=1.661)\\n if method == 'ET':\\n rfr = ExtraTreesRegressor(n_estimators=680, criterion='mse', oob_score=True, bootstrap=True)\\n #todo: importance chcek\\n if exog_data is not None and lagsx is not None:\\n print(\\\"Running exogenous mode\\\")\\n error, regressor, res, preds, ident = ML_external(train_data, lag_size, folds, rfr, exog_data, lagsx)\\n return error, regressor, res, preds, ident\\n else:\\n print(\\\"Running internal mode\\\")\\n error, regressor, res, preds, ident = ML_internal(train_data, lag_size, folds, rfr)\\n return error, regressor, res, preds, ident\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1702e51288fb7c655a73597a2d323435\",\n \"score\": \"0.6055545\",\n \"text\": \"def predict(self, *args, **kwargs):\\n return super(BaseModel, self).__call__(*args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e099193df0c90bdfdf229618cd37f144\",\n \"score\": \"0.6048183\",\n \"text\": \"def predict(model_path, X_test, is_lgbm=False, is_catboost=False, is_cnn=False, maxlen=400, lgbm_threshold=0.5):\\n\\n print('\\\\n === predict === \\\\n')\\n\\n if is_lgbm:\\n # lightgbm\\n model = lgb.Booster(model_file=model_path)\\n elif is_catboost:\\n model = CatBoostClassifier()\\n model = model.load_model(model_path)\\n elif is_cnn:\\n model = load_model(model_path)\\n else:\\n # sklearn\\n # xgboost\\n model = joblib.load(model_path)\\n\\n # y_pred = model.predict_prob(X_test)\\n y_pred = model.predict(X_test)\\n\\n if is_lgbm:\\n #print('==')\\n #print(y_pred)\\n y_output = []\\n for y in y_pred:\\n if y > lgbm_threshold:\\n y_output.append(1)\\n else:\\n y_output.append(0)\\n #print('==')\\n #print(y_output)\\n return(np.array(y_output))\\n #return np.array([np.argmax(y) for y in y_pred])\\n elif is_cnn:\\n # X_test = sequence.pad_sequences(X_test, maxlen=maxlen)\\n y_pred = model.predict(X_test)\\n y_pred = [np.argmax(y) for y in y_pred]\\n return np.array(y_pred)\\n else:\\n return y_pred\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f59a20d13d905ebe2d422eaf5d80203\",\n \"score\": \"0.6029188\",\n \"text\": \"def predict(self, desc):\\n return np.argmax(self.predict_proba(desc))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a9140a47719762b0340b00cb18685c8\",\n \"score\": \"0.60189235\",\n \"text\": \"def predict(self, X): \\n return super().predict(X)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e43c9b4b75ad7b16481d0a5ec106380a\",\n \"score\": \"0.6013293\",\n \"text\": \"def fit(self, **kwargs):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72e0aafa0846d3766820601c3d84c1fa\",\n \"score\": \"0.6012597\",\n \"text\": \"def predict(self,traindata,verbose=False):\\n\\t\\tif not self.isFitted:\\n\\t\\t\\traise UnFittedModelError(\\\"Attempting to predict on a unfitted model\\\")\\n\\n\\t\\tmodelResults=[]\\n\\t\\tensembleResult=[]\\n\\t\\tfor model in self.model:\\n\\t\\t\\tmodelResults.append(model.predict(traindata))\\n\\t\\tfor i in range(len(modelResults[0])):\\n\\t\\t\\ttmpResult=[]\\n\\t\\t\\tfor j in range(len(modelResults)):\\n\\t\\t\\t\\ttmpResult.append(int(modelResults[j][i]))\\n\\t\\t\\tensembleResult.append(str(resolveSign(tmpResult,self.model_confidence)))\\n\\t\\treturn(ensembleResult)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11702257ac9a1ce0d270be0b257f1324\",\n \"score\": \"0.59984756\",\n \"text\": \"def predict_next(self, **kwargs):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf375ac95d684c33a6d83e61969d5096\",\n \"score\": \"0.5974168\",\n \"text\": \"def cmdl(ctx, provider, filename, model_id, threshold, locally, docker, output):\\n ctx.setup_logger(format='')\\n if docker:\\n locally = \\\"docker\\\"\\n A2ML(ctx, provider).predict( model_id, filename=filename, \\n threshold=threshold, score=score, score_true_data=score_true_path,\\n locally=locally, output=output)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc77a7942d9d4d4b24a109182e4963f5\",\n \"score\": \"0.59619737\",\n \"text\": \"def predict_data(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ac7c975aaefab59c2b67c651780c379\",\n \"score\": \"0.594237\",\n \"text\": \"def predict_(self, X, i):\\n raise NotImplementedError(\\\"\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e272e5fee03c4bde643b35da3aea8732\",\n \"score\": \"0.59126353\",\n \"text\": \"def predict(self, X):\\r\\n Util.abstract()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fdde0f15ca65629fae9545114631dbef\",\n \"score\": \"0.59091765\",\n \"text\": \"def predict(self, X):\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d80e5a8d43987c11edf17105874b8d4\",\n \"score\": \"0.5893054\",\n \"text\": \"def predict():\\n # load the save model\\n classifier = pickle.load(open('best_model.pkl'))\\n\\n # compile a predictor function\\n predict_model = theano.function(inputs=[classifier.input], outputs=classifier.y_pred)\\n\\n # we can test it on some examples from test test\\n dataset = 'mnist.pkl.gz'\\n datasets = load_data(dataset)\\n test_set_x, test_set_y = datasets[2]\\n test_set_x = test_set_x.get_value()\\n\\n predict_values = predict_model(test_set_x[:10])\\n print \\\"predict values for the first example in test set:\\\"\\n print predict_values\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19a8fd64f12968f0dc46a82244000ad8\",\n \"score\": \"0.587905\",\n \"text\": \"def predict(self, x, *args, **kwargs):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50ce39d34d2b8540ffc295463e5572df\",\n \"score\": \"0.586703\",\n \"text\": \"def predict(self, X): \\n if self.mode == \\\"regex\\\":\\n X = self._regex_subview(X)\\n elif self.mode == \\\"index\\\":\\n X = self._index_subview(X)\\n elif self.mode == \\\"include\\\":\\n X = self._include_subview(X)\\n\\n return self.base_estimator_.predict(X)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab565e0069fb0bf28d6b6dbef8aa8716\",\n \"score\": \"0.58650553\",\n \"text\": \"def prediction(inp):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"05de433bc715d57fa0bde1a32b058213\",\n \"score\": \"0.58586246\",\n \"text\": \"def update_parametrized_models(self):\\n\\n logger.log(\\\"fitting baseline...\\\")\\n self.algo.baseline.fit(self.experience_replay[-1])\\n logger.log(\\\"fitted\\\")\\n\\n if self.algo.exploration_bonus:\\n logger.log(\\\"fitting exploration bonus model...\\\")\\n self.algo.exploration_bonus.fit(self.experience_replay[-1])\\n logger.log(\\\"fitted\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e6e9d2a8365e377a56c6ee7066953fe\",\n \"score\": \"0.5853979\",\n \"text\": \"def tuning_model():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e4e68237167bb972832ff6d9698b412\",\n \"score\": \"0.5841047\",\n \"text\": \"def predict_recommender(self, data):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"382e4836b988bfb226b586f894466d0e\",\n \"score\": \"0.58212394\",\n \"text\": \"def model(Xtrain,Ytrain,Xtest,Ytest):\\n model = linear_model.SGDClassifier(loss='hinge', penalty='l2', alpha=OPT_ALPHA,\\n l1_ratio=L1_RATIO, fit_intercept=True, n_iter=N_ITER, shuffle=True, verbose=0,\\n epsilon=0.1, n_jobs=1, random_state=None, learning_rate='optimal',\\n eta0=0.0, power_t=0.5, class_weight=None, warm_start=False, average=False)\\n #model = MultinomialNB(alpha=OPT_ALPHA)\\n model.fit(Xtrain,Ytrain)\\n\\n pred = model.predict(Xtest)\\n\\n i=0\\n for a,b in zip(pred,Ytest):\\n if i>20:\\n break\\n i+=1\\n print(a,b)\\n\\n R0_Baseline = mode(Ytest)[1]/len(Ytest)\\n print(\\\"Accuracy: \\\",metrics.accuracy_score(Ytest,pred))\\n print(metrics.accuracy_score(Ytest,pred)*(len(Ytest)))\\n print(\\\"Baseline: \\\",R0_Baseline,\\\" Guessing \\\",mode(Ytest)[0])\\n print(metrics.classification_report(Ytest,pred))\\n\\n \\\"\\\"\\\"\\n #hyper-parameter optimization for MultinomialNB\\n\\n parameters = {'alpha':np.linspace(-10,10,50)}\\n\\n meta_opt = GridSearchCV(model, parameters)\\n meta_opt.fit(Xtrain, Ytrain)\\n\\n print(meta_opt.best_params_)\\n \\\"\\\"\\\" \\n\\n \\\"\\\"\\\"\\n #hyper-parameter optimization for SGDClassifier\\n\\n parameters = {'alpha':np.linspace(0.0001,10,10),\\n 'l1_ratio':np.linspace(0,1,10),\\n 'n_iter':np.linspace(50,200,4)}\\n\\n meta_opt = GridSearchCV(model, parameters,verbose=1)\\n meta_opt.fit(Xtrain, Ytrain)\\n\\n print(meta_opt.best_params_)\\n \\\"\\\"\\\"\\n return metrics.accuracy_score(Ytest,pred),Ytest,pred\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3bf69a27c84f0c8c33301379ece05b33\",\n \"score\": \"0.5812485\",\n \"text\": \"def predict(self, X):\\n\\t\\t### YOUR CODE HERE\\n\\n\\t\\t### END YOUR CODE\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a6d37ad418489ee2666675aa2d71714\",\n \"score\": \"0.58112264\",\n \"text\": \"def classify(trait_arg, params_arg, performance_dict):\\n\\n activation_type, learning_rate_type, solver_type = params_arg\\n print(\\\"Using Parameters : \\\", params_arg)\\n x = df['essay'][1:]\\n x = x.str.lower()\\n y = df[trait_arg][1:]\\n\\n print(\\\"Predicting \\\", trait_arg)\\n\\n # TF-IDF vectorizer\\n vectorizer = TfidfVectorizer()\\n\\n # 10 fold\\n kf = KFold(n_splits=10)\\n\\n # MLP classifier\\n classifier = MLPClassifier(activation=activation_type, batch_size='auto',\\n hidden_layer_sizes=(60), learning_rate=learning_rate_type, max_iter=20,\\n random_state=None, solver=solver_type)\\n\\n ind = 0\\n precision_dict = dict()\\n recall_dict = dict()\\n accuracy_dict = dict()\\n for train_indices, test_indices in kf.split(x, y):\\n x_train, x_test, y_train, y_test = x[train_indices][1:], x[test_indices].tolist()[1:], \\\\\\n y[train_indices][1:], y[test_indices].tolist()[1:]\\n train_x_vector = vectorizer.fit_transform(x_train)\\n test_X_vector = vectorizer.transform(x_test)\\n classifier.fit(train_x_vector, y_train)\\n guess = classifier.predict(test_X_vector)\\n rep = classification_report(y_test, guess)\\n a, b, c, d = dict(parse_classification_report(rep))['avg']\\n precision_dict[ind] = a\\n recall_dict[ind] = b\\n accuracy_dict[ind] = accuracy_score(y_test, guess)\\n ind = ind + 1\\n\\n p, r, a = (float(sum(precision_dict.values())) / 10), (float(sum(recall_dict.values())) / 10)\\\\\\n , (float(sum(accuracy_dict.values())) / 10)\\n performance_dict[params_arg] = (p + r + a) * 33.33\\n print(\\\"Precision : \\\", p * 100, \\\", Recall : \\\", r * 100, \\\", Accuracy : \\\", a * 100)\\n return performance_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8f564d1efc31a35f3becb69aa3e7ce2\",\n \"score\": \"0.57959634\",\n \"text\": \"def predict(self, data):\\n prediction = super().predict(data)\\n\\n if self.predict_strategy == 'argmax':\\n predicted = prediction\\n elif self.predict_strategy == 'relative':\\n predicted = self._get_relative_proba(prediction)\\n elif self.predict_strategy == 'class_distribution':\\n predicted = self._get_class_distribution_proba(prediction)\\n else:\\n raise ValueError(\\n f'Strategy \\\\'{self.predict_strategy}\\\\' not implemented.')\\n\\n predicted = np.argmax(predicted, axis=1)\\n\\n return self._classes_to_labels(predicted)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c280054b53891488ef4ae1fe1bbb9589\",\n \"score\": \"0.57914215\",\n \"text\": \"def predict(data, model):\\n print('{\\\"value\\\": ' + model['p'] + '}')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f53eb539efb62c04ac71f83aa60b9be\",\n \"score\": \"0.5765077\",\n \"text\": \"def estimator_info(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9160cd10038a30e2f62e653e4a098765\",\n \"score\": \"0.5764108\",\n \"text\": \"def predict(self, features):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c7036d3edcc22b5ea42d413aaa5c1cf\",\n \"score\": \"0.57541066\",\n \"text\": \"def viz_val_decorated(*args, **kwargs):\\r\\n\\r\\n if self.reason.lower().find('val')>=0: \\r\\n self.reason ='val'\\r\\n train_score , val_score, switch, param_range,\\\\\\r\\n pname, val_kws, train_kws =func(*args, **kwargs)\\r\\n \\r\\n elif self.reason.lower().find('learn')>=0: \\r\\n self.reason ='learn'\\r\\n param_range, train_score , val_score, switch,\\\\\\r\\n pname, val_kws, train_kws=func(*args, **kwargs)\\r\\n \\r\\n if val_kws is not None : \\r\\n self.val_kws =val_kws \\r\\n if train_kws is not None: \\r\\n self.train_kws = train_kws\\r\\n \\r\\n # add the name of parameters.\\r\\n if pname !='': \\r\\n self.xlabel = {'xlabel':'Evaluation of parameter %s'%pname , \\r\\n 'fontsize': self.font_size}\\r\\n\\r\\n if switch is not None :\\r\\n self.turn =switch \\r\\n \\r\\n # if param_range is not None :\\r\\n # k= param_range \\r\\n \\r\\n plt.figure(figsize=self.fig_size)\\r\\n\\r\\n if self.turn in ['on', 1, True]: \\r\\n # if not isinstance(param_range, bool): \\r\\n self._plot_train_val_score(train_score, val_score, k=param_range )\\r\\n \\r\\n if self.savefig is not None: \\r\\n if isinstance(self.savefig, dict):\\r\\n plt.savefig(**self.savefig)\\r\\n else : \\r\\n plt.savefig(self.savefig)\\r\\n \\r\\n # initialize the trainscore_dict \\r\\n train_score=dict() \\r\\n return func(*args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f73a13c67a6b92449c48f0494ac377ab\",\n \"score\": \"0.5751417\",\n \"text\": \"def predict(self, x):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f676f56d2a4b7c3e913dd16feefb20e\",\n \"score\": \"0.57491755\",\n \"text\": \"def predict(self, test_data, fcn_obs, par=None):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f676f56d2a4b7c3e913dd16feefb20e\",\n \"score\": \"0.57491755\",\n \"text\": \"def predict(self, test_data, fcn_obs, par=None):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f676f56d2a4b7c3e913dd16feefb20e\",\n \"score\": \"0.57491755\",\n \"text\": \"def predict(self, test_data, fcn_obs, par=None):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"26ed9b101db6c4996c1dcb275ca985f7\",\n \"score\": \"0.5746194\",\n \"text\": \"def predict(example):\\n print([predictor(example) for predictor in predictors])\\n return mode(predictor(example) for predictor in predictors)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b652494908b3c3c6a26dc5ba19522b0a\",\n \"score\": \"0.5743264\",\n \"text\": \"def get_predictions(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f242e56bd1d384e111136055f019716\",\n \"score\": \"0.57417655\",\n \"text\": \"def predict(image_data):\\n \\n \\n \\n \\n PAYLOAD = {}\\n PAYLOAD[\\\"timestamp\\\"] = str(datetime.datetime.now())\\n PAYLOAD[\\\"inferencetype\\\"] = \\\"object-detection\\\"\\n PAYLOAD[\\\"inferencedescription\\\"] = \\\"Top {} predictions with score {} or above \\\".format(\\n config_utils.MAX_NO_OF_RESULTS, config_utils.SCORE_THRESHOLD\\n )\\n \\n PAYLOAD[\\\"inferenceresults\\\"] = []\\n \\n \\n try:\\n image_data = np.expand_dims(image_data, axis=0).astype(np.uint8)\\n interpreter.set_tensor(input_details[0][\\\"index\\\"], image_data)\\n interpreter.invoke()\\n boxes = interpreter.get_tensor(output_details[0][\\\"index\\\"])[0]\\n classes = interpreter.get_tensor(output_details[1][\\\"index\\\"])[0]\\n scores = interpreter.get_tensor(output_details[2][\\\"index\\\"])[0]\\n predicted = []\\n \\n for i in range(len(scores)):\\n c = int(classes[i])\\n if len(predicted) == config_utils.MAX_NO_OF_RESULTS:\\n break\\n if scores[i] >= config_utils.SCORE_THRESHOLD:\\n predicted.append(c)\\n result = {\\n \\\"Label\\\": str(labels[c]),\\n \\\"Score\\\": str(scores[i]),\\n \\\"Boundaries\\\": str(boxes[i]),\\n }\\n PAYLOAD[\\\"inferenceresults\\\"].append(result)\\n config_utils.logger.info(json.dumps(PAYLOAD))\\n \\n PAYLOAD[\\\"position\\\"] = getPositionData()\\n \\n if config_utils.TOPIC.strip() != \\\"\\\":\\n ipc_utils.IPCUtils().publish_results_to_cloud(PAYLOAD)\\n else:\\n config_utils.logger.info(\\\"No topic set to publish the inference results to the cloud.\\\") \\n return PAYLOAD[\\\"inferenceresults\\\"]\\n except Exception as e:\\n config_utils.logger.error(\\\"Exception occured during prediction: {}\\\".format(e))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee204aa532cbed03d2f369b7858014b7\",\n \"score\": \"0.5720965\",\n \"text\": \"def predict(self, data):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d9b2b7e330b7711ddd24679bc24587ac\",\n \"score\": \"0.57202613\",\n \"text\": \"def train(self, trainingData, trainingLabels, validationData, validationLabels): \\n \\n # might be useful in your code later...\\n # this is a list of all features in the training set.\\n self.features = list(set([ f for datum in trainingData for f in datum.keys() ]))\\n svr = svm.SVC()\\n parameters = {'kernel':('linear', 'rbf', 'sigmoid'), 'C':[1, 10]}\\n self.clf = grid_search.GridSearchCV(svr, parameters)\\n tdata = self.formattingData(trainingData, self.features, None, None)\\n X = tdata\\n #print(tdata)\\n Y = trainingLabels\\n #print(trainingLabels)\\n #print(trainingData)\\n #for i in range(len(trainingData)):\\n #data = self.formattingData(trainingData, self.features, None, None)\\n #label = self.legalLabels\\n self.clf.fit(X, Y)\\n #clf.score(tdata, trainingLabels)\\n \\n #print(validationLabels)\\n #print(clf.score(X,Y))\\n #self.myclassifier = clf.predict(X)\\n \\n #print(Y)\\n #print(self.myclassifier)\\n \\n \\n #print(clf.predict(X1))\\n #print(self.myclassfier.predict(X1))\\n \\n #self.classify(X)\\n #for x in self.classifer.predict(X)\\n #self.weight[\\n #print(trainingLabels[x])\\n #print(self.myclassfier.predict(X)[x])\\n \\n\\n \\n \\n \\n \\n \\n \\n \\n \\n \\n\\n \\n #method fit, grab sym always linear and call fit on it\",\n \"title\": \"\"\n },\n {\n \"docid\": \"97261af3ed24c77a1d912925dd9fca43\",\n \"score\": \"0.571568\",\n \"text\": \"def on_predict_end(self, logs=None):\\n super().on_predict_end(logs)\\n self._set_logging_mode(LoggingMode.PREDICT)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"64e86fa195cde726a6c51bac6f440bf0\",\n \"score\": \"0.5710891\",\n \"text\": \"def get_best( clf, hyper, score, X_train, y_train ):\\r\\n best = customGridSearch( clf, param_grid=hyper, score=score ) #uses cross validation\\r\\n best.fit(X_train,y_train)\\r\\n return best.best_estimator_\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e374be4895d74c285cea112611b6659e\",\n \"score\": \"0.571062\",\n \"text\": \"def predict(self,X):\\n if self.approach == \\\"always\\\":\\n value = self.value\\n y_pred = [value]*len(X)\\n return y_pred\\n elif self.approach == \\\"most\\\":\\n most_common = self.most_common\\n y_pred = most_common*len(X)\\n return y_pred\\n elif self.approach == \\\"distribution\\\":\\n distribution=self.distribution\\n y_pred = [predict_from_distribution(distribution) for i in range(len(X))]\\n return y_pred\\n elif self.approach == \\\"equal\\\":\\n distributioneq=self.distributioneq\\n y_pred = [predict_from_distribution(distributioneq) for i in range(len(X))]\\n return y_pred\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fbb2858246e1f1ca6d19e787bab0b707\",\n \"score\": \"0.57083166\",\n \"text\": \"def predict(self, dataset):\\n\\n data = np.column_stack([dataset.features, dataset.labels])\\n columns = dataset.feature_names + dataset.label_names\\n test_df = pd.DataFrame(data=data, columns=columns)\\n x_test = dataset.features\\n #y_test = np.array([1 if y == [dataset.favorable_label] else -1 for y in dataset.labels])\\n #x_control_test = np.array(test_df[self.sensitive_attr])\\n\\n all_sensitive_attributes = dataset.protected_attribute_names\\n \\n model = self.model_name\\n predictions, y_res, scores = [], [], []\\n for x in x_test:\\n t = model(x)\\n if t > 0 :\\n predictions.append(1)\\n y_res.append(1)\\n else:\\n predictions.append(0)\\n y_res.append(-1)\\n scores.append((t+1)/2)\\n\\n\\n #print(\\\"Gamma: \\\", self.obj.getGamma(y_test, y_res, x_control_test))\\n pred_dataset = dataset.copy()\\n pred_dataset.labels = np.array(predictions)\\n pred_dataset.scores = np.array(scores)\\n\\n return pred_dataset\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a113d1de58af574b69cface15bec07f2\",\n \"score\": \"0.5704807\",\n \"text\": \"def predict(\\n fitted_model, test_data_x: pd.DataFrame, test_data_y: pd.DataFrame\\n) -> np.ndarray:\\n preds = fitted_model.predict(test_data_x)\\n conf_mat = pd.crosstab(\\n test_data_y, preds, rownames=[\\\"Actual Species\\\"], colnames=[\\\"Predicted Species\\\"]\\n )\\n\\n # 5. Save performance metric\\n pai.log_metrics({\\\"accuracy\\\": accuracy_score(test_data_y, preds)})\\n pai.log_metrics({\\\"f1\\\": f1_score(test_data_y, preds, average=\\\"macro\\\")})\\n pai.log_metrics({\\\"precision\\\": precision_score(test_data_y, preds, average=\\\"macro\\\")})\\n pai.log_metrics({\\\"recall\\\": recall_score(test_data_y, preds, average=\\\"macro\\\")})\\n\\n # 6. Save other artifacts, e.g. the confusion matrix\\n pai.log_artifacts({\\\"confusion_matrix\\\": conf_mat})\\n pai.log_artifacts({\\\"plot\\\": generate_plot()})\\n\\n return preds\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30c89510eb77ed1f02ed9fdccdb8f847\",\n \"score\": \"0.5704508\",\n \"text\": \"def _fit_and_predict(estimator, X, y, train, test, verbose, fit_params,\\n parameters=None, method='predict'):\\n # Adjust length of sample weights\\n fit_params = fit_params if fit_params is not None else {}\\n fit_params = dict([(k, _index_param_value(X, v, train))\\n for k, v in fit_params.items()])\\n\\n X_train, y_train = _safe_split(estimator, X, y, train)\\n X_test, _ = _safe_split(estimator, X, y, test, train)\\n\\n if parameters is not None:\\n estimator.set_params(**parameters)\\n\\n if y_train is None:\\n estimator.fit(X_train, **fit_params)\\n else:\\n estimator.fit(X_train, y_train, **fit_params)\\n func = getattr(estimator, method)\\n predictions = func(X_test)\\n if method in ['decision_function', 'predict_proba', 'predict_log_proba']:\\n n_classes = len(set(y))\\n if n_classes != len(estimator.classes_):\\n recommendation = (\\n 'To fix this, use a cross-validation '\\n 'technique resulting in properly '\\n 'stratified folds')\\n warnings.warn('Number of classes in training fold ({}) does '\\n 'not match total number of classes ({}). '\\n 'Results may not be appropriate for your use case. '\\n '{}'.format(len(estimator.classes_),\\n n_classes, recommendation),\\n RuntimeWarning)\\n if method == 'decision_function':\\n if (predictions.ndim == 2 and\\n predictions.shape[1] != len(estimator.classes_)):\\n # This handles the case when the shape of predictions\\n # does not match the number of classes used to train\\n # it with. This case is found when sklearn.svm.SVC is\\n # set to `decision_function_shape='ovo'`.\\n raise ValueError('Output shape {} of {} does not match '\\n 'number of classes ({}) in fold. '\\n 'Irregular decision_function outputs '\\n 'are not currently supported by '\\n 'cross_val_predict'.format(\\n predictions.shape, method,\\n len(estimator.classes_),\\n recommendation))\\n if len(estimator.classes_) <= 2:\\n # In this special case, `predictions` contains a 1D array.\\n raise ValueError('Only {} class/es in training fold, this '\\n 'is not supported for decision_function '\\n 'with imbalanced folds. {}'.format(\\n len(estimator.classes_),\\n recommendation))\\n\\n float_min = np.finfo(predictions.dtype).min\\n default_values = {'decision_function': float_min,\\n 'predict_log_proba': float_min,\\n 'predict_proba': 0}\\n predictions_for_all_classes = np.full((_num_samples(predictions),\\n n_classes),\\n default_values[method])\\n predictions_for_all_classes[:, estimator.classes_] = predictions\\n predictions = predictions_for_all_classes\\n return predictions, test\",\n \"title\": \"\"\n },\n {\n \"docid\": \"73f3b377317cf0e721704a3551a5a07a\",\n \"score\": \"0.57019013\",\n \"text\": \"def analyseChangeInHyperParameter(n_est_lst, max_features_lst, min_samples_split_lst, X_train, Y_train, X_val, Y_val, X_test, Y_test, smooth_garph=True, repeat_exp=3):\\n val_score_lst, train_score_lst, test_score_lst, oob_score_lst = [], [], [], []\\n params = list(itertools.product(n_est_lst, max_features_lst, min_samples_split_lst))\\n for n_est, max_feat, min_sam_split in tqdm(params):\\n train_score, test_score, val_score, oob_score = 0,0,0,0\\n for i in range(repeat_exp):\\n clf = RandomForestClassifier(n_estimators=n_est, max_features=max_feat, min_samples_split=min_sam_split\\\\\\n , oob_score=False, n_jobs=-2).fit(X_train, Y_train)\\n train_score+=(clf.score(X_train, Y_train)*100)\\n test_score+=(clf.score(X_test, Y_test)*100)\\n val_score+=(clf.score(X_val, Y_val)*100)\\n train_score_lst.append(train_score/3)\\n test_score_lst.append(test_score/3)\\n val_score_lst.append(val_score/3)\\n if len(n_est_lst)!=1:\\n xlabel = 'n_estimators'\\n title = 'n_estimators v/s Accuracy'\\n X=n_est_lst\\n elif len(max_features_lst)!=1:\\n xlabel = 'max_features'\\n title = 'max_features v/s Accuracy'\\n X=max_features_lst\\n else:\\n xlabel = 'min_samples_split'\\n title = 'min_samples_split v/s Accuracy'\\n X=min_samples_split_lst\\n\\n Y_train_smoothed = make_interp_spline(X, train_score_lst, k=3)(xnew)\\n Y_test_smoothed = make_interp_spline(X, test_score_lst, k=3)(xnew)\\n Y_val_smoothed = make_interp_spline(X, val_score_lst, k=3)(xnew)\\n if not smooth_garph:\\n xnew = X\\n Y_train_smoothed=train_score_lst\\n Y_test_smoothed=test_score_lst\\n Y_val_smoothed=val_score_lst\\n gs = gridspec.GridSpec(2, 2)\\n\\n fig = plt.figure(figsize=(30,20))\\n ax1 = fig.add_subplot(gs[0, 0])\\n ax1.plot(xnew, Y_train_smoothed, label='Train Score')\\n ax1.legend()\\n ax1.set_title(xlabel+' v/s Train Accuracy')\\n ax1.set_xlabel(xlabel)\\n ax1.set_ylabel('Train Accuracy')\\n\\n ax1 = fig.add_subplot(gs[0, 1])\\n ax1.plot(xnew, Y_test_smoothed, label='Tes Score')\\n ax1.legend()\\n ax1.set_title(xlabel+' v/s Test Accuracy')\\n ax1.set_xlabel(xlabel)\\n ax1.set_ylabel('Test Accuracy')\\n\\n ax1 = fig.add_subplot(gs[1, 0])\\n ax1.plot(xnew, Y_val_smoothed, label='Validation Score')\\n ax1.legend()\\n ax1.set_title(xlabel+' v/s Validation Accuracy')\\n ax1.set_xlabel(xlabel)\\n ax1.set_ylabel('Validation Accuracy')\\n\\n ax1 = fig.add_subplot(gs[1, 1])\\n ax1.plot(xnew, Y_test_smoothed, label='Test Score')\\n ax1.plot(xnew, Y_val_smoothed, label='Validation Score')\\n ax1.plot(xnew, Y_train_smoothed, label='Train Score')\\n ax1.legend()\\n ax1.set_title(xlabel+' v/s Test, Validation and Train Accuracy')\\n ax1.set_xlabel(xlabel)\\n ax1.set_ylabel('Accuracy')\\n\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de700cd20c85daf4b5e49be92109aad6\",\n \"score\": \"0.5695684\",\n \"text\": \"def ALM(eval_pts, uneval_pts,model,k ):\\n \\n post_means, post_vars = model.predict(uneval_pts)\\n \\n # Point with highest variance\\n new_point = uneval_pts[np.argmax(post_vars)]\\n indx = np.argmax(post_vars)\\n return [new_point], [indx]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14845b911679b2746072b19bb443c0f9\",\n \"score\": \"0.56943643\",\n \"text\": \"def predict(self, uedata):\\n return \\\"Overloaded!\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0dbe749244a9371cb2af13d397a014cc\",\n \"score\": \"0.5690516\",\n \"text\": \"def predict(self, X):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0dbe749244a9371cb2af13d397a014cc\",\n \"score\": \"0.5690516\",\n \"text\": \"def predict(self, X):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0dbe749244a9371cb2af13d397a014cc\",\n \"score\": \"0.5690516\",\n \"text\": \"def predict(self, X):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c72d2e4eb76eef156fded7cb49da58ef\",\n \"score\": \"0.56883866\",\n \"text\": \"def FindPredictions(self):\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"03a11d8bb078babc5bf7471cbff68b44\",\n \"score\": \"0.56841344\",\n \"text\": \"def predict(self,X):\\n\\t\\t# below for outputting the index with the highest probability\\n\\t\\t#labels = self.clf.decision_function(X)\\n\\t\\t#return list(labels).index(max(labels))\\n\\n\\t\\t# below for outputting the index of first 1\\n\\t\\tprint self.clf.predict(X)\\n\\t\\treturn np.argmax(self.clf.predict(X))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4dfb3e73c50241f53e88a0e63a57132\",\n \"score\": \"0.56840223\",\n \"text\": \"def display_results(self,index):\\n self.best_features = literal_eval(self.res_df.iloc[index][\\\"Feauteres_decor_ind\\\"])\\n \\n if self.cv is not None:\\n # if task is regression\\n if is_regressor(self.model):\\n# =============================================================================\\n# Running with general algorithm and calculating accuracies\\n# ============================================================================= \\n\\n # calculating accuracies for generalized model \\n self.model.fit(self.x,self.y)\\n self.y_pred_cal_gen = np.squeeze(self.model.predict(self.x)).squeeze()\\n self.r2_cal_gen = r2_score(self.y,self.y_pred_cal_gen)\\n self.r2_adj_cal_gen = adj_r2_score(self.y,self.y_pred_cal_gen, self.x, self.r2_cal_gen)\\n self.rmse_cal_gen = mean_squared_error(self.y, self.y_pred_cal_gen, squared = False)\\n self.rmspe_cal_gen = rmspe_score(self.y, self.y_pred_cal_gen)\\n\\n if self.cv.__str__() == \\\"LeaveOneOut()\\\":\\n score_r2 = cross_validate_LOO(self.model,self.x,self.y,self.cv,\\n scoring=r2_score) \\n\\n score_rmse = cross_validate_LOO(self.model,self.x,self.y,self.cv,\\n scoring=mean_squared_error)\\n \\n self.y_pred_cv_gen = cross_val_predict(self.model, self.x, self.y, n_jobs = -1, cv=self.cv).squeeze() \\n self.r2_cv_gen = score_r2 \\n self.r2_adj_cv_gen = adj_r2_score(self.y, x=self.x, r2=self.r2_cv_gen)\\n self.rmse_cv_gen = np.sqrt(score_rmse)\\n self.rmspe_cv_gen = rmspe_score(self.y, self.y_pred_cv_gen) \\n \\n else:\\n scores = cross_validate(self.model,self.x,self.y,\\n n_jobs = -1, \\n cv=self.cv,\\n scoring=['r2','neg_mean_squared_error']) \\n \\n self.y_pred_cv_gen = cross_val_predict(self.model, self.x, self.y, n_jobs = -1, cv=self.cv).squeeze() \\n self.r2_cv_gen = scores['test_r2'].mean() \\n self.r2_adj_cv_gen = adj_r2_score(self.y, x=self.x, r2=self.r2_cv_gen)\\n self.rmse_cv_gen = np.sqrt(scores['test_neg_mean_squared_error'].mean())\\n self.rmspe_cv_gen = rmspe_score(self.y, self.y_pred_cv_gen) \\n\\n \\n # =============================================================================\\n # Running optimized model and calculate accuracies\\n # =============================================================================\\n \\n self.model_best.fit(self.x[:,self.best_features],self.y) \\n self.y_pred_cal_op = np.squeeze(self.model_best.predict(self.x[:,self.best_features])).squeeze()\\n self.r2_cal_op = r2_score(self.y,self.y_pred_cal_op)\\n self.r2_adj_cal_op = adj_r2_score(self.y,self.y_pred_cal_op,self.x[:,self.best_features],self.r2_cal_op)\\n self.rmse_cal_op = mean_squared_error(self.y, self.y_pred_cal_op, squared = False)\\n self.rmspe_cal_op = rmspe_score(self.y, self.y_pred_cal_op)\\n \\n if self.cv.__str__() == \\\"LeaveOneOut()\\\":\\n \\n score_r2 = cross_validate_LOO(self.model_best,self.x[:,self.best_features],self.y,self.cv,\\n scoring=r2_score) \\n\\n score_rmse = cross_validate_LOO(self.model_best,self.x[:,self.best_features],self.y,self.cv,\\n scoring=mean_squared_error) \\n \\n self.y_pred_cv_op = cross_val_predict(self.model_best, self.x[:,self.best_features], self.y, n_jobs = -1, cv=self.cv).squeeze() \\n self.r2_cv_op = score_r2 \\n self.r2_adj_cv_op = adj_r2_score(self.y,x = self.x[:,self.best_features],r2= self.r2_cv_op)\\n self.rmse_cv_op = np.sqrt(score_rmse)\\n self.rmspe_cv_op = rmspe_score(self.y, self.y_pred_cv_op) \\n \\n\\n else:\\n scores = cross_validate(self.model_best,self.x[:,self.best_features],self.y,\\n n_jobs = -1, \\n cv=self.cv,\\n scoring=['r2','neg_mean_squared_error']) \\n \\n self.y_pred_cv_op = cross_val_predict(self.model_best, self.x[:,self.best_features], self.y, n_jobs = -1, cv=self.cv).squeeze() \\n self.r2_cv_op = scores['test_r2'].mean() \\n self.r2_adj_cv_op = adj_r2_score(self.y,x = self.x[:,self.best_features],r2= self.r2_cv_op)\\n self.rmse_cv_op = np.sqrt(scores['test_neg_mean_squared_error'].mean())\\n self.rmspe_cv_op = rmspe_score(self.y, self.y_pred_cv_op)\\n\\n # =============================================================================\\n # Plotting generalized model and optimized model \\n # =============================================================================\\n # generalized model\\n fig,axes = plt.subplots(2,3,figsize=(15,10))\\n sns.regplot(x=self.y, y=self.y_pred_cal_gen, color=\\\"k\\\",ax=axes[0, 0])\\n axes[0,0].plot([np.min([self.y, self.y_pred_cal_gen]) - 0.1*np.std(self.y), np.max([self.y, self.y_pred_cal_gen]) + 0.1*np.std(self.y)],\\n [np.min([self.y, self.y_pred_cal_gen]) - 0.1*np.std(self.y), np.max([self.y, self.y_pred_cal_gen]) + 0.1*np.std(self.y)],'r--')\\n axes[0,0].text(0.05,0.80,r\\\"RMSE={:.3f}\\\".format(self.rmse_cal_gen) + \\\"\\\\n\\\"+ \\n r\\\"RMSPE={:.3f}\\\".format(self.rmspe_cal_gen) + \\\"\\\\n\\\"+ \\n r\\\"$R^2$={:.3f}\\\".format(self.r2_cal_gen) + \\\"\\\\n\\\"+\\n r\\\"$Adj. R^2$={:.3f}\\\".format(self.r2_adj_cal_gen),\\n transform=axes[0, 0].transAxes,\\n color='k',\\n bbox=dict(facecolor=(1,1,1,0.5),edgecolor=(0,0,0,1)))\\n axes[0,0].set_xlabel(\\\"Measured\\\")\\n axes[0,0].set_ylabel(\\\"Predicted\\\")\\n axes[0,0].set_title(\\\"General model - Cal.\\\")\\n axes[0,0].ticklabel_format(axis=\\\"both\\\", style = \\\"sci\\\", scilimits = (0,2))\\n \\n sns.regplot(x=self.y, y=self.y_pred_cv_gen, color=\\\"k\\\",ax=axes[1, 0])\\n axes[1,0].plot([np.min([self.y, self.y_pred_cv_gen]) - 0.1*np.std(self.y), np.max([self.y, self.y_pred_cv_gen]) + 0.1*np.std(self.y)],\\n [np.min([self.y, self.y_pred_cv_gen]) - 0.1*np.std(self.y), np.max([self.y, self.y_pred_cv_gen]) + 0.1*np.std(self.y)],'r--') \\n axes[1,0].text(0.05,0.80,r\\\"RMSE={:.3f}\\\".format(self.rmse_cv_gen) + \\\"\\\\n\\\"+ \\n r\\\"RMSPE={:.3f}\\\".format(self.rmspe_cv_gen) + \\\"\\\\n\\\"+ \\n r\\\"$R^2$={:.3f}\\\".format(self.r2_cv_gen) + \\\"\\\\n\\\"+\\n r\\\"$Adj. R^2$={:.3f}\\\".format(self.r2_adj_cv_gen),\\n transform=axes[1, 0].transAxes,\\n color='k',\\n bbox=dict(facecolor=(1,1,1,0.5),edgecolor=(0,0,0,1)))\\n axes[1,0].set_xlabel(\\\"Measured\\\")\\n axes[1,0].set_ylabel(\\\"Predicted\\\")\\n axes[1,0].set_title(\\\"General model - CV\\\")\\n axes[1,0].ticklabel_format(axis=\\\"both\\\", style = \\\"sci\\\", scilimits = (0,2))\\n \\n # optimized model\\n sns.regplot(x=self.y, y=self.y_pred_cal_op, color=\\\"k\\\",ax=axes[0, 1])\\n axes[0,1].plot([np.min([self.y, self.y_pred_cal_op]) - 0.1*np.std(self.y), np.max([self.y, self.y_pred_cal_op]) + 0.1*np.std(self.y)],\\n [np.min([self.y, self.y_pred_cal_op]) - 0.1*np.std(self.y), np.max([self.y, self.y_pred_cal_op]) + 0.1*np.std(self.y)],'r--') \\n axes[0,1].text(0.05,0.80,r\\\"RMSE={:.3f}\\\".format(self.rmse_cal_op) + \\\"\\\\n\\\"+ \\n r\\\"RMSPE={:.3f}\\\".format(self.rmspe_cal_op) + \\\"\\\\n\\\"+ \\n r\\\"$R^2$={:.3f}\\\".format(self.r2_cal_op)+ \\\"\\\\n\\\"+\\n r\\\"$Adj. R^2$={:.3f}\\\".format(self.r2_adj_cal_op),\\n transform=axes[0, 1].transAxes,\\n color='k',\\n bbox=dict(facecolor=(1,1,1,0.5),edgecolor=(0,0,0,1)))\\n axes[0,1].set_xlabel(\\\"Measured\\\")\\n axes[0,1].set_ylabel(\\\"Predicted\\\")\\n axes[0,1].set_title(\\\"Optimized model - Cal.\\\")\\n axes[0,1].ticklabel_format(axis=\\\"both\\\", style = \\\"sci\\\", scilimits = (0,2)) \\n \\n sns.regplot(x=self.y, y=self.y_pred_cv_op, color=\\\"k\\\",ax=axes[1, 1])\\n axes[1,1].plot([np.min([self.y, self.y_pred_cv_op]) - 0.1*np.std(self.y), np.max([self.y, self.y_pred_cv_op]) + 0.1*np.std(self.y)],\\n [np.min([self.y, self.y_pred_cv_op]) - 0.1*np.std(self.y), np.max([self.y, self.y_pred_cv_op]) + 0.1*np.std(self.y)],'r--') \\n axes[1,1].text(0.05,0.80,r\\\"RMSE={:.3f}\\\".format(self.rmse_cv_op) + \\\"\\\\n\\\"+ \\n r\\\"RMSPE={:.3f}\\\".format(self.rmspe_cv_op) + \\\"\\\\n\\\"+ \\n r\\\"$R^2$={:.3f}\\\".format(self.r2_cv_op)+ \\\"\\\\n\\\"+\\n r\\\"$Adj. R^2$={:.3f}\\\".format(self.r2_adj_cv_op),\\n transform=axes[1, 1].transAxes,\\n color='k',\\n bbox=dict(facecolor=(1,1,1,0.5),edgecolor=(0,0,0,1)))\\n axes[1,1].set_xlabel(\\\"Measured\\\")\\n axes[1,1].set_ylabel(\\\"Predicted\\\")\\n axes[1,1].set_title(\\\"Optimized model - CV\\\")\\n axes[1,1].ticklabel_format(axis=\\\"both\\\", style = \\\"sci\\\", scilimits = (0,2)) \\n \\n #residuals plot\\n sns.residplot(self.y_pred_cal_op, self.y - self.y_pred_cal_op, lowess=False, color=\\\"k\\\",ax=axes[0, 2])\\n axes[0,2].set_xlabel(\\\"Predicted\\\")\\n axes[0,2].set_ylabel(\\\"Residuals\\\")\\n axes[0,2].set_title(\\\"Optimized model - Cal. Residuals\\\")\\n axes[0,2].ticklabel_format(axis=\\\"both\\\", style = \\\"sci\\\", scilimits = (0,2)) \\n\\n \\n sns.residplot(self.y_pred_cv_op, self.y - self.y_pred_cv_op, lowess=False, color=\\\"k\\\",ax=axes[1, 2])\\n axes[1,2].set_xlabel(\\\"Predicted\\\")\\n axes[1,2].set_ylabel(\\\"Residuals\\\")\\n axes[1,2].set_title(\\\"Optimized model - CV Residuals\\\")\\n axes[1,2].ticklabel_format(axis=\\\"both\\\", style = \\\"sci\\\", scilimits = (0,2)) \\n \\n \\n\\n results = {\\\"r2_cal_gen\\\":[self.r2_cal_gen],'rmse_cal_gen':[self.rmse_cal_gen],'rmspe_cal_gen':[self.rmspe_cal_gen],'r2_adj_cal_gen':[self.r2_adj_cal_gen],\\n 'r2_cv_gen':[self.r2_cv_gen],'rmse_cv_gen':[self.rmse_cv_gen],'rmspe_cv_gen':[self.rmspe_cv_gen],'r2_adj_cv_gen':[self.r2_adj_cv_gen],\\n \\\"r2_cal_op\\\":[self.r2_cal_op],'rmse_cal_op':[self.rmse_cal_op],'rmspe_cal_op':[self.rmspe_cal_op],'r2_adj_cal_op':[self.r2_adj_cal_op],\\n 'r2_cv_op':[self.r2_cv_op],'rmse_cv_op':[self.rmse_cv_op],'rmspe_cv_op':[self.rmspe_cv_op],'r2_adj_cv_op':[self.r2_adj_cv_op]} \\n \\n # if task is classification\\n else:\\n if len(self.class_sep) == 2:\\n fig,axes = plt.subplots(2,3,figsize=(15,10))\\n else:\\n fig,axes = plt.subplots(2,2,figsize=(10 + len(self.class_sep),10 + len(self.class_sep)))\\n # general model\\n \\n self.model.fit(self.x,self.y)\\n y_train_pred = self.model.predict(self.x)\\n self.acc_train_gen = self.model.score(self.x,self.y)\\n cm = confusion_matrix(self.y,y_train_pred.squeeze())\\n cm = np.stack(list(map(lambda x: x/x.sum(),cm)))\\n conf_matrix=pd.DataFrame(cm,columns = self.class_sep,index= self.class_sep)\\n sns.heatmap(conf_matrix,cmap=\\\"coolwarm\\\", annot=True, ax=axes[0,0],vmin=0,vmax=1)\\n axes[0,0].set_title('General model - Cal. confusion matrix')\\n axes[0,0].set_ylabel('True label')\\n axes[0,0].set_xlabel('Predicted label') \\n \\n \\n y_test_pred = cross_val_predict(self.model, self.x, self.y, \\n n_jobs = -1,\\n cv=self.cv).squeeze() \\n\\n if self.cv.__str__() == \\\"LeaveOneOut()\\\": \\n score_acc = cross_validate_LOO(self.model,self.x,self.y,self.cv,\\n scoring=accuracy_score) \\n\\n self.acc_test_gen = score_acc \\n else:\\n scores = cross_validate(self.model, self.x, self.y, \\n n_jobs = -1,\\n cv=self.cv,\\n scoring = \\\"accuracy\\\") \\n \\n self.acc_test_gen = scores['test_score'].mean() \\n\\n cm = confusion_matrix(self.y,y_test_pred)\\n cm = np.stack(list(map(lambda x: x/x.sum(),cm)))\\n conf_matrix=pd.DataFrame(cm,columns = self.class_sep,index= self.class_sep)\\n sns.heatmap(conf_matrix,cmap=\\\"coolwarm\\\", annot=True,ax = axes[1,0],vmin=0,vmax=1)\\n axes[1,0].set_title('General model - CV confusion matrix') \\n axes[1,0].set_ylabel('True label')\\n axes[1,0].set_xlabel('Predicted label') \\n \\n \\n # optimized model\\n self.model_best.fit(self.x[:,self.best_features],self.y)\\n y_train_pred = self.model_best.predict(self.x[:,self.best_features])\\n self.acc_train_op = self.model_best.score(self.x[:,self.best_features],self.y)\\n cm = confusion_matrix(self.y, y_train_pred.squeeze())\\n cm = np.stack(list(map(lambda x: x/x.sum(),cm)))\\n conf_matrix=pd.DataFrame(cm,columns = self.class_sep,index= self.class_sep)\\n sns.heatmap(conf_matrix,cmap=\\\"coolwarm\\\", annot=True, ax=axes[0,1],vmin=0,vmax=1)\\n axes[0,1].set_title('Optimized model - Cal. confusion matrix') \\n axes[0,1].set_ylabel('True label')\\n axes[0,1].set_xlabel('Predicted label') \\n \\n \\n y_test_pred = cross_val_predict(self.model_best, self.x[:,self.best_features], self.y, \\n n_jobs = -1,\\n cv=self.cv).squeeze() \\n \\n if self.cv.__str__() == \\\"LeaveOneOut()\\\": \\n score_acc = cross_validate_LOO(self.model_best,self.x[:,self.best_features],self.y,\\n self.cv,\\n scoring=accuracy_score) \\n \\n self.acc_test_op = score_acc \\n else:\\n scores = cross_validate(self.model_best, self.x[:,self.best_features], self.y, \\n n_jobs = -1,\\n cv=self.cv,\\n scoring = \\\"accuracy\\\") \\n\\n self.acc_test_op = scores['test_score'].mean()\\n \\n cm = confusion_matrix(self.y,y_test_pred)\\n cm = np.stack(list(map(lambda x: x/x.sum(),cm)))\\n conf_matrix=pd.DataFrame(cm,columns = self.class_sep,index= self.class_sep)\\n sns.heatmap(conf_matrix,cmap=\\\"coolwarm\\\", annot=True,ax = axes[1,1],vmin=0,vmax=1)\\n axes[1,1].set_title('Optimized model - CV confusion matrix') \\n axes[1,1].set_ylabel('True label')\\n axes[1,1].set_xlabel('Predicted label') \\n \\n if len(self.class_sep) == 2:\\n #optimized model Roc curves\\n self.model_best.fit(self.x[:,self.best_features],self.y)\\n y_train_pred_prob = self.model_best.predict_proba(self.x[:,self.best_features])\\n fpr, tpr, thr = roc_curve(self.y, y_train_pred_prob[:, 1].squeeze()) \\n self.auc_train_op = auc(fpr, tpr)\\n axes[0,2].plot([0,1],[0,1],linestyle = '--',lw = 2,color = 'black')\\n axes[0,2].plot(fpr, tpr, color='blue',\\n label=r'$ROC_{train}$ (AUC = %0.2f)' % (auc(fpr, tpr)),lw=2, alpha=1)\\n axes[0,2].set_xlabel('False Positive Rate')\\n axes[0,2].set_ylabel('True Positive Rate')\\n axes[0,2].set_title('Optimized model - Cal. ROC Curve')\\n axes[0,2].legend(loc=\\\"lower right\\\")\\n \\n y_test_pred_prob = cross_val_predict(self.model_best, self.x[:,self.best_features], self.y, \\n n_jobs = -1,\\n cv=self.cv,\\n method='predict_proba') \\n \\n \\n fpr, tpr, thr = roc_curve(self.y, y_test_pred_prob[:, 1].squeeze()) \\n self.auc_test_op = auc(fpr, tpr)\\n axes[1,2].plot([0,1],[0,1],linestyle = '--',lw = 2,color = 'black')\\n axes[1,2].plot(fpr, tpr, color='blue',\\n label=r'$ROC_{test}$ (AUC = %0.2f)' % (auc(fpr, tpr)),lw=2, alpha=1)\\n axes[1,2].set_xlabel('False Positive Rate')\\n axes[1,2].set_ylabel('True Positive Rate')\\n axes[1,2].set_title('Optimized model- CV ROC Curve')\\n axes[1,2].legend(loc=\\\"lower right\\\")\\n \\n results = {\\\"acc_gen\\\":[self.acc_train_gen],'acc_cv_gen':[self.acc_test_gen],\\n 'acc_op':[self.acc_train_op],'acc_cv_op':[self.acc_test_op],\\n \\\"auc_op\\\":[self.auc_train_op],'auc_cv_op':[self.auc_test_op]}\\n \\n results = {\\\"acc_gen\\\":[self.acc_train_gen],'acc_cv_gen':[self.acc_test_gen],\\n 'acc_op':[self.acc_train_op],'acc_cv_op':[self.acc_test_op]} \\n \\n # if cv is not defined\\n else:\\n if is_regressor(self.model):\\n if hasattr(self, 'x_train') and hasattr(self, 'x_test') and hasattr(self, 'y_train') and hasattr(self, 'y_test'): \\n\\n # calculating accuracies for generalized model\\n self.model.fit(self.x_train,self.y_train)\\n self.y_pred_cal_gen = np.squeeze(self.model.predict(self.x_train)).squeeze()\\n self.r2_cal_gen = r2_score(self.y_train,self.y_pred_cal_gen)\\n #self.r2_adj_cal_gen = adj_r2_score(self.y_train,self.y_pred_cal_gen,x = self.x_train, r2= self.r2_cal_gen)\\n self.r2_adj_cal_gen = adj_r2_score(self.y_train,self.y_pred_cal_gen,x = self.x, r2= self.r2_cal_gen)\\n self.rmse_cal_gen = mean_squared_error(self.y_train, self.y_pred_cal_gen, squared = False)\\n self.rmspe_cal_gen = rmspe_score(self.y_train, self.y_pred_cal_gen)\\n \\n self.y_pred_val_gen = np.squeeze(self.model.predict(self.x_test)).squeeze()\\n self.r2_val_gen = r2_score(self.y_test,self.y_pred_val_gen)\\n #self.r2_adj_val_gen = adj_r2_score(self.y_test, x=self.x_test, r2=self.r2_val_gen)\\n self.r2_adj_val_gen = adj_r2_score(self.y_test, x=self.x, r2=self.r2_val_gen)\\n self.rmse_val_gen = mean_squared_error(self.y_test, self.y_pred_val_gen, squared = False)\\n self.rmspe_val_gen = rmspe_score(self.y_test, self.y_pred_val_gen)\\n \\n # calculating accuracies for optimized model\\n self.model_best.fit(self.x_train[:,self.best_features],self.y_train)\\n self.y_pred_cal_op = np.squeeze(self.model_best.predict(self.x_train[:,self.best_features])).squeeze()\\n self.r2_cal_op = r2_score(self.y_train,self.y_pred_cal_op)\\n #self.r2_adj_cal_op = adj_r2_score(self.y_train, x=self.x_train[:,self.best_features],r2=self.r2_cal_op)\\n self.r2_adj_cal_op = adj_r2_score(self.y_train, x=self.x[:,self.best_features],r2=self.r2_cal_op)\\n self.rmse_cal_op = mean_squared_error(self.y_train, self.y_pred_cal_op, squared = False) \\n self.rmspe_cal_op = rmspe_score(self.y_train, self.y_pred_cal_op)\\n \\n self.y_pred_val_op = np.squeeze(self.model_best.predict(self.x_test[:,self.best_features])).squeeze()\\n self.r2_val_op = r2_score(self.y_test,self.y_pred_val_op)\\n #self.r2_adj_val_op = adj_r2_score(self.y_test, x= self.x_test[:,self.best_features],r2=self.r2_val_op)\\n self.r2_adj_val_op = adj_r2_score(self.y_test, x= self.x[:,self.best_features],r2=self.r2_val_op)\\n self.rmse_val_op = mean_squared_error(self.y_test, self.y_pred_val_op, squared = False)\\n self.rmspe_val_op = rmspe_score(self.y_test, self.y_pred_val_op)\\n \\n # generalized model\\n fig,axes = plt.subplots(2,3,figsize=(15,10))\\n sns.regplot(x=self.y_train, y=self.y_pred_cal_gen, color=\\\"k\\\",ax=axes[0, 0])\\n axes[0,0].plot([np.min([self.y_train, self.y_pred_cal_gen]) - 0.1*np.std(self.y), np.max([self.y_train, self.y_pred_cal_gen]) + 0.1*np.std(self.y)],\\n [np.min([self.y_train, self.y_pred_cal_gen]) - 0.1*np.std(self.y), np.max([self.y_train, self.y_pred_cal_gen]) + 0.1*np.std(self.y)],'r--')\\n axes[0,0].text(0.05,0.80,r\\\"RMSE={:.3f}\\\".format(self.rmse_cal_gen) + \\\"\\\\n\\\"+ \\n r\\\"RMSPE={:.3f}\\\".format(self.rmspe_cal_gen) + \\\"\\\\n\\\"+ \\n r\\\"$R^2$={:.3f}\\\".format(self.r2_cal_gen)+ \\\"\\\\n\\\"+\\n r\\\"$Adj. R^2$={:.3f}\\\".format(self.r2_adj_cal_gen),\\n transform=axes[0, 0].transAxes,\\n color='k',\\n bbox=dict(facecolor=(1,1,1,0.5),edgecolor=(0,0,0,1)))\\n axes[0,0].set_xlabel(\\\"Measured\\\")\\n axes[0,0].set_ylabel(\\\"Predicted\\\")\\n axes[0,0].set_title(\\\"General model - Cal.\\\")\\n axes[0,0].ticklabel_format(axis=\\\"both\\\", style = \\\"sci\\\", scilimits = (0,2)) \\n \\n sns.regplot(x=self.y_test, y=self.y_pred_val_gen, color=\\\"k\\\",ax=axes[1, 0])\\n axes[1,0].plot([np.min([self.y_test, self.y_pred_val_gen]) - 0.1*np.std(self.y), np.max([self.y_test, self.y_pred_val_gen]) + 0.1*np.std(self.y)],\\n [np.min([self.y_test, self.y_pred_val_gen]) - 0.1*np.std(self.y), np.max([self.y_test, self.y_pred_val_gen]) + 0.1*np.std(self.y)],'r--') \\n axes[1,0].text(0.05,0.80,r\\\"RMSE={:.3f}\\\".format(self.rmse_val_gen) + \\\"\\\\n\\\"+ \\n r\\\"RMSPE={:.3f}\\\".format(self.rmspe_val_gen) + \\\"\\\\n\\\"+ \\n r\\\"$R^2$={:.3f}\\\".format(self.r2_val_gen)+ \\\"\\\\n\\\"+\\n r\\\"$Adj. R^2$={:.3f}\\\".format(self.r2_adj_val_gen),\\n transform=axes[1, 0].transAxes,\\n color='k',\\n bbox=dict(facecolor=(1,1,1,0.5),edgecolor=(0,0,0,1)))\\n axes[1,0].set_xlabel(\\\"Measured\\\")\\n axes[1,0].set_ylabel(\\\"Predicted\\\")\\n axes[1,0].set_title(\\\"General model - Val.\\\")\\n axes[1,0].ticklabel_format(axis=\\\"both\\\", style = \\\"sci\\\", scilimits = (0,2)) \\n\\n # optimized model\\n sns.regplot(x=self.y_train, y=self.y_pred_cal_op, color=\\\"k\\\",ax=axes[0, 1])\\n axes[0,1].plot([np.min([self.y_train, self.y_pred_cal_op]) - 0.1*np.std(self.y), np.max([self.y_train, self.y_pred_cal_op]) + 0.1*np.std(self.y)],\\n [np.min([self.y_train, self.y_pred_cal_op]) - 0.1*np.std(self.y), np.max([self.y_train, self.y_pred_cal_op]) + 0.1*np.std(self.y)],'r--') \\n axes[0,1].text(0.05,0.80,r\\\"RMSE={:.3f}\\\".format(self.rmse_cal_op) + \\\"\\\\n\\\"+ \\n r\\\"RMSPE={:.3f}\\\".format(self.rmspe_cal_op) + \\\"\\\\n\\\"+ \\n r\\\"$R^2$={:.3f}\\\".format(self.r2_cal_op)+ \\\"\\\\n\\\"+\\n r\\\"$Adj. R^2$={:.3f}\\\".format(self.r2_adj_cal_op),\\n transform=axes[0, 1].transAxes,\\n color='k',\\n bbox=dict(facecolor=(1,1,1,0.5),edgecolor=(0,0,0,1)))\\n axes[0,1].set_xlabel(\\\"Measured\\\")\\n axes[0,1].set_ylabel(\\\"Predicted\\\")\\n axes[0,1].set_title(\\\"Optimized model - Cal.\\\")\\n axes[0,1].ticklabel_format(axis=\\\"both\\\", style = \\\"sci\\\", scilimits = (0,2)) \\n \\n sns.regplot(x=self.y_test, y=self.y_pred_val_op, color=\\\"k\\\",ax=axes[1, 1])\\n axes[1,1].plot([np.min([self.y_test, self.y_pred_val_op]) - 0.1*np.std(self.y), np.max([self.y_test, self.y_pred_val_op]) + 0.1*np.std(self.y)],\\n [np.min([self.y_test, self.y_pred_val_op]) - 0.1*np.std(self.y), np.max([self.y_test, self.y_pred_val_op]) + 0.1*np.std(self.y)],'r--') \\n axes[1,1].text(0.05,0.80,r\\\"RMSE={:.3f}\\\".format(self.rmse_val_op) + \\\"\\\\n\\\"+ \\n r\\\"RMSPE={:.3f}\\\".format(self.rmspe_val_op) + \\\"\\\\n\\\"+ \\n r\\\"$R^2$={:.3f}\\\".format(self.r2_val_op)+ \\\"\\\\n\\\"+\\n r\\\"$Adj. R^2$={:.3f}\\\".format(self.r2_adj_val_op),\\n transform=axes[1, 1].transAxes,\\n color='k',\\n bbox=dict(facecolor=(1,1,1,0.5),edgecolor=(0,0,0,1)))\\n axes[1,1].set_xlabel(\\\"Measured\\\")\\n axes[1,1].set_ylabel(\\\"Predicted\\\")\\n axes[1,1].set_title(\\\"Optimized model - Val.\\\")\\n axes[1,1].ticklabel_format(axis=\\\"both\\\", style = \\\"sci\\\", scilimits = (0,2)) \\n \\n #residuals plot\\n sns.residplot(self.y_pred_cal_op, self.y_train - self.y_pred_cal_op, lowess=False, color=\\\"k\\\",ax=axes[0, 2])\\n axes[0,2].set_xlabel(\\\"Predicted\\\")\\n axes[0,2].set_ylabel(\\\"Residuals\\\")\\n axes[0,2].set_title(\\\"Optimized model - Cal. Residuals\\\")\\n axes[0,2].ticklabel_format(axis=\\\"both\\\", style = \\\"sci\\\", scilimits = (0,2)) \\n \\n \\n sns.residplot(self.y_pred_val_op, self.y_test - self.y_pred_val_op, lowess=False, color=\\\"k\\\",ax=axes[1, 2])\\n axes[1,2].set_xlabel(\\\"Predicted\\\")\\n axes[1,2].set_ylabel(\\\"Residuals\\\")\\n axes[1,2].set_title(\\\"Optimized model - Val. Residuals\\\") \\n axes[1,2].ticklabel_format(axis=\\\"both\\\", style = \\\"sci\\\", scilimits = (0,2)) \\n \\n results = {\\\"r2_cal_gen\\\":[self.r2_cal_gen],'rmse_cal_gen':[self.rmse_cal_gen], 'rmspe_cal_gen':[self.rmspe_cal_gen], 'r2_adj_cal_gen':[self.r2_adj_cal_gen],\\n 'r2_val_gen':[self.r2_val_gen],'rmse_val_gen':[self.rmse_val_gen],'rmspe_val_gen':[self.rmspe_val_gen], 'r2_adj_val_gen':[self.r2_adj_val_gen],\\n \\\"r2_cal_op\\\":[self.r2_cal_op],'rmse_cal_op':[self.rmse_cal_op],'rmspe_cal_op':[self.rmspe_cal_op], 'r2_adj_cal_op':[self.r2_adj_cal_op],\\n 'r2_val_op':[self.r2_val_op],'rmse_val_op':[self.rmse_val_op],'rmspe_val_op':[self.rmspe_val_op], 'r2_adj_val_op':[self.r2_adj_val_op]} \\n \\n # test partitions not defined \\n else:\\n # calculating accuracies for generalized model\\n self.model.fit(self.x,self.y)\\n self.y_pred_cal_gen = np.squeeze(self.model.predict(self.x)).squeeze()\\n self.r2_cal_gen = r2_score(self.y,self.y_pred_cal_gen)\\n self.r2_adj_cal_gen = adj_r2_score(self.y,x=self.x, r2=self.r2_cal_gen)\\n self.rmse_cal_gen = mean_squared_error(self.y, self.y_pred_cal_gen, squared = False) \\n self.rmspe_cal_gen = rmspe_score(self.y, self.y_pred_cal_gen) \\n \\n # calculating accuracies for optimized model\\n self.model_best.fit(self.x[:,self.best_features],self.y)\\n self.y_pred_cal_op = np.squeeze(self.model_best.predict(self.x[:,self.best_features])).squeeze()\\n self.r2_cal_op = r2_score(self.y,self.y_pred_cal_op)\\n self.r2_adj_cal_op = adj_r2_score(self.y,x=self.x[:,self.best_features], r2=self.r2_cal_op)\\n self.rmse_cal_op = mean_squared_error(self.y, self.y_pred_cal_op, squared = False) \\n self.rmspe_cal_op = rmspe_score(self.y, self.y_pred_cal_op) \\n \\n # generalized model\\n fig,axes = plt.subplots(1,3,figsize=(15,5))\\n sns.regplot(x=self.y, y=self.y_pred_cal_gen, color=\\\"k\\\",ax=axes[0])\\n axes[0].plot([np.min([self.y, self.y_pred_cal_gen]) - 0.1*np.std(self.y), np.max([self.y, self.y_pred_cal_gen]) + 0.1*np.std(self.y)],\\n [np.min([self.y, self.y_pred_cal_gen]) - 0.1*np.std(self.y), np.max([self.y, self.y_pred_cal_gen]) + 0.1*np.std(self.y)],'r--')\\n axes[0].text(0.05,0.80,r\\\"RMSE={:.3f}\\\".format(self.rmse_cal_gen) + \\\"\\\\n\\\"+ \\n r\\\"RMSPE={:.3f}\\\".format(self.rmspe_cal_gen) + \\\"\\\\n\\\"+ \\n r\\\"$R^2$={:.3f}\\\".format(self.r2_cal_gen)+ \\\"\\\\n\\\"+\\n r\\\"$Adj. R^2$={:.3f}\\\".format(self.r2_adj_cal_gen),\\n transform=axes[0].transAxes,\\n color='k',\\n bbox=dict(facecolor=(1,1,1,0.5),edgecolor=(0,0,0,1)))\\n axes[0].set_xlabel(\\\"Measured\\\")\\n axes[0].set_ylabel(\\\"Predicted\\\")\\n axes[0].set_title(\\\"General model - Cal.\\\")\\n axes[0].ticklabel_format(axis=\\\"both\\\", style = \\\"sci\\\", scilimits = (0,2)) \\n \\n \\n # optimized model\\n sns.regplot(x=self.y, y=self.y_pred_cal_op, color=\\\"k\\\",ax=axes[1])\\n axes[1].text(0.05,0.80,r\\\"RMSE={:.3f}\\\".format(self.rmse_cal_op) + \\\"\\\\n\\\"+ \\n r\\\"RMSPE={:.3f}\\\".format(self.rmspe_cal_op) + \\\"\\\\n\\\"+ \\n r\\\"$R^2$={:.3f}\\\".format(self.r2_cal_op)+ \\\"\\\\n\\\"+\\n r\\\"$Adj. R^2$={:.3f}\\\".format(self.r2_adj_cal_op),\\n transform=axes[1].transAxes,\\n color='k',\\n bbox=dict(facecolor=(1,1,1,0.5),edgecolor=(0,0,0,1)))\\n axes[1].plot([np.min([self.y, self.y_pred_cal_op]) - 0.1*np.std(self.y), np.max([self.y, self.y_pred_cal_op]) + 0.1*np.std(self.y)],\\n [np.min([self.y, self.y_pred_cal_op]) - 0.1*np.std(self.y), np.max([self.y, self.y_pred_cal_op]) + 0.1*np.std(self.y)],'r--')\\n axes[1].set_xlabel(\\\"Measured\\\")\\n axes[1].set_ylabel(\\\"Predicted\\\")\\n axes[1].set_title(\\\"Optimized model - Cal.\\\")\\n axes[1].ticklabel_format(axis=\\\"both\\\", style = \\\"sci\\\", scilimits = (0,2)) \\n plt.show(block=False)\\n \\n sns.residplot(self.y_pred_cal_op, self.y - self.y_pred_cal_op, lowess=False, color=\\\"r\\\",ax=axes[2])\\n axes[2].set_xlabel(\\\"Predicted\\\")\\n axes[2].set_ylabel(\\\"Residuals\\\")\\n axes[2].set_title(\\\"Optimized model - Cal. Residuals\\\") \\n axes[2].ticklabel_format(axis=\\\"both\\\", style = \\\"sci\\\", scilimits = (0,2)) \\n\\n results = {\\\"r2_cal_gen\\\":[self.r2_cal_gen],'rmse_cal_gen':[self.rmse_cal_gen],'rmspe_cal_gen':[self.rmspe_cal_gen],\\\"r2_adj_cal_gen\\\":[self.r2_adj_cal_gen],\\n \\\"r2_cal_op\\\":[self.r2_cal_op],'rmse_cal_op':[self.rmse_cal_op],'rmspe_cal_op':[self.rmspe_cal_op], \\\"r2_adj_cal_op\\\":[self.r2_adj_cal_op]} \\n \\n # if the task is classification\\n else:\\n if hasattr(self, 'x_train') and hasattr(self, 'x_test') and hasattr(self, 'y_train') and hasattr(self, 'y_test'): \\n if len(self.class_sep) == 2:\\n fig,axes = plt.subplots(2,3,figsize=(15,10))\\n else:\\n fig,axes = plt.subplots(2,2,figsize=(10 + len(self.class_sep),10 + len(self.class_sep)))\\n # general model\\n self.model.fit(self.x_train,self.y_train)\\n y_train_pred = self.model.predict(self.x_train)\\n self.acc_train_gen = self.model.score(self.x_train,self.y_train)\\n cm = confusion_matrix(self.y_train,y_train_pred.squeeze())\\n cm = np.stack(list(map(lambda x: x/x.sum(),cm)))\\n conf_matrix=pd.DataFrame(cm,columns = self.class_sep,index= self.class_sep)\\n sns.heatmap(conf_matrix,cmap=\\\"coolwarm\\\", annot=True, ax=axes[0,0],vmin=0,vmax=1)\\n axes[0,0].set_title('General model - Cal. confusion matrix')\\n axes[0,0].set_ylabel('True label')\\n axes[0,0].set_xlabel('Predicted label')\\n\\n self.model.fit(self.x_train,self.y_train)\\n y_test_pred = self.model.predict(self.x_test)\\n self.acc_test_gen = self.model.score(self.x_test,self.y_test)\\n cm = confusion_matrix(self.y_test,y_test_pred.squeeze())\\n cm = np.stack(list(map(lambda x: x/x.sum(),cm)))\\n conf_matrix=pd.DataFrame(cm,columns = self.class_sep,index= self.class_sep)\\n sns.heatmap(conf_matrix,cmap=\\\"coolwarm\\\", annot=True,ax = axes[1,0],vmin=0,vmax=1)\\n axes[1,0].set_title('General model - Val. confusion matrix') \\n axes[1,0].set_ylabel('True label')\\n axes[1,0].set_xlabel('Predicted label')\\n \\n # optimized model\\n self.model_best.fit(self.x_train[:,self.best_features],self.y_train)\\n y_train_pred = self.model_best.predict(self.x_train[:,self.best_features])\\n self.acc_train_op = self.model_best.score(self.x_train[:,self.best_features],self.y_train)\\n cm = confusion_matrix(self.y_train,y_train_pred.squeeze())\\n cm = np.stack(list(map(lambda x: x/x.sum(),cm)))\\n conf_matrix=pd.DataFrame(cm,columns = self.class_sep,index= self.class_sep)\\n sns.heatmap(conf_matrix,cmap=\\\"coolwarm\\\", annot=True, ax=axes[0,1],vmin=0,vmax=1)\\n axes[0,1].set_title('Optimized model - Cal. confusion matrix') \\n axes[0,1].set_ylabel('True label')\\n axes[0,1].set_xlabel('Predicted label') \\n \\n self.model_best.fit(self.x_train[:,self.best_features],self.y_train)\\n y_test_pred = self.model_best.predict(self.x_test[:,self.best_features])\\n self.acc_test_op = self.model_best.score(self.x_test[:,self.best_features],self.y_test)\\n cm = confusion_matrix(self.y_test,y_test_pred.squeeze())\\n cm = np.stack(list(map(lambda x: x/x.sum(),cm)))\\n conf_matrix=pd.DataFrame(cm,columns = self.class_sep,index= self.class_sep)\\n sns.heatmap(conf_matrix,cmap=\\\"coolwarm\\\", annot=True,ax = axes[1,1],vmin=0,vmax=1)\\n axes[1,1].set_title('Optimized model - Val. confusion matrix') \\n axes[1,1].set_ylabel('True label')\\n axes[1,1].set_xlabel('Predicted label')\\n \\n if len(self.class_sep) == 2:\\n #optimized model Roc curves\\n self.model_best.fit(self.x_train[:,self.best_features],self.y_train)\\n y_train_pred_prob = self.model_best.predict_proba(self.x_train[:,self.best_features])\\n fpr, tpr, thr = roc_curve(self.y_train, y_train_pred_prob[:, 1].squeeze()) \\n self.auc_train_op = auc(fpr, tpr)\\n axes[0,2].plot([0,1],[0,1],linestyle = '--',lw = 2,color = 'black')\\n axes[0,2].plot(fpr, tpr, color='blue',\\n label=r'$ROC_{train}$ (AUC = %0.2f)' % (auc(fpr, tpr)),lw=2, alpha=1)\\n axes[0,2].set_xlabel('False Positive Rate')\\n axes[0,2].set_ylabel('True Positive Rate')\\n axes[0,2].set_title('ROC Curve')\\n axes[0,2].legend(loc=\\\"lower right\\\")\\n \\n self.model_best.fit(self.x_train[:,self.best_features],self.y_train)\\n y_test_pred_prob = self.model_best.predict_proba(self.x_test[:,self.best_features])\\n fpr, tpr, thr = roc_curve(self.y_test, y_test_pred_prob[:, 1].squeeze()) \\n self.auc_test_op = auc(fpr, tpr)\\n axes[1,2].plot([0,1],[0,1],linestyle = '--',lw = 2,color = 'black')\\n axes[1,2].plot(fpr, tpr, color='blue',\\n label=r'$ROC_{test}$ (AUC = %0.2f)' % (auc(fpr, tpr)),lw=2, alpha=1)\\n axes[1,2].set_xlabel('False Positive Rate')\\n axes[1,2].set_ylabel('True Positive Rate')\\n axes[1,2].set_title('ROC Curve')\\n axes[1,2].legend(loc=\\\"lower right\\\")\\n \\n results = {\\\"acc_train_gen\\\":[self.acc_train_gen],'acc_test_gen':[self.acc_test_gen],\\n 'acc_train_op':[self.acc_train_op],'acc_test_op':[self.acc_test_op],\\n \\\"auc_train_op\\\":[self.auc_train_op],'auc_test_op':[self.auc_test_op]}\\n \\n results = {\\\"acc_train_gen\\\":[self.acc_train_gen],'acc_test_gen':[self.acc_test_gen],\\n 'acc_train_op':[self.acc_train_op],'acc_test_op':[self.acc_test_op]}\\n \\n # if train and test not defined\\n else:\\n warnings.warn(\\\"Neither cv_model nor test partition (x_test/y_test) are defined, predicting on x/y.\\\")\\n if len(self.class_sep) == 2:\\n fig,axes = plt.subplots(1,3,figsize=(15,5))\\n else:\\n fig,axes = plt.subplots(1,2,figsize=(10 + len(self.class_sep),5 + len(self.class_sep))) \\n # general model\\n self.model.fit(self.x,self.y)\\n y_train_pred = self.model.predict(self.x)\\n self.acc_train_gen = self.model.score(self.x,self.y)\\n cm = confusion_matrix(self.y,y_train_pred.squeeze())\\n cm = np.stack(list(map(lambda x: x/x.sum(),cm)))\\n conf_matrix=pd.DataFrame(cm,columns = self.class_sep,index= self.class_sep)\\n sns.heatmap(conf_matrix,cmap=\\\"coolwarm\\\", annot=True, ax=axes[0],vmin=0,vmax=1)\\n axes[0].set_title('General model - Cal. confusion matrix')\\n axes[0].set_ylabel('True label')\\n axes[0].set_xlabel('Predicted label')\\n \\n # optimized model\\n self.model_best.fit(self.x[:,self.best_features],self.y)\\n y_train_pred = self.model_best.predict(self.x[:,self.best_features])\\n self.acc_train_op = self.model_best.score(self.x[:,self.best_features],self.y)\\n cm = confusion_matrix(self.y,y_train_pred.squeeze())\\n cm = np.stack(list(map(lambda x: x/x.sum(),cm)))\\n conf_matrix=pd.DataFrame(cm,columns = self.class_sep,index= self.class_sep)\\n sns.heatmap(conf_matrix,cmap=\\\"coolwarm\\\", annot=True, ax=axes[1],vmin=0,vmax=1)\\n axes[1].set_title('Optimized model - Cal. confusion matrix') \\n axes[1].set_ylabel('True label')\\n axes[1].set_xlabel('Predicted label') \\n \\n \\n if len(self.class_sep) == 2:\\n #optimized model Roc curves\\n self.model_best.fit(self.x[:,self.best_features],self.y)\\n y_train_pred_prob = self.model_best.predict_proba(self.x[:,self.best_features])\\n fpr, tpr, thr = roc_curve(self.y, y_train_pred_prob[:, 1].squeeze()) \\n self.auc_train_op = auc(fpr, tpr)\\n axes[2].plot([0,1],[0,1],linestyle = '--',lw = 2,color = 'black')\\n axes[2].plot(fpr, tpr, color='blue',\\n label=r'$ROC_{train}$ (AUC = %0.2f)' % (auc(fpr, tpr)),lw=2, alpha=1)\\n axes[2].set_xlabel('False Positive Rate')\\n axes[2].set_ylabel('True Positive Rate')\\n axes[2].set_title('ROC Curve')\\n axes[2].legend(loc=\\\"lower right\\\")\\n \\n \\n results = {\\\"acc_train_gen\\\":[self.acc_train_gen],\\n 'acc_train_op':[self.acc_train_op],\\n \\\"auc_train_op\\\":[self.auc_train_op]}\\n \\n results = {\\\"acc_train_gen\\\":[self.acc_train_gen],\\n 'acc_train_op':[self.acc_train_op]}\\n \\n plt.show(block=False) \\n return results, fig\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66dc9873ef8a207c1b06acb2dbcc70b8\",\n \"score\": \"0.5680571\",\n \"text\": \"def predict(self, features):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8b532b5ed1811e46e5fe12407f4c93b\",\n \"score\": \"0.5679583\",\n \"text\": \"def fit_predict(self, X, y=None, **kwargs):\\n # we do not route parameters here, since consumers don't route. But\\n # since it's possible for a `predict` method to also consume\\n # metadata, we check if that's the case, and we raise a warning telling\\n # users that they should implement a custom `fit_predict` method\\n # to forward metadata to `predict` as well.\\n #\\n # For that, we calculate routing and check if anything would be routed\\n # to `predict` if we were to route them.\\n if _routing_enabled():\\n transform_params = self.get_metadata_routing().consumes(\\n method=\\\"predict\\\", params=kwargs.keys()\\n )\\n if transform_params:\\n warnings.warn(\\n (\\n f\\\"This object ({self.__class__.__name__}) has a `predict` \\\"\\n \\\"method which consumes metadata, but `fit_predict` does not \\\"\\n \\\"forward metadata to `predict`. Please implement a custom \\\"\\n \\\"`fit_predict` method to forward metadata to `predict` as well.\\\"\\n \\\"Alternatively, you can explicitly do `set_predict_request`\\\"\\n \\\"and set all values to `False` to disable metadata routed to \\\"\\n \\\"`predict`, if that's an option.\\\"\\n ),\\n UserWarning,\\n )\\n\\n # override for transductive outlier detectors like LocalOulierFactor\\n return self.fit(X, **kwargs).predict(X)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0826d3305203badafb8621785541b35\",\n \"score\": \"0.5678741\",\n \"text\": \"def predict(\\r\\n self, \\r\\n image, \\r\\n top_idxes=[0],\\r\\n class_names=None\\r\\n ):\\r\\n image1 = tf.expand_dims(image, axis=0)\\r\\n \\r\\n # the ensemble model outputs, len(logits) = top\\r\\n logits = self.model(image1)\\r\\n logits = [logits[i] for i in top_idxes]\\r\\n \\r\\n # predicted class IDs, [top, ]\\r\\n class_ids = []\\r\\n for logit in logits:\\r\\n class_ids.append(np.argmax(logit, axis=1))\\r\\n class_ids = np.array(class_ids)\\r\\n unique_class_ids, counts = np.unique(class_ids, return_counts=True)\\r\\n idx = np.argmax(counts)\\r\\n ensemble_class_id = unique_class_ids[idx]\\r\\n \\r\\n def vis(image, class_names, probs):\\r\\n \\\"\\\"\\\"\\r\\n Displays the image with the predicted probabilities.\\r\\n\\r\\n Parameters\\r\\n ----------\\r\\n image, class_names : same as above.\\r\\n probs : numpy array, [top, num_classes]\\r\\n The predicted probabilities over ensembled class ids.\\r\\n\\r\\n Returns\\r\\n -------\\r\\n None.\\r\\n\\r\\n \\\"\\\"\\\"\\r\\n plt.imshow(tf.cast(image, tf.int32))\\r\\n for i in range(len(class_names)):\\r\\n if i == np.argmax(probs):\\r\\n plt.text(\\r\\n image.shape[1] + 10, \\r\\n 10 + i*20,\\r\\n \\\"%s: %.2f%%\\\" % (class_names[i], probs[i]*100),\\r\\n bbox={'facecolor':'gray', 'alpha':0.3, 'pad':5})\\r\\n else:\\r\\n plt.text(\\r\\n image.shape[1] + 10, \\r\\n 10 + i*20,\\r\\n \\\"%s: %.2f%%\\\" % (class_names[i], probs[i]*100))\\r\\n plt.axis('off')\\r\\n \\r\\n if class_names is not None:\\r\\n # probs, [top, num_classes]\\r\\n probs = tf.squeeze(tf.nn.softmax(logits))\\r\\n # probs corresponding to ensemble_class_id\\r\\n idxes = tf.where(tf.argmax(probs, axis=1) == ensemble_class_id)[:,0]\\r\\n probs = tf.gather(probs, idxes, axis=0)\\r\\n # mean probs over ensembled class ids, [num_classes, ]\\r\\n mean_probs = tf.reduce_mean(probs, axis=0).numpy()\\r\\n vis(image, class_names, mean_probs)\\r\\n plt.title(class_names[ensemble_class_id])\\r\\n plt.show()\\r\\n \\r\\n return ensemble_class_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7215b58dcfc54c3b899ed45c8a141789\",\n \"score\": \"0.5678406\",\n \"text\": \"def predict(self):\\n self.kf.predict()\\n\\n if self.kf.x[3] >= np.pi: self.kf.x[3] -= np.pi * 2\\n if self.kf.x[3] < -np.pi: self.kf.x[3] += np.pi * 2\\n\\n self.age += 1\\n\\n self.history.append(self.kf.x)\\n\\n #KalmanBoxTracker predict\\n # Multiply by p_s\\n self.w *= self.ps\\n\\n return self.history[-1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a8e263d3c7637350e745862c2ae1c09\",\n \"score\": \"0.56733644\",\n \"text\": \"def __init__(self):\\n super().__init__()\\n self.printTag = 'KerasRegression'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"483433132212a5d9e3ed0686784aba07\",\n \"score\": \"0.56711423\",\n \"text\": \"def __init__(self):\\n super().__init__()\\n self.multioutputWrapper = True\\n import sklearn\\n import sklearn.ensemble\\n self.model = sklearn.ensemble.AdaBoostRegressor\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44f85f3f7034268e3da0bcee6055440a\",\n \"score\": \"0.5667656\",\n \"text\": \"def model_fn(features, labels, mode, params):\\n model = create_model(params['data_format'])\\n image = features\\n if isinstance(image, dict):\\n image = features['image']\\n\\n if mode == tf.estimator.ModeKeys.PREDICT:\\n logits = model(image, training=False)\\n predictions = {\\n 'classes': tf.argmax(logits, axis=1),\\n 'probabilities': tf.nn.softmax(logits),\\n }\\n return tf.estimator.EstimatorSpec(\\n mode=tf.estimator.ModeKeys.PREDICT,\\n predictions=predictions,\\n export_outputs={\\n 'classify': tf.estimator.export.PredictOutput(predictions)\\n })\\n if mode == tf.estimator.ModeKeys.TRAIN:\\n optimizer = tf.train.AdamOptimizer(learning_rate=params['learning_rate'])\\n #optimizer = tf.train.SyncReplicasOptimizer(optimizer, replicas_to_aggregate=2)\\n\\n # If we are running multi-GPU, we need to wrap the optimizer.\\n optimizer = tf.contrib.estimator.TowerOptimizer(optimizer)\\n\\n logits = model(image, training=True)\\n loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits)\\n accuracy = tf.metrics.accuracy(\\n labels=labels, predictions=tf.argmax(logits, axis=1))\\n\\n # Name tensors to be logged with LoggingTensorHook.\\n tf.identity(loss, 'cross_entropy')\\n tf.identity(accuracy[1], name='train_accuracy')\\n\\n # Save accuracy scalar to Tensorboard output.\\n tf.summary.scalar('train_accuracy', accuracy[1])\\n update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)\\n with tf.control_dependencies(update_ops):\\n train_op = optimizer.minimize(\\n loss=loss, global_step=tf.train.get_global_step())\\n\\n return tf.estimator.EstimatorSpec(\\n mode=tf.estimator.ModeKeys.TRAIN,\\n loss=loss,\\n train_op=train_op)\\n\\n if mode == tf.estimator.ModeKeys.EVAL:\\n logits = model(image, training=False)\\n loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits)\\n return tf.estimator.EstimatorSpec(\\n mode=tf.estimator.ModeKeys.EVAL,\\n loss=loss,\\n eval_metric_ops={\\n 'accuracy':\\n tf.metrics.accuracy(\\n labels=labels, predictions=tf.argmax(logits, axis=1)),\\n })\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f2079e1eeb7e4e8628bf88b3ee629f31\",\n \"score\": \"0.56617534\",\n \"text\": \"def test_model(model,\\n model_name,\\n predictors: np.ndarray,\\n y_true: np.ndarray,\\n threshold: float,\\n plot: bool = True,\\n verbose: bool = True):\\n y_pred = model.predict(predictors)\\n metrics = calculate_metrics(y_true, y_pred, threshold)\\n\\n if plot:\\n plot_model_performance(y_true, y_pred, metrics, model_name)\\n\\n if verbose:\\n print_metrics({model_name: metrics})\\n\\n return model, y_pred, metrics\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4c68d6e67534b14a430283a0d941f7e\",\n \"score\": \"0.5650589\",\n \"text\": \"def predict(self, instances):\\n\\n if self.verbose: logging.info(\\\"Obtaining final predictions from {} models.\\\".format(\\n len(self.ensemble_of_learners)))\\n\\n if not self.ensemble_of_learners:\\n if self.verbose: logging.info(\\\"Please, evolve the model first!\\\")\\n return None\\n\\n else:\\n\\n instances = self.return_dataframe_from_text(instances)\\n transformed_instances = self.vectorizer.transform(instances)\\n prediction_space = []\\n\\n # transformed_instances = self.update_intermediary_feature_space(custom_space = transformed_instances)\\n if self.verbose: logging.info(\\\"Representation obtained ..\\\")\\n for learner_tuple in self.ensemble_of_learners:\\n\\n try:\\n\\n ## get the solution.\\n learner, individual, score = learner_tuple\\n learner = learner.best_estimator_\\n\\n ## Subset the matrix.\\n subsetted_space = self.apply_weights(\\n individual,\\n custom_feature_space=True,\\n custom_feature_matrix=transformed_instances)\\n\\n ## obtain the predictions.\\n if not prediction_space is None:\\n prediction_space.append(\\n learner.predict(subsetted_space).tolist())\\n\\n else:\\n prediction_space.append(\\n learner.predict(subsetted_space).tolist())\\n\\n except Exception as es:\\n print(\\n es,\\n \\\"Please, re-check the data you are predicting from!\\\")\\n\\n ## generate the prediction matrix by maximum voting scheme.\\n pspace = np.matrix(prediction_space).T\\n all_predictions = pd.DataFrame(pspace).mode(\\n axis=1).values.reshape(-1)\\n if self.verbose: logging.info(\\\"Predictions obtained\\\")\\n all_predictions = self.label_encoder.inverse_transform(\\n all_predictions)\\n return all_predictions\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ec6f54818d4b362c2befd1f6e5dfdd7\",\n \"score\": \"0.5650204\",\n \"text\": \"def classifier(X_Train, Y_Train, X_Test):\\n\\n \\\"\\\"\\\"\\n Create a Logistic Regression instance and fit it to the kernelized data.\\n ==========================================================================\\n \\\"\\\"\\\"\\n\\n regression = ElasticNet(l1_ratio=0.05, max_iter=10000)\\n regression.fit(X_Train, Y_Train)\\n\\n \\\"\\\"\\\"\\n ==========================================================================\\n \\\"\\\"\\\"\\n\\n Y_Predict = regression.predict(X_Test)\\n \\n for y in Y_Predict:\\n label = 0\\n if y > 0.5:\\n label += 1\\n print label\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c3aa60b2c61cd62b65a4e32429589d9\",\n \"score\": \"0.56437147\",\n \"text\": \"def tune_hyperparam(self, estimator, random_state, verbose):\\n\\n model_type = _get_model_type(estimator)\\n\\n if model_type == \\\"DecisionTreeClassifier\\\":\\n param_distributions = {\\\"max_features\\\": [\\\"auto\\\", \\\"sqrt\\\", \\\"log2\\\", None], \\\"max_depth\\\": range(1, 15)}\\n if model_type == \\\"SVC\\\":\\n param_distributions = [{'kernel': ['rbf'], 'gamma': [1e-3, 1e-4],\\n 'C': [1, 10, 100, 1000]},\\n {'kernel': ['linear'], 'C': [1, 10, 100, 1000]}]\\n if model_type == \\\"RandomForestClassifier\\\":\\n param_distributions = {\\\"n_estimators\\\": [1, 10, 25,50, 100, 200, 500],\\n \\\"max_features\\\": [\\\"auto\\\", \\\"sqrt\\\", \\\"log2\\\", None],\\n \\\"max_depth\\\": range(1, 15)}\\n\\n clf = RandomizedSearchCV(estimator=estimator, param_distributions=param_distributions,\\n random_state=random_state, n_iter=5, n_jobs=-1, cv=5, scoring=SCORING,\\n verbose=verbose, refit='f1_macro')\\n\\n clf.fit(self.X_val, self.y_val)\\n\\n if verbose > 0:\\n print(f\\\"Best {model_type} estimator:\\\")\\n print(f\\\"accuracy: {clf.cv_results_['mean_test_accuracy'].mean()}\\\")\\n print(f\\\"precision: {clf.cv_results_['mean_test_precision'].mean()}\\\")\\n print(f\\\"recall: {clf.cv_results_['mean_test_recall'].mean()}\\\")\\n print(f\\\"F1 Score: {clf.best_score_}\\\\n\\\")\\n\\n return clf.best_estimator_\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf537d6632dc29876dad1260e7e8d7f9\",\n \"score\": \"0.5638667\",\n \"text\": \"def run_demo():\\n clf = InOutClassifier(verbose=True)\\n indoor_a = [130, 9, -1, -1]\\n indoor_b = [1400, 9, 2.0, -1]\\n outdoor_a = [120, 4, 2.0, 20]\\n outdoor_b = [40, 4, 2.0, 1.5]\\n prediction = clf.predict([indoor_a, indoor_b, outdoor_a, outdoor_b])\\n print prediction\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2fc2025814e3709c9d8994eb05766669\",\n \"score\": \"0.56380373\",\n \"text\": \"def predicting(model, x_train, y_train, x_test, y_test, smp_idx_train, smp_idx_test, unlabelled_data, impl_type, **fit_params):\\n # set y_pred_prob to None in case the probabilities can't be predicted\\n y_pred_prob = None\\n\\n # fitting and prediction based on the model implementation type\\n if impl_type == \\\"scikit\\\":\\n # fitting the model\\n start_time = time.time()\\n model.fit(x_train, y_train)\\n fit_time = time.time() - start_time\\n # predicting\\n start_time = time.time()\\n y_pred = model.predict(x_test)\\n score_time = time.time() - start_time\\n # predict proba - special case SVM: change model, fit newly and predict\\n if isinstance(model, SVC):\\n # change model\\n model.probability = True\\n model.fit(x_train, y_train)\\n # predicting probability\\n if hasattr(model, \\\"predict_proba\\\"): y_pred_prob = model.predict_proba(x_test)\\n\\n elif impl_type == \\\"keras\\\":\\n # fitting the model\\n start_time = time.time()\\n fit_ann_model(model, x_train, y_train, smp_idx_train, **fit_params)\\n fit_time = time.time() - start_time\\n # predicting + predicting probability\\n start_time = time.time()\\n y_pred, y_pred_prob = predict_ann_model(model, x_test, y_test, smp_idx_test, predict_proba=True, **fit_params)\\n score_time = time.time() - start_time\\n\\n elif impl_type == \\\"baseline\\\":\\n # fitting the model\\n start_time = time.time()\\n majority_vote = fit_baseline(y_train)\\n fit_time = time.time() - start_time\\n # predicting + predicting probability\\n start_time = time.time()\\n y_pred = predict_baseline(majority_vote, x_test)\\n score_time = time.time() - start_time\\n\\n elif impl_type == \\\"semi_manual\\\":\\n # fitting the model\\n start_time = time.time()\\n # concat unlabelled and labelled data and fit it\\n model = model.fit(pd.concat([unlabelled_data, x_train]))\\n fit_time = time.time() - start_time\\n\\n # determine the number of clusters/components\\n if hasattr(model, \\\"cluster_centers_\\\"):\\n num_components = model.cluster_centers_.shape[0]\\n elif hasattr(model, \\\"weights_\\\"):\\n num_components = model.weights_.shape[0]\\n # prediction\\n start_time = time.time()\\n test_clusters = model.predict(x_test)\\n y_pred = assign_clusters(y_test, test_clusters, num_components)\\n score_time = time.time() - start_time\\n\\n else:\\n raise ValueError(\\\"\\\"\\\"This Model implementation types does not exist.\\n Choose one of the following: \\\\\\\"scikit\\\\\\\" (for rf, svm, knn, easy_ensemble, self_trainer),\\n \\\\\\\"semi_manual\\\\\\\" (for kmean, gmm, bmm), \\\\\\\"keras\\\\\\\" (for lstm, blsm, enc_dec),\\n or \\\\\\\"baseline\\\\\\\" (for the majority vote baseline)\\\"\\\"\\\")\\n\\n return y_pred, y_pred_prob, fit_time, score_time\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4097451071cb691bbbce23c30e41d76\",\n \"score\": \"0.56368095\",\n \"text\": \"def test(self):\\r\\n # self.clf = AdaBoostClassifier(base_estimator=DecisionTreeClassifier(), n_estimators=100, learning_rate=1.5, algorithm='SAMME.R')\\r\\n self.clf = AdaBoostClassifier()\\r\\n self.clf.set_params(**self.best_parameter)\\r\\n print(\\\"*** Test Result for AdaBoost ***\\\")\\r\\n ModelEvaluation.evaluateModelWithCV(self.clf, self.dataset_x, self.dataset_y, cv=10)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd85d38012931561e78fe6b730e361ad\",\n \"score\": \"0.5633781\",\n \"text\": \"def predict(self, x):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83067c1af22ecaa91632ff21b24d0130\",\n \"score\": \"0.5632875\",\n \"text\": \"def predict(self, dataset):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d9c7908c4c729833a789ef8d83a7f31e\",\n \"score\": \"0.56293464\",\n \"text\": \"def postprocessing(self, model, input):\\n pred_num = np.argmax(input)\\n pred_score = np.max(input)\\n return json.dumps({\\\"pred_num\\\": int(pred_num), \\\"pred_score\\\": float(pred_score)})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa01c7483eeda73f7b5e3ef415fdf65e\",\n \"score\": \"0.5629328\",\n \"text\": \"def on_predict_begin(self, logs=None):\\n super().on_predict_begin(logs)\\n self._set_logging_mode(LoggingMode.PREDICT)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38291060a639a2424d3b57024da1dc1d\",\n \"score\": \"0.5629008\",\n \"text\": \"def predict(self, x, y):\\n\\t\\tpass\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1188,"cells":{"query_id":{"kind":"string","value":"cf7821d1283da30486f382a5c57d3344"},"query":{"kind":"string","value":"For synching saved procedures with mobile clients. Request Params username A valid username. password A valid password."},"positive_passages":{"kind":"list like","value":[{"docid":"48da06603589bead9d42a2649bc268cc","score":"0.0","text":"def saved_procedure_list(request):\n logging.info(\"entering patient list proc\")\n username = request.REQUEST.get(\"username\", None)\n password = request.REQUEST.get(\"password\", None)\n url = settings.OPENMRS_SERVER_URL\n try:\n saved_procedures = SavedProcedure.objects.get(encounter=id)\n data = parseSavedProcedureAll(saved_procedures)\n logging.info(\"we finished getting the patient list\")\n response = json_succeed(data)\n except Exception, e:\n logging.error(\"Got exception while fetching patient list: %s\" % e)\n response = json_fail(\"Problem while getting patient list: %s\" % e)\n return render_json_response(response)","title":""}],"string":"[\n {\n \"docid\": \"48da06603589bead9d42a2649bc268cc\",\n \"score\": \"0.0\",\n \"text\": \"def saved_procedure_list(request):\\n logging.info(\\\"entering patient list proc\\\")\\n username = request.REQUEST.get(\\\"username\\\", None)\\n password = request.REQUEST.get(\\\"password\\\", None)\\n url = settings.OPENMRS_SERVER_URL\\n try:\\n saved_procedures = SavedProcedure.objects.get(encounter=id)\\n data = parseSavedProcedureAll(saved_procedures)\\n logging.info(\\\"we finished getting the patient list\\\")\\n response = json_succeed(data)\\n except Exception, e:\\n logging.error(\\\"Got exception while fetching patient list: %s\\\" % e)\\n response = json_fail(\\\"Problem while getting patient list: %s\\\" % e)\\n return render_json_response(response)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"e58d47573d0131e4705220e41ccb057e","score":"0.58704257","text":"def set_params(usr, password):\n\n\tglobal user\n\tglobal pwd\n\tuser = usr\n\tpwd = password","title":""},{"docid":"3239ac6fbe8975fc0fdbabef8fa9de8c","score":"0.5728726","text":"def store_auth_info(self, username, password):","title":""},{"docid":"7a1e6ac577a90ce2e680d7c0e2c1ddfb","score":"0.5668911","text":"def LoginToServer():\n print(\"To start data streaming, please login:\")\n print(\"username:\")\n username = input()\n password = getpass.getpass('password:')\n template = JsonTemplate(\"Login\")\n return template(username, password, device_id, device_type, device_model, system_version, app_version)","title":""},{"docid":"ca094914a226460e9c1671f52cd02ae3","score":"0.55512995","text":"def auth(u, p):\n\tglobal user, password\n\tuser = u\n\tpassword = p","title":""},{"docid":"f56624ce0c14b1250762941adf49d694","score":"0.5452777","text":"def post(self):\n user_mobilephone = self.get_argument(\"mobilephone\")\n user_name = self.get_argument(\"username\")\n user_password = self.get_argument(\"password\")\n user_org = self.get_argument(\"org\")\n user_starttime = self.get_argument(\"starttime\")\n user_endtime = self.get_argument(\"endtime\")\n \n result = selectDataWhere(\"users\", \"id\", mobilephone=user_mobilephone)\n if result:\n self.write(\"-1\")\n else:\n orgname_id = selectDataWhere(\"orgs\", \"id\", name=user_org)[0][0]\n try:\n insertData(\"users\", username=user_mobilephone, mobilephone=user_mobilephone, name=user_name, orgnameID=orgname_id, password=user_password, starttime=user_starttime, endtime=user_endtime, status=1)\n self.write(\"1\")\n except:\n self.write(\"0\")","title":""},{"docid":"dee9b8cb44e652ca6bba5241ec08c0f5","score":"0.54525715","text":"def server_auth(realm, username):","title":""},{"docid":"2d82c6a7b01e68c3e165a9abdff25fd7","score":"0.5442518","text":"def login(self, username, password):\n pass","title":""},{"docid":"b56d17dbcb933043ce13a79897b26967","score":"0.54303306","text":"def Login(self, username, password, onSuccess, onFailure):\n pass","title":""},{"docid":"fb3933852065263c55bac2b76d14b0fb","score":"0.5415997","text":"def update(self, userguid, name=\"\", password=None, jobguid=\"\", executionparams=dict()):","title":""},{"docid":"bc0c7e3660c9c3d6e05b809b06f0c1bc","score":"0.5377909","text":"def sendAuth(username, password):\n print('Sending auth data to backend...')\n if username == \"ump_hacker\" and password == \"aH-KeE-aY-Me_h0seA\":\n return 'True'\n return 'False'","title":""},{"docid":"9a9c5fe14f17b3ec8822a26252bd2211","score":"0.53764623","text":"def client_auth_rpc(username, password):\n auth_server = jsonrpclib.Server(rhconf.AUTH_SERVER_URL)\n res = auth_server.rh_auth(str(username), str(password))\n return res","title":""},{"docid":"39737627c56b7e373aaa40e3de14de10","score":"0.53605914","text":"def __call__(self, username, dataDir = None):\n try:\n name = \"pgsql-8.3\"\n cmdbtypename = 'postgresql8server'\n configFileDir = dataDir and dataDir or q.system.fs.joinPaths(os.sep, 'etc', 'postgresql', '8.4', 'main')\n binDir = q.system.fs.joinPaths(os.sep, 'usr', 'lib', 'postgresql', '8.4', 'bin')\n exitCode = 1\n output = ''\n\n q.logger.log(\"Applying %s credentials for [%s]\"%(username, name))\n\n if not q.system.fs.isDir(configFileDir):\n\n q.logger.log(\"Creating configuration directory [%s]\"%configFileDir, 1)\n q.system.fs.createDir(configFileDir)\n\n if q.platform.isUnix():\n q.logger.log(\"Setting owner for [%s] to [%s]\"%(configFileDir, username), 5)\n q.system.unix.chown(configFileDir, username, None)\n q.logger.log(\"Setting owner for [%s] to [%s]\"%(q.dirs.baseDir, username), 5)\n q.system.unix.chown(binDir, username, None)\n\n binPath = q.system.fs.joinPaths(binDir,\"initdb\")\n initDBCommand = \"%s -E=%s --no-locale -D %s\"%(binPath, \"UTF-8\", configFileDir)\n\n if q.platform.isUnix():\n exitCode, output = q.system.unix.executeAsUser(\"%(initDBCommand)s\"%{\"initDBCommand\":initDBCommand},username, dieOnNonZeroExitCode = False)\n if exitCode:\n raise RuntimeError,('initdb failed with error %s'%output, exitCode)\n\n elif q.platform.isWindows():\n if not q.system.windows.isServiceInstalled(name):\n q.logger.log(\"Registering [%s] that located at [%s]as a windows service\"%(name, q.system.fs.joinPaths(binDir,\"pg_ctl.exe\")), 1)\n result = q.system.windows.createService(name, name, binDir+\"\\pg_ctl.exe\", 'runservice -W -N %(serviceName)s -D \\\"%(configFileDir)s\\\"'%{'serviceName':name, 'configFileDir':configFileDir})\n if result :\n q.system.windows.startService(name)\n else:\n q.logger.log(\"cannot regsiter service %s\"%name, 6)\n\n if q.system.fs.isEmptyDir(configFileDir):\n command = \"%(initDBCommand)s -U %(login)s\"%{\"login\":username, \"initDBCommand\":initDBCommand}\n exitCode, output = q.system.process.execute(command, dieOnNonZeroExitCode = False)\n if exitCode:\n raise RuntimeError,('initdb failed with error %s'%output, exitCode)\n return exitCode, output\n except:\n print q.errorconditionhandler.getCurrentExceptionString()\n raise","title":""},{"docid":"88549b2ac2400ec9083e997f1270713b","score":"0.53469175","text":"def authenticate(self, handler, data):\n username = data['username']\n password = data['password']\n if data.get('otp'):\n password = [password, data['otp']]\n data = {'username' : username, 'password' : password}\n return super().authenticate(handler, data)","title":""},{"docid":"6f9cfdeac5cab2a9af7e68aae3d53a11","score":"0.53066355","text":"def lock (self, synchronizedCall):\n self._islock = synchronizedCall \n \"\"\" execute the command on the smd server \"\"\"\n self._connection.marshall(Connector.SET_METHOD, self.METHOD_LOCK)\n # populate parameters\n self._connection.marshall(Connector.DEF_STRING, self.METHOD_PARAM_LOCK)\n if synchronizedCall:\n self._connection.marshall(Connector.SET_VALUE, self.METHOD_PARAM_LOCK, \"true\")\n else :\n self._connection.marshall(Connector.SET_VALUE, self.METHOD_PARAM_LOCK, \"false\")\n # proceed with call\n return self._connection.marshall(Connector.CALL)","title":""},{"docid":"18aacd3af67040d5839af31cf045e71c","score":"0.5306084","text":"def user_manager(isession, username, password):\r\n isession.user = UserManager.User\r\n print('Incoming Connection with Authentication')\r\n print('Stolen Credentials:')\r\n print('Username: ' + username)\r\n print('Password: ' + password)\r\n stolen_credentials = {}\r\n stolen_credentials['username'] = username\r\n stolen_credentials['password'] = password\r\n with open('stolen_credentials.txt', 'w') as f:\r\n f.write(str(stolen_credentials))\r\n f.close\r\n return True","title":""},{"docid":"facb9715ca1189fd31363b1361f3a682","score":"0.5304645","text":"def localsys_prompt_login(sid=\"\",pin=\"\",temporary=False):\n if sid == \"\":\n sid = eval(input(\"Enter your Minerva ID number: \"))\n if pin == \"\":\n pin = getpass.getpass(\"Enter your PIN number: \")\n if not temporary:\n local_credentials.set_password('minervaclient_sid','minerva',str(sid))\n local_credentials.set_password('minervalcient_pin','minerva',str(pin))\n return sid, pin","title":""},{"docid":"ac007628ef872117b5dd2704e703307e","score":"0.52901584","text":"def SRP_Auth_2 (req,data):\r\n\t\r\n\t#~ try:\r\n\t## Recuperar K \r\n\tdata = JSONDecoder(\"UTF-8\").decode(data)\r\n\tMc = data[\"M\"]\r\n\t\r\n\t## Recuperar las variables de la sesion\r\n\tcryptovars = retrieve_crypto_vars(req)\r\n\t\r\n\tB = cryptovars[\"B\"]\r\n\tv = cryptovars[\"v\"]\r\n\tb = cryptovars[\"b\"]\r\n\tA = cryptovars[\"A\"]\r\n\tuser = cryptovars[\"user\"].upper()\r\n\tsalt = cryptovars[\"salt\"].upper()\r\n\t\r\n\t\t\t\t\r\n\t\r\n\t## u = H(A, B)\r\n\tu = calc_u (A,B,N)\r\n\tu = hex2dec(hash(dec2hex(A)+dec2hex(B)))\r\n\t\r\n\t## S = (Av^u) ^ b \r\n\tS = powMod(A*powMod(v,u,N),b,N)\r\n\t\r\n\t## Clave de sesion K = H(S)\r\n\tK = hash(dec2hex(S).upper()).upper()\r\n\t\r\n\t\r\n\t## Comprobar que la prueba del cliente es igual a lo que tenemos nosotros\r\n\t## M = H(H(N) xor H(g), H(I), s, A, B, K)\r\n\tHN = hex2dec(hash(dec2hex(N)))\r\n\tHg = hex2dec(hash(dec2hex(g)))\r\n\tHI = hash(user).upper()\r\n\tinnerhash = dec2hex((HN^Hg)).upper()+HI+salt+dec2hex(A)+dec2hex(B)+K\r\n\tMc_s = hash(innerhash).upper()\r\n\t\r\n\t\r\n\t\r\n\t## si no coincide lo que calculamos nosotros con lo que nos envian... nanay\t\r\n\tif(Mc != Mc_s):\r\n\t\trdata = {\"status\":\"FAILED\"};\r\n\t\treq.write(JSONEncoder(\"UTF-8\").encode(rdata))\r\n\telse:\r\n\t\t## Si son iguales enviamos nuestra prueba al cliente\r\n\t\t\r\n\t\t## Generar la prueba del servidor\r\n\t\tMs = hash(dec2hex(A)+Mc+K)\t\r\n\t\t\r\n\t\t\r\n\t\t\r\n\t\t## Hacer efectivo el login en el servidor y emitir cookie si es necesario\r\n\t\tsession_login(req)\r\n\t\t\r\n\t\tif(data[\"remember\"]== \"yes\"):\r\n\t\t\tmessage = createLoginCookie(req,req.session['SRP_crypto']['user'])\r\n\t\t\tif message != \"OK\":\r\n\t\t\t\trdata = {\"status\": message};\r\n\t\t\t\t\r\n\t\t## Guardar la clave de sesion y proceder a la comprobacion por parte del cliente\r\n\t\trdata = {\"M\":Ms.upper(),\"status\":\"OK\"};\r\n\t\treq.write(JSONEncoder(\"UTF-8\").encode(rdata))\r\n\t\t\r\n\t#~ except:\r\n\t\t#~ rdata = {\"status\": \"Second auth step failed.\"};\r\n\t\t#~ req.write(JSONEncoder(\"UTF-8\").encode(rdata))\r\n\t\t\r\n\t\r\n\t\t\r\n\t## Todo completado, eliminar la info de la variable de sesion\r\n\tdelete_crypto_vars(req)","title":""},{"docid":"4438e5ce40e940e71924c2666f55f998","score":"0.52751994","text":"def db_password(self):","title":""},{"docid":"79b732b5d53114e6bab8254d75b47408","score":"0.5275168","text":"def authenticate():","title":""},{"docid":"034ed65cce6d6c5924af5d730a6b4bc6","score":"0.52732956","text":"def call(self, method_name, password, args=None) -> dict:\n raise NotImplementedError","title":""},{"docid":"81cc1c97438d232d20ccc176353a725b","score":"0.5271543","text":"def manual_auth(getpass):\r\n return ManualSPAuth(getpass)","title":""},{"docid":"7c63b749fd775e5f5e46c5bf095db943","score":"0.52469605","text":"def __init__(self,username,password):\n\n self.username = username\n self.password = password","title":""},{"docid":"1171177f49ac50984bee90d94d42265c","score":"0.5235322","text":"async def ask_json_rpc(method, params, user, pwd):\n json = {\n 'method': method,\n 'params': params,\n 'id': None,\n }\n async with aiohttp.ClientSession() as session:\n auth = aiohttp.BasicAuth(user, pwd)\n async with session.post(Const.REST_API, json=json, auth=auth) as response:\n if response.status == 200:\n data = await response.json()\n log.debug(f\"JSON-RPC method={method} params={params} success={data}\")\n return data['result']\n else:\n text = await response.text()\n log.error(f\"JSON-RPC method={method} params={params} error={text}\")\n raise ConnectionError(text)","title":""},{"docid":"5a48ebfcd975860c66ba4602059f6a1b","score":"0.52174294","text":"def spwd_add(self):\n self.service=self.getItem2()\n self.UsrName=self.ui.sha_username.text()\n self.add=shd()\n if(self.add.share(self.username,self.UsrName,self.service)):\n self.ui.sha_username.clear()\n self.success=Msg(\"Password Shared\").exec_()\n else:\n self.failed=Msg(\"User does not exist\").exec_()","title":""},{"docid":"5a3e34eedaf0f19d0e31d70823ca1eb4","score":"0.52056724","text":"def test_post_sync_simpleuser(self):\n self.client.login(username='homer', password='doooh')\n response = self.client.post(reverse('common:sync_randos'), data={})\n self.assertRedirects(response, '/login/?next=/commands/sync')","title":""},{"docid":"d2d821c7808a2371f76d459006601d5e","score":"0.51969475","text":"def __init__(self):\n HOST = 'managesql'\n HOST = 'karttur'\n secrets = netrc.netrc()\n username, account, password = secrets.authenticators( HOST )\n pswd = b64encode(password.encode())\n #create a query dictionary for connecting to the Postgres server\n query = {'db':'postgres','user':username,'pswd':pswd}\n #Connect to the Postgres Server\n self.session = PGsession.__init__(self,query,'ManageSMAP')","title":""},{"docid":"f7f9ae56e902a58d3bf224aea3cca3a4","score":"0.51919574","text":"def __init__(self, username, password):\r\n\r\n self.username = username\r\n self.password = password","title":""},{"docid":"f089d2c6925eacb12c8a342959a49010","score":"0.5189284","text":"def login(self):","title":""},{"docid":"f089d2c6925eacb12c8a342959a49010","score":"0.5189284","text":"def login(self):","title":""},{"docid":"1b7776f1650f04390d379de2f57ae428","score":"0.51867163","text":"def setCredentials (self, username, password, jobguid = \"\", executionparams = {}):\n params =dict()\n params['username'] = username\n params['password'] = password\n return q.workflowengine.actionmanager.startActorAction('systemnas', 'setCredentials', params, jobguid=jobguid, executionparams=executionparams)","title":""},{"docid":"f6a9917467b08733b4522fc665d07ed1","score":"0.5185898","text":"def __init__(self, user, server, password):\n self.session = self.login(user, server, password)","title":""},{"docid":"a14598081db630ddb3983443d81ce214","score":"0.5181821","text":"def getUser(name, password=None):","title":""},{"docid":"2bb2b0c1e4b20a795827cb1e37861b51","score":"0.5178116","text":"def create(self, login, name=None, password=None, jobguid=\"\", executionparams=None):","title":""},{"docid":"53afc3e5bfcff4722e65011eec4b63a8","score":"0.51725245","text":"def loginUser(self, username: str, password: hash):","title":""},{"docid":"57a9a3594d75e5447988cadde122ff21","score":"0.5157121","text":"def __init__(self, username, password):\n self.username = username\n self.password = password","title":""},{"docid":"57a9a3594d75e5447988cadde122ff21","score":"0.5157121","text":"def __init__(self, username, password):\n self.username = username\n self.password = password","title":""},{"docid":"57a9a3594d75e5447988cadde122ff21","score":"0.5157121","text":"def __init__(self, username, password):\n self.username = username\n self.password = password","title":""},{"docid":"57a9a3594d75e5447988cadde122ff21","score":"0.5157121","text":"def __init__(self, username, password):\n self.username = username\n self.password = password","title":""},{"docid":"57a9a3594d75e5447988cadde122ff21","score":"0.5157121","text":"def __init__(self, username, password):\n self.username = username\n self.password = password","title":""},{"docid":"04fdb94b85aa109db0e92d10ee162870","score":"0.5156488","text":"def authenticate(self, username, password):\n return None","title":""},{"docid":"c5087acf3c289b01a2fc3e39a1fc8580","score":"0.5154638","text":"def __login__(self):\n try:\n resp = self.login(\n self.config.get('checkpoint', 'cp_server'),\n self.config.get('checkpoint', 'username'),\n self.config.get('checkpoint', 'password'),\n self.config.getboolean('checkpoint', 'resume_session', fallback=False),\n **{'domain': self.config.get('checkpoint', 'domain', fallback=\"\"),\n 'session-comments': self.config.get('checkpoint', 'session-comments', fallback=\"\"),\n 'session-description': self.config.get('checkpoint', 'session-description', fallback=\"\"),\n 'session-name': \"CPbot_%s_%s_%d\" % (gethostname(), self.config.get('checkpoint', 'session-name',\n fallback= Path(self.configfile).name), int(time.time())),\n 'session-timeout': self.config.getint('checkpoint', 'session-timeout', fallback=3600)}\n )\n except Exception as err:\n raise err\n else:\n if not resp.success:\n resp.data['_action'] = whoami()\n self.logger.error(resp.data)\n raise Exception(\"Authentication Failed. %s\" % resp.data)","title":""},{"docid":"0047b1e16a0e0b40f8c6003060c97426","score":"0.5154146","text":"def create_shared_credential_command(client: Client, name: str, site_assignment: str, service: str,\n database: str | None = None, description: str | None = None,\n domain: str | None = None, host_restriction: str | None = None,\n http_realm: str | None = None, notes_id_password: str | None = None,\n ntlm_hash: str | None = None, oracle_enumerate_sids: str | None = None,\n oracle_listener_password: str | None = None, oracle_sid: str | None = None,\n password: str | None = None, port_restriction: str | None = None,\n sites: str | None = None, authentication_type: str | None = None,\n community_name: str | None = None, privacy_password: str | None = None,\n privacy_type: str | None = None, ssh_key_pem: str | None = None,\n ssh_permission_elevation: str | None = None,\n ssh_permission_elevation_password: str | None = None,\n ssh_permission_elevation_username: str | None = None,\n ssh_private_key_password: str | None = None,\n use_windows_authentication: str | None = None,\n username: str | None = None) -> CommandResults:\n oracle_enumerate_sids_bool = None\n sites_list = None\n snmpv3_authentication_type_enum = None\n snmpv3_privacy_type_enum = None\n ssh_permission_elevation_enum = None\n use_windows_authentication_bool = None\n\n if oracle_enumerate_sids is not None:\n oracle_enumerate_sids_bool = argToBoolean(oracle_enumerate_sids)\n\n if sites is not None:\n sites_list = [int(item) for item in argToList(sites)]\n\n if authentication_type is not None:\n snmpv3_authentication_type_enum = SNMPv3AuthenticationType[authentication_type]\n\n if privacy_type is not None:\n snmpv3_privacy_type_enum = SNMPv3PrivacyType[privacy_type]\n\n if ssh_permission_elevation is not None:\n ssh_permission_elevation_enum = SSHElevationType[ssh_permission_elevation]\n\n if use_windows_authentication is not None:\n use_windows_authentication_bool = argToBoolean(use_windows_authentication)\n\n response_data = client.create_shared_credential(\n name=name,\n site_assignment=SharedCredentialSiteAssignment[site_assignment],\n service=CredentialService[service],\n database_name=database,\n description=description,\n domain=domain,\n host_restriction=host_restriction,\n http_realm=http_realm,\n notes_id_password=notes_id_password,\n ntlm_hash=ntlm_hash,\n oracle_enumerate_sids=oracle_enumerate_sids_bool,\n oracle_listener_password=oracle_listener_password,\n oracle_sid=oracle_sid,\n password=password,\n port_restriction=port_restriction,\n sites=sites_list,\n snmp_community_name=community_name,\n snmpv3_authentication_type=snmpv3_authentication_type_enum,\n snmpv3_privacy_password=privacy_password,\n snmpv3_privacy_type=snmpv3_privacy_type_enum,\n ssh_key_pem=ssh_key_pem,\n ssh_permission_elevation=ssh_permission_elevation_enum,\n ssh_permission_elevation_password=ssh_permission_elevation_password,\n ssh_permission_elevation_username=ssh_permission_elevation_username,\n ssh_private_key_password=ssh_private_key_password,\n use_windows_authentication=use_windows_authentication_bool,\n username=username,\n )\n\n return CommandResults(\n readable_output=f\"New shared credential has been created with ID {response_data['id']}.\",\n outputs_prefix=\"Nexpose.SharedCredential\",\n outputs_key_field=\"id\",\n outputs={\"id\": response_data['id']},\n raw_response=response_data,\n )","title":""},{"docid":"eced28271063e4ed44cff9330708be0b","score":"0.51505387","text":"def check_auth(username, password):\n print username\n conn=PhenotipsClient()\n response=conn.get_patient(auth='%s:%s' % (username, password,),number=1)\n if response:\n session['password2'] = password\n password=md5.new(password).hexdigest()\n session['user'] = username\n session['password'] = password\n return True\n else: return False\n # check that user name and hash of password exist in database\n db_users=get_db('users')\n # setting a session key for pubmedBatch to save result\n session['password2'] = password\n password=md5.new(password).hexdigest()\n session['user'] = username\n session['password'] = password\n r=db_users.users.find_one({'user':username})\n if r is None:\n return False\n elif md5.new(r['password']).hexdigest() == md5.new(password).hexdigest():\n print('LOGIN', session['user'])\n return True\n else:\n return False","title":""},{"docid":"e5a1c5550719cd705dfa8219748585e3","score":"0.51445407","text":"def __init__(self, procname, aeskey, password):\n self.procname = procname\n self.aeskey = aeskey\n self.password = password\n self.client = None","title":""},{"docid":"46529ad2eab83bb520c5be69e70371c9","score":"0.51330745","text":"def _update(self, args: dict):\n self.PROTOCOL = args.get('PROTOCOL', Connection_String.PROTOCOL)\n self.USERNAME = args.get('USERNAME', Connection_String.USERNAME)\n self.PASSWORD = args.get('PASSWORD', Connection_String.PASSWORD)\n self.ADDRESS = args.get('ADDRESS', Connection_String.ADDRESS)\n self.PORT = args.get('PORT', Connection_String.PORT)\n self.SCHEMA = args.get('SCHEMA', Connection_String.SCHEMA)\n self.OPTIONS = args.get('OPTIONS', Connection_String.OPTIONS)","title":""},{"docid":"09f9153db409d05ad8192e17f18963c6","score":"0.51304585","text":"def SRP_Auth_1 (req,data):\r\n\ttry:\r\n\t\tsession = req.session \r\n\t\tsession.lock()\r\n\t\tif session.has_key(\"logged\"):\r\n\t\t\tif session[\"logged\"]:\r\n\t\t\t\trdata = {\"status\": \"Already logged in.\"};\r\n\t\t\t\treq.write(JSONEncoder(\"UTF-8\").encode(rdata))\r\n\t\t\t\treturn \r\n\t\tsession.unlock()\r\n\t\t\r\n\t\t\r\n\t\tdata = JSONDecoder(\"UTF-8\").decode(data)\r\n\r\n\t\t## recogemos A\r\n\t\tA = hex2dec(data[\"A\"])\r\n\t\t\r\n\t\t## Abortar si A%N == 0\r\n\t\tif(A%N == 0):\r\n\t\t\trdata = {\"status\": \"Server step 1 failed.\"};\r\n\t\t\treq.write(JSONEncoder(\"UTF-8\").encode(rdata))\r\n\t\t\r\n\t\t## Conectar\r\n\t\tdb=MySQLdb.connect(host=\"localhost\",user=\"root\",passwd=\"1234\",db=\"prueba\")\r\n\t\t\r\n\t\t## Preparar transaccion\r\n\t\tsql = \"\"\"\r\n\t\tSELECT * \r\n\t\tFROM usuarios \r\n\t\tWHERE userName = \"%s\" \r\n\t\t\"\"\"%(data['username'])\r\n\t\t\r\n\t\tcursor=db.cursor()\r\n\t\tcursor.execute(sql)\r\n\t\tresultado=cursor.fetchall()\r\n\r\n\t\t## Desconectar\r\n\t\tdb.close()\r\n\t\t\r\n\t\tuser_id = None\r\n\t\t## Asegurarse que no hay mas de un resultado\r\n\t\tif len(resultado)>1:\r\n\t\t\trdata = {\"status\": \"Server step 1 failed: Duplicated username!.\"};\r\n\t\t\treq.write(JSONEncoder(\"UTF-8\").encode(rdata))\r\n\t\t\treturn\r\n\t\telse:\r\n\t\t\tif len(resultado) == 0:\r\n\t\t\t\trdata = {\"status\": \"No existe el usuario.\"};\r\n\t\t\t\treq.write(JSONEncoder(\"UTF-8\").encode(rdata))\r\n\t\t\t\treturn\r\n\t\t\telse:\r\n\t\t\t\tuser_id = resultado[0][0]\r\n\t\t\t\r\n\t\t## Devuelve el salt (2a columna) y B\r\n\t\t## k = H(N, g)\r\n\t\t## Si se ha de calcular k, lo podemos hacer aqui\r\n\t\tdbsalt = resultado[0][3]\r\n\t\tdbv =resultado[0][2]\r\n\t\tv = hex2dec(dbv)\r\n\t\tsalt = hex2dec(dbsalt)\r\n\t\t\r\n\t\t## b es un numero al azar de como maximo 32 bytes\r\n\t\trandom.seed()\r\n\t\tb = random.getrandbits(256)\r\n\t\t\r\n\t\t## B=\tkv + g^b (mod N)\r\n\t\tkv = k*v\r\n\t\tgb = powMod(g,b,N)\r\n\t\tB = (kv+gb)%N\r\n\t\t\r\n\t\trdata = {\"salt\":dbsalt.upper(),\"B\":dec2hex(B).upper(),\"status\": \"OK\"};\r\n\t\treq.write(JSONEncoder(\"UTF-8\").encode(rdata))\r\n\t\t\r\n\t\t\r\n\t\t## Create crypto data structure\r\n\t\tcryptovars = {\"A\":A,\"B\":B,\"v\":v,\"b\":b,\"user\":data['username'],\"salt\":dbsalt.upper(),\"user_id\":user_id}\r\n\t\tsave_crypto_vars(req, cryptovars);\r\n\t\t\r\n\texcept:\r\n\t\trdata = {\"status\": \"Server step 1 failed.\"};\r\n\t\treq.write(JSONEncoder(\"UTF-8\").encode(rdata))","title":""},{"docid":"c0029b83c9e0cf0a2d30d7636b1aa22e","score":"0.51254386","text":"def post(self):\n username = self.get_argument(\"emailAddress\",\"\")\n password = self.get_argument(\"password\",\"\")\n loginInfo = {\"username\": username,\n \"password\": password}\n self.loginUser(loginInfo)","title":""},{"docid":"f63b693f598436f8e8cdc6529402d622","score":"0.5113076","text":"def acquire_token(self, login, password):","title":""},{"docid":"667b16f1c1e2964fc2b24a7a9793809d","score":"0.51049674","text":"def set_credentials(username, password):\n global USERNAME\n global PASSWORD\n USERNAME = username\n PASSWORD = password","title":""},{"docid":"3dca23c5d890332dd2fa3311f0902105","score":"0.5101368","text":"def login(self, uname, pword) :\n\t\tprint \"connecting to client manager\"\n\t\tcmSock = socket(AF_INET, SOCK_STREAM);\n\t\tcmSock.connect((self.cliMgrIp,30122))\n\t\tprint \"connected to client manager\"\n\t\n\t\tcmSock.sendall(\"Forest-login-v1\\nlogin: \" + uname + \\\n \t \"\\npassword: \" + pword + \"\\nover\\n\")\n\n\n\t\tbuf = \"\"\n\t\tprint \"reading line\"\n\t\tline,buf = self.readLine(cmSock,buf)\n\t\tif line != \"success\" :\n\t\t\tprint \"oops\", line\n\t\t\treturn False\n\t\tline,buf = self.readLine(cmSock,buf)\n\t\tif line != \"over\" :\n\t\t\tprint \"oops\", line\n\t\t\treturn False\n\n\t\tcmSock.sendall(\"newSession\\nover\\n\")\n\n\t\tline,buf = self.readLine(cmSock,buf)\n\n\t\tchunks = line.partition(\":\")\n\t\tif chunks[0].strip() != \"yourAddress\" or chunks[1] != \":\" :\n\t\t\treturn False\n\t\tself.myFadr = string2fadr(chunks[2].strip())\n\n\t\tline,buf = self.readLine(cmSock,buf)\n\t\tchunks = line.partition(\":\")\n\t\tif chunks[0].strip() != \"yourRouter\" or chunks[1] != \":\" :\n\t\t\treturn False\n\t\ttriple = chunks[2].strip(); triple = triple[1:-1].strip()\n\t\tchunks = triple.split(\",\")\n\t\tif len(chunks) != 3 : return False\n\t\tself.rtrIp = string2ip(chunks[0].strip())\n\t\tself.rtrPort = int(chunks[1].strip())\n\t\tself.rtrFadr = string2fadr(chunks[2].strip())\n\n\t\tline,buf = self.readLine(cmSock,buf)\n\t\tchunks = line.partition(\":\")\n\t\tif chunks[0].strip() != \"comtCtlAddress\" or chunks[1] != \":\" :\n\t\t\treturn False\n\t\tself.comtCtlFadr = string2fadr(chunks[2].strip())\n\n\t\tline,buf = self.readLine(cmSock,buf)\n\t\tchunks = line.partition(\":\")\n\t\tif chunks[0].strip() != \"connectNonce\" or chunks[1] != \":\" :\n\t\t\treturn False\n\t\tself.nonce = int(chunks[2].strip())\n\n\t\tline,buf = self.readLine(cmSock,buf) \n\t\tif line != \"overAndOut\" : return False\n\n\t\tprint \"login successful\"\n\n\t\tcmSock.close()\n\n\t\tif self.debug >= 1 :\n\t\t\tprint \"avatar address =\", fadr2string(self.myFadr)\n\t\t\tprint \"router info = (\", ip2string(self.rtrIp), \\\n\t\t\t\t\t str(self.rtrPort), \\\n\t\t\t\t\t fadr2string(self.rtrFadr), \")\"\n\t\t\tprint \"comtCtl address = \",fadr2string(self.comtCtlFadr)\n\t\t\tprint \"nonce = \", self.nonce\n\n\t\treturn True","title":""},{"docid":"f6beddfb03196a65c5854a5e2a7c9482","score":"0.50947905","text":"def authenticate(self,user,passwd):\n\n\tself.AddAttr(1,user)\n\tself.AddPasswordAttr(password=passwd)\n\tself.calledfinal=0\n\n\tself.send()\t\t# Send the packet\n\tself.receive()\t\t# Wait for reply\n\treturn self.verify()\t# Validate packet","title":""},{"docid":"1792e73bbfeca58c05e01d77bd2b68d7","score":"0.50855505","text":"def __init__(self, username, password):\n\n self.username = username\n self.password = password\n self.account = username\n self.mailbox = self.login()","title":""},{"docid":"2d10989815cad087f72b3c688c0b5089","score":"0.50829554","text":"def login(self, uname, pword) :\n\t\tprint \"connecting to client manager\"\n\t\tcmSock = socket(AF_INET, SOCK_STREAM);\n\t\tcmSock.connect((self.cliMgrIp,30122))\n\t\tprint \"connected to client manager\"\n\t\n\t\tcmSock.sendall(\"Forest-login-v1\\nlogin: \" + uname + \\\n \t \"\\npassword: \" + pword + \"\\nover\\n\")\n\n\n\t\tbuf = \"\"\n\t\tprint \"reading line\"\n\t\tline,buf = self.readLine(cmSock,buf)\n\t\tif line != \"login successful\" :\n\t\t\treturn False\n\t\tline,buf = self.readLine(cmSock,buf)\n\t\tif line != \"over\" :\n\t\t\treturn False\n\n\t\tcmSock.sendall(\"newSession\\nover\\n\")\n\n\t\tline,buf = self.readLine(cmSock,buf)\n\t\tchunks = line.partition(\":\")\n\t\tif chunks[0].strip() != \"yourAddress\" or chunks[1] != \":\" :\n\t\t\treturn False\n\t\tself.myFadr = string2fadr(chunks[2].strip())\n\n\t\tline,buf = self.readLine(cmSock,buf)\n\t\tchunks = line.partition(\":\")\n\t\tif chunks[0].strip() != \"yourRouter\" or chunks[1] != \":\" :\n\t\t\treturn False\n\t\ttriple = chunks[2].strip(); triple = triple[1:-1].strip()\n\t\tchunks = triple.split(\",\")\n\t\tif len(chunks) != 3 : return False\n\t\tself.rtrIp = string2ip(chunks[0].strip())\n\t\tself.rtrPort = int(chunks[1].strip())\n\t\tself.rtrFadr = string2fadr(chunks[2].strip())\n\n\t\tline,buf = self.readLine(cmSock,buf)\n\t\tchunks = line.partition(\":\")\n\t\tif chunks[0].strip() != \"comtCtlAddress\" or chunks[1] != \":\" :\n\t\t\treturn False\n\t\tself.comtCtlFadr = string2fadr(chunks[2].strip())\n\n\t\tline,buf = self.readLine(cmSock,buf)\n\t\tchunks = line.partition(\":\")\n\t\tif chunks[0].strip() != \"connectNonce\" or chunks[1] != \":\" :\n\t\t\treturn False\n\t\tself.nonce = int(chunks[2].strip())\n\n\t\tline,buf = self.readLine(cmSock,buf) \n\t\tif line != \"overAndOut\" : return False\n\n\t\tprint \"login successful\"\n\n\t\tcmSock.close()\n\n\t\tif self.debug >= 1 :\n\t\t\tprint \"avatar address =\", fadr2string(self.myFadr)\n\t\t\tprint \"router info = (\", ip2string(self.rtrIp), \\\n\t\t\t\t\t str(self.rtrPort), \\\n\t\t\t\t\t fadr2string(self.rtrFadr), \")\"\n\t\t\tprint \"comtCtl address = \",fadr2string(self.comtCtlFadr)\n\t\t\tprint \"nonce = \", self.nonce\n\n\t\treturn True","title":""},{"docid":"ea2ecc1d4a0963156e1836168cc5641e","score":"0.50822693","text":"def PATCH(self):\n uri = self.request.get_URI()\n clientID = self.request.get_user_id()\n programID = self.URI.match(uri).group(1)\n data = self.request.get_data_as_dictionery()\n username, password = data.get(USERNAME), data.get(PASS)\n if database.cange_record(clientID, programID, username, password):\n return Responce.ok()\n else:\n # TODO: dont have user not valid/ program not valid difference\n return Responce.not_found()","title":""},{"docid":"11736cb92640b9b110cae82773f6e546","score":"0.5076801","text":"def __init__(self, username, password):\n \n self._cli = cli.Cli()\n self._django_sr = django_sendrcv.DjangoSendRcv(base_url)\n self._tornado_sr = tornado_sendrcv.TornadoSendRcv()\n \n # Store off the username and password.\n self._username = username\n self._password = password\n \n return","title":""},{"docid":"e506eea3b92de5e0c9183bde9d58ebca","score":"0.5074458","text":"def login(self) -> LoginProtocol:\n ...","title":""},{"docid":"21242db00b0f49b0cfc0057a0b961cc8","score":"0.50736403","text":"def login(self):\r\n (username, password) = self.client_flags.split(' ')\r\n self.connect_server(username, password)","title":""},{"docid":"b1a97bfd6593fcfbbe5ab28071ca8860","score":"0.5073414","text":"def get_params(self):\n return {\n 'username': self.username,\n 'password': self.password\n }","title":""},{"docid":"bdf4ccc995278a53202b6be5b21c43b3","score":"0.5069431","text":"def authenticate(environ, identity):","title":""},{"docid":"9d331bb9608237f737f16285403242f3","score":"0.50693864","text":"def register_client(self, pin):\n # optional imports required for authentication\n from ..util import parse_plist_from_bytes, write_plist_to_bytes\n\n def get_plist_request_data(plist_data):\n \"\"\"\n Create a request header and body to send a plist to the server.\n :param plist_data: dictionary holding the plist data\n :return: plist rtsp request header, plist rtsp request body\n \"\"\"\n header = {\n \"User-Agent\": self.user_agent,\n \"Content-Type:\": \"application/x-apple-binary-plist\",\n \"Connection\": \"keep-alive\",\n }\n return header, write_plist_to_bytes(plist_data)\n\n # create new unique credentials\n auth_identifier, auth_secret = new_credentials()\n\n # 2. send plist data to receiver\n header, body = get_plist_request_data({\"user\": auth_identifier, \"method\": \"pin\"})\n req = RTSPRequest(\"/pair-setup-pin\", \"POST\", header, body=body, protocol_version=self.protocol_version)\n res = self.send_and_recv(req)\n\n if res.code != 200:\n self.cleanup(RTSPReason.AUTHENTICATION)\n raise DeviceAuthenticationError(\"Can not receive public key and salt.\")\n\n # 3. read the public key and salt\n res_plist = parse_plist_from_bytes(res.body)\n public_key = res_plist[\"pk\"]\n salt = res_plist[\"salt\"]\n\n # 4. Run Secure Remote Password procedure\n srp_handler = SRPAuthenticationHandler()\n srp_handler.initialize(auth_secret)\n srp_handler.step1(auth_identifier, pin)\n pub_key, m1_proof = srp_handler.step2(public_key, salt)\n\n # send the public key and the proof to the server\n header, body = get_plist_request_data({\"pk\": pub_key, \"proof\": m1_proof})\n req = RTSPRequest(\"/pair-setup-pin\", \"POST\", header, body=body, protocol_version=self.protocol_version)\n res = self.send_and_recv(req)\n\n if res.code != 200:\n self.cleanup(RTSPReason.AUTHENTICATION)\n raise DeviceAuthenticationWrongPinCodeError(\"Wrong pin code used.\")\n\n # 5. Run AES\n epk, tag = srp_handler.step3()\n header, body = get_plist_request_data({\"epk\": epk, \"authTag\": tag})\n req = RTSPRequest(\"/pair-setup-pin\", \"POST\", header, body=body, protocol_version=self.protocol_version)\n res = self.send_and_recv(req)\n\n if res.code != 200:\n self.cleanup(RTSPReason.AUTHENTICATION)\n raise DeviceAuthenticationWrongPinCodeError(\"Can not confirm authentication secret.\")\n\n return auth_identifier, auth_secret","title":""},{"docid":"0c9b350a1f953975eeefbfc48118e349","score":"0.5067358","text":"def __init__(self, username = \"\", password = \"\"):\n self.username = username\n self.password = password","title":""},{"docid":"cc2ba31a6636f4bb8376da197e305f82","score":"0.5063522","text":"def auth(self):\r\n#\t\tprint(\"auth called\")\r\n\t\tparams = \"{\\\"username\\\": \\\"\" + self.username.text + \"\\\", \\\"password\\\": \\\"\"+ self.password.text+ \"\\\"}\"\r\n\t\theaders = {'Content-Type': 'application/json', 'Accept': 'text/plain'}\r\n\t\treq = UrlRequest('http://138.68.21.198:5000/broschat/login', \r\n\t\t\ton_success=self.loginsuccess, req_body=params, req_headers=headers)\r\n\t\treq.wait()","title":""},{"docid":"d897a530f42a983d3cb4aac52561b8ff","score":"0.5063386","text":"def login(user, password=_marker):","title":""},{"docid":"193c9ce2a3afe7f2373fffc4b0004717","score":"0.5062788","text":"def authenticate(self):\n\n # gui = threading.Thread(target=verify, args=(self.setData, ))\n # gui.start()\n # gui.join()\n\n parent_conn, child_conn = Pipe()\n gui = Process(target=verify, args=(child_conn,))\n print(\"Starting authentication\")\n gui.start()\n # waiting till authentication is over\n gui.join()\n self.customerID, self.name = parent_conn.recv()\n parent_conn.close()\n print(\"Done\")\n if self.customerID is False or self.name is False:\n return False, False, False, False\n\n today = datetime.datetime.today()\n now = datetime.datetime.now()\n date = today.strftime(\"%Y-%m-%d\")\n current_time = now.strftime(\"%H-%M\")\n\n update = \"UPDATE Customer SET login_date=%s WHERE customerID=%s\"\n val = (date, self.customerID)\n cursor, myconn = c()\n cursor.execute(update, val)\n update = \"UPDATE Customer SET login_time=%s WHERE customerID=%s\"\n val = (current_time, self.customerID)\n cursor.execute(update, val)\n myconn.commit()\n return self.customerID, self.name, date, current_time","title":""},{"docid":"d6c3756bf50414fd38467554c180711f","score":"0.50592935","text":"def haspassword(self):\r\n raise BaseException('Not a JSON-RPC command')","title":""},{"docid":"79f1fbbcad530b6eb1a92cb952cc50b8","score":"0.50524914","text":"def remote_authenticate(self, username, password):\n if not self.collects_password:\n # Even if we were somehow given a password, we won't be\n # passing it on.\n password = None\n info = self.patron_information(username, password)\n return self.info_to_patrondata(info)","title":""},{"docid":"36ced8ecc2dc5f7c333f78ee8209a4c0","score":"0.50493103","text":"def login(self):\n if self.provider == CurveThrift.Provider.LINE: # LINE\n j = self._get_json(self.LINE_SESSION_LINE_URL)\n else: # NAVER\n j = self._get_json(self.LINE_SESSION_NAVER_URL)\n\n session_key = j['session_key']\n message = (chr(len(session_key)) + session_key +\n chr(len(self.id)) + self.id +\n chr(len(self.password)) + self.password).encode('utf-8')\n\n keyname, n, e = j['rsa_key'].split(\",\")\n pub_key = rsa.PublicKey(int(n,16), int(e,16))\n crypto = rsa.encrypt(message, pub_key).encode('hex')\n\n self._headers['X-Line-Application'] = self.app\n self.transport = THttpClient.THttpClient(self.LINE_HTTP_URL)\n self.transport.setCustomHeaders(self._headers)\n\n self.protocol = TCompactProtocol.TCompactProtocol(self.transport)\n self._client = CurveThrift.Client(self.protocol)\n\n try:\n with open(self.CERT_FILE,'r') as f:\n self.certificate = f.read()\n f.close()\n except:\n self.certificate = \"\"\n\n msg = self._client.loginWithIdentityCredentialForCertificate(\n self.id, self.password, keyname, crypto, True, self.ip,\n self.com_name, self.provider, self.certificate)\n\n if msg.type == 1:\n self.certificate = msg.certificate\n self.authToken = self._headers['X-Line-Access'] = msg.authToken\n elif msg.type == 2:\n msg = \"require QR code\"\n self.raise_error(msg)\n elif msg.type == 3:\n self._headers['X-Line-Access'] = msg.verifier\n self._pinCode = msg.pinCode\n\n print \"Enter PinCode '%s' to your mobile phone in 2 minutes\"\\\n % self._pinCode\n\n j = self.get_json(self.LINE_CERTIFICATE_URL)\n self.verifier = j['result']['verifier']\n\n msg = self._client.loginWithVerifierForCertificate(self.verifier)\n if msg.type == 1:\n if msg.certificate is not None:\n with open(self.CERT_FILE,'w') as f:\n f.write(msg.certificate)\n self.certificate = msg.certificate\n if msg.authToken is not None:\n self.authToken = self._headers['X-Line-Access'] = msg.authToken\n return True\n else:\n return False\n else:\n msg = \"Require device confirm\"\n self.raise_error(msg)\n\n #raise Exception(\"Code is removed because of the request of LINE corporation\")\n else:\n self.authToken = self._headers['X-Line-Access'] = msg.authToken\n\n return True","title":""},{"docid":"c0184d3429d5b6cea47d295931734ded","score":"0.5048686","text":"def svn_auth_invoke_simple_prompt_func(svn_auth_simple_prompt_func_t__obj, svn_auth_cred_simple_t_cred, void_baton, char_realm, char_username, svn_boolean_t_may_save, apr_pool_t_pool): # real signature unknown; restored from __doc__\n pass","title":""},{"docid":"6d475061d4dcdedcbc28cd6061fa436f","score":"0.5046636","text":"def setup_method(self):\n self.user = User.objects.create_user(\n \"username\", password=\"123\", email=\"user@user.cl\"\n )\n self.token = Token.objects.create(user=self.user)\n self.user.user_permissions.add(Permission.objects.get(name=\"Execute Commands\"))\n self.url = \"manager/ws/subscription/?token={}\".format(self.token)","title":""},{"docid":"7d6f6f566643fa034ed924c76ece3dcc","score":"0.5045806","text":"def _method_init(self, host, username, password, **kwargs):\n pass","title":""},{"docid":"d282103f938d0705e32c54065a7d1285","score":"0.5042683","text":"def __init__(self,konti,username,password):\n self.konti = konti\n self.username = username\n self.password = password","title":""},{"docid":"66a7b4a4e0027245401458d99736f1c2","score":"0.5039774","text":"def set_credentials(cb_username, cb_password):\n _SESSION_STUFF[\"calcbench_user_name\"] = cb_username\n _SESSION_STUFF[\"calcbench_password\"] = cb_password\n _calcbench_session() # Make sure credentials work.","title":""},{"docid":"be02e3889419d27aaf2dc574b0412c9c","score":"0.5035096","text":"def __init__(self,user_name, password):\n self.user_name = user_name\n self.password = password","title":""},{"docid":"d8dc6b3be4543e37660e8886e5ba105b","score":"0.5020277","text":"def __init__(self, username, password):\n self.auth = (username, password)","title":""},{"docid":"1f09fe597ed39ca969d5d387757103d4","score":"0.5018498","text":"def savePassword(param, self):\r\n try:\r\n\r\n if localHost or checkSession(self, role = ['ADM']):\r\n if param.get(\"id\") and param.get(\"pass\"):\r\n \r\n o_id = getID(param[\"id\"][0])\r\n passw = param[\"pass\"][0]\r\n \r\n coll = dataBase.users\r\n docr = coll.update_one({\"_id\": o_id}, { \"$set\": {'motpass': passw } })\r\n if param.get(\"user_mail\"):\r\n email = param[\"user_mail\"][0]\r\n name = param[\"user_name\"][0] if param.get(\"user_name\") else \"\" \r\n sendRecupPassMail(email, name, passw)\r\n return dumps(docr)\r\n else:\r\n return dumps({'ok': 0}) # No param\r\n else: \r\n return ('{\"n\":0,\"ok\":0, \"message\": \"S0062\"}')\r\n except Exception as ex:\r\n return except_handler(\"savePassword\", ex)","title":""},{"docid":"7109e769b0db5143cf1c0ca3dbd2bcc7","score":"0.50172305","text":"def authenticate():\n Gtasks()","title":""},{"docid":"774c824edb03b3e86d5f997742685ead","score":"0.50086737","text":"def svn_auth_provider_invoke_save_credentials(svn_auth_provider_t__obj, svn_boolean_t_saved, void_credentials, void_provider_baton, apr_hash_t_parameters, char_realmstring, apr_pool_t_pool): # real signature unknown; restored from __doc__\n pass","title":""},{"docid":"2babc28486d675c6ccff9c6f0d5c1af6","score":"0.49991006","text":"def post(self):\n if not (\n request.data\n and \"username\" in request.json\n and \"password\" in request.json\n ):\n return \"Bad request\", 400\n # If no mechanism is present in request body, default to simple\n if not (\"mechanism\" in request.json):\n request.json[\"mechanism\"] = \"simple\"\n try:\n return {\"sessionID\": backend.login(request.json)}, 201\n except AuthenticationError:\n return \"Forbidden\", 403","title":""},{"docid":"7fbfee3f4ebf7aa1c08d383d4a2d0eed","score":"0.4994469","text":"def post(self):\n parser = reqparse.RequestParser()\n parser.add_argument('username', type=str, required=True)\n parser.add_argument('password', type=str, required=True)\n args = parser.parse_args(strict=True)\n\n query = db_session.query(UserModel).filter(\n UserModel.username == args['username'])\n if query.count() > 0:\n user = query.one()\n if user.password == args['password']:\n user.apikey = generate_apikey(user.username)\n db_session.merge(user)\n db_session.commit()\n return {'apikey': user.apikey}, 200\n return {\"message\": {\"pass\": \"User or Password is Incorrect\",\n \"user\": \"User or Password is Incorrect\", }}, 400","title":""},{"docid":"aa4428e9e53f6c9c2edb8cda3985d903","score":"0.4993907","text":"def svn_auth_provider_invoke_save_credentials(*args) -> \"svn_boolean_t *\":\n return _core.svn_auth_provider_invoke_save_credentials(*args)","title":""},{"docid":"7ecd1449e84cc7df5975c9b99eb6fb66","score":"0.4988027","text":"def user_login(self, **kwargs):\n\n for key, val in kwargs.iteritems():\n if key == 'user': astr_user = val.translate(None, '\\'\\\"')\n if key == 'passwd': astr_passwd = val.translate(None, '\\'\\\"')\n s = self.DB\n s.cdnode('/users')\n\n # login/session/canCall structure\n ret = {}\n now = datetime.datetime.today()\n ret['loginTimeStamp'] = now.strftime('%Y-%m-%d_%H:%M:%S.%f')\n ret['loginStatus'] = False\n ret['loginMessage'] = \"\"\n ret['logoutMessage'] = \"\"\n ret['sessionStatus'] = False\n ret['sessionToken'] = \"ABCDEF\"\n ret['sessionSeed'] = \"1\"\n ret['APIcanCall'] = False\n\n s = self.DB\n s.cdnode('/users')\n if not s.cdnode(astr_user):\n ret['loginMessage'] = 'User %s not found in database.' % astr_user\n else:\n if s.cat('login/passwd') != astr_passwd:\n ret['loginMessage'] = 'Incorrect password.'\n else:\n ret['loginStatus'] = True\n ret['loginMessage'] = 'Successful login for user %s at %s.' % (astr_user, datetime.datetime.now())\n ret['logoutMessage'] = \"\"\n ret['sessionStatus'] = True\n ret['sessionToken'] = \"ABCDEF\"\n ret['sessionSeed'] = \"1\"\n\n self.debug(\"in login... writing persistent for user '%s'.\\n\" % astr_user)\n # print(ret)\n self.login_writePersistent( sessionInfo = ret,\n user = astr_user)\n return {\n 'status' : True,\n 'path' : '/',\n 'payload' : {\n 'message': 'login credentials parsed',\n 'loginDetail': ret\n }\n }","title":""},{"docid":"9a1cb62472c9e54c37c584db4ecb1179","score":"0.49866235","text":"def authenticate(self, argv):\n pass","title":""},{"docid":"8ba66048e89bd741950d43ccdbc29b3b","score":"0.49839464","text":"def connect():\n\n HANDLE.Login(IP_ADDRESS, USERNAME, PASSWORD)","title":""},{"docid":"194e28c8cac4d46c2a5c8fc3f20dd141","score":"0.49794582","text":"def pop_login(self,username,passwd): \n \n #import pop_user\n #import pop_pass\n \n self.username = username\n self.passwd = passwd\n self.pop_user(self.username)\n self.pop_pass(self.passwd)\n #self.pop.quit() ","title":""},{"docid":"008460a84728d08bd71d323b40a78648","score":"0.49792752","text":"def xmlrpc_auth(self, arg):\n \n return \"\"","title":""},{"docid":"b2fb50f3109ba86f6cdcb0261a422cca","score":"0.4976081","text":"def pop_auth_plain(self,username,passwd):\n \n self.username = username\n self.passwd = passwd\n #authstring = 'AHh4MQBw'\n #authstring = (base64.b64encode(('{}'.format('\\000'+self.username+'\\000'+self.passwd)).encode())).decode()\n basic_class.mylogger_record.info('command:<auth plain '+self.username+self.passwd+'>') \n try:\n self.resp = self.pop3.auth_plain(self.username,self.passwd)\n finally: \n [basic_class.mylogger_record.debug(self.resp.decode())]\n #self.pop.quit() ","title":""},{"docid":"adff00463d020b9d32717fa5c6d13b75","score":"0.49724698","text":"def _set_info(self, username, password):\n self.username = username\n self.password = password","title":""},{"docid":"1b52e06b61505bf167a189d4afef8dc6","score":"0.49700513","text":"def get_remote_client(self, username=None, password=None, **kwargs):","title":""},{"docid":"27f58e509187acd7f16a2d58c036e50f","score":"0.49647045","text":"def Authenticate(self, req, user, password):\n raise NotImplementedError()","title":""},{"docid":"d4ad4387df9942c702dc14ed147efde1","score":"0.49610782","text":"def connection_params(self):\n print(\"\\nVeuillez saisir les paramètres de connection au serveur sql:\")\n host = input(\"host : \")\n user = input(\"user : \")\n password = getpass()\n return host, user, password","title":""},{"docid":"02153d7f1bb96432c357bf38cfd59369","score":"0.49598175","text":"def login(user_name, user_password):\n pass","title":""},{"docid":"87997aee412c8a0dda7c477d3bf28aa1","score":"0.4950943","text":"def login(self):\n pass","title":""},{"docid":"11c2bd3de6b68ade1e3242299abd6d38","score":"0.49490416","text":"def svn_auth_invoke_username_prompt_func(svn_auth_username_prompt_func_t__obj, svn_auth_cred_username_t_cred, void_baton, char_realm, svn_boolean_t_may_save, apr_pool_t_pool): # real signature unknown; restored from __doc__\n pass","title":""},{"docid":"2efe9d4f8d68b211503d9a1556f8bc42","score":"0.4942509","text":"def authUser(param, self):\r\n\t#pdb.set_trace()\r\n\ttry:\r\n\t\tdef writeCook(mess, sessID):\r\n\t\t\tself.send_response(200)\r\n\t\t\tself.send_header('Content-type','text/html')\r\n\t\t\tself.send_header('Access-Control-Allow-Origin', '*')\r\n\t\t\t# Set cookie\r\n\t\t\tcookInfo = 'sessID=' + sessID + ';max-age=31536000'\r\n\t\t\tself.send_header('Set-Cookie', cookInfo)\r\n\t\t\tself.end_headers()\r\n\t\t\t# Write content as utf-8 data\r\n\t\t\tself.wfile.write(bytes(mess, \"utf8\"))\r\n\t\t\treturn\t\r\n\t\t\r\n\t\tif param:\r\n\t\t\tif not isinstance(param, (list)) and param.get(\"user\"):\r\n\t\t\t\tuser = param[\"user\"][0]\r\n\t\t\t\t\r\n\t\t\t\tcoll = data.users\r\n\t\t\t\tdoc = coll.find({\"courriel\": user, \"actif\": True}, [\"_id\",\"Nom\", \"courriel\", \"motpass\", \"niveau\"])\r\n\t\t\t\t\r\n\t\t\t\tdef setSessID(mess):\r\n\t\t\t\t\tsessID = str(ObjectId())\r\n\t\t\t\t\tres = coll.update({\"courriel\": user}, { \"$set\": {\"sessID\": sessID}})\r\n\t\t\t\t\twriteCook(mess, sessID)\r\n\t\t\t\t\t\r\n\t\t\t\tif doc.count() == 0:\r\n\t\t\t\t\treturn dumps({'resp': {\"result\": 0} })\t# Authenticate fail\r\n\t\t\t\telse:\r\n\t\t\t\t\tdic = cursorTOdict(doc)\r\n\t\t\t\t\tdic['_id'] = str(dic['_id'])\r\n\t\t\t\tif param.get(\"pass\") and not isinstance(param, (list)):\r\n\t\t\t\t\tpassw = param[\"pass\"][0]\r\n\t\t\t\t\tif str(dic['motpass']) == passw:\r\n\t\t\t\t\t\tdel dic['motpass']\r\n\t\t\t\t\t\tdocs = {\"resp\": {\"result\":True, \"user\": dic} }\r\n\t\t\t\t\t\tres = dumps(docs)\t# Authenticated\r\n\t\t\t\t\t\tsetSessID(res)\r\n\t\t\t\t\t\treturn False\r\n\t\t\t\t\telse:\r\n\t\t\t\t\t\treturn dumps({'resp': {\"result\": 0} })\t# Authenticate fail\r\n\t\t\t\telse:\r\n\t\t\t\t\t#pdb.set_trace()\r\n\t\t\t\t\tif param.get(\"action\"):\r\n\t\t\t\t\t\taction = param[\"action\"][0]\r\n\t\t\t\t\t\tif action == \"1\":\t# To modifiy account\r\n\t\t\t\t\t\t\tdel dic['motpass']\r\n\t\t\t\t\t\t\treturn dumps(dic) # To modifiy\r\n\t\t\t\t\telse:\r\n\t\t\t\t\t\treturn dumps({'resp': {\"result\": 0} })\t# Authenticate fail password empty\r\n\t\t\telse:\r\n\t\t\t\treturn dumps({'resp': {\"result\": 0} })\t# User is empty\r\n\t\telse:\r\n\t\t\treturn dumps({'resp': {\"result\": 0} })\t# No param\r\n\texcept:\r\n\t\tlog_Info(self.path + \" ERROR: \" + sys.exc_info()[0] + \" ; \" + str(sys.exc_info()[1]))","title":""},{"docid":"e428f47f73297dc1988e6cef258205f7","score":"0.49419478","text":"def _auth(self, method, params, recv_window):\n\n api_key = self.api_key\n api_secret = self.api_secret\n\n if api_key is None or api_secret is None:\n raise PermissionError('Authenticated endpoints require keys.')\n\n # Append required parameters.\n params['api_key'] = api_key\n params['recv_window'] = recv_window\n params['timestamp'] = int(time.time() * 10 ** 3)\n\n # Sort dictionary alphabetically to create querystring.\n _val = '&'.join(\n [str(k) + '=' + str(v) for k, v in sorted(params.items()) if\n (k != 'sign') and (v is not None)]\n )\n\n # Bug fix. Replaces all capitalized booleans with lowercase.\n if method == 'POST':\n _val = _val.replace('True', 'true').replace('False', 'false')\n\n # Return signature.\n return str(hmac.new(\n bytes(api_secret, 'utf-8'),\n bytes(_val, 'utf-8'), digestmod='sha256'\n ).hexdigest())","title":""},{"docid":"975c8fb3c2b995be7143657762f416d6","score":"0.49413943","text":"def authenticate(username, password):\n return None","title":""},{"docid":"ae1c84da29a371f42d79b1dd3b79e044","score":"0.49368295","text":"def start_user_commands(self):\n self.client = LoginCommands(self.root_directory,self.current_directory,\n self.username,self.password)","title":""},{"docid":"1a842eb28043b8b3e1869d2a72a89324","score":"0.49332032","text":"def __init__(self, username, password):\n self.client = None\n self.username = username\n self.password = password","title":""},{"docid":"0f70f3f36362a340ac1f92a06750d2fd","score":"0.49316657","text":"def user_credential_update() -> None:","title":""},{"docid":"958ef1a7263781852b550901e5484a5d","score":"0.4925496","text":"def ReceivedSprayArguments(self, serverIP, username, sprayPassword):\n simpleServer = Server(serverIP, get_info='ALL')\n self.simpleConnspray = Connection(simpleServer, user=username, password=sprayPassword)","title":""},{"docid":"b24e82301daa9ff2e26e2fadce017ef1","score":"0.49236986","text":"def Login():\n global s\n \n # Send the connecting client a vague login prompt\n s.send('Login: ')\n \n # Expect 1024 bytes\n pwd = s.recv(1024)\n\n # If the provided input does not match the specified password we just call this same function again \n if pwd.strip() != password:\n Login()\n else:\n \n # If the user got the password correct, we send an eager prompt to the assailant over socket\n s.send('Connected #> ')\n \n # Start the `Shell` function\n Shell()","title":""}],"string":"[\n {\n \"docid\": \"e58d47573d0131e4705220e41ccb057e\",\n \"score\": \"0.58704257\",\n \"text\": \"def set_params(usr, password):\\n\\n\\tglobal user\\n\\tglobal pwd\\n\\tuser = usr\\n\\tpwd = password\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3239ac6fbe8975fc0fdbabef8fa9de8c\",\n \"score\": \"0.5728726\",\n \"text\": \"def store_auth_info(self, username, password):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a1e6ac577a90ce2e680d7c0e2c1ddfb\",\n \"score\": \"0.5668911\",\n \"text\": \"def LoginToServer():\\n print(\\\"To start data streaming, please login:\\\")\\n print(\\\"username:\\\")\\n username = input()\\n password = getpass.getpass('password:')\\n template = JsonTemplate(\\\"Login\\\")\\n return template(username, password, device_id, device_type, device_model, system_version, app_version)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca094914a226460e9c1671f52cd02ae3\",\n \"score\": \"0.55512995\",\n \"text\": \"def auth(u, p):\\n\\tglobal user, password\\n\\tuser = u\\n\\tpassword = p\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f56624ce0c14b1250762941adf49d694\",\n \"score\": \"0.5452777\",\n \"text\": \"def post(self):\\n user_mobilephone = self.get_argument(\\\"mobilephone\\\")\\n user_name = self.get_argument(\\\"username\\\")\\n user_password = self.get_argument(\\\"password\\\")\\n user_org = self.get_argument(\\\"org\\\")\\n user_starttime = self.get_argument(\\\"starttime\\\")\\n user_endtime = self.get_argument(\\\"endtime\\\")\\n \\n result = selectDataWhere(\\\"users\\\", \\\"id\\\", mobilephone=user_mobilephone)\\n if result:\\n self.write(\\\"-1\\\")\\n else:\\n orgname_id = selectDataWhere(\\\"orgs\\\", \\\"id\\\", name=user_org)[0][0]\\n try:\\n insertData(\\\"users\\\", username=user_mobilephone, mobilephone=user_mobilephone, name=user_name, orgnameID=orgname_id, password=user_password, starttime=user_starttime, endtime=user_endtime, status=1)\\n self.write(\\\"1\\\")\\n except:\\n self.write(\\\"0\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dee9b8cb44e652ca6bba5241ec08c0f5\",\n \"score\": \"0.54525715\",\n \"text\": \"def server_auth(realm, username):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d82c6a7b01e68c3e165a9abdff25fd7\",\n \"score\": \"0.5442518\",\n \"text\": \"def login(self, username, password):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b56d17dbcb933043ce13a79897b26967\",\n \"score\": \"0.54303306\",\n \"text\": \"def Login(self, username, password, onSuccess, onFailure):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb3933852065263c55bac2b76d14b0fb\",\n \"score\": \"0.5415997\",\n \"text\": \"def update(self, userguid, name=\\\"\\\", password=None, jobguid=\\\"\\\", executionparams=dict()):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc0c7e3660c9c3d6e05b809b06f0c1bc\",\n \"score\": \"0.5377909\",\n \"text\": \"def sendAuth(username, password):\\n print('Sending auth data to backend...')\\n if username == \\\"ump_hacker\\\" and password == \\\"aH-KeE-aY-Me_h0seA\\\":\\n return 'True'\\n return 'False'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a9c5fe14f17b3ec8822a26252bd2211\",\n \"score\": \"0.53764623\",\n \"text\": \"def client_auth_rpc(username, password):\\n auth_server = jsonrpclib.Server(rhconf.AUTH_SERVER_URL)\\n res = auth_server.rh_auth(str(username), str(password))\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39737627c56b7e373aaa40e3de14de10\",\n \"score\": \"0.53605914\",\n \"text\": \"def __call__(self, username, dataDir = None):\\n try:\\n name = \\\"pgsql-8.3\\\"\\n cmdbtypename = 'postgresql8server'\\n configFileDir = dataDir and dataDir or q.system.fs.joinPaths(os.sep, 'etc', 'postgresql', '8.4', 'main')\\n binDir = q.system.fs.joinPaths(os.sep, 'usr', 'lib', 'postgresql', '8.4', 'bin')\\n exitCode = 1\\n output = ''\\n\\n q.logger.log(\\\"Applying %s credentials for [%s]\\\"%(username, name))\\n\\n if not q.system.fs.isDir(configFileDir):\\n\\n q.logger.log(\\\"Creating configuration directory [%s]\\\"%configFileDir, 1)\\n q.system.fs.createDir(configFileDir)\\n\\n if q.platform.isUnix():\\n q.logger.log(\\\"Setting owner for [%s] to [%s]\\\"%(configFileDir, username), 5)\\n q.system.unix.chown(configFileDir, username, None)\\n q.logger.log(\\\"Setting owner for [%s] to [%s]\\\"%(q.dirs.baseDir, username), 5)\\n q.system.unix.chown(binDir, username, None)\\n\\n binPath = q.system.fs.joinPaths(binDir,\\\"initdb\\\")\\n initDBCommand = \\\"%s -E=%s --no-locale -D %s\\\"%(binPath, \\\"UTF-8\\\", configFileDir)\\n\\n if q.platform.isUnix():\\n exitCode, output = q.system.unix.executeAsUser(\\\"%(initDBCommand)s\\\"%{\\\"initDBCommand\\\":initDBCommand},username, dieOnNonZeroExitCode = False)\\n if exitCode:\\n raise RuntimeError,('initdb failed with error %s'%output, exitCode)\\n\\n elif q.platform.isWindows():\\n if not q.system.windows.isServiceInstalled(name):\\n q.logger.log(\\\"Registering [%s] that located at [%s]as a windows service\\\"%(name, q.system.fs.joinPaths(binDir,\\\"pg_ctl.exe\\\")), 1)\\n result = q.system.windows.createService(name, name, binDir+\\\"\\\\pg_ctl.exe\\\", 'runservice -W -N %(serviceName)s -D \\\\\\\"%(configFileDir)s\\\\\\\"'%{'serviceName':name, 'configFileDir':configFileDir})\\n if result :\\n q.system.windows.startService(name)\\n else:\\n q.logger.log(\\\"cannot regsiter service %s\\\"%name, 6)\\n\\n if q.system.fs.isEmptyDir(configFileDir):\\n command = \\\"%(initDBCommand)s -U %(login)s\\\"%{\\\"login\\\":username, \\\"initDBCommand\\\":initDBCommand}\\n exitCode, output = q.system.process.execute(command, dieOnNonZeroExitCode = False)\\n if exitCode:\\n raise RuntimeError,('initdb failed with error %s'%output, exitCode)\\n return exitCode, output\\n except:\\n print q.errorconditionhandler.getCurrentExceptionString()\\n raise\",\n \"title\": \"\"\n },\n {\n \"docid\": \"88549b2ac2400ec9083e997f1270713b\",\n \"score\": \"0.53469175\",\n \"text\": \"def authenticate(self, handler, data):\\n username = data['username']\\n password = data['password']\\n if data.get('otp'):\\n password = [password, data['otp']]\\n data = {'username' : username, 'password' : password}\\n return super().authenticate(handler, data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f9cfdeac5cab2a9af7e68aae3d53a11\",\n \"score\": \"0.53066355\",\n \"text\": \"def lock (self, synchronizedCall):\\n self._islock = synchronizedCall \\n \\\"\\\"\\\" execute the command on the smd server \\\"\\\"\\\"\\n self._connection.marshall(Connector.SET_METHOD, self.METHOD_LOCK)\\n # populate parameters\\n self._connection.marshall(Connector.DEF_STRING, self.METHOD_PARAM_LOCK)\\n if synchronizedCall:\\n self._connection.marshall(Connector.SET_VALUE, self.METHOD_PARAM_LOCK, \\\"true\\\")\\n else :\\n self._connection.marshall(Connector.SET_VALUE, self.METHOD_PARAM_LOCK, \\\"false\\\")\\n # proceed with call\\n return self._connection.marshall(Connector.CALL)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18aacd3af67040d5839af31cf045e71c\",\n \"score\": \"0.5306084\",\n \"text\": \"def user_manager(isession, username, password):\\r\\n isession.user = UserManager.User\\r\\n print('Incoming Connection with Authentication')\\r\\n print('Stolen Credentials:')\\r\\n print('Username: ' + username)\\r\\n print('Password: ' + password)\\r\\n stolen_credentials = {}\\r\\n stolen_credentials['username'] = username\\r\\n stolen_credentials['password'] = password\\r\\n with open('stolen_credentials.txt', 'w') as f:\\r\\n f.write(str(stolen_credentials))\\r\\n f.close\\r\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"facb9715ca1189fd31363b1361f3a682\",\n \"score\": \"0.5304645\",\n \"text\": \"def localsys_prompt_login(sid=\\\"\\\",pin=\\\"\\\",temporary=False):\\n if sid == \\\"\\\":\\n sid = eval(input(\\\"Enter your Minerva ID number: \\\"))\\n if pin == \\\"\\\":\\n pin = getpass.getpass(\\\"Enter your PIN number: \\\")\\n if not temporary:\\n local_credentials.set_password('minervaclient_sid','minerva',str(sid))\\n local_credentials.set_password('minervalcient_pin','minerva',str(pin))\\n return sid, pin\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac007628ef872117b5dd2704e703307e\",\n \"score\": \"0.52901584\",\n \"text\": \"def SRP_Auth_2 (req,data):\\r\\n\\t\\r\\n\\t#~ try:\\r\\n\\t## Recuperar K \\r\\n\\tdata = JSONDecoder(\\\"UTF-8\\\").decode(data)\\r\\n\\tMc = data[\\\"M\\\"]\\r\\n\\t\\r\\n\\t## Recuperar las variables de la sesion\\r\\n\\tcryptovars = retrieve_crypto_vars(req)\\r\\n\\t\\r\\n\\tB = cryptovars[\\\"B\\\"]\\r\\n\\tv = cryptovars[\\\"v\\\"]\\r\\n\\tb = cryptovars[\\\"b\\\"]\\r\\n\\tA = cryptovars[\\\"A\\\"]\\r\\n\\tuser = cryptovars[\\\"user\\\"].upper()\\r\\n\\tsalt = cryptovars[\\\"salt\\\"].upper()\\r\\n\\t\\r\\n\\t\\t\\t\\t\\r\\n\\t\\r\\n\\t## u = H(A, B)\\r\\n\\tu = calc_u (A,B,N)\\r\\n\\tu = hex2dec(hash(dec2hex(A)+dec2hex(B)))\\r\\n\\t\\r\\n\\t## S = (Av^u) ^ b \\r\\n\\tS = powMod(A*powMod(v,u,N),b,N)\\r\\n\\t\\r\\n\\t## Clave de sesion K = H(S)\\r\\n\\tK = hash(dec2hex(S).upper()).upper()\\r\\n\\t\\r\\n\\t\\r\\n\\t## Comprobar que la prueba del cliente es igual a lo que tenemos nosotros\\r\\n\\t## M = H(H(N) xor H(g), H(I), s, A, B, K)\\r\\n\\tHN = hex2dec(hash(dec2hex(N)))\\r\\n\\tHg = hex2dec(hash(dec2hex(g)))\\r\\n\\tHI = hash(user).upper()\\r\\n\\tinnerhash = dec2hex((HN^Hg)).upper()+HI+salt+dec2hex(A)+dec2hex(B)+K\\r\\n\\tMc_s = hash(innerhash).upper()\\r\\n\\t\\r\\n\\t\\r\\n\\t\\r\\n\\t## si no coincide lo que calculamos nosotros con lo que nos envian... nanay\\t\\r\\n\\tif(Mc != Mc_s):\\r\\n\\t\\trdata = {\\\"status\\\":\\\"FAILED\\\"};\\r\\n\\t\\treq.write(JSONEncoder(\\\"UTF-8\\\").encode(rdata))\\r\\n\\telse:\\r\\n\\t\\t## Si son iguales enviamos nuestra prueba al cliente\\r\\n\\t\\t\\r\\n\\t\\t## Generar la prueba del servidor\\r\\n\\t\\tMs = hash(dec2hex(A)+Mc+K)\\t\\r\\n\\t\\t\\r\\n\\t\\t\\r\\n\\t\\t\\r\\n\\t\\t## Hacer efectivo el login en el servidor y emitir cookie si es necesario\\r\\n\\t\\tsession_login(req)\\r\\n\\t\\t\\r\\n\\t\\tif(data[\\\"remember\\\"]== \\\"yes\\\"):\\r\\n\\t\\t\\tmessage = createLoginCookie(req,req.session['SRP_crypto']['user'])\\r\\n\\t\\t\\tif message != \\\"OK\\\":\\r\\n\\t\\t\\t\\trdata = {\\\"status\\\": message};\\r\\n\\t\\t\\t\\t\\r\\n\\t\\t## Guardar la clave de sesion y proceder a la comprobacion por parte del cliente\\r\\n\\t\\trdata = {\\\"M\\\":Ms.upper(),\\\"status\\\":\\\"OK\\\"};\\r\\n\\t\\treq.write(JSONEncoder(\\\"UTF-8\\\").encode(rdata))\\r\\n\\t\\t\\r\\n\\t#~ except:\\r\\n\\t\\t#~ rdata = {\\\"status\\\": \\\"Second auth step failed.\\\"};\\r\\n\\t\\t#~ req.write(JSONEncoder(\\\"UTF-8\\\").encode(rdata))\\r\\n\\t\\t\\r\\n\\t\\r\\n\\t\\t\\r\\n\\t## Todo completado, eliminar la info de la variable de sesion\\r\\n\\tdelete_crypto_vars(req)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4438e5ce40e940e71924c2666f55f998\",\n \"score\": \"0.52751994\",\n \"text\": \"def db_password(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79b732b5d53114e6bab8254d75b47408\",\n \"score\": \"0.5275168\",\n \"text\": \"def authenticate():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"034ed65cce6d6c5924af5d730a6b4bc6\",\n \"score\": \"0.52732956\",\n \"text\": \"def call(self, method_name, password, args=None) -> dict:\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81cc1c97438d232d20ccc176353a725b\",\n \"score\": \"0.5271543\",\n \"text\": \"def manual_auth(getpass):\\r\\n return ManualSPAuth(getpass)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c63b749fd775e5f5e46c5bf095db943\",\n \"score\": \"0.52469605\",\n \"text\": \"def __init__(self,username,password):\\n\\n self.username = username\\n self.password = password\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1171177f49ac50984bee90d94d42265c\",\n \"score\": \"0.5235322\",\n \"text\": \"async def ask_json_rpc(method, params, user, pwd):\\n json = {\\n 'method': method,\\n 'params': params,\\n 'id': None,\\n }\\n async with aiohttp.ClientSession() as session:\\n auth = aiohttp.BasicAuth(user, pwd)\\n async with session.post(Const.REST_API, json=json, auth=auth) as response:\\n if response.status == 200:\\n data = await response.json()\\n log.debug(f\\\"JSON-RPC method={method} params={params} success={data}\\\")\\n return data['result']\\n else:\\n text = await response.text()\\n log.error(f\\\"JSON-RPC method={method} params={params} error={text}\\\")\\n raise ConnectionError(text)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a48ebfcd975860c66ba4602059f6a1b\",\n \"score\": \"0.52174294\",\n \"text\": \"def spwd_add(self):\\n self.service=self.getItem2()\\n self.UsrName=self.ui.sha_username.text()\\n self.add=shd()\\n if(self.add.share(self.username,self.UsrName,self.service)):\\n self.ui.sha_username.clear()\\n self.success=Msg(\\\"Password Shared\\\").exec_()\\n else:\\n self.failed=Msg(\\\"User does not exist\\\").exec_()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a3e34eedaf0f19d0e31d70823ca1eb4\",\n \"score\": \"0.52056724\",\n \"text\": \"def test_post_sync_simpleuser(self):\\n self.client.login(username='homer', password='doooh')\\n response = self.client.post(reverse('common:sync_randos'), data={})\\n self.assertRedirects(response, '/login/?next=/commands/sync')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d2d821c7808a2371f76d459006601d5e\",\n \"score\": \"0.51969475\",\n \"text\": \"def __init__(self):\\n HOST = 'managesql'\\n HOST = 'karttur'\\n secrets = netrc.netrc()\\n username, account, password = secrets.authenticators( HOST )\\n pswd = b64encode(password.encode())\\n #create a query dictionary for connecting to the Postgres server\\n query = {'db':'postgres','user':username,'pswd':pswd}\\n #Connect to the Postgres Server\\n self.session = PGsession.__init__(self,query,'ManageSMAP')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f7f9ae56e902a58d3bf224aea3cca3a4\",\n \"score\": \"0.51919574\",\n \"text\": \"def __init__(self, username, password):\\r\\n\\r\\n self.username = username\\r\\n self.password = password\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f089d2c6925eacb12c8a342959a49010\",\n \"score\": \"0.5189284\",\n \"text\": \"def login(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f089d2c6925eacb12c8a342959a49010\",\n \"score\": \"0.5189284\",\n \"text\": \"def login(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b7776f1650f04390d379de2f57ae428\",\n \"score\": \"0.51867163\",\n \"text\": \"def setCredentials (self, username, password, jobguid = \\\"\\\", executionparams = {}):\\n params =dict()\\n params['username'] = username\\n params['password'] = password\\n return q.workflowengine.actionmanager.startActorAction('systemnas', 'setCredentials', params, jobguid=jobguid, executionparams=executionparams)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6a9917467b08733b4522fc665d07ed1\",\n \"score\": \"0.5185898\",\n \"text\": \"def __init__(self, user, server, password):\\n self.session = self.login(user, server, password)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a14598081db630ddb3983443d81ce214\",\n \"score\": \"0.5181821\",\n \"text\": \"def getUser(name, password=None):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2bb2b0c1e4b20a795827cb1e37861b51\",\n \"score\": \"0.5178116\",\n \"text\": \"def create(self, login, name=None, password=None, jobguid=\\\"\\\", executionparams=None):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53afc3e5bfcff4722e65011eec4b63a8\",\n \"score\": \"0.51725245\",\n \"text\": \"def loginUser(self, username: str, password: hash):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"57a9a3594d75e5447988cadde122ff21\",\n \"score\": \"0.5157121\",\n \"text\": \"def __init__(self, username, password):\\n self.username = username\\n self.password = password\",\n \"title\": \"\"\n },\n {\n \"docid\": \"57a9a3594d75e5447988cadde122ff21\",\n \"score\": \"0.5157121\",\n \"text\": \"def __init__(self, username, password):\\n self.username = username\\n self.password = password\",\n \"title\": \"\"\n },\n {\n \"docid\": \"57a9a3594d75e5447988cadde122ff21\",\n \"score\": \"0.5157121\",\n \"text\": \"def __init__(self, username, password):\\n self.username = username\\n self.password = password\",\n \"title\": \"\"\n },\n {\n \"docid\": \"57a9a3594d75e5447988cadde122ff21\",\n \"score\": \"0.5157121\",\n \"text\": \"def __init__(self, username, password):\\n self.username = username\\n self.password = password\",\n \"title\": \"\"\n },\n {\n \"docid\": \"57a9a3594d75e5447988cadde122ff21\",\n \"score\": \"0.5157121\",\n \"text\": \"def __init__(self, username, password):\\n self.username = username\\n self.password = password\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04fdb94b85aa109db0e92d10ee162870\",\n \"score\": \"0.5156488\",\n \"text\": \"def authenticate(self, username, password):\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5087acf3c289b01a2fc3e39a1fc8580\",\n \"score\": \"0.5154638\",\n \"text\": \"def __login__(self):\\n try:\\n resp = self.login(\\n self.config.get('checkpoint', 'cp_server'),\\n self.config.get('checkpoint', 'username'),\\n self.config.get('checkpoint', 'password'),\\n self.config.getboolean('checkpoint', 'resume_session', fallback=False),\\n **{'domain': self.config.get('checkpoint', 'domain', fallback=\\\"\\\"),\\n 'session-comments': self.config.get('checkpoint', 'session-comments', fallback=\\\"\\\"),\\n 'session-description': self.config.get('checkpoint', 'session-description', fallback=\\\"\\\"),\\n 'session-name': \\\"CPbot_%s_%s_%d\\\" % (gethostname(), self.config.get('checkpoint', 'session-name',\\n fallback= Path(self.configfile).name), int(time.time())),\\n 'session-timeout': self.config.getint('checkpoint', 'session-timeout', fallback=3600)}\\n )\\n except Exception as err:\\n raise err\\n else:\\n if not resp.success:\\n resp.data['_action'] = whoami()\\n self.logger.error(resp.data)\\n raise Exception(\\\"Authentication Failed. %s\\\" % resp.data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0047b1e16a0e0b40f8c6003060c97426\",\n \"score\": \"0.5154146\",\n \"text\": \"def create_shared_credential_command(client: Client, name: str, site_assignment: str, service: str,\\n database: str | None = None, description: str | None = None,\\n domain: str | None = None, host_restriction: str | None = None,\\n http_realm: str | None = None, notes_id_password: str | None = None,\\n ntlm_hash: str | None = None, oracle_enumerate_sids: str | None = None,\\n oracle_listener_password: str | None = None, oracle_sid: str | None = None,\\n password: str | None = None, port_restriction: str | None = None,\\n sites: str | None = None, authentication_type: str | None = None,\\n community_name: str | None = None, privacy_password: str | None = None,\\n privacy_type: str | None = None, ssh_key_pem: str | None = None,\\n ssh_permission_elevation: str | None = None,\\n ssh_permission_elevation_password: str | None = None,\\n ssh_permission_elevation_username: str | None = None,\\n ssh_private_key_password: str | None = None,\\n use_windows_authentication: str | None = None,\\n username: str | None = None) -> CommandResults:\\n oracle_enumerate_sids_bool = None\\n sites_list = None\\n snmpv3_authentication_type_enum = None\\n snmpv3_privacy_type_enum = None\\n ssh_permission_elevation_enum = None\\n use_windows_authentication_bool = None\\n\\n if oracle_enumerate_sids is not None:\\n oracle_enumerate_sids_bool = argToBoolean(oracle_enumerate_sids)\\n\\n if sites is not None:\\n sites_list = [int(item) for item in argToList(sites)]\\n\\n if authentication_type is not None:\\n snmpv3_authentication_type_enum = SNMPv3AuthenticationType[authentication_type]\\n\\n if privacy_type is not None:\\n snmpv3_privacy_type_enum = SNMPv3PrivacyType[privacy_type]\\n\\n if ssh_permission_elevation is not None:\\n ssh_permission_elevation_enum = SSHElevationType[ssh_permission_elevation]\\n\\n if use_windows_authentication is not None:\\n use_windows_authentication_bool = argToBoolean(use_windows_authentication)\\n\\n response_data = client.create_shared_credential(\\n name=name,\\n site_assignment=SharedCredentialSiteAssignment[site_assignment],\\n service=CredentialService[service],\\n database_name=database,\\n description=description,\\n domain=domain,\\n host_restriction=host_restriction,\\n http_realm=http_realm,\\n notes_id_password=notes_id_password,\\n ntlm_hash=ntlm_hash,\\n oracle_enumerate_sids=oracle_enumerate_sids_bool,\\n oracle_listener_password=oracle_listener_password,\\n oracle_sid=oracle_sid,\\n password=password,\\n port_restriction=port_restriction,\\n sites=sites_list,\\n snmp_community_name=community_name,\\n snmpv3_authentication_type=snmpv3_authentication_type_enum,\\n snmpv3_privacy_password=privacy_password,\\n snmpv3_privacy_type=snmpv3_privacy_type_enum,\\n ssh_key_pem=ssh_key_pem,\\n ssh_permission_elevation=ssh_permission_elevation_enum,\\n ssh_permission_elevation_password=ssh_permission_elevation_password,\\n ssh_permission_elevation_username=ssh_permission_elevation_username,\\n ssh_private_key_password=ssh_private_key_password,\\n use_windows_authentication=use_windows_authentication_bool,\\n username=username,\\n )\\n\\n return CommandResults(\\n readable_output=f\\\"New shared credential has been created with ID {response_data['id']}.\\\",\\n outputs_prefix=\\\"Nexpose.SharedCredential\\\",\\n outputs_key_field=\\\"id\\\",\\n outputs={\\\"id\\\": response_data['id']},\\n raw_response=response_data,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eced28271063e4ed44cff9330708be0b\",\n \"score\": \"0.51505387\",\n \"text\": \"def check_auth(username, password):\\n print username\\n conn=PhenotipsClient()\\n response=conn.get_patient(auth='%s:%s' % (username, password,),number=1)\\n if response:\\n session['password2'] = password\\n password=md5.new(password).hexdigest()\\n session['user'] = username\\n session['password'] = password\\n return True\\n else: return False\\n # check that user name and hash of password exist in database\\n db_users=get_db('users')\\n # setting a session key for pubmedBatch to save result\\n session['password2'] = password\\n password=md5.new(password).hexdigest()\\n session['user'] = username\\n session['password'] = password\\n r=db_users.users.find_one({'user':username})\\n if r is None:\\n return False\\n elif md5.new(r['password']).hexdigest() == md5.new(password).hexdigest():\\n print('LOGIN', session['user'])\\n return True\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5a1c5550719cd705dfa8219748585e3\",\n \"score\": \"0.51445407\",\n \"text\": \"def __init__(self, procname, aeskey, password):\\n self.procname = procname\\n self.aeskey = aeskey\\n self.password = password\\n self.client = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46529ad2eab83bb520c5be69e70371c9\",\n \"score\": \"0.51330745\",\n \"text\": \"def _update(self, args: dict):\\n self.PROTOCOL = args.get('PROTOCOL', Connection_String.PROTOCOL)\\n self.USERNAME = args.get('USERNAME', Connection_String.USERNAME)\\n self.PASSWORD = args.get('PASSWORD', Connection_String.PASSWORD)\\n self.ADDRESS = args.get('ADDRESS', Connection_String.ADDRESS)\\n self.PORT = args.get('PORT', Connection_String.PORT)\\n self.SCHEMA = args.get('SCHEMA', Connection_String.SCHEMA)\\n self.OPTIONS = args.get('OPTIONS', Connection_String.OPTIONS)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"09f9153db409d05ad8192e17f18963c6\",\n \"score\": \"0.51304585\",\n \"text\": \"def SRP_Auth_1 (req,data):\\r\\n\\ttry:\\r\\n\\t\\tsession = req.session \\r\\n\\t\\tsession.lock()\\r\\n\\t\\tif session.has_key(\\\"logged\\\"):\\r\\n\\t\\t\\tif session[\\\"logged\\\"]:\\r\\n\\t\\t\\t\\trdata = {\\\"status\\\": \\\"Already logged in.\\\"};\\r\\n\\t\\t\\t\\treq.write(JSONEncoder(\\\"UTF-8\\\").encode(rdata))\\r\\n\\t\\t\\t\\treturn \\r\\n\\t\\tsession.unlock()\\r\\n\\t\\t\\r\\n\\t\\t\\r\\n\\t\\tdata = JSONDecoder(\\\"UTF-8\\\").decode(data)\\r\\n\\r\\n\\t\\t## recogemos A\\r\\n\\t\\tA = hex2dec(data[\\\"A\\\"])\\r\\n\\t\\t\\r\\n\\t\\t## Abortar si A%N == 0\\r\\n\\t\\tif(A%N == 0):\\r\\n\\t\\t\\trdata = {\\\"status\\\": \\\"Server step 1 failed.\\\"};\\r\\n\\t\\t\\treq.write(JSONEncoder(\\\"UTF-8\\\").encode(rdata))\\r\\n\\t\\t\\r\\n\\t\\t## Conectar\\r\\n\\t\\tdb=MySQLdb.connect(host=\\\"localhost\\\",user=\\\"root\\\",passwd=\\\"1234\\\",db=\\\"prueba\\\")\\r\\n\\t\\t\\r\\n\\t\\t## Preparar transaccion\\r\\n\\t\\tsql = \\\"\\\"\\\"\\r\\n\\t\\tSELECT * \\r\\n\\t\\tFROM usuarios \\r\\n\\t\\tWHERE userName = \\\"%s\\\" \\r\\n\\t\\t\\\"\\\"\\\"%(data['username'])\\r\\n\\t\\t\\r\\n\\t\\tcursor=db.cursor()\\r\\n\\t\\tcursor.execute(sql)\\r\\n\\t\\tresultado=cursor.fetchall()\\r\\n\\r\\n\\t\\t## Desconectar\\r\\n\\t\\tdb.close()\\r\\n\\t\\t\\r\\n\\t\\tuser_id = None\\r\\n\\t\\t## Asegurarse que no hay mas de un resultado\\r\\n\\t\\tif len(resultado)>1:\\r\\n\\t\\t\\trdata = {\\\"status\\\": \\\"Server step 1 failed: Duplicated username!.\\\"};\\r\\n\\t\\t\\treq.write(JSONEncoder(\\\"UTF-8\\\").encode(rdata))\\r\\n\\t\\t\\treturn\\r\\n\\t\\telse:\\r\\n\\t\\t\\tif len(resultado) == 0:\\r\\n\\t\\t\\t\\trdata = {\\\"status\\\": \\\"No existe el usuario.\\\"};\\r\\n\\t\\t\\t\\treq.write(JSONEncoder(\\\"UTF-8\\\").encode(rdata))\\r\\n\\t\\t\\t\\treturn\\r\\n\\t\\t\\telse:\\r\\n\\t\\t\\t\\tuser_id = resultado[0][0]\\r\\n\\t\\t\\t\\r\\n\\t\\t## Devuelve el salt (2a columna) y B\\r\\n\\t\\t## k = H(N, g)\\r\\n\\t\\t## Si se ha de calcular k, lo podemos hacer aqui\\r\\n\\t\\tdbsalt = resultado[0][3]\\r\\n\\t\\tdbv =resultado[0][2]\\r\\n\\t\\tv = hex2dec(dbv)\\r\\n\\t\\tsalt = hex2dec(dbsalt)\\r\\n\\t\\t\\r\\n\\t\\t## b es un numero al azar de como maximo 32 bytes\\r\\n\\t\\trandom.seed()\\r\\n\\t\\tb = random.getrandbits(256)\\r\\n\\t\\t\\r\\n\\t\\t## B=\\tkv + g^b (mod N)\\r\\n\\t\\tkv = k*v\\r\\n\\t\\tgb = powMod(g,b,N)\\r\\n\\t\\tB = (kv+gb)%N\\r\\n\\t\\t\\r\\n\\t\\trdata = {\\\"salt\\\":dbsalt.upper(),\\\"B\\\":dec2hex(B).upper(),\\\"status\\\": \\\"OK\\\"};\\r\\n\\t\\treq.write(JSONEncoder(\\\"UTF-8\\\").encode(rdata))\\r\\n\\t\\t\\r\\n\\t\\t\\r\\n\\t\\t## Create crypto data structure\\r\\n\\t\\tcryptovars = {\\\"A\\\":A,\\\"B\\\":B,\\\"v\\\":v,\\\"b\\\":b,\\\"user\\\":data['username'],\\\"salt\\\":dbsalt.upper(),\\\"user_id\\\":user_id}\\r\\n\\t\\tsave_crypto_vars(req, cryptovars);\\r\\n\\t\\t\\r\\n\\texcept:\\r\\n\\t\\trdata = {\\\"status\\\": \\\"Server step 1 failed.\\\"};\\r\\n\\t\\treq.write(JSONEncoder(\\\"UTF-8\\\").encode(rdata))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0029b83c9e0cf0a2d30d7636b1aa22e\",\n \"score\": \"0.51254386\",\n \"text\": \"def post(self):\\n username = self.get_argument(\\\"emailAddress\\\",\\\"\\\")\\n password = self.get_argument(\\\"password\\\",\\\"\\\")\\n loginInfo = {\\\"username\\\": username,\\n \\\"password\\\": password}\\n self.loginUser(loginInfo)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f63b693f598436f8e8cdc6529402d622\",\n \"score\": \"0.5113076\",\n \"text\": \"def acquire_token(self, login, password):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"667b16f1c1e2964fc2b24a7a9793809d\",\n \"score\": \"0.51049674\",\n \"text\": \"def set_credentials(username, password):\\n global USERNAME\\n global PASSWORD\\n USERNAME = username\\n PASSWORD = password\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3dca23c5d890332dd2fa3311f0902105\",\n \"score\": \"0.5101368\",\n \"text\": \"def login(self, uname, pword) :\\n\\t\\tprint \\\"connecting to client manager\\\"\\n\\t\\tcmSock = socket(AF_INET, SOCK_STREAM);\\n\\t\\tcmSock.connect((self.cliMgrIp,30122))\\n\\t\\tprint \\\"connected to client manager\\\"\\n\\t\\n\\t\\tcmSock.sendall(\\\"Forest-login-v1\\\\nlogin: \\\" + uname + \\\\\\n \\t \\\"\\\\npassword: \\\" + pword + \\\"\\\\nover\\\\n\\\")\\n\\n\\n\\t\\tbuf = \\\"\\\"\\n\\t\\tprint \\\"reading line\\\"\\n\\t\\tline,buf = self.readLine(cmSock,buf)\\n\\t\\tif line != \\\"success\\\" :\\n\\t\\t\\tprint \\\"oops\\\", line\\n\\t\\t\\treturn False\\n\\t\\tline,buf = self.readLine(cmSock,buf)\\n\\t\\tif line != \\\"over\\\" :\\n\\t\\t\\tprint \\\"oops\\\", line\\n\\t\\t\\treturn False\\n\\n\\t\\tcmSock.sendall(\\\"newSession\\\\nover\\\\n\\\")\\n\\n\\t\\tline,buf = self.readLine(cmSock,buf)\\n\\n\\t\\tchunks = line.partition(\\\":\\\")\\n\\t\\tif chunks[0].strip() != \\\"yourAddress\\\" or chunks[1] != \\\":\\\" :\\n\\t\\t\\treturn False\\n\\t\\tself.myFadr = string2fadr(chunks[2].strip())\\n\\n\\t\\tline,buf = self.readLine(cmSock,buf)\\n\\t\\tchunks = line.partition(\\\":\\\")\\n\\t\\tif chunks[0].strip() != \\\"yourRouter\\\" or chunks[1] != \\\":\\\" :\\n\\t\\t\\treturn False\\n\\t\\ttriple = chunks[2].strip(); triple = triple[1:-1].strip()\\n\\t\\tchunks = triple.split(\\\",\\\")\\n\\t\\tif len(chunks) != 3 : return False\\n\\t\\tself.rtrIp = string2ip(chunks[0].strip())\\n\\t\\tself.rtrPort = int(chunks[1].strip())\\n\\t\\tself.rtrFadr = string2fadr(chunks[2].strip())\\n\\n\\t\\tline,buf = self.readLine(cmSock,buf)\\n\\t\\tchunks = line.partition(\\\":\\\")\\n\\t\\tif chunks[0].strip() != \\\"comtCtlAddress\\\" or chunks[1] != \\\":\\\" :\\n\\t\\t\\treturn False\\n\\t\\tself.comtCtlFadr = string2fadr(chunks[2].strip())\\n\\n\\t\\tline,buf = self.readLine(cmSock,buf)\\n\\t\\tchunks = line.partition(\\\":\\\")\\n\\t\\tif chunks[0].strip() != \\\"connectNonce\\\" or chunks[1] != \\\":\\\" :\\n\\t\\t\\treturn False\\n\\t\\tself.nonce = int(chunks[2].strip())\\n\\n\\t\\tline,buf = self.readLine(cmSock,buf) \\n\\t\\tif line != \\\"overAndOut\\\" : return False\\n\\n\\t\\tprint \\\"login successful\\\"\\n\\n\\t\\tcmSock.close()\\n\\n\\t\\tif self.debug >= 1 :\\n\\t\\t\\tprint \\\"avatar address =\\\", fadr2string(self.myFadr)\\n\\t\\t\\tprint \\\"router info = (\\\", ip2string(self.rtrIp), \\\\\\n\\t\\t\\t\\t\\t str(self.rtrPort), \\\\\\n\\t\\t\\t\\t\\t fadr2string(self.rtrFadr), \\\")\\\"\\n\\t\\t\\tprint \\\"comtCtl address = \\\",fadr2string(self.comtCtlFadr)\\n\\t\\t\\tprint \\\"nonce = \\\", self.nonce\\n\\n\\t\\treturn True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6beddfb03196a65c5854a5e2a7c9482\",\n \"score\": \"0.50947905\",\n \"text\": \"def authenticate(self,user,passwd):\\n\\n\\tself.AddAttr(1,user)\\n\\tself.AddPasswordAttr(password=passwd)\\n\\tself.calledfinal=0\\n\\n\\tself.send()\\t\\t# Send the packet\\n\\tself.receive()\\t\\t# Wait for reply\\n\\treturn self.verify()\\t# Validate packet\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1792e73bbfeca58c05e01d77bd2b68d7\",\n \"score\": \"0.50855505\",\n \"text\": \"def __init__(self, username, password):\\n\\n self.username = username\\n self.password = password\\n self.account = username\\n self.mailbox = self.login()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d10989815cad087f72b3c688c0b5089\",\n \"score\": \"0.50829554\",\n \"text\": \"def login(self, uname, pword) :\\n\\t\\tprint \\\"connecting to client manager\\\"\\n\\t\\tcmSock = socket(AF_INET, SOCK_STREAM);\\n\\t\\tcmSock.connect((self.cliMgrIp,30122))\\n\\t\\tprint \\\"connected to client manager\\\"\\n\\t\\n\\t\\tcmSock.sendall(\\\"Forest-login-v1\\\\nlogin: \\\" + uname + \\\\\\n \\t \\\"\\\\npassword: \\\" + pword + \\\"\\\\nover\\\\n\\\")\\n\\n\\n\\t\\tbuf = \\\"\\\"\\n\\t\\tprint \\\"reading line\\\"\\n\\t\\tline,buf = self.readLine(cmSock,buf)\\n\\t\\tif line != \\\"login successful\\\" :\\n\\t\\t\\treturn False\\n\\t\\tline,buf = self.readLine(cmSock,buf)\\n\\t\\tif line != \\\"over\\\" :\\n\\t\\t\\treturn False\\n\\n\\t\\tcmSock.sendall(\\\"newSession\\\\nover\\\\n\\\")\\n\\n\\t\\tline,buf = self.readLine(cmSock,buf)\\n\\t\\tchunks = line.partition(\\\":\\\")\\n\\t\\tif chunks[0].strip() != \\\"yourAddress\\\" or chunks[1] != \\\":\\\" :\\n\\t\\t\\treturn False\\n\\t\\tself.myFadr = string2fadr(chunks[2].strip())\\n\\n\\t\\tline,buf = self.readLine(cmSock,buf)\\n\\t\\tchunks = line.partition(\\\":\\\")\\n\\t\\tif chunks[0].strip() != \\\"yourRouter\\\" or chunks[1] != \\\":\\\" :\\n\\t\\t\\treturn False\\n\\t\\ttriple = chunks[2].strip(); triple = triple[1:-1].strip()\\n\\t\\tchunks = triple.split(\\\",\\\")\\n\\t\\tif len(chunks) != 3 : return False\\n\\t\\tself.rtrIp = string2ip(chunks[0].strip())\\n\\t\\tself.rtrPort = int(chunks[1].strip())\\n\\t\\tself.rtrFadr = string2fadr(chunks[2].strip())\\n\\n\\t\\tline,buf = self.readLine(cmSock,buf)\\n\\t\\tchunks = line.partition(\\\":\\\")\\n\\t\\tif chunks[0].strip() != \\\"comtCtlAddress\\\" or chunks[1] != \\\":\\\" :\\n\\t\\t\\treturn False\\n\\t\\tself.comtCtlFadr = string2fadr(chunks[2].strip())\\n\\n\\t\\tline,buf = self.readLine(cmSock,buf)\\n\\t\\tchunks = line.partition(\\\":\\\")\\n\\t\\tif chunks[0].strip() != \\\"connectNonce\\\" or chunks[1] != \\\":\\\" :\\n\\t\\t\\treturn False\\n\\t\\tself.nonce = int(chunks[2].strip())\\n\\n\\t\\tline,buf = self.readLine(cmSock,buf) \\n\\t\\tif line != \\\"overAndOut\\\" : return False\\n\\n\\t\\tprint \\\"login successful\\\"\\n\\n\\t\\tcmSock.close()\\n\\n\\t\\tif self.debug >= 1 :\\n\\t\\t\\tprint \\\"avatar address =\\\", fadr2string(self.myFadr)\\n\\t\\t\\tprint \\\"router info = (\\\", ip2string(self.rtrIp), \\\\\\n\\t\\t\\t\\t\\t str(self.rtrPort), \\\\\\n\\t\\t\\t\\t\\t fadr2string(self.rtrFadr), \\\")\\\"\\n\\t\\t\\tprint \\\"comtCtl address = \\\",fadr2string(self.comtCtlFadr)\\n\\t\\t\\tprint \\\"nonce = \\\", self.nonce\\n\\n\\t\\treturn True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea2ecc1d4a0963156e1836168cc5641e\",\n \"score\": \"0.50822693\",\n \"text\": \"def PATCH(self):\\n uri = self.request.get_URI()\\n clientID = self.request.get_user_id()\\n programID = self.URI.match(uri).group(1)\\n data = self.request.get_data_as_dictionery()\\n username, password = data.get(USERNAME), data.get(PASS)\\n if database.cange_record(clientID, programID, username, password):\\n return Responce.ok()\\n else:\\n # TODO: dont have user not valid/ program not valid difference\\n return Responce.not_found()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11736cb92640b9b110cae82773f6e546\",\n \"score\": \"0.5076801\",\n \"text\": \"def __init__(self, username, password):\\n \\n self._cli = cli.Cli()\\n self._django_sr = django_sendrcv.DjangoSendRcv(base_url)\\n self._tornado_sr = tornado_sendrcv.TornadoSendRcv()\\n \\n # Store off the username and password.\\n self._username = username\\n self._password = password\\n \\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e506eea3b92de5e0c9183bde9d58ebca\",\n \"score\": \"0.5074458\",\n \"text\": \"def login(self) -> LoginProtocol:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"21242db00b0f49b0cfc0057a0b961cc8\",\n \"score\": \"0.50736403\",\n \"text\": \"def login(self):\\r\\n (username, password) = self.client_flags.split(' ')\\r\\n self.connect_server(username, password)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1a97bfd6593fcfbbe5ab28071ca8860\",\n \"score\": \"0.5073414\",\n \"text\": \"def get_params(self):\\n return {\\n 'username': self.username,\\n 'password': self.password\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bdf4ccc995278a53202b6be5b21c43b3\",\n \"score\": \"0.5069431\",\n \"text\": \"def authenticate(environ, identity):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d331bb9608237f737f16285403242f3\",\n \"score\": \"0.50693864\",\n \"text\": \"def register_client(self, pin):\\n # optional imports required for authentication\\n from ..util import parse_plist_from_bytes, write_plist_to_bytes\\n\\n def get_plist_request_data(plist_data):\\n \\\"\\\"\\\"\\n Create a request header and body to send a plist to the server.\\n :param plist_data: dictionary holding the plist data\\n :return: plist rtsp request header, plist rtsp request body\\n \\\"\\\"\\\"\\n header = {\\n \\\"User-Agent\\\": self.user_agent,\\n \\\"Content-Type:\\\": \\\"application/x-apple-binary-plist\\\",\\n \\\"Connection\\\": \\\"keep-alive\\\",\\n }\\n return header, write_plist_to_bytes(plist_data)\\n\\n # create new unique credentials\\n auth_identifier, auth_secret = new_credentials()\\n\\n # 2. send plist data to receiver\\n header, body = get_plist_request_data({\\\"user\\\": auth_identifier, \\\"method\\\": \\\"pin\\\"})\\n req = RTSPRequest(\\\"/pair-setup-pin\\\", \\\"POST\\\", header, body=body, protocol_version=self.protocol_version)\\n res = self.send_and_recv(req)\\n\\n if res.code != 200:\\n self.cleanup(RTSPReason.AUTHENTICATION)\\n raise DeviceAuthenticationError(\\\"Can not receive public key and salt.\\\")\\n\\n # 3. read the public key and salt\\n res_plist = parse_plist_from_bytes(res.body)\\n public_key = res_plist[\\\"pk\\\"]\\n salt = res_plist[\\\"salt\\\"]\\n\\n # 4. Run Secure Remote Password procedure\\n srp_handler = SRPAuthenticationHandler()\\n srp_handler.initialize(auth_secret)\\n srp_handler.step1(auth_identifier, pin)\\n pub_key, m1_proof = srp_handler.step2(public_key, salt)\\n\\n # send the public key and the proof to the server\\n header, body = get_plist_request_data({\\\"pk\\\": pub_key, \\\"proof\\\": m1_proof})\\n req = RTSPRequest(\\\"/pair-setup-pin\\\", \\\"POST\\\", header, body=body, protocol_version=self.protocol_version)\\n res = self.send_and_recv(req)\\n\\n if res.code != 200:\\n self.cleanup(RTSPReason.AUTHENTICATION)\\n raise DeviceAuthenticationWrongPinCodeError(\\\"Wrong pin code used.\\\")\\n\\n # 5. Run AES\\n epk, tag = srp_handler.step3()\\n header, body = get_plist_request_data({\\\"epk\\\": epk, \\\"authTag\\\": tag})\\n req = RTSPRequest(\\\"/pair-setup-pin\\\", \\\"POST\\\", header, body=body, protocol_version=self.protocol_version)\\n res = self.send_and_recv(req)\\n\\n if res.code != 200:\\n self.cleanup(RTSPReason.AUTHENTICATION)\\n raise DeviceAuthenticationWrongPinCodeError(\\\"Can not confirm authentication secret.\\\")\\n\\n return auth_identifier, auth_secret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c9b350a1f953975eeefbfc48118e349\",\n \"score\": \"0.5067358\",\n \"text\": \"def __init__(self, username = \\\"\\\", password = \\\"\\\"):\\n self.username = username\\n self.password = password\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc2ba31a6636f4bb8376da197e305f82\",\n \"score\": \"0.5063522\",\n \"text\": \"def auth(self):\\r\\n#\\t\\tprint(\\\"auth called\\\")\\r\\n\\t\\tparams = \\\"{\\\\\\\"username\\\\\\\": \\\\\\\"\\\" + self.username.text + \\\"\\\\\\\", \\\\\\\"password\\\\\\\": \\\\\\\"\\\"+ self.password.text+ \\\"\\\\\\\"}\\\"\\r\\n\\t\\theaders = {'Content-Type': 'application/json', 'Accept': 'text/plain'}\\r\\n\\t\\treq = UrlRequest('http://138.68.21.198:5000/broschat/login', \\r\\n\\t\\t\\ton_success=self.loginsuccess, req_body=params, req_headers=headers)\\r\\n\\t\\treq.wait()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d897a530f42a983d3cb4aac52561b8ff\",\n \"score\": \"0.5063386\",\n \"text\": \"def login(user, password=_marker):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"193c9ce2a3afe7f2373fffc4b0004717\",\n \"score\": \"0.5062788\",\n \"text\": \"def authenticate(self):\\n\\n # gui = threading.Thread(target=verify, args=(self.setData, ))\\n # gui.start()\\n # gui.join()\\n\\n parent_conn, child_conn = Pipe()\\n gui = Process(target=verify, args=(child_conn,))\\n print(\\\"Starting authentication\\\")\\n gui.start()\\n # waiting till authentication is over\\n gui.join()\\n self.customerID, self.name = parent_conn.recv()\\n parent_conn.close()\\n print(\\\"Done\\\")\\n if self.customerID is False or self.name is False:\\n return False, False, False, False\\n\\n today = datetime.datetime.today()\\n now = datetime.datetime.now()\\n date = today.strftime(\\\"%Y-%m-%d\\\")\\n current_time = now.strftime(\\\"%H-%M\\\")\\n\\n update = \\\"UPDATE Customer SET login_date=%s WHERE customerID=%s\\\"\\n val = (date, self.customerID)\\n cursor, myconn = c()\\n cursor.execute(update, val)\\n update = \\\"UPDATE Customer SET login_time=%s WHERE customerID=%s\\\"\\n val = (current_time, self.customerID)\\n cursor.execute(update, val)\\n myconn.commit()\\n return self.customerID, self.name, date, current_time\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d6c3756bf50414fd38467554c180711f\",\n \"score\": \"0.50592935\",\n \"text\": \"def haspassword(self):\\r\\n raise BaseException('Not a JSON-RPC command')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79f1fbbcad530b6eb1a92cb952cc50b8\",\n \"score\": \"0.50524914\",\n \"text\": \"def remote_authenticate(self, username, password):\\n if not self.collects_password:\\n # Even if we were somehow given a password, we won't be\\n # passing it on.\\n password = None\\n info = self.patron_information(username, password)\\n return self.info_to_patrondata(info)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36ced8ecc2dc5f7c333f78ee8209a4c0\",\n \"score\": \"0.50493103\",\n \"text\": \"def login(self):\\n if self.provider == CurveThrift.Provider.LINE: # LINE\\n j = self._get_json(self.LINE_SESSION_LINE_URL)\\n else: # NAVER\\n j = self._get_json(self.LINE_SESSION_NAVER_URL)\\n\\n session_key = j['session_key']\\n message = (chr(len(session_key)) + session_key +\\n chr(len(self.id)) + self.id +\\n chr(len(self.password)) + self.password).encode('utf-8')\\n\\n keyname, n, e = j['rsa_key'].split(\\\",\\\")\\n pub_key = rsa.PublicKey(int(n,16), int(e,16))\\n crypto = rsa.encrypt(message, pub_key).encode('hex')\\n\\n self._headers['X-Line-Application'] = self.app\\n self.transport = THttpClient.THttpClient(self.LINE_HTTP_URL)\\n self.transport.setCustomHeaders(self._headers)\\n\\n self.protocol = TCompactProtocol.TCompactProtocol(self.transport)\\n self._client = CurveThrift.Client(self.protocol)\\n\\n try:\\n with open(self.CERT_FILE,'r') as f:\\n self.certificate = f.read()\\n f.close()\\n except:\\n self.certificate = \\\"\\\"\\n\\n msg = self._client.loginWithIdentityCredentialForCertificate(\\n self.id, self.password, keyname, crypto, True, self.ip,\\n self.com_name, self.provider, self.certificate)\\n\\n if msg.type == 1:\\n self.certificate = msg.certificate\\n self.authToken = self._headers['X-Line-Access'] = msg.authToken\\n elif msg.type == 2:\\n msg = \\\"require QR code\\\"\\n self.raise_error(msg)\\n elif msg.type == 3:\\n self._headers['X-Line-Access'] = msg.verifier\\n self._pinCode = msg.pinCode\\n\\n print \\\"Enter PinCode '%s' to your mobile phone in 2 minutes\\\"\\\\\\n % self._pinCode\\n\\n j = self.get_json(self.LINE_CERTIFICATE_URL)\\n self.verifier = j['result']['verifier']\\n\\n msg = self._client.loginWithVerifierForCertificate(self.verifier)\\n if msg.type == 1:\\n if msg.certificate is not None:\\n with open(self.CERT_FILE,'w') as f:\\n f.write(msg.certificate)\\n self.certificate = msg.certificate\\n if msg.authToken is not None:\\n self.authToken = self._headers['X-Line-Access'] = msg.authToken\\n return True\\n else:\\n return False\\n else:\\n msg = \\\"Require device confirm\\\"\\n self.raise_error(msg)\\n\\n #raise Exception(\\\"Code is removed because of the request of LINE corporation\\\")\\n else:\\n self.authToken = self._headers['X-Line-Access'] = msg.authToken\\n\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0184d3429d5b6cea47d295931734ded\",\n \"score\": \"0.5048686\",\n \"text\": \"def svn_auth_invoke_simple_prompt_func(svn_auth_simple_prompt_func_t__obj, svn_auth_cred_simple_t_cred, void_baton, char_realm, char_username, svn_boolean_t_may_save, apr_pool_t_pool): # real signature unknown; restored from __doc__\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d475061d4dcdedcbc28cd6061fa436f\",\n \"score\": \"0.5046636\",\n \"text\": \"def setup_method(self):\\n self.user = User.objects.create_user(\\n \\\"username\\\", password=\\\"123\\\", email=\\\"user@user.cl\\\"\\n )\\n self.token = Token.objects.create(user=self.user)\\n self.user.user_permissions.add(Permission.objects.get(name=\\\"Execute Commands\\\"))\\n self.url = \\\"manager/ws/subscription/?token={}\\\".format(self.token)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d6f6f566643fa034ed924c76ece3dcc\",\n \"score\": \"0.5045806\",\n \"text\": \"def _method_init(self, host, username, password, **kwargs):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d282103f938d0705e32c54065a7d1285\",\n \"score\": \"0.5042683\",\n \"text\": \"def __init__(self,konti,username,password):\\n self.konti = konti\\n self.username = username\\n self.password = password\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66a7b4a4e0027245401458d99736f1c2\",\n \"score\": \"0.5039774\",\n \"text\": \"def set_credentials(cb_username, cb_password):\\n _SESSION_STUFF[\\\"calcbench_user_name\\\"] = cb_username\\n _SESSION_STUFF[\\\"calcbench_password\\\"] = cb_password\\n _calcbench_session() # Make sure credentials work.\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be02e3889419d27aaf2dc574b0412c9c\",\n \"score\": \"0.5035096\",\n \"text\": \"def __init__(self,user_name, password):\\n self.user_name = user_name\\n self.password = password\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d8dc6b3be4543e37660e8886e5ba105b\",\n \"score\": \"0.5020277\",\n \"text\": \"def __init__(self, username, password):\\n self.auth = (username, password)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f09fe597ed39ca969d5d387757103d4\",\n \"score\": \"0.5018498\",\n \"text\": \"def savePassword(param, self):\\r\\n try:\\r\\n\\r\\n if localHost or checkSession(self, role = ['ADM']):\\r\\n if param.get(\\\"id\\\") and param.get(\\\"pass\\\"):\\r\\n \\r\\n o_id = getID(param[\\\"id\\\"][0])\\r\\n passw = param[\\\"pass\\\"][0]\\r\\n \\r\\n coll = dataBase.users\\r\\n docr = coll.update_one({\\\"_id\\\": o_id}, { \\\"$set\\\": {'motpass': passw } })\\r\\n if param.get(\\\"user_mail\\\"):\\r\\n email = param[\\\"user_mail\\\"][0]\\r\\n name = param[\\\"user_name\\\"][0] if param.get(\\\"user_name\\\") else \\\"\\\" \\r\\n sendRecupPassMail(email, name, passw)\\r\\n return dumps(docr)\\r\\n else:\\r\\n return dumps({'ok': 0}) # No param\\r\\n else: \\r\\n return ('{\\\"n\\\":0,\\\"ok\\\":0, \\\"message\\\": \\\"S0062\\\"}')\\r\\n except Exception as ex:\\r\\n return except_handler(\\\"savePassword\\\", ex)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7109e769b0db5143cf1c0ca3dbd2bcc7\",\n \"score\": \"0.50172305\",\n \"text\": \"def authenticate():\\n Gtasks()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"774c824edb03b3e86d5f997742685ead\",\n \"score\": \"0.50086737\",\n \"text\": \"def svn_auth_provider_invoke_save_credentials(svn_auth_provider_t__obj, svn_boolean_t_saved, void_credentials, void_provider_baton, apr_hash_t_parameters, char_realmstring, apr_pool_t_pool): # real signature unknown; restored from __doc__\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2babc28486d675c6ccff9c6f0d5c1af6\",\n \"score\": \"0.49991006\",\n \"text\": \"def post(self):\\n if not (\\n request.data\\n and \\\"username\\\" in request.json\\n and \\\"password\\\" in request.json\\n ):\\n return \\\"Bad request\\\", 400\\n # If no mechanism is present in request body, default to simple\\n if not (\\\"mechanism\\\" in request.json):\\n request.json[\\\"mechanism\\\"] = \\\"simple\\\"\\n try:\\n return {\\\"sessionID\\\": backend.login(request.json)}, 201\\n except AuthenticationError:\\n return \\\"Forbidden\\\", 403\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7fbfee3f4ebf7aa1c08d383d4a2d0eed\",\n \"score\": \"0.4994469\",\n \"text\": \"def post(self):\\n parser = reqparse.RequestParser()\\n parser.add_argument('username', type=str, required=True)\\n parser.add_argument('password', type=str, required=True)\\n args = parser.parse_args(strict=True)\\n\\n query = db_session.query(UserModel).filter(\\n UserModel.username == args['username'])\\n if query.count() > 0:\\n user = query.one()\\n if user.password == args['password']:\\n user.apikey = generate_apikey(user.username)\\n db_session.merge(user)\\n db_session.commit()\\n return {'apikey': user.apikey}, 200\\n return {\\\"message\\\": {\\\"pass\\\": \\\"User or Password is Incorrect\\\",\\n \\\"user\\\": \\\"User or Password is Incorrect\\\", }}, 400\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa4428e9e53f6c9c2edb8cda3985d903\",\n \"score\": \"0.4993907\",\n \"text\": \"def svn_auth_provider_invoke_save_credentials(*args) -> \\\"svn_boolean_t *\\\":\\n return _core.svn_auth_provider_invoke_save_credentials(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ecd1449e84cc7df5975c9b99eb6fb66\",\n \"score\": \"0.4988027\",\n \"text\": \"def user_login(self, **kwargs):\\n\\n for key, val in kwargs.iteritems():\\n if key == 'user': astr_user = val.translate(None, '\\\\'\\\\\\\"')\\n if key == 'passwd': astr_passwd = val.translate(None, '\\\\'\\\\\\\"')\\n s = self.DB\\n s.cdnode('/users')\\n\\n # login/session/canCall structure\\n ret = {}\\n now = datetime.datetime.today()\\n ret['loginTimeStamp'] = now.strftime('%Y-%m-%d_%H:%M:%S.%f')\\n ret['loginStatus'] = False\\n ret['loginMessage'] = \\\"\\\"\\n ret['logoutMessage'] = \\\"\\\"\\n ret['sessionStatus'] = False\\n ret['sessionToken'] = \\\"ABCDEF\\\"\\n ret['sessionSeed'] = \\\"1\\\"\\n ret['APIcanCall'] = False\\n\\n s = self.DB\\n s.cdnode('/users')\\n if not s.cdnode(astr_user):\\n ret['loginMessage'] = 'User %s not found in database.' % astr_user\\n else:\\n if s.cat('login/passwd') != astr_passwd:\\n ret['loginMessage'] = 'Incorrect password.'\\n else:\\n ret['loginStatus'] = True\\n ret['loginMessage'] = 'Successful login for user %s at %s.' % (astr_user, datetime.datetime.now())\\n ret['logoutMessage'] = \\\"\\\"\\n ret['sessionStatus'] = True\\n ret['sessionToken'] = \\\"ABCDEF\\\"\\n ret['sessionSeed'] = \\\"1\\\"\\n\\n self.debug(\\\"in login... writing persistent for user '%s'.\\\\n\\\" % astr_user)\\n # print(ret)\\n self.login_writePersistent( sessionInfo = ret,\\n user = astr_user)\\n return {\\n 'status' : True,\\n 'path' : '/',\\n 'payload' : {\\n 'message': 'login credentials parsed',\\n 'loginDetail': ret\\n }\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a1cb62472c9e54c37c584db4ecb1179\",\n \"score\": \"0.49866235\",\n \"text\": \"def authenticate(self, argv):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ba66048e89bd741950d43ccdbc29b3b\",\n \"score\": \"0.49839464\",\n \"text\": \"def connect():\\n\\n HANDLE.Login(IP_ADDRESS, USERNAME, PASSWORD)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"194e28c8cac4d46c2a5c8fc3f20dd141\",\n \"score\": \"0.49794582\",\n \"text\": \"def pop_login(self,username,passwd): \\n \\n #import pop_user\\n #import pop_pass\\n \\n self.username = username\\n self.passwd = passwd\\n self.pop_user(self.username)\\n self.pop_pass(self.passwd)\\n #self.pop.quit() \",\n \"title\": \"\"\n },\n {\n \"docid\": \"008460a84728d08bd71d323b40a78648\",\n \"score\": \"0.49792752\",\n \"text\": \"def xmlrpc_auth(self, arg):\\n \\n return \\\"\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2fb50f3109ba86f6cdcb0261a422cca\",\n \"score\": \"0.4976081\",\n \"text\": \"def pop_auth_plain(self,username,passwd):\\n \\n self.username = username\\n self.passwd = passwd\\n #authstring = 'AHh4MQBw'\\n #authstring = (base64.b64encode(('{}'.format('\\\\000'+self.username+'\\\\000'+self.passwd)).encode())).decode()\\n basic_class.mylogger_record.info('command:<auth plain '+self.username+self.passwd+'>') \\n try:\\n self.resp = self.pop3.auth_plain(self.username,self.passwd)\\n finally: \\n [basic_class.mylogger_record.debug(self.resp.decode())]\\n #self.pop.quit() \",\n \"title\": \"\"\n },\n {\n \"docid\": \"adff00463d020b9d32717fa5c6d13b75\",\n \"score\": \"0.49724698\",\n \"text\": \"def _set_info(self, username, password):\\n self.username = username\\n self.password = password\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b52e06b61505bf167a189d4afef8dc6\",\n \"score\": \"0.49700513\",\n \"text\": \"def get_remote_client(self, username=None, password=None, **kwargs):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"27f58e509187acd7f16a2d58c036e50f\",\n \"score\": \"0.49647045\",\n \"text\": \"def Authenticate(self, req, user, password):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4ad4387df9942c702dc14ed147efde1\",\n \"score\": \"0.49610782\",\n \"text\": \"def connection_params(self):\\n print(\\\"\\\\nVeuillez saisir les paramètres de connection au serveur sql:\\\")\\n host = input(\\\"host : \\\")\\n user = input(\\\"user : \\\")\\n password = getpass()\\n return host, user, password\",\n \"title\": \"\"\n },\n {\n \"docid\": \"02153d7f1bb96432c357bf38cfd59369\",\n \"score\": \"0.49598175\",\n \"text\": \"def login(user_name, user_password):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87997aee412c8a0dda7c477d3bf28aa1\",\n \"score\": \"0.4950943\",\n \"text\": \"def login(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11c2bd3de6b68ade1e3242299abd6d38\",\n \"score\": \"0.49490416\",\n \"text\": \"def svn_auth_invoke_username_prompt_func(svn_auth_username_prompt_func_t__obj, svn_auth_cred_username_t_cred, void_baton, char_realm, svn_boolean_t_may_save, apr_pool_t_pool): # real signature unknown; restored from __doc__\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2efe9d4f8d68b211503d9a1556f8bc42\",\n \"score\": \"0.4942509\",\n \"text\": \"def authUser(param, self):\\r\\n\\t#pdb.set_trace()\\r\\n\\ttry:\\r\\n\\t\\tdef writeCook(mess, sessID):\\r\\n\\t\\t\\tself.send_response(200)\\r\\n\\t\\t\\tself.send_header('Content-type','text/html')\\r\\n\\t\\t\\tself.send_header('Access-Control-Allow-Origin', '*')\\r\\n\\t\\t\\t# Set cookie\\r\\n\\t\\t\\tcookInfo = 'sessID=' + sessID + ';max-age=31536000'\\r\\n\\t\\t\\tself.send_header('Set-Cookie', cookInfo)\\r\\n\\t\\t\\tself.end_headers()\\r\\n\\t\\t\\t# Write content as utf-8 data\\r\\n\\t\\t\\tself.wfile.write(bytes(mess, \\\"utf8\\\"))\\r\\n\\t\\t\\treturn\\t\\r\\n\\t\\t\\r\\n\\t\\tif param:\\r\\n\\t\\t\\tif not isinstance(param, (list)) and param.get(\\\"user\\\"):\\r\\n\\t\\t\\t\\tuser = param[\\\"user\\\"][0]\\r\\n\\t\\t\\t\\t\\r\\n\\t\\t\\t\\tcoll = data.users\\r\\n\\t\\t\\t\\tdoc = coll.find({\\\"courriel\\\": user, \\\"actif\\\": True}, [\\\"_id\\\",\\\"Nom\\\", \\\"courriel\\\", \\\"motpass\\\", \\\"niveau\\\"])\\r\\n\\t\\t\\t\\t\\r\\n\\t\\t\\t\\tdef setSessID(mess):\\r\\n\\t\\t\\t\\t\\tsessID = str(ObjectId())\\r\\n\\t\\t\\t\\t\\tres = coll.update({\\\"courriel\\\": user}, { \\\"$set\\\": {\\\"sessID\\\": sessID}})\\r\\n\\t\\t\\t\\t\\twriteCook(mess, sessID)\\r\\n\\t\\t\\t\\t\\t\\r\\n\\t\\t\\t\\tif doc.count() == 0:\\r\\n\\t\\t\\t\\t\\treturn dumps({'resp': {\\\"result\\\": 0} })\\t# Authenticate fail\\r\\n\\t\\t\\t\\telse:\\r\\n\\t\\t\\t\\t\\tdic = cursorTOdict(doc)\\r\\n\\t\\t\\t\\t\\tdic['_id'] = str(dic['_id'])\\r\\n\\t\\t\\t\\tif param.get(\\\"pass\\\") and not isinstance(param, (list)):\\r\\n\\t\\t\\t\\t\\tpassw = param[\\\"pass\\\"][0]\\r\\n\\t\\t\\t\\t\\tif str(dic['motpass']) == passw:\\r\\n\\t\\t\\t\\t\\t\\tdel dic['motpass']\\r\\n\\t\\t\\t\\t\\t\\tdocs = {\\\"resp\\\": {\\\"result\\\":True, \\\"user\\\": dic} }\\r\\n\\t\\t\\t\\t\\t\\tres = dumps(docs)\\t# Authenticated\\r\\n\\t\\t\\t\\t\\t\\tsetSessID(res)\\r\\n\\t\\t\\t\\t\\t\\treturn False\\r\\n\\t\\t\\t\\t\\telse:\\r\\n\\t\\t\\t\\t\\t\\treturn dumps({'resp': {\\\"result\\\": 0} })\\t# Authenticate fail\\r\\n\\t\\t\\t\\telse:\\r\\n\\t\\t\\t\\t\\t#pdb.set_trace()\\r\\n\\t\\t\\t\\t\\tif param.get(\\\"action\\\"):\\r\\n\\t\\t\\t\\t\\t\\taction = param[\\\"action\\\"][0]\\r\\n\\t\\t\\t\\t\\t\\tif action == \\\"1\\\":\\t# To modifiy account\\r\\n\\t\\t\\t\\t\\t\\t\\tdel dic['motpass']\\r\\n\\t\\t\\t\\t\\t\\t\\treturn dumps(dic) # To modifiy\\r\\n\\t\\t\\t\\t\\telse:\\r\\n\\t\\t\\t\\t\\t\\treturn dumps({'resp': {\\\"result\\\": 0} })\\t# Authenticate fail password empty\\r\\n\\t\\t\\telse:\\r\\n\\t\\t\\t\\treturn dumps({'resp': {\\\"result\\\": 0} })\\t# User is empty\\r\\n\\t\\telse:\\r\\n\\t\\t\\treturn dumps({'resp': {\\\"result\\\": 0} })\\t# No param\\r\\n\\texcept:\\r\\n\\t\\tlog_Info(self.path + \\\" ERROR: \\\" + sys.exc_info()[0] + \\\" ; \\\" + str(sys.exc_info()[1]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e428f47f73297dc1988e6cef258205f7\",\n \"score\": \"0.49419478\",\n \"text\": \"def _auth(self, method, params, recv_window):\\n\\n api_key = self.api_key\\n api_secret = self.api_secret\\n\\n if api_key is None or api_secret is None:\\n raise PermissionError('Authenticated endpoints require keys.')\\n\\n # Append required parameters.\\n params['api_key'] = api_key\\n params['recv_window'] = recv_window\\n params['timestamp'] = int(time.time() * 10 ** 3)\\n\\n # Sort dictionary alphabetically to create querystring.\\n _val = '&'.join(\\n [str(k) + '=' + str(v) for k, v in sorted(params.items()) if\\n (k != 'sign') and (v is not None)]\\n )\\n\\n # Bug fix. Replaces all capitalized booleans with lowercase.\\n if method == 'POST':\\n _val = _val.replace('True', 'true').replace('False', 'false')\\n\\n # Return signature.\\n return str(hmac.new(\\n bytes(api_secret, 'utf-8'),\\n bytes(_val, 'utf-8'), digestmod='sha256'\\n ).hexdigest())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"975c8fb3c2b995be7143657762f416d6\",\n \"score\": \"0.49413943\",\n \"text\": \"def authenticate(username, password):\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae1c84da29a371f42d79b1dd3b79e044\",\n \"score\": \"0.49368295\",\n \"text\": \"def start_user_commands(self):\\n self.client = LoginCommands(self.root_directory,self.current_directory,\\n self.username,self.password)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a842eb28043b8b3e1869d2a72a89324\",\n \"score\": \"0.49332032\",\n \"text\": \"def __init__(self, username, password):\\n self.client = None\\n self.username = username\\n self.password = password\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f70f3f36362a340ac1f92a06750d2fd\",\n \"score\": \"0.49316657\",\n \"text\": \"def user_credential_update() -> None:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"958ef1a7263781852b550901e5484a5d\",\n \"score\": \"0.4925496\",\n \"text\": \"def ReceivedSprayArguments(self, serverIP, username, sprayPassword):\\n simpleServer = Server(serverIP, get_info='ALL')\\n self.simpleConnspray = Connection(simpleServer, user=username, password=sprayPassword)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b24e82301daa9ff2e26e2fadce017ef1\",\n \"score\": \"0.49236986\",\n \"text\": \"def Login():\\n global s\\n \\n # Send the connecting client a vague login prompt\\n s.send('Login: ')\\n \\n # Expect 1024 bytes\\n pwd = s.recv(1024)\\n\\n # If the provided input does not match the specified password we just call this same function again \\n if pwd.strip() != password:\\n Login()\\n else:\\n \\n # If the user got the password correct, we send an eager prompt to the assailant over socket\\n s.send('Connected #> ')\\n \\n # Start the `Shell` function\\n Shell()\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1189,"cells":{"query_id":{"kind":"string","value":"76343038078799a671baedba71af10ec"},"query":{"kind":"string","value":"return length of rope in O(logn) Rope.size will also returns the same in O(1) but this function is also required at several palces. Users should use Rope.size."},"positive_passages":{"kind":"list like","value":[{"docid":"9b058c778dce24f1e7290a4696583400","score":"0.0","text":"def __len__(self,root=False):\n if root==False:\n root=self.root\n head=root\n count=0\n while head:\n count+=head.weight\n head=head.rc\n return count","title":""}],"string":"[\n {\n \"docid\": \"9b058c778dce24f1e7290a4696583400\",\n \"score\": \"0.0\",\n \"text\": \"def __len__(self,root=False):\\n if root==False:\\n root=self.root\\n head=root\\n count=0\\n while head:\\n count+=head.weight\\n head=head.rc\\n return count\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"83ecf4f070784835708b257862d12647","score":"0.7059134","text":"def house_number(self, rope_len):\n len_sum = 0\n \n for rope in self.rope_array:\n len_sum += rope // rope_len\n \n return len_sum","title":""},{"docid":"59abb5d2cbc77c96781c70a1d371fdbb","score":"0.6563532","text":"def Length():","title":""},{"docid":"422172fa4cbc7b0f9a71e3309d0a0772","score":"0.6517626","text":"def size(self) -> int:","title":""},{"docid":"6781665fd9dad9ee1e935997bd41b51c","score":"0.6431818","text":"def __len__(self):\n return len(self.rles)","title":""},{"docid":"8fb6de5b6c336bcc884a1ede0e9eecd8","score":"0.6430111","text":"def length(self) -> int:\n return self.longeur","title":""},{"docid":"8fb6de5b6c336bcc884a1ede0e9eecd8","score":"0.6430111","text":"def length(self) -> int:\n return self.longeur","title":""},{"docid":"8fb6de5b6c336bcc884a1ede0e9eecd8","score":"0.6430111","text":"def length(self) -> int:\n return self.longeur","title":""},{"docid":"8fb6de5b6c336bcc884a1ede0e9eecd8","score":"0.6430111","text":"def length(self) -> int:\n return self.longeur","title":""},{"docid":"8fb6de5b6c336bcc884a1ede0e9eecd8","score":"0.6430111","text":"def length(self) -> int:\n return self.longeur","title":""},{"docid":"fd4fac73e231381f866c9ab03f8849ac","score":"0.6427842","text":"def total_length(nrn_pop, neurite_type=NeuriteType.all):\n nrns = neuron_population(nrn_pop)\n return list(sum(neuritefunc.section_lengths(n, neurite_type=neurite_type)) for n in nrns)","title":""},{"docid":"8cd53b578e292c7300915445607cac39","score":"0.6379495","text":"def length(list_, length_ = 1):\n linked_list = list_[0]\n if not linked_list:\n return 0\n \n if isinstance(linked_list, Pair):\n if linked_list.cdr is None:\n return length_ # Once we are at the last element in the list, we have counted the length and can return it.\n else:\n return length([linked_list.cdr], length_ + 1)\n else:\n raise SnekEvaluationError","title":""},{"docid":"8b22c23c9e78875df1baf6ab93e2802a","score":"0.63575554","text":"def get_length(self) -> int:\n return len(bin(self.modulus)) - 2","title":""},{"docid":"91e51c846f896d868fd18ca9f62139cb","score":"0.63415134","text":"def __len__(self):\n if self.length is None:\n self.length = 0\n for _ in self:\n self.length += 1\n\n return self.length","title":""},{"docid":"91e51c846f896d868fd18ca9f62139cb","score":"0.63415134","text":"def __len__(self):\n if self.length is None:\n self.length = 0\n for _ in self:\n self.length += 1\n\n return self.length","title":""},{"docid":"4d7bffb989a97d31b9a6afffc2e3a1c7","score":"0.63357717","text":"def get_length_count(self):\n import collections\n data = [this.rlen for this in self]\n return collections.Counter(data)","title":""},{"docid":"67c13e7701fb89dae18a0edae811db58","score":"0.6323059","text":"def getLength(self) -> int:\n ...","title":""},{"docid":"67c13e7701fb89dae18a0edae811db58","score":"0.6323059","text":"def getLength(self) -> int:\n ...","title":""},{"docid":"5b19d5f98ed48b1e6629022a61260cc7","score":"0.6311363","text":"def length(self):\n # store a variable to keep track of the running count (contant time)\n count = 0\n # loop through the entire list of buckets (O(n) in all cases for n buckets)\n for bucket in self.buckets:\n # start at the head of the current bucket and loop through the entire bucket\n # O(m) in all cases for m items in bucket\n current = bucket.head\n while (current):\n count += 1\n current = current.next\n # total runtime: O(N) for N items in hash table\n return count","title":""},{"docid":"1028a71faff9f6258110d46dd58efadf","score":"0.6306666","text":"def __len__(self):\n\n size = len(self.direct_members)\n stack = list(self.subpopulations)\n while stack:\n subpop = stack.pop()\n stack.extend(subpop.subpopulations)\n size += len(subpop.direct_members)\n return size","title":""},{"docid":"592a119c72c8bf796ffe6de13ea6dbc4","score":"0.63044894","text":"def slice_length(orange):\n t = type(orange)\n if t is slice:\n if orange.step:\n return int((orange.stop-orange.start)/orange.step) # This will also work for negative steps\n else: # Step size is 1 by default\n return orange.stop - orange.start\n else:\n return len(orange)","title":""},{"docid":"cc15e38749dd70a6ca9ef8c8d725f537","score":"0.6298064","text":"def length(n):\r\n _length = 0\r\n for i in n: _length += 1\r\n return _length","title":""},{"docid":"2f990ebcaeec3c4c03fa67325a4474b1","score":"0.6269684","text":"def __len__(self):\n return len(self.lh) + len(self.rh)","title":""},{"docid":"bb1136982a5108900a9d6ce439f903b8","score":"0.6246994","text":"def length(self):\n return self.list.length() # use linkedlists length method","title":""},{"docid":"c68e74ac575d5bffb9ce1386b69c65cb","score":"0.62044287","text":"def getLength(self) -> int:\n\n # NOTE - Uncomment when running full test\n pCur = self.head\n count = 0\n\n while pCur != None:\n count += 1\n pCur = pCur.next\n\n return count\n\n # return self.length","title":""},{"docid":"d049fd5672daa5ab7daa8107cd144fc8","score":"0.6154258","text":"def __len__(self) -> int:\n # We want to find the number of nodes in our linked list!\n # The only thing we have access to is self._first (first node in our list)\n # Set some count variable.\n length = 0\n curr = self._first\n # Loop through linked list, O(n)\n while curr is not None:\n curr = curr.next\n # increment our length, we discovered a new node.\n length += 1\n\n # Return the length that we computed.\n return length","title":""},{"docid":"9c02e12be95a35be04f258f11db12b74","score":"0.6144763","text":"def length(self):\n total = 0\n\n for bucket in self.buckets:\n total += bucket.length()\n\n return total","title":""},{"docid":"7b4c27520bc45bd882b04af539a3981d","score":"0.6136287","text":"def size(self):\n position_head = self.head\n length = 0\n\n while position_head:\n position_head = position_head.previous_block\n length += 1\n\n return length","title":""},{"docid":"9ba1e4cd6266677a07d48f5f420b0ef5","score":"0.6122053","text":"def rle_len( rle ):\r\n\r\n run = 0\r\n for v in rle:\r\n assert type(v) == tuple, repr(rle)\r\n a, r = v\r\n run += r\r\n return run","title":""},{"docid":"958bf9d88e8537852ef93b4a1ce4acea","score":"0.61050284","text":"def __len__(self): #{\n return self._total - self._pos","title":""},{"docid":"27c0dd4cc20454a0ac9971447e2f585d","score":"0.6102254","text":"def __len__(self) -> int:\n\n return self.num_rays","title":""},{"docid":"4ea9f11fb9a6cfd618fe82c2dfa14b31","score":"0.6087288","text":"def size(self):\r\n\t\t\"\"\"Runtime is O(1) or constant time, because we're only returning the length of the list\"\"\"\r\n\t\treturn len(self.items)","title":""},{"docid":"980f6d8effc02a51b8479c6a513eea22","score":"0.60781455","text":"def length(self):\n square_values = [x**2 for x in self.numbers]\n return math.sqrt(sum(square_values))","title":""},{"docid":"c562dfc965500405e3a2daeeac0a94a1","score":"0.6076665","text":"def pathlen(start_pos, goal_pos):\n d = len(pathfind(start_pos, goal_pos))\n if d == 0:\n return 10e9\n else:\n return d","title":""},{"docid":"1accfe69f749b275f72c4954f23cc3f6","score":"0.6069495","text":"def getLength(self):\n return _libBornAgainCore.IProfileRipple_getLength(self)","title":""},{"docid":"03a04bad30f621e83ac64e01589ab1bb","score":"0.6067646","text":"def __len__(self):\n\n o = 0\n for _ in self:\n o += 1\n return o","title":""},{"docid":"f3515bb432de675219da267e17f0cd41","score":"0.60671216","text":"def __len__():","title":""},{"docid":"f3515bb432de675219da267e17f0cd41","score":"0.60671216","text":"def __len__():","title":""},{"docid":"f3515bb432de675219da267e17f0cd41","score":"0.60671216","text":"def __len__():","title":""},{"docid":"f3515bb432de675219da267e17f0cd41","score":"0.60671216","text":"def __len__():","title":""},{"docid":"969d7e9b3fb9ce3b3dad01ed9c1d975d","score":"0.60599595","text":"def len_rlist(s):\n length = 0\n while s != empty_rlist:\n s, length = rest(s), length + 1\n return length","title":""},{"docid":"adc1ba201aa8638ef9e2aa214a77cae1","score":"0.6058164","text":"def size(self) -> int:\n ...","title":""},{"docid":"adc1ba201aa8638ef9e2aa214a77cae1","score":"0.6058164","text":"def size(self) -> int:\n ...","title":""},{"docid":"adc1ba201aa8638ef9e2aa214a77cae1","score":"0.6058164","text":"def size(self) -> int:\n ...","title":""},{"docid":"d2b789abf60cd04fbb58bb4e2a3d41c8","score":"0.6052649","text":"def length(self):\n return self._complement.length","title":""},{"docid":"d2b789abf60cd04fbb58bb4e2a3d41c8","score":"0.6052649","text":"def length(self):\n return self._complement.length","title":""},{"docid":"d9f4c5417bf3786e03be2440b55712d2","score":"0.6038919","text":"def calculated_length(self) -> int:\n return HPAI.LENGTH + HPAI.LENGTH + self.cri.calculated_length()","title":""},{"docid":"1042d0348a285c1fb711429599ca7d7a","score":"0.60379875","text":"def __len__(self):\n\n return self.get_left_item_count() + self.get_right_item_count()","title":""},{"docid":"7d6a5995b1f844606315a0e269c0fa7c","score":"0.6028256","text":"def get_size(self):\n return self.size # O(1)","title":""},{"docid":"23134eb85523b6a23d2d968a500086a6","score":"0.6020768","text":"def __len__(self):\n with self._lock:\n total_len = len(self.links)\n for lst in self.links_by_addr:\n total_len = total_len + len(lst)\n return total_len","title":""},{"docid":"de1f79bb9c47b261f5462dd6528d7309","score":"0.6016707","text":"def __len__(self):\n return sum(self.lengths)","title":""},{"docid":"6ebe2adc73b4a10ab2a884c317cfb3b0","score":"0.60161734","text":"def len(u):\n return math.sqrt(u[0]*u[0]+u[1]*u[1])","title":""},{"docid":"6fd87dd53f5e6bedd845620b8d27bb33","score":"0.60079384","text":"def length(self) -> float:\n return super().length","title":""},{"docid":"6fd87dd53f5e6bedd845620b8d27bb33","score":"0.60079384","text":"def length(self) -> float:\n return super().length","title":""},{"docid":"97feedfe897b0fa6a8e885c0320c3edd","score":"0.60067505","text":"def get_length(self):\n return self.__len__()","title":""},{"docid":"7a66c1dc8a5099c6a30fff9d03b11499","score":"0.59950584","text":"def get_general_length(records):\n res = 0\n for item in records:\n res += item.length\n return res","title":""},{"docid":"baee535bb129c101e8537a978e24ca38","score":"0.5990683","text":"def length(self):\n \n return self._radius * self.angle()","title":""},{"docid":"ab73ad48230e7b2add6f5698609709dc","score":"0.5989535","text":"def n(self):\n return len(self)","title":""},{"docid":"994be8be14d16261004517c0e53d509f","score":"0.59888554","text":"def __len__(self):\n try:\n if self.rest:\n return 1 + len(self.rest)\n else:\n return 1\n except:\n # print(\"cycled linked list has no length\")\n raise ValueError","title":""},{"docid":"a3523825982cac9ebcf8a813bbb44700","score":"0.5981335","text":"def length(self):\n if not self.vertices:\n return 0\n\n length = 0\n prev_vert = self.vertices[0]\n\n for vertex in self.vertices:\n length = length + (vertex - prev_vert).length\n prev_vert = vertex\n\n return length","title":""},{"docid":"a3523825982cac9ebcf8a813bbb44700","score":"0.5981335","text":"def length(self):\n if not self.vertices:\n return 0\n\n length = 0\n prev_vert = self.vertices[0]\n\n for vertex in self.vertices:\n length = length + (vertex - prev_vert).length\n prev_vert = vertex\n\n return length","title":""},{"docid":"4f38f4e96f628fab88ccb1b350fc3c76","score":"0.59797347","text":"def __len__(self):\n c = 0\n for x in self:\n c += 1\n return c","title":""},{"docid":"09820e21a2bd0213fb544bbf2d324a12","score":"0.5979546","text":"def size(self):\n\n if self.first == None:\n return 0\n else:\n count = 1\n count += self.rest.size()\n return count","title":""},{"docid":"ada40693dd6435ff65f94b84a2b14257","score":"0.59763014","text":"def get_length(self):\n count = 0\n iterator = self.head\n\n while iterator:\n count += 1\n iterator = iterator.next\n\n return count","title":""},{"docid":"52815e00933b435528d20b53029f1a67","score":"0.5970291","text":"def __len__(self):\n if self.is_empty():\n return 0\n else:\n size = 0\n for subtree in self._subtrees:\n size += len(subtree)\n size += 1\n return size","title":""},{"docid":"c979694a426feffe08ccdf66e54c37f6","score":"0.59636265","text":"def size(self):\n return len(self.to_lsl())","title":""},{"docid":"ce7cf5d886f0e86e4577b86b10e552e4","score":"0.5962414","text":"def get_length(self):\n return self.__dict__[\"shipLength\"]","title":""},{"docid":"38d649321e7f9d354924085165e7c2d6","score":"0.59600914","text":"def __len__(self):\n return sum(1 for _ in self.traverse())","title":""},{"docid":"0ecdead6b6e3514a160e3202eee82127","score":"0.59556746","text":"def __len__(self) -> int:\n if self.is_empty():\n return 0\n elif self.is_leaf():\n return 1\n else:\n size = 0\n for subtree in self.subtrees:\n size += subtree.__len__() # could also do len(subtree) here\n return size","title":""},{"docid":"0ecdead6b6e3514a160e3202eee82127","score":"0.59556746","text":"def __len__(self) -> int:\n if self.is_empty():\n return 0\n elif self.is_leaf():\n return 1\n else:\n size = 0\n for subtree in self.subtrees:\n size += subtree.__len__() # could also do len(subtree) here\n return size","title":""},{"docid":"9598be1cfebaad2a2aeba92fad0cc179","score":"0.59484273","text":"def _get_length(self) -> \"double\" :\n return _fusion.BRepEdge__get_length(self)","title":""},{"docid":"cc91e59366d238f07ccaedb2beb058dc","score":"0.5947809","text":"def length(self):\n # Loop through all nodes and count one for each\n node_counter = 0\n for item in self.items():\n node_counter += 1\n return node_counter\n \n # for current_node != None:\n # count+=1","title":""},{"docid":"25d6362adea1ac62c9b2d92b5cdeb8f4","score":"0.5944904","text":"def length(self):\n return np.sqrt(np.sum(self ** 2, axis=-1))","title":""},{"docid":"dfb627c1896710d5091a7c45418af17a","score":"0.59440136","text":"def length(self):\n return self._length","title":""},{"docid":"dfb627c1896710d5091a7c45418af17a","score":"0.59440136","text":"def length(self):\n return self._length","title":""},{"docid":"0c8dea351ad054d51b1ad3b6228bb3ac","score":"0.5924835","text":"def __len__(self):\n args = self.args\n return self.n_rollout * (args.time_step - args.sequence_length + 1)","title":""},{"docid":"eee5b573d639c1b98f048aa4fb5577e5","score":"0.59205246","text":"def __len__(self):\n length = len(self.data) * ((self.utils.walk_length - self.span) * self.span)\n return length","title":""},{"docid":"aa2f386ab7d9dc72873e63ebeb1fa8d9","score":"0.59177715","text":"def get_length(self):\n raise(\"not implemented\")","title":""},{"docid":"c80a91c833da4beaa5309c95f87119d5","score":"0.5914325","text":"def lena(self) -> int:\n return self._core.lena()","title":""},{"docid":"8ca27c5b74dcf1190db7d052f6c84c46","score":"0.5907244","text":"def get_total_length(self):\n return sum(node.blen for node in self.preorder() if node.blen)","title":""},{"docid":"998c3341867849c3fc17ade08181e45e","score":"0.5906084","text":"def __len__(self):\n ctr = 0\n for entry in self:\n ctr += 1\n return ctr","title":""},{"docid":"b747a454d1585de058792219e3f50d0a","score":"0.59033394","text":"def size(self):\n\t\treturn (self.length + 1) * 4","title":""},{"docid":"0594d1d1d666f587e1784ecf7e9af53f","score":"0.5903229","text":"def calc_length(self):\n\n return len(self.path)","title":""},{"docid":"57a5249093cb4fc1f67b6665732a1fff","score":"0.59027517","text":"def _get_length(self) -> \"double\" :\n return _fusion.SketchCircle__get_length(self)","title":""},{"docid":"46337cc2786864e6130620289843aec8","score":"0.5899676","text":"def length(self):\n pass","title":""},{"docid":"46337cc2786864e6130620289843aec8","score":"0.5899676","text":"def length(self):\n pass","title":""},{"docid":"4548bddeaac6dc8bbeb231c6d5c1c4f4","score":"0.58961296","text":"def length(node):\n len = 0\n if node is not None:\n len += 1\n if node.next_node is not None:\n len += length(node.next_node)\n return len","title":""},{"docid":"ed6876e61d6c0c90489d8062d59359ff","score":"0.5893407","text":"def total_size(self):\n return reduce(lambda a, b: a + b.size, self.robots.values(), 0.0)","title":""},{"docid":"4f364d8d51dc26fe1af2fdb3d8bae322","score":"0.5890376","text":"def get_length(self):\n return self._length","title":""},{"docid":"aba6e32e26207c209a1bd40ade4c6a2f","score":"0.5888008","text":"def __len__(self) -> int:\n if self.slice_ids is not None:\n return len(self.slice_ids)\n else:\n return self._full_length()","title":""},{"docid":"e3bcf2bf10614c8474a310baae04f1da","score":"0.5885656","text":"def __len__(self) -> int:\n return self._length","title":""},{"docid":"e1193226d6850dbcfae93e3dd9dc0424","score":"0.58807516","text":"def __len__(self):\n return self.repo.array_size ** self.repo.array_size","title":""},{"docid":"11dc2addddc4cd4b7a7072e14e39c549","score":"0.5880631","text":"def __len__(self):\n tracks = self.tracks\n if tracks is None:\n length = 0\n else:\n length = len(tracks)\n \n return length","title":""},{"docid":"12e91c215a1c1ad740bddfb029773fed","score":"0.58802754","text":"def __len__(me):\n n = 0\n for rows,_ in me._tabs.values():\n n += len(rows)\n return n","title":""},{"docid":"7ba8f6ea7a5dec6419cbf1ffd4149e18","score":"0.58779216","text":"def _get_count(self) -> \"size_t\" :\n return _fusion.SketchCircles__get_count(self)","title":""},{"docid":"da5de6145c6298e4363967d19ab11fb2","score":"0.5877365","text":"def length(self):\n if not hasattr(self, '_length'):\n self._length = math.sqrt(self.length2)\n\n return self._length","title":""},{"docid":"15a7eb4775983bf0e154bd6e21c3c457","score":"0.5875917","text":"def getLength(self):\n return _libBornAgainCore.FormFactorAnisoPyramid_getLength(self)","title":""},{"docid":"a58a6d1fb6f032da8f32612dc4919e70","score":"0.5875638","text":"def __len__(self) -> int:\n return len(self.population)","title":""},{"docid":"831e556ccc56e5f0d97e0ed71457f41e","score":"0.5871503","text":"def __len__(self) -> int:\n # Grab some values from *simple_polygon* (i.e. *self*):\n simple_polygon: SimplePolygon = self\n points: List[P2D] = simple_polygon.points\n size: int = len(points)\n return size","title":""},{"docid":"536d040ce90beab9c46702511ae1414c","score":"0.5869267","text":"def size(self):\n return self.length","title":""},{"docid":"541c9d897437826e09655ff806af65de","score":"0.58664095","text":"def length(self):\n # # Node counter initialized to zero\n # node_count = 0\n # # Start at the head node\n # node = self.head\n # # Loop until the node is None, which is one node too far past the tail\n # while node is not None:\n # # Count one for this node\n # node_count += 1\n # # Skip to the next node\n # node = node.next\n # # Now node_count contains the number of nodes\n # return node_count\n return self.size","title":""},{"docid":"2ee662bbf359a717f2134f49bd441ce7","score":"0.5858225","text":"def length(self):\n return self.list.lenght()","title":""}],"string":"[\n {\n \"docid\": \"83ecf4f070784835708b257862d12647\",\n \"score\": \"0.7059134\",\n \"text\": \"def house_number(self, rope_len):\\n len_sum = 0\\n \\n for rope in self.rope_array:\\n len_sum += rope // rope_len\\n \\n return len_sum\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59abb5d2cbc77c96781c70a1d371fdbb\",\n \"score\": \"0.6563532\",\n \"text\": \"def Length():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"422172fa4cbc7b0f9a71e3309d0a0772\",\n \"score\": \"0.6517626\",\n \"text\": \"def size(self) -> int:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6781665fd9dad9ee1e935997bd41b51c\",\n \"score\": \"0.6431818\",\n \"text\": \"def __len__(self):\\n return len(self.rles)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8fb6de5b6c336bcc884a1ede0e9eecd8\",\n \"score\": \"0.6430111\",\n \"text\": \"def length(self) -> int:\\n return self.longeur\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8fb6de5b6c336bcc884a1ede0e9eecd8\",\n \"score\": \"0.6430111\",\n \"text\": \"def length(self) -> int:\\n return self.longeur\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8fb6de5b6c336bcc884a1ede0e9eecd8\",\n \"score\": \"0.6430111\",\n \"text\": \"def length(self) -> int:\\n return self.longeur\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8fb6de5b6c336bcc884a1ede0e9eecd8\",\n \"score\": \"0.6430111\",\n \"text\": \"def length(self) -> int:\\n return self.longeur\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8fb6de5b6c336bcc884a1ede0e9eecd8\",\n \"score\": \"0.6430111\",\n \"text\": \"def length(self) -> int:\\n return self.longeur\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd4fac73e231381f866c9ab03f8849ac\",\n \"score\": \"0.6427842\",\n \"text\": \"def total_length(nrn_pop, neurite_type=NeuriteType.all):\\n nrns = neuron_population(nrn_pop)\\n return list(sum(neuritefunc.section_lengths(n, neurite_type=neurite_type)) for n in nrns)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8cd53b578e292c7300915445607cac39\",\n \"score\": \"0.6379495\",\n \"text\": \"def length(list_, length_ = 1):\\n linked_list = list_[0]\\n if not linked_list:\\n return 0\\n \\n if isinstance(linked_list, Pair):\\n if linked_list.cdr is None:\\n return length_ # Once we are at the last element in the list, we have counted the length and can return it.\\n else:\\n return length([linked_list.cdr], length_ + 1)\\n else:\\n raise SnekEvaluationError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b22c23c9e78875df1baf6ab93e2802a\",\n \"score\": \"0.63575554\",\n \"text\": \"def get_length(self) -> int:\\n return len(bin(self.modulus)) - 2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"91e51c846f896d868fd18ca9f62139cb\",\n \"score\": \"0.63415134\",\n \"text\": \"def __len__(self):\\n if self.length is None:\\n self.length = 0\\n for _ in self:\\n self.length += 1\\n\\n return self.length\",\n \"title\": \"\"\n },\n {\n \"docid\": \"91e51c846f896d868fd18ca9f62139cb\",\n \"score\": \"0.63415134\",\n \"text\": \"def __len__(self):\\n if self.length is None:\\n self.length = 0\\n for _ in self:\\n self.length += 1\\n\\n return self.length\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d7bffb989a97d31b9a6afffc2e3a1c7\",\n \"score\": \"0.63357717\",\n \"text\": \"def get_length_count(self):\\n import collections\\n data = [this.rlen for this in self]\\n return collections.Counter(data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"67c13e7701fb89dae18a0edae811db58\",\n \"score\": \"0.6323059\",\n \"text\": \"def getLength(self) -> int:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"67c13e7701fb89dae18a0edae811db58\",\n \"score\": \"0.6323059\",\n \"text\": \"def getLength(self) -> int:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b19d5f98ed48b1e6629022a61260cc7\",\n \"score\": \"0.6311363\",\n \"text\": \"def length(self):\\n # store a variable to keep track of the running count (contant time)\\n count = 0\\n # loop through the entire list of buckets (O(n) in all cases for n buckets)\\n for bucket in self.buckets:\\n # start at the head of the current bucket and loop through the entire bucket\\n # O(m) in all cases for m items in bucket\\n current = bucket.head\\n while (current):\\n count += 1\\n current = current.next\\n # total runtime: O(N) for N items in hash table\\n return count\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1028a71faff9f6258110d46dd58efadf\",\n \"score\": \"0.6306666\",\n \"text\": \"def __len__(self):\\n\\n size = len(self.direct_members)\\n stack = list(self.subpopulations)\\n while stack:\\n subpop = stack.pop()\\n stack.extend(subpop.subpopulations)\\n size += len(subpop.direct_members)\\n return size\",\n \"title\": \"\"\n },\n {\n \"docid\": \"592a119c72c8bf796ffe6de13ea6dbc4\",\n \"score\": \"0.63044894\",\n \"text\": \"def slice_length(orange):\\n t = type(orange)\\n if t is slice:\\n if orange.step:\\n return int((orange.stop-orange.start)/orange.step) # This will also work for negative steps\\n else: # Step size is 1 by default\\n return orange.stop - orange.start\\n else:\\n return len(orange)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc15e38749dd70a6ca9ef8c8d725f537\",\n \"score\": \"0.6298064\",\n \"text\": \"def length(n):\\r\\n _length = 0\\r\\n for i in n: _length += 1\\r\\n return _length\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f990ebcaeec3c4c03fa67325a4474b1\",\n \"score\": \"0.6269684\",\n \"text\": \"def __len__(self):\\n return len(self.lh) + len(self.rh)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb1136982a5108900a9d6ce439f903b8\",\n \"score\": \"0.6246994\",\n \"text\": \"def length(self):\\n return self.list.length() # use linkedlists length method\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c68e74ac575d5bffb9ce1386b69c65cb\",\n \"score\": \"0.62044287\",\n \"text\": \"def getLength(self) -> int:\\n\\n # NOTE - Uncomment when running full test\\n pCur = self.head\\n count = 0\\n\\n while pCur != None:\\n count += 1\\n pCur = pCur.next\\n\\n return count\\n\\n # return self.length\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d049fd5672daa5ab7daa8107cd144fc8\",\n \"score\": \"0.6154258\",\n \"text\": \"def __len__(self) -> int:\\n # We want to find the number of nodes in our linked list!\\n # The only thing we have access to is self._first (first node in our list)\\n # Set some count variable.\\n length = 0\\n curr = self._first\\n # Loop through linked list, O(n)\\n while curr is not None:\\n curr = curr.next\\n # increment our length, we discovered a new node.\\n length += 1\\n\\n # Return the length that we computed.\\n return length\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c02e12be95a35be04f258f11db12b74\",\n \"score\": \"0.6144763\",\n \"text\": \"def length(self):\\n total = 0\\n\\n for bucket in self.buckets:\\n total += bucket.length()\\n\\n return total\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b4c27520bc45bd882b04af539a3981d\",\n \"score\": \"0.6136287\",\n \"text\": \"def size(self):\\n position_head = self.head\\n length = 0\\n\\n while position_head:\\n position_head = position_head.previous_block\\n length += 1\\n\\n return length\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ba1e4cd6266677a07d48f5f420b0ef5\",\n \"score\": \"0.6122053\",\n \"text\": \"def rle_len( rle ):\\r\\n\\r\\n run = 0\\r\\n for v in rle:\\r\\n assert type(v) == tuple, repr(rle)\\r\\n a, r = v\\r\\n run += r\\r\\n return run\",\n \"title\": \"\"\n },\n {\n \"docid\": \"958bf9d88e8537852ef93b4a1ce4acea\",\n \"score\": \"0.61050284\",\n \"text\": \"def __len__(self): #{\\n return self._total - self._pos\",\n \"title\": \"\"\n },\n {\n \"docid\": \"27c0dd4cc20454a0ac9971447e2f585d\",\n \"score\": \"0.6102254\",\n \"text\": \"def __len__(self) -> int:\\n\\n return self.num_rays\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ea9f11fb9a6cfd618fe82c2dfa14b31\",\n \"score\": \"0.6087288\",\n \"text\": \"def size(self):\\r\\n\\t\\t\\\"\\\"\\\"Runtime is O(1) or constant time, because we're only returning the length of the list\\\"\\\"\\\"\\r\\n\\t\\treturn len(self.items)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"980f6d8effc02a51b8479c6a513eea22\",\n \"score\": \"0.60781455\",\n \"text\": \"def length(self):\\n square_values = [x**2 for x in self.numbers]\\n return math.sqrt(sum(square_values))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c562dfc965500405e3a2daeeac0a94a1\",\n \"score\": \"0.6076665\",\n \"text\": \"def pathlen(start_pos, goal_pos):\\n d = len(pathfind(start_pos, goal_pos))\\n if d == 0:\\n return 10e9\\n else:\\n return d\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1accfe69f749b275f72c4954f23cc3f6\",\n \"score\": \"0.6069495\",\n \"text\": \"def getLength(self):\\n return _libBornAgainCore.IProfileRipple_getLength(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"03a04bad30f621e83ac64e01589ab1bb\",\n \"score\": \"0.6067646\",\n \"text\": \"def __len__(self):\\n\\n o = 0\\n for _ in self:\\n o += 1\\n return o\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3515bb432de675219da267e17f0cd41\",\n \"score\": \"0.60671216\",\n \"text\": \"def __len__():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3515bb432de675219da267e17f0cd41\",\n \"score\": \"0.60671216\",\n \"text\": \"def __len__():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3515bb432de675219da267e17f0cd41\",\n \"score\": \"0.60671216\",\n \"text\": \"def __len__():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3515bb432de675219da267e17f0cd41\",\n \"score\": \"0.60671216\",\n \"text\": \"def __len__():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"969d7e9b3fb9ce3b3dad01ed9c1d975d\",\n \"score\": \"0.60599595\",\n \"text\": \"def len_rlist(s):\\n length = 0\\n while s != empty_rlist:\\n s, length = rest(s), length + 1\\n return length\",\n \"title\": \"\"\n },\n {\n \"docid\": \"adc1ba201aa8638ef9e2aa214a77cae1\",\n \"score\": \"0.6058164\",\n \"text\": \"def size(self) -> int:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"adc1ba201aa8638ef9e2aa214a77cae1\",\n \"score\": \"0.6058164\",\n \"text\": \"def size(self) -> int:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"adc1ba201aa8638ef9e2aa214a77cae1\",\n \"score\": \"0.6058164\",\n \"text\": \"def size(self) -> int:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d2b789abf60cd04fbb58bb4e2a3d41c8\",\n \"score\": \"0.6052649\",\n \"text\": \"def length(self):\\n return self._complement.length\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d2b789abf60cd04fbb58bb4e2a3d41c8\",\n \"score\": \"0.6052649\",\n \"text\": \"def length(self):\\n return self._complement.length\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d9f4c5417bf3786e03be2440b55712d2\",\n \"score\": \"0.6038919\",\n \"text\": \"def calculated_length(self) -> int:\\n return HPAI.LENGTH + HPAI.LENGTH + self.cri.calculated_length()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1042d0348a285c1fb711429599ca7d7a\",\n \"score\": \"0.60379875\",\n \"text\": \"def __len__(self):\\n\\n return self.get_left_item_count() + self.get_right_item_count()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d6a5995b1f844606315a0e269c0fa7c\",\n \"score\": \"0.6028256\",\n \"text\": \"def get_size(self):\\n return self.size # O(1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23134eb85523b6a23d2d968a500086a6\",\n \"score\": \"0.6020768\",\n \"text\": \"def __len__(self):\\n with self._lock:\\n total_len = len(self.links)\\n for lst in self.links_by_addr:\\n total_len = total_len + len(lst)\\n return total_len\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de1f79bb9c47b261f5462dd6528d7309\",\n \"score\": \"0.6016707\",\n \"text\": \"def __len__(self):\\n return sum(self.lengths)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ebe2adc73b4a10ab2a884c317cfb3b0\",\n \"score\": \"0.60161734\",\n \"text\": \"def len(u):\\n return math.sqrt(u[0]*u[0]+u[1]*u[1])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6fd87dd53f5e6bedd845620b8d27bb33\",\n \"score\": \"0.60079384\",\n \"text\": \"def length(self) -> float:\\n return super().length\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6fd87dd53f5e6bedd845620b8d27bb33\",\n \"score\": \"0.60079384\",\n \"text\": \"def length(self) -> float:\\n return super().length\",\n \"title\": \"\"\n },\n {\n \"docid\": \"97feedfe897b0fa6a8e885c0320c3edd\",\n \"score\": \"0.60067505\",\n \"text\": \"def get_length(self):\\n return self.__len__()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a66c1dc8a5099c6a30fff9d03b11499\",\n \"score\": \"0.59950584\",\n \"text\": \"def get_general_length(records):\\n res = 0\\n for item in records:\\n res += item.length\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"baee535bb129c101e8537a978e24ca38\",\n \"score\": \"0.5990683\",\n \"text\": \"def length(self):\\n \\n return self._radius * self.angle()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab73ad48230e7b2add6f5698609709dc\",\n \"score\": \"0.5989535\",\n \"text\": \"def n(self):\\n return len(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"994be8be14d16261004517c0e53d509f\",\n \"score\": \"0.59888554\",\n \"text\": \"def __len__(self):\\n try:\\n if self.rest:\\n return 1 + len(self.rest)\\n else:\\n return 1\\n except:\\n # print(\\\"cycled linked list has no length\\\")\\n raise ValueError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3523825982cac9ebcf8a813bbb44700\",\n \"score\": \"0.5981335\",\n \"text\": \"def length(self):\\n if not self.vertices:\\n return 0\\n\\n length = 0\\n prev_vert = self.vertices[0]\\n\\n for vertex in self.vertices:\\n length = length + (vertex - prev_vert).length\\n prev_vert = vertex\\n\\n return length\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3523825982cac9ebcf8a813bbb44700\",\n \"score\": \"0.5981335\",\n \"text\": \"def length(self):\\n if not self.vertices:\\n return 0\\n\\n length = 0\\n prev_vert = self.vertices[0]\\n\\n for vertex in self.vertices:\\n length = length + (vertex - prev_vert).length\\n prev_vert = vertex\\n\\n return length\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f38f4e96f628fab88ccb1b350fc3c76\",\n \"score\": \"0.59797347\",\n \"text\": \"def __len__(self):\\n c = 0\\n for x in self:\\n c += 1\\n return c\",\n \"title\": \"\"\n },\n {\n \"docid\": \"09820e21a2bd0213fb544bbf2d324a12\",\n \"score\": \"0.5979546\",\n \"text\": \"def size(self):\\n\\n if self.first == None:\\n return 0\\n else:\\n count = 1\\n count += self.rest.size()\\n return count\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ada40693dd6435ff65f94b84a2b14257\",\n \"score\": \"0.59763014\",\n \"text\": \"def get_length(self):\\n count = 0\\n iterator = self.head\\n\\n while iterator:\\n count += 1\\n iterator = iterator.next\\n\\n return count\",\n \"title\": \"\"\n },\n {\n \"docid\": \"52815e00933b435528d20b53029f1a67\",\n \"score\": \"0.5970291\",\n \"text\": \"def __len__(self):\\n if self.is_empty():\\n return 0\\n else:\\n size = 0\\n for subtree in self._subtrees:\\n size += len(subtree)\\n size += 1\\n return size\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c979694a426feffe08ccdf66e54c37f6\",\n \"score\": \"0.59636265\",\n \"text\": \"def size(self):\\n return len(self.to_lsl())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce7cf5d886f0e86e4577b86b10e552e4\",\n \"score\": \"0.5962414\",\n \"text\": \"def get_length(self):\\n return self.__dict__[\\\"shipLength\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38d649321e7f9d354924085165e7c2d6\",\n \"score\": \"0.59600914\",\n \"text\": \"def __len__(self):\\n return sum(1 for _ in self.traverse())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ecdead6b6e3514a160e3202eee82127\",\n \"score\": \"0.59556746\",\n \"text\": \"def __len__(self) -> int:\\n if self.is_empty():\\n return 0\\n elif self.is_leaf():\\n return 1\\n else:\\n size = 0\\n for subtree in self.subtrees:\\n size += subtree.__len__() # could also do len(subtree) here\\n return size\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ecdead6b6e3514a160e3202eee82127\",\n \"score\": \"0.59556746\",\n \"text\": \"def __len__(self) -> int:\\n if self.is_empty():\\n return 0\\n elif self.is_leaf():\\n return 1\\n else:\\n size = 0\\n for subtree in self.subtrees:\\n size += subtree.__len__() # could also do len(subtree) here\\n return size\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9598be1cfebaad2a2aeba92fad0cc179\",\n \"score\": \"0.59484273\",\n \"text\": \"def _get_length(self) -> \\\"double\\\" :\\n return _fusion.BRepEdge__get_length(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc91e59366d238f07ccaedb2beb058dc\",\n \"score\": \"0.5947809\",\n \"text\": \"def length(self):\\n # Loop through all nodes and count one for each\\n node_counter = 0\\n for item in self.items():\\n node_counter += 1\\n return node_counter\\n \\n # for current_node != None:\\n # count+=1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25d6362adea1ac62c9b2d92b5cdeb8f4\",\n \"score\": \"0.5944904\",\n \"text\": \"def length(self):\\n return np.sqrt(np.sum(self ** 2, axis=-1))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dfb627c1896710d5091a7c45418af17a\",\n \"score\": \"0.59440136\",\n \"text\": \"def length(self):\\n return self._length\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dfb627c1896710d5091a7c45418af17a\",\n \"score\": \"0.59440136\",\n \"text\": \"def length(self):\\n return self._length\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c8dea351ad054d51b1ad3b6228bb3ac\",\n \"score\": \"0.5924835\",\n \"text\": \"def __len__(self):\\n args = self.args\\n return self.n_rollout * (args.time_step - args.sequence_length + 1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eee5b573d639c1b98f048aa4fb5577e5\",\n \"score\": \"0.59205246\",\n \"text\": \"def __len__(self):\\n length = len(self.data) * ((self.utils.walk_length - self.span) * self.span)\\n return length\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa2f386ab7d9dc72873e63ebeb1fa8d9\",\n \"score\": \"0.59177715\",\n \"text\": \"def get_length(self):\\n raise(\\\"not implemented\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c80a91c833da4beaa5309c95f87119d5\",\n \"score\": \"0.5914325\",\n \"text\": \"def lena(self) -> int:\\n return self._core.lena()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ca27c5b74dcf1190db7d052f6c84c46\",\n \"score\": \"0.5907244\",\n \"text\": \"def get_total_length(self):\\n return sum(node.blen for node in self.preorder() if node.blen)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"998c3341867849c3fc17ade08181e45e\",\n \"score\": \"0.5906084\",\n \"text\": \"def __len__(self):\\n ctr = 0\\n for entry in self:\\n ctr += 1\\n return ctr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b747a454d1585de058792219e3f50d0a\",\n \"score\": \"0.59033394\",\n \"text\": \"def size(self):\\n\\t\\treturn (self.length + 1) * 4\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0594d1d1d666f587e1784ecf7e9af53f\",\n \"score\": \"0.5903229\",\n \"text\": \"def calc_length(self):\\n\\n return len(self.path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"57a5249093cb4fc1f67b6665732a1fff\",\n \"score\": \"0.59027517\",\n \"text\": \"def _get_length(self) -> \\\"double\\\" :\\n return _fusion.SketchCircle__get_length(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46337cc2786864e6130620289843aec8\",\n \"score\": \"0.5899676\",\n \"text\": \"def length(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46337cc2786864e6130620289843aec8\",\n \"score\": \"0.5899676\",\n \"text\": \"def length(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4548bddeaac6dc8bbeb231c6d5c1c4f4\",\n \"score\": \"0.58961296\",\n \"text\": \"def length(node):\\n len = 0\\n if node is not None:\\n len += 1\\n if node.next_node is not None:\\n len += length(node.next_node)\\n return len\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed6876e61d6c0c90489d8062d59359ff\",\n \"score\": \"0.5893407\",\n \"text\": \"def total_size(self):\\n return reduce(lambda a, b: a + b.size, self.robots.values(), 0.0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f364d8d51dc26fe1af2fdb3d8bae322\",\n \"score\": \"0.5890376\",\n \"text\": \"def get_length(self):\\n return self._length\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aba6e32e26207c209a1bd40ade4c6a2f\",\n \"score\": \"0.5888008\",\n \"text\": \"def __len__(self) -> int:\\n if self.slice_ids is not None:\\n return len(self.slice_ids)\\n else:\\n return self._full_length()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3bcf2bf10614c8474a310baae04f1da\",\n \"score\": \"0.5885656\",\n \"text\": \"def __len__(self) -> int:\\n return self._length\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1193226d6850dbcfae93e3dd9dc0424\",\n \"score\": \"0.58807516\",\n \"text\": \"def __len__(self):\\n return self.repo.array_size ** self.repo.array_size\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11dc2addddc4cd4b7a7072e14e39c549\",\n \"score\": \"0.5880631\",\n \"text\": \"def __len__(self):\\n tracks = self.tracks\\n if tracks is None:\\n length = 0\\n else:\\n length = len(tracks)\\n \\n return length\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12e91c215a1c1ad740bddfb029773fed\",\n \"score\": \"0.58802754\",\n \"text\": \"def __len__(me):\\n n = 0\\n for rows,_ in me._tabs.values():\\n n += len(rows)\\n return n\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ba8f6ea7a5dec6419cbf1ffd4149e18\",\n \"score\": \"0.58779216\",\n \"text\": \"def _get_count(self) -> \\\"size_t\\\" :\\n return _fusion.SketchCircles__get_count(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da5de6145c6298e4363967d19ab11fb2\",\n \"score\": \"0.5877365\",\n \"text\": \"def length(self):\\n if not hasattr(self, '_length'):\\n self._length = math.sqrt(self.length2)\\n\\n return self._length\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15a7eb4775983bf0e154bd6e21c3c457\",\n \"score\": \"0.5875917\",\n \"text\": \"def getLength(self):\\n return _libBornAgainCore.FormFactorAnisoPyramid_getLength(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a58a6d1fb6f032da8f32612dc4919e70\",\n \"score\": \"0.5875638\",\n \"text\": \"def __len__(self) -> int:\\n return len(self.population)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"831e556ccc56e5f0d97e0ed71457f41e\",\n \"score\": \"0.5871503\",\n \"text\": \"def __len__(self) -> int:\\n # Grab some values from *simple_polygon* (i.e. *self*):\\n simple_polygon: SimplePolygon = self\\n points: List[P2D] = simple_polygon.points\\n size: int = len(points)\\n return size\",\n \"title\": \"\"\n },\n {\n \"docid\": \"536d040ce90beab9c46702511ae1414c\",\n \"score\": \"0.5869267\",\n \"text\": \"def size(self):\\n return self.length\",\n \"title\": \"\"\n },\n {\n \"docid\": \"541c9d897437826e09655ff806af65de\",\n \"score\": \"0.58664095\",\n \"text\": \"def length(self):\\n # # Node counter initialized to zero\\n # node_count = 0\\n # # Start at the head node\\n # node = self.head\\n # # Loop until the node is None, which is one node too far past the tail\\n # while node is not None:\\n # # Count one for this node\\n # node_count += 1\\n # # Skip to the next node\\n # node = node.next\\n # # Now node_count contains the number of nodes\\n # return node_count\\n return self.size\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ee662bbf359a717f2134f49bd441ce7\",\n \"score\": \"0.5858225\",\n \"text\": \"def length(self):\\n return self.list.lenght()\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1190,"cells":{"query_id":{"kind":"string","value":"84cc43a4fbc9223bee4e7fc6e0f497de"},"query":{"kind":"string","value":"self the test_case in question id the id number of the test_case 'self' in the database created the date when the test_case 'self' was created created_by the user that created the test_case 'self' question the question associated with 'self' weight the weight given to this test_case content the code for this test_case active a bit telling whether the test_case is active this function acts as the constructor to define a new test_case object"},"positive_passages":{"kind":"list like","value":[{"docid":"fe6bf431b10601c1924d6d6f91e4e7f6","score":"0.7035684","text":"def __init__(self, id, created, created_by, question, weight, content, active):\n self.id = id\n self.created = created\n self.created_by = created_by\n self.question = question\n self.weight = weight\n self.content = content\n self.active = active","title":""}],"string":"[\n {\n \"docid\": \"fe6bf431b10601c1924d6d6f91e4e7f6\",\n \"score\": \"0.7035684\",\n \"text\": \"def __init__(self, id, created, created_by, question, weight, content, active):\\n self.id = id\\n self.created = created\\n self.created_by = created_by\\n self.question = question\\n self.weight = weight\\n self.content = content\\n self.active = active\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"10e245c73b225e7a8ab6b9696c7f322a","score":"0.5976718","text":"def __init__(self, test: TriangleTest, subject_id: int, correct: bool):\n\n self.test = test\n self.subject_id = subject_id\n self.correct = correct","title":""},{"docid":"3825ebf774494f9eefca1a7461d7ef3c","score":"0.5914988","text":"def __init__(self, question, correct_answer):\n self.question = question\n self.correct_answer = correct_answer","title":""},{"docid":"78736b44494d6c60a5cec412a9af58e5","score":"0.5877017","text":"def __init__(self, question, correct_answer):\n\n self.question = question\n self.correct_answer = correct_answer","title":""},{"docid":"32a337687bded16618ba0fab15c34697","score":"0.5843788","text":"def create_case(self, title, description):\n session[\"case_id\"] = self.case_db_proxy.create_case(user_id = session[\"user_id\"], user_token = session[\"user_token\"], title = title, description = description)\n return self.case_status_dialogue_transition(),","title":""},{"docid":"4a234814b2bd03a5eef992dc79da1f1d","score":"0.58090466","text":"def __init__(self, id, type, question, answer, explanation):\n self.id = id\n self.type = type\n self.question = question\n self.answer = answer\n self.explanation = explanation","title":""},{"docid":"a538d066004a24a9077e4ed153bca98e","score":"0.5701454","text":"def create_testcase(self, case_data, func_name, file_name,\n class_name=None, init_kwargs={}):\n case = Case()\n case.func_name = func_name\n case.input = case_data['text']\n case.desc = case_data['description']\n case.expected = case_data['expected']\n case.class_name = class_name\n case.file_name = file_name\n case.init_kwargs = init_kwargs\n return case","title":""},{"docid":"9e90d3ea6cc1a8688c4971f6744b505d","score":"0.5669605","text":"def setUp(self):\n self.takenquiz = TakenQuiz.objects.create(\n name=\"Test TakenQuiz\",\n visibility=True,\n order_rank=1\n )\n self.quiz = Quiz.objects.create(\n name=\"Test Quiz\"\n )\n self.answer = Answer.objects.create(\n takenquiz=self.c,\n company=self.company,\n name=\"Test Product\"\n )","title":""},{"docid":"eb7e5e1870249a130ea31840292f6069","score":"0.56272227","text":"def __init__(self):\n self.db = QuestionerDB","title":""},{"docid":"472a4d966f3942c20a0031354299e227","score":"0.5623586","text":"def noID(self, created_by, question, weight, content, active):\n return self(None, None, created_by, question, weight, content, active)","title":""},{"docid":"7363e13d7d8e7f3bbc145129999752cc","score":"0.56167024","text":"def test_create_single_quiz_question(self):\r\n # This method utilises the POST request method and will make changes to the Canvas instance. This needs consideration.\r\n pass","title":""},{"docid":"e62a9f0625ef2da937ca6055a7fbf7a8","score":"0.5615972","text":"def create_trial_from_quiz(self, original_quiz_id, user, godmode):\n trial_quiz_name = \"Trial_orig_id_{0}_{1}\".format(\n original_quiz_id,\n \"godmode\" if godmode else \"usermode\"\n )\n\n if self.filter(description=trial_quiz_name).exists():\n trial_quiz = self.get(description=trial_quiz_name)\n\n else:\n trial_quiz = self.get(id=original_quiz_id)\n trial_quiz.course.enroll(False, user)\n trial_quiz.pk = None\n trial_quiz.description = trial_quiz_name\n trial_quiz.is_trial = True\n trial_quiz.prerequisite = None\n if godmode:\n trial_quiz.time_between_attempts = 0\n trial_quiz.duration = 1000\n trial_quiz.attempts_allowed = -1\n trial_quiz.active = True\n trial_quiz.start_date_time = timezone.now()\n trial_quiz.end_date_time = datetime(\n 2199, 1, 1, 0, 0, 0, 0, tzinfo=pytz.utc\n )\n trial_quiz.save()\n return trial_quiz","title":""},{"docid":"03cfb5ec3c7ae944411a6fa44b9ec48f","score":"0.5604998","text":"def test_note_on_case(self, request: FixtureRequest):\n # [Pre-Requisite] - create case\n case = self.v3_helper.create_case()\n self._test_note_on_obj(request, case)","title":""},{"docid":"8bb1571f504405b13c839fc83655b29c","score":"0.5604607","text":"def setUp(self):\n result = create_data(1, 0, False)\n self.new_class_session = result['new_class_session']\n self.new_obj = result['new_obj']\n self.new_sub_obj = result['new_sub_obj']\n self.new_activity = result['new_activity']\n self.new_sub_activity = result['new_sub_activity']\n self.new_session = result['new_session']\n self.original_mc3_bank_id = result['original_mc3_bank_id']\n \n self.obj_bank_id = self.new_class_session.obj_bank_id","title":""},{"docid":"5ca7e6c995be99afd1bd50a44e0e48f1","score":"0.55942297","text":"def test_create_question(self):\n newQuestion = Question.objects.create(\n question=\"Could this question pass the test?\",\n category=test_category(),\n author=test_user()\n )\n\n self.assertEqual(\n newQuestion.question,\n \"Could this question pass the test?\"\n )","title":""},{"docid":"ea21cb06d53558bf11800e412003e297","score":"0.5572724","text":"def __init__(self, sample_id, issue_id, date, title, user, status):\n self.sample_id = sample_id\n self.issue_id = issue_id\n self.created_at = datetime.strptime(date, '%Y-%m-%dT%H:%M:%SZ')\n self.title = title\n self.user = user\n self.status = status","title":""},{"docid":"5b799e33bdfc71b3e80261f928b4ed3b","score":"0.55564713","text":"def create_instance_for_test_set(self, case_id, test_set_id):\n self._logger.debug(\"Creating test instance for test case:{} and test set:{}\".format(case_id, test_set_id))\n xml_string = \"<Entity Type=\\\"test-instance\\\">\" \\\n + \" <Fields>\" \\\n + \" <Field Name=\\\"cycle-id\\\"><Value>\" + test_set_id + \"</Value></Field>\" \\\n + \" <Field Name=\\\"status\\\"><Value>No Run</Value></Field>\" \\\n + \" <Field Name=\\\"test-id\\\"><Value>\" + case_id + \"</Value></Field>\" \\\n + \" <Field Name=\\\"test-instance\\\"><Value>1</Value></Field>\" \\\n + \" <Field Name=\\\"subtype-id\\\"><Value>hp.qc.test-instance.MANUAL</Value></Field>\" \\\n + \" <Field Name=\\\"test-order\\\"><Value>1</Value></Field>\" \\\n + \" <Field Name=\\\"owner\\\"><Value>yanb</Value></Field>\" \\\n + \" </Fields>\" \\\n + \"</Entity>\"\n request_url = \"http://\" + self.http_server + \"/test-instances/\" + self.domain + \"/\" + self.project\n test_set_node = self._post_xml_node_from_url(request_url, xml_string)\n #self._print_xml_node(test_set_node)\n return test_set_node","title":""},{"docid":"27832d98d40650b34350c8a0d2f4e6b8","score":"0.55429137","text":"def __init__(self, name, assessment_type = None):\n\n # will store a list of question object types\n # assuming each exam object type will have \n # its own unique set of questions\n self.questions = [] \n self.name = name\n\n if assessment_type:\n self.assessment_type = assessment_type","title":""},{"docid":"dd41b90c1e466fcbd9852f2c8198411e","score":"0.5537577","text":"def _create_trial_from_questionpaper(self, original_quiz_id):\n trial_questionpaper = self.get(quiz_id=original_quiz_id)\n fixed_ques = trial_questionpaper.get_ordered_questions()\n trial_questions = {\"fixed_questions\": fixed_ques,\n \"random_questions\": trial_questionpaper\n .random_questions.all()\n }\n trial_questionpaper.pk = None\n trial_questionpaper.save()\n return trial_questionpaper, trial_questions","title":""},{"docid":"568bd0e0c26220ce5835afaa62b5b616","score":"0.55371195","text":"def test_create_question(self):\n\n res = self.client().post('/questions', json=self.new_question)\n data = json.loads(res.data)\n\n # get created question\n question_created = Question.query.filter_by(\n id=data['created']).one_or_none()\n\n # check status code and message\n self.assertEqual(res.status_code, 200)\n self.assertEqual(data['success'], True)\n self.assertTrue(data['created'])\n self.assertTrue(len(data['questions']))\n self.assertTrue(data['total_questions'])\n\n # check if created question is available after create\n self.assertNotEqual(question_created, None)","title":""},{"docid":"dc826cd3e650033aa760dc5762b2b006","score":"0.54966915","text":"def test_init(self):\n testCase = Case('caseType', 'problemNumber', 'caseNumber', 'input')\n self.assertEqual(testCase.caseType, 'caseType')\n self.assertEqual(testCase.problemNumber, 'problemNumber')\n self.assertEqual(testCase.caseNumber, 'caseNumber')\n self.assertEqual(testCase.inputContents, 'input')","title":""},{"docid":"66f1cbbdaad247db51abeb2830a51e96","score":"0.5490732","text":"def __init__(self, date, detail, identifier, reasonCodeableConcept,\n reasonReference, source, subject, target, when,):\n self.date = date\n self.detail = detail\n self.identifier = identifier\n self.reasonCodeableConcept = reasonCodeableConcept\n self.reasonReference = reasonReference\n self.source = source\n self.subject = subject\n self.target = target\n self.when = when","title":""},{"docid":"d7bf28bcbc982f20acb72fbf6d56967d","score":"0.54734844","text":"def create_instance(this_slug):\n obj = Test(this_slug)\n obj.test_field = value\n return obj","title":""},{"docid":"797da9eebd57577f69a27b62c1ca6d44","score":"0.54714197","text":"def __init__(self, is_correct: bool, answer: str):\n self.is_correct = is_correct\n self.answer = answer","title":""},{"docid":"25c2ada369c7632a9d803d73780c19c5","score":"0.5425045","text":"def create_trial_paper_to_test_quiz(self, trial_quiz, original_quiz_id):\n if self.filter(quiz=trial_quiz).exists():\n trial_questionpaper = self.get(quiz=trial_quiz)\n else:\n trial_questionpaper, trial_questions = \\\n self._create_trial_from_questionpaper(original_quiz_id)\n trial_questionpaper.quiz = trial_quiz\n trial_questionpaper.fixed_questions\\\n .add(*trial_questions[\"fixed_questions\"])\n trial_questionpaper.random_questions\\\n .add(*trial_questions[\"random_questions\"])\n trial_questionpaper.save()\n return trial_questionpaper","title":""},{"docid":"8318f90dcf89ad4c00efd47dc7ed361f","score":"0.5413448","text":"def addQuestion(self):\n self.questions.append(TestQuestion())","title":""},{"docid":"e77ae5087bf970d89f2c7509ef7d727d","score":"0.5397173","text":"def create(self):","title":""},{"docid":"e77ae5087bf970d89f2c7509ef7d727d","score":"0.5397173","text":"def create(self):","title":""},{"docid":"0e93ea8f0c11842e26adfb75ce20e5f7","score":"0.53951424","text":"def create_contest(self, name, creator, start_date, end_date):\n contest = self.create(name=name, creator=creator,\n start_date=start_date, end_date=end_date,\n active=False, created_on=timezone.now(),\n last_updated=timezone.now(),\n user_count=0, beer_count=0)\n # Add the creator as a player\n contest_player = contest.add_player(creator)\n return contest","title":""},{"docid":"4fb21498284313ac32da4ed382ab3bbe","score":"0.5393054","text":"def test_questionnaire_test1_created(self):\n test1 = Questionnaire.objects.get(title='test1')\n\n self.assertEqual(test1.questions.count(), 12)","title":""},{"docid":"d142dacdec90a7c01e1696e730788516","score":"0.53886545","text":"def __init__(self, difficulty):\n self.difficulty = difficulty","title":""},{"docid":"5f6e9dfeaaa8f92e8c96187dd82cef54","score":"0.53595054","text":"def _new_trial(self):\n pass","title":""},{"docid":"1c76974522f19ce08af1a0fe2e0b216e","score":"0.5355575","text":"def _create_proctored_exam(self, course):\n chapter = ItemFactory.create(\n parent=course, category='chapter', display_name='Test Section', publish_item=True\n )\n ItemFactory.create(\n parent=chapter, category='sequential', display_name='Test Proctored Exam',\n graded=True, is_time_limited=True, default_time_limit_minutes=10,\n is_proctored_exam=True, publish_item=True\n )","title":""},{"docid":"c93260464946f43cd51780deddd5932f","score":"0.5348018","text":"def __init__(self, first_name, last_name, date, reason):\r\n self.first_name = first_name\r\n self.last_name = last_name\r\n self.date = date\r\n self.reason = reason\r\n self.date_created = datetime.datetime.now()","title":""},{"docid":"3aa892274f4492c5363cd60389c3ca8c","score":"0.534652","text":"def __eq__(self, other):\n return (\n self.id == other.id and\n self.created == other.created and\n self.created_by == other.created_by and\n self.question == other.question and\n self.weight == other.weight and\n self.content == other.content and\n self.active == other.active\n ) if type(other) is Test_Case else False","title":""},{"docid":"b1ec96b8c466f5d11183872ef1175b28","score":"0.5335523","text":"def __init__(self):\n \n self.id = 0\n self.title = \"\"\n self.location = \"\"\n self.scheduled_on = \"\"\n self.scheduled_on_long = 0\n self.reminder = \"\"\n self.repeat = \"\"\n self.occurrences = 0\n self.occured = 0\n self.duration_hour = \"\"\n self.duration_minutes = \"\"\n self.is_open = None\n self.participants = []\n self.hour = \"\"\n self.minutes = \"\"\n self.ampm = \"\"","title":""},{"docid":"94be6a5bf026fe59e27b311cb06545a0","score":"0.52954364","text":"def setUp(self):\n self.app = create_app()\n self.client = self.app.test_client\n self.database_name = \"trivia_test\"\n self.database_path = \"postgres://{}/{}\".format('localhost:5432', self.database_name)\n setup_db(self.app, self.database_path)\n\n # binds the app to the current context\n with self.app.app_context():\n self.db = SQLAlchemy()\n self.db.init_app(self.app)\n # create all tables\n self.db.create_all()\n\n self.new_question = {'question':'who is the best for ever','answer':'Saad is the best','category':'3','difficulty':'1'}","title":""},{"docid":"3d210f7ad7e6fe39576ccab2fb6e70f1","score":"0.5284293","text":"def test_create_a_recommendation(self):\n recommendation = Recommendation(product_1=100, product_2=200, recommendation_type=\"accessory\", active=True)\n self.assertTrue(recommendation != None)\n self.assertEqual(recommendation.id, None)\n self.assertEqual(recommendation.product_1, 100)\n self.assertEqual(recommendation.product_2, 200)\n self.assertEqual(recommendation.recommendation_type, \"accessory\")\n self.assertEqual(recommendation.active, True)\n recommendation = Recommendation(product_1=100, product_2=200, recommendation_type=\"accessory\", active=False)\n self.assertEqual(recommendation.active, False)","title":""},{"docid":"b967be838fb484ae76a667bcef973f4a","score":"0.5284249","text":"def create_trial_quiz(self, trial_course, user):\n trial_quiz = self.create(course=trial_course,\n duration=1000,\n description=\"trial_questions\",\n is_trial=True,\n time_between_attempts=0\n )\n return trial_quiz","title":""},{"docid":"703b4b9622467818aa3b54d3a09b36a4","score":"0.52795863","text":"def __init__(self):\n self._title = None\n \"\"\" An Exercise has a detailed textual description\n set by the author. \"\"\"\n self._description = None\n \"\"\" An Exercise is characterized by an icon set by the author. \"\"\"\n self._icon = None\n \"\"\" An Exercise stores data on evaluation, i.e. on how often the\n Exercise has been done, what errors have been made TODO \"\"\"\n self._eval = None\n \"\"\" The settings determine the generation of calculations.\n The fields of the settings are determined by self._sett['topic'];\n the values of the settings are different for different Exercises.\n self._sett['calclines'] determines the # of lines in a calculation.\"\"\"\n self._sett = None\n \"\"\" Calculations are generated according to the settings. \"\"\"\n self._calcs = None\n \"\"\" An Exercise needs the Display for updating the settings\"\"\"\n self._display = None","title":""},{"docid":"e4c29f2fc2051deba92aa03f980af7dd","score":"0.52756625","text":"def _create_tc(tcase_class, tcase_conf, global_config):\n test_case = tcase_class(tcase_conf, global_config)\n return Global.SUCCESS, test_case","title":""},{"docid":"95ead1d81e337809374f5110e7653aa7","score":"0.52518284","text":"def __init__(self, test_uid: int, odorants: list, dilution: float, correct: bool):\n\n self.id = test_uid\n self.odorants = odorants\n self.dilution = dilution\n self.correct = correct","title":""},{"docid":"9b687f3b4e2caf2812b47afb79a8d985","score":"0.52497303","text":"def test_first_question_with_one_parent_question(self):\n test = Test.objects.create(title=\"my test\", description=\"my test description\")\n question = Question.objects.create(test=test,question_text=\"my question\")\n self.assertIs(test.first_question().id, question.id)","title":""},{"docid":"316692f33318659219badfc20912f6d4","score":"0.5228202","text":"def test_db_create(self):\n question_db = create_question()\n question_db.id.should.be.greater_than(0)\n questions.find(id=question_db.id).shouldnt.be(None)\n questions.get(id=question_db.id).shouldnt.be(None)","title":""},{"docid":"04ed56d8f62f8f88789ccb4c8ad6c13f","score":"0.52279586","text":"def test_creation():\n ticket = Ticket()\n ticket.randomize()\n ticket.is_rational(0)\n ticket.is_valid()","title":""},{"docid":"bb9a96f85de4f2a708ca1264cb8da958","score":"0.52230555","text":"def __init__(self, id, name, is_test_bot=False):\n self.id = id\n self.name = name\n self.is_test_bot = is_test_bot","title":""},{"docid":"254cf2a5ab8660726f7b36dcf3fabbb3","score":"0.52224666","text":"def test_should_create_a_question(self):\n response = self.app.post('/question', data=json.dumps(dict(\n description='Java has multiple inheritance?'\n )), content_type=AppTestCase.APPLICATION_JSON_CONTENT_TYPE, follow_redirects=True)\n self.assertEqual(response.status_code, 200)\n json_response = json.loads(response.get_data(as_text=True))\n message = str(json_response['message'])\n id_question = str(json_response['id'])\n self.assertTrue(message.__eq__('New Question created!'))\n self.assertFalse(id_question.__eq__(''))\n print('Should create question.')\n print('')","title":""},{"docid":"a92ad267b397a3baa390b1085c7c7489","score":"0.52180153","text":"def setUp(self):\r\n self.app = create_app()\r\n self.client = self.app.test_client\r\n self.database_name = \"trivia_test\"\r\n self.database_path = \"postgres://{}:{}@{}/{}\".format('postgres', '1234','localhost:5432', self.database_name)\r\n setup_db(self.app, self.database_path)\r\n\r\n # set data for testing\r\n self.new_question = {'question':'Who is the inventor of the light bulb?',\r\n 'answer' : 'Thomas Edison',\r\n 'difficulty' : 1,\r\n 'category' : 1}\r\n\r\n self.new_question2 = {'id':1,\r\n 'question':'What is the capital of Saudi Arabia',\r\n 'answer' : 'Riyadh',\r\n 'difficulty' : 1,\r\n 'category' : 3}\r\n\r\n self.invalid_question = {'question':'What is the capital of Saudi Arabia',\r\n 'answer' : 'Riyadh'}\r\n\r\n self.search_term = {'searchTerm':'capital'}\r\n\r\n self.play_quiz = {\r\n 'previous_questions': [],\r\n 'quiz_category': {'type': 'Science', 'id': 1}\r\n }\r\n self.invalid_play_quiz = {\r\n 'previous_questions': [],\r\n 'quiz_category': None\r\n }\r\n self.non_existent_question = {'id':1000,\r\n 'question':' ',\r\n 'answer' : ' ',\r\n 'difficulty' : 1,\r\n 'category' : 3}\r\n\r\n\r\n # binds the app to the current context\r\n with self.app.app_context():\r\n self.db = SQLAlchemy()\r\n self.db.init_app(self.app)\r\n # create all tables\r\n self.db.create_all()","title":""},{"docid":"9f148a8f7a6fb5d1f55d58173dc064ce","score":"0.52166456","text":"def tests_referred1(self):\n report_datetime = datetime.today()\n panel = Panel.objects.get(name='Microtube')\n SubjectRequisitionFactory(subject_visit=self.subject_visit_female, site=self.study_site, panel=panel,\n aliquot_type=AliquotType.objects.get(alpha_code='WB'))\n HivTestReviewFactory(subject_visit=self.subject_visit_female, recorded_hiv_result=POS,\n hiv_test_date=date.today())\n PimaFactory(subject_visit=self.subject_visit_female, cd4_value=501, report_datetime=datetime.today())\n subject_referral = SubjectReferralFactory(\n subject_visit=self.subject_visit_female,\n report_datetime=report_datetime)\n if self.get_intervention:\n self.assertIn('POS#-HI', subject_referral.referral_code)\n else:\n subject_referral = SubjectReferral.objects.all()[0]\n subject_referral.save()\n self.assertIn('POS#-HI', subject_referral.referral_code)","title":""},{"docid":"213078b1216aed5cf06da3191d3a23ed","score":"0.52160597","text":"def save(self, *args, **kwargs):\n if self.instance:\n question = self.instance\n if self.cleaned_data['verified'] == '0':\n question.is_verified = False\n else:\n question.is_verified = True\n\n question.updated_by = self.request.user\n if self.cleaned_data['verified'] == '1':\n question.verified_by = self.request.user\n else:\n question = Question()\n question.created_by = self.request.user\n\n question.text = self.cleaned_data['text']\n question.image = self.cleaned_data['image']\n question.audio = self.cleaned_data['audio']\n question.video = self.cleaned_data['video']\n question.difficulty = self.cleaned_data['difficulty']\n question.article = Article.objects.get(\n slug=self.cleaned_data['article']\n )\n question.category = question.article.category\n\n # question.question_type = self.cleaned_data['question_type']\n question.question_type = 'objective'\n # if question.question_type == 'objective':\n question.correct = self.cleaned_data['correct']\n # else:\n # question.correct = None\n\n question.save()\n return question","title":""},{"docid":"5886c6dd427877c299f168c6a941a5a5","score":"0.52137774","text":"def setUp(self):\n self.app = create_app()\n self.client = self.app.test_client\n self.database_path = \"postgres://{}:{}@{}/{}\".format('postgres', 'localhost:5432', 'trivia_test')\n setup_db(self.app, self.database_path)\n \n self.new_question = {\n 'question': 'ohhhh favorite pie',\n 'answer': 'imagine pie', \n 'category': 3, \n 'difficulty': 2\n }\n \n self.incomplete_question = {\n 'question': 'ohhhh favorite pie',\n 'answer': 'imagine pie', \n 'category': 3\n }\n\n # binds the app to the current context\n with self.app.app_context():\n self.db = SQLAlchemy()\n self.db.init_app(self.app)\n # create all tables\n self.db.create_all()","title":""},{"docid":"d3ec987901a0081ef0e59ab9f101cae8","score":"0.5196682","text":"def create(self):\n \n pass","title":""},{"docid":"cd56918f72efc605b9b10512f354bd66","score":"0.5186739","text":"def get_and_create_testcase(self, case_path, team_name, owner, script_name = None, description = None):\n self._logger.debug(\"Fetching the case \" + case_path)\n case_node = self.find_test_case_node(case_path)\n if(case_node == None):\n self._logger.debug(\"Creating test case :\" + case_path)\n case_name = os.path.basename((case_path))\n if(script_name == None):\n script_name = case_name + \".json\"\n if(description == None):\n description = \"Generate \" + case_name +\" automatically by dpsmoke_automatos\"\n folder_path = os.path.dirname(case_path)\n parent_id = self.get_and_create_folder_id(folder_path)\n xml_string = \"<?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"yes\\\"?>\" \\\n + \"<Entity Type=\\\"test\\\">\" \\\n + \" <Fields>\" \\\n + \" <Field Name=\\\"test-name\\\"><Value>\" + case_name + \"</Value></Field>\" \\\n + \" <Field Name=\\\"execution-type\\\"><Value>MANUAL</Value></Field>\" \\\n + \" <Field Name=\\\"parent-id\\\"><Value>\" + parent_id + \"</Value></Field>\" \\\n + \" <Field Name=\\\"subtype-id\\\"><Value>MANUAL</Value></Field>\" \\\n + \" <Field Name=\\\"team\\\"><Value>\" + team_name + \"</Value></Field>\" \\\n + \" <Field Name=\\\"old-team\\\"><Value>\" + team_name + \"</Value></Field>\" \\\n + \" <Field Name=\\\"status\\\"><Value>\" + \"Ready\" + \"</Value></Field>\" \\\n + \" <Field Name=\\\"exclusive\\\"><Value>N</Value></Field>\" \\\n + \" <Field Name=\\\"priority\\\"><Value>1-High</Value></Field>\" \\\n + \" <Field Name=\\\"remotely-executable\\\"><Value>Yes</Value></Field>\" \\\n + \" <Field Name=\\\"test-state\\\"><Value>Automated</Value></Field>\" \\\n + \" <Field Name=\\\"automation-dev-framework\\\"><Value>Automatos-Python</Value></Field>\" \\\n + \" <Field Name=\\\"script-name\\\"><Value>\" + script_name + \"</Value></Field>\" \\\n + \" <Field Name=\\\"execution-engine\\\"><Value>Automatos</Value></Field>\" \\\n + \" <Field Name=\\\"automation-script-owner\\\"><Value>\" + owner + \"</Value></Field>\" \\\n + \" <Field Name=\\\"description\\\"><Value>\" + description + \"</Value></Field>\" \\\n + \" </Fields>\" \\\n + \"</Entity>\"\n request_url = \"http://\" + self.http_server + \"/tests/\" + self.domain + \"/\" + self.project\n case_node = self._post_xml_node_from_url(request_url, xml_string)\n #self._print_xml_node(case_node)\n return case_node","title":""},{"docid":"695be56d1c4b0633a156f78a8c088759","score":"0.51864606","text":"def __init__(self, test):\n self.test = test","title":""},{"docid":"695be56d1c4b0633a156f78a8c088759","score":"0.51864606","text":"def __init__(self, test):\n self.test = test","title":""},{"docid":"f01315f19a045298013b53ae74a6813e","score":"0.51836497","text":"def initialize(self):\n\n operation = self.inspect.stack()[0][3]\n self.log.info(\"Initializing Testcase %s ...\" % self.id)\n result = {'successful': False}\n\n try:\n # get testcase data from database\n returned = self.get_testcase_data_from_database(self.id)\n\n # assign unique name for build server test result tracking\n self.build_test_name = \"%s: %s: %s: %s: %s\" % (self.module, self.feature, self.story,\n self.test, self.name)\n\n # assign procedure\n # parse procedure data\n procedure_data = returned['testcase data']['procedure']\n procedure_raw_list = procedure_data.split(',')\n # build procedure data list\n procedure_list = []\n for item in procedure_raw_list:\n procedure_list.append(int(item.strip()))\n # build procedure function list\n for procedure_step_id in procedure_list:\n data = self.get_procedure_step_data_from_database(procedure_step_id)\n # parse the data\n step_data = data['step data']\n function_data = data['function data']\n submodule_data = data['submodule data']\n # translate function call\n function = function_data['function'].lower().replace(' ', '_')\n # handle arguments\n if str(step_data['arguments']).strip() == '': step_data['arguments'] = None\n if step_data['arguments'] is not None:\n arguments = str(step_data['arguments'])\n arguments += ','\n else:\n arguments = ''\n arguments += \"testcase=self\"\n # handle verification status\n verification = eval(step_data['verification'])\n # combine into one data dict of relevant data\n translated_step_data = {\n 'id': procedure_step_id,\n 'name': step_data['name'],\n 'function': \"%s.%s(%s)\" % (submodule_data['code'], function,\n arguments),\n 'verification': verification\n }\n self.procedure.append(translated_step_data)\n\n # report\n self.log.trace(\"Testcase initialized:\")\n self.log.trace(\"\\tName: %s\" % self.name)\n self.log.trace(\"\\tBuild Test name: %s\" % self.build_test_name)\n self.log.trace(\"\\tProcedure:\")\n for step in self.procedure:\n self.log.trace(\"\\t\\t%s\" % step['name'])\n result['successful'] = True\n except BaseException, e:\n self.handle_exception(e, operation)\n\n # return\n return result","title":""},{"docid":"4c3e022909298e3313a47e2d194cef9c","score":"0.5181774","text":"def test_category(name='Test Category', description=\"Category for testing\"):\n return QuestionCategory.objects.create(name=name, description=description)","title":""},{"docid":"09c01f992b654d2f8689eae1388cddcd","score":"0.5180956","text":"def __init__(self):\n self.details = {}\n self.test_results = {}\n self.test_results_values = {}\n self.test_units = {}","title":""},{"docid":"cf3996759b4a63878a3f510c1821434a","score":"0.51779884","text":"def setUp(self):\n self.app = create_app()\n self.client = self.app.test_client\n self.database_name = \"trivia_test\"\n self.database_path = \"postgres://{}:{}@{}/{}\".format('postgres', 'postgres','localhost:5432', self.database_name)\n setup_db(self.app, self.database_path)\n\n self.new_question = {\n 'question': 'How are you?',\n 'answer': 'All good, and you?',\n 'category': 1,\n 'difficulty': 1\n }\n\n self.new_question_missing = {\n 'question': None,\n 'answer': None,\n 'category': 1,\n 'difficulty': 1\n } \n\n # binds the app to the current context\n with self.app.app_context():\n self.db = SQLAlchemy()\n self.db.init_app(self.app)\n # create all tables\n self.db.create_all()","title":""},{"docid":"0395f0c11c46917c5715d2d2057c4b46","score":"0.51658934","text":"def __init__(self, question, correctAnswer, incorrectAnswers):\r\n # Defining the question as an attribute of the class\r\n self.question = question\r\n # Defining the correct answer as an attribute of the class\r\n self.correctAnswer = correctAnswer\r\n # Defining a list of incorrect answers as an attribute of the class\r\n self.incorrectAnswers = incorrectAnswers","title":""},{"docid":"22027230b81e5f6643af2242b6998f5e","score":"0.51635367","text":"def setUp(self):\n self.testcase = models.Review()","title":""},{"docid":"285905160f3d4ea29ba20fbc6b7b45bb","score":"0.5162068","text":"def __init__(self, name, parent, test_result):\n super(CTestItem, self).__init__(name, parent)\n self.test_result = test_result","title":""},{"docid":"7e900596849ff31b7251c39bf19539d9","score":"0.5151337","text":"def test_create_question(self):\n\n # simple q\n question = {\n 'question': 'This is a simple question',\n 'answer': 'this is a simple answer',\n 'difficulty': 1,\n 'category': 1,\n }\n\n # make request and process response\n response = self.client().post('/questions/new', json=question)\n data = json.loads(response.data)\n\n self.assertEqual(response.status_code, 201)\n self.assertEqual(data['success'], True)\n self.assertEqual(data['message'], 'Question successfully created!')","title":""},{"docid":"36785c99f49ba066c6738fc20faf971e","score":"0.51466835","text":"def create_question(self, request):\n\n question = request.question\n answers = {'correct': request.correct,\n 'wrong1': request.wrong1,\n 'wrong2': request.wrong2,\n 'wrong3': request.wrong3}\n\n clues = [request.clue1, request.clue2]\n\n newQuestion = Question.new_question(question, answers, clues)\n\n return newQuestion.to_form()","title":""},{"docid":"a2a5f1041090541a974bedfcca3b2c74","score":"0.5146572","text":"def __init__(self):\n self.rid_1 = \"\"\n self.rid_2 = \"\"\n self.temp_rid = \"\"\n self.role_1 = 0\n self.role_2 = 0\n self.trans_id = \"\"\n self.go = False","title":""},{"docid":"20ef730804fd46b216401fab5f3f7dd6","score":"0.5142977","text":"def initialize(self, bid_level, bid_trump, bid_team, game_case):\n self.bid_level = bid_level\n self.bid_trump = bid_trump\n self.bid_team = bid_team\n self.game_case = game_case\n self.player_list = ['p_00', 'p_11', 'p_01', 'p_10']\n self.round_index = -1\n self.win_count = [0,0]\n self.identity = self.game_case['identity']\n\n self.open_player = [k for k, v in self.identity.items() if v == [0, 1, 0]][0]\n self.banker_player = [k for k, v in self.identity.items() if v == [1, 0, 0]][0]\n\n\n #calculate the index of the current bid\n self.bid_index = ['C','D','H','S', None].index(bid_trump)*7 + (bid_level-7)\n\n #create a list of cards in sorted order (an index -> card map)\n self.index_to_card = sorted([Card(rank=x[0], suit=x[1], trump=bid_trump) \n for x in product(Card.ranks, Card.suits)])\n\n #create a dictionary mapping cards to index\n self.card_to_index = {}\n for index,card in enumerate(self.index_to_card):\n self.card_to_index[card] = index\n\n #now initialize relevant variables for starting state (beginning of round)\n self.trick_history = []\n self.current_trick = []\n self.trick_winner = None\n self.round_over = False\n\n #determine the starting player\n # self.current_player = 'p_%d%d' % (bid_team, 1 if random.random() < 0.5 else 0)\n\n self.current_player = self.game_case['starting_player']\n\n #keep track of each team's score\n self.team0_num_tricks = 0\n self.team1_num_tricks = 0\n self.team0_score = None\n self.team1_score = None\n\n #some variables to keep track of the state representation given to neural networks\n self.played_cards = {'p_00': [], 'p_01': [], 'p_10': [], 'p_11': []}\n self.played_cards_vector = ({'p_00': np.zeros((52,)), 'p_01': np.zeros((52,)),\n 'p_10': np.zeros((52,)), 'p_11': np.zeros((52,))})\n\n self.played_this_trick = {'p_00': None, 'p_01': None, 'p_10': None, 'p_11': None}\n self.hands = {'p_00': [], 'p_01': [], 'p_10': [], 'p_11': []}\n self.hands_vector = ({'p_00': np.zeros((52,)), 'p_01': np.zeros((52,)),\n 'p_10': np.zeros((52,)), 'p_11': np.zeros((52,))})\n\n self._deal()","title":""},{"docid":"59d8b309d552f82d8f94a9ecb5a96a62","score":"0.5134122","text":"def __init__(self, patient, resident, attending, begin_date, end_date):\n\t\tfaker = Factory.create()\n\n\t\ttry:\n\t\t\tassert patient != None and resident != None and attending != None\n\t\t\t# assert type(patient) == Patient and type(resident) == Resident and type(attending) == Attending\n\t\t\t# Make a random exam modality\n\t\t\tself.examMod = ''\n\t\t\tself.randExam = random.randint(1, 4)\n\t\t\tif self.randExam is 1:\n\t\t\t\tself.examMod = 'CT'\n\t\t\telif self.randExam is 2:\n\t\t\t\tself.examMod = 'MR'\n\t\t\telif self.randExam is 3:\n\t\t\t\tself.examMod = 'XR'\n\t\t\telse:\n\t\t\t\tself.examMod = 'US'\n\t\t\t\n\t\t\t# Make a random exam date and time\n\t\t\t# Precondition: both begin_date and end_date should be in the format of yyyy-mm-dd\n\t\t\tbegin_Y_M_D = begin_date.split('-') # split the date so that it's [yyyy, mm, dd]\n\t\t\tend_Y_M_D = end_date.split('-')\n\t\t\texam_year = str(random.randint(int(begin_Y_M_D[0]), int(end_Y_M_D[0])))\n\t\t\t# For the date, if the begin and end have the same years then make the months in between; else just random from 1~12\n\t\t\texam_month = random.randint(int(begin_Y_M_D[1]), int(end_Y_M_D[1])) if int(begin_Y_M_D[0]) == int(end_Y_M_D[0]) else random.randint(1,12)\n\t\t\texam_month = str(exam_month).zfill(2) # make it so that all months will be two numbers, ex.: 01, 03, 08, 10, 12\n\t\t\t# Same logic for the day\n\t\t\texam_day = ''\n\t\t\tif (int(begin_Y_M_D[0]) is int(end_Y_M_D[0]) and int(begin_Y_M_D[1]) is int(end_Y_M_D[1])):\n\t\t\t\texam_day = str(random.randint(int(begin_Y_M_D[2]), int(end_Y_M_D[2]))).zfill(2)\n\t\t\telse:\n\t\t\t\t# need to consider the difference in days in different months, but we'll leave it for now.\n\t\t\t\texam_day = str(random.randint(1,31)).zfill(2)\n\t\t\tself.examDateTime = exam_year + \"-\" + exam_month + \"-\" + exam_day + \" \" + faker.time()\n\n\t\t\t\"\"\"\n\t\t\t# need to check the DOB of the Patient so that the DOB < examDateTime\n\t\t\twhile int(self.examDateTime[:self.examDateTime.index('-')])<int(patient.DOB()[:patient.DOB().index('-')]):\n\t\t\t\tself.examDateTime = str(faker.date_time()) \"\"\"\n\n\t\t\t# Make a random examID - should be unique.\n\t\t\tself.examID = str(random.randint(0,99999999)).zfill(8)\n\t\t\t# Check if the examID already exists.\n\t\t\tif self.examID in examID_List:\n\t\t\t\tself.examID = str(random.randint(0,99999999)).zfill(8)\n\t\t\telse:\n\t\t\t\texamID_List.append(self.examID)\n\t\t\t\n\t\t\t# Make a random exam report date and time.\n\t\t\tself.randReport = random.randint(1, 6)\n\t\t\tself.examReportDateTimeNum = int(self.examDateTime[self.examDateTime.find(' ')+1:self.examDateTime.find(':')])+self.randReport\n\t\t\tself.examReportDateTime = self.examDateTime[:self.examDateTime.find(' ')+1] + str(self.examReportDateTimeNum) + self.examDateTime[self.examDateTime.find(':'):]\n\t\t\t\n\t\t\t# Add the Resident and Attending and vice versa.\n\t\t\tself.examResident = resident\n\t\t\tself.examAttending = attending\n\t\t\tself.examPatient = patient\n\t\t\tpatient.addExam(self)\n\t\t\tresident.addExam(self)\n\t\t\tattending.addExam(self)\n\t\t\tc = conn.cursor()\n\t\t\tc.executescript(\"\"\"INSERT INTO Exams VALUES ('self.examMod', 'self.examdDateTime', 'self.examID', 'self.examReportDateTime');\"\"\")\n\t\t\tconn.commit()\n\n\t\texcept:\n\t\t\tprint \"Invalid error: Please check your inputs and try again.\"","title":""},{"docid":"828ece2925d374c2869dfba32245bcb8","score":"0.5132347","text":"def __init__(self, date, exercises):\n self.date = date\n self.exercises = exercises","title":""},{"docid":"96cbf1cf9a26485283040a408fb926b8","score":"0.51307094","text":"def test_first_question_with_multiple_parent_questions(self):\n test = Test.objects.create(title=\"my test\", description=\"my test description\")\n question1 = Question.objects.create(test=test,question_text=\"my question 1\")\n Question.objects.create(test=test, question_text=\"my question 2\")\n Question.objects.create(test=test, question_text=\"my question 3\")\n self.assertIs(test.first_question().id, question1.id)","title":""},{"docid":"77ee001e696582bebbb81d063149edba","score":"0.5127161","text":"def tests_referred3(self):\n report_datetime = datetime.today()\n panel = Panel.objects.get(name='Microtube')\n SubjectRequisitionFactory(subject_visit=self.subject_visit_female, site=self.study_site, panel=panel,\n aliquot_type=AliquotType.objects.get(alpha_code='WB'))\n HivTestReviewFactory(subject_visit=self.subject_visit_female, recorded_hiv_result=NEG)\n PimaFactory(subject_visit=self.subject_visit_female, cd4_value=501, report_datetime=datetime.today())\n subject_referral = SubjectReferralFactory(\n subject_visit=self.subject_visit_female,\n report_datetime=report_datetime)\n self.assertEqual('TST-HIV', subject_referral.referral_code)","title":""},{"docid":"051ef25554089ef698123af48204f09c","score":"0.51244783","text":"def setUp(self):\n self.app = create_app()\n self.client = self.app.test_client\n self.database_name = \"trivia_test\"\n self.database_path = \"postgres://postgres:hallo@{}/{}\".format('localhost:5432', self.database_name)\n setup_db(self.app, self.database_path)\n\n # binds the app to the current context\n with self.app.app_context():\n self.db = SQLAlchemy()\n self.db.init_app(self.app)\n # create all tables\n self.db.create_all()\n \n #create dummy question:\n self.new_question = {\n 'question': 'Which stupid bird creates havoc on our balcony?',\n 'answer': 'Meisi',\n 'category': 1,\n 'difficulty': 2\n }","title":""},{"docid":"50f973970c8adbffb91e95f3f5231ce3","score":"0.5118032","text":"def setUp(self):\n self.app = create_app()\n self.client = self.app.test_client\n self.database_name = \"trivia_test\"\n self.database_path = \"postgres://{}/{}\".format('postgres:root@localhost:5432', self.database_name)\n setup_db(self.app, self.database_path)\n\n self.new_question={\n 'question':'test question',\n 'answer': 'test answer',\n 'category': 1,\n 'difficulty': 1\n }\n\n # binds the app to the current context\n with self.app.app_context():\n self.db = SQLAlchemy()\n self.db.init_app(self.app)\n # create all tables\n self.db.create_all()","title":""},{"docid":"6517deabe158da197ff13cea152cd341","score":"0.5117872","text":"def setUp(self):\n self.app = create_app()\n self.client = self.app.test_client\n self.database_name = \"trivia_test\"\n self.database_path = \"postgresql://{}:{}@{}/{}\".format(DB_USER,DB_PASSWORD,'localhost:5432', DB_NAME)\n setup_db(self.app, self.database_path)\n\n # binds the app to the current context\n with self.app.app_context():\n self.db = SQLAlchemy()\n self.db.init_app(self.app)\n # create all tables\n self.db.create_all()\n\n self.new_question = {\n 'question': 'Alifbeda nechta harf bor?',\n 'answer': '31',\n 'category': 5,\n 'difficulty': 2,\n }","title":""},{"docid":"4fb2d2b8dd2c9140f6a6780159ee0561","score":"0.5117735","text":"def example():\n\n # create a new student\n andrea = Student('Andrea', 'Pineapple', '1234 Street, Dallas, TX 74209')\n\n # -------------------------------------------------------\n # creates a new exam object\n new_exam = Exam('basic math exam', assessment_type='Exam')\n\n # add a question and a correct answer to the exam\n new_exam.insert_an_exam_question(\"What's 1+1?\", \"2\")\n new_exam.insert_an_exam_question(\"What's 2+2?\", \"4\")\n new_exam.insert_an_exam_question(\"What's 3+3?\", \"6\")\n\n # administer exam\n take_test(new_exam, andrea)\n\n # -------------------------------------------------------\n # creates a new quiz object\n new_quiz = Quiz('basic math quiz 2', assessment_type='Quiz')\n\n # add a question and a correct answer to the exam\n new_quiz.insert_an_exam_question(\"What's 1*1?\", \"1\")\n new_quiz.insert_an_exam_question(\"What's 2*2?\", \"4\")\n new_quiz.insert_an_exam_question(\"What's 3*3?\", \"9\")\n\n print \"This is the list of quiz questions:\", new_quiz.questions\n\n # administer exam\n take_test(new_quiz, andrea)","title":""},{"docid":"9511d8458693f204041e43296b1f6ee4","score":"0.5115062","text":"def __init__(self, jsonfile: str = 'bank/cissp_questions.json',\n questionisrandom: bool = True,\n answerisalpha: bool = True,\n answerisrandom: bool = True):\n\n self._jsonFile = jsonfile\n self._questionIsRandom = questionisrandom\n self._answerIsAlpha = answerisalpha\n self._answerIsRandom = answerisrandom\n self._jsonData = self.__loadJsonFile()\n\n self.correct = 0\n self.incorrect = 0\n self.totalQuestions = len(self._jsonData['questions'])\n self.questionSet = self.__complieQuestions()","title":""},{"docid":"59ca96f36249968b10497dc485353bf5","score":"0.5114386","text":"def __init__(self,question):\n\t\tself.question = question\n\t\tself.responses = []","title":""},{"docid":"c838fc1e852de81e32e02daa59d8787e","score":"0.51141316","text":"def setUp(self):\n self.app = create_app()\n self.client = self.app.test_client\n self.database_name = \"trivia_test\"\n self.database_path = \"postgres://{}:{}@{}/{}\".format('postgres','postgres','localhost:5432', self.database_name)\n setup_db(self.app, self.database_path)\n\n self.new_question = {\n 'question' : 'What is the name of the Late Night Host with orange hair',\n 'answer' : 'Conan O\\'Brien',\n 'category' : '5',\n 'difficulty' : 2\n }\n\n # binds the app to the current context\n with self.app.app_context():\n self.db = SQLAlchemy()\n self.db.init_app(self.app)\n # create all tables\n self.db.create_all()","title":""},{"docid":"8fbb47ae90dddb7e94628ae53031a43b","score":"0.5110197","text":"def new( self, user_id, question_id, text ):\n \"\"\"unique answer id is returned\"\"\"\n \"\"\"KeyError exception should be thrown if user_id or question_id not found\"\"\"\n aid = str(uuid.uuid4())\n if user_id is not '0' and question_id is not '0':\n try:\n self.cursor.execute('''INSERT INTO answer (id, qid, uid, body)\n VALUES (?,?,?,?)''', (aid, question_id, user_id, text))\n except sqlite3.IntegrityError:\n raise KeyError\n else:\n raise KeyError\n return aid","title":""},{"docid":"74705ab235aff4c7724ccccfb479f7d1","score":"0.51067877","text":"def test_instantiation(self):\n setuprule = factories.SetupRuleFactory()\n self.assertTrue(setuprule.pk)","title":""},{"docid":"273c9c494ffc2a2a629981099e28d6a2","score":"0.5105797","text":"def create_question(question, days):\n time = timezone.now() + datetime.timedelta(days)\n return Question.objects.create(question = question, pub_date = time)","title":""},{"docid":"066e4e5b3cb2668c0799805a84d27fcd","score":"0.50995284","text":"def setUp(self):\n self.app = create_app()\n self.client = self.app.test_client\n self.database_name = \"trivia\"\n # self.database_path = \"postgres://{}/{}\".format('localhost:5432', self.database_name)\n self.database_path = \"postgresql://postgres:root@localhost:5432/trivia\"\n setup_db(self.app, self.database_path)\n\n # binds the app to the current context\n with self.app.app_context():\n self.db = SQLAlchemy()\n self.db.init_app(self.app)\n # create all tables\n self.db.create_all()\n \n self.new_question = {\n 'question': 'this is match question',\n 'answer': 'this is math answer',\n 'category': 'Math',\n 'difficulty': '10'\n }","title":""},{"docid":"04fe67026fab7516d9f940a9ae44821b","score":"0.5097631","text":"def test_instantiation(self):\n flow = factories.FlowFactory()\n self.assertTrue(flow.pk)","title":""},{"docid":"2202f1dfeedbc492fcff2124db754c22","score":"0.50964504","text":"def create_trial_paper_to_test_questions(self, trial_quiz,\n questions_list):\n if questions_list is not None:\n trial_questionpaper = self.create(quiz=trial_quiz,\n total_marks=10,\n )\n trial_questionpaper.fixed_question_order = \",\".join(questions_list)\n trial_questionpaper.fixed_questions.add(*questions_list)\n return trial_questionpaper","title":""},{"docid":"9a46a46d663c4a1ef5b0871f8678439d","score":"0.5090909","text":"def __init__(self, parent, idevice):\n Block.__init__(self, parent, idevice)\n self.idevice = idevice\n self.questionElements = []\n self.message = False\n i = 0\n for question in idevice.questions:\n self.questionElements.append(TestquestionElement(i, idevice, \n question))\n i += 1","title":""},{"docid":"74933514b1493fe99c5eda47521dd4f9","score":"0.50898194","text":"def setUp(self):\n self.app = create_app()\n self.client = self.app.test_client\n self.database_name = \"trivia_test\"\n setup_db(self.app)\n\n self.new_question = {\n 'question': 'What color is gree?',\n 'answer': 'green',\n 'difficulty': 3,\n 'category': 1\n }\n\n self.empty_question = {\n 'question': False,\n 'answer': False,\n 'difficulty': 3,\n 'category': 1\n }\n self.quiz = {\n 'quiz_category': {'id': 2},\n 'previous_questions': [],\n }\n # binds the app to the current context\n with self.app.app_context():\n self.db = SQLAlchemy()\n self.db.init_app(self.app)\n # create all tables\n self.db.create_all()","title":""},{"docid":"5a22e604758c812d0696c8448f2a1d25","score":"0.50820285","text":"def __init__(self, skillID=-1, f=None):\n super(Test, self).__init__(skillID)\n self.skillName = \"test\"\n self.f: SkillObject = f","title":""},{"docid":"7f30d27807249443caa73aaa2ff32995","score":"0.5081338","text":"def _create_dummy_question(\n self, question_id, question_content, linked_skill_ids):\n state = state_domain.State.create_default_state(\n 'ABC', is_initial_state=True)\n state.update_interaction_id('TextInput')\n state.update_interaction_customization_args({\n 'placeholder': {\n 'value': {\n 'content_id': 'ca_placeholder_0',\n 'unicode_str': ''\n }\n },\n 'rows': {'value': 1}\n })\n\n state.update_next_content_id_index(1)\n state.update_linked_skill_id(None)\n state.update_content(state_domain.SubtitledHtml('1', question_content))\n recorded_voiceovers = state_domain.RecordedVoiceovers({})\n written_translations = state_domain.WrittenTranslations({})\n recorded_voiceovers.add_content_id_for_voiceover('ca_placeholder_0')\n recorded_voiceovers.add_content_id_for_voiceover('1')\n recorded_voiceovers.add_content_id_for_voiceover('default_outcome')\n written_translations.add_content_id_for_translation('ca_placeholder_0')\n written_translations.add_content_id_for_translation('1')\n written_translations.add_content_id_for_translation('default_outcome')\n\n state.update_recorded_voiceovers(recorded_voiceovers)\n state.update_written_translations(written_translations)\n solution = state_domain.Solution(\n 'TextInput', False, 'Solution', state_domain.SubtitledHtml(\n 'solution', '<p>This is a solution.</p>'))\n hints_list = [\n state_domain.Hint(\n state_domain.SubtitledHtml('hint_1', '<p>This is a hint.</p>')\n )\n ]\n\n state.update_interaction_solution(solution)\n state.update_interaction_hints(hints_list)\n state.update_interaction_default_outcome(\n state_domain.Outcome(\n None, state_domain.SubtitledHtml(\n 'feedback_id', '<p>Dummy Feedback</p>'),\n True, [], None, None\n )\n )\n question = question_domain.Question(\n question_id, state,\n feconf.CURRENT_STATE_SCHEMA_VERSION,\n constants.DEFAULT_LANGUAGE_CODE, 0, linked_skill_ids, [])\n return question","title":""},{"docid":"a65a6ddc4b571c4ae36498d251e23ccc","score":"0.5079879","text":"def test_flow(self):\n course = Course.objects.create(title='test course')\n lesson1_webinar = Webinar.objects.create(name='webinar of 1st lesson')\n for number in range(7):\n lesson = Lesson.objects.create(title=f'lesson {number+1}', )\n if number + 1 == 1:\n lesson.webinar = lesson1_webinar\n lesson.save()\n course.add_lesson(lesson)\n\n flow = course.create_flow()\n self.assertEqual(flow.flow_lessons.count(), 7, \"Lessons must be copied from course\")\n self.assertEqual('lesson 1', flow.flow_lessons.first().lesson.title)\n self.assertEqual(lesson1_webinar, flow.flow_lessons.first().webinar)\n self.assertEqual('lesson 7', flow.flow_lessons.last().lesson.title)\n\n flow_2 = course.create_flow()\n # this flow has own webinar instead of default\n flow2_lesson1_webinar = Webinar.objects.create(name='webinar of 1st lesson for flow_2')\n flow2_lessons_first = flow_2.flow_lessons.first()\n flow2_lessons_first.webinar = flow2_lesson1_webinar\n flow2_lessons_first.save()\n\n self.assertEqual(flow.flow_lessons.count(), 7, \"Lessons must be copied from course\")\n self.assertEqual(flow2_lesson1_webinar, flow_2.flow_lessons.first().webinar,\n \"Shall override webinar\")\n self.assertEqual(lesson1_webinar, flow.flow_lessons.first().webinar,\n \"Shall keep default lesson webinar\")","title":""},{"docid":"d3a8cbde929d5b9d988ea3fbf369c330","score":"0.50783646","text":"def setUp(self):\n self.candidate = cand.Candidate()","title":""},{"docid":"3f805f4127817a182d3e361131d51fdc","score":"0.5078282","text":"def setUp(self):\n self.app = create_app()\n self.client = self.app.test_client\n self.database_name = \"trivia_test\"\n self.database_path = \"postgres://{}/{}\".format('postgres:12345@localhost:5432', self.database_name)\n setup_db(self.app, self.database_path)\n\n # binds the app to the current context\n with self.app.app_context():\n self.db = SQLAlchemy()\n self.db.init_app(self.app)\n # create all tables\n self.db.create_all()\n\n self.new_question = {\n 'question': 'q1',\n 'answer': 'a1',\n 'category': 2,\n 'difficulty': 3\n }\n\n self.new_question_missing_fields = {\n 'question': 'q1m',\n 'category': 3,\n 'difficulty': 1\n }\n\n self.play = {\n 'previous_questions': [6, 11, 19, 21],\n 'category': 0\n }\n\n self.play_with_invalid_category = {\n 'previous_questions': 1000,\n 'category': 2\n }","title":""},{"docid":"f39cd3107991db1f9511e61444828189","score":"0.507792","text":"def Create(self, parent):","title":""},{"docid":"0c23a71098ef096dc76b76488c569e64","score":"0.5076449","text":"def create():","title":""},{"docid":"faca8f019d9e1f40258fccef8f3b740a","score":"0.5071746","text":"def test_instantiation(self):\n setupmatrix = factories.SetupMatrixFactory()\n self.assertTrue(setupmatrix.pk)","title":""},{"docid":"5f0c9c105c64fc0f13fb11feedf96cfa","score":"0.5071715","text":"def __init__(self, exercise_type: dict):\n self.type = exercise_type.get(\"type\")","title":""},{"docid":"7c3c759ae4e927c2e602a4ad915bdf5e","score":"0.50699073","text":"def test_teacher_create_class(self):\n\n import pdb; pdb.set_trace()\n\n new_teacher = Teacher(username=\"wdisney\", password=\"password\", fname=\"Walt\", lname=\"Disney\")\n db.session.add(new_teacher)\n db.session.commit()\n\n teacher_id = Teacher.query.filter_by(username='wdisney').one().teacher_id\n\n with app.test_client() as c:\n with c.session_transaction() as sess:\n sess['teacher_id'] = teacher_id\n\n create_class_info = {'registration code': \"999\", 'name': \"6th Period Band\", 'type_class': \"marching_band\"}\n result=self.client.post(\"/create-class\", data=create_class_info, follow_redirects=True)\n self.assertIn(\"6th Period Band\", result.data)","title":""},{"docid":"8dab3b66ed8567f84433e0f649693ad8","score":"0.50670725","text":"def test_create_questions_category(self):\n name = \"Example Category\"\n description = \"Category for testing purposes\"\n category = QuestionCategory.objects.create(\n name=name,\n description=description\n )\n self.assertEqual(category.name, name)\n self.assertEqual(category.description, description)","title":""},{"docid":"66e3822b67a515110c23758b6fc7263b","score":"0.5066986","text":"def setUpClass(cls):\n super().setUpClass()\n cls.start_events_isolation()\n cls.course = CourseFactory.create()\n cls.course_limited = CourseFactory.create()\n cls.proctored_course = CourseFactory(\n enable_proctored_exams=True, enable_timed_exams=True\n )\n cls.proctored_course_no_exam = CourseFactory(\n enable_proctored_exams=True, enable_timed_exams=True\n )","title":""},{"docid":"b5fcd6b207c32445c3b3e12ff3d1d522","score":"0.50592625","text":"def test_candidate_with_testid(self):\n self.client.login(email=self.config['login']['email'],\n password=self.config['login']['password'])\n resp = question_create(self)\n q_id = resp.data.get('id')\n data = {'title': self.config[\n 'testPostData']['title'], 'duration': self.config['testPostData'][\n 'duration'], 'question': [q_id]}\n _ = self.client.post(self.config['test']['api'], data=json.dumps(data),\n content_type=\"application/json\")\n test_id = _.data['id']\n response = self.client.post(self.config['user']['api'],\n {'email': self.config['userPostData'][\n 'email'],\n 'password': self.config[\n 'userPostData']['password'],\n 'user_type': self.config['userPostData'][\n 'candidate'], 'test': test_id,\n 'schedule': datetime.now()})\n self.assertEqual(response.status_code, 201)\n self.assertEqual(CandidateTestMapping.objects.count(), 1)","title":""},{"docid":"90656089699842e99ca8587d87a3f0cc","score":"0.5057354","text":"def _extend_questions(cls, questions):\n # Update _questions from Sampling by adding an additional question\n questions.add_questions_to_block(\"\"\"\n # State on which trajectories start\n initial state = 0 :: int\n \"\"\")\n questions.generate_cases(\"method\", {name: method.questions\n for name, method in cls._methods.items()})","title":""},{"docid":"dcc6dd99966904134977020fe3fbffde","score":"0.5056758","text":"def case(self, **kwargs):\n return Case(self.tcex, **kwargs)","title":""},{"docid":"197716cbf77a83e9935ce15e6684ddd9","score":"0.5054177","text":"def new( self, user_id, text ):\n \"\"\"unique question id is returned\"\"\"\n \"\"\"KeyError exception should be thrown if user_id not found\"\"\"\n if user_id is not '0':\n try:\n qid = str(uuid.uuid4())\n self.cursor.execute('INSERT INTO question (id, uid, body) VALUES (?,?,?)', (qid, user_id, text))\n except sqlite3.IntegrityError:\n raise KeyError\n else:\n raise KeyError\n return qid","title":""}],"string":"[\n {\n \"docid\": \"10e245c73b225e7a8ab6b9696c7f322a\",\n \"score\": \"0.5976718\",\n \"text\": \"def __init__(self, test: TriangleTest, subject_id: int, correct: bool):\\n\\n self.test = test\\n self.subject_id = subject_id\\n self.correct = correct\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3825ebf774494f9eefca1a7461d7ef3c\",\n \"score\": \"0.5914988\",\n \"text\": \"def __init__(self, question, correct_answer):\\n self.question = question\\n self.correct_answer = correct_answer\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78736b44494d6c60a5cec412a9af58e5\",\n \"score\": \"0.5877017\",\n \"text\": \"def __init__(self, question, correct_answer):\\n\\n self.question = question\\n self.correct_answer = correct_answer\",\n \"title\": \"\"\n },\n {\n \"docid\": \"32a337687bded16618ba0fab15c34697\",\n \"score\": \"0.5843788\",\n \"text\": \"def create_case(self, title, description):\\n session[\\\"case_id\\\"] = self.case_db_proxy.create_case(user_id = session[\\\"user_id\\\"], user_token = session[\\\"user_token\\\"], title = title, description = description)\\n return self.case_status_dialogue_transition(),\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a234814b2bd03a5eef992dc79da1f1d\",\n \"score\": \"0.58090466\",\n \"text\": \"def __init__(self, id, type, question, answer, explanation):\\n self.id = id\\n self.type = type\\n self.question = question\\n self.answer = answer\\n self.explanation = explanation\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a538d066004a24a9077e4ed153bca98e\",\n \"score\": \"0.5701454\",\n \"text\": \"def create_testcase(self, case_data, func_name, file_name,\\n class_name=None, init_kwargs={}):\\n case = Case()\\n case.func_name = func_name\\n case.input = case_data['text']\\n case.desc = case_data['description']\\n case.expected = case_data['expected']\\n case.class_name = class_name\\n case.file_name = file_name\\n case.init_kwargs = init_kwargs\\n return case\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e90d3ea6cc1a8688c4971f6744b505d\",\n \"score\": \"0.5669605\",\n \"text\": \"def setUp(self):\\n self.takenquiz = TakenQuiz.objects.create(\\n name=\\\"Test TakenQuiz\\\",\\n visibility=True,\\n order_rank=1\\n )\\n self.quiz = Quiz.objects.create(\\n name=\\\"Test Quiz\\\"\\n )\\n self.answer = Answer.objects.create(\\n takenquiz=self.c,\\n company=self.company,\\n name=\\\"Test Product\\\"\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb7e5e1870249a130ea31840292f6069\",\n \"score\": \"0.56272227\",\n \"text\": \"def __init__(self):\\n self.db = QuestionerDB\",\n \"title\": \"\"\n },\n {\n \"docid\": \"472a4d966f3942c20a0031354299e227\",\n \"score\": \"0.5623586\",\n \"text\": \"def noID(self, created_by, question, weight, content, active):\\n return self(None, None, created_by, question, weight, content, active)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7363e13d7d8e7f3bbc145129999752cc\",\n \"score\": \"0.56167024\",\n \"text\": \"def test_create_single_quiz_question(self):\\r\\n # This method utilises the POST request method and will make changes to the Canvas instance. This needs consideration.\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e62a9f0625ef2da937ca6055a7fbf7a8\",\n \"score\": \"0.5615972\",\n \"text\": \"def create_trial_from_quiz(self, original_quiz_id, user, godmode):\\n trial_quiz_name = \\\"Trial_orig_id_{0}_{1}\\\".format(\\n original_quiz_id,\\n \\\"godmode\\\" if godmode else \\\"usermode\\\"\\n )\\n\\n if self.filter(description=trial_quiz_name).exists():\\n trial_quiz = self.get(description=trial_quiz_name)\\n\\n else:\\n trial_quiz = self.get(id=original_quiz_id)\\n trial_quiz.course.enroll(False, user)\\n trial_quiz.pk = None\\n trial_quiz.description = trial_quiz_name\\n trial_quiz.is_trial = True\\n trial_quiz.prerequisite = None\\n if godmode:\\n trial_quiz.time_between_attempts = 0\\n trial_quiz.duration = 1000\\n trial_quiz.attempts_allowed = -1\\n trial_quiz.active = True\\n trial_quiz.start_date_time = timezone.now()\\n trial_quiz.end_date_time = datetime(\\n 2199, 1, 1, 0, 0, 0, 0, tzinfo=pytz.utc\\n )\\n trial_quiz.save()\\n return trial_quiz\",\n \"title\": \"\"\n },\n {\n \"docid\": \"03cfb5ec3c7ae944411a6fa44b9ec48f\",\n \"score\": \"0.5604998\",\n \"text\": \"def test_note_on_case(self, request: FixtureRequest):\\n # [Pre-Requisite] - create case\\n case = self.v3_helper.create_case()\\n self._test_note_on_obj(request, case)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8bb1571f504405b13c839fc83655b29c\",\n \"score\": \"0.5604607\",\n \"text\": \"def setUp(self):\\n result = create_data(1, 0, False)\\n self.new_class_session = result['new_class_session']\\n self.new_obj = result['new_obj']\\n self.new_sub_obj = result['new_sub_obj']\\n self.new_activity = result['new_activity']\\n self.new_sub_activity = result['new_sub_activity']\\n self.new_session = result['new_session']\\n self.original_mc3_bank_id = result['original_mc3_bank_id']\\n \\n self.obj_bank_id = self.new_class_session.obj_bank_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ca7e6c995be99afd1bd50a44e0e48f1\",\n \"score\": \"0.55942297\",\n \"text\": \"def test_create_question(self):\\n newQuestion = Question.objects.create(\\n question=\\\"Could this question pass the test?\\\",\\n category=test_category(),\\n author=test_user()\\n )\\n\\n self.assertEqual(\\n newQuestion.question,\\n \\\"Could this question pass the test?\\\"\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea21cb06d53558bf11800e412003e297\",\n \"score\": \"0.5572724\",\n \"text\": \"def __init__(self, sample_id, issue_id, date, title, user, status):\\n self.sample_id = sample_id\\n self.issue_id = issue_id\\n self.created_at = datetime.strptime(date, '%Y-%m-%dT%H:%M:%SZ')\\n self.title = title\\n self.user = user\\n self.status = status\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b799e33bdfc71b3e80261f928b4ed3b\",\n \"score\": \"0.55564713\",\n \"text\": \"def create_instance_for_test_set(self, case_id, test_set_id):\\n self._logger.debug(\\\"Creating test instance for test case:{} and test set:{}\\\".format(case_id, test_set_id))\\n xml_string = \\\"<Entity Type=\\\\\\\"test-instance\\\\\\\">\\\" \\\\\\n + \\\" <Fields>\\\" \\\\\\n + \\\" <Field Name=\\\\\\\"cycle-id\\\\\\\"><Value>\\\" + test_set_id + \\\"</Value></Field>\\\" \\\\\\n + \\\" <Field Name=\\\\\\\"status\\\\\\\"><Value>No Run</Value></Field>\\\" \\\\\\n + \\\" <Field Name=\\\\\\\"test-id\\\\\\\"><Value>\\\" + case_id + \\\"</Value></Field>\\\" \\\\\\n + \\\" <Field Name=\\\\\\\"test-instance\\\\\\\"><Value>1</Value></Field>\\\" \\\\\\n + \\\" <Field Name=\\\\\\\"subtype-id\\\\\\\"><Value>hp.qc.test-instance.MANUAL</Value></Field>\\\" \\\\\\n + \\\" <Field Name=\\\\\\\"test-order\\\\\\\"><Value>1</Value></Field>\\\" \\\\\\n + \\\" <Field Name=\\\\\\\"owner\\\\\\\"><Value>yanb</Value></Field>\\\" \\\\\\n + \\\" </Fields>\\\" \\\\\\n + \\\"</Entity>\\\"\\n request_url = \\\"http://\\\" + self.http_server + \\\"/test-instances/\\\" + self.domain + \\\"/\\\" + self.project\\n test_set_node = self._post_xml_node_from_url(request_url, xml_string)\\n #self._print_xml_node(test_set_node)\\n return test_set_node\",\n \"title\": \"\"\n },\n {\n \"docid\": \"27832d98d40650b34350c8a0d2f4e6b8\",\n \"score\": \"0.55429137\",\n \"text\": \"def __init__(self, name, assessment_type = None):\\n\\n # will store a list of question object types\\n # assuming each exam object type will have \\n # its own unique set of questions\\n self.questions = [] \\n self.name = name\\n\\n if assessment_type:\\n self.assessment_type = assessment_type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd41b90c1e466fcbd9852f2c8198411e\",\n \"score\": \"0.5537577\",\n \"text\": \"def _create_trial_from_questionpaper(self, original_quiz_id):\\n trial_questionpaper = self.get(quiz_id=original_quiz_id)\\n fixed_ques = trial_questionpaper.get_ordered_questions()\\n trial_questions = {\\\"fixed_questions\\\": fixed_ques,\\n \\\"random_questions\\\": trial_questionpaper\\n .random_questions.all()\\n }\\n trial_questionpaper.pk = None\\n trial_questionpaper.save()\\n return trial_questionpaper, trial_questions\",\n \"title\": \"\"\n },\n {\n \"docid\": \"568bd0e0c26220ce5835afaa62b5b616\",\n \"score\": \"0.55371195\",\n \"text\": \"def test_create_question(self):\\n\\n res = self.client().post('/questions', json=self.new_question)\\n data = json.loads(res.data)\\n\\n # get created question\\n question_created = Question.query.filter_by(\\n id=data['created']).one_or_none()\\n\\n # check status code and message\\n self.assertEqual(res.status_code, 200)\\n self.assertEqual(data['success'], True)\\n self.assertTrue(data['created'])\\n self.assertTrue(len(data['questions']))\\n self.assertTrue(data['total_questions'])\\n\\n # check if created question is available after create\\n self.assertNotEqual(question_created, None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc826cd3e650033aa760dc5762b2b006\",\n \"score\": \"0.54966915\",\n \"text\": \"def test_init(self):\\n testCase = Case('caseType', 'problemNumber', 'caseNumber', 'input')\\n self.assertEqual(testCase.caseType, 'caseType')\\n self.assertEqual(testCase.problemNumber, 'problemNumber')\\n self.assertEqual(testCase.caseNumber, 'caseNumber')\\n self.assertEqual(testCase.inputContents, 'input')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66f1cbbdaad247db51abeb2830a51e96\",\n \"score\": \"0.5490732\",\n \"text\": \"def __init__(self, date, detail, identifier, reasonCodeableConcept,\\n reasonReference, source, subject, target, when,):\\n self.date = date\\n self.detail = detail\\n self.identifier = identifier\\n self.reasonCodeableConcept = reasonCodeableConcept\\n self.reasonReference = reasonReference\\n self.source = source\\n self.subject = subject\\n self.target = target\\n self.when = when\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d7bf28bcbc982f20acb72fbf6d56967d\",\n \"score\": \"0.54734844\",\n \"text\": \"def create_instance(this_slug):\\n obj = Test(this_slug)\\n obj.test_field = value\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"797da9eebd57577f69a27b62c1ca6d44\",\n \"score\": \"0.54714197\",\n \"text\": \"def __init__(self, is_correct: bool, answer: str):\\n self.is_correct = is_correct\\n self.answer = answer\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25c2ada369c7632a9d803d73780c19c5\",\n \"score\": \"0.5425045\",\n \"text\": \"def create_trial_paper_to_test_quiz(self, trial_quiz, original_quiz_id):\\n if self.filter(quiz=trial_quiz).exists():\\n trial_questionpaper = self.get(quiz=trial_quiz)\\n else:\\n trial_questionpaper, trial_questions = \\\\\\n self._create_trial_from_questionpaper(original_quiz_id)\\n trial_questionpaper.quiz = trial_quiz\\n trial_questionpaper.fixed_questions\\\\\\n .add(*trial_questions[\\\"fixed_questions\\\"])\\n trial_questionpaper.random_questions\\\\\\n .add(*trial_questions[\\\"random_questions\\\"])\\n trial_questionpaper.save()\\n return trial_questionpaper\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8318f90dcf89ad4c00efd47dc7ed361f\",\n \"score\": \"0.5413448\",\n \"text\": \"def addQuestion(self):\\n self.questions.append(TestQuestion())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e77ae5087bf970d89f2c7509ef7d727d\",\n \"score\": \"0.5397173\",\n \"text\": \"def create(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e77ae5087bf970d89f2c7509ef7d727d\",\n \"score\": \"0.5397173\",\n \"text\": \"def create(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e93ea8f0c11842e26adfb75ce20e5f7\",\n \"score\": \"0.53951424\",\n \"text\": \"def create_contest(self, name, creator, start_date, end_date):\\n contest = self.create(name=name, creator=creator,\\n start_date=start_date, end_date=end_date,\\n active=False, created_on=timezone.now(),\\n last_updated=timezone.now(),\\n user_count=0, beer_count=0)\\n # Add the creator as a player\\n contest_player = contest.add_player(creator)\\n return contest\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4fb21498284313ac32da4ed382ab3bbe\",\n \"score\": \"0.5393054\",\n \"text\": \"def test_questionnaire_test1_created(self):\\n test1 = Questionnaire.objects.get(title='test1')\\n\\n self.assertEqual(test1.questions.count(), 12)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d142dacdec90a7c01e1696e730788516\",\n \"score\": \"0.53886545\",\n \"text\": \"def __init__(self, difficulty):\\n self.difficulty = difficulty\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f6e9dfeaaa8f92e8c96187dd82cef54\",\n \"score\": \"0.53595054\",\n \"text\": \"def _new_trial(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c76974522f19ce08af1a0fe2e0b216e\",\n \"score\": \"0.5355575\",\n \"text\": \"def _create_proctored_exam(self, course):\\n chapter = ItemFactory.create(\\n parent=course, category='chapter', display_name='Test Section', publish_item=True\\n )\\n ItemFactory.create(\\n parent=chapter, category='sequential', display_name='Test Proctored Exam',\\n graded=True, is_time_limited=True, default_time_limit_minutes=10,\\n is_proctored_exam=True, publish_item=True\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c93260464946f43cd51780deddd5932f\",\n \"score\": \"0.5348018\",\n \"text\": \"def __init__(self, first_name, last_name, date, reason):\\r\\n self.first_name = first_name\\r\\n self.last_name = last_name\\r\\n self.date = date\\r\\n self.reason = reason\\r\\n self.date_created = datetime.datetime.now()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3aa892274f4492c5363cd60389c3ca8c\",\n \"score\": \"0.534652\",\n \"text\": \"def __eq__(self, other):\\n return (\\n self.id == other.id and\\n self.created == other.created and\\n self.created_by == other.created_by and\\n self.question == other.question and\\n self.weight == other.weight and\\n self.content == other.content and\\n self.active == other.active\\n ) if type(other) is Test_Case else False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1ec96b8c466f5d11183872ef1175b28\",\n \"score\": \"0.5335523\",\n \"text\": \"def __init__(self):\\n \\n self.id = 0\\n self.title = \\\"\\\"\\n self.location = \\\"\\\"\\n self.scheduled_on = \\\"\\\"\\n self.scheduled_on_long = 0\\n self.reminder = \\\"\\\"\\n self.repeat = \\\"\\\"\\n self.occurrences = 0\\n self.occured = 0\\n self.duration_hour = \\\"\\\"\\n self.duration_minutes = \\\"\\\"\\n self.is_open = None\\n self.participants = []\\n self.hour = \\\"\\\"\\n self.minutes = \\\"\\\"\\n self.ampm = \\\"\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"94be6a5bf026fe59e27b311cb06545a0\",\n \"score\": \"0.52954364\",\n \"text\": \"def setUp(self):\\n self.app = create_app()\\n self.client = self.app.test_client\\n self.database_name = \\\"trivia_test\\\"\\n self.database_path = \\\"postgres://{}/{}\\\".format('localhost:5432', self.database_name)\\n setup_db(self.app, self.database_path)\\n\\n # binds the app to the current context\\n with self.app.app_context():\\n self.db = SQLAlchemy()\\n self.db.init_app(self.app)\\n # create all tables\\n self.db.create_all()\\n\\n self.new_question = {'question':'who is the best for ever','answer':'Saad is the best','category':'3','difficulty':'1'}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d210f7ad7e6fe39576ccab2fb6e70f1\",\n \"score\": \"0.5284293\",\n \"text\": \"def test_create_a_recommendation(self):\\n recommendation = Recommendation(product_1=100, product_2=200, recommendation_type=\\\"accessory\\\", active=True)\\n self.assertTrue(recommendation != None)\\n self.assertEqual(recommendation.id, None)\\n self.assertEqual(recommendation.product_1, 100)\\n self.assertEqual(recommendation.product_2, 200)\\n self.assertEqual(recommendation.recommendation_type, \\\"accessory\\\")\\n self.assertEqual(recommendation.active, True)\\n recommendation = Recommendation(product_1=100, product_2=200, recommendation_type=\\\"accessory\\\", active=False)\\n self.assertEqual(recommendation.active, False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b967be838fb484ae76a667bcef973f4a\",\n \"score\": \"0.5284249\",\n \"text\": \"def create_trial_quiz(self, trial_course, user):\\n trial_quiz = self.create(course=trial_course,\\n duration=1000,\\n description=\\\"trial_questions\\\",\\n is_trial=True,\\n time_between_attempts=0\\n )\\n return trial_quiz\",\n \"title\": \"\"\n },\n {\n \"docid\": \"703b4b9622467818aa3b54d3a09b36a4\",\n \"score\": \"0.52795863\",\n \"text\": \"def __init__(self):\\n self._title = None\\n \\\"\\\"\\\" An Exercise has a detailed textual description\\n set by the author. \\\"\\\"\\\"\\n self._description = None\\n \\\"\\\"\\\" An Exercise is characterized by an icon set by the author. \\\"\\\"\\\"\\n self._icon = None\\n \\\"\\\"\\\" An Exercise stores data on evaluation, i.e. on how often the\\n Exercise has been done, what errors have been made TODO \\\"\\\"\\\"\\n self._eval = None\\n \\\"\\\"\\\" The settings determine the generation of calculations.\\n The fields of the settings are determined by self._sett['topic'];\\n the values of the settings are different for different Exercises.\\n self._sett['calclines'] determines the # of lines in a calculation.\\\"\\\"\\\"\\n self._sett = None\\n \\\"\\\"\\\" Calculations are generated according to the settings. \\\"\\\"\\\"\\n self._calcs = None\\n \\\"\\\"\\\" An Exercise needs the Display for updating the settings\\\"\\\"\\\"\\n self._display = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4c29f2fc2051deba92aa03f980af7dd\",\n \"score\": \"0.52756625\",\n \"text\": \"def _create_tc(tcase_class, tcase_conf, global_config):\\n test_case = tcase_class(tcase_conf, global_config)\\n return Global.SUCCESS, test_case\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95ead1d81e337809374f5110e7653aa7\",\n \"score\": \"0.52518284\",\n \"text\": \"def __init__(self, test_uid: int, odorants: list, dilution: float, correct: bool):\\n\\n self.id = test_uid\\n self.odorants = odorants\\n self.dilution = dilution\\n self.correct = correct\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b687f3b4e2caf2812b47afb79a8d985\",\n \"score\": \"0.52497303\",\n \"text\": \"def test_first_question_with_one_parent_question(self):\\n test = Test.objects.create(title=\\\"my test\\\", description=\\\"my test description\\\")\\n question = Question.objects.create(test=test,question_text=\\\"my question\\\")\\n self.assertIs(test.first_question().id, question.id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"316692f33318659219badfc20912f6d4\",\n \"score\": \"0.5228202\",\n \"text\": \"def test_db_create(self):\\n question_db = create_question()\\n question_db.id.should.be.greater_than(0)\\n questions.find(id=question_db.id).shouldnt.be(None)\\n questions.get(id=question_db.id).shouldnt.be(None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04ed56d8f62f8f88789ccb4c8ad6c13f\",\n \"score\": \"0.52279586\",\n \"text\": \"def test_creation():\\n ticket = Ticket()\\n ticket.randomize()\\n ticket.is_rational(0)\\n ticket.is_valid()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb9a96f85de4f2a708ca1264cb8da958\",\n \"score\": \"0.52230555\",\n \"text\": \"def __init__(self, id, name, is_test_bot=False):\\n self.id = id\\n self.name = name\\n self.is_test_bot = is_test_bot\",\n \"title\": \"\"\n },\n {\n \"docid\": \"254cf2a5ab8660726f7b36dcf3fabbb3\",\n \"score\": \"0.52224666\",\n \"text\": \"def test_should_create_a_question(self):\\n response = self.app.post('/question', data=json.dumps(dict(\\n description='Java has multiple inheritance?'\\n )), content_type=AppTestCase.APPLICATION_JSON_CONTENT_TYPE, follow_redirects=True)\\n self.assertEqual(response.status_code, 200)\\n json_response = json.loads(response.get_data(as_text=True))\\n message = str(json_response['message'])\\n id_question = str(json_response['id'])\\n self.assertTrue(message.__eq__('New Question created!'))\\n self.assertFalse(id_question.__eq__(''))\\n print('Should create question.')\\n print('')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a92ad267b397a3baa390b1085c7c7489\",\n \"score\": \"0.52180153\",\n \"text\": \"def setUp(self):\\r\\n self.app = create_app()\\r\\n self.client = self.app.test_client\\r\\n self.database_name = \\\"trivia_test\\\"\\r\\n self.database_path = \\\"postgres://{}:{}@{}/{}\\\".format('postgres', '1234','localhost:5432', self.database_name)\\r\\n setup_db(self.app, self.database_path)\\r\\n\\r\\n # set data for testing\\r\\n self.new_question = {'question':'Who is the inventor of the light bulb?',\\r\\n 'answer' : 'Thomas Edison',\\r\\n 'difficulty' : 1,\\r\\n 'category' : 1}\\r\\n\\r\\n self.new_question2 = {'id':1,\\r\\n 'question':'What is the capital of Saudi Arabia',\\r\\n 'answer' : 'Riyadh',\\r\\n 'difficulty' : 1,\\r\\n 'category' : 3}\\r\\n\\r\\n self.invalid_question = {'question':'What is the capital of Saudi Arabia',\\r\\n 'answer' : 'Riyadh'}\\r\\n\\r\\n self.search_term = {'searchTerm':'capital'}\\r\\n\\r\\n self.play_quiz = {\\r\\n 'previous_questions': [],\\r\\n 'quiz_category': {'type': 'Science', 'id': 1}\\r\\n }\\r\\n self.invalid_play_quiz = {\\r\\n 'previous_questions': [],\\r\\n 'quiz_category': None\\r\\n }\\r\\n self.non_existent_question = {'id':1000,\\r\\n 'question':' ',\\r\\n 'answer' : ' ',\\r\\n 'difficulty' : 1,\\r\\n 'category' : 3}\\r\\n\\r\\n\\r\\n # binds the app to the current context\\r\\n with self.app.app_context():\\r\\n self.db = SQLAlchemy()\\r\\n self.db.init_app(self.app)\\r\\n # create all tables\\r\\n self.db.create_all()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f148a8f7a6fb5d1f55d58173dc064ce\",\n \"score\": \"0.52166456\",\n \"text\": \"def tests_referred1(self):\\n report_datetime = datetime.today()\\n panel = Panel.objects.get(name='Microtube')\\n SubjectRequisitionFactory(subject_visit=self.subject_visit_female, site=self.study_site, panel=panel,\\n aliquot_type=AliquotType.objects.get(alpha_code='WB'))\\n HivTestReviewFactory(subject_visit=self.subject_visit_female, recorded_hiv_result=POS,\\n hiv_test_date=date.today())\\n PimaFactory(subject_visit=self.subject_visit_female, cd4_value=501, report_datetime=datetime.today())\\n subject_referral = SubjectReferralFactory(\\n subject_visit=self.subject_visit_female,\\n report_datetime=report_datetime)\\n if self.get_intervention:\\n self.assertIn('POS#-HI', subject_referral.referral_code)\\n else:\\n subject_referral = SubjectReferral.objects.all()[0]\\n subject_referral.save()\\n self.assertIn('POS#-HI', subject_referral.referral_code)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"213078b1216aed5cf06da3191d3a23ed\",\n \"score\": \"0.52160597\",\n \"text\": \"def save(self, *args, **kwargs):\\n if self.instance:\\n question = self.instance\\n if self.cleaned_data['verified'] == '0':\\n question.is_verified = False\\n else:\\n question.is_verified = True\\n\\n question.updated_by = self.request.user\\n if self.cleaned_data['verified'] == '1':\\n question.verified_by = self.request.user\\n else:\\n question = Question()\\n question.created_by = self.request.user\\n\\n question.text = self.cleaned_data['text']\\n question.image = self.cleaned_data['image']\\n question.audio = self.cleaned_data['audio']\\n question.video = self.cleaned_data['video']\\n question.difficulty = self.cleaned_data['difficulty']\\n question.article = Article.objects.get(\\n slug=self.cleaned_data['article']\\n )\\n question.category = question.article.category\\n\\n # question.question_type = self.cleaned_data['question_type']\\n question.question_type = 'objective'\\n # if question.question_type == 'objective':\\n question.correct = self.cleaned_data['correct']\\n # else:\\n # question.correct = None\\n\\n question.save()\\n return question\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5886c6dd427877c299f168c6a941a5a5\",\n \"score\": \"0.52137774\",\n \"text\": \"def setUp(self):\\n self.app = create_app()\\n self.client = self.app.test_client\\n self.database_path = \\\"postgres://{}:{}@{}/{}\\\".format('postgres', 'localhost:5432', 'trivia_test')\\n setup_db(self.app, self.database_path)\\n \\n self.new_question = {\\n 'question': 'ohhhh favorite pie',\\n 'answer': 'imagine pie', \\n 'category': 3, \\n 'difficulty': 2\\n }\\n \\n self.incomplete_question = {\\n 'question': 'ohhhh favorite pie',\\n 'answer': 'imagine pie', \\n 'category': 3\\n }\\n\\n # binds the app to the current context\\n with self.app.app_context():\\n self.db = SQLAlchemy()\\n self.db.init_app(self.app)\\n # create all tables\\n self.db.create_all()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3ec987901a0081ef0e59ab9f101cae8\",\n \"score\": \"0.5196682\",\n \"text\": \"def create(self):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd56918f72efc605b9b10512f354bd66\",\n \"score\": \"0.5186739\",\n \"text\": \"def get_and_create_testcase(self, case_path, team_name, owner, script_name = None, description = None):\\n self._logger.debug(\\\"Fetching the case \\\" + case_path)\\n case_node = self.find_test_case_node(case_path)\\n if(case_node == None):\\n self._logger.debug(\\\"Creating test case :\\\" + case_path)\\n case_name = os.path.basename((case_path))\\n if(script_name == None):\\n script_name = case_name + \\\".json\\\"\\n if(description == None):\\n description = \\\"Generate \\\" + case_name +\\\" automatically by dpsmoke_automatos\\\"\\n folder_path = os.path.dirname(case_path)\\n parent_id = self.get_and_create_folder_id(folder_path)\\n xml_string = \\\"<?xml version=\\\\\\\"1.0\\\\\\\" encoding=\\\\\\\"UTF-8\\\\\\\" standalone=\\\\\\\"yes\\\\\\\"?>\\\" \\\\\\n + \\\"<Entity Type=\\\\\\\"test\\\\\\\">\\\" \\\\\\n + \\\" <Fields>\\\" \\\\\\n + \\\" <Field Name=\\\\\\\"test-name\\\\\\\"><Value>\\\" + case_name + \\\"</Value></Field>\\\" \\\\\\n + \\\" <Field Name=\\\\\\\"execution-type\\\\\\\"><Value>MANUAL</Value></Field>\\\" \\\\\\n + \\\" <Field Name=\\\\\\\"parent-id\\\\\\\"><Value>\\\" + parent_id + \\\"</Value></Field>\\\" \\\\\\n + \\\" <Field Name=\\\\\\\"subtype-id\\\\\\\"><Value>MANUAL</Value></Field>\\\" \\\\\\n + \\\" <Field Name=\\\\\\\"team\\\\\\\"><Value>\\\" + team_name + \\\"</Value></Field>\\\" \\\\\\n + \\\" <Field Name=\\\\\\\"old-team\\\\\\\"><Value>\\\" + team_name + \\\"</Value></Field>\\\" \\\\\\n + \\\" <Field Name=\\\\\\\"status\\\\\\\"><Value>\\\" + \\\"Ready\\\" + \\\"</Value></Field>\\\" \\\\\\n + \\\" <Field Name=\\\\\\\"exclusive\\\\\\\"><Value>N</Value></Field>\\\" \\\\\\n + \\\" <Field Name=\\\\\\\"priority\\\\\\\"><Value>1-High</Value></Field>\\\" \\\\\\n + \\\" <Field Name=\\\\\\\"remotely-executable\\\\\\\"><Value>Yes</Value></Field>\\\" \\\\\\n + \\\" <Field Name=\\\\\\\"test-state\\\\\\\"><Value>Automated</Value></Field>\\\" \\\\\\n + \\\" <Field Name=\\\\\\\"automation-dev-framework\\\\\\\"><Value>Automatos-Python</Value></Field>\\\" \\\\\\n + \\\" <Field Name=\\\\\\\"script-name\\\\\\\"><Value>\\\" + script_name + \\\"</Value></Field>\\\" \\\\\\n + \\\" <Field Name=\\\\\\\"execution-engine\\\\\\\"><Value>Automatos</Value></Field>\\\" \\\\\\n + \\\" <Field Name=\\\\\\\"automation-script-owner\\\\\\\"><Value>\\\" + owner + \\\"</Value></Field>\\\" \\\\\\n + \\\" <Field Name=\\\\\\\"description\\\\\\\"><Value>\\\" + description + \\\"</Value></Field>\\\" \\\\\\n + \\\" </Fields>\\\" \\\\\\n + \\\"</Entity>\\\"\\n request_url = \\\"http://\\\" + self.http_server + \\\"/tests/\\\" + self.domain + \\\"/\\\" + self.project\\n case_node = self._post_xml_node_from_url(request_url, xml_string)\\n #self._print_xml_node(case_node)\\n return case_node\",\n \"title\": \"\"\n },\n {\n \"docid\": \"695be56d1c4b0633a156f78a8c088759\",\n \"score\": \"0.51864606\",\n \"text\": \"def __init__(self, test):\\n self.test = test\",\n \"title\": \"\"\n },\n {\n \"docid\": \"695be56d1c4b0633a156f78a8c088759\",\n \"score\": \"0.51864606\",\n \"text\": \"def __init__(self, test):\\n self.test = test\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f01315f19a045298013b53ae74a6813e\",\n \"score\": \"0.51836497\",\n \"text\": \"def initialize(self):\\n\\n operation = self.inspect.stack()[0][3]\\n self.log.info(\\\"Initializing Testcase %s ...\\\" % self.id)\\n result = {'successful': False}\\n\\n try:\\n # get testcase data from database\\n returned = self.get_testcase_data_from_database(self.id)\\n\\n # assign unique name for build server test result tracking\\n self.build_test_name = \\\"%s: %s: %s: %s: %s\\\" % (self.module, self.feature, self.story,\\n self.test, self.name)\\n\\n # assign procedure\\n # parse procedure data\\n procedure_data = returned['testcase data']['procedure']\\n procedure_raw_list = procedure_data.split(',')\\n # build procedure data list\\n procedure_list = []\\n for item in procedure_raw_list:\\n procedure_list.append(int(item.strip()))\\n # build procedure function list\\n for procedure_step_id in procedure_list:\\n data = self.get_procedure_step_data_from_database(procedure_step_id)\\n # parse the data\\n step_data = data['step data']\\n function_data = data['function data']\\n submodule_data = data['submodule data']\\n # translate function call\\n function = function_data['function'].lower().replace(' ', '_')\\n # handle arguments\\n if str(step_data['arguments']).strip() == '': step_data['arguments'] = None\\n if step_data['arguments'] is not None:\\n arguments = str(step_data['arguments'])\\n arguments += ','\\n else:\\n arguments = ''\\n arguments += \\\"testcase=self\\\"\\n # handle verification status\\n verification = eval(step_data['verification'])\\n # combine into one data dict of relevant data\\n translated_step_data = {\\n 'id': procedure_step_id,\\n 'name': step_data['name'],\\n 'function': \\\"%s.%s(%s)\\\" % (submodule_data['code'], function,\\n arguments),\\n 'verification': verification\\n }\\n self.procedure.append(translated_step_data)\\n\\n # report\\n self.log.trace(\\\"Testcase initialized:\\\")\\n self.log.trace(\\\"\\\\tName: %s\\\" % self.name)\\n self.log.trace(\\\"\\\\tBuild Test name: %s\\\" % self.build_test_name)\\n self.log.trace(\\\"\\\\tProcedure:\\\")\\n for step in self.procedure:\\n self.log.trace(\\\"\\\\t\\\\t%s\\\" % step['name'])\\n result['successful'] = True\\n except BaseException, e:\\n self.handle_exception(e, operation)\\n\\n # return\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c3e022909298e3313a47e2d194cef9c\",\n \"score\": \"0.5181774\",\n \"text\": \"def test_category(name='Test Category', description=\\\"Category for testing\\\"):\\n return QuestionCategory.objects.create(name=name, description=description)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"09c01f992b654d2f8689eae1388cddcd\",\n \"score\": \"0.5180956\",\n \"text\": \"def __init__(self):\\n self.details = {}\\n self.test_results = {}\\n self.test_results_values = {}\\n self.test_units = {}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf3996759b4a63878a3f510c1821434a\",\n \"score\": \"0.51779884\",\n \"text\": \"def setUp(self):\\n self.app = create_app()\\n self.client = self.app.test_client\\n self.database_name = \\\"trivia_test\\\"\\n self.database_path = \\\"postgres://{}:{}@{}/{}\\\".format('postgres', 'postgres','localhost:5432', self.database_name)\\n setup_db(self.app, self.database_path)\\n\\n self.new_question = {\\n 'question': 'How are you?',\\n 'answer': 'All good, and you?',\\n 'category': 1,\\n 'difficulty': 1\\n }\\n\\n self.new_question_missing = {\\n 'question': None,\\n 'answer': None,\\n 'category': 1,\\n 'difficulty': 1\\n } \\n\\n # binds the app to the current context\\n with self.app.app_context():\\n self.db = SQLAlchemy()\\n self.db.init_app(self.app)\\n # create all tables\\n self.db.create_all()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0395f0c11c46917c5715d2d2057c4b46\",\n \"score\": \"0.51658934\",\n \"text\": \"def __init__(self, question, correctAnswer, incorrectAnswers):\\r\\n # Defining the question as an attribute of the class\\r\\n self.question = question\\r\\n # Defining the correct answer as an attribute of the class\\r\\n self.correctAnswer = correctAnswer\\r\\n # Defining a list of incorrect answers as an attribute of the class\\r\\n self.incorrectAnswers = incorrectAnswers\",\n \"title\": \"\"\n },\n {\n \"docid\": \"22027230b81e5f6643af2242b6998f5e\",\n \"score\": \"0.51635367\",\n \"text\": \"def setUp(self):\\n self.testcase = models.Review()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"285905160f3d4ea29ba20fbc6b7b45bb\",\n \"score\": \"0.5162068\",\n \"text\": \"def __init__(self, name, parent, test_result):\\n super(CTestItem, self).__init__(name, parent)\\n self.test_result = test_result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e900596849ff31b7251c39bf19539d9\",\n \"score\": \"0.5151337\",\n \"text\": \"def test_create_question(self):\\n\\n # simple q\\n question = {\\n 'question': 'This is a simple question',\\n 'answer': 'this is a simple answer',\\n 'difficulty': 1,\\n 'category': 1,\\n }\\n\\n # make request and process response\\n response = self.client().post('/questions/new', json=question)\\n data = json.loads(response.data)\\n\\n self.assertEqual(response.status_code, 201)\\n self.assertEqual(data['success'], True)\\n self.assertEqual(data['message'], 'Question successfully created!')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36785c99f49ba066c6738fc20faf971e\",\n \"score\": \"0.51466835\",\n \"text\": \"def create_question(self, request):\\n\\n question = request.question\\n answers = {'correct': request.correct,\\n 'wrong1': request.wrong1,\\n 'wrong2': request.wrong2,\\n 'wrong3': request.wrong3}\\n\\n clues = [request.clue1, request.clue2]\\n\\n newQuestion = Question.new_question(question, answers, clues)\\n\\n return newQuestion.to_form()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2a5f1041090541a974bedfcca3b2c74\",\n \"score\": \"0.5146572\",\n \"text\": \"def __init__(self):\\n self.rid_1 = \\\"\\\"\\n self.rid_2 = \\\"\\\"\\n self.temp_rid = \\\"\\\"\\n self.role_1 = 0\\n self.role_2 = 0\\n self.trans_id = \\\"\\\"\\n self.go = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20ef730804fd46b216401fab5f3f7dd6\",\n \"score\": \"0.5142977\",\n \"text\": \"def initialize(self, bid_level, bid_trump, bid_team, game_case):\\n self.bid_level = bid_level\\n self.bid_trump = bid_trump\\n self.bid_team = bid_team\\n self.game_case = game_case\\n self.player_list = ['p_00', 'p_11', 'p_01', 'p_10']\\n self.round_index = -1\\n self.win_count = [0,0]\\n self.identity = self.game_case['identity']\\n\\n self.open_player = [k for k, v in self.identity.items() if v == [0, 1, 0]][0]\\n self.banker_player = [k for k, v in self.identity.items() if v == [1, 0, 0]][0]\\n\\n\\n #calculate the index of the current bid\\n self.bid_index = ['C','D','H','S', None].index(bid_trump)*7 + (bid_level-7)\\n\\n #create a list of cards in sorted order (an index -> card map)\\n self.index_to_card = sorted([Card(rank=x[0], suit=x[1], trump=bid_trump) \\n for x in product(Card.ranks, Card.suits)])\\n\\n #create a dictionary mapping cards to index\\n self.card_to_index = {}\\n for index,card in enumerate(self.index_to_card):\\n self.card_to_index[card] = index\\n\\n #now initialize relevant variables for starting state (beginning of round)\\n self.trick_history = []\\n self.current_trick = []\\n self.trick_winner = None\\n self.round_over = False\\n\\n #determine the starting player\\n # self.current_player = 'p_%d%d' % (bid_team, 1 if random.random() < 0.5 else 0)\\n\\n self.current_player = self.game_case['starting_player']\\n\\n #keep track of each team's score\\n self.team0_num_tricks = 0\\n self.team1_num_tricks = 0\\n self.team0_score = None\\n self.team1_score = None\\n\\n #some variables to keep track of the state representation given to neural networks\\n self.played_cards = {'p_00': [], 'p_01': [], 'p_10': [], 'p_11': []}\\n self.played_cards_vector = ({'p_00': np.zeros((52,)), 'p_01': np.zeros((52,)),\\n 'p_10': np.zeros((52,)), 'p_11': np.zeros((52,))})\\n\\n self.played_this_trick = {'p_00': None, 'p_01': None, 'p_10': None, 'p_11': None}\\n self.hands = {'p_00': [], 'p_01': [], 'p_10': [], 'p_11': []}\\n self.hands_vector = ({'p_00': np.zeros((52,)), 'p_01': np.zeros((52,)),\\n 'p_10': np.zeros((52,)), 'p_11': np.zeros((52,))})\\n\\n self._deal()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59d8b309d552f82d8f94a9ecb5a96a62\",\n \"score\": \"0.5134122\",\n \"text\": \"def __init__(self, patient, resident, attending, begin_date, end_date):\\n\\t\\tfaker = Factory.create()\\n\\n\\t\\ttry:\\n\\t\\t\\tassert patient != None and resident != None and attending != None\\n\\t\\t\\t# assert type(patient) == Patient and type(resident) == Resident and type(attending) == Attending\\n\\t\\t\\t# Make a random exam modality\\n\\t\\t\\tself.examMod = ''\\n\\t\\t\\tself.randExam = random.randint(1, 4)\\n\\t\\t\\tif self.randExam is 1:\\n\\t\\t\\t\\tself.examMod = 'CT'\\n\\t\\t\\telif self.randExam is 2:\\n\\t\\t\\t\\tself.examMod = 'MR'\\n\\t\\t\\telif self.randExam is 3:\\n\\t\\t\\t\\tself.examMod = 'XR'\\n\\t\\t\\telse:\\n\\t\\t\\t\\tself.examMod = 'US'\\n\\t\\t\\t\\n\\t\\t\\t# Make a random exam date and time\\n\\t\\t\\t# Precondition: both begin_date and end_date should be in the format of yyyy-mm-dd\\n\\t\\t\\tbegin_Y_M_D = begin_date.split('-') # split the date so that it's [yyyy, mm, dd]\\n\\t\\t\\tend_Y_M_D = end_date.split('-')\\n\\t\\t\\texam_year = str(random.randint(int(begin_Y_M_D[0]), int(end_Y_M_D[0])))\\n\\t\\t\\t# For the date, if the begin and end have the same years then make the months in between; else just random from 1~12\\n\\t\\t\\texam_month = random.randint(int(begin_Y_M_D[1]), int(end_Y_M_D[1])) if int(begin_Y_M_D[0]) == int(end_Y_M_D[0]) else random.randint(1,12)\\n\\t\\t\\texam_month = str(exam_month).zfill(2) # make it so that all months will be two numbers, ex.: 01, 03, 08, 10, 12\\n\\t\\t\\t# Same logic for the day\\n\\t\\t\\texam_day = ''\\n\\t\\t\\tif (int(begin_Y_M_D[0]) is int(end_Y_M_D[0]) and int(begin_Y_M_D[1]) is int(end_Y_M_D[1])):\\n\\t\\t\\t\\texam_day = str(random.randint(int(begin_Y_M_D[2]), int(end_Y_M_D[2]))).zfill(2)\\n\\t\\t\\telse:\\n\\t\\t\\t\\t# need to consider the difference in days in different months, but we'll leave it for now.\\n\\t\\t\\t\\texam_day = str(random.randint(1,31)).zfill(2)\\n\\t\\t\\tself.examDateTime = exam_year + \\\"-\\\" + exam_month + \\\"-\\\" + exam_day + \\\" \\\" + faker.time()\\n\\n\\t\\t\\t\\\"\\\"\\\"\\n\\t\\t\\t# need to check the DOB of the Patient so that the DOB < examDateTime\\n\\t\\t\\twhile int(self.examDateTime[:self.examDateTime.index('-')])<int(patient.DOB()[:patient.DOB().index('-')]):\\n\\t\\t\\t\\tself.examDateTime = str(faker.date_time()) \\\"\\\"\\\"\\n\\n\\t\\t\\t# Make a random examID - should be unique.\\n\\t\\t\\tself.examID = str(random.randint(0,99999999)).zfill(8)\\n\\t\\t\\t# Check if the examID already exists.\\n\\t\\t\\tif self.examID in examID_List:\\n\\t\\t\\t\\tself.examID = str(random.randint(0,99999999)).zfill(8)\\n\\t\\t\\telse:\\n\\t\\t\\t\\texamID_List.append(self.examID)\\n\\t\\t\\t\\n\\t\\t\\t# Make a random exam report date and time.\\n\\t\\t\\tself.randReport = random.randint(1, 6)\\n\\t\\t\\tself.examReportDateTimeNum = int(self.examDateTime[self.examDateTime.find(' ')+1:self.examDateTime.find(':')])+self.randReport\\n\\t\\t\\tself.examReportDateTime = self.examDateTime[:self.examDateTime.find(' ')+1] + str(self.examReportDateTimeNum) + self.examDateTime[self.examDateTime.find(':'):]\\n\\t\\t\\t\\n\\t\\t\\t# Add the Resident and Attending and vice versa.\\n\\t\\t\\tself.examResident = resident\\n\\t\\t\\tself.examAttending = attending\\n\\t\\t\\tself.examPatient = patient\\n\\t\\t\\tpatient.addExam(self)\\n\\t\\t\\tresident.addExam(self)\\n\\t\\t\\tattending.addExam(self)\\n\\t\\t\\tc = conn.cursor()\\n\\t\\t\\tc.executescript(\\\"\\\"\\\"INSERT INTO Exams VALUES ('self.examMod', 'self.examdDateTime', 'self.examID', 'self.examReportDateTime');\\\"\\\"\\\")\\n\\t\\t\\tconn.commit()\\n\\n\\t\\texcept:\\n\\t\\t\\tprint \\\"Invalid error: Please check your inputs and try again.\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"828ece2925d374c2869dfba32245bcb8\",\n \"score\": \"0.5132347\",\n \"text\": \"def __init__(self, date, exercises):\\n self.date = date\\n self.exercises = exercises\",\n \"title\": \"\"\n },\n {\n \"docid\": \"96cbf1cf9a26485283040a408fb926b8\",\n \"score\": \"0.51307094\",\n \"text\": \"def test_first_question_with_multiple_parent_questions(self):\\n test = Test.objects.create(title=\\\"my test\\\", description=\\\"my test description\\\")\\n question1 = Question.objects.create(test=test,question_text=\\\"my question 1\\\")\\n Question.objects.create(test=test, question_text=\\\"my question 2\\\")\\n Question.objects.create(test=test, question_text=\\\"my question 3\\\")\\n self.assertIs(test.first_question().id, question1.id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77ee001e696582bebbb81d063149edba\",\n \"score\": \"0.5127161\",\n \"text\": \"def tests_referred3(self):\\n report_datetime = datetime.today()\\n panel = Panel.objects.get(name='Microtube')\\n SubjectRequisitionFactory(subject_visit=self.subject_visit_female, site=self.study_site, panel=panel,\\n aliquot_type=AliquotType.objects.get(alpha_code='WB'))\\n HivTestReviewFactory(subject_visit=self.subject_visit_female, recorded_hiv_result=NEG)\\n PimaFactory(subject_visit=self.subject_visit_female, cd4_value=501, report_datetime=datetime.today())\\n subject_referral = SubjectReferralFactory(\\n subject_visit=self.subject_visit_female,\\n report_datetime=report_datetime)\\n self.assertEqual('TST-HIV', subject_referral.referral_code)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"051ef25554089ef698123af48204f09c\",\n \"score\": \"0.51244783\",\n \"text\": \"def setUp(self):\\n self.app = create_app()\\n self.client = self.app.test_client\\n self.database_name = \\\"trivia_test\\\"\\n self.database_path = \\\"postgres://postgres:hallo@{}/{}\\\".format('localhost:5432', self.database_name)\\n setup_db(self.app, self.database_path)\\n\\n # binds the app to the current context\\n with self.app.app_context():\\n self.db = SQLAlchemy()\\n self.db.init_app(self.app)\\n # create all tables\\n self.db.create_all()\\n \\n #create dummy question:\\n self.new_question = {\\n 'question': 'Which stupid bird creates havoc on our balcony?',\\n 'answer': 'Meisi',\\n 'category': 1,\\n 'difficulty': 2\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50f973970c8adbffb91e95f3f5231ce3\",\n \"score\": \"0.5118032\",\n \"text\": \"def setUp(self):\\n self.app = create_app()\\n self.client = self.app.test_client\\n self.database_name = \\\"trivia_test\\\"\\n self.database_path = \\\"postgres://{}/{}\\\".format('postgres:root@localhost:5432', self.database_name)\\n setup_db(self.app, self.database_path)\\n\\n self.new_question={\\n 'question':'test question',\\n 'answer': 'test answer',\\n 'category': 1,\\n 'difficulty': 1\\n }\\n\\n # binds the app to the current context\\n with self.app.app_context():\\n self.db = SQLAlchemy()\\n self.db.init_app(self.app)\\n # create all tables\\n self.db.create_all()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6517deabe158da197ff13cea152cd341\",\n \"score\": \"0.5117872\",\n \"text\": \"def setUp(self):\\n self.app = create_app()\\n self.client = self.app.test_client\\n self.database_name = \\\"trivia_test\\\"\\n self.database_path = \\\"postgresql://{}:{}@{}/{}\\\".format(DB_USER,DB_PASSWORD,'localhost:5432', DB_NAME)\\n setup_db(self.app, self.database_path)\\n\\n # binds the app to the current context\\n with self.app.app_context():\\n self.db = SQLAlchemy()\\n self.db.init_app(self.app)\\n # create all tables\\n self.db.create_all()\\n\\n self.new_question = {\\n 'question': 'Alifbeda nechta harf bor?',\\n 'answer': '31',\\n 'category': 5,\\n 'difficulty': 2,\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4fb2d2b8dd2c9140f6a6780159ee0561\",\n \"score\": \"0.5117735\",\n \"text\": \"def example():\\n\\n # create a new student\\n andrea = Student('Andrea', 'Pineapple', '1234 Street, Dallas, TX 74209')\\n\\n # -------------------------------------------------------\\n # creates a new exam object\\n new_exam = Exam('basic math exam', assessment_type='Exam')\\n\\n # add a question and a correct answer to the exam\\n new_exam.insert_an_exam_question(\\\"What's 1+1?\\\", \\\"2\\\")\\n new_exam.insert_an_exam_question(\\\"What's 2+2?\\\", \\\"4\\\")\\n new_exam.insert_an_exam_question(\\\"What's 3+3?\\\", \\\"6\\\")\\n\\n # administer exam\\n take_test(new_exam, andrea)\\n\\n # -------------------------------------------------------\\n # creates a new quiz object\\n new_quiz = Quiz('basic math quiz 2', assessment_type='Quiz')\\n\\n # add a question and a correct answer to the exam\\n new_quiz.insert_an_exam_question(\\\"What's 1*1?\\\", \\\"1\\\")\\n new_quiz.insert_an_exam_question(\\\"What's 2*2?\\\", \\\"4\\\")\\n new_quiz.insert_an_exam_question(\\\"What's 3*3?\\\", \\\"9\\\")\\n\\n print \\\"This is the list of quiz questions:\\\", new_quiz.questions\\n\\n # administer exam\\n take_test(new_quiz, andrea)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9511d8458693f204041e43296b1f6ee4\",\n \"score\": \"0.5115062\",\n \"text\": \"def __init__(self, jsonfile: str = 'bank/cissp_questions.json',\\n questionisrandom: bool = True,\\n answerisalpha: bool = True,\\n answerisrandom: bool = True):\\n\\n self._jsonFile = jsonfile\\n self._questionIsRandom = questionisrandom\\n self._answerIsAlpha = answerisalpha\\n self._answerIsRandom = answerisrandom\\n self._jsonData = self.__loadJsonFile()\\n\\n self.correct = 0\\n self.incorrect = 0\\n self.totalQuestions = len(self._jsonData['questions'])\\n self.questionSet = self.__complieQuestions()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59ca96f36249968b10497dc485353bf5\",\n \"score\": \"0.5114386\",\n \"text\": \"def __init__(self,question):\\n\\t\\tself.question = question\\n\\t\\tself.responses = []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c838fc1e852de81e32e02daa59d8787e\",\n \"score\": \"0.51141316\",\n \"text\": \"def setUp(self):\\n self.app = create_app()\\n self.client = self.app.test_client\\n self.database_name = \\\"trivia_test\\\"\\n self.database_path = \\\"postgres://{}:{}@{}/{}\\\".format('postgres','postgres','localhost:5432', self.database_name)\\n setup_db(self.app, self.database_path)\\n\\n self.new_question = {\\n 'question' : 'What is the name of the Late Night Host with orange hair',\\n 'answer' : 'Conan O\\\\'Brien',\\n 'category' : '5',\\n 'difficulty' : 2\\n }\\n\\n # binds the app to the current context\\n with self.app.app_context():\\n self.db = SQLAlchemy()\\n self.db.init_app(self.app)\\n # create all tables\\n self.db.create_all()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8fbb47ae90dddb7e94628ae53031a43b\",\n \"score\": \"0.5110197\",\n \"text\": \"def new( self, user_id, question_id, text ):\\n \\\"\\\"\\\"unique answer id is returned\\\"\\\"\\\"\\n \\\"\\\"\\\"KeyError exception should be thrown if user_id or question_id not found\\\"\\\"\\\"\\n aid = str(uuid.uuid4())\\n if user_id is not '0' and question_id is not '0':\\n try:\\n self.cursor.execute('''INSERT INTO answer (id, qid, uid, body)\\n VALUES (?,?,?,?)''', (aid, question_id, user_id, text))\\n except sqlite3.IntegrityError:\\n raise KeyError\\n else:\\n raise KeyError\\n return aid\",\n \"title\": \"\"\n },\n {\n \"docid\": \"74705ab235aff4c7724ccccfb479f7d1\",\n \"score\": \"0.51067877\",\n \"text\": \"def test_instantiation(self):\\n setuprule = factories.SetupRuleFactory()\\n self.assertTrue(setuprule.pk)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"273c9c494ffc2a2a629981099e28d6a2\",\n \"score\": \"0.5105797\",\n \"text\": \"def create_question(question, days):\\n time = timezone.now() + datetime.timedelta(days)\\n return Question.objects.create(question = question, pub_date = time)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"066e4e5b3cb2668c0799805a84d27fcd\",\n \"score\": \"0.50995284\",\n \"text\": \"def setUp(self):\\n self.app = create_app()\\n self.client = self.app.test_client\\n self.database_name = \\\"trivia\\\"\\n # self.database_path = \\\"postgres://{}/{}\\\".format('localhost:5432', self.database_name)\\n self.database_path = \\\"postgresql://postgres:root@localhost:5432/trivia\\\"\\n setup_db(self.app, self.database_path)\\n\\n # binds the app to the current context\\n with self.app.app_context():\\n self.db = SQLAlchemy()\\n self.db.init_app(self.app)\\n # create all tables\\n self.db.create_all()\\n \\n self.new_question = {\\n 'question': 'this is match question',\\n 'answer': 'this is math answer',\\n 'category': 'Math',\\n 'difficulty': '10'\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04fe67026fab7516d9f940a9ae44821b\",\n \"score\": \"0.5097631\",\n \"text\": \"def test_instantiation(self):\\n flow = factories.FlowFactory()\\n self.assertTrue(flow.pk)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2202f1dfeedbc492fcff2124db754c22\",\n \"score\": \"0.50964504\",\n \"text\": \"def create_trial_paper_to_test_questions(self, trial_quiz,\\n questions_list):\\n if questions_list is not None:\\n trial_questionpaper = self.create(quiz=trial_quiz,\\n total_marks=10,\\n )\\n trial_questionpaper.fixed_question_order = \\\",\\\".join(questions_list)\\n trial_questionpaper.fixed_questions.add(*questions_list)\\n return trial_questionpaper\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a46a46d663c4a1ef5b0871f8678439d\",\n \"score\": \"0.5090909\",\n \"text\": \"def __init__(self, parent, idevice):\\n Block.__init__(self, parent, idevice)\\n self.idevice = idevice\\n self.questionElements = []\\n self.message = False\\n i = 0\\n for question in idevice.questions:\\n self.questionElements.append(TestquestionElement(i, idevice, \\n question))\\n i += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"74933514b1493fe99c5eda47521dd4f9\",\n \"score\": \"0.50898194\",\n \"text\": \"def setUp(self):\\n self.app = create_app()\\n self.client = self.app.test_client\\n self.database_name = \\\"trivia_test\\\"\\n setup_db(self.app)\\n\\n self.new_question = {\\n 'question': 'What color is gree?',\\n 'answer': 'green',\\n 'difficulty': 3,\\n 'category': 1\\n }\\n\\n self.empty_question = {\\n 'question': False,\\n 'answer': False,\\n 'difficulty': 3,\\n 'category': 1\\n }\\n self.quiz = {\\n 'quiz_category': {'id': 2},\\n 'previous_questions': [],\\n }\\n # binds the app to the current context\\n with self.app.app_context():\\n self.db = SQLAlchemy()\\n self.db.init_app(self.app)\\n # create all tables\\n self.db.create_all()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a22e604758c812d0696c8448f2a1d25\",\n \"score\": \"0.50820285\",\n \"text\": \"def __init__(self, skillID=-1, f=None):\\n super(Test, self).__init__(skillID)\\n self.skillName = \\\"test\\\"\\n self.f: SkillObject = f\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f30d27807249443caa73aaa2ff32995\",\n \"score\": \"0.5081338\",\n \"text\": \"def _create_dummy_question(\\n self, question_id, question_content, linked_skill_ids):\\n state = state_domain.State.create_default_state(\\n 'ABC', is_initial_state=True)\\n state.update_interaction_id('TextInput')\\n state.update_interaction_customization_args({\\n 'placeholder': {\\n 'value': {\\n 'content_id': 'ca_placeholder_0',\\n 'unicode_str': ''\\n }\\n },\\n 'rows': {'value': 1}\\n })\\n\\n state.update_next_content_id_index(1)\\n state.update_linked_skill_id(None)\\n state.update_content(state_domain.SubtitledHtml('1', question_content))\\n recorded_voiceovers = state_domain.RecordedVoiceovers({})\\n written_translations = state_domain.WrittenTranslations({})\\n recorded_voiceovers.add_content_id_for_voiceover('ca_placeholder_0')\\n recorded_voiceovers.add_content_id_for_voiceover('1')\\n recorded_voiceovers.add_content_id_for_voiceover('default_outcome')\\n written_translations.add_content_id_for_translation('ca_placeholder_0')\\n written_translations.add_content_id_for_translation('1')\\n written_translations.add_content_id_for_translation('default_outcome')\\n\\n state.update_recorded_voiceovers(recorded_voiceovers)\\n state.update_written_translations(written_translations)\\n solution = state_domain.Solution(\\n 'TextInput', False, 'Solution', state_domain.SubtitledHtml(\\n 'solution', '<p>This is a solution.</p>'))\\n hints_list = [\\n state_domain.Hint(\\n state_domain.SubtitledHtml('hint_1', '<p>This is a hint.</p>')\\n )\\n ]\\n\\n state.update_interaction_solution(solution)\\n state.update_interaction_hints(hints_list)\\n state.update_interaction_default_outcome(\\n state_domain.Outcome(\\n None, state_domain.SubtitledHtml(\\n 'feedback_id', '<p>Dummy Feedback</p>'),\\n True, [], None, None\\n )\\n )\\n question = question_domain.Question(\\n question_id, state,\\n feconf.CURRENT_STATE_SCHEMA_VERSION,\\n constants.DEFAULT_LANGUAGE_CODE, 0, linked_skill_ids, [])\\n return question\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a65a6ddc4b571c4ae36498d251e23ccc\",\n \"score\": \"0.5079879\",\n \"text\": \"def test_flow(self):\\n course = Course.objects.create(title='test course')\\n lesson1_webinar = Webinar.objects.create(name='webinar of 1st lesson')\\n for number in range(7):\\n lesson = Lesson.objects.create(title=f'lesson {number+1}', )\\n if number + 1 == 1:\\n lesson.webinar = lesson1_webinar\\n lesson.save()\\n course.add_lesson(lesson)\\n\\n flow = course.create_flow()\\n self.assertEqual(flow.flow_lessons.count(), 7, \\\"Lessons must be copied from course\\\")\\n self.assertEqual('lesson 1', flow.flow_lessons.first().lesson.title)\\n self.assertEqual(lesson1_webinar, flow.flow_lessons.first().webinar)\\n self.assertEqual('lesson 7', flow.flow_lessons.last().lesson.title)\\n\\n flow_2 = course.create_flow()\\n # this flow has own webinar instead of default\\n flow2_lesson1_webinar = Webinar.objects.create(name='webinar of 1st lesson for flow_2')\\n flow2_lessons_first = flow_2.flow_lessons.first()\\n flow2_lessons_first.webinar = flow2_lesson1_webinar\\n flow2_lessons_first.save()\\n\\n self.assertEqual(flow.flow_lessons.count(), 7, \\\"Lessons must be copied from course\\\")\\n self.assertEqual(flow2_lesson1_webinar, flow_2.flow_lessons.first().webinar,\\n \\\"Shall override webinar\\\")\\n self.assertEqual(lesson1_webinar, flow.flow_lessons.first().webinar,\\n \\\"Shall keep default lesson webinar\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3a8cbde929d5b9d988ea3fbf369c330\",\n \"score\": \"0.50783646\",\n \"text\": \"def setUp(self):\\n self.candidate = cand.Candidate()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f805f4127817a182d3e361131d51fdc\",\n \"score\": \"0.5078282\",\n \"text\": \"def setUp(self):\\n self.app = create_app()\\n self.client = self.app.test_client\\n self.database_name = \\\"trivia_test\\\"\\n self.database_path = \\\"postgres://{}/{}\\\".format('postgres:12345@localhost:5432', self.database_name)\\n setup_db(self.app, self.database_path)\\n\\n # binds the app to the current context\\n with self.app.app_context():\\n self.db = SQLAlchemy()\\n self.db.init_app(self.app)\\n # create all tables\\n self.db.create_all()\\n\\n self.new_question = {\\n 'question': 'q1',\\n 'answer': 'a1',\\n 'category': 2,\\n 'difficulty': 3\\n }\\n\\n self.new_question_missing_fields = {\\n 'question': 'q1m',\\n 'category': 3,\\n 'difficulty': 1\\n }\\n\\n self.play = {\\n 'previous_questions': [6, 11, 19, 21],\\n 'category': 0\\n }\\n\\n self.play_with_invalid_category = {\\n 'previous_questions': 1000,\\n 'category': 2\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f39cd3107991db1f9511e61444828189\",\n \"score\": \"0.507792\",\n \"text\": \"def Create(self, parent):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c23a71098ef096dc76b76488c569e64\",\n \"score\": \"0.5076449\",\n \"text\": \"def create():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"faca8f019d9e1f40258fccef8f3b740a\",\n \"score\": \"0.5071746\",\n \"text\": \"def test_instantiation(self):\\n setupmatrix = factories.SetupMatrixFactory()\\n self.assertTrue(setupmatrix.pk)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f0c9c105c64fc0f13fb11feedf96cfa\",\n \"score\": \"0.5071715\",\n \"text\": \"def __init__(self, exercise_type: dict):\\n self.type = exercise_type.get(\\\"type\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c3c759ae4e927c2e602a4ad915bdf5e\",\n \"score\": \"0.50699073\",\n \"text\": \"def test_teacher_create_class(self):\\n\\n import pdb; pdb.set_trace()\\n\\n new_teacher = Teacher(username=\\\"wdisney\\\", password=\\\"password\\\", fname=\\\"Walt\\\", lname=\\\"Disney\\\")\\n db.session.add(new_teacher)\\n db.session.commit()\\n\\n teacher_id = Teacher.query.filter_by(username='wdisney').one().teacher_id\\n\\n with app.test_client() as c:\\n with c.session_transaction() as sess:\\n sess['teacher_id'] = teacher_id\\n\\n create_class_info = {'registration code': \\\"999\\\", 'name': \\\"6th Period Band\\\", 'type_class': \\\"marching_band\\\"}\\n result=self.client.post(\\\"/create-class\\\", data=create_class_info, follow_redirects=True)\\n self.assertIn(\\\"6th Period Band\\\", result.data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8dab3b66ed8567f84433e0f649693ad8\",\n \"score\": \"0.50670725\",\n \"text\": \"def test_create_questions_category(self):\\n name = \\\"Example Category\\\"\\n description = \\\"Category for testing purposes\\\"\\n category = QuestionCategory.objects.create(\\n name=name,\\n description=description\\n )\\n self.assertEqual(category.name, name)\\n self.assertEqual(category.description, description)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66e3822b67a515110c23758b6fc7263b\",\n \"score\": \"0.5066986\",\n \"text\": \"def setUpClass(cls):\\n super().setUpClass()\\n cls.start_events_isolation()\\n cls.course = CourseFactory.create()\\n cls.course_limited = CourseFactory.create()\\n cls.proctored_course = CourseFactory(\\n enable_proctored_exams=True, enable_timed_exams=True\\n )\\n cls.proctored_course_no_exam = CourseFactory(\\n enable_proctored_exams=True, enable_timed_exams=True\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5fcd6b207c32445c3b3e12ff3d1d522\",\n \"score\": \"0.50592625\",\n \"text\": \"def test_candidate_with_testid(self):\\n self.client.login(email=self.config['login']['email'],\\n password=self.config['login']['password'])\\n resp = question_create(self)\\n q_id = resp.data.get('id')\\n data = {'title': self.config[\\n 'testPostData']['title'], 'duration': self.config['testPostData'][\\n 'duration'], 'question': [q_id]}\\n _ = self.client.post(self.config['test']['api'], data=json.dumps(data),\\n content_type=\\\"application/json\\\")\\n test_id = _.data['id']\\n response = self.client.post(self.config['user']['api'],\\n {'email': self.config['userPostData'][\\n 'email'],\\n 'password': self.config[\\n 'userPostData']['password'],\\n 'user_type': self.config['userPostData'][\\n 'candidate'], 'test': test_id,\\n 'schedule': datetime.now()})\\n self.assertEqual(response.status_code, 201)\\n self.assertEqual(CandidateTestMapping.objects.count(), 1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90656089699842e99ca8587d87a3f0cc\",\n \"score\": \"0.5057354\",\n \"text\": \"def _extend_questions(cls, questions):\\n # Update _questions from Sampling by adding an additional question\\n questions.add_questions_to_block(\\\"\\\"\\\"\\n # State on which trajectories start\\n initial state = 0 :: int\\n \\\"\\\"\\\")\\n questions.generate_cases(\\\"method\\\", {name: method.questions\\n for name, method in cls._methods.items()})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dcc6dd99966904134977020fe3fbffde\",\n \"score\": \"0.5056758\",\n \"text\": \"def case(self, **kwargs):\\n return Case(self.tcex, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"197716cbf77a83e9935ce15e6684ddd9\",\n \"score\": \"0.5054177\",\n \"text\": \"def new( self, user_id, text ):\\n \\\"\\\"\\\"unique question id is returned\\\"\\\"\\\"\\n \\\"\\\"\\\"KeyError exception should be thrown if user_id not found\\\"\\\"\\\"\\n if user_id is not '0':\\n try:\\n qid = str(uuid.uuid4())\\n self.cursor.execute('INSERT INTO question (id, uid, body) VALUES (?,?,?)', (qid, user_id, text))\\n except sqlite3.IntegrityError:\\n raise KeyError\\n else:\\n raise KeyError\\n return qid\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1191,"cells":{"query_id":{"kind":"string","value":"89fa3e4594421fa702c0fb4c1dd03800"},"query":{"kind":"string","value":"Get Fuel grade details by FuelRewardId or RewardName"},"positive_passages":{"kind":"list like","value":[{"docid":"3f250a5ac03b4c98a51f71f930ab65d5","score":"0.76041454","text":"def GetFuelGradesById(FuelRewardId=\"1\", RewardName=\"FuelReward1\"):\n result = \"\"\n if (FuelRewardId == \"\" and RewardName == \"\"):\n log.info(\"FuelRewardId or RewardName must be not empty\")\n return result\n\n result = Loyalty().GetFuelGradesById(FuelRewardId, RewardName)\n return result","title":""}],"string":"[\n {\n \"docid\": \"3f250a5ac03b4c98a51f71f930ab65d5\",\n \"score\": \"0.76041454\",\n \"text\": \"def GetFuelGradesById(FuelRewardId=\\\"1\\\", RewardName=\\\"FuelReward1\\\"):\\n result = \\\"\\\"\\n if (FuelRewardId == \\\"\\\" and RewardName == \\\"\\\"):\\n log.info(\\\"FuelRewardId or RewardName must be not empty\\\")\\n return result\\n\\n result = Loyalty().GetFuelGradesById(FuelRewardId, RewardName)\\n return result\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"48ee6941c505a3dd30e77f5efa10c4aa","score":"0.74738145","text":"def GetFuelRewardsById(FuelRewardId=\"1\", RewardName=\"FuelReward1\"):\n result = \"\"\n if (FuelRewardId == \"\" and RewardName == \"\"):\n log.info(\"FuelRewardId or RewardName must be not empty\")\n return result\n\n result = Loyalty().GetFuelRewardsById(FuelRewardId, RewardName)\n return result","title":""},{"docid":"a6fce3f133976e6cfdac0b44435e5f07","score":"0.64665705","text":"def GetNonFuelRewardsById(NonfuelRewardId=\"1\", RewardName=\"15 cents DryStock\"):\n result = \"\"\n if (NonfuelRewardId == \"\" and RewardName == \"\"):\n log.info(\"NonfuelRewardId or RewardName must be not empty\")\n return result\n\n result = Loyalty().GetNonFuelRewardsById(NonfuelRewardId, RewardName)\n return result","title":""},{"docid":"5dba9aa7b0e1bfc926245451db7b5ee8","score":"0.60220605","text":"def AssignFuelReward(LoyatyID=\"1\", FuelID=\"1\", RewardName=\"15 cents DryStock\"):\n result = False\n\n if (FuelID == \"\" and RewardName == \"\"):\n log.info(\"FuelID or RewardName must be not empty\")\n return result\n\n result = Loyalty().AssignFuelReward(LoyatyID, FuelID, RewardName)\n if (result == 'true'):\n result = True\n return result","title":""},{"docid":"d7449d58c2895ccf6651bb7fc9e67ecf","score":"0.5910867","text":"def GetTransactionRewardsById(TransLevelRewardID=\"1\", RewardName=\"20 cents Trans.Rwd\"):\n result = \"\"\n if (TransLevelRewardID == \"\" and RewardName == \"\"):\n log.info(\"TransLevelRewardID or RewardName must be not empty\")\n return result\n\n result = Loyalty().GetTransactionRewardsById(TransLevelRewardID, RewardName)\n return result","title":""},{"docid":"c2d8445e8bc3849662eb47674a4d9ab9","score":"0.5756781","text":"def AddFuelRewards(RewardName=\"FuelReward1\", TypeReward=\"Instant\", TypeRewardDescription=\"\", DiscountMethod=\"amountOffPPU\", RewardLimit=\"30\", ReceiptText=\"25 cents Discount\", FuelGrades=\"001:0.25, 002:0.25, 003:0.25\"):\n result = \"\"\n if not(TypeReward == \"Instant\" or TypeReward == \"Optional\"):\n log.info(\"TypeReward (string) must be - {'Instant', 'Optional'}\")\n return result\n if not(DiscountMethod == \"amountOffPPU\" or DiscountMethod == \"newPrice\"):\n log.info(\"DiscountMethod (string) - should be - {'amountOffPPU', 'newPrice'}\")\n return result\n\n result = Loyalty().AddFuelRewards(RewardName, TypeReward, TypeRewardDescription, DiscountMethod, RewardLimit, ReceiptText, FuelGrades)\n return result","title":""},{"docid":"932b4c5ef5ce6caf6ad844008aad0734","score":"0.57084525","text":"def _getReward(self, a, s=None):\n raise NotImplementedError(\"_getReward should be implemented in child classes\")","title":""},{"docid":"cbe8e27e1e78265832c077d4561a451a","score":"0.5655711","text":"def UpdateFuelRewards(FuelRewardId=\"1\", RewardName=\"FuelReward1\", TypeReward=\"Instant\", TypeRewardDescription=\"\", DiscountMethod=\"amountOffPPU\", RewardLimit=\"30\", ReceiptText=\"25 cents Discount\", FuelGrades=\"001:0.25, 002:0.25, 003:0.25\"):\n result = False\n if not(TypeReward == \"Instant\" or TypeReward == \"Optional\"):\n log.info(\"TypeReward (string) must be - {'Instant', 'Optional'}\")\n return result\n if not(DiscountMethod == \"amountOffPPU\" or DiscountMethod == \"newPrice\"):\n log.info(\"DiscountMethod (string) - should be - {'amountOffPPU', 'newPrice'}\")\n return result\n\n result = Loyalty().UpdateFuelRewards(FuelRewardId, RewardName, TypeReward, TypeRewardDescription, DiscountMethod, RewardLimit, ReceiptText, FuelGrades)\n if (result == 'true'):\n result = True\n return result","title":""},{"docid":"76e8636dea09004afdaa40e1334a8a48","score":"0.563607","text":"def GetFuelRewardsList():\n result = Loyalty().GetFuelRewardsList()\n return result","title":""},{"docid":"a47c4a604c38416399583c88efb515c8","score":"0.5306753","text":"def _get_reward(self):\n if self.reward_function == 'play':\n return self._play_reward()\n elif self.reward_function == 'rules':\n return self._rules_reward()\n else:\n logger.error(\"Please specify a valid reward function. Choosing play reward now!\")\n return self._play_reward()","title":""},{"docid":"1ed54a8cc70b5a035430e2b5ee361c34","score":"0.523644","text":"def _get_reward(self):\n pass","title":""},{"docid":"0567aea06c7bd049d8a476466d4d36e2","score":"0.5212868","text":"def AddFuelGrades(FuelRewardId, FuelGrades=\"001:0.25, 002:0.25, 003:0.25\"):\n result = False\n result = Loyalty().AddFuelGrades(FuelRewardId, FuelGrades)\n if (result == 'true'):\n result = True\n return result","title":""},{"docid":"d8c7a7776e39a70c546461231d415c49","score":"0.5155305","text":"def UnAssignFuelReward(LoyatyID=\"1\", FuelID=\"1\", RewardName=\"15 cents DryStock\"):\n result = False\n\n if (FuelID == \"\" and RewardName == \"\"):\n log.info(\"FuelID or RewardName must be not empty\")\n return result\n\n result = Loyalty().UnAssignFuelReward(LoyatyID, FuelID, RewardName)\n if (result == 'true'):\n result = True\n return result","title":""},{"docid":"86a7ef58dd0767e33c811091f127b849","score":"0.51505774","text":"def get_reward_info(self):\n raise NotImplementedError","title":""},{"docid":"7c78946902449bf1c1fa6f4ecebfe716","score":"0.5148206","text":"def get(self, *args, **kwargs):\n reward = self.reward\n if self.cb_get is not None:\n reward = self.cb_get(self.reward, *args, **kwargs)\n\n if self.activate_bonus:\n reward = max(self.reward + self.bonus, self.rmax)\n return reward","title":""},{"docid":"d05bcceba1966187e98a7969bbe61478","score":"0.5139826","text":"def getFuel(self):\n return getItem(4)","title":""},{"docid":"81175436ca1ae7a98dff6728aaa9eeaf","score":"0.5052786","text":"def AddNonFuelReward(RewardName=\"15 cents DryStock\", TypeReward=\"Optional\", TypeRewardDescription=\"R u Sure?\", DiscountMethod=\"amountOff\", Discount=\"0.15\", ItemType=\"merchandiseLine\", CodeFormat=\"plu\", CodeValue=\"1234\", RewardLimit=\"10.10\", ReceiptText=\"Dry stock 10 cents amount off\"):\n result = \"\"\n if not(TypeReward == \"Instant\" or TypeReward == \"Optional\"):\n log.info(\"TypeReward (string) must be - {'Instant', 'Optional'}\")\n return result\n if not(DiscountMethod == \"amountOff\" or DiscountMethod == \"percentOff\" or DiscountMethod == \"newPrice\"):\n log.info(\"DiscountMethod (string) - should be - {'amountOff', 'percentOff', 'newPrice'}\")\n return result\n if not(ItemType == \"itemLine\" or ItemType == \"merchandiseLine\"):\n log.info(\"ItemType (string) - should be - {'itemLine', 'merchandiseLine'}\")\n return result\n if not(CodeFormat == \"upcA\" or CodeFormat == \"upcA\" or CodeFormat == \"upcE\" or CodeFormat == \"ean8\" or CodeFormat == \"ean13\" or CodeFormat == \"plu\" or CodeFormat == \"gtin\" or CodeFormat == \"rss14\" or CodeFormat == \"none\"):\n log.info(\"CodeFormat (string) - should be - {'upcA', 'upcE', 'ean8', 'ean13', 'plu', 'gtin', 'rss14', 'none'}\")\n return result\n\n result = Loyalty().AddNonFuelReward(RewardName, TypeReward, TypeRewardDescription, DiscountMethod, Discount, ItemType, CodeFormat, CodeValue, RewardLimit, ReceiptText)\n return result","title":""},{"docid":"f9e3e9bd6a7d114e11bed434ebf43356","score":"0.50421715","text":"def get_genre(cls, genre_name):\n\n try:\n genre = Genre.query.filter_by(genre=genre_name).one()\n except NoResultFound:\n print \"No genre instance was found with that name.\"\n\n return genre","title":""},{"docid":"0dc829a9524d0ce2b9ecbe0ece942bec","score":"0.50084245","text":"def get_reward(self, physics):\n return self._get_reward(physics, sparse=self._sparse)","title":""},{"docid":"58732be00301516907851a671a06c6d8","score":"0.4999932","text":"def getReward(self, s):\n cell = self.grid[s]\n if type(cell) == int or type(cell) == float:\n return float(cell)\n return self.livingReward","title":""},{"docid":"d20616b024cccdb09bf24723ded31ea5","score":"0.49980584","text":"def get(organisation_id, grade_id):\n grade = Grade.query.get_or_404(str(grade_id))\n\n return Response(repr(grade), mimetype=\"application/json\", status=200)","title":""},{"docid":"4c7808db06ac0a0da7e3b1e206cdf313","score":"0.4980085","text":"def UpdateNonFuelReward(NonfuelRewardId=\"1\", RewardName=\"15 cents DryStock\", TypeReward=\"Optional\", TypeRewardDescription=\"R u Sure?\", DiscountMethod=\"amountOff\", Discount=\"0.15\", ItemType=\"merchandiseLine\", CodeFormat=\"plu\", CodeValue=\"1234\", RewardLimit=\"10.10\", ReceiptText=\"Dry stock 10 cents amount off\"):\n result = False\n if not(TypeReward == \"Instant\" or TypeReward == \"Optional\"):\n log.info(\"TypeReward (string) must be - {'Instant', 'Optional'}\")\n return result\n if not(DiscountMethod == \"amountOff\" or DiscountMethod == \"percentOff\" or DiscountMethod == \"newPrice\"):\n log.info(\"DiscountMethod (string) - should be - {'amountOff', 'percentOff', 'newPrice'}\")\n return result\n if not(ItemType == \"itemLine\" or ItemType == \"merchandiseLine\"):\n log.info(\"ItemType (string) - should be - {'itemLine', 'merchandiseLine'}\")\n return result\n if not(CodeFormat == \"upcA\" or CodeFormat == \"upcA\" or CodeFormat == \"upcE\" or CodeFormat == \"ean8\" or CodeFormat == \"ean13\" or CodeFormat == \"plu\" or CodeFormat == \"gtin\" or CodeFormat == \"rss14\" or CodeFormat == \"none\"):\n log.info(\"CodeFormat (string) - should be - {'upcA', 'upcE', 'ean8', 'ean13', 'plu', 'gtin', 'rss14', 'none'}\")\n return result\n\n result = Loyalty().UpdateNonFuelReward(NonfuelRewardId, RewardName, TypeReward, TypeRewardDescription, DiscountMethod, Discount, ItemType, CodeFormat, CodeValue, RewardLimit, ReceiptText)\n if (result == 'true'):\n result = True\n return result","title":""},{"docid":"ddbda1c1632579b94c968ed5f84414e5","score":"0.49531397","text":"def get(self, mf_credit_id):\n le_mf_credit = get_an_mf_credit(mf_credit_id)\n if not le_mf_credit:\n return {'success': False, 'message': 'mf_credit not found'}\n else:\n return le_mf_credit","title":""},{"docid":"5893b1302a8af88dec197f8d60a69c99","score":"0.48749417","text":"def reward(self, s = None):\n s = self.state(as_tuple=True) if s == None else s\n s = s if isinstance(s, tuple) else self.legal_states[s]\n return self.rewards[s]","title":""},{"docid":"a2d56aca9453bb752152034204c872d0","score":"0.48682547","text":"def DeleteFuelReward(FuelRewardId=\"1\"):\n result = False\n result = Loyalty().DeleteFuelReward(FuelRewardId)\n if (result == 'true'):\n result = True\n return result","title":""},{"docid":"37970c1b6dbd8c886b8e5b8a00a9aa33","score":"0.4867022","text":"def _get_reward(self):\n print('get reward mai hai ham')\n\n # reward for speed tracking\n v = self.ego.get_velocity()\n speed = np.sqrt(v.x**2 + v.y**2)\n r_speed = -abs(speed - self.desired_speed)\n\n # reward for collision\n r_collision = 0\n if len(self.collision_hist) > 0:\n r_collision = -1\n\n # reward for steering:\n r_steer = -self.ego.get_control().steer**2\n\n # reward for out of lane\n ego_x, ego_y = get_pos(self.ego)\n dis, w = get_lane_dis(self.waypoints, ego_x, ego_y)\n r_out = 0\n if abs(dis) > self.out_lane_thres:\n r_out = -1\n\n # longitudinal speed\n lspeed = np.array([v.x, v.y])\n lspeed_lon = np.dot(lspeed, w)\n\n # cost for too fast\n r_fast = 0\n if lspeed_lon > self.desired_speed:\n r_fast = -1\n\n # cost for lateral acceleration\n r_lat = - abs(self.ego.get_control().steer) * lspeed_lon**2\n\n r = 200*r_collision + 1*lspeed_lon + 10 * \\\n r_fast + 1*r_out + r_steer*5 + 0.2*r_lat - 0.1\n\n return r","title":""},{"docid":"6c8d5d0868341fc6898adde4b6c2a25d","score":"0.4814422","text":"def get(id):\n params = rest.get_params(_spec)\n obj = Supplier.query.get_or_404(id)\n filtered = rest.filter_fields(obj.query, params)\n return jsonify(rest.to_dict(obj, filtered))","title":""},{"docid":"68ca786cdcf3e6b115d5b7d165c073f6","score":"0.48093444","text":"def get_reward(self):\n return self.reward","title":""},{"docid":"51c739b6297630efbd5df7f7b445f064","score":"0.48086652","text":"def reward_info(user):\n reward = Reward.query.get(user)\n\n if not reward:\n raise NotFound\n\n serialized_object = reward_schema.dumps(reward, sort_keys=True, indent=4)\n\n return Response(\n response=serialized_object,\n status=http_status.OK,\n mimetype=\"application/json\"\n )","title":""},{"docid":"1a17732c4ae1a7ad51d3358a36cdf7a4","score":"0.4802178","text":"def detail(self, request, pk=None):\n queryset = Supplier.objects.get(pk=pk)\n data = SupplierDetailSerializer(queryset).data\n return Response(data)","title":""},{"docid":"5a019cec2446875d401dbd439e5a168a","score":"0.4793602","text":"def test_grade_retrieve(slu1, student, student2):\n Grade.objects.create(\n user=student,\n unit=slu1,\n status=\"graded\",\n score=16,\n )\n Grade.objects.create(\n user=student,\n unit=slu1,\n status=\"graded\",\n score=14,\n )\n Grade.objects.create(\n user=student, unit=slu1, status=\"graded\", score=20, on_time=False\n )\n\n assert get_last_grade(slu1, student).score == 20\n assert get_best_grade(slu1, student).score == 16\n\n assert get_last_grade(slu1, student2).score == None\n assert get_best_grade(slu1, student2).score == None","title":""},{"docid":"a773d609f81882930613411904af1ef0","score":"0.478699","text":"def get_rewards():\n episode = int(request.args.get('user'))\n rewards = data_preprocessor.get_scalar_values_by_tag(\n \"reward-e\"+str(episode))\n return rewards, 200, JSON_TYPE","title":""},{"docid":"4b75b96742af1d982fbc47a9aa2f12b5","score":"0.47646138","text":"def _get_reward(ver_dict, stance, debate_method, reward_method) -> torch.Tensor:\n debate_method = debate_method.lower() # Evaluate Oracle rewards too\n j_score = ver_dict[reward_method]\n if debate_method in {'a', 'r'}:\n rewards = j_score\n elif debate_method in {'b'}:\n rewards = (1. - j_score)\n elif debate_method in {'e', 'l', 'w', 'ⅰ', 'ⅱ', 'ⅲ', 'ⅳ'}:\n rewards = (ver_dict['prob_dist'] * stance.to(ver_dict['prob_dist'])).sum(dim=1)\n else:\n raise NotImplementedError('_get_reward not implemented for debate_method', debate_method)\n return rewards.detach() # Judge shouldn't get gradients through j_score, used to reward debaters","title":""},{"docid":"bdd5658790de512de85be9379366c704","score":"0.4743473","text":"def get_reward(self, state, action, next_state):\n return self.model_reward.get((state, action, next_state), (0, 0))","title":""},{"docid":"bdd5658790de512de85be9379366c704","score":"0.4743473","text":"def get_reward(self, state, action, next_state):\n return self.model_reward.get((state, action, next_state), (0, 0))","title":""},{"docid":"4c6af3459308335d253ece9ff3e750f5","score":"0.4736354","text":"def get_grade_by_id(user_id):\n\n return Grade.query.filter(Grade.user_id == user_id).all()","title":""},{"docid":"2c34ba50471ecbc73a642ff0508ce841","score":"0.47319853","text":"def getReward(self, gameState):\n score = 0\n # rewards\n # add if we eat the ghost\n score += 1.25 if self.ateFood(gameState) else 0\n score += self.getScoreIncrease(gameState)\n # punishment\n score -= self.numFoodCarrying if self.checkDeath(gameState) else 0\n foodList = self.getFood(gameState).asList()\n if len(foodList) > 0:\n minDistance = min([self.getMazeDistance(gameState.getAgentState(\n self.index).getPosition(), food) for food in foodList])\n score += np.reciprocal(float(minDistance)) # distance to closest food\n # if the game is over big reward if we win, else penalty if we lose\n if gameState.isOver():\n score += self.getScore(gameState)*2\n return score","title":""},{"docid":"17fcfe317a419e8979cc06fd1e8a6c34","score":"0.47302","text":"def reward(self):\n return self._reward","title":""},{"docid":"aed91178f872cb79606cadab31c704b6","score":"0.47075465","text":"def get_reward(self, st):\n if st in self._rewards:\n return self._rewards[st]\n # print(\"Warning. Could not find state {} in rewards dictionary. Returning 0 reward.\")\n # set default reward as zero\n return 0","title":""},{"docid":"8d6238e4a8f4990367259e5482573818","score":"0.4705804","text":"def info_from_name(ref_df, ref_dict, name):\n\n # get first id from name\n recipe_id = list(ref_dict.keys())[list(ref_dict.values()).index(name)]\n # filter df with id\n sub_df = ref_df[ref_df.id == recipe_id]\n\n # return name, url, weight_list, ingredient_list, impact_list, total_impact\n recipe_name = sub_df.title_raw.iloc[0]\n recipe_url = sub_df.url.iloc[0]\n recipe_impact = sum(sub_df.ingredient_impact)\n ingredient_weights = list(sub_df.weight_per_ingr)\n ingredient_names = list(sub_df.ingredient_raw)\n ingredient_impacts = list(sub_df.ingredient_impact)\n\n return recipe_name, recipe_url, recipe_impact, ingredient_weights, ingredient_names, ingredient_impacts","title":""},{"docid":"f8e25b1aea4aebce86812e5a2a6787fb","score":"0.46987453","text":"def grade(self, grade):\n return self._db[grade]","title":""},{"docid":"1ba77915592607266394d5fc264fc559","score":"0.46956807","text":"def reward(s, rewards={' ': 0, 'w': None, 't': trapReward, 'r': treasureReward}):\n return rewards[board[s]]","title":""},{"docid":"5ffb385d644e980bbd90d018ed996214","score":"0.46879724","text":"def get_grade(self, grading_instance_key, grading_event_id=1):\n grade_info = self.get_grade_info()\n grade_value = grade_info.get_grade(grading_instance_key, \\\n grading_event_id)\n return grade_value","title":""},{"docid":"24463dff589e2e8bdefed3e3a4602a68","score":"0.4677687","text":"def get_grade(self):\n return # osid.grading.Grade","title":""},{"docid":"e406b65bb273ca42ed536950cb37a388","score":"0.4653771","text":"def _get_reward(self):\n\n return self.calc_reward()\n # TODO: generalize for different network","title":""},{"docid":"38993f5398b1816e4d9cf368c342d5fa","score":"0.46492845","text":"def extract_reward(srew, arew):\n if srew >= arew:\n reward = 1\n else:\n reward = -1\n return reward","title":""},{"docid":"9d5e482aa7bc2854e3cac513c7808acd","score":"0.46311963","text":"def get_score(student, assessment_name):\n return dict_get(student.scores, assessment_name)","title":""},{"docid":"4e0ef4eb8c8b8a331688aa754194af7f","score":"0.46127132","text":"def reward(self, action=None):\n reward = 0.\n\n # sparse completion reward\n if self._check_success():\n reward = 1.0\n\n # use a shaping reward\n if self.reward_shaping:\n\n # reaching reward\n cube_pos = self.sim.data.body_xpos[self.cube_body_id]\n gripper_site_pos = self.sim.data.site_xpos[self.eef_site_id]\n dist = np.linalg.norm(gripper_site_pos - cube_pos)\n reaching_reward = 1 - np.tanh(10.0 * dist)\n reward += reaching_reward\n\n # grasping reward\n touch_left_finger = False\n touch_right_finger = False\n for i in range(self.sim.data.ncon):\n c = self.sim.data.contact[i]\n if c.geom1 in self.l_finger_geom_ids and c.geom2 == self.cube_geom_id:\n touch_left_finger = True\n if c.geom1 == self.cube_geom_id and c.geom2 in self.l_finger_geom_ids:\n touch_left_finger = True\n if c.geom1 in self.r_finger_geom_ids and c.geom2 == self.cube_geom_id:\n touch_right_finger = True\n if c.geom1 == self.cube_geom_id and c.geom2 in self.r_finger_geom_ids:\n touch_right_finger = True\n if touch_left_finger and touch_right_finger:\n reward += 0.25\n\n return reward","title":""},{"docid":"202989e6828ee36a1e2ea4f1b3fd4dfb","score":"0.461233","text":"def get_reward(self):\n return self._reward","title":""},{"docid":"be03d7ed5a50c223b03677fdab020b45","score":"0.45986578","text":"def get(self, mf_id):\n return get_all_mf_credits_by_mf(mf_id)","title":""},{"docid":"5d6bea1cc8b0926fde84675279625ba9","score":"0.45925483","text":"def get_dish_details():\n user_id=session[\"user_id\"]\n score =request.form.get(\"rate\")\n if request.method=='POST': \n dish_id = request.form.get(\"dish_id\")\n else:\n dish_id = request.args.get(\"dish_id\")\n if score!=None: \n crud.create_rating(score=score,dish_id=dish_id,user_id=user_id)\n flash(\"you rated!!!\")\n \n \n\n dish= Dish.query.get(dish_id)\n summary = get_dish_summary(dish_id)\n \n ingredients=get_ingredients(dish_id)\n equipments=get_equipments(dish_id)\n instructions=get_instructions(dish_id)\n \n \n \n\n\n print(dish_id)\n print('*'*20)\n\n print(score)\n print('*'*20)\n \n print('*'*20)\n\n return render_template('dish_details.html', summary=summary,ingredients=ingredients,equipments=equipments,instructions=instructions,dish=dish,rate=score)","title":""},{"docid":"6d13b921fd3d54ff502972d6e304be53","score":"0.45728022","text":"def get_reward(agent_id: int, factory: Factory, episodes: int) -> float:\n\n # only configured rewards get picked up\n rewards_to_use = get_reward_names_and_weights()\n reward_names = rewards_to_use.keys()\n rewards = {}\n\n if \"rew_tighten_max_steps\" in reward_names:\n # After 300 episodes we cut the allowed step length to 1/4th.\n episode_discount = max(0.25, 1 - (episodes / 400.))\n factory.max_num_steps = int(factory.initial_max_num_steps * episode_discount)\n # We set this to 0, as this is just a mean to decrease the max step count over\n # time. This leads to shorter episodes and is reflected in \"rew_punish_slow_tables\".\n rewards[\"rew_tighten_max_steps\"] = 0\n\n max_num_steps = factory.max_num_steps\n steps = factory.agent_step_counter.get(agent_id)\n agent: Table = factory.tables[agent_id]\n\n # sum negative rewards due to collisions and illegal moves\n if factory.moves.get(agent_id):\n move = factory.moves[agent_id].pop(-1)\n rewards[\"rew_collisions\"] = move.reward()\n\n # high incentive for reaching a target, quickly\n time_taken = steps / float(max_num_steps)\n if agent.is_at_target:\n rewards[\"rew_found_target\"] = (1.0 - time_taken)\n rewards[\"rew_found_target_squared\"] = (1.0 - time_taken) ** 2\n\n # punish if too slow\n if steps == max_num_steps:\n num_cores_left = len([t for t in factory.tables if t.has_core()])\n rewards[\"rew_punish_slow_tables\"] = - 1 * num_cores_left\n\n if not agent.has_core():\n # If an agent without core is close to one with core, let it shy away...\n rewards[\"rew_avoid_cores\"] = -1.0 * has_core_neighbour(agent.node, factory)\n\n # ... and if it sits on any current target, punish it\n # TODO: how can we account for the fact that tables might be \"on the path\"\n # to a target, given that we don't know what the path is?\n # Maybe we can account for situation in which ALL paths to a core target are obstructed\n # by this table?\n all_targets = [c.current_target for c in factory.cores]\n rewards[\"rew_blocking_target\"] = -1.0 * int(agent.node in all_targets)\n\n reward = 0\n for reward_name, weight in rewards_to_use.items():\n # multiply rewards by configured weight terms\n reward += rewards.get(reward_name, 0) * weight\n\n return reward","title":""},{"docid":"016bb5f1285f5f18e287c3064599f84a","score":"0.456851","text":"def lookup_name_facc(self, name):\n self.__init_coll_sf_facc()\n res = self.__coll_sf_facc.find_by_id(name.lower())\n return res if res else {}","title":""},{"docid":"886999ea875963c357bcabfaead2ad76","score":"0.45552728","text":"def get_reward(state):\n if found_gold(state):\n return 100\n elif oh_no_dragon(state):\n return -10\n else:\n return -1.0","title":""},{"docid":"3b99903a069fec44fbb788427c83c11d","score":"0.45493957","text":"def getStudentByRegId(self, request):\n\t\tS = Student.objects.get(dtuRegId = request[\"dtuRegId\"])\n\t\treturn S","title":""},{"docid":"5ff00adf1a2c8a9eeb4759a4f3265099","score":"0.4542256","text":"def _get_reward(self):\n reward = -np.abs(self.powergrid.res_bus.iloc[1, 2] - self.target_load)\n return reward\n # TODO: generalize for different network","title":""},{"docid":"82a47506e328d80348974d74e924b65d","score":"0.4531861","text":"def AssignNonFuelReward(LoyatyID=\"1\", NonFuelID=\"1\", RewardName=\"15 cents DryStock\"):\n result = False\n\n if (NonFuelID == \"\" and RewardName == \"\"):\n log.info(\"NonFuelID or RewardName must be not empty\")\n return result\n\n result = Loyalty().AssignNonFuelReward(LoyatyID, NonFuelID, RewardName)\n if (result == 'true'):\n result = True\n return result","title":""},{"docid":"e6e30fd9282cf31891e8aa8bd68a9dc1","score":"0.4516599","text":"def get_reward(self, position=None):\r\n if position is None:\r\n position = self.position\r\n\r\n return self.rewards[position[0], position[1]]","title":""},{"docid":"e0e29c88a45b321124a82b6e40db5f7b","score":"0.45160243","text":"def get_reward(self):\n # Calculate the reward\n total_reward = self.reward.mission_reward(self.state_manager.ugv,\n self.state_manager.uav,\n self.config)\n for vehicle in self.state_manager.uav:\n vehicle.reward = total_reward\n\n for vehicle in self.state_manager.ugv:\n vehicle.reward = total_reward\n\n return total_reward","title":""},{"docid":"e0fa77ced0a64dad306025ceebcef722","score":"0.45129043","text":"def _get_reward(self):\n # if self.status == FOOBAR:\n # return 1\n # elif self.status == ABC:\n # return self.somestate ** 2\n # else:\n return self._reward # -1 for the step/time increment ","title":""},{"docid":"dec87f7070152de75c183659bc422d13","score":"0.4502535","text":"def get_score(self, name):\n score = self.db.scores.find_one({\"name\": name}).get(\"score\")\n # print(score)\n return(score)","title":""},{"docid":"84a43e7bcc744c84b56a8b6e12748bff","score":"0.44988525","text":"def reward(self, score):\n return score","title":""},{"docid":"d00aa2d714af106e42d500e36e57c4a0","score":"0.44877315","text":"def get(self, supplier_id):\n app.logger.info(\"Request to Retrieve a supplier with id [%s]\", supplier_id)\n supplier = Supplier.find(supplier_id)\n if not supplier:\n abort(status.HTTP_404_NOT_FOUND, \"Supplier with id '{}' was not found.\".format(supplier_id))\n return supplier.serialize(), status.HTTP_200_OK","title":""},{"docid":"b53c9d7a7392b405177ae5d6e4b43a58","score":"0.44803327","text":"def get_grade(self):\n return self._grade","title":""},{"docid":"d54320b4e9b65988d51e2ea96d1083db","score":"0.44785026","text":"def get_done_reward():\n rospy.wait_for_service('/drl/get_done_reward')\n\n try:\n get_done_reward_proxy = rospy.ServiceProxy(\n '/drl/get_done_reward', GetDoneAndReward)\n resp = get_done_reward_proxy()\n return resp\n except rospy.ServiceException, ex:\n print \"Service call get_reward_done failed: %e\" % ex","title":""},{"docid":"8847f45c19ac55e327ccce24a45a1573","score":"0.4476952","text":"def get_fuel_supplier(self, obj):\n if obj['type'] == 'Credit Trade':\n history = CreditTradeHistory.objects.get(id=obj['id'])\n\n if history.credit_trade.type.id in [1, 3, 5]:\n fuel_supplier = history.credit_trade.initiator\n else:\n fuel_supplier = history.credit_trade.respondent\n else:\n history = ComplianceReportHistory.objects.get(id=obj['id'])\n\n fuel_supplier = history.compliance_report.organization\n\n serializer = OrganizationMinSerializer(fuel_supplier, read_only=True)\n return serializer.data","title":""},{"docid":"fcd45b7b22192f08d1fbba60d2e17e23","score":"0.4473189","text":"def get(self, good_id):\n shablon = (\"id\", \"barcode\", \"name\", \"grup\", \"delimeter\")\n query1 = db.session.query(Goods.id, Goods.barcode, Goods.name, Goods.grup, Goods.delimeter).filter((Goods.name.like(\"%\" + good_id + \"%\")) | (Goods.barcode == (good_id)) | (Goods.id == (good_id))).all()\n if not query1:\n\n return good_id, 404\n resp = {\"good\": list(dict(zip(shablon, x))for x in query1)}\n print(resp)\n return resp","title":""},{"docid":"d081e18c650c698b0e2df0702678c192","score":"0.44646215","text":"def getValue(self, fsr_name):\n try:\n return self.fsr_dict[fsr_name].getValue()\n\n except KeyError:\n raise pybullet.error(\"The required Fsr does not exist\")","title":""},{"docid":"bda26bcfd7cebda1c1143889bd671e4b","score":"0.44618854","text":"def _rew_drug_penalty(self) -> float:\n\n # Get params\n params = self.params_reward[\"drug_penalty\"]\n\n # Compute reward, only discrete\n reward = params[\"low_rew\"]\n if 0 < self.action <= 3 or self.action == 6:\n reward = params[\"intermed_rew\"]\n if 3 < self.action < 6 or self.action > 6:\n reward = params[\"high_rew\"]\n return reward","title":""},{"docid":"3268870de45c2da1e3e84e290bfbe0b7","score":"0.4454963","text":"def getReward(self):\n # this function should be deterministic and without side effects\n if self.env.state.click != Click.NOT_CLICKED:\n if self.env.clicked_item.item_relevance == ItemRelevance.TARGET_RELEVANCE:\n # reward for clicking the correct item after seeing it\n # target item should always have correct length\n return self.reward_success\n else:\n # penalty for clicking the wrong item\n return self.reward_failure\n elif self.env.state.quit == Quit.HAS_QUIT:\n if self.env.target_present is False:\n # reward for quitting when target is absent\n return self.reward_success\n else:\n # penalty for quitting when target is present\n return self.reward_failure\n # default penalty for spending time\n return int(-1 * self.env.action_duration)","title":""},{"docid":"6d81756e4baa70f4b9431f62df067a20","score":"0.44533724","text":"def get(self, foodName):\n\t\ttry:\n\t\t\tfoodName = foodName.lower()\n\t\t\tfood = Food.query.filter_by(name=foodName).one()\n\t\texcept:\n\t\t\treturn {\"message\": \"food resource not found\", \"status\":404}, 404\n\t\treturn food.foodRepresentation()","title":""},{"docid":"09c542ab251035a07388e52083378727","score":"0.44508135","text":"def reward(self, action=None):\n # reaching reward\n cube_pos = self.sim.data.body_xpos[self.cube_body_id]\n gripper_site_pos = self.sim.data.site_xpos[self.eef_site_id]\n dist = np.linalg.norm(gripper_site_pos - cube_pos) # Eucledian distance b/w cube and grip site\n reaching_reward = 1 - np.tanh(10.0 * dist) # tanh function on distance\n\n # slow down reward\n vel = np.sum(abs(self.ee_v)) / 6\n vel_reward = 1 - np.tanh(10.0 * vel)\n\n # Two phases of the task (0.Reaching and 1.Grasping+Lifiting)\n if self.phase == 0:\n\n reward = 0.6 * reaching_reward\n\n if dist < 0.08:\n reward += 0.3 * vel_reward\n\n # gripper open reward\n if action[-1] < 0:\n reward += 0.1 * abs(action[-1])\n\n if dist < 0.025:\n self.phase = 1\n\n elif self.phase == 1:\n\n reward = reaching_reward + vel_reward\n\n # gripper closing reward\n if action[-1] > 0:\n reward += 0.5 * action[-1]\n\n # check contact between fingers and cube\n touch_left_finger = False\n touch_right_finger = False\n\n for i in range(self.sim.data.ncon):\n c = self.sim.data.contact[i]\n if c.geom1 in self.l_finger_geom_ids and c.geom2 == self.cube_geom_id:\n touch_left_finger = True\n #reward += 0.1\n if c.geom1 == self.cube_geom_id and c.geom2 in self.l_finger_geom_ids:\n touch_left_finger = True\n # reward += 0.1\n if c.geom1 in self.r_finger_geom_ids and c.geom2 == self.cube_geom_id:\n touch_right_finger = True\n #reward += 0.1\n if c.geom1 == self.cube_geom_id and c.geom2 in self.r_finger_geom_ids:\n touch_right_finger = True\n #reward += 0.1\n\n self.has_grasp = touch_left_finger and touch_right_finger\n\n # grasping reward\n if self.has_grasp:\n reward += 0.5\n\n # success reward\n if self._check_success():\n reward += 2.0\n\n # stay within joint limits! This is used only in retraining condition\n #if self._check_q_limits():\n #reward -= 1.0\n\n # Collision avoidance scene\n if self.config.mode == 2:\n # collision penalty\n collision = False\n for contact in self.sim.data.contact[:self.sim.data.ncon]:\n # hand collision check\n if self.sim.model.geom_id2name(contact.geom1) in self.hand_names and contact.geom2 == self.cyl_geom_id:\n collision = True\n if self.sim.model.geom_id2name(contact.geom2) in self.hand_names and contact.geom1 == self.cyl_geom_id:\n collision = True\n if self.sim.model.geom_id2name(contact.geom1) in self.hand_names and contact.geom2 == self.cyl2_geom_id:\n collision = True\n if self.sim.model.geom_id2name(contact.geom2) in self.hand_names and contact.geom1 == self.cyl2_geom_id:\n collision = True\n # cube collision check\n if contact.geom1 == self.cyl_geom_id and contact.geom2 == self.cube_geom_id:\n collision = True\n if contact.geom1 == self.cube_geom_id and contact.geom2 == self.cyl_geom_id:\n collision = True\n if contact.geom1 == self.cyl2_geom_id and contact.geom2 == self.cube_geom_id:\n collision = True\n if contact.geom1 == self.cube_geom_id and contact.geom2 == self.cyl2_geom_id:\n collision = True\n\n if collision:\n reward -= 5.0\n\n return reward","title":""},{"docid":"8c2592a6a2a8cd319a92cbddc60fc22a","score":"0.44498286","text":"def get_award(user, slug):\n try:\n old_badge = Badge.objects.get(slug=slug)\n return Award.objects.get(user=user, badge=old_badge)\n except:\n # Award not granted\n return None","title":""},{"docid":"5e8d2b1203a358efc17d707ea3a1102d","score":"0.44433272","text":"def get_ref():\n id = request.args.get('id')\n try:\n ref = g.db.query(Referee).filter_by(id=id).one()\n except NoResultFound:\n print \"No result found for {0}\".format(id)\n ref = None\n return render_template('ref_info.html', ref=ref)","title":""},{"docid":"44c1612104a847710bb0b4de71ae3613","score":"0.44368294","text":"def get_grade_info(self):\n if (self.grade_info_store):\n grade_info = \\\n SchoolDB.assistant_classes.InformationContainer.get_data(\n stored_data = self.grade_info_store, \n compressed = True, current_version = 1)\n else:\n grade_info = None\n return grade_info","title":""},{"docid":"8ea8fb868b0b31262e9ff599516b6c82","score":"0.4435227","text":"def get_Furniture(id): \r\n urnitures = Furniture.query.all()\r\n furniture = next(furniture for furniture in urnitures if furniture.id==id)\r\n return furniture","title":""},{"docid":"92d40cfb4d528bb263cdd6b0df4b8da3","score":"0.4434018","text":"def badges(self, request, pk=None):\n queryset = User.objects.all()\n user = get_object_or_404(queryset, pk=pk)\n awards = user.badges.all()\n return Response([BadgeSerializer(award.badge, context={'request': request}).data for award in awards])","title":""},{"docid":"96c0521d1f5dfb35dcd9f53891120ac0","score":"0.44329074","text":"def reward_bonus(self, assignment_id, amount, reason):\n from psiturk.amt_services import MTurkServices\n\n self.amt_services = MTurkServices(\n self.aws_access_key_id,\n self.aws_secret_access_key,\n self.config.getboolean(\n 'Shell Parameters', 'launch_in_sandbox_mode'))\n return self.amt_services.bonus_worker(assignment_id, amount, reason)","title":""},{"docid":"25649211343054d2198885421fa22fab","score":"0.44319484","text":"def _get_grade_comparison():\n\n my_grade = lrs.find({\n \"type\": \"GradeEvent\",\n \"actor.name\": session['username'],\n \"object.assignable.id\": \"http://example.org/assessment/1\"\n }, {\n \"generated.scoreGiven\": 1\n })[0]\n\n average_grade = list(lrs.aggregate([\n {\n \"$match\": {\n \"type\": \"GradeEvent\",\n \"object.assignable.id\": \"http://example.org/assessment/1\"\n }\n },\n {\n \"$group\": {\n \"_id\": None,\n \"average_score\": {\n \"$avg\": \"$generated.scoreGiven\"\n }\n }\n }\n ]))[0]\n\n return {\n \"my_score\": my_grade['generated']['scoreGiven'],\n \"average_score\": average_grade['average_score']\n }","title":""},{"docid":"a43c5554e5d083e11d499532157badb3","score":"0.44311404","text":"def find_fishery_name(fishery_id: str) -> str:\n try:\n fishery = Lookups.get(Lookups.lookup_type == 'FISHERY',\n Lookups.lookup_value == fishery_id)\n return fishery.description\n\n except Lookups.DoesNotExist:\n return '?'","title":""},{"docid":"f87b86b71e16c6361b7209efa5acd13c","score":"0.44212267","text":"def info_by_rfid(request, form):\n rfid = rfid_reader()\n try:\n user = Client.objects.get(rfid_code=rfid)\n return {\n 'first_name': user.first_name,\n 'last_name': user.last_name,\n 'email': user.email,\n 'rfid_code': user.rfid_code\n }\n except Client.DoesNotExist:\n return {}","title":""},{"docid":"f6e4cf720b6b115821305d7dd533f446","score":"0.4400915","text":"def fuel_cost(hr, frc_eia923, gen_eia923):\n # Split up the plants on the basis of how many different primary energy\n # sources the component generators have:\n gen_w_ft = pd.merge(gen_eia923,\n hr[['plant_id_eia', 'report_date', 'generator_id',\n 'fuel_type_pudl', 'fuel_type_count',\n 'heat_rate_mmbtu_mwh']],\n how='inner',\n on=['plant_id_eia', 'report_date', 'generator_id'])\n\n one_fuel = gen_w_ft[gen_w_ft.fuel_type_count == 1]\n multi_fuel = gen_w_ft[gen_w_ft.fuel_type_count > 1]\n\n # Bring the single fuel cost & generation information together for just\n # the one fuel plants:\n one_fuel = pd.merge(one_fuel, frc_eia923[['plant_id_eia', 'report_date',\n 'fuel_cost_per_mmbtu',\n 'fuel_type_pudl',\n 'total_fuel_cost',\n 'total_heat_content_mmbtu']],\n how='left', on=['plant_id_eia', 'report_date'])\n # We need to retain the different energy_source information from the\n # generators (primary for the generator) and the fuel receipts (which is\n # per-delivery), and in the one_fuel case, there will only be a single\n # generator getting all of the fuels:\n one_fuel.rename(columns={'fuel_type_pudl_x': 'ftp_gen',\n 'fuel_type_pudl_y': 'ftp_frc'},\n inplace=True)\n\n # Do the same thing for the multi fuel plants, but also merge based on\n # the different fuel types within the plant, so that we keep that info\n # as separate records:\n multi_fuel = pd.merge(multi_fuel,\n frc_eia923[['plant_id_eia', 'report_date',\n 'fuel_cost_per_mmbtu',\n 'fuel_type_pudl']],\n how='left', on=['plant_id_eia', 'report_date',\n 'fuel_type_pudl'])\n\n # At this point, within each plant, we should have one record per\n # combination of generator & fuel type, which includes the heat rate of\n # each generator, as well as *plant* level fuel cost per unit heat input\n # for *each* fuel, which we can combine to figure out the fuel cost per\n # unit net electricity generation on a generator basis.\n\n # We have to do these calculations separately for the single and multi-fuel\n # plants because in the case of the one fuel plants we need to sum up all\n # of the fuel costs -- including both primary and secondary fuel\n # consumption -- whereas in the multi-fuel plants we are going to look at\n # fuel costs on a per-fuel basis (this is very close to being correct,\n # since secondary fuels are typically a fraction of a percent of the\n # plant's overall costs).\n\n one_fuel_gb = one_fuel.groupby(by=['report_date', 'plant_id_eia'])\n one_fuel_agg = one_fuel_gb.agg({\n 'total_fuel_cost': np.sum,\n 'total_heat_content_mmbtu': np.sum\n })\n one_fuel_agg['fuel_cost_per_mmbtu'] = \\\n one_fuel_agg['total_fuel_cost'] / \\\n one_fuel_agg['total_heat_content_mmbtu']\n one_fuel_agg = one_fuel_agg.reset_index()\n one_fuel = pd.merge(one_fuel[['plant_id_eia', 'report_date',\n 'generator_id', 'heat_rate_mmbtu_mwh']],\n one_fuel_agg[['plant_id_eia', 'report_date',\n 'fuel_cost_per_mmbtu']],\n on=['plant_id_eia', 'report_date'])\n one_fuel = one_fuel.drop_duplicates(\n subset=['plant_id_eia', 'report_date', 'generator_id'])\n\n multi_fuel = multi_fuel[['plant_id_eia', 'report_date', 'generator_id',\n 'fuel_cost_per_mmbtu', 'heat_rate_mmbtu_mwh']]\n\n fuel_cost = one_fuel.append(multi_fuel)\n fuel_cost['fuel_cost_per_mwh'] = \\\n fuel_cost['fuel_cost_per_mmbtu'] * fuel_cost['heat_rate_mmbtu_mwh']\n fuel_cost = \\\n fuel_cost.sort_values(['report_date', 'plant_id_eia', 'generator_id'])\n\n out_df = gen_w_ft.drop('heat_rate_mmbtu_mwh', axis=1)\n out_df = pd.merge(out_df.drop_duplicates(), fuel_cost,\n on=['report_date', 'plant_id_eia', 'generator_id'])\n\n return(out_df)","title":""},{"docid":"b3d131974bf26ee9185fac8b921cba5e","score":"0.43875495","text":"def test_detail_return(self):\n resp = self.c.get('/minerals/1/')\n self.assertIsNotNone(resp.context.get('mineral'))\n self.assertEqual(resp.status_code, 200)\n self.assertEqual(resp.context.get('mineral').get('name'), 'Abelsonite')","title":""},{"docid":"7a33d0506c0c0236d97751c60eac2ab8","score":"0.4380622","text":"def get_reward(self):\n reward = 0\n for health in self.asset_healths:\n if health < 0.75:\n reward = -1 * math.exp(3 * (0.75 - health))\n reward -= self.maintenanceCost\n return reward","title":""},{"docid":"478c472b1c18f374d01220eceb811ea0","score":"0.4379156","text":"def get_restaurant(self, restaurant_id):\n if restaurant_id not in self.restaurants: return\n return self.restaurants[restaurant_id]","title":""},{"docid":"a3d7938428c2129371b48b716e05dafb","score":"0.43633485","text":"def rewardFunction(self):\n logging.warning(\"rewardFunction not defined!\")\n return 0","title":""},{"docid":"aa369dbfd62378faee925ab706652aff","score":"0.43599656","text":"def get_reward(self, state, sim):\n # reward = self.get_staggered_reward_altitude_and_heading_with_negatives(\n # state, sim, numPositiveAltitudeStaggerLevels=25,\n # numPositiveHeadingStaggerLevels=25, altitudeWorth=0.5,\n # headingWorth=0.5, numNegativeAltitudeStaggerLevels=25,\n # numNegativeHeadingStaggerLevels=25, penaltyMultiplier=0.1)\n reward = self.get_staggered_reward_altitude_and_heading(state, sim,\n numAltitudeStaggerLevels=10, numHeadingStaggerLevels=10,\n headingWorth=0.5, altitudeWorth=0.5)\n\n self.stepCount += 1\n self.simTime = sim.get_property_value(c.simulation_sim_time_sec)\n\n return reward","title":""},{"docid":"3ef236dace19179d840d8175846989ef","score":"0.43581444","text":"def getStudentByName(self, request):\n\t\tN = Name.objects.get(preferredName = request['preferredName'])\n\t\tS = Student.objects.get(name = N)\n\t\t# rollNoList = []\n\t\t# for n in N:\n\t\t#\tS = Student.objects.filter(name = n)\n\t\t#\tfor s in S:\n\t\t#\t\trollNoList.append(s.rollNo)\n\t\treturn S","title":""},{"docid":"8cd3bb383be69e31079f40285a36b7d9","score":"0.4357834","text":"def fort_details(bot, fort_id, latitude, longitude):\n\n if fort_id not in FORT_CACHE:\n \"\"\"\n Lookup the fort details and cache the response for future use.\n \"\"\"\n request = bot.api.create_request()\n request.fort_details(fort_id=fort_id, latitude=latitude, longitude=longitude)\n try:\n response_dict = request.call()\n FORT_CACHE[fort_id] = response_dict['responses']['FORT_DETAILS']\n except Exception:\n pass\n\n # Just to avoid KeyErrors\n return FORT_CACHE.get(fort_id, {})","title":""},{"docid":"c92749640c35baabcb20f91f144d8267","score":"0.43537796","text":"def get(self, penalty_id):\n le_penalty = get_an_penalty(penalty_id)\n if not le_penalty:\n return {'success': False, 'message': 'penalty not found'}\n else:\n return le_penalty","title":""},{"docid":"b8d408240a63d0b562d75fe477bcc861","score":"0.4353404","text":"def getReward(self, gameState, action):\n successor = self.getSuccessor(gameState, action)\n currentCarrying = gameState.getAgentState(self.index).numCarrying\n reward = self.reward\n currentPos = gameState.getAgentState(self.index).getPosition()\n nextPos = successor.getAgentState(self.index).getPosition()\n\n nextDistance = 99999\n for pos in range(len(self.boundary)):\n distance = self.agent.getMazeDistance(nextPos, self.boundary[pos])\n if (nextDistance > distance):\n nextDistance = distance\n\n currentDistance = 99999\n for pos in range(len(self.boundary)):\n distance = self.agent.getMazeDistance(currentPos, self.boundary[pos])\n if (currentDistance > distance):\n currentDistance = distance\n\n move = self.agent.getMazeDistance(currentPos, nextPos)\n # eaten\n if move > 1:\n if currentCarrying > 0:\n reward = -20 * currentCarrying\n else:\n reward = -100\n else:\n if currentDistance > nextDistance:\n reward = 5 * currentCarrying\n if currentCarrying > 0:\n reward = 100 * currentCarrying\n\n return reward","title":""},{"docid":"6820baf1aaa734fc712e3cdb944cd821","score":"0.43472552","text":"def get_genre_id_by_name(genre_name):\r\n\r\n genre = Genre.query.filter(Genre.name == genre_name).first() \r\n\r\n return genre.id","title":""},{"docid":"6798a7125432345d7481f85e1714f468","score":"0.43407738","text":"def _get_grades(self):\n if (self.target_object):\n json_data = self.argsDict.get(\"encoded_data\",\"\")\n grade_handler = \\\n SchoolDB.assistant_classes.AjaxGetGradeHandler(\n self.target_object, json_data)\n successful, self.return_string = \\\n grade_handler.service_request()\n if (not successful):\n self.error_string = \"Grade load failed\"\n raise AjaxError, self.error_string\n else:\n self.error_string = \"Incorrect or missing class session db key.\"\n raise AjaxError, self.error_string","title":""},{"docid":"b9f1d95d8f809b8c4c6dbaa4a4a02391","score":"0.43302628","text":"def get_recipe_by_name(self, name):\n for _, recipe in self.recipe_list.items():\n if recipe.name == name:\n print(recipe)\n return recipe","title":""},{"docid":"d245288213622e92c875b2b72d0ad9a2","score":"0.43282163","text":"def giveReward(self, rewardName):\n if rewardName in self.rewardDict:\n sleepTime = self.rewardDict.get(rewardName)\n else:\n sleepTime = self.rewardDict.get('default')\n GPIO.output(self.rewardPin, 1)\n sleep(sleepTime) # not very accurate timing, but good enough\n GPIO.output(self.rewardPin, 0)\n self.totalsDict[rewardName] += 1\n return sleepTime","title":""},{"docid":"04827169500554c292a96cdcbb991d01","score":"0.43266624","text":"def _get_reward(self):\n current_state = self.env.getState()\n #print(\"State =\",current_state)\n #print(\"len State =\",len(current_state))\n ball_proximity = current_state[53]\n goal_proximity = current_state[15]\n ball_dist = 1.0 - ball_proximity\n goal_dist = 1.0 - goal_proximity\n kickable = current_state[12]\n ball_ang_sin_rad = current_state[51]\n ball_ang_cos_rad = current_state[52]\n ball_ang_rad = math.acos(ball_ang_cos_rad)\n if ball_ang_sin_rad < 0:\n ball_ang_rad *= -1.\n goal_ang_sin_rad = current_state[13]\n goal_ang_cos_rad = current_state[14]\n goal_ang_rad = math.acos(goal_ang_cos_rad)\n if goal_ang_sin_rad < 0:\n goal_ang_rad *= -1.\n alpha = max(ball_ang_rad, goal_ang_rad) - min(ball_ang_rad, goal_ang_rad)\n ball_dist_goal = math.sqrt(ball_dist*ball_dist + goal_dist*goal_dist -\n 2.*ball_dist*goal_dist*math.cos(alpha))\n # Compute the difference in ball proximity from the last step\n if not self.first_step:\n ball_prox_delta = ball_proximity - self.old_ball_prox\n kickable_delta = kickable - self.old_kickable\n ball_dist_goal_delta = ball_dist_goal - self.old_ball_dist_goal\n self.old_ball_prox = ball_proximity\n self.old_kickable = kickable\n self.old_ball_dist_goal = ball_dist_goal\n #print(self.env.playerOnBall())\n #print(self.env.playerOnBall().unum)\n #print(self.env.getUnum())\n reward = 0\n if not self.first_step:\n '''# Reward the agent for moving towards the ball\n reward += ball_prox_delta\n if kickable_delta > 0 and not self.got_kickable_reward:\n reward += 1.\n self.got_kickable_reward = True\n # Reward the agent for kicking towards the goal\n reward += 0.6 * -ball_dist_goal_delta\n # Reward the agent for scoring\n if self.status == hfo_py.GOAL:\n reward += 5.0'''\n '''reward = self.__move_to_ball_reward(kickable_delta, ball_prox_delta) + \\\n 3. * self.__kick_to_goal_reward(ball_dist_goal_delta) + \\\n self.__EOT_reward();'''\n mtb = self.__move_to_ball_reward(kickable_delta, ball_prox_delta)\n ktg = 3. * self.__kick_to_goal_reward(ball_dist_goal_delta)\n eot = self.__EOT_reward()\n reward = mtb + ktg + eot\n #print(\"mtb: %.06f ktg: %.06f eot: %.06f\"%(mtb,ktg,eot))\n \n self.first_step = False\n #print(\"r =\",reward)\n return reward","title":""},{"docid":"8dfe3f2b94bad1109ef8b861a1985681","score":"0.43243834","text":"def get_fedex_shipping_cost(self):\n Currency = Pool().get('currency.currency')\n\n fedex_credentials = self.carrier.get_fedex_credentials()\n\n if not all([\n self.fedex_drop_off_type, self.fedex_packaging_type,\n self.fedex_service_type\n ]):\n self.raise_user_error('fedex_settings_missing')\n\n rate_request = RateService(fedex_credentials)\n requested_shipment = rate_request.RequestedShipment\n\n requested_shipment.DropoffType = self.fedex_drop_off_type.value\n requested_shipment.ServiceType = self.fedex_service_type.value\n requested_shipment.PackagingType = self.fedex_packaging_type.value\n requested_shipment.PreferredCurrency = self.cost_currency.code\n\n # Shipper and Recipient\n requested_shipment.Shipper.AccountNumber = \\\n fedex_credentials.AccountNumber\n # From location is the warehouse location. So it must be filled.\n if not self.warehouse.address:\n self.raise_user_error('warehouse_address_required')\n self.warehouse.address.set_fedex_address(requested_shipment.Shipper)\n self.delivery_address.set_fedex_address(requested_shipment.Recipient)\n\n # Shipping Charges Payment\n shipping_charges = requested_shipment.ShippingChargesPayment\n shipping_charges.PaymentType = 'SENDER'\n shipping_charges.Payor.ResponsibleParty = requested_shipment.Shipper\n\n # Express Freight Detail\n fright_detail = requested_shipment.ExpressFreightDetail\n fright_detail.PackingListEnclosed = 1\n fright_detail.ShippersLoadAndCount = 2\n fright_detail.BookingConfirmationNumber = 'Ref-%s' % self.reference\n\n # Customs Clearance Detail\n self.get_fedex_customs_details(rate_request)\n\n # Label Specification\n requested_shipment.LabelSpecification.LabelFormatType = 'COMMON2D'\n requested_shipment.LabelSpecification.ImageType = 'PNG'\n requested_shipment.LabelSpecification.LabelStockType = 'PAPER_4X6'\n\n requested_shipment.RateRequestTypes = ['ACCOUNT']\n\n self.get_fedex_items_details(rate_request)\n\n try:\n response = rate_request.send_request(int(self.id))\n except RequestError, exc:\n self.raise_user_error(\n 'fedex_shipping_cost_error', error_args=(exc.message, )\n )\n\n currency, = Currency.search([\n ('code', '=', str(\n response.RateReplyDetails[0].RatedShipmentDetails[0].\n ShipmentRateDetail.TotalNetCharge.Currency\n ))\n ])\n\n return Decimal(str(\n response.RateReplyDetails[0].RatedShipmentDetails[0].ShipmentRateDetail.TotalNetCharge.Amount # noqa\n )), currency.id","title":""},{"docid":"844947cba443f3009cb3496bd5e278e3","score":"0.43232056","text":"def get_recipe_by_name(recipe_name):\r\n\r\n return Recipes.query.get(recipe_name)","title":""},{"docid":"c4155a3ccfed1dd03c723703b35d4d97","score":"0.43177205","text":"def flesch_grade (index):\n grades = [ (100, 90), (89, 80), (79, 70), (69, 65), (64, 50), (49, 30), (30, 0)]\n grade_level = ['5th grader', '6th grader', '7th grader', '8th grader', 'High school student', \\\n 'College student', 'College graduate', 'Law school graduate']\n i = 0\n if index > 100:\n return grade_level [0]\n elif index < 0:\n return grade_level [-1]\n while i < len (grades):\n if index <= grades [i] [0] and index >= grades [i] [1]:\n return grade_level [i]\n else:\n i += 1","title":""},{"docid":"ab4545b2f96e08737bdbe61524dfe5fb","score":"0.43112177","text":"def get_info(self):\n\t\tprint(self.name)\n\t\tprint(\"Grants a bonus of {} to SKILL.\\nGrants a bonus of {} to WILL.\\n\".format(self.skill_bonus, self.will_bonus))\n\t\treturn","title":""},{"docid":"6ea00b4f386339a0d0d1d62ef1185e4a","score":"0.43094763","text":"def get_reward_list(self):\n return self._gameRewards","title":""}],"string":"[\n {\n \"docid\": \"48ee6941c505a3dd30e77f5efa10c4aa\",\n \"score\": \"0.74738145\",\n \"text\": \"def GetFuelRewardsById(FuelRewardId=\\\"1\\\", RewardName=\\\"FuelReward1\\\"):\\n result = \\\"\\\"\\n if (FuelRewardId == \\\"\\\" and RewardName == \\\"\\\"):\\n log.info(\\\"FuelRewardId or RewardName must be not empty\\\")\\n return result\\n\\n result = Loyalty().GetFuelRewardsById(FuelRewardId, RewardName)\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a6fce3f133976e6cfdac0b44435e5f07\",\n \"score\": \"0.64665705\",\n \"text\": \"def GetNonFuelRewardsById(NonfuelRewardId=\\\"1\\\", RewardName=\\\"15 cents DryStock\\\"):\\n result = \\\"\\\"\\n if (NonfuelRewardId == \\\"\\\" and RewardName == \\\"\\\"):\\n log.info(\\\"NonfuelRewardId or RewardName must be not empty\\\")\\n return result\\n\\n result = Loyalty().GetNonFuelRewardsById(NonfuelRewardId, RewardName)\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5dba9aa7b0e1bfc926245451db7b5ee8\",\n \"score\": \"0.60220605\",\n \"text\": \"def AssignFuelReward(LoyatyID=\\\"1\\\", FuelID=\\\"1\\\", RewardName=\\\"15 cents DryStock\\\"):\\n result = False\\n\\n if (FuelID == \\\"\\\" and RewardName == \\\"\\\"):\\n log.info(\\\"FuelID or RewardName must be not empty\\\")\\n return result\\n\\n result = Loyalty().AssignFuelReward(LoyatyID, FuelID, RewardName)\\n if (result == 'true'):\\n result = True\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d7449d58c2895ccf6651bb7fc9e67ecf\",\n \"score\": \"0.5910867\",\n \"text\": \"def GetTransactionRewardsById(TransLevelRewardID=\\\"1\\\", RewardName=\\\"20 cents Trans.Rwd\\\"):\\n result = \\\"\\\"\\n if (TransLevelRewardID == \\\"\\\" and RewardName == \\\"\\\"):\\n log.info(\\\"TransLevelRewardID or RewardName must be not empty\\\")\\n return result\\n\\n result = Loyalty().GetTransactionRewardsById(TransLevelRewardID, RewardName)\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c2d8445e8bc3849662eb47674a4d9ab9\",\n \"score\": \"0.5756781\",\n \"text\": \"def AddFuelRewards(RewardName=\\\"FuelReward1\\\", TypeReward=\\\"Instant\\\", TypeRewardDescription=\\\"\\\", DiscountMethod=\\\"amountOffPPU\\\", RewardLimit=\\\"30\\\", ReceiptText=\\\"25 cents Discount\\\", FuelGrades=\\\"001:0.25, 002:0.25, 003:0.25\\\"):\\n result = \\\"\\\"\\n if not(TypeReward == \\\"Instant\\\" or TypeReward == \\\"Optional\\\"):\\n log.info(\\\"TypeReward (string) must be - {'Instant', 'Optional'}\\\")\\n return result\\n if not(DiscountMethod == \\\"amountOffPPU\\\" or DiscountMethod == \\\"newPrice\\\"):\\n log.info(\\\"DiscountMethod (string) - should be - {'amountOffPPU', 'newPrice'}\\\")\\n return result\\n\\n result = Loyalty().AddFuelRewards(RewardName, TypeReward, TypeRewardDescription, DiscountMethod, RewardLimit, ReceiptText, FuelGrades)\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"932b4c5ef5ce6caf6ad844008aad0734\",\n \"score\": \"0.57084525\",\n \"text\": \"def _getReward(self, a, s=None):\\n raise NotImplementedError(\\\"_getReward should be implemented in child classes\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cbe8e27e1e78265832c077d4561a451a\",\n \"score\": \"0.5655711\",\n \"text\": \"def UpdateFuelRewards(FuelRewardId=\\\"1\\\", RewardName=\\\"FuelReward1\\\", TypeReward=\\\"Instant\\\", TypeRewardDescription=\\\"\\\", DiscountMethod=\\\"amountOffPPU\\\", RewardLimit=\\\"30\\\", ReceiptText=\\\"25 cents Discount\\\", FuelGrades=\\\"001:0.25, 002:0.25, 003:0.25\\\"):\\n result = False\\n if not(TypeReward == \\\"Instant\\\" or TypeReward == \\\"Optional\\\"):\\n log.info(\\\"TypeReward (string) must be - {'Instant', 'Optional'}\\\")\\n return result\\n if not(DiscountMethod == \\\"amountOffPPU\\\" or DiscountMethod == \\\"newPrice\\\"):\\n log.info(\\\"DiscountMethod (string) - should be - {'amountOffPPU', 'newPrice'}\\\")\\n return result\\n\\n result = Loyalty().UpdateFuelRewards(FuelRewardId, RewardName, TypeReward, TypeRewardDescription, DiscountMethod, RewardLimit, ReceiptText, FuelGrades)\\n if (result == 'true'):\\n result = True\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"76e8636dea09004afdaa40e1334a8a48\",\n \"score\": \"0.563607\",\n \"text\": \"def GetFuelRewardsList():\\n result = Loyalty().GetFuelRewardsList()\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a47c4a604c38416399583c88efb515c8\",\n \"score\": \"0.5306753\",\n \"text\": \"def _get_reward(self):\\n if self.reward_function == 'play':\\n return self._play_reward()\\n elif self.reward_function == 'rules':\\n return self._rules_reward()\\n else:\\n logger.error(\\\"Please specify a valid reward function. Choosing play reward now!\\\")\\n return self._play_reward()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ed54a8cc70b5a035430e2b5ee361c34\",\n \"score\": \"0.523644\",\n \"text\": \"def _get_reward(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0567aea06c7bd049d8a476466d4d36e2\",\n \"score\": \"0.5212868\",\n \"text\": \"def AddFuelGrades(FuelRewardId, FuelGrades=\\\"001:0.25, 002:0.25, 003:0.25\\\"):\\n result = False\\n result = Loyalty().AddFuelGrades(FuelRewardId, FuelGrades)\\n if (result == 'true'):\\n result = True\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d8c7a7776e39a70c546461231d415c49\",\n \"score\": \"0.5155305\",\n \"text\": \"def UnAssignFuelReward(LoyatyID=\\\"1\\\", FuelID=\\\"1\\\", RewardName=\\\"15 cents DryStock\\\"):\\n result = False\\n\\n if (FuelID == \\\"\\\" and RewardName == \\\"\\\"):\\n log.info(\\\"FuelID or RewardName must be not empty\\\")\\n return result\\n\\n result = Loyalty().UnAssignFuelReward(LoyatyID, FuelID, RewardName)\\n if (result == 'true'):\\n result = True\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"86a7ef58dd0767e33c811091f127b849\",\n \"score\": \"0.51505774\",\n \"text\": \"def get_reward_info(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c78946902449bf1c1fa6f4ecebfe716\",\n \"score\": \"0.5148206\",\n \"text\": \"def get(self, *args, **kwargs):\\n reward = self.reward\\n if self.cb_get is not None:\\n reward = self.cb_get(self.reward, *args, **kwargs)\\n\\n if self.activate_bonus:\\n reward = max(self.reward + self.bonus, self.rmax)\\n return reward\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d05bcceba1966187e98a7969bbe61478\",\n \"score\": \"0.5139826\",\n \"text\": \"def getFuel(self):\\n return getItem(4)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81175436ca1ae7a98dff6728aaa9eeaf\",\n \"score\": \"0.5052786\",\n \"text\": \"def AddNonFuelReward(RewardName=\\\"15 cents DryStock\\\", TypeReward=\\\"Optional\\\", TypeRewardDescription=\\\"R u Sure?\\\", DiscountMethod=\\\"amountOff\\\", Discount=\\\"0.15\\\", ItemType=\\\"merchandiseLine\\\", CodeFormat=\\\"plu\\\", CodeValue=\\\"1234\\\", RewardLimit=\\\"10.10\\\", ReceiptText=\\\"Dry stock 10 cents amount off\\\"):\\n result = \\\"\\\"\\n if not(TypeReward == \\\"Instant\\\" or TypeReward == \\\"Optional\\\"):\\n log.info(\\\"TypeReward (string) must be - {'Instant', 'Optional'}\\\")\\n return result\\n if not(DiscountMethod == \\\"amountOff\\\" or DiscountMethod == \\\"percentOff\\\" or DiscountMethod == \\\"newPrice\\\"):\\n log.info(\\\"DiscountMethod (string) - should be - {'amountOff', 'percentOff', 'newPrice'}\\\")\\n return result\\n if not(ItemType == \\\"itemLine\\\" or ItemType == \\\"merchandiseLine\\\"):\\n log.info(\\\"ItemType (string) - should be - {'itemLine', 'merchandiseLine'}\\\")\\n return result\\n if not(CodeFormat == \\\"upcA\\\" or CodeFormat == \\\"upcA\\\" or CodeFormat == \\\"upcE\\\" or CodeFormat == \\\"ean8\\\" or CodeFormat == \\\"ean13\\\" or CodeFormat == \\\"plu\\\" or CodeFormat == \\\"gtin\\\" or CodeFormat == \\\"rss14\\\" or CodeFormat == \\\"none\\\"):\\n log.info(\\\"CodeFormat (string) - should be - {'upcA', 'upcE', 'ean8', 'ean13', 'plu', 'gtin', 'rss14', 'none'}\\\")\\n return result\\n\\n result = Loyalty().AddNonFuelReward(RewardName, TypeReward, TypeRewardDescription, DiscountMethod, Discount, ItemType, CodeFormat, CodeValue, RewardLimit, ReceiptText)\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f9e3e9bd6a7d114e11bed434ebf43356\",\n \"score\": \"0.50421715\",\n \"text\": \"def get_genre(cls, genre_name):\\n\\n try:\\n genre = Genre.query.filter_by(genre=genre_name).one()\\n except NoResultFound:\\n print \\\"No genre instance was found with that name.\\\"\\n\\n return genre\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0dc829a9524d0ce2b9ecbe0ece942bec\",\n \"score\": \"0.50084245\",\n \"text\": \"def get_reward(self, physics):\\n return self._get_reward(physics, sparse=self._sparse)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58732be00301516907851a671a06c6d8\",\n \"score\": \"0.4999932\",\n \"text\": \"def getReward(self, s):\\n cell = self.grid[s]\\n if type(cell) == int or type(cell) == float:\\n return float(cell)\\n return self.livingReward\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d20616b024cccdb09bf24723ded31ea5\",\n \"score\": \"0.49980584\",\n \"text\": \"def get(organisation_id, grade_id):\\n grade = Grade.query.get_or_404(str(grade_id))\\n\\n return Response(repr(grade), mimetype=\\\"application/json\\\", status=200)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c7808db06ac0a0da7e3b1e206cdf313\",\n \"score\": \"0.4980085\",\n \"text\": \"def UpdateNonFuelReward(NonfuelRewardId=\\\"1\\\", RewardName=\\\"15 cents DryStock\\\", TypeReward=\\\"Optional\\\", TypeRewardDescription=\\\"R u Sure?\\\", DiscountMethod=\\\"amountOff\\\", Discount=\\\"0.15\\\", ItemType=\\\"merchandiseLine\\\", CodeFormat=\\\"plu\\\", CodeValue=\\\"1234\\\", RewardLimit=\\\"10.10\\\", ReceiptText=\\\"Dry stock 10 cents amount off\\\"):\\n result = False\\n if not(TypeReward == \\\"Instant\\\" or TypeReward == \\\"Optional\\\"):\\n log.info(\\\"TypeReward (string) must be - {'Instant', 'Optional'}\\\")\\n return result\\n if not(DiscountMethod == \\\"amountOff\\\" or DiscountMethod == \\\"percentOff\\\" or DiscountMethod == \\\"newPrice\\\"):\\n log.info(\\\"DiscountMethod (string) - should be - {'amountOff', 'percentOff', 'newPrice'}\\\")\\n return result\\n if not(ItemType == \\\"itemLine\\\" or ItemType == \\\"merchandiseLine\\\"):\\n log.info(\\\"ItemType (string) - should be - {'itemLine', 'merchandiseLine'}\\\")\\n return result\\n if not(CodeFormat == \\\"upcA\\\" or CodeFormat == \\\"upcA\\\" or CodeFormat == \\\"upcE\\\" or CodeFormat == \\\"ean8\\\" or CodeFormat == \\\"ean13\\\" or CodeFormat == \\\"plu\\\" or CodeFormat == \\\"gtin\\\" or CodeFormat == \\\"rss14\\\" or CodeFormat == \\\"none\\\"):\\n log.info(\\\"CodeFormat (string) - should be - {'upcA', 'upcE', 'ean8', 'ean13', 'plu', 'gtin', 'rss14', 'none'}\\\")\\n return result\\n\\n result = Loyalty().UpdateNonFuelReward(NonfuelRewardId, RewardName, TypeReward, TypeRewardDescription, DiscountMethod, Discount, ItemType, CodeFormat, CodeValue, RewardLimit, ReceiptText)\\n if (result == 'true'):\\n result = True\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ddbda1c1632579b94c968ed5f84414e5\",\n \"score\": \"0.49531397\",\n \"text\": \"def get(self, mf_credit_id):\\n le_mf_credit = get_an_mf_credit(mf_credit_id)\\n if not le_mf_credit:\\n return {'success': False, 'message': 'mf_credit not found'}\\n else:\\n return le_mf_credit\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5893b1302a8af88dec197f8d60a69c99\",\n \"score\": \"0.48749417\",\n \"text\": \"def reward(self, s = None):\\n s = self.state(as_tuple=True) if s == None else s\\n s = s if isinstance(s, tuple) else self.legal_states[s]\\n return self.rewards[s]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2d56aca9453bb752152034204c872d0\",\n \"score\": \"0.48682547\",\n \"text\": \"def DeleteFuelReward(FuelRewardId=\\\"1\\\"):\\n result = False\\n result = Loyalty().DeleteFuelReward(FuelRewardId)\\n if (result == 'true'):\\n result = True\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37970c1b6dbd8c886b8e5b8a00a9aa33\",\n \"score\": \"0.4867022\",\n \"text\": \"def _get_reward(self):\\n print('get reward mai hai ham')\\n\\n # reward for speed tracking\\n v = self.ego.get_velocity()\\n speed = np.sqrt(v.x**2 + v.y**2)\\n r_speed = -abs(speed - self.desired_speed)\\n\\n # reward for collision\\n r_collision = 0\\n if len(self.collision_hist) > 0:\\n r_collision = -1\\n\\n # reward for steering:\\n r_steer = -self.ego.get_control().steer**2\\n\\n # reward for out of lane\\n ego_x, ego_y = get_pos(self.ego)\\n dis, w = get_lane_dis(self.waypoints, ego_x, ego_y)\\n r_out = 0\\n if abs(dis) > self.out_lane_thres:\\n r_out = -1\\n\\n # longitudinal speed\\n lspeed = np.array([v.x, v.y])\\n lspeed_lon = np.dot(lspeed, w)\\n\\n # cost for too fast\\n r_fast = 0\\n if lspeed_lon > self.desired_speed:\\n r_fast = -1\\n\\n # cost for lateral acceleration\\n r_lat = - abs(self.ego.get_control().steer) * lspeed_lon**2\\n\\n r = 200*r_collision + 1*lspeed_lon + 10 * \\\\\\n r_fast + 1*r_out + r_steer*5 + 0.2*r_lat - 0.1\\n\\n return r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c8d5d0868341fc6898adde4b6c2a25d\",\n \"score\": \"0.4814422\",\n \"text\": \"def get(id):\\n params = rest.get_params(_spec)\\n obj = Supplier.query.get_or_404(id)\\n filtered = rest.filter_fields(obj.query, params)\\n return jsonify(rest.to_dict(obj, filtered))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"68ca786cdcf3e6b115d5b7d165c073f6\",\n \"score\": \"0.48093444\",\n \"text\": \"def get_reward(self):\\n return self.reward\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51c739b6297630efbd5df7f7b445f064\",\n \"score\": \"0.48086652\",\n \"text\": \"def reward_info(user):\\n reward = Reward.query.get(user)\\n\\n if not reward:\\n raise NotFound\\n\\n serialized_object = reward_schema.dumps(reward, sort_keys=True, indent=4)\\n\\n return Response(\\n response=serialized_object,\\n status=http_status.OK,\\n mimetype=\\\"application/json\\\"\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a17732c4ae1a7ad51d3358a36cdf7a4\",\n \"score\": \"0.4802178\",\n \"text\": \"def detail(self, request, pk=None):\\n queryset = Supplier.objects.get(pk=pk)\\n data = SupplierDetailSerializer(queryset).data\\n return Response(data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a019cec2446875d401dbd439e5a168a\",\n \"score\": \"0.4793602\",\n \"text\": \"def test_grade_retrieve(slu1, student, student2):\\n Grade.objects.create(\\n user=student,\\n unit=slu1,\\n status=\\\"graded\\\",\\n score=16,\\n )\\n Grade.objects.create(\\n user=student,\\n unit=slu1,\\n status=\\\"graded\\\",\\n score=14,\\n )\\n Grade.objects.create(\\n user=student, unit=slu1, status=\\\"graded\\\", score=20, on_time=False\\n )\\n\\n assert get_last_grade(slu1, student).score == 20\\n assert get_best_grade(slu1, student).score == 16\\n\\n assert get_last_grade(slu1, student2).score == None\\n assert get_best_grade(slu1, student2).score == None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a773d609f81882930613411904af1ef0\",\n \"score\": \"0.478699\",\n \"text\": \"def get_rewards():\\n episode = int(request.args.get('user'))\\n rewards = data_preprocessor.get_scalar_values_by_tag(\\n \\\"reward-e\\\"+str(episode))\\n return rewards, 200, JSON_TYPE\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b75b96742af1d982fbc47a9aa2f12b5\",\n \"score\": \"0.47646138\",\n \"text\": \"def _get_reward(ver_dict, stance, debate_method, reward_method) -> torch.Tensor:\\n debate_method = debate_method.lower() # Evaluate Oracle rewards too\\n j_score = ver_dict[reward_method]\\n if debate_method in {'a', 'r'}:\\n rewards = j_score\\n elif debate_method in {'b'}:\\n rewards = (1. - j_score)\\n elif debate_method in {'e', 'l', 'w', 'ⅰ', 'ⅱ', 'ⅲ', 'ⅳ'}:\\n rewards = (ver_dict['prob_dist'] * stance.to(ver_dict['prob_dist'])).sum(dim=1)\\n else:\\n raise NotImplementedError('_get_reward not implemented for debate_method', debate_method)\\n return rewards.detach() # Judge shouldn't get gradients through j_score, used to reward debaters\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bdd5658790de512de85be9379366c704\",\n \"score\": \"0.4743473\",\n \"text\": \"def get_reward(self, state, action, next_state):\\n return self.model_reward.get((state, action, next_state), (0, 0))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bdd5658790de512de85be9379366c704\",\n \"score\": \"0.4743473\",\n \"text\": \"def get_reward(self, state, action, next_state):\\n return self.model_reward.get((state, action, next_state), (0, 0))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c6af3459308335d253ece9ff3e750f5\",\n \"score\": \"0.4736354\",\n \"text\": \"def get_grade_by_id(user_id):\\n\\n return Grade.query.filter(Grade.user_id == user_id).all()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c34ba50471ecbc73a642ff0508ce841\",\n \"score\": \"0.47319853\",\n \"text\": \"def getReward(self, gameState):\\n score = 0\\n # rewards\\n # add if we eat the ghost\\n score += 1.25 if self.ateFood(gameState) else 0\\n score += self.getScoreIncrease(gameState)\\n # punishment\\n score -= self.numFoodCarrying if self.checkDeath(gameState) else 0\\n foodList = self.getFood(gameState).asList()\\n if len(foodList) > 0:\\n minDistance = min([self.getMazeDistance(gameState.getAgentState(\\n self.index).getPosition(), food) for food in foodList])\\n score += np.reciprocal(float(minDistance)) # distance to closest food\\n # if the game is over big reward if we win, else penalty if we lose\\n if gameState.isOver():\\n score += self.getScore(gameState)*2\\n return score\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17fcfe317a419e8979cc06fd1e8a6c34\",\n \"score\": \"0.47302\",\n \"text\": \"def reward(self):\\n return self._reward\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aed91178f872cb79606cadab31c704b6\",\n \"score\": \"0.47075465\",\n \"text\": \"def get_reward(self, st):\\n if st in self._rewards:\\n return self._rewards[st]\\n # print(\\\"Warning. Could not find state {} in rewards dictionary. Returning 0 reward.\\\")\\n # set default reward as zero\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d6238e4a8f4990367259e5482573818\",\n \"score\": \"0.4705804\",\n \"text\": \"def info_from_name(ref_df, ref_dict, name):\\n\\n # get first id from name\\n recipe_id = list(ref_dict.keys())[list(ref_dict.values()).index(name)]\\n # filter df with id\\n sub_df = ref_df[ref_df.id == recipe_id]\\n\\n # return name, url, weight_list, ingredient_list, impact_list, total_impact\\n recipe_name = sub_df.title_raw.iloc[0]\\n recipe_url = sub_df.url.iloc[0]\\n recipe_impact = sum(sub_df.ingredient_impact)\\n ingredient_weights = list(sub_df.weight_per_ingr)\\n ingredient_names = list(sub_df.ingredient_raw)\\n ingredient_impacts = list(sub_df.ingredient_impact)\\n\\n return recipe_name, recipe_url, recipe_impact, ingredient_weights, ingredient_names, ingredient_impacts\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f8e25b1aea4aebce86812e5a2a6787fb\",\n \"score\": \"0.46987453\",\n \"text\": \"def grade(self, grade):\\n return self._db[grade]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ba77915592607266394d5fc264fc559\",\n \"score\": \"0.46956807\",\n \"text\": \"def reward(s, rewards={' ': 0, 'w': None, 't': trapReward, 'r': treasureReward}):\\n return rewards[board[s]]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ffb385d644e980bbd90d018ed996214\",\n \"score\": \"0.46879724\",\n \"text\": \"def get_grade(self, grading_instance_key, grading_event_id=1):\\n grade_info = self.get_grade_info()\\n grade_value = grade_info.get_grade(grading_instance_key, \\\\\\n grading_event_id)\\n return grade_value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24463dff589e2e8bdefed3e3a4602a68\",\n \"score\": \"0.4677687\",\n \"text\": \"def get_grade(self):\\n return # osid.grading.Grade\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e406b65bb273ca42ed536950cb37a388\",\n \"score\": \"0.4653771\",\n \"text\": \"def _get_reward(self):\\n\\n return self.calc_reward()\\n # TODO: generalize for different network\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38993f5398b1816e4d9cf368c342d5fa\",\n \"score\": \"0.46492845\",\n \"text\": \"def extract_reward(srew, arew):\\n if srew >= arew:\\n reward = 1\\n else:\\n reward = -1\\n return reward\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d5e482aa7bc2854e3cac513c7808acd\",\n \"score\": \"0.46311963\",\n \"text\": \"def get_score(student, assessment_name):\\n return dict_get(student.scores, assessment_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e0ef4eb8c8b8a331688aa754194af7f\",\n \"score\": \"0.46127132\",\n \"text\": \"def reward(self, action=None):\\n reward = 0.\\n\\n # sparse completion reward\\n if self._check_success():\\n reward = 1.0\\n\\n # use a shaping reward\\n if self.reward_shaping:\\n\\n # reaching reward\\n cube_pos = self.sim.data.body_xpos[self.cube_body_id]\\n gripper_site_pos = self.sim.data.site_xpos[self.eef_site_id]\\n dist = np.linalg.norm(gripper_site_pos - cube_pos)\\n reaching_reward = 1 - np.tanh(10.0 * dist)\\n reward += reaching_reward\\n\\n # grasping reward\\n touch_left_finger = False\\n touch_right_finger = False\\n for i in range(self.sim.data.ncon):\\n c = self.sim.data.contact[i]\\n if c.geom1 in self.l_finger_geom_ids and c.geom2 == self.cube_geom_id:\\n touch_left_finger = True\\n if c.geom1 == self.cube_geom_id and c.geom2 in self.l_finger_geom_ids:\\n touch_left_finger = True\\n if c.geom1 in self.r_finger_geom_ids and c.geom2 == self.cube_geom_id:\\n touch_right_finger = True\\n if c.geom1 == self.cube_geom_id and c.geom2 in self.r_finger_geom_ids:\\n touch_right_finger = True\\n if touch_left_finger and touch_right_finger:\\n reward += 0.25\\n\\n return reward\",\n \"title\": \"\"\n },\n {\n \"docid\": \"202989e6828ee36a1e2ea4f1b3fd4dfb\",\n \"score\": \"0.461233\",\n \"text\": \"def get_reward(self):\\n return self._reward\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be03d7ed5a50c223b03677fdab020b45\",\n \"score\": \"0.45986578\",\n \"text\": \"def get(self, mf_id):\\n return get_all_mf_credits_by_mf(mf_id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d6bea1cc8b0926fde84675279625ba9\",\n \"score\": \"0.45925483\",\n \"text\": \"def get_dish_details():\\n user_id=session[\\\"user_id\\\"]\\n score =request.form.get(\\\"rate\\\")\\n if request.method=='POST': \\n dish_id = request.form.get(\\\"dish_id\\\")\\n else:\\n dish_id = request.args.get(\\\"dish_id\\\")\\n if score!=None: \\n crud.create_rating(score=score,dish_id=dish_id,user_id=user_id)\\n flash(\\\"you rated!!!\\\")\\n \\n \\n\\n dish= Dish.query.get(dish_id)\\n summary = get_dish_summary(dish_id)\\n \\n ingredients=get_ingredients(dish_id)\\n equipments=get_equipments(dish_id)\\n instructions=get_instructions(dish_id)\\n \\n \\n \\n\\n\\n print(dish_id)\\n print('*'*20)\\n\\n print(score)\\n print('*'*20)\\n \\n print('*'*20)\\n\\n return render_template('dish_details.html', summary=summary,ingredients=ingredients,equipments=equipments,instructions=instructions,dish=dish,rate=score)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d13b921fd3d54ff502972d6e304be53\",\n \"score\": \"0.45728022\",\n \"text\": \"def get_reward(agent_id: int, factory: Factory, episodes: int) -> float:\\n\\n # only configured rewards get picked up\\n rewards_to_use = get_reward_names_and_weights()\\n reward_names = rewards_to_use.keys()\\n rewards = {}\\n\\n if \\\"rew_tighten_max_steps\\\" in reward_names:\\n # After 300 episodes we cut the allowed step length to 1/4th.\\n episode_discount = max(0.25, 1 - (episodes / 400.))\\n factory.max_num_steps = int(factory.initial_max_num_steps * episode_discount)\\n # We set this to 0, as this is just a mean to decrease the max step count over\\n # time. This leads to shorter episodes and is reflected in \\\"rew_punish_slow_tables\\\".\\n rewards[\\\"rew_tighten_max_steps\\\"] = 0\\n\\n max_num_steps = factory.max_num_steps\\n steps = factory.agent_step_counter.get(agent_id)\\n agent: Table = factory.tables[agent_id]\\n\\n # sum negative rewards due to collisions and illegal moves\\n if factory.moves.get(agent_id):\\n move = factory.moves[agent_id].pop(-1)\\n rewards[\\\"rew_collisions\\\"] = move.reward()\\n\\n # high incentive for reaching a target, quickly\\n time_taken = steps / float(max_num_steps)\\n if agent.is_at_target:\\n rewards[\\\"rew_found_target\\\"] = (1.0 - time_taken)\\n rewards[\\\"rew_found_target_squared\\\"] = (1.0 - time_taken) ** 2\\n\\n # punish if too slow\\n if steps == max_num_steps:\\n num_cores_left = len([t for t in factory.tables if t.has_core()])\\n rewards[\\\"rew_punish_slow_tables\\\"] = - 1 * num_cores_left\\n\\n if not agent.has_core():\\n # If an agent without core is close to one with core, let it shy away...\\n rewards[\\\"rew_avoid_cores\\\"] = -1.0 * has_core_neighbour(agent.node, factory)\\n\\n # ... and if it sits on any current target, punish it\\n # TODO: how can we account for the fact that tables might be \\\"on the path\\\"\\n # to a target, given that we don't know what the path is?\\n # Maybe we can account for situation in which ALL paths to a core target are obstructed\\n # by this table?\\n all_targets = [c.current_target for c in factory.cores]\\n rewards[\\\"rew_blocking_target\\\"] = -1.0 * int(agent.node in all_targets)\\n\\n reward = 0\\n for reward_name, weight in rewards_to_use.items():\\n # multiply rewards by configured weight terms\\n reward += rewards.get(reward_name, 0) * weight\\n\\n return reward\",\n \"title\": \"\"\n },\n {\n \"docid\": \"016bb5f1285f5f18e287c3064599f84a\",\n \"score\": \"0.456851\",\n \"text\": \"def lookup_name_facc(self, name):\\n self.__init_coll_sf_facc()\\n res = self.__coll_sf_facc.find_by_id(name.lower())\\n return res if res else {}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"886999ea875963c357bcabfaead2ad76\",\n \"score\": \"0.45552728\",\n \"text\": \"def get_reward(state):\\n if found_gold(state):\\n return 100\\n elif oh_no_dragon(state):\\n return -10\\n else:\\n return -1.0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b99903a069fec44fbb788427c83c11d\",\n \"score\": \"0.45493957\",\n \"text\": \"def getStudentByRegId(self, request):\\n\\t\\tS = Student.objects.get(dtuRegId = request[\\\"dtuRegId\\\"])\\n\\t\\treturn S\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ff00adf1a2c8a9eeb4759a4f3265099\",\n \"score\": \"0.4542256\",\n \"text\": \"def _get_reward(self):\\n reward = -np.abs(self.powergrid.res_bus.iloc[1, 2] - self.target_load)\\n return reward\\n # TODO: generalize for different network\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82a47506e328d80348974d74e924b65d\",\n \"score\": \"0.4531861\",\n \"text\": \"def AssignNonFuelReward(LoyatyID=\\\"1\\\", NonFuelID=\\\"1\\\", RewardName=\\\"15 cents DryStock\\\"):\\n result = False\\n\\n if (NonFuelID == \\\"\\\" and RewardName == \\\"\\\"):\\n log.info(\\\"NonFuelID or RewardName must be not empty\\\")\\n return result\\n\\n result = Loyalty().AssignNonFuelReward(LoyatyID, NonFuelID, RewardName)\\n if (result == 'true'):\\n result = True\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e6e30fd9282cf31891e8aa8bd68a9dc1\",\n \"score\": \"0.4516599\",\n \"text\": \"def get_reward(self, position=None):\\r\\n if position is None:\\r\\n position = self.position\\r\\n\\r\\n return self.rewards[position[0], position[1]]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0e29c88a45b321124a82b6e40db5f7b\",\n \"score\": \"0.45160243\",\n \"text\": \"def get_reward(self):\\n # Calculate the reward\\n total_reward = self.reward.mission_reward(self.state_manager.ugv,\\n self.state_manager.uav,\\n self.config)\\n for vehicle in self.state_manager.uav:\\n vehicle.reward = total_reward\\n\\n for vehicle in self.state_manager.ugv:\\n vehicle.reward = total_reward\\n\\n return total_reward\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0fa77ced0a64dad306025ceebcef722\",\n \"score\": \"0.45129043\",\n \"text\": \"def _get_reward(self):\\n # if self.status == FOOBAR:\\n # return 1\\n # elif self.status == ABC:\\n # return self.somestate ** 2\\n # else:\\n return self._reward # -1 for the step/time increment \",\n \"title\": \"\"\n },\n {\n \"docid\": \"dec87f7070152de75c183659bc422d13\",\n \"score\": \"0.4502535\",\n \"text\": \"def get_score(self, name):\\n score = self.db.scores.find_one({\\\"name\\\": name}).get(\\\"score\\\")\\n # print(score)\\n return(score)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"84a43e7bcc744c84b56a8b6e12748bff\",\n \"score\": \"0.44988525\",\n \"text\": \"def reward(self, score):\\n return score\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d00aa2d714af106e42d500e36e57c4a0\",\n \"score\": \"0.44877315\",\n \"text\": \"def get(self, supplier_id):\\n app.logger.info(\\\"Request to Retrieve a supplier with id [%s]\\\", supplier_id)\\n supplier = Supplier.find(supplier_id)\\n if not supplier:\\n abort(status.HTTP_404_NOT_FOUND, \\\"Supplier with id '{}' was not found.\\\".format(supplier_id))\\n return supplier.serialize(), status.HTTP_200_OK\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b53c9d7a7392b405177ae5d6e4b43a58\",\n \"score\": \"0.44803327\",\n \"text\": \"def get_grade(self):\\n return self._grade\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d54320b4e9b65988d51e2ea96d1083db\",\n \"score\": \"0.44785026\",\n \"text\": \"def get_done_reward():\\n rospy.wait_for_service('/drl/get_done_reward')\\n\\n try:\\n get_done_reward_proxy = rospy.ServiceProxy(\\n '/drl/get_done_reward', GetDoneAndReward)\\n resp = get_done_reward_proxy()\\n return resp\\n except rospy.ServiceException, ex:\\n print \\\"Service call get_reward_done failed: %e\\\" % ex\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8847f45c19ac55e327ccce24a45a1573\",\n \"score\": \"0.4476952\",\n \"text\": \"def get_fuel_supplier(self, obj):\\n if obj['type'] == 'Credit Trade':\\n history = CreditTradeHistory.objects.get(id=obj['id'])\\n\\n if history.credit_trade.type.id in [1, 3, 5]:\\n fuel_supplier = history.credit_trade.initiator\\n else:\\n fuel_supplier = history.credit_trade.respondent\\n else:\\n history = ComplianceReportHistory.objects.get(id=obj['id'])\\n\\n fuel_supplier = history.compliance_report.organization\\n\\n serializer = OrganizationMinSerializer(fuel_supplier, read_only=True)\\n return serializer.data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fcd45b7b22192f08d1fbba60d2e17e23\",\n \"score\": \"0.4473189\",\n \"text\": \"def get(self, good_id):\\n shablon = (\\\"id\\\", \\\"barcode\\\", \\\"name\\\", \\\"grup\\\", \\\"delimeter\\\")\\n query1 = db.session.query(Goods.id, Goods.barcode, Goods.name, Goods.grup, Goods.delimeter).filter((Goods.name.like(\\\"%\\\" + good_id + \\\"%\\\")) | (Goods.barcode == (good_id)) | (Goods.id == (good_id))).all()\\n if not query1:\\n\\n return good_id, 404\\n resp = {\\\"good\\\": list(dict(zip(shablon, x))for x in query1)}\\n print(resp)\\n return resp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d081e18c650c698b0e2df0702678c192\",\n \"score\": \"0.44646215\",\n \"text\": \"def getValue(self, fsr_name):\\n try:\\n return self.fsr_dict[fsr_name].getValue()\\n\\n except KeyError:\\n raise pybullet.error(\\\"The required Fsr does not exist\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bda26bcfd7cebda1c1143889bd671e4b\",\n \"score\": \"0.44618854\",\n \"text\": \"def _rew_drug_penalty(self) -> float:\\n\\n # Get params\\n params = self.params_reward[\\\"drug_penalty\\\"]\\n\\n # Compute reward, only discrete\\n reward = params[\\\"low_rew\\\"]\\n if 0 < self.action <= 3 or self.action == 6:\\n reward = params[\\\"intermed_rew\\\"]\\n if 3 < self.action < 6 or self.action > 6:\\n reward = params[\\\"high_rew\\\"]\\n return reward\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3268870de45c2da1e3e84e290bfbe0b7\",\n \"score\": \"0.4454963\",\n \"text\": \"def getReward(self):\\n # this function should be deterministic and without side effects\\n if self.env.state.click != Click.NOT_CLICKED:\\n if self.env.clicked_item.item_relevance == ItemRelevance.TARGET_RELEVANCE:\\n # reward for clicking the correct item after seeing it\\n # target item should always have correct length\\n return self.reward_success\\n else:\\n # penalty for clicking the wrong item\\n return self.reward_failure\\n elif self.env.state.quit == Quit.HAS_QUIT:\\n if self.env.target_present is False:\\n # reward for quitting when target is absent\\n return self.reward_success\\n else:\\n # penalty for quitting when target is present\\n return self.reward_failure\\n # default penalty for spending time\\n return int(-1 * self.env.action_duration)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d81756e4baa70f4b9431f62df067a20\",\n \"score\": \"0.44533724\",\n \"text\": \"def get(self, foodName):\\n\\t\\ttry:\\n\\t\\t\\tfoodName = foodName.lower()\\n\\t\\t\\tfood = Food.query.filter_by(name=foodName).one()\\n\\t\\texcept:\\n\\t\\t\\treturn {\\\"message\\\": \\\"food resource not found\\\", \\\"status\\\":404}, 404\\n\\t\\treturn food.foodRepresentation()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"09c542ab251035a07388e52083378727\",\n \"score\": \"0.44508135\",\n \"text\": \"def reward(self, action=None):\\n # reaching reward\\n cube_pos = self.sim.data.body_xpos[self.cube_body_id]\\n gripper_site_pos = self.sim.data.site_xpos[self.eef_site_id]\\n dist = np.linalg.norm(gripper_site_pos - cube_pos) # Eucledian distance b/w cube and grip site\\n reaching_reward = 1 - np.tanh(10.0 * dist) # tanh function on distance\\n\\n # slow down reward\\n vel = np.sum(abs(self.ee_v)) / 6\\n vel_reward = 1 - np.tanh(10.0 * vel)\\n\\n # Two phases of the task (0.Reaching and 1.Grasping+Lifiting)\\n if self.phase == 0:\\n\\n reward = 0.6 * reaching_reward\\n\\n if dist < 0.08:\\n reward += 0.3 * vel_reward\\n\\n # gripper open reward\\n if action[-1] < 0:\\n reward += 0.1 * abs(action[-1])\\n\\n if dist < 0.025:\\n self.phase = 1\\n\\n elif self.phase == 1:\\n\\n reward = reaching_reward + vel_reward\\n\\n # gripper closing reward\\n if action[-1] > 0:\\n reward += 0.5 * action[-1]\\n\\n # check contact between fingers and cube\\n touch_left_finger = False\\n touch_right_finger = False\\n\\n for i in range(self.sim.data.ncon):\\n c = self.sim.data.contact[i]\\n if c.geom1 in self.l_finger_geom_ids and c.geom2 == self.cube_geom_id:\\n touch_left_finger = True\\n #reward += 0.1\\n if c.geom1 == self.cube_geom_id and c.geom2 in self.l_finger_geom_ids:\\n touch_left_finger = True\\n # reward += 0.1\\n if c.geom1 in self.r_finger_geom_ids and c.geom2 == self.cube_geom_id:\\n touch_right_finger = True\\n #reward += 0.1\\n if c.geom1 == self.cube_geom_id and c.geom2 in self.r_finger_geom_ids:\\n touch_right_finger = True\\n #reward += 0.1\\n\\n self.has_grasp = touch_left_finger and touch_right_finger\\n\\n # grasping reward\\n if self.has_grasp:\\n reward += 0.5\\n\\n # success reward\\n if self._check_success():\\n reward += 2.0\\n\\n # stay within joint limits! This is used only in retraining condition\\n #if self._check_q_limits():\\n #reward -= 1.0\\n\\n # Collision avoidance scene\\n if self.config.mode == 2:\\n # collision penalty\\n collision = False\\n for contact in self.sim.data.contact[:self.sim.data.ncon]:\\n # hand collision check\\n if self.sim.model.geom_id2name(contact.geom1) in self.hand_names and contact.geom2 == self.cyl_geom_id:\\n collision = True\\n if self.sim.model.geom_id2name(contact.geom2) in self.hand_names and contact.geom1 == self.cyl_geom_id:\\n collision = True\\n if self.sim.model.geom_id2name(contact.geom1) in self.hand_names and contact.geom2 == self.cyl2_geom_id:\\n collision = True\\n if self.sim.model.geom_id2name(contact.geom2) in self.hand_names and contact.geom1 == self.cyl2_geom_id:\\n collision = True\\n # cube collision check\\n if contact.geom1 == self.cyl_geom_id and contact.geom2 == self.cube_geom_id:\\n collision = True\\n if contact.geom1 == self.cube_geom_id and contact.geom2 == self.cyl_geom_id:\\n collision = True\\n if contact.geom1 == self.cyl2_geom_id and contact.geom2 == self.cube_geom_id:\\n collision = True\\n if contact.geom1 == self.cube_geom_id and contact.geom2 == self.cyl2_geom_id:\\n collision = True\\n\\n if collision:\\n reward -= 5.0\\n\\n return reward\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c2592a6a2a8cd319a92cbddc60fc22a\",\n \"score\": \"0.44498286\",\n \"text\": \"def get_award(user, slug):\\n try:\\n old_badge = Badge.objects.get(slug=slug)\\n return Award.objects.get(user=user, badge=old_badge)\\n except:\\n # Award not granted\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e8d2b1203a358efc17d707ea3a1102d\",\n \"score\": \"0.44433272\",\n \"text\": \"def get_ref():\\n id = request.args.get('id')\\n try:\\n ref = g.db.query(Referee).filter_by(id=id).one()\\n except NoResultFound:\\n print \\\"No result found for {0}\\\".format(id)\\n ref = None\\n return render_template('ref_info.html', ref=ref)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44c1612104a847710bb0b4de71ae3613\",\n \"score\": \"0.44368294\",\n \"text\": \"def get_grade_info(self):\\n if (self.grade_info_store):\\n grade_info = \\\\\\n SchoolDB.assistant_classes.InformationContainer.get_data(\\n stored_data = self.grade_info_store, \\n compressed = True, current_version = 1)\\n else:\\n grade_info = None\\n return grade_info\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ea8fb868b0b31262e9ff599516b6c82\",\n \"score\": \"0.4435227\",\n \"text\": \"def get_Furniture(id): \\r\\n urnitures = Furniture.query.all()\\r\\n furniture = next(furniture for furniture in urnitures if furniture.id==id)\\r\\n return furniture\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92d40cfb4d528bb263cdd6b0df4b8da3\",\n \"score\": \"0.4434018\",\n \"text\": \"def badges(self, request, pk=None):\\n queryset = User.objects.all()\\n user = get_object_or_404(queryset, pk=pk)\\n awards = user.badges.all()\\n return Response([BadgeSerializer(award.badge, context={'request': request}).data for award in awards])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"96c0521d1f5dfb35dcd9f53891120ac0\",\n \"score\": \"0.44329074\",\n \"text\": \"def reward_bonus(self, assignment_id, amount, reason):\\n from psiturk.amt_services import MTurkServices\\n\\n self.amt_services = MTurkServices(\\n self.aws_access_key_id,\\n self.aws_secret_access_key,\\n self.config.getboolean(\\n 'Shell Parameters', 'launch_in_sandbox_mode'))\\n return self.amt_services.bonus_worker(assignment_id, amount, reason)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25649211343054d2198885421fa22fab\",\n \"score\": \"0.44319484\",\n \"text\": \"def _get_grade_comparison():\\n\\n my_grade = lrs.find({\\n \\\"type\\\": \\\"GradeEvent\\\",\\n \\\"actor.name\\\": session['username'],\\n \\\"object.assignable.id\\\": \\\"http://example.org/assessment/1\\\"\\n }, {\\n \\\"generated.scoreGiven\\\": 1\\n })[0]\\n\\n average_grade = list(lrs.aggregate([\\n {\\n \\\"$match\\\": {\\n \\\"type\\\": \\\"GradeEvent\\\",\\n \\\"object.assignable.id\\\": \\\"http://example.org/assessment/1\\\"\\n }\\n },\\n {\\n \\\"$group\\\": {\\n \\\"_id\\\": None,\\n \\\"average_score\\\": {\\n \\\"$avg\\\": \\\"$generated.scoreGiven\\\"\\n }\\n }\\n }\\n ]))[0]\\n\\n return {\\n \\\"my_score\\\": my_grade['generated']['scoreGiven'],\\n \\\"average_score\\\": average_grade['average_score']\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a43c5554e5d083e11d499532157badb3\",\n \"score\": \"0.44311404\",\n \"text\": \"def find_fishery_name(fishery_id: str) -> str:\\n try:\\n fishery = Lookups.get(Lookups.lookup_type == 'FISHERY',\\n Lookups.lookup_value == fishery_id)\\n return fishery.description\\n\\n except Lookups.DoesNotExist:\\n return '?'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f87b86b71e16c6361b7209efa5acd13c\",\n \"score\": \"0.44212267\",\n \"text\": \"def info_by_rfid(request, form):\\n rfid = rfid_reader()\\n try:\\n user = Client.objects.get(rfid_code=rfid)\\n return {\\n 'first_name': user.first_name,\\n 'last_name': user.last_name,\\n 'email': user.email,\\n 'rfid_code': user.rfid_code\\n }\\n except Client.DoesNotExist:\\n return {}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6e4cf720b6b115821305d7dd533f446\",\n \"score\": \"0.4400915\",\n \"text\": \"def fuel_cost(hr, frc_eia923, gen_eia923):\\n # Split up the plants on the basis of how many different primary energy\\n # sources the component generators have:\\n gen_w_ft = pd.merge(gen_eia923,\\n hr[['plant_id_eia', 'report_date', 'generator_id',\\n 'fuel_type_pudl', 'fuel_type_count',\\n 'heat_rate_mmbtu_mwh']],\\n how='inner',\\n on=['plant_id_eia', 'report_date', 'generator_id'])\\n\\n one_fuel = gen_w_ft[gen_w_ft.fuel_type_count == 1]\\n multi_fuel = gen_w_ft[gen_w_ft.fuel_type_count > 1]\\n\\n # Bring the single fuel cost & generation information together for just\\n # the one fuel plants:\\n one_fuel = pd.merge(one_fuel, frc_eia923[['plant_id_eia', 'report_date',\\n 'fuel_cost_per_mmbtu',\\n 'fuel_type_pudl',\\n 'total_fuel_cost',\\n 'total_heat_content_mmbtu']],\\n how='left', on=['plant_id_eia', 'report_date'])\\n # We need to retain the different energy_source information from the\\n # generators (primary for the generator) and the fuel receipts (which is\\n # per-delivery), and in the one_fuel case, there will only be a single\\n # generator getting all of the fuels:\\n one_fuel.rename(columns={'fuel_type_pudl_x': 'ftp_gen',\\n 'fuel_type_pudl_y': 'ftp_frc'},\\n inplace=True)\\n\\n # Do the same thing for the multi fuel plants, but also merge based on\\n # the different fuel types within the plant, so that we keep that info\\n # as separate records:\\n multi_fuel = pd.merge(multi_fuel,\\n frc_eia923[['plant_id_eia', 'report_date',\\n 'fuel_cost_per_mmbtu',\\n 'fuel_type_pudl']],\\n how='left', on=['plant_id_eia', 'report_date',\\n 'fuel_type_pudl'])\\n\\n # At this point, within each plant, we should have one record per\\n # combination of generator & fuel type, which includes the heat rate of\\n # each generator, as well as *plant* level fuel cost per unit heat input\\n # for *each* fuel, which we can combine to figure out the fuel cost per\\n # unit net electricity generation on a generator basis.\\n\\n # We have to do these calculations separately for the single and multi-fuel\\n # plants because in the case of the one fuel plants we need to sum up all\\n # of the fuel costs -- including both primary and secondary fuel\\n # consumption -- whereas in the multi-fuel plants we are going to look at\\n # fuel costs on a per-fuel basis (this is very close to being correct,\\n # since secondary fuels are typically a fraction of a percent of the\\n # plant's overall costs).\\n\\n one_fuel_gb = one_fuel.groupby(by=['report_date', 'plant_id_eia'])\\n one_fuel_agg = one_fuel_gb.agg({\\n 'total_fuel_cost': np.sum,\\n 'total_heat_content_mmbtu': np.sum\\n })\\n one_fuel_agg['fuel_cost_per_mmbtu'] = \\\\\\n one_fuel_agg['total_fuel_cost'] / \\\\\\n one_fuel_agg['total_heat_content_mmbtu']\\n one_fuel_agg = one_fuel_agg.reset_index()\\n one_fuel = pd.merge(one_fuel[['plant_id_eia', 'report_date',\\n 'generator_id', 'heat_rate_mmbtu_mwh']],\\n one_fuel_agg[['plant_id_eia', 'report_date',\\n 'fuel_cost_per_mmbtu']],\\n on=['plant_id_eia', 'report_date'])\\n one_fuel = one_fuel.drop_duplicates(\\n subset=['plant_id_eia', 'report_date', 'generator_id'])\\n\\n multi_fuel = multi_fuel[['plant_id_eia', 'report_date', 'generator_id',\\n 'fuel_cost_per_mmbtu', 'heat_rate_mmbtu_mwh']]\\n\\n fuel_cost = one_fuel.append(multi_fuel)\\n fuel_cost['fuel_cost_per_mwh'] = \\\\\\n fuel_cost['fuel_cost_per_mmbtu'] * fuel_cost['heat_rate_mmbtu_mwh']\\n fuel_cost = \\\\\\n fuel_cost.sort_values(['report_date', 'plant_id_eia', 'generator_id'])\\n\\n out_df = gen_w_ft.drop('heat_rate_mmbtu_mwh', axis=1)\\n out_df = pd.merge(out_df.drop_duplicates(), fuel_cost,\\n on=['report_date', 'plant_id_eia', 'generator_id'])\\n\\n return(out_df)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b3d131974bf26ee9185fac8b921cba5e\",\n \"score\": \"0.43875495\",\n \"text\": \"def test_detail_return(self):\\n resp = self.c.get('/minerals/1/')\\n self.assertIsNotNone(resp.context.get('mineral'))\\n self.assertEqual(resp.status_code, 200)\\n self.assertEqual(resp.context.get('mineral').get('name'), 'Abelsonite')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a33d0506c0c0236d97751c60eac2ab8\",\n \"score\": \"0.4380622\",\n \"text\": \"def get_reward(self):\\n reward = 0\\n for health in self.asset_healths:\\n if health < 0.75:\\n reward = -1 * math.exp(3 * (0.75 - health))\\n reward -= self.maintenanceCost\\n return reward\",\n \"title\": \"\"\n },\n {\n \"docid\": \"478c472b1c18f374d01220eceb811ea0\",\n \"score\": \"0.4379156\",\n \"text\": \"def get_restaurant(self, restaurant_id):\\n if restaurant_id not in self.restaurants: return\\n return self.restaurants[restaurant_id]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3d7938428c2129371b48b716e05dafb\",\n \"score\": \"0.43633485\",\n \"text\": \"def rewardFunction(self):\\n logging.warning(\\\"rewardFunction not defined!\\\")\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa369dbfd62378faee925ab706652aff\",\n \"score\": \"0.43599656\",\n \"text\": \"def get_reward(self, state, sim):\\n # reward = self.get_staggered_reward_altitude_and_heading_with_negatives(\\n # state, sim, numPositiveAltitudeStaggerLevels=25,\\n # numPositiveHeadingStaggerLevels=25, altitudeWorth=0.5,\\n # headingWorth=0.5, numNegativeAltitudeStaggerLevels=25,\\n # numNegativeHeadingStaggerLevels=25, penaltyMultiplier=0.1)\\n reward = self.get_staggered_reward_altitude_and_heading(state, sim,\\n numAltitudeStaggerLevels=10, numHeadingStaggerLevels=10,\\n headingWorth=0.5, altitudeWorth=0.5)\\n\\n self.stepCount += 1\\n self.simTime = sim.get_property_value(c.simulation_sim_time_sec)\\n\\n return reward\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ef236dace19179d840d8175846989ef\",\n \"score\": \"0.43581444\",\n \"text\": \"def getStudentByName(self, request):\\n\\t\\tN = Name.objects.get(preferredName = request['preferredName'])\\n\\t\\tS = Student.objects.get(name = N)\\n\\t\\t# rollNoList = []\\n\\t\\t# for n in N:\\n\\t\\t#\\tS = Student.objects.filter(name = n)\\n\\t\\t#\\tfor s in S:\\n\\t\\t#\\t\\trollNoList.append(s.rollNo)\\n\\t\\treturn S\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8cd3bb383be69e31079f40285a36b7d9\",\n \"score\": \"0.4357834\",\n \"text\": \"def fort_details(bot, fort_id, latitude, longitude):\\n\\n if fort_id not in FORT_CACHE:\\n \\\"\\\"\\\"\\n Lookup the fort details and cache the response for future use.\\n \\\"\\\"\\\"\\n request = bot.api.create_request()\\n request.fort_details(fort_id=fort_id, latitude=latitude, longitude=longitude)\\n try:\\n response_dict = request.call()\\n FORT_CACHE[fort_id] = response_dict['responses']['FORT_DETAILS']\\n except Exception:\\n pass\\n\\n # Just to avoid KeyErrors\\n return FORT_CACHE.get(fort_id, {})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c92749640c35baabcb20f91f144d8267\",\n \"score\": \"0.43537796\",\n \"text\": \"def get(self, penalty_id):\\n le_penalty = get_an_penalty(penalty_id)\\n if not le_penalty:\\n return {'success': False, 'message': 'penalty not found'}\\n else:\\n return le_penalty\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8d408240a63d0b562d75fe477bcc861\",\n \"score\": \"0.4353404\",\n \"text\": \"def getReward(self, gameState, action):\\n successor = self.getSuccessor(gameState, action)\\n currentCarrying = gameState.getAgentState(self.index).numCarrying\\n reward = self.reward\\n currentPos = gameState.getAgentState(self.index).getPosition()\\n nextPos = successor.getAgentState(self.index).getPosition()\\n\\n nextDistance = 99999\\n for pos in range(len(self.boundary)):\\n distance = self.agent.getMazeDistance(nextPos, self.boundary[pos])\\n if (nextDistance > distance):\\n nextDistance = distance\\n\\n currentDistance = 99999\\n for pos in range(len(self.boundary)):\\n distance = self.agent.getMazeDistance(currentPos, self.boundary[pos])\\n if (currentDistance > distance):\\n currentDistance = distance\\n\\n move = self.agent.getMazeDistance(currentPos, nextPos)\\n # eaten\\n if move > 1:\\n if currentCarrying > 0:\\n reward = -20 * currentCarrying\\n else:\\n reward = -100\\n else:\\n if currentDistance > nextDistance:\\n reward = 5 * currentCarrying\\n if currentCarrying > 0:\\n reward = 100 * currentCarrying\\n\\n return reward\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6820baf1aaa734fc712e3cdb944cd821\",\n \"score\": \"0.43472552\",\n \"text\": \"def get_genre_id_by_name(genre_name):\\r\\n\\r\\n genre = Genre.query.filter(Genre.name == genre_name).first() \\r\\n\\r\\n return genre.id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6798a7125432345d7481f85e1714f468\",\n \"score\": \"0.43407738\",\n \"text\": \"def _get_grades(self):\\n if (self.target_object):\\n json_data = self.argsDict.get(\\\"encoded_data\\\",\\\"\\\")\\n grade_handler = \\\\\\n SchoolDB.assistant_classes.AjaxGetGradeHandler(\\n self.target_object, json_data)\\n successful, self.return_string = \\\\\\n grade_handler.service_request()\\n if (not successful):\\n self.error_string = \\\"Grade load failed\\\"\\n raise AjaxError, self.error_string\\n else:\\n self.error_string = \\\"Incorrect or missing class session db key.\\\"\\n raise AjaxError, self.error_string\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9f1d95d8f809b8c4c6dbaa4a4a02391\",\n \"score\": \"0.43302628\",\n \"text\": \"def get_recipe_by_name(self, name):\\n for _, recipe in self.recipe_list.items():\\n if recipe.name == name:\\n print(recipe)\\n return recipe\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d245288213622e92c875b2b72d0ad9a2\",\n \"score\": \"0.43282163\",\n \"text\": \"def giveReward(self, rewardName):\\n if rewardName in self.rewardDict:\\n sleepTime = self.rewardDict.get(rewardName)\\n else:\\n sleepTime = self.rewardDict.get('default')\\n GPIO.output(self.rewardPin, 1)\\n sleep(sleepTime) # not very accurate timing, but good enough\\n GPIO.output(self.rewardPin, 0)\\n self.totalsDict[rewardName] += 1\\n return sleepTime\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04827169500554c292a96cdcbb991d01\",\n \"score\": \"0.43266624\",\n \"text\": \"def _get_reward(self):\\n current_state = self.env.getState()\\n #print(\\\"State =\\\",current_state)\\n #print(\\\"len State =\\\",len(current_state))\\n ball_proximity = current_state[53]\\n goal_proximity = current_state[15]\\n ball_dist = 1.0 - ball_proximity\\n goal_dist = 1.0 - goal_proximity\\n kickable = current_state[12]\\n ball_ang_sin_rad = current_state[51]\\n ball_ang_cos_rad = current_state[52]\\n ball_ang_rad = math.acos(ball_ang_cos_rad)\\n if ball_ang_sin_rad < 0:\\n ball_ang_rad *= -1.\\n goal_ang_sin_rad = current_state[13]\\n goal_ang_cos_rad = current_state[14]\\n goal_ang_rad = math.acos(goal_ang_cos_rad)\\n if goal_ang_sin_rad < 0:\\n goal_ang_rad *= -1.\\n alpha = max(ball_ang_rad, goal_ang_rad) - min(ball_ang_rad, goal_ang_rad)\\n ball_dist_goal = math.sqrt(ball_dist*ball_dist + goal_dist*goal_dist -\\n 2.*ball_dist*goal_dist*math.cos(alpha))\\n # Compute the difference in ball proximity from the last step\\n if not self.first_step:\\n ball_prox_delta = ball_proximity - self.old_ball_prox\\n kickable_delta = kickable - self.old_kickable\\n ball_dist_goal_delta = ball_dist_goal - self.old_ball_dist_goal\\n self.old_ball_prox = ball_proximity\\n self.old_kickable = kickable\\n self.old_ball_dist_goal = ball_dist_goal\\n #print(self.env.playerOnBall())\\n #print(self.env.playerOnBall().unum)\\n #print(self.env.getUnum())\\n reward = 0\\n if not self.first_step:\\n '''# Reward the agent for moving towards the ball\\n reward += ball_prox_delta\\n if kickable_delta > 0 and not self.got_kickable_reward:\\n reward += 1.\\n self.got_kickable_reward = True\\n # Reward the agent for kicking towards the goal\\n reward += 0.6 * -ball_dist_goal_delta\\n # Reward the agent for scoring\\n if self.status == hfo_py.GOAL:\\n reward += 5.0'''\\n '''reward = self.__move_to_ball_reward(kickable_delta, ball_prox_delta) + \\\\\\n 3. * self.__kick_to_goal_reward(ball_dist_goal_delta) + \\\\\\n self.__EOT_reward();'''\\n mtb = self.__move_to_ball_reward(kickable_delta, ball_prox_delta)\\n ktg = 3. * self.__kick_to_goal_reward(ball_dist_goal_delta)\\n eot = self.__EOT_reward()\\n reward = mtb + ktg + eot\\n #print(\\\"mtb: %.06f ktg: %.06f eot: %.06f\\\"%(mtb,ktg,eot))\\n \\n self.first_step = False\\n #print(\\\"r =\\\",reward)\\n return reward\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8dfe3f2b94bad1109ef8b861a1985681\",\n \"score\": \"0.43243834\",\n \"text\": \"def get_fedex_shipping_cost(self):\\n Currency = Pool().get('currency.currency')\\n\\n fedex_credentials = self.carrier.get_fedex_credentials()\\n\\n if not all([\\n self.fedex_drop_off_type, self.fedex_packaging_type,\\n self.fedex_service_type\\n ]):\\n self.raise_user_error('fedex_settings_missing')\\n\\n rate_request = RateService(fedex_credentials)\\n requested_shipment = rate_request.RequestedShipment\\n\\n requested_shipment.DropoffType = self.fedex_drop_off_type.value\\n requested_shipment.ServiceType = self.fedex_service_type.value\\n requested_shipment.PackagingType = self.fedex_packaging_type.value\\n requested_shipment.PreferredCurrency = self.cost_currency.code\\n\\n # Shipper and Recipient\\n requested_shipment.Shipper.AccountNumber = \\\\\\n fedex_credentials.AccountNumber\\n # From location is the warehouse location. So it must be filled.\\n if not self.warehouse.address:\\n self.raise_user_error('warehouse_address_required')\\n self.warehouse.address.set_fedex_address(requested_shipment.Shipper)\\n self.delivery_address.set_fedex_address(requested_shipment.Recipient)\\n\\n # Shipping Charges Payment\\n shipping_charges = requested_shipment.ShippingChargesPayment\\n shipping_charges.PaymentType = 'SENDER'\\n shipping_charges.Payor.ResponsibleParty = requested_shipment.Shipper\\n\\n # Express Freight Detail\\n fright_detail = requested_shipment.ExpressFreightDetail\\n fright_detail.PackingListEnclosed = 1\\n fright_detail.ShippersLoadAndCount = 2\\n fright_detail.BookingConfirmationNumber = 'Ref-%s' % self.reference\\n\\n # Customs Clearance Detail\\n self.get_fedex_customs_details(rate_request)\\n\\n # Label Specification\\n requested_shipment.LabelSpecification.LabelFormatType = 'COMMON2D'\\n requested_shipment.LabelSpecification.ImageType = 'PNG'\\n requested_shipment.LabelSpecification.LabelStockType = 'PAPER_4X6'\\n\\n requested_shipment.RateRequestTypes = ['ACCOUNT']\\n\\n self.get_fedex_items_details(rate_request)\\n\\n try:\\n response = rate_request.send_request(int(self.id))\\n except RequestError, exc:\\n self.raise_user_error(\\n 'fedex_shipping_cost_error', error_args=(exc.message, )\\n )\\n\\n currency, = Currency.search([\\n ('code', '=', str(\\n response.RateReplyDetails[0].RatedShipmentDetails[0].\\n ShipmentRateDetail.TotalNetCharge.Currency\\n ))\\n ])\\n\\n return Decimal(str(\\n response.RateReplyDetails[0].RatedShipmentDetails[0].ShipmentRateDetail.TotalNetCharge.Amount # noqa\\n )), currency.id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"844947cba443f3009cb3496bd5e278e3\",\n \"score\": \"0.43232056\",\n \"text\": \"def get_recipe_by_name(recipe_name):\\r\\n\\r\\n return Recipes.query.get(recipe_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c4155a3ccfed1dd03c723703b35d4d97\",\n \"score\": \"0.43177205\",\n \"text\": \"def flesch_grade (index):\\n grades = [ (100, 90), (89, 80), (79, 70), (69, 65), (64, 50), (49, 30), (30, 0)]\\n grade_level = ['5th grader', '6th grader', '7th grader', '8th grader', 'High school student', \\\\\\n 'College student', 'College graduate', 'Law school graduate']\\n i = 0\\n if index > 100:\\n return grade_level [0]\\n elif index < 0:\\n return grade_level [-1]\\n while i < len (grades):\\n if index <= grades [i] [0] and index >= grades [i] [1]:\\n return grade_level [i]\\n else:\\n i += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab4545b2f96e08737bdbe61524dfe5fb\",\n \"score\": \"0.43112177\",\n \"text\": \"def get_info(self):\\n\\t\\tprint(self.name)\\n\\t\\tprint(\\\"Grants a bonus of {} to SKILL.\\\\nGrants a bonus of {} to WILL.\\\\n\\\".format(self.skill_bonus, self.will_bonus))\\n\\t\\treturn\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ea00b4f386339a0d0d1d62ef1185e4a\",\n \"score\": \"0.43094763\",\n \"text\": \"def get_reward_list(self):\\n return self._gameRewards\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1192,"cells":{"query_id":{"kind":"string","value":"a5eb1395ef001d2fb07bc4f9a4250243"},"query":{"kind":"string","value":"Try to get application name, that we're connected to. If it couldn't get the name, it returns empty string."},"positive_passages":{"kind":"list like","value":[{"docid":"5e8df666c206686584ea9c6dc5fd265c","score":"0.77397156","text":"def __get_app_name():\n executer = service_manager.CommandExecuter()\n try:\n app_info = executer.execute_command('app.introduce()')\n return app_info['name']\n except Exception:\n #In case of any error, return empty string.\n return ''","title":""}],"string":"[\n {\n \"docid\": \"5e8df666c206686584ea9c6dc5fd265c\",\n \"score\": \"0.77397156\",\n \"text\": \"def __get_app_name():\\n executer = service_manager.CommandExecuter()\\n try:\\n app_info = executer.execute_command('app.introduce()')\\n return app_info['name']\\n except Exception:\\n #In case of any error, return empty string.\\n return ''\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"c00fe9e1c4301ef07e40d89efba46a17","score":"0.7748835","text":"def _get_application_name(self):\n return self.__application_name","title":""},{"docid":"6b05b966e198a38ec887dd965578dedd","score":"0.7464836","text":"def application_name(self):\n if \"applicationName\" in self._prop_dict:\n return self._prop_dict[\"applicationName\"]\n else:\n return None","title":""},{"docid":"39732734cbc131374cd2a449cf1fed5a","score":"0.7341654","text":"def get_name(self):\n return self.application_name","title":""},{"docid":"2589e1db31eb2fe0787f28a78ba691a1","score":"0.73404837","text":"def app_name(self):\r\n return self._app_name","title":""},{"docid":"33997c4ef835d016d1df7819309009c4","score":"0.73021233","text":"def app_name(self):\n return self._app_name","title":""},{"docid":"33997c4ef835d016d1df7819309009c4","score":"0.73021233","text":"def app_name(self):\n return self._app_name","title":""},{"docid":"b7e9e5e60b1b3eceb8a32295ebd8d6e3","score":"0.72788143","text":"def get_app_name(self):\n return self.appname + \" \"","title":""},{"docid":"6bc759d5867708ac97db4b77daa3669c","score":"0.7241777","text":"def app_name(self) -> str:\n return self._app_name","title":""},{"docid":"7fd660b74a55952d7e343d4935b13fc5","score":"0.7158058","text":"def app_name(self):\n if self._app_name is None:\n app_name = self.manifest.app_name or self.name\n self._app_name = app_name.split(\".\")[-1]\n return self._app_name","title":""},{"docid":"f693aa2667c12a5c2af8b6785d83c61e","score":"0.6965545","text":"def get_app(self):\n head, _ = os.path.split(self.dcpath)\n _, name = os.path.split(head)\n return name","title":""},{"docid":"e678b6ecb2cdc2ee34de80283a5b0d33","score":"0.6716414","text":"def app_name():\n appname = run(\"basename -s .git `git config --get remote.origin.url`\", shell=True)\n return appname","title":""},{"docid":"4070d5fbc8cdca3f2d5fe8f4c14a076c","score":"0.6642829","text":"def application_name():\n return os.path.split(sys.argv[0])[1]","title":""},{"docid":"f8314858e60f4506b6cab51803ed44af","score":"0.66051465","text":"def name():\n if _state.PLATFORM_NAME is None:\n _state.PLATFORM_NAME = _get_name()\n return _state.PLATFORM_NAME","title":""},{"docid":"81e1fcdf9fadf73df53e1971cf1b7aee","score":"0.6405698","text":"def get_prompt_string():\n global APP_NAME\n if APP_NAME is None:\n #Try getting application name.\n APP_NAME = __get_app_name()\n \n prompt = SETTINGS_DICT['command_prefix']\n# if prompt.endswith('.'):\n# prompt = prompt[:len(prompt)-1]\n if prompt:\n prompt = prompt.replace('.', '/')\n \n return '{0}/{1}> '.format(APP_NAME, prompt)","title":""},{"docid":"e8e9aaaf1f418d8bba2f39c99c7c5520","score":"0.6371456","text":"def device_name(self) -> Optional[str]:\n return pulumi.get(self, \"device_name\")","title":""},{"docid":"eed31c02e821e59d6f0245d9fdb9b0e7","score":"0.6357206","text":"def get_app_name(bundle_id):\n app_path = NSWorkspace.sharedWorkspace().absolutePathForAppBundleWithIdentifier_(bundle_id)\n if not app_path:\n app_name = \"SYSTEM\"\n else:\n app_name = NSFileManager.defaultManager().displayNameAtPath_(app_path)\n return app_name","title":""},{"docid":"9f4ce5b960c5dba96bdc412ffb096c2e","score":"0.63334155","text":"def get_app(self):\n git_root = self.git_root()\n remotes = subprocess.check_output(['git', 'remote', '-v'], cwd=git_root)\n if remotes is None:\n raise EnvironmentError('No git remotes found.')\n for remote in remotes.splitlines():\n if 'github.com' in remote:\n url = remote.split()[1]\n break\n if url is None:\n raise EnvironmentError('No Github remotes found.')\n pieces = url.split('/')\n owner = pieces[-2].split(':')[-1]\n repo = pieces[-1].replace('.git', '')\n app_raw = owner + '/' + repo\n app_name = app_raw.replace('-', '--').replace('/', '-')\n return app_name","title":""},{"docid":"81cee35fedec115bc577934b9094bd3a","score":"0.62990165","text":"def device_name(self):\n return self.info['name']","title":""},{"docid":"43865d3f6b778209f988d3367dc5473f","score":"0.6284212","text":"def get_application_name(self, participant):\n for app in self.deploy_map:\n for site in self.deploy_map[app]:\n if site == participant:\n return app\n return None","title":""},{"docid":"d80a70a72e5cedda3b605b8b841be146","score":"0.6266978","text":"def get_device_name(self, device):\n for client in self.online_clients:\n if client[\"mac\"] == device:\n return client[\"name\"]\n return None","title":""},{"docid":"b073f199a9fc0c02a253c893ccfeb5c1","score":"0.62309957","text":"def get_app_label(app):\n return app.__name__.split('.')[-2]","title":""},{"docid":"bed32e44819836ca28bc90f30f6a1d6c","score":"0.62280446","text":"def get_host_name():\n\n return socket.gethostname()","title":""},{"docid":"e7b0f2d3145a5a2b27b0408226d8d361","score":"0.62162864","text":"def computer_name(self) -> Optional[str]:\n return pulumi.get(self, \"computer_name\")","title":""},{"docid":"9754eaf511f2ba729e36370b4748bd94","score":"0.6197015","text":"def device_name(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"device_name\")","title":""},{"docid":"9754eaf511f2ba729e36370b4748bd94","score":"0.6197015","text":"def device_name(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"device_name\")","title":""},{"docid":"e142e974bc89c14c4261c7fcffae50c6","score":"0.619661","text":"def peer_connection_name(self) -> pulumi.Output[Optional[str]]:\n return pulumi.get(self, \"peer_connection_name\")","title":""},{"docid":"9c2d7d794966c9cfdd25a96d56f8beb2","score":"0.6192331","text":"def getWirelessName(self):\n i, result = self.iwstruct.iw_get_ext(self.ifname, \n SIOCGIWNAME)\n if i > 0:\n return result\n return result.split('\\0')[0]","title":""},{"docid":"f26e1d9b3e6f44997e7586ebe2cf5573","score":"0.6181147","text":"def get_device_name(self, device):\n if not self.last_results:\n return None\n for client in self.last_results:\n if client[\"mac\"] == device:\n return client[\"name\"]\n return None","title":""},{"docid":"7124186deb8da9b5a4fd9890ebf11954","score":"0.61766523","text":"def client_application(self) -> Optional[str]:\n return pulumi.get(self, \"client_application\")","title":""},{"docid":"3c092bc2a37adf4531d5d700498fb068","score":"0.6174697","text":"def peer_connection_name(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"peer_connection_name\")","title":""},{"docid":"3c092bc2a37adf4531d5d700498fb068","score":"0.6174697","text":"def peer_connection_name(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"peer_connection_name\")","title":""},{"docid":"99cb556b2971576fdfbe73c1854fd616","score":"0.6173118","text":"def device_name(self) -> str:\n assert isinstance(self._env['device_name'], str)\n return self._env['device_name']","title":""},{"docid":"20105cb5b666656fc33a3303e2193245","score":"0.61559826","text":"def get_device_name(self, device):\n if not self.last_results:\n return None\n for client in self.last_results:\n if client['mac'] == device:\n return client['name']\n return None","title":""},{"docid":"015cf96a3692504172c46ea009a34757","score":"0.61285526","text":"def get_base_name(self):\r\n\t\ttry:\r\n\t\t\tself.base_name\r\n\t\texcept:\r\n\t\t\tappname = getattr(Context.g_module, Context.APPNAME, 'noname')\r\n\t\t\tversion = getattr(Context.g_module, Context.VERSION, '1.0')\r\n\t\t\tself.base_name = appname + '-' + version\r\n\t\treturn self.base_name","title":""},{"docid":"1d4caf5c9ce3db8646c5783cf4423863","score":"0.6120303","text":"def get_user_name() -> str:\n return connexion.context[USER_NAME]","title":""},{"docid":"18211b943ce4100d690a8fd123ba5d7d","score":"0.6117219","text":"def name(self):\n if self._siteName == \"\":\n return self._deviceName\n return \"{}: {}\".format(self._siteName, self._deviceName)","title":""},{"docid":"c22a11ca00bf105df88f6107d7903250","score":"0.61168706","text":"def get_daemon_name(self):\n return self.daemon_name if self.daemon_name else self.appname","title":""},{"docid":"048ddb79aa574ba9ed23d37a441fb38a","score":"0.6110233","text":"def get_con_name(self):\n return self.get_name()","title":""},{"docid":"b1e739b3a0a8861acba66eb60fb1aa05","score":"0.60953015","text":"def device_name(self) -> str:\n return pulumi.get(self, \"device_name\")","title":""},{"docid":"0e3ff17e9c592f907bd2535f064250b4","score":"0.60936487","text":"def name(self) -> str:\n return self.client.name or self.client.hostname","title":""},{"docid":"a236ab714327fc1d1efed618877292c2","score":"0.6070656","text":"def computer_name(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"computer_name\")","title":""},{"docid":"a236ab714327fc1d1efed618877292c2","score":"0.6070656","text":"def computer_name(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"computer_name\")","title":""},{"docid":"7d07596a5aadc6e25fb0bcf2ff2aab44","score":"0.60612166","text":"def appName(self):\n raise NotImplementedError()","title":""},{"docid":"6eca66f78d3998afae5869c33077fbb9","score":"0.60582924","text":"def servername():\n return socket.gethostname().split(\".\")[0]","title":""},{"docid":"b50c0d00f726e325516ae6af52e1eee4","score":"0.6039016","text":"def primary_connection_string(self) -> Optional[str]:\n return pulumi.get(self, \"primary_connection_string\")","title":""},{"docid":"b7119fa54670e31888bbadb3c230cb9d","score":"0.60336596","text":"def host_name(self) -> Optional[str]:\n return pulumi.get(self, \"host_name\")","title":""},{"docid":"b7119fa54670e31888bbadb3c230cb9d","score":"0.60336596","text":"def host_name(self) -> Optional[str]:\n return pulumi.get(self, \"host_name\")","title":""},{"docid":"d2b9909f6a8b7aedc3acc693df6f125b","score":"0.6032223","text":"def derive_launch_name(self, app_name):\n if self.LAUNCH_NAME in self._configoptions:\n return self._configoptions[self.LAUNCH_NAME]\n else:\n launch_names = self._find_all_entity_names(\"sample\")\n if not launch_names:\n launch_names = self._find_all_entity_names(\"appresult\")\n if len(launch_names) > 3:\n contracted_names = launch_names[:3] + [\"%dmore\" % (len(launch_names) - 3)]\n launch_instance_name = \"+\".join(contracted_names)\n else:\n launch_instance_name = \"+\".join(launch_names)\n return \"%s : %s\" % (app_name, launch_instance_name)","title":""},{"docid":"35bd002ae7c38c394af9cf2af2cd3bc9","score":"0.6005484","text":"def host_name():\n return platform.node()","title":""},{"docid":"08503125b7945c3f23cddc71265b2fb6","score":"0.6001447","text":"def name(self):\n return self._device_info.device_name","title":""},{"docid":"7a1eecac07954a0deb0f5641b011fcdb","score":"0.5994097","text":"def _get_system_name(self):\n return self.__system_name","title":""},{"docid":"7a1eecac07954a0deb0f5641b011fcdb","score":"0.5994097","text":"def _get_system_name(self):\n return self.__system_name","title":""},{"docid":"7a1eecac07954a0deb0f5641b011fcdb","score":"0.5994097","text":"def _get_system_name(self):\n return self.__system_name","title":""},{"docid":"b3459fc407487c2549708441f174a619","score":"0.5981338","text":"def get_host_name():\n global host_name\n return host_name","title":""},{"docid":"3176ed2507525d35efa32b3fe9c5c409","score":"0.5979681","text":"def name(self) -> str | None:\n return self._device.name","title":""},{"docid":"de72c2e7a87f28baab3d1251b8a69f1a","score":"0.59776795","text":"def getUserName(self):\r\n if self._activeUser is not None:\r\n return self._activeUser.getUserName()\r\n return 'None'","title":""},{"docid":"64827eb999ea4ef6f72921d6f3d410b3","score":"0.59765327","text":"def get_app(name_or_uri, global_conf=None):","title":""},{"docid":"00266b48b6e3d89a3d26a3774c34155b","score":"0.59574443","text":"def get_host_name(self):\n return_value = self.current_node.current_controller.get_host_name()\n return return_value","title":""},{"docid":"13574afab5e9b9d1ca7bfebf502b0339","score":"0.59555405","text":"def host_name(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"host_name\")","title":""},{"docid":"8fdaa5f69ef5f8e1d3c520ab83eeeb3a","score":"0.5950556","text":"def device_name(self):\n return self._device_name","title":""},{"docid":"8fdaa5f69ef5f8e1d3c520ab83eeeb3a","score":"0.5950556","text":"def device_name(self):\n return self._device_name","title":""},{"docid":"b671d3f577e298be54fbab6775d49218","score":"0.59486705","text":"def active_name():\n return env_vars().get(\"CONDA_DEFAULT_ENV\", None)","title":""},{"docid":"05cf739971c874dd4a9c249fabe36574","score":"0.59392995","text":"async def _read_app_state(self, appname):\n import xml.etree.ElementTree as ET\n url = self.broadcast.get_app_url()\n if url:\n try:\n async with aiohttp.ClientSession() as session:\n async with session.get(url + appname) as resp:\n text = await resp.text()\n root = ET.fromstring(text)\n return root[2].text\n except aiohttp.ClientError:\n pass\n return \"stopped\"","title":""},{"docid":"b4ced5adab3f6357b4b2746e8c2f2085","score":"0.5923644","text":"def name(self):\n return self.device_name","title":""},{"docid":"b4ced5adab3f6357b4b2746e8c2f2085","score":"0.5923644","text":"def name(self):\n return self.device_name","title":""},{"docid":"693da69ec3adf6db5564168994421c65","score":"0.5923249","text":"def get_app_label(self):\n return self.get_ct().app_label","title":""},{"docid":"cd4e8c866569999c852daf1cfefcd4ef","score":"0.59044594","text":"def resolveApplicationName(self, name):\n if not self.directory.has_key(name):\n raise KeyError(\"Application %s not found\" % name)\n return self.directory[name]","title":""},{"docid":"432cca8b2838f44ec9593e04cd28f395","score":"0.58990604","text":"def get_device_name(self, device):\n _LOGGER.debug(\"[\"+self.server+\"] \"+\"Called get_device_name with device = \"+str(device))\n filter_named = [result.last_ssid for result in self.last_results\n if result.mac == device]\n\n if filter_named:\n _LOGGER.debug(\"[\"+self.server+\"] \"+\"Returning name \" + str(filter_named[0]) + \" for client \"+ str(device) )\n return filter_named[0]\n return None","title":""},{"docid":"2c48bec2ce83f7518adf8e5a96d60f96","score":"0.5879398","text":"def thing_name(self) -> str:\n\t\treturn self._device_information[\"data\"][\"getDevice\"][\"thing_name\"]","title":""},{"docid":"880dd4cfaf1c6739a33a5eba8cf2379c","score":"0.58686143","text":"def connector_name(self) -> pulumi.Output[Optional[str]]:\n return pulumi.get(self, \"connector_name\")","title":""},{"docid":"dfa72f446e720e58318783895d9c905b","score":"0.5863034","text":"def __str__(self):\n return self.app_name","title":""},{"docid":"f2fdcb3e5f3fa3946b9c451a13842495","score":"0.5861583","text":"def welcome_get_name():\n\n print('Welcome to the Brain Games!')\n\n name = prompt.string('May I have your name? ')\n\n print('Hello, {0}!'.format(name))\n\n return name","title":""},{"docid":"868ae3c038d31cc8de7308767b6b0792","score":"0.585989","text":"def appconfig_application(self) -> str:\n return self._appconfig_application","title":""},{"docid":"eee9130362d7b4cb5886e0d550cdde95","score":"0.58585393","text":"def app_id(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"app_id\")","title":""},{"docid":"7cf04b92370f335addde60a63e4ef68c","score":"0.58526415","text":"def GetHostName():\r\n pass","title":""},{"docid":"fb5935763de00ca73927a461cec2cdf3","score":"0.5852345","text":"def detect_app(self, context, name):\n if isinstance(name, Application):\n return name\n if not name:\n raise errors.UnknownAppError(\n \"Value {} is not a valid app or lib name\".format(context.expr(name))\n )\n try:\n if \".\" in name:\n apps = self.apps_by_lib[name]\n if not apps:\n raise KeyError(\"No app called '{}'\".format(name))\n if len(apps) != 1:\n _app = context[\".app\"]\n if _app and _app.name in apps:\n return _app\n for c in reversed(context[\"._callstack\"]):\n if c.app.name in apps:\n return c.app\n raise errors.AmbiguousAppError(name, apps)\n return self.apps[apps[0]]\n else:\n return self.apps[name]\n except KeyError:\n raise errors.UnknownAppError(app=name)","title":""},{"docid":"d5c6416eb0ed0cd7d504f20a15d3ae48","score":"0.58498573","text":"def _get_app_by_name(self, aws_info, appname):\n for i, app in enumerate(aws_info):\n if app[\"name\"] == appname:\n return app","title":""},{"docid":"6214a921bb439745540fba27caf00841","score":"0.5842732","text":"def computer_name(self) -> str:\n return pulumi.get(self, \"computer_name\")","title":""},{"docid":"0b12a0ca51f19813bafd02504f3918ca","score":"0.58268625","text":"def app_version_name(self) -> str:\n return self.app_metadata[log_extras.app_version_name]","title":""},{"docid":"e141f7aa7f0767fb4be1e29613a2eaac","score":"0.58194864","text":"def get_appinfo_from_name(app_name):\n info = app_names.ROOK_APP_NAME_LOOKUP.get(app_name, None)\n if info:\n return RAppInfo(info)\n\n return None","title":""},{"docid":"63c777713e01121009814e9d6922fe20","score":"0.58150524","text":"def get_connection_name(short_name):\n # When running tests, always use the default connection. This avoids\n # problems with having both a default connection and a metro-specific one.\n if getattr(settings, 'TESTING', False):\n return 'default'\n return short_name","title":""},{"docid":"2d8601384e7f33f6a9a0dc7bfabd19d1","score":"0.5810202","text":"def client_getname(self, encoding=_NOTSET):\n return self.execute(b'CLIENT', b'GETNAME', encoding=encoding)","title":""},{"docid":"643578d6e9f3095cc0ccb69276a6d6ae","score":"0.5806756","text":"def get_host(self):\n return os.uname()[1]","title":""},{"docid":"effcbeae9cca77b1727f314e6d6002fa","score":"0.5797212","text":"def host_name(self) -> str:\n return pulumi.get(self, \"host_name\")","title":""},{"docid":"0b78d55dcabb4dbd6e85297ebea31d38","score":"0.5796956","text":"def program_name():\n return os.path.basename(sys.argv[0])","title":""},{"docid":"b5dd6efb714162ba49678e83e2df5823","score":"0.57863903","text":"def server_name(self) -> Optional[str]:\n return pulumi.get(self, \"server_name\")","title":""},{"docid":"066cb9d74e8f22231670e16f27eff142","score":"0.5782645","text":"def get_client_name_from_hostname(self, host_name):\n try:\n\n self.log.info(\"Getting client name for Client for %s \" % host_name)\n _query = \"select name from APP_Client where net_hostname = '%s'\" % host_name\n\n self.csdb.execute(_query)\n _results = self.csdb.fetch_one_row()\n return _results[0]\n\n except Exception as err:\n self.log.exception(\n \"An exception occured in getting the client ID %s\" % err)\n raise err","title":""},{"docid":"883129c194a0dce1d7b8b8b6c6f4ad58","score":"0.5778496","text":"def name(self):\n # ask the nameserver for the name that corresponds to my key\n return self.pyre_nameserver.getName(self.key) if self.key else None","title":""},{"docid":"55e8c1c35cc47eb07ad0167296e6ee55","score":"0.57768255","text":"def app_id(self):\n return self._app_id or self._services['default'].data.get('application')","title":""},{"docid":"7113839fa471d449764ae342ca56998e","score":"0.5776333","text":"def userDefinedDeviceName(self):\n\n return self.device.get(\"userDefinedDeviceName\")","title":""},{"docid":"5fa18214d92470bfb732f4611a84d6c8","score":"0.5775967","text":"def name(self):\n return self._coordinator.get_device_name() + \" \" + self._name","title":""},{"docid":"5fa18214d92470bfb732f4611a84d6c8","score":"0.5775967","text":"def name(self):\n return self._coordinator.get_device_name() + \" \" + self._name","title":""},{"docid":"5fa18214d92470bfb732f4611a84d6c8","score":"0.5775967","text":"def name(self):\n return self._coordinator.get_device_name() + \" \" + self._name","title":""},{"docid":"df21ff691084be4f4c99100aa7cea964","score":"0.57756644","text":"def get_hostname():\n request = get_request()\n if request is None:\n return\n return request.get_host().split(':')[0].lower()","title":""},{"docid":"c0acfda7932e4880dd0bef944d91b4cd","score":"0.57738626","text":"def get_name(self):\n self._usb_ser.write(\"?\")\n str = self._usb_ser.read(15)\n return str","title":""},{"docid":"295e4c13a5b7503fed23feeab46cd16e","score":"0.57702196","text":"def name(self) -> str:\n return self.common_name or self.username","title":""},{"docid":"510c8e0788934744950295644f5f15e4","score":"0.57675886","text":"def appCodeName(self):\n raise NotImplementedError()","title":""},{"docid":"d28b7347df0e978ea56926826a13c40c","score":"0.5767375","text":"def get_progname():\n progname = sys.argv[0]\n if len(progname) == 0:\n raise ConfigError(\"No programe name available\")\n progname = os.path.basename(progname)\n mtch = re.match('(\\w+)', progname)\n if not mtch:\n raise ConfigError(\"Cannot convert prog name \" + progname)\n return mtch.group(0)","title":""},{"docid":"7ef26060209dd4c494714b750b283fe7","score":"0.576134","text":"def name(self) -> Optional[str]:\n return self.api.name","title":""},{"docid":"8788dd2f24e4ad751d09ee59c4600314","score":"0.57508713","text":"def servername(self) -> Optional[str]:\n return pulumi.get(self, \"servername\")","title":""}],"string":"[\n {\n \"docid\": \"c00fe9e1c4301ef07e40d89efba46a17\",\n \"score\": \"0.7748835\",\n \"text\": \"def _get_application_name(self):\\n return self.__application_name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b05b966e198a38ec887dd965578dedd\",\n \"score\": \"0.7464836\",\n \"text\": \"def application_name(self):\\n if \\\"applicationName\\\" in self._prop_dict:\\n return self._prop_dict[\\\"applicationName\\\"]\\n else:\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39732734cbc131374cd2a449cf1fed5a\",\n \"score\": \"0.7341654\",\n \"text\": \"def get_name(self):\\n return self.application_name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2589e1db31eb2fe0787f28a78ba691a1\",\n \"score\": \"0.73404837\",\n \"text\": \"def app_name(self):\\r\\n return self._app_name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33997c4ef835d016d1df7819309009c4\",\n \"score\": \"0.73021233\",\n \"text\": \"def app_name(self):\\n return self._app_name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33997c4ef835d016d1df7819309009c4\",\n \"score\": \"0.73021233\",\n \"text\": \"def app_name(self):\\n return self._app_name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7e9e5e60b1b3eceb8a32295ebd8d6e3\",\n \"score\": \"0.72788143\",\n \"text\": \"def get_app_name(self):\\n return self.appname + \\\" \\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6bc759d5867708ac97db4b77daa3669c\",\n \"score\": \"0.7241777\",\n \"text\": \"def app_name(self) -> str:\\n return self._app_name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7fd660b74a55952d7e343d4935b13fc5\",\n \"score\": \"0.7158058\",\n \"text\": \"def app_name(self):\\n if self._app_name is None:\\n app_name = self.manifest.app_name or self.name\\n self._app_name = app_name.split(\\\".\\\")[-1]\\n return self._app_name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f693aa2667c12a5c2af8b6785d83c61e\",\n \"score\": \"0.6965545\",\n \"text\": \"def get_app(self):\\n head, _ = os.path.split(self.dcpath)\\n _, name = os.path.split(head)\\n return name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e678b6ecb2cdc2ee34de80283a5b0d33\",\n \"score\": \"0.6716414\",\n \"text\": \"def app_name():\\n appname = run(\\\"basename -s .git `git config --get remote.origin.url`\\\", shell=True)\\n return appname\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4070d5fbc8cdca3f2d5fe8f4c14a076c\",\n \"score\": \"0.6642829\",\n \"text\": \"def application_name():\\n return os.path.split(sys.argv[0])[1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f8314858e60f4506b6cab51803ed44af\",\n \"score\": \"0.66051465\",\n \"text\": \"def name():\\n if _state.PLATFORM_NAME is None:\\n _state.PLATFORM_NAME = _get_name()\\n return _state.PLATFORM_NAME\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81e1fcdf9fadf73df53e1971cf1b7aee\",\n \"score\": \"0.6405698\",\n \"text\": \"def get_prompt_string():\\n global APP_NAME\\n if APP_NAME is None:\\n #Try getting application name.\\n APP_NAME = __get_app_name()\\n \\n prompt = SETTINGS_DICT['command_prefix']\\n# if prompt.endswith('.'):\\n# prompt = prompt[:len(prompt)-1]\\n if prompt:\\n prompt = prompt.replace('.', '/')\\n \\n return '{0}/{1}> '.format(APP_NAME, prompt)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e8e9aaaf1f418d8bba2f39c99c7c5520\",\n \"score\": \"0.6371456\",\n \"text\": \"def device_name(self) -> Optional[str]:\\n return pulumi.get(self, \\\"device_name\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eed31c02e821e59d6f0245d9fdb9b0e7\",\n \"score\": \"0.6357206\",\n \"text\": \"def get_app_name(bundle_id):\\n app_path = NSWorkspace.sharedWorkspace().absolutePathForAppBundleWithIdentifier_(bundle_id)\\n if not app_path:\\n app_name = \\\"SYSTEM\\\"\\n else:\\n app_name = NSFileManager.defaultManager().displayNameAtPath_(app_path)\\n return app_name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f4ce5b960c5dba96bdc412ffb096c2e\",\n \"score\": \"0.63334155\",\n \"text\": \"def get_app(self):\\n git_root = self.git_root()\\n remotes = subprocess.check_output(['git', 'remote', '-v'], cwd=git_root)\\n if remotes is None:\\n raise EnvironmentError('No git remotes found.')\\n for remote in remotes.splitlines():\\n if 'github.com' in remote:\\n url = remote.split()[1]\\n break\\n if url is None:\\n raise EnvironmentError('No Github remotes found.')\\n pieces = url.split('/')\\n owner = pieces[-2].split(':')[-1]\\n repo = pieces[-1].replace('.git', '')\\n app_raw = owner + '/' + repo\\n app_name = app_raw.replace('-', '--').replace('/', '-')\\n return app_name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81cee35fedec115bc577934b9094bd3a\",\n \"score\": \"0.62990165\",\n \"text\": \"def device_name(self):\\n return self.info['name']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43865d3f6b778209f988d3367dc5473f\",\n \"score\": \"0.6284212\",\n \"text\": \"def get_application_name(self, participant):\\n for app in self.deploy_map:\\n for site in self.deploy_map[app]:\\n if site == participant:\\n return app\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d80a70a72e5cedda3b605b8b841be146\",\n \"score\": \"0.6266978\",\n \"text\": \"def get_device_name(self, device):\\n for client in self.online_clients:\\n if client[\\\"mac\\\"] == device:\\n return client[\\\"name\\\"]\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b073f199a9fc0c02a253c893ccfeb5c1\",\n \"score\": \"0.62309957\",\n \"text\": \"def get_app_label(app):\\n return app.__name__.split('.')[-2]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bed32e44819836ca28bc90f30f6a1d6c\",\n \"score\": \"0.62280446\",\n \"text\": \"def get_host_name():\\n\\n return socket.gethostname()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e7b0f2d3145a5a2b27b0408226d8d361\",\n \"score\": \"0.62162864\",\n \"text\": \"def computer_name(self) -> Optional[str]:\\n return pulumi.get(self, \\\"computer_name\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9754eaf511f2ba729e36370b4748bd94\",\n \"score\": \"0.6197015\",\n \"text\": \"def device_name(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"device_name\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9754eaf511f2ba729e36370b4748bd94\",\n \"score\": \"0.6197015\",\n \"text\": \"def device_name(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"device_name\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e142e974bc89c14c4261c7fcffae50c6\",\n \"score\": \"0.619661\",\n \"text\": \"def peer_connection_name(self) -> pulumi.Output[Optional[str]]:\\n return pulumi.get(self, \\\"peer_connection_name\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c2d7d794966c9cfdd25a96d56f8beb2\",\n \"score\": \"0.6192331\",\n \"text\": \"def getWirelessName(self):\\n i, result = self.iwstruct.iw_get_ext(self.ifname, \\n SIOCGIWNAME)\\n if i > 0:\\n return result\\n return result.split('\\\\0')[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f26e1d9b3e6f44997e7586ebe2cf5573\",\n \"score\": \"0.6181147\",\n \"text\": \"def get_device_name(self, device):\\n if not self.last_results:\\n return None\\n for client in self.last_results:\\n if client[\\\"mac\\\"] == device:\\n return client[\\\"name\\\"]\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7124186deb8da9b5a4fd9890ebf11954\",\n \"score\": \"0.61766523\",\n \"text\": \"def client_application(self) -> Optional[str]:\\n return pulumi.get(self, \\\"client_application\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c092bc2a37adf4531d5d700498fb068\",\n \"score\": \"0.6174697\",\n \"text\": \"def peer_connection_name(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"peer_connection_name\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c092bc2a37adf4531d5d700498fb068\",\n \"score\": \"0.6174697\",\n \"text\": \"def peer_connection_name(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"peer_connection_name\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99cb556b2971576fdfbe73c1854fd616\",\n \"score\": \"0.6173118\",\n \"text\": \"def device_name(self) -> str:\\n assert isinstance(self._env['device_name'], str)\\n return self._env['device_name']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20105cb5b666656fc33a3303e2193245\",\n \"score\": \"0.61559826\",\n \"text\": \"def get_device_name(self, device):\\n if not self.last_results:\\n return None\\n for client in self.last_results:\\n if client['mac'] == device:\\n return client['name']\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"015cf96a3692504172c46ea009a34757\",\n \"score\": \"0.61285526\",\n \"text\": \"def get_base_name(self):\\r\\n\\t\\ttry:\\r\\n\\t\\t\\tself.base_name\\r\\n\\t\\texcept:\\r\\n\\t\\t\\tappname = getattr(Context.g_module, Context.APPNAME, 'noname')\\r\\n\\t\\t\\tversion = getattr(Context.g_module, Context.VERSION, '1.0')\\r\\n\\t\\t\\tself.base_name = appname + '-' + version\\r\\n\\t\\treturn self.base_name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d4caf5c9ce3db8646c5783cf4423863\",\n \"score\": \"0.6120303\",\n \"text\": \"def get_user_name() -> str:\\n return connexion.context[USER_NAME]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18211b943ce4100d690a8fd123ba5d7d\",\n \"score\": \"0.6117219\",\n \"text\": \"def name(self):\\n if self._siteName == \\\"\\\":\\n return self._deviceName\\n return \\\"{}: {}\\\".format(self._siteName, self._deviceName)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c22a11ca00bf105df88f6107d7903250\",\n \"score\": \"0.61168706\",\n \"text\": \"def get_daemon_name(self):\\n return self.daemon_name if self.daemon_name else self.appname\",\n \"title\": \"\"\n },\n {\n \"docid\": \"048ddb79aa574ba9ed23d37a441fb38a\",\n \"score\": \"0.6110233\",\n \"text\": \"def get_con_name(self):\\n return self.get_name()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1e739b3a0a8861acba66eb60fb1aa05\",\n \"score\": \"0.60953015\",\n \"text\": \"def device_name(self) -> str:\\n return pulumi.get(self, \\\"device_name\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e3ff17e9c592f907bd2535f064250b4\",\n \"score\": \"0.60936487\",\n \"text\": \"def name(self) -> str:\\n return self.client.name or self.client.hostname\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a236ab714327fc1d1efed618877292c2\",\n \"score\": \"0.6070656\",\n \"text\": \"def computer_name(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"computer_name\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a236ab714327fc1d1efed618877292c2\",\n \"score\": \"0.6070656\",\n \"text\": \"def computer_name(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"computer_name\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d07596a5aadc6e25fb0bcf2ff2aab44\",\n \"score\": \"0.60612166\",\n \"text\": \"def appName(self):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6eca66f78d3998afae5869c33077fbb9\",\n \"score\": \"0.60582924\",\n \"text\": \"def servername():\\n return socket.gethostname().split(\\\".\\\")[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b50c0d00f726e325516ae6af52e1eee4\",\n \"score\": \"0.6039016\",\n \"text\": \"def primary_connection_string(self) -> Optional[str]:\\n return pulumi.get(self, \\\"primary_connection_string\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7119fa54670e31888bbadb3c230cb9d\",\n \"score\": \"0.60336596\",\n \"text\": \"def host_name(self) -> Optional[str]:\\n return pulumi.get(self, \\\"host_name\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7119fa54670e31888bbadb3c230cb9d\",\n \"score\": \"0.60336596\",\n \"text\": \"def host_name(self) -> Optional[str]:\\n return pulumi.get(self, \\\"host_name\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d2b9909f6a8b7aedc3acc693df6f125b\",\n \"score\": \"0.6032223\",\n \"text\": \"def derive_launch_name(self, app_name):\\n if self.LAUNCH_NAME in self._configoptions:\\n return self._configoptions[self.LAUNCH_NAME]\\n else:\\n launch_names = self._find_all_entity_names(\\\"sample\\\")\\n if not launch_names:\\n launch_names = self._find_all_entity_names(\\\"appresult\\\")\\n if len(launch_names) > 3:\\n contracted_names = launch_names[:3] + [\\\"%dmore\\\" % (len(launch_names) - 3)]\\n launch_instance_name = \\\"+\\\".join(contracted_names)\\n else:\\n launch_instance_name = \\\"+\\\".join(launch_names)\\n return \\\"%s : %s\\\" % (app_name, launch_instance_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35bd002ae7c38c394af9cf2af2cd3bc9\",\n \"score\": \"0.6005484\",\n \"text\": \"def host_name():\\n return platform.node()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08503125b7945c3f23cddc71265b2fb6\",\n \"score\": \"0.6001447\",\n \"text\": \"def name(self):\\n return self._device_info.device_name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a1eecac07954a0deb0f5641b011fcdb\",\n \"score\": \"0.5994097\",\n \"text\": \"def _get_system_name(self):\\n return self.__system_name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a1eecac07954a0deb0f5641b011fcdb\",\n \"score\": \"0.5994097\",\n \"text\": \"def _get_system_name(self):\\n return self.__system_name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a1eecac07954a0deb0f5641b011fcdb\",\n \"score\": \"0.5994097\",\n \"text\": \"def _get_system_name(self):\\n return self.__system_name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b3459fc407487c2549708441f174a619\",\n \"score\": \"0.5981338\",\n \"text\": \"def get_host_name():\\n global host_name\\n return host_name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3176ed2507525d35efa32b3fe9c5c409\",\n \"score\": \"0.5979681\",\n \"text\": \"def name(self) -> str | None:\\n return self._device.name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de72c2e7a87f28baab3d1251b8a69f1a\",\n \"score\": \"0.59776795\",\n \"text\": \"def getUserName(self):\\r\\n if self._activeUser is not None:\\r\\n return self._activeUser.getUserName()\\r\\n return 'None'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"64827eb999ea4ef6f72921d6f3d410b3\",\n \"score\": \"0.59765327\",\n \"text\": \"def get_app(name_or_uri, global_conf=None):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00266b48b6e3d89a3d26a3774c34155b\",\n \"score\": \"0.59574443\",\n \"text\": \"def get_host_name(self):\\n return_value = self.current_node.current_controller.get_host_name()\\n return return_value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"13574afab5e9b9d1ca7bfebf502b0339\",\n \"score\": \"0.59555405\",\n \"text\": \"def host_name(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"host_name\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8fdaa5f69ef5f8e1d3c520ab83eeeb3a\",\n \"score\": \"0.5950556\",\n \"text\": \"def device_name(self):\\n return self._device_name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8fdaa5f69ef5f8e1d3c520ab83eeeb3a\",\n \"score\": \"0.5950556\",\n \"text\": \"def device_name(self):\\n return self._device_name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b671d3f577e298be54fbab6775d49218\",\n \"score\": \"0.59486705\",\n \"text\": \"def active_name():\\n return env_vars().get(\\\"CONDA_DEFAULT_ENV\\\", None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"05cf739971c874dd4a9c249fabe36574\",\n \"score\": \"0.59392995\",\n \"text\": \"async def _read_app_state(self, appname):\\n import xml.etree.ElementTree as ET\\n url = self.broadcast.get_app_url()\\n if url:\\n try:\\n async with aiohttp.ClientSession() as session:\\n async with session.get(url + appname) as resp:\\n text = await resp.text()\\n root = ET.fromstring(text)\\n return root[2].text\\n except aiohttp.ClientError:\\n pass\\n return \\\"stopped\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b4ced5adab3f6357b4b2746e8c2f2085\",\n \"score\": \"0.5923644\",\n \"text\": \"def name(self):\\n return self.device_name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b4ced5adab3f6357b4b2746e8c2f2085\",\n \"score\": \"0.5923644\",\n \"text\": \"def name(self):\\n return self.device_name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"693da69ec3adf6db5564168994421c65\",\n \"score\": \"0.5923249\",\n \"text\": \"def get_app_label(self):\\n return self.get_ct().app_label\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd4e8c866569999c852daf1cfefcd4ef\",\n \"score\": \"0.59044594\",\n \"text\": \"def resolveApplicationName(self, name):\\n if not self.directory.has_key(name):\\n raise KeyError(\\\"Application %s not found\\\" % name)\\n return self.directory[name]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"432cca8b2838f44ec9593e04cd28f395\",\n \"score\": \"0.58990604\",\n \"text\": \"def get_device_name(self, device):\\n _LOGGER.debug(\\\"[\\\"+self.server+\\\"] \\\"+\\\"Called get_device_name with device = \\\"+str(device))\\n filter_named = [result.last_ssid for result in self.last_results\\n if result.mac == device]\\n\\n if filter_named:\\n _LOGGER.debug(\\\"[\\\"+self.server+\\\"] \\\"+\\\"Returning name \\\" + str(filter_named[0]) + \\\" for client \\\"+ str(device) )\\n return filter_named[0]\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c48bec2ce83f7518adf8e5a96d60f96\",\n \"score\": \"0.5879398\",\n \"text\": \"def thing_name(self) -> str:\\n\\t\\treturn self._device_information[\\\"data\\\"][\\\"getDevice\\\"][\\\"thing_name\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"880dd4cfaf1c6739a33a5eba8cf2379c\",\n \"score\": \"0.58686143\",\n \"text\": \"def connector_name(self) -> pulumi.Output[Optional[str]]:\\n return pulumi.get(self, \\\"connector_name\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dfa72f446e720e58318783895d9c905b\",\n \"score\": \"0.5863034\",\n \"text\": \"def __str__(self):\\n return self.app_name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f2fdcb3e5f3fa3946b9c451a13842495\",\n \"score\": \"0.5861583\",\n \"text\": \"def welcome_get_name():\\n\\n print('Welcome to the Brain Games!')\\n\\n name = prompt.string('May I have your name? ')\\n\\n print('Hello, {0}!'.format(name))\\n\\n return name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"868ae3c038d31cc8de7308767b6b0792\",\n \"score\": \"0.585989\",\n \"text\": \"def appconfig_application(self) -> str:\\n return self._appconfig_application\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eee9130362d7b4cb5886e0d550cdde95\",\n \"score\": \"0.58585393\",\n \"text\": \"def app_id(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"app_id\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7cf04b92370f335addde60a63e4ef68c\",\n \"score\": \"0.58526415\",\n \"text\": \"def GetHostName():\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb5935763de00ca73927a461cec2cdf3\",\n \"score\": \"0.5852345\",\n \"text\": \"def detect_app(self, context, name):\\n if isinstance(name, Application):\\n return name\\n if not name:\\n raise errors.UnknownAppError(\\n \\\"Value {} is not a valid app or lib name\\\".format(context.expr(name))\\n )\\n try:\\n if \\\".\\\" in name:\\n apps = self.apps_by_lib[name]\\n if not apps:\\n raise KeyError(\\\"No app called '{}'\\\".format(name))\\n if len(apps) != 1:\\n _app = context[\\\".app\\\"]\\n if _app and _app.name in apps:\\n return _app\\n for c in reversed(context[\\\"._callstack\\\"]):\\n if c.app.name in apps:\\n return c.app\\n raise errors.AmbiguousAppError(name, apps)\\n return self.apps[apps[0]]\\n else:\\n return self.apps[name]\\n except KeyError:\\n raise errors.UnknownAppError(app=name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5c6416eb0ed0cd7d504f20a15d3ae48\",\n \"score\": \"0.58498573\",\n \"text\": \"def _get_app_by_name(self, aws_info, appname):\\n for i, app in enumerate(aws_info):\\n if app[\\\"name\\\"] == appname:\\n return app\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6214a921bb439745540fba27caf00841\",\n \"score\": \"0.5842732\",\n \"text\": \"def computer_name(self) -> str:\\n return pulumi.get(self, \\\"computer_name\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b12a0ca51f19813bafd02504f3918ca\",\n \"score\": \"0.58268625\",\n \"text\": \"def app_version_name(self) -> str:\\n return self.app_metadata[log_extras.app_version_name]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e141f7aa7f0767fb4be1e29613a2eaac\",\n \"score\": \"0.58194864\",\n \"text\": \"def get_appinfo_from_name(app_name):\\n info = app_names.ROOK_APP_NAME_LOOKUP.get(app_name, None)\\n if info:\\n return RAppInfo(info)\\n\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"63c777713e01121009814e9d6922fe20\",\n \"score\": \"0.58150524\",\n \"text\": \"def get_connection_name(short_name):\\n # When running tests, always use the default connection. This avoids\\n # problems with having both a default connection and a metro-specific one.\\n if getattr(settings, 'TESTING', False):\\n return 'default'\\n return short_name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d8601384e7f33f6a9a0dc7bfabd19d1\",\n \"score\": \"0.5810202\",\n \"text\": \"def client_getname(self, encoding=_NOTSET):\\n return self.execute(b'CLIENT', b'GETNAME', encoding=encoding)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"643578d6e9f3095cc0ccb69276a6d6ae\",\n \"score\": \"0.5806756\",\n \"text\": \"def get_host(self):\\n return os.uname()[1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"effcbeae9cca77b1727f314e6d6002fa\",\n \"score\": \"0.5797212\",\n \"text\": \"def host_name(self) -> str:\\n return pulumi.get(self, \\\"host_name\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b78d55dcabb4dbd6e85297ebea31d38\",\n \"score\": \"0.5796956\",\n \"text\": \"def program_name():\\n return os.path.basename(sys.argv[0])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5dd6efb714162ba49678e83e2df5823\",\n \"score\": \"0.57863903\",\n \"text\": \"def server_name(self) -> Optional[str]:\\n return pulumi.get(self, \\\"server_name\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"066cb9d74e8f22231670e16f27eff142\",\n \"score\": \"0.5782645\",\n \"text\": \"def get_client_name_from_hostname(self, host_name):\\n try:\\n\\n self.log.info(\\\"Getting client name for Client for %s \\\" % host_name)\\n _query = \\\"select name from APP_Client where net_hostname = '%s'\\\" % host_name\\n\\n self.csdb.execute(_query)\\n _results = self.csdb.fetch_one_row()\\n return _results[0]\\n\\n except Exception as err:\\n self.log.exception(\\n \\\"An exception occured in getting the client ID %s\\\" % err)\\n raise err\",\n \"title\": \"\"\n },\n {\n \"docid\": \"883129c194a0dce1d7b8b8b6c6f4ad58\",\n \"score\": \"0.5778496\",\n \"text\": \"def name(self):\\n # ask the nameserver for the name that corresponds to my key\\n return self.pyre_nameserver.getName(self.key) if self.key else None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55e8c1c35cc47eb07ad0167296e6ee55\",\n \"score\": \"0.57768255\",\n \"text\": \"def app_id(self):\\n return self._app_id or self._services['default'].data.get('application')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7113839fa471d449764ae342ca56998e\",\n \"score\": \"0.5776333\",\n \"text\": \"def userDefinedDeviceName(self):\\n\\n return self.device.get(\\\"userDefinedDeviceName\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5fa18214d92470bfb732f4611a84d6c8\",\n \"score\": \"0.5775967\",\n \"text\": \"def name(self):\\n return self._coordinator.get_device_name() + \\\" \\\" + self._name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5fa18214d92470bfb732f4611a84d6c8\",\n \"score\": \"0.5775967\",\n \"text\": \"def name(self):\\n return self._coordinator.get_device_name() + \\\" \\\" + self._name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5fa18214d92470bfb732f4611a84d6c8\",\n \"score\": \"0.5775967\",\n \"text\": \"def name(self):\\n return self._coordinator.get_device_name() + \\\" \\\" + self._name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df21ff691084be4f4c99100aa7cea964\",\n \"score\": \"0.57756644\",\n \"text\": \"def get_hostname():\\n request = get_request()\\n if request is None:\\n return\\n return request.get_host().split(':')[0].lower()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0acfda7932e4880dd0bef944d91b4cd\",\n \"score\": \"0.57738626\",\n \"text\": \"def get_name(self):\\n self._usb_ser.write(\\\"?\\\")\\n str = self._usb_ser.read(15)\\n return str\",\n \"title\": \"\"\n },\n {\n \"docid\": \"295e4c13a5b7503fed23feeab46cd16e\",\n \"score\": \"0.57702196\",\n \"text\": \"def name(self) -> str:\\n return self.common_name or self.username\",\n \"title\": \"\"\n },\n {\n \"docid\": \"510c8e0788934744950295644f5f15e4\",\n \"score\": \"0.57675886\",\n \"text\": \"def appCodeName(self):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d28b7347df0e978ea56926826a13c40c\",\n \"score\": \"0.5767375\",\n \"text\": \"def get_progname():\\n progname = sys.argv[0]\\n if len(progname) == 0:\\n raise ConfigError(\\\"No programe name available\\\")\\n progname = os.path.basename(progname)\\n mtch = re.match('(\\\\w+)', progname)\\n if not mtch:\\n raise ConfigError(\\\"Cannot convert prog name \\\" + progname)\\n return mtch.group(0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ef26060209dd4c494714b750b283fe7\",\n \"score\": \"0.576134\",\n \"text\": \"def name(self) -> Optional[str]:\\n return self.api.name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8788dd2f24e4ad751d09ee59c4600314\",\n \"score\": \"0.57508713\",\n \"text\": \"def servername(self) -> Optional[str]:\\n return pulumi.get(self, \\\"servername\\\")\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1193,"cells":{"query_id":{"kind":"string","value":"78db8c500389249841d2390ed7b6e221"},"query":{"kind":"string","value":"Test a simple circuit with a single sample measurement for shot vectors"},"positive_passages":{"kind":"list like","value":[{"docid":"4d2d4d61ee906fb9a0477bedab95d5f0","score":"0.64724594","text":"def test_sample_shot_vector(self, max_workers, shots):\n x = np.array(0.732)\n shots = qml.measurements.Shots(shots)\n qs = qml.tape.QuantumScript([qml.RY(x, wires=0)], [qml.sample(wires=range(2))], shots=shots)\n\n dev = DefaultQubit2(max_workers=max_workers)\n result = dev.execute(qs)\n\n assert isinstance(result, tuple)\n assert len(result) == len(list(shots))\n\n assert all(isinstance(res, (float, np.ndarray)) for res in result)\n assert all(res.shape == (s, 2) for res, s in zip(result, shots))\n assert all(\n np.allclose(\n np.sum(res, axis=0).astype(np.float32) / s, [np.sin(x / 2) ** 2, 0], atol=0.1\n )\n for res, s in zip(result, shots)\n )","title":""}],"string":"[\n {\n \"docid\": \"4d2d4d61ee906fb9a0477bedab95d5f0\",\n \"score\": \"0.64724594\",\n \"text\": \"def test_sample_shot_vector(self, max_workers, shots):\\n x = np.array(0.732)\\n shots = qml.measurements.Shots(shots)\\n qs = qml.tape.QuantumScript([qml.RY(x, wires=0)], [qml.sample(wires=range(2))], shots=shots)\\n\\n dev = DefaultQubit2(max_workers=max_workers)\\n result = dev.execute(qs)\\n\\n assert isinstance(result, tuple)\\n assert len(result) == len(list(shots))\\n\\n assert all(isinstance(res, (float, np.ndarray)) for res in result)\\n assert all(res.shape == (s, 2) for res, s in zip(result, shots))\\n assert all(\\n np.allclose(\\n np.sum(res, axis=0).astype(np.float32) / s, [np.sin(x / 2) ** 2, 0], atol=0.1\\n )\\n for res, s in zip(result, shots)\\n )\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"839434dfcbb582513f5d2a98de523b9a","score":"0.6360883","text":"def test_sample_values(self, device, shots, tol):\n dev = device(1)\n par = 1.5708\n\n observable = qml.PauliZ(wires=[0])\n\n dev.apply(\n [\n qml.RX(par, wires=[0]),\n ],\n rotations=[*observable.diagonalizing_gates()]\n )\n\n dev._samples = dev.generate_samples()\n\n s1 = dev.sample(observable)\n\n # s1 should only contain 1 and -1\n assert np.allclose(s1 ** 2, 1, **tol)","title":""},{"docid":"2e4c317bcab30689d66d445862b215c7","score":"0.62718225","text":"def test_measurement_process_sample(self):\n dev = qml.device(\"default.qubit\", wires=2, shots=20)\n prod_op = Prod(qml.PauliX(wires=0), qml.PauliX(wires=1))\n\n @qml.qnode(dev)\n def my_circ():\n Prod(qml.Hadamard(0), qml.Hadamard(1))\n return qml.sample(op=prod_op)\n\n results = my_circ()\n\n assert len(results) == 20\n assert (results == 1).all()","title":""},{"docid":"e730f94d89cc5da552f6cc9e520e5ba5","score":"0.61450756","text":"def test_detect_metal(self):","title":""},{"docid":"b7f64f1af290ddfeb177c89b52b9a9fc","score":"0.6129349","text":"def testGetOneParticulateSampleAndDoBitChecking(self):\n \n request=\"spectrum=CURR, analysis=CURR\"\n cpt = 0\n total_t0 = time.time()\n \n #listOfSamplesToTest = self.getListOfSampleIDs('2008-12-24',endDate='2008-12-25',spectralQualif='FULL',nbOfElem='1')\n \n #sampleID = listOfSamplesToTest[0]\n sampleID = 967273\n \n # fetchnoble particulate\n fetcher = DBDataFetcher.getDataFetcher(self.mainConn,self.archConn,sampleID)\n \n fetcher.fetch(request,'PAR')\n \n renderer = GenieParticulateRenderer(fetcher)\n \n xmlStr = renderer.asXmlStr(request)\n \n path = \"/tmp/samples/sampml-full-%s.xml\"%(sampleID)\n \n ctbto.common.xml_utils.pretty_print_xml(StringIO.StringIO(xmlStr),path)\n \n # check if no tags are left\n self.assertIfNoTagsLeft(path)\n \n self.assertAllCalibrationInfo(path)\n \n self.assertFileContentEquals(\"%s/samples/sampml-full-%s.xml.master\"%(self._get_tests_dir_path(),sampleID),path)\n \n cpt +=1\n \n total_t1 = time.time()\n \n TestSAMPMLCreator.c_log.info(\"\\n****************************************************************************\\n****************************************************************************\\n****** EXECUTED %d FULL SAMPLE RETRIEVALS in %s seconds ********\\n****************************************************************************\\n****************************************************************************\\n\"%(cpt,total_t1-total_t0))","title":""},{"docid":"3ee23e3c5098de7a51c66aa468fa1bfe","score":"0.6090496","text":"def test_init_circuit(self):\n # random unitaries\n u0 = random_unitary(2).data\n u1 = random_unitary(2).data\n # add to circuit\n qr = QuantumRegister(2)\n circ = QuantumCircuit(qr)\n circ.unitary(u0, [qr[0]])\n circ.unitary(u1, [qr[1]])\n target_vec = Statevector(np.kron(u1, u0).dot([1, 0, 0, 0]))\n target = DensityMatrix(target_vec)\n rho = DensityMatrix(circ)\n self.assertEqual(rho, target)\n\n # Test tensor product of 1-qubit gates\n circuit = QuantumCircuit(3)\n circuit.h(0)\n circuit.x(1)\n circuit.ry(np.pi / 2, 2)\n target = DensityMatrix.from_label(\"000\").evolve(Operator(circuit))\n rho = DensityMatrix(circuit)\n self.assertEqual(rho, target)\n\n # Test decomposition of Controlled-Phase gate\n lam = np.pi / 4\n circuit = QuantumCircuit(2)\n circuit.h(0)\n circuit.h(1)\n circuit.cp(lam, 0, 1)\n target = DensityMatrix.from_label(\"00\").evolve(Operator(circuit))\n rho = DensityMatrix(circuit)\n self.assertEqual(rho, target)","title":""},{"docid":"695faebb1d2c829c41a32a085f378eaa","score":"0.601816","text":"def test_mocked_engine_run_samples_one_mode(self, monkeypatch):\n modes = [0]\n shots = 10\n monkeypatch.setattr(\"strawberryfields.RemoteEngine\", MockEngine)\n dev = qml.device(\"strawberryfields.remote\", backend=\"X8\", shots=shots)\n\n @qml.qnode(dev)\n def quantum_function(theta, phi):\n qml.Beamsplitter(theta, phi, wires=[0, 1])\n qml.Beamsplitter(theta, phi, wires=[4, 5])\n return qml.sample(qml.TensorN(wires=modes))\n\n a = quantum_function(1.0, 0)\n\n expected = np.array([3, 4, 2, 4, 4, 1, 2, 1, 1, 1])\n assert a.shape == (shots,)\n assert np.array_equal(a, expected)","title":""},{"docid":"ca3ff68422b1d59423edbb89aa2516af","score":"0.6017791","text":"def test_synth_full(self, num_qubits):\n rng = np.random.default_rng(1234)\n samples = 50\n for _ in range(samples):\n circ = random_clifford_circuit(num_qubits, 5 * num_qubits, seed=rng)\n target = Clifford(circ)\n value = Clifford(synth_clifford_full(target))\n self.assertEqual(value, target)","title":""},{"docid":"d0f319df35692bd92e266a9ac08e218d","score":"0.5985815","text":"def test_dummy_vector(test_client):\n assert len(test_client.dummy_vector(512)) == 512","title":""},{"docid":"b09de90b43642e1aad94fe3f08efbc0f","score":"0.59637326","text":"def test_execute_one_circuit_simulator_online(self, qe_token, qe_url):\n provider = IBMQProvider(qe_token, qe_url)\n backend = provider.get_backend('ibmq_qasm_simulator')\n qr = QuantumRegister(1)\n cr = ClassicalRegister(1)\n qc = QuantumCircuit(qr, cr)\n qc.h(qr[0])\n qc.measure(qr[0], cr[0])\n qobj = transpiler.compile(qc, backend, seed=73846087)\n shots = qobj.config.shots\n job = backend.run(qobj)\n result = job.result()\n counts = result.get_counts(qc)\n target = {'0': shots / 2, '1': shots / 2}\n threshold = 0.04 * shots\n self.assertDictAlmostEqual(counts, target, threshold)","title":""},{"docid":"c5112c9ae2261ab2138233133b82b1a9","score":"0.59583545","text":"def sample(self):\n pass","title":""},{"docid":"55a9ab7f2b07159b02d7759d6ec67a3a","score":"0.59305805","text":"def sample(self):\n\t\tpass","title":""},{"docid":"9ff215c4e58d959e769d1f395c50380f","score":"0.5930204","text":"def test_single_sample(self, max_workers):\n x = np.array(0.732)\n qs = qml.tape.QuantumScript([qml.RY(x, wires=0)], [qml.sample(wires=range(2))], shots=10000)\n\n dev = DefaultQubit2(max_workers=max_workers)\n result = dev.execute(qs)\n\n assert isinstance(result, (float, np.ndarray))\n assert result.shape == (10000, 2)\n assert np.allclose(\n np.sum(result, axis=0).astype(np.float32) / 10000, [np.sin(x / 2) ** 2, 0], atol=0.1\n )","title":""},{"docid":"01187d06c5e8d13815d6643947e060bb","score":"0.59286565","text":"def test_sample(self):\n case = self.reader.read(PSSE_DATA_FILE)\n\n self.assertEqual(len(case.buses), 42 + 4) # 4x 3-winding trx\n pl = 5\n self.assertAlmostEqual(case.buses[0].v_base, 21.6, pl)\n self.assertAlmostEqual(case.buses[41].v_base, 0.69, pl)\n self.assertAlmostEqual(case.buses[0].v_magnitude, 1.01, pl)\n self.assertAlmostEqual(case.buses[40].v_magnitude, 1.04738, pl)\n self.assertAlmostEqual(case.buses[0].v_angle, -10.4286, pl)\n self.assertAlmostEqual(case.buses[1].v_angle, -10.7806, pl)\n\n # Loads.\n load_buses = [b for b in case.buses if\n b.p_demand > 0.0 or b.q_demand > 0.0]\n self.assertEqual(len(load_buses), 15)\n self.assertAlmostEqual(case.buses[3].p_demand, 1200.0, pl)\n self.assertAlmostEqual(case.buses[3].q_demand, 360.0, pl)\n self.assertAlmostEqual(case.buses[34].p_demand, 12.0, pl)\n self.assertAlmostEqual(case.buses[34].q_demand, 5.0, pl)\n\n # Generators.\n self.assertEqual(len(case.generators), 15)\n self.assertAlmostEqual(case.generators[0].p, 750.0, pl)\n self.assertAlmostEqual(case.generators[0].q, 125.648, pl)\n self.assertAlmostEqual(case.generators[0].q_max, 400.0, pl)\n self.assertAlmostEqual(case.generators[0].q_min, -100.0, pl)\n self.assertAlmostEqual(case.generators[0].v_magnitude, 1.01, pl)\n self.assertAlmostEqual(case.generators[0].base_mva, 900.0, pl)\n self.assertTrue(case.generators[0].online)\n self.assertAlmostEqual(case.generators[0].p_max, 800.0, pl)\n self.assertAlmostEqual(case.generators[0].p_min, 50.0, pl)\n\n self.assertAlmostEqual(case.generators[14].p, 3.24, pl)\n self.assertAlmostEqual(case.generators[14].q, -1.475, pl)\n\n # Branches.\n self.assertEqual(case.buses.index(case.branches[0].from_bus), 2)\n self.assertEqual(case.buses.index(case.branches[0].to_bus), 3)\n self.assertEqual(case.branches[0].r, 0.00260, pl)\n self.assertEqual(case.branches[0].x, 0.04600, pl)\n self.assertEqual(case.branches[0].b, 3.50000, pl)\n self.assertEqual(case.branches[0].rate_a, 1200.00, pl)\n self.assertEqual(case.branches[0].rate_b, 1100.00, pl)\n self.assertEqual(case.branches[0].rate_c, 1000.00, pl)\n self.assertTrue(case.branches[0].online)\n\n self.assertEqual(case.buses.index(case.branches[29].from_bus), 32)\n self.assertEqual(case.buses.index(case.branches[29].to_bus), 33)\n\n # Transformers.\n self.assertEqual(case.buses.index(case.branches[30].from_bus), 0)\n self.assertEqual(case.buses.index(case.branches[30].to_bus), 2)\n self.assertEqual(case.branches[30].b, -0.10288, pl)\n self.assertEqual(case.branches[30].r, 0.00009, pl)\n self.assertEqual(case.branches[30].x, 0.00758, pl)\n self.assertEqual(case.branches[30].ratio, 1.0, pl)\n self.assertEqual(case.branches[30].phase_shift, 0.0, pl)\n self.assertEqual(case.branches[30].rate_a, 1200.00, pl)\n self.assertEqual(case.branches[30].rate_b, 1100.00, pl)\n self.assertEqual(case.branches[30].rate_c, 1000.00, pl)\n\n self.assertEqual(len(case.branches), 30 + 14 + (4*3)) # 4x 3-winding trx.","title":""},{"docid":"7645c0e0440dd1e07d66eb27a13a19f8","score":"0.5928295","text":"def test_instrument_autosample_samples(self):\n self.assert_initialize_driver()\n\n # command the instrument to auto-sample mode.\n self.driver_client.cmd_dvr('execute_resource', ProtocolEvent.START_AUTOSAMPLE)\n\n self.assert_current_state(ProtocolStates.AUTOSAMPLE)\n \n # wait for some samples to be generated\n log.debug('test_instrument_start_stop_autosample: waiting 5 seconds for samples')\n gevent.sleep(5)\n\n # Verify we received at least 2 samples.\n sample_events = [evt for evt in self.events if evt['type']==DriverAsyncEvent.SAMPLE]\n log.debug('test_instrument_start_stop_autosample: # 0f samples = %d' %len(sample_events))\n for sample in sample_events:\n if sample['value'].find(DataParticleType.SAMPLE) != -1:\n log.debug('parsed sample=%s\\n' %sample)\n sample_dict = eval(sample['value']) # turn string into dictionary\n values = sample_dict['values'] # get particle dictionary\n # pull timestamp out of particle\n ntp_timestamp = [item for item in values if item[\"value_id\"] == \"timestamp\"][0]['value']\n float_timestamp = ntplib.ntp_to_system_time(ntp_timestamp)\n log.debug('dt=%s' %time.ctime(float_timestamp))\n self.assertTrue(len(sample_events) >= 2)\n\n # stop autosample and return to command mode\n self.driver_client.cmd_dvr('execute_resource', ProtocolEvent.STOP_AUTOSAMPLE)\n \n self.assert_current_state(ProtocolStates.COMMAND)","title":""},{"docid":"42a492c72a480dca370a859c84c32176","score":"0.5896839","text":"def test_from_circuit(self):\n # random unitaries\n u0 = random_unitary(2).data\n u1 = random_unitary(2).data\n # add to circuit\n qr = QuantumRegister(2)\n circ = QuantumCircuit(qr)\n circ.unitary(u0, [qr[0]])\n circ.unitary(u1, [qr[1]])\n\n # Test decomposition of controlled-H gate\n circuit = QuantumCircuit(2)\n circ.x(0)\n circuit.ch(0, 1)\n target = DensityMatrix.from_label(\"00\").evolve(Operator(circuit))\n rho = DensityMatrix.from_instruction(circuit)\n self.assertEqual(rho, target)\n\n # Test initialize instruction\n init = Statevector([1, 0, 0, 1j]) / np.sqrt(2)\n target = DensityMatrix(init)\n circuit = QuantumCircuit(2)\n circuit.initialize(init.data, [0, 1])\n rho = DensityMatrix.from_instruction(circuit)\n self.assertEqual(rho, target)\n\n # Test reset instruction\n target = DensityMatrix([1, 0])\n circuit = QuantumCircuit(1)\n circuit.h(0)\n circuit.reset(0)\n rho = DensityMatrix.from_instruction(circuit)\n self.assertEqual(rho, target)","title":""},{"docid":"37a3b963a2ddeac481c81af517396d54","score":"0.58894324","text":"def test_fock_basis_samples(self, monkeypatch):\n shots = 10\n monkeypatch.setattr(\"strawberryfields.RemoteEngine\", MockEngine)\n dev = qml.device(\"strawberryfields.remote\", backend=\"X8\", shots=shots)\n\n sampled_modes = np.array([0, 3, 5])\n\n @qml.qnode(dev)\n def quantum_function(theta, phi):\n qml.Beamsplitter(theta, phi, wires=[0, 1])\n qml.Beamsplitter(theta, phi, wires=[4, 5])\n return [qml.sample(qml.NumberOperator(i)) for i in sampled_modes]\n\n a = quantum_function(1.0, 0)\n\n assert a.shape == (len(sampled_modes), shots)\n assert np.array_equal(a, MOCK_SAMPLES.T[sampled_modes])","title":""},{"docid":"6c02b1cc90b587d9016ab7191d3795e4","score":"0.58732194","text":"def test_measure_deterministic_with_sampling(self):\n shots = 100\n circuits = ref_measure.measure_circuits_deterministic(\n allow_sampling=True)\n targets = ref_measure.measure_counts_deterministic(shots)\n qobj = assemble(circuits, self.SIMULATOR, shots=shots)\n result = self.SIMULATOR.run(\n qobj, backend_options=self.BACKEND_OPTS).result()\n self.is_completed(result)\n self.compare_counts(result, circuits, targets, delta=0)\n # Test sampling was enabled\n for res in result.results:\n self.assertIn(\"measure_sampling\", res.metadata)\n self.assertEqual(res.metadata[\"measure_sampling\"], True)","title":""},{"docid":"9fc2c2865e75a74b18402a24007b210b","score":"0.586133","text":"def test_measure_nondeterministic_with_sampling(self):\n shots = 2000\n circuits = ref_measure.measure_circuits_nondeterministic(\n allow_sampling=True)\n targets = ref_measure.measure_counts_nondeterministic(shots)\n qobj = assemble(circuits, self.SIMULATOR, shots=shots)\n result = self.SIMULATOR.run(\n qobj, backend_options=self.BACKEND_OPTS).result()\n self.is_completed(result)\n self.compare_counts(result, circuits, targets, delta=0.05 * shots)\n # Test sampling was enabled\n for res in result.results:\n self.assertIn(\"measure_sampling\", res.metadata)\n self.assertEqual(res.metadata[\"measure_sampling\"], True)","title":""},{"docid":"1274e76c0963b153550760a5a6822c52","score":"0.5850311","text":"def sample(self):\n pass","title":""},{"docid":"1274e76c0963b153550760a5a6822c52","score":"0.5850311","text":"def sample(self):\n pass","title":""},{"docid":"c54b0cba89618a3496446a436942f457","score":"0.5829635","text":"def test_identity(self, monkeypatch):\n shots = 10\n monkeypatch.setattr(\"strawberryfields.RemoteEngine\", MockEngine)\n dev = qml.device(\"strawberryfields.remote\", backend=\"X8\", shots=shots)\n\n @qml.qnode(dev)\n def quantum_function(theta, phi):\n qml.Beamsplitter(theta, phi, wires=[0, 1])\n qml.Beamsplitter(theta, phi, wires=[4, 5])\n return qml.sample(qml.Identity(wires=0))\n\n a = quantum_function(1.0, 0)\n\n expected = np.ones(shots)\n assert a.shape == (shots,)\n assert np.array_equal(a, expected)","title":""},{"docid":"5208787a6bef2cdf1d74826e89d950a9","score":"0.58218944","text":"def run_simulation(sample, scan_size=200,is_reflectometry=False):\n #This is you walking towards the instrument:\n instrument = define_instrument(is_reflectometry=is_reflectometry)\n\n #These are your hands putting the sample on the instrument:\n instrument.setSample(sample)\n\n #This is you starting the beam:\n instrument.runSimulation()\n\n #This is the instrument sending your results back:\n return instrument.result()","title":""},{"docid":"9fa222649055de2346a3fe21244c2cf0","score":"0.58113545","text":"def test_samples(self):\n super(TestResult2d, self).test_samples()","title":""},{"docid":"75b3b7f3a1653e2e72812b41d89b22c7","score":"0.5805721","text":"def test_mocked_engine_run_samples(self, monkeypatch):\n modes = 8\n shots = 10\n monkeypatch.setattr(\"strawberryfields.RemoteEngine\", MockEngine)\n dev = qml.device(\"strawberryfields.remote\", backend=\"X8\", shots=shots)\n\n @qml.qnode(dev)\n def quantum_function(theta, phi):\n qml.Beamsplitter(theta, phi, wires=[0, 1])\n qml.Beamsplitter(theta, phi, wires=[4, 5])\n return qml.sample(qml.TensorN(wires=list(range(modes))))\n\n a = quantum_function(1.0, 0)\n\n assert a.shape == (shots,)\n assert np.array_equal(a, MOCK_SAMPLES_PROD)","title":""},{"docid":"173217355e3df15f8d87427284c35e84","score":"0.5800243","text":"def sample(self):","title":""},{"docid":"e884e9dae31adc0ebae6d44b3e444d2e","score":"0.579368","text":"def test_VentriculostomySurfaceCut1(self):\n\n self.delayDisplay(\"Starting the test\")\n #\n # first, get some data\n #\n import urllib\n downloads = (\n ('http://slicer.kitware.com/midas3/download?items=5767', 'FA.nrrd', slicer.util.loadVolume),\n )\n\n for url,name,loader in downloads:\n filePath = slicer.app.temporaryPath + '/' + name\n if not os.path.exists(filePath) or os.stat(filePath).st_size == 0:\n logging.info('Requesting download %s from %s...\\n' % (name, url))\n urllib.urlretrieve(url, filePath)\n if loader:\n logging.info('Loading %s...' % (name,))\n loader(filePath)\n self.delayDisplay('Finished with download and loading')\n\n volumeNode = slicer.util.getNode(pattern=\"FA\")\n logic = VentriculostomySurfaceCutLogic()\n self.assertIsNotNone( logic.hasImageData(volumeNode) )\n self.delayDisplay('Test passed!')","title":""},{"docid":"8f6b80e98a74a3b0222eacd4272e72d5","score":"0.57886356","text":"def sample(self, n_shot, m):\n pass","title":""},{"docid":"34b5daad90c6abd6f576b15fa82107c9","score":"0.5735137","text":"def test_biosample_samples_get(self):\n pass","title":""},{"docid":"76b83d9c26e62e1ffc302c28f9ae5844","score":"0.57321715","text":"def test_supported_gate_single_wire_no_parameters(self, device, tol, name, expected_output):\n n_wires = 1\n dev = device(n_wires)\n\n op = getattr(qml.ops, name)\n\n @qml.qnode(dev)\n def circuit():\n op(wires=0)\n return qml.expval(qml.PauliZ(0))\n\n assert np.isclose(circuit(), expected_output, atol=tol(dev.shots))","title":""},{"docid":"1c70dcaa2048d1517695dc8305f26b69","score":"0.5713772","text":"def simulate():\n pass","title":""},{"docid":"a34db359f9053af975f8e35402e84f3d","score":"0.5697099","text":"def test_run(self, tmpdir, extraction):\n extraction.run(outdir=tmpdir)\n testobs = SpectrumObservation.read(tmpdir / 'ogip_data' / 'pha_obs23523.fits')\n assert_quantity_allclose(testobs.aeff.data.data,\n extraction.observations[0].aeff.data.data)\n assert_quantity_allclose(testobs.on_vector.data.data,\n extraction.observations[0].on_vector.data.data)\n assert_quantity_allclose(testobs.on_vector.energy.nodes,\n extraction.observations[0].on_vector.energy.nodes)","title":""},{"docid":"39c9dcb74547fbb8b6c7170dcbe7099e","score":"0.568977","text":"def test_int(self):\n shots = Shots(100)\n assert shots.shot_vector == (ShotCopies(100, 1),)\n assert shots.total_shots == 100","title":""},{"docid":"74c4e7bd79ec20cac2c2103519ee66e0","score":"0.56746376","text":"def test_vector(self):","title":""},{"docid":"71f134f275137b401ae536beeffe216a","score":"0.5662659","text":"def test_autosample_particle_generation(self):\n self.assert_initialize_driver(DriverProtocolState.AUTOSAMPLE)\n\n\n self.assert_async_particle_generation(DataParticleType.OPTAA_SAMPLE, self.assert_sample_data_particle, timeout=120)","title":""},{"docid":"9f95e643802a8dbdac41288250de5244","score":"0.565523","text":"def main():\n config = global_conf.load_global_config('config.yml')\n db_client = database.client.DatabaseClient(config=config)\n\n controller = sample_controller.CombinatorialSampleController(\n x_samples=range(20, 271, 50), # 5 samples\n y_samples=range(40, 221, 36), # 5 samples\n z_samples=range(80, 181, 20), # 5 samples\n roll_samples=(-np.pi/2, -np.pi/4, -np.pi/6, 0, np.pi/6, np.pi/4, np.pi/2), # 6 samples\n pitch_samples=np.linspace(-np.pi/2, np.pi/2, 7), # 7 samples\n yaw_samples=np.linspace(-np.pi, np.pi, 7), # 7 samples\n fov_samples=(30, 60, 90),\n aperture_samples=(2.2, 22, 120),\n subject_pose=tf.Transform(location=(130.00, 140.00, 70.37))\n )\n simulator = ue_sim.UnrealCVSimulator(controller, config={\n 'provide_rgb': True,\n 'provide_depth': False,\n 'provide_labels': True,\n 'provide_world_normals': False,\n })\n builder = collection_builder.ImageCollectionBuilder(db_client)\n builder.add_from_image_source(simulator, filter_function=image_filter)\n builder.save()","title":""},{"docid":"f4db4b6c39718908e71a73bca11e32bc","score":"0.5635579","text":"def test_example_shots():\n hamiltonian = qml.Hamiltonian(\n coeffs=[-1.0] * 3,\n observables=[qml.PauliX(0), qml.PauliZ(1), qml.PauliY(0) @ qml.PauliX(1)],\n )\n\n @qml.qnode(qml.device(\"default.qubit\", wires=2, shots=1000))\n def quant_fun():\n qml.RX(0.1, wires=[0])\n qml.RY(0.5, wires=[1])\n qml.CNOT(wires=[0, 1])\n qml.RY(0.6, wires=[0])\n return qml.expval(hamiltonian)\n\n opt = LieAlgebraOptimizer(circuit=quant_fun, stepsize=0.1, exact=False)\n\n for _ in range(3):\n opt.step_and_cost()","title":""},{"docid":"9fc171a0b1b11836fe6ae791ad69e18d","score":"0.5634411","text":"def test_run(tmpdir, extraction):\n extraction.run()\n extraction.write(outdir=str(tmpdir), overwrite=True)\n testobs = SpectrumDatasetOnOff.from_ogip_files(\n str(tmpdir / \"ogip_data\" / \"pha_obs23523.fits\")\n )\n assert_quantity_allclose(\n testobs.aeff.data.data, extraction.spectrum_observations[0].aeff.data.data\n )\n assert_quantity_allclose(\n testobs.counts.data, extraction.spectrum_observations[0].counts.data\n )\n assert_allclose(\n testobs.counts.energy.center,\n extraction.spectrum_observations[0].counts.energy.center,\n )","title":""},{"docid":"01e0e31f5e12210e755b8d13052f249f","score":"0.56336546","text":"def testFullNobleGazSamples(self):\n \n request=\"spectrum=CURR/DETBK/GASBK/QC, analysis=CURR\"\n \n # get full\n listOfSamplesToTest = self.getListOfSaunaSampleIDs('2008-08-11',endDate='2008-12-12',spectralQualif='FULL',nbOfElem='2')\n \n # remove sampleID for which data isn't available\n if \"141372\" in listOfSamplesToTest:\n listOfSamplesToTest.remove(\"141372\")\n \n TestSAMPMLCreator.c_log.info(\"list samples :%s\"%(listOfSamplesToTest))\n \n cpt = 0\n total_t0 = time.time()\n \n for sampleID in listOfSamplesToTest:\n \n TestSAMPMLCreator.c_log.info(\"Start Test %d for SampleID %s.\\n\"%(cpt,sampleID))\n \n t0 = time.time()\n \n # fetchnoble particulate\n fetcher = DBDataFetcher.getDataFetcher(self.nbConn,self.archConn,sampleID)\n \n #modify remoteHost\n fetcher.setRemoteHost(self.conf.get('RemoteAccess','nobleGazRemoteHost','dls007'))\n \n fetcher.fetch(request,'GAS')\n \n renderer = SaunaRenderer(fetcher)\n \n xmlStr = renderer.asXmlStr(request)\n \n #print \"Non Formatted String [%s]\\n\"%(xmlStr)\n \n #f = open(\"/tmp/xmlStr.xml\",\"w\")\n \n #f.write(xmlStr)\n #f.close()\n \n path = \"/tmp/samples/sampml-full-%s.xml\"%(sampleID)\n \n ctbto.common.xml_utils.pretty_print_xml(StringIO.StringIO(xmlStr),path)\n \n # check if no tags are left\n self.assertIfNoTagsLeft(path)\n \n #self.assertAllCalibrationInfo(path)\n \n t1 = time.time()\n \n #TestSAMPMLCreator.c_log.info(\"End of Test %d for SampleID %s.\\nTest executed in %s seconds.\\n\\n**************************************************************** \\n**************************************************************** \\n\"%(cpt,sampleID,(t1-t0)))\n TestSAMPMLCreator.c_log.info(\"\\n********************************************************************************\\n End of Test %d for SampleID %s. Test executed in %s seconds.\\n********************************************************************************\\n\"%(cpt,sampleID,(t1-t0)))\n \n cpt +=1\n \n total_t1 = time.time()\n \n TestSAMPMLCreator.c_log.info(\"\\n****************************************************************************\\n****************************************************************************\\n****** EXECUTED %d FULL SAMPLE RETRIEVALS in %s seconds ********\\n****************************************************************************\\n****************************************************************************\\n\"%(cpt,total_t1-total_t0))","title":""},{"docid":"c256a90e9a0b78acdba418173418a0ff","score":"0.56321967","text":"def testBasics(self):\n bbox = Box2I(Point2I(256, 100), Extent2I(128, 127))\n minCounts = 5000\n maxCounts = 50000\n starSigma = 1.5\n numX = 5\n numY = 5\n coordList = self.makeCoordList(\n bbox = bbox,\n numX = numX,\n numY = numY,\n minCounts = minCounts,\n maxCounts = maxCounts,\n sigma = starSigma,\n )\n kwid = 11 # kernel width\n sky = 2000\n # create an exposure without a Wcs; add the Wcs later\n exposure = plantSources(bbox=bbox, kwid=kwid, sky=sky, coordList=coordList, addPoissonNoise=True)\n\n schema = SourceTable.makeMinimalSchema()\n\n config = DetectAndMeasureTask.ConfigClass()\n task = DetectAndMeasureTask(config=config, schema=schema)\n\n butler = Butler(root=InputDir)\n dataRef = butler.dataRef(\"calexp\", dataId=dict(visit=1))\n wcs = dataRef.get(\"raw\").getWcs()\n exposure.setWcs(wcs)\n exposureIdInfo = dataRef.get(\"expIdInfo\")\n taskRes = task.run(exposure=exposure, exposureIdInfo=exposureIdInfo)\n self.assertEqual(len(taskRes.sourceCat), numX * numY)\n schema = taskRes.sourceCat.schema\n centroidFlagKey = schema.find(\"slot_Centroid_flag\").getKey()\n parentKey = schema.find(\"parent\").getKey()\n psfFluxFlagKey = schema.find(\"slot_PsfFlux_flag\").getKey()\n psfFluxKey = schema.find(\"slot_PsfFlux_flux\").getKey()\n for src in taskRes.sourceCat:\n self.assertFalse(src.get(centroidFlagKey)) # centroid found\n self.assertEqual(src.get(parentKey), 0) # not debelended\n self.assertFalse(src.get(psfFluxFlagKey)) # flux measured\n self.assertGreater(src.get(psfFluxKey), 4000) # flux sane","title":""},{"docid":"d688b0c1d26e35c3481e4d0029cf34d4","score":"0.5627072","text":"def test_instrumment_start_stop_autosample(self):\n self.assert_initialize_driver()\n \n # start auto-sample.\n reply = self.driver_client.cmd_dvr('execute_resource', ProtocolEvent.START_AUTOSAMPLE)\n\n # Test the driver is in autosample mode.\n state = self.driver_client.cmd_dvr('get_resource_state')\n self.assertEqual(state, ProtocolStates.AUTOSAMPLE)\n \n # stop auto-sample.\n reply = self.driver_client.cmd_dvr('execute_resource', ProtocolEvent.STOP_AUTOSAMPLE)\n\n # Test the driver is in command mode.\n state = self.driver_client.cmd_dvr('get_resource_state')\n self.assertEqual(state, ProtocolStates.COMMAND)","title":""},{"docid":"ee01f8ccb90613d66d5b1b71eeb4bb27","score":"0.561529","text":"def runTest(self):\n self.setUp()\n self.test_VentriculostomySurfaceCut1()","title":""},{"docid":"bd1c37e2289789da22f067c3cc789ff9","score":"0.5610199","text":"def test_step_by_step_circuit_inspection(self):\n \n qubits = QuantumRegister(3)\n bits = ClassicalRegister(3)\n circuit = QuantumCircuit(qubits, bits)\n circuit.h(qubits[0])\n circuit.snapshot(\"step 1\") # This is a snapshot - the simulator will store the statevector at this point\n circuit.x(qubits[2])\n circuit.cx(qubits[0], qubits[1])\n circuit.snapshot(\"step 2\")\n circuit.barrier(qubits)\n circuit.measure(qubits, bits)\n circuit.snapshot(\"step 3\")\n \n simulator = Aer.get_backend('qasm_simulator')\n simulation = execute(circuit, simulator, shots=1)\n snapshots = simulation.result().results[0].data.snapshots.statevector # Get the statevectors for all of the snapshots\n for(name, state) in snapshots.items():\n print(f\"{name}: {state}\")\n print(\"\")","title":""},{"docid":"3185ba7b3793fa44d93ae3deec3f5934","score":"0.5604561","text":"def testFullGenieParticulateSamples(self):\n \n request=\"spectrum=ALL, analysis=ALL\"\n \n # get full 2003-10-24 to 2003-10-26\n listOfSamplesToTest = self.getListOfSampleIDs('2007-10-24',endDate='2008-10-26',spectralQualif='FULL',nbOfElem='2')\n \n # error\n #listOfSamplesToTest = [ \"700637\" ]\n \n #transform in numbers and retransform in str to remove the 0 at the beginning of the number\"\n #intifiedlist = map(int,listOfSamplesToTest)\n \n #listOfSamplesToTest = map(str,intifiedlist)\n \n TestSAMPMLCreator.c_log.info(\"list samples: %s\"%listOfSamplesToTest)\n \n cpt = 0\n total_t0 = time.time()\n \n for sampleID in listOfSamplesToTest:\n \n TestSAMPMLCreator.c_log.info(\"\\n********************************************************************************\\n Start Test %d for SampleID %s.\\n********************************************************************************\\n\"%(cpt,sampleID))\n \n t0 = time.time()\n \n # fetchnoble particulate\n fetcher = DBDataFetcher.getDataFetcher(self.mainConn,self.archConn,sampleID)\n \n fetcher.fetch(request,'PAR')\n \n renderer = GenieParticulateRenderer(fetcher)\n \n xmlStr = renderer.asXmlStr(request)\n \n path = \"/tmp/samples/sampml-full-%s.xml\"%(sampleID)\n \n ctbto.common.xml_utils.pretty_print_xml(StringIO.StringIO(xmlStr),path)\n \n # check if no tags are left\n self.assertIfNoTagsLeft(path)\n \n self.assertAllCalibrationInfo(path)\n \n t1 = time.time()\n \n TestSAMPMLCreator.c_log.info(\"\\n********************************************************************************\\n End of Test %d for SampleID %s. Test executed in %s seconds.\\n********************************************************************************\\n\"%(cpt,sampleID,(t1-t0)))\n \n cpt +=1\n \n total_t1 = time.time()\n \n TestSAMPMLCreator.c_log.info(\"\\n****************************************************************************\\n****************************************************************************\\n****** EXECUTED %d FULL SAMPLE RETRIEVALS in %s seconds ********\\n****************************************************************************\\n****************************************************************************\\n\"%(cpt,total_t1-total_t0))","title":""},{"docid":"e4df36beaf108f557f9455a4c35e30c2","score":"0.5595684","text":"def test_class():\n\tfrom Summary import make_raster_plot\n\n\t# use model to generate a phenotype\n\tpop = Population.Population(1, 6)\n\tmodel = CellularAutomataModel()\n\ts = time.time()\n\toutput = model.run_simulation()\n\n\tprint(f\"{time.time() - s:.2f} seconds\")\n\t\n\t# generate reference phenotype from experimental data\n\treference_file = {\n\t\t\"small\": \"../Resources/Small - 7-1-35.spk.txt\",\n\t\t\"dense\": \"../Resources/Dense - 2-1-20.spk.txt\"\n\t}\n\n\t# Compare model output with experimental data\n\tmake_raster_plot(reference_file[\"small\"], output, DURATION)","title":""},{"docid":"f803462e26d0054a09049590dec03b88","score":"0.5592262","text":"def testing_full_framework(\n red, blue,\n output_file,\n l_s, h_s, count,\n vparam=\"eps\",\n eps=.01,\n alpha=.01,\n max_disk_r=None,\n min_disk_r=None,\n disc_name=\"disc\",\n region_name=\"halfplane\",\n sample_method=\"halfplane\",\n fast_disk = True,\n two_level_sample=True,\n max_time=None):\n\n fieldnames = [\"vparam\", \"disc\", \"region\", \"n\", \"s\", \"n_pts\", \"m_pts\", \"b_pts\", \"alpha\", \"time\",\n \"m_disc\", \"m_disc_approx\", \"sample_method\"]\n with open(output_file, 'w') as f:\n writer = csv.DictWriter(f, fieldnames=fieldnames)\n writer.writeheader()\n\n for i in np.logspace(l_s, h_s, count):\n\n if vparam == \"eps\":\n eps = i\n elif vparam == \"alpha\":\n alpha = i\n n = 1 / eps\n s = 1 / (2 * eps * eps)\n n = int(round(n) + .1)\n s = int(round(s) + .1)\n\n\n disc = utils.disc_to_func(disc_name)\n\n red_sample = pyscan.my_sample(red, s)\n blue_sample = pyscan.my_sample(blue, s)\n if two_level_sample:\n red_net = pyscan.my_sample(red, n)\n blue_net = pyscan.my_sample(blue, n)\n else:\n red_net = red_sample\n blue_net = blue_sample\n\n net = red_net + blue_net\n\n print(\"Running: {} {}\".format(n, s))\n\n start_time = time.time()\n\n if region_name == \"multiscale_disk\":\n if max_disk_r is not None and alpha > max_disk_r:\n print(\"Max Disk Radius is greater than alpha\")\n continue\n reg, mx = multiscale_disk(min_disk_r, max_disk_r, alpha, red_sample, blue_sample, net, disc, fast_disk)\n m_sample, b_sample, net_set = [], [], []\n else:\n if sample_method == \"halfplane\":\n m_sample = [pyscan.halfplane_kernel([pyscan.Point(pt[0], pt[1], 1.0) for pt in traj], alpha) for traj in red_sample]\n b_sample = [pyscan.halfplane_kernel([pyscan.Point(pt[0], pt[1], 1.0) for pt in traj], alpha) for traj in blue_sample]\n pt_net = [pyscan.halfplane_kernel([pyscan.Point(pt[0], pt[1], 1.0) for pt in traj], alpha) for traj in net]\n elif sample_method == \"dp\":\n m_sample = [pyscan.dp_compress(traj, alpha) for traj in red_sample]\n b_sample = [pyscan.dp_compress(traj, alpha) for traj in blue_sample]\n pt_net = [pyscan.dp_compress(traj, alpha) for traj in net]\n elif sample_method == \"hull\":\n m_sample = [pyscan.convex_hull([pyscan.Point(pt[0], pt[1], 1.0) for pt in traj]) for traj in red_sample]\n b_sample = [pyscan.convex_hull([pyscan.Point(pt[0], pt[1], 1.0) for pt in traj]) for traj in blue_sample]\n pt_net = [pyscan.convex_hull([pyscan.Point(pt[0], pt[1], 1.0) for pt in traj]) for traj in net]\n elif sample_method is None:\n #just takes the waypoints.\n m_sample = [[pyscan.Point(pt[0], pt[1], 1.0) for pt in traj] for traj in red_sample]\n b_sample = [[pyscan.Point(pt[0], pt[1], 1.0) for pt in traj] for traj in blue_sample]\n pt_net = [[pyscan.Point(pt[0], pt[1], 1.0) for pt in traj] for traj in net]\n elif sample_method == \"grid\":\n m_sample = [pyscan.grid_kernel(traj, alpha) for traj in red_sample]\n b_sample = [pyscan.grid_kernel(traj, alpha) for traj in blue_sample]\n pt_net = [pyscan.grid_kernel(traj, alpha) for traj in net]\n elif sample_method == \"lifting\":\n m_sample = [pyscan.lifting_kernel(traj, alpha) for traj in red_sample]\n b_sample = [pyscan.lifting_kernel(traj, alpha) for traj in blue_sample]\n pt_net = [pyscan.lifting_kernel(traj, alpha) for traj in net]\n elif sample_method == \"grid_direc\":\n\n if max_disk_r is not None and alpha > max_disk_r:\n print(\"Max Disk Radius is greater than alpha\")\n continue\n chord_l = math.sqrt(4 * alpha * max(min_disk_r, alpha) - 2 * alpha * alpha)\n m_sample = [pyscan.grid_direc_kernel(pyscan.dp_compress(traj, alpha), chord_l, alpha) for traj in\n red_sample]\n b_sample = [pyscan.grid_direc_kernel(pyscan.dp_compress(traj, alpha), chord_l, alpha) for traj in\n blue_sample]\n pt_net = [pyscan.grid_direc_kernel(pyscan.dp_compress(traj, alpha), chord_l, alpha) for traj in net]\n elif sample_method == \"even\":\n m_sample = [pyscan.even_sample_error(traj, alpha, False) for traj in red_sample]\n b_sample = [pyscan.even_sample_error(traj, alpha, False) for traj in blue_sample]\n pt_net = [pyscan.even_sample_error(traj, alpha, False) for traj in net]\n else:\n return\n\n if region_name == \"multiscale_disk_fixed\":\n m_sample = list(pyscan.trajectories_to_labels(m_sample))\n b_sample = list(pyscan.trajectories_to_labels(b_sample))\n net_set = list(pyscan.trajectories_to_labels(pt_net))\n reg, mx = multiscale_disk_fixed(min_disk_r, max_disk_r, m_sample, b_sample, net_set, disc, fast_disk)\n else:\n m_sample = list(pyscan.trajectories_to_labels(m_sample))\n b_sample = list(pyscan.trajectories_to_labels(b_sample))\n net_set = list(itertools.chain.from_iterable(pt_net))\n if region_name == \"halfplane\":\n reg, mx = pyscan.max_halfplane_labeled(net_set, m_sample, b_sample, disc)\n elif region_name == \"disk\":\n reg, mx = pyscan.max_disk_labeled(net_set, m_sample, b_sample, disc)\n elif region_name == \"rectangle\":\n reg, mx = pyscan.max_rect_labeled(n, 2 * max_disk_r, m_sample, b_sample, disc)\n elif region_name == \"rectangle_scale\":\n reg, mx = pyscan.max_rect_labeled_scale(n, 2 * max_disk_r, alpha, net_set, m_sample, b_sample, disc)\n else:\n return\n\n end_time = time.time()\n actual_mx = pyscan.evaluate_range_trajectory(reg, red, blue, disc)\n row = {\"vparam\": vparam,\n \"disc\": disc_name,\n \"region\": region_name,\n \"n\": n, \"s\": s,\n \"n_pts\": len(net_set), \"m_pts\":len(m_sample), \"b_pts\":len(b_sample),\n \"alpha\":alpha,\n \"time\": end_time - start_time,\n \"m_disc_approx\": mx,\n \"m_disc\": actual_mx,\n \"sample_method\": sample_method}\n writer.writerow(row)\n f.flush()\n print(row)\n if max_time is not None and end_time - start_time > max_time:\n return","title":""},{"docid":"3cdd104871deaea7a6ab82e021f83687","score":"0.558972","text":"def test_sampler_object():\n \n os.chdir(data_path())\n \n spec = spectrum.SpectrumSampler(SPECTRUM_FILE)\n sampler_checks(spec)\n \n new = spec.redo_1d_extraction()\n sampler_checks(new)\n \n # Initialized from HDUList\n with pyfits.open(SPECTRUM_FILE) as hdul:\n spec = spectrum.SpectrumSampler(hdul)\n sampler_checks(spec)","title":""},{"docid":"cd6b68b08a08b2283250a83b3e774615","score":"0.55812806","text":"def test_samples(self):\n super(TestResult1d, self).test_samples()","title":""},{"docid":"4c75a9f02d0c7b3ed9d77dc715cd7dde","score":"0.558012","text":"def _sample(self):\n pass","title":""},{"docid":"f01cb8dd1b259780c04c9d51beff877e","score":"0.5571169","text":"def test_vs_update(self):\n random.seed(5)\n p1 = axelrod.CautiousQLearner()\n p2 = axelrod.Cooperator()\n simulate_play(p1, p2)\n self.assertEqual(p1.Vs, {'': 0.1, '0.0': 0})\n simulate_play(p1, p2)\n self.assertEqual(p1.Vs,{'': 0.1, '0.0': 0.30000000000000004, 'C1.0': 0})","title":""},{"docid":"a7b1e7db483c6d9d2c366f0b68026c2f","score":"0.55548793","text":"def test_multi_measurement_shot_vector(self, max_workers, shots):\n x, y = np.array(0.732), np.array(0.488)\n shots = qml.measurements.Shots(shots)\n qs = qml.tape.QuantumScript(\n [qml.RX(x, wires=0), qml.CNOT(wires=[0, 1]), qml.RY(y, wires=1)],\n [qml.expval(qml.Hadamard(0)), qml.probs(wires=range(2)), qml.sample(wires=range(2))],\n shots=shots,\n )\n\n dev = DefaultQubit2(max_workers=max_workers)\n result = dev.execute(qs)\n\n assert isinstance(result, tuple)\n assert len(result) == len(list(shots))\n\n for shot_res, s in zip(result, shots):\n assert isinstance(shot_res, tuple)\n assert len(shot_res) == 3\n\n assert all(isinstance(meas_res, (float, np.ndarray)) for meas_res in shot_res)\n\n assert shot_res[0].shape == ()\n assert np.allclose(shot_res[0], np.cos(x) / np.sqrt(2), atol=0.1)\n\n assert shot_res[1].shape == (4,)\n assert np.allclose(\n shot_res[1],\n [\n np.cos(x / 2) ** 2 * np.cos(y / 2) ** 2,\n np.cos(x / 2) ** 2 * np.sin(y / 2) ** 2,\n np.sin(x / 2) ** 2 * np.sin(y / 2) ** 2,\n np.sin(x / 2) ** 2 * np.cos(y / 2) ** 2,\n ],\n atol=0.1,\n )\n\n assert shot_res[2].shape == (s, 2)","title":""},{"docid":"02c57ea60fd7847b15e58dbd524282b7","score":"0.55475247","text":"def main():\n\n # robot = rb.Snatch3rRobot()\n # robot.drive_system.go_straight_inches(10)\n # make_polygon(5)\n # follow_the_black_line()\n # beep_when_area_is_bigger_than(20)\n # IR_sensor(10)\n\n print('------SPRINT ONE-------')\n print(\"test drive system\") # OK\n # test_drive_system()\n # time.sleep(5)\n\n print('test follow black line')\n print('in clockwise')\n # follow_black_line()\n # follow_black_line_fancy_version()\n time.sleep(5)\n\n print('move forward until see black:')\n # until_color_is(1)\n # time.sleep(5)\n print('----end of sprint one------')\n\n print('------SPRINT TWO---------')\n print('TEST ARM HERE') # OK\n # test_arm()\n # time.sleep(5)\n print('TEST IR HERE')\n # beep_when_wave_hands()\n # time.sleep(5)\n print('TEST CAMERA HERE')\n # beep_if_bigger_than_1000()\n # time.sleep(5)\n print('----end of sprint two------')","title":""},{"docid":"33860dfba6a628a6429ddb2d84e3fb3f","score":"0.55372095","text":"def test_go_straight_inches_method():\n my_robot = rb.Snatch3rRobot()\n\n print('Test 1: Forward 5 Inches')\n my_robot.drive_system.go_straight_inches(5, 50)\n\n time.sleep(10)\n\n print('Test 2: Backward 5 Inches')\n my_robot.drive_system.go_straight_inches(-5, -50)\n\n time.sleep(10)\n\n print('Test 3: Forward 0.5 Inches')\n my_robot.drive_system.go_straight_inches(0.5, 20)\n\n time.sleep(10)\n\n print('Test 4: Forward 0 Inches')\n my_robot.drive_system.go_straight_inches(0, 100)\n\n time.sleep(10)\n\n print('Test 5: Forward 60 Inches')\n my_robot.drive_system.go_straight_inches(60, 100)\n\n time.sleep(10)","title":""},{"docid":"5c36077f239d0d266b68694cddd5e192","score":"0.5535899","text":"def test_synth_ag(self, num_qubits):\n rng = np.random.default_rng(1234)\n samples = 50\n for _ in range(samples):\n circ = random_clifford_circuit(num_qubits, 5 * num_qubits, seed=rng)\n target = Clifford(circ)\n value = Clifford(synth_clifford_ag(target))\n self.assertEqual(value, target)","title":""},{"docid":"aa2d20807c0b2424e1aaa8853ed6308c","score":"0.55336535","text":"def sampleWrold():\n pass","title":""},{"docid":"0dc7caf4c04d56d64fb1a478599b66de","score":"0.5493892","text":"def main():\n\n test_surface_normal()","title":""},{"docid":"f3788e7da72fdd3d0578b04f990d63c9","score":"0.54914016","text":"async def test_discrete_true(hass, mock_hub):\n register_config = {\n CONF_INPUT_TYPE: CALL_TYPE_DISCRETE,\n }\n await run_sensor_test(\n hass,\n mock_hub,\n register_config,\n [0xFF],\n expected=\"on\",\n )","title":""},{"docid":"a056d4b43d62405f7743c4b83c15917a","score":"0.5490275","text":"def test_num_shots(self):\n # number of qubits\n nqubits = 4\n\n # number of shots\n nshots = 40\n\n # get an outcome from simulating a circuit\n result = self.get_all_ones_outcome(nqubits, nshots)\n\n # create a MeasurementOutcome\n outcome = MeasurementOutcome(result)\n\n # trivial check\n self.assertEqual(outcome.shots, nshots)","title":""},{"docid":"852ed519f12dce4f82ceaf600a3dcf2e","score":"0.54843324","text":"def test_sample_paths_wiener(self, watch_params, use_time_step,\n use_time_grid, supply_normal_draws):\n dtype = tf.float64\n\n def drift_fn(_, x):\n return tf.zeros_like(x)\n\n def vol_fn(_, x):\n return tf.expand_dims(tf.ones_like(x), -1)\n\n times = np.array([0.1, 0.2, 0.3])\n num_samples = 10000\n if watch_params:\n watch_params = []\n else:\n watch_params = None\n if use_time_step:\n time_step = 0.01\n num_time_steps = None\n else:\n time_step = None\n num_time_steps = 30\n if use_time_grid:\n time_step = None\n times_grid = tf.linspace(tf.constant(0.0, dtype=dtype), 0.3, 31)\n else:\n times_grid = None\n if supply_normal_draws:\n num_samples = 1\n # Use antithetic sampling\n normal_draws = tf.random.stateless_normal(\n shape=[5000, 30, 1],\n seed=[1, 42],\n dtype=dtype)\n normal_draws = tf.concat([normal_draws, -normal_draws], axis=0)\n else:\n normal_draws = None\n paths = euler_sampling.sample(\n dim=1, drift_fn=drift_fn, volatility_fn=vol_fn,\n times=times,\n num_samples=num_samples,\n time_step=time_step,\n num_time_steps=num_time_steps,\n watch_params=watch_params,\n normal_draws=normal_draws,\n times_grid=times_grid,\n random_type=random.RandomType.STATELESS_ANTITHETIC,\n seed=[1, 42])\n\n # The correct number of samples\n num_samples = 10000\n with self.subTest('Shape'):\n self.assertAllEqual(paths.shape.as_list(), [num_samples, 3, 1])\n paths = self.evaluate(paths)\n means = np.mean(paths, axis=0).reshape([-1])\n covars = np.cov(paths.reshape([num_samples, -1]), rowvar=False)\n expected_means = np.zeros((3,))\n expected_covars = np.minimum(times.reshape([-1, 1]), times.reshape([1, -1]))\n with self.subTest('Means'):\n self.assertAllClose(means, expected_means, rtol=1e-2, atol=1e-2)\n with self.subTest('Covariance'):\n self.assertAllClose(covars, expected_covars, rtol=1e-2, atol=1e-2)","title":""},{"docid":"a38e1b89f0bbe81cb22fbee64d196777","score":"0.54838246","text":"def test_measure_deterministic_multi_qubit_with_sampling(self):\n shots = 100\n circuits = ref_measure.multiqubit_measure_circuits_deterministic(\n allow_sampling=True)\n targets = ref_measure.multiqubit_measure_counts_deterministic(shots)\n qobj = assemble(circuits, self.SIMULATOR, shots=shots)\n result = self.SIMULATOR.run(\n qobj, backend_options=self.BACKEND_OPTS).result()\n self.is_completed(result)\n self.compare_counts(result, circuits, targets, delta=0)\n # Test sampling was enabled\n for res in result.results:\n self.assertIn(\"measure_sampling\", res.metadata)\n self.assertEqual(res.metadata[\"measure_sampling\"], True)","title":""},{"docid":"5e50f1d96ffe7ed1438e506554c776b1","score":"0.54747975","text":"def test_measure_nondeterministic_multi_qubit_with_sampling(self):\n shots = 2000\n circuits = ref_measure.multiqubit_measure_circuits_nondeterministic(\n allow_sampling=True)\n targets = ref_measure.multiqubit_measure_counts_nondeterministic(shots)\n qobj = assemble(circuits, self.SIMULATOR, shots=shots)\n result = self.SIMULATOR.run(\n qobj, backend_options=self.BACKEND_OPTS).result()\n self.is_completed(result)\n self.compare_counts(result, circuits, targets, delta=0.05 * shots)\n # Test sampling was enabled\n for res in result.results:\n self.assertIn(\"measure_sampling\", res.metadata)\n self.assertEqual(res.metadata[\"measure_sampling\"], True)","title":""},{"docid":"2782f906db22bb9a25d5e138c5a41cd7","score":"0.5460231","text":"def oneSample_tTest(imgList, maskFile, outImage, brik=''):\n if type(imgList) == list:\n imgFile = ' '.join([x + brik for x in imgList])\n else:\n imgFile = imgList + brik\n\n os.system('3dttest++ -setA ' + imgFile + ' -mask ' + maskFile + ' -prefix ' + outImage)","title":""},{"docid":"81d17b4054b92b08588cb5e5910b0ab2","score":"0.54576665","text":"def test_measure_deterministic_without_sampling(self):\n shots = 100\n circuits = ref_measure.measure_circuits_deterministic(\n allow_sampling=False)\n targets = ref_measure.measure_counts_deterministic(shots)\n qobj = assemble(circuits, self.SIMULATOR, shots=shots)\n result = self.SIMULATOR.run(\n qobj, backend_options=self.BACKEND_OPTS).result()\n self.is_completed(result)\n self.compare_counts(result, circuits, targets, delta=0)\n # Test sampling was disabled\n for res in result.results:\n self.assertIn(\"measure_sampling\", res.metadata)\n self.assertEqual(res.metadata[\"measure_sampling\"], False)","title":""},{"docid":"2e675b850cbdafb859ae47468f82d084","score":"0.545464","text":"def test_vs_update(self):\n random.seed(5)\n p1 = axelrod.HesitantQLearner()\n p2 = axelrod.Cooperator()\n simulate_play(p1, p2)\n self.assertEqual(p1.Vs, {'': 0.1, '0.0': 0})\n simulate_play(p1, p2)\n self.assertEqual(p1.Vs,{'': 0.1, '0.0': 0.30000000000000004, 'C1.0': 0})","title":""},{"docid":"276ce37d391a9673edfc24bc4be36f02","score":"0.54525274","text":"def test_stream_random(context):\n checker = Checker()\n vpw.register(checker)\n\n for x in range(5000):\n checker.set(random.getrandbits(Param.IMG_WIDTH), random.getrandbits(Param.KER_WIDTH))\n vpw.tick()\n\n vpw.idle(10) # wait for longer then the pipelined depth of module","title":""},{"docid":"903dfd2d14b26b2197245c419d6f54e7","score":"0.5449353","text":"def run(wrk_dir, radUnit, outerCup, innerCupSer, innerCupNum, coll, x_range, y_range, z_range, steps, shot):\n\n logging.info(\"Single shot run\")\n logging.debug(wrk_dir)\n logging.debug(radUnit)\n logging.debug(outerCup)\n logging.debug(innerCupSer)\n logging.debug(innerCupNum)\n logging.debug(str(coll))\n logging.debug(str(x_range))\n logging.debug(str(y_range))\n logging.debug(str(z_range))\n logging.debug(str(steps))\n logging.debug(str(shot))\n\n mats = materials.materials(\"Materials.txt\")\n\n logging.info(\"Materials initialized\")\n\n cl = collimator.collimator(coll)\n\n if not XcConstants.IsQACup(innerCupSer):\n file_prefix = clinical.make_cup_name(radUnit, outerCup, innerCupSer, innerCupNum)\n else:\n file_prefix = qa.make_cup_name(radUnit, outerCup, innerCupSer, innerCupNum)\n\n liA = None\n liB = None\n liC = None\n if not XcConstants.IsQACup(innerCupSer):\n #cdown = cup_downloader.cup_downloader(\"192.168.1.217\", \"./\", wrk_dir, file_prefix, \"kriol\", \"Proton31\")\n cdown = cup_downloader.cup_downloader(\"192.168.1.230\", \"/Programs_n_Docs/Kdd_CupGeometry/Out/\", wrk_dir, file_prefix, \"beamuser\", \"beamuser\")\n cdown.load()\n #cdown.load()\n if (cdown.rc() != 0):\n raise RuntimeError(\"run_single_shot\", \"unable to load files\")\n\n logging.info(\"Cups downloaded\")\n\n cupA = cc.curve(os.path.join( wrk_dir, file_prefix + \"_\" + \"KddCurveA.txt\"))\n cupB = cc.curve(os.path.join( wrk_dir, file_prefix + \"_\" + \"KddCurveB.txt\"))\n cupC = cc.curve(os.path.join( wrk_dir, file_prefix + \"_\" + \"KddCurveC.txt\"))\n\n liA = linint.linint(cupA)\n liB = linint.linint(cupB)\n liC = linint.linint(cupC)\n\n logging.info(\"Interpolators done\")\n\n if not XcConstants.IsQACup(innerCupSer):\n nr = int(cl.size()*1.2/steps[0])\n else:\n nr = int(40.0/steps[0])\n\n z_max = z_range[1]\n if not XcConstants.IsQACup(innerCupSer):\n z_max = liA.zmax() # z_max = max(liA.zmax(), liB.zmax(), liC.zmax())\n\n # phantom dimensions and boundaries\n pdim = build_phandim.build_phandim(shot, x_range, y_range, (z_range[0], z_max), steps, nr)\n\n logging.info(\"Phantom dimensions\")\n\n # phantom in memory\n if not XcConstants.IsQACup(innerCupSer):\n phntom = clinical.make_phantom(pdim, liA, liB, liC, mats, (z_range[0], z_max))\n else:\n phntom = qa.make_phantom(pdim, liA, liB, liC, mats, (z_range[0], z_max))\n\n full_prefix = names_helper.make_qualified_name(file_prefix, cl, shot)\n\n write_egs_phantom.write_phantom(wrk_dir, full_prefix, phntom, mats)\n\n logging.info(\"Phantom saved\")\n\n egsinp_template = None\n if not XcConstants.IsQACup(innerCupSer):\n egsinp_template = \"template.egsinp\"\n else:\n egsinp_template = \"templateQA.egsinp\"\n\n egsinp_name = write_egs_input.write_input(wrk_dir, egsinp_template, full_prefix, cl, shot)\n\n logging.info(\"Making EGS input\")\n\n logging.info(\"And DosXYZ is about to run\")\n\n rc = run_dosxyz.run_dosxyz(wrk_dir, egsinp_name, \"700jin.pegs4dat\")\n if rc != 0:\n logging.info(\"DosXYZ error: {0}\".format(rc))\n raise RuntimeError(\"run_single_shot\", \"Dose was not computed\")\n logging.info(\"And DosXYZ is done\")\n\n logging.info(\"Data uploader is going up\")\n logging.info(\"...right now, no upload\")\n\n host, port, user, pswd, dest = read_credentials(\"config_sftp.json\")\n dupload = data_uploader.data_uploader(wrk_dir, host, port, dest, file_prefix, user, pswd)\n\n dupload.upload(cl)\n\n rc = dupload.rc()\n\n if (rc != 0):\n logging.info(\"Data upload failure {0}\", rc)\n raise RuntimeError(\"run_single_shot\", \"unable to upload files\")\n\n logging.info(\"Data uploaded\")\n logging.info(\"Finita la comedia\")","title":""},{"docid":"d35bbd2f913d9d9b32232c199baa46e2","score":"0.5448022","text":"def testVtiInputOutput(self):\n for i in xrange(3 * len(self.vtis.values())):\n vti = random.choice(self.vtis.values())\n self._CheckVtiInputOutput(vti, 4)\n self._CheckVtiInputOutput(vti, 6)","title":""},{"docid":"a90f5515ec3684fd69f0b668cc31a454","score":"0.5447884","text":"def test_get_item(self):\n # number of qubits\n nqubits = 5\n\n # number of shots\n nshots = 40\n\n # get an outcome from simulating a circuit\n result = self.get_all_ones_outcome(nqubits, nshots)\n\n # create a MeasurementOutcome\n outcome = MeasurementOutcome(result)\n\n self.assertEqual(len(outcome[0]), 5)","title":""},{"docid":"5bdcbf66b42547154e6dcea32cbcd8ed","score":"0.54401785","text":"def setUp(self):\n slicer.mrmlScene.Clear(0)\n import SampleData\n sampleDataLogic = SampleData.SampleDataLogic()\n self.inputAngioVolume = sampleDataLogic.downloadCTACardio()\n \n self.vesselPositionRas = [176.9, -17.4, 52.7]\n\n # make the output volume appear in all the slice views\n selectionNode = slicer.app.applicationLogic().GetSelectionNode()\n selectionNode.SetReferenceActiveVolumeID(self.inputAngioVolume.GetID())\n slicer.app.applicationLogic().PropagateVolumeSelection(1)","title":""},{"docid":"bacab49ca12934b5c8313b279df2be5b","score":"0.54308283","text":"def test_noise(self):\n qr = qiskit.QuantumRegister(3)\n cr = qiskit.ClassicalRegister(3)\n circuit = qiskit.QuantumCircuit(qr, cr)\n circuit.iden(qr[0])\n circuit.noise(0)\n circuit.iden(qr[1])\n circuit.noise(1)\n circuit.iden(qr[2])\n circuit.measure(qr, cr)\n\n config = {\n 'noise_params': {\n 'id': {'p_pauli': [1.0, 0.0, 0.0]}\n }\n }\n sim = 'local_qasm_simulator_cpp'\n shots = 1000\n result = execute(circuit, sim, config=config, shots=shots).result()\n counts = result.get_counts()\n target = {'101': shots}\n self.assertEqual(counts, target)","title":""},{"docid":"392391c6356a5dba381ef417d98c1e09","score":"0.54300576","text":"def test_keywordargs_used(self, qubit_device_1_wire, tol):\n\n def circuit(w, x=None):\n qml.RX(x, wires=[0])\n return qml.expval(qml.PauliZ(0))\n\n circuit = qml.QNode(circuit, qubit_device_1_wire).to_tf()\n\n c = circuit(tf.constant(1.), x=np.pi)\n assert np.allclose(c.numpy(), -1., atol=tol, rtol=0)","title":""},{"docid":"5cdc053b40e4b0cf961cdbcee9cf22e0","score":"0.54287535","text":"def test_sample_scattering():\n test_data_dir = os.path.dirname(__file__)\n test_data_filename = os.path.join(test_data_dir, 'test_data_1d.pt')\n data = torch.load(test_data_filename, map_location='cpu')\n\n x = data['x']\n J = data['J']\n Q = data['Q']\n Sx0 = data['Sx']\n\n T = x.shape[2]\n\n scattering = Scattering1D(T, J, Q)\n\n if force_gpu:\n scattering = scattering.cuda()\n x = x.cuda()\n\n Sx = scattering.forward(x)\n\n if force_gpu:\n Sx = Sx.cpu()\n\n assert (Sx - Sx0).abs().max() < 1e-6","title":""},{"docid":"f705206215b48506113d178a0332622c","score":"0.5427243","text":"def test_measure_2qubit(self):\n\n state = DensityMatrix.from_label(\"+0\")\n seed = 200\n shots = 100\n\n with self.subTest(msg=\"measure\"):\n for i in range(shots):\n rho = state.copy()\n rho.seed(seed + i)\n outcome, value = rho.measure()\n self.assertIn(outcome, [\"00\", \"10\"])\n if outcome == \"00\":\n target = DensityMatrix.from_label(\"00\")\n self.assertEqual(value, target)\n else:\n target = DensityMatrix.from_label(\"10\")\n self.assertEqual(value, target)\n\n with self.subTest(msg=\"measure [0, 1]\"):\n for i in range(shots):\n rho = state.copy()\n outcome, value = rho.measure([0, 1])\n self.assertIn(outcome, [\"00\", \"10\"])\n if outcome == \"00\":\n target = DensityMatrix.from_label(\"00\")\n self.assertEqual(value, target)\n else:\n target = DensityMatrix.from_label(\"10\")\n self.assertEqual(value, target)\n\n with self.subTest(msg=\"measure [1, 0]\"):\n for i in range(shots):\n rho = state.copy()\n outcome, value = rho.measure([1, 0])\n self.assertIn(outcome, [\"00\", \"01\"])\n if outcome == \"00\":\n target = DensityMatrix.from_label(\"00\")\n self.assertEqual(value, target)\n else:\n target = DensityMatrix.from_label(\"10\")\n self.assertEqual(value, target)\n with self.subTest(msg=\"measure [0]\"):\n for i in range(shots):\n rho = state.copy()\n outcome, value = rho.measure([0])\n self.assertEqual(outcome, \"0\")\n target = DensityMatrix.from_label(\"+0\")\n self.assertEqual(value, target)\n\n with self.subTest(msg=\"measure [1]\"):\n for i in range(shots):\n rho = state.copy()\n outcome, value = rho.measure([1])\n self.assertIn(outcome, [\"0\", \"1\"])\n if outcome == \"0\":\n target = DensityMatrix.from_label(\"00\")\n self.assertEqual(value, target)\n else:\n target = DensityMatrix.from_label(\"10\")\n self.assertEqual(value, target)","title":""},{"docid":"ed3d03f2dc2e2f9f657964c3bfcb77a4","score":"0.54211783","text":"def test_basic():\n this = os.path.dirname(__file__)\n l = \"../samples/images/random4_4k/1l.jpg\"\n c = \"../samples/images/random4_4k/1c.jpg\"\n r = \"../samples/images/random4_4k/1r.jpg\"\n pathl = os.path.join(this, l)\n pathc = os.path.join(this, c)\n pathr = os.path.join(this, r)\n\n det = YOLOSimpleDetector()\n dw = DetectWrapper(det, write_partials=False)\n dw.run_detection(pathl, pathc, pathr)\n # dw.write(\"result.jpg\")\n # TODO: Add more asserts here\n assert dw.result","title":""},{"docid":"89dd16053cc658c1b1e208c27c380515","score":"0.5416006","text":"def test_device_simple_query(self):\n pass","title":""},{"docid":"d94f38b71828e6fc2bb97894caf1e4d9","score":"0.5412627","text":"def test_supported_gate_single_wire_with_parameters(\n self, device, tol, name, par, expected_output\n ):\n\n n_wires = 1\n dev = device(n_wires)\n op = getattr(qml.ops, name)\n\n @qml.qnode(dev)\n def circuit():\n op(*par, wires=0)\n return qml.expval(qml.PauliZ(0))\n\n assert np.isclose(circuit(), expected_output, atol=tol(dev.shots))","title":""},{"docid":"cc15c40460c7b6db76696c533caded41","score":"0.5405369","text":"def test_measure_simple(self):\n bk = Backend(name='ibmq', device='qasm_simulator')\n qc = QComp(qubit_num=2, backend=bk)\n res = qc.h(0).cx(0,1).measure([0,1]).run(shots=10)\n self.assertEqual(res['measured_qid'], [0,1])\n self.assertEqual(res['frequency']['00']+res['frequency']['11'], 10)","title":""},{"docid":"a528e89c162169ec90a5562a71818e44","score":"0.5403991","text":"def simulate_single_param(args): \n from neuron import h\n from neuron import gui\n h.load_file(\"nrngui.hoc\")\n h.load_file(\"import3d.hoc\")\n \n param_dict = args[0]\n \n hoc_code = '''h(\"create soma\")\nh(\"access soma\")\nh(\"nseg = 1\")\nh(\"L = 20\")\nh(\"diam = 20\")\nh(\"insert pas\")\nh(\"cm = 1\")\nh(\"Ra = 150\")\nh(\"forall nseg = 1\")\nh(\"forall e_pas = -90\")\n\nh(\"soma insert Ca_LVAst \")\nh(\"soma insert Ca_HVA \")\nh(\"soma insert SKv3_1 \")\nh(\"soma insert SK_E2 \")\nh(\"soma insert K_Tst \")\nh(\"soma insert K_Pst \")\nh(\"soma insert Nap_Et2 \")\nh(\"soma insert NaTa_t\")\nh(\"soma insert CaDynamics_E2\")\nh(\"soma insert Ih\")\n\nh(\"ek = -85\")\nh(\"ena = 50\")\nh(\"gIhbar_Ih = 0.0002\")\nh(\"g_pas = 1.0 / 12000 \")\nh(\"celsius = 36\")\n'''\n \n exec(hoc_code)\n exec('h(\"tstop = {}\")'.format(simulation_length))\n exec('h(\"v_init = {}\")'.format(v_init))\n\n for key in param_dict.keys():\n h(key + \" = \" + str(param_dict[key]))\n \n iclamp = h.IClamp(0.5)\n iclamp.delay = 500\n iclamp.dur = 1400\n\n amp = 0.05\n iclamp.amp = amp\n im = h.Impedance()\n h(\"access soma\")\n h(\"nseg = 1\")\n im.loc(0.5)\n im.compute(0)\n Ri = im.input(0.5)\n \n semitrial_start = time.time()\n \n ### Set the protocol\n \n h.finitialize()\n \n ### Simulate!\n \n voltageVector = h.Vector()\n voltageVector.record(eval(\"h.soma(0.5)._ref_v\"))\n timeVector = h.Vector()\n timeVector.record(h._ref_t)\n\n h.run()\n \n timeVector = np.array(timeVector)\n voltageVector = np.array(voltageVector)\n\n trace = {}\n trace['T'] = timeVector[4000:]\n trace['V'] = np.array(voltageVector)[4000:]\n trace['stim_start'] = [500]\n trace['stim_end'] = [1900]\n del voltageVector, timeVector\n return trace","title":""},{"docid":"aa33fb75582c20da8bbe0be23f519625","score":"0.5402854","text":"def test_create_sample():\n sample = flowsym.create_sample(10, ['blue', 'green', 'NIR']) # Params for test function\n\n assert len(sample) == 10 # Is dataframe correct size from input step?\n assert type(sample) == type(pd.DataFrame()) # Did we actually output a dataframe?\n assert list(sample.columns) == ['Wavelength', 'Excitation Efficiency', 'Emission Efficiency',\n 'Copy number'] # Are these the columns?\n\n # Check to make sure excitation wavelengths aren't the same as emission\n for index, val in sample.iterrows():\n assert val['Excitation Efficiency'] != val['Emission Efficiency']","title":""},{"docid":"6a41a9115b1f4f7d5dbc80391af85bea","score":"0.53991514","text":"def test_measure_nondeterministic_without_sampling(self):\n shots = 2000\n circuits = ref_measure.measure_circuits_nondeterministic(\n allow_sampling=False)\n targets = ref_measure.measure_counts_nondeterministic(shots)\n qobj = assemble(circuits, self.SIMULATOR, shots=shots)\n result = self.SIMULATOR.run(\n qobj, backend_options=self.BACKEND_OPTS).result()\n self.is_completed(result)\n self.compare_counts(result, circuits, targets, delta=0.05 * shots)\n # Test sampling was disabled\n for res in result.results:\n self.assertIn(\"measure_sampling\", res.metadata)\n self.assertEqual(res.metadata[\"measure_sampling\"], False)","title":""},{"docid":"df4283da3ffae441b2759fa28fc305e8","score":"0.5398783","text":"def test_spw_value_velocity(self):\n self._default_test()","title":""},{"docid":"fe2cc7de270405c81c8582adb4662132","score":"0.5395273","text":"def sample(self):\n raise NotImplementedError","title":""},{"docid":"fe2cc7de270405c81c8582adb4662132","score":"0.5395273","text":"def sample(self):\n raise NotImplementedError","title":""},{"docid":"8d60c3dd54d260eabaa318cc9f102dcc","score":"0.5393843","text":"def testSingleFramePlugin(self):\n task = self.makeSingleFrameMeasurementTask(\"base_SdssCentroid\")\n exposure, catalog = self.dataset.realize(10.0, task.schema, randomSeed=0)\n task.run(catalog, exposure)\n record = catalog[0]\n self.assertFalse(record.get(\"base_SdssCentroid_flag\"))\n self.assertFalse(record.get(\"base_SdssCentroid_flag_edge\"))\n self.assertFloatsAlmostEqual(record.get(\"base_SdssCentroid_x\"), record.get(\"truth_x\"), rtol=0.005)\n self.assertFloatsAlmostEqual(record.get(\"base_SdssCentroid_y\"), record.get(\"truth_y\"), rtol=0.005)","title":""},{"docid":"b9189405811d2aff1945c404f9c53e37","score":"0.53937644","text":"def test_voq_switch(self, vct):\n\n if vct is None:\n return\n\n dvss = vct.dvss\n for name in dvss.keys():\n dvs = dvss[name]\n # Get the config info\n config_db = dvs.get_config_db()\n metatbl = config_db.get_entry(\"DEVICE_METADATA\", \"localhost\")\n\n cfg_switch_type = metatbl.get(\"switch_type\")\n\n # Test only for line cards\n if cfg_switch_type == \"voq\":\n print(\"VOQ Switch test for {}\".format(name))\n cfg_switch_id = metatbl.get(\"switch_id\")\n assert cfg_switch_id != \"\", \"Got error in getting switch_id from CONFIG_DB DEVICE_METADATA\"\n\n cfg_max_cores = metatbl.get(\"max_cores\")\n assert cfg_max_cores != \"\", \"Got error in getting max_cores from CONFIG_DB DEVICE_METADATA\"\n\n cfgspkeys = config_db.get_keys(\"SYSTEM_PORT\")\n sp_count = len(cfgspkeys)\n\n asic_db = dvs.get_asic_db()\n keys = asic_db.get_keys(\"ASIC_STATE:SAI_OBJECT_TYPE_SWITCH\")\n switch_oid_key = keys[0]\n\n switch_entry = asic_db.get_entry(\"ASIC_STATE:SAI_OBJECT_TYPE_SWITCH\", switch_oid_key)\n\n value = switch_entry.get(\"SAI_SWITCH_ATTR_TYPE\")\n assert value == \"SAI_SWITCH_TYPE_VOQ\", \"Switch type is not VOQ\"\n\n value = switch_entry.get(\"SAI_SWITCH_ATTR_SWITCH_ID\")\n assert value == cfg_switch_id, \"VOQ switch id is invalid\"\n\n value = switch_entry.get(\"SAI_SWITCH_ATTR_MAX_SYSTEM_CORES\")\n assert value == cfg_max_cores, \"Max system cores is invalid\"\n\n value = switch_entry.get(\"SAI_SWITCH_ATTR_SYSTEM_PORT_CONFIG_LIST\")\n assert value != \"\", \"Empty system port config list\"\n # Convert the spcfg string to dictionary\n spcfg = ast.literal_eval(value)\n assert spcfg['count'] == sp_count, \"Number of systems ports configured is invalid\"","title":""},{"docid":"9ad86b8073695a1bd9cc4ad4f93b1bf0","score":"0.53883","text":"def test_smoke_embed_draw_sparse(self):\n\n demo_file = os.path.join(project_dir, 'Pegasus_Embedding_Video/embed_draw_sparse.py')\n subprocess.check_output([sys.executable, demo_file])","title":""},{"docid":"598a59ae84ee7f261aec563bdb521f6c","score":"0.5388235","text":"def test_basic(self):\n # get an outcome from simulating a circuit\n result = self.get_all_ones_outcome(4, 10)\n\n # create a MeasurementOutcome\n outcome = MeasurementOutcome(result)\n\n # trivial check\n self.assertTrue((outcome.raw_outcome == result).all())","title":""},{"docid":"9156d7a402e8f9c39b8aefb8f1bb16e7","score":"0.5387616","text":"def test_shot_vectors(self, max_workers, n_qubits, shots):\n dev = DefaultQubit2(max_workers=max_workers)\n shots = qml.measurements.Shots(shots)\n\n ops = [qml.Hadamard(i) for i in range(n_qubits)]\n qs = qml.tape.QuantumScript(ops, [qml.classical_shadow(range(n_qubits))], shots=shots)\n res = dev.execute(qs)\n\n assert isinstance(res, tuple)\n assert len(res) == len(list(shots))\n\n for r, s in zip(res, shots):\n assert r.shape == (2, s, n_qubits)\n assert r.dtype == np.int8\n\n # test that the bits are either 0 and 1\n assert np.all(np.logical_or(r[0] == 0, r[0] == 1))\n\n # test that the recipes are either 0, 1, or 2 (X, Y, or Z)\n assert np.all(np.logical_or(np.logical_or(r[1] == 0, r[1] == 1), r[1] == 2))","title":""},{"docid":"012a4366b6fcd5368f903549f97144bb","score":"0.53797024","text":"def main():\n NAME = os.path.basename(__file__).split(\".\")[0]\n\n NUM_STEPS = 5\n\n sensitivities = None\n r_means = []\n g_means = []\n b_means = []\n\n with its.device.ItsSession() as cam:\n props = cam.get_camera_properties()\n if not its.caps.compute_target_exposure(props):\n print \"Test skipped\"\n return\n\n expt,_ = its.target.get_target_exposure_combos(cam)[\"midSensitivity\"]\n sens_range = props['android.sensor.info.sensitivityRange']\n sens_step = (sens_range[1] - sens_range[0]) / float(NUM_STEPS-1)\n sensitivities = [sens_range[0] + i * sens_step for i in range(NUM_STEPS)]\n\n for s in sensitivities:\n req = its.objects.manual_capture_request(s, expt)\n cap = cam.do_capture(req)\n img = its.image.convert_capture_to_rgb_image(cap)\n its.image.write_image(\n img, \"%s_iso=%04d.jpg\" % (NAME, s))\n tile = its.image.get_image_patch(img, 0.45, 0.45, 0.1, 0.1)\n rgb_means = its.image.compute_image_means(tile)\n r_means.append(rgb_means[0])\n g_means.append(rgb_means[1])\n b_means.append(rgb_means[2])\n\n # Draw a plot.\n pylab.plot(sensitivities, r_means, 'r')\n pylab.plot(sensitivities, g_means, 'g')\n pylab.plot(sensitivities, b_means, 'b')\n pylab.ylim([0,1])\n matplotlib.pyplot.savefig(\"%s_plot_means.png\" % (NAME))\n\n # Test for pass/fail: check that each shot is brighter than the previous.\n for means in [r_means, g_means, b_means]:\n for i in range(len(means)-1):\n assert(means[i+1] > means[i])","title":""},{"docid":"46c5d6612a7f00bb15e1cc1916dcfdf2","score":"0.5374479","text":"def sample(self, inputs):\n pass","title":""},{"docid":"d8bd541c779ba20e07af8bdf6fd5ab93","score":"0.5371352","text":"def test_synth_bm(self, num_qubits):\n rng = np.random.default_rng(1234)\n samples = 50\n for _ in range(samples):\n circ = random_clifford_circuit(num_qubits, 5 * num_qubits, seed=rng)\n target = Clifford(circ)\n value = Clifford(synth_clifford_bm(target))\n self.assertEqual(value, target)","title":""},{"docid":"fde6a53a230f4be51116c8218142f53c","score":"0.53705996","text":"def SingleShot(qubit: qreg):\n init(qubit)\n Id(qubit)\n MEAS(qubit)\n init(qubit)\n X(qubit)\n MEAS(qubit)","title":""},{"docid":"1f0ffa433fd6a89f7b1d0cebd3732c81","score":"0.53701806","text":"def test_got_data(self):\n # Create and initialize the instrument driver with a mock port agent\n driver = mavs4InstrumentDriver(self._got_data_event_callback)\n self.assert_initialize_driver(driver) # defaults to autosample mode, so sample generated\n\n self.assert_raw_particle_published(driver, True)\n\n # validate data particle\n self.assert_particle_published(driver, self.SAMPLE, self.assert_particle_sample, True)","title":""},{"docid":"e69fdaa0395ee7a8d04fa189f0df4848","score":"0.53696066","text":"def test(): \n\t\treturn [\"vice.singlezone\", \n\t\t\t[ \n\t\t\t\ttest_from_output(), \n\t\t\t\t_singlezone.test(run = False), \n\t\t\t\ttrials.test(run = False), \n\t\t\t\tsrc_test(run = False) \n\t\t\t] \n\t\t]","title":""},{"docid":"1e838b7c5a1dc880005021d104b13215","score":"0.5368527","text":"def simulate(self):\n pass","title":""},{"docid":"84be85f1717c65fefb48dba824d7ff4d","score":"0.5367002","text":"def test_stress_strain_curve():\n pass","title":""},{"docid":"2693114bd2af7a464dd27f56fc594846","score":"0.53657436","text":"def test_inventory(clients):\n station = clients[\"station\"].get_stations()\n assert isinstance(station, obspy.Inventory)\n assert len(station)\n # each channel should have a sampling rate\n for net in station:\n for sta in net:\n for chan in sta:\n assert chan.sample_rate == 250","title":""},{"docid":"3e1af52db6c963aa1c790a5e1a781cb8","score":"0.5363447","text":"def test_logical_circuit_on_5_1_code():\n n, k = 5, 1\n binary_rep = np.array([[1, 0, 0, 1, 0, 0, 1, 1, 0, 0],\n [0, 1, 0, 0, 1, 0, 0, 1, 1, 0],\n [1, 0, 1, 0, 0, 0, 0, 0, 1, 1],\n [0, 1, 0, 1, 0, 1, 0, 0, 0, 1]])\n code = StabilizerCode(binary_rep, n, k)\n\n # Test apply \"X0\" To encoded |0> Is equivalent to encoded |1>\n eng = MainEngine()\n quantum_reg = eng.allocate_qureg(n)\n eng.flush()\n code.encoding_circuit(eng, quantum_reg, [0])\n wavefunc_zero = np.array(eng.backend.cheat()[1])\n code.logical_circuit(eng, quantum_reg, \"X0\")\n wave_function_actual = np.array(eng.backend.cheat()[1])\n All(Measure) | quantum_reg\n\n eng = MainEngine()\n quantum_reg = eng.allocate_qureg(n)\n eng.flush()\n code.encoding_circuit(eng, quantum_reg, [1])\n wave_function_desired = np.array(eng.backend.cheat()[1])\n\n assert np.all(np.abs(wave_function_actual - wave_function_desired) < 1e-4)\n All(Measure) | quantum_reg\n\n # Test apply \"X0\" To encoded |1> Is equivalent to encoded |0>.\n eng = MainEngine()\n quantum_reg = eng.allocate_qureg(n)\n eng.flush()\n code.encoding_circuit(eng, quantum_reg, [1])\n code.logical_circuit(eng, quantum_reg, \"X0\")\n wave_function_actual = np.array(eng.backend.cheat()[1])\n assert np.all(np.abs(wave_function_actual - wavefunc_zero) < 1e-4)\n All(Measure) | quantum_reg\n\n # Test apply \"Z0\" to encoded |1> is equivalent to encoded -|1>\n eng = MainEngine()\n quantum_reg = eng.allocate_qureg(n)\n eng.flush()\n code.encoding_circuit(eng, quantum_reg, [1])\n wave_func_before = np.array(eng.backend.cheat()[1])\n code.logical_circuit(eng, quantum_reg, \"Z0\")\n wave_function_actual = np.array(eng.backend.cheat()[1])\n assert np.all(np.abs(wave_function_actual + wave_func_before) < 1e-4)\n All(Measure) | quantum_reg\n\n # Test apply \"Z0\" to encoded |0> is equivalent to encoded |0>\n eng = MainEngine()\n quantum_reg = eng.allocate_qureg(n)\n eng.flush()\n code.encoding_circuit(eng, quantum_reg, [0])\n wave_func_before = np.array(eng.backend.cheat()[1])\n code.logical_circuit(eng, quantum_reg, \"Z0\")\n wave_function_actual = np.array(eng.backend.cheat()[1])\n assert np.all(np.abs(wave_function_actual - wave_func_before) < 1e-4)\n All(Measure) | quantum_reg","title":""},{"docid":"4105d7a53781700fe692ea1e941c1763","score":"0.53629273","text":"def test_instantaneous(empty_model, inst_spill):\n model = empty_model\n model.spills += inst_spill\n model.full_run()\n results = model.outputters[0]\n\n data = results.data_buffer.certain\n\n # there should be 100 Elements at all times\n for ts in data:\n assert len(ts['mass']) == NUM_ELEMENTS","title":""},{"docid":"2752efe784286dd24a33974fa6ff8d02","score":"0.53608817","text":"def test_spw_id_default_velocity(self):\n self._default_test()","title":""},{"docid":"bc3cb9fb4630541d6f1c609d864c51d3","score":"0.5357145","text":"def test_calibrate():\n with DeviceTestContext(PowerSupply, process=True) as proxy:\n proxy.calibrate()\n assert proxy.voltage == 1.23","title":""}],"string":"[\n {\n \"docid\": \"839434dfcbb582513f5d2a98de523b9a\",\n \"score\": \"0.6360883\",\n \"text\": \"def test_sample_values(self, device, shots, tol):\\n dev = device(1)\\n par = 1.5708\\n\\n observable = qml.PauliZ(wires=[0])\\n\\n dev.apply(\\n [\\n qml.RX(par, wires=[0]),\\n ],\\n rotations=[*observable.diagonalizing_gates()]\\n )\\n\\n dev._samples = dev.generate_samples()\\n\\n s1 = dev.sample(observable)\\n\\n # s1 should only contain 1 and -1\\n assert np.allclose(s1 ** 2, 1, **tol)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e4c317bcab30689d66d445862b215c7\",\n \"score\": \"0.62718225\",\n \"text\": \"def test_measurement_process_sample(self):\\n dev = qml.device(\\\"default.qubit\\\", wires=2, shots=20)\\n prod_op = Prod(qml.PauliX(wires=0), qml.PauliX(wires=1))\\n\\n @qml.qnode(dev)\\n def my_circ():\\n Prod(qml.Hadamard(0), qml.Hadamard(1))\\n return qml.sample(op=prod_op)\\n\\n results = my_circ()\\n\\n assert len(results) == 20\\n assert (results == 1).all()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e730f94d89cc5da552f6cc9e520e5ba5\",\n \"score\": \"0.61450756\",\n \"text\": \"def test_detect_metal(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7f64f1af290ddfeb177c89b52b9a9fc\",\n \"score\": \"0.6129349\",\n \"text\": \"def testGetOneParticulateSampleAndDoBitChecking(self):\\n \\n request=\\\"spectrum=CURR, analysis=CURR\\\"\\n cpt = 0\\n total_t0 = time.time()\\n \\n #listOfSamplesToTest = self.getListOfSampleIDs('2008-12-24',endDate='2008-12-25',spectralQualif='FULL',nbOfElem='1')\\n \\n #sampleID = listOfSamplesToTest[0]\\n sampleID = 967273\\n \\n # fetchnoble particulate\\n fetcher = DBDataFetcher.getDataFetcher(self.mainConn,self.archConn,sampleID)\\n \\n fetcher.fetch(request,'PAR')\\n \\n renderer = GenieParticulateRenderer(fetcher)\\n \\n xmlStr = renderer.asXmlStr(request)\\n \\n path = \\\"/tmp/samples/sampml-full-%s.xml\\\"%(sampleID)\\n \\n ctbto.common.xml_utils.pretty_print_xml(StringIO.StringIO(xmlStr),path)\\n \\n # check if no tags are left\\n self.assertIfNoTagsLeft(path)\\n \\n self.assertAllCalibrationInfo(path)\\n \\n self.assertFileContentEquals(\\\"%s/samples/sampml-full-%s.xml.master\\\"%(self._get_tests_dir_path(),sampleID),path)\\n \\n cpt +=1\\n \\n total_t1 = time.time()\\n \\n TestSAMPMLCreator.c_log.info(\\\"\\\\n****************************************************************************\\\\n****************************************************************************\\\\n****** EXECUTED %d FULL SAMPLE RETRIEVALS in %s seconds ********\\\\n****************************************************************************\\\\n****************************************************************************\\\\n\\\"%(cpt,total_t1-total_t0))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ee23e3c5098de7a51c66aa468fa1bfe\",\n \"score\": \"0.6090496\",\n \"text\": \"def test_init_circuit(self):\\n # random unitaries\\n u0 = random_unitary(2).data\\n u1 = random_unitary(2).data\\n # add to circuit\\n qr = QuantumRegister(2)\\n circ = QuantumCircuit(qr)\\n circ.unitary(u0, [qr[0]])\\n circ.unitary(u1, [qr[1]])\\n target_vec = Statevector(np.kron(u1, u0).dot([1, 0, 0, 0]))\\n target = DensityMatrix(target_vec)\\n rho = DensityMatrix(circ)\\n self.assertEqual(rho, target)\\n\\n # Test tensor product of 1-qubit gates\\n circuit = QuantumCircuit(3)\\n circuit.h(0)\\n circuit.x(1)\\n circuit.ry(np.pi / 2, 2)\\n target = DensityMatrix.from_label(\\\"000\\\").evolve(Operator(circuit))\\n rho = DensityMatrix(circuit)\\n self.assertEqual(rho, target)\\n\\n # Test decomposition of Controlled-Phase gate\\n lam = np.pi / 4\\n circuit = QuantumCircuit(2)\\n circuit.h(0)\\n circuit.h(1)\\n circuit.cp(lam, 0, 1)\\n target = DensityMatrix.from_label(\\\"00\\\").evolve(Operator(circuit))\\n rho = DensityMatrix(circuit)\\n self.assertEqual(rho, target)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"695faebb1d2c829c41a32a085f378eaa\",\n \"score\": \"0.601816\",\n \"text\": \"def test_mocked_engine_run_samples_one_mode(self, monkeypatch):\\n modes = [0]\\n shots = 10\\n monkeypatch.setattr(\\\"strawberryfields.RemoteEngine\\\", MockEngine)\\n dev = qml.device(\\\"strawberryfields.remote\\\", backend=\\\"X8\\\", shots=shots)\\n\\n @qml.qnode(dev)\\n def quantum_function(theta, phi):\\n qml.Beamsplitter(theta, phi, wires=[0, 1])\\n qml.Beamsplitter(theta, phi, wires=[4, 5])\\n return qml.sample(qml.TensorN(wires=modes))\\n\\n a = quantum_function(1.0, 0)\\n\\n expected = np.array([3, 4, 2, 4, 4, 1, 2, 1, 1, 1])\\n assert a.shape == (shots,)\\n assert np.array_equal(a, expected)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca3ff68422b1d59423edbb89aa2516af\",\n \"score\": \"0.6017791\",\n \"text\": \"def test_synth_full(self, num_qubits):\\n rng = np.random.default_rng(1234)\\n samples = 50\\n for _ in range(samples):\\n circ = random_clifford_circuit(num_qubits, 5 * num_qubits, seed=rng)\\n target = Clifford(circ)\\n value = Clifford(synth_clifford_full(target))\\n self.assertEqual(value, target)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d0f319df35692bd92e266a9ac08e218d\",\n \"score\": \"0.5985815\",\n \"text\": \"def test_dummy_vector(test_client):\\n assert len(test_client.dummy_vector(512)) == 512\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b09de90b43642e1aad94fe3f08efbc0f\",\n \"score\": \"0.59637326\",\n \"text\": \"def test_execute_one_circuit_simulator_online(self, qe_token, qe_url):\\n provider = IBMQProvider(qe_token, qe_url)\\n backend = provider.get_backend('ibmq_qasm_simulator')\\n qr = QuantumRegister(1)\\n cr = ClassicalRegister(1)\\n qc = QuantumCircuit(qr, cr)\\n qc.h(qr[0])\\n qc.measure(qr[0], cr[0])\\n qobj = transpiler.compile(qc, backend, seed=73846087)\\n shots = qobj.config.shots\\n job = backend.run(qobj)\\n result = job.result()\\n counts = result.get_counts(qc)\\n target = {'0': shots / 2, '1': shots / 2}\\n threshold = 0.04 * shots\\n self.assertDictAlmostEqual(counts, target, threshold)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5112c9ae2261ab2138233133b82b1a9\",\n \"score\": \"0.59583545\",\n \"text\": \"def sample(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55a9ab7f2b07159b02d7759d6ec67a3a\",\n \"score\": \"0.59305805\",\n \"text\": \"def sample(self):\\n\\t\\tpass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ff215c4e58d959e769d1f395c50380f\",\n \"score\": \"0.5930204\",\n \"text\": \"def test_single_sample(self, max_workers):\\n x = np.array(0.732)\\n qs = qml.tape.QuantumScript([qml.RY(x, wires=0)], [qml.sample(wires=range(2))], shots=10000)\\n\\n dev = DefaultQubit2(max_workers=max_workers)\\n result = dev.execute(qs)\\n\\n assert isinstance(result, (float, np.ndarray))\\n assert result.shape == (10000, 2)\\n assert np.allclose(\\n np.sum(result, axis=0).astype(np.float32) / 10000, [np.sin(x / 2) ** 2, 0], atol=0.1\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"01187d06c5e8d13815d6643947e060bb\",\n \"score\": \"0.59286565\",\n \"text\": \"def test_sample(self):\\n case = self.reader.read(PSSE_DATA_FILE)\\n\\n self.assertEqual(len(case.buses), 42 + 4) # 4x 3-winding trx\\n pl = 5\\n self.assertAlmostEqual(case.buses[0].v_base, 21.6, pl)\\n self.assertAlmostEqual(case.buses[41].v_base, 0.69, pl)\\n self.assertAlmostEqual(case.buses[0].v_magnitude, 1.01, pl)\\n self.assertAlmostEqual(case.buses[40].v_magnitude, 1.04738, pl)\\n self.assertAlmostEqual(case.buses[0].v_angle, -10.4286, pl)\\n self.assertAlmostEqual(case.buses[1].v_angle, -10.7806, pl)\\n\\n # Loads.\\n load_buses = [b for b in case.buses if\\n b.p_demand > 0.0 or b.q_demand > 0.0]\\n self.assertEqual(len(load_buses), 15)\\n self.assertAlmostEqual(case.buses[3].p_demand, 1200.0, pl)\\n self.assertAlmostEqual(case.buses[3].q_demand, 360.0, pl)\\n self.assertAlmostEqual(case.buses[34].p_demand, 12.0, pl)\\n self.assertAlmostEqual(case.buses[34].q_demand, 5.0, pl)\\n\\n # Generators.\\n self.assertEqual(len(case.generators), 15)\\n self.assertAlmostEqual(case.generators[0].p, 750.0, pl)\\n self.assertAlmostEqual(case.generators[0].q, 125.648, pl)\\n self.assertAlmostEqual(case.generators[0].q_max, 400.0, pl)\\n self.assertAlmostEqual(case.generators[0].q_min, -100.0, pl)\\n self.assertAlmostEqual(case.generators[0].v_magnitude, 1.01, pl)\\n self.assertAlmostEqual(case.generators[0].base_mva, 900.0, pl)\\n self.assertTrue(case.generators[0].online)\\n self.assertAlmostEqual(case.generators[0].p_max, 800.0, pl)\\n self.assertAlmostEqual(case.generators[0].p_min, 50.0, pl)\\n\\n self.assertAlmostEqual(case.generators[14].p, 3.24, pl)\\n self.assertAlmostEqual(case.generators[14].q, -1.475, pl)\\n\\n # Branches.\\n self.assertEqual(case.buses.index(case.branches[0].from_bus), 2)\\n self.assertEqual(case.buses.index(case.branches[0].to_bus), 3)\\n self.assertEqual(case.branches[0].r, 0.00260, pl)\\n self.assertEqual(case.branches[0].x, 0.04600, pl)\\n self.assertEqual(case.branches[0].b, 3.50000, pl)\\n self.assertEqual(case.branches[0].rate_a, 1200.00, pl)\\n self.assertEqual(case.branches[0].rate_b, 1100.00, pl)\\n self.assertEqual(case.branches[0].rate_c, 1000.00, pl)\\n self.assertTrue(case.branches[0].online)\\n\\n self.assertEqual(case.buses.index(case.branches[29].from_bus), 32)\\n self.assertEqual(case.buses.index(case.branches[29].to_bus), 33)\\n\\n # Transformers.\\n self.assertEqual(case.buses.index(case.branches[30].from_bus), 0)\\n self.assertEqual(case.buses.index(case.branches[30].to_bus), 2)\\n self.assertEqual(case.branches[30].b, -0.10288, pl)\\n self.assertEqual(case.branches[30].r, 0.00009, pl)\\n self.assertEqual(case.branches[30].x, 0.00758, pl)\\n self.assertEqual(case.branches[30].ratio, 1.0, pl)\\n self.assertEqual(case.branches[30].phase_shift, 0.0, pl)\\n self.assertEqual(case.branches[30].rate_a, 1200.00, pl)\\n self.assertEqual(case.branches[30].rate_b, 1100.00, pl)\\n self.assertEqual(case.branches[30].rate_c, 1000.00, pl)\\n\\n self.assertEqual(len(case.branches), 30 + 14 + (4*3)) # 4x 3-winding trx.\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7645c0e0440dd1e07d66eb27a13a19f8\",\n \"score\": \"0.5928295\",\n \"text\": \"def test_instrument_autosample_samples(self):\\n self.assert_initialize_driver()\\n\\n # command the instrument to auto-sample mode.\\n self.driver_client.cmd_dvr('execute_resource', ProtocolEvent.START_AUTOSAMPLE)\\n\\n self.assert_current_state(ProtocolStates.AUTOSAMPLE)\\n \\n # wait for some samples to be generated\\n log.debug('test_instrument_start_stop_autosample: waiting 5 seconds for samples')\\n gevent.sleep(5)\\n\\n # Verify we received at least 2 samples.\\n sample_events = [evt for evt in self.events if evt['type']==DriverAsyncEvent.SAMPLE]\\n log.debug('test_instrument_start_stop_autosample: # 0f samples = %d' %len(sample_events))\\n for sample in sample_events:\\n if sample['value'].find(DataParticleType.SAMPLE) != -1:\\n log.debug('parsed sample=%s\\\\n' %sample)\\n sample_dict = eval(sample['value']) # turn string into dictionary\\n values = sample_dict['values'] # get particle dictionary\\n # pull timestamp out of particle\\n ntp_timestamp = [item for item in values if item[\\\"value_id\\\"] == \\\"timestamp\\\"][0]['value']\\n float_timestamp = ntplib.ntp_to_system_time(ntp_timestamp)\\n log.debug('dt=%s' %time.ctime(float_timestamp))\\n self.assertTrue(len(sample_events) >= 2)\\n\\n # stop autosample and return to command mode\\n self.driver_client.cmd_dvr('execute_resource', ProtocolEvent.STOP_AUTOSAMPLE)\\n \\n self.assert_current_state(ProtocolStates.COMMAND)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42a492c72a480dca370a859c84c32176\",\n \"score\": \"0.5896839\",\n \"text\": \"def test_from_circuit(self):\\n # random unitaries\\n u0 = random_unitary(2).data\\n u1 = random_unitary(2).data\\n # add to circuit\\n qr = QuantumRegister(2)\\n circ = QuantumCircuit(qr)\\n circ.unitary(u0, [qr[0]])\\n circ.unitary(u1, [qr[1]])\\n\\n # Test decomposition of controlled-H gate\\n circuit = QuantumCircuit(2)\\n circ.x(0)\\n circuit.ch(0, 1)\\n target = DensityMatrix.from_label(\\\"00\\\").evolve(Operator(circuit))\\n rho = DensityMatrix.from_instruction(circuit)\\n self.assertEqual(rho, target)\\n\\n # Test initialize instruction\\n init = Statevector([1, 0, 0, 1j]) / np.sqrt(2)\\n target = DensityMatrix(init)\\n circuit = QuantumCircuit(2)\\n circuit.initialize(init.data, [0, 1])\\n rho = DensityMatrix.from_instruction(circuit)\\n self.assertEqual(rho, target)\\n\\n # Test reset instruction\\n target = DensityMatrix([1, 0])\\n circuit = QuantumCircuit(1)\\n circuit.h(0)\\n circuit.reset(0)\\n rho = DensityMatrix.from_instruction(circuit)\\n self.assertEqual(rho, target)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37a3b963a2ddeac481c81af517396d54\",\n \"score\": \"0.58894324\",\n \"text\": \"def test_fock_basis_samples(self, monkeypatch):\\n shots = 10\\n monkeypatch.setattr(\\\"strawberryfields.RemoteEngine\\\", MockEngine)\\n dev = qml.device(\\\"strawberryfields.remote\\\", backend=\\\"X8\\\", shots=shots)\\n\\n sampled_modes = np.array([0, 3, 5])\\n\\n @qml.qnode(dev)\\n def quantum_function(theta, phi):\\n qml.Beamsplitter(theta, phi, wires=[0, 1])\\n qml.Beamsplitter(theta, phi, wires=[4, 5])\\n return [qml.sample(qml.NumberOperator(i)) for i in sampled_modes]\\n\\n a = quantum_function(1.0, 0)\\n\\n assert a.shape == (len(sampled_modes), shots)\\n assert np.array_equal(a, MOCK_SAMPLES.T[sampled_modes])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c02b1cc90b587d9016ab7191d3795e4\",\n \"score\": \"0.58732194\",\n \"text\": \"def test_measure_deterministic_with_sampling(self):\\n shots = 100\\n circuits = ref_measure.measure_circuits_deterministic(\\n allow_sampling=True)\\n targets = ref_measure.measure_counts_deterministic(shots)\\n qobj = assemble(circuits, self.SIMULATOR, shots=shots)\\n result = self.SIMULATOR.run(\\n qobj, backend_options=self.BACKEND_OPTS).result()\\n self.is_completed(result)\\n self.compare_counts(result, circuits, targets, delta=0)\\n # Test sampling was enabled\\n for res in result.results:\\n self.assertIn(\\\"measure_sampling\\\", res.metadata)\\n self.assertEqual(res.metadata[\\\"measure_sampling\\\"], True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9fc2c2865e75a74b18402a24007b210b\",\n \"score\": \"0.586133\",\n \"text\": \"def test_measure_nondeterministic_with_sampling(self):\\n shots = 2000\\n circuits = ref_measure.measure_circuits_nondeterministic(\\n allow_sampling=True)\\n targets = ref_measure.measure_counts_nondeterministic(shots)\\n qobj = assemble(circuits, self.SIMULATOR, shots=shots)\\n result = self.SIMULATOR.run(\\n qobj, backend_options=self.BACKEND_OPTS).result()\\n self.is_completed(result)\\n self.compare_counts(result, circuits, targets, delta=0.05 * shots)\\n # Test sampling was enabled\\n for res in result.results:\\n self.assertIn(\\\"measure_sampling\\\", res.metadata)\\n self.assertEqual(res.metadata[\\\"measure_sampling\\\"], True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1274e76c0963b153550760a5a6822c52\",\n \"score\": \"0.5850311\",\n \"text\": \"def sample(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1274e76c0963b153550760a5a6822c52\",\n \"score\": \"0.5850311\",\n \"text\": \"def sample(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c54b0cba89618a3496446a436942f457\",\n \"score\": \"0.5829635\",\n \"text\": \"def test_identity(self, monkeypatch):\\n shots = 10\\n monkeypatch.setattr(\\\"strawberryfields.RemoteEngine\\\", MockEngine)\\n dev = qml.device(\\\"strawberryfields.remote\\\", backend=\\\"X8\\\", shots=shots)\\n\\n @qml.qnode(dev)\\n def quantum_function(theta, phi):\\n qml.Beamsplitter(theta, phi, wires=[0, 1])\\n qml.Beamsplitter(theta, phi, wires=[4, 5])\\n return qml.sample(qml.Identity(wires=0))\\n\\n a = quantum_function(1.0, 0)\\n\\n expected = np.ones(shots)\\n assert a.shape == (shots,)\\n assert np.array_equal(a, expected)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5208787a6bef2cdf1d74826e89d950a9\",\n \"score\": \"0.58218944\",\n \"text\": \"def run_simulation(sample, scan_size=200,is_reflectometry=False):\\n #This is you walking towards the instrument:\\n instrument = define_instrument(is_reflectometry=is_reflectometry)\\n\\n #These are your hands putting the sample on the instrument:\\n instrument.setSample(sample)\\n\\n #This is you starting the beam:\\n instrument.runSimulation()\\n\\n #This is the instrument sending your results back:\\n return instrument.result()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9fa222649055de2346a3fe21244c2cf0\",\n \"score\": \"0.58113545\",\n \"text\": \"def test_samples(self):\\n super(TestResult2d, self).test_samples()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"75b3b7f3a1653e2e72812b41d89b22c7\",\n \"score\": \"0.5805721\",\n \"text\": \"def test_mocked_engine_run_samples(self, monkeypatch):\\n modes = 8\\n shots = 10\\n monkeypatch.setattr(\\\"strawberryfields.RemoteEngine\\\", MockEngine)\\n dev = qml.device(\\\"strawberryfields.remote\\\", backend=\\\"X8\\\", shots=shots)\\n\\n @qml.qnode(dev)\\n def quantum_function(theta, phi):\\n qml.Beamsplitter(theta, phi, wires=[0, 1])\\n qml.Beamsplitter(theta, phi, wires=[4, 5])\\n return qml.sample(qml.TensorN(wires=list(range(modes))))\\n\\n a = quantum_function(1.0, 0)\\n\\n assert a.shape == (shots,)\\n assert np.array_equal(a, MOCK_SAMPLES_PROD)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"173217355e3df15f8d87427284c35e84\",\n \"score\": \"0.5800243\",\n \"text\": \"def sample(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e884e9dae31adc0ebae6d44b3e444d2e\",\n \"score\": \"0.579368\",\n \"text\": \"def test_VentriculostomySurfaceCut1(self):\\n\\n self.delayDisplay(\\\"Starting the test\\\")\\n #\\n # first, get some data\\n #\\n import urllib\\n downloads = (\\n ('http://slicer.kitware.com/midas3/download?items=5767', 'FA.nrrd', slicer.util.loadVolume),\\n )\\n\\n for url,name,loader in downloads:\\n filePath = slicer.app.temporaryPath + '/' + name\\n if not os.path.exists(filePath) or os.stat(filePath).st_size == 0:\\n logging.info('Requesting download %s from %s...\\\\n' % (name, url))\\n urllib.urlretrieve(url, filePath)\\n if loader:\\n logging.info('Loading %s...' % (name,))\\n loader(filePath)\\n self.delayDisplay('Finished with download and loading')\\n\\n volumeNode = slicer.util.getNode(pattern=\\\"FA\\\")\\n logic = VentriculostomySurfaceCutLogic()\\n self.assertIsNotNone( logic.hasImageData(volumeNode) )\\n self.delayDisplay('Test passed!')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f6b80e98a74a3b0222eacd4272e72d5\",\n \"score\": \"0.57886356\",\n \"text\": \"def sample(self, n_shot, m):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"34b5daad90c6abd6f576b15fa82107c9\",\n \"score\": \"0.5735137\",\n \"text\": \"def test_biosample_samples_get(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"76b83d9c26e62e1ffc302c28f9ae5844\",\n \"score\": \"0.57321715\",\n \"text\": \"def test_supported_gate_single_wire_no_parameters(self, device, tol, name, expected_output):\\n n_wires = 1\\n dev = device(n_wires)\\n\\n op = getattr(qml.ops, name)\\n\\n @qml.qnode(dev)\\n def circuit():\\n op(wires=0)\\n return qml.expval(qml.PauliZ(0))\\n\\n assert np.isclose(circuit(), expected_output, atol=tol(dev.shots))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c70dcaa2048d1517695dc8305f26b69\",\n \"score\": \"0.5713772\",\n \"text\": \"def simulate():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a34db359f9053af975f8e35402e84f3d\",\n \"score\": \"0.5697099\",\n \"text\": \"def test_run(self, tmpdir, extraction):\\n extraction.run(outdir=tmpdir)\\n testobs = SpectrumObservation.read(tmpdir / 'ogip_data' / 'pha_obs23523.fits')\\n assert_quantity_allclose(testobs.aeff.data.data,\\n extraction.observations[0].aeff.data.data)\\n assert_quantity_allclose(testobs.on_vector.data.data,\\n extraction.observations[0].on_vector.data.data)\\n assert_quantity_allclose(testobs.on_vector.energy.nodes,\\n extraction.observations[0].on_vector.energy.nodes)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39c9dcb74547fbb8b6c7170dcbe7099e\",\n \"score\": \"0.568977\",\n \"text\": \"def test_int(self):\\n shots = Shots(100)\\n assert shots.shot_vector == (ShotCopies(100, 1),)\\n assert shots.total_shots == 100\",\n \"title\": \"\"\n },\n {\n \"docid\": \"74c4e7bd79ec20cac2c2103519ee66e0\",\n \"score\": \"0.56746376\",\n \"text\": \"def test_vector(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"71f134f275137b401ae536beeffe216a\",\n \"score\": \"0.5662659\",\n \"text\": \"def test_autosample_particle_generation(self):\\n self.assert_initialize_driver(DriverProtocolState.AUTOSAMPLE)\\n\\n\\n self.assert_async_particle_generation(DataParticleType.OPTAA_SAMPLE, self.assert_sample_data_particle, timeout=120)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f95e643802a8dbdac41288250de5244\",\n \"score\": \"0.565523\",\n \"text\": \"def main():\\n config = global_conf.load_global_config('config.yml')\\n db_client = database.client.DatabaseClient(config=config)\\n\\n controller = sample_controller.CombinatorialSampleController(\\n x_samples=range(20, 271, 50), # 5 samples\\n y_samples=range(40, 221, 36), # 5 samples\\n z_samples=range(80, 181, 20), # 5 samples\\n roll_samples=(-np.pi/2, -np.pi/4, -np.pi/6, 0, np.pi/6, np.pi/4, np.pi/2), # 6 samples\\n pitch_samples=np.linspace(-np.pi/2, np.pi/2, 7), # 7 samples\\n yaw_samples=np.linspace(-np.pi, np.pi, 7), # 7 samples\\n fov_samples=(30, 60, 90),\\n aperture_samples=(2.2, 22, 120),\\n subject_pose=tf.Transform(location=(130.00, 140.00, 70.37))\\n )\\n simulator = ue_sim.UnrealCVSimulator(controller, config={\\n 'provide_rgb': True,\\n 'provide_depth': False,\\n 'provide_labels': True,\\n 'provide_world_normals': False,\\n })\\n builder = collection_builder.ImageCollectionBuilder(db_client)\\n builder.add_from_image_source(simulator, filter_function=image_filter)\\n builder.save()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4db4b6c39718908e71a73bca11e32bc\",\n \"score\": \"0.5635579\",\n \"text\": \"def test_example_shots():\\n hamiltonian = qml.Hamiltonian(\\n coeffs=[-1.0] * 3,\\n observables=[qml.PauliX(0), qml.PauliZ(1), qml.PauliY(0) @ qml.PauliX(1)],\\n )\\n\\n @qml.qnode(qml.device(\\\"default.qubit\\\", wires=2, shots=1000))\\n def quant_fun():\\n qml.RX(0.1, wires=[0])\\n qml.RY(0.5, wires=[1])\\n qml.CNOT(wires=[0, 1])\\n qml.RY(0.6, wires=[0])\\n return qml.expval(hamiltonian)\\n\\n opt = LieAlgebraOptimizer(circuit=quant_fun, stepsize=0.1, exact=False)\\n\\n for _ in range(3):\\n opt.step_and_cost()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9fc171a0b1b11836fe6ae791ad69e18d\",\n \"score\": \"0.5634411\",\n \"text\": \"def test_run(tmpdir, extraction):\\n extraction.run()\\n extraction.write(outdir=str(tmpdir), overwrite=True)\\n testobs = SpectrumDatasetOnOff.from_ogip_files(\\n str(tmpdir / \\\"ogip_data\\\" / \\\"pha_obs23523.fits\\\")\\n )\\n assert_quantity_allclose(\\n testobs.aeff.data.data, extraction.spectrum_observations[0].aeff.data.data\\n )\\n assert_quantity_allclose(\\n testobs.counts.data, extraction.spectrum_observations[0].counts.data\\n )\\n assert_allclose(\\n testobs.counts.energy.center,\\n extraction.spectrum_observations[0].counts.energy.center,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"01e0e31f5e12210e755b8d13052f249f\",\n \"score\": \"0.56336546\",\n \"text\": \"def testFullNobleGazSamples(self):\\n \\n request=\\\"spectrum=CURR/DETBK/GASBK/QC, analysis=CURR\\\"\\n \\n # get full\\n listOfSamplesToTest = self.getListOfSaunaSampleIDs('2008-08-11',endDate='2008-12-12',spectralQualif='FULL',nbOfElem='2')\\n \\n # remove sampleID for which data isn't available\\n if \\\"141372\\\" in listOfSamplesToTest:\\n listOfSamplesToTest.remove(\\\"141372\\\")\\n \\n TestSAMPMLCreator.c_log.info(\\\"list samples :%s\\\"%(listOfSamplesToTest))\\n \\n cpt = 0\\n total_t0 = time.time()\\n \\n for sampleID in listOfSamplesToTest:\\n \\n TestSAMPMLCreator.c_log.info(\\\"Start Test %d for SampleID %s.\\\\n\\\"%(cpt,sampleID))\\n \\n t0 = time.time()\\n \\n # fetchnoble particulate\\n fetcher = DBDataFetcher.getDataFetcher(self.nbConn,self.archConn,sampleID)\\n \\n #modify remoteHost\\n fetcher.setRemoteHost(self.conf.get('RemoteAccess','nobleGazRemoteHost','dls007'))\\n \\n fetcher.fetch(request,'GAS')\\n \\n renderer = SaunaRenderer(fetcher)\\n \\n xmlStr = renderer.asXmlStr(request)\\n \\n #print \\\"Non Formatted String [%s]\\\\n\\\"%(xmlStr)\\n \\n #f = open(\\\"/tmp/xmlStr.xml\\\",\\\"w\\\")\\n \\n #f.write(xmlStr)\\n #f.close()\\n \\n path = \\\"/tmp/samples/sampml-full-%s.xml\\\"%(sampleID)\\n \\n ctbto.common.xml_utils.pretty_print_xml(StringIO.StringIO(xmlStr),path)\\n \\n # check if no tags are left\\n self.assertIfNoTagsLeft(path)\\n \\n #self.assertAllCalibrationInfo(path)\\n \\n t1 = time.time()\\n \\n #TestSAMPMLCreator.c_log.info(\\\"End of Test %d for SampleID %s.\\\\nTest executed in %s seconds.\\\\n\\\\n**************************************************************** \\\\n**************************************************************** \\\\n\\\"%(cpt,sampleID,(t1-t0)))\\n TestSAMPMLCreator.c_log.info(\\\"\\\\n********************************************************************************\\\\n End of Test %d for SampleID %s. Test executed in %s seconds.\\\\n********************************************************************************\\\\n\\\"%(cpt,sampleID,(t1-t0)))\\n \\n cpt +=1\\n \\n total_t1 = time.time()\\n \\n TestSAMPMLCreator.c_log.info(\\\"\\\\n****************************************************************************\\\\n****************************************************************************\\\\n****** EXECUTED %d FULL SAMPLE RETRIEVALS in %s seconds ********\\\\n****************************************************************************\\\\n****************************************************************************\\\\n\\\"%(cpt,total_t1-total_t0))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c256a90e9a0b78acdba418173418a0ff\",\n \"score\": \"0.56321967\",\n \"text\": \"def testBasics(self):\\n bbox = Box2I(Point2I(256, 100), Extent2I(128, 127))\\n minCounts = 5000\\n maxCounts = 50000\\n starSigma = 1.5\\n numX = 5\\n numY = 5\\n coordList = self.makeCoordList(\\n bbox = bbox,\\n numX = numX,\\n numY = numY,\\n minCounts = minCounts,\\n maxCounts = maxCounts,\\n sigma = starSigma,\\n )\\n kwid = 11 # kernel width\\n sky = 2000\\n # create an exposure without a Wcs; add the Wcs later\\n exposure = plantSources(bbox=bbox, kwid=kwid, sky=sky, coordList=coordList, addPoissonNoise=True)\\n\\n schema = SourceTable.makeMinimalSchema()\\n\\n config = DetectAndMeasureTask.ConfigClass()\\n task = DetectAndMeasureTask(config=config, schema=schema)\\n\\n butler = Butler(root=InputDir)\\n dataRef = butler.dataRef(\\\"calexp\\\", dataId=dict(visit=1))\\n wcs = dataRef.get(\\\"raw\\\").getWcs()\\n exposure.setWcs(wcs)\\n exposureIdInfo = dataRef.get(\\\"expIdInfo\\\")\\n taskRes = task.run(exposure=exposure, exposureIdInfo=exposureIdInfo)\\n self.assertEqual(len(taskRes.sourceCat), numX * numY)\\n schema = taskRes.sourceCat.schema\\n centroidFlagKey = schema.find(\\\"slot_Centroid_flag\\\").getKey()\\n parentKey = schema.find(\\\"parent\\\").getKey()\\n psfFluxFlagKey = schema.find(\\\"slot_PsfFlux_flag\\\").getKey()\\n psfFluxKey = schema.find(\\\"slot_PsfFlux_flux\\\").getKey()\\n for src in taskRes.sourceCat:\\n self.assertFalse(src.get(centroidFlagKey)) # centroid found\\n self.assertEqual(src.get(parentKey), 0) # not debelended\\n self.assertFalse(src.get(psfFluxFlagKey)) # flux measured\\n self.assertGreater(src.get(psfFluxKey), 4000) # flux sane\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d688b0c1d26e35c3481e4d0029cf34d4\",\n \"score\": \"0.5627072\",\n \"text\": \"def test_instrumment_start_stop_autosample(self):\\n self.assert_initialize_driver()\\n \\n # start auto-sample.\\n reply = self.driver_client.cmd_dvr('execute_resource', ProtocolEvent.START_AUTOSAMPLE)\\n\\n # Test the driver is in autosample mode.\\n state = self.driver_client.cmd_dvr('get_resource_state')\\n self.assertEqual(state, ProtocolStates.AUTOSAMPLE)\\n \\n # stop auto-sample.\\n reply = self.driver_client.cmd_dvr('execute_resource', ProtocolEvent.STOP_AUTOSAMPLE)\\n\\n # Test the driver is in command mode.\\n state = self.driver_client.cmd_dvr('get_resource_state')\\n self.assertEqual(state, ProtocolStates.COMMAND)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee01f8ccb90613d66d5b1b71eeb4bb27\",\n \"score\": \"0.561529\",\n \"text\": \"def runTest(self):\\n self.setUp()\\n self.test_VentriculostomySurfaceCut1()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd1c37e2289789da22f067c3cc789ff9\",\n \"score\": \"0.5610199\",\n \"text\": \"def test_step_by_step_circuit_inspection(self):\\n \\n qubits = QuantumRegister(3)\\n bits = ClassicalRegister(3)\\n circuit = QuantumCircuit(qubits, bits)\\n circuit.h(qubits[0])\\n circuit.snapshot(\\\"step 1\\\") # This is a snapshot - the simulator will store the statevector at this point\\n circuit.x(qubits[2])\\n circuit.cx(qubits[0], qubits[1])\\n circuit.snapshot(\\\"step 2\\\")\\n circuit.barrier(qubits)\\n circuit.measure(qubits, bits)\\n circuit.snapshot(\\\"step 3\\\")\\n \\n simulator = Aer.get_backend('qasm_simulator')\\n simulation = execute(circuit, simulator, shots=1)\\n snapshots = simulation.result().results[0].data.snapshots.statevector # Get the statevectors for all of the snapshots\\n for(name, state) in snapshots.items():\\n print(f\\\"{name}: {state}\\\")\\n print(\\\"\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3185ba7b3793fa44d93ae3deec3f5934\",\n \"score\": \"0.5604561\",\n \"text\": \"def testFullGenieParticulateSamples(self):\\n \\n request=\\\"spectrum=ALL, analysis=ALL\\\"\\n \\n # get full 2003-10-24 to 2003-10-26\\n listOfSamplesToTest = self.getListOfSampleIDs('2007-10-24',endDate='2008-10-26',spectralQualif='FULL',nbOfElem='2')\\n \\n # error\\n #listOfSamplesToTest = [ \\\"700637\\\" ]\\n \\n #transform in numbers and retransform in str to remove the 0 at the beginning of the number\\\"\\n #intifiedlist = map(int,listOfSamplesToTest)\\n \\n #listOfSamplesToTest = map(str,intifiedlist)\\n \\n TestSAMPMLCreator.c_log.info(\\\"list samples: %s\\\"%listOfSamplesToTest)\\n \\n cpt = 0\\n total_t0 = time.time()\\n \\n for sampleID in listOfSamplesToTest:\\n \\n TestSAMPMLCreator.c_log.info(\\\"\\\\n********************************************************************************\\\\n Start Test %d for SampleID %s.\\\\n********************************************************************************\\\\n\\\"%(cpt,sampleID))\\n \\n t0 = time.time()\\n \\n # fetchnoble particulate\\n fetcher = DBDataFetcher.getDataFetcher(self.mainConn,self.archConn,sampleID)\\n \\n fetcher.fetch(request,'PAR')\\n \\n renderer = GenieParticulateRenderer(fetcher)\\n \\n xmlStr = renderer.asXmlStr(request)\\n \\n path = \\\"/tmp/samples/sampml-full-%s.xml\\\"%(sampleID)\\n \\n ctbto.common.xml_utils.pretty_print_xml(StringIO.StringIO(xmlStr),path)\\n \\n # check if no tags are left\\n self.assertIfNoTagsLeft(path)\\n \\n self.assertAllCalibrationInfo(path)\\n \\n t1 = time.time()\\n \\n TestSAMPMLCreator.c_log.info(\\\"\\\\n********************************************************************************\\\\n End of Test %d for SampleID %s. Test executed in %s seconds.\\\\n********************************************************************************\\\\n\\\"%(cpt,sampleID,(t1-t0)))\\n \\n cpt +=1\\n \\n total_t1 = time.time()\\n \\n TestSAMPMLCreator.c_log.info(\\\"\\\\n****************************************************************************\\\\n****************************************************************************\\\\n****** EXECUTED %d FULL SAMPLE RETRIEVALS in %s seconds ********\\\\n****************************************************************************\\\\n****************************************************************************\\\\n\\\"%(cpt,total_t1-total_t0))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4df36beaf108f557f9455a4c35e30c2\",\n \"score\": \"0.5595684\",\n \"text\": \"def test_class():\\n\\tfrom Summary import make_raster_plot\\n\\n\\t# use model to generate a phenotype\\n\\tpop = Population.Population(1, 6)\\n\\tmodel = CellularAutomataModel()\\n\\ts = time.time()\\n\\toutput = model.run_simulation()\\n\\n\\tprint(f\\\"{time.time() - s:.2f} seconds\\\")\\n\\t\\n\\t# generate reference phenotype from experimental data\\n\\treference_file = {\\n\\t\\t\\\"small\\\": \\\"../Resources/Small - 7-1-35.spk.txt\\\",\\n\\t\\t\\\"dense\\\": \\\"../Resources/Dense - 2-1-20.spk.txt\\\"\\n\\t}\\n\\n\\t# Compare model output with experimental data\\n\\tmake_raster_plot(reference_file[\\\"small\\\"], output, DURATION)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f803462e26d0054a09049590dec03b88\",\n \"score\": \"0.5592262\",\n \"text\": \"def testing_full_framework(\\n red, blue,\\n output_file,\\n l_s, h_s, count,\\n vparam=\\\"eps\\\",\\n eps=.01,\\n alpha=.01,\\n max_disk_r=None,\\n min_disk_r=None,\\n disc_name=\\\"disc\\\",\\n region_name=\\\"halfplane\\\",\\n sample_method=\\\"halfplane\\\",\\n fast_disk = True,\\n two_level_sample=True,\\n max_time=None):\\n\\n fieldnames = [\\\"vparam\\\", \\\"disc\\\", \\\"region\\\", \\\"n\\\", \\\"s\\\", \\\"n_pts\\\", \\\"m_pts\\\", \\\"b_pts\\\", \\\"alpha\\\", \\\"time\\\",\\n \\\"m_disc\\\", \\\"m_disc_approx\\\", \\\"sample_method\\\"]\\n with open(output_file, 'w') as f:\\n writer = csv.DictWriter(f, fieldnames=fieldnames)\\n writer.writeheader()\\n\\n for i in np.logspace(l_s, h_s, count):\\n\\n if vparam == \\\"eps\\\":\\n eps = i\\n elif vparam == \\\"alpha\\\":\\n alpha = i\\n n = 1 / eps\\n s = 1 / (2 * eps * eps)\\n n = int(round(n) + .1)\\n s = int(round(s) + .1)\\n\\n\\n disc = utils.disc_to_func(disc_name)\\n\\n red_sample = pyscan.my_sample(red, s)\\n blue_sample = pyscan.my_sample(blue, s)\\n if two_level_sample:\\n red_net = pyscan.my_sample(red, n)\\n blue_net = pyscan.my_sample(blue, n)\\n else:\\n red_net = red_sample\\n blue_net = blue_sample\\n\\n net = red_net + blue_net\\n\\n print(\\\"Running: {} {}\\\".format(n, s))\\n\\n start_time = time.time()\\n\\n if region_name == \\\"multiscale_disk\\\":\\n if max_disk_r is not None and alpha > max_disk_r:\\n print(\\\"Max Disk Radius is greater than alpha\\\")\\n continue\\n reg, mx = multiscale_disk(min_disk_r, max_disk_r, alpha, red_sample, blue_sample, net, disc, fast_disk)\\n m_sample, b_sample, net_set = [], [], []\\n else:\\n if sample_method == \\\"halfplane\\\":\\n m_sample = [pyscan.halfplane_kernel([pyscan.Point(pt[0], pt[1], 1.0) for pt in traj], alpha) for traj in red_sample]\\n b_sample = [pyscan.halfplane_kernel([pyscan.Point(pt[0], pt[1], 1.0) for pt in traj], alpha) for traj in blue_sample]\\n pt_net = [pyscan.halfplane_kernel([pyscan.Point(pt[0], pt[1], 1.0) for pt in traj], alpha) for traj in net]\\n elif sample_method == \\\"dp\\\":\\n m_sample = [pyscan.dp_compress(traj, alpha) for traj in red_sample]\\n b_sample = [pyscan.dp_compress(traj, alpha) for traj in blue_sample]\\n pt_net = [pyscan.dp_compress(traj, alpha) for traj in net]\\n elif sample_method == \\\"hull\\\":\\n m_sample = [pyscan.convex_hull([pyscan.Point(pt[0], pt[1], 1.0) for pt in traj]) for traj in red_sample]\\n b_sample = [pyscan.convex_hull([pyscan.Point(pt[0], pt[1], 1.0) for pt in traj]) for traj in blue_sample]\\n pt_net = [pyscan.convex_hull([pyscan.Point(pt[0], pt[1], 1.0) for pt in traj]) for traj in net]\\n elif sample_method is None:\\n #just takes the waypoints.\\n m_sample = [[pyscan.Point(pt[0], pt[1], 1.0) for pt in traj] for traj in red_sample]\\n b_sample = [[pyscan.Point(pt[0], pt[1], 1.0) for pt in traj] for traj in blue_sample]\\n pt_net = [[pyscan.Point(pt[0], pt[1], 1.0) for pt in traj] for traj in net]\\n elif sample_method == \\\"grid\\\":\\n m_sample = [pyscan.grid_kernel(traj, alpha) for traj in red_sample]\\n b_sample = [pyscan.grid_kernel(traj, alpha) for traj in blue_sample]\\n pt_net = [pyscan.grid_kernel(traj, alpha) for traj in net]\\n elif sample_method == \\\"lifting\\\":\\n m_sample = [pyscan.lifting_kernel(traj, alpha) for traj in red_sample]\\n b_sample = [pyscan.lifting_kernel(traj, alpha) for traj in blue_sample]\\n pt_net = [pyscan.lifting_kernel(traj, alpha) for traj in net]\\n elif sample_method == \\\"grid_direc\\\":\\n\\n if max_disk_r is not None and alpha > max_disk_r:\\n print(\\\"Max Disk Radius is greater than alpha\\\")\\n continue\\n chord_l = math.sqrt(4 * alpha * max(min_disk_r, alpha) - 2 * alpha * alpha)\\n m_sample = [pyscan.grid_direc_kernel(pyscan.dp_compress(traj, alpha), chord_l, alpha) for traj in\\n red_sample]\\n b_sample = [pyscan.grid_direc_kernel(pyscan.dp_compress(traj, alpha), chord_l, alpha) for traj in\\n blue_sample]\\n pt_net = [pyscan.grid_direc_kernel(pyscan.dp_compress(traj, alpha), chord_l, alpha) for traj in net]\\n elif sample_method == \\\"even\\\":\\n m_sample = [pyscan.even_sample_error(traj, alpha, False) for traj in red_sample]\\n b_sample = [pyscan.even_sample_error(traj, alpha, False) for traj in blue_sample]\\n pt_net = [pyscan.even_sample_error(traj, alpha, False) for traj in net]\\n else:\\n return\\n\\n if region_name == \\\"multiscale_disk_fixed\\\":\\n m_sample = list(pyscan.trajectories_to_labels(m_sample))\\n b_sample = list(pyscan.trajectories_to_labels(b_sample))\\n net_set = list(pyscan.trajectories_to_labels(pt_net))\\n reg, mx = multiscale_disk_fixed(min_disk_r, max_disk_r, m_sample, b_sample, net_set, disc, fast_disk)\\n else:\\n m_sample = list(pyscan.trajectories_to_labels(m_sample))\\n b_sample = list(pyscan.trajectories_to_labels(b_sample))\\n net_set = list(itertools.chain.from_iterable(pt_net))\\n if region_name == \\\"halfplane\\\":\\n reg, mx = pyscan.max_halfplane_labeled(net_set, m_sample, b_sample, disc)\\n elif region_name == \\\"disk\\\":\\n reg, mx = pyscan.max_disk_labeled(net_set, m_sample, b_sample, disc)\\n elif region_name == \\\"rectangle\\\":\\n reg, mx = pyscan.max_rect_labeled(n, 2 * max_disk_r, m_sample, b_sample, disc)\\n elif region_name == \\\"rectangle_scale\\\":\\n reg, mx = pyscan.max_rect_labeled_scale(n, 2 * max_disk_r, alpha, net_set, m_sample, b_sample, disc)\\n else:\\n return\\n\\n end_time = time.time()\\n actual_mx = pyscan.evaluate_range_trajectory(reg, red, blue, disc)\\n row = {\\\"vparam\\\": vparam,\\n \\\"disc\\\": disc_name,\\n \\\"region\\\": region_name,\\n \\\"n\\\": n, \\\"s\\\": s,\\n \\\"n_pts\\\": len(net_set), \\\"m_pts\\\":len(m_sample), \\\"b_pts\\\":len(b_sample),\\n \\\"alpha\\\":alpha,\\n \\\"time\\\": end_time - start_time,\\n \\\"m_disc_approx\\\": mx,\\n \\\"m_disc\\\": actual_mx,\\n \\\"sample_method\\\": sample_method}\\n writer.writerow(row)\\n f.flush()\\n print(row)\\n if max_time is not None and end_time - start_time > max_time:\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3cdd104871deaea7a6ab82e021f83687\",\n \"score\": \"0.558972\",\n \"text\": \"def test_sampler_object():\\n \\n os.chdir(data_path())\\n \\n spec = spectrum.SpectrumSampler(SPECTRUM_FILE)\\n sampler_checks(spec)\\n \\n new = spec.redo_1d_extraction()\\n sampler_checks(new)\\n \\n # Initialized from HDUList\\n with pyfits.open(SPECTRUM_FILE) as hdul:\\n spec = spectrum.SpectrumSampler(hdul)\\n sampler_checks(spec)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd6b68b08a08b2283250a83b3e774615\",\n \"score\": \"0.55812806\",\n \"text\": \"def test_samples(self):\\n super(TestResult1d, self).test_samples()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c75a9f02d0c7b3ed9d77dc715cd7dde\",\n \"score\": \"0.558012\",\n \"text\": \"def _sample(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f01cb8dd1b259780c04c9d51beff877e\",\n \"score\": \"0.5571169\",\n \"text\": \"def test_vs_update(self):\\n random.seed(5)\\n p1 = axelrod.CautiousQLearner()\\n p2 = axelrod.Cooperator()\\n simulate_play(p1, p2)\\n self.assertEqual(p1.Vs, {'': 0.1, '0.0': 0})\\n simulate_play(p1, p2)\\n self.assertEqual(p1.Vs,{'': 0.1, '0.0': 0.30000000000000004, 'C1.0': 0})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a7b1e7db483c6d9d2c366f0b68026c2f\",\n \"score\": \"0.55548793\",\n \"text\": \"def test_multi_measurement_shot_vector(self, max_workers, shots):\\n x, y = np.array(0.732), np.array(0.488)\\n shots = qml.measurements.Shots(shots)\\n qs = qml.tape.QuantumScript(\\n [qml.RX(x, wires=0), qml.CNOT(wires=[0, 1]), qml.RY(y, wires=1)],\\n [qml.expval(qml.Hadamard(0)), qml.probs(wires=range(2)), qml.sample(wires=range(2))],\\n shots=shots,\\n )\\n\\n dev = DefaultQubit2(max_workers=max_workers)\\n result = dev.execute(qs)\\n\\n assert isinstance(result, tuple)\\n assert len(result) == len(list(shots))\\n\\n for shot_res, s in zip(result, shots):\\n assert isinstance(shot_res, tuple)\\n assert len(shot_res) == 3\\n\\n assert all(isinstance(meas_res, (float, np.ndarray)) for meas_res in shot_res)\\n\\n assert shot_res[0].shape == ()\\n assert np.allclose(shot_res[0], np.cos(x) / np.sqrt(2), atol=0.1)\\n\\n assert shot_res[1].shape == (4,)\\n assert np.allclose(\\n shot_res[1],\\n [\\n np.cos(x / 2) ** 2 * np.cos(y / 2) ** 2,\\n np.cos(x / 2) ** 2 * np.sin(y / 2) ** 2,\\n np.sin(x / 2) ** 2 * np.sin(y / 2) ** 2,\\n np.sin(x / 2) ** 2 * np.cos(y / 2) ** 2,\\n ],\\n atol=0.1,\\n )\\n\\n assert shot_res[2].shape == (s, 2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"02c57ea60fd7847b15e58dbd524282b7\",\n \"score\": \"0.55475247\",\n \"text\": \"def main():\\n\\n # robot = rb.Snatch3rRobot()\\n # robot.drive_system.go_straight_inches(10)\\n # make_polygon(5)\\n # follow_the_black_line()\\n # beep_when_area_is_bigger_than(20)\\n # IR_sensor(10)\\n\\n print('------SPRINT ONE-------')\\n print(\\\"test drive system\\\") # OK\\n # test_drive_system()\\n # time.sleep(5)\\n\\n print('test follow black line')\\n print('in clockwise')\\n # follow_black_line()\\n # follow_black_line_fancy_version()\\n time.sleep(5)\\n\\n print('move forward until see black:')\\n # until_color_is(1)\\n # time.sleep(5)\\n print('----end of sprint one------')\\n\\n print('------SPRINT TWO---------')\\n print('TEST ARM HERE') # OK\\n # test_arm()\\n # time.sleep(5)\\n print('TEST IR HERE')\\n # beep_when_wave_hands()\\n # time.sleep(5)\\n print('TEST CAMERA HERE')\\n # beep_if_bigger_than_1000()\\n # time.sleep(5)\\n print('----end of sprint two------')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33860dfba6a628a6429ddb2d84e3fb3f\",\n \"score\": \"0.55372095\",\n \"text\": \"def test_go_straight_inches_method():\\n my_robot = rb.Snatch3rRobot()\\n\\n print('Test 1: Forward 5 Inches')\\n my_robot.drive_system.go_straight_inches(5, 50)\\n\\n time.sleep(10)\\n\\n print('Test 2: Backward 5 Inches')\\n my_robot.drive_system.go_straight_inches(-5, -50)\\n\\n time.sleep(10)\\n\\n print('Test 3: Forward 0.5 Inches')\\n my_robot.drive_system.go_straight_inches(0.5, 20)\\n\\n time.sleep(10)\\n\\n print('Test 4: Forward 0 Inches')\\n my_robot.drive_system.go_straight_inches(0, 100)\\n\\n time.sleep(10)\\n\\n print('Test 5: Forward 60 Inches')\\n my_robot.drive_system.go_straight_inches(60, 100)\\n\\n time.sleep(10)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c36077f239d0d266b68694cddd5e192\",\n \"score\": \"0.5535899\",\n \"text\": \"def test_synth_ag(self, num_qubits):\\n rng = np.random.default_rng(1234)\\n samples = 50\\n for _ in range(samples):\\n circ = random_clifford_circuit(num_qubits, 5 * num_qubits, seed=rng)\\n target = Clifford(circ)\\n value = Clifford(synth_clifford_ag(target))\\n self.assertEqual(value, target)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa2d20807c0b2424e1aaa8853ed6308c\",\n \"score\": \"0.55336535\",\n \"text\": \"def sampleWrold():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0dc7caf4c04d56d64fb1a478599b66de\",\n \"score\": \"0.5493892\",\n \"text\": \"def main():\\n\\n test_surface_normal()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3788e7da72fdd3d0578b04f990d63c9\",\n \"score\": \"0.54914016\",\n \"text\": \"async def test_discrete_true(hass, mock_hub):\\n register_config = {\\n CONF_INPUT_TYPE: CALL_TYPE_DISCRETE,\\n }\\n await run_sensor_test(\\n hass,\\n mock_hub,\\n register_config,\\n [0xFF],\\n expected=\\\"on\\\",\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a056d4b43d62405f7743c4b83c15917a\",\n \"score\": \"0.5490275\",\n \"text\": \"def test_num_shots(self):\\n # number of qubits\\n nqubits = 4\\n\\n # number of shots\\n nshots = 40\\n\\n # get an outcome from simulating a circuit\\n result = self.get_all_ones_outcome(nqubits, nshots)\\n\\n # create a MeasurementOutcome\\n outcome = MeasurementOutcome(result)\\n\\n # trivial check\\n self.assertEqual(outcome.shots, nshots)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"852ed519f12dce4f82ceaf600a3dcf2e\",\n \"score\": \"0.54843324\",\n \"text\": \"def test_sample_paths_wiener(self, watch_params, use_time_step,\\n use_time_grid, supply_normal_draws):\\n dtype = tf.float64\\n\\n def drift_fn(_, x):\\n return tf.zeros_like(x)\\n\\n def vol_fn(_, x):\\n return tf.expand_dims(tf.ones_like(x), -1)\\n\\n times = np.array([0.1, 0.2, 0.3])\\n num_samples = 10000\\n if watch_params:\\n watch_params = []\\n else:\\n watch_params = None\\n if use_time_step:\\n time_step = 0.01\\n num_time_steps = None\\n else:\\n time_step = None\\n num_time_steps = 30\\n if use_time_grid:\\n time_step = None\\n times_grid = tf.linspace(tf.constant(0.0, dtype=dtype), 0.3, 31)\\n else:\\n times_grid = None\\n if supply_normal_draws:\\n num_samples = 1\\n # Use antithetic sampling\\n normal_draws = tf.random.stateless_normal(\\n shape=[5000, 30, 1],\\n seed=[1, 42],\\n dtype=dtype)\\n normal_draws = tf.concat([normal_draws, -normal_draws], axis=0)\\n else:\\n normal_draws = None\\n paths = euler_sampling.sample(\\n dim=1, drift_fn=drift_fn, volatility_fn=vol_fn,\\n times=times,\\n num_samples=num_samples,\\n time_step=time_step,\\n num_time_steps=num_time_steps,\\n watch_params=watch_params,\\n normal_draws=normal_draws,\\n times_grid=times_grid,\\n random_type=random.RandomType.STATELESS_ANTITHETIC,\\n seed=[1, 42])\\n\\n # The correct number of samples\\n num_samples = 10000\\n with self.subTest('Shape'):\\n self.assertAllEqual(paths.shape.as_list(), [num_samples, 3, 1])\\n paths = self.evaluate(paths)\\n means = np.mean(paths, axis=0).reshape([-1])\\n covars = np.cov(paths.reshape([num_samples, -1]), rowvar=False)\\n expected_means = np.zeros((3,))\\n expected_covars = np.minimum(times.reshape([-1, 1]), times.reshape([1, -1]))\\n with self.subTest('Means'):\\n self.assertAllClose(means, expected_means, rtol=1e-2, atol=1e-2)\\n with self.subTest('Covariance'):\\n self.assertAllClose(covars, expected_covars, rtol=1e-2, atol=1e-2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a38e1b89f0bbe81cb22fbee64d196777\",\n \"score\": \"0.54838246\",\n \"text\": \"def test_measure_deterministic_multi_qubit_with_sampling(self):\\n shots = 100\\n circuits = ref_measure.multiqubit_measure_circuits_deterministic(\\n allow_sampling=True)\\n targets = ref_measure.multiqubit_measure_counts_deterministic(shots)\\n qobj = assemble(circuits, self.SIMULATOR, shots=shots)\\n result = self.SIMULATOR.run(\\n qobj, backend_options=self.BACKEND_OPTS).result()\\n self.is_completed(result)\\n self.compare_counts(result, circuits, targets, delta=0)\\n # Test sampling was enabled\\n for res in result.results:\\n self.assertIn(\\\"measure_sampling\\\", res.metadata)\\n self.assertEqual(res.metadata[\\\"measure_sampling\\\"], True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e50f1d96ffe7ed1438e506554c776b1\",\n \"score\": \"0.54747975\",\n \"text\": \"def test_measure_nondeterministic_multi_qubit_with_sampling(self):\\n shots = 2000\\n circuits = ref_measure.multiqubit_measure_circuits_nondeterministic(\\n allow_sampling=True)\\n targets = ref_measure.multiqubit_measure_counts_nondeterministic(shots)\\n qobj = assemble(circuits, self.SIMULATOR, shots=shots)\\n result = self.SIMULATOR.run(\\n qobj, backend_options=self.BACKEND_OPTS).result()\\n self.is_completed(result)\\n self.compare_counts(result, circuits, targets, delta=0.05 * shots)\\n # Test sampling was enabled\\n for res in result.results:\\n self.assertIn(\\\"measure_sampling\\\", res.metadata)\\n self.assertEqual(res.metadata[\\\"measure_sampling\\\"], True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2782f906db22bb9a25d5e138c5a41cd7\",\n \"score\": \"0.5460231\",\n \"text\": \"def oneSample_tTest(imgList, maskFile, outImage, brik=''):\\n if type(imgList) == list:\\n imgFile = ' '.join([x + brik for x in imgList])\\n else:\\n imgFile = imgList + brik\\n\\n os.system('3dttest++ -setA ' + imgFile + ' -mask ' + maskFile + ' -prefix ' + outImage)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81d17b4054b92b08588cb5e5910b0ab2\",\n \"score\": \"0.54576665\",\n \"text\": \"def test_measure_deterministic_without_sampling(self):\\n shots = 100\\n circuits = ref_measure.measure_circuits_deterministic(\\n allow_sampling=False)\\n targets = ref_measure.measure_counts_deterministic(shots)\\n qobj = assemble(circuits, self.SIMULATOR, shots=shots)\\n result = self.SIMULATOR.run(\\n qobj, backend_options=self.BACKEND_OPTS).result()\\n self.is_completed(result)\\n self.compare_counts(result, circuits, targets, delta=0)\\n # Test sampling was disabled\\n for res in result.results:\\n self.assertIn(\\\"measure_sampling\\\", res.metadata)\\n self.assertEqual(res.metadata[\\\"measure_sampling\\\"], False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e675b850cbdafb859ae47468f82d084\",\n \"score\": \"0.545464\",\n \"text\": \"def test_vs_update(self):\\n random.seed(5)\\n p1 = axelrod.HesitantQLearner()\\n p2 = axelrod.Cooperator()\\n simulate_play(p1, p2)\\n self.assertEqual(p1.Vs, {'': 0.1, '0.0': 0})\\n simulate_play(p1, p2)\\n self.assertEqual(p1.Vs,{'': 0.1, '0.0': 0.30000000000000004, 'C1.0': 0})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"276ce37d391a9673edfc24bc4be36f02\",\n \"score\": \"0.54525274\",\n \"text\": \"def test_stream_random(context):\\n checker = Checker()\\n vpw.register(checker)\\n\\n for x in range(5000):\\n checker.set(random.getrandbits(Param.IMG_WIDTH), random.getrandbits(Param.KER_WIDTH))\\n vpw.tick()\\n\\n vpw.idle(10) # wait for longer then the pipelined depth of module\",\n \"title\": \"\"\n },\n {\n \"docid\": \"903dfd2d14b26b2197245c419d6f54e7\",\n \"score\": \"0.5449353\",\n \"text\": \"def run(wrk_dir, radUnit, outerCup, innerCupSer, innerCupNum, coll, x_range, y_range, z_range, steps, shot):\\n\\n logging.info(\\\"Single shot run\\\")\\n logging.debug(wrk_dir)\\n logging.debug(radUnit)\\n logging.debug(outerCup)\\n logging.debug(innerCupSer)\\n logging.debug(innerCupNum)\\n logging.debug(str(coll))\\n logging.debug(str(x_range))\\n logging.debug(str(y_range))\\n logging.debug(str(z_range))\\n logging.debug(str(steps))\\n logging.debug(str(shot))\\n\\n mats = materials.materials(\\\"Materials.txt\\\")\\n\\n logging.info(\\\"Materials initialized\\\")\\n\\n cl = collimator.collimator(coll)\\n\\n if not XcConstants.IsQACup(innerCupSer):\\n file_prefix = clinical.make_cup_name(radUnit, outerCup, innerCupSer, innerCupNum)\\n else:\\n file_prefix = qa.make_cup_name(radUnit, outerCup, innerCupSer, innerCupNum)\\n\\n liA = None\\n liB = None\\n liC = None\\n if not XcConstants.IsQACup(innerCupSer):\\n #cdown = cup_downloader.cup_downloader(\\\"192.168.1.217\\\", \\\"./\\\", wrk_dir, file_prefix, \\\"kriol\\\", \\\"Proton31\\\")\\n cdown = cup_downloader.cup_downloader(\\\"192.168.1.230\\\", \\\"/Programs_n_Docs/Kdd_CupGeometry/Out/\\\", wrk_dir, file_prefix, \\\"beamuser\\\", \\\"beamuser\\\")\\n cdown.load()\\n #cdown.load()\\n if (cdown.rc() != 0):\\n raise RuntimeError(\\\"run_single_shot\\\", \\\"unable to load files\\\")\\n\\n logging.info(\\\"Cups downloaded\\\")\\n\\n cupA = cc.curve(os.path.join( wrk_dir, file_prefix + \\\"_\\\" + \\\"KddCurveA.txt\\\"))\\n cupB = cc.curve(os.path.join( wrk_dir, file_prefix + \\\"_\\\" + \\\"KddCurveB.txt\\\"))\\n cupC = cc.curve(os.path.join( wrk_dir, file_prefix + \\\"_\\\" + \\\"KddCurveC.txt\\\"))\\n\\n liA = linint.linint(cupA)\\n liB = linint.linint(cupB)\\n liC = linint.linint(cupC)\\n\\n logging.info(\\\"Interpolators done\\\")\\n\\n if not XcConstants.IsQACup(innerCupSer):\\n nr = int(cl.size()*1.2/steps[0])\\n else:\\n nr = int(40.0/steps[0])\\n\\n z_max = z_range[1]\\n if not XcConstants.IsQACup(innerCupSer):\\n z_max = liA.zmax() # z_max = max(liA.zmax(), liB.zmax(), liC.zmax())\\n\\n # phantom dimensions and boundaries\\n pdim = build_phandim.build_phandim(shot, x_range, y_range, (z_range[0], z_max), steps, nr)\\n\\n logging.info(\\\"Phantom dimensions\\\")\\n\\n # phantom in memory\\n if not XcConstants.IsQACup(innerCupSer):\\n phntom = clinical.make_phantom(pdim, liA, liB, liC, mats, (z_range[0], z_max))\\n else:\\n phntom = qa.make_phantom(pdim, liA, liB, liC, mats, (z_range[0], z_max))\\n\\n full_prefix = names_helper.make_qualified_name(file_prefix, cl, shot)\\n\\n write_egs_phantom.write_phantom(wrk_dir, full_prefix, phntom, mats)\\n\\n logging.info(\\\"Phantom saved\\\")\\n\\n egsinp_template = None\\n if not XcConstants.IsQACup(innerCupSer):\\n egsinp_template = \\\"template.egsinp\\\"\\n else:\\n egsinp_template = \\\"templateQA.egsinp\\\"\\n\\n egsinp_name = write_egs_input.write_input(wrk_dir, egsinp_template, full_prefix, cl, shot)\\n\\n logging.info(\\\"Making EGS input\\\")\\n\\n logging.info(\\\"And DosXYZ is about to run\\\")\\n\\n rc = run_dosxyz.run_dosxyz(wrk_dir, egsinp_name, \\\"700jin.pegs4dat\\\")\\n if rc != 0:\\n logging.info(\\\"DosXYZ error: {0}\\\".format(rc))\\n raise RuntimeError(\\\"run_single_shot\\\", \\\"Dose was not computed\\\")\\n logging.info(\\\"And DosXYZ is done\\\")\\n\\n logging.info(\\\"Data uploader is going up\\\")\\n logging.info(\\\"...right now, no upload\\\")\\n\\n host, port, user, pswd, dest = read_credentials(\\\"config_sftp.json\\\")\\n dupload = data_uploader.data_uploader(wrk_dir, host, port, dest, file_prefix, user, pswd)\\n\\n dupload.upload(cl)\\n\\n rc = dupload.rc()\\n\\n if (rc != 0):\\n logging.info(\\\"Data upload failure {0}\\\", rc)\\n raise RuntimeError(\\\"run_single_shot\\\", \\\"unable to upload files\\\")\\n\\n logging.info(\\\"Data uploaded\\\")\\n logging.info(\\\"Finita la comedia\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d35bbd2f913d9d9b32232c199baa46e2\",\n \"score\": \"0.5448022\",\n \"text\": \"def testVtiInputOutput(self):\\n for i in xrange(3 * len(self.vtis.values())):\\n vti = random.choice(self.vtis.values())\\n self._CheckVtiInputOutput(vti, 4)\\n self._CheckVtiInputOutput(vti, 6)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a90f5515ec3684fd69f0b668cc31a454\",\n \"score\": \"0.5447884\",\n \"text\": \"def test_get_item(self):\\n # number of qubits\\n nqubits = 5\\n\\n # number of shots\\n nshots = 40\\n\\n # get an outcome from simulating a circuit\\n result = self.get_all_ones_outcome(nqubits, nshots)\\n\\n # create a MeasurementOutcome\\n outcome = MeasurementOutcome(result)\\n\\n self.assertEqual(len(outcome[0]), 5)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5bdcbf66b42547154e6dcea32cbcd8ed\",\n \"score\": \"0.54401785\",\n \"text\": \"def setUp(self):\\n slicer.mrmlScene.Clear(0)\\n import SampleData\\n sampleDataLogic = SampleData.SampleDataLogic()\\n self.inputAngioVolume = sampleDataLogic.downloadCTACardio()\\n \\n self.vesselPositionRas = [176.9, -17.4, 52.7]\\n\\n # make the output volume appear in all the slice views\\n selectionNode = slicer.app.applicationLogic().GetSelectionNode()\\n selectionNode.SetReferenceActiveVolumeID(self.inputAngioVolume.GetID())\\n slicer.app.applicationLogic().PropagateVolumeSelection(1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bacab49ca12934b5c8313b279df2be5b\",\n \"score\": \"0.54308283\",\n \"text\": \"def test_noise(self):\\n qr = qiskit.QuantumRegister(3)\\n cr = qiskit.ClassicalRegister(3)\\n circuit = qiskit.QuantumCircuit(qr, cr)\\n circuit.iden(qr[0])\\n circuit.noise(0)\\n circuit.iden(qr[1])\\n circuit.noise(1)\\n circuit.iden(qr[2])\\n circuit.measure(qr, cr)\\n\\n config = {\\n 'noise_params': {\\n 'id': {'p_pauli': [1.0, 0.0, 0.0]}\\n }\\n }\\n sim = 'local_qasm_simulator_cpp'\\n shots = 1000\\n result = execute(circuit, sim, config=config, shots=shots).result()\\n counts = result.get_counts()\\n target = {'101': shots}\\n self.assertEqual(counts, target)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"392391c6356a5dba381ef417d98c1e09\",\n \"score\": \"0.54300576\",\n \"text\": \"def test_keywordargs_used(self, qubit_device_1_wire, tol):\\n\\n def circuit(w, x=None):\\n qml.RX(x, wires=[0])\\n return qml.expval(qml.PauliZ(0))\\n\\n circuit = qml.QNode(circuit, qubit_device_1_wire).to_tf()\\n\\n c = circuit(tf.constant(1.), x=np.pi)\\n assert np.allclose(c.numpy(), -1., atol=tol, rtol=0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5cdc053b40e4b0cf961cdbcee9cf22e0\",\n \"score\": \"0.54287535\",\n \"text\": \"def test_sample_scattering():\\n test_data_dir = os.path.dirname(__file__)\\n test_data_filename = os.path.join(test_data_dir, 'test_data_1d.pt')\\n data = torch.load(test_data_filename, map_location='cpu')\\n\\n x = data['x']\\n J = data['J']\\n Q = data['Q']\\n Sx0 = data['Sx']\\n\\n T = x.shape[2]\\n\\n scattering = Scattering1D(T, J, Q)\\n\\n if force_gpu:\\n scattering = scattering.cuda()\\n x = x.cuda()\\n\\n Sx = scattering.forward(x)\\n\\n if force_gpu:\\n Sx = Sx.cpu()\\n\\n assert (Sx - Sx0).abs().max() < 1e-6\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f705206215b48506113d178a0332622c\",\n \"score\": \"0.5427243\",\n \"text\": \"def test_measure_2qubit(self):\\n\\n state = DensityMatrix.from_label(\\\"+0\\\")\\n seed = 200\\n shots = 100\\n\\n with self.subTest(msg=\\\"measure\\\"):\\n for i in range(shots):\\n rho = state.copy()\\n rho.seed(seed + i)\\n outcome, value = rho.measure()\\n self.assertIn(outcome, [\\\"00\\\", \\\"10\\\"])\\n if outcome == \\\"00\\\":\\n target = DensityMatrix.from_label(\\\"00\\\")\\n self.assertEqual(value, target)\\n else:\\n target = DensityMatrix.from_label(\\\"10\\\")\\n self.assertEqual(value, target)\\n\\n with self.subTest(msg=\\\"measure [0, 1]\\\"):\\n for i in range(shots):\\n rho = state.copy()\\n outcome, value = rho.measure([0, 1])\\n self.assertIn(outcome, [\\\"00\\\", \\\"10\\\"])\\n if outcome == \\\"00\\\":\\n target = DensityMatrix.from_label(\\\"00\\\")\\n self.assertEqual(value, target)\\n else:\\n target = DensityMatrix.from_label(\\\"10\\\")\\n self.assertEqual(value, target)\\n\\n with self.subTest(msg=\\\"measure [1, 0]\\\"):\\n for i in range(shots):\\n rho = state.copy()\\n outcome, value = rho.measure([1, 0])\\n self.assertIn(outcome, [\\\"00\\\", \\\"01\\\"])\\n if outcome == \\\"00\\\":\\n target = DensityMatrix.from_label(\\\"00\\\")\\n self.assertEqual(value, target)\\n else:\\n target = DensityMatrix.from_label(\\\"10\\\")\\n self.assertEqual(value, target)\\n with self.subTest(msg=\\\"measure [0]\\\"):\\n for i in range(shots):\\n rho = state.copy()\\n outcome, value = rho.measure([0])\\n self.assertEqual(outcome, \\\"0\\\")\\n target = DensityMatrix.from_label(\\\"+0\\\")\\n self.assertEqual(value, target)\\n\\n with self.subTest(msg=\\\"measure [1]\\\"):\\n for i in range(shots):\\n rho = state.copy()\\n outcome, value = rho.measure([1])\\n self.assertIn(outcome, [\\\"0\\\", \\\"1\\\"])\\n if outcome == \\\"0\\\":\\n target = DensityMatrix.from_label(\\\"00\\\")\\n self.assertEqual(value, target)\\n else:\\n target = DensityMatrix.from_label(\\\"10\\\")\\n self.assertEqual(value, target)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed3d03f2dc2e2f9f657964c3bfcb77a4\",\n \"score\": \"0.54211783\",\n \"text\": \"def test_basic():\\n this = os.path.dirname(__file__)\\n l = \\\"../samples/images/random4_4k/1l.jpg\\\"\\n c = \\\"../samples/images/random4_4k/1c.jpg\\\"\\n r = \\\"../samples/images/random4_4k/1r.jpg\\\"\\n pathl = os.path.join(this, l)\\n pathc = os.path.join(this, c)\\n pathr = os.path.join(this, r)\\n\\n det = YOLOSimpleDetector()\\n dw = DetectWrapper(det, write_partials=False)\\n dw.run_detection(pathl, pathc, pathr)\\n # dw.write(\\\"result.jpg\\\")\\n # TODO: Add more asserts here\\n assert dw.result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"89dd16053cc658c1b1e208c27c380515\",\n \"score\": \"0.5416006\",\n \"text\": \"def test_device_simple_query(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d94f38b71828e6fc2bb97894caf1e4d9\",\n \"score\": \"0.5412627\",\n \"text\": \"def test_supported_gate_single_wire_with_parameters(\\n self, device, tol, name, par, expected_output\\n ):\\n\\n n_wires = 1\\n dev = device(n_wires)\\n op = getattr(qml.ops, name)\\n\\n @qml.qnode(dev)\\n def circuit():\\n op(*par, wires=0)\\n return qml.expval(qml.PauliZ(0))\\n\\n assert np.isclose(circuit(), expected_output, atol=tol(dev.shots))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc15c40460c7b6db76696c533caded41\",\n \"score\": \"0.5405369\",\n \"text\": \"def test_measure_simple(self):\\n bk = Backend(name='ibmq', device='qasm_simulator')\\n qc = QComp(qubit_num=2, backend=bk)\\n res = qc.h(0).cx(0,1).measure([0,1]).run(shots=10)\\n self.assertEqual(res['measured_qid'], [0,1])\\n self.assertEqual(res['frequency']['00']+res['frequency']['11'], 10)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a528e89c162169ec90a5562a71818e44\",\n \"score\": \"0.5403991\",\n \"text\": \"def simulate_single_param(args): \\n from neuron import h\\n from neuron import gui\\n h.load_file(\\\"nrngui.hoc\\\")\\n h.load_file(\\\"import3d.hoc\\\")\\n \\n param_dict = args[0]\\n \\n hoc_code = '''h(\\\"create soma\\\")\\nh(\\\"access soma\\\")\\nh(\\\"nseg = 1\\\")\\nh(\\\"L = 20\\\")\\nh(\\\"diam = 20\\\")\\nh(\\\"insert pas\\\")\\nh(\\\"cm = 1\\\")\\nh(\\\"Ra = 150\\\")\\nh(\\\"forall nseg = 1\\\")\\nh(\\\"forall e_pas = -90\\\")\\n\\nh(\\\"soma insert Ca_LVAst \\\")\\nh(\\\"soma insert Ca_HVA \\\")\\nh(\\\"soma insert SKv3_1 \\\")\\nh(\\\"soma insert SK_E2 \\\")\\nh(\\\"soma insert K_Tst \\\")\\nh(\\\"soma insert K_Pst \\\")\\nh(\\\"soma insert Nap_Et2 \\\")\\nh(\\\"soma insert NaTa_t\\\")\\nh(\\\"soma insert CaDynamics_E2\\\")\\nh(\\\"soma insert Ih\\\")\\n\\nh(\\\"ek = -85\\\")\\nh(\\\"ena = 50\\\")\\nh(\\\"gIhbar_Ih = 0.0002\\\")\\nh(\\\"g_pas = 1.0 / 12000 \\\")\\nh(\\\"celsius = 36\\\")\\n'''\\n \\n exec(hoc_code)\\n exec('h(\\\"tstop = {}\\\")'.format(simulation_length))\\n exec('h(\\\"v_init = {}\\\")'.format(v_init))\\n\\n for key in param_dict.keys():\\n h(key + \\\" = \\\" + str(param_dict[key]))\\n \\n iclamp = h.IClamp(0.5)\\n iclamp.delay = 500\\n iclamp.dur = 1400\\n\\n amp = 0.05\\n iclamp.amp = amp\\n im = h.Impedance()\\n h(\\\"access soma\\\")\\n h(\\\"nseg = 1\\\")\\n im.loc(0.5)\\n im.compute(0)\\n Ri = im.input(0.5)\\n \\n semitrial_start = time.time()\\n \\n ### Set the protocol\\n \\n h.finitialize()\\n \\n ### Simulate!\\n \\n voltageVector = h.Vector()\\n voltageVector.record(eval(\\\"h.soma(0.5)._ref_v\\\"))\\n timeVector = h.Vector()\\n timeVector.record(h._ref_t)\\n\\n h.run()\\n \\n timeVector = np.array(timeVector)\\n voltageVector = np.array(voltageVector)\\n\\n trace = {}\\n trace['T'] = timeVector[4000:]\\n trace['V'] = np.array(voltageVector)[4000:]\\n trace['stim_start'] = [500]\\n trace['stim_end'] = [1900]\\n del voltageVector, timeVector\\n return trace\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa33fb75582c20da8bbe0be23f519625\",\n \"score\": \"0.5402854\",\n \"text\": \"def test_create_sample():\\n sample = flowsym.create_sample(10, ['blue', 'green', 'NIR']) # Params for test function\\n\\n assert len(sample) == 10 # Is dataframe correct size from input step?\\n assert type(sample) == type(pd.DataFrame()) # Did we actually output a dataframe?\\n assert list(sample.columns) == ['Wavelength', 'Excitation Efficiency', 'Emission Efficiency',\\n 'Copy number'] # Are these the columns?\\n\\n # Check to make sure excitation wavelengths aren't the same as emission\\n for index, val in sample.iterrows():\\n assert val['Excitation Efficiency'] != val['Emission Efficiency']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a41a9115b1f4f7d5dbc80391af85bea\",\n \"score\": \"0.53991514\",\n \"text\": \"def test_measure_nondeterministic_without_sampling(self):\\n shots = 2000\\n circuits = ref_measure.measure_circuits_nondeterministic(\\n allow_sampling=False)\\n targets = ref_measure.measure_counts_nondeterministic(shots)\\n qobj = assemble(circuits, self.SIMULATOR, shots=shots)\\n result = self.SIMULATOR.run(\\n qobj, backend_options=self.BACKEND_OPTS).result()\\n self.is_completed(result)\\n self.compare_counts(result, circuits, targets, delta=0.05 * shots)\\n # Test sampling was disabled\\n for res in result.results:\\n self.assertIn(\\\"measure_sampling\\\", res.metadata)\\n self.assertEqual(res.metadata[\\\"measure_sampling\\\"], False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df4283da3ffae441b2759fa28fc305e8\",\n \"score\": \"0.5398783\",\n \"text\": \"def test_spw_value_velocity(self):\\n self._default_test()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe2cc7de270405c81c8582adb4662132\",\n \"score\": \"0.5395273\",\n \"text\": \"def sample(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe2cc7de270405c81c8582adb4662132\",\n \"score\": \"0.5395273\",\n \"text\": \"def sample(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d60c3dd54d260eabaa318cc9f102dcc\",\n \"score\": \"0.5393843\",\n \"text\": \"def testSingleFramePlugin(self):\\n task = self.makeSingleFrameMeasurementTask(\\\"base_SdssCentroid\\\")\\n exposure, catalog = self.dataset.realize(10.0, task.schema, randomSeed=0)\\n task.run(catalog, exposure)\\n record = catalog[0]\\n self.assertFalse(record.get(\\\"base_SdssCentroid_flag\\\"))\\n self.assertFalse(record.get(\\\"base_SdssCentroid_flag_edge\\\"))\\n self.assertFloatsAlmostEqual(record.get(\\\"base_SdssCentroid_x\\\"), record.get(\\\"truth_x\\\"), rtol=0.005)\\n self.assertFloatsAlmostEqual(record.get(\\\"base_SdssCentroid_y\\\"), record.get(\\\"truth_y\\\"), rtol=0.005)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9189405811d2aff1945c404f9c53e37\",\n \"score\": \"0.53937644\",\n \"text\": \"def test_voq_switch(self, vct):\\n\\n if vct is None:\\n return\\n\\n dvss = vct.dvss\\n for name in dvss.keys():\\n dvs = dvss[name]\\n # Get the config info\\n config_db = dvs.get_config_db()\\n metatbl = config_db.get_entry(\\\"DEVICE_METADATA\\\", \\\"localhost\\\")\\n\\n cfg_switch_type = metatbl.get(\\\"switch_type\\\")\\n\\n # Test only for line cards\\n if cfg_switch_type == \\\"voq\\\":\\n print(\\\"VOQ Switch test for {}\\\".format(name))\\n cfg_switch_id = metatbl.get(\\\"switch_id\\\")\\n assert cfg_switch_id != \\\"\\\", \\\"Got error in getting switch_id from CONFIG_DB DEVICE_METADATA\\\"\\n\\n cfg_max_cores = metatbl.get(\\\"max_cores\\\")\\n assert cfg_max_cores != \\\"\\\", \\\"Got error in getting max_cores from CONFIG_DB DEVICE_METADATA\\\"\\n\\n cfgspkeys = config_db.get_keys(\\\"SYSTEM_PORT\\\")\\n sp_count = len(cfgspkeys)\\n\\n asic_db = dvs.get_asic_db()\\n keys = asic_db.get_keys(\\\"ASIC_STATE:SAI_OBJECT_TYPE_SWITCH\\\")\\n switch_oid_key = keys[0]\\n\\n switch_entry = asic_db.get_entry(\\\"ASIC_STATE:SAI_OBJECT_TYPE_SWITCH\\\", switch_oid_key)\\n\\n value = switch_entry.get(\\\"SAI_SWITCH_ATTR_TYPE\\\")\\n assert value == \\\"SAI_SWITCH_TYPE_VOQ\\\", \\\"Switch type is not VOQ\\\"\\n\\n value = switch_entry.get(\\\"SAI_SWITCH_ATTR_SWITCH_ID\\\")\\n assert value == cfg_switch_id, \\\"VOQ switch id is invalid\\\"\\n\\n value = switch_entry.get(\\\"SAI_SWITCH_ATTR_MAX_SYSTEM_CORES\\\")\\n assert value == cfg_max_cores, \\\"Max system cores is invalid\\\"\\n\\n value = switch_entry.get(\\\"SAI_SWITCH_ATTR_SYSTEM_PORT_CONFIG_LIST\\\")\\n assert value != \\\"\\\", \\\"Empty system port config list\\\"\\n # Convert the spcfg string to dictionary\\n spcfg = ast.literal_eval(value)\\n assert spcfg['count'] == sp_count, \\\"Number of systems ports configured is invalid\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ad86b8073695a1bd9cc4ad4f93b1bf0\",\n \"score\": \"0.53883\",\n \"text\": \"def test_smoke_embed_draw_sparse(self):\\n\\n demo_file = os.path.join(project_dir, 'Pegasus_Embedding_Video/embed_draw_sparse.py')\\n subprocess.check_output([sys.executable, demo_file])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"598a59ae84ee7f261aec563bdb521f6c\",\n \"score\": \"0.5388235\",\n \"text\": \"def test_basic(self):\\n # get an outcome from simulating a circuit\\n result = self.get_all_ones_outcome(4, 10)\\n\\n # create a MeasurementOutcome\\n outcome = MeasurementOutcome(result)\\n\\n # trivial check\\n self.assertTrue((outcome.raw_outcome == result).all())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9156d7a402e8f9c39b8aefb8f1bb16e7\",\n \"score\": \"0.5387616\",\n \"text\": \"def test_shot_vectors(self, max_workers, n_qubits, shots):\\n dev = DefaultQubit2(max_workers=max_workers)\\n shots = qml.measurements.Shots(shots)\\n\\n ops = [qml.Hadamard(i) for i in range(n_qubits)]\\n qs = qml.tape.QuantumScript(ops, [qml.classical_shadow(range(n_qubits))], shots=shots)\\n res = dev.execute(qs)\\n\\n assert isinstance(res, tuple)\\n assert len(res) == len(list(shots))\\n\\n for r, s in zip(res, shots):\\n assert r.shape == (2, s, n_qubits)\\n assert r.dtype == np.int8\\n\\n # test that the bits are either 0 and 1\\n assert np.all(np.logical_or(r[0] == 0, r[0] == 1))\\n\\n # test that the recipes are either 0, 1, or 2 (X, Y, or Z)\\n assert np.all(np.logical_or(np.logical_or(r[1] == 0, r[1] == 1), r[1] == 2))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"012a4366b6fcd5368f903549f97144bb\",\n \"score\": \"0.53797024\",\n \"text\": \"def main():\\n NAME = os.path.basename(__file__).split(\\\".\\\")[0]\\n\\n NUM_STEPS = 5\\n\\n sensitivities = None\\n r_means = []\\n g_means = []\\n b_means = []\\n\\n with its.device.ItsSession() as cam:\\n props = cam.get_camera_properties()\\n if not its.caps.compute_target_exposure(props):\\n print \\\"Test skipped\\\"\\n return\\n\\n expt,_ = its.target.get_target_exposure_combos(cam)[\\\"midSensitivity\\\"]\\n sens_range = props['android.sensor.info.sensitivityRange']\\n sens_step = (sens_range[1] - sens_range[0]) / float(NUM_STEPS-1)\\n sensitivities = [sens_range[0] + i * sens_step for i in range(NUM_STEPS)]\\n\\n for s in sensitivities:\\n req = its.objects.manual_capture_request(s, expt)\\n cap = cam.do_capture(req)\\n img = its.image.convert_capture_to_rgb_image(cap)\\n its.image.write_image(\\n img, \\\"%s_iso=%04d.jpg\\\" % (NAME, s))\\n tile = its.image.get_image_patch(img, 0.45, 0.45, 0.1, 0.1)\\n rgb_means = its.image.compute_image_means(tile)\\n r_means.append(rgb_means[0])\\n g_means.append(rgb_means[1])\\n b_means.append(rgb_means[2])\\n\\n # Draw a plot.\\n pylab.plot(sensitivities, r_means, 'r')\\n pylab.plot(sensitivities, g_means, 'g')\\n pylab.plot(sensitivities, b_means, 'b')\\n pylab.ylim([0,1])\\n matplotlib.pyplot.savefig(\\\"%s_plot_means.png\\\" % (NAME))\\n\\n # Test for pass/fail: check that each shot is brighter than the previous.\\n for means in [r_means, g_means, b_means]:\\n for i in range(len(means)-1):\\n assert(means[i+1] > means[i])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46c5d6612a7f00bb15e1cc1916dcfdf2\",\n \"score\": \"0.5374479\",\n \"text\": \"def sample(self, inputs):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d8bd541c779ba20e07af8bdf6fd5ab93\",\n \"score\": \"0.5371352\",\n \"text\": \"def test_synth_bm(self, num_qubits):\\n rng = np.random.default_rng(1234)\\n samples = 50\\n for _ in range(samples):\\n circ = random_clifford_circuit(num_qubits, 5 * num_qubits, seed=rng)\\n target = Clifford(circ)\\n value = Clifford(synth_clifford_bm(target))\\n self.assertEqual(value, target)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fde6a53a230f4be51116c8218142f53c\",\n \"score\": \"0.53705996\",\n \"text\": \"def SingleShot(qubit: qreg):\\n init(qubit)\\n Id(qubit)\\n MEAS(qubit)\\n init(qubit)\\n X(qubit)\\n MEAS(qubit)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f0ffa433fd6a89f7b1d0cebd3732c81\",\n \"score\": \"0.53701806\",\n \"text\": \"def test_got_data(self):\\n # Create and initialize the instrument driver with a mock port agent\\n driver = mavs4InstrumentDriver(self._got_data_event_callback)\\n self.assert_initialize_driver(driver) # defaults to autosample mode, so sample generated\\n\\n self.assert_raw_particle_published(driver, True)\\n\\n # validate data particle\\n self.assert_particle_published(driver, self.SAMPLE, self.assert_particle_sample, True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e69fdaa0395ee7a8d04fa189f0df4848\",\n \"score\": \"0.53696066\",\n \"text\": \"def test(): \\n\\t\\treturn [\\\"vice.singlezone\\\", \\n\\t\\t\\t[ \\n\\t\\t\\t\\ttest_from_output(), \\n\\t\\t\\t\\t_singlezone.test(run = False), \\n\\t\\t\\t\\ttrials.test(run = False), \\n\\t\\t\\t\\tsrc_test(run = False) \\n\\t\\t\\t] \\n\\t\\t]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e838b7c5a1dc880005021d104b13215\",\n \"score\": \"0.5368527\",\n \"text\": \"def simulate(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"84be85f1717c65fefb48dba824d7ff4d\",\n \"score\": \"0.5367002\",\n \"text\": \"def test_stress_strain_curve():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2693114bd2af7a464dd27f56fc594846\",\n \"score\": \"0.53657436\",\n \"text\": \"def test_inventory(clients):\\n station = clients[\\\"station\\\"].get_stations()\\n assert isinstance(station, obspy.Inventory)\\n assert len(station)\\n # each channel should have a sampling rate\\n for net in station:\\n for sta in net:\\n for chan in sta:\\n assert chan.sample_rate == 250\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e1af52db6c963aa1c790a5e1a781cb8\",\n \"score\": \"0.5363447\",\n \"text\": \"def test_logical_circuit_on_5_1_code():\\n n, k = 5, 1\\n binary_rep = np.array([[1, 0, 0, 1, 0, 0, 1, 1, 0, 0],\\n [0, 1, 0, 0, 1, 0, 0, 1, 1, 0],\\n [1, 0, 1, 0, 0, 0, 0, 0, 1, 1],\\n [0, 1, 0, 1, 0, 1, 0, 0, 0, 1]])\\n code = StabilizerCode(binary_rep, n, k)\\n\\n # Test apply \\\"X0\\\" To encoded |0> Is equivalent to encoded |1>\\n eng = MainEngine()\\n quantum_reg = eng.allocate_qureg(n)\\n eng.flush()\\n code.encoding_circuit(eng, quantum_reg, [0])\\n wavefunc_zero = np.array(eng.backend.cheat()[1])\\n code.logical_circuit(eng, quantum_reg, \\\"X0\\\")\\n wave_function_actual = np.array(eng.backend.cheat()[1])\\n All(Measure) | quantum_reg\\n\\n eng = MainEngine()\\n quantum_reg = eng.allocate_qureg(n)\\n eng.flush()\\n code.encoding_circuit(eng, quantum_reg, [1])\\n wave_function_desired = np.array(eng.backend.cheat()[1])\\n\\n assert np.all(np.abs(wave_function_actual - wave_function_desired) < 1e-4)\\n All(Measure) | quantum_reg\\n\\n # Test apply \\\"X0\\\" To encoded |1> Is equivalent to encoded |0>.\\n eng = MainEngine()\\n quantum_reg = eng.allocate_qureg(n)\\n eng.flush()\\n code.encoding_circuit(eng, quantum_reg, [1])\\n code.logical_circuit(eng, quantum_reg, \\\"X0\\\")\\n wave_function_actual = np.array(eng.backend.cheat()[1])\\n assert np.all(np.abs(wave_function_actual - wavefunc_zero) < 1e-4)\\n All(Measure) | quantum_reg\\n\\n # Test apply \\\"Z0\\\" to encoded |1> is equivalent to encoded -|1>\\n eng = MainEngine()\\n quantum_reg = eng.allocate_qureg(n)\\n eng.flush()\\n code.encoding_circuit(eng, quantum_reg, [1])\\n wave_func_before = np.array(eng.backend.cheat()[1])\\n code.logical_circuit(eng, quantum_reg, \\\"Z0\\\")\\n wave_function_actual = np.array(eng.backend.cheat()[1])\\n assert np.all(np.abs(wave_function_actual + wave_func_before) < 1e-4)\\n All(Measure) | quantum_reg\\n\\n # Test apply \\\"Z0\\\" to encoded |0> is equivalent to encoded |0>\\n eng = MainEngine()\\n quantum_reg = eng.allocate_qureg(n)\\n eng.flush()\\n code.encoding_circuit(eng, quantum_reg, [0])\\n wave_func_before = np.array(eng.backend.cheat()[1])\\n code.logical_circuit(eng, quantum_reg, \\\"Z0\\\")\\n wave_function_actual = np.array(eng.backend.cheat()[1])\\n assert np.all(np.abs(wave_function_actual - wave_func_before) < 1e-4)\\n All(Measure) | quantum_reg\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4105d7a53781700fe692ea1e941c1763\",\n \"score\": \"0.53629273\",\n \"text\": \"def test_instantaneous(empty_model, inst_spill):\\n model = empty_model\\n model.spills += inst_spill\\n model.full_run()\\n results = model.outputters[0]\\n\\n data = results.data_buffer.certain\\n\\n # there should be 100 Elements at all times\\n for ts in data:\\n assert len(ts['mass']) == NUM_ELEMENTS\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2752efe784286dd24a33974fa6ff8d02\",\n \"score\": \"0.53608817\",\n \"text\": \"def test_spw_id_default_velocity(self):\\n self._default_test()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc3cb9fb4630541d6f1c609d864c51d3\",\n \"score\": \"0.5357145\",\n \"text\": \"def test_calibrate():\\n with DeviceTestContext(PowerSupply, process=True) as proxy:\\n proxy.calibrate()\\n assert proxy.voltage == 1.23\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1194,"cells":{"query_id":{"kind":"string","value":"924948d35b7fe68ec1ca9f608b6f8a54"},"query":{"kind":"string","value":"Returns the 'base' silo object that an object operates on. Includes special handling for Twitter photo URLs, e.g."},"positive_passages":{"kind":"list like","value":[{"docid":"4ad0af60344efd2b2cfcd871cde66a90","score":"0.70898885","text":"def base_object(self, obj):\n base_obj = super(Twitter, self).base_object(obj)\n url = base_obj.get('url')\n if url:\n try:\n parsed = urlparse.urlparse(url)\n parts = parsed.path.split('/')\n if len(parts) >= 3 and parts[-2] == 'photo':\n base_obj['id'] = parts[-3]\n parsed = list(parsed)\n parsed[2] = '/'.join(parts[:-2])\n base_obj['url'] = urlparse.urlunparse(parsed)\n except BaseException, e:\n logging.error(\n \"Couldn't parse object URL %s : %s. Falling back to default logic.\",\n url, e)\n\n return base_obj","title":""}],"string":"[\n {\n \"docid\": \"4ad0af60344efd2b2cfcd871cde66a90\",\n \"score\": \"0.70898885\",\n \"text\": \"def base_object(self, obj):\\n base_obj = super(Twitter, self).base_object(obj)\\n url = base_obj.get('url')\\n if url:\\n try:\\n parsed = urlparse.urlparse(url)\\n parts = parsed.path.split('/')\\n if len(parts) >= 3 and parts[-2] == 'photo':\\n base_obj['id'] = parts[-3]\\n parsed = list(parsed)\\n parsed[2] = '/'.join(parts[:-2])\\n base_obj['url'] = urlparse.urlunparse(parsed)\\n except BaseException, e:\\n logging.error(\\n \\\"Couldn't parse object URL %s : %s. Falling back to default logic.\\\",\\n url, e)\\n\\n return base_obj\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"495f4ed4f383e264827d73752466ae8b","score":"0.619478","text":"def get_base():\r\n return _base","title":""},{"docid":"edf7a898d9f719bbf07cf25c2d5703fe","score":"0.59034413","text":"def get_base_image(self):\n return self.get_image_and_tag()[0]","title":""},{"docid":"b2d657de6ca0fd188032e8c08cab6d49","score":"0.5715031","text":"def base(self):\n return self._base","title":""},{"docid":"1dab10b911f5d3dc9d72ee7c5c060a32","score":"0.5683419","text":"def get_base(self):\r\n base = self.params.get('base', None)\r\n if base and isinstance(base, (Account, Repository)):\r\n return base\r\n return None","title":""},{"docid":"917ac5eab2cf9d0161ad9137abd1968a","score":"0.5676508","text":"def base(self):\n return self.__base","title":""},{"docid":"917ac5eab2cf9d0161ad9137abd1968a","score":"0.5676508","text":"def base(self):\n return self.__base","title":""},{"docid":"129e6fae3a2da19ffa778dad45b54b2b","score":"0.5605025","text":"def _get_base_url(self):\n raise NotImplementedError","title":""},{"docid":"c69d36aed5d6ba372973de29a6ee2f98","score":"0.5503673","text":"def base(self):\n return self._base","title":""},{"docid":"c69d36aed5d6ba372973de29a6ee2f98","score":"0.5503673","text":"def base(self):\n return self._base","title":""},{"docid":"235fcb1b0733cf986b5852527a63b01f","score":"0.54643565","text":"def get_object(self):","title":""},{"docid":"e150f35355e89d1079a07d6cdc11fdd7","score":"0.54401696","text":"def __init__(self, props=None, base_obj=None):\n self._base = None\n if base_obj is not None:\n self._base = base_obj\n self._log = logging.getLogger(__name__)\n\n self._image_id = None\n self._width = 0\n self._height = 0\n self._url = None\n self._error = None\n\n if props:\n # account for alternate variable names from raw API response\n if 'imageId' in props:\n self.image_id = props['imageId']\n if 'image_id' in props:\n self.image_id = props['image_id']\n if 'width' in props:\n self.width = props['width']\n if 'height' in props:\n self.height = props['height']\n if 'url' in props:\n self.url = props['url']\n if 'error' in props:\n self.error = props['error']","title":""},{"docid":"b8969e18d110b7f82f26da0ce02f34cd","score":"0.53879964","text":"def get_base(label):\n base = _registry.get(label, None)\n if base is None:\n raise ValueError(\"No base registered under label '{0}'!\".format(label))\n else:\n return base","title":""},{"docid":"da22c4fa28a6b84e05319c34e8f0366a","score":"0.5382678","text":"def getBase(self):\n return self.base","title":""},{"docid":"da22c4fa28a6b84e05319c34e8f0366a","score":"0.5382678","text":"def getBase(self):\n return self.base","title":""},{"docid":"da22c4fa28a6b84e05319c34e8f0366a","score":"0.5382678","text":"def getBase(self):\n return self.base","title":""},{"docid":"d1dbbb756332fbfa10df82c028508d09","score":"0.5361202","text":"def __init__(self, base_url):\n self.base = base_url","title":""},{"docid":"54602cab302c2852b982da5fd36f1801","score":"0.532844","text":"def base_info(self):\n return self._base_info","title":""},{"docid":"0dd24ce39fa9cbb90a6a4fbf35e043eb","score":"0.5283953","text":"def get_handle_base(self) -> str:\n return get_handle_base(self)","title":""},{"docid":"ce6c42e4a1fd1fd1ea9041b65f9e8603","score":"0.5273263","text":"def getBaseObjects(self):\n \n pass","title":""},{"docid":"a7a84d0c400e17e6987486730709f498","score":"0.52586496","text":"def base_image(self) -> Optional[str]:\n return pulumi.get(self, \"base_image\")","title":""},{"docid":"cfbe481217c2ed03f860874ee0fac95d","score":"0.52474546","text":"def getAlternateObject(self, **kwargs):\n \n pass","title":""},{"docid":"f598545328cba6aab60c5af491af39d3","score":"0.52453506","text":"def __init__(self, source=None):\n PrimaryObject.__init__(self, source)\n SourceBase.__init__(self, source)\n NoteBase.__init__(self, source)\n DateBase.__init__(self, source)\n AttributeBase.__init__(self, source)\n\n if source:\n self.path = source.path\n self.mime = source.mime\n self.desc = source.desc\n self.thumb = source.thumb\n else:\n self.path = \"\"\n self.mime = \"\"\n self.desc = \"\"\n self.thumb = None","title":""},{"docid":"de928bf3d93109e50f6a79187104e7f2","score":"0.5229754","text":"def __init__(self, props=None, base_obj=None):\n self._base = None\n if base_obj is not None:\n self._base = base_obj\n\n self.__id = None\n self._width = 0\n self._height = 0\n self._alt_text = None\n\n if props:\n # account for alternate variable names from raw API response\n if 'id' in props:\n self.id = props['id']\n if 'width' in props:\n self.width = props['width']\n if 'height' in props:\n self.height = props['height']\n if 'altText' in props:\n self.alt_text = props['altText']\n if 'alt_text' in props:\n self.alt_text = props['alt_text']","title":""},{"docid":"8d6ad6e5662a3d4c6e3ac4210452c0a0","score":"0.52187616","text":"def _get_object(self):\n if self.project is not None: return self.project\n if self.dataset is not None: return self.dataset\n if self.image is not None: return self.image\n if self.screen is not None: return self.screen\n if self.acquisition is not None: return self.acquisition\n if self.plate is not None: return self.plate\n if self.well is not None: return self.well\n if self.tag is not None: return self.tag\n if self.file is not None: return self.file","title":""},{"docid":"ecec53c5e063beb3f4f8dd0303b741cc","score":"0.5185176","text":"def base_uri(self):\r\n return self._base_uri","title":""},{"docid":"42b99bc0fdc631b44870c194343c6ffb","score":"0.51783407","text":"def get_base_uri(self):\n return self._get()","title":""},{"docid":"e1d0123c216d0d58632a83023ec2184c","score":"0.51763153","text":"def __init__(self, base_url=\"http://www.BoxOfficeMojo.com/\"):\n self.base_url = base_url","title":""},{"docid":"966887444fa7202bc71dccb3391f2d2c","score":"0.51737046","text":"def get_handle_base(instance: 'User') -> str:\n if instance.name:\n return instance.name\n elif instance.email:\n return instance.email.split('@')[0]\n else:\n return instance.username","title":""},{"docid":"d4ead5b17c2a954cb20f22476bf8c71b","score":"0.5171402","text":"def object(self, *args, **kwargs):\n kwargs[\"api\"] = self.api\n return Object(*args, **kwargs)","title":""},{"docid":"413f63a92b1743ebb5a1c18328deac87","score":"0.5162696","text":"def getBase(self):\r\n try:\r\n return self.modDict['base'][0]\r\n except KeyError:\r\n return None","title":""},{"docid":"7da3201c5b247ea952a36f8c4fe196b8","score":"0.5126898","text":"def get_full_object(self, crunch_entity):\n ret = self.entity(namespace=crunch_entity.namespace, permalink=crunch_entity.permalink)\n return crunch_entity.__class__(ret)","title":""},{"docid":"289061502792bdb42b44e21d797c0975","score":"0.510712","text":"def get_base_model(self):\n return self.modelstr['full']","title":""},{"docid":"b4f4ad5ab0eb01a121665302105c299f","score":"0.51042","text":"def uri(self):","title":""},{"docid":"301a7dbb048808ccb0a7daeb86c41d1a","score":"0.5092325","text":"def get_from(self, obj):\n # type: (Any) -> Any\n raise NotImplementedError","title":""},{"docid":"1ab0bebae02ffe56bbad767962f4a9f1","score":"0.5088766","text":"def getBaseObject(object):\n if inspect.isbuiltin(object):\n # Builtin functions don't have an argspec that we can get.\n dropSelf = 0\n elif inspect.ismethod(object):\n # Get the function from the object otherwise inspect.getargspec()\n # complains that the object isn't a Python function.\n try:\n if object.im_self is None:\n # This is an unbound method so we do not drop self from the\n # argspec, since an instance must be passed as the first arg.\n dropSelf = 0\n else:\n dropSelf = 1\n object = object.im_func\n except AttributeError:\n dropSelf = 0\n elif inspect.isclass(object):\n # Get the __init__ method function for the class.\n constructor = getConstructor(object)\n if constructor is not None:\n object = constructor\n dropSelf = 1\n else:\n dropSelf = 0\n elif callable(object):\n # Get the __call__ method instead.\n try:\n object = object.__call__.im_func\n dropSelf = 1\n except AttributeError:\n dropSelf = 0\n else:\n dropSelf = 0\n return object, dropSelf","title":""},{"docid":"84fea28f56c5828b944ee2d4f49f2417","score":"0.5070376","text":"def get_base_uri_obj(base_uri):\n base_uri_obj = _get_base_uri_obj(base_uri)\n if base_uri_obj is None:\n raise (ValidationError(\"Base URI {} not registered\".format(base_uri)))\n return base_uri_obj","title":""},{"docid":"4e2db9fe1cd82570434809bf513bb8ad","score":"0.5066215","text":"def _base_model(self, path):\n return {\n \"name\": path.rsplit('/', 1)[-1],\n \"path\": path,\n \"writable\": True,\n \"last_modified\": None,\n \"created\": None,\n \"content\": None,\n \"format\": None,\n \"mimetype\": None,\n }","title":""},{"docid":"3acf4cef8b32c116012170a36ff4c2a8","score":"0.50498116","text":"def _wrap(obj, parent=None):\n if isinstance(obj, tb.group.RootGroup):\n return HDF5Group(obj, parent)\n if isinstance(obj, tb.Group):\n return HDF5Group(obj, parent)\n if isinstance(obj, tb.Table):\n return HDF5Table(obj)\n if isinstance(obj, tb.file.File):\n return HDF5Handle(obj)\n return obj","title":""},{"docid":"12463fc22d7089cf0fca06d8068b2be0","score":"0.5031422","text":"def get_is_base(self):\n\n\t\treturn self.__is_base","title":""},{"docid":"8facb6029101d3928ab01df714dbb8a0","score":"0.50097185","text":"def get_uri_obj(uri, storage_args={}):\n\n if isinstance(uri, BaseURI): return uri\n uri_obj = None\n\n o = urlparse(uri)\n for storage in STORAGES:\n uri_obj = storage.parse_uri(o, storage_args=storage_args)\n if uri_obj is not None:\n break\n #end for\n if uri_obj is None:\n raise TypeError('<{}> is an unsupported URI.'.format(uri))\n\n return uri_obj","title":""},{"docid":"442f13ef152130955ae443fc5d57b273","score":"0.4983435","text":"def base_url(self):\n return self._resource","title":""},{"docid":"e7b34caec513abc997dc6938294adce0","score":"0.4978478","text":"def get_obj_of_own_type(self):\n raise NotImplementedError","title":""},{"docid":"705b9bcae618f3416df6df6d29e48f4f","score":"0.4966472","text":"def base(self):\r\n if isinstance(self._base, str):\r\n return self.store[self._base]\r\n else:\r\n return self._base","title":""},{"docid":"423451cbeed84a77bb7520501197910e","score":"0.49364698","text":"def base_url(self):\n return self.__base_url","title":""},{"docid":"9990eb4085720adb5e31f3b24d5f539d","score":"0.4933659","text":"def get_object(self):\n photo = super(PhotoDetailView, self).get_object()\n if photo.published != 'PUBLIC':\n if photo.user.username != self.request.user.get_username():\n raise Http404('This Photo does not belong to you')\n return photo","title":""},{"docid":"20a8949a767ec0b960a9bf8c4e62f729","score":"0.49267602","text":"def _polymorphic(self):\n return self.__polymorphic","title":""},{"docid":"6061944c4a8dbea3c6979d9d64c56a55","score":"0.49247247","text":"def base_url(self):","title":""},{"docid":"e0febff626d40e6eeeef9202f6d0078c","score":"0.4924086","text":"def get_object(self):\n return get_object_or_404(self.model, url=self.kwargs['url'])","title":""},{"docid":"4e92f4da86388b02889fb2cf77aba00d","score":"0.49229723","text":"def social_media(self):\n self.__check_image_server()\n\n if self.image_server is None:\n return None\n\n return '{h}/{c}/full/600,/0/default.jpg'.format(\n h=self.image_server.server_base,\n c=self.resource\n )","title":""},{"docid":"de76d73e76abca5e6baf3f37c6ec4ea1","score":"0.4915651","text":"def base_model(name, path):\n\n return {\n \"name\": name,\n \"path\": path,\n \"last_modified\": \"00-00-0000\",\n \"created\": \"00-00-0000\",\n \"content\": None,\n \"format\": None,\n \"mimetype\": None,\n # XXX - if this is a \"real\" path to a narrative, it should\n # look up the narrative and see if it's really writable.\n # For now, assume it's not, and set it otherwise.\n \"writable\": False,\n \"type\": None,\n }","title":""},{"docid":"11cf9b673de7bb435be9f1b0bd7c0363","score":"0.49141443","text":"def _get_stream_object(self):\n\n return self.streamClass(*self.args, **self.kwargs)","title":""},{"docid":"9dfea7a0e59f02e7d8abb5142156a862","score":"0.49080947","text":"def _get_thumb():\n pass","title":""},{"docid":"0844ddc0cfabb05374e216c367f67c4c","score":"0.49078128","text":"def get_service(self):\n\n if self.binary_field is not None and self.object_type is not None and self.id is None:\n return SOBJ_SERVICE % (\n self.api_version, '/' + self.object_type)\n elif self.id is not None and self.object_type is not None and self.external_id is None:\n return '/%s/%s' % (self.object_type, self.id)\n elif self.id is not None and self.object_type is not None and self.external_id is not None:\n return '/%s/%s/%s' % (self.object_type, self.external_id, self.id)\n elif self.object_type is None:\n return ''\n return '/%s' % self.object_type","title":""},{"docid":"b78443c3267865f94d98fa9fe872cc11","score":"0.49077907","text":"def __repr__(self):\n cls_name = self.__class__.__name__\n base_url = self.api.base_url\n return f\"{cls_name}: {base_url}\"","title":""},{"docid":"479b9200679074ef0eacb03b0c2a9738","score":"0.48943108","text":"def object(self) -> Optional[str]:\n return pulumi.get(self, \"object\")","title":""},{"docid":"694388aef4218098ed02ed0d68cd9d4d","score":"0.4888513","text":"def uri(self):\n pass","title":""},{"docid":"972dc2e0f1dac3d96ca3fdec8e36dd1f","score":"0.48878708","text":"def derived_from(self):","title":""},{"docid":"972dc2e0f1dac3d96ca3fdec8e36dd1f","score":"0.48878708","text":"def derived_from(self):","title":""},{"docid":"972dc2e0f1dac3d96ca3fdec8e36dd1f","score":"0.48878708","text":"def derived_from(self):","title":""},{"docid":"972dc2e0f1dac3d96ca3fdec8e36dd1f","score":"0.48878708","text":"def derived_from(self):","title":""},{"docid":"972dc2e0f1dac3d96ca3fdec8e36dd1f","score":"0.48878708","text":"def derived_from(self):","title":""},{"docid":"972dc2e0f1dac3d96ca3fdec8e36dd1f","score":"0.48878708","text":"def derived_from(self):","title":""},{"docid":"972dc2e0f1dac3d96ca3fdec8e36dd1f","score":"0.48878708","text":"def derived_from(self):","title":""},{"docid":"972dc2e0f1dac3d96ca3fdec8e36dd1f","score":"0.48878708","text":"def derived_from(self):","title":""},{"docid":"bcf110b4c7721d11a29662e63119d7de","score":"0.48841444","text":"def getDesiredImageBase(self) -> long:\n ...","title":""},{"docid":"0456ea96f3aee49602513a6ebf482ccc","score":"0.4863484","text":"def current_base_view(self):\n return self.get_class_name('base')","title":""},{"docid":"6014162958de7db7a60fbffbdb6ccc4f","score":"0.485996","text":"def _get_obj_info(self, obj_type, obj=None, obj_name=None,\n api_to_use=None, tenant_uuid=None,\n cloud_uuid=None):\n\n api_to_use = api_to_use or self.api\n\n if obj:\n obj_name = obj.get('name', None)\n obj_uuid = obj.get('uuid', None)\n else:\n if obj_name is None:\n obj = None\n obj_uuid = None\n else:\n if obj_name.startswith('/%s/' % obj_type):\n # If name begins with /<obj_type>/ assume it is a\n # URL path to the object.\n obj_resp = api_to_use.get(\n obj_name, tenant_uuid=tenant_uuid)\n if obj_resp.status_code == 404:\n # Object was not found; return a pseudo-object with\n # UUID derived from the URI as a fallback where the\n # user may not have access to read the object but the\n # reference is still usable.\n\n logger.debug('Object URI inaccessible - assuming a '\n 'permission issue and creating a '\n 'pseudo-object for %s', obj_name)\n\n obj = dict()\n obj['name'] = obj_name\n obj['uuid'] = obj_name.split('/api/%s/' % obj_type)[1]\n else:\n obj = obj_resp.json()\n obj_name = obj.get('name', None)\n obj_uuid = obj.get('uuid', None)\n else:\n obj = api_to_use.get_object_by_name(obj_type, obj_name,\n tenant_uuid=tenant_uuid,\n params=({\"cloud_uuid\": cloud_uuid}\n if cloud_uuid else {}))\n\n if obj is None and obj_type == 'tenant':\n # User may not have access to lookup tenants by name\n # so try tenant list API\n\n tl_obj = api_to_use.get('/user-tenant-list').json()\n for tenant in tl_obj['tenants']:\n if tenant['name'] == obj_name:\n logger.debug('Found tenant %s in user '\n 'tenant list', obj_name)\n obj = dict()\n obj['name'] = obj_name\n obj['uuid'] = tenant['uuid']\n break\n\n if obj is None:\n raise Exception('A %s with name %s could not be found'\n % (obj_type, obj_name))\n obj_uuid = obj.get('uuid', None)\n\n return obj, obj_name, obj_uuid","title":""},{"docid":"64ec01961a1b070ce37042f101077186","score":"0.4853492","text":"def _base_model(self, path):\n info = self.odfs.getinfo(path, namespaces=['details'])\n\n try:\n # size of file\n size = info.size\n except (ValueError, OSError):\n self.log.warning('Unable to get size.')\n size = None\n\n try:\n last_modified = info.modified\n except (ValueError, OSError):\n # Files can rarely have an invalid timestamp\n # https://github.com/jupyter/notebook/issues/2539\n # https://github.com/jupyter/notebook/issues/2757\n # Use the Unix epoch as a fallback so we don't crash.\n self.log.warning('Invalid mtime %s for %s', str(info.modified), path)\n last_modified = datetime(1970, 1, 1, 0, 0, tzinfo=tz.UTC)\n\n try:\n created = info.created\n except (ValueError, OSError): # See above\n self.log.warning('Invalid ctime %s for %s', str(info.created), path)\n created = datetime(1970, 1, 1, 0, 0, tzinfo=tz.UTC)\n\n # Create the base model.\n model = {}\n model['name'] = path.rsplit('/', 1)[-1]\n model['path'] = path\n model['last_modified'] = last_modified\n model['created'] = created\n model['content'] = None\n model['format'] = None\n model['mimetype'] = None\n model['size'] = size\n\n try:\n model['writable'] = True # TODO\n except OSError:\n self.log.error(\"Failed to check write permissions on %s\", path)\n model['writable'] = False\n return model","title":""},{"docid":"60a45aeb0898bc88c5c95e2425f8f41f","score":"0.48457372","text":"def get_base_class_name(obj):\n return obj.__class__.__name__.rsplit('.', 1)[-1]","title":""},{"docid":"7dfb46f4b67dac9353d7ead6ed02c1a6","score":"0.48391798","text":"def _get_parent_class():\n return ConcreteOverlay","title":""},{"docid":"cd43f6b787d634d3d58bdb8e19504e0d","score":"0.48336053","text":"def get_object(self, bits):\n if len(bits)==1:\n return Tag.objects.get(name__iexact=bits[0])\n elif len(bits)==0:\n return None\n else:\n raise ObjectDoesNotExist","title":""},{"docid":"48e47b62dbadb8eb55028745a86f822a","score":"0.48249474","text":"def _get_base_widget(self):\r\n w = self\r\n while hasattr(w, '_original_widget'):\r\n w = w._original_widget\r\n return w","title":""},{"docid":"bf2679b8a7a1166928144089a1cba225","score":"0.48244423","text":"def map_object(self, object):\r\n return object","title":""},{"docid":"5338d7cc0a91eb85d1b6cb2ebb4bcc5b","score":"0.48237678","text":"def get_animal_base():\n return \"List of animals by common name\"","title":""},{"docid":"f80400488c7d0f5ea7926ca5a87e27a2","score":"0.48155412","text":"def getObject(self) -> typing.Any:\n ...","title":""},{"docid":"2c499f06f8d8950617e978c2f2cb8c8f","score":"0.4811933","text":"def get_obj(self):\n return self.obj","title":""},{"docid":"04228d17d7450d5682aca88bfe1f2d72","score":"0.4810663","text":"def _base_model(self):\n if self.model == 'MobileNetV2':\n base_model = MobileNetV2(\n weights=\"imagenet\", include_top=False,\n input_tensor=Input(shape=base.IMAGE_SIZE[self.model])\n )\n elif self.model == 'VGG16':\n base_model = VGG16(\n weights=\"imagenet\", include_top=False,\n input_tensor=Input(shape=base.IMAGE_SIZE[self.model])\n )\n elif self.model == 'Xception':\n base_model = Xception(\n weights=\"imagenet\", include_top=False,\n input_tensor=Input(shape=base.IMAGE_SIZE[self.model])\n )\n\n # Loop over all layers in the base model and freeze them so they will\n # not be updated during training process\n for layer in base_model.layers:\n layer.trainable = False\n\n return base_model","title":""},{"docid":"bf552e1b965b4705471465d0529a75bf","score":"0.48099104","text":"def obj_type_from_url(obj_type):\r\n if obj_type == 'accounts':\r\n return 'account'\r\n elif obj_type == 'repositories':\r\n return 'repository'\r\n else:\r\n return obj_type","title":""},{"docid":"ea48dbd84d16c301c08ca8f235c62950","score":"0.48056346","text":"def __init__(self):\n #Base Object\n self.bo = None\n self.ref = None\n self.descrpition = \"\"","title":""},{"docid":"58dfa50cf7c7183ee9bd26c2dafe30e4","score":"0.48045555","text":"def base_url(self):\n\n\t\treturn self._api_base_url","title":""},{"docid":"3d7a02fa3de2728ab2dc842afeba3a62","score":"0.47965908","text":"def get_object(self):\n if self.url_type.has_id_value:\n Model = self.get_model()\n return Model.objects.get(pk=self.type_value)\n else:\n return None","title":""},{"docid":"53967581427c8b9ab97ffcdde00355d8","score":"0.47938448","text":"def homogenize(self):\n return self.__class__(self.apicls.homogenize(self))","title":""},{"docid":"50aca3c383d12a913949f00a17b9608e","score":"0.47930536","text":"def inherit_from(self):\n return self._inherit_from","title":""},{"docid":"a5f23796e0f5dcb1c54dc74e6955183c","score":"0.4790182","text":"def storage_factory(klass, location, base_url):\r\n return klass(location=location, base_url=base_url)","title":""},{"docid":"57757e5b0523d1ee8ea05702958a64dd","score":"0.47876573","text":"def __init__(self, object=''):\n pass","title":""},{"docid":"05dafe17a3ef33a9ff48efe349fab948","score":"0.4784957","text":"def base_url(self):\r\n if self._base_url is not None:\r\n return self._base_url\r\n if self._parent is not no_default:\r\n return self._parent.base_url","title":""},{"docid":"0f22fe378968231caca4a80b8c857753","score":"0.47839153","text":"def base_uri(self):\n return self._base_uri_model.get_base_uri()","title":""},{"docid":"49fdacb0df5f39e725e6031585487853","score":"0.4780638","text":"def __init__(self, *args, **kwargs):\n newobj = _gizmos.new_StaticPicture(*args, **kwargs)\n self.this = newobj.this\n self.thisown = 1\n del newobj.thisown\n self._setOORInfo(self)","title":""},{"docid":"ec90b86924ebac11d172906e260fb799","score":"0.47795218","text":"def target_for(cls, obj, shared=False):\n # TODO: we have entities in prod that fail this, eg\n # https://indieweb.social/users/bismark has source_protocol webmention\n # assert obj.source_protocol in (cls.LABEL, cls.ABBREV, 'ui', None), str(obj)\n\n if not util.is_web(obj.key.id()):\n logger.warning(f\"{obj.key} is source_protocol web but id isn't a URL!\")\n return None\n\n return obj.key.id()","title":""},{"docid":"e6ee30591400740cee55794600ead285","score":"0.4778724","text":"def get_scheme_obj(self):\n\n return self.scheme_obj","title":""},{"docid":"b238f81ea388cd95f9e44fcca4a3ed16","score":"0.47706872","text":"def __instance(self):\r\n instance = self.__api_request('GET', '/api/v1/instance/')\r\n return instance","title":""},{"docid":"350e62e6415f05b22b2b950f9efe6eae","score":"0.47650775","text":"def get_library_instance():\n return zoom.tools.zoompath('zoom/_assets/web')","title":""},{"docid":"ae96cbe811b9554697275304d0d71741","score":"0.47635433","text":"def getServerInstance(self):\n return self.http","title":""},{"docid":"dc1a75e6e170fec69bdef34787ece32c","score":"0.47558844","text":"def get_by_name(self, **kwargs):\n # Retrieve the object with the given arguments\n obj = super(SuperTagManager, self).get(**kwargs)\n # If object has a substitute speficied, use that tag enstead\n obj = obj.substitute or obj\n \n # Return object if freebase is not used\n if not st_settings.USE_FREEBASE:\n return obj\n \n # Try to find the name using freebase\n fb_name = retrieve_freebase_name(obj.name, obj.stype)\n try:\n # Try to retrieve the existing name given by freebase \n # in our database\n new_tag = self.get(name__iexact=fb_name)\n # Return the new tag or the new tags substitute\n return new_tag.substitute or new_tag\n except:\n # Simply return the obj if something went wrong\n return obj","title":""},{"docid":"cfd90d23de780855a04443fd73347895","score":"0.4755379","text":"def basedocument(self) -> str:\n return self._basedocument","title":""},{"docid":"9dfc84943bc309addd47f14f2fe53f10","score":"0.47483093","text":"def _make_one(self, request):\n inst = RESTView(request)\n return inst","title":""},{"docid":"c76998aa92422ad19c0ea44616a064b5","score":"0.47469887","text":"def retrieve_image(self):\n res = requests.get(self.url_lite, stream=True)\n res.raw.decode_content = True\n return Image.open(res.raw)","title":""},{"docid":"006c7d08e15f35dd7514df7605a898bc","score":"0.47409326","text":"def get_base_context(self):\n try:\n return {'site': Site.objects.get_current()}\n except Site.DoesNotExist:\n return {'site': None}","title":""},{"docid":"3673cf36ad6ca3710d4ec4af6d68865f","score":"0.47378477","text":"def __apiobject__(self):\n \n pass","title":""},{"docid":"3673cf36ad6ca3710d4ec4af6d68865f","score":"0.47378477","text":"def __apiobject__(self):\n \n pass","title":""}],"string":"[\n {\n \"docid\": \"495f4ed4f383e264827d73752466ae8b\",\n \"score\": \"0.619478\",\n \"text\": \"def get_base():\\r\\n return _base\",\n \"title\": \"\"\n },\n {\n \"docid\": \"edf7a898d9f719bbf07cf25c2d5703fe\",\n \"score\": \"0.59034413\",\n \"text\": \"def get_base_image(self):\\n return self.get_image_and_tag()[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2d657de6ca0fd188032e8c08cab6d49\",\n \"score\": \"0.5715031\",\n \"text\": \"def base(self):\\n return self._base\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1dab10b911f5d3dc9d72ee7c5c060a32\",\n \"score\": \"0.5683419\",\n \"text\": \"def get_base(self):\\r\\n base = self.params.get('base', None)\\r\\n if base and isinstance(base, (Account, Repository)):\\r\\n return base\\r\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"917ac5eab2cf9d0161ad9137abd1968a\",\n \"score\": \"0.5676508\",\n \"text\": \"def base(self):\\n return self.__base\",\n \"title\": \"\"\n },\n {\n \"docid\": \"917ac5eab2cf9d0161ad9137abd1968a\",\n \"score\": \"0.5676508\",\n \"text\": \"def base(self):\\n return self.__base\",\n \"title\": \"\"\n },\n {\n \"docid\": \"129e6fae3a2da19ffa778dad45b54b2b\",\n \"score\": \"0.5605025\",\n \"text\": \"def _get_base_url(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c69d36aed5d6ba372973de29a6ee2f98\",\n \"score\": \"0.5503673\",\n \"text\": \"def base(self):\\n return self._base\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c69d36aed5d6ba372973de29a6ee2f98\",\n \"score\": \"0.5503673\",\n \"text\": \"def base(self):\\n return self._base\",\n \"title\": \"\"\n },\n {\n \"docid\": \"235fcb1b0733cf986b5852527a63b01f\",\n \"score\": \"0.54643565\",\n \"text\": \"def get_object(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e150f35355e89d1079a07d6cdc11fdd7\",\n \"score\": \"0.54401696\",\n \"text\": \"def __init__(self, props=None, base_obj=None):\\n self._base = None\\n if base_obj is not None:\\n self._base = base_obj\\n self._log = logging.getLogger(__name__)\\n\\n self._image_id = None\\n self._width = 0\\n self._height = 0\\n self._url = None\\n self._error = None\\n\\n if props:\\n # account for alternate variable names from raw API response\\n if 'imageId' in props:\\n self.image_id = props['imageId']\\n if 'image_id' in props:\\n self.image_id = props['image_id']\\n if 'width' in props:\\n self.width = props['width']\\n if 'height' in props:\\n self.height = props['height']\\n if 'url' in props:\\n self.url = props['url']\\n if 'error' in props:\\n self.error = props['error']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8969e18d110b7f82f26da0ce02f34cd\",\n \"score\": \"0.53879964\",\n \"text\": \"def get_base(label):\\n base = _registry.get(label, None)\\n if base is None:\\n raise ValueError(\\\"No base registered under label '{0}'!\\\".format(label))\\n else:\\n return base\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da22c4fa28a6b84e05319c34e8f0366a\",\n \"score\": \"0.5382678\",\n \"text\": \"def getBase(self):\\n return self.base\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da22c4fa28a6b84e05319c34e8f0366a\",\n \"score\": \"0.5382678\",\n \"text\": \"def getBase(self):\\n return self.base\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da22c4fa28a6b84e05319c34e8f0366a\",\n \"score\": \"0.5382678\",\n \"text\": \"def getBase(self):\\n return self.base\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d1dbbb756332fbfa10df82c028508d09\",\n \"score\": \"0.5361202\",\n \"text\": \"def __init__(self, base_url):\\n self.base = base_url\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54602cab302c2852b982da5fd36f1801\",\n \"score\": \"0.532844\",\n \"text\": \"def base_info(self):\\n return self._base_info\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0dd24ce39fa9cbb90a6a4fbf35e043eb\",\n \"score\": \"0.5283953\",\n \"text\": \"def get_handle_base(self) -> str:\\n return get_handle_base(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce6c42e4a1fd1fd1ea9041b65f9e8603\",\n \"score\": \"0.5273263\",\n \"text\": \"def getBaseObjects(self):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a7a84d0c400e17e6987486730709f498\",\n \"score\": \"0.52586496\",\n \"text\": \"def base_image(self) -> Optional[str]:\\n return pulumi.get(self, \\\"base_image\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cfbe481217c2ed03f860874ee0fac95d\",\n \"score\": \"0.52474546\",\n \"text\": \"def getAlternateObject(self, **kwargs):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f598545328cba6aab60c5af491af39d3\",\n \"score\": \"0.52453506\",\n \"text\": \"def __init__(self, source=None):\\n PrimaryObject.__init__(self, source)\\n SourceBase.__init__(self, source)\\n NoteBase.__init__(self, source)\\n DateBase.__init__(self, source)\\n AttributeBase.__init__(self, source)\\n\\n if source:\\n self.path = source.path\\n self.mime = source.mime\\n self.desc = source.desc\\n self.thumb = source.thumb\\n else:\\n self.path = \\\"\\\"\\n self.mime = \\\"\\\"\\n self.desc = \\\"\\\"\\n self.thumb = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de928bf3d93109e50f6a79187104e7f2\",\n \"score\": \"0.5229754\",\n \"text\": \"def __init__(self, props=None, base_obj=None):\\n self._base = None\\n if base_obj is not None:\\n self._base = base_obj\\n\\n self.__id = None\\n self._width = 0\\n self._height = 0\\n self._alt_text = None\\n\\n if props:\\n # account for alternate variable names from raw API response\\n if 'id' in props:\\n self.id = props['id']\\n if 'width' in props:\\n self.width = props['width']\\n if 'height' in props:\\n self.height = props['height']\\n if 'altText' in props:\\n self.alt_text = props['altText']\\n if 'alt_text' in props:\\n self.alt_text = props['alt_text']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d6ad6e5662a3d4c6e3ac4210452c0a0\",\n \"score\": \"0.52187616\",\n \"text\": \"def _get_object(self):\\n if self.project is not None: return self.project\\n if self.dataset is not None: return self.dataset\\n if self.image is not None: return self.image\\n if self.screen is not None: return self.screen\\n if self.acquisition is not None: return self.acquisition\\n if self.plate is not None: return self.plate\\n if self.well is not None: return self.well\\n if self.tag is not None: return self.tag\\n if self.file is not None: return self.file\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ecec53c5e063beb3f4f8dd0303b741cc\",\n \"score\": \"0.5185176\",\n \"text\": \"def base_uri(self):\\r\\n return self._base_uri\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42b99bc0fdc631b44870c194343c6ffb\",\n \"score\": \"0.51783407\",\n \"text\": \"def get_base_uri(self):\\n return self._get()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1d0123c216d0d58632a83023ec2184c\",\n \"score\": \"0.51763153\",\n \"text\": \"def __init__(self, base_url=\\\"http://www.BoxOfficeMojo.com/\\\"):\\n self.base_url = base_url\",\n \"title\": \"\"\n },\n {\n \"docid\": \"966887444fa7202bc71dccb3391f2d2c\",\n \"score\": \"0.51737046\",\n \"text\": \"def get_handle_base(instance: 'User') -> str:\\n if instance.name:\\n return instance.name\\n elif instance.email:\\n return instance.email.split('@')[0]\\n else:\\n return instance.username\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4ead5b17c2a954cb20f22476bf8c71b\",\n \"score\": \"0.5171402\",\n \"text\": \"def object(self, *args, **kwargs):\\n kwargs[\\\"api\\\"] = self.api\\n return Object(*args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"413f63a92b1743ebb5a1c18328deac87\",\n \"score\": \"0.5162696\",\n \"text\": \"def getBase(self):\\r\\n try:\\r\\n return self.modDict['base'][0]\\r\\n except KeyError:\\r\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7da3201c5b247ea952a36f8c4fe196b8\",\n \"score\": \"0.5126898\",\n \"text\": \"def get_full_object(self, crunch_entity):\\n ret = self.entity(namespace=crunch_entity.namespace, permalink=crunch_entity.permalink)\\n return crunch_entity.__class__(ret)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"289061502792bdb42b44e21d797c0975\",\n \"score\": \"0.510712\",\n \"text\": \"def get_base_model(self):\\n return self.modelstr['full']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b4f4ad5ab0eb01a121665302105c299f\",\n \"score\": \"0.51042\",\n \"text\": \"def uri(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"301a7dbb048808ccb0a7daeb86c41d1a\",\n \"score\": \"0.5092325\",\n \"text\": \"def get_from(self, obj):\\n # type: (Any) -> Any\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ab0bebae02ffe56bbad767962f4a9f1\",\n \"score\": \"0.5088766\",\n \"text\": \"def getBaseObject(object):\\n if inspect.isbuiltin(object):\\n # Builtin functions don't have an argspec that we can get.\\n dropSelf = 0\\n elif inspect.ismethod(object):\\n # Get the function from the object otherwise inspect.getargspec()\\n # complains that the object isn't a Python function.\\n try:\\n if object.im_self is None:\\n # This is an unbound method so we do not drop self from the\\n # argspec, since an instance must be passed as the first arg.\\n dropSelf = 0\\n else:\\n dropSelf = 1\\n object = object.im_func\\n except AttributeError:\\n dropSelf = 0\\n elif inspect.isclass(object):\\n # Get the __init__ method function for the class.\\n constructor = getConstructor(object)\\n if constructor is not None:\\n object = constructor\\n dropSelf = 1\\n else:\\n dropSelf = 0\\n elif callable(object):\\n # Get the __call__ method instead.\\n try:\\n object = object.__call__.im_func\\n dropSelf = 1\\n except AttributeError:\\n dropSelf = 0\\n else:\\n dropSelf = 0\\n return object, dropSelf\",\n \"title\": \"\"\n },\n {\n \"docid\": \"84fea28f56c5828b944ee2d4f49f2417\",\n \"score\": \"0.5070376\",\n \"text\": \"def get_base_uri_obj(base_uri):\\n base_uri_obj = _get_base_uri_obj(base_uri)\\n if base_uri_obj is None:\\n raise (ValidationError(\\\"Base URI {} not registered\\\".format(base_uri)))\\n return base_uri_obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e2db9fe1cd82570434809bf513bb8ad\",\n \"score\": \"0.5066215\",\n \"text\": \"def _base_model(self, path):\\n return {\\n \\\"name\\\": path.rsplit('/', 1)[-1],\\n \\\"path\\\": path,\\n \\\"writable\\\": True,\\n \\\"last_modified\\\": None,\\n \\\"created\\\": None,\\n \\\"content\\\": None,\\n \\\"format\\\": None,\\n \\\"mimetype\\\": None,\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3acf4cef8b32c116012170a36ff4c2a8\",\n \"score\": \"0.50498116\",\n \"text\": \"def _wrap(obj, parent=None):\\n if isinstance(obj, tb.group.RootGroup):\\n return HDF5Group(obj, parent)\\n if isinstance(obj, tb.Group):\\n return HDF5Group(obj, parent)\\n if isinstance(obj, tb.Table):\\n return HDF5Table(obj)\\n if isinstance(obj, tb.file.File):\\n return HDF5Handle(obj)\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12463fc22d7089cf0fca06d8068b2be0\",\n \"score\": \"0.5031422\",\n \"text\": \"def get_is_base(self):\\n\\n\\t\\treturn self.__is_base\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8facb6029101d3928ab01df714dbb8a0\",\n \"score\": \"0.50097185\",\n \"text\": \"def get_uri_obj(uri, storage_args={}):\\n\\n if isinstance(uri, BaseURI): return uri\\n uri_obj = None\\n\\n o = urlparse(uri)\\n for storage in STORAGES:\\n uri_obj = storage.parse_uri(o, storage_args=storage_args)\\n if uri_obj is not None:\\n break\\n #end for\\n if uri_obj is None:\\n raise TypeError('<{}> is an unsupported URI.'.format(uri))\\n\\n return uri_obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"442f13ef152130955ae443fc5d57b273\",\n \"score\": \"0.4983435\",\n \"text\": \"def base_url(self):\\n return self._resource\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e7b34caec513abc997dc6938294adce0\",\n \"score\": \"0.4978478\",\n \"text\": \"def get_obj_of_own_type(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"705b9bcae618f3416df6df6d29e48f4f\",\n \"score\": \"0.4966472\",\n \"text\": \"def base(self):\\r\\n if isinstance(self._base, str):\\r\\n return self.store[self._base]\\r\\n else:\\r\\n return self._base\",\n \"title\": \"\"\n },\n {\n \"docid\": \"423451cbeed84a77bb7520501197910e\",\n \"score\": \"0.49364698\",\n \"text\": \"def base_url(self):\\n return self.__base_url\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9990eb4085720adb5e31f3b24d5f539d\",\n \"score\": \"0.4933659\",\n \"text\": \"def get_object(self):\\n photo = super(PhotoDetailView, self).get_object()\\n if photo.published != 'PUBLIC':\\n if photo.user.username != self.request.user.get_username():\\n raise Http404('This Photo does not belong to you')\\n return photo\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20a8949a767ec0b960a9bf8c4e62f729\",\n \"score\": \"0.49267602\",\n \"text\": \"def _polymorphic(self):\\n return self.__polymorphic\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6061944c4a8dbea3c6979d9d64c56a55\",\n \"score\": \"0.49247247\",\n \"text\": \"def base_url(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0febff626d40e6eeeef9202f6d0078c\",\n \"score\": \"0.4924086\",\n \"text\": \"def get_object(self):\\n return get_object_or_404(self.model, url=self.kwargs['url'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e92f4da86388b02889fb2cf77aba00d\",\n \"score\": \"0.49229723\",\n \"text\": \"def social_media(self):\\n self.__check_image_server()\\n\\n if self.image_server is None:\\n return None\\n\\n return '{h}/{c}/full/600,/0/default.jpg'.format(\\n h=self.image_server.server_base,\\n c=self.resource\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de76d73e76abca5e6baf3f37c6ec4ea1\",\n \"score\": \"0.4915651\",\n \"text\": \"def base_model(name, path):\\n\\n return {\\n \\\"name\\\": name,\\n \\\"path\\\": path,\\n \\\"last_modified\\\": \\\"00-00-0000\\\",\\n \\\"created\\\": \\\"00-00-0000\\\",\\n \\\"content\\\": None,\\n \\\"format\\\": None,\\n \\\"mimetype\\\": None,\\n # XXX - if this is a \\\"real\\\" path to a narrative, it should\\n # look up the narrative and see if it's really writable.\\n # For now, assume it's not, and set it otherwise.\\n \\\"writable\\\": False,\\n \\\"type\\\": None,\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11cf9b673de7bb435be9f1b0bd7c0363\",\n \"score\": \"0.49141443\",\n \"text\": \"def _get_stream_object(self):\\n\\n return self.streamClass(*self.args, **self.kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9dfea7a0e59f02e7d8abb5142156a862\",\n \"score\": \"0.49080947\",\n \"text\": \"def _get_thumb():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0844ddc0cfabb05374e216c367f67c4c\",\n \"score\": \"0.49078128\",\n \"text\": \"def get_service(self):\\n\\n if self.binary_field is not None and self.object_type is not None and self.id is None:\\n return SOBJ_SERVICE % (\\n self.api_version, '/' + self.object_type)\\n elif self.id is not None and self.object_type is not None and self.external_id is None:\\n return '/%s/%s' % (self.object_type, self.id)\\n elif self.id is not None and self.object_type is not None and self.external_id is not None:\\n return '/%s/%s/%s' % (self.object_type, self.external_id, self.id)\\n elif self.object_type is None:\\n return ''\\n return '/%s' % self.object_type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b78443c3267865f94d98fa9fe872cc11\",\n \"score\": \"0.49077907\",\n \"text\": \"def __repr__(self):\\n cls_name = self.__class__.__name__\\n base_url = self.api.base_url\\n return f\\\"{cls_name}: {base_url}\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"479b9200679074ef0eacb03b0c2a9738\",\n \"score\": \"0.48943108\",\n \"text\": \"def object(self) -> Optional[str]:\\n return pulumi.get(self, \\\"object\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"694388aef4218098ed02ed0d68cd9d4d\",\n \"score\": \"0.4888513\",\n \"text\": \"def uri(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"972dc2e0f1dac3d96ca3fdec8e36dd1f\",\n \"score\": \"0.48878708\",\n \"text\": \"def derived_from(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"972dc2e0f1dac3d96ca3fdec8e36dd1f\",\n \"score\": \"0.48878708\",\n \"text\": \"def derived_from(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"972dc2e0f1dac3d96ca3fdec8e36dd1f\",\n \"score\": \"0.48878708\",\n \"text\": \"def derived_from(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"972dc2e0f1dac3d96ca3fdec8e36dd1f\",\n \"score\": \"0.48878708\",\n \"text\": \"def derived_from(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"972dc2e0f1dac3d96ca3fdec8e36dd1f\",\n \"score\": \"0.48878708\",\n \"text\": \"def derived_from(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"972dc2e0f1dac3d96ca3fdec8e36dd1f\",\n \"score\": \"0.48878708\",\n \"text\": \"def derived_from(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"972dc2e0f1dac3d96ca3fdec8e36dd1f\",\n \"score\": \"0.48878708\",\n \"text\": \"def derived_from(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"972dc2e0f1dac3d96ca3fdec8e36dd1f\",\n \"score\": \"0.48878708\",\n \"text\": \"def derived_from(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bcf110b4c7721d11a29662e63119d7de\",\n \"score\": \"0.48841444\",\n \"text\": \"def getDesiredImageBase(self) -> long:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0456ea96f3aee49602513a6ebf482ccc\",\n \"score\": \"0.4863484\",\n \"text\": \"def current_base_view(self):\\n return self.get_class_name('base')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6014162958de7db7a60fbffbdb6ccc4f\",\n \"score\": \"0.485996\",\n \"text\": \"def _get_obj_info(self, obj_type, obj=None, obj_name=None,\\n api_to_use=None, tenant_uuid=None,\\n cloud_uuid=None):\\n\\n api_to_use = api_to_use or self.api\\n\\n if obj:\\n obj_name = obj.get('name', None)\\n obj_uuid = obj.get('uuid', None)\\n else:\\n if obj_name is None:\\n obj = None\\n obj_uuid = None\\n else:\\n if obj_name.startswith('/%s/' % obj_type):\\n # If name begins with /<obj_type>/ assume it is a\\n # URL path to the object.\\n obj_resp = api_to_use.get(\\n obj_name, tenant_uuid=tenant_uuid)\\n if obj_resp.status_code == 404:\\n # Object was not found; return a pseudo-object with\\n # UUID derived from the URI as a fallback where the\\n # user may not have access to read the object but the\\n # reference is still usable.\\n\\n logger.debug('Object URI inaccessible - assuming a '\\n 'permission issue and creating a '\\n 'pseudo-object for %s', obj_name)\\n\\n obj = dict()\\n obj['name'] = obj_name\\n obj['uuid'] = obj_name.split('/api/%s/' % obj_type)[1]\\n else:\\n obj = obj_resp.json()\\n obj_name = obj.get('name', None)\\n obj_uuid = obj.get('uuid', None)\\n else:\\n obj = api_to_use.get_object_by_name(obj_type, obj_name,\\n tenant_uuid=tenant_uuid,\\n params=({\\\"cloud_uuid\\\": cloud_uuid}\\n if cloud_uuid else {}))\\n\\n if obj is None and obj_type == 'tenant':\\n # User may not have access to lookup tenants by name\\n # so try tenant list API\\n\\n tl_obj = api_to_use.get('/user-tenant-list').json()\\n for tenant in tl_obj['tenants']:\\n if tenant['name'] == obj_name:\\n logger.debug('Found tenant %s in user '\\n 'tenant list', obj_name)\\n obj = dict()\\n obj['name'] = obj_name\\n obj['uuid'] = tenant['uuid']\\n break\\n\\n if obj is None:\\n raise Exception('A %s with name %s could not be found'\\n % (obj_type, obj_name))\\n obj_uuid = obj.get('uuid', None)\\n\\n return obj, obj_name, obj_uuid\",\n \"title\": \"\"\n },\n {\n \"docid\": \"64ec01961a1b070ce37042f101077186\",\n \"score\": \"0.4853492\",\n \"text\": \"def _base_model(self, path):\\n info = self.odfs.getinfo(path, namespaces=['details'])\\n\\n try:\\n # size of file\\n size = info.size\\n except (ValueError, OSError):\\n self.log.warning('Unable to get size.')\\n size = None\\n\\n try:\\n last_modified = info.modified\\n except (ValueError, OSError):\\n # Files can rarely have an invalid timestamp\\n # https://github.com/jupyter/notebook/issues/2539\\n # https://github.com/jupyter/notebook/issues/2757\\n # Use the Unix epoch as a fallback so we don't crash.\\n self.log.warning('Invalid mtime %s for %s', str(info.modified), path)\\n last_modified = datetime(1970, 1, 1, 0, 0, tzinfo=tz.UTC)\\n\\n try:\\n created = info.created\\n except (ValueError, OSError): # See above\\n self.log.warning('Invalid ctime %s for %s', str(info.created), path)\\n created = datetime(1970, 1, 1, 0, 0, tzinfo=tz.UTC)\\n\\n # Create the base model.\\n model = {}\\n model['name'] = path.rsplit('/', 1)[-1]\\n model['path'] = path\\n model['last_modified'] = last_modified\\n model['created'] = created\\n model['content'] = None\\n model['format'] = None\\n model['mimetype'] = None\\n model['size'] = size\\n\\n try:\\n model['writable'] = True # TODO\\n except OSError:\\n self.log.error(\\\"Failed to check write permissions on %s\\\", path)\\n model['writable'] = False\\n return model\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60a45aeb0898bc88c5c95e2425f8f41f\",\n \"score\": \"0.48457372\",\n \"text\": \"def get_base_class_name(obj):\\n return obj.__class__.__name__.rsplit('.', 1)[-1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7dfb46f4b67dac9353d7ead6ed02c1a6\",\n \"score\": \"0.48391798\",\n \"text\": \"def _get_parent_class():\\n return ConcreteOverlay\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd43f6b787d634d3d58bdb8e19504e0d\",\n \"score\": \"0.48336053\",\n \"text\": \"def get_object(self, bits):\\n if len(bits)==1:\\n return Tag.objects.get(name__iexact=bits[0])\\n elif len(bits)==0:\\n return None\\n else:\\n raise ObjectDoesNotExist\",\n \"title\": \"\"\n },\n {\n \"docid\": \"48e47b62dbadb8eb55028745a86f822a\",\n \"score\": \"0.48249474\",\n \"text\": \"def _get_base_widget(self):\\r\\n w = self\\r\\n while hasattr(w, '_original_widget'):\\r\\n w = w._original_widget\\r\\n return w\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf2679b8a7a1166928144089a1cba225\",\n \"score\": \"0.48244423\",\n \"text\": \"def map_object(self, object):\\r\\n return object\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5338d7cc0a91eb85d1b6cb2ebb4bcc5b\",\n \"score\": \"0.48237678\",\n \"text\": \"def get_animal_base():\\n return \\\"List of animals by common name\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f80400488c7d0f5ea7926ca5a87e27a2\",\n \"score\": \"0.48155412\",\n \"text\": \"def getObject(self) -> typing.Any:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c499f06f8d8950617e978c2f2cb8c8f\",\n \"score\": \"0.4811933\",\n \"text\": \"def get_obj(self):\\n return self.obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04228d17d7450d5682aca88bfe1f2d72\",\n \"score\": \"0.4810663\",\n \"text\": \"def _base_model(self):\\n if self.model == 'MobileNetV2':\\n base_model = MobileNetV2(\\n weights=\\\"imagenet\\\", include_top=False,\\n input_tensor=Input(shape=base.IMAGE_SIZE[self.model])\\n )\\n elif self.model == 'VGG16':\\n base_model = VGG16(\\n weights=\\\"imagenet\\\", include_top=False,\\n input_tensor=Input(shape=base.IMAGE_SIZE[self.model])\\n )\\n elif self.model == 'Xception':\\n base_model = Xception(\\n weights=\\\"imagenet\\\", include_top=False,\\n input_tensor=Input(shape=base.IMAGE_SIZE[self.model])\\n )\\n\\n # Loop over all layers in the base model and freeze them so they will\\n # not be updated during training process\\n for layer in base_model.layers:\\n layer.trainable = False\\n\\n return base_model\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf552e1b965b4705471465d0529a75bf\",\n \"score\": \"0.48099104\",\n \"text\": \"def obj_type_from_url(obj_type):\\r\\n if obj_type == 'accounts':\\r\\n return 'account'\\r\\n elif obj_type == 'repositories':\\r\\n return 'repository'\\r\\n else:\\r\\n return obj_type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea48dbd84d16c301c08ca8f235c62950\",\n \"score\": \"0.48056346\",\n \"text\": \"def __init__(self):\\n #Base Object\\n self.bo = None\\n self.ref = None\\n self.descrpition = \\\"\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58dfa50cf7c7183ee9bd26c2dafe30e4\",\n \"score\": \"0.48045555\",\n \"text\": \"def base_url(self):\\n\\n\\t\\treturn self._api_base_url\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d7a02fa3de2728ab2dc842afeba3a62\",\n \"score\": \"0.47965908\",\n \"text\": \"def get_object(self):\\n if self.url_type.has_id_value:\\n Model = self.get_model()\\n return Model.objects.get(pk=self.type_value)\\n else:\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53967581427c8b9ab97ffcdde00355d8\",\n \"score\": \"0.47938448\",\n \"text\": \"def homogenize(self):\\n return self.__class__(self.apicls.homogenize(self))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50aca3c383d12a913949f00a17b9608e\",\n \"score\": \"0.47930536\",\n \"text\": \"def inherit_from(self):\\n return self._inherit_from\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5f23796e0f5dcb1c54dc74e6955183c\",\n \"score\": \"0.4790182\",\n \"text\": \"def storage_factory(klass, location, base_url):\\r\\n return klass(location=location, base_url=base_url)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"57757e5b0523d1ee8ea05702958a64dd\",\n \"score\": \"0.47876573\",\n \"text\": \"def __init__(self, object=''):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"05dafe17a3ef33a9ff48efe349fab948\",\n \"score\": \"0.4784957\",\n \"text\": \"def base_url(self):\\r\\n if self._base_url is not None:\\r\\n return self._base_url\\r\\n if self._parent is not no_default:\\r\\n return self._parent.base_url\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f22fe378968231caca4a80b8c857753\",\n \"score\": \"0.47839153\",\n \"text\": \"def base_uri(self):\\n return self._base_uri_model.get_base_uri()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49fdacb0df5f39e725e6031585487853\",\n \"score\": \"0.4780638\",\n \"text\": \"def __init__(self, *args, **kwargs):\\n newobj = _gizmos.new_StaticPicture(*args, **kwargs)\\n self.this = newobj.this\\n self.thisown = 1\\n del newobj.thisown\\n self._setOORInfo(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec90b86924ebac11d172906e260fb799\",\n \"score\": \"0.47795218\",\n \"text\": \"def target_for(cls, obj, shared=False):\\n # TODO: we have entities in prod that fail this, eg\\n # https://indieweb.social/users/bismark has source_protocol webmention\\n # assert obj.source_protocol in (cls.LABEL, cls.ABBREV, 'ui', None), str(obj)\\n\\n if not util.is_web(obj.key.id()):\\n logger.warning(f\\\"{obj.key} is source_protocol web but id isn't a URL!\\\")\\n return None\\n\\n return obj.key.id()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e6ee30591400740cee55794600ead285\",\n \"score\": \"0.4778724\",\n \"text\": \"def get_scheme_obj(self):\\n\\n return self.scheme_obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b238f81ea388cd95f9e44fcca4a3ed16\",\n \"score\": \"0.47706872\",\n \"text\": \"def __instance(self):\\r\\n instance = self.__api_request('GET', '/api/v1/instance/')\\r\\n return instance\",\n \"title\": \"\"\n },\n {\n \"docid\": \"350e62e6415f05b22b2b950f9efe6eae\",\n \"score\": \"0.47650775\",\n \"text\": \"def get_library_instance():\\n return zoom.tools.zoompath('zoom/_assets/web')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae96cbe811b9554697275304d0d71741\",\n \"score\": \"0.47635433\",\n \"text\": \"def getServerInstance(self):\\n return self.http\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc1a75e6e170fec69bdef34787ece32c\",\n \"score\": \"0.47558844\",\n \"text\": \"def get_by_name(self, **kwargs):\\n # Retrieve the object with the given arguments\\n obj = super(SuperTagManager, self).get(**kwargs)\\n # If object has a substitute speficied, use that tag enstead\\n obj = obj.substitute or obj\\n \\n # Return object if freebase is not used\\n if not st_settings.USE_FREEBASE:\\n return obj\\n \\n # Try to find the name using freebase\\n fb_name = retrieve_freebase_name(obj.name, obj.stype)\\n try:\\n # Try to retrieve the existing name given by freebase \\n # in our database\\n new_tag = self.get(name__iexact=fb_name)\\n # Return the new tag or the new tags substitute\\n return new_tag.substitute or new_tag\\n except:\\n # Simply return the obj if something went wrong\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cfd90d23de780855a04443fd73347895\",\n \"score\": \"0.4755379\",\n \"text\": \"def basedocument(self) -> str:\\n return self._basedocument\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9dfc84943bc309addd47f14f2fe53f10\",\n \"score\": \"0.47483093\",\n \"text\": \"def _make_one(self, request):\\n inst = RESTView(request)\\n return inst\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c76998aa92422ad19c0ea44616a064b5\",\n \"score\": \"0.47469887\",\n \"text\": \"def retrieve_image(self):\\n res = requests.get(self.url_lite, stream=True)\\n res.raw.decode_content = True\\n return Image.open(res.raw)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"006c7d08e15f35dd7514df7605a898bc\",\n \"score\": \"0.47409326\",\n \"text\": \"def get_base_context(self):\\n try:\\n return {'site': Site.objects.get_current()}\\n except Site.DoesNotExist:\\n return {'site': None}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3673cf36ad6ca3710d4ec4af6d68865f\",\n \"score\": \"0.47378477\",\n \"text\": \"def __apiobject__(self):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3673cf36ad6ca3710d4ec4af6d68865f\",\n \"score\": \"0.47378477\",\n \"text\": \"def __apiobject__(self):\\n \\n pass\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1195,"cells":{"query_id":{"kind":"string","value":"639ae350ec8ef52a509371edd6bce2af"},"query":{"kind":"string","value":"Tests that writing text like 'lrename from to' calls the put action and passes 'from' as the original filename and 'to' as the new filename."},"positive_passages":{"kind":"list like","value":[{"docid":"57d5a5b7e9c035083711aa25d4d048e4","score":"0.7171982","text":"def test_lrename_invokes_rename_action(capsys, monkeypatch, sftp):\n\n # Replace the real action with our mocked function\n monkeypatch.setattr(rename_files_local, \"rename_local_file\", mock_rename)\n\n # Pass in a valid input\n with mock_input(\"lrename {} {}\".format(FROM, TO)):\n main_loop(sftp)\n\n assert SUCCESS_STRING in capsys.readouterr().out","title":""}],"string":"[\n {\n \"docid\": \"57d5a5b7e9c035083711aa25d4d048e4\",\n \"score\": \"0.7171982\",\n \"text\": \"def test_lrename_invokes_rename_action(capsys, monkeypatch, sftp):\\n\\n # Replace the real action with our mocked function\\n monkeypatch.setattr(rename_files_local, \\\"rename_local_file\\\", mock_rename)\\n\\n # Pass in a valid input\\n with mock_input(\\\"lrename {} {}\\\".format(FROM, TO)):\\n main_loop(sftp)\\n\\n assert SUCCESS_STRING in capsys.readouterr().out\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"5070ce0417bc7fba6eda6cafe103ebc2","score":"0.68994135","text":"def mock_rename(before: str, after: str):\n\n assert before == FROM\n assert after == TO\n print(SUCCESS_STRING)","title":""},{"docid":"80e9e5047d64afe6b307ffc273d28fe6","score":"0.66226196","text":"def rename(old,new,test_run):\n from os import makedirs,rename\n from os.path import dirname,exists\n\n renamed[old] = new\n info(\"Renaming %s\" % old)\n info(\"-> %s\" % new)\n if not test_run:\n if not exists(dirname(new)): makedirs(dirname(new))\n rename(old,new)","title":""},{"docid":"f9783a766eed3e379a2634032acceacf","score":"0.66076416","text":"def testRename(self):\n def _check(results):\n self.assertEqual(results[0], '')\n self.assertEqual(results[1], 'testfile2')\n return self.runCommand('rename testfile2 testfile1')\n\n d = self.runScript('rename testfile1 testfile2', 'ls testfile?')\n d.addCallback(_check)\n d.addCallback(self.assertEqual, '')\n return d","title":""},{"docid":"fe4b47f8087c77cdcf08ae331388b648","score":"0.66037583","text":"def test_renaming(self):\n self.fw.writeRecord('{\"a\" : 1}')\n self.fw.close()\n self.assertTrue(os.path.exists(TESTDATA_TARGET_FILENAME), \"File must be found!\")","title":""},{"docid":"9bdcd08ed9cbf3ed9c971f805b3434e8","score":"0.6523673","text":"def test_rename(self):\n bundy.util.process.rename(\"rename-test\")\n self.assertEqual(\"rename-test\", self.__get_self_name())\n bundy.util.process.rename()\n self.assertEqual(\"process_test.py\", self.__get_self_name())","title":""},{"docid":"706349de23285a25036675910e7de29b","score":"0.6372971","text":"def test_rename_overwrite(getch, y_or_n, cleandir, fake_db, funk_dict):\n getch.side_effect = lambda x: y_or_n\n fnames = [name for name in funk_dict]\n OLD, NEW = fnames[0], fnames[1]\n cmd = commands.Rename([OLD, NEW])\n cmd()\n\n loaded_funks = shared.load_funks()\n if y_or_n == \"y\":\n cmd_string = funk_dict[OLD]\n else:\n cmd_string = funk_dict[NEW]\n\n assert loaded_funks[NEW] == cmd_string","title":""},{"docid":"fedd82e061c6c8dd01783dbc2755ae72","score":"0.6352608","text":"def test_rename(self):\n self.activity_1.rename(\"Have fun!\")\n self.assertEqual(self.activity_1.name, \"Have fun!\", msg='The object name should be `Have fun!`')","title":""},{"docid":"8cbd2c1900904b124b505a441160539c","score":"0.6212922","text":"def rename(lib: 'Library', *args):\n logger.debug('rename(%r, %r)', lib, args)\n parser = argparse.ArgumentParser(prog=\"dantalian rename\", add_help=False)\n parser.add_argument('file')\n parser.add_argument('new')\n args = parser.parse_args(args)\n lib.rename(args.file, args.new)","title":""},{"docid":"b9af9ab2b578fcd5b9346c7c5de0226c","score":"0.607795","text":"def test_rename_config():\r\n sample_rename = f\"{os.path.dirname(sample)}/sample.rename\"\r\n shutil.copy(sample, sample_rename)\r\n runner = CliRunner()\r\n result = runner.invoke(cli.cli,\r\n ['-f', sample_rename, 'rename', 'server_cmd_1', 'server_cmd_rename'], input=\"y\")\r\n assert result.exit_code ==0\r\n os.remove(sample_rename)","title":""},{"docid":"2e7d0f3380a1c374d188e092a3906f10","score":"0.60744256","text":"def fileRenamed(self, p_str, p_str_1, p_str_2): # real signature unknown; restored from __doc__\n pass","title":""},{"docid":"12ac779fed12e80fa5bae1b1e640a148","score":"0.6072542","text":"def thing_rename(self, event):\n\n self.thing_rename_action()","title":""},{"docid":"8180873a589ada6b0d45a5efdc8ee694","score":"0.606389","text":"def renameFile(oldName, newName):\n\tos.rename(oldName, newName);\n\tprintHere (\"The file \" + oldName + \" has been renamed as \" + newName);","title":""},{"docid":"5d10953efb49b1355f7c72b37202cb8f","score":"0.60150146","text":"def test_invalid_commands_do_not_invoke_rename_function(capsys, sftp):\n\n # See tests/integration/test_close.py for details.\n test_inputs = [\n \"doesnotexist\",\n \"lrename\", # No files\n \"lrename onefile\",\n \"lrename onefile twofile threefile\"\n ]\n for test in test_inputs:\n with mock_input(test):\n main_loop(sftp)\n assert \"not recognized\" in capsys.readouterr().out","title":""},{"docid":"b0b1246f6842fd6395ef32f90ef4f83f","score":"0.6009825","text":"def _rename(src: str, dst: str):\n if os.path.isfile(src):\n os.path.isfile(dst) and os.unlink(dst)\n os.rename(src, dst)","title":""},{"docid":"50ec76f7a6b1c3ed9dc54354b514a4ad","score":"0.60048604","text":"def testPut(self):\n # XXX - not actually a unit test\n expectedOutput = ('Transferred %s/testfile1 to %s/%s/test\"file2'\n % (self.testDir, os.getcwd(), self.testDir))\n def _checkPut(result):\n self.assertFilesEqual(self.testDir + '/testfile1',\n self.testDir + '/test\"file2')\n self.failUnless(result.endswith(expectedOutput))\n return self.runCommand('rm \"test\\\\\"file2\"')\n\n d = self.runCommand('put %s/testfile1 \"test\\\\\"file2\"'\n % (self.testDir,))\n d.addCallback(_checkPut)\n d.addCallback(lambda _: self.failIf(\n os.path.exists(self.testDir + '/test\"file2')))\n return d","title":""},{"docid":"4786b1e99d530e2652dd8efafb953aac","score":"0.5983363","text":"def rename(self, oldName, newName):\r\n os.rename( oldName, newName )\r\n return TRUE","title":""},{"docid":"0e13ca09208b25a20dee6592cdb31f43","score":"0.59827757","text":"def rename_command(*, login_manager: LoginManager, endpoint_id, source, destination):\n transfer_client = login_manager.get_transfer_client()\n autoactivate(transfer_client, endpoint_id, if_expires_in=60)\n\n res = transfer_client.operation_rename(\n endpoint_id, oldpath=source, newpath=destination\n )\n formatted_print(res, text_format=FORMAT_TEXT_RAW, response_key=\"message\")","title":""},{"docid":"a080f61aa2f8fb5055db9769d9446025","score":"0.59646535","text":"def edit(self, fileName, newFile):","title":""},{"docid":"f300d8a727dafa44435be280e7289f76","score":"0.5956121","text":"def _fancy_rename(oldname, newname):\n if not os.path.exists(newname):\n try:\n os.rename(oldname, newname)\n except OSError:\n raise\n return\n\n # destination file exists\n try:\n (fd, tmpfile) = tempfile.mkstemp(\".tmp\", prefix=oldname, dir=\".\")\n os.close(fd)\n os.remove(tmpfile)\n except OSError:\n # either file could not be created (e.g. permission problem)\n # or could not be deleted (e.g. rude virus scanner)\n raise\n try:\n os.rename(newname, tmpfile)\n except OSError:\n raise # no rename occurred\n try:\n os.rename(oldname, newname)\n except OSError:\n os.rename(tmpfile, newname)\n raise\n os.remove(tmpfile)","title":""},{"docid":"9f616167b21ea3cf0d4dcd594e1767d9","score":"0.59474653","text":"def testWildcardPut(self):\n def check(results):\n self.assertEqual(results[0], '')\n self.assertEqual(results[2], '')\n self.assertFilesEqual(self.testDir + '/testRemoveFile',\n self.testDir + '/../testRemoveFile',\n 'testRemoveFile get failed')\n self.assertFilesEqual(self.testDir + '/testRenameFile',\n self.testDir + '/../testRenameFile',\n 'testRenameFile get failed')\n\n d = self.runScript('cd ..',\n 'put %s/testR*' % (self.testDir,),\n 'cd %s' % os.path.basename(self.testDir))\n d.addCallback(check)\n return d","title":""},{"docid":"7d0a4bca54edeb9699c09bd9e94a70fd","score":"0.5939229","text":"def rename(self, source, target):\n json = {\n \"from\": source,\n \"to\": target,\n }\n return self.transport.post(url=self.links[\"action:rename\"], json=json)","title":""},{"docid":"171cc54590b63f074474dc522418c767","score":"0.5933106","text":"def rename_file(file_source, file_dest):\n os.rename(file_source, file_dest)\n print('File renamed from \"{}\" to \"{}\".'.format(file_source, file_dest))","title":""},{"docid":"527ec6a433ba26a26c478646213cd996","score":"0.59083015","text":"def MoveFile(source, destination):\n assert(len(source) > 0)\n assert(len(destination) > 0)\n # Check if we can use the atomic \"rename\" function: \n logger = logging.getLogger(\"pynt.output\")\n canuserename = True\n if os.path.exists(destination):\n # file already exists\n # check if the permission of the two files are the same\n srcstat = os.stat(source)\n dststat = os.stat(destination)\n try:\n if srcstat.st_mode != dststat.st_mode:\n os.chmod(source, dststat.st_mode)\n if srcstat.st_uid != dststat.st_uid:\n os.chown(source, dststat.st_uid, -1)\n if srcstat.st_gid != dststat.st_gid:\n os.chown(source, -1, dststat.st_gid)\n except OSError:\n canuserename = False\n if canuserename:\n # if so, do a move in the finder\n logger.debug(\"Doing an atomic rename from %s to %s\" % (source, destination))\n try:\n # os.rename(source, destination)\n # we use shutil.move instead of os.rename, since os.rename() can handle moving between two file systems\n shutil.move(source, destination)\n except OSError:\n logger.debug(\"Caught OSError while renaming %s to %s\" % (source, destination))\n canuserename = False\n if not canuserename:\n # read contents from file\n \n logger.debug(\"Doing a non-atomic rename from %s to %s\" % (source, destination))\n infile = file(source, 'r+b')\n contents = infile.read()\n infile.close()\n outfile = file(destination, 'w+b')\n outfile.write(contents)\n outfile.close()\n try:\n os.remove(source)\n except OSError:\n pass\n # self.logger.debug(\"Could not delete temporary file %s\" % (source))","title":""},{"docid":"2cb386903c11563b52cc0131bc0baebc","score":"0.59001464","text":"def rename(self, new_name):\n pass","title":""},{"docid":"5317796a0865ba970c0c375668281b82","score":"0.58949876","text":"def _rename(view: View, defx: Defx, context: Context) -> None:\n for target in context.targets:\n path = target['action__path']\n filename = cwd_input(\n view._vim, defx._cwd,\n ('New name: {} -> '.format(path)), path, 'file')\n if not filename or filename == path:\n continue\n if os.path.exists(filename):\n error(view._vim, '{} is already exists'.format(filename))\n continue\n\n os.rename(path, filename)\n\n view.redraw(True)\n view.search_file(filename, defx._index)","title":""},{"docid":"cc29288ae1c6268344f5394c59745aa4","score":"0.5873752","text":"def test_rename_file(self):\n filename = \"cute_doge.jpg\"\n renamer = PathAndRename(\"images/\")\n uuid_overwrite = \"9087assd9f87\"\n with patch('apps.projects.models.uuid.uuid4', return_value=uuid_overwrite):\n new_path = renamer(None, filename)\n self.assertEqual(new_path, f'images/{uuid_overwrite}.jpg')","title":""},{"docid":"43eaa4e3bbea2426d86c348360c67f57","score":"0.58690107","text":"def put(source, target):\n return client.put_file(source, target)","title":""},{"docid":"d73137a541c876ef558b3a10e9e0a906","score":"0.58486813","text":"def test_rename_according_to_time_and_date():\n files = get_files(1)\n now = time.time()\n data_source_func = partial(get_files_names, files)\n data = get_file_rename_data(data_source_func, 0, False)\n # tempfile would fail on attemp to rename, don't perform actual rename\n rename_according_to_time_and_date(data, False)\n\n files = get_files(5)\n now = time.time()\n data_source_func = partial(get_files_names, files)\n data = get_file_rename_data(data_source_func, 0, False)\n # tempfile would fail on attemp to rename, don't perform actual rename\n rename_according_to_time_and_date(data, False)","title":""},{"docid":"ea05c67cb419f8d40b8e22f4aba50c80","score":"0.5841246","text":"def test_mv_noop(self):\n fs = self.make_one()\n fs.open('foo', 'wb').write(b'Hello!')\n fs.mv('foo', 'foo')\n self.assertEqual(fs.open('foo', 'rb').read(), b'Hello!')\n transaction.commit()\n self.assertEqual(fs.open('foo', 'rb').read(), b'Hello!')\n\n fs.mv('foo', 'foo')\n self.assertEqual(fs.open('foo', 'rb').read(), b'Hello!')\n transaction.commit()\n self.assertEqual(fs.open('foo', 'rb').read(), b'Hello!')","title":""},{"docid":"5af2e4c9bde6bfa532797a0ce9b4903f","score":"0.5838941","text":"def main():\n script_dir = os.path.dirname(os.path.realpath(__file__))\n for filename in os.listdir(script_dir):\n basename, extension = os.path.splitext(filename)\n if basename.startswith(\"Test\") and extension == '.park':\n source_path = os.path.join(script_dir, filename)\n dest_path = os.path.join(script_dir, basename)\n sys.stdout.write(\"renaming {} to {}\\n\".format(\n source_path, dest_path))\n os.rename(source_path, dest_path)","title":""},{"docid":"732413ec04e51aee12c676abc0110bd3","score":"0.582419","text":"def rename(trial, param):\n param.name = self.new_name\n return True","title":""},{"docid":"9768cc00a52480f4ba43faac979814eb","score":"0.581262","text":"def rename(self, old, new):\n assert not self._readonly\n return os.rename(self._relpath(old), self._relpath(new))","title":""},{"docid":"44ec31e32fb5cdd5f0bed160b6e5017b","score":"0.5810397","text":"def rename(self, src, dst):\n raise UnsupportedError(\"rename resource\")","title":""},{"docid":"d52ec75e01b5d3910589b1c114055cd6","score":"0.5805513","text":"def rename(self, path, new_path):\n raise NotImplementedError('FileSystem.rename')","title":""},{"docid":"3fe8885e9834d38d7e8c89281c239f6a","score":"0.5803796","text":"def test_filesystem_rename(self):\n pool_name = p_n()\n pool_path = make_test_pool(pool_name, DISKS[0:1])\n\n fs_name = fs_n()\n make_test_filesystem(pool_path, fs_name)\n\n fs_name_rename = fs_n()\n\n (_, return_code, _) = StratisDbus.fs_rename(fs_name, fs_name_rename)\n self.assertEqual(return_code, dbus.UInt16(0))","title":""},{"docid":"2c13051c9176e01ea84668921f5baa23","score":"0.57980865","text":"def Rename(oldpath, newpath, overwrite=False):\n success = pywrapfile.File.Rename(oldpath, newpath, overwrite)\n if not success:\n raise GOSError(\"Failed to rename: %s to %s\" % (oldpath, newpath))","title":""},{"docid":"fd8725e297c5518646e73fac9e5cabfc","score":"0.57819134","text":"def r_rename(old, new):\n with closing(connections[env.host_string].open_sftp()) as ftp:\n return ftp.rename (_r_normalizepath( old ), _r_normalizepath( new ))","title":""},{"docid":"5993630b3d503e3891c29ab3caaae17d","score":"0.5762428","text":"def test_write_dynamic_file_names(self):\n skel = DynamicFileName(baz=\"replaced-name\")\n with TempDir() as tmp_dir:\n skel.write(tmp_dir.path)\n self.assertEqual(\n open(tmp_dir.join('foo.txt')).read().strip(),\n 'foo'\n )\n self.assertEqual(\n open(tmp_dir.join('bar/replaced-name.txt')).read().strip(),\n 'baz'\n )","title":""},{"docid":"de22199919fc7dca8b56a4713d17dfe5","score":"0.57378876","text":"def rename_file(self, old_path, new_path):\n self.log.debug(\"MongoContents.GenericManager: Init rename of '%s' to '%s'\", old_path, new_path)\n if self.file_exists(new_path) or self.dir_exists(new_path):\n self.already_exists(new_path)\n elif self.file_exists(old_path) or self.dir_exists(old_path):\n self.log.debug(\"MongoContents.GenericManager: Actually renaming '%s' to '%s'\", old_path,\n new_path)\n self.fs.mv(old_path, new_path)\n else:\n self.no_such_entity(old_path)","title":""},{"docid":"994768cef5357b843ad0991706946b44","score":"0.57122236","text":"def do_rename(self, remote_path):\n\t\tremote_src_dest=remote_path.split(\" \")\n\t\tif len(remote_src_dest)!=2:\n\t\t\tprint \"Rename command expects exactly two arguments\"\n\t\telse:\n\t\t\ttry: \n\t\t\t\tresponse=self._perform_ftp_command('rename',remote_src_dest[0], remote_src_dest[1])\n print (\"Renamed: \" + remote_src_dest[0] + \" -> \" + remote_src_dest[1])\n\t\t\texcept IOError as e:\n print(\"Failure\")\n\t\t\t\tprint(e)","title":""},{"docid":"04633e9fc909cd703e7c1b6827247429","score":"0.5693519","text":"def rename(from_segments, to_segments):","title":""},{"docid":"a6f69b49c7e91aead5105fad97309119","score":"0.56745493","text":"def mv(source, destination):\n for filename in source:\n click.echo('Moving {} to {}'.format(filename, destination))","title":""},{"docid":"f9c436dbe7974bf2e8940375c3f55e53","score":"0.56428856","text":"def effect_rename(self, old_name: str, new_name: str):\n data = {\"write\": {\"command\": \"rename\",\n \"animName\": old_name,\n \"newName\": new_name}}\n self.__put(\"effects\", data)","title":""},{"docid":"8a32fa8b8f1d24a5ea167365fc90e165","score":"0.5632927","text":"def thing_rename_action(self):\n\n thing_name = self.patch.things.names[self.selected_index]\n new_name = wx.GetTextFromUser('Enter a new name for ' + thing_name, caption='Change name',\n default_value=thing_name, parent=self)\n\n if new_name != '':\n self.undo_add()\n\n self.patch.things.names[self.selected_index] = new_name\n\n self.update_properties()\n self.is_modified(True)\n\n parent = self.GetParent()\n parent.editor_windows[windows.MAIN_TOOL_STATES].update_filterlist()","title":""},{"docid":"031b7a1520571d0b182a069ba70c9d09","score":"0.56318015","text":"def main():\n print(\"Starting directory is: {}\".format(os.getcwd()))\n os.chdir('Lyrics/Christmas')\n print(\"Files in {}:\\n{}\\n\".format(os.getcwd(), os.listdir('.')))\n\n for filename in os.listdir('.'):\n for count, character in enumerate(filename):\n try:\n if filename[count].islower() and filename[count + 1].isupper():\n filename = filename[:count + 1] + \"_\" + filename[count + 1:]\n if filename[count].isdigit() and filename[count+1].isalpha():\n filename = filename[:count + 1] + \"_\" + filename[count + 1:]\n except IndexError:\n pass\n\n new_name = get_fixed_filename(filename)\n print(\"Renaming {} to {}\".format(filename, new_name))","title":""},{"docid":"ad99cb37294915723003400a6cb50f71","score":"0.5616454","text":"def notify_rename(self, ea, new_name):\n return 0","title":""},{"docid":"45613e39d5089f78f259c1a4333e5018","score":"0.5612399","text":"def rename(src):\n index = 0; dst = os.path.normpath(src)\n while os.path.exists(dst):\n index += 1\n dst = \"%s.%d\"%(os.path.normpath(src),index)\n if index > 0:\n print >> sys.stderr, \"Warning: %s already exists, renaming the existing one to %s and continuing...\"%(src,dst)\n os.renames(src,dst)","title":""},{"docid":"c5310a1f1944b5ca6c8d886fe78ce26a","score":"0.56091917","text":"def rename(self, src, dst):\n params = {'op': 'RENAME', 'destination': dst}\n r = requests.put(\"%s%s\" % (self.namenode_url, src), params=params)\n if r.status_code == 200:\n return True\n return False","title":""},{"docid":"c450006c000d70e19974f64b84549618","score":"0.56062555","text":"def test_autorename():\n input_filenames = [1, \"apple\"]\n dest = create_env([])\n\n result = Pipe(input_filenames, dest, output_suffix=\".pdf\", autoname=True)(\n touch_output\n )\n\n expected = [\n \"c4ca4238a0b923820dcc509a6f75849b.pdf\",\n \"1f3870be274f6c49b3e31a0c6728957f.pdf\",\n ]\n\n assert result == expected","title":""},{"docid":"db942329b6ed8561a79b63236b7d821c","score":"0.5604357","text":"def test_write_utf8_file_fails_at_rename(\n tmpdir: py.path.local, caplog: pytest.LogCaptureFixture\n) -> None:\n test_dir = tmpdir.mkdir(\"files\")\n test_file = Path(test_dir / \"test.json\")\n\n with pytest.raises(WriteError), patch(\n \"homeassistant.util.file.os.replace\", side_effect=OSError\n ):\n write_utf8_file(test_file, '{\"some\":\"data\"}', False)\n\n assert not os.path.exists(test_file)\n\n assert \"File replacement cleanup failed\" not in caplog.text","title":""},{"docid":"f78efa74654a357b3a4c0347826e8cec","score":"0.558254","text":"def test_rename(self):\n self.create_data()\n self.assertEqual(Photo.objects.get(pk=1).name, 'photo1')\n # invalid photo, redisplay\n data = {'name': 'dd'*500} # name is too long\n self.json_post_value(\n reverse('photo_rename', kwargs=dict(pk=1)), 'html', data)\n # valid photo, redirect\n data = {'name': 'new-name'}\n self.json_post_value(\n reverse('photo_rename', kwargs=dict(pk=1)), 'url', data)\n self.assertEqual(Photo.objects.get(pk=1).name, 'new-name')","title":""},{"docid":"875f1d7fb7591ffab03194a2822d0c7a","score":"0.5579691","text":"def write2newfile(self, fname, s):\n if os.path.exists(fname):\n for i in range(100):\n new_fname = '%s,%s' % (fname, i)\n if not os.path.exists(new_fname):\n os.rename(fname, new_fname)\n print('renamed old file to %s' % new_fname)\n break\n self.write2file(fname, s)","title":""},{"docid":"42f1e70972ced709ef761ff0b36313b0","score":"0.556879","text":"def put(self, group, group_root, path=\"\"):\n parser = reqparse.RequestParser()\n parser.add_argument('rename_to', type=str, required=True)\n args = parser.parse_args()\n\n new_name = args['rename_to']\n file_path = os.path.join(group_root, path)\n if not os.path.exists(file_path):\n abort(400, \"%s does not exist\" % path)\n\n new_path = os.path.join(os.path.dirname(file_path), new_name)\n if os.path.exists(new_path):\n abort(400, (\"%s already exists at %s\"\n % (new_name, os.path.dirname(path))))\n self.rename(file_path, new_name)\n return success(\"Successfully renamed to %s\" % args['rename_to'])","title":""},{"docid":"8d19dabd20c0b709edd4cb93bcf2d877","score":"0.5558562","text":"def rename_file(src, dest):\n try:\n if os.path.exists(src):\n shutil.move(src, dest)\n logging.info(\"moved tmp file \" + src + \" to \" + dest)\n return {'success': True}\n except Exception as e:\n logging.error(\"Cannot rename file \"+ src + \" to \"+ dest)\n logging.exception(e)\n return {'success' : False}","title":""},{"docid":"4157b147b0868b17ebce7df7318deb20","score":"0.55525565","text":"def renamer(old, new):\n\n try:\n mkdirs(os.path.dirname(new))\n os.rename(old, new)\n except OSError:\n mkdirs(os.path.dirname(new))\n os.rename(old, new)","title":""},{"docid":"6e6336893bfcd8b6a1d1117af9f31805","score":"0.5551752","text":"def test_rename_fail(cleandir, fake_db):\n cmd = commands.Rename(['bad_funk', 'NEW'])\n with pytest.raises(errors.FunkNotDefinedError):\n cmd()","title":""},{"docid":"cab392345a418d984244217c6fb8fb32","score":"0.5538901","text":"def rename_command(new_path, old_path, endpoint_id):\n client = get_client()\n autoactivate(client, endpoint_id, if_expires_in=60)\n\n res = client.operation_rename(endpoint_id, oldpath=old_path,\n newpath=new_path)\n\n if outformat_is_json():\n print_json_response(res)\n else:\n safeprint(res['message'])","title":""},{"docid":"a263643fcf54e5553c9d827b993ee528","score":"0.55375606","text":"def _mv(src, dest, logstream=None):\n assert ' ' not in src and ' ' not in dest,\\\n \"_mv: can't handle paths in spaces: src=%r, dest=%r\" % (src, dest)\n if sys.platform == \"win32\":\n _run(\"move %s %s\" % (src, dest), logstream=logstream)\n else:\n _run(\"mv %s %s\" % (src, dest), logstream=logstream)","title":""},{"docid":"ffcad37aa0221042a90048667fc21853","score":"0.5529329","text":"def _correct_name(self, action, obj_name):\n action_name = action.name.split('.')[0]\n correct_name = '{0}.{1}'.format(action_name, obj_name)\n if action.name != correct_name:\n action.name = correct_name","title":""},{"docid":"828e56417de3037faa99c093755f9772","score":"0.55150414","text":"def test_file_move(self):\n try:\n source_url = saga.url(\"file:///\"+self.filename_a)\n target_url = saga.url(\"file:///\"+self.filename_d)\n \n my_file = saga.filesystem.file(source_url)\n my_file.move(target_url)\n my_file.close()\n \n except saga.exception, e: \n self.fail(e.get_full_message())\n\n self.failUnless(os.path.exists(self.filename_d))","title":""},{"docid":"56d8975a775dd6aa4ce0ce9f65906a29","score":"0.5489117","text":"def rename_handler(event):\n if not event:\n return\n if 'target' not in event:\n print('ERROR: %s: missing \"target\" in %s event' % (sys.argv[0], event['type']))\n return\n if 'newname' not in event:\n print('ERROR: %s: missing \"newname\" in %s event' % (sys.argv[0], event['type']))\n return\n\n write_event_log(rename_event_tmpl % event)\n\n return","title":""},{"docid":"f03ac6eb56d106852ae0ee25b867c9df","score":"0.54791427","text":"def rename(self, srcfile, destfile):\n\n # Don't rename until all are ready\n self.barrier()\n\n # We'll capture any exception in this variable\n err = None\n\n # Rank 0 renames the file.\n if self.rank == 0:\n try:\n if os.path.exists(srcfile):\n os.rename(srcfile, destfile)\n except Exception as e:\n err = e\n\n # Verify that the rename succeeded\n self.allraise_if(err)","title":""},{"docid":"20bdc4f0062dcbe2bdfc74b45fa0f925","score":"0.54716074","text":"def renames(old: str, new: str) -> None:\n if isfile(old):\n makedirs(dirname(new))\n elif isdir(old):\n makedirs(dirname(dirname(new))) # return parent directory name of dir which is passed as new argument\n os.rename(old, new)","title":""},{"docid":"60947f1f0ad6b402478a7751a9ebc24f","score":"0.54592294","text":"def mv(src, dst):\n # move the file\n try:\n shutil.move(src, dst)\n except:\n print(\"filetools.mv: Unable to move {:s} to {:s}\".format(src, dst))\n return False\n return True","title":""},{"docid":"b35257c04f4b4d9618ec3e858f0898bb","score":"0.54465586","text":"def rename(self, pfn, new_pfn):\n try:\n bucket, key = self.get_bucket_key(pfn)\n if key is None:\n raise exception.SourceNotFound('Cannot get the source key from S3')\n bucket_name, key_name = self.get_bucket_key_name(new_pfn)\n key.copy(bucket_name, key_name)\n key.delete()\n except exception.SourceNotFound as e:\n raise exception.SourceNotFound(e)\n except boto.exception.S3ResponseError as e:\n if e.status in [404, 403]:\n raise exception.DestinationNotAccessible(e)\n else:\n raise exception.ServiceUnavailable(e)\n except Exception as e:\n raise exception.ServiceUnavailable(e)","title":""},{"docid":"ece82843038df31bb3a6b09ec990f732","score":"0.5437126","text":"def test_filesystem_rename_same_name(self):\n pool_name = p_n()\n pool_path = make_test_pool(pool_name, DISKS[0:1])\n\n fs_name = fs_n()\n make_test_filesystem(pool_path, fs_name)\n\n (_, return_code, _) = StratisDbus.fs_rename(fs_name, fs_name)\n self.assertEqual(return_code, dbus.UInt16(0))","title":""},{"docid":"3ec583ce483376951bd4ded0d4633ea4","score":"0.5421914","text":"def mv(self, from_path, to_path):\n self.cli.file_move(self.path + \"/\" + from_path, self.path + \"/\" + to_path)","title":""},{"docid":"d389080f2b6932797026eb626a0d6fab","score":"0.5420785","text":"def test_patch_move(self):\n pass","title":""},{"docid":"8f04f58844ed0e47d5f1049b29097150","score":"0.5412139","text":"def test_rename_default(self, client):\n translation.activate('en')\n root = Node.root()\n r1 = root.add(\"r1\")\n\n r1.rename(\"rr1\")\n\n assert r1.paths.count() == 4 ## \"any\" gets added implicitly\n assert r1.path == \"/rr1\"\n translation.activate('nl')\n assert r1.path == \"/rr1\"\n translation.activate('fr')\n assert r1.path == \"/rr1\"\n translation.activate('any')\n assert r1.path == \"/rr1\"","title":""},{"docid":"76182be220bb6676824aaa66a6d1b215","score":"0.54071873","text":"def rename(self, src, dst):\n return self.execute_command('RENAME', src, dst)","title":""},{"docid":"bf0b2847734525863ccfbc5558277f59","score":"0.5391521","text":"def renameFile (self, mainPath, oldFileName, newFileName, extFile, \n project=0, update_location=False):\n RCI.instance().renameFileTests( filePath=mainPath, \n fileName=oldFileName, \n fileExt=extFile, \n fileProject=project,\n newName=newFileName,\n update_location=update_location )","title":""},{"docid":"a22355c9693c80dbef3d0fdb581a5727","score":"0.5384263","text":"def move_and_rename(source, destination, remove_old):\r\n if remove_old:\r\n try:\r\n if destination.exists():\r\n os.remove(str(destination))\r\n except PermissionError as e:\r\n print(e)\r\n\r\n output = True\r\n try:\r\n shutil.move(str(source), str(destination))\r\n except IOError as e:\r\n print(e)\r\n output = False\r\n except TypeError as e:\r\n print(e)\r\n output = False\r\n raise\r\n\r\n return output","title":""},{"docid":"863cec51ed11c500e4bb935a614892de","score":"0.5373303","text":"def test_audiobook_name(self, os_walk, mkdir_p,\n copyfile): # pylint: disable=W0621\n assert self\n assert mkdir_p # to appease pylint\n\n def single_file(filename, expected):\n \"\"\" Test a single file \"\"\"\n os_walk.return_value = [\n # if os.walk returns a root with leading /, os.path.join\n # doesn't add the filemanager base_dir to the path. This\n # makes it a bit easier to write these tests\n ('/foo', [], (filename,)),\n ]\n filemgr.copy_audiobook(\"source\")\n copyfile.assert_called_with(os.path.join('/foo', filename),\n os.path.join('/foo', expected))\n\n copy_base = 'copy_base'\n filemgr = pyres.filemanager.FileManager(base_dir=copy_base)\n assert filemgr\n # one digit\n single_file('1one', '01one')\n single_file('o1ne', 'o01ne')\n single_file('one1', 'one01')\n\n # two digits\n single_file('11two', '11two')\n single_file('t11wo', 't11wo')\n single_file('two11', 'two11')\n\n # three digits\n single_file('123three', '123three')\n single_file('th123ree', 'th123ree')\n single_file('three123', 'three123')\n\n # mp3 extension not changed\n single_file('1baz.mp3', '01baz.mp3')\n single_file('b1az.mp3', 'b01az.mp3')\n single_file('baz1.mp3', 'baz01.mp3')\n\n # test multiple substitutions\n single_file('1b1a1z.1mp3', '01b01a01z.01mp3')","title":""},{"docid":"b0048ec58a9204054c7a32a32d0bd222","score":"0.5367443","text":"def test_substitute(tmpdir, td):\n pytest.debug_func()\n q = editor.editor(td.filename.strpath)\n assert K[\"orig_l\"] == q.buffer\n q.sub(K[\"lowe\"], K[\"uppE\"])\n q.quit()\n backup = py.path.local(q.backup_filename())\n assert backup.exists()\n assert td.filename.exists()\n assert written_format(K[\"orig_l\"]) == backup.read()\n exp = written_format([_.replace(K[\"lowe\"], K[\"uppE\"])\n for _ in K[\"orig_l\"]])\n assert exp == td.filename.read()","title":""},{"docid":"bc23c2ef7ee93c76487b58cc87644c6e","score":"0.5360535","text":"def rename(old_file_name, old_format):\n old_file = old_file_name + old_format\n check_file = check_existing_file(old_file)\n if check_file:\n print \"\\nThe file is available\\n\"\n\n print \"\\nEnter the new file name: \"\n new_name = sys.stdin.readline()\n new_name = new_name.lower().strip()\n\n print \"\\nEnter the new format: \"\n new_format = sys.stdin.readline()\n new_format = new_format.lower().strip()\n\n new_file = new_name + new_format\n\n if new_file != \"\":\n if format == new_format:\n os.rename(old_file, new_file)\n else:\n container = read_file(old_file)\n if old_format == '.html':\n query = re.search(r'(?<=<div style=\"display: None;\">)[* \\w\\d+]+', container).group()\n else:\n os.rename(old_file,new_file)\n else:\n print \"The file does not exist. You can use this name.\\n\\n\"","title":""},{"docid":"2aba1bbba45ca817395e10bc6027a4ac","score":"0.5354556","text":"def test_another(tmpdir, td, fx_chdir):\n pytest.debug_func()\n q = editor.editor(td.basename)\n other = tmpdir.join(K[\"altfile\"])\n q.sub(K[\"test\"], K[\"frib\"])\n q.quit(filepath=other.strpath)\n edited = [x.replace(K[\"test\"], K[\"frib\"]) for x in K[\"orig_l\"]]\n assert q.backup_filename() is None\n assert td.filename.exists()\n assert other.exists()\n assert written_format(K[\"orig_l\"]) == td.filename.read()\n assert written_format(edited) == other.read()","title":""},{"docid":"c49bcfe0265eae2991e6467d6938f17f","score":"0.5354362","text":"def testChangesTakeEffect(self):\n newName = 'Kathpakalaxmikanthan'\n body = {'name': newName}\n response = self.fluiddb.db('PUT', ['users', 'testuser1'], body)\n userID = self.fluiddb.users['testuser1'].get().value['id']\n response = self.fluiddb.objects[userID]['fluiddb/users/name'].get()\n self.assertEqual(newName, response.value)","title":""},{"docid":"92b1e65a116f636ab81e55557d897911","score":"0.5350848","text":"def setName(self, newName):\n \n pass","title":""},{"docid":"1c8deda9a61cdc6e73e230a232321faa","score":"0.53483593","text":"def ev_rename(self, ea, new_name):\n self.__on_rename(ea, new_name)\n return super(IdaReIDPHooks, self).ev_rename(ea, new_name)","title":""},{"docid":"7f98384b4695a39f459a7895c1c226aa","score":"0.53336656","text":"def edit_fname(self, case):\n raise NotImplementedError","title":""},{"docid":"1639cc3018a55b6018859dceb90a1dc6","score":"0.5325321","text":"def mv(self, from_path, to_path):\n os.replace(from_path, to_path)","title":""},{"docid":"c198eee40a394c9510a0cfa2db670d23","score":"0.53162706","text":"def modify_name(roi,which,name):\n manager,index = roi.mapper(which)\n source = roi.get_source(which)\n\n manager.names[index]=name\n source.name=name","title":""},{"docid":"ae5e04b8cf5da95f6183f841b9ceea21","score":"0.5304809","text":"def change_filename(self, filepath, entities):\n exts = Path(filepath).suffixes\n old_ext = \"\"\n for ext in exts:\n old_ext += ext\n\n suffix = entities['suffix']\n entity_file_keys = []\n file_keys = ['task', 'acquisition', 'direction',\n 'reconstruction', 'run']\n\n for key in file_keys:\n if key in list(entities.keys()):\n entity_file_keys.append(key)\n\n sub = get_key_name(filepath, 'sub')\n ses = get_key_name(filepath, 'ses')\n sub_ses = sub + '_' + ses\n\n if 'run' in list(entities.keys()) and 'run-0' in filepath:\n entities['run'] = '0' + str(entities['run'])\n\n filename = \"_\".join([\"{}-{}\".format(key, entities[key])\n for key in entity_file_keys])\n filename = filename.replace('acquisition', 'acq') \\\n .replace('direction', 'dir') \\\n .replace('reconstruction', 'rec')\n if len(filename) > 0:\n filename = sub_ses + '_' + filename + '_' + suffix + old_ext\n else:\n filename = sub_ses + filename + '_' + suffix + old_ext\n\n # CHECK TO SEE IF DATATYPE CHANGED\n dtypes = ['anat', 'func', 'perf', 'fmap', 'dwi']\n old = ''\n for dtype in dtypes:\n if dtype in filepath:\n old = dtype\n\n if 'datatype' in entities.keys():\n dtype = entities['datatype']\n if entities['datatype'] != old:\n print(\"WARNING: DATATYPE CHANGE DETECETD\")\n else:\n dtype = old\n new_path = str(self.path) + '/' + sub + '/' + ses \\\n + '/' + dtype + '/' + filename\n\n # add the scan path + new path to the lists of old, new filenames\n self.old_filenames.append(filepath)\n self.new_filenames.append(new_path)\n\n # NOW NEED TO RENAME ASSOCIATIONS\n # bids_file = self.layout.get_file(filepath)\n bids_file = filepath\n # associations = bids_file.get_associations()\n associations = self.get_nifti_associations(str(bids_file))\n for assoc_path in associations:\n # assoc_path = assoc.path\n if Path(assoc_path).exists():\n # print(\"FILE: \", filepath)\n # print(\"ASSOC: \", assoc.path)\n # ensure assoc not an IntendedFor reference\n if '.nii' not in str(assoc_path):\n self.old_filenames.append(assoc_path)\n new_ext_path = img_to_new_ext(new_path,\n ''.join(Path(assoc_path)\n .suffixes))\n self.new_filenames.append(new_ext_path)\n\n # MAKE SURE THESE AREN'T COVERED BY get_associations!!!\n if '/dwi/' in filepath:\n # add the bval and bvec if there\n if Path(img_to_new_ext(filepath, '.bval')).exists() \\\n and img_to_new_ext(filepath, '.bval') \\\n not in self.old_filenames:\n self.old_filenames.append(img_to_new_ext(filepath,\n '.bval'))\n self.new_filenames.append(img_to_new_ext(new_path,\n '.bval'))\n\n if Path(img_to_new_ext(filepath, '.bvec')).exists() \\\n and img_to_new_ext(filepath, '.bvec') \\\n not in self.old_filenames:\n self.old_filenames.append(img_to_new_ext(filepath,\n '.bvec'))\n self.new_filenames.append(img_to_new_ext(new_path,\n '.bvec'))\n\n # now rename _events and _physio files!\n old_suffix = parse_file_entities(filepath)['suffix']\n scan_end = '_' + old_suffix + old_ext\n\n if '_task-' in filepath:\n old_events = filepath.replace(scan_end, '_events.tsv')\n old_ejson = filepath.replace(scan_end, '_events.json')\n if Path(old_events).exists():\n self.old_filenames.append(old_events)\n new_scan_end = '_' + suffix + old_ext\n new_events = new_path.replace(new_scan_end, '_events.tsv')\n self.new_filenames.append(new_events)\n if Path(old_ejson).exists():\n self.old_filenames.append(old_ejson)\n new_scan_end = '_' + suffix + old_ext\n new_ejson = new_path.replace(new_scan_end, '_events.json')\n self.new_filenames.append(new_ejson)\n\n old_physio = filepath.replace(scan_end, '_physio.tsv.gz')\n if Path(old_physio).exists():\n self.old_filenames.append(old_physio)\n new_scan_end = '_' + suffix + old_ext\n new_physio = new_path.replace(new_scan_end, '_physio.tsv.gz')\n self.new_filenames.append(new_physio)\n\n # RENAME INTENDED FORS!\n ses_path = self.path + '/' + sub + '/' + ses\n for path in Path(ses_path).rglob(\"fmap/*.json\"):\n self.IF_rename_paths.append(str(path))\n # json_file = self.layout.get_file(str(path))\n # data = json_file.get_dict()\n data = get_sidecar_metadata(str(path))\n if data == \"Erroneous sidecar\":\n print('Error parsing sidecar: ', str(path))\n continue\n\n if 'IntendedFor' in data.keys():\n # check if IntendedFor field is a str or list\n if isinstance(data['IntendedFor'], str):\n if data['IntendedFor'] == \\\n _get_intended_for_reference(filepath):\n # replace old filename with new one (overwrite string)\n data['IntendedFor'] = \\\n _get_intended_for_reference(new_path)\n\n # update the json with the new data dictionary\n _update_json(str(path), data)\n\n if isinstance(data['IntendedFor'], list):\n for item in data['IntendedFor']:\n if item in _get_intended_for_reference(filepath):\n\n # remove old filename\n data['IntendedFor'].remove(item)\n # add new filename\n data['IntendedFor'].append(\n _get_intended_for_reference(new_path))\n\n # update the json with the new data dictionary\n _update_json(str(path), data)\n\n # save IntendedFor purges so that you can datalad run the\n # remove association file commands on a clean dataset\n # if self.use_datalad:\n # if not self.is_datalad_clean():\n # self.datalad_save(message=\"Renamed IntendedFors\")\n # self.reset_bids_layout()\n # else:\n # print(\"No IntendedFor References to Rename\")","title":""},{"docid":"b7763449f66409706e06e349ef5e8210","score":"0.52938694","text":"def safe_rename(filename):\n old_filename = filename + '.old'\n try:\n shutil.move(filename, old_filename)\n print \"Renamed existing file from %s to %s\" % (filename, old_filename)\n except IOError, exc:\n if exc.errno != errno.ENOENT:\n charlimit_textwrap(exc.message)\n raise RuntimeError('ERROR: Failed to rename %s to %s' % (filename, old_filename))","title":""},{"docid":"87e41c074ec408e0b3d10c861372ebb6","score":"0.52835345","text":"def test_write_utf8_file_fails_at_rename_and_remove(\n tmpdir: py.path.local, caplog: pytest.LogCaptureFixture\n) -> None:\n test_dir = tmpdir.mkdir(\"files\")\n test_file = Path(test_dir / \"test.json\")\n\n with pytest.raises(WriteError), patch(\n \"homeassistant.util.file.os.remove\", side_effect=OSError\n ), patch(\"homeassistant.util.file.os.replace\", side_effect=OSError):\n write_utf8_file(test_file, '{\"some\":\"data\"}', False)\n\n assert \"File replacement cleanup failed\" in caplog.text","title":""},{"docid":"0f9f8ebe29c4ba932129f4678e0a4d57","score":"0.5277333","text":"def rename(self, pfn, new_pfn):\n raise NotImplementedError","title":""},{"docid":"9111ece2a7536af991f856c75bd610ae","score":"0.5268497","text":"def rollName(self, arg, dirname, fnames):\n\t\tdirname = self.changeName(\"\", dirname)\n\n\t\tfor file in fnames:\n\t\t\tfullname = os.path.join(dirname, file)\n\n\t\t\tif string.find(fullname, \"images/\") > 0 :\n\t\t\t\t# don't change image files names\n\t\t\t\tcontinue\n\n\t\t\tif os.path.isfile(fullname):\n\t\t\t\tnewname = self.changeName(\"\", fullname)\n\t\t\t\tself.update(newname, newname)","title":""},{"docid":"17c0d6a465541542a25959bab123b21e","score":"0.526777","text":"def move_file(filename, old_path, new_path):\n if not os.path.exists(new_path):\n os.makedirs(new_path)\n og_loc = os.path.join(old_path, filename)\n new_loc = os.path.join(new_path, filename)\n shutil.move(og_loc, new_loc)","title":""},{"docid":"645cae768d841f4e7d7ccb828b53728f","score":"0.5259965","text":"def rename(self, fromname: str, toname: str) -> bool:\n self.instance.rename(fromname, toname)","title":""},{"docid":"f03d3e8284e4dc66263978b2d6e67123","score":"0.5259022","text":"def test_modify_task_name(self):\n lastname = self.taskbody[\"name\"]\n self.taskbody[\"name\"] = \"newname\"\n self.taskbody[\"id\"] = self.taskid\n response =self.client.modify_task(job_data=json.dumps(self.taskbody))\n logger.info(response)\n if isinstance(self.check_schema(resp=response), str):\n self.assertTrue(False, \"jsonschema check failed\")\n self.assertEqual(response.get(\"code\"), 0, msg=\"expect code = 0\")\n self.assertEqual(response.get(\"subCode\"), None, msg=\"expect subCode = Null\")\n response = self.client.get_task(taskid=self.taskid)\n logger.info(response)\n self.assertEqual(response.get(\"data\").get(\"name\"), \"newname\",\n msg=\"expect name = newname\")\n self.taskbody[\"name\"] = lastname","title":""},{"docid":"e0aaf5c664df53e37dc35080189f4c96","score":"0.5258637","text":"def rename(self, ea, new_name):\n self.__on_rename(ea, new_name)\n return super(IdaReIDPHooks, self).rename(ea, new_name)","title":""},{"docid":"fbd27121713fd004b5c2713d35d64aec","score":"0.52583665","text":"def rename_cmd(args):\n cmd = commands.Rename([args.args[0], 'NEW'])\n return cmd","title":""},{"docid":"5adaf7d96b58c731ba208278fe4a909a","score":"0.5253459","text":"def test_to_camel_case(source: str, expected: str):\n assert expected == definitions.to_camel_case(source)","title":""},{"docid":"16aa0af88505264c4897f0552ebbe2af","score":"0.5249994","text":"def rename(self, oldName, newName):\r\n return self.execCmd(\"svn rename \" + oldName + \" \" + newName)","title":""},{"docid":"b9c90e453a0c66a793d9e7ed6fe01df4","score":"0.5238427","text":"def _rename_test(node, filename):\n old_name = node.value\n root = find_root(node)\n def_statement = find_binding(old_name, root)\n\n if old_name in (\"None\",):\n # why are these there?\n return\n\n if not old_name.startswith(\"test_\"):\n new_name = f\"test_{old_name}\"\n\n # def_statement can be None if the test doesn't exist.\n # Could happen if it was referenced in multiple places;\n # the first time we came across it we renamed it.\n if def_statement is not None:\n # Rename the function\n def_statement.children[1].value = new_name\n def_statement.children[1].changed()\n\n # Rename all references, including `node`\n for n in root.leaves():\n if n.type == TOKEN.NAME and n.value == old_name:\n # Don't include dotted names\n if (\n n.parent.type == syms.trailer\n and n.prev_sibling\n and n.prev_sibling.value == '.'\n ):\n continue\n\n # This is probably a reference to the test function\n # However, we need to check in case it's a separate local var.\n # Figure out if we're in a function, and see if there's a binding for\n # the same name\n func_node = get_ancestor_of_type(n, syms.funcdef)\n if func_node and find_binding(old_name, func_node):\n # This is a local var with the same name, don't rename it\n continue\n\n n.value = new_name\n n.changed()","title":""},{"docid":"69698acb33eb0d1874be3e5b70360a5b","score":"0.5238101","text":"def move_file(source, destiny):\n\n try:\n os.rename(source, destiny)\n except FileExistsError:\n base = os.path.basename(destiny)\n name, ext = os.path.splitext(destiny)\n alt_destiny = os.path.join(base, name + '_' + ext)\n move_file(source, alt_destiny)","title":""},{"docid":"eb32031456f021821021c17fd0b907ee","score":"0.5237962","text":"def test_filename_spaces(self):\n temp_filename = spaced_filename = self.get_temp_filename()\n spaced_filename += \" a filename with spaces.\" + self.extension\n shutil.copyfile(self.raw_text_filename, spaced_filename)\n self.compare_cli_output(\n spaced_filename,\n self.get_expected_filename(self.raw_text_filename),\n )\n os.remove(temp_filename)\n os.remove(spaced_filename)","title":""},{"docid":"3a9010e032c5157b120cc9d68d479b80","score":"0.5222952","text":"def _add_word_to_filename(file_path, word, to_path, copy=False):\n if file_path is None:\n raise ValueError(\"Input file does not exist\")\n _to_path = os.path.join(to_path, word+'_'+os.path.basename(file_path))\n if copy:\n shutil.copyfile(file_path, _to_path)\n else:\n os.rename(file_path, _to_path)","title":""},{"docid":"94729dff72780fe643f015821426b2ba","score":"0.5219495","text":"def rename_file(self, old_filename, new_filename):\n\n old_key = self.subfolder + old_filename\n new_key = self.subfolder + new_filename\n\n if old_key not in self.list_contents():\n logger.exception(f\"File {old_filename} does not exist in path s3://{self.bucket_name}/{self.subfolder}\")\n raise ValueError(f\"File {old_filename} does not exist in path s3://{self.bucket_name}/{self.subfolder}\")\n\n source_file = f\"{self.bucket_name}/{self.subfolder}{old_filename}\"\n self.s3.Object(self.bucket, new_key).copy_from(CopySource=source_file)\n self.s3.Object(self.bucket, old_key).delete()","title":""},{"docid":"5998384f143ef547146e4ede3da01864","score":"0.52135426","text":"def command_mv(user: UserMessages, m: MicroPythonRepl, args):\n args.SRC.connect_repl(m)\n args.DST.connect_repl(m)\n args.SRC.rename(args.DST)","title":""}],"string":"[\n {\n \"docid\": \"5070ce0417bc7fba6eda6cafe103ebc2\",\n \"score\": \"0.68994135\",\n \"text\": \"def mock_rename(before: str, after: str):\\n\\n assert before == FROM\\n assert after == TO\\n print(SUCCESS_STRING)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"80e9e5047d64afe6b307ffc273d28fe6\",\n \"score\": \"0.66226196\",\n \"text\": \"def rename(old,new,test_run):\\n from os import makedirs,rename\\n from os.path import dirname,exists\\n\\n renamed[old] = new\\n info(\\\"Renaming %s\\\" % old)\\n info(\\\"-> %s\\\" % new)\\n if not test_run:\\n if not exists(dirname(new)): makedirs(dirname(new))\\n rename(old,new)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f9783a766eed3e379a2634032acceacf\",\n \"score\": \"0.66076416\",\n \"text\": \"def testRename(self):\\n def _check(results):\\n self.assertEqual(results[0], '')\\n self.assertEqual(results[1], 'testfile2')\\n return self.runCommand('rename testfile2 testfile1')\\n\\n d = self.runScript('rename testfile1 testfile2', 'ls testfile?')\\n d.addCallback(_check)\\n d.addCallback(self.assertEqual, '')\\n return d\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe4b47f8087c77cdcf08ae331388b648\",\n \"score\": \"0.66037583\",\n \"text\": \"def test_renaming(self):\\n self.fw.writeRecord('{\\\"a\\\" : 1}')\\n self.fw.close()\\n self.assertTrue(os.path.exists(TESTDATA_TARGET_FILENAME), \\\"File must be found!\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9bdcd08ed9cbf3ed9c971f805b3434e8\",\n \"score\": \"0.6523673\",\n \"text\": \"def test_rename(self):\\n bundy.util.process.rename(\\\"rename-test\\\")\\n self.assertEqual(\\\"rename-test\\\", self.__get_self_name())\\n bundy.util.process.rename()\\n self.assertEqual(\\\"process_test.py\\\", self.__get_self_name())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"706349de23285a25036675910e7de29b\",\n \"score\": \"0.6372971\",\n \"text\": \"def test_rename_overwrite(getch, y_or_n, cleandir, fake_db, funk_dict):\\n getch.side_effect = lambda x: y_or_n\\n fnames = [name for name in funk_dict]\\n OLD, NEW = fnames[0], fnames[1]\\n cmd = commands.Rename([OLD, NEW])\\n cmd()\\n\\n loaded_funks = shared.load_funks()\\n if y_or_n == \\\"y\\\":\\n cmd_string = funk_dict[OLD]\\n else:\\n cmd_string = funk_dict[NEW]\\n\\n assert loaded_funks[NEW] == cmd_string\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fedd82e061c6c8dd01783dbc2755ae72\",\n \"score\": \"0.6352608\",\n \"text\": \"def test_rename(self):\\n self.activity_1.rename(\\\"Have fun!\\\")\\n self.assertEqual(self.activity_1.name, \\\"Have fun!\\\", msg='The object name should be `Have fun!`')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8cbd2c1900904b124b505a441160539c\",\n \"score\": \"0.6212922\",\n \"text\": \"def rename(lib: 'Library', *args):\\n logger.debug('rename(%r, %r)', lib, args)\\n parser = argparse.ArgumentParser(prog=\\\"dantalian rename\\\", add_help=False)\\n parser.add_argument('file')\\n parser.add_argument('new')\\n args = parser.parse_args(args)\\n lib.rename(args.file, args.new)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9af9ab2b578fcd5b9346c7c5de0226c\",\n \"score\": \"0.607795\",\n \"text\": \"def test_rename_config():\\r\\n sample_rename = f\\\"{os.path.dirname(sample)}/sample.rename\\\"\\r\\n shutil.copy(sample, sample_rename)\\r\\n runner = CliRunner()\\r\\n result = runner.invoke(cli.cli,\\r\\n ['-f', sample_rename, 'rename', 'server_cmd_1', 'server_cmd_rename'], input=\\\"y\\\")\\r\\n assert result.exit_code ==0\\r\\n os.remove(sample_rename)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e7d0f3380a1c374d188e092a3906f10\",\n \"score\": \"0.60744256\",\n \"text\": \"def fileRenamed(self, p_str, p_str_1, p_str_2): # real signature unknown; restored from __doc__\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12ac779fed12e80fa5bae1b1e640a148\",\n \"score\": \"0.6072542\",\n \"text\": \"def thing_rename(self, event):\\n\\n self.thing_rename_action()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8180873a589ada6b0d45a5efdc8ee694\",\n \"score\": \"0.606389\",\n \"text\": \"def renameFile(oldName, newName):\\n\\tos.rename(oldName, newName);\\n\\tprintHere (\\\"The file \\\" + oldName + \\\" has been renamed as \\\" + newName);\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d10953efb49b1355f7c72b37202cb8f\",\n \"score\": \"0.60150146\",\n \"text\": \"def test_invalid_commands_do_not_invoke_rename_function(capsys, sftp):\\n\\n # See tests/integration/test_close.py for details.\\n test_inputs = [\\n \\\"doesnotexist\\\",\\n \\\"lrename\\\", # No files\\n \\\"lrename onefile\\\",\\n \\\"lrename onefile twofile threefile\\\"\\n ]\\n for test in test_inputs:\\n with mock_input(test):\\n main_loop(sftp)\\n assert \\\"not recognized\\\" in capsys.readouterr().out\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0b1246f6842fd6395ef32f90ef4f83f\",\n \"score\": \"0.6009825\",\n \"text\": \"def _rename(src: str, dst: str):\\n if os.path.isfile(src):\\n os.path.isfile(dst) and os.unlink(dst)\\n os.rename(src, dst)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50ec76f7a6b1c3ed9dc54354b514a4ad\",\n \"score\": \"0.60048604\",\n \"text\": \"def testPut(self):\\n # XXX - not actually a unit test\\n expectedOutput = ('Transferred %s/testfile1 to %s/%s/test\\\"file2'\\n % (self.testDir, os.getcwd(), self.testDir))\\n def _checkPut(result):\\n self.assertFilesEqual(self.testDir + '/testfile1',\\n self.testDir + '/test\\\"file2')\\n self.failUnless(result.endswith(expectedOutput))\\n return self.runCommand('rm \\\"test\\\\\\\\\\\"file2\\\"')\\n\\n d = self.runCommand('put %s/testfile1 \\\"test\\\\\\\\\\\"file2\\\"'\\n % (self.testDir,))\\n d.addCallback(_checkPut)\\n d.addCallback(lambda _: self.failIf(\\n os.path.exists(self.testDir + '/test\\\"file2')))\\n return d\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4786b1e99d530e2652dd8efafb953aac\",\n \"score\": \"0.5983363\",\n \"text\": \"def rename(self, oldName, newName):\\r\\n os.rename( oldName, newName )\\r\\n return TRUE\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e13ca09208b25a20dee6592cdb31f43\",\n \"score\": \"0.59827757\",\n \"text\": \"def rename_command(*, login_manager: LoginManager, endpoint_id, source, destination):\\n transfer_client = login_manager.get_transfer_client()\\n autoactivate(transfer_client, endpoint_id, if_expires_in=60)\\n\\n res = transfer_client.operation_rename(\\n endpoint_id, oldpath=source, newpath=destination\\n )\\n formatted_print(res, text_format=FORMAT_TEXT_RAW, response_key=\\\"message\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a080f61aa2f8fb5055db9769d9446025\",\n \"score\": \"0.59646535\",\n \"text\": \"def edit(self, fileName, newFile):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f300d8a727dafa44435be280e7289f76\",\n \"score\": \"0.5956121\",\n \"text\": \"def _fancy_rename(oldname, newname):\\n if not os.path.exists(newname):\\n try:\\n os.rename(oldname, newname)\\n except OSError:\\n raise\\n return\\n\\n # destination file exists\\n try:\\n (fd, tmpfile) = tempfile.mkstemp(\\\".tmp\\\", prefix=oldname, dir=\\\".\\\")\\n os.close(fd)\\n os.remove(tmpfile)\\n except OSError:\\n # either file could not be created (e.g. permission problem)\\n # or could not be deleted (e.g. rude virus scanner)\\n raise\\n try:\\n os.rename(newname, tmpfile)\\n except OSError:\\n raise # no rename occurred\\n try:\\n os.rename(oldname, newname)\\n except OSError:\\n os.rename(tmpfile, newname)\\n raise\\n os.remove(tmpfile)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f616167b21ea3cf0d4dcd594e1767d9\",\n \"score\": \"0.59474653\",\n \"text\": \"def testWildcardPut(self):\\n def check(results):\\n self.assertEqual(results[0], '')\\n self.assertEqual(results[2], '')\\n self.assertFilesEqual(self.testDir + '/testRemoveFile',\\n self.testDir + '/../testRemoveFile',\\n 'testRemoveFile get failed')\\n self.assertFilesEqual(self.testDir + '/testRenameFile',\\n self.testDir + '/../testRenameFile',\\n 'testRenameFile get failed')\\n\\n d = self.runScript('cd ..',\\n 'put %s/testR*' % (self.testDir,),\\n 'cd %s' % os.path.basename(self.testDir))\\n d.addCallback(check)\\n return d\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d0a4bca54edeb9699c09bd9e94a70fd\",\n \"score\": \"0.5939229\",\n \"text\": \"def rename(self, source, target):\\n json = {\\n \\\"from\\\": source,\\n \\\"to\\\": target,\\n }\\n return self.transport.post(url=self.links[\\\"action:rename\\\"], json=json)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"171cc54590b63f074474dc522418c767\",\n \"score\": \"0.5933106\",\n \"text\": \"def rename_file(file_source, file_dest):\\n os.rename(file_source, file_dest)\\n print('File renamed from \\\"{}\\\" to \\\"{}\\\".'.format(file_source, file_dest))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"527ec6a433ba26a26c478646213cd996\",\n \"score\": \"0.59083015\",\n \"text\": \"def MoveFile(source, destination):\\n assert(len(source) > 0)\\n assert(len(destination) > 0)\\n # Check if we can use the atomic \\\"rename\\\" function: \\n logger = logging.getLogger(\\\"pynt.output\\\")\\n canuserename = True\\n if os.path.exists(destination):\\n # file already exists\\n # check if the permission of the two files are the same\\n srcstat = os.stat(source)\\n dststat = os.stat(destination)\\n try:\\n if srcstat.st_mode != dststat.st_mode:\\n os.chmod(source, dststat.st_mode)\\n if srcstat.st_uid != dststat.st_uid:\\n os.chown(source, dststat.st_uid, -1)\\n if srcstat.st_gid != dststat.st_gid:\\n os.chown(source, -1, dststat.st_gid)\\n except OSError:\\n canuserename = False\\n if canuserename:\\n # if so, do a move in the finder\\n logger.debug(\\\"Doing an atomic rename from %s to %s\\\" % (source, destination))\\n try:\\n # os.rename(source, destination)\\n # we use shutil.move instead of os.rename, since os.rename() can handle moving between two file systems\\n shutil.move(source, destination)\\n except OSError:\\n logger.debug(\\\"Caught OSError while renaming %s to %s\\\" % (source, destination))\\n canuserename = False\\n if not canuserename:\\n # read contents from file\\n \\n logger.debug(\\\"Doing a non-atomic rename from %s to %s\\\" % (source, destination))\\n infile = file(source, 'r+b')\\n contents = infile.read()\\n infile.close()\\n outfile = file(destination, 'w+b')\\n outfile.write(contents)\\n outfile.close()\\n try:\\n os.remove(source)\\n except OSError:\\n pass\\n # self.logger.debug(\\\"Could not delete temporary file %s\\\" % (source))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2cb386903c11563b52cc0131bc0baebc\",\n \"score\": \"0.59001464\",\n \"text\": \"def rename(self, new_name):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5317796a0865ba970c0c375668281b82\",\n \"score\": \"0.58949876\",\n \"text\": \"def _rename(view: View, defx: Defx, context: Context) -> None:\\n for target in context.targets:\\n path = target['action__path']\\n filename = cwd_input(\\n view._vim, defx._cwd,\\n ('New name: {} -> '.format(path)), path, 'file')\\n if not filename or filename == path:\\n continue\\n if os.path.exists(filename):\\n error(view._vim, '{} is already exists'.format(filename))\\n continue\\n\\n os.rename(path, filename)\\n\\n view.redraw(True)\\n view.search_file(filename, defx._index)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc29288ae1c6268344f5394c59745aa4\",\n \"score\": \"0.5873752\",\n \"text\": \"def test_rename_file(self):\\n filename = \\\"cute_doge.jpg\\\"\\n renamer = PathAndRename(\\\"images/\\\")\\n uuid_overwrite = \\\"9087assd9f87\\\"\\n with patch('apps.projects.models.uuid.uuid4', return_value=uuid_overwrite):\\n new_path = renamer(None, filename)\\n self.assertEqual(new_path, f'images/{uuid_overwrite}.jpg')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43eaa4e3bbea2426d86c348360c67f57\",\n \"score\": \"0.58690107\",\n \"text\": \"def put(source, target):\\n return client.put_file(source, target)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d73137a541c876ef558b3a10e9e0a906\",\n \"score\": \"0.58486813\",\n \"text\": \"def test_rename_according_to_time_and_date():\\n files = get_files(1)\\n now = time.time()\\n data_source_func = partial(get_files_names, files)\\n data = get_file_rename_data(data_source_func, 0, False)\\n # tempfile would fail on attemp to rename, don't perform actual rename\\n rename_according_to_time_and_date(data, False)\\n\\n files = get_files(5)\\n now = time.time()\\n data_source_func = partial(get_files_names, files)\\n data = get_file_rename_data(data_source_func, 0, False)\\n # tempfile would fail on attemp to rename, don't perform actual rename\\n rename_according_to_time_and_date(data, False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea05c67cb419f8d40b8e22f4aba50c80\",\n \"score\": \"0.5841246\",\n \"text\": \"def test_mv_noop(self):\\n fs = self.make_one()\\n fs.open('foo', 'wb').write(b'Hello!')\\n fs.mv('foo', 'foo')\\n self.assertEqual(fs.open('foo', 'rb').read(), b'Hello!')\\n transaction.commit()\\n self.assertEqual(fs.open('foo', 'rb').read(), b'Hello!')\\n\\n fs.mv('foo', 'foo')\\n self.assertEqual(fs.open('foo', 'rb').read(), b'Hello!')\\n transaction.commit()\\n self.assertEqual(fs.open('foo', 'rb').read(), b'Hello!')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5af2e4c9bde6bfa532797a0ce9b4903f\",\n \"score\": \"0.5838941\",\n \"text\": \"def main():\\n script_dir = os.path.dirname(os.path.realpath(__file__))\\n for filename in os.listdir(script_dir):\\n basename, extension = os.path.splitext(filename)\\n if basename.startswith(\\\"Test\\\") and extension == '.park':\\n source_path = os.path.join(script_dir, filename)\\n dest_path = os.path.join(script_dir, basename)\\n sys.stdout.write(\\\"renaming {} to {}\\\\n\\\".format(\\n source_path, dest_path))\\n os.rename(source_path, dest_path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"732413ec04e51aee12c676abc0110bd3\",\n \"score\": \"0.582419\",\n \"text\": \"def rename(trial, param):\\n param.name = self.new_name\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9768cc00a52480f4ba43faac979814eb\",\n \"score\": \"0.581262\",\n \"text\": \"def rename(self, old, new):\\n assert not self._readonly\\n return os.rename(self._relpath(old), self._relpath(new))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44ec31e32fb5cdd5f0bed160b6e5017b\",\n \"score\": \"0.5810397\",\n \"text\": \"def rename(self, src, dst):\\n raise UnsupportedError(\\\"rename resource\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d52ec75e01b5d3910589b1c114055cd6\",\n \"score\": \"0.5805513\",\n \"text\": \"def rename(self, path, new_path):\\n raise NotImplementedError('FileSystem.rename')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3fe8885e9834d38d7e8c89281c239f6a\",\n \"score\": \"0.5803796\",\n \"text\": \"def test_filesystem_rename(self):\\n pool_name = p_n()\\n pool_path = make_test_pool(pool_name, DISKS[0:1])\\n\\n fs_name = fs_n()\\n make_test_filesystem(pool_path, fs_name)\\n\\n fs_name_rename = fs_n()\\n\\n (_, return_code, _) = StratisDbus.fs_rename(fs_name, fs_name_rename)\\n self.assertEqual(return_code, dbus.UInt16(0))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c13051c9176e01ea84668921f5baa23\",\n \"score\": \"0.57980865\",\n \"text\": \"def Rename(oldpath, newpath, overwrite=False):\\n success = pywrapfile.File.Rename(oldpath, newpath, overwrite)\\n if not success:\\n raise GOSError(\\\"Failed to rename: %s to %s\\\" % (oldpath, newpath))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd8725e297c5518646e73fac9e5cabfc\",\n \"score\": \"0.57819134\",\n \"text\": \"def r_rename(old, new):\\n with closing(connections[env.host_string].open_sftp()) as ftp:\\n return ftp.rename (_r_normalizepath( old ), _r_normalizepath( new ))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5993630b3d503e3891c29ab3caaae17d\",\n \"score\": \"0.5762428\",\n \"text\": \"def test_write_dynamic_file_names(self):\\n skel = DynamicFileName(baz=\\\"replaced-name\\\")\\n with TempDir() as tmp_dir:\\n skel.write(tmp_dir.path)\\n self.assertEqual(\\n open(tmp_dir.join('foo.txt')).read().strip(),\\n 'foo'\\n )\\n self.assertEqual(\\n open(tmp_dir.join('bar/replaced-name.txt')).read().strip(),\\n 'baz'\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de22199919fc7dca8b56a4713d17dfe5\",\n \"score\": \"0.57378876\",\n \"text\": \"def rename_file(self, old_path, new_path):\\n self.log.debug(\\\"MongoContents.GenericManager: Init rename of '%s' to '%s'\\\", old_path, new_path)\\n if self.file_exists(new_path) or self.dir_exists(new_path):\\n self.already_exists(new_path)\\n elif self.file_exists(old_path) or self.dir_exists(old_path):\\n self.log.debug(\\\"MongoContents.GenericManager: Actually renaming '%s' to '%s'\\\", old_path,\\n new_path)\\n self.fs.mv(old_path, new_path)\\n else:\\n self.no_such_entity(old_path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"994768cef5357b843ad0991706946b44\",\n \"score\": \"0.57122236\",\n \"text\": \"def do_rename(self, remote_path):\\n\\t\\tremote_src_dest=remote_path.split(\\\" \\\")\\n\\t\\tif len(remote_src_dest)!=2:\\n\\t\\t\\tprint \\\"Rename command expects exactly two arguments\\\"\\n\\t\\telse:\\n\\t\\t\\ttry: \\n\\t\\t\\t\\tresponse=self._perform_ftp_command('rename',remote_src_dest[0], remote_src_dest[1])\\n print (\\\"Renamed: \\\" + remote_src_dest[0] + \\\" -> \\\" + remote_src_dest[1])\\n\\t\\t\\texcept IOError as e:\\n print(\\\"Failure\\\")\\n\\t\\t\\t\\tprint(e)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04633e9fc909cd703e7c1b6827247429\",\n \"score\": \"0.5693519\",\n \"text\": \"def rename(from_segments, to_segments):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a6f69b49c7e91aead5105fad97309119\",\n \"score\": \"0.56745493\",\n \"text\": \"def mv(source, destination):\\n for filename in source:\\n click.echo('Moving {} to {}'.format(filename, destination))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f9c436dbe7974bf2e8940375c3f55e53\",\n \"score\": \"0.56428856\",\n \"text\": \"def effect_rename(self, old_name: str, new_name: str):\\n data = {\\\"write\\\": {\\\"command\\\": \\\"rename\\\",\\n \\\"animName\\\": old_name,\\n \\\"newName\\\": new_name}}\\n self.__put(\\\"effects\\\", data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a32fa8b8f1d24a5ea167365fc90e165\",\n \"score\": \"0.5632927\",\n \"text\": \"def thing_rename_action(self):\\n\\n thing_name = self.patch.things.names[self.selected_index]\\n new_name = wx.GetTextFromUser('Enter a new name for ' + thing_name, caption='Change name',\\n default_value=thing_name, parent=self)\\n\\n if new_name != '':\\n self.undo_add()\\n\\n self.patch.things.names[self.selected_index] = new_name\\n\\n self.update_properties()\\n self.is_modified(True)\\n\\n parent = self.GetParent()\\n parent.editor_windows[windows.MAIN_TOOL_STATES].update_filterlist()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"031b7a1520571d0b182a069ba70c9d09\",\n \"score\": \"0.56318015\",\n \"text\": \"def main():\\n print(\\\"Starting directory is: {}\\\".format(os.getcwd()))\\n os.chdir('Lyrics/Christmas')\\n print(\\\"Files in {}:\\\\n{}\\\\n\\\".format(os.getcwd(), os.listdir('.')))\\n\\n for filename in os.listdir('.'):\\n for count, character in enumerate(filename):\\n try:\\n if filename[count].islower() and filename[count + 1].isupper():\\n filename = filename[:count + 1] + \\\"_\\\" + filename[count + 1:]\\n if filename[count].isdigit() and filename[count+1].isalpha():\\n filename = filename[:count + 1] + \\\"_\\\" + filename[count + 1:]\\n except IndexError:\\n pass\\n\\n new_name = get_fixed_filename(filename)\\n print(\\\"Renaming {} to {}\\\".format(filename, new_name))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad99cb37294915723003400a6cb50f71\",\n \"score\": \"0.5616454\",\n \"text\": \"def notify_rename(self, ea, new_name):\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45613e39d5089f78f259c1a4333e5018\",\n \"score\": \"0.5612399\",\n \"text\": \"def rename(src):\\n index = 0; dst = os.path.normpath(src)\\n while os.path.exists(dst):\\n index += 1\\n dst = \\\"%s.%d\\\"%(os.path.normpath(src),index)\\n if index > 0:\\n print >> sys.stderr, \\\"Warning: %s already exists, renaming the existing one to %s and continuing...\\\"%(src,dst)\\n os.renames(src,dst)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5310a1f1944b5ca6c8d886fe78ce26a\",\n \"score\": \"0.56091917\",\n \"text\": \"def rename(self, src, dst):\\n params = {'op': 'RENAME', 'destination': dst}\\n r = requests.put(\\\"%s%s\\\" % (self.namenode_url, src), params=params)\\n if r.status_code == 200:\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c450006c000d70e19974f64b84549618\",\n \"score\": \"0.56062555\",\n \"text\": \"def test_autorename():\\n input_filenames = [1, \\\"apple\\\"]\\n dest = create_env([])\\n\\n result = Pipe(input_filenames, dest, output_suffix=\\\".pdf\\\", autoname=True)(\\n touch_output\\n )\\n\\n expected = [\\n \\\"c4ca4238a0b923820dcc509a6f75849b.pdf\\\",\\n \\\"1f3870be274f6c49b3e31a0c6728957f.pdf\\\",\\n ]\\n\\n assert result == expected\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db942329b6ed8561a79b63236b7d821c\",\n \"score\": \"0.5604357\",\n \"text\": \"def test_write_utf8_file_fails_at_rename(\\n tmpdir: py.path.local, caplog: pytest.LogCaptureFixture\\n) -> None:\\n test_dir = tmpdir.mkdir(\\\"files\\\")\\n test_file = Path(test_dir / \\\"test.json\\\")\\n\\n with pytest.raises(WriteError), patch(\\n \\\"homeassistant.util.file.os.replace\\\", side_effect=OSError\\n ):\\n write_utf8_file(test_file, '{\\\"some\\\":\\\"data\\\"}', False)\\n\\n assert not os.path.exists(test_file)\\n\\n assert \\\"File replacement cleanup failed\\\" not in caplog.text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f78efa74654a357b3a4c0347826e8cec\",\n \"score\": \"0.558254\",\n \"text\": \"def test_rename(self):\\n self.create_data()\\n self.assertEqual(Photo.objects.get(pk=1).name, 'photo1')\\n # invalid photo, redisplay\\n data = {'name': 'dd'*500} # name is too long\\n self.json_post_value(\\n reverse('photo_rename', kwargs=dict(pk=1)), 'html', data)\\n # valid photo, redirect\\n data = {'name': 'new-name'}\\n self.json_post_value(\\n reverse('photo_rename', kwargs=dict(pk=1)), 'url', data)\\n self.assertEqual(Photo.objects.get(pk=1).name, 'new-name')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"875f1d7fb7591ffab03194a2822d0c7a\",\n \"score\": \"0.5579691\",\n \"text\": \"def write2newfile(self, fname, s):\\n if os.path.exists(fname):\\n for i in range(100):\\n new_fname = '%s,%s' % (fname, i)\\n if not os.path.exists(new_fname):\\n os.rename(fname, new_fname)\\n print('renamed old file to %s' % new_fname)\\n break\\n self.write2file(fname, s)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42f1e70972ced709ef761ff0b36313b0\",\n \"score\": \"0.556879\",\n \"text\": \"def put(self, group, group_root, path=\\\"\\\"):\\n parser = reqparse.RequestParser()\\n parser.add_argument('rename_to', type=str, required=True)\\n args = parser.parse_args()\\n\\n new_name = args['rename_to']\\n file_path = os.path.join(group_root, path)\\n if not os.path.exists(file_path):\\n abort(400, \\\"%s does not exist\\\" % path)\\n\\n new_path = os.path.join(os.path.dirname(file_path), new_name)\\n if os.path.exists(new_path):\\n abort(400, (\\\"%s already exists at %s\\\"\\n % (new_name, os.path.dirname(path))))\\n self.rename(file_path, new_name)\\n return success(\\\"Successfully renamed to %s\\\" % args['rename_to'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d19dabd20c0b709edd4cb93bcf2d877\",\n \"score\": \"0.5558562\",\n \"text\": \"def rename_file(src, dest):\\n try:\\n if os.path.exists(src):\\n shutil.move(src, dest)\\n logging.info(\\\"moved tmp file \\\" + src + \\\" to \\\" + dest)\\n return {'success': True}\\n except Exception as e:\\n logging.error(\\\"Cannot rename file \\\"+ src + \\\" to \\\"+ dest)\\n logging.exception(e)\\n return {'success' : False}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4157b147b0868b17ebce7df7318deb20\",\n \"score\": \"0.55525565\",\n \"text\": \"def renamer(old, new):\\n\\n try:\\n mkdirs(os.path.dirname(new))\\n os.rename(old, new)\\n except OSError:\\n mkdirs(os.path.dirname(new))\\n os.rename(old, new)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e6336893bfcd8b6a1d1117af9f31805\",\n \"score\": \"0.5551752\",\n \"text\": \"def test_rename_fail(cleandir, fake_db):\\n cmd = commands.Rename(['bad_funk', 'NEW'])\\n with pytest.raises(errors.FunkNotDefinedError):\\n cmd()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cab392345a418d984244217c6fb8fb32\",\n \"score\": \"0.5538901\",\n \"text\": \"def rename_command(new_path, old_path, endpoint_id):\\n client = get_client()\\n autoactivate(client, endpoint_id, if_expires_in=60)\\n\\n res = client.operation_rename(endpoint_id, oldpath=old_path,\\n newpath=new_path)\\n\\n if outformat_is_json():\\n print_json_response(res)\\n else:\\n safeprint(res['message'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a263643fcf54e5553c9d827b993ee528\",\n \"score\": \"0.55375606\",\n \"text\": \"def _mv(src, dest, logstream=None):\\n assert ' ' not in src and ' ' not in dest,\\\\\\n \\\"_mv: can't handle paths in spaces: src=%r, dest=%r\\\" % (src, dest)\\n if sys.platform == \\\"win32\\\":\\n _run(\\\"move %s %s\\\" % (src, dest), logstream=logstream)\\n else:\\n _run(\\\"mv %s %s\\\" % (src, dest), logstream=logstream)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ffcad37aa0221042a90048667fc21853\",\n \"score\": \"0.5529329\",\n \"text\": \"def _correct_name(self, action, obj_name):\\n action_name = action.name.split('.')[0]\\n correct_name = '{0}.{1}'.format(action_name, obj_name)\\n if action.name != correct_name:\\n action.name = correct_name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"828e56417de3037faa99c093755f9772\",\n \"score\": \"0.55150414\",\n \"text\": \"def test_file_move(self):\\n try:\\n source_url = saga.url(\\\"file:///\\\"+self.filename_a)\\n target_url = saga.url(\\\"file:///\\\"+self.filename_d)\\n \\n my_file = saga.filesystem.file(source_url)\\n my_file.move(target_url)\\n my_file.close()\\n \\n except saga.exception, e: \\n self.fail(e.get_full_message())\\n\\n self.failUnless(os.path.exists(self.filename_d))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"56d8975a775dd6aa4ce0ce9f65906a29\",\n \"score\": \"0.5489117\",\n \"text\": \"def rename_handler(event):\\n if not event:\\n return\\n if 'target' not in event:\\n print('ERROR: %s: missing \\\"target\\\" in %s event' % (sys.argv[0], event['type']))\\n return\\n if 'newname' not in event:\\n print('ERROR: %s: missing \\\"newname\\\" in %s event' % (sys.argv[0], event['type']))\\n return\\n\\n write_event_log(rename_event_tmpl % event)\\n\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f03ac6eb56d106852ae0ee25b867c9df\",\n \"score\": \"0.54791427\",\n \"text\": \"def rename(self, srcfile, destfile):\\n\\n # Don't rename until all are ready\\n self.barrier()\\n\\n # We'll capture any exception in this variable\\n err = None\\n\\n # Rank 0 renames the file.\\n if self.rank == 0:\\n try:\\n if os.path.exists(srcfile):\\n os.rename(srcfile, destfile)\\n except Exception as e:\\n err = e\\n\\n # Verify that the rename succeeded\\n self.allraise_if(err)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20bdc4f0062dcbe2bdfc74b45fa0f925\",\n \"score\": \"0.54716074\",\n \"text\": \"def renames(old: str, new: str) -> None:\\n if isfile(old):\\n makedirs(dirname(new))\\n elif isdir(old):\\n makedirs(dirname(dirname(new))) # return parent directory name of dir which is passed as new argument\\n os.rename(old, new)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60947f1f0ad6b402478a7751a9ebc24f\",\n \"score\": \"0.54592294\",\n \"text\": \"def mv(src, dst):\\n # move the file\\n try:\\n shutil.move(src, dst)\\n except:\\n print(\\\"filetools.mv: Unable to move {:s} to {:s}\\\".format(src, dst))\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b35257c04f4b4d9618ec3e858f0898bb\",\n \"score\": \"0.54465586\",\n \"text\": \"def rename(self, pfn, new_pfn):\\n try:\\n bucket, key = self.get_bucket_key(pfn)\\n if key is None:\\n raise exception.SourceNotFound('Cannot get the source key from S3')\\n bucket_name, key_name = self.get_bucket_key_name(new_pfn)\\n key.copy(bucket_name, key_name)\\n key.delete()\\n except exception.SourceNotFound as e:\\n raise exception.SourceNotFound(e)\\n except boto.exception.S3ResponseError as e:\\n if e.status in [404, 403]:\\n raise exception.DestinationNotAccessible(e)\\n else:\\n raise exception.ServiceUnavailable(e)\\n except Exception as e:\\n raise exception.ServiceUnavailable(e)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ece82843038df31bb3a6b09ec990f732\",\n \"score\": \"0.5437126\",\n \"text\": \"def test_filesystem_rename_same_name(self):\\n pool_name = p_n()\\n pool_path = make_test_pool(pool_name, DISKS[0:1])\\n\\n fs_name = fs_n()\\n make_test_filesystem(pool_path, fs_name)\\n\\n (_, return_code, _) = StratisDbus.fs_rename(fs_name, fs_name)\\n self.assertEqual(return_code, dbus.UInt16(0))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ec583ce483376951bd4ded0d4633ea4\",\n \"score\": \"0.5421914\",\n \"text\": \"def mv(self, from_path, to_path):\\n self.cli.file_move(self.path + \\\"/\\\" + from_path, self.path + \\\"/\\\" + to_path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d389080f2b6932797026eb626a0d6fab\",\n \"score\": \"0.5420785\",\n \"text\": \"def test_patch_move(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f04f58844ed0e47d5f1049b29097150\",\n \"score\": \"0.5412139\",\n \"text\": \"def test_rename_default(self, client):\\n translation.activate('en')\\n root = Node.root()\\n r1 = root.add(\\\"r1\\\")\\n\\n r1.rename(\\\"rr1\\\")\\n\\n assert r1.paths.count() == 4 ## \\\"any\\\" gets added implicitly\\n assert r1.path == \\\"/rr1\\\"\\n translation.activate('nl')\\n assert r1.path == \\\"/rr1\\\"\\n translation.activate('fr')\\n assert r1.path == \\\"/rr1\\\"\\n translation.activate('any')\\n assert r1.path == \\\"/rr1\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"76182be220bb6676824aaa66a6d1b215\",\n \"score\": \"0.54071873\",\n \"text\": \"def rename(self, src, dst):\\n return self.execute_command('RENAME', src, dst)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf0b2847734525863ccfbc5558277f59\",\n \"score\": \"0.5391521\",\n \"text\": \"def renameFile (self, mainPath, oldFileName, newFileName, extFile, \\n project=0, update_location=False):\\n RCI.instance().renameFileTests( filePath=mainPath, \\n fileName=oldFileName, \\n fileExt=extFile, \\n fileProject=project,\\n newName=newFileName,\\n update_location=update_location )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a22355c9693c80dbef3d0fdb581a5727\",\n \"score\": \"0.5384263\",\n \"text\": \"def move_and_rename(source, destination, remove_old):\\r\\n if remove_old:\\r\\n try:\\r\\n if destination.exists():\\r\\n os.remove(str(destination))\\r\\n except PermissionError as e:\\r\\n print(e)\\r\\n\\r\\n output = True\\r\\n try:\\r\\n shutil.move(str(source), str(destination))\\r\\n except IOError as e:\\r\\n print(e)\\r\\n output = False\\r\\n except TypeError as e:\\r\\n print(e)\\r\\n output = False\\r\\n raise\\r\\n\\r\\n return output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"863cec51ed11c500e4bb935a614892de\",\n \"score\": \"0.5373303\",\n \"text\": \"def test_audiobook_name(self, os_walk, mkdir_p,\\n copyfile): # pylint: disable=W0621\\n assert self\\n assert mkdir_p # to appease pylint\\n\\n def single_file(filename, expected):\\n \\\"\\\"\\\" Test a single file \\\"\\\"\\\"\\n os_walk.return_value = [\\n # if os.walk returns a root with leading /, os.path.join\\n # doesn't add the filemanager base_dir to the path. This\\n # makes it a bit easier to write these tests\\n ('/foo', [], (filename,)),\\n ]\\n filemgr.copy_audiobook(\\\"source\\\")\\n copyfile.assert_called_with(os.path.join('/foo', filename),\\n os.path.join('/foo', expected))\\n\\n copy_base = 'copy_base'\\n filemgr = pyres.filemanager.FileManager(base_dir=copy_base)\\n assert filemgr\\n # one digit\\n single_file('1one', '01one')\\n single_file('o1ne', 'o01ne')\\n single_file('one1', 'one01')\\n\\n # two digits\\n single_file('11two', '11two')\\n single_file('t11wo', 't11wo')\\n single_file('two11', 'two11')\\n\\n # three digits\\n single_file('123three', '123three')\\n single_file('th123ree', 'th123ree')\\n single_file('three123', 'three123')\\n\\n # mp3 extension not changed\\n single_file('1baz.mp3', '01baz.mp3')\\n single_file('b1az.mp3', 'b01az.mp3')\\n single_file('baz1.mp3', 'baz01.mp3')\\n\\n # test multiple substitutions\\n single_file('1b1a1z.1mp3', '01b01a01z.01mp3')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0048ec58a9204054c7a32a32d0bd222\",\n \"score\": \"0.5367443\",\n \"text\": \"def test_substitute(tmpdir, td):\\n pytest.debug_func()\\n q = editor.editor(td.filename.strpath)\\n assert K[\\\"orig_l\\\"] == q.buffer\\n q.sub(K[\\\"lowe\\\"], K[\\\"uppE\\\"])\\n q.quit()\\n backup = py.path.local(q.backup_filename())\\n assert backup.exists()\\n assert td.filename.exists()\\n assert written_format(K[\\\"orig_l\\\"]) == backup.read()\\n exp = written_format([_.replace(K[\\\"lowe\\\"], K[\\\"uppE\\\"])\\n for _ in K[\\\"orig_l\\\"]])\\n assert exp == td.filename.read()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc23c2ef7ee93c76487b58cc87644c6e\",\n \"score\": \"0.5360535\",\n \"text\": \"def rename(old_file_name, old_format):\\n old_file = old_file_name + old_format\\n check_file = check_existing_file(old_file)\\n if check_file:\\n print \\\"\\\\nThe file is available\\\\n\\\"\\n\\n print \\\"\\\\nEnter the new file name: \\\"\\n new_name = sys.stdin.readline()\\n new_name = new_name.lower().strip()\\n\\n print \\\"\\\\nEnter the new format: \\\"\\n new_format = sys.stdin.readline()\\n new_format = new_format.lower().strip()\\n\\n new_file = new_name + new_format\\n\\n if new_file != \\\"\\\":\\n if format == new_format:\\n os.rename(old_file, new_file)\\n else:\\n container = read_file(old_file)\\n if old_format == '.html':\\n query = re.search(r'(?<=<div style=\\\"display: None;\\\">)[* \\\\w\\\\d+]+', container).group()\\n else:\\n os.rename(old_file,new_file)\\n else:\\n print \\\"The file does not exist. You can use this name.\\\\n\\\\n\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2aba1bbba45ca817395e10bc6027a4ac\",\n \"score\": \"0.5354556\",\n \"text\": \"def test_another(tmpdir, td, fx_chdir):\\n pytest.debug_func()\\n q = editor.editor(td.basename)\\n other = tmpdir.join(K[\\\"altfile\\\"])\\n q.sub(K[\\\"test\\\"], K[\\\"frib\\\"])\\n q.quit(filepath=other.strpath)\\n edited = [x.replace(K[\\\"test\\\"], K[\\\"frib\\\"]) for x in K[\\\"orig_l\\\"]]\\n assert q.backup_filename() is None\\n assert td.filename.exists()\\n assert other.exists()\\n assert written_format(K[\\\"orig_l\\\"]) == td.filename.read()\\n assert written_format(edited) == other.read()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c49bcfe0265eae2991e6467d6938f17f\",\n \"score\": \"0.5354362\",\n \"text\": \"def testChangesTakeEffect(self):\\n newName = 'Kathpakalaxmikanthan'\\n body = {'name': newName}\\n response = self.fluiddb.db('PUT', ['users', 'testuser1'], body)\\n userID = self.fluiddb.users['testuser1'].get().value['id']\\n response = self.fluiddb.objects[userID]['fluiddb/users/name'].get()\\n self.assertEqual(newName, response.value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92b1e65a116f636ab81e55557d897911\",\n \"score\": \"0.5350848\",\n \"text\": \"def setName(self, newName):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c8deda9a61cdc6e73e230a232321faa\",\n \"score\": \"0.53483593\",\n \"text\": \"def ev_rename(self, ea, new_name):\\n self.__on_rename(ea, new_name)\\n return super(IdaReIDPHooks, self).ev_rename(ea, new_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f98384b4695a39f459a7895c1c226aa\",\n \"score\": \"0.53336656\",\n \"text\": \"def edit_fname(self, case):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1639cc3018a55b6018859dceb90a1dc6\",\n \"score\": \"0.5325321\",\n \"text\": \"def mv(self, from_path, to_path):\\n os.replace(from_path, to_path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c198eee40a394c9510a0cfa2db670d23\",\n \"score\": \"0.53162706\",\n \"text\": \"def modify_name(roi,which,name):\\n manager,index = roi.mapper(which)\\n source = roi.get_source(which)\\n\\n manager.names[index]=name\\n source.name=name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae5e04b8cf5da95f6183f841b9ceea21\",\n \"score\": \"0.5304809\",\n \"text\": \"def change_filename(self, filepath, entities):\\n exts = Path(filepath).suffixes\\n old_ext = \\\"\\\"\\n for ext in exts:\\n old_ext += ext\\n\\n suffix = entities['suffix']\\n entity_file_keys = []\\n file_keys = ['task', 'acquisition', 'direction',\\n 'reconstruction', 'run']\\n\\n for key in file_keys:\\n if key in list(entities.keys()):\\n entity_file_keys.append(key)\\n\\n sub = get_key_name(filepath, 'sub')\\n ses = get_key_name(filepath, 'ses')\\n sub_ses = sub + '_' + ses\\n\\n if 'run' in list(entities.keys()) and 'run-0' in filepath:\\n entities['run'] = '0' + str(entities['run'])\\n\\n filename = \\\"_\\\".join([\\\"{}-{}\\\".format(key, entities[key])\\n for key in entity_file_keys])\\n filename = filename.replace('acquisition', 'acq') \\\\\\n .replace('direction', 'dir') \\\\\\n .replace('reconstruction', 'rec')\\n if len(filename) > 0:\\n filename = sub_ses + '_' + filename + '_' + suffix + old_ext\\n else:\\n filename = sub_ses + filename + '_' + suffix + old_ext\\n\\n # CHECK TO SEE IF DATATYPE CHANGED\\n dtypes = ['anat', 'func', 'perf', 'fmap', 'dwi']\\n old = ''\\n for dtype in dtypes:\\n if dtype in filepath:\\n old = dtype\\n\\n if 'datatype' in entities.keys():\\n dtype = entities['datatype']\\n if entities['datatype'] != old:\\n print(\\\"WARNING: DATATYPE CHANGE DETECETD\\\")\\n else:\\n dtype = old\\n new_path = str(self.path) + '/' + sub + '/' + ses \\\\\\n + '/' + dtype + '/' + filename\\n\\n # add the scan path + new path to the lists of old, new filenames\\n self.old_filenames.append(filepath)\\n self.new_filenames.append(new_path)\\n\\n # NOW NEED TO RENAME ASSOCIATIONS\\n # bids_file = self.layout.get_file(filepath)\\n bids_file = filepath\\n # associations = bids_file.get_associations()\\n associations = self.get_nifti_associations(str(bids_file))\\n for assoc_path in associations:\\n # assoc_path = assoc.path\\n if Path(assoc_path).exists():\\n # print(\\\"FILE: \\\", filepath)\\n # print(\\\"ASSOC: \\\", assoc.path)\\n # ensure assoc not an IntendedFor reference\\n if '.nii' not in str(assoc_path):\\n self.old_filenames.append(assoc_path)\\n new_ext_path = img_to_new_ext(new_path,\\n ''.join(Path(assoc_path)\\n .suffixes))\\n self.new_filenames.append(new_ext_path)\\n\\n # MAKE SURE THESE AREN'T COVERED BY get_associations!!!\\n if '/dwi/' in filepath:\\n # add the bval and bvec if there\\n if Path(img_to_new_ext(filepath, '.bval')).exists() \\\\\\n and img_to_new_ext(filepath, '.bval') \\\\\\n not in self.old_filenames:\\n self.old_filenames.append(img_to_new_ext(filepath,\\n '.bval'))\\n self.new_filenames.append(img_to_new_ext(new_path,\\n '.bval'))\\n\\n if Path(img_to_new_ext(filepath, '.bvec')).exists() \\\\\\n and img_to_new_ext(filepath, '.bvec') \\\\\\n not in self.old_filenames:\\n self.old_filenames.append(img_to_new_ext(filepath,\\n '.bvec'))\\n self.new_filenames.append(img_to_new_ext(new_path,\\n '.bvec'))\\n\\n # now rename _events and _physio files!\\n old_suffix = parse_file_entities(filepath)['suffix']\\n scan_end = '_' + old_suffix + old_ext\\n\\n if '_task-' in filepath:\\n old_events = filepath.replace(scan_end, '_events.tsv')\\n old_ejson = filepath.replace(scan_end, '_events.json')\\n if Path(old_events).exists():\\n self.old_filenames.append(old_events)\\n new_scan_end = '_' + suffix + old_ext\\n new_events = new_path.replace(new_scan_end, '_events.tsv')\\n self.new_filenames.append(new_events)\\n if Path(old_ejson).exists():\\n self.old_filenames.append(old_ejson)\\n new_scan_end = '_' + suffix + old_ext\\n new_ejson = new_path.replace(new_scan_end, '_events.json')\\n self.new_filenames.append(new_ejson)\\n\\n old_physio = filepath.replace(scan_end, '_physio.tsv.gz')\\n if Path(old_physio).exists():\\n self.old_filenames.append(old_physio)\\n new_scan_end = '_' + suffix + old_ext\\n new_physio = new_path.replace(new_scan_end, '_physio.tsv.gz')\\n self.new_filenames.append(new_physio)\\n\\n # RENAME INTENDED FORS!\\n ses_path = self.path + '/' + sub + '/' + ses\\n for path in Path(ses_path).rglob(\\\"fmap/*.json\\\"):\\n self.IF_rename_paths.append(str(path))\\n # json_file = self.layout.get_file(str(path))\\n # data = json_file.get_dict()\\n data = get_sidecar_metadata(str(path))\\n if data == \\\"Erroneous sidecar\\\":\\n print('Error parsing sidecar: ', str(path))\\n continue\\n\\n if 'IntendedFor' in data.keys():\\n # check if IntendedFor field is a str or list\\n if isinstance(data['IntendedFor'], str):\\n if data['IntendedFor'] == \\\\\\n _get_intended_for_reference(filepath):\\n # replace old filename with new one (overwrite string)\\n data['IntendedFor'] = \\\\\\n _get_intended_for_reference(new_path)\\n\\n # update the json with the new data dictionary\\n _update_json(str(path), data)\\n\\n if isinstance(data['IntendedFor'], list):\\n for item in data['IntendedFor']:\\n if item in _get_intended_for_reference(filepath):\\n\\n # remove old filename\\n data['IntendedFor'].remove(item)\\n # add new filename\\n data['IntendedFor'].append(\\n _get_intended_for_reference(new_path))\\n\\n # update the json with the new data dictionary\\n _update_json(str(path), data)\\n\\n # save IntendedFor purges so that you can datalad run the\\n # remove association file commands on a clean dataset\\n # if self.use_datalad:\\n # if not self.is_datalad_clean():\\n # self.datalad_save(message=\\\"Renamed IntendedFors\\\")\\n # self.reset_bids_layout()\\n # else:\\n # print(\\\"No IntendedFor References to Rename\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7763449f66409706e06e349ef5e8210\",\n \"score\": \"0.52938694\",\n \"text\": \"def safe_rename(filename):\\n old_filename = filename + '.old'\\n try:\\n shutil.move(filename, old_filename)\\n print \\\"Renamed existing file from %s to %s\\\" % (filename, old_filename)\\n except IOError, exc:\\n if exc.errno != errno.ENOENT:\\n charlimit_textwrap(exc.message)\\n raise RuntimeError('ERROR: Failed to rename %s to %s' % (filename, old_filename))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87e41c074ec408e0b3d10c861372ebb6\",\n \"score\": \"0.52835345\",\n \"text\": \"def test_write_utf8_file_fails_at_rename_and_remove(\\n tmpdir: py.path.local, caplog: pytest.LogCaptureFixture\\n) -> None:\\n test_dir = tmpdir.mkdir(\\\"files\\\")\\n test_file = Path(test_dir / \\\"test.json\\\")\\n\\n with pytest.raises(WriteError), patch(\\n \\\"homeassistant.util.file.os.remove\\\", side_effect=OSError\\n ), patch(\\\"homeassistant.util.file.os.replace\\\", side_effect=OSError):\\n write_utf8_file(test_file, '{\\\"some\\\":\\\"data\\\"}', False)\\n\\n assert \\\"File replacement cleanup failed\\\" in caplog.text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f9f8ebe29c4ba932129f4678e0a4d57\",\n \"score\": \"0.5277333\",\n \"text\": \"def rename(self, pfn, new_pfn):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9111ece2a7536af991f856c75bd610ae\",\n \"score\": \"0.5268497\",\n \"text\": \"def rollName(self, arg, dirname, fnames):\\n\\t\\tdirname = self.changeName(\\\"\\\", dirname)\\n\\n\\t\\tfor file in fnames:\\n\\t\\t\\tfullname = os.path.join(dirname, file)\\n\\n\\t\\t\\tif string.find(fullname, \\\"images/\\\") > 0 :\\n\\t\\t\\t\\t# don't change image files names\\n\\t\\t\\t\\tcontinue\\n\\n\\t\\t\\tif os.path.isfile(fullname):\\n\\t\\t\\t\\tnewname = self.changeName(\\\"\\\", fullname)\\n\\t\\t\\t\\tself.update(newname, newname)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17c0d6a465541542a25959bab123b21e\",\n \"score\": \"0.526777\",\n \"text\": \"def move_file(filename, old_path, new_path):\\n if not os.path.exists(new_path):\\n os.makedirs(new_path)\\n og_loc = os.path.join(old_path, filename)\\n new_loc = os.path.join(new_path, filename)\\n shutil.move(og_loc, new_loc)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"645cae768d841f4e7d7ccb828b53728f\",\n \"score\": \"0.5259965\",\n \"text\": \"def rename(self, fromname: str, toname: str) -> bool:\\n self.instance.rename(fromname, toname)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f03d3e8284e4dc66263978b2d6e67123\",\n \"score\": \"0.5259022\",\n \"text\": \"def test_modify_task_name(self):\\n lastname = self.taskbody[\\\"name\\\"]\\n self.taskbody[\\\"name\\\"] = \\\"newname\\\"\\n self.taskbody[\\\"id\\\"] = self.taskid\\n response =self.client.modify_task(job_data=json.dumps(self.taskbody))\\n logger.info(response)\\n if isinstance(self.check_schema(resp=response), str):\\n self.assertTrue(False, \\\"jsonschema check failed\\\")\\n self.assertEqual(response.get(\\\"code\\\"), 0, msg=\\\"expect code = 0\\\")\\n self.assertEqual(response.get(\\\"subCode\\\"), None, msg=\\\"expect subCode = Null\\\")\\n response = self.client.get_task(taskid=self.taskid)\\n logger.info(response)\\n self.assertEqual(response.get(\\\"data\\\").get(\\\"name\\\"), \\\"newname\\\",\\n msg=\\\"expect name = newname\\\")\\n self.taskbody[\\\"name\\\"] = lastname\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0aaf5c664df53e37dc35080189f4c96\",\n \"score\": \"0.5258637\",\n \"text\": \"def rename(self, ea, new_name):\\n self.__on_rename(ea, new_name)\\n return super(IdaReIDPHooks, self).rename(ea, new_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fbd27121713fd004b5c2713d35d64aec\",\n \"score\": \"0.52583665\",\n \"text\": \"def rename_cmd(args):\\n cmd = commands.Rename([args.args[0], 'NEW'])\\n return cmd\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5adaf7d96b58c731ba208278fe4a909a\",\n \"score\": \"0.5253459\",\n \"text\": \"def test_to_camel_case(source: str, expected: str):\\n assert expected == definitions.to_camel_case(source)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"16aa0af88505264c4897f0552ebbe2af\",\n \"score\": \"0.5249994\",\n \"text\": \"def rename(self, oldName, newName):\\r\\n return self.execCmd(\\\"svn rename \\\" + oldName + \\\" \\\" + newName)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9c90e453a0c66a793d9e7ed6fe01df4\",\n \"score\": \"0.5238427\",\n \"text\": \"def _rename_test(node, filename):\\n old_name = node.value\\n root = find_root(node)\\n def_statement = find_binding(old_name, root)\\n\\n if old_name in (\\\"None\\\",):\\n # why are these there?\\n return\\n\\n if not old_name.startswith(\\\"test_\\\"):\\n new_name = f\\\"test_{old_name}\\\"\\n\\n # def_statement can be None if the test doesn't exist.\\n # Could happen if it was referenced in multiple places;\\n # the first time we came across it we renamed it.\\n if def_statement is not None:\\n # Rename the function\\n def_statement.children[1].value = new_name\\n def_statement.children[1].changed()\\n\\n # Rename all references, including `node`\\n for n in root.leaves():\\n if n.type == TOKEN.NAME and n.value == old_name:\\n # Don't include dotted names\\n if (\\n n.parent.type == syms.trailer\\n and n.prev_sibling\\n and n.prev_sibling.value == '.'\\n ):\\n continue\\n\\n # This is probably a reference to the test function\\n # However, we need to check in case it's a separate local var.\\n # Figure out if we're in a function, and see if there's a binding for\\n # the same name\\n func_node = get_ancestor_of_type(n, syms.funcdef)\\n if func_node and find_binding(old_name, func_node):\\n # This is a local var with the same name, don't rename it\\n continue\\n\\n n.value = new_name\\n n.changed()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69698acb33eb0d1874be3e5b70360a5b\",\n \"score\": \"0.5238101\",\n \"text\": \"def move_file(source, destiny):\\n\\n try:\\n os.rename(source, destiny)\\n except FileExistsError:\\n base = os.path.basename(destiny)\\n name, ext = os.path.splitext(destiny)\\n alt_destiny = os.path.join(base, name + '_' + ext)\\n move_file(source, alt_destiny)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb32031456f021821021c17fd0b907ee\",\n \"score\": \"0.5237962\",\n \"text\": \"def test_filename_spaces(self):\\n temp_filename = spaced_filename = self.get_temp_filename()\\n spaced_filename += \\\" a filename with spaces.\\\" + self.extension\\n shutil.copyfile(self.raw_text_filename, spaced_filename)\\n self.compare_cli_output(\\n spaced_filename,\\n self.get_expected_filename(self.raw_text_filename),\\n )\\n os.remove(temp_filename)\\n os.remove(spaced_filename)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a9010e032c5157b120cc9d68d479b80\",\n \"score\": \"0.5222952\",\n \"text\": \"def _add_word_to_filename(file_path, word, to_path, copy=False):\\n if file_path is None:\\n raise ValueError(\\\"Input file does not exist\\\")\\n _to_path = os.path.join(to_path, word+'_'+os.path.basename(file_path))\\n if copy:\\n shutil.copyfile(file_path, _to_path)\\n else:\\n os.rename(file_path, _to_path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"94729dff72780fe643f015821426b2ba\",\n \"score\": \"0.5219495\",\n \"text\": \"def rename_file(self, old_filename, new_filename):\\n\\n old_key = self.subfolder + old_filename\\n new_key = self.subfolder + new_filename\\n\\n if old_key not in self.list_contents():\\n logger.exception(f\\\"File {old_filename} does not exist in path s3://{self.bucket_name}/{self.subfolder}\\\")\\n raise ValueError(f\\\"File {old_filename} does not exist in path s3://{self.bucket_name}/{self.subfolder}\\\")\\n\\n source_file = f\\\"{self.bucket_name}/{self.subfolder}{old_filename}\\\"\\n self.s3.Object(self.bucket, new_key).copy_from(CopySource=source_file)\\n self.s3.Object(self.bucket, old_key).delete()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5998384f143ef547146e4ede3da01864\",\n \"score\": \"0.52135426\",\n \"text\": \"def command_mv(user: UserMessages, m: MicroPythonRepl, args):\\n args.SRC.connect_repl(m)\\n args.DST.connect_repl(m)\\n args.SRC.rename(args.DST)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1196,"cells":{"query_id":{"kind":"string","value":"de97c516d14954426965ccc70629522e"},"query":{"kind":"string","value":"Do not return anything, modify s inplace instead."},"positive_passages":{"kind":"list like","value":[{"docid":"ad50449cb5ff4102482424c51fef92e2","score":"0.0","text":"def reverseWords(self, s: List[str]) -> None:\n if not s:\n return\n def reverse(s,start,end):\n i,j=start,end\n while i<j:\n s[i],s[j]=s[j],s[i]\n i+=1\n j-=1\n start,end=0,-1\n for i in range(len(s)):\n if i==len(s)-1:\n reverse(s,start,len(s)-1)\n elif s[i]!=\" \":\n end+=1\n else:\n reverse(s,start,end)\n start=i+1\n end=i\n return reverse(s,0,len(s)-1)","title":""}],"string":"[\n {\n \"docid\": \"ad50449cb5ff4102482424c51fef92e2\",\n \"score\": \"0.0\",\n \"text\": \"def reverseWords(self, s: List[str]) -> None:\\n if not s:\\n return\\n def reverse(s,start,end):\\n i,j=start,end\\n while i<j:\\n s[i],s[j]=s[j],s[i]\\n i+=1\\n j-=1\\n start,end=0,-1\\n for i in range(len(s)):\\n if i==len(s)-1:\\n reverse(s,start,len(s)-1)\\n elif s[i]!=\\\" \\\":\\n end+=1\\n else:\\n reverse(s,start,end)\\n start=i+1\\n end=i\\n return reverse(s,0,len(s)-1)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"31d79ffbb36821823f943aee39e09f63","score":"0.62795085","text":"def modifiable_chars(self, s):\n self.replace_in_range((self.x_modifiable, len(self._buffer)), s)","title":""},{"docid":"16389be01038261a31222f1a48d6bab5","score":"0.59871805","text":"def replace(self, s, a, b):\n # easy answer, there are much more fun ways to do this manually\n s = s.replace(a,b)\n return s","title":""},{"docid":"cc5a13df09277c4119842efd4f11f680","score":"0.59394085","text":"def with_replacement(self):","title":""},{"docid":"a2232304d4410f6ce8a47139f2d03c59","score":"0.58474874","text":"def modify_s(self, *args, **kwds):\n\t\treturn []","title":""},{"docid":"6bf3f897a90d606bd787ae2c51f3b08d","score":"0.5711943","text":"def copy(self):\n return self.replace()","title":""},{"docid":"bc24d0db8cd79d17a76085e9c1410786","score":"0.5656627","text":"def back_replace(self, s, repmap):\n for placeholder, rep in repmap:\n s = s.replace(placeholder, rep)\n return s","title":""},{"docid":"1c87e20fa3249b8dab30ae7799872beb","score":"0.56050366","text":"def updateS(self,x):\n #self.S.add(x)\n self.S.append(x)\n self.Remainder.remove(x)","title":""},{"docid":"29b24e53f0d5ffc094915269c4372096","score":"0.55281585","text":"def perform(self, s):\n for repl in self.replacements:\n s = repl.convert(s)\n return s","title":""},{"docid":"a7cacd64edf5adfea3d5d4a8f5037324","score":"0.54220396","text":"def _sed_copy(self):\n return copy.deepcopy(self.sed_unnormed)","title":""},{"docid":"03ab2440a59951b1239a3998f15b6634","score":"0.54196125","text":"def replace(self, data):\n raise NotImplementedError()","title":""},{"docid":"ae8c9edba6640ab1c84a6495b6f2e6ca","score":"0.53523684","text":"def final_replace(self, s, n):\n for i in range(n):\n s = s.replace(self.proto_placeholder(i), \" {} \".format(self.placeholder(i)))\n return s","title":""},{"docid":"9a3348d6f3ed4e8e9b6e6858d37054ea","score":"0.53392696","text":"def replace(a, old, new): # real signature unknown; restored from __doc__\n pass","title":""},{"docid":"2f8af02436cdaef49bc5a114a2438322","score":"0.5293701","text":"def _mimic_inplace(self, result, inplace=False):\n if inplace:\n self._data = result._data\n self._mask = result._mask\n self._null_count = result._null_count\n else:\n return result","title":""},{"docid":"eb8ebb235ba91a34d6731fdcb79e6bd5","score":"0.5273301","text":"def simplify(self):\n self.data = self.simplify_data(self.data)\n return self","title":""},{"docid":"bb2204f22b594755ecce94cbcf70d2d1","score":"0.5217706","text":"def modifystring(s, sub, offset):\n return s[:offset] + sub + s[offset + len(sub):]","title":""},{"docid":"0afca63893abb8a679c04c94cd471081","score":"0.5201338","text":"def mutate(self, bit_string, inplace=False):\n n = self.n\n bit_string_len = len(bit_string)\n if not bit_string_len == n:\n raise ValueError(\"Wrong length for input bit string : expected {}, got {}.\"\n .format(n, bit_string_len))\n if not inplace:\n bit_string = bit_string.copy()\n self._mutate(bit_string)\n if not inplace:\n return bit_string","title":""},{"docid":"97e0e58b6531c45bb1381e37138a2a59","score":"0.5198011","text":"def Replace(s, src, dst):\n ctx = _get_ctx2(dst, s)\n if ctx is None and is_expr(src):\n ctx = src.ctx\n src = _coerce_seq(src, ctx)\n dst = _coerce_seq(dst, ctx)\n s = _coerce_seq(s, ctx)\n return SeqRef(Z3_mk_seq_replace(src.ctx_ref(), s.as_ast(), src.as_ast(), dst.as_ast()), s.ctx)","title":""},{"docid":"980a07643101f3e1ce9f8de9d2e659a9","score":"0.51972073","text":"def _replace(self, p, e):\n node = self._validiate(p)\n old = node._element\n node._element = e\n return old","title":""},{"docid":"23bf10de7d29309b7168fb4144be3942","score":"0.5188707","text":"def copy_data(self):\n return self.replace(data=self.data.copy())","title":""},{"docid":"687a68acfe17c91f653cf1f350cd2df1","score":"0.5186593","text":"def replace(self, fn):\n pass","title":""},{"docid":"b6ef10fbc17ba061ab23eb07ed839201","score":"0.51484907","text":"def copy(self):\n\n return replace(self)","title":""},{"docid":"77fef4326c1d74f2facf397a75d38e14","score":"0.51361006","text":"def replace(self, old, new, count=None): # real signature unknown; restored from __doc__\n return \"\"","title":""},{"docid":"e3b51751779625cf696ce1c22d4f0e1c","score":"0.51354384","text":"def replace(self, node: Node, s: str, values: list[list[Token]]) -> None:\n # Replace the tokens...\n tokens = tokens_for_node(self.filename, node, self.tokens)\n i1 = i = tokens[0].index\n replace_token(self.tokens[i], 'string', s)\n j = 1\n while j < len(tokens):\n replace_token(self.tokens[i1 + j], 'killed', '')\n j += 1\n # Replace the node.\n new_node = ast.Str()\n new_node.s = s\n replace_node(new_node, node)\n # Update the token.\n token = self.tokens[i1]\n token.node = new_node\n # Update the token list.\n add_token_to_token_list(token, new_node)","title":""},{"docid":"e61cfcffa941383be0e3fb1d01af7308","score":"0.51313186","text":"def _mutate(self, bit_string):\n n = self.n\n mutation_rate = self.get_mutation_rate()\n index = sample_waiting_time(mutation_rate) - 1 # We need to subtract 1 to get a chance\n # to mutate the first index.\n while index < n:\n flip(bit_string, index)\n index += sample_waiting_time(mutation_rate)","title":""},{"docid":"a1c524a8625444aba6b8bb16c83c62b0","score":"0.5126679","text":"def apply(self, s):\n return self.rule(self.needle, s)","title":""},{"docid":"5bee49f51380027a614cbe3916296da0","score":"0.51236445","text":"def substitute(self):\n self.content = self.content.replace(self.search, self.replace)\n return self","title":""},{"docid":"c616c12e2d1650cb8a65a2fb059cc52a","score":"0.5112068","text":"def replace_in_range(self, rng, s, relative_to_x_modifiable=False, set_drawend=False):\n if rng is None:\n rng = (len(self._buffer), len(self._buffer))\n\n elif relative_to_x_modifiable: # Convert to absolute location if necessary\n rng = rng[0] + self.x_modifiable, rng[1] + self.x_modifiable\n\n # Update the right bound if necessary\n if rng[0] < self.intact_right_bound:\n self.intact_right_bound = rng[0]\n\n rotate_n = max(len(self._buffer) - rng[1], 0)\n try:\n self._buffer.rotate(rotate_n) # rotate buffer first so deletion is possible\n if rng[0] != rng[1]: # delete chars if necessary\n self._pop_chars(rng[1] - rng[0])\n # The newly inserted chars are always of default properties\n self._buffer.extend(DEFAULT_CHAR._replace(data=c) for c in s)\n\n finally:\n self._buffer.rotate(-rotate_n)\n\n # Update cursor to the end of this replacement\n self.cursor_x = rng[0] + len(s)\n\n # Normally the draw end is not set\n if set_drawend:\n self.x_drawend = self.cursor_x\n\n nlf = s.count('\\n')\n if nlf > 0:\n self.nlines += nlf\n self._ensure_nlines_max()","title":""},{"docid":"953ce1211b84755a3379c63508c75c68","score":"0.51009995","text":"def reverse(s):","title":""},{"docid":"64537731edda91fc54467464905b86cd","score":"0.5100021","text":"def update_buffer(self, string):\n return string[:-1]","title":""},{"docid":"8287f899f324f185c9e374f6e361e0fd","score":"0.50904584","text":"def change(s, l, r, c):\n\n replacement_c = '0' if c == 'a' else '1'\n\n first, _, right = lyse(s, l, r)\n middle = replacement_c * (r - l + 1)\n return first + middle + right","title":""},{"docid":"3ca23a4ddbb911c11f292a5cf18ae8ff","score":"0.5084901","text":"def inplace_identity(X):\n # Nothing to do","title":""},{"docid":"33bc91874c57a1a6ccb04ba480cb3616","score":"0.50834745","text":"def mutate(self):\n raise NotImplementedError","title":""},{"docid":"d4eb271d219c37685831dda4bc7bb7f4","score":"0.50829947","text":"def replace(self, content):\r\n return self.apply(ReplaceTransformation(content))","title":""},{"docid":"b56af4b270327acf4fdef35c009bc5d6","score":"0.50705564","text":"def _replace(self,p,e):\n node=self._validate(p)\n old=node._element\n node._element=e\n return old","title":""},{"docid":"f4ef3b967cd681d88d0165aa10feed1b","score":"0.5064258","text":"def replace(self, p, e):\n original = self._validate(p)\n old_value = original._element\n original._element = e\n return old_value","title":""},{"docid":"582132892796f69a6de2f25ad009de3d","score":"0.5059555","text":"def replace(s, old, new):\n return string.join(string.split(s, old), new)","title":""},{"docid":"6d017cda71cb0861b7a9072080509ff5","score":"0.50540483","text":"def simplify(self):\n return self.copy()","title":""},{"docid":"bdb2cfbb99d0f9b8c90465f3b7aa04a6","score":"0.50504404","text":"def normalize(self, inplace=False):\n s = self._inplacer(inplace)\n if s.is_ket():\n s.data /= norm(s.data)\n else:\n s.data /= trace(s.data)\n return s","title":""},{"docid":"f465dbd605198b38d380b3e399aa8729","score":"0.5046654","text":"def _replace(self, p, e):\n node = self._validate(p)\n old = node._element\n node._element = e\n return old","title":""},{"docid":"8959b55feca8da6bdb83f7707ad16444","score":"0.50377786","text":"def _compact(self, s, start, end):\n if start == 0:\n return s[end:]\n\n if end == len(s):\n return s[0:start]\n\n start -= 1\n\n while start >= 0 and end < len(s):\n if s[start] != s[end]:\n break\n\n ball = s[start]\n num = 0\n newStart = start\n newEnd = end\n while newStart >= 0 and s[newStart] == ball:\n newStart -= 1\n num += 1\n\n while newEnd < len(s) and s[newEnd] == ball:\n newEnd += 1\n num += 1\n\n if num < 3:\n break\n\n start = newStart\n end = newEnd\n\n if start < 0:\n return s[end:]\n else:\n return s[0:start + 1] + s[end:]","title":""},{"docid":"79cbce4d1d1403f1c6e7f22dc7dea3a0","score":"0.50364983","text":"def replace(self, *args, **kwargs): # real signature unknown\n pass","title":""},{"docid":"79cbce4d1d1403f1c6e7f22dc7dea3a0","score":"0.50364983","text":"def replace(self, *args, **kwargs): # real signature unknown\n pass","title":""},{"docid":"69bf5f4c62dcac1817667c7161fa2a40","score":"0.50361246","text":"def mutate(self):\n self.value = not self.value","title":""},{"docid":"a45d27e0f71e60f1f6768c7c12468d1b","score":"0.5033477","text":"def replace(self, sig):\n ...","title":""},{"docid":"9152cbb773a23376ee8d5aa8218b08e5","score":"0.50321686","text":"def set_s(self, s: Union[Number, Iterable[Number]]):\n self.s_i = s","title":""},{"docid":"03088ccdc1405197f26e6e5a9bad1f5b","score":"0.50253236","text":"def reverseString(self, s) -> None:\n def helper(i, j):\n if i >= j:\n return\n\n s[i], s[j] = s[j], s[i]\n helper(i+1, j-1)\n\n helper(0, len(s) - 1)","title":""},{"docid":"26464daae2c41d898d17fb67d43d5f68","score":"0.501373","text":"def s(self, s: str):\n\n self._s = s","title":""},{"docid":"bfd1df4124cfbb49dc6edc86aeaa05bf","score":"0.5011341","text":"def mutate(self):\n\n\t\tpass","title":""},{"docid":"41aa0a63e05471fb416f969f558688c8","score":"0.50031644","text":"def bulk_insert(s, x, y, str_list):\n s_rev = s[::-1]\n for i in range(len(s_rev)):\n c = s_rev[i]\n str_list = insert(c, x, y, str_list)\n return str_list","title":""},{"docid":"c864f9039fbd7866a151a8f1884730d5","score":"0.4998985","text":"def sensing(self):","title":""},{"docid":"637fed6d13b170a8412fe513fe15959c","score":"0.49961746","text":"def __call__(self, line):\n for (_re, repl) in self._re:\n line = _re.sub(repl, line)\n\n # no leading or trailing spaces\n return line.strip()","title":""},{"docid":"54851120bf5025aa2b23890c6e40d9ed","score":"0.49952963","text":"def f(a: str) -> str:\n return a.replace(\"abc\", \"x\", 1)","title":""},{"docid":"fe74f9251494b979f1105732787fd555","score":"0.49858895","text":"def to_replacement(self) -> Replacement:\n ...","title":""},{"docid":"63a4009515162ffb2d3587c10f856c65","score":"0.49820688","text":"def replace(self, item):\n self.data[-1] = item","title":""},{"docid":"d17780152eceb8032ef756dfa233e1ff","score":"0.49681655","text":"def __call__(self, s, a=None, d=None):\n if a is None: a = self.allchars\n if d is None: d = self.delchars\n return s.translate(a,d)","title":""},{"docid":"1723deb9f4b4408be7e8fc0343a89cb4","score":"0.4963671","text":"def reverseString(self, s: List[str]) -> None:\n min_count = 0\n max_count = len(s) - 1\n\n while min_count < len(s) / 2:\n first_item = s[min_count]\n last_item = s[max_count]\n #override\n s[min_count] = last_item\n s[max_count] = first_item\n \n min_count += 1\n max_count -= 1","title":""},{"docid":"8cdc2d4543a768b6da401ec8a6471c72","score":"0.4958718","text":"def replace(orig, position, char):\n str_len = len(orig)\n if position < 0 or position > str_len - 1:\n return orig\n\n # Method 1\n # orig = list(orig)\n # orig[position] = char\n # return ''.join(orig)\n\n # Method 2 - Faster\n return orig[:position] + char + orig[position + 1:]","title":""},{"docid":"305ab0b9d41a2f411000d27edc11d18e","score":"0.49516863","text":"def keepOriginalText(s,startLoc,t):\r\n try:\r\n endloc = getTokensEndLoc()\r\n except ParseException:\r\n raise ParseFatalException(\"incorrect usage of keepOriginalText - may only be called as a parse action\")\r\n del t[:]\r\n t += ParseResults(s[startLoc:endloc])\r\n return t","title":""},{"docid":"e34de886fc79dee6aa98ad82211eb341","score":"0.4947436","text":"def replace(self,s):\n q = u'update replace $i in %s ' % (self._path,)\n q += ' with %s' % (s,)\n self._cursor.execute(q, pretty_print=True, nsmap=self._nsmap)","title":""},{"docid":"78a02f4541ca467c3ad3e0f6ae877379","score":"0.49387455","text":"def f2(self, s, x):\r\n return (s * (int(x / len(s)) + x))[:x]","title":""},{"docid":"f5c2dea3bc103b17ad96012f8789cb96","score":"0.49378243","text":"def rewrite(s):\n for k in REWRITE_RULES:\n s = s.replace(k, REWRITE_RULES[k])\n return s","title":""},{"docid":"58ea587d3f722c328ef0adbc64daa947","score":"0.49121037","text":"def extend(s, var, val):\r\n s2 = s.copy()\r\n s2[var] = val\r\n return s2","title":""},{"docid":"bdd69ab94427274135570bc1bf6d49f9","score":"0.49049163","text":"def reverseString(self, s: List[str]) -> None:\n \n \"\"\"\n In this question, I perform flips, but extra care is taken to not repeat flip,\n otherwise this could result in no flip being performed. Therefore, I only flip\n if i has not passed the midpoint of the string.\n \"\"\"\n \n len_s = len(s)\n mid_s = len(s) // 2\n \n for i in range(len_s):\n if i < mid_s: # prevents extra flips\n temp = s[i]\n s[i] = s[len_s - 1 - i]\n s[len_s - 1 - i] = temp","title":""},{"docid":"cdd6664c0a318f8129376a827ee98ff9","score":"0.4901787","text":"def flipside(s):\n x = len(s) / 2\n return s[x:] + s[:x]","title":""},{"docid":"25205f30880d3f09aa4bba6090d7974c","score":"0.48937893","text":"def reverseString(self, s: List[str]) -> None:\n\n #using two pointersRuntime\n # 226 ms\n # Beats\n # 37.86%\n # left, right = 0, len(s)-1\n # while left < right:\n # s[left], s[right] = s[right], s[left]\n # left += 1\n # right -= 1\n\n #python\n #Runtime\n # 217 ms\n # Beats\n # 68.64%\n# s.reverse()\n\n\n # Runtime\n # 209 ms\n # Beats\n # 84.27%\n s[:] = s[::-1]","title":""},{"docid":"7a735450f9461e1a5cdce4b4daed634b","score":"0.48910204","text":"def set(self, s):\r\n if len(s) != self._size * self._size:\r\n raise ValueError(\"String to set should be {0} characters; {1} given\"\r\n .format(self._size * self._size, len(s)))\r\n\r\n def gen_digit():\r\n valid = self.digits | { '.' if ('.' in s) else ' ' }\r\n for digit in s:\r\n if digit in valid:\r\n yield digit\r\n\r\n gen = gen_digit()\r\n for row in range(self._size):\r\n for col in range(self._size):\r\n digit = next(gen)\r\n if digit in self.digits:\r\n if not self._try_add(row, col, digit):\r\n raise ValueError(\"Column {0} of row {1} cannot contain '{2}'\".format(col, row, digit))\r\n self._mods.clear()","title":""},{"docid":"d7044e2f9264cb1cec7da4d1ffe141c9","score":"0.48897186","text":"def binary_back_substitute(W, s):\n # iterate backwards, starting from second to last row for back-substitution\n s_copy = np.array(s)\n n = len(s)\n for row_num in xrange(n - 2, -1, -1):\n row = W[row_num]\n for col_num in xrange(row_num + 1, n):\n if row[col_num] == 1:\n s_copy[row_num] = (s_copy[row_num] + s_copy[col_num]) % 2\n\n return s_copy","title":""},{"docid":"018697a2981852ef5e5fa34b08e135f1","score":"0.4886204","text":"def reverseString(self, s: List[str]) -> None:\n # if not s:\n # return s\n # idxl = 0\n # idxr = len(s)-1\n # while(idxr>idxl):\n # tmp = s[idxl]\n # s[idxl] = s[idxr]\n # s[idxr] = tmp\n s[:] = s[::-1]","title":""},{"docid":"8855746d7a3fb30324f41ffa3dde2d2f","score":"0.4885424","text":"def replace(self, p, e):\n node = self._validate(p)\n elem = node._element # Old element to return.\n node._element = e # Assigning new element to node.\n return elem","title":""},{"docid":"0541877089ecf60fab6a0ad3728c9b18","score":"0.48800692","text":"def filter_inplace(\n self,\n value: Any,\n storage: Any,\n strict: bool = False,\n allow_downcast: Optional[bool] = None,\n ):\n raise NotImplementedError()","title":""},{"docid":"07012f45af7d5dcc4debfa88f230b441","score":"0.48747844","text":"def reverseString(self, s) -> None:\n s_len = len(s)\n for index in range(s_len // 2):\n s[index], s[s_len - index - 1] = s[s_len - index - 1], s[index]","title":""},{"docid":"a4da12fd04f17d9beff49ae23fee605a","score":"0.48719764","text":"def replace(self, old, new):\r\n for i in range(len(self.data)):\r\n self.data[i] = self.data[i].replace(old, new)","title":""},{"docid":"d7abb05dcf4e87d17c9fb2900b62acba","score":"0.48677066","text":"def update_sequence(s, n, x):\n t = tuple(s)\n if -len(t) <= n < len(t):\n return t[0:n] + (x,) + t[n + 1 : 0 if n == -1 else None]\n else:\n raise IndexError(\"sequence index out of range\")","title":""},{"docid":"a75eaeafe7f5d9959b3411e0672c440c","score":"0.48648545","text":"def _backup_ ( s ) :\n return LoKi.Dicts.DictsLoop.backup ( s )","title":""},{"docid":"3b3da1bf023a5a0639d592244d5e16c1","score":"0.48617312","text":"def transform(self, S, typ):\n #Defer everything to LindbladOp's\n # `spam_tranform` function, which applies either\n # error_map -> inv(S) * error_map (\"prep\" case) OR\n # error_map -> error_map * S (\"effect\" case)\n self.error_map.spam_transform(S, typ)\n self.dirty = True","title":""},{"docid":"29ce2118627da87087579bb06bc0ab46","score":"0.48601794","text":"def replace(self, oldtext, newtext):\r\n return self","title":""},{"docid":"638b8794e0dcacc8d144a5001445eca1","score":"0.48575413","text":"def replaceWith(replStr):\r\n return lambda s,l,t: [replStr]","title":""},{"docid":"9d30e401c73e0565e148c351d5103a54","score":"0.48521605","text":"def test_inplace_setitem(self):\n node = ast.parse('x[n] += 1')\n IndexingToFunctions('op').visit(node)\n self.assertEqual(to_source(node), dedent(\"\"\"\\\n op.setitem(x, n, op.iadd(op.getitem(x, n), 1))\n \"\"\"))","title":""},{"docid":"cc52330c462cadfe42d69f43904dd476","score":"0.4847357","text":"def patch(self, start, end):\n self.fh.seek(start)\n region = self.fh.read(end - start + 1)\n assert region[-1] == b'\\0'\n substituted = region.replace(self.search, self.replace)\n substituted+= b'\\0' * (end - start - len(substituted))\n self.fh.seek(start)\n self.fh.write(substituted)\n return self","title":""},{"docid":"8cd8d52d6080e47ba14e6f097e5400c7","score":"0.48469257","text":"def mark_safe(s):\n if isinstance(s, SafeData):\n return s\n if isinstance(s, str):\n return SafeString(s)\n if isinstance(s, unicode):\n return SafeUnicode(s)\n return SafeString(str(s))","title":""},{"docid":"0e92f4a508b25ad54679875f1d080fb0","score":"0.48462895","text":"def _copy_and_replace(self, args):\n\n n = copy.deepcopy(self.replace_with)\n ReplaceParameters(n).replace(self.name, args)\n\n return n","title":""},{"docid":"f4a2c1168a9091f8eab1331ca553c40a","score":"0.4840211","text":"def rreplace(s: str, old: str, new: str) -> str:\n return (s[::-1].replace(old[::-1], new[::-1], 1))[::-1]","title":""},{"docid":"d510912b7e4d7b69bccfc7077f4416ea","score":"0.4838308","text":"def replace_content(g):\n g[0] = 88\n g[1] = 22\n g[2] = 44\n print(\"g = \", g)","title":""},{"docid":"07ba5be5f475e23604ec8804d10cbb19","score":"0.4823161","text":"def update(self, s, a, r, s_p):\n if s is not None and a is not None:\n if self.counter[s][a] < self.n_known:\n self.counter[s][a] += 1\n normalizer = 1. / float(self.counter[s][a])\n\n self.R[s][a] = self.R[s][a] + normalizer * (r - self.R[s][a])\n self.T[s][a][s_p] = self.T[s][a][s_p] + normalizer * (1. - self.T[s][a][s_p])\n for _s_p in self.T[s][a]:\n if _s_p not in [s_p]:\n self.T[s][a][_s_p] = self.T[s][a][_s_p] * (1 - normalizer)\n\n if self.counter[s][a] == self.n_known:\n self.update_upper_bound()","title":""},{"docid":"0bc523f2a1000041a2318361c35a6807","score":"0.48209307","text":"def replace_homonyms(s):\n for val in homonyms.keys():\n s = s.replace(val, homonyms[val])\n return s","title":""},{"docid":"1cdce6d8002ba96e113559604122a8ad","score":"0.48192036","text":"def rreplace(s, old, new, occurrence):\n li = s.rsplit(old, occurrence)\n return new.join(li)","title":""},{"docid":"ca2a7dcc16e6b1cb1392b778cfa4303e","score":"0.48156697","text":"def test_inplace_setitem_gensym_both(self):\n node = ast.parse('x.data[m:m+n] += 1')\n gensym.reset()\n IndexingToFunctions('op').visit(node)\n self.assertEqual(to_source(node), dedent(\"\"\"\\\n __gensym_1 = x.data\n __gensym_2 = slice(m, m + n)\n op.setitem(__gensym_1, __gensym_2, op.iadd(op.getitem(__gensym_1,\n __gensym_2), 1))\n \"\"\"))","title":""},{"docid":"df93de88c28e8fb7353c9804ca17e821","score":"0.4810758","text":"def reverseString(s: List[str]) -> None:\n '''\n Idea is to use double pointers to swap first/last and then work inwards\n '''\n \n i = 0\n j = len(s) - 1\n\n while i < j:\n t = s[i]\n s[i] = s[j]\n s[j] = t\n\n i = i + 1\n j = j - 1 \n\n # for testing\n return s","title":""},{"docid":"5714eb9ca2c077880bd9198f9f4a7534","score":"0.48094067","text":"def reverse_string(self, s: [str]) -> None:\n tmp = None\n length = len(s)\n for i in range(0, length // 2):\n tmp = s[i]\n s[i] = s[length - i - 1]\n s[length - i - 1] = tmp","title":""},{"docid":"3012c07f5f75b05c0b8bc23a1901a715","score":"0.48090547","text":"def reverseString(self, s: List[str]) -> None:\n if not s or len(s) == 0:\n return\n for idx in range(len(s) // 2):\n s[idx], s[-1 - idx] = s[-1 - idx], s[idx]","title":""},{"docid":"33b0d96e5c1be7abae8978e4d608cc70","score":"0.4807088","text":"def _write(self, s):\n raise NotImplementedError()","title":""},{"docid":"9d02088c7a7400cd98e3ea7ea43d1a37","score":"0.4806133","text":"def replace_with_spaces(self, s):\n return s.translate(self.space_table)","title":""},{"docid":"591a4759ece6d31d37c85a92fd8faa35","score":"0.48044842","text":"def mutate(self, state):\n raise NotImplementedError","title":""},{"docid":"4e49df8f2ae022756fafd14c1fdb5c88","score":"0.47979444","text":"def modify(self, text):\n\n text = ' '.join(text)\n text = '\\n'.join([line for line in text.splitlines() if line])\n\n return text","title":""},{"docid":"61786cd165ce7454d004141248a5131c","score":"0.47941357","text":"def test_cons_replacing():\n cons = HyCons(\"foo\", \"bar\")\n cons[0] = \"car\"\n\n assert cons == HyCons(\"car\", \"bar\")\n\n cons[1:] = \"cdr\"\n assert cons == HyCons(\"car\", \"cdr\")\n\n try:\n cons[:] = \"foo\"\n assert True is False\n except IndexError:\n pass","title":""},{"docid":"03f7de513eec43c1fdc8daf534035c1c","score":"0.4790297","text":"def singularize(self):\n temp = [TextBlob(word) for word in self.text]\n self.text = [list(word.words.singularize())[0] for word in temp]\n tempv = [Word(word) for word in self.text]\n self.text = [word.lemmatize(\"v\") for word in tempv]","title":""},{"docid":"6247818c5583298e987c0a353cd98381","score":"0.47774732","text":"def reverseString(self, s: List[str]) -> None:\r\n s.reverse()","title":""},{"docid":"86e6289d2f383786d118fe835511e774","score":"0.47745445","text":"def prepare_for_replace(self):\n pass","title":""},{"docid":"ae3af43ca1b1b940091f6358f2692482","score":"0.47745287","text":"def update_replace(self, *args, **kwds):\n data_new = self._process_upddata(args, kwds)\n keys = {key for key, val in data_new}\n pre_data = tuple((key, val) for key, val in self._data if key not in keys)\n self._data_checked = pre_data + data_new","title":""},{"docid":"f59ade267e427f0b31542db37269e6e5","score":"0.47729003","text":"def reverseString(self, s: List[str]) -> None:\n\n for i in range(len(s) // 2):\n s[i], s[-(i+1)] = s[-(i+1)], s[i]\n\n #","title":""},{"docid":"21f31b943e2e556635f39f5c77da920a","score":"0.47650313","text":"def reverseString(self, s) -> None:\n n = len(s)\n for i in range(n//2):\n s[i], s[n-i-1] = s[n-i-1], s[i]","title":""}],"string":"[\n {\n \"docid\": \"31d79ffbb36821823f943aee39e09f63\",\n \"score\": \"0.62795085\",\n \"text\": \"def modifiable_chars(self, s):\\n self.replace_in_range((self.x_modifiable, len(self._buffer)), s)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"16389be01038261a31222f1a48d6bab5\",\n \"score\": \"0.59871805\",\n \"text\": \"def replace(self, s, a, b):\\n # easy answer, there are much more fun ways to do this manually\\n s = s.replace(a,b)\\n return s\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc5a13df09277c4119842efd4f11f680\",\n \"score\": \"0.59394085\",\n \"text\": \"def with_replacement(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2232304d4410f6ce8a47139f2d03c59\",\n \"score\": \"0.58474874\",\n \"text\": \"def modify_s(self, *args, **kwds):\\n\\t\\treturn []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6bf3f897a90d606bd787ae2c51f3b08d\",\n \"score\": \"0.5711943\",\n \"text\": \"def copy(self):\\n return self.replace()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc24d0db8cd79d17a76085e9c1410786\",\n \"score\": \"0.5656627\",\n \"text\": \"def back_replace(self, s, repmap):\\n for placeholder, rep in repmap:\\n s = s.replace(placeholder, rep)\\n return s\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c87e20fa3249b8dab30ae7799872beb\",\n \"score\": \"0.56050366\",\n \"text\": \"def updateS(self,x):\\n #self.S.add(x)\\n self.S.append(x)\\n self.Remainder.remove(x)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"29b24e53f0d5ffc094915269c4372096\",\n \"score\": \"0.55281585\",\n \"text\": \"def perform(self, s):\\n for repl in self.replacements:\\n s = repl.convert(s)\\n return s\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a7cacd64edf5adfea3d5d4a8f5037324\",\n \"score\": \"0.54220396\",\n \"text\": \"def _sed_copy(self):\\n return copy.deepcopy(self.sed_unnormed)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"03ab2440a59951b1239a3998f15b6634\",\n \"score\": \"0.54196125\",\n \"text\": \"def replace(self, data):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae8c9edba6640ab1c84a6495b6f2e6ca\",\n \"score\": \"0.53523684\",\n \"text\": \"def final_replace(self, s, n):\\n for i in range(n):\\n s = s.replace(self.proto_placeholder(i), \\\" {} \\\".format(self.placeholder(i)))\\n return s\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a3348d6f3ed4e8e9b6e6858d37054ea\",\n \"score\": \"0.53392696\",\n \"text\": \"def replace(a, old, new): # real signature unknown; restored from __doc__\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f8af02436cdaef49bc5a114a2438322\",\n \"score\": \"0.5293701\",\n \"text\": \"def _mimic_inplace(self, result, inplace=False):\\n if inplace:\\n self._data = result._data\\n self._mask = result._mask\\n self._null_count = result._null_count\\n else:\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb8ebb235ba91a34d6731fdcb79e6bd5\",\n \"score\": \"0.5273301\",\n \"text\": \"def simplify(self):\\n self.data = self.simplify_data(self.data)\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb2204f22b594755ecce94cbcf70d2d1\",\n \"score\": \"0.5217706\",\n \"text\": \"def modifystring(s, sub, offset):\\n return s[:offset] + sub + s[offset + len(sub):]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0afca63893abb8a679c04c94cd471081\",\n \"score\": \"0.5201338\",\n \"text\": \"def mutate(self, bit_string, inplace=False):\\n n = self.n\\n bit_string_len = len(bit_string)\\n if not bit_string_len == n:\\n raise ValueError(\\\"Wrong length for input bit string : expected {}, got {}.\\\"\\n .format(n, bit_string_len))\\n if not inplace:\\n bit_string = bit_string.copy()\\n self._mutate(bit_string)\\n if not inplace:\\n return bit_string\",\n \"title\": \"\"\n },\n {\n \"docid\": \"97e0e58b6531c45bb1381e37138a2a59\",\n \"score\": \"0.5198011\",\n \"text\": \"def Replace(s, src, dst):\\n ctx = _get_ctx2(dst, s)\\n if ctx is None and is_expr(src):\\n ctx = src.ctx\\n src = _coerce_seq(src, ctx)\\n dst = _coerce_seq(dst, ctx)\\n s = _coerce_seq(s, ctx)\\n return SeqRef(Z3_mk_seq_replace(src.ctx_ref(), s.as_ast(), src.as_ast(), dst.as_ast()), s.ctx)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"980a07643101f3e1ce9f8de9d2e659a9\",\n \"score\": \"0.51972073\",\n \"text\": \"def _replace(self, p, e):\\n node = self._validiate(p)\\n old = node._element\\n node._element = e\\n return old\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23bf10de7d29309b7168fb4144be3942\",\n \"score\": \"0.5188707\",\n \"text\": \"def copy_data(self):\\n return self.replace(data=self.data.copy())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"687a68acfe17c91f653cf1f350cd2df1\",\n \"score\": \"0.5186593\",\n \"text\": \"def replace(self, fn):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b6ef10fbc17ba061ab23eb07ed839201\",\n \"score\": \"0.51484907\",\n \"text\": \"def copy(self):\\n\\n return replace(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77fef4326c1d74f2facf397a75d38e14\",\n \"score\": \"0.51361006\",\n \"text\": \"def replace(self, old, new, count=None): # real signature unknown; restored from __doc__\\n return \\\"\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3b51751779625cf696ce1c22d4f0e1c\",\n \"score\": \"0.51354384\",\n \"text\": \"def replace(self, node: Node, s: str, values: list[list[Token]]) -> None:\\n # Replace the tokens...\\n tokens = tokens_for_node(self.filename, node, self.tokens)\\n i1 = i = tokens[0].index\\n replace_token(self.tokens[i], 'string', s)\\n j = 1\\n while j < len(tokens):\\n replace_token(self.tokens[i1 + j], 'killed', '')\\n j += 1\\n # Replace the node.\\n new_node = ast.Str()\\n new_node.s = s\\n replace_node(new_node, node)\\n # Update the token.\\n token = self.tokens[i1]\\n token.node = new_node\\n # Update the token list.\\n add_token_to_token_list(token, new_node)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e61cfcffa941383be0e3fb1d01af7308\",\n \"score\": \"0.51313186\",\n \"text\": \"def _mutate(self, bit_string):\\n n = self.n\\n mutation_rate = self.get_mutation_rate()\\n index = sample_waiting_time(mutation_rate) - 1 # We need to subtract 1 to get a chance\\n # to mutate the first index.\\n while index < n:\\n flip(bit_string, index)\\n index += sample_waiting_time(mutation_rate)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1c524a8625444aba6b8bb16c83c62b0\",\n \"score\": \"0.5126679\",\n \"text\": \"def apply(self, s):\\n return self.rule(self.needle, s)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5bee49f51380027a614cbe3916296da0\",\n \"score\": \"0.51236445\",\n \"text\": \"def substitute(self):\\n self.content = self.content.replace(self.search, self.replace)\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c616c12e2d1650cb8a65a2fb059cc52a\",\n \"score\": \"0.5112068\",\n \"text\": \"def replace_in_range(self, rng, s, relative_to_x_modifiable=False, set_drawend=False):\\n if rng is None:\\n rng = (len(self._buffer), len(self._buffer))\\n\\n elif relative_to_x_modifiable: # Convert to absolute location if necessary\\n rng = rng[0] + self.x_modifiable, rng[1] + self.x_modifiable\\n\\n # Update the right bound if necessary\\n if rng[0] < self.intact_right_bound:\\n self.intact_right_bound = rng[0]\\n\\n rotate_n = max(len(self._buffer) - rng[1], 0)\\n try:\\n self._buffer.rotate(rotate_n) # rotate buffer first so deletion is possible\\n if rng[0] != rng[1]: # delete chars if necessary\\n self._pop_chars(rng[1] - rng[0])\\n # The newly inserted chars are always of default properties\\n self._buffer.extend(DEFAULT_CHAR._replace(data=c) for c in s)\\n\\n finally:\\n self._buffer.rotate(-rotate_n)\\n\\n # Update cursor to the end of this replacement\\n self.cursor_x = rng[0] + len(s)\\n\\n # Normally the draw end is not set\\n if set_drawend:\\n self.x_drawend = self.cursor_x\\n\\n nlf = s.count('\\\\n')\\n if nlf > 0:\\n self.nlines += nlf\\n self._ensure_nlines_max()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"953ce1211b84755a3379c63508c75c68\",\n \"score\": \"0.51009995\",\n \"text\": \"def reverse(s):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"64537731edda91fc54467464905b86cd\",\n \"score\": \"0.5100021\",\n \"text\": \"def update_buffer(self, string):\\n return string[:-1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8287f899f324f185c9e374f6e361e0fd\",\n \"score\": \"0.50904584\",\n \"text\": \"def change(s, l, r, c):\\n\\n replacement_c = '0' if c == 'a' else '1'\\n\\n first, _, right = lyse(s, l, r)\\n middle = replacement_c * (r - l + 1)\\n return first + middle + right\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ca23a4ddbb911c11f292a5cf18ae8ff\",\n \"score\": \"0.5084901\",\n \"text\": \"def inplace_identity(X):\\n # Nothing to do\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33bc91874c57a1a6ccb04ba480cb3616\",\n \"score\": \"0.50834745\",\n \"text\": \"def mutate(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4eb271d219c37685831dda4bc7bb7f4\",\n \"score\": \"0.50829947\",\n \"text\": \"def replace(self, content):\\r\\n return self.apply(ReplaceTransformation(content))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b56af4b270327acf4fdef35c009bc5d6\",\n \"score\": \"0.50705564\",\n \"text\": \"def _replace(self,p,e):\\n node=self._validate(p)\\n old=node._element\\n node._element=e\\n return old\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4ef3b967cd681d88d0165aa10feed1b\",\n \"score\": \"0.5064258\",\n \"text\": \"def replace(self, p, e):\\n original = self._validate(p)\\n old_value = original._element\\n original._element = e\\n return old_value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"582132892796f69a6de2f25ad009de3d\",\n \"score\": \"0.5059555\",\n \"text\": \"def replace(s, old, new):\\n return string.join(string.split(s, old), new)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d017cda71cb0861b7a9072080509ff5\",\n \"score\": \"0.50540483\",\n \"text\": \"def simplify(self):\\n return self.copy()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bdb2cfbb99d0f9b8c90465f3b7aa04a6\",\n \"score\": \"0.50504404\",\n \"text\": \"def normalize(self, inplace=False):\\n s = self._inplacer(inplace)\\n if s.is_ket():\\n s.data /= norm(s.data)\\n else:\\n s.data /= trace(s.data)\\n return s\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f465dbd605198b38d380b3e399aa8729\",\n \"score\": \"0.5046654\",\n \"text\": \"def _replace(self, p, e):\\n node = self._validate(p)\\n old = node._element\\n node._element = e\\n return old\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8959b55feca8da6bdb83f7707ad16444\",\n \"score\": \"0.50377786\",\n \"text\": \"def _compact(self, s, start, end):\\n if start == 0:\\n return s[end:]\\n\\n if end == len(s):\\n return s[0:start]\\n\\n start -= 1\\n\\n while start >= 0 and end < len(s):\\n if s[start] != s[end]:\\n break\\n\\n ball = s[start]\\n num = 0\\n newStart = start\\n newEnd = end\\n while newStart >= 0 and s[newStart] == ball:\\n newStart -= 1\\n num += 1\\n\\n while newEnd < len(s) and s[newEnd] == ball:\\n newEnd += 1\\n num += 1\\n\\n if num < 3:\\n break\\n\\n start = newStart\\n end = newEnd\\n\\n if start < 0:\\n return s[end:]\\n else:\\n return s[0:start + 1] + s[end:]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79cbce4d1d1403f1c6e7f22dc7dea3a0\",\n \"score\": \"0.50364983\",\n \"text\": \"def replace(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79cbce4d1d1403f1c6e7f22dc7dea3a0\",\n \"score\": \"0.50364983\",\n \"text\": \"def replace(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69bf5f4c62dcac1817667c7161fa2a40\",\n \"score\": \"0.50361246\",\n \"text\": \"def mutate(self):\\n self.value = not self.value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a45d27e0f71e60f1f6768c7c12468d1b\",\n \"score\": \"0.5033477\",\n \"text\": \"def replace(self, sig):\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9152cbb773a23376ee8d5aa8218b08e5\",\n \"score\": \"0.50321686\",\n \"text\": \"def set_s(self, s: Union[Number, Iterable[Number]]):\\n self.s_i = s\",\n \"title\": \"\"\n },\n {\n \"docid\": \"03088ccdc1405197f26e6e5a9bad1f5b\",\n \"score\": \"0.50253236\",\n \"text\": \"def reverseString(self, s) -> None:\\n def helper(i, j):\\n if i >= j:\\n return\\n\\n s[i], s[j] = s[j], s[i]\\n helper(i+1, j-1)\\n\\n helper(0, len(s) - 1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"26464daae2c41d898d17fb67d43d5f68\",\n \"score\": \"0.501373\",\n \"text\": \"def s(self, s: str):\\n\\n self._s = s\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bfd1df4124cfbb49dc6edc86aeaa05bf\",\n \"score\": \"0.5011341\",\n \"text\": \"def mutate(self):\\n\\n\\t\\tpass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"41aa0a63e05471fb416f969f558688c8\",\n \"score\": \"0.50031644\",\n \"text\": \"def bulk_insert(s, x, y, str_list):\\n s_rev = s[::-1]\\n for i in range(len(s_rev)):\\n c = s_rev[i]\\n str_list = insert(c, x, y, str_list)\\n return str_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c864f9039fbd7866a151a8f1884730d5\",\n \"score\": \"0.4998985\",\n \"text\": \"def sensing(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"637fed6d13b170a8412fe513fe15959c\",\n \"score\": \"0.49961746\",\n \"text\": \"def __call__(self, line):\\n for (_re, repl) in self._re:\\n line = _re.sub(repl, line)\\n\\n # no leading or trailing spaces\\n return line.strip()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54851120bf5025aa2b23890c6e40d9ed\",\n \"score\": \"0.49952963\",\n \"text\": \"def f(a: str) -> str:\\n return a.replace(\\\"abc\\\", \\\"x\\\", 1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe74f9251494b979f1105732787fd555\",\n \"score\": \"0.49858895\",\n \"text\": \"def to_replacement(self) -> Replacement:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"63a4009515162ffb2d3587c10f856c65\",\n \"score\": \"0.49820688\",\n \"text\": \"def replace(self, item):\\n self.data[-1] = item\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d17780152eceb8032ef756dfa233e1ff\",\n \"score\": \"0.49681655\",\n \"text\": \"def __call__(self, s, a=None, d=None):\\n if a is None: a = self.allchars\\n if d is None: d = self.delchars\\n return s.translate(a,d)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1723deb9f4b4408be7e8fc0343a89cb4\",\n \"score\": \"0.4963671\",\n \"text\": \"def reverseString(self, s: List[str]) -> None:\\n min_count = 0\\n max_count = len(s) - 1\\n\\n while min_count < len(s) / 2:\\n first_item = s[min_count]\\n last_item = s[max_count]\\n #override\\n s[min_count] = last_item\\n s[max_count] = first_item\\n \\n min_count += 1\\n max_count -= 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8cdc2d4543a768b6da401ec8a6471c72\",\n \"score\": \"0.4958718\",\n \"text\": \"def replace(orig, position, char):\\n str_len = len(orig)\\n if position < 0 or position > str_len - 1:\\n return orig\\n\\n # Method 1\\n # orig = list(orig)\\n # orig[position] = char\\n # return ''.join(orig)\\n\\n # Method 2 - Faster\\n return orig[:position] + char + orig[position + 1:]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"305ab0b9d41a2f411000d27edc11d18e\",\n \"score\": \"0.49516863\",\n \"text\": \"def keepOriginalText(s,startLoc,t):\\r\\n try:\\r\\n endloc = getTokensEndLoc()\\r\\n except ParseException:\\r\\n raise ParseFatalException(\\\"incorrect usage of keepOriginalText - may only be called as a parse action\\\")\\r\\n del t[:]\\r\\n t += ParseResults(s[startLoc:endloc])\\r\\n return t\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e34de886fc79dee6aa98ad82211eb341\",\n \"score\": \"0.4947436\",\n \"text\": \"def replace(self,s):\\n q = u'update replace $i in %s ' % (self._path,)\\n q += ' with %s' % (s,)\\n self._cursor.execute(q, pretty_print=True, nsmap=self._nsmap)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78a02f4541ca467c3ad3e0f6ae877379\",\n \"score\": \"0.49387455\",\n \"text\": \"def f2(self, s, x):\\r\\n return (s * (int(x / len(s)) + x))[:x]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5c2dea3bc103b17ad96012f8789cb96\",\n \"score\": \"0.49378243\",\n \"text\": \"def rewrite(s):\\n for k in REWRITE_RULES:\\n s = s.replace(k, REWRITE_RULES[k])\\n return s\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58ea587d3f722c328ef0adbc64daa947\",\n \"score\": \"0.49121037\",\n \"text\": \"def extend(s, var, val):\\r\\n s2 = s.copy()\\r\\n s2[var] = val\\r\\n return s2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bdd69ab94427274135570bc1bf6d49f9\",\n \"score\": \"0.49049163\",\n \"text\": \"def reverseString(self, s: List[str]) -> None:\\n \\n \\\"\\\"\\\"\\n In this question, I perform flips, but extra care is taken to not repeat flip,\\n otherwise this could result in no flip being performed. Therefore, I only flip\\n if i has not passed the midpoint of the string.\\n \\\"\\\"\\\"\\n \\n len_s = len(s)\\n mid_s = len(s) // 2\\n \\n for i in range(len_s):\\n if i < mid_s: # prevents extra flips\\n temp = s[i]\\n s[i] = s[len_s - 1 - i]\\n s[len_s - 1 - i] = temp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cdd6664c0a318f8129376a827ee98ff9\",\n \"score\": \"0.4901787\",\n \"text\": \"def flipside(s):\\n x = len(s) / 2\\n return s[x:] + s[:x]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25205f30880d3f09aa4bba6090d7974c\",\n \"score\": \"0.48937893\",\n \"text\": \"def reverseString(self, s: List[str]) -> None:\\n\\n #using two pointersRuntime\\n # 226 ms\\n # Beats\\n # 37.86%\\n # left, right = 0, len(s)-1\\n # while left < right:\\n # s[left], s[right] = s[right], s[left]\\n # left += 1\\n # right -= 1\\n\\n #python\\n #Runtime\\n # 217 ms\\n # Beats\\n # 68.64%\\n# s.reverse()\\n\\n\\n # Runtime\\n # 209 ms\\n # Beats\\n # 84.27%\\n s[:] = s[::-1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a735450f9461e1a5cdce4b4daed634b\",\n \"score\": \"0.48910204\",\n \"text\": \"def set(self, s):\\r\\n if len(s) != self._size * self._size:\\r\\n raise ValueError(\\\"String to set should be {0} characters; {1} given\\\"\\r\\n .format(self._size * self._size, len(s)))\\r\\n\\r\\n def gen_digit():\\r\\n valid = self.digits | { '.' if ('.' in s) else ' ' }\\r\\n for digit in s:\\r\\n if digit in valid:\\r\\n yield digit\\r\\n\\r\\n gen = gen_digit()\\r\\n for row in range(self._size):\\r\\n for col in range(self._size):\\r\\n digit = next(gen)\\r\\n if digit in self.digits:\\r\\n if not self._try_add(row, col, digit):\\r\\n raise ValueError(\\\"Column {0} of row {1} cannot contain '{2}'\\\".format(col, row, digit))\\r\\n self._mods.clear()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d7044e2f9264cb1cec7da4d1ffe141c9\",\n \"score\": \"0.48897186\",\n \"text\": \"def binary_back_substitute(W, s):\\n # iterate backwards, starting from second to last row for back-substitution\\n s_copy = np.array(s)\\n n = len(s)\\n for row_num in xrange(n - 2, -1, -1):\\n row = W[row_num]\\n for col_num in xrange(row_num + 1, n):\\n if row[col_num] == 1:\\n s_copy[row_num] = (s_copy[row_num] + s_copy[col_num]) % 2\\n\\n return s_copy\",\n \"title\": \"\"\n },\n {\n \"docid\": \"018697a2981852ef5e5fa34b08e135f1\",\n \"score\": \"0.4886204\",\n \"text\": \"def reverseString(self, s: List[str]) -> None:\\n # if not s:\\n # return s\\n # idxl = 0\\n # idxr = len(s)-1\\n # while(idxr>idxl):\\n # tmp = s[idxl]\\n # s[idxl] = s[idxr]\\n # s[idxr] = tmp\\n s[:] = s[::-1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8855746d7a3fb30324f41ffa3dde2d2f\",\n \"score\": \"0.4885424\",\n \"text\": \"def replace(self, p, e):\\n node = self._validate(p)\\n elem = node._element # Old element to return.\\n node._element = e # Assigning new element to node.\\n return elem\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0541877089ecf60fab6a0ad3728c9b18\",\n \"score\": \"0.48800692\",\n \"text\": \"def filter_inplace(\\n self,\\n value: Any,\\n storage: Any,\\n strict: bool = False,\\n allow_downcast: Optional[bool] = None,\\n ):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07012f45af7d5dcc4debfa88f230b441\",\n \"score\": \"0.48747844\",\n \"text\": \"def reverseString(self, s) -> None:\\n s_len = len(s)\\n for index in range(s_len // 2):\\n s[index], s[s_len - index - 1] = s[s_len - index - 1], s[index]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4da12fd04f17d9beff49ae23fee605a\",\n \"score\": \"0.48719764\",\n \"text\": \"def replace(self, old, new):\\r\\n for i in range(len(self.data)):\\r\\n self.data[i] = self.data[i].replace(old, new)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d7abb05dcf4e87d17c9fb2900b62acba\",\n \"score\": \"0.48677066\",\n \"text\": \"def update_sequence(s, n, x):\\n t = tuple(s)\\n if -len(t) <= n < len(t):\\n return t[0:n] + (x,) + t[n + 1 : 0 if n == -1 else None]\\n else:\\n raise IndexError(\\\"sequence index out of range\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a75eaeafe7f5d9959b3411e0672c440c\",\n \"score\": \"0.48648545\",\n \"text\": \"def _backup_ ( s ) :\\n return LoKi.Dicts.DictsLoop.backup ( s )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b3da1bf023a5a0639d592244d5e16c1\",\n \"score\": \"0.48617312\",\n \"text\": \"def transform(self, S, typ):\\n #Defer everything to LindbladOp's\\n # `spam_tranform` function, which applies either\\n # error_map -> inv(S) * error_map (\\\"prep\\\" case) OR\\n # error_map -> error_map * S (\\\"effect\\\" case)\\n self.error_map.spam_transform(S, typ)\\n self.dirty = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"29ce2118627da87087579bb06bc0ab46\",\n \"score\": \"0.48601794\",\n \"text\": \"def replace(self, oldtext, newtext):\\r\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"638b8794e0dcacc8d144a5001445eca1\",\n \"score\": \"0.48575413\",\n \"text\": \"def replaceWith(replStr):\\r\\n return lambda s,l,t: [replStr]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d30e401c73e0565e148c351d5103a54\",\n \"score\": \"0.48521605\",\n \"text\": \"def test_inplace_setitem(self):\\n node = ast.parse('x[n] += 1')\\n IndexingToFunctions('op').visit(node)\\n self.assertEqual(to_source(node), dedent(\\\"\\\"\\\"\\\\\\n op.setitem(x, n, op.iadd(op.getitem(x, n), 1))\\n \\\"\\\"\\\"))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc52330c462cadfe42d69f43904dd476\",\n \"score\": \"0.4847357\",\n \"text\": \"def patch(self, start, end):\\n self.fh.seek(start)\\n region = self.fh.read(end - start + 1)\\n assert region[-1] == b'\\\\0'\\n substituted = region.replace(self.search, self.replace)\\n substituted+= b'\\\\0' * (end - start - len(substituted))\\n self.fh.seek(start)\\n self.fh.write(substituted)\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8cd8d52d6080e47ba14e6f097e5400c7\",\n \"score\": \"0.48469257\",\n \"text\": \"def mark_safe(s):\\n if isinstance(s, SafeData):\\n return s\\n if isinstance(s, str):\\n return SafeString(s)\\n if isinstance(s, unicode):\\n return SafeUnicode(s)\\n return SafeString(str(s))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e92f4a508b25ad54679875f1d080fb0\",\n \"score\": \"0.48462895\",\n \"text\": \"def _copy_and_replace(self, args):\\n\\n n = copy.deepcopy(self.replace_with)\\n ReplaceParameters(n).replace(self.name, args)\\n\\n return n\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4a2c1168a9091f8eab1331ca553c40a\",\n \"score\": \"0.4840211\",\n \"text\": \"def rreplace(s: str, old: str, new: str) -> str:\\n return (s[::-1].replace(old[::-1], new[::-1], 1))[::-1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d510912b7e4d7b69bccfc7077f4416ea\",\n \"score\": \"0.4838308\",\n \"text\": \"def replace_content(g):\\n g[0] = 88\\n g[1] = 22\\n g[2] = 44\\n print(\\\"g = \\\", g)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07ba5be5f475e23604ec8804d10cbb19\",\n \"score\": \"0.4823161\",\n \"text\": \"def update(self, s, a, r, s_p):\\n if s is not None and a is not None:\\n if self.counter[s][a] < self.n_known:\\n self.counter[s][a] += 1\\n normalizer = 1. / float(self.counter[s][a])\\n\\n self.R[s][a] = self.R[s][a] + normalizer * (r - self.R[s][a])\\n self.T[s][a][s_p] = self.T[s][a][s_p] + normalizer * (1. - self.T[s][a][s_p])\\n for _s_p in self.T[s][a]:\\n if _s_p not in [s_p]:\\n self.T[s][a][_s_p] = self.T[s][a][_s_p] * (1 - normalizer)\\n\\n if self.counter[s][a] == self.n_known:\\n self.update_upper_bound()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0bc523f2a1000041a2318361c35a6807\",\n \"score\": \"0.48209307\",\n \"text\": \"def replace_homonyms(s):\\n for val in homonyms.keys():\\n s = s.replace(val, homonyms[val])\\n return s\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1cdce6d8002ba96e113559604122a8ad\",\n \"score\": \"0.48192036\",\n \"text\": \"def rreplace(s, old, new, occurrence):\\n li = s.rsplit(old, occurrence)\\n return new.join(li)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca2a7dcc16e6b1cb1392b778cfa4303e\",\n \"score\": \"0.48156697\",\n \"text\": \"def test_inplace_setitem_gensym_both(self):\\n node = ast.parse('x.data[m:m+n] += 1')\\n gensym.reset()\\n IndexingToFunctions('op').visit(node)\\n self.assertEqual(to_source(node), dedent(\\\"\\\"\\\"\\\\\\n __gensym_1 = x.data\\n __gensym_2 = slice(m, m + n)\\n op.setitem(__gensym_1, __gensym_2, op.iadd(op.getitem(__gensym_1,\\n __gensym_2), 1))\\n \\\"\\\"\\\"))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df93de88c28e8fb7353c9804ca17e821\",\n \"score\": \"0.4810758\",\n \"text\": \"def reverseString(s: List[str]) -> None:\\n '''\\n Idea is to use double pointers to swap first/last and then work inwards\\n '''\\n \\n i = 0\\n j = len(s) - 1\\n\\n while i < j:\\n t = s[i]\\n s[i] = s[j]\\n s[j] = t\\n\\n i = i + 1\\n j = j - 1 \\n\\n # for testing\\n return s\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5714eb9ca2c077880bd9198f9f4a7534\",\n \"score\": \"0.48094067\",\n \"text\": \"def reverse_string(self, s: [str]) -> None:\\n tmp = None\\n length = len(s)\\n for i in range(0, length // 2):\\n tmp = s[i]\\n s[i] = s[length - i - 1]\\n s[length - i - 1] = tmp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3012c07f5f75b05c0b8bc23a1901a715\",\n \"score\": \"0.48090547\",\n \"text\": \"def reverseString(self, s: List[str]) -> None:\\n if not s or len(s) == 0:\\n return\\n for idx in range(len(s) // 2):\\n s[idx], s[-1 - idx] = s[-1 - idx], s[idx]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33b0d96e5c1be7abae8978e4d608cc70\",\n \"score\": \"0.4807088\",\n \"text\": \"def _write(self, s):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d02088c7a7400cd98e3ea7ea43d1a37\",\n \"score\": \"0.4806133\",\n \"text\": \"def replace_with_spaces(self, s):\\n return s.translate(self.space_table)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"591a4759ece6d31d37c85a92fd8faa35\",\n \"score\": \"0.48044842\",\n \"text\": \"def mutate(self, state):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e49df8f2ae022756fafd14c1fdb5c88\",\n \"score\": \"0.47979444\",\n \"text\": \"def modify(self, text):\\n\\n text = ' '.join(text)\\n text = '\\\\n'.join([line for line in text.splitlines() if line])\\n\\n return text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"61786cd165ce7454d004141248a5131c\",\n \"score\": \"0.47941357\",\n \"text\": \"def test_cons_replacing():\\n cons = HyCons(\\\"foo\\\", \\\"bar\\\")\\n cons[0] = \\\"car\\\"\\n\\n assert cons == HyCons(\\\"car\\\", \\\"bar\\\")\\n\\n cons[1:] = \\\"cdr\\\"\\n assert cons == HyCons(\\\"car\\\", \\\"cdr\\\")\\n\\n try:\\n cons[:] = \\\"foo\\\"\\n assert True is False\\n except IndexError:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"03f7de513eec43c1fdc8daf534035c1c\",\n \"score\": \"0.4790297\",\n \"text\": \"def singularize(self):\\n temp = [TextBlob(word) for word in self.text]\\n self.text = [list(word.words.singularize())[0] for word in temp]\\n tempv = [Word(word) for word in self.text]\\n self.text = [word.lemmatize(\\\"v\\\") for word in tempv]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6247818c5583298e987c0a353cd98381\",\n \"score\": \"0.47774732\",\n \"text\": \"def reverseString(self, s: List[str]) -> None:\\r\\n s.reverse()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"86e6289d2f383786d118fe835511e774\",\n \"score\": \"0.47745445\",\n \"text\": \"def prepare_for_replace(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae3af43ca1b1b940091f6358f2692482\",\n \"score\": \"0.47745287\",\n \"text\": \"def update_replace(self, *args, **kwds):\\n data_new = self._process_upddata(args, kwds)\\n keys = {key for key, val in data_new}\\n pre_data = tuple((key, val) for key, val in self._data if key not in keys)\\n self._data_checked = pre_data + data_new\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f59ade267e427f0b31542db37269e6e5\",\n \"score\": \"0.47729003\",\n \"text\": \"def reverseString(self, s: List[str]) -> None:\\n\\n for i in range(len(s) // 2):\\n s[i], s[-(i+1)] = s[-(i+1)], s[i]\\n\\n #\",\n \"title\": \"\"\n },\n {\n \"docid\": \"21f31b943e2e556635f39f5c77da920a\",\n \"score\": \"0.47650313\",\n \"text\": \"def reverseString(self, s) -> None:\\n n = len(s)\\n for i in range(n//2):\\n s[i], s[n-i-1] = s[n-i-1], s[i]\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1197,"cells":{"query_id":{"kind":"string","value":"92fdae44e288b1efac5c4c32ca43cd36"},"query":{"kind":"string","value":"method that does the same as test on old page templates"},"positive_passages":{"kind":"list like","value":[{"docid":"19a65991270b27665e8da46d5b6d675b","score":"0.0","text":"def test(self, condition, true_value, false_value):\n\n if condition:\n return true_value\n else:\n return false_value","title":""}],"string":"[\n {\n \"docid\": \"19a65991270b27665e8da46d5b6d675b\",\n \"score\": \"0.0\",\n \"text\": \"def test(self, condition, true_value, false_value):\\n\\n if condition:\\n return true_value\\n else:\\n return false_value\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"116ee17c0eac19f4dc5c111ec94c6a03","score":"0.7356265","text":"def test_templates(self):\n\t\tpass","title":""},{"docid":"6a5eb435727277a1882a9d380302dcf0","score":"0.69098026","text":"def _assertPageTemplatesUsed(self, response):\n self.assertTemplateUsed(\n response, 'melange/landing_page/_program_section.html')\n self.assertTemplateUsed(\n response, 'melange/landing_page/_contact_us_section.html')","title":""},{"docid":"fe37e98dc5e162593ee3c915fc25fc55","score":"0.68025625","text":"def test_template_usado(self):\n self.assertTemplateUsed(self.response, 'search/index.html')","title":""},{"docid":"3ea10c32cd038a7e2551f2a6de19740d","score":"0.6735","text":"def test_template(self):\n self.assertTemplateUsed(self.resp, 'index.html')","title":""},{"docid":"95e8a1423ff9dbea530b83741a7fcb8e","score":"0.6627654","text":"def test_template_usado(self):\n self.assertTemplateUsed(self.response, 'search/search_map.html')","title":""},{"docid":"9ef9e6fb3e51c5434def9ec1b902282a","score":"0.6601774","text":"def test_view_uses_correct_template(self):\n response = self.client.get(reverse('home'))\n self.assertEqual(response.status_code, 200)\n self.assertTemplateUsed(response, 'home.html')","title":""},{"docid":"242319f2d81738ba6dc037d57de4ae81","score":"0.6590009","text":"def test_template_usado(self):\n self.assertTemplateUsed(self.response, 'search/search_filters.html')","title":""},{"docid":"9028718d9f70633dc93963be1f23651a","score":"0.6552989","text":"def test_templates_used(self):\n response = self.client.get(reverse('home'))\n self.assertEqual(response.status_code, 200)\n self.assertTemplateUsed(response, 'home/home.html')\n self.assertTemplateUsed(response, 'base.html')","title":""},{"docid":"29950777339f71f3880609f167c99fa3","score":"0.6528326","text":"def test_template_home(self):\n url = reverse('home')\n response = self.client.get(url)\n actual= 'home.html'\n self.assertTemplateUsed(response,actual)","title":""},{"docid":"38c1a95f7923c17a29dcc1657677019c","score":"0.65246904","text":"def test_template_usage(self):\n self.assertTemplateUsed(self.client.get(reverse('home')),\n 'index.html')","title":""},{"docid":"0038661826218905bd5b30402469fedb","score":"0.6483396","text":"def test_page_output(self):\r\n # Test initial state\r\n from fluent_pages.tests.testapp.page_type_plugins import SimpleTextPagePlugin # Import here as it needs an existing DB\r\n self.assertEquals(SimpleTextPagePlugin.render_template, 'testapp/simpletextpage.html')\r\n\r\n # Test how a normal page is rendered\r\n response = self.client.get('/sibling1/')\r\n self.assertTemplateUsed(response, 'testapp/simpletextpage.html')\r\n self.assertContains(response, '<div id=\"test_contents\">TEST_CONTENTS</div>')","title":""},{"docid":"72f9863e3c37328d2619e3920698212a","score":"0.6448849","text":"def test_should_use_template_index_html(self):\n self.assertTemplateUsed(self.response, 'index.html')","title":""},{"docid":"e98d825cd19a752d3633b5df84cf58ce","score":"0.6426471","text":"def before(self, request, tmpl_content):","title":""},{"docid":"9e617e31f38622d95ee31e3216eb2797","score":"0.6376796","text":"def test_home_list(self):\n url = reverse('list')\n response = self.client.get(url)\n actual= 'list.html'\n self.assertTemplateUsed(response,actual)","title":""},{"docid":"aba952416c4ce206b3cf0bb03a1099d2","score":"0.6367485","text":"def test_template_used(self):\n self.assertTemplateUsed(self.resp, 'accounts/signup.html')","title":""},{"docid":"12273e68e00a3f09c4165872e9202777","score":"0.6344034","text":"def test_namespaced_html(self):\n ...","title":""},{"docid":"18f36c23c24159d6f082ebc29e235709","score":"0.63392204","text":"def test_home_route_uses_right_templates(self):\n response = self.client.get(reverse_lazy(\"home\"))\n self.assertTemplateUsed(response, \"imagersite/base.html\")\n self.assertTemplateUsed(response, \"imagersite/home.html\")","title":""},{"docid":"494a9a00b9f4e6e04de9271422533a8a","score":"0.63349473","text":"def test_only_ajax(self):\n request = RequestFactory().get(\"/\")\n response = self.view(request)\n assert response.template_name == \"normal_template.html\"","title":""},{"docid":"b2a45c83549d57ca2c9ab1df8d9f8b8f","score":"0.6330742","text":"def assertPageTemplatesUsed(self, response):\n self.assertGCITemplatesUsed(response)\n self.assertTemplateUsed(response, 'modules/gci/org_list/base.html')\n self.assertTemplateUsed(\n response, 'modules/gci/accepted_orgs/_project_list.html')\n self.assertTemplateUsed(response, 'soc/list/lists.html')\n self.assertTemplateUsed(response, 'soc/list/list.html')","title":""},{"docid":"ed7f0d57d0e266a409ae59c23ec088de","score":"0.6298556","text":"def test_template_tag(self):\n entries = Entry.objects.all()\n entries = get_published_entries(entries, 'en')\n self.assertEqual(len(entries), 1, msg=(\n 'Should return the entries that are published.'))","title":""},{"docid":"77f5adbf4f512ef2b2334155e6d79b52","score":"0.62533784","text":"def test_home_template_use(self):\n response = self.client.get('/')\n self.assertTemplateUsed(response, 'mainRecycleApp/home.html')","title":""},{"docid":"34c05b6c2cd308848038273b6b17fece","score":"0.6252499","text":"def test_homepage_pulls_from_base_template(self):\n response = self.client.get(reverse_lazy('homepage'))\n self.assertTemplateUsed(response, 'imagersite/base.html')","title":""},{"docid":"6505c443dbc56c1e5e1031dc76121093","score":"0.62293416","text":"def test_custom_template(self):\n response = self.client.get(self.customized_url)\n self.assertTemplateUsed(response, \"customized_contact.html\")","title":""},{"docid":"a0b82ff0933aa92666b99b0f270ee93b","score":"0.6224032","text":"def test_home_view_uses_correct_template(self):\n response = self.client.get(reverse('home'))\n self.assertEqual(response.status_code, 200)\n self.assertTemplateUsed(\n response, template_name=\"home/index.html\")","title":""},{"docid":"7206040a475bca8fb6d8c15877afeb4a","score":"0.61902326","text":"def test_template_display(self):\n response = self.client.get('/webtest/')\n self.assertEqual(response.status_code, 200)\n content = response.content\n self.assertEqual(\"<h1>Web test of TransAct api</h1>\" in content, True)\n #static files not easly testable\n #response = self.client.get('/static/webtest/js/main.js')\n #self.assertEqual(response.status_code, 200)","title":""},{"docid":"25769d2b08e9b8b751381d857b5306ce","score":"0.61889523","text":"def test_translate_view(self):\r\n # Check page status\r\n resp = self.client['maintainer'].get(self.translate_view_url)\r\n self.assertEqual(resp.status_code, 200)\r\n self.assertTemplateUsed(resp, 'translate.html')","title":""},{"docid":"7e1a4787a38c9da0fdb3ece80d6041a3","score":"0.6176841","text":"def test_restricted_template_inherits(self):\n template_base_path = os.path.join(settings.TEMPLATE_DIR, 'rango')\n template_path = os.path.join(template_base_path, 'restricted.html')\n\n template_str = get_template(template_path)\n full_title_pattern = r'<title>(\\s*|\\n*)Rango(\\s*|\\n*)-(\\s*|\\n*)Restricted Page(\\s*|\\n*)</title>'\n block_title_pattern = r'{% block title_block %}(\\s*|\\n*)Restricted Page(\\s*|\\n*){% (endblock|endblock title_block) %}'\n\n user_object = create_user_object()\n self.client.login(username='testuser', password='testabc123')\n request = self.client.get(reverse('rango:restricted'))\n content = request.content.decode('utf-8')\n\n self.assertTrue(re.search(full_title_pattern, content), f\"{FAILURE_HEADER}The <title> of the response for 'rango:restricted' is not correct. Check your restricted.html template, and try again.{FAILURE_FOOTER}\")\n self.assertTrue(re.search(block_title_pattern, template_str), f\"{FAILURE_HEADER}Is restricted.html using template inheritance? Is your <title> block correct?{FAILURE_FOOTER}\")","title":""},{"docid":"1721962048b6a32d5c45fc1344314ed7","score":"0.6148524","text":"def test_that_template_is_used(self):\n client = Client()\n test_superuser = User.objects.get(username=test_superuser_username)\n client.force_login(user=test_superuser)\n response = client.get(reverse('file_manager:note_index'), follow=True)\n self.assertTemplateUsed(\n response=response,\n template_name='file_manager/note_index.html'\n )","title":""},{"docid":"1dbb20bcd403f88689653cf849c98c3a","score":"0.6143415","text":"def test_products_page(self):\n response = self.client.get('/products/')\n self.assertEqual(response.status_code, 200)\n self. assertTemplateUsed(response, 'products/products.html')","title":""},{"docid":"6bab94157b95186296e606f591cd6142","score":"0.61313236","text":"def test_about_template_use(self):\n response = self.client.get('/about/')\n self.assertTemplateUsed(response, 'mainRecycleApp/about.html')","title":""},{"docid":"19d4cf8cd4bd312223205cf2f7fa104f","score":"0.61263794","text":"def test_post_landing_page(self):\n pass","title":""},{"docid":"023c0c0d55ed2201caab5c81a2f7ea67","score":"0.61125535","text":"def test_render_template(mock_render_template):\n mock_self = Mock()\n template.Page.render_template(mock_self)\n mock_self.build.assert_called_once_with()\n mock_render_template.assert_called_once_with(mock_self.build())","title":""},{"docid":"2ceb8e13675eccea952c241666a72042","score":"0.60705966","text":"def template():\n pass","title":""},{"docid":"67fef3f4123471bfae7bb88944d7b749","score":"0.60630566","text":"def test_assign_linguists_from_template(self):\n pass","title":""},{"docid":"cf933e1c3c2a535625a5853cf3615716","score":"0.60584915","text":"def test_category_list_page_without_category(self): \n\n c = Client()\n response = c.get(\"/personal/blog/category/\")\n self.assertEqual(response.status_code, 200) \n self.assertEqual(response.template_name, ['blog/category_list.html'])\n self.assertIn('blog/category_list.html', response.template_name)","title":""},{"docid":"388a5f4300812f6fc913175fcccc1895","score":"0.6051886","text":"def test_render_template(self):\n template = self.block.meta.template\n self.assertEqual(template, 'common/blocks/centered_text.html', 'The templates were not the same')","title":""},{"docid":"73b55d3bd929a57f82b9c3972cc098db","score":"0.60433125","text":"def test_uses_home_template(self):\n # instead of manually creating an HttpRequest object (as we did\n # in the earlier version of this test, we can use self.client.get\n # (part of the Django extensions to TestCase)\n response = self.client.get('/') # the view returns a HttpResponse\n # object\n\n # We don't need to test constants, we can just test that the right\n # template gets loaded (the functional test will still prove that\n # the right content is shown to the user).\n self.assertTemplateUsed(response, 'lists/home.html')","title":""},{"docid":"f5761af0a81a12b4fed429822f5e942a","score":"0.6033479","text":"def skip_page(self, page: pywikibot.Page) -> bool:\n if page.namespace() != 118:\n pywikibot.warning('{} is not a draft.'.format(page))\n return True\n if has_template(page, self.opt.template):\n pywikibot.warning('{} already has the template.'.format(page))\n return True\n return super().skip_page(page)","title":""},{"docid":"87092f8949648ce6c6e8427d60a307e4","score":"0.6005766","text":"def testAccessTemplate(self):\n parser = templateparser.Parser()\n self.assertEqual(len(parser), 0)\n self.assertEqual(parser[self.name], self.template)\n self.assertEqual(len(parser), 1)","title":""},{"docid":"1a2daa77221361d9e5b92153ee87d379","score":"0.600456","text":"def test_get_nonexistent_template(self):\n self.template_set.get('nonexistenttemplate.xml')","title":""},{"docid":"61eb4743e2bbecbcd22dd26d491cc756","score":"0.6002779","text":"def test_renders(self):\n page = PageFactory.create(parent=None, title=\"foo\")\n SiteFactory.create(root_page=page)\n with self.assertTemplateUsed(template_name=\"blocks/page_chooser_block.html\"):\n response = self.block.render_basic(page)\n\n self.assertIn(page.title, response)\n self.assertIn(page.url, response)","title":""},{"docid":"25adda85d9e96164e4344abacac430b9","score":"0.6001421","text":"def test_view_uses_correct_template(self):\n response = self.client.get(reverse('shopping_bag'))\n self.assertEqual(response.status_code, 302)","title":""},{"docid":"c0c456fb8f449ed30e4e69cf3cd85e4b","score":"0.5974716","text":"def test_blog_loaded_templates(self):\n response = self.client.get('/blog/')\n templates = response.templates\n names = fetch_template_names(templates)\n\n self.assertIn('base.html', str(names))\n self.assertIn('blog.html', str(names))","title":""},{"docid":"9816fd87fd7741cab381c39329606a49","score":"0.59736246","text":"def test_real_xhtml_document(self):\n ...","title":""},{"docid":"9816fd87fd7741cab381c39329606a49","score":"0.59736246","text":"def test_real_xhtml_document(self):\n ...","title":""},{"docid":"484ba3963007ff28277d0061fe9a14f7","score":"0.5956028","text":"def test_get_home(self):\n page = self.client.get(reverse(\"home\"))\n self.assertEqual(page.status_code, 200)\n self.assertTemplateUsed(page, \"home.html\")","title":""},{"docid":"096ca896d1e0a5ee3c204275cae2fe3a","score":"0.59459","text":"def test_swaps_template(self):\n request = RequestFactory().get(\n \"/\", HTTP_X_REQUESTED_WITH=\"XMLHttpRequest\")\n response = self.view(request)\n assert response.template_name == \"ajax_template.html\"","title":""},{"docid":"89258d0eb44c4baa1cef74fac2cb8690","score":"0.5941432","text":"def testGenHtml(self):\n html = self.gen_html('')\n self.assertIn('test1', html)\n self.assertIn('test2', html)\n self.assertIn('release', html)\n self.assertIn('debug', html)","title":""},{"docid":"dad68ffb46cae9daf09fa4b69b766738","score":"0.59371394","text":"def test_render(self):\n\n\t\ttemplate_content = self.app.render('test_render.html', test_var='Testing templates')\n\t\tself.assertEqual(template_content, 'Testing templates')","title":""},{"docid":"f36518e5504a35ed39478058305ac064","score":"0.5929788","text":"def test_projects_home_status_code_correct_template(self):\n self.assertEqual(self.response.status_code, 200)\n self.assertTemplateUsed(self.response, 'projects/projects_home.html')","title":""},{"docid":"32b6533d37c24744378c05b1215a9d29","score":"0.5927937","text":"def test_get_page_empty_page(self):\n data = {'page': 1231}\n req = self.request.get('/', data=data)\n response = self.view.get(req)\n html = response.content.decode('utf-8')\n self.assertIn(\n 'chosen\">3</a>',\n html\n )","title":""},{"docid":"f54817c6d0e45887c425eb4473fa946b","score":"0.5924586","text":"def test_get(self):\r\n response = self._get()\r\n self.assertEquals(response.status_code, 200)\r\n self.assertTemplateUsed(response, self.template_name)\r\n self.assertFalse(response.context['form'].is_bound)","title":""},{"docid":"464ae1fb511f2ed49bbf30442d94cf7f","score":"0.59194297","text":"def test_login_template_content(self):\n template_base_path = os.path.join(settings.TEMPLATE_DIR, 'rango')\n template_path = os.path.join(template_base_path, 'login.html')\n self.assertTrue(os.path.exists(template_path), f\"{FAILURE_HEADER}We couldn't find the 'login.html' template in the 'templates/rango/' directory. Did you put it in the right place?{FAILURE_FOOTER}\")\n \n template_str = get_template(template_path)\n self.assertTrue('<h1>Login to Rango</h1>' in template_str, f\"{FAILURE_HEADER}We couldn't find the '<h1>Login to Rango</h1>' in the login.html template.{FAILURE_FOOTER}\")\n self.assertTrue('action=\"{% url \\'rango:login\\' %}\"' in template_str, f\"{FAILURE_HEADER}We couldn't find the url lookup for 'rango:login' in your login.html <form>.{FAILURE_FOOTER}\")\n self.assertTrue('<input type=\"submit\" value=\"submit\" />' in template_str, f\"{FAILURE_HEADER}We couldn't find the submit button in your login.html template. Check it matches what is in the book, and try again.{FAILURE_FOOTER}\")","title":""},{"docid":"787af99704ca80667d6b47eb450b3d4a","score":"0.5914893","text":"def test_render_template(self):\n template = self.block.meta.template\n self.assertEqual(template, 'common/blocks/button.html', 'Templates did not match')","title":""},{"docid":"9fa976ec82cd196cd96382a48067c2c1","score":"0.5914765","text":"def test_section_page_gets_created(section_page):\n assert section_page is not None","title":""},{"docid":"0d2755e58a3fbc8b7585512531958812","score":"0.589026","text":"def setUp(self):\n self.tmpl = templateparser.Template","title":""},{"docid":"0d2755e58a3fbc8b7585512531958812","score":"0.589026","text":"def setUp(self):\n self.tmpl = templateparser.Template","title":""},{"docid":"099e336942fe4f4994e65f52df9615d0","score":"0.58871114","text":"def test_homepage(self):\n\n with self.client as client:\n response = client.get('/')\n html = response.get_data(as_text=True)\n # test that you're getting a template\n \n self.assertIn('id=\"boggle_game_board\"', html)","title":""},{"docid":"a40f890858d01f91b9f9a9d596abdfb8","score":"0.588143","text":"def test_app_page_output(self):\r\n # Test initial state\r\n from fluent_pages.tests.testapp.page_type_plugins import WebShopPagePlugin\r\n self.assertEquals(WebShopPagePlugin.urls, 'fluent_pages.tests.testapp.urls_webshop')\r\n\r\n response = self.client.get('/shop/')\r\n self.assertContains(response, 'test_webshop: index_page') # The URLconf is an overlay over the standard get_response()\r","title":""},{"docid":"b342bf9886d7f1a1307128ee5a10e735","score":"0.5877771","text":"def testPageLoads(self):\n self.site.latest_gsoc = self.gsoc_program.key().name()\n self.site.latest_gci = self.gci_program.key().name()\n self.site.mailing_list = 'dev@test.com'\n self.site.put()\n\n response = self.get('/')\n self.assertResponseOK(response)\n self._assertPageTemplatesUsed(response)","title":""},{"docid":"e0f30f91675a0b25051fe89a75ad4a27","score":"0.5870789","text":"def test_Create_route(self):\n url = reverse('create')\n response = self.client.get(url)\n actual= 'create.html'\n self.assertTemplateUsed(response,actual)","title":""},{"docid":"2bba2e05914ffcafe4a457c699686016","score":"0.5870573","text":"def assert_test_get(self, response):\n self.assertEqual(response.status_code, 200)\n self.assertTemplateUsed(\n response, 'firestorm/display_recommend_enroll.html')\n self.assertTemplateUsed(response, \n 'include/frm/frm_recommend_ad_rep.html')\n self.assertTemplateUsed(response, 'include/dsp/dsp_live_help.html')","title":""},{"docid":"e17480cc00fc8fc1680b166029e38c54","score":"0.5867035","text":"def test_page_basics(self):\n rv = self.app.get('/')\n assert b'<!DOCTYPE html>' in rv.data\n assert b'<title>jotquote</title>' in rv.data\n assert b'<div class=\"quote\">They that can give up essential liberty to obtain a little temporary safety deserve neither liberty nor safety.</div>' in rv.data\n assert b'<div class=\"author\">Ben Franklin</div>' in rv.data","title":""},{"docid":"f5cd03c07fe89aeeb9d4f09f4860568f","score":"0.5866824","text":"def test_home_page_response_and_template(self):\n response = self.client.get(reverse('hello:home'))\n\n self.assertTemplateUsed(response, 'home.html')\n self.assertEqual(response.status_code, 200)\n self.assertContains(response,\n '<h1>42 Coffee Cups Test Assignment</h1>',\n html=True)","title":""},{"docid":"35b5e1ec66c8c92869543e57082b9004","score":"0.58607686","text":"def test_portals_id_designs_nk_template_get(self):\n pass","title":""},{"docid":"b6c3c4f07114f7d67e0309a1e919554d","score":"0.58506745","text":"def test_assignable_templates(self):\n pass","title":""},{"docid":"c1546ac89718891ef0f1dba45657d4b1","score":"0.5845095","text":"def test_app_page_url(self):\r\n response = self.client.get('/shop/foobar/')\r\n self.assertContains(response, 'test_webshop: article: foobar')","title":""},{"docid":"dbb2df73e92278bbac92be5728967b06","score":"0.58383566","text":"def test_en_page(self):\n response = self.client.get('/test/')\n self.assertEqual(response.status_code, 200)\n self.assertTrue('page' in response.context)\n self.assertContains(response, 'A test page for English pages.')","title":""},{"docid":"f0de4f8434de7c0931c357e0011ce7ea","score":"0.58278805","text":"def testTaglessTemplate(self):\n template = 'Template without any tags'\n self.assertEqual(self.tmpl(template).Parse(), template)","title":""},{"docid":"bbc8531d32a8c74688ab79df1fe7be28","score":"0.58140105","text":"def test_hide_unpublished(self):\r\n self.assertTrue(SimpleTextPage.objects.filter(translations__slug='unpublished').exists(), \"page /unpublished/ should exist in the database.\")\r\n self.assert404('/unpublished/')\r\n self.assert404('/unpublished') # With default APPEND_SLASH=True\r","title":""},{"docid":"43fc588ef24e82870574cdb1c050d06d","score":"0.58100694","text":"def test_normal(self):\n response = self.client.get(HOME_URL)\n for entry in NORMAL_ENTRIES:\n node = '<a href=\"%s\">%s</a>' % (reverse(entry['view']), entry['name'])\n self.assertContains(response, node, html=True)","title":""},{"docid":"e1d368817505dc20f5fe9ef78225625a","score":"0.5809132","text":"def test_get_template(self):\n import hardest\n instance = hardest.Templator(self.package_name)\n context = {'first': 'a', 'second': 'b'}\n template = instance.get_template(self.template_path, context)\n self.assertIsInstance(template, hardest.Template)","title":""},{"docid":"f3a69f82adb48aad0bd61023d9703698","score":"0.58033097","text":"def test_project_about_status_code_correct_template(self):\n self.assertEqual(self.response.status_code, 200)\n self.assertTemplateUsed(self.response, 'projects/project_about.html')","title":""},{"docid":"4733f883575a48460fd00fa9af622c0c","score":"0.58021456","text":"def test_html_for(self):\n message = factory.get_html_for('test', {'title': 'Et hop'})\n self.assertIn('Et hop', message)","title":""},{"docid":"8c10f71035eafe02b12190546ac86827","score":"0.58018196","text":"def test_about_us_page(self):\n about_us_url = reverse('about-us')\n response = self.client.get(about_us_url)\n self.assertEqual(response.status_code, 200)\n self.assertTemplateUsed(response, 'common/about_us.html')","title":""},{"docid":"4c9e4f98b6477deca7dc8bfeb709b565","score":"0.5797842","text":"def test_template(self):\n self.assertIsInstance(self.changelist.template, Template)\n render = self.changelist.template.render(Context())\n to_json = json.loads(render)\n self.assertEqual(to_json, {\n 'test': {\n 'name': 'whee!',\n 'models': {\n 'embeds.Iframe': None,\n }\n }\n })","title":""},{"docid":"a73513a95311b180c8c944f1b3986442","score":"0.57956564","text":"def test_template():\n tpl = Template()\n assert tpl.hello().my_name_is(\"Christophe\") == \"hello Christophe\"","title":""},{"docid":"7b8445c962a436885410532c6d5b4124","score":"0.57843786","text":"def test__call__():\n mock_self = Mock()\n template.Page.__call__(mock_self)\n mock_self.build.assert_called_once_with()","title":""},{"docid":"e54fa1c883ce52789c21e09f9c86332a","score":"0.5778468","text":"def test_take_action_template(self):\n new_campaign = Campaign(campaign_url='test.com')\n new_campaign.save() \n response = self.client.get('/takeaction/?campaign_id=%s' % (new_campaign.id))\n self.assertEqual('takeaction.html', response.templates[0].name)","title":""},{"docid":"2b3932b75f3c7c239b5bd7063b766d44","score":"0.5758571","text":"def _test_template(self, template_path):\n full_path = os.path.join(self.template_dir, template_path)\n doc = lxml.html.parse(full_path)\n expecting_vuln = _get_expecting_vuln(doc)\n templ = loader.get_template(template_path)\n context = parse_template.get_default_context()\n templ.render(context)\n methods = [\n parse_template.get_non_js_escaped_results_for_template,\n parse_template.get_non_quoted_attr_vars_for_template\n ]\n for method in methods:\n for result in method(templ):\n self.csw.handle_callback(result)\n self.assertEqual(len(self.csw.results), len(expecting_vuln))\n for result, expected in zip(self.csw.results, expecting_vuln):\n line_no = result.get_line_number()\n part = result.get_vulnerability_text()\n filename = result.get_filename()\n var = str(result._var_node.filter_expression.var)\n self.assertEqual(line_no, expected['line_number'])\n self.assertEqual(var, expected['name'])\n self.assertEqual(filename, full_path)\n self.assertTrue(var in part)","title":""},{"docid":"b664f6303b06f997a5135fe34353b728","score":"0.57584506","text":"def test_view(self):\n pass","title":""},{"docid":"f422b99e00b8a8a9899ae1fa36840d36","score":"0.575402","text":"def test_normal_doctypes(self):\n ...","title":""},{"docid":"d3d12f1ad668aa0ee0239f0f696383c3","score":"0.57506454","text":"def test_portals_id_templates_get(self):\n pass","title":""},{"docid":"8bd237ed8fee294ff7d9fca6ba340050","score":"0.57318455","text":"def test_render_template_doc(mock_rts, mock_markup):\n test_string = 'some string'\n test_doc = Mock(spec=yattag.Doc)\n test_doc.getvalue.return_value = test_string\n template.render_template(test_doc)\n test_doc.getvalue.assert_called_once_with()\n mock_rts.assert_called_once_with(test_string)\n mock_markup.assert_called_once_with(mock_rts(test_string))","title":""},{"docid":"f2a5178b586baabdde4ed5213aa7dac0","score":"0.5729704","text":"def regularpage(pagename=None):\n if pagename==None:\n raise ExperimentError('page_not_found')\n return render_template(pagename)","title":""},{"docid":"e264aaeca3d086b3910ac486d2234f0c","score":"0.5728226","text":"def test_base_for_register_link(self):\n template_base_path = os.path.join(settings.TEMPLATE_DIR, 'rango')\n base_path = os.path.join(template_base_path, 'base.html')\n template_str = get_template(base_path)\n self.assertTrue('<li><a href=\"{% url \\'rango:register\\' %}\">Sign Up</a></li>' in template_str)","title":""},{"docid":"742ef51da6a59f2639264569bfcfd05c","score":"0.57240415","text":"def test_contact_template_use(self):\n response = self.client.get('/contact/')\n self.assertTemplateUsed(response, 'mainRecycleApp/contact.html')","title":""},{"docid":"724a9e8df265ac6baca6102fe76d0ac3","score":"0.57095826","text":"def WhatsNewPage(request):\n return render_to_response('new_in_2_0.html', {})","title":""},{"docid":"df4a11c52c3aeef4cb579aedeb115a3b","score":"0.5706879","text":"def get_uses_template_and_defaults(self, template):\n self.login()\n self.response = self.client.get(reverse(self.name))\n super().get_uses_template_and_defaults(template)","title":""},{"docid":"835e95bba390acceb669f2fb55565bf7","score":"0.5706813","text":"def test_restricted_template_exists(self):\n template_base_path = os.path.join(settings.TEMPLATE_DIR, 'rango')\n template_path = os.path.join(template_base_path, 'restricted.html')\n self.assertTrue(os.path.exists(template_path), f\"{FAILURE_HEADER}We couldn't find the 'restricted.html' template in the 'templates/rango/' directory. Did you put it in the right place? Did you complete the exercises?{FAILURE_FOOTER}\")","title":""},{"docid":"40056130eadf591fecc87bc79a486301","score":"0.56994915","text":"def test_wagtail_pages(self):\n\n pages = Page.objects.live()\n for page in pages:\n url = page.relative_url(page.get_site())\n if url is not None:\n self.get(url)\n self.assert_status_code('200')","title":""},{"docid":"2b262cf90396f100bbbdc4b49fd0a4da","score":"0.56977457","text":"def test_template_loader_postmortem(self):\n template_name = \"notfound.html\"\n with tempfile.NamedTemporaryFile(prefix=template_name) as tmpfile:\n tempdir = os.path.dirname(tmpfile.name)\n template_path = os.path.join(tempdir, template_name)\n with override_settings(\n TEMPLATES=[\n {\n \"BACKEND\": \"django.template.backends.django.DjangoTemplates\",\n \"DIRS\": [tempdir],\n }\n ]\n ), self.assertLogs(\"django.request\", \"ERROR\"):\n response = self.client.get(\n reverse(\n \"raises_template_does_not_exist\", kwargs={\"path\": template_name}\n )\n )\n self.assertContains(\n response,\n \"%s (Source does not exist)\" % template_path,\n status_code=500,\n count=2,\n )\n # Assert as HTML.\n self.assertContains(\n response,\n \"<li><code>django.template.loaders.filesystem.Loader</code>: \"\n \"%s (Source does not exist)</li>\"\n % os.path.join(tempdir, \"notfound.html\"),\n status_code=500,\n html=True,\n )","title":""},{"docid":"23a3110f24df1959a363564579c74175","score":"0.5697314","text":"def test_template_text(self):\n t = Template(\"\"\"{% spaceless %}{% load addendum_tags %}{% snippet 'django' template=True %}Hello world{% endsnippet %}{% endspaceless %}\"\"\")\n c = Context({'dog': 'woof'})\n result = t.render(c)\n self.assertEqual(result, \"WOOF\")","title":""},{"docid":"e9ffd02f0105b7ef64a50cee775d53a5","score":"0.5693967","text":"def testGenHtmlFilterExact(self):\n html = self.gen_html('release', True)\n self.assertIn('release', html)\n self.assertNotIn('debug', html)","title":""},{"docid":"86bfb1fbf5d1df2c30b60e7c8b8b80d9","score":"0.5693639","text":"def test_get_template_no_context(self):\n import hardest\n instance = hardest.Templator(self.package_name)\n template = instance.get_template(self.template_path)\n self.assertIsInstance(template, hardest.Template)","title":""},{"docid":"8a92cf152a228d2f2349b5f68e92e07e","score":"0.56882656","text":"def test_get_template_directory(self):\n self.template_set.get('directory')","title":""},{"docid":"2b7c6474d31f27f95da5c9f0afcfb85a","score":"0.568337","text":"def testPreloadTemplates(self):\n parser = templateparser.Parser(templates=[self.name])\n self.assertEqual(len(parser), 1)\n self.assertEqual(parser[self.name], self.template)","title":""},{"docid":"4235c847ca4cf67bcfb14d65bfce7d70","score":"0.5681217","text":"def test_get(self):\r\n response = self._get()\r\n self.assertEquals(response.status_code, 200)\r\n self.assertTemplateUsed(response, self.template_name)\r\n self.assertTrue('object' in response.context)\r\n self.assertEquals(response.context['object'], self.obj)","title":""},{"docid":"4235c847ca4cf67bcfb14d65bfce7d70","score":"0.5681217","text":"def test_get(self):\r\n response = self._get()\r\n self.assertEquals(response.status_code, 200)\r\n self.assertTemplateUsed(response, self.template_name)\r\n self.assertTrue('object' in response.context)\r\n self.assertEquals(response.context['object'], self.obj)","title":""},{"docid":"33aa2d4a8fce7a55b806d01098462f42","score":"0.567803","text":"def test_users_page(self):\n\n with self.client as client:\n response = client.get('/users')\n html = response.get_data(as_text=True)\n\n self.assertEqual(response.status_code, 200)\n self.assertIn('Users Template.', html)","title":""},{"docid":"d4b2a4b3c1d93f3eabc8d4d098ddf6d7","score":"0.5676243","text":"def verify_template_is_not_available(self,template):\n field=npsp_lex_locators[\"adv_mappings\"][\"field_mapping\"].format(\"Template\")\n self.selenium.click_element(field)\n element=self.selenium.get_webelement(field)\n status=element.get_attribute(\"aria-activedescendant\")\n if status is not None:\n self.selenium.page_should_not_contain(template)\n else:\n self.selenium.wait_until_page_contains(\"Default Gift Entry Template\")\n self.selenium.page_should_not_contain(template) \n self.selenium.click_button(\"Cancel\")","title":""}],"string":"[\n {\n \"docid\": \"116ee17c0eac19f4dc5c111ec94c6a03\",\n \"score\": \"0.7356265\",\n \"text\": \"def test_templates(self):\\n\\t\\tpass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a5eb435727277a1882a9d380302dcf0\",\n \"score\": \"0.69098026\",\n \"text\": \"def _assertPageTemplatesUsed(self, response):\\n self.assertTemplateUsed(\\n response, 'melange/landing_page/_program_section.html')\\n self.assertTemplateUsed(\\n response, 'melange/landing_page/_contact_us_section.html')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe37e98dc5e162593ee3c915fc25fc55\",\n \"score\": \"0.68025625\",\n \"text\": \"def test_template_usado(self):\\n self.assertTemplateUsed(self.response, 'search/index.html')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ea10c32cd038a7e2551f2a6de19740d\",\n \"score\": \"0.6735\",\n \"text\": \"def test_template(self):\\n self.assertTemplateUsed(self.resp, 'index.html')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95e8a1423ff9dbea530b83741a7fcb8e\",\n \"score\": \"0.6627654\",\n \"text\": \"def test_template_usado(self):\\n self.assertTemplateUsed(self.response, 'search/search_map.html')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ef9e6fb3e51c5434def9ec1b902282a\",\n \"score\": \"0.6601774\",\n \"text\": \"def test_view_uses_correct_template(self):\\n response = self.client.get(reverse('home'))\\n self.assertEqual(response.status_code, 200)\\n self.assertTemplateUsed(response, 'home.html')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"242319f2d81738ba6dc037d57de4ae81\",\n \"score\": \"0.6590009\",\n \"text\": \"def test_template_usado(self):\\n self.assertTemplateUsed(self.response, 'search/search_filters.html')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9028718d9f70633dc93963be1f23651a\",\n \"score\": \"0.6552989\",\n \"text\": \"def test_templates_used(self):\\n response = self.client.get(reverse('home'))\\n self.assertEqual(response.status_code, 200)\\n self.assertTemplateUsed(response, 'home/home.html')\\n self.assertTemplateUsed(response, 'base.html')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"29950777339f71f3880609f167c99fa3\",\n \"score\": \"0.6528326\",\n \"text\": \"def test_template_home(self):\\n url = reverse('home')\\n response = self.client.get(url)\\n actual= 'home.html'\\n self.assertTemplateUsed(response,actual)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38c1a95f7923c17a29dcc1657677019c\",\n \"score\": \"0.65246904\",\n \"text\": \"def test_template_usage(self):\\n self.assertTemplateUsed(self.client.get(reverse('home')),\\n 'index.html')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0038661826218905bd5b30402469fedb\",\n \"score\": \"0.6483396\",\n \"text\": \"def test_page_output(self):\\r\\n # Test initial state\\r\\n from fluent_pages.tests.testapp.page_type_plugins import SimpleTextPagePlugin # Import here as it needs an existing DB\\r\\n self.assertEquals(SimpleTextPagePlugin.render_template, 'testapp/simpletextpage.html')\\r\\n\\r\\n # Test how a normal page is rendered\\r\\n response = self.client.get('/sibling1/')\\r\\n self.assertTemplateUsed(response, 'testapp/simpletextpage.html')\\r\\n self.assertContains(response, '<div id=\\\"test_contents\\\">TEST_CONTENTS</div>')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72f9863e3c37328d2619e3920698212a\",\n \"score\": \"0.6448849\",\n \"text\": \"def test_should_use_template_index_html(self):\\n self.assertTemplateUsed(self.response, 'index.html')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e98d825cd19a752d3633b5df84cf58ce\",\n \"score\": \"0.6426471\",\n \"text\": \"def before(self, request, tmpl_content):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e617e31f38622d95ee31e3216eb2797\",\n \"score\": \"0.6376796\",\n \"text\": \"def test_home_list(self):\\n url = reverse('list')\\n response = self.client.get(url)\\n actual= 'list.html'\\n self.assertTemplateUsed(response,actual)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aba952416c4ce206b3cf0bb03a1099d2\",\n \"score\": \"0.6367485\",\n \"text\": \"def test_template_used(self):\\n self.assertTemplateUsed(self.resp, 'accounts/signup.html')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12273e68e00a3f09c4165872e9202777\",\n \"score\": \"0.6344034\",\n \"text\": \"def test_namespaced_html(self):\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18f36c23c24159d6f082ebc29e235709\",\n \"score\": \"0.63392204\",\n \"text\": \"def test_home_route_uses_right_templates(self):\\n response = self.client.get(reverse_lazy(\\\"home\\\"))\\n self.assertTemplateUsed(response, \\\"imagersite/base.html\\\")\\n self.assertTemplateUsed(response, \\\"imagersite/home.html\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"494a9a00b9f4e6e04de9271422533a8a\",\n \"score\": \"0.63349473\",\n \"text\": \"def test_only_ajax(self):\\n request = RequestFactory().get(\\\"/\\\")\\n response = self.view(request)\\n assert response.template_name == \\\"normal_template.html\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2a45c83549d57ca2c9ab1df8d9f8b8f\",\n \"score\": \"0.6330742\",\n \"text\": \"def assertPageTemplatesUsed(self, response):\\n self.assertGCITemplatesUsed(response)\\n self.assertTemplateUsed(response, 'modules/gci/org_list/base.html')\\n self.assertTemplateUsed(\\n response, 'modules/gci/accepted_orgs/_project_list.html')\\n self.assertTemplateUsed(response, 'soc/list/lists.html')\\n self.assertTemplateUsed(response, 'soc/list/list.html')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed7f0d57d0e266a409ae59c23ec088de\",\n \"score\": \"0.6298556\",\n \"text\": \"def test_template_tag(self):\\n entries = Entry.objects.all()\\n entries = get_published_entries(entries, 'en')\\n self.assertEqual(len(entries), 1, msg=(\\n 'Should return the entries that are published.'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77f5adbf4f512ef2b2334155e6d79b52\",\n \"score\": \"0.62533784\",\n \"text\": \"def test_home_template_use(self):\\n response = self.client.get('/')\\n self.assertTemplateUsed(response, 'mainRecycleApp/home.html')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"34c05b6c2cd308848038273b6b17fece\",\n \"score\": \"0.6252499\",\n \"text\": \"def test_homepage_pulls_from_base_template(self):\\n response = self.client.get(reverse_lazy('homepage'))\\n self.assertTemplateUsed(response, 'imagersite/base.html')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6505c443dbc56c1e5e1031dc76121093\",\n \"score\": \"0.62293416\",\n \"text\": \"def test_custom_template(self):\\n response = self.client.get(self.customized_url)\\n self.assertTemplateUsed(response, \\\"customized_contact.html\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0b82ff0933aa92666b99b0f270ee93b\",\n \"score\": \"0.6224032\",\n \"text\": \"def test_home_view_uses_correct_template(self):\\n response = self.client.get(reverse('home'))\\n self.assertEqual(response.status_code, 200)\\n self.assertTemplateUsed(\\n response, template_name=\\\"home/index.html\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7206040a475bca8fb6d8c15877afeb4a\",\n \"score\": \"0.61902326\",\n \"text\": \"def test_template_display(self):\\n response = self.client.get('/webtest/')\\n self.assertEqual(response.status_code, 200)\\n content = response.content\\n self.assertEqual(\\\"<h1>Web test of TransAct api</h1>\\\" in content, True)\\n #static files not easly testable\\n #response = self.client.get('/static/webtest/js/main.js')\\n #self.assertEqual(response.status_code, 200)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25769d2b08e9b8b751381d857b5306ce\",\n \"score\": \"0.61889523\",\n \"text\": \"def test_translate_view(self):\\r\\n # Check page status\\r\\n resp = self.client['maintainer'].get(self.translate_view_url)\\r\\n self.assertEqual(resp.status_code, 200)\\r\\n self.assertTemplateUsed(resp, 'translate.html')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e1a4787a38c9da0fdb3ece80d6041a3\",\n \"score\": \"0.6176841\",\n \"text\": \"def test_restricted_template_inherits(self):\\n template_base_path = os.path.join(settings.TEMPLATE_DIR, 'rango')\\n template_path = os.path.join(template_base_path, 'restricted.html')\\n\\n template_str = get_template(template_path)\\n full_title_pattern = r'<title>(\\\\s*|\\\\n*)Rango(\\\\s*|\\\\n*)-(\\\\s*|\\\\n*)Restricted Page(\\\\s*|\\\\n*)</title>'\\n block_title_pattern = r'{% block title_block %}(\\\\s*|\\\\n*)Restricted Page(\\\\s*|\\\\n*){% (endblock|endblock title_block) %}'\\n\\n user_object = create_user_object()\\n self.client.login(username='testuser', password='testabc123')\\n request = self.client.get(reverse('rango:restricted'))\\n content = request.content.decode('utf-8')\\n\\n self.assertTrue(re.search(full_title_pattern, content), f\\\"{FAILURE_HEADER}The <title> of the response for 'rango:restricted' is not correct. Check your restricted.html template, and try again.{FAILURE_FOOTER}\\\")\\n self.assertTrue(re.search(block_title_pattern, template_str), f\\\"{FAILURE_HEADER}Is restricted.html using template inheritance? Is your <title> block correct?{FAILURE_FOOTER}\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1721962048b6a32d5c45fc1344314ed7\",\n \"score\": \"0.6148524\",\n \"text\": \"def test_that_template_is_used(self):\\n client = Client()\\n test_superuser = User.objects.get(username=test_superuser_username)\\n client.force_login(user=test_superuser)\\n response = client.get(reverse('file_manager:note_index'), follow=True)\\n self.assertTemplateUsed(\\n response=response,\\n template_name='file_manager/note_index.html'\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1dbb20bcd403f88689653cf849c98c3a\",\n \"score\": \"0.6143415\",\n \"text\": \"def test_products_page(self):\\n response = self.client.get('/products/')\\n self.assertEqual(response.status_code, 200)\\n self. assertTemplateUsed(response, 'products/products.html')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6bab94157b95186296e606f591cd6142\",\n \"score\": \"0.61313236\",\n \"text\": \"def test_about_template_use(self):\\n response = self.client.get('/about/')\\n self.assertTemplateUsed(response, 'mainRecycleApp/about.html')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19d4cf8cd4bd312223205cf2f7fa104f\",\n \"score\": \"0.61263794\",\n \"text\": \"def test_post_landing_page(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"023c0c0d55ed2201caab5c81a2f7ea67\",\n \"score\": \"0.61125535\",\n \"text\": \"def test_render_template(mock_render_template):\\n mock_self = Mock()\\n template.Page.render_template(mock_self)\\n mock_self.build.assert_called_once_with()\\n mock_render_template.assert_called_once_with(mock_self.build())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ceb8e13675eccea952c241666a72042\",\n \"score\": \"0.60705966\",\n \"text\": \"def template():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"67fef3f4123471bfae7bb88944d7b749\",\n \"score\": \"0.60630566\",\n \"text\": \"def test_assign_linguists_from_template(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf933e1c3c2a535625a5853cf3615716\",\n \"score\": \"0.60584915\",\n \"text\": \"def test_category_list_page_without_category(self): \\n\\n c = Client()\\n response = c.get(\\\"/personal/blog/category/\\\")\\n self.assertEqual(response.status_code, 200) \\n self.assertEqual(response.template_name, ['blog/category_list.html'])\\n self.assertIn('blog/category_list.html', response.template_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"388a5f4300812f6fc913175fcccc1895\",\n \"score\": \"0.6051886\",\n \"text\": \"def test_render_template(self):\\n template = self.block.meta.template\\n self.assertEqual(template, 'common/blocks/centered_text.html', 'The templates were not the same')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"73b55d3bd929a57f82b9c3972cc098db\",\n \"score\": \"0.60433125\",\n \"text\": \"def test_uses_home_template(self):\\n # instead of manually creating an HttpRequest object (as we did\\n # in the earlier version of this test, we can use self.client.get\\n # (part of the Django extensions to TestCase)\\n response = self.client.get('/') # the view returns a HttpResponse\\n # object\\n\\n # We don't need to test constants, we can just test that the right\\n # template gets loaded (the functional test will still prove that\\n # the right content is shown to the user).\\n self.assertTemplateUsed(response, 'lists/home.html')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5761af0a81a12b4fed429822f5e942a\",\n \"score\": \"0.6033479\",\n \"text\": \"def skip_page(self, page: pywikibot.Page) -> bool:\\n if page.namespace() != 118:\\n pywikibot.warning('{} is not a draft.'.format(page))\\n return True\\n if has_template(page, self.opt.template):\\n pywikibot.warning('{} already has the template.'.format(page))\\n return True\\n return super().skip_page(page)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87092f8949648ce6c6e8427d60a307e4\",\n \"score\": \"0.6005766\",\n \"text\": \"def testAccessTemplate(self):\\n parser = templateparser.Parser()\\n self.assertEqual(len(parser), 0)\\n self.assertEqual(parser[self.name], self.template)\\n self.assertEqual(len(parser), 1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a2daa77221361d9e5b92153ee87d379\",\n \"score\": \"0.600456\",\n \"text\": \"def test_get_nonexistent_template(self):\\n self.template_set.get('nonexistenttemplate.xml')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"61eb4743e2bbecbcd22dd26d491cc756\",\n \"score\": \"0.6002779\",\n \"text\": \"def test_renders(self):\\n page = PageFactory.create(parent=None, title=\\\"foo\\\")\\n SiteFactory.create(root_page=page)\\n with self.assertTemplateUsed(template_name=\\\"blocks/page_chooser_block.html\\\"):\\n response = self.block.render_basic(page)\\n\\n self.assertIn(page.title, response)\\n self.assertIn(page.url, response)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25adda85d9e96164e4344abacac430b9\",\n \"score\": \"0.6001421\",\n \"text\": \"def test_view_uses_correct_template(self):\\n response = self.client.get(reverse('shopping_bag'))\\n self.assertEqual(response.status_code, 302)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0c456fb8f449ed30e4e69cf3cd85e4b\",\n \"score\": \"0.5974716\",\n \"text\": \"def test_blog_loaded_templates(self):\\n response = self.client.get('/blog/')\\n templates = response.templates\\n names = fetch_template_names(templates)\\n\\n self.assertIn('base.html', str(names))\\n self.assertIn('blog.html', str(names))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9816fd87fd7741cab381c39329606a49\",\n \"score\": \"0.59736246\",\n \"text\": \"def test_real_xhtml_document(self):\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9816fd87fd7741cab381c39329606a49\",\n \"score\": \"0.59736246\",\n \"text\": \"def test_real_xhtml_document(self):\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"484ba3963007ff28277d0061fe9a14f7\",\n \"score\": \"0.5956028\",\n \"text\": \"def test_get_home(self):\\n page = self.client.get(reverse(\\\"home\\\"))\\n self.assertEqual(page.status_code, 200)\\n self.assertTemplateUsed(page, \\\"home.html\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"096ca896d1e0a5ee3c204275cae2fe3a\",\n \"score\": \"0.59459\",\n \"text\": \"def test_swaps_template(self):\\n request = RequestFactory().get(\\n \\\"/\\\", HTTP_X_REQUESTED_WITH=\\\"XMLHttpRequest\\\")\\n response = self.view(request)\\n assert response.template_name == \\\"ajax_template.html\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"89258d0eb44c4baa1cef74fac2cb8690\",\n \"score\": \"0.5941432\",\n \"text\": \"def testGenHtml(self):\\n html = self.gen_html('')\\n self.assertIn('test1', html)\\n self.assertIn('test2', html)\\n self.assertIn('release', html)\\n self.assertIn('debug', html)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dad68ffb46cae9daf09fa4b69b766738\",\n \"score\": \"0.59371394\",\n \"text\": \"def test_render(self):\\n\\n\\t\\ttemplate_content = self.app.render('test_render.html', test_var='Testing templates')\\n\\t\\tself.assertEqual(template_content, 'Testing templates')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f36518e5504a35ed39478058305ac064\",\n \"score\": \"0.5929788\",\n \"text\": \"def test_projects_home_status_code_correct_template(self):\\n self.assertEqual(self.response.status_code, 200)\\n self.assertTemplateUsed(self.response, 'projects/projects_home.html')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"32b6533d37c24744378c05b1215a9d29\",\n \"score\": \"0.5927937\",\n \"text\": \"def test_get_page_empty_page(self):\\n data = {'page': 1231}\\n req = self.request.get('/', data=data)\\n response = self.view.get(req)\\n html = response.content.decode('utf-8')\\n self.assertIn(\\n 'chosen\\\">3</a>',\\n html\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f54817c6d0e45887c425eb4473fa946b\",\n \"score\": \"0.5924586\",\n \"text\": \"def test_get(self):\\r\\n response = self._get()\\r\\n self.assertEquals(response.status_code, 200)\\r\\n self.assertTemplateUsed(response, self.template_name)\\r\\n self.assertFalse(response.context['form'].is_bound)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"464ae1fb511f2ed49bbf30442d94cf7f\",\n \"score\": \"0.59194297\",\n \"text\": \"def test_login_template_content(self):\\n template_base_path = os.path.join(settings.TEMPLATE_DIR, 'rango')\\n template_path = os.path.join(template_base_path, 'login.html')\\n self.assertTrue(os.path.exists(template_path), f\\\"{FAILURE_HEADER}We couldn't find the 'login.html' template in the 'templates/rango/' directory. Did you put it in the right place?{FAILURE_FOOTER}\\\")\\n \\n template_str = get_template(template_path)\\n self.assertTrue('<h1>Login to Rango</h1>' in template_str, f\\\"{FAILURE_HEADER}We couldn't find the '<h1>Login to Rango</h1>' in the login.html template.{FAILURE_FOOTER}\\\")\\n self.assertTrue('action=\\\"{% url \\\\'rango:login\\\\' %}\\\"' in template_str, f\\\"{FAILURE_HEADER}We couldn't find the url lookup for 'rango:login' in your login.html <form>.{FAILURE_FOOTER}\\\")\\n self.assertTrue('<input type=\\\"submit\\\" value=\\\"submit\\\" />' in template_str, f\\\"{FAILURE_HEADER}We couldn't find the submit button in your login.html template. Check it matches what is in the book, and try again.{FAILURE_FOOTER}\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"787af99704ca80667d6b47eb450b3d4a\",\n \"score\": \"0.5914893\",\n \"text\": \"def test_render_template(self):\\n template = self.block.meta.template\\n self.assertEqual(template, 'common/blocks/button.html', 'Templates did not match')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9fa976ec82cd196cd96382a48067c2c1\",\n \"score\": \"0.5914765\",\n \"text\": \"def test_section_page_gets_created(section_page):\\n assert section_page is not None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d2755e58a3fbc8b7585512531958812\",\n \"score\": \"0.589026\",\n \"text\": \"def setUp(self):\\n self.tmpl = templateparser.Template\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d2755e58a3fbc8b7585512531958812\",\n \"score\": \"0.589026\",\n \"text\": \"def setUp(self):\\n self.tmpl = templateparser.Template\",\n \"title\": \"\"\n },\n {\n \"docid\": \"099e336942fe4f4994e65f52df9615d0\",\n \"score\": \"0.58871114\",\n \"text\": \"def test_homepage(self):\\n\\n with self.client as client:\\n response = client.get('/')\\n html = response.get_data(as_text=True)\\n # test that you're getting a template\\n \\n self.assertIn('id=\\\"boggle_game_board\\\"', html)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a40f890858d01f91b9f9a9d596abdfb8\",\n \"score\": \"0.588143\",\n \"text\": \"def test_app_page_output(self):\\r\\n # Test initial state\\r\\n from fluent_pages.tests.testapp.page_type_plugins import WebShopPagePlugin\\r\\n self.assertEquals(WebShopPagePlugin.urls, 'fluent_pages.tests.testapp.urls_webshop')\\r\\n\\r\\n response = self.client.get('/shop/')\\r\\n self.assertContains(response, 'test_webshop: index_page') # The URLconf is an overlay over the standard get_response()\\r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b342bf9886d7f1a1307128ee5a10e735\",\n \"score\": \"0.5877771\",\n \"text\": \"def testPageLoads(self):\\n self.site.latest_gsoc = self.gsoc_program.key().name()\\n self.site.latest_gci = self.gci_program.key().name()\\n self.site.mailing_list = 'dev@test.com'\\n self.site.put()\\n\\n response = self.get('/')\\n self.assertResponseOK(response)\\n self._assertPageTemplatesUsed(response)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0f30f91675a0b25051fe89a75ad4a27\",\n \"score\": \"0.5870789\",\n \"text\": \"def test_Create_route(self):\\n url = reverse('create')\\n response = self.client.get(url)\\n actual= 'create.html'\\n self.assertTemplateUsed(response,actual)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2bba2e05914ffcafe4a457c699686016\",\n \"score\": \"0.5870573\",\n \"text\": \"def assert_test_get(self, response):\\n self.assertEqual(response.status_code, 200)\\n self.assertTemplateUsed(\\n response, 'firestorm/display_recommend_enroll.html')\\n self.assertTemplateUsed(response, \\n 'include/frm/frm_recommend_ad_rep.html')\\n self.assertTemplateUsed(response, 'include/dsp/dsp_live_help.html')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e17480cc00fc8fc1680b166029e38c54\",\n \"score\": \"0.5867035\",\n \"text\": \"def test_page_basics(self):\\n rv = self.app.get('/')\\n assert b'<!DOCTYPE html>' in rv.data\\n assert b'<title>jotquote</title>' in rv.data\\n assert b'<div class=\\\"quote\\\">They that can give up essential liberty to obtain a little temporary safety deserve neither liberty nor safety.</div>' in rv.data\\n assert b'<div class=\\\"author\\\">Ben Franklin</div>' in rv.data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5cd03c07fe89aeeb9d4f09f4860568f\",\n \"score\": \"0.5866824\",\n \"text\": \"def test_home_page_response_and_template(self):\\n response = self.client.get(reverse('hello:home'))\\n\\n self.assertTemplateUsed(response, 'home.html')\\n self.assertEqual(response.status_code, 200)\\n self.assertContains(response,\\n '<h1>42 Coffee Cups Test Assignment</h1>',\\n html=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35b5e1ec66c8c92869543e57082b9004\",\n \"score\": \"0.58607686\",\n \"text\": \"def test_portals_id_designs_nk_template_get(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b6c3c4f07114f7d67e0309a1e919554d\",\n \"score\": \"0.58506745\",\n \"text\": \"def test_assignable_templates(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c1546ac89718891ef0f1dba45657d4b1\",\n \"score\": \"0.5845095\",\n \"text\": \"def test_app_page_url(self):\\r\\n response = self.client.get('/shop/foobar/')\\r\\n self.assertContains(response, 'test_webshop: article: foobar')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dbb2df73e92278bbac92be5728967b06\",\n \"score\": \"0.58383566\",\n \"text\": \"def test_en_page(self):\\n response = self.client.get('/test/')\\n self.assertEqual(response.status_code, 200)\\n self.assertTrue('page' in response.context)\\n self.assertContains(response, 'A test page for English pages.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f0de4f8434de7c0931c357e0011ce7ea\",\n \"score\": \"0.58278805\",\n \"text\": \"def testTaglessTemplate(self):\\n template = 'Template without any tags'\\n self.assertEqual(self.tmpl(template).Parse(), template)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bbc8531d32a8c74688ab79df1fe7be28\",\n \"score\": \"0.58140105\",\n \"text\": \"def test_hide_unpublished(self):\\r\\n self.assertTrue(SimpleTextPage.objects.filter(translations__slug='unpublished').exists(), \\\"page /unpublished/ should exist in the database.\\\")\\r\\n self.assert404('/unpublished/')\\r\\n self.assert404('/unpublished') # With default APPEND_SLASH=True\\r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43fc588ef24e82870574cdb1c050d06d\",\n \"score\": \"0.58100694\",\n \"text\": \"def test_normal(self):\\n response = self.client.get(HOME_URL)\\n for entry in NORMAL_ENTRIES:\\n node = '<a href=\\\"%s\\\">%s</a>' % (reverse(entry['view']), entry['name'])\\n self.assertContains(response, node, html=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1d368817505dc20f5fe9ef78225625a\",\n \"score\": \"0.5809132\",\n \"text\": \"def test_get_template(self):\\n import hardest\\n instance = hardest.Templator(self.package_name)\\n context = {'first': 'a', 'second': 'b'}\\n template = instance.get_template(self.template_path, context)\\n self.assertIsInstance(template, hardest.Template)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3a69f82adb48aad0bd61023d9703698\",\n \"score\": \"0.58033097\",\n \"text\": \"def test_project_about_status_code_correct_template(self):\\n self.assertEqual(self.response.status_code, 200)\\n self.assertTemplateUsed(self.response, 'projects/project_about.html')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4733f883575a48460fd00fa9af622c0c\",\n \"score\": \"0.58021456\",\n \"text\": \"def test_html_for(self):\\n message = factory.get_html_for('test', {'title': 'Et hop'})\\n self.assertIn('Et hop', message)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c10f71035eafe02b12190546ac86827\",\n \"score\": \"0.58018196\",\n \"text\": \"def test_about_us_page(self):\\n about_us_url = reverse('about-us')\\n response = self.client.get(about_us_url)\\n self.assertEqual(response.status_code, 200)\\n self.assertTemplateUsed(response, 'common/about_us.html')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c9e4f98b6477deca7dc8bfeb709b565\",\n \"score\": \"0.5797842\",\n \"text\": \"def test_template(self):\\n self.assertIsInstance(self.changelist.template, Template)\\n render = self.changelist.template.render(Context())\\n to_json = json.loads(render)\\n self.assertEqual(to_json, {\\n 'test': {\\n 'name': 'whee!',\\n 'models': {\\n 'embeds.Iframe': None,\\n }\\n }\\n })\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a73513a95311b180c8c944f1b3986442\",\n \"score\": \"0.57956564\",\n \"text\": \"def test_template():\\n tpl = Template()\\n assert tpl.hello().my_name_is(\\\"Christophe\\\") == \\\"hello Christophe\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b8445c962a436885410532c6d5b4124\",\n \"score\": \"0.57843786\",\n \"text\": \"def test__call__():\\n mock_self = Mock()\\n template.Page.__call__(mock_self)\\n mock_self.build.assert_called_once_with()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e54fa1c883ce52789c21e09f9c86332a\",\n \"score\": \"0.5778468\",\n \"text\": \"def test_take_action_template(self):\\n new_campaign = Campaign(campaign_url='test.com')\\n new_campaign.save() \\n response = self.client.get('/takeaction/?campaign_id=%s' % (new_campaign.id))\\n self.assertEqual('takeaction.html', response.templates[0].name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b3932b75f3c7c239b5bd7063b766d44\",\n \"score\": \"0.5758571\",\n \"text\": \"def _test_template(self, template_path):\\n full_path = os.path.join(self.template_dir, template_path)\\n doc = lxml.html.parse(full_path)\\n expecting_vuln = _get_expecting_vuln(doc)\\n templ = loader.get_template(template_path)\\n context = parse_template.get_default_context()\\n templ.render(context)\\n methods = [\\n parse_template.get_non_js_escaped_results_for_template,\\n parse_template.get_non_quoted_attr_vars_for_template\\n ]\\n for method in methods:\\n for result in method(templ):\\n self.csw.handle_callback(result)\\n self.assertEqual(len(self.csw.results), len(expecting_vuln))\\n for result, expected in zip(self.csw.results, expecting_vuln):\\n line_no = result.get_line_number()\\n part = result.get_vulnerability_text()\\n filename = result.get_filename()\\n var = str(result._var_node.filter_expression.var)\\n self.assertEqual(line_no, expected['line_number'])\\n self.assertEqual(var, expected['name'])\\n self.assertEqual(filename, full_path)\\n self.assertTrue(var in part)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b664f6303b06f997a5135fe34353b728\",\n \"score\": \"0.57584506\",\n \"text\": \"def test_view(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f422b99e00b8a8a9899ae1fa36840d36\",\n \"score\": \"0.575402\",\n \"text\": \"def test_normal_doctypes(self):\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3d12f1ad668aa0ee0239f0f696383c3\",\n \"score\": \"0.57506454\",\n \"text\": \"def test_portals_id_templates_get(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8bd237ed8fee294ff7d9fca6ba340050\",\n \"score\": \"0.57318455\",\n \"text\": \"def test_render_template_doc(mock_rts, mock_markup):\\n test_string = 'some string'\\n test_doc = Mock(spec=yattag.Doc)\\n test_doc.getvalue.return_value = test_string\\n template.render_template(test_doc)\\n test_doc.getvalue.assert_called_once_with()\\n mock_rts.assert_called_once_with(test_string)\\n mock_markup.assert_called_once_with(mock_rts(test_string))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f2a5178b586baabdde4ed5213aa7dac0\",\n \"score\": \"0.5729704\",\n \"text\": \"def regularpage(pagename=None):\\n if pagename==None:\\n raise ExperimentError('page_not_found')\\n return render_template(pagename)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e264aaeca3d086b3910ac486d2234f0c\",\n \"score\": \"0.5728226\",\n \"text\": \"def test_base_for_register_link(self):\\n template_base_path = os.path.join(settings.TEMPLATE_DIR, 'rango')\\n base_path = os.path.join(template_base_path, 'base.html')\\n template_str = get_template(base_path)\\n self.assertTrue('<li><a href=\\\"{% url \\\\'rango:register\\\\' %}\\\">Sign Up</a></li>' in template_str)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"742ef51da6a59f2639264569bfcfd05c\",\n \"score\": \"0.57240415\",\n \"text\": \"def test_contact_template_use(self):\\n response = self.client.get('/contact/')\\n self.assertTemplateUsed(response, 'mainRecycleApp/contact.html')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"724a9e8df265ac6baca6102fe76d0ac3\",\n \"score\": \"0.57095826\",\n \"text\": \"def WhatsNewPage(request):\\n return render_to_response('new_in_2_0.html', {})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df4a11c52c3aeef4cb579aedeb115a3b\",\n \"score\": \"0.5706879\",\n \"text\": \"def get_uses_template_and_defaults(self, template):\\n self.login()\\n self.response = self.client.get(reverse(self.name))\\n super().get_uses_template_and_defaults(template)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"835e95bba390acceb669f2fb55565bf7\",\n \"score\": \"0.5706813\",\n \"text\": \"def test_restricted_template_exists(self):\\n template_base_path = os.path.join(settings.TEMPLATE_DIR, 'rango')\\n template_path = os.path.join(template_base_path, 'restricted.html')\\n self.assertTrue(os.path.exists(template_path), f\\\"{FAILURE_HEADER}We couldn't find the 'restricted.html' template in the 'templates/rango/' directory. Did you put it in the right place? Did you complete the exercises?{FAILURE_FOOTER}\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"40056130eadf591fecc87bc79a486301\",\n \"score\": \"0.56994915\",\n \"text\": \"def test_wagtail_pages(self):\\n\\n pages = Page.objects.live()\\n for page in pages:\\n url = page.relative_url(page.get_site())\\n if url is not None:\\n self.get(url)\\n self.assert_status_code('200')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b262cf90396f100bbbdc4b49fd0a4da\",\n \"score\": \"0.56977457\",\n \"text\": \"def test_template_loader_postmortem(self):\\n template_name = \\\"notfound.html\\\"\\n with tempfile.NamedTemporaryFile(prefix=template_name) as tmpfile:\\n tempdir = os.path.dirname(tmpfile.name)\\n template_path = os.path.join(tempdir, template_name)\\n with override_settings(\\n TEMPLATES=[\\n {\\n \\\"BACKEND\\\": \\\"django.template.backends.django.DjangoTemplates\\\",\\n \\\"DIRS\\\": [tempdir],\\n }\\n ]\\n ), self.assertLogs(\\\"django.request\\\", \\\"ERROR\\\"):\\n response = self.client.get(\\n reverse(\\n \\\"raises_template_does_not_exist\\\", kwargs={\\\"path\\\": template_name}\\n )\\n )\\n self.assertContains(\\n response,\\n \\\"%s (Source does not exist)\\\" % template_path,\\n status_code=500,\\n count=2,\\n )\\n # Assert as HTML.\\n self.assertContains(\\n response,\\n \\\"<li><code>django.template.loaders.filesystem.Loader</code>: \\\"\\n \\\"%s (Source does not exist)</li>\\\"\\n % os.path.join(tempdir, \\\"notfound.html\\\"),\\n status_code=500,\\n html=True,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23a3110f24df1959a363564579c74175\",\n \"score\": \"0.5697314\",\n \"text\": \"def test_template_text(self):\\n t = Template(\\\"\\\"\\\"{% spaceless %}{% load addendum_tags %}{% snippet 'django' template=True %}Hello world{% endsnippet %}{% endspaceless %}\\\"\\\"\\\")\\n c = Context({'dog': 'woof'})\\n result = t.render(c)\\n self.assertEqual(result, \\\"WOOF\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9ffd02f0105b7ef64a50cee775d53a5\",\n \"score\": \"0.5693967\",\n \"text\": \"def testGenHtmlFilterExact(self):\\n html = self.gen_html('release', True)\\n self.assertIn('release', html)\\n self.assertNotIn('debug', html)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"86bfb1fbf5d1df2c30b60e7c8b8b80d9\",\n \"score\": \"0.5693639\",\n \"text\": \"def test_get_template_no_context(self):\\n import hardest\\n instance = hardest.Templator(self.package_name)\\n template = instance.get_template(self.template_path)\\n self.assertIsInstance(template, hardest.Template)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a92cf152a228d2f2349b5f68e92e07e\",\n \"score\": \"0.56882656\",\n \"text\": \"def test_get_template_directory(self):\\n self.template_set.get('directory')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b7c6474d31f27f95da5c9f0afcfb85a\",\n \"score\": \"0.568337\",\n \"text\": \"def testPreloadTemplates(self):\\n parser = templateparser.Parser(templates=[self.name])\\n self.assertEqual(len(parser), 1)\\n self.assertEqual(parser[self.name], self.template)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4235c847ca4cf67bcfb14d65bfce7d70\",\n \"score\": \"0.5681217\",\n \"text\": \"def test_get(self):\\r\\n response = self._get()\\r\\n self.assertEquals(response.status_code, 200)\\r\\n self.assertTemplateUsed(response, self.template_name)\\r\\n self.assertTrue('object' in response.context)\\r\\n self.assertEquals(response.context['object'], self.obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4235c847ca4cf67bcfb14d65bfce7d70\",\n \"score\": \"0.5681217\",\n \"text\": \"def test_get(self):\\r\\n response = self._get()\\r\\n self.assertEquals(response.status_code, 200)\\r\\n self.assertTemplateUsed(response, self.template_name)\\r\\n self.assertTrue('object' in response.context)\\r\\n self.assertEquals(response.context['object'], self.obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33aa2d4a8fce7a55b806d01098462f42\",\n \"score\": \"0.567803\",\n \"text\": \"def test_users_page(self):\\n\\n with self.client as client:\\n response = client.get('/users')\\n html = response.get_data(as_text=True)\\n\\n self.assertEqual(response.status_code, 200)\\n self.assertIn('Users Template.', html)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4b2a4b3c1d93f3eabc8d4d098ddf6d7\",\n \"score\": \"0.5676243\",\n \"text\": \"def verify_template_is_not_available(self,template):\\n field=npsp_lex_locators[\\\"adv_mappings\\\"][\\\"field_mapping\\\"].format(\\\"Template\\\")\\n self.selenium.click_element(field)\\n element=self.selenium.get_webelement(field)\\n status=element.get_attribute(\\\"aria-activedescendant\\\")\\n if status is not None:\\n self.selenium.page_should_not_contain(template)\\n else:\\n self.selenium.wait_until_page_contains(\\\"Default Gift Entry Template\\\")\\n self.selenium.page_should_not_contain(template) \\n self.selenium.click_button(\\\"Cancel\\\")\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1198,"cells":{"query_id":{"kind":"string","value":"d7ea8a8e35003f145cb58edd2f6bb6df"},"query":{"kind":"string","value":"Returns the command's stderr as a string."},"positive_passages":{"kind":"list like","value":[{"docid":"008c90d9c024641373debcdb186bb746","score":"0.74979025","text":"def stderr(self): \n return self._stderr","title":""}],"string":"[\n {\n \"docid\": \"008c90d9c024641373debcdb186bb746\",\n \"score\": \"0.74979025\",\n \"text\": \"def stderr(self): \\n return self._stderr\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"374a0b366ef567928cf96393fb1a88ac","score":"0.8082867","text":"def stderr(self):\n if not self._stderr_str:\n self._stderr_str = self.raw_stderr.decode(encoding=self._encoding)\n return self._stderr_str","title":""},{"docid":"0ab8ea5c425e8a450dd8f3d2d307b30b","score":"0.7926002","text":"def get_stderr(self):\n return self.cstderr.getvalue().rstrip('\\n')","title":""},{"docid":"a8ea2d4c2262113c9e4043f9a1295318","score":"0.7743329","text":"def stderr(self):\n\n return psys.u(self.raw_stderr())","title":""},{"docid":"dd18803d89b9efb74d09e2b47039116b","score":"0.74740756","text":"def stderr(self) -> str:\n\n\t\treturn super().stderr","title":""},{"docid":"5af372a332e27242aa8cda01625a63ad","score":"0.74736005","text":"def raw_stderr(self):\n\n self.__ensure_terminated()\n return self.__stderr.getvalue()","title":""},{"docid":"0f10ce86c0cfa36dc0748d846c63f62e","score":"0.73806137","text":"def stderr(self):\n self.wait()\n return self._stderr","title":""},{"docid":"0ce5b6643721fb964813fe2b6f984d85","score":"0.71499336","text":"def error(self):\n import os\n\n try:\n return self._process.stderr.read()\n except:\n _Log.debug(\"Nothing to read stderr {}\".format(self._executable))\n # there is nothing to read, return None\n return None","title":""},{"docid":"37b5183cc776a9152319ef5ba535c665","score":"0.7051798","text":"def get_error(execute_command_result):\n\n if not is_error(execute_command_result):\n raise ValueError(\"execute_command_result has no error entry. before using get_error use is_error\")\n\n if isinstance(execute_command_result, dict):\n return execute_command_result['Contents']\n\n error_messages = []\n for entry in execute_command_result:\n is_error_entry = type(entry) == dict and entry['Type'] == entryTypes['error']\n if is_error_entry:\n error_messages.append(entry['Contents'])\n\n return '\\n'.join(error_messages)","title":""},{"docid":"509d926e18e423852fc7613fc35b3726","score":"0.69838387","text":"def stderr_output(cmd):\n\n handle, gpg_stderr = stderr_handle()\n try:\n output = subprocess.check_output(cmd, stderr=gpg_stderr) # nosec\n if handle:\n handle.close()\n\n return output\n except subprocess.CalledProcessError as exception:\n LOGGER.debug(\"GPG Command %s\", ' '.join(exception.cmd))\n LOGGER.debug(\"GPG Output %s\", exception.output)\n raise CryptoritoError('GPG Execution')","title":""},{"docid":"b02de1b410481b602e6ebbfd87a53ad2","score":"0.6944275","text":"def get_run_stderr(self, id):\n stderr_url = self.get_run(id)['run_log']['stderr']\n auth = wes_config()[self.id]['auth']\n res = requests.get(stderr_url, headers=auth)\n return res.text","title":""},{"docid":"c25b17af6e961a5771694ae5a3d8f003","score":"0.6927808","text":"def get_err_msg(self):\n return self.get_error_message()","title":""},{"docid":"28b2ec2580aabb7ed0a690d08b7ed21c","score":"0.67414993","text":"def process_stderr_command(args, host, port):\n resp = send_command(const.CMD_GET_STDERR, args, host, port)\n if isdata(resp):\n return resp[1:].decode().split(\"\\n\")\n else:\n print_msg(resp[1:].decode())","title":""},{"docid":"78bef0f5c400c6d591b6870eb125d180","score":"0.6646245","text":"def getFailedMessage(self):\n errorMessage = ''\n try:\n with open(self.exitErrorMessageFile) as errorFile:\n errorMessage = errorFile.read()\n except IOError:\n pass\n try:\n with open(self.stderrFile) as stderrFile:\n errorMessage += stderrFile.read()\n except IOError:\n pass\n if len(errorMessage):\n errorMessage += ' '\n # add exit code inforamtion\n if self.subprocess.returncode == -6:\n errorMessage += \"SIGABRT received\"\n elif self.subprocess.returncode == -9:\n errorMessage += \"SIGKILL received\"\n elif self.subprocess.returncode == -11:\n errorMessage += \"Segmentation fault\"\n else:\n errorMessage += \"exit code {0}\".format(self.subprocess.returncode)\n return errorMessage if errorMessage else 'Unexpected termination of {0}'.format(self.subprocessName)","title":""},{"docid":"c6eb05ffbf38651459ff2b5bad72eb68","score":"0.6562889","text":"def err_msg(self) -> str:\n return self._err_msg","title":""},{"docid":"120bc5027348c6815300ba7cdbfb203c","score":"0.65458125","text":"def getoutputerror(cmd, verbose=False, debug=False, header='', split=False):\n\n if verbose or debug: \n print(header+cmd)\n\n if not cmd:\n # Return empty lists or strings.\n if split:\n return [], []\n else:\n return '', ''\n\n if not debug:\n # fixme: use subprocess.\n pin,pout,perr = os.popen3(cmd)\n tout = pout.read().rstrip()\n terr = perr.read().rstrip()\n pin.close()\n pout.close()\n perr.close()\n if split:\n return tout.split('\\n'), terr.split('\\n')\n else:\n return tout, terr","title":""},{"docid":"9dfbfad73bedd02da77ebc3dbf828165","score":"0.64598876","text":"def get_error(self):\n\n return self.err","title":""},{"docid":"e54f0cf09cac00729530e496660a52af","score":"0.6414143","text":"def get_stderr(self):\n with open(os.path.join(self.directory, 'log.err')) as f:\n for line in f:\n yield line.rstrip()","title":""},{"docid":"f4785dce715d74927c427cd9c5cabcbc","score":"0.6375055","text":"def getoutputoserror(cmd):\n p = subprocess.Popen(cmd.split(), stdout=subprocess.PIPE, stderr=subprocess.STDOUT)\n p.wait()\n output = p.communicate()[0].decode()\n if p.returncode:\n raise OSError(p.returncode, '{0}: {1}'.format(cmd, output))\n return output","title":""},{"docid":"0f5d155c3e3c79f68fb8ef81413c885b","score":"0.6362348","text":"def err(self):\n \n return self._err.get()","title":""},{"docid":"80dd65bda8f279941c69b88b877c3bd1","score":"0.635026","text":"def GetUnixErrorOutput(filename, error, new_error=False):\n\n line = ''\n\n if error.token:\n line = '%d' % error.token.line_number\n\n error_code = '%04d' % error.code\n if new_error:\n error_code = 'New Error ' + error_code\n return '%s:%s:(%s) %s' % (filename, line, error_code, error.message)","title":""},{"docid":"eb2dce4c90436aef3dc87c72fa421535","score":"0.6336272","text":"def GetErrorOutput(error, new_error=False):\n\n line = ''\n if error.token:\n line = 'Line %d, ' % error.token.line_number\n\n code = 'E:%04d' % error.code\n\n error_message = error.message\n if new_error:\n error_message = 'New Error ' + error_message\n\n return '%s%s: %s' % (line, code, error.message)","title":""},{"docid":"e7905aa356bc0918317428a5f07bcb41","score":"0.62756157","text":"def get_process_failure_msg(proc):\n return \"Execution failed (return code={}): {}\\n\".format(\n proc.returncode, util.repr_command(proc.args)\n )","title":""},{"docid":"9ac89ef978a83a439fb3c60191c6c1ff","score":"0.6249727","text":"def message(self) -> str:\n return self._err_message","title":""},{"docid":"2b1ab7c725ee5dbd26149cd1c4cb14ee","score":"0.6230253","text":"def read_stderr(self):\r\n return self._read_all_from_queue(self._stderr_queue)","title":""},{"docid":"6e6435d6f6605460b8e6c82ee7f3c93c","score":"0.6192707","text":"def __readStderr(self):\n self.process.setReadChannel(QProcess.StandardError)\n \n while self.process.canReadLine():\n self.errorGroup.show()\n s = str(self.process.readAllStandardError(),\n Preferences.getSystem(\"IOEncoding\"),\n 'replace')\n self.errors.insertPlainText(s)\n self.errors.ensureCursorVisible()","title":""},{"docid":"2c3eb8ed828ae20134a6bcecca0a2ab1","score":"0.61628324","text":"def getLastError(self):\t\n\n\t\treturn self.__error_buffer.value.decode('utf-8')","title":""},{"docid":"c5bcd892cbefecc89cbdac7547e8a3d5","score":"0.6160232","text":"def error_msg(self) -> str:\n return self._error_msg","title":""},{"docid":"0092e9b278fa83060bc950526a7d18f7","score":"0.61584425","text":"def get_err_msgs(self) -> str:\n msgs = []\n for i in range(self._num_procs):\n if (\n os.path.exists(self._stdout_files[i])\n and os.stat(self._stdout_files[i]).st_size > 0\n ):\n if self._args.method == Method.OPTIMIZE:\n msgs.append('console log output:\\n')\n with open(self._stdout_files[0], 'r') as fd:\n msgs.append(fd.read())\n else:\n with open(self._stdout_files[i], 'r') as fd:\n contents = fd.read()\n # pattern matches initial \"Exception\" or \"Error\" msg\n pat = re.compile(r'^E[rx].*$', re.M)\n errors = re.findall(pat, contents)\n if len(errors) > 0:\n msgs.append('\\n\\t'.join(errors))\n return '\\n'.join(msgs)","title":""},{"docid":"28ced4b083efd827911979d09e84c8a9","score":"0.61578876","text":"def get_stdout_stderr(self, typ, datetime, dir):\n out = '%s_%s_stdout.log' % (datetime, typ)\n err = '%s_%s_stderr.log' % (datetime, typ)\n return os.path.join(dir, out), os.path.join(dir, err)","title":""},{"docid":"83588cc38dd0dd5293bb521712b1d589","score":"0.6130121","text":"def geterr():\n return None","title":""},{"docid":"f6d5f235cace8ad9768a685b5b46da8d","score":"0.61210054","text":"def GetCommandOutput(env_cmd):\n\n # Disables exception pop-ups on Windows.\n environ, cmdline = env_cmd\n environ = dict(environ) # Ensures we are modifying a copy.\n environ[CATCH_EXCEPTIONS_ENV_VAR_NAME] = '1'\n return NormalizeOutput(GetShellCommandOutput((environ, cmdline)))","title":""},{"docid":"2e153c4d6d2085d872f70310b5b1d3cc","score":"0.6110246","text":"def get_last_error_message(cls):\n drmaa2_lib = LibraryManager.get_instance().get_drmaa2_library()\n string_ptr = drmaa2_lib.drmaa2_lasterror_text()\n message = ''\n if string_ptr:\n message = ByteString(string_ptr.value).decode()\n drmaa2_lib.drmaa2_string_free(string_ptr)\n return message","title":""},{"docid":"6ff44576b68389b3e91ed4507cdc3fe2","score":"0.6076596","text":"def strerror(self):\n return self.__str__()","title":""},{"docid":"11a510e194a57d7e989fc135d4d534a3","score":"0.6044898","text":"def get_error_msg(self):\n \n raise NotImplementedError(\"Must be implemented\")","title":""},{"docid":"99e8e875342ad381ea7562dddf836d60","score":"0.6023411","text":"def write_err(s):\n sys.stderr.write(s)","title":""},{"docid":"139983475f518b197a3f0fe613aa52dd","score":"0.60152155","text":"def stderr_message(self, message: str) -> None:\n pass","title":""},{"docid":"58fc060d37a008218e1312110657d7b0","score":"0.60105383","text":"def parsed_error_msg(self):\n return self.error_msg","title":""},{"docid":"35e9f32652168cefb2b051ef5c1c36a3","score":"0.6006651","text":"def get_error_message(self):\n return self.__error_observer.get_error_message()","title":""},{"docid":"29a3198e0e824a13e956e55174b754b6","score":"0.5999936","text":"def get_stdout_stderr(typ, datetime, dir):\n out = '%s_%s_stdout.log' % (datetime, typ)\n err = '%s_%s_stderr.log' % (datetime, typ)\n return os.path.join(dir, out), os.path.join(dir, err)","title":""},{"docid":"6db05c0fcc0b3ca6776cc647490fa889","score":"0.59967333","text":"def get_error(self):\n retval = self.ask('SYST:ERR:ALL?').strip().split(\"\\n\")\n return retval","title":""},{"docid":"9d58d5e3b7b0b9e2eb3d6766278f5dad","score":"0.5988061","text":"def error_desc(self):\n return self._error_desc","title":""},{"docid":"6ebe3b5ba363e9146cb5701a55c8ae08","score":"0.59517837","text":"def stderr_to(replacement=None):\n return redirect_file_obj('sys.stderr', replacement)","title":""},{"docid":"324f2a51a7bf28d4f39352defa1ace39","score":"0.59016526","text":"def to_stderr(self, message):\n\t\tprint >>sys.stderr, message.encode(preferredencoding())","title":""},{"docid":"69d31b31e0f28f46930e37fdcacd0681","score":"0.5899151","text":"def log_message(self) -> str:\n return self._err_message","title":""},{"docid":"ecc9785efcf72ba0f900904f2812f13d","score":"0.5881488","text":"def readouterr(self):\n out = err = \"\"\n if self.out:\n out = self.out.getvalue()\n self.out.truncate(0)\n self.out.seek(0)\n if self.err:\n err = self.err.getvalue()\n self.err.truncate(0)\n self.err.seek(0)\n return out, err","title":""},{"docid":"84d4b828ab724ed7b4fe253db0c8d7dd","score":"0.58774126","text":"def get_exitcode_stdout_stderr(cmd):\n command = cmd.split(\" \")\n args = [x.strip() for x in command if x.strip()]\n\n proc = Popen(args, stdout=PIPE, stderr=PIPE)\n out, err = proc.communicate()\n exitcode = proc.returncode\n #\n return exitcode, out, err","title":""},{"docid":"e4e39ecb1aa20140c9ce355a1cddeb9c","score":"0.5872963","text":"def error_message(self) -> str:\n return self._error_message","title":""},{"docid":"8f8beda27d4ea55f3f24ec4c75076e14","score":"0.5870949","text":"def error_str(rc):\n return '{}: {}'.format(rc, mqtt.error_string(rc))","title":""},{"docid":"8f8beda27d4ea55f3f24ec4c75076e14","score":"0.5870949","text":"def error_str(rc):\n return '{}: {}'.format(rc, mqtt.error_string(rc))","title":""},{"docid":"8f8beda27d4ea55f3f24ec4c75076e14","score":"0.5870949","text":"def error_str(rc):\n return '{}: {}'.format(rc, mqtt.error_string(rc))","title":""},{"docid":"8f8beda27d4ea55f3f24ec4c75076e14","score":"0.5870949","text":"def error_str(rc):\n return '{}: {}'.format(rc, mqtt.error_string(rc))","title":""},{"docid":"8f8beda27d4ea55f3f24ec4c75076e14","score":"0.5870949","text":"def error_str(rc):\n return '{}: {}'.format(rc, mqtt.error_string(rc))","title":""},{"docid":"9eaf43003d105c66077e31fc9d181763","score":"0.5857871","text":"def readouterr(self):\n out = self._readsnapshot('out')\n err = self._readsnapshot('err')\n return out, err","title":""},{"docid":"432559a6050b5f488b8ded9074fe0e6c","score":"0.58549106","text":"def error_message(self) -> str:\n return pulumi.get(self, \"error_message\")","title":""},{"docid":"060b0b301ab2726900cd65273548ba58","score":"0.5840013","text":"def error(self):\r\n return self.args[1]","title":""},{"docid":"10b14b3d467c6deb0cc3cff9b2e033ae","score":"0.5831012","text":"def test_execute_command_stderr(self):\n response = utility.ExecutorResponse()\n response.execute_command([\"ls\", \"not_a_real_dir\"])\n\n self.assertEqual(response._stdout, \"\")\n self.assertIn(\"No such file\", response._stderr)","title":""},{"docid":"bb89361a69c558b949d975c0783596ad","score":"0.5825796","text":"def GetErrorSummary(self):\n if self.fail_num == 0:\n return ''\n output = StringIO.StringIO()\n for testcase in self.testcases:\n if not testcase.failures:\n continue\n print >>output, '%s.%s:' % (self.name, testcase.name)\n for failure in testcase.failures:\n print >>output, failure.contents.encode('utf-8')\n return output.getvalue()","title":""},{"docid":"5bf47f2f1e71329ded73cb17813f4e97","score":"0.58255994","text":"def getErrorInfo(ex):\n if isinstance(ex, DBGPError):\n #XXX _Try_ to get the error message out of the DBGPError. There\n # is no good standard for DBGPError args right now though,\n # hence the pain here.\n if len(ex.args) == 2: # typically this is (errmsg, errno)\n errmsg = ex.args[0]\n try:\n errno = int(ex.args[1]) # sometimes this is a string\n except ValueError:\n errno = 0\n elif len(ex.args) == 1 and not hasattr(ex.args[0], \"__len__\"):\n # bug94837: if we're hovering over an unevaluate-able string,\n # we only get the numeric code in ex.args, so pull the message\n # out of ex.msg\n try:\n errmsg = ex.msg\n errno = ex.args[0]\n except:\n pass\n else:\n errmsg = ex.args[0]\n errno = 0\n elif isinstance(ex, COMException):\n from xpcom import components\n lastErrorSvc = components.classes[\"@activestate.com/koLastErrorService;1\"].\\\n getService(components.interfaces.koILastErrorService)\n errno, errmsg = lastErrorSvc.getLastError() \n return (errno, errmsg)","title":""},{"docid":"e406db8d8b58659a55e14d03419b351f","score":"0.58045083","text":"def getError(self):\r\n return self.lastError;","title":""},{"docid":"fb7c5bfaa940910975308bf002c65b8e","score":"0.5794283","text":"def get_error_message(error):\n return error.args[0] if error.args else \"\"","title":""},{"docid":"f07990e515500a118abd39103232364e","score":"0.5788405","text":"def error_message(self) -> \"str\":\n return self._attrs.get(\"errorMessage\")","title":""},{"docid":"715b3f74237de059bde63c8b480ce896","score":"0.5784418","text":"def get_error_message(error):\n try:\n return error.args[0]\n except IndexError:\n return ''","title":""},{"docid":"f9a40c17adbc26b2447768ee207a9a36","score":"0.5769262","text":"def return_error(message, error='', outputs=None):\n is_command = hasattr(demisto, 'command')\n try:\n is_server_handled = is_command and demisto.command() in ('fetch-incidents',\n 'fetch-credentials',\n 'long-running-execution',\n 'fetch-indicators')\n except Exception:\n is_server_handled = False\n message = LOG(message)\n if error:\n LOG(str(error))\n if any(sys.exc_info()): # Checking that an exception occurred\n fixed_traceback = fix_traceback_line_numbers(traceback.format_exc())\n LOG('\\n{}'.format(fixed_traceback))\n if is_debug_mode():\n message = '{}\\n\\n{}'.format(message, fixed_traceback)\n\n LOG.print_log()\n if not isinstance(message, str):\n message = message.encode('utf8') if hasattr(message, 'encode') else str(message)\n\n if is_command and demisto.command() == 'get-modified-remote-data':\n if (error and not isinstance(error, NotImplementedError)) or sys.exc_info()[0] != NotImplementedError:\n message = 'skip update. error: ' + message\n\n if is_server_handled:\n raise Exception(message)\n else:\n demisto.results({\n 'Type': entryTypes['error'],\n 'ContentsFormat': formats['text'],\n 'Contents': message,\n 'EntryContext': outputs,\n })\n sys.exit(0)","title":""},{"docid":"0441cff54e1b9f31595bb4ff316353b2","score":"0.57572985","text":"def redirectStdErr():\r\n sys.stderr=StringIO.StringIO()","title":""},{"docid":"9f0ba47f645615ffbc328d7fa40509db","score":"0.575696","text":"def get_error_output(self, error):\n if isinstance(error, dnf.exceptions.TransactionCheckError):\n if self.opts.transactions_action == 'undo':\n id_, = self.opts.transactions\n return (_('Cannot undo transaction %s, doing so would result '\n 'in an inconsistent package database.') % id_,)\n elif self.opts.transactions_action == 'rollback':\n id_, = (self.opts.transactions if self.opts.transactions[0] != 'force'\n else self.opts.transactions[1:])\n return (_('Cannot rollback transaction %s, doing so would '\n 'result in an inconsistent package database.') % id_,)\n\n return Command.get_error_output(self, error)","title":""},{"docid":"700d38204e17e8fd9841b22d1cdd2734","score":"0.57561153","text":"def get_error(self):\n exc_info = sys.exc_info()\n if exc_info[0] is None:\n return None\n else:\n err_type, err_value, err_trace = exc_info[0], exc_info[1], None\n if self.verbose and len(exc_info) > 2:\n err_trace = exc_info[2]\n return format_error(err_type, err_value, err_trace)","title":""},{"docid":"700d38204e17e8fd9841b22d1cdd2734","score":"0.57561153","text":"def get_error(self):\n exc_info = sys.exc_info()\n if exc_info[0] is None:\n return None\n else:\n err_type, err_value, err_trace = exc_info[0], exc_info[1], None\n if self.verbose and len(exc_info) > 2:\n err_trace = exc_info[2]\n return format_error(err_type, err_value, err_trace)","title":""},{"docid":"e7b2c669c2b34d679b43a51936e770ab","score":"0.57500446","text":"def error(self) -> Optional[Sequence[str]]:\n return pulumi.get(self, \"error\")","title":""},{"docid":"6621be1fc4c2ef3465d41fba2ec3a180","score":"0.57465094","text":"def get_error_message(self):\n\n arInfo = sys.exc_info()\n if len(arInfo) == 3 and arInfo[0] != None:\n sMsg = str(arInfo[1])\n if arInfo[2] != None:\n sMsg += \" at line \" + str(arInfo[2].tb_lineno)\n return sMsg\n else:\n return \"\"","title":""},{"docid":"f2af659188ec6f3e8acc3b50a77c9f00","score":"0.5745881","text":"def errRun( *cmd, **kwargs ):\n # By default we separate stderr, don't run in a shell, and don't echo\n stderr = kwargs.get( 'stderr', PIPE )\n shell = kwargs.get( 'shell', False )\n echo = kwargs.get( 'echo', False )\n if echo:\n # cmd goes to stderr, output goes to stdout\n info( cmd, '\\n' )\n if len( cmd ) == 1:\n cmd = cmd[ 0 ]\n # Allow passing in a list or a string\n if isinstance( cmd, BaseString ) and not shell:\n cmd = cmd.split( ' ' )\n cmd = [ str( arg ) for arg in cmd ]\n elif isinstance( cmd, list ) and shell:\n cmd = \" \".join( arg for arg in cmd )\n debug( '*** errRun:', cmd, '\\n' )\n popen = Popen( cmd, stdout=PIPE, stderr=stderr, shell=shell )\n # We use poll() because select() doesn't work with large fd numbers,\n # and thus communicate() doesn't work either\n out, err = '', ''\n poller = poll()\n poller.register( popen.stdout, POLLIN )\n fdToFile = { popen.stdout.fileno(): popen.stdout }\n fdToDecoder = { popen.stdout.fileno(): getincrementaldecoder() }\n outDone, errDone = False, True\n if popen.stderr:\n fdToFile[ popen.stderr.fileno() ] = popen.stderr\n fdToDecoder[ popen.stderr.fileno() ] = getincrementaldecoder()\n poller.register( popen.stderr, POLLIN )\n errDone = False\n while not outDone or not errDone:\n readable = poller.poll()\n for fd, event in readable:\n f = fdToFile[ fd ]\n decoder = fdToDecoder[ fd ]\n if event & POLLIN:\n data = decoder.decode( f.read( 1024 ) )\n if echo:\n output( data )\n if f == popen.stdout:\n out += data\n if data == '':\n outDone = True\n elif f == popen.stderr:\n err += data\n if data == '':\n errDone = True\n else: # POLLHUP or something unexpected\n if f == popen.stdout:\n outDone = True\n elif f == popen.stderr:\n errDone = True\n poller.unregister( fd )\n\n returncode = popen.wait()\n # Python 3 complains if we don't explicitly close these\n popen.stdout.close()\n if stderr == PIPE:\n popen.stderr.close()\n debug( out, err, returncode )\n return out, err, returncode","title":""},{"docid":"ac9e092a4e7cd8f8f585fcd22395970e","score":"0.5724777","text":"def run_or_die(command):\n (status, stdio) = commands.getstatusoutput(command)\n if status != 0:\n raise Exception(\"command '%s' failed with exit status %d and output '%s'\" % (command, status, stdio))\n return stdio","title":""},{"docid":"ac9e092a4e7cd8f8f585fcd22395970e","score":"0.5724777","text":"def run_or_die(command):\n (status, stdio) = commands.getstatusoutput(command)\n if status != 0:\n raise Exception(\"command '%s' failed with exit status %d and output '%s'\" % (command, status, stdio))\n return stdio","title":""},{"docid":"4bc9d2f04259b031a2a15e3c7aea1b99","score":"0.5722492","text":"def GetError(self):\n return self.qERR()","title":""},{"docid":"4bc9d2f04259b031a2a15e3c7aea1b99","score":"0.5722492","text":"def GetError(self):\n return self.qERR()","title":""},{"docid":"966c5523cf59e0a9c2ce5f37a50abca3","score":"0.572064","text":"def stderr(self, output, done=False, once=None):\n stderr = output if isinstance(output, (str, unicode)) else unicode8(output)\n self._conn.send_json(message.output(stderr=stderr, done=done, id=self._id, once=once))\n return self","title":""},{"docid":"92b16c16c4689002ca4db5787c506b00","score":"0.57193947","text":"def print_err(out):\n print(out, file=sys.stderr)","title":""},{"docid":"d87a927f1005021838aeae5b2b18b5e3","score":"0.56802905","text":"def check_errors(self):\n return self.error_str","title":""},{"docid":"c8fd0c5e4655646bfe6162b151f213a1","score":"0.56768036","text":"def error(self, command, message):\n return command.response(message)","title":""},{"docid":"2e5ead070e5517b22c57fddfe102e2bd","score":"0.56755966","text":"def get_command_output(self, command):\n p = subprocess.Popen(command, stdout=subprocess.PIPE, shell=True)\n (output, error) = p.communicate()\n return output","title":""},{"docid":"8097dbd0b2b0d185f8ded67efb5d15ec","score":"0.5672942","text":"def error(self):\n err = self.values(\":system:error?\")\n if len(err) < 2:\n err = self.read() # Try reading again\n code = err[0]\n message = err[1].replace('\"', '')\n return (code, message)","title":""},{"docid":"c919e7b5f37d7e356c75b29bf766969a","score":"0.56675595","text":"def mrc3_get_error_message(err):\n \n \n size = 256\n result = create_string_buffer(size)\n \n mrc3GetErrorMessage(err,result,size)\n \n return result.value","title":""},{"docid":"30a654128ae807805b29f4882a036ef9","score":"0.5663933","text":"def _get_errorOrWarningMessage(self) -> \"std::string\" :\n return _fusion.ShellFeature__get_errorOrWarningMessage(self)","title":""},{"docid":"5f13d086c69d5386178e8d46f1944341","score":"0.5650838","text":"def load_stderr(self):\n\n if self.fa_method.lower() != \"ml\":\n msg = \"Standard errors only available under ML estimation\"\n raise ValueError(msg)\n\n if self.nobs is None:\n msg = \"nobs is required to obtain standard errors.\"\n raise ValueError(msg)\n\n v = np.outer(self.uniqueness, np.ones(self.loadings.shape[1]))\n return np.sqrt(v / self.nobs)","title":""},{"docid":"3e8fa6c7c48a86b64e75881e140e591d","score":"0.5630652","text":"def get_error_message(self):\n assert self.has_error(),\\\n \"Please check that '.has_error()' is True before using this method\"\n\n return self.error_message","title":""},{"docid":"6b2c8611b730f768c45106547c8472c9","score":"0.5625867","text":"def rexec(cmd):\n try:\n output = subprocess.check_output(cmd.split(), stderr=subprocess.STDOUT)\n except subprocess.CalledProcessError as exc:\n return exc.output[:-1]\n else:\n return output[:-1]","title":""},{"docid":"2f90c71e29bec58d32ff1f3124a97f51","score":"0.5621934","text":"def find_exception_details(self):\n if self.status != \"failed\" or not self.output:\n return None, None\n\n output = \"\".join(self.output)\n\n # not an exception\n if \"Traceback (most recent call last):\" not in output:\n return None, None\n\n\n if \":\" in self.output[-1]:\n exctype, message = self.output[-1].split(\":\", 1)\n\n return exctype.strip(), message.strip()","title":""},{"docid":"3cab7562d55dc005d01c9858c04fbd8f","score":"0.5606115","text":"def stderr_lines(self):\n for line in self.stderr.split(\"\\n\"):\n yield line","title":""},{"docid":"3126be22d659d301e51f28238a9f5f52","score":"0.5602675","text":"def error(message):\n sys.stderr.write(\"{}\\n\".format(message))","title":""},{"docid":"93297d37f7992a2316fc0e5929ea1aa7","score":"0.5592672","text":"def message(self):\n return \"Consult NI VeriStand help for error code 0x%x error message %s\" % (\n self.errcode,\n self.errstring,\n )","title":""},{"docid":"f6f0699fbc49565a9a9b83b948257f79","score":"0.55814415","text":"def fetch_output_error(self, ex):\n return ex","title":""},{"docid":"4fce164e3055e736b091f331c677cf1f","score":"0.5568845","text":"def _execute_popen(command):\n (_, child_stdout_and_stderr) = os.popen4(command)\n return ''.join(child_stdout_and_stderr.readlines())","title":""},{"docid":"faa19b28b5e80e64c70036edcbdbf026","score":"0.55659664","text":"def get_error(self):\n task = get_validation_task_result_from_revision_id(self.object.id)\n\n if task is None:\n return None\n\n description = ERROR_DESCRIPTIONS.get(task.result, None)\n if description is None:\n return None\n\n return {\"description\": description}","title":""},{"docid":"28827962fb2fae3680ce58dfcac17005","score":"0.55450606","text":"def embederr(self):\n return self.__embederr","title":""},{"docid":"df56df594477152ed99701aaca2417fb","score":"0.5542965","text":"def errmsg(self):\n return _openfast.w_fast_data__get__errmsg()","title":""},{"docid":"79f0c1a4ca8bb6f5cb75492d4961f0fd","score":"0.55363333","text":"def GetCommandOutput(command):\n try:\n return subprocess.check_output(\n shlex.split(command), stderr=subprocess.STDOUT).strip()\n except subprocess.CalledProcessError:\n pass\n return None","title":""},{"docid":"c5757702e485b705e2e4fda193c6a626","score":"0.55274606","text":"def dump_stderr(payload):\n if isinstance(payload, Serializable):\n payload = payload.serialize()\n sys.stderr.write(json.dumps(payload))\n sys.stderr.write('\\n')","title":""},{"docid":"dc6966185b2ba218b5c83475763c1d04","score":"0.5511661","text":"def GetErrorSummary(self):\n return '\\n'.join(suite.GetErrorSummary() for suite in self.testsuites if\n suite.fail_num != 0)","title":""},{"docid":"e99de17bd23ed777ca8827ae219bea1c","score":"0.5511438","text":"def get_error(self):\n\t\tx = c_char_p(_libwte.wte_get_error(self.w))\n\t\treturn x.value","title":""},{"docid":"4300eb9df175925ddab7daff9212db88","score":"0.54962593","text":"def test_container_with_stderr(self):\n client = make_fake_client()\n container = client.containers.get(FAKE_CONTAINER_ID)\n command = \"echo Hello World\"\n exit_status = 42\n image = FAKE_IMAGE_ID\n stderr = \"Something went wrong\"\n\n err = ContainerError(container, exit_status, command, image, stderr)\n msg = (\"Command '{}' in image '{}' returned non-zero exit status {}: \"\n \"{}\").format(command, image, exit_status, stderr)\n assert str(err) == msg","title":""},{"docid":"63aea52cf167bdb84cfa772abf0e5177","score":"0.54929495","text":"def _get_errorOrWarningMessage(self) -> \"std::string\" :\n return _fusion.PipeFeature__get_errorOrWarningMessage(self)","title":""}],"string":"[\n {\n \"docid\": \"374a0b366ef567928cf96393fb1a88ac\",\n \"score\": \"0.8082867\",\n \"text\": \"def stderr(self):\\n if not self._stderr_str:\\n self._stderr_str = self.raw_stderr.decode(encoding=self._encoding)\\n return self._stderr_str\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ab8ea5c425e8a450dd8f3d2d307b30b\",\n \"score\": \"0.7926002\",\n \"text\": \"def get_stderr(self):\\n return self.cstderr.getvalue().rstrip('\\\\n')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a8ea2d4c2262113c9e4043f9a1295318\",\n \"score\": \"0.7743329\",\n \"text\": \"def stderr(self):\\n\\n return psys.u(self.raw_stderr())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd18803d89b9efb74d09e2b47039116b\",\n \"score\": \"0.74740756\",\n \"text\": \"def stderr(self) -> str:\\n\\n\\t\\treturn super().stderr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5af372a332e27242aa8cda01625a63ad\",\n \"score\": \"0.74736005\",\n \"text\": \"def raw_stderr(self):\\n\\n self.__ensure_terminated()\\n return self.__stderr.getvalue()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f10ce86c0cfa36dc0748d846c63f62e\",\n \"score\": \"0.73806137\",\n \"text\": \"def stderr(self):\\n self.wait()\\n return self._stderr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ce5b6643721fb964813fe2b6f984d85\",\n \"score\": \"0.71499336\",\n \"text\": \"def error(self):\\n import os\\n\\n try:\\n return self._process.stderr.read()\\n except:\\n _Log.debug(\\\"Nothing to read stderr {}\\\".format(self._executable))\\n # there is nothing to read, return None\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37b5183cc776a9152319ef5ba535c665\",\n \"score\": \"0.7051798\",\n \"text\": \"def get_error(execute_command_result):\\n\\n if not is_error(execute_command_result):\\n raise ValueError(\\\"execute_command_result has no error entry. before using get_error use is_error\\\")\\n\\n if isinstance(execute_command_result, dict):\\n return execute_command_result['Contents']\\n\\n error_messages = []\\n for entry in execute_command_result:\\n is_error_entry = type(entry) == dict and entry['Type'] == entryTypes['error']\\n if is_error_entry:\\n error_messages.append(entry['Contents'])\\n\\n return '\\\\n'.join(error_messages)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"509d926e18e423852fc7613fc35b3726\",\n \"score\": \"0.69838387\",\n \"text\": \"def stderr_output(cmd):\\n\\n handle, gpg_stderr = stderr_handle()\\n try:\\n output = subprocess.check_output(cmd, stderr=gpg_stderr) # nosec\\n if handle:\\n handle.close()\\n\\n return output\\n except subprocess.CalledProcessError as exception:\\n LOGGER.debug(\\\"GPG Command %s\\\", ' '.join(exception.cmd))\\n LOGGER.debug(\\\"GPG Output %s\\\", exception.output)\\n raise CryptoritoError('GPG Execution')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b02de1b410481b602e6ebbfd87a53ad2\",\n \"score\": \"0.6944275\",\n \"text\": \"def get_run_stderr(self, id):\\n stderr_url = self.get_run(id)['run_log']['stderr']\\n auth = wes_config()[self.id]['auth']\\n res = requests.get(stderr_url, headers=auth)\\n return res.text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c25b17af6e961a5771694ae5a3d8f003\",\n \"score\": \"0.6927808\",\n \"text\": \"def get_err_msg(self):\\n return self.get_error_message()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"28b2ec2580aabb7ed0a690d08b7ed21c\",\n \"score\": \"0.67414993\",\n \"text\": \"def process_stderr_command(args, host, port):\\n resp = send_command(const.CMD_GET_STDERR, args, host, port)\\n if isdata(resp):\\n return resp[1:].decode().split(\\\"\\\\n\\\")\\n else:\\n print_msg(resp[1:].decode())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78bef0f5c400c6d591b6870eb125d180\",\n \"score\": \"0.6646245\",\n \"text\": \"def getFailedMessage(self):\\n errorMessage = ''\\n try:\\n with open(self.exitErrorMessageFile) as errorFile:\\n errorMessage = errorFile.read()\\n except IOError:\\n pass\\n try:\\n with open(self.stderrFile) as stderrFile:\\n errorMessage += stderrFile.read()\\n except IOError:\\n pass\\n if len(errorMessage):\\n errorMessage += ' '\\n # add exit code inforamtion\\n if self.subprocess.returncode == -6:\\n errorMessage += \\\"SIGABRT received\\\"\\n elif self.subprocess.returncode == -9:\\n errorMessage += \\\"SIGKILL received\\\"\\n elif self.subprocess.returncode == -11:\\n errorMessage += \\\"Segmentation fault\\\"\\n else:\\n errorMessage += \\\"exit code {0}\\\".format(self.subprocess.returncode)\\n return errorMessage if errorMessage else 'Unexpected termination of {0}'.format(self.subprocessName)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c6eb05ffbf38651459ff2b5bad72eb68\",\n \"score\": \"0.6562889\",\n \"text\": \"def err_msg(self) -> str:\\n return self._err_msg\",\n \"title\": \"\"\n },\n {\n \"docid\": \"120bc5027348c6815300ba7cdbfb203c\",\n \"score\": \"0.65458125\",\n \"text\": \"def getoutputerror(cmd, verbose=False, debug=False, header='', split=False):\\n\\n if verbose or debug: \\n print(header+cmd)\\n\\n if not cmd:\\n # Return empty lists or strings.\\n if split:\\n return [], []\\n else:\\n return '', ''\\n\\n if not debug:\\n # fixme: use subprocess.\\n pin,pout,perr = os.popen3(cmd)\\n tout = pout.read().rstrip()\\n terr = perr.read().rstrip()\\n pin.close()\\n pout.close()\\n perr.close()\\n if split:\\n return tout.split('\\\\n'), terr.split('\\\\n')\\n else:\\n return tout, terr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9dfbfad73bedd02da77ebc3dbf828165\",\n \"score\": \"0.64598876\",\n \"text\": \"def get_error(self):\\n\\n return self.err\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e54f0cf09cac00729530e496660a52af\",\n \"score\": \"0.6414143\",\n \"text\": \"def get_stderr(self):\\n with open(os.path.join(self.directory, 'log.err')) as f:\\n for line in f:\\n yield line.rstrip()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4785dce715d74927c427cd9c5cabcbc\",\n \"score\": \"0.6375055\",\n \"text\": \"def getoutputoserror(cmd):\\n p = subprocess.Popen(cmd.split(), stdout=subprocess.PIPE, stderr=subprocess.STDOUT)\\n p.wait()\\n output = p.communicate()[0].decode()\\n if p.returncode:\\n raise OSError(p.returncode, '{0}: {1}'.format(cmd, output))\\n return output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f5d155c3e3c79f68fb8ef81413c885b\",\n \"score\": \"0.6362348\",\n \"text\": \"def err(self):\\n \\n return self._err.get()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"80dd65bda8f279941c69b88b877c3bd1\",\n \"score\": \"0.635026\",\n \"text\": \"def GetUnixErrorOutput(filename, error, new_error=False):\\n\\n line = ''\\n\\n if error.token:\\n line = '%d' % error.token.line_number\\n\\n error_code = '%04d' % error.code\\n if new_error:\\n error_code = 'New Error ' + error_code\\n return '%s:%s:(%s) %s' % (filename, line, error_code, error.message)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb2dce4c90436aef3dc87c72fa421535\",\n \"score\": \"0.6336272\",\n \"text\": \"def GetErrorOutput(error, new_error=False):\\n\\n line = ''\\n if error.token:\\n line = 'Line %d, ' % error.token.line_number\\n\\n code = 'E:%04d' % error.code\\n\\n error_message = error.message\\n if new_error:\\n error_message = 'New Error ' + error_message\\n\\n return '%s%s: %s' % (line, code, error.message)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e7905aa356bc0918317428a5f07bcb41\",\n \"score\": \"0.62756157\",\n \"text\": \"def get_process_failure_msg(proc):\\n return \\\"Execution failed (return code={}): {}\\\\n\\\".format(\\n proc.returncode, util.repr_command(proc.args)\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ac89ef978a83a439fb3c60191c6c1ff\",\n \"score\": \"0.6249727\",\n \"text\": \"def message(self) -> str:\\n return self._err_message\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b1ab7c725ee5dbd26149cd1c4cb14ee\",\n \"score\": \"0.6230253\",\n \"text\": \"def read_stderr(self):\\r\\n return self._read_all_from_queue(self._stderr_queue)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e6435d6f6605460b8e6c82ee7f3c93c\",\n \"score\": \"0.6192707\",\n \"text\": \"def __readStderr(self):\\n self.process.setReadChannel(QProcess.StandardError)\\n \\n while self.process.canReadLine():\\n self.errorGroup.show()\\n s = str(self.process.readAllStandardError(),\\n Preferences.getSystem(\\\"IOEncoding\\\"),\\n 'replace')\\n self.errors.insertPlainText(s)\\n self.errors.ensureCursorVisible()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c3eb8ed828ae20134a6bcecca0a2ab1\",\n \"score\": \"0.61628324\",\n \"text\": \"def getLastError(self):\\t\\n\\n\\t\\treturn self.__error_buffer.value.decode('utf-8')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5bcd892cbefecc89cbdac7547e8a3d5\",\n \"score\": \"0.6160232\",\n \"text\": \"def error_msg(self) -> str:\\n return self._error_msg\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0092e9b278fa83060bc950526a7d18f7\",\n \"score\": \"0.61584425\",\n \"text\": \"def get_err_msgs(self) -> str:\\n msgs = []\\n for i in range(self._num_procs):\\n if (\\n os.path.exists(self._stdout_files[i])\\n and os.stat(self._stdout_files[i]).st_size > 0\\n ):\\n if self._args.method == Method.OPTIMIZE:\\n msgs.append('console log output:\\\\n')\\n with open(self._stdout_files[0], 'r') as fd:\\n msgs.append(fd.read())\\n else:\\n with open(self._stdout_files[i], 'r') as fd:\\n contents = fd.read()\\n # pattern matches initial \\\"Exception\\\" or \\\"Error\\\" msg\\n pat = re.compile(r'^E[rx].*$', re.M)\\n errors = re.findall(pat, contents)\\n if len(errors) > 0:\\n msgs.append('\\\\n\\\\t'.join(errors))\\n return '\\\\n'.join(msgs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"28ced4b083efd827911979d09e84c8a9\",\n \"score\": \"0.61578876\",\n \"text\": \"def get_stdout_stderr(self, typ, datetime, dir):\\n out = '%s_%s_stdout.log' % (datetime, typ)\\n err = '%s_%s_stderr.log' % (datetime, typ)\\n return os.path.join(dir, out), os.path.join(dir, err)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83588cc38dd0dd5293bb521712b1d589\",\n \"score\": \"0.6130121\",\n \"text\": \"def geterr():\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6d5f235cace8ad9768a685b5b46da8d\",\n \"score\": \"0.61210054\",\n \"text\": \"def GetCommandOutput(env_cmd):\\n\\n # Disables exception pop-ups on Windows.\\n environ, cmdline = env_cmd\\n environ = dict(environ) # Ensures we are modifying a copy.\\n environ[CATCH_EXCEPTIONS_ENV_VAR_NAME] = '1'\\n return NormalizeOutput(GetShellCommandOutput((environ, cmdline)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e153c4d6d2085d872f70310b5b1d3cc\",\n \"score\": \"0.6110246\",\n \"text\": \"def get_last_error_message(cls):\\n drmaa2_lib = LibraryManager.get_instance().get_drmaa2_library()\\n string_ptr = drmaa2_lib.drmaa2_lasterror_text()\\n message = ''\\n if string_ptr:\\n message = ByteString(string_ptr.value).decode()\\n drmaa2_lib.drmaa2_string_free(string_ptr)\\n return message\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ff44576b68389b3e91ed4507cdc3fe2\",\n \"score\": \"0.6076596\",\n \"text\": \"def strerror(self):\\n return self.__str__()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11a510e194a57d7e989fc135d4d534a3\",\n \"score\": \"0.6044898\",\n \"text\": \"def get_error_msg(self):\\n \\n raise NotImplementedError(\\\"Must be implemented\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99e8e875342ad381ea7562dddf836d60\",\n \"score\": \"0.6023411\",\n \"text\": \"def write_err(s):\\n sys.stderr.write(s)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"139983475f518b197a3f0fe613aa52dd\",\n \"score\": \"0.60152155\",\n \"text\": \"def stderr_message(self, message: str) -> None:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58fc060d37a008218e1312110657d7b0\",\n \"score\": \"0.60105383\",\n \"text\": \"def parsed_error_msg(self):\\n return self.error_msg\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35e9f32652168cefb2b051ef5c1c36a3\",\n \"score\": \"0.6006651\",\n \"text\": \"def get_error_message(self):\\n return self.__error_observer.get_error_message()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"29a3198e0e824a13e956e55174b754b6\",\n \"score\": \"0.5999936\",\n \"text\": \"def get_stdout_stderr(typ, datetime, dir):\\n out = '%s_%s_stdout.log' % (datetime, typ)\\n err = '%s_%s_stderr.log' % (datetime, typ)\\n return os.path.join(dir, out), os.path.join(dir, err)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6db05c0fcc0b3ca6776cc647490fa889\",\n \"score\": \"0.59967333\",\n \"text\": \"def get_error(self):\\n retval = self.ask('SYST:ERR:ALL?').strip().split(\\\"\\\\n\\\")\\n return retval\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d58d5e3b7b0b9e2eb3d6766278f5dad\",\n \"score\": \"0.5988061\",\n \"text\": \"def error_desc(self):\\n return self._error_desc\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ebe3b5ba363e9146cb5701a55c8ae08\",\n \"score\": \"0.59517837\",\n \"text\": \"def stderr_to(replacement=None):\\n return redirect_file_obj('sys.stderr', replacement)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"324f2a51a7bf28d4f39352defa1ace39\",\n \"score\": \"0.59016526\",\n \"text\": \"def to_stderr(self, message):\\n\\t\\tprint >>sys.stderr, message.encode(preferredencoding())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69d31b31e0f28f46930e37fdcacd0681\",\n \"score\": \"0.5899151\",\n \"text\": \"def log_message(self) -> str:\\n return self._err_message\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ecc9785efcf72ba0f900904f2812f13d\",\n \"score\": \"0.5881488\",\n \"text\": \"def readouterr(self):\\n out = err = \\\"\\\"\\n if self.out:\\n out = self.out.getvalue()\\n self.out.truncate(0)\\n self.out.seek(0)\\n if self.err:\\n err = self.err.getvalue()\\n self.err.truncate(0)\\n self.err.seek(0)\\n return out, err\",\n \"title\": \"\"\n },\n {\n \"docid\": \"84d4b828ab724ed7b4fe253db0c8d7dd\",\n \"score\": \"0.58774126\",\n \"text\": \"def get_exitcode_stdout_stderr(cmd):\\n command = cmd.split(\\\" \\\")\\n args = [x.strip() for x in command if x.strip()]\\n\\n proc = Popen(args, stdout=PIPE, stderr=PIPE)\\n out, err = proc.communicate()\\n exitcode = proc.returncode\\n #\\n return exitcode, out, err\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4e39ecb1aa20140c9ce355a1cddeb9c\",\n \"score\": \"0.5872963\",\n \"text\": \"def error_message(self) -> str:\\n return self._error_message\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f8beda27d4ea55f3f24ec4c75076e14\",\n \"score\": \"0.5870949\",\n \"text\": \"def error_str(rc):\\n return '{}: {}'.format(rc, mqtt.error_string(rc))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f8beda27d4ea55f3f24ec4c75076e14\",\n \"score\": \"0.5870949\",\n \"text\": \"def error_str(rc):\\n return '{}: {}'.format(rc, mqtt.error_string(rc))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f8beda27d4ea55f3f24ec4c75076e14\",\n \"score\": \"0.5870949\",\n \"text\": \"def error_str(rc):\\n return '{}: {}'.format(rc, mqtt.error_string(rc))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f8beda27d4ea55f3f24ec4c75076e14\",\n \"score\": \"0.5870949\",\n \"text\": \"def error_str(rc):\\n return '{}: {}'.format(rc, mqtt.error_string(rc))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f8beda27d4ea55f3f24ec4c75076e14\",\n \"score\": \"0.5870949\",\n \"text\": \"def error_str(rc):\\n return '{}: {}'.format(rc, mqtt.error_string(rc))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9eaf43003d105c66077e31fc9d181763\",\n \"score\": \"0.5857871\",\n \"text\": \"def readouterr(self):\\n out = self._readsnapshot('out')\\n err = self._readsnapshot('err')\\n return out, err\",\n \"title\": \"\"\n },\n {\n \"docid\": \"432559a6050b5f488b8ded9074fe0e6c\",\n \"score\": \"0.58549106\",\n \"text\": \"def error_message(self) -> str:\\n return pulumi.get(self, \\\"error_message\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"060b0b301ab2726900cd65273548ba58\",\n \"score\": \"0.5840013\",\n \"text\": \"def error(self):\\r\\n return self.args[1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10b14b3d467c6deb0cc3cff9b2e033ae\",\n \"score\": \"0.5831012\",\n \"text\": \"def test_execute_command_stderr(self):\\n response = utility.ExecutorResponse()\\n response.execute_command([\\\"ls\\\", \\\"not_a_real_dir\\\"])\\n\\n self.assertEqual(response._stdout, \\\"\\\")\\n self.assertIn(\\\"No such file\\\", response._stderr)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb89361a69c558b949d975c0783596ad\",\n \"score\": \"0.5825796\",\n \"text\": \"def GetErrorSummary(self):\\n if self.fail_num == 0:\\n return ''\\n output = StringIO.StringIO()\\n for testcase in self.testcases:\\n if not testcase.failures:\\n continue\\n print >>output, '%s.%s:' % (self.name, testcase.name)\\n for failure in testcase.failures:\\n print >>output, failure.contents.encode('utf-8')\\n return output.getvalue()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5bf47f2f1e71329ded73cb17813f4e97\",\n \"score\": \"0.58255994\",\n \"text\": \"def getErrorInfo(ex):\\n if isinstance(ex, DBGPError):\\n #XXX _Try_ to get the error message out of the DBGPError. There\\n # is no good standard for DBGPError args right now though,\\n # hence the pain here.\\n if len(ex.args) == 2: # typically this is (errmsg, errno)\\n errmsg = ex.args[0]\\n try:\\n errno = int(ex.args[1]) # sometimes this is a string\\n except ValueError:\\n errno = 0\\n elif len(ex.args) == 1 and not hasattr(ex.args[0], \\\"__len__\\\"):\\n # bug94837: if we're hovering over an unevaluate-able string,\\n # we only get the numeric code in ex.args, so pull the message\\n # out of ex.msg\\n try:\\n errmsg = ex.msg\\n errno = ex.args[0]\\n except:\\n pass\\n else:\\n errmsg = ex.args[0]\\n errno = 0\\n elif isinstance(ex, COMException):\\n from xpcom import components\\n lastErrorSvc = components.classes[\\\"@activestate.com/koLastErrorService;1\\\"].\\\\\\n getService(components.interfaces.koILastErrorService)\\n errno, errmsg = lastErrorSvc.getLastError() \\n return (errno, errmsg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e406db8d8b58659a55e14d03419b351f\",\n \"score\": \"0.58045083\",\n \"text\": \"def getError(self):\\r\\n return self.lastError;\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb7c5bfaa940910975308bf002c65b8e\",\n \"score\": \"0.5794283\",\n \"text\": \"def get_error_message(error):\\n return error.args[0] if error.args else \\\"\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f07990e515500a118abd39103232364e\",\n \"score\": \"0.5788405\",\n \"text\": \"def error_message(self) -> \\\"str\\\":\\n return self._attrs.get(\\\"errorMessage\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"715b3f74237de059bde63c8b480ce896\",\n \"score\": \"0.5784418\",\n \"text\": \"def get_error_message(error):\\n try:\\n return error.args[0]\\n except IndexError:\\n return ''\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f9a40c17adbc26b2447768ee207a9a36\",\n \"score\": \"0.5769262\",\n \"text\": \"def return_error(message, error='', outputs=None):\\n is_command = hasattr(demisto, 'command')\\n try:\\n is_server_handled = is_command and demisto.command() in ('fetch-incidents',\\n 'fetch-credentials',\\n 'long-running-execution',\\n 'fetch-indicators')\\n except Exception:\\n is_server_handled = False\\n message = LOG(message)\\n if error:\\n LOG(str(error))\\n if any(sys.exc_info()): # Checking that an exception occurred\\n fixed_traceback = fix_traceback_line_numbers(traceback.format_exc())\\n LOG('\\\\n{}'.format(fixed_traceback))\\n if is_debug_mode():\\n message = '{}\\\\n\\\\n{}'.format(message, fixed_traceback)\\n\\n LOG.print_log()\\n if not isinstance(message, str):\\n message = message.encode('utf8') if hasattr(message, 'encode') else str(message)\\n\\n if is_command and demisto.command() == 'get-modified-remote-data':\\n if (error and not isinstance(error, NotImplementedError)) or sys.exc_info()[0] != NotImplementedError:\\n message = 'skip update. error: ' + message\\n\\n if is_server_handled:\\n raise Exception(message)\\n else:\\n demisto.results({\\n 'Type': entryTypes['error'],\\n 'ContentsFormat': formats['text'],\\n 'Contents': message,\\n 'EntryContext': outputs,\\n })\\n sys.exit(0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0441cff54e1b9f31595bb4ff316353b2\",\n \"score\": \"0.57572985\",\n \"text\": \"def redirectStdErr():\\r\\n sys.stderr=StringIO.StringIO()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f0ba47f645615ffbc328d7fa40509db\",\n \"score\": \"0.575696\",\n \"text\": \"def get_error_output(self, error):\\n if isinstance(error, dnf.exceptions.TransactionCheckError):\\n if self.opts.transactions_action == 'undo':\\n id_, = self.opts.transactions\\n return (_('Cannot undo transaction %s, doing so would result '\\n 'in an inconsistent package database.') % id_,)\\n elif self.opts.transactions_action == 'rollback':\\n id_, = (self.opts.transactions if self.opts.transactions[0] != 'force'\\n else self.opts.transactions[1:])\\n return (_('Cannot rollback transaction %s, doing so would '\\n 'result in an inconsistent package database.') % id_,)\\n\\n return Command.get_error_output(self, error)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"700d38204e17e8fd9841b22d1cdd2734\",\n \"score\": \"0.57561153\",\n \"text\": \"def get_error(self):\\n exc_info = sys.exc_info()\\n if exc_info[0] is None:\\n return None\\n else:\\n err_type, err_value, err_trace = exc_info[0], exc_info[1], None\\n if self.verbose and len(exc_info) > 2:\\n err_trace = exc_info[2]\\n return format_error(err_type, err_value, err_trace)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"700d38204e17e8fd9841b22d1cdd2734\",\n \"score\": \"0.57561153\",\n \"text\": \"def get_error(self):\\n exc_info = sys.exc_info()\\n if exc_info[0] is None:\\n return None\\n else:\\n err_type, err_value, err_trace = exc_info[0], exc_info[1], None\\n if self.verbose and len(exc_info) > 2:\\n err_trace = exc_info[2]\\n return format_error(err_type, err_value, err_trace)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e7b2c669c2b34d679b43a51936e770ab\",\n \"score\": \"0.57500446\",\n \"text\": \"def error(self) -> Optional[Sequence[str]]:\\n return pulumi.get(self, \\\"error\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6621be1fc4c2ef3465d41fba2ec3a180\",\n \"score\": \"0.57465094\",\n \"text\": \"def get_error_message(self):\\n\\n arInfo = sys.exc_info()\\n if len(arInfo) == 3 and arInfo[0] != None:\\n sMsg = str(arInfo[1])\\n if arInfo[2] != None:\\n sMsg += \\\" at line \\\" + str(arInfo[2].tb_lineno)\\n return sMsg\\n else:\\n return \\\"\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f2af659188ec6f3e8acc3b50a77c9f00\",\n \"score\": \"0.5745881\",\n \"text\": \"def errRun( *cmd, **kwargs ):\\n # By default we separate stderr, don't run in a shell, and don't echo\\n stderr = kwargs.get( 'stderr', PIPE )\\n shell = kwargs.get( 'shell', False )\\n echo = kwargs.get( 'echo', False )\\n if echo:\\n # cmd goes to stderr, output goes to stdout\\n info( cmd, '\\\\n' )\\n if len( cmd ) == 1:\\n cmd = cmd[ 0 ]\\n # Allow passing in a list or a string\\n if isinstance( cmd, BaseString ) and not shell:\\n cmd = cmd.split( ' ' )\\n cmd = [ str( arg ) for arg in cmd ]\\n elif isinstance( cmd, list ) and shell:\\n cmd = \\\" \\\".join( arg for arg in cmd )\\n debug( '*** errRun:', cmd, '\\\\n' )\\n popen = Popen( cmd, stdout=PIPE, stderr=stderr, shell=shell )\\n # We use poll() because select() doesn't work with large fd numbers,\\n # and thus communicate() doesn't work either\\n out, err = '', ''\\n poller = poll()\\n poller.register( popen.stdout, POLLIN )\\n fdToFile = { popen.stdout.fileno(): popen.stdout }\\n fdToDecoder = { popen.stdout.fileno(): getincrementaldecoder() }\\n outDone, errDone = False, True\\n if popen.stderr:\\n fdToFile[ popen.stderr.fileno() ] = popen.stderr\\n fdToDecoder[ popen.stderr.fileno() ] = getincrementaldecoder()\\n poller.register( popen.stderr, POLLIN )\\n errDone = False\\n while not outDone or not errDone:\\n readable = poller.poll()\\n for fd, event in readable:\\n f = fdToFile[ fd ]\\n decoder = fdToDecoder[ fd ]\\n if event & POLLIN:\\n data = decoder.decode( f.read( 1024 ) )\\n if echo:\\n output( data )\\n if f == popen.stdout:\\n out += data\\n if data == '':\\n outDone = True\\n elif f == popen.stderr:\\n err += data\\n if data == '':\\n errDone = True\\n else: # POLLHUP or something unexpected\\n if f == popen.stdout:\\n outDone = True\\n elif f == popen.stderr:\\n errDone = True\\n poller.unregister( fd )\\n\\n returncode = popen.wait()\\n # Python 3 complains if we don't explicitly close these\\n popen.stdout.close()\\n if stderr == PIPE:\\n popen.stderr.close()\\n debug( out, err, returncode )\\n return out, err, returncode\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac9e092a4e7cd8f8f585fcd22395970e\",\n \"score\": \"0.5724777\",\n \"text\": \"def run_or_die(command):\\n (status, stdio) = commands.getstatusoutput(command)\\n if status != 0:\\n raise Exception(\\\"command '%s' failed with exit status %d and output '%s'\\\" % (command, status, stdio))\\n return stdio\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac9e092a4e7cd8f8f585fcd22395970e\",\n \"score\": \"0.5724777\",\n \"text\": \"def run_or_die(command):\\n (status, stdio) = commands.getstatusoutput(command)\\n if status != 0:\\n raise Exception(\\\"command '%s' failed with exit status %d and output '%s'\\\" % (command, status, stdio))\\n return stdio\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4bc9d2f04259b031a2a15e3c7aea1b99\",\n \"score\": \"0.5722492\",\n \"text\": \"def GetError(self):\\n return self.qERR()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4bc9d2f04259b031a2a15e3c7aea1b99\",\n \"score\": \"0.5722492\",\n \"text\": \"def GetError(self):\\n return self.qERR()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"966c5523cf59e0a9c2ce5f37a50abca3\",\n \"score\": \"0.572064\",\n \"text\": \"def stderr(self, output, done=False, once=None):\\n stderr = output if isinstance(output, (str, unicode)) else unicode8(output)\\n self._conn.send_json(message.output(stderr=stderr, done=done, id=self._id, once=once))\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92b16c16c4689002ca4db5787c506b00\",\n \"score\": \"0.57193947\",\n \"text\": \"def print_err(out):\\n print(out, file=sys.stderr)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d87a927f1005021838aeae5b2b18b5e3\",\n \"score\": \"0.56802905\",\n \"text\": \"def check_errors(self):\\n return self.error_str\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c8fd0c5e4655646bfe6162b151f213a1\",\n \"score\": \"0.56768036\",\n \"text\": \"def error(self, command, message):\\n return command.response(message)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e5ead070e5517b22c57fddfe102e2bd\",\n \"score\": \"0.56755966\",\n \"text\": \"def get_command_output(self, command):\\n p = subprocess.Popen(command, stdout=subprocess.PIPE, shell=True)\\n (output, error) = p.communicate()\\n return output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8097dbd0b2b0d185f8ded67efb5d15ec\",\n \"score\": \"0.5672942\",\n \"text\": \"def error(self):\\n err = self.values(\\\":system:error?\\\")\\n if len(err) < 2:\\n err = self.read() # Try reading again\\n code = err[0]\\n message = err[1].replace('\\\"', '')\\n return (code, message)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c919e7b5f37d7e356c75b29bf766969a\",\n \"score\": \"0.56675595\",\n \"text\": \"def mrc3_get_error_message(err):\\n \\n \\n size = 256\\n result = create_string_buffer(size)\\n \\n mrc3GetErrorMessage(err,result,size)\\n \\n return result.value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30a654128ae807805b29f4882a036ef9\",\n \"score\": \"0.5663933\",\n \"text\": \"def _get_errorOrWarningMessage(self) -> \\\"std::string\\\" :\\n return _fusion.ShellFeature__get_errorOrWarningMessage(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f13d086c69d5386178e8d46f1944341\",\n \"score\": \"0.5650838\",\n \"text\": \"def load_stderr(self):\\n\\n if self.fa_method.lower() != \\\"ml\\\":\\n msg = \\\"Standard errors only available under ML estimation\\\"\\n raise ValueError(msg)\\n\\n if self.nobs is None:\\n msg = \\\"nobs is required to obtain standard errors.\\\"\\n raise ValueError(msg)\\n\\n v = np.outer(self.uniqueness, np.ones(self.loadings.shape[1]))\\n return np.sqrt(v / self.nobs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e8fa6c7c48a86b64e75881e140e591d\",\n \"score\": \"0.5630652\",\n \"text\": \"def get_error_message(self):\\n assert self.has_error(),\\\\\\n \\\"Please check that '.has_error()' is True before using this method\\\"\\n\\n return self.error_message\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b2c8611b730f768c45106547c8472c9\",\n \"score\": \"0.5625867\",\n \"text\": \"def rexec(cmd):\\n try:\\n output = subprocess.check_output(cmd.split(), stderr=subprocess.STDOUT)\\n except subprocess.CalledProcessError as exc:\\n return exc.output[:-1]\\n else:\\n return output[:-1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f90c71e29bec58d32ff1f3124a97f51\",\n \"score\": \"0.5621934\",\n \"text\": \"def find_exception_details(self):\\n if self.status != \\\"failed\\\" or not self.output:\\n return None, None\\n\\n output = \\\"\\\".join(self.output)\\n\\n # not an exception\\n if \\\"Traceback (most recent call last):\\\" not in output:\\n return None, None\\n\\n\\n if \\\":\\\" in self.output[-1]:\\n exctype, message = self.output[-1].split(\\\":\\\", 1)\\n\\n return exctype.strip(), message.strip()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3cab7562d55dc005d01c9858c04fbd8f\",\n \"score\": \"0.5606115\",\n \"text\": \"def stderr_lines(self):\\n for line in self.stderr.split(\\\"\\\\n\\\"):\\n yield line\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3126be22d659d301e51f28238a9f5f52\",\n \"score\": \"0.5602675\",\n \"text\": \"def error(message):\\n sys.stderr.write(\\\"{}\\\\n\\\".format(message))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93297d37f7992a2316fc0e5929ea1aa7\",\n \"score\": \"0.5592672\",\n \"text\": \"def message(self):\\n return \\\"Consult NI VeriStand help for error code 0x%x error message %s\\\" % (\\n self.errcode,\\n self.errstring,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6f0699fbc49565a9a9b83b948257f79\",\n \"score\": \"0.55814415\",\n \"text\": \"def fetch_output_error(self, ex):\\n return ex\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4fce164e3055e736b091f331c677cf1f\",\n \"score\": \"0.5568845\",\n \"text\": \"def _execute_popen(command):\\n (_, child_stdout_and_stderr) = os.popen4(command)\\n return ''.join(child_stdout_and_stderr.readlines())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"faa19b28b5e80e64c70036edcbdbf026\",\n \"score\": \"0.55659664\",\n \"text\": \"def get_error(self):\\n task = get_validation_task_result_from_revision_id(self.object.id)\\n\\n if task is None:\\n return None\\n\\n description = ERROR_DESCRIPTIONS.get(task.result, None)\\n if description is None:\\n return None\\n\\n return {\\\"description\\\": description}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"28827962fb2fae3680ce58dfcac17005\",\n \"score\": \"0.55450606\",\n \"text\": \"def embederr(self):\\n return self.__embederr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df56df594477152ed99701aaca2417fb\",\n \"score\": \"0.5542965\",\n \"text\": \"def errmsg(self):\\n return _openfast.w_fast_data__get__errmsg()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79f0c1a4ca8bb6f5cb75492d4961f0fd\",\n \"score\": \"0.55363333\",\n \"text\": \"def GetCommandOutput(command):\\n try:\\n return subprocess.check_output(\\n shlex.split(command), stderr=subprocess.STDOUT).strip()\\n except subprocess.CalledProcessError:\\n pass\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5757702e485b705e2e4fda193c6a626\",\n \"score\": \"0.55274606\",\n \"text\": \"def dump_stderr(payload):\\n if isinstance(payload, Serializable):\\n payload = payload.serialize()\\n sys.stderr.write(json.dumps(payload))\\n sys.stderr.write('\\\\n')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc6966185b2ba218b5c83475763c1d04\",\n \"score\": \"0.5511661\",\n \"text\": \"def GetErrorSummary(self):\\n return '\\\\n'.join(suite.GetErrorSummary() for suite in self.testsuites if\\n suite.fail_num != 0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e99de17bd23ed777ca8827ae219bea1c\",\n \"score\": \"0.5511438\",\n \"text\": \"def get_error(self):\\n\\t\\tx = c_char_p(_libwte.wte_get_error(self.w))\\n\\t\\treturn x.value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4300eb9df175925ddab7daff9212db88\",\n \"score\": \"0.54962593\",\n \"text\": \"def test_container_with_stderr(self):\\n client = make_fake_client()\\n container = client.containers.get(FAKE_CONTAINER_ID)\\n command = \\\"echo Hello World\\\"\\n exit_status = 42\\n image = FAKE_IMAGE_ID\\n stderr = \\\"Something went wrong\\\"\\n\\n err = ContainerError(container, exit_status, command, image, stderr)\\n msg = (\\\"Command '{}' in image '{}' returned non-zero exit status {}: \\\"\\n \\\"{}\\\").format(command, image, exit_status, stderr)\\n assert str(err) == msg\",\n \"title\": \"\"\n },\n {\n \"docid\": \"63aea52cf167bdb84cfa772abf0e5177\",\n \"score\": \"0.54929495\",\n \"text\": \"def _get_errorOrWarningMessage(self) -> \\\"std::string\\\" :\\n return _fusion.PipeFeature__get_errorOrWarningMessage(self)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1199,"cells":{"query_id":{"kind":"string","value":"c24f08615fc30efad9f87465a3ef1f7c"},"query":{"kind":"string","value":"Invite user to group (inviteGroupPublicV1) Required valid user authentication"},"positive_passages":{"kind":"list like","value":[{"docid":"66fef9c12b9b0b4cb300b50530fd2936","score":"0.71501696","text":"def invite_group_public_v1(\n user_id: str,\n namespace: Optional[str] = None,\n x_additional_headers: Optional[Dict[str, str]] = None,\n **kwargs\n):\n if namespace is None:\n namespace, error = get_services_namespace()\n if error:\n return None, error\n request = InviteGroupPublicV1.create(\n user_id=user_id,\n namespace=namespace,\n )\n return run_request(request, additional_headers=x_additional_headers, **kwargs)","title":""}],"string":"[\n {\n \"docid\": \"66fef9c12b9b0b4cb300b50530fd2936\",\n \"score\": \"0.71501696\",\n \"text\": \"def invite_group_public_v1(\\n user_id: str,\\n namespace: Optional[str] = None,\\n x_additional_headers: Optional[Dict[str, str]] = None,\\n **kwargs\\n):\\n if namespace is None:\\n namespace, error = get_services_namespace()\\n if error:\\n return None, error\\n request = InviteGroupPublicV1.create(\\n user_id=user_id,\\n namespace=namespace,\\n )\\n return run_request(request, additional_headers=x_additional_headers, **kwargs)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"4459c330912a73c413cf5d1668233bb8","score":"0.7699805","text":"def invite_member(request):\n group = get_object_or_404(models.Group, id=request.POST.get(\"group\"))\n user = get_user_from_username(request.user, request.POST[\"user\"])\n\n if not group.user == request.user:\n return HttpResponseBadRequest()\n\n try:\n models.GroupMemberInvitation.objects.create(user=user, group=group)\n except IntegrityError:\n pass\n\n return redirect(user.local_path)","title":""},{"docid":"385a749693ab4aaacea09db62bd39d97","score":"0.7404152","text":"def invite_user_to_group(group_id):\n\n membership = Membership.get_membership_by_user_group(\n session[CURR_USER_ID], group_id)\n if membership == None:\n return restricted_not_authorized()\n\n safe_group = Group.get_safe_group(group_id)\n member_type = membership.member_type\n\n if (member_type not in safe_group['can_invite_list']):\n return restricted_group_privileges(group_id)\n\n else:\n form = forms.InviteToGroupForm()\n if form.validate_on_submit():\n\n username = form.username.data\n invited_user = User.get_user_by_username(username)\n\n # invitation = Membership.invite_by_api(\n # invited_user.id, group_id, session[CURR_USER_ID], g.user.api_token,app_url)\n\n API_URL = f\"{app_url}/api/invitations\"\n json_data = {\n \"member_id\": invited_user.id,\n \"group_id\": group_id,\n \"invited_by_id\": session[CURR_USER_ID],\n \"api_token\":g.user.api_token\n }\n response = requests.post(API_URL, json=json_data, headers={\n \"Content-Type\": \"application/json\"})\n invitation= response.json()\n\n status = invitation['status']\n message = invitation['message']\n \n if status==\"successful\":\n flash(message, 'success')\n else:\n flash(message, 'danger')\n\n safe_members = Membership.get_serialized_safe_members_info_by_group_sorted(\n group_id)\n\n else:\n safe_members = Membership.get_serialized_safe_members_info_by_group_sorted(\n group_id)\n\n return render_template('invite_to_group.html', form=form, group=safe_group, members=safe_members)","title":""},{"docid":"7a96e5ad91d11fb486ec9376d757a99f","score":"0.7260222","text":"async def invite_group_public_v1_async(\n user_id: str,\n namespace: Optional[str] = None,\n x_additional_headers: Optional[Dict[str, str]] = None,\n **kwargs\n):\n if namespace is None:\n namespace, error = get_services_namespace()\n if error:\n return None, error\n request = InviteGroupPublicV1.create(\n user_id=user_id,\n namespace=namespace,\n )\n return await run_request_async(\n request, additional_headers=x_additional_headers, **kwargs\n )","title":""},{"docid":"0dcd65512d49cb81c358f5bdcd54988d","score":"0.7227689","text":"def group_invite(request, pk, template_name='groups/group_invite.html'):\n group = get_object_or_404(Group, pk=pk, is_active=True)\n form = GroupInviteForm(initial={'group': group.pk, 'user': request.user.pk})\n return render(request, template_name, {\n 'group': group,\n 'form': form\n })","title":""},{"docid":"5ca3dfc009087125ea9d4a47fc3f9a8c","score":"0.72048044","text":"async def invite_group_public_v2_async(\n group_id: str,\n user_id: str,\n namespace: Optional[str] = None,\n x_additional_headers: Optional[Dict[str, str]] = None,\n **kwargs\n):\n if namespace is None:\n namespace, error = get_services_namespace()\n if error:\n return None, error\n request = InviteGroupPublicV2.create(\n group_id=group_id,\n user_id=user_id,\n namespace=namespace,\n )\n return await run_request_async(\n request, additional_headers=x_additional_headers, **kwargs\n )","title":""},{"docid":"9c708a909ed3c6f501859f89317056d9","score":"0.7129191","text":"def invite_group_public_v2(\n group_id: str,\n user_id: str,\n namespace: Optional[str] = None,\n x_additional_headers: Optional[Dict[str, str]] = None,\n **kwargs\n):\n if namespace is None:\n namespace, error = get_services_namespace()\n if error:\n return None, error\n request = InviteGroupPublicV2.create(\n group_id=group_id,\n user_id=user_id,\n namespace=namespace,\n )\n return run_request(request, additional_headers=x_additional_headers, **kwargs)","title":""},{"docid":"a553eb8fc3eb701cefa1d4c938fb06b6","score":"0.68725336","text":"def accept_membership(request):\n group = get_object_or_404(models.Group, id=request.POST.get(\"group\"))\n invite = get_object_or_404(\n models.GroupMemberInvitation, group=group, user=request.user\n )\n\n try:\n invite.accept()\n except IntegrityError:\n pass\n\n return redirect(group.local_path)","title":""},{"docid":"c313d2fec18103d1602ff6ec33deccbf","score":"0.67918944","text":"def group_invite(request, slug, template_name='groups/group_invite.html'):\n group = get_object_or_404(Group, slug=slug, is_active=True)\n form = InviteForm(initial={'group': group.pk, 'user': request.user.pk})\n return render_to_response(template_name, {\n 'group': group,\n 'form': form\n }, context_instance=RequestContext(request))","title":""},{"docid":"cc05bc926c522b4d146943df687fef3d","score":"0.6783538","text":"def test_get_group_invite_only(self):\n group = Group.objects.create(name='test-group', invite_only=True)\n group.users.add(self.user)\n\n rsp = self.apiGet(self.get_item_url(group.name),\n expected_mimetype=self.item_mimetype)\n self.assertEqual(rsp['stat'], 'ok')\n self.assertEqual(rsp['group']['invite_only'], True)","title":""},{"docid":"056eced7e2c66878c5312d3b2f101727","score":"0.6662698","text":"def project__user_invite(self) -> None:\n project_name = self.get_project()\n try:\n self.client.invite_project_user(\n project=project_name,\n user_email=self.args.email,\n member_type=self.args.role,\n )\n except client.Error as ex:\n print(ex.response.text)\n raise argx.UserError(\"Project '{}' invite for {} failed\".format(project_name, self.args.email))\n self.log.info(\"Invited %r into project %r\", self.args.email, project_name)","title":""},{"docid":"d05c0d5fc67b012fc635fb4f98e9ad50","score":"0.6653732","text":"def inviteToGroup(self, groupId, users, role, expiration=1440):\n params = {\n \"f\" : \"json\",\n \"token\" : self._securityHandler.token,\n \"users\" : users,\n \"role\" : role,\n \"expiration\" : expiration\n }\n return self._do_post(url=self._url + \"/%s/invite\" % groupId,\n param_dict=params,\n proxy_url=self._proxy_url,\n proxy_port=self._proxy_port)","title":""},{"docid":"4b87a930d019cfe7083eb13052b570e0","score":"0.6620096","text":"def account__team__user_invite(self) -> None:\n self.client.add_team_member(self.args.account_id, self.args.team_id, self.args.email)","title":""},{"docid":"142d8ff6f8b0f3fcd0f8eebc192f4a74","score":"0.64957047","text":"def add_invitation(request, group_id, user_id):\n group = get_object_or_404(Group, pk=group_id)\n if not is_member(request.user, group):\n raise PermissionDenied\n user_to_invite = get_object_or_404(User, pk=user_id)\n # get all instruments which that user plays AND are desired by the group\n their_instruments = UserInstrument.objects.filter(user=user_to_invite)\n possible_to_invite = group.desired_instruments.filter(user_plays__in=their_instruments)\n # check how many instruments are possible:\n instr_count = possible_to_invite.count()\n if instr_count == 0:\n # this isn't possible from the normal user interface, but is from the user messing\n # with urls manually. Give an error message and redirect the user to their dashboard\n messages.error(request, \"You tried to issue an invalid invitation!\")\n return redirect(reverse(\"dashboard\"))\n elif instr_count == 1:\n # simply send an invitation and send the user to the relevant group's page (so they\n # can see that the invitation was successful)\n try:\n Invitation.objects.create(inviting_user=request.user,\n invited_user=user_to_invite,\n invited_instrument=possible_to_invite[0],\n group=group)\n messages.success(request, \"Your invitation was sent to %s\" %user_to_invite.username)\n return redirect(reverse(\"group\", kwargs={\"id\": group_id}))\n except IntegrityError:\n # in case an invitation has already been sent\n messages.error(request,\n \"%s has already been invited to the group %s!\" %(user_to_invite.username,\n group.name))\n return redirect(reverse(\"dashboard\"))\n else:\n # there is more than one possible instrument. Redirect to the appropriate pre-populated\n # invitation form so the user can choose the instrument\n return redirect(reverse(\"invite_for_user\", kwargs={\"group_id\": group_id, \"user_id\": user_id}))","title":""},{"docid":"932aff1fecdb828efbea5a166b79426c","score":"0.64831245","text":"def _inviteIntoGroup(self, groupId, contactIds=[], seq=0):\n return self._client.inviteIntoGroup(seq, groupId, contactIds)","title":""},{"docid":"932aff1fecdb828efbea5a166b79426c","score":"0.64831245","text":"def _inviteIntoGroup(self, groupId, contactIds=[], seq=0):\n return self._client.inviteIntoGroup(seq, groupId, contactIds)","title":""},{"docid":"5c36ceb828ac5bbe8f8050c0141cc158","score":"0.6475991","text":"def new_group(request, username=\"\"):\n if request.method == \"POST\":\n form = GroupSetupForm(data=request.POST, user=request.user)\n form.order_fields([\"name\", \"description\", \"instrument\", \"invited_user\", \"desired_instruments\"])\n if form.is_valid():\n # create group and issue invitation (NB the group is created even if the user\n # declines the invitation - this is intentional)\n group = Group(name=form.cleaned_data[\"name\"],\n description=form.cleaned_data[\"description\"])\n try:\n invited_user = User.objects.get(username=form.cleaned_data[\"invited_user\"])\n if invited_user == request.user:\n form.add_error(\"invited_user\", \"You can't invite yourself!\")\n else:\n group.save()\n # It should be allowable for the \"desired_instruments\" field to be left blank\n # - to allow for groups of just 2 users. Django internally requires the\n # ManyToManyField to be an iterable - otherwise an error is raised - but if\n # left blank the value included in the POST request is None. To avoid this\n # error, we manually set it to be the empty list.\n if request.POST.get(\"desired_instruments\"):\n group.desired_instruments = form.cleaned_data[\"desired_instruments\"]\n else:\n group.desired_instruments = []\n\n my_instr = UserInstrument.objects.get(pk=form.cleaned_data[\"instrument\"])\n group.members = [my_instr]\n group.save()\n \n\n invited_instrument = UserInstrument.objects.get(pk=request.POST.get(\"invited_instrument\")) \\\n .instrument\n Invitation.objects.create(inviting_user=request.user,\n invited_user=invited_user,\n invited_instrument=invited_instrument,\n group=group)\n messages.success(request, \"\"\"Your new group %s has now been started!\n An invitation has been sent to %s\n \"\"\" % (form.cleaned_data[\"name\"], \n form.cleaned_data[\"invited_user\"]))\n return redirect(reverse(\"my_groups\"))\n \n except IntegrityError:\n # this happens if the user attempts to create a group with an already existing name\n # (since the name field has unique=True). Although this is a db error, it will make\n # most sense to the user to raise this as an error in the form itself\n form.add_error(\"name\", \"That group name is already taken!\")\n\n else:\n messages.error(request, \"Please correct the indicated errors and try again\")\n else:\n form = GroupSetupForm(user=request.user, initial={\"invited_user\": username})\n form.order_fields([\"name\", \"description\", \"instrument\", \"invited_user\", \"desired_instruments\"])\n \n args = {\"active\": \"dashboard\", \"form\": form}\n args.update(csrf(request))\n return render(request, \"groups/new.html\", args)","title":""},{"docid":"80afed92bbff9ef301c7554c97b6e3d7","score":"0.64502317","text":"def add_user_to_public_group(sender, instance, created, **kwargs):\n public_group = ExtendedGroup.objects.public_group()\n # need to check if Public group exists to avoid errors when creating\n # user accounts (like superuser and AnonymousUser) before the group\n # is created by init_refinery command\n if public_group:\n instance.groups.add(public_group)","title":""},{"docid":"7c7dd600c3f37f29a4a7916d5bd0495d","score":"0.6432827","text":"def specific_invite(request, group_id, instr_name=None, user_id=None):\n group = get_object_or_404(Group, pk=group_id)\n if not is_member(request.user, group):\n raise PermissionDenied\n\n if instr_name:\n instrument = get_object_or_404(Instrument, instrument=instr_name)\n\n else:\n instrument = None\n\n if user_id:\n user = get_object_or_404(User, pk=user_id)\n else:\n user = None\n\n # determine fields to exclude from form, depending on the circumstances this view is called:\n exclusions = [\"group\"]\n if instr_name:\n exclusions.append(\"invited_instrument\")\n if user_id:\n exclusions.append(\"invited_user\")\n\n if request.method == \"POST\":\n form = InvitationForm(request.POST, instr=instr_name, exclude=exclusions)\n if form.is_valid():\n invitation = form.save(commit=False)\n invitation.inviting_user = request.user\n invitation.group = group\n if instrument:\n invitation.invited_instrument = instrument\n else:\n invitation.invited_instrument = form.cleaned_data[\"invited_instrument\"]\n # If the group does not desire the instrument requested, return to the group page\n # with an explanatory error message.\n if instrument and instrument not in group.desired_instruments.all():\n messages.error(request, \"The %s group isn't looking for a %s player!\" %(group.name,\n instrument.instrument))\n return redirect(reverse(\"group\", kwargs={\"id\": group_id}))\n\n if user:\n invitation.invited_user = user\n else:\n invitation.invited_user = form.cleaned_data[\"invited_user\"]\n # display an error message if you are trying to invite someone already in the group\n if group in Group.objects.filter(members__user__in=[invitation.invited_user]):\n form.add_error(\"invited_user\", \"%s is already in this group!\" \\\n %invitation.invited_user.username) \n else:\n try:\n invitation.save()\n messages.success(request,\n \"Your invitation was sent to %s\" %invitation.invited_user.username)\n return redirect(reverse(\"group\", kwargs={\"id\": group_id}))\n except IntegrityError:\n # This happens when the uniqueness constraint is violated - that is,\n # you try to invite someone who has already been invited to the same\n # group\n form.add_error(\"invited_user\", \"%s has already been invited to join this group!\" \\\n %invitation.invited_user.username)\n else:\n messages.error(request, \"Please correct the indicated errors and try again\")\n else:\n if instrument:\n form = InvitationForm(instr=instrument.instrument, exclude=exclusions)\n else:\n form = InvitationForm(exclude=exclusions)\n if not instrument:\n if Invitation.objects.filter(group=group, invited_user=user).exists():\n # the user has already been invited - this can't happen via the usual interface\n # but, as before, might be possible from manual entering of URLs. Handle this with\n # an error message:\n messages.error(\"invited_user\", \"%s has already been invited to join this group!\" \\\n %invitation.invited_user.username)\n return redirect(reverse(\"group\", kwargs={\"id\": group_id}))\n form.fields[\"invited_instrument\"].queryset = group.desired_instruments \\\n .filter(user_plays__user__in=[user])\n\n args = {\"active\": \"dashboard\", \"form\": form, \"group\": group,\n \"instrument\": instr_name, \"user\": user}\n args.update(csrf(request))\n return render(request, \"groups/specific-invite.html\", args)","title":""},{"docid":"d689d040e98cfba6350dc5da0a6db1ac","score":"0.64071214","text":"def _acceptGroupInvitation(self, groupId, seq=0):\n return self._client.acceptGroupInvitation(seq, groupId)","title":""},{"docid":"d689d040e98cfba6350dc5da0a6db1ac","score":"0.64071214","text":"def _acceptGroupInvitation(self, groupId, seq=0):\n return self._client.acceptGroupInvitation(seq, groupId)","title":""},{"docid":"c79ea71e17ba6566dfd24eca43314f02","score":"0.6395867","text":"def test_user_invite(self):\n self.client.credentials(HTTP_AUTHORIZATION='JWT ' + self.token)\n response = self.client.post(reverse('test_challenge:user-invite',\n kwargs={'pk': '{}'.format(self.test_user.pk)}),\n {'email': 'email@example.com'}, format='json')\n self.assertEqual(response.status_code, status.HTTP_200_OK)\n\n no_email_response = self.client.post(reverse('test_challenge:user-invite',\n kwargs={'pk': '{}'.format(self.test_user.pk)}),\n {}, format='json')\n self.assertEqual(no_email_response.status_code, status.HTTP_400_BAD_REQUEST)","title":""},{"docid":"d7fb9049c061abcea73bfaa7814efce7","score":"0.6392299","text":"def test_p2p_go_invite_auth(dev):\n addr0 = dev[0].p2p_dev_addr()\n addr1 = dev[1].p2p_dev_addr()\n\n logger.info(\"Generate BSS table entry for old group\")\n # this adds more coverage to testing by forcing the GO to be found with an\n # older entry in the BSS table and with that entry having a different\n # operating channel.\n dev[0].p2p_start_go(freq=2432)\n dev[1].scan()\n dev[0].remove_group()\n dev[0].dump_monitor()\n dev[1].dump_monitor()\n\n logger.info(\"Discover peer\")\n dev[1].p2p_listen()\n if not dev[0].discover_peer(addr1, social=True):\n raise Exception(\"Peer \" + addr1 + \" not found\")\n dev[0].p2p_listen()\n if not dev[1].discover_peer(addr0, social=True):\n raise Exception(\"Peer \" + addr0 + \" not found\")\n dev[1].p2p_listen()\n\n logger.info(\"Authorize invitation\")\n pin = dev[1].wps_read_pin()\n dev[1].global_request(\"P2P_CONNECT \" + addr0 + \" \" + pin + \" join auth\")\n\n logger.info(\"Start GO on non-social channel\")\n res = dev[0].p2p_start_go(freq=2427)\n logger.debug(\"res: \" + str(res))\n\n logger.info(\"Invite peer to join the group\")\n dev[0].p2p_go_authorize_client(pin)\n dev[0].global_request(\"P2P_INVITE group=\" + dev[0].group_ifname + \" peer=\" + addr1)\n ev = dev[1].wait_global_event([\"P2P-INVITATION-RECEIVED\",\n \"P2P-GROUP-STARTED\"], timeout=20)\n if ev is None:\n raise Exception(\"Timeout on invitation on peer\")\n if \"P2P-INVITATION-RECEIVED\" in ev:\n raise Exception(\"Unexpected request to accept pre-authorized invitaton\")\n dev[0].dump_monitor()\n\n logger.info(\"Client connected\")\n hwsim_utils.test_connectivity_p2p(dev[0], dev[1])\n\n logger.info(\"Terminate group\")\n dev[0].remove_group()\n dev[1].wait_go_ending_session()","title":""},{"docid":"cdc22d53c06037657497389a31a3a11c","score":"0.6333147","text":"async def accept_group_invitation_public_v1_async(\n group_id: str,\n namespace: Optional[str] = None,\n x_additional_headers: Optional[Dict[str, str]] = None,\n **kwargs\n):\n if namespace is None:\n namespace, error = get_services_namespace()\n if error:\n return None, error\n request = AcceptGroupInvitationPublicV1.create(\n group_id=group_id,\n namespace=namespace,\n )\n return await run_request_async(\n request, additional_headers=x_additional_headers, **kwargs\n )","title":""},{"docid":"01a99d8be81edff5cb3c81abfcfd673f","score":"0.632663","text":"def invite_accept(\n login_manager: LoginManager, *, group_id: uuid.UUID, identity: ParsedIdentity | None\n) -> None:\n auth_client = login_manager.get_auth_client()\n groups_client = login_manager.get_groups_client()\n\n actions = build_invite_actions(\n auth_client, groups_client, \"accept\", group_id, identity\n )\n response = groups_client.batch_membership_action(group_id, actions)\n # if this failed to return at least one accepted user, figure out an error to show\n if not response.get(\"accept\", None):\n try:\n raise ValueError(response[\"errors\"][\"accept\"][0][\"detail\"])\n except LookupError:\n raise ValueError(\"Could not accept invite\")\n\n display(response, text_mode=get_invite_formatter(\"accept\"))","title":""},{"docid":"2432146a3abed818840501659b8489d7","score":"0.6276367","text":"def accept_group_invitation_public_v1(\n group_id: str,\n namespace: Optional[str] = None,\n x_additional_headers: Optional[Dict[str, str]] = None,\n **kwargs\n):\n if namespace is None:\n namespace, error = get_services_namespace()\n if error:\n return None, error\n request = AcceptGroupInvitationPublicV1.create(\n group_id=group_id,\n namespace=namespace,\n )\n return run_request(request, additional_headers=x_additional_headers, **kwargs)","title":""},{"docid":"d2a1dcff3383ea92d06cb221214ef21b","score":"0.62529373","text":"async def accept_group_invitation_public_v2_async(\n group_id: str,\n namespace: Optional[str] = None,\n x_additional_headers: Optional[Dict[str, str]] = None,\n **kwargs\n):\n if namespace is None:\n namespace, error = get_services_namespace()\n if error:\n return None, error\n request = AcceptGroupInvitationPublicV2.create(\n group_id=group_id,\n namespace=namespace,\n )\n return await run_request_async(\n request, additional_headers=x_additional_headers, **kwargs\n )","title":""},{"docid":"dcae190f718bb4f6fcec5a0262315e56","score":"0.6249498","text":"def send_invite(self, message):\n\n invitation_link = self.bot.export_chat_invite_link(SECRET_GROUP)\n if invitation_link:\n self.bot.send_message(message.chat.id, invitation_link, reply_markup=None)","title":""},{"docid":"5dea90b653b948c80729e22657e567f1","score":"0.6230298","text":"def invite():\n user_manager = current_app.user_manager\n db_adapter = user_manager.db_adapter\n\n invite_form = user_manager.invite_form(request.form)\n\n if request.method == 'POST' and invite_form.validate():\n email = invite_form.email.data\n\n User = db_adapter.UserClass\n user_class_fields = User.__dict__\n user_fields = {\n \"email\": email\n }\n\n user, user_email = user_manager.find_user_by_email(email)\n if user:\n flash(\"Usuario com este email ja foi cadastrado.\", \"error\")\n return redirect(url_for('user.invite'))\n else:\n user_invite = db_adapter \\\n .add_object(db_adapter.UserInvitationClass, **{\n \"email\": email,\n \"invited_by_user_id\": current_user.id\n })\n db_adapter.commit()\n\n token = user_manager.generate_token(user_invite.id)\n accept_invite_link = url_for('user.register',\n token=token,\n _external=True)\n\n # Store token\n if hasattr(db_adapter.UserInvitationClass, 'token'):\n user_invite.token = token\n db_adapter.commit()\n\n try:\n # Send 'invite' email\n emails.send_invite_email(user_invite, accept_invite_link)\n except Exception as e:\n # delete new User object if send fails\n db_adapter.delete_object(user_invite)\n db_adapter.commit()\n raise\n\n signals \\\n .user_sent_invitation \\\n .send(current_app._get_current_object(), user_invite=user_invite,\n form=invite_form)\n\n flash(_('Convite enviado.'), 'success')\n safe_next = _get_safe_next_param(\n 'next', user_manager.after_invite_endpoint)\n return redirect(safe_next)\n\n return render(user_manager.invite_template, form=invite_form)","title":""},{"docid":"5e52b5310701e5a8f2ffc00bb11329e4","score":"0.6216854","text":"def acceptGroupInvitation(self, groupId, username):\n params = {\n \"f\" : \"json\",\n \"token\" : self._securityHandler.token\n }\n return self._do_post(url=self._url + \"/%s/applications/%s/accept\" % (groupId,\n username),\n param_dict=params,\n proxy_url=self._proxy_url,\n proxy_port=self._proxy_port)","title":""},{"docid":"464ff7553e8f7635fbca160699ce4f62","score":"0.62121665","text":"def accept_group_invitation_public_v2(\n group_id: str,\n namespace: Optional[str] = None,\n x_additional_headers: Optional[Dict[str, str]] = None,\n **kwargs\n):\n if namespace is None:\n namespace, error = get_services_namespace()\n if error:\n return None, error\n request = AcceptGroupInvitationPublicV2.create(\n group_id=group_id,\n namespace=namespace,\n )\n return run_request(request, additional_headers=x_additional_headers, **kwargs)","title":""},{"docid":"e9186b04925dfb4ef7119fd3614313fd","score":"0.6173581","text":"def v3_addUserToGroup(request):\n pass","title":""},{"docid":"166df8693ccc39599414518f96a60527","score":"0.6160525","text":"def test_get_group_invite_only_with_permission_denied_error(self):\n group = Group.objects.create(name='test-group', invite_only=True)\n\n rsp = self.apiGet(self.get_item_url(group.name),\n expected_status=403)\n self.assertEqual(rsp['stat'], 'fail')\n self.assertEqual(rsp['err']['code'], PERMISSION_DENIED.code)","title":""},{"docid":"0319810cb5e4714439d190992a069e27","score":"0.6160085","text":"def organization__user__invite(self) -> None:\n self.client.invite_organization_user(self.args.organization_id, self.args.email)","title":""},{"docid":"208bceab80404c7fca7e727eac44a63d","score":"0.61597395","text":"def accept_invitation(self, request, new_user):\n\t\t\n\t\tinv = FacebookInvite.objects.create(user=self.user, sent=False, facebook_user_id=self.facebook_user_id, accepted_user=new_user, personal_note=self.personal_note)","title":""},{"docid":"8783862b3a03fea1152e6d63f5d72138","score":"0.61500204","text":"def inviteUserToApplet(user, appletObject, userB):\n\n groupId = appletObject['roles']['user']['groups'][0]['id']\n currentUser = authenticate(user)\n group = Group().load(id=ObjectId(groupId), force=True)\n\n Invitation().createInvitation(\n appletObject,\n currentUser,\n role=\"user\",\n profile=Profile().createProfile(appletObject, userB),\n idCode=None\n )\n return Group().inviteUser(group, userB, level=AccessType.READ)\n # inviteResp = girder.post('group/{}/invitation'.format(groupId), {\n # \"email\": userB['email']\n # })\n # assert inviteResp['public'] == False, 'invite is public!'\n # assert len(inviteResp['access']['users']), 'ths user was not added'\n # return inviteResp","title":""},{"docid":"2744485f4be12a103d165079ffe9148b","score":"0.61273444","text":"def test_p2p_go_invite(dev):\n addr0 = dev[0].p2p_dev_addr()\n addr1 = dev[1].p2p_dev_addr()\n\n logger.info(\"Generate BSS table entry for old group\")\n # this adds more coverage to testing by forcing the GO to be found with an\n # older entry in the BSS table and with that entry having a different\n # operating channel.\n dev[0].p2p_start_go(freq=2422)\n dev[1].scan()\n dev[0].remove_group()\n\n logger.info(\"Discover peer\")\n dev[1].p2p_listen()\n if not dev[0].discover_peer(addr1, social=True):\n raise Exception(\"Peer \" + addr1 + \" not found\")\n\n logger.info(\"Start GO on non-social channel\")\n res = dev[0].p2p_start_go(freq=2417)\n logger.debug(\"res: \" + str(res))\n\n logger.info(\"Invite peer to join the group\")\n dev[0].global_request(\"P2P_INVITE group=\" + dev[0].group_ifname + \" peer=\" + addr1)\n ev = dev[1].wait_global_event([\"P2P-INVITATION-RECEIVED\"], timeout=10)\n if ev is None:\n raise Exception(\"Timeout on invitation on peer\")\n ev = dev[0].wait_global_event([\"P2P-INVITATION-RESULT\"], timeout=10)\n if ev is None:\n raise Exception(\"Timeout on invitation on GO\")\n if \"status=1\" not in ev:\n raise Exception(\"Unexpected invitation result\")\n\n logger.info(\"Join the group\")\n pin = dev[1].wps_read_pin()\n dev[0].p2p_go_authorize_client(pin)\n dev[1].p2p_connect_group(addr0, pin, timeout=60)\n logger.info(\"Client connected\")\n hwsim_utils.test_connectivity_p2p(dev[0], dev[1])\n\n logger.info(\"Terminate group\")\n dev[0].remove_group()\n dev[1].wait_go_ending_session()","title":""},{"docid":"5a218701c05d63a7277ae9812ea5b296","score":"0.6124214","text":"def invite_user():\n form = InviteUserForm()\n if form.validate_on_submit():\n user = User(\n role=form.role.data,\n first_name=form.first_name.data,\n last_name=form.last_name.data,\n email=form.email.data)\n db.session.add(user)\n db.session.commit()\n token = user.generate_confirmation_token()\n invite_link = url_for(\n 'account.join_from_invite',\n user_id=user.id,\n token=token,\n _external=True)\n invite_by = User.query.filter(User.id == current_user.id).first()\n get_queue().enqueue(\n send_email,\n recipient=user.email,\n subject='You Are Invited To Join',\n template='account/email/invite',\n user=user.id,\n invite_link=invite_link,\n invite_by=invite_by\n )\n flash('User {} successfully invited'.format(user.full_name),\n 'form-success')\n return render_template('admin/new_user.html', form=form)","title":""},{"docid":"3072c3e8509780de135dd340c08910d7","score":"0.6106594","text":"def test_invite_users(self):\n pass","title":""},{"docid":"b603be6695d81acf9de59c70eaedaf76","score":"0.61043465","text":"def invite_user():\n form = InviteUserForm()\n if form.validate_on_submit():\n user = User(\n role=form.role.data,\n first_name=form.first_name.data,\n last_name=form.last_name.data,\n email=form.email.data)\n db.session.add(user)\n db.session.commit()\n token = user.generate_confirmation_token()\n invite_link = url_for(\n 'account.join_from_invite',\n user_id=user.id,\n token=token,\n _external=True)\n get_queue().enqueue(\n send_email,\n recipient=user.email,\n subject='You Are Invited To Join',\n template='account/email/invite',\n user=user,\n invite_link=invite_link,\n )\n flash('User {} successfully invited'.format(user.full_name()),\n 'form-success')\n return render_template('admin/new_user.html', form=form)","title":""},{"docid":"ba8fb5303f450984e7a5cea0597e2a03","score":"0.60186064","text":"def invitePost(self, request, context, params, group_entity, **kwargs):\n\n # get the request view parameters and populate the form using POST data\n request_params = request_view.view.getParams()\n form = request_params['invite_form'](request.POST)\n\n if not form.is_valid():\n # return the invalid form response\n return self._constructResponse(request, entity=None, context=context,\n form=form, params=request_params)\n\n # collect the cleaned data from the valid form\n _, form_fields = soc.views.helper.forms.collectCleanedFields(form)\n\n # create the fields for the new request entity\n request_fields = {\n 'user': form_fields['user_id'],\n 'group': group_entity,\n 'role': params['logic'].role_name,\n 'status': 'group_accepted'}\n\n if not request_logic.isValidNewRequest(request_fields, params['logic']):\n # not a valid invite\n context['error_message'] = self.DEF_INVITE_ERROR_MSG_FMT % (\n params)\n return super(View, self)._constructResponse(request, entity=None,\n context=context, form=form, params=request_params)\n\n # create the request entity\n request_logic.updateOrCreateFromFields(request_fields)\n\n group_view = params.get('group_view')\n\n if not group_view:\n return http.HttpResponseRedirect('/')\n else:\n # redirect to the requests list\n return http.HttpResponseRedirect(\n redirects.getListRequestsRedirect(group_entity, \n group_view.getParams()))","title":""},{"docid":"cb0c9662a413468b1b123e9b7c6bcc48","score":"0.6010423","text":"def test_successful_invite_user(self) -> None:\n self.login(\"hamlet\")\n invitee = \"alice-test@zulip.com\"\n self.assert_json_success(self.invite(invitee, [\"Denmark\"]))\n self.assertTrue(find_key_by_email(invitee))\n self.check_sent_emails([invitee])","title":""},{"docid":"d9366e5e54a43e932f08daf1f710d483","score":"0.599388","text":"def invite_user():\n\n form = InviteUserForm()\n if form.validate_on_submit():\n invited_by = db.session.query(User).filter_by(id=current_user.id).first()\n user = User(\n invited_by=invited_by.full_name,\n first_name=form.first_name.data,\n last_name=form.last_name.data,\n area_code=form.area_code.data,\n mobile_phone=form.mobile_phone.data\n )\n db.session.add(user)\n db.session.commit()\n token = user.generate_confirmation_token()\n invite_link = url_for(\n 'account.join_from_invite',\n user_id=user.id,\n token=token,\n _external=True)\n area_code = str(form.area_code.data)\n area_code = area_code.replace(' ', '')\n phone_number = str(form.mobile_phone.data)\n phone_number = phone_number.replace(' ', '')\n if str(area_code)[0] != '+':\n area_code = '+' + str(area_code)\n client.messages.create(\n body=f'Invitation to networked.ng by %s. Click link to complete registration : {invite_link}' % invited_by,\n messaging_service_sid=messaging_service_sid,\n to=str(area_code) + str(phone_number)\n )\n flash('User {} successfully invited'.format(user.full_name),\n 'form-success')\n return redirect(url_for('account.invited_users'))\n else:\n flash('Error! Invitation not sent.', 'error')\n return render_template('main/new_user.html', form=form)","title":""},{"docid":"074abfbeadf7862171a90f5127f61f00","score":"0.59922636","text":"def invite():\n form = InviteForm(request.form)\n if form.validate_on_submit():\n from flask_application import user_datastore, app, mail\n user = User.objects(id=current_user.get_id()).first()\n password = binascii.b2a_hex(os.urandom(15))\n # create an account\n user_datastore.create_user(username=\"x\",\n email=form.email.data,\n password=encrypt_password(password),\n roles=['contributor'],\n active=True,\n invited_by=user)\n user_datastore.commit()\n invitee = User.objects(email=form.email.data).first()\n # set inviter data\n user.add_invitation(invitee)\n # send an email\n msg = Message(\"An invitation to %s\" % app.config['SITE_NAME'],\n sender=app.config['DEFAULT_MAIL_SENDER'],\n recipients=[form.email.data])\n msg.body = '''\n\t\t\tYou have been invited to %s by %s. \n\t\t\tYour can log in with the following username/ password:\n\t\t\t%s\n\t\t\t%s\n\t\t\tYou can change this random, complicated password after you have logged in.\n\t\t\t''' % (app.config['SITE_NAME'], user.email, form.email.data, password)\n mail.send(msg)\n # @todo: set invited and invited by\n flash(\"An account has been created for %s and an email has been sent. You may want to let them know that it is coming.\" % form.email.data)\n return render_template('profiles/invite.html',\n register_user_form=form)","title":""},{"docid":"e89b8ef0313b9a03468a263d74869c1f","score":"0.5988099","text":"def test_invite_existing_user(self) -> None:\n self.login(\"hamlet\")\n\n hamlet_email = \"hAmLeT@zUlIp.com\"\n result = self.invite(hamlet_email, [\"Denmark\"])\n self.assert_json_error(result, \"We weren't able to invite anyone.\")\n\n self.assertFalse(\n PreregistrationUser.objects.filter(email__iexact=hamlet_email).exists(),\n )\n self.check_sent_emails([])","title":""},{"docid":"51064a0d582418ed6cd2b08eaae66e4d","score":"0.5982782","text":"def test_add_new_user_to_public_group(self):\n new_user = User.objects.create_user(self.username)\n self.assertEqual(\n new_user.groups.filter(name=self.public_group_name).count(), 1)","title":""},{"docid":"082c6a3230268914cd0c5d1918fbb5b1","score":"0.59728533","text":"def invite(self, user, admin=None):\n if admin is None or self.is_admin(admin):\n return self.add_member(user, state=MembershipState.PENDING_USER)\n return None","title":""},{"docid":"5551d9a5d7cd4afd3e1534e487d60310","score":"0.59535766","text":"def send_invite(arg):\n pass","title":""},{"docid":"85fb6fad9a6f10a9488d8d8e03ca6abd","score":"0.59390855","text":"def testUserInviteFromManager(user, appletObject, userB):\n authenticate(user)\n groupId = appletObject['roles']['user']['groups'][0]['id']\n pendingInvites = girder.get('group/{}/invitation'.format(groupId))\n value = False\n for invite in pendingInvites:\n if invite['email'] == userB['email']:\n value = True\n break\n assert value, \"email not in list of pending invites, {}\".format(pendingInvites)\n return 1","title":""},{"docid":"2fa232975772459ae64d24c219b960a2","score":"0.59250855","text":"def test_local_users_allowed(self):\n values = {\n 'email': INVITE_EMAIL,\n 'project': self.project.pk,\n 'role': self.role_contributor.pk,\n }\n with self.login(self.user):\n response = self.client.post(\n reverse(\n 'projectroles:invite_create',\n kwargs={'project': self.project.sodar_uuid},\n ),\n values,\n )\n self.assertEqual(response.status_code, 302)\n invite = ProjectInvite.objects.get(\n project=self.project, email=INVITE_EMAIL, active=True\n )\n self.assertIsNotNone(invite)","title":""},{"docid":"cbfc57c37093f6158c275fb6558ef440","score":"0.59185433","text":"def test_guest_user_invitation(self) -> None:\n self.login(\"polonius\")\n invitee = \"alice-test@zulip.com\"\n self.assert_json_error(self.invite(invitee, [\"Denmark\"]), \"Not allowed for guest users\")\n self.assertEqual(find_key_by_email(invitee), None)\n self.check_sent_emails([])","title":""},{"docid":"381dc7ec2b552dff2da6a64b99e3bc2d","score":"0.5906466","text":"def invite_user(self, project_id, user):\n url = base_url + project_id + '/users/invite'\n json_object = dumps(user.to_json())\n data = {\n 'JSONString': json_object\n }\n resp = zoho_http_client.post(url, self.details, self.headers, data)\n return parser.get_user(resp)","title":""},{"docid":"965b70bd4998a9e1a3668a4da44d9657","score":"0.59058297","text":"def user_invite(email, team1_id, user2_email): # POST\n\n user = coll(\"users\").find_one({\"_id\": user2_email})\n if not user:\n return {\"message\": f\"User ({user2_email}) does not exist\"}, 404\n team2_id = user[\"team_id\"]\n team2 = coll(\"teams\").find_one({\"_id\": team2_id})\n\n if not team2:\n return {\"message\": f\"Team {team2_id} does not exist\"}, 404\n\n if len(team2[\"members\"]) > 1:\n return {\"message\": f\"User ({user2_email}) already on a team with others\"}, 409\n\n return team_invite(email, team1_id, team2_id)","title":""},{"docid":"a9503ee24f89fc915feaad14ee5f6ec9","score":"0.588392","text":"def testUserInviteFromUser(user, appletObject):\n pending = user.get('groupInvites')\n pendingInvitesForUser = []\n userfields = [\n 'firstName',\n '_id',\n 'email',\n 'gravatar_baseUrl',\n 'login'\n ]\n for p in pending:\n groupId = p.get('groupId')\n applets = list(AppletModel().find(\n query={\n \"roles.user.groups.id\": groupId\n },\n fields=[\n 'cached.applet.skos:prefLabel',\n 'cached.applet.schema:description',\n 'cached.applet.schema:image',\n 'roles'\n ]\n ))\n for applet in applets:\n for role in ['manager', 'reviewer']:\n applet[''.join([role, 's'])] = [{\n (\n 'image' if userKey=='gravatar_baseUrl' else userKey\n ): user.get(\n userKey\n ) for userKey in user.keys()\n } for user in list(UserModel().find(\n query={\n \"groups\": {\n \"$in\": [\n group.get('id') for group in applet.get(\n 'roles',\n {}\n ).get(role, {}).get('groups', [])\n ]\n }\n },\n fields=userfields\n ))\n ]\n groupId = appletObject['roles']['user']['groups'][0]['id']\n assert len(pendingInvitesForUser), \"this user has no invites\"\n assert pendingInvitesForUser[0]['_id'] == groupId, \"this user doesn't have the invite you expect\"\n return groupId","title":""},{"docid":"9fb7b1c0464574deb127b8bffa4c0776","score":"0.5874308","text":"def invite(self, request, access_type,\n page_name=None, params=None, **kwargs):\n\n # get the context for this webpage\n context = responses.getUniversalContext(request)\n responses.useJavaScript(context, params['js_uses_all'])\n context['page_name'] = page_name\n\n group_entity = params['group_logic'].getFromKeyName(kwargs['scope_path'])\n\n context['instruction_message'] = (self.DEF_INVITE_INSTRUCTION_MSG_FMT %\n (params['name'], group_entity.name))\n\n if request.method == 'POST':\n return self.invitePost(request, context, params, group_entity, **kwargs)\n else:\n # request.method == 'GET'\n return self.inviteGet(request, context, params, **kwargs)","title":""},{"docid":"97bc4478454ff4159b6d296a6272bb50","score":"0.5851911","text":"def send_invitation(self, request):\n serializer = InvitationSerializer(data=request.data)\n serializer.is_valid(raise_exception=True)\n if self.request.user.role == User.ROLE.ADMIN:\n to_user = User.objects.filter(id=request.data['id']).first()\n if to_user:\n if to_user.status == User.STATUS.ACTIVE:\n return Response(\n data={\n 'detail': 'This user is already activated.',\n },\n status=status.HTTP_403_FORBIDDEN,\n )\n if to_user.send_invitation_email():\n to_user.status = User.STATUS.INVITATION\n to_user.save()\n return Response(\n data={\n 'detail': 'Invitation instructions have been sent to the user.',\n },\n )\n raise NotFound\n raise PermissionDenied","title":""},{"docid":"92cc5ba5f2864803a5cc47ab7996e012","score":"0.5846169","text":"def _invite(self, email, **kwargs):\n data = dict(email=email, **kwargs)\n channels = data.pop('channels', None)\n if channels is not None:\n data['channels'] = ','.join(channels)\n data['_attempts'] = 1 # required by slack for some reason\n return self.post('users.admin.invite', data=data)","title":""},{"docid":"38905225d3fe82bc260af90da4d7d4da","score":"0.58422005","text":"async def accept_group_join_request_public_v1_async(\n user_id: str,\n namespace: Optional[str] = None,\n x_additional_headers: Optional[Dict[str, str]] = None,\n **kwargs\n):\n if namespace is None:\n namespace, error = get_services_namespace()\n if error:\n return None, error\n request = AcceptGroupJoinRequestPublicV1.create(\n user_id=user_id,\n namespace=namespace,\n )\n return await run_request_async(\n request, additional_headers=x_additional_headers, **kwargs\n )","title":""},{"docid":"da5f9691880ff7f346fd2dff2f51b748","score":"0.5830894","text":"async def accept_group_join_request_public_v2_async(\n group_id: str,\n user_id: str,\n namespace: Optional[str] = None,\n x_additional_headers: Optional[Dict[str, str]] = None,\n **kwargs\n):\n if namespace is None:\n namespace, error = get_services_namespace()\n if error:\n return None, error\n request = AcceptGroupJoinRequestPublicV2.create(\n group_id=group_id,\n user_id=user_id,\n namespace=namespace,\n )\n return await run_request_async(\n request, additional_headers=x_additional_headers, **kwargs\n )","title":""},{"docid":"6a7d2d6d1a89a97fe8bd42827ecdeec0","score":"0.5811526","text":"def chat_invite_user(self):\n try:\n action = self.msg_object['action']\n chat_invite_user = False\n if action['type'] == 'chat_invite_user':\n if action['member_id'] == -int(VK_GROUP_ID):\n chat_invite_user = True\n except KeyError:\n chat_invite_user = False\n # if action not equals chat_invite_user\n if not chat_invite_user:\n return False\n self.response += '🤖 Привет! Я - чат-бот, созданный на базе бота с \\\nоткрытым исходным кодом. Вы можете изучить мои команды на странице RiBot.\\n\\n\\\nВ настоящее время я обрабатываю сообщения только с тегом @club{}. Администраторы чата могут разрешить мне \\\nпросмотр всех сообщений или назначить администратором беседы. Обещаю, что не собираюсь хулиганить!\\n\\n\\\n@ageykindmi(⁉ Техническая поддержка)\\n@ribot(✍🏻 Список команд)'.format(VK_GROUP_ID)\n return True","title":""},{"docid":"1fc026e2511630f33ff6b07771a4d5bc","score":"0.5802746","text":"def invite(self):\r\n pass","title":""},{"docid":"f45480bd4d91aa5801e6fb0c4be276bf","score":"0.5786934","text":"def reject_membership(request):\n group = get_object_or_404(models.Group, id=request.POST.get(\"group\"))\n invite = get_object_or_404(\n models.GroupMemberInvitation, group=group, user=request.user\n )\n\n invite.reject()\n return redirect(request.user.local_path)","title":""},{"docid":"5f90d292d40932f7af5dfbd259d82365","score":"0.57654643","text":"def create_invite(self,**kwargs):\r\n\r\n\t\treturn Invite(self.__bot.api(f\"/channels/{self.id}/invites\",\"POST\",json=kwargs),self.__bot)","title":""},{"docid":"2edd6868f492bf885e3376fae2b50a48","score":"0.5749992","text":"def test_invite_user_as_invalid_type(self) -> None:\n self.login(\"iago\")\n invitee = self.nonreg_email(\"alice\")\n response = self.invite(invitee, [\"Denmark\"], invite_as=10)\n self.assert_json_error(response, \"Invalid invite_as\")","title":""},{"docid":"43d63b51f33798bbcb7c714c13b1dbd0","score":"0.57447815","text":"def v3_validateUserInGroup(request):\n pass","title":""},{"docid":"4f4f99d1228b33fc54619bfbb73bb006","score":"0.5730765","text":"def test_accept_public_users_invitation(client, jwt, session, org_info, role, # pylint:disable=unused-argument\n claims, source, exp_status):\n headers = factory_auth_header(jwt=jwt, claims=claims)\n client.post('/api/v1/users', headers=headers, content_type='application/json')\n rv = client.post('/api/v1/orgs', data=json.dumps(org_info), headers=headers, content_type='application/json')\n dictionary = json.loads(rv.data)\n org_id = dictionary['id']\n rv = client.post('/api/v1/invitations', data=json.dumps(factory_invitation(org_id=org_id, membership_type=role)),\n headers=headers, content_type='application/json')\n invitation_dictionary = json.loads(rv.data)\n invitation_id = invitation_dictionary['id']\n invitation_id_token = InvitationService.generate_confirmation_token(invitation_id)\n\n request = KeycloakScenario.create_user_request()\n KEYCLOAK_SERVICE.add_user(request, return_if_exists=True)\n user = KEYCLOAK_SERVICE.get_user_by_username(request.user_name)\n user_id = user.id\n headers_invitee = factory_auth_header(jwt=jwt, claims=TestJwtClaims.get_test_user(user_id, source=source))\n client.post('/api/v1/users', headers=headers_invitee, content_type='application/json')\n rv = client.put('/api/v1/invitations/tokens/{}'.format(invitation_id_token), headers=headers_invitee,\n content_type='application/json')\n assert rv.status_code == http_status.HTTP_200_OK\n\n rv = client.get(f'/api/v1/orgs/{org_id}/members?status={exp_status.name}',\n headers=headers,\n content_type='application/json')\n dictionary = json.loads(rv.data)\n assert len(dictionary['members']) == 1\n # Assert that the user got added to the keycloak groups\n user_groups = KEYCLOAK_SERVICE.get_user_groups(user_id=user_id)\n groups = []\n for group in user_groups:\n groups.append(group.get('name'))\n assert GROUP_PUBLIC_USERS in groups","title":""},{"docid":"be8e6af3b3797aa98a7eb0216277b7c3","score":"0.57278436","text":"def test_create_accept_invitation(self):\n # Register or login another user in order to send him an invitation\n # (sometimes the database might not get cleaned up properly)\n resp_register = self.client.post(\n '/users/',\n data=json.dumps(dict(\n email='gill@gmail.com',\n username='gibilan',\n password='123456'\n )),\n content_type='application/json'\n )\n if resp_register.status_code == HTTPStatus.CONFLICT:\n resp_login = self.client.post(\n '/auth/login',\n data=json.dumps(dict(\n email='gill@gmail.com',\n password='123456'\n )),\n content_type='application/json'\n )\n self.assertEqual(resp_login.status_code, HTTPStatus.OK)\n other_user_token = resp_login.json['data']['Authorization']\n else:\n self.assertTrue(resp_register.status_code, HTTPStatus.CREATED)\n other_user_token = resp_register.json['data']['Authorization']\n\n # send an invitation to the other user for 'my_group'\n resp_invite = self.client.post(\n '/invitations',\n data=json.dumps(dict(\n username_invitee='gibilan',\n resource_type='group',\n uuid_resource=self.created_group.json['group']['_uuid']\n )),\n headers=dict(\n Authorization=self.auth\n ),\n content_type='application/json'\n )\n common_fields = ['_uuid', 'uuid_sender', 'uuid_invitee', 'uuid_resource', 'resource_type', 'token']\n\n for field in common_fields:\n self.assertTrue(resp_invite.json[field])\n self.assertEqual(\n resp_invite.json.get('status'),\n InvitationStatus.PENDING\n )\n\n # accept invitation\n resp_reject = self.client.post(\n '/invitations/{}/accept'.format(resp_invite.json['_uuid']),\n headers=dict(\n Authorization=other_user_token\n ),\n content_type='application/json'\n )\n self.assertEqual(resp_reject.status_code, HTTPStatus.OK)\n for field in common_fields:\n self.assertEqual(\n resp_invite.json.get(field),\n resp_reject.json.get(field)\n )\n self.assertEqual(\n resp_reject.json.get('status'),\n InvitationStatus.ACCEPTED\n )","title":""},{"docid":"fb7bbc10eefc79185ec65f68a044c51a","score":"0.57269514","text":"def test_can_send_email_to_invitee(self):\n user = make_user(invite_count=5)\n nickname = fake.name()\n invite = user.create_invite(nickname=nickname)\n\n url = self._get_url(invite.pk)\n headers = make_authentication_headers_for_user(user)\n with patch('django.core.mail.EmailMultiAlternatives', return_value=MockEmailMultiAlternatives):\n response = self.client.post(url, {\n 'email': fake.email()\n }, **headers)\n\n self.assertEqual(response.status_code, status.HTTP_200_OK)\n invite.refresh_from_db()\n self.assertTrue(invite.is_invite_email_sent)","title":""},{"docid":"8ab9caf4fd2393ab59ae399533169880","score":"0.5719857","text":"def subscribe(self, user):\n if self.subscription_policy == SubscriptionPolicy.OPEN:\n return self.add_member(user)\n elif self.subscription_policy == SubscriptionPolicy.APPROVAL:\n email_body = render_template_to_string(\n 'groups/join_group_email.txt',\n group=self,\n )\n subject = 'New Group Request at LifeWatch Open Science Framework'\n to = [User.query.get(a.admin_id).email for a in self.admins]\n scheduled_send_email(fromaddr=cfg['CFG_SITE_ADMIN_EMAIL'],\n toaddr=to,\n subject=subject,\n content=email_body)\n return self.add_member(user, state=MembershipState.PENDING_ADMIN)\n elif self.subscription_policy == SubscriptionPolicy.CLOSED:\n return None","title":""},{"docid":"09554e35d3120b00d08042640964775a","score":"0.5711843","text":"async def kick_group_member_public_v2_async(\n group_id: str,\n user_id: str,\n namespace: Optional[str] = None,\n x_additional_headers: Optional[Dict[str, str]] = None,\n **kwargs\n):\n if namespace is None:\n namespace, error = get_services_namespace()\n if error:\n return None, error\n request = KickGroupMemberPublicV2.create(\n group_id=group_id,\n user_id=user_id,\n namespace=namespace,\n )\n return await run_request_async(\n request, additional_headers=x_additional_headers, **kwargs\n )","title":""},{"docid":"6478fc8248270a8e611fadc0ad988ca9","score":"0.5710785","text":"def approve_request(self):\n # Create the gitlab object\n gl = gitlab.Gitlab(\n settings.GITLAB_URL, \n private_token=settings.GITLAB_ACCOUNTS_SECRET_TOKEN\n )\n try:\n new_user = gl.users.create({\n \"name\": self.username,\n \"username\": self.username,\n \"email\": self.email,\n \"reset_password\": True,\n \"can_create_group\": False,\n \"skip_confirmation\": True, # The password reset mail is enough.\n })\n new_user.projects_limit = 5\n new_user.save()\n except Exception as e:\n print(\"Error: {}\".format(e))","title":""},{"docid":"6f9202ab7afbe354136f79ba47ff2b78","score":"0.5704054","text":"def accept_group_join_request_public_v1(\n user_id: str,\n namespace: Optional[str] = None,\n x_additional_headers: Optional[Dict[str, str]] = None,\n **kwargs\n):\n if namespace is None:\n namespace, error = get_services_namespace()\n if error:\n return None, error\n request = AcceptGroupJoinRequestPublicV1.create(\n user_id=user_id,\n namespace=namespace,\n )\n return run_request(request, additional_headers=x_additional_headers, **kwargs)","title":""},{"docid":"e170be5693c5896937088af5f6307be5","score":"0.5703481","text":"def create(self, invite):\n model_invite = models.load('AccountInvite', invite)\n model_invite.account_id = self.account_id\n return self.client.invite_account(model_invite)","title":""},{"docid":"79b3ff10d0bb449ce7419b3fd38290e2","score":"0.5699924","text":"def test_multi_user_invite(self) -> None:\n self.login(\"hamlet\")\n # Intentionally use a weird string.\n self.assert_json_success(\n self.invite(\n \"\"\"bob-test@zulip.com, carol-test@zulip.com,\n dave-test@zulip.com\n\n\nearl-test@zulip.com\"\"\",\n [\"Denmark\"],\n )\n )\n for user in (\"bob\", \"carol\", \"dave\", \"earl\"):\n self.assertTrue(find_key_by_email(f\"{user}-test@zulip.com\"))\n self.check_sent_emails(\n [\n \"bob-test@zulip.com\",\n \"carol-test@zulip.com\",\n \"dave-test@zulip.com\",\n \"earl-test@zulip.com\",\n ]\n )","title":""},{"docid":"92335d66f1d62371b8810420568809c5","score":"0.5694413","text":"def accept_group_join_request_public_v2(\n group_id: str,\n user_id: str,\n namespace: Optional[str] = None,\n x_additional_headers: Optional[Dict[str, str]] = None,\n **kwargs\n):\n if namespace is None:\n namespace, error = get_services_namespace()\n if error:\n return None, error\n request = AcceptGroupJoinRequestPublicV2.create(\n group_id=group_id,\n user_id=user_id,\n namespace=namespace,\n )\n return run_request(request, additional_headers=x_additional_headers, **kwargs)","title":""},{"docid":"c24748c396342d8d0fd34d6a8852506b","score":"0.569394","text":"def InviteMember(message):\n\tif not message.isMinecraft:\n\t\tmessage.Reply(\"This command must be run in game\")\n\t\treturn\n\tusername = message.mcusername\n\tteamname = GetData(__name__, \"teamowners.{0}\".format(username))\n\tif not teamname:\n\t\tmessage.Reply(\"You aren't the owner of any teams\")\n\t\treturn\n\n\t# Max of 8 members\n\tteammembers = GetData(__name__, \"teammembers.{0}\".format(teamname))\n\tif len(teammembers) > 7:\n\t\tmessage.Reply(\"You have too many members on your team and cannot invite any more\")\n\t\treturn\n\n\tinvitee = message.GetArg(0)\n\tcurteam = GetCurrentTeam(invitee)\n\tif curteam:\n\t\tmessage.Reply(\"{0} is already in team {1}\".format(invitee, curteam))\n\t\treturn\n\tStoreData(__name__, \"teaminvites.{0}.{1}\".format(teamname, invitee), time.time()+1200)\n\n\tmessage.Reply(\"Invited {0} to team {1}\".format(invitee, teamname))","title":""},{"docid":"8a2ccb7add083f9487020c1ad6bc7f08","score":"0.56831145","text":"def test_get_reviewrequest_with_invite_only_group_and_permission_denied_error(self):\n review_request = ReviewRequest.objects.filter(public=True,\n local_site=None).exclude(submitter=self.user)[0]\n review_request.target_groups.clear()\n review_request.target_people.clear()\n\n group = Group(name='test-group', invite_only=True)\n group.save()\n\n review_request.target_groups.add(group)\n review_request.save()\n\n rsp = self.apiGet(self.get_item_url(review_request.display_id),\n expected_status=403)\n self.assertEqual(rsp['stat'], 'fail')\n self.assertEqual(rsp['err']['code'], PERMISSION_DENIED.code)","title":""},{"docid":"85411c7dd22b11002a3a5d54913e75b7","score":"0.5682659","text":"async def invite(self, ctx, *args):\n manager = MessageManager(ctx)\n username, platform_id = await self.get_user_details(args)\n\n member_db = await self.get_member_db(ctx, username)\n admin_db = await self.bot.database.get_member_by_discord_id(ctx.author.id)\n clan_db = await self.get_admin_group(ctx)\n\n if not platform_id and clan_db.platform:\n platform_id = clan_db.platform\n else:\n raise InvalidCommandError(\"Platform was not specified and clan default platform is not set\")\n\n bungie_id = None\n if member_db:\n bungie_id = member_db.bungie_id\n\n membership_id, platform_id = await self.get_bungie_details(username, bungie_id, platform_id)\n\n res = None\n try:\n res = await execute_pydest(\n self.bot.destiny.api.group_invite_member(\n group_id=clan_db.clan_id,\n membership_type=platform_id,\n membership_id=membership_id,\n message=f\"Join my clan {clan_db.name}!\",\n access_token=admin_db.bungie_access_token\n ),\n self.bot.redis\n )\n except pydest.PydestTokenException:\n tokens = await self.refresh_admin_tokens(manager, admin_db)\n res = await execute_pydest(\n self.bot.destiny.api.group_invite_member(\n group_id=clan_db.clan_id,\n membership_type=platform_id,\n membership_id=membership_id,\n message=f\"Join my clan {clan_db.name}!\",\n access_token=tokens['access_token']\n ),\n self.bot.redis\n )\n admin_db.bungie_access_token = tokens['access_token']\n admin_db.bungie_refresh_token = tokens['refresh_token']\n await self.bot.database.update(admin_db)\n\n if not res:\n raise RuntimeError(\"Unexpected empty response from the Bungie API\")\n\n if res['ErrorStatus'] == 'ClanTargetDisallowsInvites':\n message = f\"User **{username}** has disabled clan invites\"\n elif res['ErrorStatus'] != 'Success':\n message = f\"Could not invite **{username}**\"\n log.info(f\"Could not invite '{username}': {res}\")\n else:\n message = f\"Invited **{username}** to clan **{clan_db.name}**\"\n\n return await manager.send_message(message, mention=False, clean=False)","title":""},{"docid":"a0f7739828b2848ccfe272edd3d47c51","score":"0.5677255","text":"def project_add_member(project=None, luser=None, **kwargs):\n email = flask.request.form[\"email\"].strip()\n member = models.Luser.query.filter_by(email=email).first()\n\n # If the user is not yet signed up\n if member is None:\n existing_invite = models.ProjectInvite.query.filter_by(email=email).first()\n if existing_invite is None:\n # Create an invite that will be checked when the user \n # signs up and used to add them to any projects they\n # have been invited to.\n invite = models.ProjectInvite(luser_id=luser._id,\n project_id=project._id,\n email=email)\n models.db.session.add(invite)\n\n # Also create a BetaSignup and activate it so that the user\n # doesn't get stuck at the beta wall.\n beta = models.BetaSignup(email=email, is_activated=True)\n models.db.session.add(beta)\n models.db.session.commit()\n \n try:\n invitee = models.Luser.query.filter_by(email=email).first()\n is_registered = invitee is not None \n email_notify.project_invite(project, email, is_registered=is_registered)\n except:\n return respond_with_json({ \n \"status\" : \"failure\", \n \"type\" : \"error\",\n \"error\" : \"Failed to send email. Is %s added to amazon SES?\" % email\n })\n\n return respond_with_json({\n \"status\" : \"success\", \n \"type\" : \"invite\",\n \"html\" : flask.render_template(\"invite_partial.html\", invite=invite),\n \"message\": \"Invited %s to the project.\" % email\n })\n\n else: \n\n try:\n invitee = models.Luser.query.filter_by(email=email).first()\n is_registered = invitee is not None \n email_notify.project_invite(project, email, is_registered=is_registered)\n except:\n return respond_with_json({ \n \"status\" : \"failure\", \n \"type\" : \"error\",\n \"error\" : \"Failed to send email. Is %s added to amazon SES?\" % email\n })\n\n return respond_with_json({\n \"status\" : \"success\", \n \"type\" : \"already_invited\",\n \"message\" : \"%s was already invited to this project. Re-sending email.\" % email\n })\n\n # The user is signed up already, check if hes a project member. If not,\n # add him.\n else:\n existing = (models.ProjectLuser.query\n .filter(models.ProjectLuser.project_id==project._id)\n .filter(models.ProjectLuser.luser_id==models.Luser._id)\n .filter(models.Luser.email==email).first())\n member = existing\n if existing is None:\n new_member = models.Luser.query.filter_by(email=email).first()\n\n member = models.ProjectLuser(project_id=project._id,\n luser_id=new_member._id,\n is_owner=False)\n\n models.db.session.add(member)\n models.db.session.commit()\n\n return respond_with_json({\n \"status\" : \"success\", \n \"type\" : \"member\",\n \"html\" : flask.render_template(\"member_partial.html\", member=member.luser),\n \"message\": \"Added %s to the project.\" % email\n })\n\n else:\n\n return respond_with_json({\n \"status\" : \"failure\", \n \"type\" : \"error\",\n \"error\" : \"%s is already a member of this project.\" % email \n })","title":""},{"docid":"c77cfd6cb9534da4c478974a8a59bc0d","score":"0.56704414","text":"def send_invitation(UserArn=None):\n pass","title":""},{"docid":"06ff61391c939a5721f35962f372ae74","score":"0.56538683","text":"def test_get_reviewrequest_with_invite_only_group_and_target_user(self):\n review_request = ReviewRequest.objects.filter(public=True,\n local_site=None).exclude(submitter=self.user)[0]\n review_request.target_groups.clear()\n review_request.target_people.clear()\n\n group = Group(name='test-group', invite_only=True)\n group.save()\n\n review_request.target_groups.add(group)\n review_request.target_people.add(self.user)\n review_request.save()\n\n rsp = self.apiGet(self.get_item_url(review_request.display_id),\n expected_mimetype=self.item_mimetype)\n self.assertEqual(rsp['stat'], 'ok')\n self.assertEqual(rsp['review_request']['id'], review_request.display_id)\n self.assertEqual(rsp['review_request']['summary'],\n review_request.summary)","title":""},{"docid":"130f8c9a89dedc9fe3845ca9dd1b80a2","score":"0.5650841","text":"def invite(email, inviter, sendfn=send_invite, resend=True, **kwargs):\n \n if OptOut.objects.is_blocked(email): \n return None, False\n try:\n user = User.objects.get(email=email)\n if user.is_active:\n return user, False\n if not resend:\n return user, False\n except User.DoesNotExist:\n user = User.objects.create(\n username=shortuuid.uuid(),\n email=email,\n is_active=False\n )\n user.set_unusable_password()\n user.save()\n \n url_parts = int_to_base36(user.id), token_generator.make_token(user)\n url = reverse('inviter:register', args=url_parts)\n \n opt_out_url = reverse('inviter:opt-out', args=url_parts)\n kwargs.update(opt_out_url=opt_out_url)\n \n sendfn(user, inviter, url=url, **kwargs)\n return user, True","title":""},{"docid":"7ad71a428d21fef7061ef04b9c0d3bdd","score":"0.5649967","text":"def _acceptInvitePost(self, fields, request, context, params, **kwargs):\n pass","title":""},{"docid":"94b299bfbaf3770154459ab7df18d0e7","score":"0.563217","text":"def test_local_users_not_allowed(self):\n values = {\n 'email': INVITE_EMAIL,\n 'project': self.project.pk,\n 'role': self.role_contributor.pk,\n }\n with self.login(self.user):\n response = self.client.post(\n reverse(\n 'projectroles:invite_create',\n kwargs={'project': self.project.sodar_uuid},\n ),\n values,\n )\n self.assertEqual(response.status_code, 200)\n self.assertEqual(ProjectInvite.objects.all().count(), 0)","title":""},{"docid":"9efdf279825e2919c837dffe4dc03bb0","score":"0.56291455","text":"def bind_group_invitations(user):\n\n user_emails = [email.email for email in user.emails if email.confirmed]\n\n if not user_emails and not user.facebook_id:\n return\n\n query = meta.Session.query(PendingInvitation)\n if user_emails:\n query = query.filter(PendingInvitation.email.in_(user_emails))\n if user.facebook_id:\n query = query.filter(PendingInvitation.facebook_id == user.facebook_id)\n\n invitations = query.filter(PendingInvitation.active == True).all()\n\n # filter out invitations with different location\n invitations = [i for i in invitations \\\n if i.group.location.root == user.location.root]\n\n for invitation in invitations:\n invitation.user = user","title":""},{"docid":"51e1160ffbfabd83863074eaee35343b","score":"0.5626658","text":"def kick_group_member_public_v2(\n group_id: str,\n user_id: str,\n namespace: Optional[str] = None,\n x_additional_headers: Optional[Dict[str, str]] = None,\n **kwargs\n):\n if namespace is None:\n namespace, error = get_services_namespace()\n if error:\n return None, error\n request = KickGroupMemberPublicV2.create(\n group_id=group_id,\n user_id=user_id,\n namespace=namespace,\n )\n return run_request(request, additional_headers=x_additional_headers, **kwargs)","title":""},{"docid":"4402a29bf69bb1d852c5143fb61ffaec","score":"0.5614303","text":"def channel_invite_v2(token, channel_id, u_id):\n data = getData()\n # Decode the token to access auth_user_id of the channel owner\n auth_user_id = find_user_id(token)\n # Set the following as false to be checked before implementing feature\n user_valid = False\n channel_valid = False\n auth_user_valid = False\n auth_is_user = False\n \n # Ensure there is a channels key if the wrong input is sent in\n if data.get('channels') == None:\n data['channels'] = []\n\n # Only run code if there is information in data\n # Checks both u_id and auth_user_id are existing users\n # Check the channel id exists\n user_valid = check_valid_info(data['users'], u_id)\n auth_user_valid = check_valid_info(data['users'], auth_user_id)\n\n # If user id not found, raise exception\n if user_valid == False:\n raise InputError(description= \"User ID does not exist\")\n \n if auth_user_valid == False:\n raise InputError(description= 'User ID does not exist')\n\n channel_valid = check_valid_info(data['channels'], channel_id)\n # If channel id not found, raise exception\n if channel_valid == False:\n raise InputError(description= \"Channel ID does not exist\")\n \n # Check that the user wanting to add u_id is a member of the channel\n auth_is_user = check_members(data['channels'], auth_user_id)\n # If yes, then append the user as a channel member\n if auth_is_user:\n add_to_channel_member_list(u_id, channel_id)\n # If they are not a member, raise AccessError\n else: \n raise AccessError(description= \"You are not a member of this channel. You do not have permission to add users to this channel\")\n\n #THE FOLLOWING CODE IS TO SUPPORT NOTIFICATIONS_GET FUNCTION \n if data.get(\"added_info\") == None:\n data[\"added_info\"] = { }\n \n if data[\"added_info\"].get(\"channels\") == None:\n data[\"added_info\"][\"channels\"] = []\n \n info_dict = {\"u_id\": u_id,\n \"channel_id\" : channel_id,\n \"adder\" : data[\"users\"][auth_user_id - 1][\"handle_str\"]\n }\n data[\"added_info\"][\"channels\"].append(info_dict)\n \n return {\n }","title":""},{"docid":"644282c0fa8111d985fc6e2dd31469cb","score":"0.56123453","text":"def test_add_team_invite_unverified_email(self):\n user = fixture.get(User)\n fixture.get(EmailAddress, user=user, verified=False)\n\n self.assertEqual(Invitation.objects.all().count(), 0)\n\n url = reverse(\n \"organization_team_member_add\",\n args=[self.organization.slug, self.team.slug],\n )\n resp = self.client.post(url, data={\"username_or_email\": user.email})\n self.assertEqual(resp.status_code, 302)\n\n invitation = Invitation.objects.for_object(self.team).get()\n self.assertEqual(invitation.from_user, self.owner)\n self.assertEqual(invitation.to_user, None)\n self.assertEqual(invitation.to_email, user.email)\n self.assertEqual(self.team.members.count(), 0)","title":""},{"docid":"869778221004fcbeee92cd6236f01135","score":"0.56041455","text":"def addGroup(self, userguid, groupguid, jobguid=\"\", executionparams=None):","title":""},{"docid":"e048b940e0994c842b31967aa889153a","score":"0.55990416","text":"def save(self, user):\n return self.invite.accept(user)","title":""},{"docid":"aced35fdb899f8f8fa871edd9465f58c","score":"0.5598923","text":"def invite(self, channel, user):\n return self.post('channels.invite',\n data={'channel': channel, 'user': user})","title":""},{"docid":"f343b959cd689693e398601b8687dfc9","score":"0.55932057","text":"def handle_groupchat_invite(self, inv):\r\n logging.debug(\"MUC invite to %s from %s: %s\", inv['to'], inv[\"from\"], inv)\r\n if inv['from'] not in self.rooms.keys():\r\n self.xmpp.event(\"groupchat_invite\", inv)","title":""},{"docid":"8fc14cfbcf11b84544cd9fde0c2045f0","score":"0.55812365","text":"def add_to_group(userObj, groupObj):\n if not is_member_of(userObj, groupObj):\n if userObj.groups:\n userObj.groups.append(groupObj)\n else:\n userObj.groups = [groupObj]\n\n return userObj\n else:\n raise ValueError('This user is already a member of this group')","title":""},{"docid":"d634ef1c59581dc9c5ad9fac5b448561","score":"0.5559801","text":"def invite(self, user, email):\n invitation = None\n try:\n # It is possible that there is more than one invitation fitting\n # the criteria. Normally this means some older invitations are\n # expired or an email is invited consequtively.\n invitation = self.filter(user=user, email=email)[0]\n if not invitation.is_valid():\n invitation = None\n except (Invitation.DoesNotExist, IndexError):\n pass\n if invitation is None:\n key = '%s%0.16f%s%s' % (settings.SECRET_KEY, random.random(),\n user.email, email)\n key = hashlib.sha1(key).hexdigest()\n invitation = self.create(user=user, email=email, key=key)\n return invitation","title":""},{"docid":"7aa961286a0f8dc54d351ca52edb3002","score":"0.5555895","text":"def has_invite(self, invite_id):","title":""},{"docid":"10e17a8a4e37475c3709f0424201509a","score":"0.55533147","text":"def invite_user(bot, trigger):\n chanops = get_chanops(str(trigger.sender), bot.memory['channelmgnt']['jdcache'])\n channel = trigger.sender\n dodeop = False\n if chanops:\n if bot.channels[trigger.sender].privileges[bot.nick] < OP and trigger.account in chanops:\n bot.say('Please wait...')\n bot.say('op ' + trigger.sender, 'ChanServ')\n time.sleep(1)\n dodeop = True\n nick = trigger.group(2)\n if not nick:\n bot.say(f'{trigger.account}: No user specified.', trigger.sender)\n elif trigger.account in chanops:\n bot.write(['INVITE', channel, nick])\n if dodeop:\n deopbot(trigger.sender, bot)\n else:\n bot.reply('Access Denied. If in error, please contact the channel founder.')\n else:\n bot.reply(f'No ChanOps Found. Please ask for assistance in {bot.settings.channelmgnt.support_channel}')","title":""},{"docid":"9a55439f110d1b17f5022704563d0fcd","score":"0.55455256","text":"def test_accept_local_user_not_allowed(self):\n invite = self.make_invite(\n email=INVITE_EMAIL,\n project=self.project,\n role=self.role_contributor,\n issuer=self.user,\n message='',\n )\n self.assertEqual(ProjectInvite.objects.filter(active=True).count(), 1)\n\n response = self.client.get(\n reverse(\n 'projectroles:invite_accept',\n kwargs={'secret': invite.secret},\n ),\n follow=True,\n )\n self.assertListEqual(\n response.redirect_chain,\n [(reverse('home'), 302), (reverse('login') + '?next=/', 302)],\n )","title":""},{"docid":"0fea15623a6acb6064e4b99cdcee6686","score":"0.55444974","text":"def send_invitation(self, user, sender=None, **kwargs):\n if user.is_active:\n return False\n token = RegistrationTokenGenerator().make_token(user)\n self._send_email(user, self.invitation_subject, self.invitation_body,\n sender, **{'token': token})","title":""},{"docid":"241884093e871a0075441bc6f1e7f69b","score":"0.55342114","text":"def commitinvite(self,email,maker,group):\n\t\tnew = Invite(email,maker.id,group)\n\t\tdb.session.add(new)\n\t\tdb.session.commit()\n\t\treceiver = [email]\n\t\tsend_email_register(maker.email,receiver)","title":""},{"docid":"00dde02eb0ceaf7946414356a9d71f20","score":"0.5534189","text":"async def reject_group_invitation_public_v1_async(\n group_id: str,\n namespace: Optional[str] = None,\n x_additional_headers: Optional[Dict[str, str]] = None,\n **kwargs\n):\n if namespace is None:\n namespace, error = get_services_namespace()\n if error:\n return None, error\n request = RejectGroupInvitationPublicV1.create(\n group_id=group_id,\n namespace=namespace,\n )\n return await run_request_async(\n request, additional_headers=x_additional_headers, **kwargs\n )","title":""},{"docid":"3c8e912c99f713c00ab609e7bf90da94","score":"0.55245507","text":"def invite_user(self, contact):\r\n raise NotImplementedError","title":""}],"string":"[\n {\n \"docid\": \"4459c330912a73c413cf5d1668233bb8\",\n \"score\": \"0.7699805\",\n \"text\": \"def invite_member(request):\\n group = get_object_or_404(models.Group, id=request.POST.get(\\\"group\\\"))\\n user = get_user_from_username(request.user, request.POST[\\\"user\\\"])\\n\\n if not group.user == request.user:\\n return HttpResponseBadRequest()\\n\\n try:\\n models.GroupMemberInvitation.objects.create(user=user, group=group)\\n except IntegrityError:\\n pass\\n\\n return redirect(user.local_path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"385a749693ab4aaacea09db62bd39d97\",\n \"score\": \"0.7404152\",\n \"text\": \"def invite_user_to_group(group_id):\\n\\n membership = Membership.get_membership_by_user_group(\\n session[CURR_USER_ID], group_id)\\n if membership == None:\\n return restricted_not_authorized()\\n\\n safe_group = Group.get_safe_group(group_id)\\n member_type = membership.member_type\\n\\n if (member_type not in safe_group['can_invite_list']):\\n return restricted_group_privileges(group_id)\\n\\n else:\\n form = forms.InviteToGroupForm()\\n if form.validate_on_submit():\\n\\n username = form.username.data\\n invited_user = User.get_user_by_username(username)\\n\\n # invitation = Membership.invite_by_api(\\n # invited_user.id, group_id, session[CURR_USER_ID], g.user.api_token,app_url)\\n\\n API_URL = f\\\"{app_url}/api/invitations\\\"\\n json_data = {\\n \\\"member_id\\\": invited_user.id,\\n \\\"group_id\\\": group_id,\\n \\\"invited_by_id\\\": session[CURR_USER_ID],\\n \\\"api_token\\\":g.user.api_token\\n }\\n response = requests.post(API_URL, json=json_data, headers={\\n \\\"Content-Type\\\": \\\"application/json\\\"})\\n invitation= response.json()\\n\\n status = invitation['status']\\n message = invitation['message']\\n \\n if status==\\\"successful\\\":\\n flash(message, 'success')\\n else:\\n flash(message, 'danger')\\n\\n safe_members = Membership.get_serialized_safe_members_info_by_group_sorted(\\n group_id)\\n\\n else:\\n safe_members = Membership.get_serialized_safe_members_info_by_group_sorted(\\n group_id)\\n\\n return render_template('invite_to_group.html', form=form, group=safe_group, members=safe_members)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a96e5ad91d11fb486ec9376d757a99f\",\n \"score\": \"0.7260222\",\n \"text\": \"async def invite_group_public_v1_async(\\n user_id: str,\\n namespace: Optional[str] = None,\\n x_additional_headers: Optional[Dict[str, str]] = None,\\n **kwargs\\n):\\n if namespace is None:\\n namespace, error = get_services_namespace()\\n if error:\\n return None, error\\n request = InviteGroupPublicV1.create(\\n user_id=user_id,\\n namespace=namespace,\\n )\\n return await run_request_async(\\n request, additional_headers=x_additional_headers, **kwargs\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0dcd65512d49cb81c358f5bdcd54988d\",\n \"score\": \"0.7227689\",\n \"text\": \"def group_invite(request, pk, template_name='groups/group_invite.html'):\\n group = get_object_or_404(Group, pk=pk, is_active=True)\\n form = GroupInviteForm(initial={'group': group.pk, 'user': request.user.pk})\\n return render(request, template_name, {\\n 'group': group,\\n 'form': form\\n })\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ca3dfc009087125ea9d4a47fc3f9a8c\",\n \"score\": \"0.72048044\",\n \"text\": \"async def invite_group_public_v2_async(\\n group_id: str,\\n user_id: str,\\n namespace: Optional[str] = None,\\n x_additional_headers: Optional[Dict[str, str]] = None,\\n **kwargs\\n):\\n if namespace is None:\\n namespace, error = get_services_namespace()\\n if error:\\n return None, error\\n request = InviteGroupPublicV2.create(\\n group_id=group_id,\\n user_id=user_id,\\n namespace=namespace,\\n )\\n return await run_request_async(\\n request, additional_headers=x_additional_headers, **kwargs\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c708a909ed3c6f501859f89317056d9\",\n \"score\": \"0.7129191\",\n \"text\": \"def invite_group_public_v2(\\n group_id: str,\\n user_id: str,\\n namespace: Optional[str] = None,\\n x_additional_headers: Optional[Dict[str, str]] = None,\\n **kwargs\\n):\\n if namespace is None:\\n namespace, error = get_services_namespace()\\n if error:\\n return None, error\\n request = InviteGroupPublicV2.create(\\n group_id=group_id,\\n user_id=user_id,\\n namespace=namespace,\\n )\\n return run_request(request, additional_headers=x_additional_headers, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a553eb8fc3eb701cefa1d4c938fb06b6\",\n \"score\": \"0.68725336\",\n \"text\": \"def accept_membership(request):\\n group = get_object_or_404(models.Group, id=request.POST.get(\\\"group\\\"))\\n invite = get_object_or_404(\\n models.GroupMemberInvitation, group=group, user=request.user\\n )\\n\\n try:\\n invite.accept()\\n except IntegrityError:\\n pass\\n\\n return redirect(group.local_path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c313d2fec18103d1602ff6ec33deccbf\",\n \"score\": \"0.67918944\",\n \"text\": \"def group_invite(request, slug, template_name='groups/group_invite.html'):\\n group = get_object_or_404(Group, slug=slug, is_active=True)\\n form = InviteForm(initial={'group': group.pk, 'user': request.user.pk})\\n return render_to_response(template_name, {\\n 'group': group,\\n 'form': form\\n }, context_instance=RequestContext(request))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc05bc926c522b4d146943df687fef3d\",\n \"score\": \"0.6783538\",\n \"text\": \"def test_get_group_invite_only(self):\\n group = Group.objects.create(name='test-group', invite_only=True)\\n group.users.add(self.user)\\n\\n rsp = self.apiGet(self.get_item_url(group.name),\\n expected_mimetype=self.item_mimetype)\\n self.assertEqual(rsp['stat'], 'ok')\\n self.assertEqual(rsp['group']['invite_only'], True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"056eced7e2c66878c5312d3b2f101727\",\n \"score\": \"0.6662698\",\n \"text\": \"def project__user_invite(self) -> None:\\n project_name = self.get_project()\\n try:\\n self.client.invite_project_user(\\n project=project_name,\\n user_email=self.args.email,\\n member_type=self.args.role,\\n )\\n except client.Error as ex:\\n print(ex.response.text)\\n raise argx.UserError(\\\"Project '{}' invite for {} failed\\\".format(project_name, self.args.email))\\n self.log.info(\\\"Invited %r into project %r\\\", self.args.email, project_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d05c0d5fc67b012fc635fb4f98e9ad50\",\n \"score\": \"0.6653732\",\n \"text\": \"def inviteToGroup(self, groupId, users, role, expiration=1440):\\n params = {\\n \\\"f\\\" : \\\"json\\\",\\n \\\"token\\\" : self._securityHandler.token,\\n \\\"users\\\" : users,\\n \\\"role\\\" : role,\\n \\\"expiration\\\" : expiration\\n }\\n return self._do_post(url=self._url + \\\"/%s/invite\\\" % groupId,\\n param_dict=params,\\n proxy_url=self._proxy_url,\\n proxy_port=self._proxy_port)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b87a930d019cfe7083eb13052b570e0\",\n \"score\": \"0.6620096\",\n \"text\": \"def account__team__user_invite(self) -> None:\\n self.client.add_team_member(self.args.account_id, self.args.team_id, self.args.email)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"142d8ff6f8b0f3fcd0f8eebc192f4a74\",\n \"score\": \"0.64957047\",\n \"text\": \"def add_invitation(request, group_id, user_id):\\n group = get_object_or_404(Group, pk=group_id)\\n if not is_member(request.user, group):\\n raise PermissionDenied\\n user_to_invite = get_object_or_404(User, pk=user_id)\\n # get all instruments which that user plays AND are desired by the group\\n their_instruments = UserInstrument.objects.filter(user=user_to_invite)\\n possible_to_invite = group.desired_instruments.filter(user_plays__in=their_instruments)\\n # check how many instruments are possible:\\n instr_count = possible_to_invite.count()\\n if instr_count == 0:\\n # this isn't possible from the normal user interface, but is from the user messing\\n # with urls manually. Give an error message and redirect the user to their dashboard\\n messages.error(request, \\\"You tried to issue an invalid invitation!\\\")\\n return redirect(reverse(\\\"dashboard\\\"))\\n elif instr_count == 1:\\n # simply send an invitation and send the user to the relevant group's page (so they\\n # can see that the invitation was successful)\\n try:\\n Invitation.objects.create(inviting_user=request.user,\\n invited_user=user_to_invite,\\n invited_instrument=possible_to_invite[0],\\n group=group)\\n messages.success(request, \\\"Your invitation was sent to %s\\\" %user_to_invite.username)\\n return redirect(reverse(\\\"group\\\", kwargs={\\\"id\\\": group_id}))\\n except IntegrityError:\\n # in case an invitation has already been sent\\n messages.error(request,\\n \\\"%s has already been invited to the group %s!\\\" %(user_to_invite.username,\\n group.name))\\n return redirect(reverse(\\\"dashboard\\\"))\\n else:\\n # there is more than one possible instrument. Redirect to the appropriate pre-populated\\n # invitation form so the user can choose the instrument\\n return redirect(reverse(\\\"invite_for_user\\\", kwargs={\\\"group_id\\\": group_id, \\\"user_id\\\": user_id}))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"932aff1fecdb828efbea5a166b79426c\",\n \"score\": \"0.64831245\",\n \"text\": \"def _inviteIntoGroup(self, groupId, contactIds=[], seq=0):\\n return self._client.inviteIntoGroup(seq, groupId, contactIds)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"932aff1fecdb828efbea5a166b79426c\",\n \"score\": \"0.64831245\",\n \"text\": \"def _inviteIntoGroup(self, groupId, contactIds=[], seq=0):\\n return self._client.inviteIntoGroup(seq, groupId, contactIds)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c36ceb828ac5bbe8f8050c0141cc158\",\n \"score\": \"0.6475991\",\n \"text\": \"def new_group(request, username=\\\"\\\"):\\n if request.method == \\\"POST\\\":\\n form = GroupSetupForm(data=request.POST, user=request.user)\\n form.order_fields([\\\"name\\\", \\\"description\\\", \\\"instrument\\\", \\\"invited_user\\\", \\\"desired_instruments\\\"])\\n if form.is_valid():\\n # create group and issue invitation (NB the group is created even if the user\\n # declines the invitation - this is intentional)\\n group = Group(name=form.cleaned_data[\\\"name\\\"],\\n description=form.cleaned_data[\\\"description\\\"])\\n try:\\n invited_user = User.objects.get(username=form.cleaned_data[\\\"invited_user\\\"])\\n if invited_user == request.user:\\n form.add_error(\\\"invited_user\\\", \\\"You can't invite yourself!\\\")\\n else:\\n group.save()\\n # It should be allowable for the \\\"desired_instruments\\\" field to be left blank\\n # - to allow for groups of just 2 users. Django internally requires the\\n # ManyToManyField to be an iterable - otherwise an error is raised - but if\\n # left blank the value included in the POST request is None. To avoid this\\n # error, we manually set it to be the empty list.\\n if request.POST.get(\\\"desired_instruments\\\"):\\n group.desired_instruments = form.cleaned_data[\\\"desired_instruments\\\"]\\n else:\\n group.desired_instruments = []\\n\\n my_instr = UserInstrument.objects.get(pk=form.cleaned_data[\\\"instrument\\\"])\\n group.members = [my_instr]\\n group.save()\\n \\n\\n invited_instrument = UserInstrument.objects.get(pk=request.POST.get(\\\"invited_instrument\\\")) \\\\\\n .instrument\\n Invitation.objects.create(inviting_user=request.user,\\n invited_user=invited_user,\\n invited_instrument=invited_instrument,\\n group=group)\\n messages.success(request, \\\"\\\"\\\"Your new group %s has now been started!\\n An invitation has been sent to %s\\n \\\"\\\"\\\" % (form.cleaned_data[\\\"name\\\"], \\n form.cleaned_data[\\\"invited_user\\\"]))\\n return redirect(reverse(\\\"my_groups\\\"))\\n \\n except IntegrityError:\\n # this happens if the user attempts to create a group with an already existing name\\n # (since the name field has unique=True). Although this is a db error, it will make\\n # most sense to the user to raise this as an error in the form itself\\n form.add_error(\\\"name\\\", \\\"That group name is already taken!\\\")\\n\\n else:\\n messages.error(request, \\\"Please correct the indicated errors and try again\\\")\\n else:\\n form = GroupSetupForm(user=request.user, initial={\\\"invited_user\\\": username})\\n form.order_fields([\\\"name\\\", \\\"description\\\", \\\"instrument\\\", \\\"invited_user\\\", \\\"desired_instruments\\\"])\\n \\n args = {\\\"active\\\": \\\"dashboard\\\", \\\"form\\\": form}\\n args.update(csrf(request))\\n return render(request, \\\"groups/new.html\\\", args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"80afed92bbff9ef301c7554c97b6e3d7\",\n \"score\": \"0.64502317\",\n \"text\": \"def add_user_to_public_group(sender, instance, created, **kwargs):\\n public_group = ExtendedGroup.objects.public_group()\\n # need to check if Public group exists to avoid errors when creating\\n # user accounts (like superuser and AnonymousUser) before the group\\n # is created by init_refinery command\\n if public_group:\\n instance.groups.add(public_group)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c7dd600c3f37f29a4a7916d5bd0495d\",\n \"score\": \"0.6432827\",\n \"text\": \"def specific_invite(request, group_id, instr_name=None, user_id=None):\\n group = get_object_or_404(Group, pk=group_id)\\n if not is_member(request.user, group):\\n raise PermissionDenied\\n\\n if instr_name:\\n instrument = get_object_or_404(Instrument, instrument=instr_name)\\n\\n else:\\n instrument = None\\n\\n if user_id:\\n user = get_object_or_404(User, pk=user_id)\\n else:\\n user = None\\n\\n # determine fields to exclude from form, depending on the circumstances this view is called:\\n exclusions = [\\\"group\\\"]\\n if instr_name:\\n exclusions.append(\\\"invited_instrument\\\")\\n if user_id:\\n exclusions.append(\\\"invited_user\\\")\\n\\n if request.method == \\\"POST\\\":\\n form = InvitationForm(request.POST, instr=instr_name, exclude=exclusions)\\n if form.is_valid():\\n invitation = form.save(commit=False)\\n invitation.inviting_user = request.user\\n invitation.group = group\\n if instrument:\\n invitation.invited_instrument = instrument\\n else:\\n invitation.invited_instrument = form.cleaned_data[\\\"invited_instrument\\\"]\\n # If the group does not desire the instrument requested, return to the group page\\n # with an explanatory error message.\\n if instrument and instrument not in group.desired_instruments.all():\\n messages.error(request, \\\"The %s group isn't looking for a %s player!\\\" %(group.name,\\n instrument.instrument))\\n return redirect(reverse(\\\"group\\\", kwargs={\\\"id\\\": group_id}))\\n\\n if user:\\n invitation.invited_user = user\\n else:\\n invitation.invited_user = form.cleaned_data[\\\"invited_user\\\"]\\n # display an error message if you are trying to invite someone already in the group\\n if group in Group.objects.filter(members__user__in=[invitation.invited_user]):\\n form.add_error(\\\"invited_user\\\", \\\"%s is already in this group!\\\" \\\\\\n %invitation.invited_user.username) \\n else:\\n try:\\n invitation.save()\\n messages.success(request,\\n \\\"Your invitation was sent to %s\\\" %invitation.invited_user.username)\\n return redirect(reverse(\\\"group\\\", kwargs={\\\"id\\\": group_id}))\\n except IntegrityError:\\n # This happens when the uniqueness constraint is violated - that is,\\n # you try to invite someone who has already been invited to the same\\n # group\\n form.add_error(\\\"invited_user\\\", \\\"%s has already been invited to join this group!\\\" \\\\\\n %invitation.invited_user.username)\\n else:\\n messages.error(request, \\\"Please correct the indicated errors and try again\\\")\\n else:\\n if instrument:\\n form = InvitationForm(instr=instrument.instrument, exclude=exclusions)\\n else:\\n form = InvitationForm(exclude=exclusions)\\n if not instrument:\\n if Invitation.objects.filter(group=group, invited_user=user).exists():\\n # the user has already been invited - this can't happen via the usual interface\\n # but, as before, might be possible from manual entering of URLs. Handle this with\\n # an error message:\\n messages.error(\\\"invited_user\\\", \\\"%s has already been invited to join this group!\\\" \\\\\\n %invitation.invited_user.username)\\n return redirect(reverse(\\\"group\\\", kwargs={\\\"id\\\": group_id}))\\n form.fields[\\\"invited_instrument\\\"].queryset = group.desired_instruments \\\\\\n .filter(user_plays__user__in=[user])\\n\\n args = {\\\"active\\\": \\\"dashboard\\\", \\\"form\\\": form, \\\"group\\\": group,\\n \\\"instrument\\\": instr_name, \\\"user\\\": user}\\n args.update(csrf(request))\\n return render(request, \\\"groups/specific-invite.html\\\", args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d689d040e98cfba6350dc5da0a6db1ac\",\n \"score\": \"0.64071214\",\n \"text\": \"def _acceptGroupInvitation(self, groupId, seq=0):\\n return self._client.acceptGroupInvitation(seq, groupId)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d689d040e98cfba6350dc5da0a6db1ac\",\n \"score\": \"0.64071214\",\n \"text\": \"def _acceptGroupInvitation(self, groupId, seq=0):\\n return self._client.acceptGroupInvitation(seq, groupId)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c79ea71e17ba6566dfd24eca43314f02\",\n \"score\": \"0.6395867\",\n \"text\": \"def test_user_invite(self):\\n self.client.credentials(HTTP_AUTHORIZATION='JWT ' + self.token)\\n response = self.client.post(reverse('test_challenge:user-invite',\\n kwargs={'pk': '{}'.format(self.test_user.pk)}),\\n {'email': 'email@example.com'}, format='json')\\n self.assertEqual(response.status_code, status.HTTP_200_OK)\\n\\n no_email_response = self.client.post(reverse('test_challenge:user-invite',\\n kwargs={'pk': '{}'.format(self.test_user.pk)}),\\n {}, format='json')\\n self.assertEqual(no_email_response.status_code, status.HTTP_400_BAD_REQUEST)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d7fb9049c061abcea73bfaa7814efce7\",\n \"score\": \"0.6392299\",\n \"text\": \"def test_p2p_go_invite_auth(dev):\\n addr0 = dev[0].p2p_dev_addr()\\n addr1 = dev[1].p2p_dev_addr()\\n\\n logger.info(\\\"Generate BSS table entry for old group\\\")\\n # this adds more coverage to testing by forcing the GO to be found with an\\n # older entry in the BSS table and with that entry having a different\\n # operating channel.\\n dev[0].p2p_start_go(freq=2432)\\n dev[1].scan()\\n dev[0].remove_group()\\n dev[0].dump_monitor()\\n dev[1].dump_monitor()\\n\\n logger.info(\\\"Discover peer\\\")\\n dev[1].p2p_listen()\\n if not dev[0].discover_peer(addr1, social=True):\\n raise Exception(\\\"Peer \\\" + addr1 + \\\" not found\\\")\\n dev[0].p2p_listen()\\n if not dev[1].discover_peer(addr0, social=True):\\n raise Exception(\\\"Peer \\\" + addr0 + \\\" not found\\\")\\n dev[1].p2p_listen()\\n\\n logger.info(\\\"Authorize invitation\\\")\\n pin = dev[1].wps_read_pin()\\n dev[1].global_request(\\\"P2P_CONNECT \\\" + addr0 + \\\" \\\" + pin + \\\" join auth\\\")\\n\\n logger.info(\\\"Start GO on non-social channel\\\")\\n res = dev[0].p2p_start_go(freq=2427)\\n logger.debug(\\\"res: \\\" + str(res))\\n\\n logger.info(\\\"Invite peer to join the group\\\")\\n dev[0].p2p_go_authorize_client(pin)\\n dev[0].global_request(\\\"P2P_INVITE group=\\\" + dev[0].group_ifname + \\\" peer=\\\" + addr1)\\n ev = dev[1].wait_global_event([\\\"P2P-INVITATION-RECEIVED\\\",\\n \\\"P2P-GROUP-STARTED\\\"], timeout=20)\\n if ev is None:\\n raise Exception(\\\"Timeout on invitation on peer\\\")\\n if \\\"P2P-INVITATION-RECEIVED\\\" in ev:\\n raise Exception(\\\"Unexpected request to accept pre-authorized invitaton\\\")\\n dev[0].dump_monitor()\\n\\n logger.info(\\\"Client connected\\\")\\n hwsim_utils.test_connectivity_p2p(dev[0], dev[1])\\n\\n logger.info(\\\"Terminate group\\\")\\n dev[0].remove_group()\\n dev[1].wait_go_ending_session()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cdc22d53c06037657497389a31a3a11c\",\n \"score\": \"0.6333147\",\n \"text\": \"async def accept_group_invitation_public_v1_async(\\n group_id: str,\\n namespace: Optional[str] = None,\\n x_additional_headers: Optional[Dict[str, str]] = None,\\n **kwargs\\n):\\n if namespace is None:\\n namespace, error = get_services_namespace()\\n if error:\\n return None, error\\n request = AcceptGroupInvitationPublicV1.create(\\n group_id=group_id,\\n namespace=namespace,\\n )\\n return await run_request_async(\\n request, additional_headers=x_additional_headers, **kwargs\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"01a99d8be81edff5cb3c81abfcfd673f\",\n \"score\": \"0.632663\",\n \"text\": \"def invite_accept(\\n login_manager: LoginManager, *, group_id: uuid.UUID, identity: ParsedIdentity | None\\n) -> None:\\n auth_client = login_manager.get_auth_client()\\n groups_client = login_manager.get_groups_client()\\n\\n actions = build_invite_actions(\\n auth_client, groups_client, \\\"accept\\\", group_id, identity\\n )\\n response = groups_client.batch_membership_action(group_id, actions)\\n # if this failed to return at least one accepted user, figure out an error to show\\n if not response.get(\\\"accept\\\", None):\\n try:\\n raise ValueError(response[\\\"errors\\\"][\\\"accept\\\"][0][\\\"detail\\\"])\\n except LookupError:\\n raise ValueError(\\\"Could not accept invite\\\")\\n\\n display(response, text_mode=get_invite_formatter(\\\"accept\\\"))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2432146a3abed818840501659b8489d7\",\n \"score\": \"0.6276367\",\n \"text\": \"def accept_group_invitation_public_v1(\\n group_id: str,\\n namespace: Optional[str] = None,\\n x_additional_headers: Optional[Dict[str, str]] = None,\\n **kwargs\\n):\\n if namespace is None:\\n namespace, error = get_services_namespace()\\n if error:\\n return None, error\\n request = AcceptGroupInvitationPublicV1.create(\\n group_id=group_id,\\n namespace=namespace,\\n )\\n return run_request(request, additional_headers=x_additional_headers, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d2a1dcff3383ea92d06cb221214ef21b\",\n \"score\": \"0.62529373\",\n \"text\": \"async def accept_group_invitation_public_v2_async(\\n group_id: str,\\n namespace: Optional[str] = None,\\n x_additional_headers: Optional[Dict[str, str]] = None,\\n **kwargs\\n):\\n if namespace is None:\\n namespace, error = get_services_namespace()\\n if error:\\n return None, error\\n request = AcceptGroupInvitationPublicV2.create(\\n group_id=group_id,\\n namespace=namespace,\\n )\\n return await run_request_async(\\n request, additional_headers=x_additional_headers, **kwargs\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dcae190f718bb4f6fcec5a0262315e56\",\n \"score\": \"0.6249498\",\n \"text\": \"def send_invite(self, message):\\n\\n invitation_link = self.bot.export_chat_invite_link(SECRET_GROUP)\\n if invitation_link:\\n self.bot.send_message(message.chat.id, invitation_link, reply_markup=None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5dea90b653b948c80729e22657e567f1\",\n \"score\": \"0.6230298\",\n \"text\": \"def invite():\\n user_manager = current_app.user_manager\\n db_adapter = user_manager.db_adapter\\n\\n invite_form = user_manager.invite_form(request.form)\\n\\n if request.method == 'POST' and invite_form.validate():\\n email = invite_form.email.data\\n\\n User = db_adapter.UserClass\\n user_class_fields = User.__dict__\\n user_fields = {\\n \\\"email\\\": email\\n }\\n\\n user, user_email = user_manager.find_user_by_email(email)\\n if user:\\n flash(\\\"Usuario com este email ja foi cadastrado.\\\", \\\"error\\\")\\n return redirect(url_for('user.invite'))\\n else:\\n user_invite = db_adapter \\\\\\n .add_object(db_adapter.UserInvitationClass, **{\\n \\\"email\\\": email,\\n \\\"invited_by_user_id\\\": current_user.id\\n })\\n db_adapter.commit()\\n\\n token = user_manager.generate_token(user_invite.id)\\n accept_invite_link = url_for('user.register',\\n token=token,\\n _external=True)\\n\\n # Store token\\n if hasattr(db_adapter.UserInvitationClass, 'token'):\\n user_invite.token = token\\n db_adapter.commit()\\n\\n try:\\n # Send 'invite' email\\n emails.send_invite_email(user_invite, accept_invite_link)\\n except Exception as e:\\n # delete new User object if send fails\\n db_adapter.delete_object(user_invite)\\n db_adapter.commit()\\n raise\\n\\n signals \\\\\\n .user_sent_invitation \\\\\\n .send(current_app._get_current_object(), user_invite=user_invite,\\n form=invite_form)\\n\\n flash(_('Convite enviado.'), 'success')\\n safe_next = _get_safe_next_param(\\n 'next', user_manager.after_invite_endpoint)\\n return redirect(safe_next)\\n\\n return render(user_manager.invite_template, form=invite_form)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e52b5310701e5a8f2ffc00bb11329e4\",\n \"score\": \"0.6216854\",\n \"text\": \"def acceptGroupInvitation(self, groupId, username):\\n params = {\\n \\\"f\\\" : \\\"json\\\",\\n \\\"token\\\" : self._securityHandler.token\\n }\\n return self._do_post(url=self._url + \\\"/%s/applications/%s/accept\\\" % (groupId,\\n username),\\n param_dict=params,\\n proxy_url=self._proxy_url,\\n proxy_port=self._proxy_port)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"464ff7553e8f7635fbca160699ce4f62\",\n \"score\": \"0.62121665\",\n \"text\": \"def accept_group_invitation_public_v2(\\n group_id: str,\\n namespace: Optional[str] = None,\\n x_additional_headers: Optional[Dict[str, str]] = None,\\n **kwargs\\n):\\n if namespace is None:\\n namespace, error = get_services_namespace()\\n if error:\\n return None, error\\n request = AcceptGroupInvitationPublicV2.create(\\n group_id=group_id,\\n namespace=namespace,\\n )\\n return run_request(request, additional_headers=x_additional_headers, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9186b04925dfb4ef7119fd3614313fd\",\n \"score\": \"0.6173581\",\n \"text\": \"def v3_addUserToGroup(request):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"166df8693ccc39599414518f96a60527\",\n \"score\": \"0.6160525\",\n \"text\": \"def test_get_group_invite_only_with_permission_denied_error(self):\\n group = Group.objects.create(name='test-group', invite_only=True)\\n\\n rsp = self.apiGet(self.get_item_url(group.name),\\n expected_status=403)\\n self.assertEqual(rsp['stat'], 'fail')\\n self.assertEqual(rsp['err']['code'], PERMISSION_DENIED.code)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0319810cb5e4714439d190992a069e27\",\n \"score\": \"0.6160085\",\n \"text\": \"def organization__user__invite(self) -> None:\\n self.client.invite_organization_user(self.args.organization_id, self.args.email)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"208bceab80404c7fca7e727eac44a63d\",\n \"score\": \"0.61597395\",\n \"text\": \"def accept_invitation(self, request, new_user):\\n\\t\\t\\n\\t\\tinv = FacebookInvite.objects.create(user=self.user, sent=False, facebook_user_id=self.facebook_user_id, accepted_user=new_user, personal_note=self.personal_note)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8783862b3a03fea1152e6d63f5d72138\",\n \"score\": \"0.61500204\",\n \"text\": \"def inviteUserToApplet(user, appletObject, userB):\\n\\n groupId = appletObject['roles']['user']['groups'][0]['id']\\n currentUser = authenticate(user)\\n group = Group().load(id=ObjectId(groupId), force=True)\\n\\n Invitation().createInvitation(\\n appletObject,\\n currentUser,\\n role=\\\"user\\\",\\n profile=Profile().createProfile(appletObject, userB),\\n idCode=None\\n )\\n return Group().inviteUser(group, userB, level=AccessType.READ)\\n # inviteResp = girder.post('group/{}/invitation'.format(groupId), {\\n # \\\"email\\\": userB['email']\\n # })\\n # assert inviteResp['public'] == False, 'invite is public!'\\n # assert len(inviteResp['access']['users']), 'ths user was not added'\\n # return inviteResp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2744485f4be12a103d165079ffe9148b\",\n \"score\": \"0.61273444\",\n \"text\": \"def test_p2p_go_invite(dev):\\n addr0 = dev[0].p2p_dev_addr()\\n addr1 = dev[1].p2p_dev_addr()\\n\\n logger.info(\\\"Generate BSS table entry for old group\\\")\\n # this adds more coverage to testing by forcing the GO to be found with an\\n # older entry in the BSS table and with that entry having a different\\n # operating channel.\\n dev[0].p2p_start_go(freq=2422)\\n dev[1].scan()\\n dev[0].remove_group()\\n\\n logger.info(\\\"Discover peer\\\")\\n dev[1].p2p_listen()\\n if not dev[0].discover_peer(addr1, social=True):\\n raise Exception(\\\"Peer \\\" + addr1 + \\\" not found\\\")\\n\\n logger.info(\\\"Start GO on non-social channel\\\")\\n res = dev[0].p2p_start_go(freq=2417)\\n logger.debug(\\\"res: \\\" + str(res))\\n\\n logger.info(\\\"Invite peer to join the group\\\")\\n dev[0].global_request(\\\"P2P_INVITE group=\\\" + dev[0].group_ifname + \\\" peer=\\\" + addr1)\\n ev = dev[1].wait_global_event([\\\"P2P-INVITATION-RECEIVED\\\"], timeout=10)\\n if ev is None:\\n raise Exception(\\\"Timeout on invitation on peer\\\")\\n ev = dev[0].wait_global_event([\\\"P2P-INVITATION-RESULT\\\"], timeout=10)\\n if ev is None:\\n raise Exception(\\\"Timeout on invitation on GO\\\")\\n if \\\"status=1\\\" not in ev:\\n raise Exception(\\\"Unexpected invitation result\\\")\\n\\n logger.info(\\\"Join the group\\\")\\n pin = dev[1].wps_read_pin()\\n dev[0].p2p_go_authorize_client(pin)\\n dev[1].p2p_connect_group(addr0, pin, timeout=60)\\n logger.info(\\\"Client connected\\\")\\n hwsim_utils.test_connectivity_p2p(dev[0], dev[1])\\n\\n logger.info(\\\"Terminate group\\\")\\n dev[0].remove_group()\\n dev[1].wait_go_ending_session()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a218701c05d63a7277ae9812ea5b296\",\n \"score\": \"0.6124214\",\n \"text\": \"def invite_user():\\n form = InviteUserForm()\\n if form.validate_on_submit():\\n user = User(\\n role=form.role.data,\\n first_name=form.first_name.data,\\n last_name=form.last_name.data,\\n email=form.email.data)\\n db.session.add(user)\\n db.session.commit()\\n token = user.generate_confirmation_token()\\n invite_link = url_for(\\n 'account.join_from_invite',\\n user_id=user.id,\\n token=token,\\n _external=True)\\n invite_by = User.query.filter(User.id == current_user.id).first()\\n get_queue().enqueue(\\n send_email,\\n recipient=user.email,\\n subject='You Are Invited To Join',\\n template='account/email/invite',\\n user=user.id,\\n invite_link=invite_link,\\n invite_by=invite_by\\n )\\n flash('User {} successfully invited'.format(user.full_name),\\n 'form-success')\\n return render_template('admin/new_user.html', form=form)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3072c3e8509780de135dd340c08910d7\",\n \"score\": \"0.6106594\",\n \"text\": \"def test_invite_users(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b603be6695d81acf9de59c70eaedaf76\",\n \"score\": \"0.61043465\",\n \"text\": \"def invite_user():\\n form = InviteUserForm()\\n if form.validate_on_submit():\\n user = User(\\n role=form.role.data,\\n first_name=form.first_name.data,\\n last_name=form.last_name.data,\\n email=form.email.data)\\n db.session.add(user)\\n db.session.commit()\\n token = user.generate_confirmation_token()\\n invite_link = url_for(\\n 'account.join_from_invite',\\n user_id=user.id,\\n token=token,\\n _external=True)\\n get_queue().enqueue(\\n send_email,\\n recipient=user.email,\\n subject='You Are Invited To Join',\\n template='account/email/invite',\\n user=user,\\n invite_link=invite_link,\\n )\\n flash('User {} successfully invited'.format(user.full_name()),\\n 'form-success')\\n return render_template('admin/new_user.html', form=form)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba8fb5303f450984e7a5cea0597e2a03\",\n \"score\": \"0.60186064\",\n \"text\": \"def invitePost(self, request, context, params, group_entity, **kwargs):\\n\\n # get the request view parameters and populate the form using POST data\\n request_params = request_view.view.getParams()\\n form = request_params['invite_form'](request.POST)\\n\\n if not form.is_valid():\\n # return the invalid form response\\n return self._constructResponse(request, entity=None, context=context,\\n form=form, params=request_params)\\n\\n # collect the cleaned data from the valid form\\n _, form_fields = soc.views.helper.forms.collectCleanedFields(form)\\n\\n # create the fields for the new request entity\\n request_fields = {\\n 'user': form_fields['user_id'],\\n 'group': group_entity,\\n 'role': params['logic'].role_name,\\n 'status': 'group_accepted'}\\n\\n if not request_logic.isValidNewRequest(request_fields, params['logic']):\\n # not a valid invite\\n context['error_message'] = self.DEF_INVITE_ERROR_MSG_FMT % (\\n params)\\n return super(View, self)._constructResponse(request, entity=None,\\n context=context, form=form, params=request_params)\\n\\n # create the request entity\\n request_logic.updateOrCreateFromFields(request_fields)\\n\\n group_view = params.get('group_view')\\n\\n if not group_view:\\n return http.HttpResponseRedirect('/')\\n else:\\n # redirect to the requests list\\n return http.HttpResponseRedirect(\\n redirects.getListRequestsRedirect(group_entity, \\n group_view.getParams()))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb0c9662a413468b1b123e9b7c6bcc48\",\n \"score\": \"0.6010423\",\n \"text\": \"def test_successful_invite_user(self) -> None:\\n self.login(\\\"hamlet\\\")\\n invitee = \\\"alice-test@zulip.com\\\"\\n self.assert_json_success(self.invite(invitee, [\\\"Denmark\\\"]))\\n self.assertTrue(find_key_by_email(invitee))\\n self.check_sent_emails([invitee])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d9366e5e54a43e932f08daf1f710d483\",\n \"score\": \"0.599388\",\n \"text\": \"def invite_user():\\n\\n form = InviteUserForm()\\n if form.validate_on_submit():\\n invited_by = db.session.query(User).filter_by(id=current_user.id).first()\\n user = User(\\n invited_by=invited_by.full_name,\\n first_name=form.first_name.data,\\n last_name=form.last_name.data,\\n area_code=form.area_code.data,\\n mobile_phone=form.mobile_phone.data\\n )\\n db.session.add(user)\\n db.session.commit()\\n token = user.generate_confirmation_token()\\n invite_link = url_for(\\n 'account.join_from_invite',\\n user_id=user.id,\\n token=token,\\n _external=True)\\n area_code = str(form.area_code.data)\\n area_code = area_code.replace(' ', '')\\n phone_number = str(form.mobile_phone.data)\\n phone_number = phone_number.replace(' ', '')\\n if str(area_code)[0] != '+':\\n area_code = '+' + str(area_code)\\n client.messages.create(\\n body=f'Invitation to networked.ng by %s. Click link to complete registration : {invite_link}' % invited_by,\\n messaging_service_sid=messaging_service_sid,\\n to=str(area_code) + str(phone_number)\\n )\\n flash('User {} successfully invited'.format(user.full_name),\\n 'form-success')\\n return redirect(url_for('account.invited_users'))\\n else:\\n flash('Error! Invitation not sent.', 'error')\\n return render_template('main/new_user.html', form=form)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"074abfbeadf7862171a90f5127f61f00\",\n \"score\": \"0.59922636\",\n \"text\": \"def invite():\\n form = InviteForm(request.form)\\n if form.validate_on_submit():\\n from flask_application import user_datastore, app, mail\\n user = User.objects(id=current_user.get_id()).first()\\n password = binascii.b2a_hex(os.urandom(15))\\n # create an account\\n user_datastore.create_user(username=\\\"x\\\",\\n email=form.email.data,\\n password=encrypt_password(password),\\n roles=['contributor'],\\n active=True,\\n invited_by=user)\\n user_datastore.commit()\\n invitee = User.objects(email=form.email.data).first()\\n # set inviter data\\n user.add_invitation(invitee)\\n # send an email\\n msg = Message(\\\"An invitation to %s\\\" % app.config['SITE_NAME'],\\n sender=app.config['DEFAULT_MAIL_SENDER'],\\n recipients=[form.email.data])\\n msg.body = '''\\n\\t\\t\\tYou have been invited to %s by %s. \\n\\t\\t\\tYour can log in with the following username/ password:\\n\\t\\t\\t%s\\n\\t\\t\\t%s\\n\\t\\t\\tYou can change this random, complicated password after you have logged in.\\n\\t\\t\\t''' % (app.config['SITE_NAME'], user.email, form.email.data, password)\\n mail.send(msg)\\n # @todo: set invited and invited by\\n flash(\\\"An account has been created for %s and an email has been sent. You may want to let them know that it is coming.\\\" % form.email.data)\\n return render_template('profiles/invite.html',\\n register_user_form=form)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e89b8ef0313b9a03468a263d74869c1f\",\n \"score\": \"0.5988099\",\n \"text\": \"def test_invite_existing_user(self) -> None:\\n self.login(\\\"hamlet\\\")\\n\\n hamlet_email = \\\"hAmLeT@zUlIp.com\\\"\\n result = self.invite(hamlet_email, [\\\"Denmark\\\"])\\n self.assert_json_error(result, \\\"We weren't able to invite anyone.\\\")\\n\\n self.assertFalse(\\n PreregistrationUser.objects.filter(email__iexact=hamlet_email).exists(),\\n )\\n self.check_sent_emails([])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51064a0d582418ed6cd2b08eaae66e4d\",\n \"score\": \"0.5982782\",\n \"text\": \"def test_add_new_user_to_public_group(self):\\n new_user = User.objects.create_user(self.username)\\n self.assertEqual(\\n new_user.groups.filter(name=self.public_group_name).count(), 1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"082c6a3230268914cd0c5d1918fbb5b1\",\n \"score\": \"0.59728533\",\n \"text\": \"def invite(self, user, admin=None):\\n if admin is None or self.is_admin(admin):\\n return self.add_member(user, state=MembershipState.PENDING_USER)\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5551d9a5d7cd4afd3e1534e487d60310\",\n \"score\": \"0.59535766\",\n \"text\": \"def send_invite(arg):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"85fb6fad9a6f10a9488d8d8e03ca6abd\",\n \"score\": \"0.59390855\",\n \"text\": \"def testUserInviteFromManager(user, appletObject, userB):\\n authenticate(user)\\n groupId = appletObject['roles']['user']['groups'][0]['id']\\n pendingInvites = girder.get('group/{}/invitation'.format(groupId))\\n value = False\\n for invite in pendingInvites:\\n if invite['email'] == userB['email']:\\n value = True\\n break\\n assert value, \\\"email not in list of pending invites, {}\\\".format(pendingInvites)\\n return 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2fa232975772459ae64d24c219b960a2\",\n \"score\": \"0.59250855\",\n \"text\": \"def test_local_users_allowed(self):\\n values = {\\n 'email': INVITE_EMAIL,\\n 'project': self.project.pk,\\n 'role': self.role_contributor.pk,\\n }\\n with self.login(self.user):\\n response = self.client.post(\\n reverse(\\n 'projectroles:invite_create',\\n kwargs={'project': self.project.sodar_uuid},\\n ),\\n values,\\n )\\n self.assertEqual(response.status_code, 302)\\n invite = ProjectInvite.objects.get(\\n project=self.project, email=INVITE_EMAIL, active=True\\n )\\n self.assertIsNotNone(invite)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cbfc57c37093f6158c275fb6558ef440\",\n \"score\": \"0.59185433\",\n \"text\": \"def test_guest_user_invitation(self) -> None:\\n self.login(\\\"polonius\\\")\\n invitee = \\\"alice-test@zulip.com\\\"\\n self.assert_json_error(self.invite(invitee, [\\\"Denmark\\\"]), \\\"Not allowed for guest users\\\")\\n self.assertEqual(find_key_by_email(invitee), None)\\n self.check_sent_emails([])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"381dc7ec2b552dff2da6a64b99e3bc2d\",\n \"score\": \"0.5906466\",\n \"text\": \"def invite_user(self, project_id, user):\\n url = base_url + project_id + '/users/invite'\\n json_object = dumps(user.to_json())\\n data = {\\n 'JSONString': json_object\\n }\\n resp = zoho_http_client.post(url, self.details, self.headers, data)\\n return parser.get_user(resp)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"965b70bd4998a9e1a3668a4da44d9657\",\n \"score\": \"0.59058297\",\n \"text\": \"def user_invite(email, team1_id, user2_email): # POST\\n\\n user = coll(\\\"users\\\").find_one({\\\"_id\\\": user2_email})\\n if not user:\\n return {\\\"message\\\": f\\\"User ({user2_email}) does not exist\\\"}, 404\\n team2_id = user[\\\"team_id\\\"]\\n team2 = coll(\\\"teams\\\").find_one({\\\"_id\\\": team2_id})\\n\\n if not team2:\\n return {\\\"message\\\": f\\\"Team {team2_id} does not exist\\\"}, 404\\n\\n if len(team2[\\\"members\\\"]) > 1:\\n return {\\\"message\\\": f\\\"User ({user2_email}) already on a team with others\\\"}, 409\\n\\n return team_invite(email, team1_id, team2_id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a9503ee24f89fc915feaad14ee5f6ec9\",\n \"score\": \"0.588392\",\n \"text\": \"def testUserInviteFromUser(user, appletObject):\\n pending = user.get('groupInvites')\\n pendingInvitesForUser = []\\n userfields = [\\n 'firstName',\\n '_id',\\n 'email',\\n 'gravatar_baseUrl',\\n 'login'\\n ]\\n for p in pending:\\n groupId = p.get('groupId')\\n applets = list(AppletModel().find(\\n query={\\n \\\"roles.user.groups.id\\\": groupId\\n },\\n fields=[\\n 'cached.applet.skos:prefLabel',\\n 'cached.applet.schema:description',\\n 'cached.applet.schema:image',\\n 'roles'\\n ]\\n ))\\n for applet in applets:\\n for role in ['manager', 'reviewer']:\\n applet[''.join([role, 's'])] = [{\\n (\\n 'image' if userKey=='gravatar_baseUrl' else userKey\\n ): user.get(\\n userKey\\n ) for userKey in user.keys()\\n } for user in list(UserModel().find(\\n query={\\n \\\"groups\\\": {\\n \\\"$in\\\": [\\n group.get('id') for group in applet.get(\\n 'roles',\\n {}\\n ).get(role, {}).get('groups', [])\\n ]\\n }\\n },\\n fields=userfields\\n ))\\n ]\\n groupId = appletObject['roles']['user']['groups'][0]['id']\\n assert len(pendingInvitesForUser), \\\"this user has no invites\\\"\\n assert pendingInvitesForUser[0]['_id'] == groupId, \\\"this user doesn't have the invite you expect\\\"\\n return groupId\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9fb7b1c0464574deb127b8bffa4c0776\",\n \"score\": \"0.5874308\",\n \"text\": \"def invite(self, request, access_type,\\n page_name=None, params=None, **kwargs):\\n\\n # get the context for this webpage\\n context = responses.getUniversalContext(request)\\n responses.useJavaScript(context, params['js_uses_all'])\\n context['page_name'] = page_name\\n\\n group_entity = params['group_logic'].getFromKeyName(kwargs['scope_path'])\\n\\n context['instruction_message'] = (self.DEF_INVITE_INSTRUCTION_MSG_FMT %\\n (params['name'], group_entity.name))\\n\\n if request.method == 'POST':\\n return self.invitePost(request, context, params, group_entity, **kwargs)\\n else:\\n # request.method == 'GET'\\n return self.inviteGet(request, context, params, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"97bc4478454ff4159b6d296a6272bb50\",\n \"score\": \"0.5851911\",\n \"text\": \"def send_invitation(self, request):\\n serializer = InvitationSerializer(data=request.data)\\n serializer.is_valid(raise_exception=True)\\n if self.request.user.role == User.ROLE.ADMIN:\\n to_user = User.objects.filter(id=request.data['id']).first()\\n if to_user:\\n if to_user.status == User.STATUS.ACTIVE:\\n return Response(\\n data={\\n 'detail': 'This user is already activated.',\\n },\\n status=status.HTTP_403_FORBIDDEN,\\n )\\n if to_user.send_invitation_email():\\n to_user.status = User.STATUS.INVITATION\\n to_user.save()\\n return Response(\\n data={\\n 'detail': 'Invitation instructions have been sent to the user.',\\n },\\n )\\n raise NotFound\\n raise PermissionDenied\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92cc5ba5f2864803a5cc47ab7996e012\",\n \"score\": \"0.5846169\",\n \"text\": \"def _invite(self, email, **kwargs):\\n data = dict(email=email, **kwargs)\\n channels = data.pop('channels', None)\\n if channels is not None:\\n data['channels'] = ','.join(channels)\\n data['_attempts'] = 1 # required by slack for some reason\\n return self.post('users.admin.invite', data=data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38905225d3fe82bc260af90da4d7d4da\",\n \"score\": \"0.58422005\",\n \"text\": \"async def accept_group_join_request_public_v1_async(\\n user_id: str,\\n namespace: Optional[str] = None,\\n x_additional_headers: Optional[Dict[str, str]] = None,\\n **kwargs\\n):\\n if namespace is None:\\n namespace, error = get_services_namespace()\\n if error:\\n return None, error\\n request = AcceptGroupJoinRequestPublicV1.create(\\n user_id=user_id,\\n namespace=namespace,\\n )\\n return await run_request_async(\\n request, additional_headers=x_additional_headers, **kwargs\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da5f9691880ff7f346fd2dff2f51b748\",\n \"score\": \"0.5830894\",\n \"text\": \"async def accept_group_join_request_public_v2_async(\\n group_id: str,\\n user_id: str,\\n namespace: Optional[str] = None,\\n x_additional_headers: Optional[Dict[str, str]] = None,\\n **kwargs\\n):\\n if namespace is None:\\n namespace, error = get_services_namespace()\\n if error:\\n return None, error\\n request = AcceptGroupJoinRequestPublicV2.create(\\n group_id=group_id,\\n user_id=user_id,\\n namespace=namespace,\\n )\\n return await run_request_async(\\n request, additional_headers=x_additional_headers, **kwargs\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a7d2d6d1a89a97fe8bd42827ecdeec0\",\n \"score\": \"0.5811526\",\n \"text\": \"def chat_invite_user(self):\\n try:\\n action = self.msg_object['action']\\n chat_invite_user = False\\n if action['type'] == 'chat_invite_user':\\n if action['member_id'] == -int(VK_GROUP_ID):\\n chat_invite_user = True\\n except KeyError:\\n chat_invite_user = False\\n # if action not equals chat_invite_user\\n if not chat_invite_user:\\n return False\\n self.response += '🤖 Привет! Я - чат-бот, созданный на базе бота с \\\\\\nоткрытым исходным кодом. Вы можете изучить мои команды на странице RiBot.\\\\n\\\\n\\\\\\nВ настоящее время я обрабатываю сообщения только с тегом @club{}. Администраторы чата могут разрешить мне \\\\\\nпросмотр всех сообщений или назначить администратором беседы. Обещаю, что не собираюсь хулиганить!\\\\n\\\\n\\\\\\n@ageykindmi(⁉ Техническая поддержка)\\\\n@ribot(✍🏻 Список команд)'.format(VK_GROUP_ID)\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1fc026e2511630f33ff6b07771a4d5bc\",\n \"score\": \"0.5802746\",\n \"text\": \"def invite(self):\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f45480bd4d91aa5801e6fb0c4be276bf\",\n \"score\": \"0.5786934\",\n \"text\": \"def reject_membership(request):\\n group = get_object_or_404(models.Group, id=request.POST.get(\\\"group\\\"))\\n invite = get_object_or_404(\\n models.GroupMemberInvitation, group=group, user=request.user\\n )\\n\\n invite.reject()\\n return redirect(request.user.local_path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f90d292d40932f7af5dfbd259d82365\",\n \"score\": \"0.57654643\",\n \"text\": \"def create_invite(self,**kwargs):\\r\\n\\r\\n\\t\\treturn Invite(self.__bot.api(f\\\"/channels/{self.id}/invites\\\",\\\"POST\\\",json=kwargs),self.__bot)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2edd6868f492bf885e3376fae2b50a48\",\n \"score\": \"0.5749992\",\n \"text\": \"def test_invite_user_as_invalid_type(self) -> None:\\n self.login(\\\"iago\\\")\\n invitee = self.nonreg_email(\\\"alice\\\")\\n response = self.invite(invitee, [\\\"Denmark\\\"], invite_as=10)\\n self.assert_json_error(response, \\\"Invalid invite_as\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43d63b51f33798bbcb7c714c13b1dbd0\",\n \"score\": \"0.57447815\",\n \"text\": \"def v3_validateUserInGroup(request):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f4f99d1228b33fc54619bfbb73bb006\",\n \"score\": \"0.5730765\",\n \"text\": \"def test_accept_public_users_invitation(client, jwt, session, org_info, role, # pylint:disable=unused-argument\\n claims, source, exp_status):\\n headers = factory_auth_header(jwt=jwt, claims=claims)\\n client.post('/api/v1/users', headers=headers, content_type='application/json')\\n rv = client.post('/api/v1/orgs', data=json.dumps(org_info), headers=headers, content_type='application/json')\\n dictionary = json.loads(rv.data)\\n org_id = dictionary['id']\\n rv = client.post('/api/v1/invitations', data=json.dumps(factory_invitation(org_id=org_id, membership_type=role)),\\n headers=headers, content_type='application/json')\\n invitation_dictionary = json.loads(rv.data)\\n invitation_id = invitation_dictionary['id']\\n invitation_id_token = InvitationService.generate_confirmation_token(invitation_id)\\n\\n request = KeycloakScenario.create_user_request()\\n KEYCLOAK_SERVICE.add_user(request, return_if_exists=True)\\n user = KEYCLOAK_SERVICE.get_user_by_username(request.user_name)\\n user_id = user.id\\n headers_invitee = factory_auth_header(jwt=jwt, claims=TestJwtClaims.get_test_user(user_id, source=source))\\n client.post('/api/v1/users', headers=headers_invitee, content_type='application/json')\\n rv = client.put('/api/v1/invitations/tokens/{}'.format(invitation_id_token), headers=headers_invitee,\\n content_type='application/json')\\n assert rv.status_code == http_status.HTTP_200_OK\\n\\n rv = client.get(f'/api/v1/orgs/{org_id}/members?status={exp_status.name}',\\n headers=headers,\\n content_type='application/json')\\n dictionary = json.loads(rv.data)\\n assert len(dictionary['members']) == 1\\n # Assert that the user got added to the keycloak groups\\n user_groups = KEYCLOAK_SERVICE.get_user_groups(user_id=user_id)\\n groups = []\\n for group in user_groups:\\n groups.append(group.get('name'))\\n assert GROUP_PUBLIC_USERS in groups\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be8e6af3b3797aa98a7eb0216277b7c3\",\n \"score\": \"0.57278436\",\n \"text\": \"def test_create_accept_invitation(self):\\n # Register or login another user in order to send him an invitation\\n # (sometimes the database might not get cleaned up properly)\\n resp_register = self.client.post(\\n '/users/',\\n data=json.dumps(dict(\\n email='gill@gmail.com',\\n username='gibilan',\\n password='123456'\\n )),\\n content_type='application/json'\\n )\\n if resp_register.status_code == HTTPStatus.CONFLICT:\\n resp_login = self.client.post(\\n '/auth/login',\\n data=json.dumps(dict(\\n email='gill@gmail.com',\\n password='123456'\\n )),\\n content_type='application/json'\\n )\\n self.assertEqual(resp_login.status_code, HTTPStatus.OK)\\n other_user_token = resp_login.json['data']['Authorization']\\n else:\\n self.assertTrue(resp_register.status_code, HTTPStatus.CREATED)\\n other_user_token = resp_register.json['data']['Authorization']\\n\\n # send an invitation to the other user for 'my_group'\\n resp_invite = self.client.post(\\n '/invitations',\\n data=json.dumps(dict(\\n username_invitee='gibilan',\\n resource_type='group',\\n uuid_resource=self.created_group.json['group']['_uuid']\\n )),\\n headers=dict(\\n Authorization=self.auth\\n ),\\n content_type='application/json'\\n )\\n common_fields = ['_uuid', 'uuid_sender', 'uuid_invitee', 'uuid_resource', 'resource_type', 'token']\\n\\n for field in common_fields:\\n self.assertTrue(resp_invite.json[field])\\n self.assertEqual(\\n resp_invite.json.get('status'),\\n InvitationStatus.PENDING\\n )\\n\\n # accept invitation\\n resp_reject = self.client.post(\\n '/invitations/{}/accept'.format(resp_invite.json['_uuid']),\\n headers=dict(\\n Authorization=other_user_token\\n ),\\n content_type='application/json'\\n )\\n self.assertEqual(resp_reject.status_code, HTTPStatus.OK)\\n for field in common_fields:\\n self.assertEqual(\\n resp_invite.json.get(field),\\n resp_reject.json.get(field)\\n )\\n self.assertEqual(\\n resp_reject.json.get('status'),\\n InvitationStatus.ACCEPTED\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb7bbc10eefc79185ec65f68a044c51a\",\n \"score\": \"0.57269514\",\n \"text\": \"def test_can_send_email_to_invitee(self):\\n user = make_user(invite_count=5)\\n nickname = fake.name()\\n invite = user.create_invite(nickname=nickname)\\n\\n url = self._get_url(invite.pk)\\n headers = make_authentication_headers_for_user(user)\\n with patch('django.core.mail.EmailMultiAlternatives', return_value=MockEmailMultiAlternatives):\\n response = self.client.post(url, {\\n 'email': fake.email()\\n }, **headers)\\n\\n self.assertEqual(response.status_code, status.HTTP_200_OK)\\n invite.refresh_from_db()\\n self.assertTrue(invite.is_invite_email_sent)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ab9caf4fd2393ab59ae399533169880\",\n \"score\": \"0.5719857\",\n \"text\": \"def subscribe(self, user):\\n if self.subscription_policy == SubscriptionPolicy.OPEN:\\n return self.add_member(user)\\n elif self.subscription_policy == SubscriptionPolicy.APPROVAL:\\n email_body = render_template_to_string(\\n 'groups/join_group_email.txt',\\n group=self,\\n )\\n subject = 'New Group Request at LifeWatch Open Science Framework'\\n to = [User.query.get(a.admin_id).email for a in self.admins]\\n scheduled_send_email(fromaddr=cfg['CFG_SITE_ADMIN_EMAIL'],\\n toaddr=to,\\n subject=subject,\\n content=email_body)\\n return self.add_member(user, state=MembershipState.PENDING_ADMIN)\\n elif self.subscription_policy == SubscriptionPolicy.CLOSED:\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"09554e35d3120b00d08042640964775a\",\n \"score\": \"0.5711843\",\n \"text\": \"async def kick_group_member_public_v2_async(\\n group_id: str,\\n user_id: str,\\n namespace: Optional[str] = None,\\n x_additional_headers: Optional[Dict[str, str]] = None,\\n **kwargs\\n):\\n if namespace is None:\\n namespace, error = get_services_namespace()\\n if error:\\n return None, error\\n request = KickGroupMemberPublicV2.create(\\n group_id=group_id,\\n user_id=user_id,\\n namespace=namespace,\\n )\\n return await run_request_async(\\n request, additional_headers=x_additional_headers, **kwargs\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6478fc8248270a8e611fadc0ad988ca9\",\n \"score\": \"0.5710785\",\n \"text\": \"def approve_request(self):\\n # Create the gitlab object\\n gl = gitlab.Gitlab(\\n settings.GITLAB_URL, \\n private_token=settings.GITLAB_ACCOUNTS_SECRET_TOKEN\\n )\\n try:\\n new_user = gl.users.create({\\n \\\"name\\\": self.username,\\n \\\"username\\\": self.username,\\n \\\"email\\\": self.email,\\n \\\"reset_password\\\": True,\\n \\\"can_create_group\\\": False,\\n \\\"skip_confirmation\\\": True, # The password reset mail is enough.\\n })\\n new_user.projects_limit = 5\\n new_user.save()\\n except Exception as e:\\n print(\\\"Error: {}\\\".format(e))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f9202ab7afbe354136f79ba47ff2b78\",\n \"score\": \"0.5704054\",\n \"text\": \"def accept_group_join_request_public_v1(\\n user_id: str,\\n namespace: Optional[str] = None,\\n x_additional_headers: Optional[Dict[str, str]] = None,\\n **kwargs\\n):\\n if namespace is None:\\n namespace, error = get_services_namespace()\\n if error:\\n return None, error\\n request = AcceptGroupJoinRequestPublicV1.create(\\n user_id=user_id,\\n namespace=namespace,\\n )\\n return run_request(request, additional_headers=x_additional_headers, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e170be5693c5896937088af5f6307be5\",\n \"score\": \"0.5703481\",\n \"text\": \"def create(self, invite):\\n model_invite = models.load('AccountInvite', invite)\\n model_invite.account_id = self.account_id\\n return self.client.invite_account(model_invite)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79b3ff10d0bb449ce7419b3fd38290e2\",\n \"score\": \"0.5699924\",\n \"text\": \"def test_multi_user_invite(self) -> None:\\n self.login(\\\"hamlet\\\")\\n # Intentionally use a weird string.\\n self.assert_json_success(\\n self.invite(\\n \\\"\\\"\\\"bob-test@zulip.com, carol-test@zulip.com,\\n dave-test@zulip.com\\n\\n\\nearl-test@zulip.com\\\"\\\"\\\",\\n [\\\"Denmark\\\"],\\n )\\n )\\n for user in (\\\"bob\\\", \\\"carol\\\", \\\"dave\\\", \\\"earl\\\"):\\n self.assertTrue(find_key_by_email(f\\\"{user}-test@zulip.com\\\"))\\n self.check_sent_emails(\\n [\\n \\\"bob-test@zulip.com\\\",\\n \\\"carol-test@zulip.com\\\",\\n \\\"dave-test@zulip.com\\\",\\n \\\"earl-test@zulip.com\\\",\\n ]\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92335d66f1d62371b8810420568809c5\",\n \"score\": \"0.5694413\",\n \"text\": \"def accept_group_join_request_public_v2(\\n group_id: str,\\n user_id: str,\\n namespace: Optional[str] = None,\\n x_additional_headers: Optional[Dict[str, str]] = None,\\n **kwargs\\n):\\n if namespace is None:\\n namespace, error = get_services_namespace()\\n if error:\\n return None, error\\n request = AcceptGroupJoinRequestPublicV2.create(\\n group_id=group_id,\\n user_id=user_id,\\n namespace=namespace,\\n )\\n return run_request(request, additional_headers=x_additional_headers, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c24748c396342d8d0fd34d6a8852506b\",\n \"score\": \"0.569394\",\n \"text\": \"def InviteMember(message):\\n\\tif not message.isMinecraft:\\n\\t\\tmessage.Reply(\\\"This command must be run in game\\\")\\n\\t\\treturn\\n\\tusername = message.mcusername\\n\\tteamname = GetData(__name__, \\\"teamowners.{0}\\\".format(username))\\n\\tif not teamname:\\n\\t\\tmessage.Reply(\\\"You aren't the owner of any teams\\\")\\n\\t\\treturn\\n\\n\\t# Max of 8 members\\n\\tteammembers = GetData(__name__, \\\"teammembers.{0}\\\".format(teamname))\\n\\tif len(teammembers) > 7:\\n\\t\\tmessage.Reply(\\\"You have too many members on your team and cannot invite any more\\\")\\n\\t\\treturn\\n\\n\\tinvitee = message.GetArg(0)\\n\\tcurteam = GetCurrentTeam(invitee)\\n\\tif curteam:\\n\\t\\tmessage.Reply(\\\"{0} is already in team {1}\\\".format(invitee, curteam))\\n\\t\\treturn\\n\\tStoreData(__name__, \\\"teaminvites.{0}.{1}\\\".format(teamname, invitee), time.time()+1200)\\n\\n\\tmessage.Reply(\\\"Invited {0} to team {1}\\\".format(invitee, teamname))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a2ccb7add083f9487020c1ad6bc7f08\",\n \"score\": \"0.56831145\",\n \"text\": \"def test_get_reviewrequest_with_invite_only_group_and_permission_denied_error(self):\\n review_request = ReviewRequest.objects.filter(public=True,\\n local_site=None).exclude(submitter=self.user)[0]\\n review_request.target_groups.clear()\\n review_request.target_people.clear()\\n\\n group = Group(name='test-group', invite_only=True)\\n group.save()\\n\\n review_request.target_groups.add(group)\\n review_request.save()\\n\\n rsp = self.apiGet(self.get_item_url(review_request.display_id),\\n expected_status=403)\\n self.assertEqual(rsp['stat'], 'fail')\\n self.assertEqual(rsp['err']['code'], PERMISSION_DENIED.code)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"85411c7dd22b11002a3a5d54913e75b7\",\n \"score\": \"0.5682659\",\n \"text\": \"async def invite(self, ctx, *args):\\n manager = MessageManager(ctx)\\n username, platform_id = await self.get_user_details(args)\\n\\n member_db = await self.get_member_db(ctx, username)\\n admin_db = await self.bot.database.get_member_by_discord_id(ctx.author.id)\\n clan_db = await self.get_admin_group(ctx)\\n\\n if not platform_id and clan_db.platform:\\n platform_id = clan_db.platform\\n else:\\n raise InvalidCommandError(\\\"Platform was not specified and clan default platform is not set\\\")\\n\\n bungie_id = None\\n if member_db:\\n bungie_id = member_db.bungie_id\\n\\n membership_id, platform_id = await self.get_bungie_details(username, bungie_id, platform_id)\\n\\n res = None\\n try:\\n res = await execute_pydest(\\n self.bot.destiny.api.group_invite_member(\\n group_id=clan_db.clan_id,\\n membership_type=platform_id,\\n membership_id=membership_id,\\n message=f\\\"Join my clan {clan_db.name}!\\\",\\n access_token=admin_db.bungie_access_token\\n ),\\n self.bot.redis\\n )\\n except pydest.PydestTokenException:\\n tokens = await self.refresh_admin_tokens(manager, admin_db)\\n res = await execute_pydest(\\n self.bot.destiny.api.group_invite_member(\\n group_id=clan_db.clan_id,\\n membership_type=platform_id,\\n membership_id=membership_id,\\n message=f\\\"Join my clan {clan_db.name}!\\\",\\n access_token=tokens['access_token']\\n ),\\n self.bot.redis\\n )\\n admin_db.bungie_access_token = tokens['access_token']\\n admin_db.bungie_refresh_token = tokens['refresh_token']\\n await self.bot.database.update(admin_db)\\n\\n if not res:\\n raise RuntimeError(\\\"Unexpected empty response from the Bungie API\\\")\\n\\n if res['ErrorStatus'] == 'ClanTargetDisallowsInvites':\\n message = f\\\"User **{username}** has disabled clan invites\\\"\\n elif res['ErrorStatus'] != 'Success':\\n message = f\\\"Could not invite **{username}**\\\"\\n log.info(f\\\"Could not invite '{username}': {res}\\\")\\n else:\\n message = f\\\"Invited **{username}** to clan **{clan_db.name}**\\\"\\n\\n return await manager.send_message(message, mention=False, clean=False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0f7739828b2848ccfe272edd3d47c51\",\n \"score\": \"0.5677255\",\n \"text\": \"def project_add_member(project=None, luser=None, **kwargs):\\n email = flask.request.form[\\\"email\\\"].strip()\\n member = models.Luser.query.filter_by(email=email).first()\\n\\n # If the user is not yet signed up\\n if member is None:\\n existing_invite = models.ProjectInvite.query.filter_by(email=email).first()\\n if existing_invite is None:\\n # Create an invite that will be checked when the user \\n # signs up and used to add them to any projects they\\n # have been invited to.\\n invite = models.ProjectInvite(luser_id=luser._id,\\n project_id=project._id,\\n email=email)\\n models.db.session.add(invite)\\n\\n # Also create a BetaSignup and activate it so that the user\\n # doesn't get stuck at the beta wall.\\n beta = models.BetaSignup(email=email, is_activated=True)\\n models.db.session.add(beta)\\n models.db.session.commit()\\n \\n try:\\n invitee = models.Luser.query.filter_by(email=email).first()\\n is_registered = invitee is not None \\n email_notify.project_invite(project, email, is_registered=is_registered)\\n except:\\n return respond_with_json({ \\n \\\"status\\\" : \\\"failure\\\", \\n \\\"type\\\" : \\\"error\\\",\\n \\\"error\\\" : \\\"Failed to send email. Is %s added to amazon SES?\\\" % email\\n })\\n\\n return respond_with_json({\\n \\\"status\\\" : \\\"success\\\", \\n \\\"type\\\" : \\\"invite\\\",\\n \\\"html\\\" : flask.render_template(\\\"invite_partial.html\\\", invite=invite),\\n \\\"message\\\": \\\"Invited %s to the project.\\\" % email\\n })\\n\\n else: \\n\\n try:\\n invitee = models.Luser.query.filter_by(email=email).first()\\n is_registered = invitee is not None \\n email_notify.project_invite(project, email, is_registered=is_registered)\\n except:\\n return respond_with_json({ \\n \\\"status\\\" : \\\"failure\\\", \\n \\\"type\\\" : \\\"error\\\",\\n \\\"error\\\" : \\\"Failed to send email. Is %s added to amazon SES?\\\" % email\\n })\\n\\n return respond_with_json({\\n \\\"status\\\" : \\\"success\\\", \\n \\\"type\\\" : \\\"already_invited\\\",\\n \\\"message\\\" : \\\"%s was already invited to this project. Re-sending email.\\\" % email\\n })\\n\\n # The user is signed up already, check if hes a project member. If not,\\n # add him.\\n else:\\n existing = (models.ProjectLuser.query\\n .filter(models.ProjectLuser.project_id==project._id)\\n .filter(models.ProjectLuser.luser_id==models.Luser._id)\\n .filter(models.Luser.email==email).first())\\n member = existing\\n if existing is None:\\n new_member = models.Luser.query.filter_by(email=email).first()\\n\\n member = models.ProjectLuser(project_id=project._id,\\n luser_id=new_member._id,\\n is_owner=False)\\n\\n models.db.session.add(member)\\n models.db.session.commit()\\n\\n return respond_with_json({\\n \\\"status\\\" : \\\"success\\\", \\n \\\"type\\\" : \\\"member\\\",\\n \\\"html\\\" : flask.render_template(\\\"member_partial.html\\\", member=member.luser),\\n \\\"message\\\": \\\"Added %s to the project.\\\" % email\\n })\\n\\n else:\\n\\n return respond_with_json({\\n \\\"status\\\" : \\\"failure\\\", \\n \\\"type\\\" : \\\"error\\\",\\n \\\"error\\\" : \\\"%s is already a member of this project.\\\" % email \\n })\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c77cfd6cb9534da4c478974a8a59bc0d\",\n \"score\": \"0.56704414\",\n \"text\": \"def send_invitation(UserArn=None):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06ff61391c939a5721f35962f372ae74\",\n \"score\": \"0.56538683\",\n \"text\": \"def test_get_reviewrequest_with_invite_only_group_and_target_user(self):\\n review_request = ReviewRequest.objects.filter(public=True,\\n local_site=None).exclude(submitter=self.user)[0]\\n review_request.target_groups.clear()\\n review_request.target_people.clear()\\n\\n group = Group(name='test-group', invite_only=True)\\n group.save()\\n\\n review_request.target_groups.add(group)\\n review_request.target_people.add(self.user)\\n review_request.save()\\n\\n rsp = self.apiGet(self.get_item_url(review_request.display_id),\\n expected_mimetype=self.item_mimetype)\\n self.assertEqual(rsp['stat'], 'ok')\\n self.assertEqual(rsp['review_request']['id'], review_request.display_id)\\n self.assertEqual(rsp['review_request']['summary'],\\n review_request.summary)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"130f8c9a89dedc9fe3845ca9dd1b80a2\",\n \"score\": \"0.5650841\",\n \"text\": \"def invite(email, inviter, sendfn=send_invite, resend=True, **kwargs):\\n \\n if OptOut.objects.is_blocked(email): \\n return None, False\\n try:\\n user = User.objects.get(email=email)\\n if user.is_active:\\n return user, False\\n if not resend:\\n return user, False\\n except User.DoesNotExist:\\n user = User.objects.create(\\n username=shortuuid.uuid(),\\n email=email,\\n is_active=False\\n )\\n user.set_unusable_password()\\n user.save()\\n \\n url_parts = int_to_base36(user.id), token_generator.make_token(user)\\n url = reverse('inviter:register', args=url_parts)\\n \\n opt_out_url = reverse('inviter:opt-out', args=url_parts)\\n kwargs.update(opt_out_url=opt_out_url)\\n \\n sendfn(user, inviter, url=url, **kwargs)\\n return user, True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ad71a428d21fef7061ef04b9c0d3bdd\",\n \"score\": \"0.5649967\",\n \"text\": \"def _acceptInvitePost(self, fields, request, context, params, **kwargs):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"94b299bfbaf3770154459ab7df18d0e7\",\n \"score\": \"0.563217\",\n \"text\": \"def test_local_users_not_allowed(self):\\n values = {\\n 'email': INVITE_EMAIL,\\n 'project': self.project.pk,\\n 'role': self.role_contributor.pk,\\n }\\n with self.login(self.user):\\n response = self.client.post(\\n reverse(\\n 'projectroles:invite_create',\\n kwargs={'project': self.project.sodar_uuid},\\n ),\\n values,\\n )\\n self.assertEqual(response.status_code, 200)\\n self.assertEqual(ProjectInvite.objects.all().count(), 0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9efdf279825e2919c837dffe4dc03bb0\",\n \"score\": \"0.56291455\",\n \"text\": \"def bind_group_invitations(user):\\n\\n user_emails = [email.email for email in user.emails if email.confirmed]\\n\\n if not user_emails and not user.facebook_id:\\n return\\n\\n query = meta.Session.query(PendingInvitation)\\n if user_emails:\\n query = query.filter(PendingInvitation.email.in_(user_emails))\\n if user.facebook_id:\\n query = query.filter(PendingInvitation.facebook_id == user.facebook_id)\\n\\n invitations = query.filter(PendingInvitation.active == True).all()\\n\\n # filter out invitations with different location\\n invitations = [i for i in invitations \\\\\\n if i.group.location.root == user.location.root]\\n\\n for invitation in invitations:\\n invitation.user = user\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51e1160ffbfabd83863074eaee35343b\",\n \"score\": \"0.5626658\",\n \"text\": \"def kick_group_member_public_v2(\\n group_id: str,\\n user_id: str,\\n namespace: Optional[str] = None,\\n x_additional_headers: Optional[Dict[str, str]] = None,\\n **kwargs\\n):\\n if namespace is None:\\n namespace, error = get_services_namespace()\\n if error:\\n return None, error\\n request = KickGroupMemberPublicV2.create(\\n group_id=group_id,\\n user_id=user_id,\\n namespace=namespace,\\n )\\n return run_request(request, additional_headers=x_additional_headers, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4402a29bf69bb1d852c5143fb61ffaec\",\n \"score\": \"0.5614303\",\n \"text\": \"def channel_invite_v2(token, channel_id, u_id):\\n data = getData()\\n # Decode the token to access auth_user_id of the channel owner\\n auth_user_id = find_user_id(token)\\n # Set the following as false to be checked before implementing feature\\n user_valid = False\\n channel_valid = False\\n auth_user_valid = False\\n auth_is_user = False\\n \\n # Ensure there is a channels key if the wrong input is sent in\\n if data.get('channels') == None:\\n data['channels'] = []\\n\\n # Only run code if there is information in data\\n # Checks both u_id and auth_user_id are existing users\\n # Check the channel id exists\\n user_valid = check_valid_info(data['users'], u_id)\\n auth_user_valid = check_valid_info(data['users'], auth_user_id)\\n\\n # If user id not found, raise exception\\n if user_valid == False:\\n raise InputError(description= \\\"User ID does not exist\\\")\\n \\n if auth_user_valid == False:\\n raise InputError(description= 'User ID does not exist')\\n\\n channel_valid = check_valid_info(data['channels'], channel_id)\\n # If channel id not found, raise exception\\n if channel_valid == False:\\n raise InputError(description= \\\"Channel ID does not exist\\\")\\n \\n # Check that the user wanting to add u_id is a member of the channel\\n auth_is_user = check_members(data['channels'], auth_user_id)\\n # If yes, then append the user as a channel member\\n if auth_is_user:\\n add_to_channel_member_list(u_id, channel_id)\\n # If they are not a member, raise AccessError\\n else: \\n raise AccessError(description= \\\"You are not a member of this channel. You do not have permission to add users to this channel\\\")\\n\\n #THE FOLLOWING CODE IS TO SUPPORT NOTIFICATIONS_GET FUNCTION \\n if data.get(\\\"added_info\\\") == None:\\n data[\\\"added_info\\\"] = { }\\n \\n if data[\\\"added_info\\\"].get(\\\"channels\\\") == None:\\n data[\\\"added_info\\\"][\\\"channels\\\"] = []\\n \\n info_dict = {\\\"u_id\\\": u_id,\\n \\\"channel_id\\\" : channel_id,\\n \\\"adder\\\" : data[\\\"users\\\"][auth_user_id - 1][\\\"handle_str\\\"]\\n }\\n data[\\\"added_info\\\"][\\\"channels\\\"].append(info_dict)\\n \\n return {\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"644282c0fa8111d985fc6e2dd31469cb\",\n \"score\": \"0.56123453\",\n \"text\": \"def test_add_team_invite_unverified_email(self):\\n user = fixture.get(User)\\n fixture.get(EmailAddress, user=user, verified=False)\\n\\n self.assertEqual(Invitation.objects.all().count(), 0)\\n\\n url = reverse(\\n \\\"organization_team_member_add\\\",\\n args=[self.organization.slug, self.team.slug],\\n )\\n resp = self.client.post(url, data={\\\"username_or_email\\\": user.email})\\n self.assertEqual(resp.status_code, 302)\\n\\n invitation = Invitation.objects.for_object(self.team).get()\\n self.assertEqual(invitation.from_user, self.owner)\\n self.assertEqual(invitation.to_user, None)\\n self.assertEqual(invitation.to_email, user.email)\\n self.assertEqual(self.team.members.count(), 0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"869778221004fcbeee92cd6236f01135\",\n \"score\": \"0.56041455\",\n \"text\": \"def addGroup(self, userguid, groupguid, jobguid=\\\"\\\", executionparams=None):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e048b940e0994c842b31967aa889153a\",\n \"score\": \"0.55990416\",\n \"text\": \"def save(self, user):\\n return self.invite.accept(user)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aced35fdb899f8f8fa871edd9465f58c\",\n \"score\": \"0.5598923\",\n \"text\": \"def invite(self, channel, user):\\n return self.post('channels.invite',\\n data={'channel': channel, 'user': user})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f343b959cd689693e398601b8687dfc9\",\n \"score\": \"0.55932057\",\n \"text\": \"def handle_groupchat_invite(self, inv):\\r\\n logging.debug(\\\"MUC invite to %s from %s: %s\\\", inv['to'], inv[\\\"from\\\"], inv)\\r\\n if inv['from'] not in self.rooms.keys():\\r\\n self.xmpp.event(\\\"groupchat_invite\\\", inv)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8fc14cfbcf11b84544cd9fde0c2045f0\",\n \"score\": \"0.55812365\",\n \"text\": \"def add_to_group(userObj, groupObj):\\n if not is_member_of(userObj, groupObj):\\n if userObj.groups:\\n userObj.groups.append(groupObj)\\n else:\\n userObj.groups = [groupObj]\\n\\n return userObj\\n else:\\n raise ValueError('This user is already a member of this group')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d634ef1c59581dc9c5ad9fac5b448561\",\n \"score\": \"0.5559801\",\n \"text\": \"def invite(self, user, email):\\n invitation = None\\n try:\\n # It is possible that there is more than one invitation fitting\\n # the criteria. Normally this means some older invitations are\\n # expired or an email is invited consequtively.\\n invitation = self.filter(user=user, email=email)[0]\\n if not invitation.is_valid():\\n invitation = None\\n except (Invitation.DoesNotExist, IndexError):\\n pass\\n if invitation is None:\\n key = '%s%0.16f%s%s' % (settings.SECRET_KEY, random.random(),\\n user.email, email)\\n key = hashlib.sha1(key).hexdigest()\\n invitation = self.create(user=user, email=email, key=key)\\n return invitation\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7aa961286a0f8dc54d351ca52edb3002\",\n \"score\": \"0.5555895\",\n \"text\": \"def has_invite(self, invite_id):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10e17a8a4e37475c3709f0424201509a\",\n \"score\": \"0.55533147\",\n \"text\": \"def invite_user(bot, trigger):\\n chanops = get_chanops(str(trigger.sender), bot.memory['channelmgnt']['jdcache'])\\n channel = trigger.sender\\n dodeop = False\\n if chanops:\\n if bot.channels[trigger.sender].privileges[bot.nick] < OP and trigger.account in chanops:\\n bot.say('Please wait...')\\n bot.say('op ' + trigger.sender, 'ChanServ')\\n time.sleep(1)\\n dodeop = True\\n nick = trigger.group(2)\\n if not nick:\\n bot.say(f'{trigger.account}: No user specified.', trigger.sender)\\n elif trigger.account in chanops:\\n bot.write(['INVITE', channel, nick])\\n if dodeop:\\n deopbot(trigger.sender, bot)\\n else:\\n bot.reply('Access Denied. If in error, please contact the channel founder.')\\n else:\\n bot.reply(f'No ChanOps Found. Please ask for assistance in {bot.settings.channelmgnt.support_channel}')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a55439f110d1b17f5022704563d0fcd\",\n \"score\": \"0.55455256\",\n \"text\": \"def test_accept_local_user_not_allowed(self):\\n invite = self.make_invite(\\n email=INVITE_EMAIL,\\n project=self.project,\\n role=self.role_contributor,\\n issuer=self.user,\\n message='',\\n )\\n self.assertEqual(ProjectInvite.objects.filter(active=True).count(), 1)\\n\\n response = self.client.get(\\n reverse(\\n 'projectroles:invite_accept',\\n kwargs={'secret': invite.secret},\\n ),\\n follow=True,\\n )\\n self.assertListEqual(\\n response.redirect_chain,\\n [(reverse('home'), 302), (reverse('login') + '?next=/', 302)],\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fea15623a6acb6064e4b99cdcee6686\",\n \"score\": \"0.55444974\",\n \"text\": \"def send_invitation(self, user, sender=None, **kwargs):\\n if user.is_active:\\n return False\\n token = RegistrationTokenGenerator().make_token(user)\\n self._send_email(user, self.invitation_subject, self.invitation_body,\\n sender, **{'token': token})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"241884093e871a0075441bc6f1e7f69b\",\n \"score\": \"0.55342114\",\n \"text\": \"def commitinvite(self,email,maker,group):\\n\\t\\tnew = Invite(email,maker.id,group)\\n\\t\\tdb.session.add(new)\\n\\t\\tdb.session.commit()\\n\\t\\treceiver = [email]\\n\\t\\tsend_email_register(maker.email,receiver)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00dde02eb0ceaf7946414356a9d71f20\",\n \"score\": \"0.5534189\",\n \"text\": \"async def reject_group_invitation_public_v1_async(\\n group_id: str,\\n namespace: Optional[str] = None,\\n x_additional_headers: Optional[Dict[str, str]] = None,\\n **kwargs\\n):\\n if namespace is None:\\n namespace, error = get_services_namespace()\\n if error:\\n return None, error\\n request = RejectGroupInvitationPublicV1.create(\\n group_id=group_id,\\n namespace=namespace,\\n )\\n return await run_request_async(\\n request, additional_headers=x_additional_headers, **kwargs\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c8e912c99f713c00ab609e7bf90da94\",\n \"score\": \"0.55245507\",\n \"text\": \"def invite_user(self, contact):\\r\\n raise NotImplementedError\",\n \"title\": \"\"\n }\n]","isConversational":false}}}],"truncated":false,"partial":false},"paginationData":{"pageIndex":11,"numItemsPerPage":100,"numTotalItems":994482,"offset":1100,"length":100}},"jwt":"eyJhbGciOiJFZERTQSIsImtpZCI6IjVHZDBvd0g5MTM2eDZjc1FvbE1zcktNWUZoRVFoUm5PVVVybEpjOUhaUEEifQ.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc5MDcyMjk2MSwianRpIjoiZmJjOTkwNTAtMjNiYS00YjA5LTljM2ItMmMzODExNjk4Mjg4Iiwic3ViIjoiL2RhdGFzZXRzL250aGFrdXIvY29ybnN0YWNrLXB5dGhvbi12MS10ZXZhdHJvbi0xTSIsImV4cCI6MTc5MDcyNjU2MSwiaXNzIjoiaHR0cHM6Ly9odWdnaW5nZmFjZS5jbyJ9.IpytL-3rS2IHa_q-tZrW9kKSEDfi7kz7V8Bl0UeyRSzCi_uEU-cMoNqWV1A_uLWAn1QDclhqOIxmz2qz65nTDA","displayUrls":true,"splitSizeSummaries":[{"config":"default","split":"train","numRows":994482,"numBytesParquet":23972089656}]},"discussionsStats":{"closed":0,"open":1,"total":1},"fullWidth":true,"hasGatedAccess":true,"hasFullAccess":true,"isEmbedded":false,"savedQueries":{"community":[],"user":[]}}">
query_id stringlengths 32 32 | query stringlengths 9 4.01k | positive_passages listlengths 1 1 | negative_passages listlengths 88 101 |
|---|---|---|---|
6c3f7c11e36323549ee6baccc7129fab | create a RDKit molecule object from scratch info | [
{
"docid": "d63a27b58fac7413d276ab63049e13ef",
"score": "0.0",
"text": "def build(self, sort_atom=F, sanitize=True, removeHs=False):\n if self.na > 999:\n ctab = write_pdb( (self.zs, self.chgs, self.bom, self.coords), sort_atom=sort_atom)\n m = Chem.MolFromPDBBlock(ctab, san... | [
{
"docid": "f490e9fb88f43e9e2bd73e8f595c1a62",
"score": "0.5770976",
"text": "def makeRecipe(molecule, datname):\n\n ## The Profile\n profile = Profile()\n\n # Load data and add it to the profile\n profile.loadtxt(datname)\n profile.setCalculationRange(xmin=1.2, xmax=8)\n\n ## The Prof... |
56294e61d8944a90327bc76db58b1e8e | function which returns the molecular mass of a chemical species based on its name >>> molecule='nitrogen_dioxide' >>> print(molecule, get_molmass(molecule)) nitrogen_dioxide 46.00682 If attempt to get the molecular mass of a cube where the molecule name is not recognised, a value error is raised To extend this function... | [
{
"docid": "7f4d78385c3d285af5a6ca8d3ce383c0",
"score": "0.80399835",
"text": "def get_molmass(molecule):\n\n # note that we need to replace underscores with spaces\n molecule = molecule.replace('_', ' ')\n\n # Now try and look up the molecule passed in\n # if it is not in the dictionary rai... | [
{
"docid": "85ac180ccc58ee5f950a14da7ffd4e48",
"score": "0.7116969",
"text": "def getMass( self, unit ) :\n\n# FIXME Still need to correct for electron masses and binding energy.\n if len(self.mass) > 0: return self.mass[0].float(unit)\n return self.nuclide.getMass(unit)",
"title": ""
... |
78a76e3aad152a82a60cf576ac3411a8 | Only keeps rectangular possiblynested sequences of primitives. | [
{
"docid": "476137becee5dcbc0b3b6f13856c728e",
"score": "0.0",
"text": "def TENSOR_LIKE_SEQUENCE_FILTER(arg_value):\n return arg_value.is_sequence and arg_value.sequence_dtype is not None",
"title": ""
}
] | [
{
"docid": "e03bc414556dff6905fd101e1817436c",
"score": "0.5820732",
"text": "def removeContainedBoxes(boxes):\n if boxes is None:\n return\n\n inside_id = []\n for i in range(len(boxes)):\n ri_x = boxes[i][0]; ri_y = boxes[i][1]; ri_w = boxes[i][2]; ri_h = boxes[i][3]\n\n ... |
ef0dc0aafaea8e44f1f5be1248ccd400 | Return tuple of positional and keywrord argument names for a function, method, class or callable. In case of a class, its ``__init__`` method is considered. For methods the ``self`` parameter is not included. | [
{
"docid": "e6d257693c0aaa0749862f8a8ce2b631",
"score": "0.6350734",
"text": "def varnames(func):\n cache = getattr(func, \"__dict__\", {})\n try:\n return cache[\"_varnames\"]\n except KeyError:\n pass\n\n if inspect.isclass(func):\n try:\n func = func.__init... | [
{
"docid": "4c90ad3810d0e33a1daac63b151a5a3c",
"score": "0.69376844",
"text": "def _get_funcargs(cls, varname: str) -> tuple:\n return getattr(cls, varname, (...,))",
"title": ""
},
{
"docid": "eba33dc643d6ea78fe704731e895f898",
"score": "0.69193465",
"text": "def arg_names(self):... |
335758eb754227fcc0c48c709f4d0ba8 | Copies driver files from a VirtIO CD to the guest. The kernel driver (.sys), setup information file (.inf) and signed catalog file (.cat) are copied. | [
{
"docid": "b344df495b4ba0e73f9c6e721e2b0694",
"score": "0.6024463",
"text": "def install_driver(self, name, overwrite=False):\n exts = {\n \"sys\": \"system32/drivers\",\n \"inf\": \"inf\",\n \"cat\": \"system32/CatRoot\",\n }\n vio_win_name = _WIND... | [
{
"docid": "1c708092656f1cdb27930811e53e2d64",
"score": "0.59442574",
"text": "def make_add_cdrom_device_spec(cdrom):\n (c_bus_number, d_unit_number) = cdrom['vdev_node'].split(':')\n cdrom_spec = vim.vm.device.VirtualDeviceSpec()\n cdrom_spec.operation = \"add\"\n cdrom_spec.device = vim.vm... |
48bca57f33f077bf1c973e4c39d0734e | Creates the database tables. | [
{
"docid": "a0c82c0aa67bdf918dfc0fe0ee9ca9d4",
"score": "0.0",
"text": "def init_db():\n with closing(connect_db()) as db:\n with app.open_resource('schema.sql') as f:\n db.execute(f.read())",
"title": ""
}
] | [
{
"docid": "21b848f6d5f2677acb386f94c490794b",
"score": "0.8628246",
"text": "def setup_tables():\n db = config['Default']['database']\n try:\n cm = ConnectionManager(db)\n cm.connect()\n metadata.create_all(cm.engine)\n print(\"Tables successfully created.\")\n exce... |
29eb4a58d7c2a25a7cc6533a13edd3a7 | Executes a saved command. | [
{
"docid": "a263b9f0dca237ea6250865b2ec6a83e",
"score": "0.0",
"text": "def cli(ctx, pattern):\n json_path = os.path.join(os.path.expanduser('~'), '.keep', 'commands.json')\n if not os.path.exists(json_path):\n click.echo('No commands to run. Add one by `keep new`.')\n else:\n FOU... | [
{
"docid": "e5ad654b54a14bdc5b67f0b25e1d9d32",
"score": "0.73189586",
"text": "def save(self, *args, **kwargs):\n self.execute = \"\\n\".join(self.execute.splitlines())\n super(Command, self).save(*args, **kwargs)",
"title": ""
},
{
"docid": "aeda881dc630cfcb36681f5b5e2e9376",
... |
642566e4f57772c7726a487643b9c8d6 | Gets the next image. | [
{
"docid": "a40ab87ad5587261e89c0df1719fcfb8",
"score": "0.64370894",
"text": "def get_next_det_image(self): \n self.det_main_img_axis.clear()\n self.det_cur_img_index += 1\n img,label = self.det_dataloader.dataset[self.det_cur_img_index]\n img = img.unsqueeze(0)\n sel... | [
{
"docid": "c8e603529fa23ce56e4a76e0ad554ad1",
"score": "0.84023815",
"text": "def load_next_image(self):\n return self.q.get()",
"title": ""
},
{
"docid": "3ebca855ec72178a384dc8b5ffa89a9e",
"score": "0.79071504",
"text": "def nextImage(self):\n\t\tif (self.current_image_inde... |
bef603b0b2ac4818a051d293f7055c86 | Assumes all lists are same length | [
{
"docid": "38f5462a8e128b482cbf4dd3d805dc59",
"score": "0.5995612",
"text": "def addLists(list_of_lists):\n sumOfLists = []\n x = -1\n y = -1\n for i in range(0, len(list_of_lists[0])):\n x += 1\n keep = 0\n while y < len(list_of_lists) - 1:\n y += 1\n ... | [
{
"docid": "dd024997d6b82616e3c8346ee8cb74f9",
"score": "0.671178",
"text": "def listsizes():",
"title": ""
},
{
"docid": "8072e90822feb2d3ea4c7723a95116b3",
"score": "0.6422881",
"text": "def sequential_comparison(list_of_lists):\n return [sequential_neighbor(List) for List in li... |
0a12739b3ca783797695c8157478c16e | Obtiene los miembros del equipo y los guarda en la base de datos. team es Github.Team. | [
{
"docid": "c3f97272580890f6be38f882fdaeb7b5",
"score": "0.0",
"text": "def save_dev_membership(team):\n for member in team.get_members():\n query = session.query(models.Member).\\\n filter(models.Member.dev_id == member.login,\n models.Memb... | [
{
"docid": "5e74950d1fed29bedf12b29611fb7865",
"score": "0.62554234",
"text": "def save_teams(data, page):\n \n # Ver si hay un equipo que exista con los datos entrantes\n team = Team.objects.filter(name=data['club']['name']).first()\n\n # Bool para saber si es registro nuevo\n is_new = F... |
15bc3575da9286681864ce5d6191cbfd | Return a `str` version of this GatewayPortIdentity object. | [
{
"docid": "2cf9b875eb06b425502cda82c259817d",
"score": "0.0",
"text": "def __str__(self) -> str:\n return json.dumps(self.to_dict(), indent=2)",
"title": ""
}
] | [
{
"docid": "be9ce849c92e7bfb9d8d919c7210c71f",
"score": "0.7250259",
"text": "def to_string(self):\n return \"{}.{}[{}]->{}\".format(self.driver, self.port, self.pin, self.interconnect)",
"title": ""
},
{
"docid": "a28972152c2a23083b160280dc4b1ba5",
"score": "0.7187217",
"text... |
ab7fca2470ff47b20b2a98fce4c41a75 | This method returns all Event connectors related to the run Process and Event. This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please define a `callback` function to be invoked when receiving the response. | [
{
"docid": "583a885d20092dafd8acb8cd9a9ea4e3",
"score": "0.47278807",
"text": "def find_event_connector_by_id_with_http_info(self, process_id, event_id, connector_id, **kwargs):\n\n all_params = ['process_id', 'event_id', 'connector_id']\n all_params.append('callback')\n all_params.... | [
{
"docid": "761f93a80a0746b8115d597be6cdf74a",
"score": "0.6226422",
"text": "def find_event_connectors(self, process_id, event_id, **kwargs):\n kwargs['_return_http_data_only'] = True\n if kwargs.get('callback'):\n return self.find_event_connectors_with_http_info(process_id, ev... |
0809ee599aa6b07f86a053854ceee480 | Backup the games assets | [
{
"docid": "71bcd836f8cc25ecf4dbd856096a81a0",
"score": "0.6175193",
"text": "def backup_vanilla_assets(sc_path: str, vanilla_dir: str) -> bool:\n speech_dir = os.path.join(sc_path, 'fx', 'Speech')\n binks_dir = os.path.join(sc_path, 'binks')\n aiv_dir = os.path.join(sc_path, 'aiv')\n\n fx_f... | [
{
"docid": "c322445b95b73659de869e4ff9f56c11",
"score": "0.6529334",
"text": "def backup(self):\n pass",
"title": ""
},
{
"docid": "3345cd0b243a01f345c7bfec9cbc1b7a",
"score": "0.64462405",
"text": "def save_base(self, path):\n pickle.dump([self.players, self.games, sel... |
c9234869cb20d6ee80388ff29681fd58 | Test old hotfix. CMFPlone has patches/publishing.py, containing the publishing patch from Products.PloneHotfix20160419. This avoids publishing some methods inherited from Zope or CMF, which upstream does not want to fix, considering it no problem to have these methods available. I can imagine that. But in Plone we have... | [
{
"docid": "d433c3e33b6bdeb150f4119ca54eb02f",
"score": "0.6920851",
"text": "def test_old_hotfix(self):\n self.portal.invokeFactory(\"Document\", \"doc\")\n transaction.commit()\n portal_url = self.portal.absolute_url()\n doc_url = self.portal.doc.absolute_url()\n bro... | [
{
"docid": "6066b1f5d5ee09a70b578930fc54ac93",
"score": "0.58093923",
"text": "def test_bug_18045_normal(self):\n\n self.pkgsend_bulk(self.rurl, self.installed_18045)\n self.api_obj.refresh(immediate=True)\n self._api_install(self.api_obj, [\"runtime/python-2... |
4b0ed3361004aee2b49d45c7f9dd7253 | Returns True if the position passed represents a double press. i.e. The last interaction was the button was to release it, and the time to press is less than the double_press_time. | [
{
"docid": "aa8d0ed46050df0d980390b591fbede1",
"score": "0.8744822",
"text": "def is_double_press(self, position):\r\n double_press = False\r\n #was there a previous interaction\r\n if self._interaction:\r\n # was the previous interaction complete (i.e. had it been releas... | [
{
"docid": "4c91afadab945154784cf9d6e3c31d2b",
"score": "0.75115657",
"text": "def when_double_pressed(self):\r\n return self._when_double_pressed",
"title": ""
},
{
"docid": "91f5f906aa17bb31c7636e771ac8fa53",
"score": "0.73344475",
"text": "def wait_for_double_press(self, ti... |
b143cf43dc587895dae0f19526af567b | Make the Pelvis Axis function Takes in a dictionary of x,y,z positions and marker names, as well as an index. Calculates the pelvis joint center and axis and returns both. | [
{
"docid": "ef6a50cecd41320235fe57ba5a6aa9f0",
"score": "0.55933523",
"text": "def pelvis_joint_center(frame):\n # Get the Pelvis Joint Centre\n RASI = frame['RASI']\n LASI = frame['LASI']\n\n try:\n RPSI = frame['RPSI']\n LPSI = frame['LPSI']\n # If no sacrum, mean of ... | [
{
"docid": "143bc4c330ec71ffc7200ab1efd27805",
"score": "0.60773385",
"text": "def returnAxesInfoForFootTransform(transform, foot_vec_dict):\n # Get the vector crossing the length of the foot \n foot_cross_vec = foot_vec_dict['cross']\n\n # Get the vector along the aim of the foot\n foot_ai... |
d9de6404eb0e425b298065aeb305abb1 | print the json response to the console | [
{
"docid": "499f5d26bb626a07969b8dd9b850454d",
"score": "0.7942105",
"text": "def print_result(self):\n json_resp = json.dumps(self.query_response)\n print('--> JSON Response', json_resp)",
"title": ""
}
] | [
{
"docid": "5b7e4f130682f48d02726199d1818b48",
"score": "0.79296833",
"text": "def print_one_string(response):\r\n print(response.json())",
"title": ""
},
{
"docid": "86632caa905d24bccf2c05347d9605ee",
"score": "0.77987415",
"text": "def print_json(self):\n pass",
"titl... |
79f2cdf206a4b88984cbc147ede5b3d0 | Search references in the database | [
{
"docid": "61f143eebf464034b6bda01d73091e6c",
"score": "0.58271915",
"text": "def search(args):\n try:\n sys.path.append(os.getcwd())\n\n import database\n from .operations import load_work, work_to_bibtex, reload, find\n reload()\n results = {x for x in find(args.... | [
{
"docid": "792f6c0f77427c671da4682308163a61",
"score": "0.6469385",
"text": "def get_references(ref):\n table = DB.table(ref)\n return table.all()",
"title": ""
},
{
"docid": "84065e58a5dd5ed3a5ed961fa33a7667",
"score": "0.6444353",
"text": "def references(self, param=\"\"):\n... |
e4e0141d306486215cf018c6763b08ca | Performs a depthfirst traversal of the state tree, where each branch is a different fork | [
{
"docid": "eab29860c1ff62fbc987aa46172cb77d",
"score": "0.6549031",
"text": "def print_fork_tree(states: typing.Dict[int, StateDescriptor]):\n\n def df_print(state_id, depth=0):\n state = states[state_id]\n\n # Prepare a debug message about the state based on its current status\n ... | [
{
"docid": "e91cf865a445bd3ba897bdce131be220",
"score": "0.6715602",
"text": "def iterative_deepening(initial_state):\n depth = 0\n\n while True:\n ret = dfs(initial_state, depth) # return value of the `dfs`\n print(\"Depth:\", depth)\n if ret is not None:\n return... |
ce823a009f4154cc77cb7f3def4aed24 | OAuth callback. Will load the state and the response and login the user if it can. | [
{
"docid": "f551014ea28a16070c87ecb6ce1bf861",
"score": "0.6258861",
"text": "def authorize(self):\n try:\n state = json.loads(base64.b64decode(request.args.get('state')).decode('UTF-8'))\n except Exception as e:\n print(e)\n state = {'next': url_for('MetaV... | [
{
"docid": "ff24c61c1ae6dcec64d37e395c0f7a46",
"score": "0.68280095",
"text": "def authorized_callback(app, oauth_type):\n oauth = _oauth_client(app, oauth_type)\n resp = oauth.authorized_response()\n if not resp:\n pkdlog('missing oauth response')\n werkzeug.exceptions.abort(403)... |
705056acb7a2a373c56004ccbe4d94da | Downloads a single URL to a file on disk. | [
{
"docid": "7dec849541fd8f30da30d03e65c69dcb",
"score": "0.7134277",
"text": "def download_single_url(self, url):\n\n infodict = {'url': url}\n if len(url) > 0:\n filename_on_disk = self.compute_unique_filename_on_disk(url)\n\n # download file:\n try:\n ... | [
{
"docid": "99bc8585b8f8fb2913d8c58e78a0cbfe",
"score": "0.7879106",
"text": "def download_file(url, filename):\n print(\"Starting Download...\\nDownload URL: {}\".format(url))\n with requests.get(url, stream=True) as r, open(filename, 'wb') as f:\n shutil.copyfileobj(r.raw, f)\n\n print... |
b1d03757531cc0ad42ff390d76701dd9 | Forward data from one side to the other. | [
{
"docid": "14ac17326ae340271da5fdf870590f54",
"score": "0.0",
"text": "async def forward_data_task(\n self,\n dir_str: str,\n reader: StreamReader,\n writer: StreamWriter,\n exit_q: asyncio.Queue,\n exit_status: int,\n ):\n try:\n while Tru... | [
{
"docid": "d5acecabaf6d17bdd23207a7bc855a29",
"score": "0.7457351",
"text": "def forward(self):",
"title": ""
},
{
"docid": "d5acecabaf6d17bdd23207a7bc855a29",
"score": "0.7457351",
"text": "def forward(self):",
"title": ""
},
{
"docid": "c205a780d50e749fde6e6c9398dcc404... |
30a2e41d50fb7b3e87ce7e6eea0b0ba7 | Loads the scene at cursor position | [
{
"docid": "c53ae83809d367e51ec734532d2a05ce",
"score": "0.5126262",
"text": "def loadBaseScene(self, force=False):\n logger.debug(\"Func: loadBaseScene\")\n relSceneFile = self._currentSceneInfo[\"Versions\"][self._currentVersionIndex-1][\"RelativePath\"]\n absSceneFile = os.path.j... | [
{
"docid": "f886132f410f52dc182ae21dae9d0c53",
"score": "0.63543177",
"text": "def menu_load_scene(self):\n file_name = QtGui.QFileDialog().getOpenFileName(self, \"Choose Scene File\", get_data_path(), \"*.pkl\")\n with open(file_name, \"rb\") as f:\n self.scene = pickle.load(f)... |
72df6a14864fed43bf41a7c37c7c91e0 | Equivalent to 'snap rename ' | [
{
"docid": "a011e09c0d546535e6e616a2c7bef3a1",
"score": "0.6832444",
"text": "def snapshot_rename(self, current_name, new_name):\n\n self.filer.invoke('snapshot-rename',\n 'volume', self.name,\n 'current-name', current_name,\n ... | [
{
"docid": "1be7a7de8532e616b561f8f832af01ea",
"score": "0.67915624",
"text": "def rename(old, new):\n run([\"rename\", old, new])",
"title": ""
},
{
"docid": "eaefe78f79682e952d12712bd3a45ec5",
"score": "0.67752296",
"text": "def rename(src, dst):\n pass",
"title": ""
},... |
75b08366253a5f3026dc998fa26f67e4 | Given two strings, write a method to decide if one is a permutation of the other | [
{
"docid": "0d359c7af9d5371d2ad776b186e59e32",
"score": "0.82985634",
"text": "def check_permutation(first, second):\n\n # first of all, both strings must have the same length\n if len(first) != len(second):\n return False\n\n # we will use a counter dictionary to count the chars in both... | [
{
"docid": "adec46ad7c2bdb0f7aa0e2b0a5459235",
"score": "0.86076456",
"text": "def is_string_permutation(s1, s2):\n if len(s1) == len(s2): # must be same size to be a permutation of the other string\n s1_lst = list(s1)\n s2_lst = list(s2)\n for currentChr in s1_lst:\n ... |
9cd8165fec1ca7f8dffcd547b4e6f91a | UnpPack a route into a string | [
{
"docid": "628ada664ad4d8c93eb8fd7343045ce1",
"score": "0.65899783",
"text": "def routeunpack(value):\n return str(value).replace(\"!\",\"/\")",
"title": ""
}
] | [
{
"docid": "17f387f4db30ee49bf8fce84afe609c1",
"score": "0.52920175",
"text": "def make_full_route(self):\r\n regpath = ''\r\n for part in self.routelist:\r\n if isinstance(part, dict):\r\n regpath += '%(' + part['name'] + ')s'\r\n else:\r\n ... |
b2ba2ac873afeee2f0725e6f1b0652ce | Sets the specified weights to the network. | [
{
"docid": "215e22f93f0c60d30e7618d068bb133f",
"score": "0.85788",
"text": "def set_weights(self, weights: object):\n pass",
"title": ""
}
] | [
{
"docid": "5811e3ba3e60902c890d72ab3904ed76",
"score": "0.8840008",
"text": "def set_weights(self, weights):\n pass",
"title": ""
},
{
"docid": "b412838cc98f709b131ae37ef79435d2",
"score": "0.8778187",
"text": "def set_weights(self, weights):\n self.weights = weights",... |
7a8b6443d7c16773755e199995c953ef | Create a directory at the current working directory | [
{
"docid": "5a20c5ee703087727b98d0c7985ac5a5",
"score": "0.7041337",
"text": "def mkdir(name):\n os.mkdir(name)",
"title": ""
}
] | [
{
"docid": "3a138e9cc890b1c597e55c1914ee3fc7",
"score": "0.796372",
"text": "def createDir(self):\n try:\n os.makedirs(os.path.join(self.path))\n except OSError:\n # Fail silently on any OS errors\n pass",
"title": ""
},
{
"docid": "c52fee7d47d9... |
5eac6d32105cd42aa814f9b07007065f | Does a tensor contain NaNs. | [
{
"docid": "214ed8a0710bac163123c17fd80695ea",
"score": "0.83893895",
"text": "def has_nans(tensor, verbose=True):\n tensor_numpy = tensor.data.cpu().numpy().flatten()\n where_nan = numpy.argwhere(tensor_numpy != tensor_numpy)\n\n nan_count = len(where_nan)\n nan = nan_count != 0\n\n if v... | [
{
"docid": "710c8a8b9c40cbf54b856a60b56d26e7",
"score": "0.82159275",
"text": "def has_nan(x):\n return bool((x != x).sum() > 0)",
"title": ""
},
{
"docid": "7a6f17acfc46fddada1ce23bcad5da8a",
"score": "0.8094285",
"text": "def torch_isnan(x):\n if isinstance(x, numbers.Number)... |
9a6310fa7d99ceecb5c05a2681894c4b | Simply quits the program. | [
{
"docid": "4bc218877aefa6278912a926a696f8b7",
"score": "0.7919135",
"text": "def quit_the_program():\n print('Tschüss')\n sys.exit()",
"title": ""
}
] | [
{
"docid": "9ff06877bd1a23fddb29ff0853b1ffb8",
"score": "0.81753623",
"text": "def quitProgram():\r\n print(\"Program exited\")\r\n sys.exit()",
"title": ""
},
{
"docid": "26b52d0300420fea5c9af688063ed615",
"score": "0.8020633",
"text": "def exit_program():\n quit()",
... |
66b9e28684d35392a3b1e4e55560012a | Return emoji score and emoji char | [
{
"docid": "8c03fe698333ce6daceb4e54c918a6f4",
"score": "0.0",
"text": "def movie_data_2016():\n # query for the top 10 revenue movie data\n results = db.session.query(Movie.title, Movie.revenue, Movie.budget).\\\n filter(and_(Movie.release_date.like('%16'), (Movie.budget >= 1000000.0))).\\... | [
{
"docid": "aad6fb5f76d8bbf5a9458cc4e969d8f6",
"score": "0.64628303",
"text": "def nearest_emoji(score: float) -> str:\n array = np.asarray(list(EMOJI_EMOTIONS.keys()))\n index = (np.abs(array - score)).argmin()\n emoji_index = array[index]\n if emoji_index:\n return EMOJI_EMOTIONS[em... |
5a6d6b48dd51876df21beb6fa5d41174 | Returns a function that deploys a compiled contract | [
{
"docid": "2a48149318dcd140d738f77fd03a8ca1",
"score": "0.73027295",
"text": "def deploy_contract(deploy_contract_txhash):\n def fn(\n web3,\n deployer_address,\n abi,\n bytecode,\n args,\n ):\n contract = web3.eth.contract(abi=abi, by... | [
{
"docid": "e16dfdd9c5fc9c158dfe9782ba90532b",
"score": "0.71858716",
"text": "def deploy_tester_contract(\n web3,\n contracts_manager,\n deploy_contract,\n):\n def f(contract_name, libs=None, args=None):\n json_contract = contracts_manager.get_contract(contract_name)\n ... |
8973dd8bd398458801e87546276df3ad | Configure the quantization behavior by setting config variables. | [
{
"docid": "f9e8b2fad0099b851f22dd0b77d171fa",
"score": "0.6312448",
"text": "def qconfig(**kwargs):\n node_args = {k: v if k not in kwargs else kwargs[k]\n for k, v in QConfig._node_defaults.items()}\n return _make.node(\"relay.quantize.QConfig\", **node_args)",
"title": ""
... | [
{
"docid": "5e4373596af0532af838a55e803f69bc",
"score": "0.63947123",
"text": "def for_dynamic(cls) -> 'QuantizationConfig':\n return QuantizationConfig()",
"title": ""
},
{
"docid": "a5446a51cee2c0db335a7d627ce8d031",
"score": "0.61188734",
"text": "def setup(self):\n \n ... |
8c068de81227aeefa72a67b73b566180 | synthesize image from source to target | [
{
"docid": "21b1a88713452af041f2c1558227f591",
"score": "0.0",
"text": "def stereo_synthesize_loss(self, source_img, target_ms, target_depth_ms, pose_t2s, intrinsic, suffix=\"\"):\n pose_stereo = cp.pose_matr2rvec_batch(tf.expand_dims(pose_t2s, 1))\n\n # synth_target_ms: list of [batch, 1,... | [
{
"docid": "c73c5b0fb8da6c806ee50bb6afd52f59",
"score": "0.66019917",
"text": "def set_source_image(self, source_ndarray):\n raise RuntimeError(\"Unimplemented method\")",
"title": ""
},
{
"docid": "349d5921b94572a878e049b6b722a642",
"score": "0.6539507",
"text": "def set_imag... |
9d7be8d547a1edf89626848380c5d3d8 | Show available discard, choose where to get card. | [
{
"docid": "3db8855b8343e5fc0ae558ba802e9798",
"score": "0.6061682",
"text": "def turn_start(self, move: Move) -> None:\n self.print_turn_screen()\n self.action_text(\"Take discard or draw?\")\n print()\n self.context(\"Available discard: {}\".\n format(mo... | [
{
"docid": "73a6ac0425f826599468137fdf6e729f",
"score": "0.71386373",
"text": "def on_cards_chosen(self, discards):\n pass",
"title": ""
},
{
"docid": "95f503cc60ef150e05aff695428d191d",
"score": "0.70201945",
"text": "def discard(hand, dealer):\n visual.clear_screen()\n ... |
f0433c70cb26564958485c0ea3a6018b | Returns an OptionParser object with all the default options. | [
{
"docid": "a015d716708034c492ca0d950c1787ec",
"score": "0.0",
"text": "def MakeArgParser(estimators):\n parser = ArgumentParser(description='A script for running OBD analysis for a gradient of concentrations')\n parser.add_argument('-c', '--config_fname', action='store',\n ... | [
{
"docid": "d0bb6b1423d0a2de82e1c0bd68953516",
"score": "0.71966404",
"text": "def _create_parser(self):\n parser = optparse.OptionParser(usage='', add_help_option=False)\n parser.disable_interspersed_args()\n self.configure_parser(parser)\n return parser",
"title": ""
... |
5954f0514a703681399511e7be646635 | Sets the owner_references of this V1ObjectMeta. | [
{
"docid": "3ea9368cdcd9889056309e54ead156e3",
"score": "0.82682514",
"text": "def owner_references(self, owner_references):\n\n self._owner_references = owner_references",
"title": ""
}
] | [
{
"docid": "ca100a0fb99b4a3199f69f71ccd5761b",
"score": "0.65113187",
"text": "def owners(self, owners):\n\n self._owners = owners",
"title": ""
},
{
"docid": "ca100a0fb99b4a3199f69f71ccd5761b",
"score": "0.65113187",
"text": "def owners(self, owners):\n\n self._owners ... |
81cff0c557b6cfd56de7e42f977b8227 | Creates a dvport group on the specified Distributed Virtual Switch with the vlan tags supplied. | [
{
"docid": "89fb6983e88f25c738d23e155c936d19",
"score": "0.70796925",
"text": "def create_port_group(session, dvs_name, pg_name, vlan_id):\n LOG.debug(\"Entered create_port_group :\"\n \" (dvs name, portgroup name, vlanid) : (%s, %s, %s)\" %\n (dvs_name, pg_name, vlan_id))\n... | [
{
"docid": "f0a7c6396e0f9f3854dc906925af032d",
"score": "0.5728364",
"text": "def _get_add_vswitch_port_group_spec(client_factory,\n port_group_name, vlan_id):\n vswitch_port_group_spec = client_factory.create(\n 'ns0:DVPortgroupConfigSpec')\n vswitch_por... |
e56cf55cd1ac51af31afc7241fcad5a3 | Return fileId from a url. | [
{
"docid": "6ddf031d3d7f70b93a2cb984afc19374",
"score": "0.83659995",
"text": "def fileId_from_url(url):\n raw_fileId = re.findall(r'~[\\w]+/[0-9]+$', url)[0][1:]\n return raw_fileId.replace('/', ':')",
"title": ""
}
] | [
{
"docid": "c3eb208742793a70362a1015a94c54e4",
"score": "0.8446173",
"text": "def fileId_from_url(url):\r\n\r\n raw_fileId = re.findall(\"~[A-z.]+/[0-9]+\", url)[0][1: ]\r\n\r\n return raw_fileId.replace('/', ':')",
"title": ""
},
{
"docid": "b732360ee57a8234e39e6962832ef33e",
"sco... |
89d4207af1a49d95a3b2b6eafb19909c | Apply formatting to command, after escaping all arguments. | [
{
"docid": "7aa9f32ebf801ec42dd3789761f86a41",
"score": "0.7210737",
"text": "def format(cls, command, *args, **kwargs):\n fargs = [cls.escape(arg) for arg in args] # pylint: disable=E1120\n fkwargs = dict(\n (key, cls.escape(value)) # pylint: disable=E1120\n ... | [
{
"docid": "a71f651003c1aa5640ce1b258e3d7a97",
"score": "0.6286698",
"text": "def __call__(self, command, **format_args):\n git = self.set(command, **format_args)\n\n git_command = f\"git {' '.join(git._args)}\"\n git_command = re.sub(' +', ' ', git_command)\n\n # Format to s... |
e2c704f095f831535252bea14553ba67 | Converts string production to deserialized objects of all productions | [
{
"docid": "fef3538d4de52754907a61fc7ef4742f",
"score": "0.6497535",
"text": "def _parse_production(cls, production: str):\n productions = []\n string_productions = production.split('|')\n for string_production in string_productions:\n productions.append(cls._parse_symbol... | [
{
"docid": "31bc3f8939eaed9dccd3a877d1e88c68",
"score": "0.5999906",
"text": "def _parse_productions(*productions):\n return [parser_types.Production.parse(p) for p in productions]",
"title": ""
},
{
"docid": "45f08cafda83671baf031614b8533fd2",
"score": "0.5975294",
"text": "def des... |
12545d6c23df672406f3c22eb656f99f | min is the start of the range, inclusive. | [
{
"docid": "a184d7c0e02cc2cf4e2d77542ef46b67",
"score": "0.70370907",
"text": "def min(self) -> Optional[pulumi.Input[int]]:\n return pulumi.get(self, \"min\")",
"title": ""
}
] | [
{
"docid": "b05c1e2d7762ffab21e951ce3145d680",
"score": "0.81204927",
"text": "def minimum(self, start, end):\n return self.foldl1(start, end, min)",
"title": ""
},
{
"docid": "d977ee08a74221f1311eaf99d1df4cce",
"score": "0.8115792",
"text": "def min_range(self):\n retu... |
fd5f5cf260757f399b179eb623bcc45c | Method to execute an auction and return its result. | [
{
"docid": "f8935e72861a708102a1f2ad9370af27",
"score": "0.81326866",
"text": "def _execute_auction(self, auction):\r\n result = Result()\r\n if self._is_valid_auction(auction):\r\n # if we have a valid auction determine the site\r\n site = self._config.get_site(aucti... | [
{
"docid": "104cfb40cb15a5477671a8a9a73c7d62",
"score": "0.6842456",
"text": "def execute_auctions(self, auctions_json):\r\n results = list()\r\n for auction in JsonObj.loads(Auction, auctions_json):\r\n results.append(self._execute_auction(auction))\r\n # return auction ... |
26159be2323045ebd344100f2fa3af72 | Reports a given candidate as positive, effectively removing its points from the incache PointSet, making sure they never come up again, and don't misguide the rest of the search. | [
{
"docid": "2d9c4d64496d460506765dfb3c354dc3",
"score": "0.8525394",
"text": "def report_positive(self, candidate):\n for point in candidate.points:\n try:\n self.pset.remove(point)\n self.banned_starts.remove(point)\n except (ValueError, KeyErr... | [
{
"docid": "5f1b20435a1fe04c0a9537d0257eeefc",
"score": "0.64094204",
"text": "def remove_point(self, x):\n idx = np.sum(np.abs(self.proposed_points - x), axis=1).argmin()\n self.proposed_points = np.delete(self.proposed_points, idx, axis=0)\n for i in range(len(self.search_strategi... |
6d069b0bcbcb59c12d7d514a7722ef3d | Retrieve a volume attachment. This request retrieves a single volume attachment specified by the identifier in the URL. | [
{
"docid": "97f9ae20c7500d032daea06dcebc3570",
"score": "0.6519782",
"text": "def get_instance_volume_attachment(self,\n instance_id: str,\n id: str,\n **kwargs\n ) -> DetailedResponse:\n\n if instance_id is None:\n raise ValueError('instance_id must be provided... | [
{
"docid": "ad7703cff10340dff77cced6ba6097fc",
"score": "0.7413413",
"text": "def get(self):\n return attachment_service.get_attachment(get_uuid(request))",
"title": ""
},
{
"docid": "720f954ca7018f6364496865e4e79c5a",
"score": "0.69237274",
"text": "def get_attachment(self, d... |
c2bf9027ac7a97e212050ec645335c0e | Predicts new data using the pretrained classifier. | [
{
"docid": "4e2798d8e4bdd7fa046e0215bb606711",
"score": "0.0",
"text": "def predict(self, X_val, I_val=None):\n\n if not self.subgraph:\n raise e.BuildError('KNNSubgraph has not been properly created')\n\n if not self.subgraph.trained:\n raise e.BuildError('Classifier... | [
{
"docid": "7a9cae06ee961dc3a0f9f63c3e31e580",
"score": "0.77232546",
"text": "def predict():\n model = load_model()\n run_predict(lambda sents: make_preds(model, sents))",
"title": ""
},
{
"docid": "82574c2b37cc35e3adf32c2b7da304d8",
"score": "0.7268341",
"text": "def predict_... |
4a4dab7fbb0b85e114e91b9bdabd7154 | XORs two cipher block | [
{
"docid": "91f01cd0dbf4f86e0568d5ca846a0b20",
"score": "0.7651875",
"text": "def block_xor(block1, block2):\n assert len(block1) == Serpent.get_block_size() and len(block2) == len(block1)\n return bytes(block1[i] ^ block2[i] for i in range(Serpent.get_block_size()))",
"title": ""
}
... | [
{
"docid": "cb08eb881810245db8d0155d6416cb47",
"score": "0.80599743",
"text": "def xor(a: bytes, b: bytes) -> bytes:\n return bytes(x^y for x, y in zip(a, b))",
"title": ""
},
{
"docid": "377ab9a364770789c5bca001dd285278",
"score": "0.7936208",
"text": "def xor(s1: bytes, s2: byte... |
efd310992b39d2c58f3369b4291c17ec | Get whether sales person is required. | [
{
"docid": "64231859862ba5ea85e4271ca30b1007",
"score": "0.9003576",
"text": "def get_is_sales_person_required(self):\n return self.is_sales_person_required",
"title": ""
}
] | [
{
"docid": "edbe7e993eeb92fc3370e8d05e3bc252",
"score": "0.7079675",
"text": "def set_is_sales_person_required(self, is_sales_person_required):\n self.is_sales_person_required = is_sales_person_required",
"title": ""
},
{
"docid": "794608a3c516ad7479da210280b844ef",
"score": "0.66... |
602e18f1acf50fdaae4276a260c3a313 | Returns whatever theme is, from the submission. | [
{
"docid": "a9457a7caef25dc4350e7a6b02f60878",
"score": "0.6223251",
"text": "def theme(self) -> str:\n bit = self.soup.find(\"strong\", text=\"Category:\").next_sibling\n theme = CATEGORY_THEME_REGEX.match(bit).group(2)\n return clean(theme)",
"title": ""
}
] | [
{
"docid": "24964504539bfcec3ae7205b7fc87c3f",
"score": "0.6489319",
"text": "def get_theme_from_request(request):\n \n selected_theme=None\n \n theme_var = _local_thread.keys['form']\n \n #Check if is a request\n if request:\n # Check if there is in POST or HEAD a change the... |
e85279bf4032b5b70a6c0e16dfb07361 | Command launched when QPushButton 'Add' is clicked. Update parent widget | [
{
"docid": "98aff5ea19554b7a024bf454db66005e",
"score": "0.0",
"text": "def accept(self):\n stepName = str(self.leStep.text())\n stepLabel = str(self.leStepLabel.text())\n if stepName in ['', ' '] or stepLabel in ['', ' ']:\n log = \"Step name or label can not be empty !!... | [
{
"docid": "9760706cafd4939d3b4ddc15e0e36c6e",
"score": "0.78889227",
"text": "def buttonAddClicked(self):\n\t\tbutton=self.sender()\n\t\tself.emit(SIGNAL(\"addItem\"),button.text())\n\t\tself.deleteButtons(button.stackIndex)",
"title": ""
},
{
"docid": "8832e4e808f3a1ea1b63b7364cc80ddb",
... |
8125f16b08ec1559d258d73c014b3e78 | We are being sent PATH plus binary name. Strip off the PATH. | [
{
"docid": "7b404bdb51a7de911901fa012d71a50c",
"score": "0.0",
"text": "def remove_class_name(path):\n return path.split('/')[-1]",
"title": ""
}
] | [
{
"docid": "1a62985c3b2cf64735eb933e07aa3a52",
"score": "0.6172189",
"text": "def get_binary_name():\n return os.path.basename(sys.argv[0])[:16].replace(' ', '_')",
"title": ""
},
{
"docid": "2ecf451aeb402524cda278a8b0a10e81",
"score": "0.59092593",
"text": "def strip_path():\n ... |
949c6b033d9b3389bf9cb412d6e9db4b | Profiles dataframe while calling `count` action and return execution time, execution plan stage and number of rows. | [
{
"docid": "9e37daa7b6f9014e8ac92937cfa82336",
"score": "0.61490655",
"text": "def _get_rows_and_execution_time(df: DataFrame) -> Tuple[int, float]:\n\n # total time and count\n ts_start = pd.Timestamp.now()\n rows = df.count()\n ts_end = pd.Timestamp.now()\n total_tim... | [
{
"docid": "9a982092ec3e528339bc5e864445b8cf",
"score": "0.6167149",
"text": "def profiler(self):\n dfProfiler = self.__df\n return spark_df_profiling_optimus.ProfileReport(dfProfiler)",
"title": ""
},
{
"docid": "7dcb96edc0b60b73f166bcb818ab22d1",
"score": "0.61443484",
... |
b4bd2654e317fe09f3796570365993a1 | Input new position values for current position with tuple or list of length 4 that defines position for each axis [A,B,C,D]. If you do not wish to change the value of an axis, set list element to None | [
{
"docid": "444c2ff7aa2774cc861bde95fe218dad",
"score": "0.63630646",
"text": "def set_position(self, positions):\n if len(positions) == 4: # check input length\n self.galil.GCommand(list_to_galil_input(\"DP\", positions)) # Write new positio... | [
{
"docid": "774b187cc71b930a7998b8ce09b41a8b",
"score": "0.61919117",
"text": "def position(self, new_position) -> None:\n self.x, self.y, = new_position[:2]",
"title": ""
},
{
"docid": "5f4a1de7ee84e4de485236db3a5528b7",
"score": "0.6103263",
"text": "def set_position(self, p... |
fff563977116392e6e08b93e82e6ba7c | Analyze full SQuAD dataset (joined both versions). | [
{
"docid": "48b102371bfb7135f4faea0900222498",
"score": "0.74554676",
"text": "def analyze_full_squad_dataset():\n squad_1_1_json_filename = DATA_DIR / f\"squad/raw/squad_full_dev_and_train-v{VERSION1_1}.json\"\n squad_2_0_json_filename = DATA_DIR / f\"squad/raw/squad_full_dev_and_train-v{VERSION2... | [
{
"docid": "4b138f2adc461c0a9d5fcd330438e8e5",
"score": "0.58067715",
"text": "def analyze():",
"title": ""
},
{
"docid": "a35fdf3f3e44e70e39eb216ab596ab63",
"score": "0.5611722",
"text": "def run_queries(self):\n spark = SparkSession.builder.appName('BDS-DAS Spark ' + self.na... |
a2c3035cf9e15d8dbe110af7d6204d5b | This test if the input file is an unWISE file | [
{
"docid": "e960a3e53aaf5f3710a20cd8529d59ec",
"score": "0.72966903",
"text": "def is_wise_file(filename):\n # No useful info in the header! So must rely on filename itself\n return \"unwise\" in filename",
"title": ""
}
] | [
{
"docid": "5ada01101fbe39bb3cc5eb9ffd57ca4d",
"score": "0.628483",
"text": "def test_09_is_allowed_file_no_ext(self):\n\n filename = \"somename\"\n is_allowed = utils.is_allowed_file(filename)\n self.assertFalse(is_allowed)",
"title": ""
},
{
"docid": "0365cff43bcb32e30... |
dfb83bced3931a6b6d975e925fb4bf25 | Numero de componentes fuertemente conectados | [
{
"docid": "3969d0c04efd588978c104d1ab3ff783",
"score": "0.0",
"text": "def connectedComponents(analyzer):\n return model.connectedComponents(analyzer)",
"title": ""
}
] | [
{
"docid": "475f2657e0b7bb8c8cb3c824257f0fe0",
"score": "0.58194244",
"text": "def n_components(self):\n to_ret = len(self.components)\n return to_ret",
"title": ""
},
{
"docid": "8029c26b3dc0354593b6afeeae33293a",
"score": "0.5778188",
"text": "def getNumComponents(sel... |
4b0ada919b7658a4a449132b88ab5dbc | runs the model for userdefined iteration numbers. If user doesnt specify the iteration then the model run for default 1000 times. | [
{
"docid": "17456902123b4a3ffc3b7212e5d1b81b",
"score": "0.0",
"text": "def run_model(self, n_iterations=1000):\n male = female = 0\n for count in range(n_iterations):\n self.step()\n for index, value in enumerate(self.population):\n if value.sex == \"Male\" an... | [
{
"docid": "1af615c217dbc797bb9f79ad7059fd70",
"score": "0.6972713",
"text": "def run_model(self, step_count=200):\n for i in range(step_count):\n self.step()",
"title": ""
},
{
"docid": "8a848e4574a4f59bcb6245ef3fb4c824",
"score": "0.6647719",
"text": "def run_iter... |
a8571bd27a3d3fce3e6017aaf4a8925b | Return length of Big Integer | [
{
"docid": "9ab10e8539c2157c9d93ea54fabbb639",
"score": "0.0",
"text": "def __len__(self):\n return len(self.nodes)",
"title": ""
}
] | [
{
"docid": "8f12c369a7eaced3bfbf24fac348e2f2",
"score": "0.79488444",
"text": "def BinaryLength(self) -> int:",
"title": ""
},
{
"docid": "3959f2a960e6ec7276258139efce0fd4",
"score": "0.7470769",
"text": "def length(self) -> long:\n ...",
"title": ""
},
{
"docid": ... |
c1db58188a5b3ea43d26d40572fdd85d | Representation of an Dependency object in string. | [
{
"docid": "3f4036cb88b454c228d8981460eac5e8",
"score": "0.0",
"text": "def __repr__(self):\n return \"groupId: {}\\nartifactId: {}\"\\\n .format(self.root.groupId, self.root.artifactId)",
"title": ""
}
] | [
{
"docid": "204edaa40862caebc6dcd3b97a1e5544",
"score": "0.74531114",
"text": "def dep_tostring(self):\n for i in self.deps:\n print(\"{}({}, {})\".format(i.dtype, i.gov, i.dependent))",
"title": ""
},
{
"docid": "3f2662017c19df9bbc1cdfe009603ee9",
"score": "0.70947254"... |
b297c7434d86edc62542cc3945c7de28 | Computes the varPhragmen rule. Uses a Mixed Integer Quadratic Programming (MIQP) implemention and requires Gurobi 7.0+. This function returns one optimal committees (this is a limitation with the MIQP solver of Gurobi). | [
{
"docid": "0735704e9f255c55d174a893a98f7ffa",
"score": "0.69625366",
"text": "def compute_varphragmen(profile, committeesize):\n return rule_approval_ilp.compute_optphragmen_ilp(profile, committeesize, \"varphrag\")",
"title": ""
}
] | [
{
"docid": "cede51e62b21f2b67b6bf7f0fe98be71",
"score": "0.6235433",
"text": "def solve_qp(self):\n # Revert to default settings\n self.m.setParam('BarHomogeneous',-1)\n self.m.setParam('NumericFocus',0)\n # self.m.setParam('OutputFlag',0)\n\n # Solve\n self.m.o... |
d053982513f9aad36837ac2ce7ce19bb | 1) Learned clauses are not supported Return true if a contradiction occurs, otherwise false 2) Learned clauses are supported If a contradiction occurs, return a clause which causes the contradiction, otherwise return None | [
{
"docid": "3223ba7c010d97a8fe19f9507d28c294",
"score": "0.596008",
"text": "def unit_propagation(self):\n\n temp_contradiction = self.__unit_propagation()\n\n # Learned clauses are not supported\n if (not self.__use_learned_clauses):\n # No contradiction\n if ... | [
{
"docid": "351c5df084004ca3e526d1dd047dfedf",
"score": "0.61939806",
"text": "def all_clauses_satisfied(clauses):\n if(len(clauses) == 0):\n return True\n return False",
"title": ""
},
{
"docid": "47246288f3488e5971bfe3ca128f0490",
"score": "0.58200014",
"text": "def te... |
15b0cf90ab67e5d3f5137709fbafdb58 | Extract interquantile range (25%75%) Input | [
{
"docid": "ddd14f3aaba1c165ba4b128b699169bf",
"score": "0.55874294",
"text": "def iqr(series):\n Q1 = series.quantile(0.25)\n Q3 = series.quantile(0.75)\n IQR = Q3 - Q1\n return IQR",
"title": ""
}
] | [
{
"docid": "dfa9f2fc5a32246ceaafb360b77168a9",
"score": "0.76805675",
"text": "def interquartile_range(x):\n return quantile(x, 0.75) - quantile(x, 0.25)",
"title": ""
},
{
"docid": "4077e7dd30f70ad22b8bcac762c1e85b",
"score": "0.74730366",
"text": "def interquartile_range(a):\n ... |
1cc757ad3d19f9bc9b089dfb3ff50080 | Sorts a single file. | [
{
"docid": "29b0f3d843bc261dc266b991be56fcc7",
"score": "0.7040347",
"text": "def sortFile(path, newPath, filename):\n\tprint('Begin sorting file: ' + filename)\n\tnewName = newPath.replace(' ', '\\ ')+filename + '_sorted.bam'\n\tstart = time.time()\n\tos.system('samtools sort -@ 12 -o {0} {1}'.format(n... | [
{
"docid": "fe45d9da508fc27012f0e3a708bcc702",
"score": "0.7231363",
"text": "def sort_file(file_path, encoding=ENCODING):\n file_path = os.path.normpath(file_path)\n input_file = open_fr(file_path, encoding)\n lines = [line.strip().replace('\\x00', '') for line in input_file]\n input_file.c... |
913881d0daaee0e1a17a9c18ede3c603 | Test adding multiple items to an empty bimap by value. | [
{
"docid": "b79327f424d73139b0a256f944c212af",
"score": "0.8132962",
"text": "def test_add_by_value_to_empty(self):\n bimap = BiMap()\n bimap.update_by_value({\"first\": 1, \"second\": 2})\n\n self.assertEqual(2, len(bimap))\n self.assertEqual(\"first\", bimap.get_key(1))\n ... | [
{
"docid": "d4ead005ebcb3552724cf1c4daab67d6",
"score": "0.81062824",
"text": "def test_add_to_nonempty(self):\n bimap = BiMap({1: \"first\", 2: \"second\"})\n bimap.add_key(3, \"third\")\n bimap.add_value(\"fourth\", 4)\n\n self.assertEqual(4, len(bimap))\n self.asser... |
d794b04fc3e27dc1608b7f638b86f9fa | Sums the session counter upon new page request to keep user signed in | [
{
"docid": "8deae0c37d33174b9afa1c57df487e43",
"score": "0.77163893",
"text": "def sumsessioncounter():\n try:\n session['counter'] += 1\n except KeyError:\n session['counter'] = 1",
"title": ""
}
] | [
{
"docid": "b9b94e3909ddef9eeb1a8be9c009c26a",
"score": "0.7454385",
"text": "async def add_session_count(self):\n session_id = await self.ex.u_cache.get_session_id()\n self.ex.cache.current_session += 1\n self.ex.cache.total_used += 1\n await self.ex.conn.execute(\"UPDATE st... |
9c85b455cd259a9ef9630b05275d18e3 | Have some timeouts expired? | [
{
"docid": "c8efa7c5cd2d44de2688fe2aef4031b6",
"score": "0.0",
"text": "def hclock(ctx, _jso):\n #ctx[\"_clock\"]=jso\n tm=ctx[\"_tm\"]\n \n timeout_state_report=ctx[\"timeout_state_report\"]\n \n ### go through all 'ttype' and decrease their timeout\n ttypes=ctx[\"task_types\"]\n ... | [
{
"docid": "785fc845b4b028ded484f971f459d1e5",
"score": "0.7448214",
"text": "def check_time_has_expired(self, timeout: datetime.timedelta) -> bool:\n timeout_expiration = datetime.datetime.now() + timeout\n return datetime.datetime.now() <= timeout_expiration",
"title": ""
},
{
... |
fd0b274e31999af0dca7f358e36a4a64 | x, y, w, h > x, y, x+w, x+h | [
{
"docid": "237d7376b6ec0d9dbd14f025c89861b4",
"score": "0.0",
"text": "def pil_crop_rect(rect_tup):\n x, y, w, h = [int(x) for x in rect_tup]\n return (x, y, x + w, y + h)",
"title": ""
}
] | [
{
"docid": "816939a9f7ba2d48eeaf9df158ca4801",
"score": "0.67684543",
"text": "def in_bound(x, y, width, height):\n if 0 <= x < width and 0 <= y < height:\n return True\n return False",
"title": ""
},
{
"docid": "8e86f94be1a3368756238cdf614b8259",
"score": "0.67358947",
... |
b74d9d05c3c2dd3e1e817d1e3a6bb336 | Print a warning when called. | [
{
"docid": "bd298b891fa5482d793bc5b2fb2330ea",
"score": "0.0",
"text": "def _missing_warn():\n warnings.warn(\"One of the clusters is empty. \"\n \"Re-run kmeans with a different initialization.\")",
"title": ""
}
] | [
{
"docid": "de704c7adfe544749027e2604a0ae588",
"score": "0.82660335",
"text": "def warning(self, msg):\n pass",
"title": ""
},
{
"docid": "92ad80e88c1b3d3346b12d3c8542a735",
"score": "0.7983018",
"text": "def warning(self, msg, *args):\n print(str(msg) % args, file=self... |
414115057c84d443c63dc799c9808212 | Return the value of G(n), computed recursively. >>> g(1) 1 >>> g(2) 2 >>> g(3) 3 >>> g(4) 10 >>> g(5) 22 >>> from construct_check import check >>> check(HW_SOURCE_FILE, 'g', ['While', 'For']) True | [
{
"docid": "fc71e62ab3196fdab2695f5624a87eb0",
"score": "0.6365963",
"text": "def g(n):\n \"*** YOUR CODE HERE ***\"\n if n<=3:\n return n\n else:\n return g(n - 1) + 2 * g(n - 2) + 3 * g(n - 3)",
"title": ""
}
] | [
{
"docid": "0a5ca6bada61dbb8e5f5735adb7599b9",
"score": "0.6579224",
"text": "def gen_g(n):\r\n while True:\r\n a = rsa.randnum.randint(n-1)\r\n if rsa.prime.are_relatively_prime(n, a):\r\n if g_f(a,n):\r\n return a*a",
"title": ""
},
{
"docid": "c9... |
8f827431eb712e0ae50130e71ce9fafc | validate that a command is send via the websocket and the response contains expected results | [
{
"docid": "b1000da8b51b94ec1e4664d6925428bf",
"score": "0.6972805",
"text": "def validate_command_execution(websocket, command, log_obj, checking):\n\n send_a_command(websocket, command)\n print('\\nshell command and output:\\n' + log_obj.last_message + '\\n')\n for i in checking:\n ass... | [
{
"docid": "5f31467a177d89a192f6a157c14c345b",
"score": "0.6918964",
"text": "def validateConnection(self): # pylint: disable=C0103\n self._assert_connected()\n random_str = str(random.randint(0, 10000000))\n cmd = \"_vCnt\\t%s\" % random_str\n logger.debug(\"validateConnect... |
3813a0a3a6a535d2dd1322bbdfe9107a | "Only calculate the scales if needed, for performance. | [
{
"docid": "2dc7d62cdec24385882469bf68a04166",
"score": "0.64434797",
"text": "def calculate_scales(self, block_id=0):\n if self._n_refl[block_id] > 1:\n value, _, __ = self._smoother.multi_value_weight(\n self._normalised_values[block_id], self.value\n )\n ... | [
{
"docid": "b322254a56ef5e26c9de11c341d862d5",
"score": "0.6770345",
"text": "def scales(self) -> Union[paddle.Tensor, np.ndarray]:\n pass",
"title": ""
},
{
"docid": "f16e9d087d3fe5a04e852ca3acbc7b4a",
"score": "0.6761587",
"text": "def _downscale(self):\n pass",
"... |
65c919ea24a9ad705b76f5fb815ebe66 | find a package using TagCollector2 | [
{
"docid": "ce266a276de1feb706f0a35e67018d0d",
"score": "0.59297603",
"text": "def main(args):\n\n import PyUtils.AmiLib as amilib\n client = amilib.Client()\n\n pkgs = args.pkg\n if isinstance(pkgs, basestring):\n pkgs = [pkgs]\n\n for pkg in pkgs:\n client.msg.info('lookin... | [
{
"docid": "39a4e6daf5ff068dffd96a194019643f",
"score": "0.63264567",
"text": "def find_package(*args):\n pass",
"title": ""
},
{
"docid": "08179e7ad410703928e7cb679d30deac",
"score": "0.5906656",
"text": "def main():\n if TAG_ENV not in os.environ:\n print(ERROR_MSG, fi... |
59892f052fb5357c5cf20ae9778373a1 | Determine the server URL. | [
{
"docid": "8672951c4eeb562387df6ad3b9df68a2",
"score": "0.6708006",
"text": "def site_url(url):\n base_url = 'http://%s' % socket.gethostname()\n\n if server.port is not 80:\n base_url += ':%d' % server.port\n\n return urlparse.urljoin(base_url, url)",
"title": ""
}
] | [
{
"docid": "d5f0a3a6f20b696f5f7142f1197d5931",
"score": "0.8548539",
"text": "def get_server_url(self):\n return self.__server_url",
"title": ""
},
{
"docid": "5d0d4f7314234ea3d73c90fe7a619ef1",
"score": "0.8101123",
"text": "def _server():\n return urlparse.urlunparse((req... |
f553809667ff58cbb006dc3ff1570f88 | Get a random number in the range [a, b) or [a, b] depending on rounding. | [
{
"docid": "9efe57cc3670869da903e85162f9f86e",
"score": "0.0",
"text": "def uniform(self, a, b):\n pass",
"title": ""
}
] | [
{
"docid": "6f56cf354ae85a03c8e578521aacd40d",
"score": "0.76068443",
"text": "def rand (a, b):\n return a + int(round(random.random()*(b - a)))",
"title": ""
},
{
"docid": "141edcafc1fad2580301b4097d708c4e",
"score": "0.75378996",
"text": "def randint(a,b):\n return int(random()... |
9e6e4fd307fe655adbb4fb03e17753b9 | Takes sequence of constellation indices from range {0,1,2,3} and maps them to a sequence of QPSK constellation symbols | [
{
"docid": "0cd82ad1f1d14ecbaf4ce814c39063d1",
"score": "0.6679043",
"text": "def map_indices_to_constellation(constellation_indices):\r\n\r\n # UNIT TEST: check constellation indices belong to range {0,1,2,3}\r\n assert not np.isin([False], np.isin(constellation_indices, [0,1,2,3])), \"Indices do... | [
{
"docid": "d2598221522cf25d35da1b69f0b333b1",
"score": "0.61352867",
"text": "def symbolsToIq(syms, constellation):\n return constellation[syms]",
"title": ""
},
{
"docid": "2363cf12e816d5988ee0cf4ba33ef244",
"score": "0.5707883",
"text": "def per_sym_block(I,J,K):\n out = num... |
910fc38e6de509fb9b8878560199883a | Create a client that will connect to addr. Once created, this class will autoconnect and reconnect to the controller as needed. | [
{
"docid": "fd10602c59a4259aa6dd1c77417341a6",
"score": "0.0",
"text": "def __init__(self, addr):\n self.addr = addr\n self.url = 'http://%s:%s/' % self.addr\n self._proxy = webxmlrpc.Proxy(self.url)\n self._deferredIDCallbacks = {}\n # This class manages some pending ... | [
{
"docid": "6fda1795974d5a6849b2a3528b154d47",
"score": "0.6795558",
"text": "def connectClient(self, address, portNumber, clientCreator):\n raise NotImplementedError()",
"title": ""
},
{
"docid": "bc42c2097360fd3d40a411a1c2de38f2",
"score": "0.66389805",
"text": "def client()... |
1c0a72730c8b273ec5ef48dc1f7beea5 | Get the CW power value from the given channel. | [
{
"docid": "c71098f72e15b14d335afefb8075d9ab",
"score": "0.6012683",
"text": "def get_cw_val(cw_scale,gain,fft_shift,test_channel):\n local_freq = ch_list[self.n_chans-test_channel] + f_offset\n\n try:\n Aqf.step(\"Setting digital gain to: {}\".format(gain))\n ... | [
{
"docid": "4565b9102a442dffebb389111819faaa",
"score": "0.78215754",
"text": "def getPower(self, channel):\n if channel not in self.allchannels:\n raise ValueError(\n \"Invalid Laser Thorlabs channel id. Should be in %s\" %\n str(self.allchannels))\n ... |
921045a46ee596a4a476b465043c27a8 | Saves the current GNSS AzEl plot | [
{
"docid": "4d88b79ea24d23caf3feedc85a59b70b",
"score": "0.80356157",
"text": "def save_AzElPlot(self):\n file_choices = \"PNG (*.png)|*.png\"\n\n path = unicode(QtGui.QFileDialog.getSaveFileName(self,\n 'Save file', '',\n file_choices))\n ... | [
{
"docid": "3da7c11e9a1ba80e044c38e2249e9ee7",
"score": "0.6852159",
"text": "def save_plot(self):\r\n self.plot()\r\n plt.savefig('saved_graph.png', bbox_inches='tight')\r\n plt.clf()\r\n # plt.plot(x, y)\r",
"title": ""
},
{
"docid": "f933916428f4e89e0266ce7a5cd... |
6cbd04da0413b5a028fefae422175402 | A simple parser that splits data by commas | [
{
"docid": "a5b92357123d45f89a4fa01a83798b3c",
"score": "0.6035004",
"text": "def comma_decoder(data):\n if isinstance(data, bytes):\n data = data.decode('latin')\n try:\n data = np.array([float(x) for x in data.split(',')])\n except ValueError:\n return None\n return da... | [
{
"docid": "f8ebd5077eba1d8ccb531e61f27dbe17",
"score": "0.73508716",
"text": "def line_parse(line):\r\n line = line.replace(',', ' ')\r\n return line.strip().split()",
"title": ""
},
{
"docid": "b1d0fe61444734d756ac515665f0b820",
"score": "0.7275903",
"text": "def comma_parse(... |
37b5a80db4e42b79650f9914f2393e39 | Returns the dictionary of the branch order of each node. The branch order denotes the number of branch points that are crossed when tracing the path back to the soma. | [
{
"docid": "724814b922e8efd3066b12590654c2a2",
"score": "0.7339895",
"text": "def get_branch_order(self):\n return self._get_branch_order(1,0)",
"title": ""
}
] | [
{
"docid": "f3e53ab5f0c901b2650cfe45350bb7df",
"score": "0.8029624",
"text": "def _get_branch_order(self, start, bo):\n d = {}\n edges = self.edges(start)\n d[start] = bo\n if len(edges) > 1:\n for e in edges:\n d.update(self._get_branch_order(e[1], ... |
5fc48e3f864a1447a85086954100c72a | Given a symmetric, real NxN matrix, M, an eigen decomposition is always possible, such that M can be written as M = T_transp S T (orthogonal transformation) with T_transp T = 1_N and S being a diagonal matrix with the eigenvalues of M on the diagonal. Returns | [
{
"docid": "b29fb740dcbe35969f7e43c3080baa47",
"score": "0.694518",
"text": "def eigenDecomposition(mat):\n from scipy import linalg\n from numpy import matrix\n import numpy\n\n A = matrix(mat)\n S, T_trans = linalg.eig(A)\n if numpy.iscomplex(S).any():\n raise ValueError(\"Giv... | [
{
"docid": "9a33d6904c6d3ad929b3d4137e1e72ea",
"score": "0.66076136",
"text": "def sym_eig(A, dt=None):\n assert all(A == A.transpose()), \"A needs to be symmetric\"\n if dt is None:\n dt = impl.get_runtime().default_fp\n if A.n == 2:\n return sym_eig2x2(A, dt)\n if A.n == 3:\n... |
f8f1208d7de5e25851eb3d9d9abb3e25 | Get the url for a file | [
{
"docid": "06f436330056e464833500ead9705187",
"score": "0.7538725",
"text": "def getFileUrl(self, host, path):\n return self.build_url('file', host, path) + '?view=read&format=auto'",
"title": ""
}
] | [
{
"docid": "b67eb8f01f5b7342fd9c6f20b0d0597a",
"score": "0.76272726",
"text": "def url(self, filename, base_dir=None):\n filename = os.path.join(base_dir or self.base_dir, filename)\n\n file_url = urljoin(self.base_url, filename)\n\n if self.private:\n expires, token = se... |
2b6854efb52ee4dc489239424c714963 | Checks if filepath exists. | [
{
"docid": "4455d9f30dc5440658404123554c1ab4",
"score": "0.0",
"text": "def remote_filepath_exists(ssh_cmd, host, path):\n return ssh_cmd(host, 'test -e {}'.format(path))",
"title": ""
}
] | [
{
"docid": "f7e1bf97ad7f69e7bf4c2f4810440a92",
"score": "0.8392589",
"text": "def file_exists_path(self, filename):\n\n if os.path.isfile(filename):\n return True\n else:\n return False",
"title": ""
},
{
"docid": "cdc50f22a1d3e4d55433363448e09a54",
"s... |
60534a882bad6489130ae88e1b87ad99 | Calculates weighted harmonic mean from this and other densities. | [
{
"docid": "d4ee8d6a2c824d28cace73d5b256677d",
"score": "0.616609",
"text": "def _mean_scalar_density(self, other):\n\n new_density = (self._concentration + other._concentration) / \\\n (self._concentration / self._density +\n other._concentration / other._density)\n\n ... | [
{
"docid": "da685280af95152519d2289f1e64f4b8",
"score": "0.6433477",
"text": "def normalize_weight(self):\n\n # For each input synapse\n avg = 0\n # Compute the average\n for syn in self.in_syn:\n avg += syn.weight\n avg /= len(self.in_syn)\n\n # For ... |
ba4e832995d2d6719ce11f6647011340 | Returns True if this is an interface type. | [
{
"docid": "5e0f8440b0f07aa08160bfd58e96d6f1",
"score": "0.7727358",
"text": "def is_interface(self):\n return False",
"title": ""
}
] | [
{
"docid": "cf53c05b32b6516e32b063c592955d88",
"score": "0.7480001",
"text": "def has_interface(self, resource_type):\n if self.type() == resource_type:\n return True\n\n try:\n ri = self.stack.env.get_resource_info(self.type(),\n ... |
63f22b3e28f12a682ffd26858f1e0efb | The unique identifier of the pair of prices (bid and ask) the position is closed at. | [
{
"docid": "cb8a5e586b69c38c628924521be306ca",
"score": "0.5273115",
"text": "def get_close_quote_id(self, trade_id):\r\n row = self._find_row(trade_id)\r\n if row:\r\n return row.getCloseQuoteID()",
"title": ""
}
] | [
{
"docid": "fd513a4c15168533dc835befb5e43cb2",
"score": "0.5583561",
"text": "def getClosePrice(ticker):\n try:\n yesterday = time.strftime(\"%m/%d/%y\", time.localtime(time.time() - 86400))\n close = list(si.get_data(ticker, start_date=yesterday).to_dict()[\"close\"].values())[0]\n return clo... |
02ccca2b4b0c4d9786cbcb62426bbd92 | Use positive semidefiniteness to identify likely zerovalued eigenvalues due to floating point imprecision. | [
{
"docid": "bcb2f01e5855d0ebefec3ad2f381a49a",
"score": "0.0",
"text": "def from_correlations(\n cls: Type[Eigenvalues],\n data: ndarray,\n atol: float = float(1e3 * np.finfo(np.float64).eps),\n lower: bool = True,\n ) -> Eigenvalues:\n if len(data.shape) > 2:\n ... | [
{
"docid": "ffa4c774fada3e0ed2a46ad5aab82bec",
"score": "0.74455935",
"text": "def test_eigenvalues_of_too_few_points_results_in_0():\n a = np.array([5])\n pc = create_point_cloud(a, a, a)\n\n compute_features(pc, [[0]], pc, [\"eigenv_1\", \"eigenv_2\", \"eigenv_3\"], InfiniteCylind... |
014f941460669fd601096f185f218dca | estimator a la scikit but we want to customize a few thing in particular the predict we add a predict_verbose method which gives the argmax of predict and all other kind of info | [
{
"docid": "b33b48f7146ba4208a877df886f8387b",
"score": "0.0",
"text": "def __init__(self,method=\"manhattan\"):\n\t\tself.liste_of_games=[]\n\t\tself.dictofplayer={}\n\t\t#self.liste_of_players=[]\n\t\tself.liste=False #do we use a list of game or a dict of player with a list of games",
"title": ""... | [
{
"docid": "37fc8baf70ddba5073708b5e25b2031b",
"score": "0.68523747",
"text": "def meta_predict_clf():\n pass",
"title": ""
},
{
"docid": "1bdc25b629b990ea75d72e0f323a6ef5",
"score": "0.66119105",
"text": "def getPrediction(self,data_arg1,data_arg2,data_lbl):",
"title": ""
}... |
cf7821d1283da30486f382a5c57d3344 | For synching saved procedures with mobile clients. Request Params username A valid username. password A valid password. | [
{
"docid": "48da06603589bead9d42a2649bc268cc",
"score": "0.0",
"text": "def saved_procedure_list(request):\n logging.info(\"entering patient list proc\")\n username = request.REQUEST.get(\"username\", None)\n password = request.REQUEST.get(\"password\", None)\n url = settings.OPENMRS_SERVER_... | [
{
"docid": "e58d47573d0131e4705220e41ccb057e",
"score": "0.58704257",
"text": "def set_params(usr, password):\n\n\tglobal user\n\tglobal pwd\n\tuser = usr\n\tpwd = password",
"title": ""
},
{
"docid": "3239ac6fbe8975fc0fdbabef8fa9de8c",
"score": "0.5728726",
"text": "def store_auth_i... |
76343038078799a671baedba71af10ec | return length of rope in O(logn) Rope.size will also returns the same in O(1) but this function is also required at several palces. Users should use Rope.size. | [
{
"docid": "9b058c778dce24f1e7290a4696583400",
"score": "0.0",
"text": "def __len__(self,root=False):\n if root==False:\n root=self.root\n head=root\n count=0\n while head:\n count+=head.weight\n head=head.rc\n return count",
"title... | [
{
"docid": "83ecf4f070784835708b257862d12647",
"score": "0.7059134",
"text": "def house_number(self, rope_len):\n len_sum = 0\n \n for rope in self.rope_array:\n len_sum += rope // rope_len\n \n return len_sum",
"title": ""
},
{
"docid": "59a... |
84cc43a4fbc9223bee4e7fc6e0f497de | self the test_case in question id the id number of the test_case 'self' in the database created the date when the test_case 'self' was created created_by the user that created the test_case 'self' question the question associated with 'self' weight the weight given to this test_case content the code for this test_case ... | [
{
"docid": "fe6bf431b10601c1924d6d6f91e4e7f6",
"score": "0.7035684",
"text": "def __init__(self, id, created, created_by, question, weight, content, active):\n self.id = id\n self.created = created\n self.created_by = created_by\n self.question = question\n ... | [
{
"docid": "10e245c73b225e7a8ab6b9696c7f322a",
"score": "0.5976718",
"text": "def __init__(self, test: TriangleTest, subject_id: int, correct: bool):\n\n self.test = test\n self.subject_id = subject_id\n self.correct = correct",
"title": ""
},
{
"docid": "3825ebf774494f9... |
89fa3e4594421fa702c0fb4c1dd03800 | Get Fuel grade details by FuelRewardId or RewardName | [
{
"docid": "3f250a5ac03b4c98a51f71f930ab65d5",
"score": "0.76041454",
"text": "def GetFuelGradesById(FuelRewardId=\"1\", RewardName=\"FuelReward1\"):\n result = \"\"\n if (FuelRewardId == \"\" and RewardName == \"\"):\n log.info(\"FuelRewardId or RewardName must be not empty\")\n ret... | [
{
"docid": "48ee6941c505a3dd30e77f5efa10c4aa",
"score": "0.74738145",
"text": "def GetFuelRewardsById(FuelRewardId=\"1\", RewardName=\"FuelReward1\"):\n result = \"\"\n if (FuelRewardId == \"\" and RewardName == \"\"):\n log.info(\"FuelRewardId or RewardName must be not empty\")\n re... |
a5eb1395ef001d2fb07bc4f9a4250243 | Try to get application name, that we're connected to. If it couldn't get the name, it returns empty string. | [
{
"docid": "5e8df666c206686584ea9c6dc5fd265c",
"score": "0.77397156",
"text": "def __get_app_name():\n executer = service_manager.CommandExecuter()\n try:\n app_info = executer.execute_command('app.introduce()')\n return app_info['name']\n except Exception:\n #In case of an... | [
{
"docid": "c00fe9e1c4301ef07e40d89efba46a17",
"score": "0.7748835",
"text": "def _get_application_name(self):\n return self.__application_name",
"title": ""
},
{
"docid": "6b05b966e198a38ec887dd965578dedd",
"score": "0.7464836",
"text": "def application_name(self):\n if \"... |
78db8c500389249841d2390ed7b6e221 | Test a simple circuit with a single sample measurement for shot vectors | [
{
"docid": "4d2d4d61ee906fb9a0477bedab95d5f0",
"score": "0.64724594",
"text": "def test_sample_shot_vector(self, max_workers, shots):\n x = np.array(0.732)\n shots = qml.measurements.Shots(shots)\n qs = qml.tape.QuantumScript([qml.RY(x, wires=0)], [qml.sample(wires=range(2))], shots... | [
{
"docid": "839434dfcbb582513f5d2a98de523b9a",
"score": "0.6360883",
"text": "def test_sample_values(self, device, shots, tol):\n dev = device(1)\n par = 1.5708\n\n observable = qml.PauliZ(wires=[0])\n\n dev.apply(\n [\n qml.RX(par, wires=[0]),\n ... |
924948d35b7fe68ec1ca9f608b6f8a54 | Returns the 'base' silo object that an object operates on. Includes special handling for Twitter photo URLs, e.g. | [
{
"docid": "4ad0af60344efd2b2cfcd871cde66a90",
"score": "0.70898885",
"text": "def base_object(self, obj):\n base_obj = super(Twitter, self).base_object(obj)\n url = base_obj.get('url')\n if url:\n try:\n parsed = urlparse.urlparse(url)\n parts = parsed.path.split('/')\n ... | [
{
"docid": "495f4ed4f383e264827d73752466ae8b",
"score": "0.619478",
"text": "def get_base():\r\n return _base",
"title": ""
},
{
"docid": "edf7a898d9f719bbf07cf25c2d5703fe",
"score": "0.59034413",
"text": "def get_base_image(self):\n return self.get_image_and_tag()[0]",
... |
639ae350ec8ef52a509371edd6bce2af | Tests that writing text like 'lrename from to' calls the put action and passes 'from' as the original filename and 'to' as the new filename. | [
{
"docid": "57d5a5b7e9c035083711aa25d4d048e4",
"score": "0.7171982",
"text": "def test_lrename_invokes_rename_action(capsys, monkeypatch, sftp):\n\n # Replace the real action with our mocked function\n monkeypatch.setattr(rename_files_local, \"rename_local_file\", mock_rename)\n\n # Pass in a v... | [
{
"docid": "5070ce0417bc7fba6eda6cafe103ebc2",
"score": "0.68994135",
"text": "def mock_rename(before: str, after: str):\n\n assert before == FROM\n assert after == TO\n print(SUCCESS_STRING)",
"title": ""
},
{
"docid": "80e9e5047d64afe6b307ffc273d28fe6",
"score": "0.66226196",
... |
de97c516d14954426965ccc70629522e | Do not return anything, modify s inplace instead. | [
{
"docid": "ad50449cb5ff4102482424c51fef92e2",
"score": "0.0",
"text": "def reverseWords(self, s: List[str]) -> None:\n if not s:\n return\n def reverse(s,start,end):\n i,j=start,end\n while i<j:\n s[i],s[j]=s[j],s[i]\n i+=1\n ... | [
{
"docid": "31d79ffbb36821823f943aee39e09f63",
"score": "0.62795085",
"text": "def modifiable_chars(self, s):\n self.replace_in_range((self.x_modifiable, len(self._buffer)), s)",
"title": ""
},
{
"docid": "16389be01038261a31222f1a48d6bab5",
"score": "0.59871805",
"text": "def ... |
92fdae44e288b1efac5c4c32ca43cd36 | method that does the same as test on old page templates | [
{
"docid": "19a65991270b27665e8da46d5b6d675b",
"score": "0.0",
"text": "def test(self, condition, true_value, false_value):\n\n if condition:\n return true_value\n else:\n return false_value",
"title": ""
}
] | [
{
"docid": "116ee17c0eac19f4dc5c111ec94c6a03",
"score": "0.7356265",
"text": "def test_templates(self):\n\t\tpass",
"title": ""
},
{
"docid": "6a5eb435727277a1882a9d380302dcf0",
"score": "0.69098026",
"text": "def _assertPageTemplatesUsed(self, response):\n self.assertTemplateUsed... |
d7ea8a8e35003f145cb58edd2f6bb6df | Returns the command's stderr as a string. | [
{
"docid": "008c90d9c024641373debcdb186bb746",
"score": "0.74979025",
"text": "def stderr(self): \n return self._stderr",
"title": ""
}
] | [
{
"docid": "374a0b366ef567928cf96393fb1a88ac",
"score": "0.8082867",
"text": "def stderr(self):\n if not self._stderr_str:\n self._stderr_str = self.raw_stderr.decode(encoding=self._encoding)\n return self._stderr_str",
"title": ""
},
{
"docid": "0ab8ea5c425e8a450dd8... |
c24f08615fc30efad9f87465a3ef1f7c | Invite user to group (inviteGroupPublicV1) Required valid user authentication | [
{
"docid": "66fef9c12b9b0b4cb300b50530fd2936",
"score": "0.71501696",
"text": "def invite_group_public_v1(\n user_id: str,\n namespace: Optional[str] = None,\n x_additional_headers: Optional[Dict[str, str]] = None,\n **kwargs\n):\n if namespace is None:\n namespace, error = get_ser... | [
{
"docid": "4459c330912a73c413cf5d1668233bb8",
"score": "0.7699805",
"text": "def invite_member(request):\n group = get_object_or_404(models.Group, id=request.POST.get(\"group\"))\n user = get_user_from_username(request.user, request.POST[\"user\"])\n\n if not group.user == request.user:\n ... |